From 86c6312574de6513db70ef4025e5e9eb9832ced0 Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 14 Mar 2022 18:36:10 +0000 Subject: [PATCH 01/57] Added .devcontainer to gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index f2a2a4c6..de80a12f 100644 --- a/.gitignore +++ b/.gitignore @@ -114,3 +114,6 @@ settings.dcf tests/fakedata_generation/ site/ credentials.yaml + +# Docker files +.devcontainer From 50358978cca23197da678c4ca5555e207de53ae7 Mon Sep 17 00:00:00 2001 From: Primoz Date: Tue, 15 Mar 2022 12:49:51 +0000 Subject: [PATCH 02/57] Testing Git integration on PC --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index de80a12f..4ac47683 100644 --- a/.gitignore +++ b/.gitignore @@ -115,5 +115,5 @@ tests/fakedata_generation/ site/ credentials.yaml -# Docker files +# Docker container and other files .devcontainer From dc2b4621451a3d36cf08d7446f3feff3795868ec Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 16 Mar 2022 13:30:19 +0000 Subject: [PATCH 03/57] Reseting files to defaults - for Minimal Working Example --- Snakefile | 7 -- config.yaml | 126 +++++++++--------------- data/external/timesegments_periodic.csv | 6 -- rules/preprocessing.smk | 32 +----- 4 files changed, 48 insertions(+), 123 deletions(-) diff --git a/Snakefile b/Snakefile index 4ea33827..7dc5ad3a 100644 --- a/Snakefile +++ b/Snakefile @@ -5,7 +5,6 @@ include: "rules/common.smk" include: "rules/renv.smk" include: "rules/preprocessing.smk" include: "rules/features.smk" -include: "rules/models.smk" include: "rules/reports.smk" import itertools @@ -403,12 +402,6 @@ for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys(): if config["ALL_CLEANING_OVERALL"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +".csv")) -# Baseline features -files_to_compute.extend(expand("data/raw/baseline_merged.csv")) -files_to_compute.extend(expand("data/raw/{pid}/participant_baseline_raw.csv", pid=config["PIDS"])) -files_to_compute.extend(expand("data/interim/{pid}/baseline_questionnaires.csv", pid=config["PIDS"])) -files_to_compute.extend(expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"])) - rule all: input: files_to_compute diff --git a/config.yaml b/config.yaml index 4d7a699c..4b277ac5 100644 --- a/config.yaml +++ b/config.yaml @@ -3,38 +3,36 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: ['p031', 'p032', 'p033', 'p034', 'p035', 'p036', 'p037', 'p038', 'p039', 'p040', 'p042', 'p043', 'p044', 'p045', 'p046', 'p049', 'p050', 'p052', 'p053', 'p054', 'p055', 'p057', 'p058', 'p059', 'p060', 'p061', 'p062', 'p064', 'p067', 'p068', 'p069', 'p070', 'p071', 'p072', 'p073', 'p074', 'p075', 'p076', 'p077', 'p078', 'p079', 'p080', 'p081', 'p082', 'p083', 'p084', 'p085', 'p086', 'p088', 'p089', 'p090', 'p091', 'p092', 'p093', 'p106', 'p107'] +PIDS: [p01] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: - USERNAMES_CSV: "data/external/main_study_usernames.csv" - CSV_FILE_PATH: "data/external/main_study_participants.csv" # see docs for required format + CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format PHONE_SECTION: ADD: True IGNORED_DEVICE_IDS: [] FITBIT_SECTION: - ADD: False + ADD: True IGNORED_DEVICE_IDS: [] EMPATICA_SECTION: - ADD: False + ADD: True IGNORED_DEVICE_IDS: [] # See https://www.rapids.science/latest/setup/configuration/#time-segments TIME_SEGMENTS: &time_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT - FILE: "data/external/timesegments_daily.csv" - INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs + FILE: "data/external/timesegments_periodic.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study TIMEZONE: - TYPE: MULTIPLE + TYPE: SINGLE SINGLE: - TZCODE: Europe/Ljubljana + TZCODE: America/New_York MULTIPLE: - TZ_FILE: data/external/timezone.csv - TZCODES_FILE: data/external/multiple_timezones.csv - IF_MISSING_TZCODE: USE_DEFAULT - DEFAULT_TZCODE: Europe/Ljubljana + TZCODES_FILE: data/external/multiple_timezones_example.csv + IF_MISSING_TZCODE: STOP + DEFAULT_TZCODE: America/New_York FITBIT: ALLOW_MULTIPLE_TZ_PER_DEVICE: False INFER_FROM_SMARTPHONE_TZ: False @@ -45,15 +43,12 @@ TIMEZONE: # See https://www.rapids.science/latest/setup/configuration/#data-stream-configuration PHONE_DATA_STREAMS: - USE: aware_postgresql + USE: aware_csv # AVAILABLE: aware_mysql: DATABASE_GROUP: MY_GROUP - aware_postgresql: - DATABASE_GROUP: PSQL_STRAW - aware_csv: FOLDER: data/external/aware_csv @@ -82,12 +77,12 @@ PHONE_ACCELEROMETER: # See https://www.rapids.science/latest/features/phone-activity-recognition/ PHONE_ACTIVITY_RECOGNITION: CONTAINER: - ANDROID: google_ar + ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same AR episode. PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] ACTIVITY_CLASSES: STATIONARY: ["still", "tilting"] @@ -107,17 +102,16 @@ PHONE_APPLICATIONS_CRASHES: # See https://www.rapids.science/latest/features/phone-applications-foreground/ PHONE_APPLICATIONS_FOREGROUND: - CONTAINER: applications + CONTAINER: applications_foreground APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" - PACKAGE_NAMES_HASHED: True UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: - COMPUTE: True - INCLUDE_EPISODE_FEATURES: True + COMPUTE: False + INCLUDE_EPISODE_FEATURES: False SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] @@ -127,7 +121,7 @@ PHONE_APPLICATIONS_FOREGROUND: dating: ["com.tinder", "com.relance.happycouple", "com.kiwi.joyride"] SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps EXCLUDED_CATEGORIES: [] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] # TODO list system apps? + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: APP_EVENTS: ["countevent", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"] @@ -137,7 +131,7 @@ PHONE_APPLICATIONS_FOREGROUND: # See https://www.rapids.science/latest/features/phone-applications-notifications/ PHONE_APPLICATIONS_NOTIFICATIONS: - CONTAINER: notifications + CONTAINER: applications_notifications APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" @@ -151,7 +145,7 @@ PHONE_BATTERY: EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] SRC_SCRIPT: src/features/phone_battery/rapids/main.py @@ -160,12 +154,12 @@ PHONE_BLUETOOTH: CONTAINER: bluetooth PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R DORYAB: - COMPUTE: True + COMPUTE: False FEATURES: ALL: DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"] @@ -183,7 +177,7 @@ PHONE_BLUETOOTH: # See https://www.rapids.science/latest/features/phone-calls/ PHONE_CALLS: - CONTAINER: call + CONTAINER: calls.csv PROVIDERS: RAPIDS: COMPUTE: True @@ -196,7 +190,7 @@ PHONE_CALLS: SRC_SCRIPT: src/features/phone_calls/rapids/main.R # See https://www.rapids.science/latest/features/phone-conversation/ -PHONE_CONVERSATION: # TODO Adapt for speech +PHONE_CONVERSATION: CONTAINER: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio @@ -215,21 +209,10 @@ PHONE_CONVERSATION: # TODO Adapt for speech # See https://www.rapids.science/latest/features/phone-data-yield/ PHONE_DATA_YIELD: - SENSORS: [#PHONE_ACCELEROMETER, - PHONE_ACTIVITY_RECOGNITION, - PHONE_APPLICATIONS_FOREGROUND, - PHONE_APPLICATIONS_NOTIFICATIONS, - PHONE_BATTERY, - PHONE_BLUETOOTH, - PHONE_CALLS, - PHONE_LIGHT, - PHONE_LOCATIONS, - PHONE_MESSAGES, - PHONE_SCREEN, - PHONE_WIFI_VISIBLE] + SENSORS: [] PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1, minimum percentage of valid minutes in an hour to be considered valid. SRC_SCRIPT: src/features/phone_data_yield/rapids/main.R @@ -245,10 +228,10 @@ PHONE_KEYBOARD: # See https://www.rapids.science/latest/features/phone-light/ PHONE_LIGHT: - CONTAINER: light_sensor + CONTAINER: light PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_SCRIPT: src/features/phone_light/rapids/main.py @@ -262,7 +245,7 @@ PHONE_LOCATIONS: PROVIDERS: DORYAB: - COMPUTE: True + COMPUTE: False FEATURES: ["locationvariance","loglocationvariance","totaldistance","avgspeed","varspeed", "numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","avglengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","timeathome", "homelabel"] DBSCAN_EPS: 100 # meters DBSCAN_MINSAMPLES: 5 @@ -277,7 +260,7 @@ PHONE_LOCATIONS: SRC_SCRIPT: src/features/phone_locations/doryab/main.py BARNETT: - COMPUTE: True + COMPUTE: False FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features @@ -292,10 +275,10 @@ PHONE_LOG: # See https://www.rapids.science/latest/features/phone-messages/ PHONE_MESSAGES: - CONTAINER: sms + CONTAINER: messages PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] @@ -307,7 +290,7 @@ PHONE_SCREEN: CONTAINER: screen PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False REFERENCE_HOUR_FIRST_USE: 0 IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable IGNORE_EPISODES_LONGER_THAN: 360 # in minutes, set to 0 to disable @@ -329,7 +312,7 @@ PHONE_WIFI_VISIBLE: CONTAINER: wifi PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R @@ -557,16 +540,16 @@ EMPATICA_TAGS: # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#1-histograms-of-phone-data-yield HISTOGRAM_PHONE_DATA_YIELD: - PLOT: True + PLOT: False # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#2-heatmaps-of-overall-data-yield HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: - PLOT: True + PLOT: False TIME: RELATIVE_TIME # ABSOLUTE_TIME or RELATIVE_TIME # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#3-heatmap-of-recorded-phone-sensors HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT: - PLOT: True + PLOT: False # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#4-heatmap-of-sensor-row-count HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT: @@ -577,7 +560,7 @@ HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT: # See https://www.rapids.science/latest/visualizations/feature-visualizations/#1-heatmap-correlation-matrix HEATMAP_FEATURE_CORRELATION_MATRIX: - PLOT: True + PLOT: False MIN_ROWS_RATIO: 0.5 CORR_THRESHOLD: 0.1 CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} @@ -590,17 +573,17 @@ HEATMAP_FEATURE_CORRELATION_MATRIX: ALL_CLEANING_INDIVIDUAL: PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False IMPUTE_SELECTED_EVENT_FEATURES: COMPUTE: True MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33 COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable COLS_VAR_THRESHOLD: True - ROWS_NAN_THRESHOLD: 1 # set to 1 to disable + ROWS_NAN_THRESHOLD: 0.3 # set to 1 to disable DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES - DATA_YIELD_RATIO_THRESHOLD: 0.3 # set to 0 to disable + DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable DROP_HIGHLY_CORRELATED_FEATURES: - COMPUTE: False + COMPUTE: True MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5 CORR_THRESHOLD: 0.95 SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R @@ -608,32 +591,17 @@ ALL_CLEANING_INDIVIDUAL: ALL_CLEANING_OVERALL: PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False IMPUTE_SELECTED_EVENT_FEATURES: COMPUTE: True MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33 COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable COLS_VAR_THRESHOLD: True - ROWS_NAN_THRESHOLD: 1 # set to 1 to disable + ROWS_NAN_THRESHOLD: 0.3 # set to 1 to disable DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES - DATA_YIELD_RATIO_THRESHOLD: 0.3 # set to 0 to disable + DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable DROP_HIGHLY_CORRELATED_FEATURES: - COMPUTE: False + COMPUTE: True MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5 CORR_THRESHOLD: 0.95 - SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R - -######################################################################################################################## -# Analysis Workflow Example # -######################################################################################################################## - -PARAMS_FOR_ANALYSIS: - BASELINE: - FOLDER: data/external/baseline - CONTAINER: [results-survey637813_final.csv, # Slovenia - results-survey358134_final.csv, # Belgium 1 - results-survey413767_final.csv # Belgium 2 - ] - QUESTION_LIST: survey637813+question_text.csv - FEATURES: [age, gender, startlanguage, demand, control, demand_control_ratio] - CATEGORICAL_FEATURES: [gender] + SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R \ No newline at end of file diff --git a/data/external/timesegments_periodic.csv b/data/external/timesegments_periodic.csv index d52d92fa..0a26eb6b 100644 --- a/data/external/timesegments_periodic.csv +++ b/data/external/timesegments_periodic.csv @@ -1,9 +1,3 @@ label,start_time,length,repeats_on,repeats_value -threeday,00:00:00,2D 23H 59M 59S,every_day,0 daily,00:00:00,23H 59M 59S,every_day,0 -morning,06:00:00,5H 59M 59S,every_day,0 -afternoon,12:00:00,5H 59M 59S,every_day,0 -evening,18:00:00,5H 59M 59S,every_day,0 night,00:00:00,5H 59M 59S,every_day,0 -two_weeks_overlapping,00:00:00,13D 23H 59M 59S,every_day,0 -weekends,00:00:00,2D 23H 59M 59S,wday,5 diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index cd83b95b..f40495fc 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -4,36 +4,6 @@ rule create_example_participant_files: shell: "echo 'PHONE:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n PLATFORMS: [android]\n LABEL: test-01\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\nFITBIT:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n LABEL: test-01\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\n' >> ./data/external/participant_files/example01.yaml && echo 'PHONE:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n PLATFORMS: [ios]\n LABEL: test-02\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\nFITBIT:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n LABEL: test-02\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\n' >> ./data/external/participant_files/example02.yaml" -rule query_usernames_device_empatica_ids: - params: - baseline_folder = "/mnt/e/STRAWbaseline/" - output: - usernames_file = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"], - timezone_file = config["TIMEZONE"]["MULTIPLE"]["TZ_FILE"] - script: - "../../participants/prepare_usernames_file.py" - -rule prepare_tzcodes_file: - input: - timezone_file = config["TIMEZONE"]["MULTIPLE"]["TZ_FILE"] - output: - tzcodes_file = config["TIMEZONE"]["MULTIPLE"]["TZCODES_FILE"] - script: - "../tools/create_multi_timezones_file.py" - -rule prepare_participants_csv: - input: - username_list = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"] - params: - data_configuration = config["PHONE_DATA_STREAMS"][config["PHONE_DATA_STREAMS"]["USE"]], - participants_table = "participants", - device_id_table = "esm", - start_end_date_table = "esm" - output: - participants_file = config["CREATE_PARTICIPANT_FILES"]["CSV_FILE_PATH"] - script: - "../src/data/translate_usernames_into_participants_data.R" - rule create_participants_files: input: participants_file = config["CREATE_PARTICIPANT_FILES"]["CSV_FILE_PATH"] @@ -249,4 +219,4 @@ rule empatica_readable_datetime: output: "data/raw/{pid}/empatica_{sensor}_with_datetime.csv" script: - "../src/data/datetime/readable_datetime.R" + "../src/data/datetime/readable_datetime.R" \ No newline at end of file From 3f8e1cc252e8f38b7d2fedfe72faf0a7947f609d Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 16 Mar 2022 15:03:32 +0000 Subject: [PATCH 04/57] Empatica features calculations with an example ZIP --- .gitignore | 24 +++++++++---------- config.yaml | 12 +++++----- data/external/aware_csv/calls.csv | 9 +++++++ data/external/empatica/empatica1/E4 Data.zip | Bin 0 -> 1678743 bytes data/external/participant_files/p01.yaml | 11 +++++++++ 5 files changed, 38 insertions(+), 18 deletions(-) create mode 100644 data/external/aware_csv/calls.csv create mode 100644 data/external/empatica/empatica1/E4 Data.zip create mode 100644 data/external/participant_files/p01.yaml diff --git a/.gitignore b/.gitignore index 4ac47683..e15324bd 100644 --- a/.gitignore +++ b/.gitignore @@ -91,18 +91,18 @@ packrat/* !packrat/packrat.opts .snakemake/ -# exclude data from source control by default -data/external/* -!/data/external/.gitkeep -!/data/external/stachl_application_genre_catalogue.csv -!/data/external/timesegments*.csv -!/data/external/wiki_tz.csv -data/raw/* -!/data/raw/.gitkeep -data/interim/* -!/data/interim/.gitkeep -data/processed/* -!/data/processed/.gitkeep +# exclude data from source control by default -- temporarly added for working on PC and laptop (primoz) +# data/external/* +# !/data/external/.gitkeep +# !/data/external/stachl_application_genre_catalogue.csv +# !/data/external/timesegments*.csv +# !/data/external/wiki_tz.csv +# data/raw/* +# !/data/raw/.gitkeep +# data/interim/* +# !/data/interim/.gitkeep +# data/processed/* +# !/data/processed/.gitkeep reports/ .Rproj.user *.Rproj diff --git a/config.yaml b/config.yaml index 4b277ac5..d82b2f8e 100644 --- a/config.yaml +++ b/config.yaml @@ -477,7 +477,7 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py @@ -486,7 +486,7 @@ EMPATICA_HEARTRATE: CONTAINER: HR PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py @@ -495,7 +495,7 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py @@ -504,7 +504,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py @@ -513,7 +513,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py @@ -522,7 +522,7 @@ EMPATICA_INTER_BEAT_INTERVAL: CONTAINER: IBI PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py diff --git a/data/external/aware_csv/calls.csv b/data/external/aware_csv/calls.csv new file mode 100644 index 00000000..530f275a --- /dev/null +++ b/data/external/aware_csv/calls.csv @@ -0,0 +1,9 @@ +"_id","timestamp","device_id","call_type","call_duration","trace" +1,1587663260695,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,14,"d5e84f8af01b2728021d4f43f53a163c0c90000c" +2,1587739118007,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",3,0,"47c125dc7bd163b8612cdea13724a814917b6e93" +5,1587746544891,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,95,"9cc793ffd6e88b1d850ce540b5d7e000ef5650d4" +6,1587911379859,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,63,"51fb9344e988049a3fec774c7ca622358bf80264" 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START_DATE: 2020-01-01 # this can also be empty + END_DATE: 2021-01-01 # this can also be empty +EMPATICA: + DEVICE_IDS: [empatica1] + LABEL: test01 + START_DATE: + END_DATE: From d63158c199d8ed79d79081a37b0105a1d4cbfcfc Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 21 Mar 2022 08:28:28 +0000 Subject: [PATCH 05/57] Build calc features lib and related packages. --- .gitignore | 24 +- calculate_HR_features_test.py | 23 + calculatingfeatures/.gitignore | 221 + .../CalculatingFeatures/__init__.py | 0 .../calculate_acc_gyro_common_features.py | 610 + .../CalculatingFeatures/calculate_features.py | 138 + .../calculate_hrv_peaks.py | 90 + .../CalculatingFeatures/feature_functions.py | 986 + .../CalculatingFeatures/gsr.py | 669 + .../CalculatingFeatures/helper_functions.py | 131 + .../CalculatingFeatures/hrv.py | 304 + calculatingfeatures/README.md | 207 + calculatingfeatures/__init__.py | 0 calculatingfeatures/eda_explorer/.csv | 175 + calculatingfeatures/eda_explorer/.gitignore | 5 + .../AccelerometerFeatureExtractionScript.py | 309 + .../eda_explorer/ArtifactClassifiers.py | 601 + .../EDA-Artifact-Detection-Script.py | 411 + .../eda_explorer/EDA_Peak_Detection_Script.py | 250 + calculatingfeatures/eda_explorer/LICENSE.txt | 21 + calculatingfeatures/eda_explorer/README.md | 39 + calculatingfeatures/eda_explorer/__init__.py | 1 + calculatingfeatures/eda_explorer/classify.py | 90 + .../eda_explorer/load_files.py | 264 + .../eda_explorer/requirements.txt | 6 + calculatingfeatures/example_data/EDA.csv | 114320 ++++ .../example_data/S2_E4_Data/ACC.csv | 251972 +++++++ .../example_data/S2_E4_Data/BVP.csv | 503945 +++++++++++++++ .../example_data/S2_E4_Data/EDA.csv | 31496 + .../example_data/S2_E4_Data/HR.csv | 7867 + .../example_data/S2_E4_Data/IBI.csv | 3459 + .../example_data/S2_E4_Data/TEMP.csv | 31498 + .../example_data/S2_E4_Data/info.txt | 32 + .../example_data/S2_E4_Data/tags.csv | 0 calculatingfeatures/pyproject.toml | 3 + calculatingfeatures/setup.cfg | 16 + calculatingfeatures/usage_examples.ipynb | 6112 + environment.yml | 198 +- 38 files changed, 956407 insertions(+), 86 deletions(-) create mode 100644 calculate_HR_features_test.py create mode 100644 calculatingfeatures/.gitignore create mode 100644 calculatingfeatures/CalculatingFeatures/__init__.py create mode 100644 calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py create mode 100644 calculatingfeatures/CalculatingFeatures/calculate_features.py create mode 100644 calculatingfeatures/CalculatingFeatures/calculate_hrv_peaks.py create mode 100644 calculatingfeatures/CalculatingFeatures/feature_functions.py create mode 100644 calculatingfeatures/CalculatingFeatures/gsr.py create mode 100644 calculatingfeatures/CalculatingFeatures/helper_functions.py create mode 100644 calculatingfeatures/CalculatingFeatures/hrv.py create mode 100644 calculatingfeatures/README.md create mode 100644 calculatingfeatures/__init__.py create mode 100644 calculatingfeatures/eda_explorer/.csv create mode 100644 calculatingfeatures/eda_explorer/.gitignore create mode 100644 calculatingfeatures/eda_explorer/AccelerometerFeatureExtractionScript.py create mode 100644 calculatingfeatures/eda_explorer/ArtifactClassifiers.py create mode 100644 calculatingfeatures/eda_explorer/EDA-Artifact-Detection-Script.py create mode 100644 calculatingfeatures/eda_explorer/EDA_Peak_Detection_Script.py create mode 100644 calculatingfeatures/eda_explorer/LICENSE.txt create mode 100644 calculatingfeatures/eda_explorer/README.md create mode 100644 calculatingfeatures/eda_explorer/__init__.py create mode 100644 calculatingfeatures/eda_explorer/classify.py create mode 100644 calculatingfeatures/eda_explorer/load_files.py create mode 100644 calculatingfeatures/eda_explorer/requirements.txt create mode 100644 calculatingfeatures/example_data/EDA.csv create mode 100644 calculatingfeatures/example_data/S2_E4_Data/ACC.csv create mode 100644 calculatingfeatures/example_data/S2_E4_Data/BVP.csv create mode 100644 calculatingfeatures/example_data/S2_E4_Data/EDA.csv create mode 100644 calculatingfeatures/example_data/S2_E4_Data/HR.csv create mode 100644 calculatingfeatures/example_data/S2_E4_Data/IBI.csv create mode 100644 calculatingfeatures/example_data/S2_E4_Data/TEMP.csv create mode 100644 calculatingfeatures/example_data/S2_E4_Data/info.txt create mode 100644 calculatingfeatures/example_data/S2_E4_Data/tags.csv create mode 100644 calculatingfeatures/pyproject.toml create mode 100644 calculatingfeatures/setup.cfg create mode 100644 calculatingfeatures/usage_examples.ipynb diff --git a/.gitignore b/.gitignore index e15324bd..4ac47683 100644 --- a/.gitignore +++ b/.gitignore @@ -91,18 +91,18 @@ packrat/* !packrat/packrat.opts .snakemake/ -# exclude data from source control by default -- temporarly added for working on PC and laptop (primoz) -# data/external/* -# !/data/external/.gitkeep -# !/data/external/stachl_application_genre_catalogue.csv -# !/data/external/timesegments*.csv -# !/data/external/wiki_tz.csv -# data/raw/* -# !/data/raw/.gitkeep -# data/interim/* -# !/data/interim/.gitkeep -# data/processed/* -# !/data/processed/.gitkeep +# exclude data from source control by default +data/external/* +!/data/external/.gitkeep +!/data/external/stachl_application_genre_catalogue.csv +!/data/external/timesegments*.csv +!/data/external/wiki_tz.csv +data/raw/* +!/data/raw/.gitkeep +data/interim/* +!/data/interim/.gitkeep +data/processed/* +!/data/processed/.gitkeep reports/ .Rproj.user *.Rproj diff --git a/calculate_HR_features_test.py b/calculate_HR_features_test.py new file mode 100644 index 00000000..5fd91bce --- /dev/null +++ b/calculate_HR_features_test.py @@ -0,0 +1,23 @@ +from calculatingfeatures.CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, frequencyFeatureNames, hrvFeatureNames +from calculatingfeatures.CalculatingFeatures.calculate_features import calculateFeatures +import pandas as pd + +pathToHrvCsv = "calculatingfeatures/example_data/S2_E4_Data/BVP.csv" +windowLength = 500 + +# get an array of values from HRV empatica file +hrv_data, startTimeStamp, sampleRate = convert1DEmpaticaToArray(pathToHrvCsv) + +# Convert the HRV data into 2D array +hrv_data_2D = convertInputInto2d(hrv_data, windowLength) + +# Create a list with feature names +featureNames = [] +featureNames.extend(hrvFeatureNames) +featureNames.extend(frequencyFeatureNames) + +pd.set_option('display.max_columns', None) + +# Calculate features +calculatedFeatures = calculateFeatures(hrv_data_2D, fs=int(sampleRate), featureNames=featureNames) +print(calculatedFeatures) diff --git a/calculatingfeatures/.gitignore b/calculatingfeatures/.gitignore new file mode 100644 index 00000000..779580fa --- /dev/null +++ b/calculatingfeatures/.gitignore @@ -0,0 +1,221 @@ +.ipynb_checkpoints +build +CalculatingFeatures.egg-info +dist +venv + +# From the website: https://github.com/github/gitignore/blob/master/Python.gitignore +# Also from the website: https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore + + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. Uncomment if using +# auto-import. +# .idea/artifacts +# .idea/compiler.xml +# .idea/jarRepositories.xml +# .idea/modules.xml +# .idea/*.iml +# .idea/modules +# *.iml +# *.ipr + +# CMake +cmake-build-*/ + +# Mongo Explorer plugin +.idea/**/mongoSettings.xml + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +.idea/httpRequests + +# Android studio 3.1+ serialized cache file +.idea/caches/build_file_checksums.ser + diff --git a/calculatingfeatures/CalculatingFeatures/__init__.py b/calculatingfeatures/CalculatingFeatures/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py b/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py new file mode 100644 index 00000000..796cf25d --- /dev/null +++ b/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py @@ -0,0 +1,610 @@ +from calculatingfeatures.CalculatingFeatures import feature_functions as f +import numpy as np + + +def calculateFeaturesAcc(ax, ay, az, time, axBand, ayBand, azBand, axLow, ayLow, azLow, featureNames=None): + """ Calculate features for the accelerometer + + :param ax2d: 2D array including the X axis + :param ay2d: 2D array including the Y axis + :param az2d: 2D array including the Z axis + :param time2d: 2D array of times, denoting when each measurement occurred + :param afs: sampling frequency of the accelerometer + :param prefix: prefix for column names in returned dataframe + :return: features of the accelerometer + """ + if ay is not None: + magnitudes = f.magnitudeVector(ax, ay, az) + magnitudesLow = f.magnitudeVector(axLow, ayLow, azLow) + magnitudesBand = f.magnitudeVector(axBand, ayBand, azBand) + else: + magnitudes = ax + magnitudesLow = axLow + magnitudesBand = axBand + + ACsumPerXBand = f.calcSumPerComponent(axBand, time) + if ay is not None: + ACsumPerYBand = f.calcSumPerComponent(ayBand, time) + ACsumPerZBand = f.calcSumPerComponent(azBand, time) + + meanKineticEnergyX = f.calcMeanKineticEnergy(axBand, time) + if ay is not None: + meanKineticEnergyY = f.calcMeanKineticEnergy(ayBand, time) + meanKineticEnergyZ = f.calcMeanKineticEnergy(azBand, time) + totalKineticEnergy = f.calcTotalKineticEnergy(axBand, ayBand, azBand, time) + + roll = f.calcRoll(ayLow, azLow) + pitch = f.calcPitch(axLow, ayLow, azLow) + + acAbsoluteArea = f.calcAcAbsoluteArea(axBand, ayBand, azBand) + + row = {} + if f.checkForFeature("meanLow", featureNames): + row["mean_XLow"] = f.calcMean(axLow) + if ay is not None: + row["mean_YLow"] = f.calcMean(ayLow) + row["mean_ZLow"] = f.calcMean(azLow) + row["totalMeanLow"] = (row["mean_XLow"] + row["mean_YLow"] + row["mean_ZLow"]) * len(ax) / 3 + + if f.checkForFeature("areaLow", featureNames): + row["area_XLow"] = f.calcArea(axLow) + if ay is not None: + row["area_YLow"] = f.calcArea(ayLow) + row["area_ZLow"] = f.calcArea(azLow) + + if ay is not None: + if f.checkForFeature("totalAbsoluteAreaBand", featureNames): + row["totalAbsoluteAreaBand"] = f.calcTotalAbsoluteArea(f.calcAcAbsoluteArea(axBand, ayBand, azBand)) + + if f.checkForFeature("totalMagnitudeBand", featureNames): + row["totalMagnitudeBand"] = f.calcTotalMagnitude(axBand, ayBand, azBand) + + # Measures of body posture + if f.checkForFeature("postureDistanceLow", featureNames): + postureDistance = f.calcPostureDistance(row["mean_XLow"], row["mean_YLow"], + row["mean_ZLow"]) if "mean_XLow" in row \ + else f.calcPostureDistance(f.calcMean(axLow), f.calcMean(ayLow), f.calcMean(azLow)) + row["postureDistance_XLow"] = postureDistance[0] + row["postureDistance_YLow"] = postureDistance[1] + row["postureDistance_ZLow"] = postureDistance[2] + + if f.checkForFeature("entropyBand", featureNames): + row["entropy_XBand"] = f.calcEntropy(axBand) + if ay is not None: + row["entropy_YBand"] = f.calcEntropy(ayBand) + row["entropy_ZBand"] = f.calcEntropy(azBand) + + if f.checkForFeature("skewnessBand", featureNames): + row["skewness_XBand"] = f.calcSkewness(axBand) + if ay is not None: + row["skewness_YBand"] = f.calcSkewness(ayBand) + row["skewness_ZBand"] = f.calcSkewness(azBand) + + if f.checkForFeature("kurtosisBand", featureNames): + row["kurtosis_XBand"] = f.calcKurtosis(axBand) + if ay is not None: + row["kurtosis_YBand"] = f.calcKurtosis(ayBand) + row["kurtosis_ZBand"] = f.calcKurtosis(azBand) + + # Measures of motion shape + if f.checkForFeature("absoluteMeanBand", featureNames): + row["absoluteMean_XBand"] = f.calcMean(axBand) + if ay is not None: + row["absoluteMean_YBand"] = f.calcMean(ayBand) + row["absoluteMean_ZBand"] = f.calcMean(azBand) + + if f.checkForFeature("absoluteAreaBand", featureNames): + row["absoluteArea_XBand"] = f.calcArea(axBand) + if ay is not None: + row["absoluteArea_YBand"] = f.calcArea(ayBand) + row["absoluteArea_ZBand"] = f.calcArea(azBand) + row["absoluteAreaAllBand"] = f.calcTotalAbsoluteArea(acAbsoluteArea) + + acQuartilesX = f.calcQuartiles(axBand) + if ay is not None: + acQuartilesY = f.calcQuartiles(ayBand) + acQuartilesZ = f.calcQuartiles(azBand) + + if f.checkForFeature("quartilesBand", featureNames): + row["quartilesQ1_XBand"] = acQuartilesX[0] + row["quartilesQ2_XBand"] = acQuartilesX[1] + row["quartilesQ3_XBand"] = acQuartilesX[2] + if ay is not None: + row["quartilesQ1_YBand"] = acQuartilesY[0] + row["quartilesQ2_YBand"] = acQuartilesY[1] + row["quartilesQ3_YBand"] = acQuartilesY[2] + + row["quartilesQ1_ZBand"] = acQuartilesZ[0] + row["quartilesQ2_ZBand"] = acQuartilesZ[1] + row["quartilesQ3_ZBand"] = acQuartilesZ[2] + + if f.checkForFeature("interQuartileRangeBand", featureNames): + row["interQuartileRange_XBand"] = f.calcInterQuartileRange(acQuartilesX) + if ay is not None: + row["interQuartileRange_YBand"] = f.calcInterQuartileRange(acQuartilesY) + row["interQuartileRange_ZBand"] = f.calcInterQuartileRange(acQuartilesZ) + + # Measures of motion variation + if f.checkForFeature("varianceBand", featureNames): + row["variance_XBand"] = f.calcVariance(axBand) + if ay is not None: + row["variance_YBand"] = f.calcVariance(ayBand) + row["variance_ZBand"] = f.calcVariance(azBand) + + if f.checkForFeature("coefficientOfVariationBand", featureNames): + row["coefficientOfVariation_XBand"] = f.calcCoefficientOfVariation(axBand) + if ay is not None: + row["coefficientOfVariation_YBand"] = f.calcCoefficientOfVariation(ayBand) + row["coefficientOfVariation_ZBand"] = f.calcCoefficientOfVariation(azBand) + + if f.checkForFeature("amplitudeBand", featureNames): + row["amplitude_XBand"] = f.calcAmplitude(axBand) + if ay is not None: + row["amplitude_YBand"] = f.calcAmplitude(ayBand) + row["amplitude_ZBand"] = f.calcAmplitude(azBand) + + if f.checkForFeature("totalEnergyBand", featureNames): + row["totalEnergy_XBand"] = f.calcTotalEnergy(axBand) + if ay is not None: + row["totalEnergy_YBand"] = f.calcTotalEnergy(ayBand) + row["totalEnergy_ZBand"] = f.calcTotalEnergy(azBand) + + if f.checkForFeature("dominantFrequencyEnergyBand", featureNames): + row["dominantFrequencyEnergy_XBand"] = f.calcDominantFrequencyEnergy(axBand) + if ay is not None: + row["dominantFrequencyEnergy_YBand"] = f.calcDominantFrequencyEnergy(ayBand) + row["dominantFrequencyEnergy_ZBand"] = f.calcDominantFrequencyEnergy(azBand) + + if f.checkForFeature("meanCrossingRateBand", featureNames): + row["meanCrossingRate_XBand"] = f.calcMeanCrossingRate(axBand) + if ay is not None: + row["meanCrossingRate_YBand"] = f.calcMeanCrossingRate(ayBand) + row["meanCrossingRate_ZBand"] = f.calcMeanCrossingRate(azBand) + + if ay is not None: + if f.checkForFeature("correlationBand", featureNames): + row["correlation_X_YBand"] = f.calcCorrelation(axBand, ayBand) + row["correlation_X_ZBand"] = f.calcCorrelation(axBand, azBand) + row["correlation_Y_ZBand"] = f.calcCorrelation(ayBand, azBand) + + acQuartilesMagnitude = f.calcQuartiles(magnitudesBand) + if f.checkForFeature("quartilesMagnitudesBand", featureNames): + row["quartilesMagnitudes_XBand"] = acQuartilesMagnitude[0] + row["quartilesMagnitudes_YBand"] = acQuartilesMagnitude[1] + row["quartilesMagnitudes_ZBand"] = acQuartilesMagnitude[2] + + if f.checkForFeature("interQuartileRangeMagnitudesBand", featureNames): + row["interQuartileRangeMagnitudesBand"] = f.calcInterQuartileRange(acQuartilesMagnitude) + + if f.checkForFeature("areaUnderAccelerationMagnitude", featureNames): + row["areaUnderAccelerationMagnitude"] = f.calcAreaUnderAccelerationMagnitude(magnitudes, time) + + if f.checkForFeature("peaksDataLow", featureNames): + peakCount = f.calcPeakCount(magnitudesLow) + row["peaksCountLow"] = peakCount[0] + row["peaksSumLow"] = peakCount[1] + row["peaksAmplitudeAvgLow"] = peakCount[3] + row["peaksPeakAvgLow"] = peakCount[2] + + if f.checkForFeature("sumPerComponentBand", featureNames): + row["sumPerComponent_XBand"] = ACsumPerXBand + if ay is not None: + row["sumPerComponent_YBand"] = ACsumPerYBand + row["sumPerComponent_ZBand"] = ACsumPerZBand + + if f.checkForFeature("velocityBand", featureNames): + row["velocity_XBand"] = f.computeACVelocity(axBand, time) + if ay is not None: + row["velocity_YBand"] = f.computeACVelocity(ayBand, time) + row["velocity_ZBand"] = f.computeACVelocity(azBand, time) + + if f.checkForFeature("meanKineticEnergyBand", featureNames): + row["meanKineticEnergy_XBand"] = meanKineticEnergyX + if ay is not None: + row["meanKineticEnergy_YBand"] = meanKineticEnergyY + row["meanKineticEnergy_ZBand"] = meanKineticEnergyZ + + if ay is not None: + if f.checkForFeature("totalKineticEnergyBand", featureNames): + row["totalKineticEnergyBand"] = totalKineticEnergy + + ACsumPerX = f.calcSumPerComponent(ax, time) + acSumSquaredX = pow(ACsumPerX, 2) + if ay is not None: + ACsumPerY = f.calcSumPerComponent(ay, time) + ACsumPerZ = f.calcSumPerComponent(az, time) + acSumSquaredY = pow(ACsumPerY, 2) + acSumSquaredZ = pow(ACsumPerZ, 2) + + if f.checkForFeature("squareSumOfComponent", featureNames): + row["squareSumOfComponent_X"] = acSumSquaredX + if ay is not None: + row["squareSumOfComponent_Y"] = acSumSquaredY + row["squareSumOfComponent_Z"] = acSumSquaredZ + + if f.checkForFeature("averageVectorLength", featureNames): + row["averageVectorLength"] = f.calcAverageVectorLength(magnitudes) + + if f.checkForFeature("averageVectorLengthPower", featureNames): + row["averageVectorLengthPower"] = f.calcAverageVectorLengthPower(magnitudes) + + if ay is not None: + if f.checkForFeature("rollAvgLow", featureNames): + row["rollAvgLow"] = (f.max(roll) - f.min(roll)) + + if f.checkForFeature("pitchAvgLow", featureNames): + row["pitchAvgLow"] = (f.max(pitch) - f.min(pitch)) + + if f.checkForFeature("rollStdDevLow", featureNames): + row["rollStdDevLow"] = f.stdDev(roll) + + if f.checkForFeature("pitchStdDevLow", featureNames): + row["pitchStdDevLow"] = f.stdDev(pitch) + + if f.checkForFeature("rollMotionAmountLow", featureNames): + row["rollMotionAmountLow"] = f.rollMotionAmount(roll) + + if f.checkForFeature("rollMotionRegularityLow", featureNames): + row["rollMotionRegularityLow"] = f.rollMotionRegularity(roll) + + if f.checkForFeature("manipulationLow", featureNames): + row["manipulationLow"] = f.manipulation(axLow, ayLow, azLow, roll, pitch) + + if f.checkForFeature("rollPeaks", featureNames): + roll_peaks = f.calcPeakCount(roll) + row["rollPeak0"] = roll_peaks[0] + row["rollPeak1"] = roll_peaks[1] + row["rollPeak2"] = roll_peaks[2] + row["rollPeak3"] = roll_peaks[3] + + if f.checkForFeature("pitchPeaks", featureNames): + pitch_peaks = f.calcPeakCount(pitch) + row["pitchPeak0"] = pitch_peaks[0] + row["pitchPeak1"] = pitch_peaks[1] + row["pitchPeak2"] = pitch_peaks[2] + row["pitchPeak3"] = pitch_peaks[3] + + if f.checkForFeature("rollPitchCorrelation", featureNames): + row["rollPitchCorrelation"] = f.calcCorrelation(roll, pitch) + return row + + +def calcCommonFeatures(x, y, z, time, featureNames=None): + """ Calculate common features of accelerometer and gyroscope + + :param prefix: prefix of all feature names + :param x: array including the X axis + :param y: array including the Y axis + :param z: array including the Z axis + :return: pandas dataframe with the calculated features + """ + row = {} + if f.checkForFeature("autocorrelations", featureNames): + row.update(calcAutocorrelations(x, "_X")) + if y is not None: + row.update(calcAutocorrelations(y, "_Y")) + row.update(calcAutocorrelations(z, "_Z")) + + if f.checkForFeature("countAboveMean", featureNames): + row["countAboveMean_X"] = f.countAboveMean(x) + if y is not None: + row["countAboveMean_Y"] = f.countAboveMean(y) + row["countAboveMean_Z"] = f.countAboveMean(z) + + if f.checkForFeature("countBelowMean", featureNames): + row["countBelowMean_X"] = f.countBelowMean(x) + if y is not None: + row["countBelowMean_Y"] = f.countBelowMean(y) + row["countBelowMean_Z"] = f.countBelowMean(z) + + if f.checkForFeature("maximum", featureNames): + row["maximum_X"] = f.max(x) + if y is not None: + row["maximum_Y"] = f.max(y) + row["maximum_Z"] = f.max(z) + + if f.checkForFeature("minimum", featureNames): + row["minimum_X"] = f.min(x) + if y is not None: + row["minimum_Y"] = f.min(y) + row["minimum_Z"] = f.min(z) + + if f.checkForFeature("meanAbsChange", featureNames): + row["meanAbsChange_X"] = f.meanAbsChange(x) + if y is not None: + row["meanAbsChange_Y"] = f.meanAbsChange(y) + row["meanAbsChange_Z"] = f.meanAbsChange(z) + + if f.checkForFeature("longestStrikeAboveMean", featureNames): + row["longestStrikeAboveMean_X"] = f._calcMaxLengthOfSequenceTrueOrOne(x > np.mean(x)) + if y is not None: + row["longestStrikeAboveMean_Y"] = f._calcMaxLengthOfSequenceTrueOrOne(y > np.mean(y)) + row["longestStrikeAboveMean_Z"] = f._calcMaxLengthOfSequenceTrueOrOne(z > np.mean(z)) + + if f.checkForFeature("longestStrikeBelowMean", featureNames): + row["longestStrikeBelowMean_X"] = f._calcMaxLengthOfSequenceTrueOrOne(x < np.mean(x)) + if y is not None: + row["longestStrikeBelowMean_Y"] = f._calcMaxLengthOfSequenceTrueOrOne(y < np.mean(y)) + row["longestStrikeBelowMean_Z"] = f._calcMaxLengthOfSequenceTrueOrOne(z < np.mean(z)) + + if f.checkForFeature("stdDev", featureNames): + row["stdDev_X"] = f.stdDev(x) + if y is not None: + row["stdDev_Y"] = f.stdDev(y) + row["stdDev_Z"] = f.stdDev(z) + + if f.checkForFeature("median", featureNames): + row["median_X"] = np.median(x) + if y is not None: + row["median_Y"] = np.median(y) + row["median_Z"] = np.median(z) + + if f.checkForFeature("meanChange", featureNames): + row["meanChange_X"] = f.meanChange(x) + if y is not None: + row["meanChange_Y"] = f.meanChange(y) + row["meanChange_Z"] = f.meanChange(z) + + if f.checkForFeature("numberOfZeroCrossings", featureNames): + row["numberOfZeroCrossings_X"] = f.numberOfZeroCrossings(x) + if y is not None: + row["numberOfZeroCrossings_Y"] = f.numberOfZeroCrossings(y) + row["numberOfZeroCrossings_Z"] = f.numberOfZeroCrossings(z) + + if f.checkForFeature("absEnergy", featureNames): + row["absEnergy_X"] = f.absEnergy(x) + if y is not None: + row["absEnergy_Y"] = f.absEnergy(y) + row["absEnergy_Z"] = f.absEnergy(z) + + if f.checkForFeature("linearTrendSlope", featureNames): + row["linearTrendSlope_X"] = f.linearTrendSlope(x) + if y is not None: + row["linearTrendSlope_Y"] = f.linearTrendSlope(y) + row["linearTrendSlope_Z"] = f.linearTrendSlope(z) + + if f.checkForFeature("ratioBeyondRSigma", featureNames): + r = 2.5 + row["ratioBeyondRSigma_X"] = f.ratioBeyondRSigma(x, r) + if y is not None: + row["ratioBeyondRSigma_Y"] = f.ratioBeyondRSigma(y, r) + row["ratioBeyondRSigma_Z"] = f.ratioBeyondRSigma(z, r) + + if f.checkForFeature("binnedEntropy", featureNames): + max_bins = 10 + row["binnedEntropy_X"] = f.binnedEntropy(x, max_bins) + if y is not None: + row["binnedEntropy_Y"] = f.binnedEntropy(y, max_bins) + row["binnedEntropy_Z"] = f.binnedEntropy(z, max_bins) + + autocorrelationsX = f.autocorrelations(x) + if y is not None: + autocorrelationsY = f.autocorrelations(y) + autocorrelationsZ = f.autocorrelations(z) + + if f.checkForFeature("numOfPeaksAutocorr", featureNames): + row["numOfPeaksAutocorr_X"] = f.calcPeakCount(autocorrelationsX)[0] + if y is not None: + row["numOfPeaksAutocorr_Y"] = f.calcPeakCount(autocorrelationsY)[0] + row["numOfPeaksAutocorr_Z"] = f.calcPeakCount(autocorrelationsZ)[0] + + if f.checkForFeature("numberOfZeroCrossingsAutocorr", featureNames): + row["numberOfZeroCrossingsAutocorr_X"] = f.numberOfZeroCrossings(autocorrelationsX) + if y is not None: + row["numberOfZeroCrossingsAutocorr_Y"] = f.numberOfZeroCrossings(autocorrelationsY) + row["numberOfZeroCrossingsAutocorr_Z"] = f.numberOfZeroCrossings(autocorrelationsZ) + + if f.checkForFeature("areaAutocorr", featureNames): + row["areaAutocorr_X"] = f.calcArea(autocorrelationsX) + if y is not None: + row["areaAutocorr_Y"] = f.calcArea(autocorrelationsY) + row["areaAutocorr_Z"] = f.calcArea(autocorrelationsZ) + + if f.checkForFeature("calcMeanCrossingRateAutocorr", featureNames): + row["calcMeanCrossingRateAutocorr_X"] = f.calcMeanCrossingRate(autocorrelationsX) + if y is not None: + row["calcMeanCrossingRateAutocorr_Y"] = f.calcMeanCrossingRate(autocorrelationsY) + row["calcMeanCrossingRateAutocorr_Z"] = f.calcMeanCrossingRate(autocorrelationsZ) + + if f.checkForFeature("countAboveMeanAutocorr", featureNames): + row["countAboveMeanAutocorr_X"] = f.countAboveMean(autocorrelationsX) + if y is not None: + row["countAboveMeanAutocorr_Y"] = f.countAboveMean(autocorrelationsY) + row["countAboveMeanAutocorr_Z"] = f.countAboveMean(autocorrelationsZ) + + GCsumPerX_gyro = f.calcSumPerComponent(x, time) + if y is not None: + GCsumPerY_gyro = f.calcSumPerComponent(y, time) + GCsumPerZ_gyro = f.calcSumPerComponent(z, time) + + if f.checkForFeature("sumPer", featureNames): + row["sumPer_X"] = GCsumPerX_gyro + if y is not None: + row["sumPer_Y"] = GCsumPerY_gyro + row["sumPer_Z"] = GCsumPerZ_gyro + + GCsumSquaredX = pow(GCsumPerX_gyro, 2) + if y is not None: + GCsumSquaredY = pow(GCsumPerY_gyro, 2) + GCsumSquaredZ = pow(GCsumPerZ_gyro, 2) + + if f.checkForFeature("sumSquared", featureNames): + row["sumSquared_X"] = GCsumSquaredX + if y is not None: + row["sumSquared_Y"] = GCsumSquaredY + row["sumSquared_Z"] = GCsumSquaredZ + + if y is not None: + if f.checkForFeature("squareSumOfComponent", featureNames): + row["squareSumOfComponent"] = pow((GCsumSquaredX + GCsumSquaredY + GCsumSquaredZ), 2) + + if f.checkForFeature("sumOfSquareComponents", featureNames): + row["sumOfSquareComponents"] = pow(GCsumSquaredX, 2) + pow(GCsumSquaredY, 2) + pow(GCsumSquaredZ, 2) + + return row + + +def calcAutocorrelations(signal, suffix): + """ Calculate autocorrelations of the given signal with lags 5, 10, 20, 30, 50, 75 and 100 + + :param signal: signal on which to calculate the autocorrelations + :param suffix: suffix of the feature name + :return: dict with calculated autocorrelations + """ + row = {} + for i in [5, 10, 20, 30, 50, 75, 100]: + row["autocorrelation_" + str(i) + suffix] = f.autocorrelation(signal, i) + return row + + +def calculateFeaturesGyro(gx, gy, gz, time, gxLow, gyLow, gzLow, featureNames=None): + """ + + :param gx2d: 2D array including the X axis of the gyroscope + :param gy2d: 2D array including the Y axis of the gyroscope + :param gz2d: 2D array including the Z axis of the gyroscope + :param gtime2d: 2D array of times, denoting when each measurement of the gyroscope occurred + :param gFs: sampling frequency of the gyroscope + :return: pandas dataframe including the calculated features + """ + if gy is not None: + magnitudesLow_gyro = f.magnitudeVector(gxLow, gyLow, gzLow) + else: + magnitudesLow_gyro = gxLow + + row = {} + + if f.checkForFeature("meanLow", featureNames): + row["mean_XLow"] = f.calcMean(gxLow) + if gy is not None: + row["mean_YLow"] = f.calcMean(gyLow) + row["mean_ZLow"] = f.calcMean(gzLow) + row["totalMeanLow"] = (row["mean_XLow"] + row["mean_YLow"] + row["mean_ZLow"]) * len(gx) / 3 + + if f.checkForFeature("areaLow", featureNames): + row["area_XLow"] = f.calcArea(gxLow) + if gy is not None: + row["area_YLow"] = f.calcArea(gyLow) + row["area_ZLow"] = f.calcArea(gzLow) + + if gy is not None: + if f.checkForFeature("totalAbsoluteAreaLow", featureNames): + row["totalAbsoluteAreaLow"] = f.calcTotalAbsoluteArea(f.calcAcAbsoluteArea(gxLow, gyLow, gzLow)) + + if f.checkForFeature("totalMagnitudeLow", featureNames): + row["totalMagnitudeLow"] = f.calcTotalMagnitude(gxLow, gyLow, gzLow) + + if f.checkForFeature("entropyLow", featureNames): + row["entropy_XLow"] = f.calcEntropy(gxLow) + if gy is not None: + row["entropy_YLow"] = f.calcEntropy(gyLow) + row["entropy_ZLow"] = f.calcEntropy(gzLow) + + if f.checkForFeature("skewnessLow", featureNames): + row["skewness_XLow"] = f.calcSkewness(gxLow) + if gy is not None: + row["skewness_YLow"] = f.calcSkewness(gyLow) + row["skewness_ZLow"] = f.calcSkewness(gzLow) + + if f.checkForFeature("kurtosisLow", featureNames): + row["kurtosis_XLow"] = f.calcKurtosis(gxLow) + if gy is not None: + row["kurtosis_YLow"] = f.calcKurtosis(gyLow) + row["kurtosis_ZLow"] = f.calcKurtosis(gzLow) + + gcQuartilesX = f.calcQuartiles(gxLow) + if gy is not None: + gcQuartilesY = f.calcQuartiles(gyLow) + gcQuartilesZ = f.calcQuartiles(gzLow) + + if f.checkForFeature("quartilesLow", featureNames): + row["quartiles_Q1_XLow"] = gcQuartilesX[0] + row["quartiles_Q2_XLow"] = gcQuartilesX[1] + row["quartiles_Q3_XLow"] = gcQuartilesX[2] + + if gy is not None: + row["quartiles_Q1_YLow"] = gcQuartilesY[0] + row["quartiles_Q2_YLow"] = gcQuartilesY[1] + row["quartiles_Q3_YLow"] = gcQuartilesY[2] + + row["quartiles_Q1_ZLow"] = gcQuartilesZ[0] + row["quartiles_Q2_ZLow"] = gcQuartilesZ[1] + row["quartiles_Q3_ZLow"] = gcQuartilesZ[2] + + if f.checkForFeature("interQuartileRangeLow", featureNames): + row["interQuartileRange_XLow"] = f.calcInterQuartileRange(gcQuartilesX) + if gy is not None: + row["interQuartileRange_YLow"] = f.calcInterQuartileRange(gcQuartilesY) + row["interQuartileRange_ZLow"] = f.calcInterQuartileRange(gcQuartilesZ) + + # Measures of motion variation + if f.checkForFeature("varianceLow", featureNames): + row["variance_XLow"] = f.calcVariance(gxLow) + if gy is not None: + row["variance_YLow"] = f.calcVariance(gyLow) + row["variance_ZLow"] = f.calcVariance(gzLow) + + if f.checkForFeature("coefficientOfVariationLow", featureNames): + row["coefficientOfVariation_XLow"] = f.calcCoefficientOfVariation(gxLow) + if gy is not None: + row["coefficientOfVariation_YLow"] = f.calcCoefficientOfVariation(gyLow) + row["coefficientOfVariation_ZLow"] = f.calcCoefficientOfVariation(gzLow) + + if f.checkForFeature("amplitudeLow", featureNames): + row["amplitude_XLow"] = f.calcAmplitude(gxLow) + if gy is not None: + row["amplitude_YLow"] = f.calcAmplitude(gyLow) + row["amplitude_ZLow"] = f.calcAmplitude(gzLow) + + if f.checkForFeature("totalEnergyLow", featureNames): + row["totalEnergy_XLow"] = f.calcTotalEnergy(gxLow) + if gy is not None: + row["totalEnergy_YLow"] = f.calcTotalEnergy(gyLow) + row["totalEnergy_ZLow"] = f.calcTotalEnergy(gzLow) + + if f.checkForFeature("dominantFrequencyEnergyLow", featureNames): + row["dominantFrequencyEnergy_XLow"] = f.calcDominantFrequencyEnergy(gxLow) + if gy is not None: + row["dominantFrequencyEnergy_YLow"] = f.calcDominantFrequencyEnergy(gyLow) + row["dominantFrequencyEnergy_ZLow"] = f.calcDominantFrequencyEnergy(gzLow) + + if f.checkForFeature("meanCrossingRateLow", featureNames): + row["meanCrossingRate_XLow"] = f.calcMeanCrossingRate(gxLow) + if gy is not None: + row["meanCrossingRate_YLow"] = f.calcMeanCrossingRate(gyLow) + row["meanCrossingRate_ZLow"] = f.calcMeanCrossingRate(gzLow) + + if gy is not None: + if f.checkForFeature("correlationLow", featureNames): + row["correlation_X_YLow"] = f.calcCorrelation(gxLow, gyLow) + row["correlation_X_ZLow"] = f.calcCorrelation(gxLow, gzLow) + row["correlation_Y_ZLow"] = f.calcCorrelation(gyLow, gzLow) + + gcQuartilesMagnitude = f.calcQuartiles(magnitudesLow_gyro) + + if f.checkForFeature("quartilesMagnitudeLow", featureNames): + row["quartilesMagnitudeLow_Q1"] = gcQuartilesMagnitude[0] + row["quartilesMagnitudeLow_Q2"] = gcQuartilesMagnitude[1] + row["quartilesMagnitudeLow_Q3"] = gcQuartilesMagnitude[2] + + if f.checkForFeature("interQuartileRangeMagnitudesLow", featureNames): + row["interQuartileRangeMagnitudesLow"] = f.calcInterQuartileRange(gcQuartilesMagnitude) + + if gy is not None: + if f.checkForFeature("areaUnderMagnitude", featureNames): + row["areaUnderMagnitude"] = f.calcAreaUnderAccelerationMagnitude(f.magnitudeVector(gx, gy, gz), time) + + if f.checkForFeature("peaksCountLow", featureNames): + peaksCount_gyro = f.calcPeaks(magnitudesLow_gyro) + row["peaksCountLow_Q1"] = peaksCount_gyro[0] + row["peaksCountLow_Q2"] = peaksCount_gyro[1] + row["peaksCountLow_Q3"] = peaksCount_gyro[0] + row["peaksCountLow_Q4"] = peaksCount_gyro[1] + + if f.checkForFeature("averageVectorLengthLow", featureNames): + row["averageVectorLengthLow"] = f.calcAverageVectorLength(magnitudesLow_gyro) + + if f.checkForFeature("averageVectorLengthPowerLow", featureNames): + row["averageVectorLengthPowerLow"] = f.calcAverageVectorLengthPower(magnitudesLow_gyro) + + return row diff --git a/calculatingfeatures/CalculatingFeatures/calculate_features.py b/calculatingfeatures/CalculatingFeatures/calculate_features.py new file mode 100644 index 00000000..975441aa --- /dev/null +++ b/calculatingfeatures/CalculatingFeatures/calculate_features.py @@ -0,0 +1,138 @@ +from tqdm import tqdm +from calculatingfeatures.CalculatingFeatures.helper_functions import * +import calculatingfeatures.CalculatingFeatures.gsr as gsr +import calculatingfeatures.CalculatingFeatures.hrv as hrv +from calculatingfeatures.CalculatingFeatures.calculate_acc_gyro_common_features import * + +ALPHA_LOW = 0.3 +ALPHA_BAND_LOW = 0.3 +ALPHA_BAND_HIGH = 0.6 + +DEFAULT_DELTA_TIME = 0.02 + +MIN_INPUT_ARRAY_WIDTH = 256 + + +def calculateFeatures(x2d, y2d=None, z2d=None, time2d=None, fs=None, prefix=None, featureNames=None): + """ Calculate features for the given data + Feature names are stored at the end of the "helper_functions.py" file in variables frequencyFeatureNames, genericFeatureNames, + accelerometerFeatureNames, gyroscopeFeatureNames, edaFeatureNames and bvpFeatureNames. For information about features + read the README.md file + + For calculation of features with 1D input shape (one axis), input only x2d parameter. Examples of inputs with 1D input shape + are BVP and EDA signals. + For calculation of features with 3D input shape (three axes), input also y2d and z2d parameter. Examples of inputs with 3D input + shape are gyroscope and accelerometer signals. + + Each individual input axis has to be in a 2D shape. This means that input signals have to be split into rows. + If BVP features are being calculated, width of window has to be at least 256. The conversion from 1D to 2D can be + made with "convertInputInto2d()" function, located in the helper_functions.py file. + + If sampling frequency (fs) is not given, it will be calculated from time2d parameter. + If array of times (time2d) is not given, it will be calculated from fs parameter. + If none of them is given, fs will be calculated as 1/DEFAULT_DELTA_TIME + + :param x2d: 2D array including the X axis + :param y2d: 2D array including the Y axis + :param z2d: 2D array including the Z axis + :param time2d: 2D array of times, denoting when each measurement occurred + :param fs: sampling frequency + :param prefix: prefix to append before each column name + :param featureNames: list of features to calculate. If it is None, all features will be calculated + :return: pandas DataFrame of the calculated features + """ + + if len(x2d[0]) < MIN_INPUT_ARRAY_WIDTH: + raise Exception("Input 2D array width has to be at least " + str(MIN_INPUT_ARRAY_WIDTH)) + + if type(x2d) is list: + x2d = np.asarray(x2d) + + if type(y2d) is list: + y2d = np.asarray(y2d) + + if type(z2d) is list: + z2d = np.asarray(z2d) + + if type(time2d) is list: + time2d = np.asarray(time2d) + + if (x2d is not None and y2d is not None and z2d is not None) or (x2d is not None and y2d is None and z2d is None): + if y2d is not None and not (x2d.shape == y2d.shape and y2d.shape == z2d.shape): + raise Exception("x2d, y2d, z2d shapes have to be the same!") + # Verify fs and time array + if time2d is not None and fs is not None and fs != 1 / (time2d[0, 1] - time2d[0, 0]): + raise Exception("sampling frequency of the given time2D matrix and fs do not match!") + if time2d is None: + deltaTime = 1 / fs if fs is not None else DEFAULT_DELTA_TIME + time2d = np.asarray(convertInputInto2d([i * deltaTime for i in range(x2d.size)], x2d.shape[1])) + if fs is None: + fs = 1 / (time2d[0][1] - time2d[0][0]) + + else: + raise Exception("Incorrect input! Either x2d, y2d and z2d are given, or only x2d is given!") + + fs = int(fs) + + if y2d is None: + y2d = z2d = [None] * len(x2d) + + df = pd.DataFrame() + for x, y, z, time in tqdm(zip(x2d, y2d, z2d, time2d), total=len(x2d)): + xBand = f.bandPassFilter(x, ALPHA_BAND_LOW, ALPHA_BAND_HIGH) + if y is not None: + yBand = f.bandPassFilter(y, ALPHA_BAND_LOW, ALPHA_BAND_HIGH) + zBand = f.bandPassFilter(z, ALPHA_BAND_LOW, ALPHA_BAND_HIGH) + + xLow = f.lowPassFilter(x, ALPHA_LOW) + if y is not None: + yLow = f.lowPassFilter(y, ALPHA_LOW) + zLow = f.lowPassFilter(z, ALPHA_LOW) + + row = {} + + if y is not None: + row.update(calculateFeaturesAcc(x, y, z, time, xBand, yBand, zBand, xLow, yLow, zLow, featureNames)) + else: + row.update(calculateFeaturesAcc(x, y, z, time, xBand, None, None, xLow, None, None, featureNames)) + + if y is not None: + row.update(calculateFeaturesGyro(x, y, z, time, xLow, yLow, zLow, featureNames)) + else: + row.update(calculateFeaturesGyro(x, y, z, time, xLow, None, None, featureNames)) + + row.update(calcCommonFeatures(x, y, z, time, featureNames)) + + # Add frequency features + row.update({str(key) + "_X": val for key, val in f.computeFreqFeatures(x, featureNames, fs).items()}) + if y is not None: + row.update({str(key) + "_Y": val for key, val in f.computeFreqFeatures(y, featureNames, fs).items()}) + row.update({str(key) + "_Z": val for key, val in f.computeFreqFeatures(z, featureNames, fs).items()}) + + # EDA features + row.update({str(key) + "_X": val for key, val in + gsr.extractGsrFeatures(x, sampleRate=fs, featureNames=featureNames).items()}) + if y is not None: + row.update({str(key) + "_Y": val for key, val in + gsr.extractGsrFeatures(y, sampleRate=fs, featureNames=featureNames).items()}) + row.update({str(key) + "_Z": val for key, val in + gsr.extractGsrFeatures(z, sampleRate=fs, featureNames=featureNames).items()}) + + # BVP features + row.update({str(key) + "_X": val for key, val in + hrv.extractHrvFeatures(x, sampling=fs, featureNames=featureNames).items()}) + if y is not None: + row.update({str(key) + "_Y": val for key, val in + hrv.extractHrvFeatures(y, sampling=fs, featureNames=featureNames).items()}) + row.update({str(key) + "_Z": val for key, val in + hrv.extractHrvFeatures(z, sampling=fs, featureNames=featureNames).items()}) + + df = df.append(row, ignore_index=True) + + if prefix is not None: + dfNewCols = [] + for col in df.columns: + dfNewCols.append(prefix + "_" + col) + df.columns = dfNewCols + + return df diff --git a/calculatingfeatures/CalculatingFeatures/calculate_hrv_peaks.py b/calculatingfeatures/CalculatingFeatures/calculate_hrv_peaks.py new file mode 100644 index 00000000..9be80d8b --- /dev/null +++ b/calculatingfeatures/CalculatingFeatures/calculate_hrv_peaks.py @@ -0,0 +1,90 @@ +import numpy as np +import math + + +def peak_detector(ppg, Fs): + """ + Peak detector written by Gasper Slapnicar. Optimized for detecting peaks in PPG signal. + :param ppg: Signal where peaks are to be detected. 1-D array like + :param Fs: Sampling frequency + :return: Peaks and valleys in PPG. Two arrays of indices in ppg input + """ + peak_duration = int(math.floor((1.0 / 9) * Fs)) + aver_pulse_rate = int(math.floor((2.0 / 3) * Fs)) + aver_level_window = int(math.floor(2 * Fs)) + + mean_coef_for_threshold = 0.02 + signal_length = len(ppg) + + ppg_squared = np.square(ppg) + ppg_squared[ppg < 0] = 0 + + mean_peak_level = np.convolve(ppg_squared, np.ones((peak_duration,)) / peak_duration, mode='same') + mean_beat_level = np.convolve(ppg_squared, np.ones((aver_pulse_rate,)) / aver_pulse_rate, mode='same') + + thresh1 = np.add(mean_beat_level, + mean_coef_for_threshold * np.convolve(ppg_squared, + np.ones((aver_level_window,)) / aver_level_window, + mode='same')) + block_of_interest = np.zeros(signal_length) + block_of_interest[mean_peak_level > thresh1] = 1 + + block_edges = np.diff(block_of_interest) + block_start = np.add(np.where(block_edges == 1), 1)[0] + if block_start.size == 0: + return np.array([]), np.array([]) + else: + block_end = np.where(block_edges == -1)[0] + + if block_end.size == 0: + return np.array([]), np.array([]) + + if block_start[0] > block_end[0]: + block_start = np.insert(block_start, 0, 1, axis=0) + + if block_start[-1] > block_end[-1]: + block_end = np.append(block_end, signal_length) + + if len(block_start) != len(block_end): + return np.array([]), np.array([]) + + length_block = np.subtract(block_end, block_start) + correct_blocks = np.where(length_block > peak_duration) + + peak_pos = np.zeros(len(correct_blocks[0])) + i_peak = 0 + for iBlock in correct_blocks[0]: + block_of_interest = ppg_squared[block_start[iBlock]:block_end[iBlock]] + peak_pos[i_peak] = max(range(len(block_of_interest)), key=block_of_interest.__getitem__) + peak_pos[i_peak] = peak_pos[i_peak] + (block_start[iBlock] - 1) + i_peak += 1 + + interpeak_threshold_coeff = 0.65 + max_over_average = 1.15 + need_check = True + while need_check: + interpeak = np.diff(peak_pos) + + if interpeak.size == 0: + return np.array([]), np.array([]) + + mean_interpeak = np.mean(interpeak) + interpeak_double = np.insert(np.add(interpeak[0:-1], interpeak[1:]), 0, 2 * interpeak[0], axis=0) + interpeak_thresh = np.insert(interpeak_double[0:-2], 0, [2 * mean_interpeak, 2 * mean_interpeak], axis=0) + interpeak_thresh[interpeak_thresh > 2 * max_over_average * mean_interpeak] = 2 * max_over_average \ + * mean_interpeak + interpeak_thresh = interpeak_thresh * interpeak_threshold_coeff + index_short_interval = np.where(interpeak_double < interpeak_thresh)[0] + + if index_short_interval.size != 0: + peak_pos = np.delete(peak_pos, index_short_interval.tolist()) + else: + need_check = False + + # Add valley (simple detection) + valley_pos = [] + for start, end in zip(peak_pos[0:-1].astype('int'), peak_pos[1:].astype('int')): + valley_pos.append(min(range(len(ppg[start:end])), key=ppg[start:end].__getitem__) + start) + + # Addition because I missed it somewhere in the code...but i cant find where + return np.add(peak_pos, 1).astype('int'), np.array(valley_pos) diff --git a/calculatingfeatures/CalculatingFeatures/feature_functions.py b/calculatingfeatures/CalculatingFeatures/feature_functions.py new file mode 100644 index 00000000..e336703d --- /dev/null +++ b/calculatingfeatures/CalculatingFeatures/feature_functions.py @@ -0,0 +1,986 @@ +import numpy as np +from scipy.signal import welch +from scipy.stats import entropy, skew, kurtosis, iqr, linregress +import pandas as pd +import itertools +from calculatingfeatures.CalculatingFeatures.helper_functions import checkForFeature + + +def computeFreqFeatures(data, featureNames=None, Fs=50): + """ Computes frequency features of the given array. The signal is converted to power spectral density signal and + features are calculated on that signal + + :param data: data on which to calculate the features + :param featureNames: names of features to calculate + :param Fs: sampling rate of the given data + :return: the calculated features (names of the features are given in array "freqFeatureNames") + """ + features = {} + + ## 3 highest peaks and corresponding freqs + f, Pxx_den = welch(data, Fs) # f: array of sample frequencies, Pxx_den: power spectrum of data + + # arr.argsort() returns an array of indices of the same shape as arr that that would sort the array + # arr[arr.argsort()] returns the sorted array arr (if one-dimensional) + indices_of_max = Pxx_den.argsort()[-3:][::-1] # last three values (=highest peaks), reversed (?) + + if checkForFeature("fqHighestPeakFreqs", featureNames): + highestPeakFreqs = f[indices_of_max] # three frequencies corresponding to the largest peaks added to features + features["fqHighestPeakFreq1"] = highestPeakFreqs[0] + features["fqHighestPeakFreq2"] = highestPeakFreqs[1] + features["fqHighestPeakFreq3"] = highestPeakFreqs[2] + + if checkForFeature("fqHighestPeaks", featureNames): + highestPeaks = Pxx_den[indices_of_max] # three largest peaks added to features + features["fqHighestPeak1"] = highestPeaks[0] + features["fqHighestPeak2"] = highestPeaks[1] + features["fqHighestPeak3"] = highestPeaks[2] + + ## Energy and Entropy + # np.fft.fft() computes the one-dimensional n-point discrete Fourier Transform (DFT) with the efficient FFT algorithm + Y = np.fft.fft(data) + # energy calculated as the sum of the squared FFT component magnitudes, and normalized + Y_abs = np.abs(Y) + energy_feat = np.sum(np.square(Y_abs)) / len(data) # np.abs = absolute value + + entropy_feat = entropy(np.abs(Y)) if Y_abs.any() else np.NaN + + if checkForFeature("fqEnergyFeat", featureNames): + features["fqEnergyFeat"] = energy_feat + + if checkForFeature("fqEntropyFeat", featureNames): + features["fqEntropyFeat"] = entropy_feat + + # Binned distribution (histogram) + # First, the PSD is split into 10 equal-sized bins ranging from 0 Hz to 25 Hz. + # Then, the fraction of magnitudes falling into each bin is calculated. + if checkForFeature("fqHistogramBins", featureNames): + total_fft_sum = np.sum(np.square(Pxx_den)) + + def getBin(start, end): + return np.nan if total_fft_sum == 0 else np.sum(np.square(Pxx_den[start:end])) / total_fft_sum + + features["fqHistogramBin1"] = getBin(0, 5) + features["fqHistogramBin2"] = getBin(5, 10) + features["fqHistogramBin3"] = getBin(10, 15) + features["fqHistogramBin4"] = getBin(15, 20) + features["fqHistogramBin5"] = getBin(20, 25) + features["fqHistogramBin6"] = getBin(25, 30) + features["fqHistogramBin7"] = getBin(30, 35) + features["fqHistogramBin8"] = getBin(35, 40) + features["fqHistogramBin9"] = getBin(40, 45) + features["fqHistogramBin10"] = getBin(45, len(Pxx_den)) + + # Statistical features + if checkForFeature("fqAbsMean", featureNames): + features["fqAbsMean"] = np.mean(np.abs(data)) # this on raw signal + if checkForFeature("fqAbsMean", featureNames): + features["fqSkewness"] = skew(Pxx_den) # this on "distribution-like" periodogram + if checkForFeature("fqKurtosis", featureNames): + features["fqKurtosis"] = kurtosis(Pxx_den) # this on "distribution-like" periodogram + if checkForFeature("fqInterquart", featureNames): + features["fqInterquart"] = iqr(data) # this on raw signal + + return features + + +def calcAreaUnderAccelerationMagnitude(magnitudes, time): + """ + Calculates AreaUnderAccelerationMagnitude feature + :param magnitudes: vector of magnitudes + :param time: array of timestamps + :return: AreaUnderAccelerationMagnitude for the selected axis + """ + + eeArea = 0.0 + dT = 35 + + for i in range(len(magnitudes)): + eeArea += magnitudes[i] * dT # - gravity + if i > 0: + dT = time[i] - time[i - 1] + else: + dT = 35 + return eeArea + + +def calcAverageVectorLength(magnitudes): + """ + Calculates mean of magnitude vector + :param magnitudes: vector of magnitudes + :return: mean of magnitude vector + """ + return np.sum(magnitudes) / (len(magnitudes)) + + +def calcAverageVectorLengthPower(magnitudes): + """ + Calculates square mean of magnitude vector + :param magnitudes: vector of magnitudes + :return: mean of magnitude vector squared + """ + return np.square(calcAverageVectorLength(magnitudes)) + + +def calcMeanKineticEnergy(data, time): + """ + Calculates mean kinetic energy for the selected axis + :param data: data from accelerometer for selected axis (band-pass filtered) + :param time: array of timestamps + :return: mean kinetic energy 1/2*mV^2 + """ + weight = 60.0 + dT = 1.0 + velocity = 0.0 + kinetic = 0.0 + + for i in range(len(data)): + velocity += data[i] * dT + kinetic += 0.5 * weight * velocity * velocity * dT + if i < len(time) - 1: + dT = (time[i + 1] - time[i]) / 1000.0 + + return kinetic + + +def calcPeaks(magnitudes): + """ + Calculates number of peaks and sum of values + :param magnitudes: vector of magnitudes + :return: array of double - [0] number of peaks, [1] sum of peak values + """ + maxValue = 500.0 + previous = -200.0 + threshold = 3 + peaks = np.empty(0) + sumOfPeakValues = 0.0 + peak = False + + for curMagnitude in magnitudes: + if curMagnitude > threshold: + if curMagnitude >= maxValue: + maxValue = curMagnitude + peak = True + elif curMagnitude < maxValue: + if peak and previous > curMagnitude: + peaks = np.append(peaks, maxValue) + peak = False + sumOfPeakValues += maxValue + + if curMagnitude > previous and not peak: + peak = True + maxValue = -200.0 + previous = curMagnitude + + return np.array([float(len(peaks)), sumOfPeakValues]) + + +def calcTotalKineticEnergy(x, y, z, t): + """ + Calculates total kinetic energy for all axes + :param x: data from accelerometer for X axis (band-pass filtered) + :param y: data from accelerometer for Y axis (band-pass filtered) + :param z: data from accelerometer for Z axis (band-pass filtered) + :param t: array of timestamps + :return: total kinetic energy 1/2*mV^2 + """ + weight = 60.0 + totaltime = (t[-1] - t[0]) / 1000.0 + + dT = 1.0 + velocityX = 0.0 + velocityY = 0.0 + velocityZ = 0.0 + kineticX = 0.0 + kineticY = 0.0 + kineticZ = 0.0 + totalEnergy = 0.0 + + for i in range(len(x)): + velocityX += x[i] * dT + velocityY += y[i] * dT + velocityZ += z[i] * dT + + kineticX += 0.5 * weight * velocityX * velocityX * dT + kineticY += 0.5 * weight * velocityY * velocityY * dT + kineticZ += 0.5 * weight * velocityZ * velocityZ * dT + + totalEnergy += kineticX + kineticY + kineticZ + if i < t.size - 1: + dT = (t[i + 1] - t[i]) / 1000.0 + + return totalEnergy / totaltime + + +def calcAcAbsoluteArea(x, y, z): + """ + Calculates a vector with sums of absolute values for the given sensor + :param x: x component (band-pass filtered) + :param y: y component (band-pass filtered) + :param z: z component (band-pass filtered) + :return: [sumX, sumY, sumZ] + """ + return np.array([np.sum(np.absolute(x)), np.sum(np.absolute(y)), np.sum(np.absolute(z))]) + + +def calcAbsoluteMean(data): + """ + + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: mean (sum)/N + """ + return np.sum(np.absolute(data)) / len(data) + + +def calcAmplitude(data): + """ + Calculates dispersion for a given vector component + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: dispersion + """ + return np.max(data) - np.min(data) + + +def calcCoefficientOfVariation(data): + """ + Calculates dispersion for a given vector component + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: dispersion + """ + s = np.sum(np.absolute(data)) + if s == 0: + return np.NaN + return np.sqrt(calcVariance(data)) / s * 100 + + +# +def calcCorrelation(a, b): + """ + Calculates Pearson's correlation between sensor axis + :param a: first component (band-pass filtered) + :param b: second component (band-pass filtered) + :return: correlation between a and b + """ + selfCovarianceA = covariance(a, a) + selfCovarianceB = covariance(b, b) + s = np.sqrt(selfCovarianceA * selfCovarianceB) + if s == 0: + return np.NaN + return covariance(a, b) / s + + +def calcEntropy(data): + """ + Calculates the degree of disorder + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: entropy for the selected axis + """ + acc = 0 + for d in normalize(fftMagnitude(fft(data))): + if d == 0: + return np.NaN + acc += d * np.log(d) / np.log(2.0) + + return -acc + + +# +def calcInterQuartileRange(qData): + """ + Calculates interquartile range + :param qData: quartiles vector + :return: range for the selected axis + """ + return qData[2] - qData[0] + + +def calcKurtosis(data): + """ + Calculates Kurtosis for given vector + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: kurtosis for selected vector + """ + mean = calcAbsoluteMean(data) + + acc = 0 + for d in data: + acc += np.power(d - mean, 4.0) + pow4 = acc + + acc = 0 + for d in data: + acc += np.power(d - mean, 2.0) + pow2 = acc + if pow2 == 0: + return np.NaN + return len(data) * pow4 / np.square(pow2) - 3 + + +def calcMeanCrossingRate(data): + """ + Calculates the number of signal crossings with mean + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: number of mean crossings + """ + mean = np.sum(np.abs(data)) / len(data) + + crossings = 0 + last = data[0] - mean + + for i in range(len(data)): + current = data[i] - mean + if last * current < 0: + crossings += 1 + last = current + + return crossings + + +def calcQuartiles(data): + """ + Quartiles at 25%, 50% and 75% per signal + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: [accQ25, accQ50, accQ75] + """ + sorted1 = sorted(data) + size = len(data) + return np.array([sorted1[int(size / 4)], sorted1[int(size / 2)], sorted1[int(size * 3 / 4)]]) + + +def calcSkewness(data): + """ + Calculates skewness for given vector + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: skewness for selected vector + """ + mean = calcAbsoluteMean(data) + + acc = 0 + for d in data: + acc += np.power(d - mean, 3.0) + pow3 = acc + + acc = 0 + for d in data: + acc += np.power(d - mean, 2.0) + pow2 = acc + if pow2 == 0: + return np.NaN + return np.sqrt(float(len(data))) * pow3 / np.power(pow2, 1.5) + + +# +def calcTotalAbsoluteArea(area): + """ + Calculates sum of component areas for selected sensor (@see calcAcAbsoluteArea) + :param area: [sumX, sumY, sumZ] for given device (band-pass filtered) (@see calcAcAbsoluteArea) + :return: sum of component sums + """ + return np.sum(area) + + +def calcTotalEnergy(data): + """ + Calculates total magnitude of AC signal for the given sensor + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: total energy for selected axis + """ + fftMagnitudeTmp = fftMagnitude(fft(data)) + + return np.sum(np.square(fftMagnitudeTmp[1:])) / len(fftMagnitudeTmp) + + +def calcDominantFrequencyEnergy(data): + """ + Calculates ratio of energy in dominant frequency + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: energy ratio for selected axis + """ + fftMagnitudeTmp = fftMagnitude(fft(data)) + sortedTmp = sorted(fftMagnitudeTmp) + s = np.sum(sortedTmp) + if s == 0: + return np.NaN + return sortedTmp[-1] / s + + +def calcTotalMagnitude(x, y, z): + """ + Calculates total magnitude of AC signal for the given sensor + :param x: x component (band-pass filtered) + :param y: y component (band-pass filtered) + :param z: z component (band-pass filtered) + :return: sqrt(sum(x^2+y^2+z^2)) + """ + return np.sqrt(magnitudes(x) + magnitudes(y) + magnitudes(z)) + + +def calcVariance(data): + """ + Calculates variance for given vector + :param data: data from accelerometer for selected axis (band-pass filtered) + :return: variance + """ + acc = 0 + for d in data: + acc = acc + np.square(d - calcAbsoluteMean(data)) / len(data) + return acc + + +def calcArea(data): + """ + Calculates sum of component + :param data: data from accelerometer for selected axis (low-pass filtered) + :return: area + """ + return np.sum(data) + + +def calcMean(data): + """ + Calculates mean value for a given vector + :param data: data from accelerometer for selected axis (low-pass filtered) + :return: mean (sum)/N + """ + return np.sum(data) / len(data) + + +def calcPostureDistance(meanX, meanY, meanZ): + """ + Calculates difference between mean values for a given sensor (low-pass filtered) + :param meanX: mean for X components + :param meanY: mean for Y components + :param meanZ: mean for Z components + :return: [X-Y, X-Z, Y-Z] + """ + return np.array([meanX - meanY, meanX - meanZ, meanY - meanZ]) + + +def calcTotalMean(sumPhone, sumBand): + """ + Calculates mean of all sensors (low-pass filtered) + :param sumPhone: meanX + meanY + meanZ + :param sumBand: meanX + meanY + meanZ + :return: mean of all means + """ + return (sumPhone.sum() + sumBand.sum()) / 6 + + +def calcPeakCount(magnitudes): + """ + Calculates PeakCount feature + :param magnitudes: vector of magnitudes + :return: [numberOfPeaks, sumOfPeakValues, peakAvg, amplitudeAvg] + """ + previous = 0.0 + eExpenditurePeaks = 0.0 + eExpenditureAmplitude = 0.0 + + state = np.zeros(0) + peaks = np.zeros(0) + low = -1.0 + + for currentmagnitude in magnitudes: + if currentmagnitude > previous: + state = np.append(state, True) + else: + state = np.append(state, False) + + if len(state) > 2: + state = np.delete(state, 0) + if state[0] and not state[1]: + if low != -1.0: + eExpenditureAmplitude = previous - low + else: + low = previous + if previous - low > 1.0: + peaks = np.append(peaks, currentmagnitude) + eExpenditurePeaks += previous + + if not state[0] and state[1]: + low = previous + + previous = currentmagnitude + + peaksReturn0 = len(peaks) + peaksReturn1 = eExpenditurePeaks + peaksReturn2 = 0.0 + peaksReturn3 = 0.0 + if len(peaks) > 0: + peaksReturn2 = eExpenditurePeaks / len(peaks) + peaksReturn3 = eExpenditureAmplitude / len(peaks) + + return np.array([float(peaksReturn0), peaksReturn1, peaksReturn2, peaksReturn3]) + + +def calcSumPerComponent(data, time): + """ + Calculates calcSumPerComponent feature + :param data: data from accelerometer for selected axis + :param time: array of timestamps + :return: sum of Xi*dT + """ + calc = 0.0 + dT = 1.0 + + for i in range(len(data)): + calc += np.abs(data[i]) * dT + if i < len(data) - 1: + dT = (time[i + 1] - time[i]) / 1000.0 + + return calc + + +def computeACVelocity(data, time): + """ + Calculates velocity feature + :param data: data from accelerometer for selected axis (band-pass filtered) + :param time: array of timestamps + :return: velocity for selected axis + """ + calc = 0.0 + dT = 1.0 + + for i in range(len(data)): + calc += data[i] * dT + if i < data.size - 1: + dT = (time[i + 1] - time[i]) / 1000.0 + + return calc + + +def lowPassFilter(input, alpha=0.2): + """ Low-pass filter implemented in discrete time + + :param input: input signal to be filtered + :param alpha: smoothing factor + :return: filtered signal + """ + output = np.zeros(input.size) + output[0] = input[0] + for i in range(1, output.size): + output[i] = output[i - 1] + alpha * (input[i] - output[i - 1]) + return output + + +def bandPassFilter(input, alphaLpf=0.2, alphaHpf=0.6): + """ Band-pass filer implemented in discrete time + + :param input: input signal to be filtered + :param alphaLpf: smoothing factor for LPF + :param alphaHpf: smoothing factor for HPF + :return: filtered signal + """ + output = lowPassFilter(input, alphaLpf) + output = highPassFilter(output, alphaHpf) + return output + + +def highPassFilter(input, alpha=0.6): + """ High-pass filter implemented in discrete time + + :param input: input signal to be filtered + :param alpha: smoothing factor + :return: filtered signal + """ + output = np.zeros(input.size) + output[0] = input[0] + for i in range(1, output.size): + output[i] = alpha * (output[i - 1] + input[i] - input[i - 1]) + return output + + +def magnitudeVector(x, y, z): + """ Calculates magnitudes of vectors x, y and z + + :param x: x axis + :param y: y axis + :param z: z axis + :return: numpy array of magnitudes + """ + acc = np.zeros(x.size) + for i in range(acc.size): + acc[i] = np.sqrt(np.square(x[i]) + np.square(y[i]) + np.square(z[i])) + return acc + + +def sum(data): + """ Sums up the given array + + :param data: array to sum up + :return: summed value + """ + return np.sum(data) + + +def absoluteSum(data): + """ Sums up absolute values of the given array + + :param data: array to sum up + :return: summed value + """ + return np.sum(np.abs(data)) + + +def fft(data): + """ Performs fast fourier transform on the given array + + :param data: the array on which fft should be performed + :return: processed array + """ + tmpArray = [None] * (len(data) * 2) + for i in range(len(data)): + tmpArray[i] = data[i] + tmpArray = np.fft.fft(tmpArray, len(data)) + + tmpArray2 = [] + for t in tmpArray: + tmpArray2.append(t.real) + tmpArray2.append(t.imag) + + if len(data) % 2 == 0: + ret = np.zeros(len(data)) + else: + ret = np.zeros(len(data) + 1) + + for i, _ in enumerate(ret): + ret[i] = tmpArray2[i] + return ret + + +def fftMagnitude(data): + """ + + :param data: + :return: + """ + ret = np.zeros(int(len(data) / 2)) + for i, _ in enumerate(ret): + ret[i] = np.sqrt(np.square(data[2 * i]) + np.square(data[2 * i + 1])) + return ret + + +def normalize(data): + """ Normalize the given array + + :param data: the array to normalize + :return: normalized array + """ + ret = np.zeros(len(data)) + sum = np.sum(data) + for i, _ in enumerate(data): + if sum == 0: + ret[i] = np.NaN + else: + ret[i] = data[i] / sum + return ret + + +def covariance(array1, array2): + """ + Covariance between two arrays + :param array1: first array of values + :param array2: second array of values + :return: covariance(array1, array2) + """ + cov = 0.0 + + m1 = np.sum(np.abs(array1)) + m2 = np.sum(np.abs(array2)) + + for i, _ in enumerate(array1): + cov += (array1[i] - m1) * (array2[i] - m2) + + return cov + + +def magnitudes(data): + """ Calculates the sum of squares of given array + + :param data: given array + :return: sum of squares + """ + acc = 0 + for d in data: + acc += np.square(d) + return acc + + +def calcRoll(arrayAy, arrayAz): + """ Calculate the roll value of y and z axis + + :param arrayAy: array of y values + :param arrayAz: array of z values + :return: array of calculated roll values + """ + roll = np.zeros(arrayAy.size) + + for i, _ in enumerate(arrayAy): + roll[i] = np.arctan2(arrayAz[i], arrayAy[i]) + + return roll + + +def calcPitch(arrayAx, arrayAy, arrayAz): + """Calculate the pitch + + :param arrayAx: array of x values + :param arrayAy: array of y values + :param arrayAz: array of z values + :return: array of calculated pitch values + """ + pitch = np.zeros(arrayAx.size) + + for i, _ in enumerate(arrayAy): + pitch[i] = np.arctan2(-arrayAx[i], (arrayAy[i] * arrayAy[i] + arrayAz[i] * arrayAz[i])) + + return pitch + + +def stdDev(arrayX): + """ Calculates the standard deviation of the given array + + :param arrayX: the array on which to calculate standard deviation + :return: standard deviation of the given array + """ + std = 0.0 + + mean = calcMean(arrayX) + for cnt, _ in enumerate(arrayX): + std += (arrayX[cnt] - mean) * (arrayX[cnt] - mean); + + return np.sqrt(std / arrayX.size) + + +def rollMotionAmount(roll): + """ + amount of wrist roll motion + Improving the Recognition of Eating Gestures Using Intergesture Sequential Dependencies [R.I. Ramos-Garcia] + """ + meanRoll = calcMean(roll) + rollMot_mean = np.zeros(roll.size) + + for i in range(roll.size): + rollMot_mean[i] = np.abs(roll[i] - meanRoll) # - gravity; + + return calcMean(rollMot_mean) + + +def rollMotionRegularity(roll): + """ + regularity of wrist roll motion + represents the percentage of time that the wrist is in roll motion + Improving the Recognition of Eating Gestures Using Intergesture Sequential Dependencies [R.I. Ramos-Garcia] + """ + rollBoundary = 10 * (np.pi / 180) + instance_number = 0.0 + + for i in range(roll.size): + if np.abs(roll[i]) > rollBoundary: + instance_number += 1.0 + + return instance_number / roll.size + + +def manipulation(axLow, ayLow, azLow, roll, pitch): + """ + + :param axLow: low-pass filtered accelerometer's x axis + :param ayLow: low-pass filtered accelerometer's y axis + :param azLow: low-pass filtered accelerometer's z axis + :param roll: roll value + :param pitch: pitch value + :return: manipulation array + """ + man_velocity = np.zeros(roll.size) + + for i in range(roll.size): + man_velocity[i] = \ + (np.abs(roll[i]) + np.abs(pitch[i])) / (np.abs(axLow[i]) + np.abs(ayLow[i]) + np.abs(azLow[i])) + return calcMean(man_velocity) + + +def min(list): + """ Return the minimum value of the given list. + + :param list: the given list + :return: the minimum value of the list + """ + return np.min(list) + + +def max(list): + """ Return the maximum value of the given list. + + :param list: the given list + :return: the maximum value of the list + """ + return np.max(list) + + +def avg(list): + """ Return the average value of the given list. + + :param list: the given list + :return: the average value of the list + """ + return np.average(list) + + +def countAboveMean(signal): + """ Returns the number of values in x that are higher than the mean of x + + :param signal: the time series to calculate the feature of + :return: the value of this feature + """ + mean = np.mean(signal) + return np.where(signal > mean)[0].size + + +def countBelowMean(signal): + """ Returns the number of values in x that are lower than the mean of x + + :param signal: the time series to calculate the feature of + :return: the value of this feature + """ + mean = np.mean(signal) + return np.where(signal < mean)[0].size + + +def meanAbsChange(signal): + """ Returns the mean of absolute differences between subsequent time series values + + :param signal: input signal + :return: mean of absolute differences + """ + return np.mean(np.abs(np.diff(signal))) + + +def autocorrelation(signal, lag): + """Compute the lag-N autocorrelation. + + This method computes the Pearson correlation between + the Series and its shifted self. + :param signal: the signal to preform the autocorrelation on + :param lag: Number of lags to apply before performing autocorrelation. + :return: + """ + signal = pd.Series(signal) + return signal.autocorr(lag) + + +def autocorrelations(signal): + """ This method computes autocorrelations for each lag from 0 to len(signal2D[0]) * 0.7 + + :param signal: input signal on which to calculate autocorrelations + :return: array of autocorrelations + """ + nlags = int(len(signal) * 0.7) + autocorrs = np.empty(nlags) + for lag in range(nlags): + autocorrs[lag] = autocorrelation(signal, lag) + return autocorrs + + +def _calcMaxLengthOfSequenceTrueOrOne(signal): + if len(signal) == 0: + return 0 + else: + res = [len(list(group)) for value, group in itertools.groupby(signal) if value == 1] + return max(res) if len(res) > 0 else 0 + + +def meanChange(x): + """ Returns the mean over the differences between subsequent time series values + + :param x: the time series to calculate the feature of + :return: the value of this feature + """ + x = np.asarray(x) + return (x[-1] - x[0]) / (len(x) - 1) if len(x) > 1 else np.NaN + + +def numberOfZeroCrossings(x): + """ Calculates the number of crossings of x on 0. A crossing is defined as two sequential values where the first + value is lower than 0 and the next is greater, or vice-versa. + + :param x: the time series to calculate the feature of + :return: the value of this feature + """ + x = np.asarray(x) + positive = x > 0 + return np.where(np.diff(positive))[0].size + + +def ratioBeyondRSigma(x, r): + """ Ratio of values that are more than r*std(x) (so r sigma) away from the mean of x. + + :param x: the time series to calculate the feature of + :param r: the ratio to compare with + :return: the value of this feature + """ + return np.sum(np.abs(x - np.mean(x)) > r * np.std(x)) / x.size + + +def binnedEntropy(x, max_bins): + """ + First bins the values of x into max_bins equidistant bins. + Then calculates the value of + + .. math:: + + - \\sum_{k=0}^{min(max\\_bins, len(x))} p_k log(p_k) \\cdot \\mathbf{1}_{(p_k > 0)} + + where :math:`p_k` is the percentage of samples in bin :math:`k`. + + :param x: the time series to calculate the feature of + :type x: numpy.ndarray + :param max_bins: the maximal number of bins + :type max_bins: int + :return: the value of this feature + :return type: float + """ + if not isinstance(x, (np.ndarray, pd.Series)): + x = np.asarray(x) + + # nan makes no sense here + if np.isnan(x).any(): + return np.nan + + hist, bin_edges = np.histogram(x, bins=max_bins) + probs = hist / x.size + probs[probs == 0] = 1.0 + return - np.sum(probs * np.log(probs)) + + +def absEnergy(x): + """ + Returns the absolute energy of the time series which is the sum over the squared values + + .. math:: + + E = \\sum_{i=1,\\ldots, n} x_i^2 + + :param x: the time series to calculate the feature of + :type x: numpy.ndarray + :return: the value of this feature + :return type: float + """ + if not isinstance(x, (np.ndarray, pd.Series)): + x = np.asarray(x) + return np.dot(x, x) + + +def linearTrendSlope(x): + """ + Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to + length of the time series minus one. + This feature assumes the signal to be uniformly sampled. It will not use the time stamps to fit the model. + The parameters control which of the characteristics are returned. + + :param x: the time series to calculate the feature of + :return: slope of the model + """ + slope, intercept, r_value, p_value, std_err = linregress(range(len(x)), x) + + return slope diff --git a/calculatingfeatures/CalculatingFeatures/gsr.py b/calculatingfeatures/CalculatingFeatures/gsr.py new file mode 100644 index 00000000..54b87c37 --- /dev/null +++ b/calculatingfeatures/CalculatingFeatures/gsr.py @@ -0,0 +1,669 @@ +from calculatingfeatures.eda_explorer.load_files import butter_lowpass_filter +from calculatingfeatures.eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures +import numpy as np +import peakutils +import matplotlib.pyplot as plt +import scipy.signal as signal +import biosppy.signals.tools as st +from calculatingfeatures.CalculatingFeatures.helper_functions import checkForFeature + + +def extractGsrFeatures(signal, startTimestampSeconds=0, sampleRate=4, threshold=.02, offset=1, riseTime=4, decayTime=4, + featureNames=None): + """ Extract Martin's GSR features with eda-explorer peak detection + + :param signal: numpy array containing the signal + :param startTimestampSeconds: seconds from epoch when the signal stared + :param sampleRate: sampling rate of the input signal + :param threshold: threshold for detected peaks + :param offset: + :param riseTime: rise time of detected peaks + :param decayTime: decay time of detected peaks + :return: calculated GSR features + """ + filteredSignal = butter_lowpass_filter(signal, 1.0, sampleRate, 6) + + gsr_data = pd.DataFrame(signal, columns=["EDA"]) + + startTime = pd.to_datetime(startTimestampSeconds, unit="s") + gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000 / sampleRate) + 'L') + + # Filter the signal + gsr_data['filtered_eda'] = filteredSignal + # Calculate peak data with eda-explorer + peakData = calcPeakFeatures(gsr_data, offset, threshold, + riseTime, decayTime, sampleRate) + + peaks = np.where(peakData.peaks == 1.0)[0] + + if np.any(signal): + tonic = peakutils.baseline(signal, 10) + else: + tonic = signal + + # Calculate features with Martin's library + feats = calculate_GSR_features(signal, peaks, tonic, sampleRate, featureNames=featureNames) + freq_feats = GSR_freq(signal, sampleRate, False, print_flag=False, featureNames=featureNames) + peaks, ends, starts = get_peak_intervals(signal, peaks, sampleRate, False) + peak_features = get_peak_intervals_features(signal, peaks, starts, ends, sampleRate, featureNames=featureNames) + significant_change_features = significant_change(signal, sampleRate, False, False, featureNames=featureNames) + + return {**feats, **freq_feats, **peak_features, **significant_change_features} + + +def extractGsrFeatures2D(signal2D, startTimestampSeconds=0, sampleRate=4, threshold=.02, offset=1, riseTime=4, + decayTime=4): + """ Extract Martin's GSR features with eda-explorer peak detection + + :param signal2D: 2 dimensional numpy array containing the signal (each row is processed seperately) + :param startTimestampSeconds: seconds from epoch when the signal stared + :param sampleRate: sampling rate of the input signal + :param threshold: threshold for detected peaks + :param offset: + :param riseTime: rise time of detected peaks + :param decayTime: decay time of detected peaks + :return: pandas dataframe of calculated GSR features, each row corresponds with each input row + """ + data = pd.DataFrame() + + for signal in signal2D: + features = extractGsrFeatures(signal, startTimestampSeconds, sampleRate, threshold, offset, riseTime, decayTime) + data = data.append(features, ignore_index=True) + + return data + + +def filter_FIR(signal, sampling_rate, plt_flag=True, ): + filtered = st.filter_signal(signal=signal, + ftype="FIR", + band="bandpass", + frequency=(0.01, 1), + order=20, + sampling_rate=sampling_rate) + + signal_f = filtered['signal'] + if (plt_flag): + plt.plot(signal, label='raw', c="blue") + plt.plot(signal_f, label='filtered', c="red") + plt.xlabel("Sample") + plt.ylabel("GSR value") + plt.legend() + plt.show() + + return signal_f + + +def find_peaks(signal, sampling_rate, plt_flag=True): + tonic = peakutils.baseline(signal, 10) + singal_bf = signal - tonic + indexes = peakutils.indexes(singal_bf, thres=0.3, min_dist=sampling_rate) + if (plt_flag): + plt.figure(figsize=(30, 3)) + plt.plot(singal_bf, alpha=0.5, color='blue') + plt.scatter(indexes, singal_bf[indexes], color='red') # Plot detected peaks + plt.title("GSR with removed tonic") + plt.show() + plt.figure(figsize=(30, 3)) + plt.plot(signal, alpha=0.5, color='blue', label="GSR signal") + plt.scatter(indexes, signal[indexes], color='red') # Plot detected peaks + plt.plot(tonic, alpha=0.5, color='green', label="GSR tonic driver") + plt.legend() + plt.show() + return indexes, tonic + + +def find_peaks_heght_filter(signal, sampling_rate, height_threshold=.1, plt_flag=True): + tonic = peakutils.baseline(signal, 10) + singal_bf = signal - tonic + indexes = peakutils.indexes(singal_bf, thres=0.1, min_dist=sampling_rate) + + all_indexes = np.copy(indexes) + good_hights = [] + bad_indexes = [] + + good_hights = np.argwhere(singal_bf[indexes] > height_threshold) + bad_hights = np.argwhere(singal_bf[indexes] <= height_threshold) + if (len(good_hights) > 0): + indexes = np.concatenate(indexes[good_hights]) + else: + indexes = [] # all are bad + if (len(bad_hights) > 0): + bad_indexes = np.concatenate(all_indexes[bad_hights]) + # print(singal_bf[indexes]) + + if (plt_flag): + plt.figure(figsize=(30, 3)) + plt.plot(singal_bf, alpha=0.5, color='blue', label='GSR-tonic') + plt.scatter(indexes, singal_bf[indexes], color='red') # Plot detected peaks + plt.legend() + plt.show() + plt.figure(figsize=(30, 3)) + plt.plot(signal, alpha=0.5, color='blue', label="GSR signal") + plt.scatter(indexes, signal[indexes], color='red', label='Good Detected peaks') + plt.scatter(bad_indexes, signal[bad_indexes], color='purple', label='Bad detected peaks') + plt.plot(tonic, alpha=0.5, color='green', label="GSR tonic driver") + plt.legend() + plt.show() + + return indexes, tonic + + +import pandas as pd + + +def find_peaks_sliding(sig, sampling_rate, height_threshold=.1, plt_flag=True): + window_size = 60 * sampling_rate + window_count = 1 + + # detrending using sliding window. For signals in which the trend is not linear + singal_bf = np.copy(sig) + tonic_sliding = [] + while ((window_count * window_size) <= len(sig)): + start = (window_count - 1) * window_size + end = window_count * window_size + if ((len(singal_bf) - end) < window_size): + end = end + window_size + tonic_sliding.extend(peakutils.baseline(sig[start:end], 3)) + window_count = window_count + 1 + sig_df = pd.DataFrame(tonic_sliding) + tonic_sliding = sig_df.iloc[:, 0].rolling(window=(3 * sampling_rate), center=True).mean().values + tonic_sliding[np.isnan(tonic_sliding)] = np.reshape(sig_df[np.isnan(tonic_sliding)].values, + len(sig_df[np.isnan(tonic_sliding)].values)) + tonic_sliding = np.reshape(tonic_sliding, len(tonic_sliding)) + + tonic = peakutils.baseline(sig, 3) + + if (len(tonic_sliding) > 0): + singal_bf = singal_bf - tonic_sliding + else: + singal_bf = singal_bf - tonic + indexes = peakutils.indexes(singal_bf, thres=0.3, min_dist=sampling_rate) + all_indexes = np.copy(indexes) + good_hights = [] + bad_indexes = [] + good_hights = np.argwhere(singal_bf[indexes] > height_threshold) + bad_hights = np.argwhere(singal_bf[indexes] <= height_threshold) + if (len(good_hights) > 0): + indexes = np.concatenate(indexes[good_hights]) + if (len(bad_hights) > 0): + bad_indexes = np.concatenate(all_indexes[bad_hights]) + + if (plt_flag): + plt.figure(figsize=(30, 3)) + plt.plot(singal_bf, alpha=0.5, color='blue') + plt.scatter(indexes, singal_bf[indexes], color='red') # Plot detected peaks + plt.title("GSR with removed tonic") + plt.show() + plt.figure(figsize=(30, 3)) + plt.plot(sig, alpha=0.5, color='blue', label="GSR signal") + plt.scatter(indexes, sig[indexes], color='red') + plt.scatter(bad_indexes, sig[bad_indexes], color='yellow') + plt.plot(tonic, alpha=0.5, color='green', label="GSR tonic driver") # Plot semi-transparent HR + plt.plot(tonic_sliding, alpha=0.5, color='purple', + label="GSR tonic driver - sliding") # Plot semi-transparent HR + plt.legend() + plt.show() + return indexes, tonic + + +def calculate_GSR_features(signal, peaks, tonic, sampling_rate, featureNames=None): + q25 = np.percentile(signal, 0.25) + q75 = np.percentile(signal, 0.75) + derivative = np.gradient(signal) + pos_idx = np.where(derivative > 0)[0] + + out = {} + if checkForFeature('mean', featureNames): + out['mean'] = np.mean(signal) + + if checkForFeature('std', featureNames): + out['std'] = np.std(signal) + + if checkForFeature('q25', featureNames): + out['q25'] = q25 + + if checkForFeature('q75', featureNames): + out['q75'] = q75 + + if checkForFeature('qd', featureNames): + out['qd'] = q75 - q25 + + if checkForFeature('deriv', featureNames): + out['deriv'] = np.sum(np.gradient(signal)) + + if checkForFeature('power', featureNames): + out['power'] = np.mean(signal * signal) + + if checkForFeature('numPeaks', featureNames): + out['numPeaks'] = len(peaks) + + if checkForFeature('ratePeaks', featureNames): + out['ratePeaks'] = len(peaks) / (len(signal) / sampling_rate) + + if checkForFeature('powerPeaks', featureNames): + if len(signal[peaks]) == 0: + out['powerPeaks'] = np.nan + else: + out['powerPeaks'] = np.mean(signal[peaks]) + + if checkForFeature('sumPosDeriv', featureNames): + out['sumPosDeriv'] = np.sum(derivative[pos_idx]) / len(derivative) + + if checkForFeature('propPosDeriv', featureNames): + out['propPosDeriv'] = len(pos_idx) / len(derivative) + + if checkForFeature('derivTonic', featureNames): + out['derivTonic'] = np.sum(np.gradient(tonic)) + + if checkForFeature('sigTonicDifference', featureNames): + out['sigTonicDifference'] = np.mean(signal - tonic) + + return out + + +# In[7]: + +def get_GSR_features(signal, sampling_rate, height_threshold=.1, plt_flag=True): + # signal_f =filter_FIR(signal,sampling_rate,plt_flag) + # signal_f = mean_filter(signal,3*sampling_rate,1,sampling_rate,plt_flag) + signal_f = signal + peaks, tonic = find_peaks_heght_filter(signal_f, sampling_rate, height_threshold, plt_flag) + + feats = calculate_GSR_features(signal_f, peaks, tonic, sampling_rate) + freq_feats = GSR_freq(signal_f, sampling_rate, plt_flag, print_flag=plt_flag) + + peaks, ends, starts = get_peak_intervals(signal_f, peaks, sampling_rate, plt_flag) + peak_features = get_peak_intervals_features(signal_f, peaks, starts, ends, sampling_rate) + significant_change_features = significant_change(signal, sampling_rate, plt_flag, plt_flag) + # print('significant_change_features',significant_change_features) + + return np.concatenate((feats, freq_feats, peak_features, significant_change_features)) + + +def get_GSR_features_old(signal, sampling_rate, plt_flag=True): + signal_f = filter_FIR(signal, sampling_rate, plt_flag) + peaks, tonic = find_peaks(signal_f, sampling_rate, plt_flag) + feats = calculate_GSR_features(signal_f, peaks, tonic, sampling_rate) + # freq_feats = GSR_freq(signal_f,sampling_rate,plt_flag,print_flag=plt_flag) + + return feats + + +def GSR_freq(s, fs, plot_flag, print_flag, featureNames=None): + if not checkForFeature('freqFeats', featureNames): + return dict() + + ff, Pxx_spec = signal.periodogram(s, fs, 'flattop', scaling='spectrum') + if (plot_flag): + # plt.plot(s,label="Signal freq") + # plt.legend() + # plt.show() + plt.semilogy(ff, Pxx_spec) + plt.xlabel('frequency [Hz]') + plt.ylabel('PSD [V**2/Hz]') + plt.xlim(0, fs // 2) + plt.show() + # get the power in the band [0-0.5] + current_f = 0.0 + increment = 0.1 + feats = [] + while (current_f < 0.6): + feat = np.trapz(abs(Pxx_spec[(ff >= current_f) & (ff <= current_f + increment)])) + feats.append(feat) + # if(print_flag): + # print(current_f,"-",current_f+increment, feat) + current_f = current_f + increment + + return dict(zip(['fp01', 'fp02', 'fp03', 'fp04', 'fp05', 'fp06'], feats)) + + +def significant_increase(sig, fs, print_flag): + # 5 seconds + win_size = 5 * fs + sig_change_threshold = 1.05 # 5% + sig_counter = 0 + sig_duration_threshold = 15 * fs # 10% change should be sustained for a duration of 15 seconds + sig_duration = 0 + sig_windows = [] + sig_windows_all = [] + + for idx in range(len(sig) // win_size - 1): + # print('inside') + win_prev = sig[idx * win_size] + win_next = sig[(idx + 1) * win_size] + if win_prev == 0: + win_prev = win_prev + 0.00001 + if (win_next / win_prev) > sig_change_threshold: + sig_counter = sig_counter + 1 + # print("Sig increase") + sig_windows.append(win_prev) + else: + if sig_counter * win_size >= sig_duration_threshold: # foe how manu windows there was a sig change? + sig_duration = sig_duration + (sig_counter * win_size) + # if(print_flag): + # print("Significant increase ended") + sig_windows_all.extend(sig_windows) + sig_counter = 0 + sig_windows = [] + # if(print_flag): + # print(idx*win_size,(idx+1)*win_size,win_next/win_prev) + if (sig_counter * win_size >= sig_duration_threshold): + sig_duration = sig_duration + (sig_counter * win_size) + + # how many seconds there has been a significant increase + mean = 0 + intensity = 0 + change = 0 + speed = 0 + if len(sig_windows_all) > 0: + mean = np.mean(sig_windows_all) + intensity = np.mean(sig_windows_all) * sig_duration + change = max(sig_windows_all) - min(sig_windows_all) + speed = change / sig_duration + return [sig_duration, mean, intensity, change, speed] + + +def significant_decrease(sig, fs, print_flag): + # 5 seconds + win_size = 5 * fs + sig_change_threshold = 1.05 + sig_counter = 0 + sig_duration_threshold = 15 * fs # 10% change should be sustained for a duration of 15 seconds + sig_duration = 0 + + sig_windows = [] + sig_windows_all = [] + + for idx in range(len(sig) // win_size - 1): + win_prev = sig[idx * win_size] + win_next = sig[(idx + 1) * win_size] + if win_next == 0: + win_next = win_prev + 0.00001 + if (win_prev / win_next) > sig_change_threshold: + sig_counter = sig_counter + 1 + sig_windows.append(win_prev) + else: + if (sig_counter * win_size) >= sig_duration_threshold: + sig_duration = sig_duration + (sig_counter * win_size) + # if(print_flag): + # print("Significant decrease ended") + sig_windows_all.extend(sig_windows) + sig_counter = 0 + sig_windows = [] + # if(print_flag): + # print(idx*win_size,(idx+1)*win_size,win_prev/win_next) + if (sig_counter * win_size >= sig_duration_threshold): + sig_duration = sig_duration + (sig_counter * win_size) + + # how many seconds there has been a significant decrease + mean = 0 + intensity = 0 + change = 0 + speed = 0 + if len(sig_windows_all) > 0: + mean = np.mean(sig_windows_all) + intensity = np.mean(sig_windows_all) * sig_duration + change = min(sig_windows_all) - max(sig_windows_all) + speed = change / sig_duration + return [sig_duration, mean, intensity, change, speed] + + +def significant_change(sig, fs, plt_flag, print_flag, featureNames=None): + out = {} + + if checkForFeature('significantIncrease', featureNames): + a = significant_increase(sig, fs, print_flag) + out['significantIncreaseDuration'] = a[0] + out['significantIncreaseMean'] = a[1] + out['significantIncreaseIntensity'] = a[2] + out['significantIncreaseChange'] = a[3] + out['significantIncreaseSpeed'] = a[4] + + if checkForFeature('significantDecrease', featureNames): + b = significant_decrease(sig, fs, print_flag) + out['significantDecreaseDuration'] = b[0] + out['significantDecreaseMean'] = b[1] + out['significantDecreaseIntensity'] = b[2] + out['significantDecreaseChange'] = b[3] + out['significantDecreaseSpeed'] = b[4] + + return out + + +def get_peak_intervals(sig, peak_indexes, sampling_frequency, plt_flag): + window_size = 4 + window_slide = 1 + inertion = .01 + ends = [] + starts = [] + for start_idx in peak_indexes: + # go backwards + mean_prev = np.mean(sig[start_idx:(start_idx + window_size)]) + window_start = start_idx - window_size + window_end = start_idx + mean_current = np.mean(sig[window_start:window_end]) + while (window_start >= 0 and (mean_current + inertion) <= mean_prev): + window_end = window_end - window_slide + window_start = window_start - window_slide + mean_prev = mean_current + mean_current = np.mean(sig[window_start:window_end]) + if (window_end < 0): + window_end = 0 + value = window_end + if (value > start_idx): + value = start_idx - window_size + if (value < 0): + value = 0 + starts.append(value) + + # go forward + mean_prev = np.mean(sig[start_idx:(start_idx + window_size)]) + window_start = start_idx + window_slide + window_end = window_start + window_size + mean_current = np.mean(sig[window_start:window_end]) + while (window_end <= len(sig) and (mean_current + inertion) <= mean_prev): + window_start = window_start + window_slide + window_end = window_end + window_slide + mean_prev = mean_current + mean_current = np.mean(sig[window_start:window_end]) + if (window_start >= len(sig)): + window_start = len(sig) - 1 + + value = window_start + if (value <= start_idx): + value = start_idx + window_size + if (value >= len(sig)): + value = len(sig) - 1 + + ends.append(value) + + # #filter bad-short peaks + # inc_duration_threshold = 1 + # dec_duration_threshold = 1 + # inc_amplitude_threshold = .1 + # dec_amplitude_threshold = .1 + good_indexes = [] + bad_indexes = [] + for i in range(len(peak_indexes)): + good_indexes.append(i) + # inc_duration = (peak_indexes[i]-starts[i])/sampling_frequency + # dec_duration = (ends[i]-peak_indexes[i])/sampling_frequency + # inc_amplitude = (sig[peak_indexes[i]]-sig[starts[i]]) + # dec_amplitude = (sig[peak_indexes[i]]-sig[ends[i]]) + # # print(i,inc_duration,dec_duration,inc_amplitude,dec_amplitude) + # if (inc_duration>=inc_duration_threshold and + # dec_duration>=dec_duration_threshold and + # inc_amplitude>=inc_amplitude_threshold and + # dec_amplitude>=dec_amplitude_threshold): + # good_indexes.append(i) + # else: + # bad_indexes.append(i) + peak_indexes = np.array(peak_indexes) + bad_peak_indexes = peak_indexes[bad_indexes] + peak_indexes = peak_indexes[good_indexes] + ends = np.array(ends) + starts = np.array(starts) + ends = ends[good_indexes] + starts = starts[good_indexes] + + if (plt_flag and len(peak_indexes) > 0): + plt.figure(figsize=(30, 3)) + plt.plot(sig, label='GSR') + plt.scatter(peak_indexes, sig[peak_indexes], color='red', label='Good Detected peaks') # Plot detected peaks + plt.scatter(bad_peak_indexes, sig[bad_peak_indexes], color='purple', + label='Bad Detected peaks') # Plot detected peaks + + plt.scatter(ends, .001 + sig[ends], color='orange', label='Peak end') # Plot detected peaks + plt.scatter(starts, .001 + sig[starts], color='green', label='Peak start') # Plot detected peaks + plt.legend() + plt.show() + + return peak_indexes, np.array(ends), np.array(starts) + + +def get_peak_intervals_features(sig, peak_indexes, starts, ends, sampling_frequency, featureNames=None): + if (len(peak_indexes) > 0): + + max_peak_idx = np.argmax(sig[peak_indexes]) + + max_peak_start = starts[max_peak_idx] + max_peak_end = ends[max_peak_idx] + + max_peak_amlitude_change_before = sig[peak_indexes[max_peak_idx]] - sig[max_peak_start] + max_peak_amlitude_change_after = sig[peak_indexes[max_peak_idx]] - sig[max_peak_end] + + # max_peak_change_ratio = max_peak_amlitude_change_before/max_peak_amlitude_change_after + + avg_peak_amlitude_change_before = np.median(sig[peak_indexes] - sig[starts]) + avg_peak_amlitude_change_after = np.median(sig[peak_indexes] - sig[ends]) + + # avg_peak_change_ratio=0 + # if avg_peak_amlitude_change_after!=0: + # avg_peak_change_ratio = avg_peak_amlitude_change_before/avg_peak_amlitude_change_after + + max_peak_increase_time = (peak_indexes[max_peak_idx] - max_peak_start) / sampling_frequency + max_peak_decrease_time = (max_peak_end - peak_indexes[max_peak_idx]) / sampling_frequency + + max_peak_duration = (max_peak_end - max_peak_start) / sampling_frequency + + max_peak_change_ratio = 0 + if max_peak_decrease_time != 0: + max_peak_change_ratio = max_peak_increase_time / max_peak_decrease_time + + avg_peak_increase_time = np.mean(peak_indexes - starts) / sampling_frequency + avg_peak_decrease_time = np.mean(ends - peak_indexes) / sampling_frequency + avg_peak_duration = np.mean(ends - starts) * sampling_frequency + avg_peak_change_ratio = 0 + if (avg_peak_decrease_time != 0): + avg_peak_change_ratio = avg_peak_increase_time / avg_peak_decrease_time + + dif = np.diff(sig[max_peak_start:peak_indexes[max_peak_idx]]) + # prevent "Mean of empty slice" warning + if len(dif) == 0: + max_peak_response_slope_before = np.nan + else: + max_peak_response_slope_before = np.mean(dif) + + # if np.isnan(max_peak_response_slope_before): + # max_peak_response_slope_before = 0 + + dif = np.diff(sig[peak_indexes[max_peak_idx]:max_peak_end]) + + # prevent "Mean of empty slice" warning + if len(dif) == 0: + max_peak_response_slope_after = np.nan + else: + max_peak_response_slope_after = np.mean(dif) + + # if np.isnan(max_peak_response_slope_after): + # max_peak_response_slope_after = 0 + + signal_overall_change = np.max(sig) - np.min(sig) + change_duration = np.abs((np.argmax(sig) - np.argmin(sig))) / sampling_frequency + if (signal_overall_change != 0): + change_rate = change_duration / signal_overall_change + gsr_peak_features = [max_peak_amlitude_change_before, max_peak_amlitude_change_after, + avg_peak_amlitude_change_before, avg_peak_amlitude_change_after, + avg_peak_change_ratio, max_peak_increase_time, max_peak_decrease_time, + max_peak_duration, max_peak_change_ratio, + avg_peak_increase_time, avg_peak_decrease_time, avg_peak_duration, + max_peak_response_slope_before, max_peak_response_slope_after, signal_overall_change, + change_duration, change_rate] + else: + num_features = 17 + gsr_peak_features = np.array([np.nan] * num_features) + else: + num_features = 17 + gsr_peak_features = np.array([np.nan] * num_features) + # print('bad features',gsr_peak_features) + + out = {} + if checkForFeature('maxPeakAmplitudeChangeBefore', featureNames): + out['maxPeakAmplitudeChangeBefore'] = gsr_peak_features[0] + + if checkForFeature('maxPeakAmplitudeChangeAfter', featureNames): + out['maxPeakAmplitudeChangeAfter'] = gsr_peak_features[1] + + if checkForFeature('avgPeakAmplitudeChangeBefore', featureNames): + out['avgPeakAmplitudeChangeBefore'] = gsr_peak_features[2] + + if checkForFeature('avgPeakAmplitudeChangeAfter', featureNames): + out['avgPeakAmplitudeChangeAfter'] = gsr_peak_features[3] + + if checkForFeature('avgPeakChangeRatio', featureNames): + out['avgPeakChangeRatio'] = gsr_peak_features[4] + + if checkForFeature('maxPeakIncreaseTime', featureNames): + out['maxPeakIncreaseTime'] = gsr_peak_features[5] + + if checkForFeature('maxPeakDecreaseTime', featureNames): + out['maxPeakDecreaseTime'] = gsr_peak_features[6] + + if checkForFeature('maxPeakDuration', featureNames): + out['maxPeakDuration'] = gsr_peak_features[7] + + if checkForFeature('maxPeakChangeRatio', featureNames): + out['maxPeakChangeRatio'] = gsr_peak_features[8] + + if checkForFeature('avgPeakIncreaseTime', featureNames): + out['avgPeakIncreaseTime'] = gsr_peak_features[9] + + if checkForFeature('avgPeakDecreaseTime', featureNames): + out['avgPeakDecreaseTime'] = gsr_peak_features[10] + + if checkForFeature('avgPeakDuration', featureNames): + out['avgPeakDuration'] = gsr_peak_features[11] + + if checkForFeature('maxPeakResponseSlopeBefore', featureNames): + out['maxPeakResponseSlopeBefore'] = gsr_peak_features[12] + + if checkForFeature('maxPeakResponseSlopeAfter', featureNames): + out['maxPeakResponseSlopeAfter'] = gsr_peak_features[13] + + if checkForFeature('signalOverallChange', featureNames): + out['signalOverallChange'] = gsr_peak_features[14] + + if checkForFeature('changeDuration', featureNames): + out['changeDuration'] = gsr_peak_features[15] + + if checkForFeature('changeRate', featureNames): + out['changeRate'] = gsr_peak_features[16] + + return out + + +def mean_filter(s, windows_size, window_slide, sampling_rate, plt_flag=True): + mean_s = [] + start = 0 + end = windows_size + while (end <= len(s)): + mean_s.append(np.mean(s[start:end])) + start = start + window_slide + end = start + windows_size + if (plt_flag): + plt.plot(s, label='original') + plt.plot(mean_s, label='mean_filter') + plt.legend() + plt.show() + + return np.array(mean_s) diff --git a/calculatingfeatures/CalculatingFeatures/helper_functions.py b/calculatingfeatures/CalculatingFeatures/helper_functions.py new file mode 100644 index 00000000..bb2be020 --- /dev/null +++ b/calculatingfeatures/CalculatingFeatures/helper_functions.py @@ -0,0 +1,131 @@ +import numpy as np +import pandas as pd + + +def convertInputInto2d(input, windowLength, overlap=0): + """Convert input into 2d matrix with width = numCols. The last row is padded with zeros to match the other rows. + + Overlap has to be smaller than window length + + :param input: the one dimensional array + :param windowLength: window length, expressed in number of samples + :param overlap: Amount of overlap + :return: 2D matrix + """ + + if windowLength <= overlap: + raise Exception("Overlap has to be smaller than window length") + + inputWasList = True + if type(input) != list: + inputWasList = False + input = input.tolist() + + out = [input[i: i + windowLength] for i in range(0, len(input), windowLength - overlap)] + out[-1].extend([0] * (windowLength - len(out[-1]))) + return out if inputWasList else np.asarray(out) + + +def convertInputInto2dTime(input, timeThreshold): + """ Convert input array into 2D matrix by time interval. When the timeThreshold is reached in each row, + the process continues in the next row. + + :param input: the pandas dataframe with rows "time" and "data" + :param timeThreshold: the threshold with which the row width is defined + :return: 2D matrix + """ + outData = [[]] + outTime = [[]] + startTime = 0 + + for index, row in input.iterrows(): + t = row["time"] + data = row["data"] + + if t - startTime >= timeThreshold: + startTime = t + outData.append([]) + outTime.append([]) + + outData[-1].append(data) + outTime[-1].append(t) + + return outData, outTime + + +def convert1DEmpaticaToArray(pathToEmpaticaCsvFile): + """ Convert 1D empatica file to array + + :param pathToEmpaticaCsvFile: path to Empatica csv file + :return: array of data, starting timestamp of data, sample rate of data + """ + df = pd.read_csv(pathToEmpaticaCsvFile, names=["name"]) + startTimeStamp = df.name[0] + sampleRate = df.name[1] + df.drop([0, 1], inplace=True) + data = df.name.ravel() + return data, startTimeStamp, sampleRate + + +def convert3DEmpaticaToArray(pathToEmpaticaCsvFile): + """ Convert 3D empatica file to array + + :param pathToEmpaticaCsvFile: path to Empatica csv file + :return: array of data, starting timestamp of data, sample rate of data + """ + df = pd.read_csv(pathToEmpaticaCsvFile, names=["x", "y", "z"]) + startTimeStamp = df.x[0] + sampleRate = df.x[1] + df.drop([0, 1], inplace=True) + data = np.vstack((df.x.ravel(), df.y.ravel(), df.z.ravel())) + return data, startTimeStamp, sampleRate + + +def checkForFeature(featureName, featureNames): + return featureNames is None or featureName in featureNames + + +frequencyFeatureNames = ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins", + "fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart"] + +genericFeatureNames = ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", + "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", + "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", "ratioBeyondRSigma", "binnedEntropy", + "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", + "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", + "squareSumOfComponent", "sumOfSquareComponents"] + +accelerometerFeatureNames = ["meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", + "skewnessBand", "kurtosisBand", "postureDistanceLow", "absoluteMeanBand", + "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", + "varianceBand", "coefficientOfVariationBand", "amplitudeBand", "totalEnergyBand", + "dominantFrequencyEnergyBand", "meanCrossingRateBand", "correlationBand", + "quartilesMagnitudesBand", + "interQuartileRangeMagnitudesBand", "areaUnderAccelerationMagnitude", "peaksDataLow", + "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", + "totalKineticEnergyBand", "squareSumOfComponent", "sumOfSquareComponents", + "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", + "rollStdDevLow", "pitchStdDevLow", + "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", + "pitchPeaks", + "rollPitchCorrelation"] + +gyroscopeFeatureNames = ["meanLow", "areaLow", "totalAbsoluteAreaLow", "totalMagnitudeLow", "entropyLow", "skewnessLow", + "kurtosisLow", + "quartilesLow", "interQuartileRangeLow", "varianceLow", "coefficientOfVariationLow", + "amplitudeLow", + "totalEnergyLow", "dominantFrequencyEnergyLow", "meanCrossingRateLow", "correlationLow", + "quartilesMagnitudeLow", "interQuartileRangeMagnitudesLow", "areaUnderMagnitude", + "peaksCountLow", + "averageVectorLengthLow", "averageVectorLengthPowerLow"] + +gsrFeatureNames = ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', + 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats', + 'maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', + 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', + 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', + 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', + 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', + 'significantIncrease', 'significantDecrease'] + +hrvFeatureNames = ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR'] diff --git a/calculatingfeatures/CalculatingFeatures/hrv.py b/calculatingfeatures/CalculatingFeatures/hrv.py new file mode 100644 index 00000000..af807da8 --- /dev/null +++ b/calculatingfeatures/CalculatingFeatures/hrv.py @@ -0,0 +1,304 @@ +import pandas as pd +import numpy as np +import scipy.signal as _signal + +from calculatingfeatures.CalculatingFeatures.helper_functions import checkForFeature, hrvFeatureNames +from calculatingfeatures.CalculatingFeatures.calculate_hrv_peaks import peak_detector + + +def extractHrvFeatures(_sample, ma=False, detrend=False, m_deternd=False, low_pass=False, winsorize=False, + winsorize_value=25, hampel_fiter=True, sampling=64, featureNames=None): + """ Extract Martin's HRV features + + Warning: Input sample length has to be at least 256! + + :param _sample: array containing the HRV signal + :param ma: should moving average filter be used prior the calculation + :param detrend: should overall detrending be used prior the calculation + :param m_deternd: should moving detrending be used prior the calculation + :param low_pass: should low pass filter be used prior the calculation + :param winsorize: should winsorize filter be used prior the calculation + :param winsorize_value: winsorize value + :param hampel_fiter: hould winsorize filter be used after the calculation + :param sampling: the sampling frequency of the signal + :param featureNames: + :return: HRV features + """ + + hrv_time_features, sample, rr, timings, peak_indx = get_HRV_features(_sample, ma, detrend, m_deternd, low_pass, + winsorize, winsorize_value, + hampel_fiter, sampling, + featureNames=featureNames) + return hrv_time_features + + +def extractHrvFeatures2D(signal2D, ma=False, detrend=False, m_deternd=False, low_pass=False, winsorize=False, + winsorize_value=25, hampel_fiter=True, sampling=64): + """ Extract Martin's HRV features + + Warning: Input 2D array width (column count) has to be at least 200! + + :param signal2D: array containing the HRV signal in 2D (each row is processed seperately) + :param ma: should moving average filter be used prior the calculation + :param detrend: should overall detrending be used prior the calculation + :param m_deternd: should moving detrending be used prior the calculation + :param low_pass: should low pass filter be used prior the calculation + :param winsorize: should winsorize filter be used prior the calculation + :param winsorize_value: winsorize value + :param hampel_fiter: hould winsorize filter be used after the calculation + :param sampling: the sampling frequency of the signal + :return: pandas dataframe of calculated HRV features, each row corresponds with each input row + """ + outData = pd.DataFrame() + + for sample in signal2D: + features = extractHrvFeatures(sample, ma, detrend, m_deternd, low_pass, + winsorize, winsorize_value, + hampel_fiter, sampling) + + outData = outData.append(features, ignore_index=True) + + return outData + + +# filter signala and calculate HRV features in time and in frequency domain +def get_HRV_features(_sample, ma=False, detrend=False, m_deternd=False, low_pass=False, winsorize=True, + winsorize_value=25, hampel_fiter=True, sampling=1000, featureNames=None): + if featureNames is not None and len(set(featureNames).intersection(set(hrvFeatureNames))) == 0: + return dict(), 0, 0, 0, 0 + + sample = _sample.copy() + + if low_pass: # lowpass filter + sample = butter_lowpass_filter(sample) + if m_deternd: # moving detrending + sample = moving_detrending(sample, sampling) + if detrend: # overall detrending + sample = _signal.detrend(sample) + if ma: # moving average + sample = moving_average(sample) + + # winsorize the signal + if winsorize: + sample = winsorize_signal(sample, winsorize_value) + # if dynamic_threshold: #find the median of the min-max normalized signal + # thres = dynamic_threshold_value*np.median((sample - sample.min())/(sample.max() - sample.min())) + + rr, timings, peak_indx = detect_RR(sample, sampling) + + if hampel_fiter: + rr, outlier_indeces = hampel_filtering(rr) + + timings, rr = medianFilter(timings, rr) + + bad_signal = False + + if len(rr) < len(sample) / (2 * sampling): # check whether HR is>30 + #print("Bad signal. Too little RRs detected.") + bad_signal = True + elif len(rr) > len(sample) / (sampling / 4): # check whether HR is<240 + #print("Bad signal. Too much RRs detected.") + bad_signal = True + hrv_time_features = HRV_time(rr, print_flag=False, badSignal=bad_signal, featureNames=featureNames) + + return hrv_time_features, sample, rr, timings, peak_indx + + +def butter_lowpass(cutoff, fs, order): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + b, a = _signal.butter(order, normal_cutoff, btype='low', analog=False) + return b, a + + +def butter_lowpass_filter(data, cutoff=5, fs=64, order=3): + b, a = butter_lowpass(cutoff, fs, order=order) + y = _signal.lfilter(b, a, data) + return pd.Series(y[1000:]) + + +# perfrom detrending using sliding window +def moving_detrending(sig_input, sampling_rate=64): + sig = np.copy(sig_input) + window_size = 1 * sampling_rate + window_count = 1 + start = (window_count - 1) * window_size + end = window_count * window_size + while (end <= len(sig)): + if ((len(sig) - end) < window_size): + end = end + window_size + sig[start:end] = _signal.detrend(sig[start:end]) + window_count = window_count + 1 + start = (window_count - 1) * window_size + end = window_count * window_size + return sig + + +# perform moving average +def moving_average(sample, ma_size=10): + sample = pd.Series(sample) + sample_ma = sample.rolling(ma_size).mean() + sample_ma = sample_ma.iloc[ma_size:].values + return sample_ma + + +def winsorize_signal(sample, winsorize_value): + p_min = np.percentile(sample, winsorize_value) + p_max = np.percentile(sample, 100 - winsorize_value) + + sample[sample > p_max] = p_max + sample[sample < p_min] = p_min + + return sample + + +# https://www.mathworks.com/help/signal/ref/hampel.html +# compute median and standard deviation +# of a window composed of the sample and its six surrounding samples +# If a sample differs from the median by more than three standard deviations, +# it is replaced with the median. +# reutn fistered RRs and outlier indices +def hampel_filtering(sample_rr): + outlier_indicies = [] + filtered_rr = [] + for i in range(len(sample_rr)): + start = i - 3 + end = i + 3 + if start < 0: # for the first 3 samples calculate median and std using the closest 6 samples + start = 0 + end = end + 3 - i + if end > len(sample_rr) - 1: # for the last 3 samples calculate median and std using the first 6 samples + start = len(sample_rr) - 7 + end = len(sample_rr) - 1 + + sample_rr_part = sample_rr[start:end] + + # Prevent "Mean of empty slice" warning + if len(sample_rr_part) == 0: + sample_med = np.nan + sample_std = np.nan + else: + sample_med = np.median(sample_rr_part) + sample_std = np.std(sample_rr_part) + + if abs(sample_rr[i] - sample_med) > 3 * sample_std: + outlier_indicies.append(i) + filtered_rr.append(sample_med) + # print('outlier') + filtered_rr.append(sample_rr[i]) + return np.array(filtered_rr), outlier_indicies + + +def medianFilter(time, rr): + percentageBorder = 0.8 + if len(rr) == 0: + median = np.nan + else: + median = np.median(rr) + idx = (rr / median >= percentageBorder) & (rr / median <= (2 - percentageBorder)) + # f_rr = rr[(rr/median>=percentageBorder) & (rr/median<=(2-percentageBorder))] + f_rr = np.copy(rr) + # f_rr[~idx]=median + f_rr = f_rr[idx] + f_time = timestamps_from_RR(f_rr) + # f_time = time[(rr/median>=percentageBorder) & (rr/median<=(2-percentageBorder))] + return f_time, f_rr + + +def detect_RR(sig, sampling_rate): + # peak_indx = peakutils.indexes(sig, thres=thres, min_dist=sampling_rate/2.5) + peak_indx, _ = peak_detector(sig, sampling_rate) + if len(peak_indx) == 0: + return [], [], [] + time = np.arange(len(sig)) + tmp = time[peak_indx] + timings1 = tmp[0:] + timings = tmp[1:] + RR_intervals = timings - timings1[:len(timings1) - 1] + + return RR_intervals / sampling_rate, timings / sampling_rate, peak_indx + + +# extract HRV features in time domain +def HRV_time(RR_intervals, print_flag, badSignal=False, featureNames=None): + if not badSignal: + ibi = np.mean(RR_intervals) # Take the mean of RR_list to get the mean Inter Beat Interval + mean_hr = 60 / ibi + sdnn = np.std(RR_intervals) # Take standard deviation of all R-R intervals + # find successive/neighbouring RRs (i.e., filter noise) + RR_diff = [] + RR_sqdiff = [] + for i in range(len(RR_intervals) - 1): + RR_diff.append(np.absolute(RR_intervals[i + 1] - RR_intervals[i])) + RR_sqdiff.append(np.power(np.absolute(RR_intervals[i + 1] - RR_intervals[i]), 2)) + RR_diff = np.array(RR_diff) + RR_sqdiff = np.array(RR_sqdiff) + sdsd = np.std(RR_diff) # Take standard deviation of the differences between all subsequent R-R intervals + rmssd = np.sqrt(np.mean(RR_sqdiff)) # Take root of the mean of the list of squared differences + nn20 = [x for x in RR_diff if (x > 0.02)] # First create a list of all values over 20, 50 + nn50 = [x for x in RR_diff if (x > 0.05)] + pnn20 = 100 * float(len(nn20)) / float(len(RR_diff)) if len( + RR_diff) > 0 else np.nan # Calculate the proportion of NN20, NN50 intervals to all intervals + pnn50 = 100 * float(len(nn50)) / float(len(RR_diff)) if len(RR_diff) > 0 else np.nan + sd1 = np.sqrt(0.5 * sdnn * sdnn) + sd2 = np.nan + tmp = 2.0 * sdsd * sdsd - 0.5 * sdnn * sdnn + if tmp > 0: # avoid sqrt of negative values + sd2 = np.sqrt(2.0 * sdsd * sdsd - 0.5 * sdnn * sdnn) + + if (print_flag): + print("menHR:", mean_hr) + print("IBI:", ibi) + print("SDNN:", sdnn) + print("sdsd", sdsd) + print("RMSSD:", rmssd) + print("pNN20:", pnn20) + print("pNN50:", pnn50) + print("sd1:", sd1) + print("sd2:", sd2) + print("sd1/sd2:", sd1 / sd2) + + out = {} + if checkForFeature("meanHr", featureNames): + out['meanHr'] = mean_hr if not badSignal else np.NaN + + if checkForFeature("ibi", featureNames): + out['ibi'] = ibi if not badSignal else np.NaN + + if checkForFeature("sdnn", featureNames): + out['sdnn'] = sdnn if not badSignal else np.NaN + + if checkForFeature("sdsd", featureNames): + out['sdsd'] = sdsd if not badSignal else np.NaN + + if checkForFeature("rmssd", featureNames): + out['rmssd'] = rmssd if not badSignal else np.NaN + + if checkForFeature("pnn20", featureNames): + out['pnn20'] = pnn20 if not badSignal else np.NaN + + if checkForFeature("pnn50", featureNames): + out['pnn50'] = pnn50 if not badSignal else np.NaN + + if checkForFeature("sd", featureNames): + out['sd'] = sd1 if not badSignal else np.NaN + + if checkForFeature("sd2", featureNames): + out['sd2'] = sd2 if not badSignal else np.NaN + + if checkForFeature("sd1/sd2", featureNames): + out['sd1/sd2'] = sd1 / sd2 if not badSignal else np.NaN + + if checkForFeature("numRR", featureNames): + out['numRR'] = len(RR_intervals) + + return out + + +def timestamps_from_RR(rr_intervals): + time = [] + current_time = 0.0 + for rr in rr_intervals: + current_time = current_time + rr + time.append(current_time) + return np.array(time) diff --git a/calculatingfeatures/README.md b/calculatingfeatures/README.md new file mode 100644 index 00000000..8fd333e3 --- /dev/null +++ b/calculatingfeatures/README.md @@ -0,0 +1,207 @@ +# Calculating features + +## Usage +- Install the library with: + ``` + pip install pep517 + python -m pep517.build . + + Alternative: + pip install build + python -m build + ``` +- Basic usage is: + ``` + from calculatingfeatures.CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, frequencyFeatureNames, hrvFeatureNames + from calculatingfeatures.CalculatingFeatures.calculate_features import calculateFeatures + import pandas as pd + + pathToHrvCsv = "example_data/S2_E4_Data/BVP.csv" + windowLength = 500 + + # get an array of values from HRV empatica file + hrv_data, startTimeStamp, sampleRate = convert1DEmpaticaToArray(pathToHrvCsv) + + # Convert the HRV data into 2D array + hrv_data_2D = convertInputInto2d(hrv_data, windowLength) + + # Create a list with feature names + featureNames = [] + featureNames.extend(hrvFeatureNames) + featureNames.extend(frequencyFeatureNames) + + pd.set_option('display.max_columns', None) + + # Calculate features + calculatedFeatures = calculateFeatures(hrv_data_2D, fs=int(sampleRate), featureNames=featureNames) + ``` +- More usage examples are located in **usage_examples.ipynb** file + +## Features +- Features are returned (from calculateFeatures() function) in a Pandas DataFrame object. +- In the case if a feature couldn't be calculated (for example, if input signal is invalid), NaN value is returned. +- Further in this section, the list with descriptions of all possible features is presented. + +### GSR features: +These features are useful for 1D GSR(EDA) signals +- `mean`: mean of the signal +- `std`: standard deviation of signal +- `q25`: 0.25 quantile +- `q75`: 0.75 quantile +- `qd`: q75 - q25 +- `deriv`: sum of gradients of the signal +- `power`: power of the signal (mean of squared signal) +- `numPeaks`: number of EDA peaks +- `ratePeaks`: average number of peaks per second +- `powerPeaks`: power of peaks (mean of signal at indexes of peaks) +- `sumPosDeriv`: sum of positive derivatives divided by number of all derivatives +- `propPosDeriv`: proportion of positive derivatives per all derivatives +- `derivTonic`: sum of gradients of the tonic +- `sigTonicDifference`: mean of tonic subtracted from signal +- `freqFeats`: +- `maxPeakAmplitudeChangeBefore`: maximum peak amplitude change before peak +- `maxPeakAmplitudeChangeAfter`: maximum peak amplitude change after peak +- `avgPeakAmplitudeChangeBefore`: average peak amplitude change before peak +- `avgPeakAmplitudeChangeAfter`: average peak amplitude change after peak +- `avgPeakChangeRatio`: avg_peak_increase_time / avg_peak_decrease_time +- `maxPeakIncreaseTime`: maximum peak increase time +- `maxPeakDecreaseTime`: maximum peak decrease time +- `maxPeakDuration`: maximum peak duration +- `maxPeakChangeRatio`: max_peak_increase_time / max_peak_decrease_time +- `avgPeakIncreaseTime`: average peak increase time +- `avgPeakDecreaseTime`: average peak decreade time +- `avgPeakDuration`: average peak duration +- `maxPeakResponseSlopeBefore`: maximum peak response slope before peak +- `maxPeakResponseSlopeAfter`: maximum peak response slope after peak +- `signalOverallChange`: maximum difference between samples (max(sig)-min(sig)) +- `changeDuration`: duration between maximum and minimum values +- `changeRate`: change_duration / signal_overall_change +- `significantIncrease`: +- `significantDecrease`: + +### HRV features: +These features are useful for 1D HRV(BVP) signals. + +If number of RR intervals (numRR) is less than `length of sample / (2 * sampling rate)` (30 BPM) or greater than `length of sample / (sampling rate / 4)` (240 BPM), BPM value is incorrect and thus, all other HRV features are set to NaN. + +- `meanHr`: mean heart rate +- `ibi`: mean interbeat interval +- `sdnn`: standard deviation of the ibi +- `sdsd`: standard deviation of the differences between all subsequent R-R intervals +- `rmssd`: root of the mean of the list of squared differences +- `pnn20`: the proportion of NN20 intervals to all intervals +- `pnn50`: the proportion of NN50 intervals to all intervals +- `sd`: +- `sd2`: +- `sd1/sd2`: sd / sd2 ratio +- `numRR`: number of RR intervals + +### Accelerometer features: +These features are useful for 3D signals from accelerometer +- `meanLow`: mean of low-pass filtered signal +- `areaLow`: area under the low-pass filtered signal +- `totalAbsoluteAreaBand`: sum of absolute areas under the band-pass filtered x, y and z signal +- `totalMagnitudeBand`: square root of sum of squared band-pass filtered x, y and z components +- `entropyBand`: entropy of band-pass filtered signal +- `skewnessBand`: skewness of band-pass filtered signal +- `kurtosisBand`: kurtosis of band-pass filtered signal +- `postureDistanceLow`: calculates difference between mean values for a given sensor (low-pass filtered) +- `absoluteMeanBand`: mean of band-pass filtered signal +- `absoluteAreaBand`: area under the band-pass filtered signal +- `quartilesBand`: quartiles of band-pass filtered signal +- `interQuartileRangeBand`: inter quartile range of band-pass filtered signal +- `varianceBand`: variance of band-pass filtered signal +- `coefficientOfVariationBand`: dispersion of band-pass filtered signal +- `amplitudeBand`: difference between maximum and minimum sample of band-pass filtered signal +- `totalEnergyBand`: total magnitude of band-pass filtered signal +- `dominantFrequencyEnergyBand`: ratio of energy in dominant frequency +- `meanCrossingRateBand`: the number of signal crossings with mean of band-pass filtered signal +- `correlationBand`: Pearson's correlation between band-pass filtered axis +- `quartilesMagnitudesBand`: quartiles at 25%, 50% and 75% per band-pass filtered signal +- `interQuartileRangeMagnitudesBand`: interquartile range of band-pass filtered signal +- `areaUnderAccelerationMagnitude`: area under acceleration magnitude +- `peaksDataLow`: number of peaks, sum of peak values, peak avg, amplitude avg +- `sumPerComponentBand`: sum per component of band-pass filtered signal +- `velocityBand`: velocity of the band-pass filtered signal +- `meanKineticEnergyBand`: mean kinetic energy 1/2*mV^2 of band-pass filtered signal +- `totalKineticEnergyBand`: total kinetic energy 1/2*mV^2 for all axes (band-pass filtered) +- `squareSumOfComponent`: squared sum of component +- `sumOfSquareComponents`: sum of squared components +- `averageVectorLength`: mean of magnitude vector +- `averageVectorLengthPower`: square mean of magnitude vector +- `rollAvgLow`: maximum difference of low-pass filtered roll samples +- `pitchAvgLow`: maximum difference of low-pass filtered pitch samples +- `rollStdDevLow`: standard deviation of roll (calculated from low-pass filtered signal) +- `pitchStdDevLow`: standard deviation of pitch (calculated from low-pass filtered signal) +- `rollMotionAmountLow`: amount of wrist roll (from low-pass filtered signal) motion +- `rollMotionRegularityLow`: regularity of wrist roll motion +- `manipulationLow`: manipulation of low-pass filtered signals +- `rollPeaks`: number of roll peaks, sum of roll peak values, roll peak avg, roll amplitude avg +- `pitchPeaks`: number of pitch peaks, sum of pitch peak values, pitch peak avg, pitch amplitude avg +- `rollPitchCorrelation`: correlation between roll and peak (obtained from low-pass filtered signal) + +### Gyroscope features: +These features are useful for 3D signals from gyroscope +- `meanLow`: mean of low-pass filtered signal +- `areaLow`: area under the low-pass filtered signal +- `totalAbsoluteAreaLow`: sum of absolute areas under the low-pass filtered x, y and z signal +- `totalMagnitudeLow`: square root of sum of squared band-pass filtered x, y and z components +- `entropyLow`: entropy of low-pass filtered signal +- `skewnessLow`: skewness of low-pass filtered signal +- `kurtosisLow`: kurtosis of low-pass filtered signal +- `quartilesLow`: quartiles of low-pass filtered signal +- `interQuartileRangeLow`: inter quartile range of low-pass filtered signal +- `varianceLow`: variance of low-pass filtered signal +- `coefficientOfVariationLow`: dispersion of low-pass filtered signal +- `amplitudeLow`: difference between maximum and minimum sample of low-pass filtered signal +- `totalEnergyLow`: total magnitude of low-pass filtered signal +- `dominantFrequencyEnergyLow`: ratio of energy in dominant frequency +- `meanCrossingRateLow`: the number of signal crossings with mean of low-pass filtered signal +- `correlationLow`: Pearson's correlation between low-pass filtered axis +- `quartilesMagnitudeLow`: quartiles at 25%, 50% and 75% per low-pass filtered signal +- `interQuartileRangeMagnitudesLow`: interquartile range of band-pass filtered signal +- `areaUnderMagnitude`: area under magnitude +- `peaksCountLow`: number of peaks in low-pass filtered signal +- `averageVectorLengthLow`: mean of low-pass filtered magnitude vector +- `averageVectorLengthPowerLow`: square mean of low-pass filtered magnitude vector + + +### Generic features: +These are generic features, useful for many different types of signals +- `autocorrelations`: autocorrelations of the given signal with lags 5, 10, 20, 30, 50, 75 and 100 +- `countAboveMean`: number of values in signal that are higher than the mean of signal +- `countBelowMean`: number of values in signal that are lower than the mean of signal +- `maximum`: maximum value of the signal +- `minimum`: minimum value of the signal +- `meanAbsChange`: the mean of absolute differences between subsequent time series values +- `longestStrikeAboveMean`: longest part of signal above mean +- `longestStrikeBelowMean`: longest part of signal below mean +- `stdDev`: standard deviation of the signal +- `median`: median of the signal +- `meanChange`: the mean over the differences between subsequent time series values +- `numberOfZeroCrossings`: number of crossings of signal on 0 +- `absEnergy`: the absolute energy of the time series which is the sum over the squared values +- `linearTrendSlope`: a linear least-squares regression for the values of the time series versus the sequence from 0 to length of the time series minus one +- `ratioBeyondRSigma`: ratio of values that are more than r*std(x) (so r sigma) away from the mean of signal. r in this case is 2.5 +- `binnedEntropy`: entropy of binned values +- `numOfPeaksAutocorr`: number of peaks of autocorrelations +- `numberOfZeroCrossingsAutocorr`: number of crossings of autocorrelations on 0 +- `areaAutocorr`: area under autocorrelations +- `calcMeanCrossingRateAutocorr`: the number of autocorrelation crossings with mean +- `countAboveMeanAutocorr`: umber of values in signal that are higher than the mean of autocorrelation +- `sumPer`: sum per component +- `sumSquared`: squared sum per component +- `squareSumOfComponent`: square sum of component +- `sumOfSquareComponents`:sum of square components + +### Frequency features: +These are frequency features, useful for many different types of signals. The signal is converted to power spectral density signal and features are calculated on this signal +- `fqHighestPeakFreqs`: three frequencies corresponding to the largest peaks added to features +- `fqHighestPeaks`: three largest peaks added to features +- `fqEnergyFeat`: energy calculated as the sum of the squared FFT component magnitudes, and normalized +- `fqEntropyFeat`: entropy of the FFT of the signal +- `fqHistogramBins`: Binned distribution (histogram) +- `fqAbsMean`: absolute mean of the raw signal +- `fqSkewness`: skewness of the power spectrum of the data +- `fqKurtosis`: kurtosis of the power spectrum of the data +- `fqInterquart`: inter quartile range of the raw signal \ No newline at end of file diff --git a/calculatingfeatures/__init__.py b/calculatingfeatures/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/calculatingfeatures/eda_explorer/.csv b/calculatingfeatures/eda_explorer/.csv new file mode 100644 index 00000000..6fd23aef --- /dev/null +++ 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09:26:27.125,0.473078,1.875,0.03726277291054325,0.023063999999999973,2.0,3.125,0.07207499999999992 diff --git a/calculatingfeatures/eda_explorer/.gitignore b/calculatingfeatures/eda_explorer/.gitignore new file mode 100644 index 00000000..1d6e4473 --- /dev/null +++ b/calculatingfeatures/eda_explorer/.gitignore @@ -0,0 +1,5 @@ +*.pyc +.idea +env-python2 +env-python3 +tests \ No newline at end of file diff --git a/calculatingfeatures/eda_explorer/AccelerometerFeatureExtractionScript.py b/calculatingfeatures/eda_explorer/AccelerometerFeatureExtractionScript.py new file mode 100644 index 00000000..6412ae1c --- /dev/null +++ b/calculatingfeatures/eda_explorer/AccelerometerFeatureExtractionScript.py @@ -0,0 +1,309 @@ +import numpy as np +import pandas as pd +import scipy.signal as scisig +import os +import matplotlib.pyplot as plt + +from load_files import getInputLoadFile, getOutputPath, get_user_input + +DEBUG = True + +SAMPLING_RATE = 8 + +ONE_MINUTE_S = 60 +THIRTY_MIN_S = ONE_MINUTE_S*30 +SECONDS_IN_DAY = 24*60*60 + +STILLNESS_MOTION_THRESHOLD = .1 +PERCENT_STILLNESS_THRESHOLD = .95 + +STEP_DIFFERENCE_THRESHOLD = 0.3 + + + +def computeAllAccelerometerFeatures(data, time_frames): + if DEBUG: print("\t\tcomputing motion...") + motion = computeMotion(data['AccelX'], data['AccelY'], data['AccelZ']) + + if DEBUG: print("\t\tcomputing steps...") + steps = computeSteps(motion) + + if DEBUG: print("\t\tcomputing stillness...") + stillness = computeStillness(motion) + + features = [] + + for time_frame in time_frames: + start = time_frame[0] + end = time_frame[1] + start1Hz = int(start / SAMPLING_RATE) + end1Hz = end if end == -1 else int(end / SAMPLING_RATE) + if DEBUG: print("\t\tcomputing features for time frame. Start index: "+ str(start)+ " end index: "+ str(end)) + + time_frame_feats = computeAccelerometerFeaturesOverOneTimeFrame(motion[start:end], + steps[start:end], + stillness[start1Hz:end1Hz]) + features.append(time_frame_feats) + + return features, steps, motion + +def computeMotion(acc1, acc2, acc3): + '''Aggregates 3-axis accelerometer signal into a single motion signal''' + return np.sqrt(np.array(acc1)**2 + np.array(acc2)**2 + np.array(acc3)**2) + +def computeSteps(motion): + '''Determines the location of steps from the aggregated accelerometer signal. + Signal is low-pass filtered, then minimums are located in the signal. For each + min, if the max absolute derivative (first difference) immediately surrounding + it is greater than a threshold, it is counted as a step. + + Args: + motion: root mean squared 3 axis acceleration + Returns: + steps: binary array at 8Hz which is 1 everywhere there is a step''' + + filtered_signal = filterSignalFIR(motion, 2, 256) + diff = filtered_signal[1:]-filtered_signal[:-1] + + mins = scisig.argrelextrema(filtered_signal, np.less)[0] + + steps = [0] * len(filtered_signal) + for m in mins: + if m <= 4 or m >= len(diff) - 4: + continue + if max(abs(diff[m-4:m+4])) > STEP_DIFFERENCE_THRESHOLD: + steps[m] = 1.0 + + return steps + +def filterSignalFIR(eda, cutoff=0.4, numtaps=64): + f = cutoff/(SAMPLING_RATE/2.0) + FIR_coeff = scisig.firwin(numtaps,f) + + return scisig.lfilter(FIR_coeff,1,eda) + +def computeStillness(motion): + '''Locates periods in which the person is still or motionless. + Total acceleration must be less than a threshold for 95 percent of one + minute in order for that minute to count as still + + Args: + motion: an array containing the root mean squared acceleration + Returns: + A 1Hz array that is 1 for each second belonging to a still period, 0 otherwise + ''' + diff = motion[1:]-motion[:-1] + momentary_stillness = diff < STILLNESS_MOTION_THRESHOLD + np.append(momentary_stillness,0) # to ensure list is the same size as the full day signal + num_minutes_in_day = 24*60 + + #create array indicating whether person was still or not for each second of the day + #to be still the momentary_stillness signal must be true for more than 95% of the minute + #containing that second + second_stillness = [0]*SECONDS_IN_DAY + + for i in range(num_minutes_in_day): + hours_start = int(i / 60) + mins_start = i % 60 + hours_end = int((i+1) / 60) + mins_end = (i+1) % 60 + + start_idx = getIndexFromTimestamp(hours_start, mins_start) + end_idx = getIndexFromTimestamp(hours_end, mins_end) + + this_minute = momentary_stillness[start_idx:end_idx] + minute_stillness = sum(this_minute) > PERCENT_STILLNESS_THRESHOLD*(60*SAMPLING_RATE) + + second_idx = int(start_idx/8) + for si in range(second_idx,second_idx+60): + second_stillness[si] = float(minute_stillness) + + return second_stillness + +def computeAccelerometerFeaturesOverOneTimeFrame(motion, steps, stillness): + ''' Computes all available features for a time period. Incoming signals are assumed to be from + only that time period. + + Args: + motion: 8Hz root mean squared 3 axis acceleration + steps: 8Hz binary signal that is 1 if there is a step + stillness: 1Hz 1 if the person was still during this second, 0 otherwise + Returns: + A list of features containing (in order): + -Step count number of steps detected + -mean step time during movement average number of samples between two steps (aggregated first to 1 minute, + then we take the mean of only the parts of this signal occuring during movement) + -percent stillness percentage of time the person spent nearly motionless + ''' + + features = [] + + features.extend(computeStepFeatures(steps,stillness)) + features.append(countStillness(stillness)) + + return features + +def computeStepFeatures(steps,stillness): + '''Counts the total number of steps over a given period, + as well as the average time between steps (meant to approximate walking speed) + + Args: + steps: an binary array at 8 Hz that is 1 every time there is a step + Returns: + sum: the number of steps in a period + median time: average number of samples between two steps''' + + sum_steps = float(sum(steps)) + + step_indices = np.nonzero(steps)[0] + diff = step_indices[1:]-step_indices[:-1] + + #ensure length of step difference array is the same so we can get the actual locations of step differences + timed_step_diff = np.empty(len(steps)) * np.nan + timed_step_diff[step_indices[:len(diff)]] = diff + + signal_length_1s = len(stillness) + signal_length_1min = int(signal_length_1s / 60) + + # if there aren't enough steps during this period, cannot accurately compute mean step diff + if len(timed_step_diff) < signal_length_1min: + return [sum_steps, np.nan] + + agg_stillness = aggregateSignal(stillness, signal_length_1min, 'max') + agg_step_diff = aggregateSignal(timed_step_diff, signal_length_1min, 'mean') + + movement_indices = [i for i in range(len(agg_stillness)) if agg_stillness[i] == 0.0] + step_diff_during_movement = agg_step_diff[movement_indices] + + return [sum_steps,round(np.nanmean(step_diff_during_movement),10)] + +def countStillness(stillness): + '''Counts the total percentage of time spent still over a period + + Args: + stillness: an binary array at 1Hz that is 1 if that second is part of a still period + Returns: + the percentage time spent still over a period''' + + return float(sum(stillness)) / float(len(stillness)) + +def aggregateSignal(signal, new_signal_length, agg_method='sum'): + new_signal = np.zeros(new_signal_length) + samples_per_bucket = int(len(signal) / new_signal_length) + + #the new signal length must be large enough that there is at least 1 sample per bucket + assert(samples_per_bucket > 0) + + for i in range(new_signal_length): + if agg_method == 'sum': + new_signal[i] = np.nansum(signal[i*samples_per_bucket:(i+1)*samples_per_bucket]) + elif agg_method == 'percent': + new_signal[i] = np.nansum(signal[i*samples_per_bucket:(i+1)*samples_per_bucket]) / samples_per_bucket + elif agg_method == 'mean': + new_signal[i] = np.nanmean(signal[i*samples_per_bucket:(i+1)*samples_per_bucket]) + elif agg_method == 'max': + new_signal[i] = np.nanmax(signal[i*samples_per_bucket:(i+1)*samples_per_bucket]) + return new_signal + +def getIndexFromTimestamp(hours, mins=0): + return ((hours * 60) + mins) * 60 * SAMPLING_RATE + +def inputTimeFrames(): + '''Allows user to choose the time frames over which they compute accelerometer features.''' + + time_frames = [] + print("Accelerometer features can be extracted over different time periods.") + cont = get_user_input("If you would like to enter a time period over which to compute features, enter 'y', or press enter to compute features over the entire file.") + while cont == 'y' or cont == 'Y': + start = int(get_user_input("Enter the starting hour of the time period (hour 0 is when the file starts):")) + end = int(get_user_input("Enter the ending hour of the time period (hour 0 is when the file starts; use -1 for the end of the file):")) + start = getIndexFromTimestamp(int(start)) + if end != -1: + end = getIndexFromTimestamp(int(end)) + time_frames.append([start,end]) + print("Great! Now computing features for the following time periods:"+ str(time_frames)) + cont = get_user_input("To add another time period, enter 'y'. To finish, press enter.") + + if len(time_frames) == 0: + time_frames = [[0,-1]] # the whole file + + return time_frames + +def saveFeaturesToFile(features, time_frames, output_file): + of = open(output_file, 'w') + of.write("Time period start hour, Time period end hour, Step count, Mean step time during movement, Percent stillness\n") + tf_i = 0 + for tf in time_frames: + output_str = str(tf[0]) + ' , ' + str(tf[1]) + for feat in features[tf_i]: + output_str += ' , ' + str(feat) + tf_i += 1 + of.write(output_str + '\n') + of.close() + print("Saved features to file"+ output_file) + +# draws a graph of the data with the peaks marked on it +# assumes that 'data' dataframe already contains the 'peaks' column +def plotSteps(data, x_seconds, sampleRate = SAMPLING_RATE): + if x_seconds: + time_m = np.arange(0,len(data))/float(sampleRate) + realign = 128/(sampleRate) + else: + time_m = np.arange(0,len(data))/(sampleRate*60.) + realign = 128/(sampleRate*60.) + + data_min = data['motion'].min() + data_max = data['motion'].max() + + #Plot the data with the Peaks marked + plt.figure(1,figsize=(20, 5)) + + plt.plot(time_m,data['motion']) + + for i in range(len(data)): + if data.iloc[i]["steps"]==1: + x_loc = time_m[i] - realign + plt.plot([x_loc,x_loc],[data_min,data_max],"k") + step_height = data_max * 1.15 + #data['steps_plot'] = data['steps'] * step_height + #plt.plot(time_m,data['steps_plot'],'k') + + plt.xlim([0,time_m[-1]]) + plt.ylim([data_min-.1,data_max+.1]) + plt.title('Motion with Detected "Steps" marked') + plt.ylabel('g') + if x_seconds: + plt.xlabel('Time (s)') + else: + plt.xlabel('Time (min)') + + plt.show() + +if __name__ == "__main__": + print("This script will extract features related to accelerometer data.") + + data, filepath_confirm = getInputLoadFile() + + output_path = getOutputPath() + + time_frames = inputTimeFrames() + + features, steps, motion = computeAllAccelerometerFeatures(data, time_frames) + + data["steps"] = steps + data["motion"] = motion + + saveFeaturesToFile(features, time_frames, output_path) + + print("") + plot_ans = get_user_input("Do you want to plot the detected steps? (y/n): ") + if 'y' in plot_ans: + secs_ans = get_user_input("Would you like the x-axis to be in seconds or minutes? (sec/min): ") + if 'sec' in secs_ans: + x_seconds=True + else: + x_seconds=False + plotSteps(data, x_seconds) + else: + print("\tOkay, script will not produce a plot") + diff --git a/calculatingfeatures/eda_explorer/ArtifactClassifiers.py b/calculatingfeatures/eda_explorer/ArtifactClassifiers.py new file mode 100644 index 00000000..045f4f59 --- /dev/null +++ b/calculatingfeatures/eda_explorer/ArtifactClassifiers.py @@ -0,0 +1,601 @@ +import numpy as np +from sklearn.metrics.pairwise import rbf_kernel + + +def predict_binary_classifier(X): + '''' + X: num test data by 13 features + ''' + + # Get params + params = binary_classifier() + + # compute kernel for all data points + K = rbf_kernel(params['support_vec'], X, gamma=params['gamma']) + + # Prediction = sign((sum_{i=1}^n y_i*alpha*K(x_i,x)) + rho) + predictions = np.zeros(X.shape[0]) + for i in range(X.shape[0]): + predictions[i] = np.sign(np.sum(params['dual_coef']*K[:, i]) + params['intercept']) + + return predictions + + +def predict_multiclass_classifier(X): + ''' + X: num test data by 10 features + ''' + # Get params + params = multiclass_classifier() + + K = rbf_kernel(params['support_vec'], X, gamma=params['gamma']) + + # define the start and end index for support vectors for each class + nv = params['num_support_vec'] + start = [sum(nv[:i]) for i in range(len(nv))] + end = [start[i] + nv[i] for i in range(len(nv))] + + # calculate: sum(a_p * k(x_p, x)) between every 2 classes + dual_coef = params['dual_coef'].T + predictions_0_1 = np.zeros(X.shape[0]) + for i in range(X.shape[0]): + temp_prediction = np.sum(dual_coef[start[0]:end[0], 0] * K[start[0]:end[0], i]) + \ + np.sum(dual_coef[start[1]:end[1], 0] * K[start[1]:end[1], i]) + params['intercept'][0] + predictions_0_1[i] = 0 if temp_prediction > 0 else 1 + + predictions_0_2 = np.zeros(X.shape[0]) + for i in range(X.shape[0]): + temp_prediction = np.sum(dual_coef[start[0]:end[0], 1] * K[start[0]:end[0], i]) + \ + np.sum(dual_coef[start[2]:end[2], 0] * K[start[2]:end[2], i]) + params['intercept'][1] + predictions_0_2[i] = 0 if temp_prediction > 0 else 2 + + predictions_1_2 = np.zeros(X.shape[0]) + for i in range(X.shape[0]): + temp_prediction = np.sum(dual_coef[start[1]:end[1], 1] * K[start[1]:end[1], i]) + \ + np.sum(dual_coef[start[2]:end[2], 1] * K[start[2]:end[2], i]) + params['intercept'][2] + predictions_1_2[i] = 1 if temp_prediction > 0 else 2 + + decision_function = np.vstack([predictions_0_1, predictions_0_2, predictions_1_2]).T + + # Majority Vote to find the best class + predictions = np.zeros(X.shape[0]) + for i in range(X.shape[0]): + lst = decision_function[i,:].tolist() + predictions[i] = max(set(lst), key=lst.count)-1 + + return predictions + + + +def binary_classifier(): + gamma = 0.1 + + # dual coef = y_i*alpha_i + dual_coef = np.array([[-1.12775599e+02, -1.00000000e+03, -1.00000000e+03, + -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, + -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, + -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, + -4.65947457e+02, -1.00000000e+03, -1.00000000e+03, + -1.00000000e+03, -1.17935400e+02, -1.00000000e+03, + -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, + -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, + -1.00000000e+03, -2.92534132e+02, -1.00000000e+03, + -1.00000000e+03, -3.69965631e+01, -1.00000000e+03, + -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, + -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, + -1.00000000e+03, -1.00000000e+03, 1.00000000e+03, + 1.00000000e+03, 1.00000000e+03, 1.00000000e+03, + 7.92366387e+02, 3.00553142e+02, 2.22950860e-01, + 1.00000000e+03, 1.00000000e+03, 5.58636056e+02, + 1.21751544e+02, 1.00000000e+03, 1.00000000e+03, + 2.61920652e+00, 9.96570403e+02, 1.00000000e+03, + 1.00000000e+03, 1.00000000e+03, 1.00000000e+03, + 1.00000000e+03, 1.00000000e+03, 1.02270060e+02, + 5.41288840e+01, 1.91650287e+02, 1.00000000e+03, + 1.00000000e+03, 1.00000000e+03, 1.00000000e+03, + 1.00000000e+03, 2.45152637e+02, 7.53766346e+02, + 1.00000000e+03, 1.00000000e+03, 3.63211198e+00, + 1.00000000e+03, 3.31675798e+01, 5.64620367e+02, + 1.00000000e+03, 1.00000000e+03, 1.00000000e+03, + 2.66900636e+02, 1.00000000e+03, 6.54763900e+02, + 3.38216549e+02, 6.86434772e+01, 2.78998678e+02, + 6.97557950e+02, 1.00000000e+03]]) + + # intercept = rho + intercept = np.array([-2.63232929]) + + # support vectors = x_i + support_vec = np.array([[0.02809756, 0.0455, 0.025, 0.00866667, 0.03799132, -0.00799413, 0.01061208, 0.016263, 0.00671743, 0.00572262, 0.00578504, 0.00542415, 0.00318195], + [0.00060976, 0.0035, 0.007, 0.00087179, 0.00024191, -0.0005069, 0.0005069, 0.0070711, 0.00306413, 0.0031833, 0.0107827, 0.0066959, 0.0022981], + [3.49731707, 0.092, 0.054, 0.01923077, 3.53815367, -0.02236652, 0.02659884, 0.062225, 0.0316782, 0.01818914, 0.06607571, 0.03342241, 0.099702], + [2.52643902, 0.058, 0.055, 0.0114359, 2.54031008, -0.01070662, 0.01296803, 0.043134, 0.01649923, 0.01579683, 0.03326171, 0.05004163, 0.013965], + [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -2.74622599e-18, -2.42947453e-17, 3.36047450e-17, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], + [3.89758537, 0.167, 0.27, 0.06717949, 3.87923565, -0.04130143, 0.05403825, 0.047376, 0.0328098, 0.01255584, 0.03676955, 0.14237773, 0.11031], + [0.93326829, 0.0855, 0.106, 0.01169231, 0.92669874, -0.02740927, 0.02740927, 0.043841, 0.01131377, 0.01595008, 0.0231871, 0.02414775, 0.0139655], + [4.64253659, 0.106, 0.13, 0.03661538, 4.63806066, -0.03168223, 0.03168223, 0.10182, 0.0559785, 0.03369301, 0.06341563, 0.08583294, 0.0251025], + [0.29312195, 0.028, 0.039, 0.00682051, 0.28575076, -0.00648365, 0.00648365, 0.0056569, 0.00367694, 0.00126494, 0.00364005, 0.01814984, 0.006364], + [3.08187805, 0.0615, 0.123, 0.03435897, 3.11862292, -0.02260403, 0.02260403, 0.053033, 0.0397394, 0.01570345, 0.0338851, 0.10069204, 0.16652], + [2.43902439e-05, 5.00000000e-04, 1.00000000e-03, 1.02564103e-04, 2.43769719e-05, -7.19856842e-05, 7.19856842e-05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], + [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -4.05052739e-10, -2.77557303e-09, 5.77955577e-09, 7.07110000e-04, 1.17851667e-04, 2.88676449e-04, 2.04124145e-04, 1.44336183e-04, 0.00000000e+00], + [0.83290244, 0.099, 0.172, 0.02610256, 0.82408369, -0.0168393, 0.0168393, 0.13011, 0.02875613, 0.04987211, 0.03786379, 0.02684837, 0.0155565], + [0.92597561, 0.017, 0.009, 0.00369231, 0.92583814, -0.00670974, 0.00670974, 0.012021, 0.00506763, 0.00420523, 0.01259266, 0.0115391, 0.00265165], + [2.43902439e-05, 5.00000000e-04, 1.00000000e-03, 2.56410256e-05, 2.18000765e-04, -5.56411248e-04, 5.56411248e-04, 9.19240000e-03, 2.71058333e-03, 4.25246049e-03, 2.49833278e-03, 7.64311464e-03, 0.00000000e+00], + [0.88760976, 0.0205, 0.022, 0.00489744, 0.88799505, -0.00346772, 0.00461828, 0.011314, 0.00447838, 0.00394135, 0.01327278, 0.01434142, 0.00406585], + [9.21263415, 0.118, 0.472, 0.0695641, 9.19153391, -0.02181738, 0.02181738, 0.16688, 0.07130037, 0.06135461, 0.04328934, 0.04277416, 0.0829085], + [0.48378049, 0.017, 0.026, 0.00794872, 0.48333175, -0.00337375, 0.00350864, 0.016971, 0.0089568, 0.00472601, 0.01168189, 0.01629524, 0.0226275], + [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 9.65026603e-122, -2.00921455e-120, 4.22507597e-120, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000], + [0.10897561, 0.03, 0.033, 0.00553846, 0.12761266, -0.00442938, 0.00556735, 0.025456, 0.00872107, 0.00870258, 0.01130487, 0.01554551, 0.0123745], + [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -1.38812548e-09, -2.34438020e-08, 2.34438020e-08, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], + [0.66663415, 0.052, 0.05, 0.00510256, 0.66182973, -0.01361869, 0.01361869, 0.0049497, 0.00296982, 0.00208565, 0.00424264, 0.00961131, 0.012374], + [3.74146341e+00, 6.60000000e-02, 7.00000000e-02, 2.41025641e-02, 3.72790310e+00, -1.65194036e-02, 1.65194036e-02, 2.33350000e-02, 2.29102000e-02, 3.87787571e-04, 7.25086202e-03, 8.04828002e-03, 2.26270000e-02], + [2.43902439e-05, 5.00000000e-04, 1.00000000e-03, 1.02564103e-04, 2.44149661e-05, -7.19856850e-05, 7.19856850e-05, 7.07110000e-04, 1.17851667e-04, 2.88676449e-04, 2.04124145e-04, 1.44336183e-04, 0.00000000e+00], + [1.14713659e+01, 1.68000000e-01, 3.24000000e-01, 8.83589744e-02, 1.13977278e+01, -4.35202063e-02, 4.35202063e-02, 1.20920000e-01, 1.15826000e-01, 5.32593935e-03, 4.29825546e-02, 1.11681949e-01, 1.82080000e-01], + [1.63631707, 0.0825, 0.138, 0.02410256, 1.65473267, -0.02914746, 0.02927458, 0.074953, 0.02899134, 0.03271076, 0.02718317, 0.09610564, 0.012728], + [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.01460518e-42, -2.71490067e-40, 2.71490067e-40, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], + [0.52358537, 0.038, 0.03, 0.00769231, 0.52319376, -0.01066405, 0.01066405, 0.026163, 0.01025307, 0.00912966, 0.02678697, 0.04011893, 0.00866185], + [0.10931707, 0.103, 0.407, 0.04461538, 0.13188551, -0.01686662, 0.02506229, 0.1492, 0.0384195, 0.06327203, 0.06411448, 0.05508901, 0], + [0.0444878, 0.0245, 0.04, 0.00984615, 0.03577326, -0.00573919, 0.00573919, 0.013435, 0.0078961, 0.00418135, 0.01136515, 0.01291603, 0.0134352], + [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.03127202e-08, -2.56175141e-07, 5.37317466e-07, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.27917545e-05, -7.79437718e-04, 7.79437718e-04, 3.04060000e-02, 5.06766667e-03, 1.24131975e-02, 1.34721936e-02, 5.34029589e-02, 0.00000000e+00], + 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2.35933619e-04, 1.41420000e-03, 1.41420000e-03, 2.60312573e-11, 4.08248290e-04, 2.88668284e-04, 7.07110000e-04], + [0.82373171, 0.048, 0.121, 0.01853846, 0.82149219, -0.0053288, 0.00684639, 0.041012, 0.0208598, 0.01423898, 0.02609294, 0.02676908, 0.01078335], + [4.39680488, 0.223, 0.354, 0.09258974, 4.35973108, -0.03206468, 0.03450864, 0.20506, 0.0971572, 0.07235446, 0.13713059, 0.23019854, 0.32138], + [5.66058537, 0.0285, 0.093, 0.01282051, 5.66682734, -0.00633008, 0.00633008, 0.040305, 0.01513214, 0.01889847, 0.01503912, 0.03383458, 0], + [0.13329268, 0.011, 0.021, 0.00338462, 0.13419267, -0.00262455, 0.00262455, 0.0035355, 0.00226272, 0.00092195, 0.00772172, 0.00411547, 0.0038891], + [0.15463415, 0.0325, 0.065, 0.01617949, 0.15422134, -0.00766504, 0.00766504, 0.067882, 0.02286322, 0.02270081, 0.02939288, 0.0224428, 0.017501], + [1.47902439e-01, 1.50000000e-03, 2.00000000e-03, 3.84615385e-04, 1.48269290e-01, -1.36058722e-04, 1.36058722e-04, 2.12130000e-03, 8.24950000e-04, 9.39849132e-04, 5.16397779e-04, 5.91603500e-04, 0.00000000e+00], + [2.76797561, 0.071, 0.17, 0.03212821, 2.84223399, -0.01692731, 0.01692731, 0.04879, 0.03441267, 0.00934515, 0.03221283, 0.05768286, 0.092806], + [1.30939024, 0.044, 0.066, 0.0165641, 1.2967273, -0.01727205, 0.01727205, 0.03182, 0.01456652, 0.01056655, 0.00732632, 0.02987207, 0.038891], + [0.0914878, 0.038, 0.028, 0.00364103, 0.08295897, -0.00877545, 0.00877545, 0.032527, 0.00648182, 0.01277828, 0.01289089, 0.01040763, 0.0042426], + [0.13621951, 0.0015, 0.006, 0.00174359, 0.13689296, -0.00036169, 0.00040731, 0.0021213, 0.00153205, 0.00082663, 0.00058452, 0.00069522, 0.00088391], + [0.05692683, 0.007, 0.006, 0.00189744, 0.05532006, -0.00145672, 0.00145672, 0.0056569, 0.00311126, 0.00184393, 0.00420714, 0.00465287, 0.0070711], + [0.07460976, 0.002, 0.006, 0.00097436, 0.07430141, -0.00035004, 0.00038011, 0.0028284, 0.00113136, 0.0011832, 0.00070711, 0.0005916, 0.00070711], + [0.04782927, 0.006, 0.011, 0.00353846, 0.04406202, -0.00232859, 0.00232859, 0.012021, 0.00438408, 0.00442728, 0.00363318, 0.00540593, 0.0091924], + [4.443, 0.141, 0.076, 0.02310256, 4.40858239, -0.03710778, 0.03710778, 0.03182, 0.0271528, 0.00465324, 0.03506173, 0.07970664, 0.11278], + [8.79678049, 0.057, 0.208, 0.04194872, 8.784878, -0.01132933, 0.01132933, 0.08061, 0.04695182, 0.039817, 0.0405623, 0.01937402, 0.033234], + [2.58236585, 0.063, 0.128, 0.02112821, 2.5705713, -0.0079298, 0.01979542, 0.062225, 0.0309712, 0.02172778, 0.02949491, 0.02741888, 0.02687], + [0.08992683, 0.0015, 0.006, 0.00030769, 0.09000535, -0.00020308, 0.00020308, 0.0021213, 0.00106065, 0.00116188, 0.0007746, 0.00086603, 0.00053035], + [0.09085366, 0.0175, 0.037, 0.00694872, 0.09607742, -0.00456388, 0.00456388, 0.0098995, 0.00523258, 0.00310646, 0.01357571, 0.0133944, 0.0056569], + [1.34473171, 0.0255, 0.022, 0.00953846, 1.37010789, -0.00558419, 0.00558419, 0.030406, 0.0134351, 0.00877511, 0.00929516, 0.03188089, 0.0265165], + [0.14253659, 0.001, 0.004, 0.00097436, 0.14237889, -0.0002998, 0.0002998, 0.0014142, 0.0011785, 0.00057734, 0.0005164, 0.00069521, 0.00106066], + [0.07617073, 0.001, 0.004, 0.00179487, 0.07597272, -0.00025949, 0.00025949, 0.0014142, 0.0011785, 0.00057734, 0.0005164, 0.00063245, 0.00070711], + [0.28502439, 0.0025, 0.01, 0.00241026, 0.28596915, -0.000355, 0.000355, 0.12869, 0.02333393, 0.05162999, 0.0313152, 0.13233722, 0.0044194], + [5.97658537, 0.0645, 0.106, 0.02925641, 5.95365623, -0.01454886, 0.01454886, 0.045962, 0.02913296, 0.02145587, 0.04602717, 0.06410626, 0.053033], + [4.19787805, 0.0405, 0.072, 0.02764103, 4.21230508, -0.01456906, 0.01468492, 0.030406, 0.02206174, 0.01003006, 0.02031748, 0.03873656, 0.034295], + [0.06904878, 0.0025, 0.005, 0.00117949, 0.06819891, -0.00023428, 0.00033805, 0.0035355, 0.00098994, 0.00154918, 0.001, 0.0007071, 0.00070711], + [2.07410488e+01, 1.10000000e-02, 4.40000000e-02, 1.24102564e-02, 2.07288498e+01, -5.11402880e-02, 5.11402880e-02, 1.55560000e-02, 1.55560000e-02, 0.00000000e+00, 5.68037557e-03, 3.17543685e-03, 7.77820000e-03], + [0.15141463, 0.0025, 0.008, 0.00161538, 0.15286961, -0.00066236, 0.00066236, 0.0049497, 0.0021213, 0.00180276, 0.00235584, 0.01268589, 0.0021213], + [1.07970732, 0.0275, 0.046, 0.00725641, 1.0819483, -0.0025949, 0.00261392, 0.026163, 0.00754248, 0.00945165, 0.01400506, 0.00566908, 0.011137], + [1.45278049e+00, 2.50000000e-02, 3.40000000e-02, 8.23076923e-03, 1.46401853e+00, -5.22375992e-03, 7.56803574e-03, 8.48530000e-03, 6.71755000e-03, 1.39641061e-03, 4.14024959e-03, 1.47976972e-02, 2.03295000e-02], + [1.18829268e-01, 1.00000000e-03, 4.00000000e-03, 1.17948718e-03, 1.18657803e-01, -3.33958979e-04, 3.55599268e-04, 1.41420000e-03, 1.41420000e-03, 2.60312573e-11, 6.32455532e-04, 5.32284214e-04, 7.07110000e-04], + [0.09217073, 0.0085, 0.007, 0.00258974, 0.07952256, -0.00104703, 0.00138337, 0.006364, 0.00466692, 0.00203719, 0.00509166, 0.01307342, 0.021213], + [0.06936585, 0.0095, 0.015, 0.00394872, 0.06837444, -0.00205373, 0.00205373, 0.0084853, 0.00296984, 0.0030984, 0.00234521, 0.00419839, 0.0017678], + [5.05807317, 0.049, 0.082, 0.02402564, 5.06327737, -0.01120311, 0.01120311, 0.031113, 0.0239, 0.01338272, 0.01117139, 0.04351642, 0.020506], + [0.26421951, 0.04, 0.068, 0.00902564, 0.2587529, -0.01040894, 0.01040894, 0.025456, 0.01060666, 0.00890233, 0.01111643, 0.04563416, 0.011314], + [3.59336585, 0.0575, 0.054, 0.02094872, 3.58195886, -0.01804095, 0.01838506, 0.043134, 0.0336584, 0.01240579, 0.01683523, 0.04717173, 0.038184], + [1.29187805, 0.026, 0.016, 0.00689744, 1.27916244, -0.00322078, 0.00490015, 0.025456, 0.01032378, 0.00861112, 0.01863263, 0.0636921, 0.038537], + [6.28670732, 0.1245, 0.127, 0.03102564, 6.35501978, -0.01747513, 0.02813757, 0.084146, 0.04690465, 0.0254467, 0.06541464, 0.18275149, 0.15008], + [10.64578049, 0.079, 0.284, 0.04564103, 10.64447668, -0.01946271, 0.01947497, 0.10889, 0.04186, 0.05739752, 0.06891299, 0.05417812, 0.050205], + [3.32470732, 0.092, 0.046, 0.01687179, 3.32977984, -0.02794509, 0.02794509, 0.072125, 0.0288498, 0.02428699, 0.06277798, 0.10343739, 0.061518], + [0.07358537, 0.001, 0.004, 0.00153846, 0.0735262, -0.00027514, 0.00027514, 0.0014142, 0.0009428, 0.00073029, 0.00075277, 0.00053228, 0.00070711]]) + + return {'dual_coef': dual_coef, + 'support_vec': support_vec, + 'intercept': intercept, + 'gamma': gamma} + + +def multiclass_classifier(): + gamma = 0.1 + + # dual coef = y_i*alpha_i + dual_coef = np.array([[1.00000000e+02, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, 2.19164051e-01, + 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, + 2.73972798e+00, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 5.78184818e+01, + 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, + 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 4.43824790e+01, + 0.00000000e+00, 0.00000000e+00, 8.90021137e+01, 1.00000000e+02, 3.38829336e+01, + 1.00000000e+02, 7.35308055e+01, 5.00832282e+01, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 9.04295253e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, + 1.00000000e+02, 1.00000000e+02, 7.37255035e+01, 1.00000000e+02, 0.00000000e+00, + 1.00000000e+02, -1.00000000e+02, -4.59726588e+01, -9.10060871e+01, -0.00000000e+00, + -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, + -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, + -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, + -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, + -1.00000000e+02, -1.00000000e+02, -2.32473120e-01, -1.00000000e+02, -0.00000000e+00, + -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, + -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, + -2.01478019e-01, -1.00000000e+02, -5.32795432e+01, -0.00000000e+00, -0.00000000e+00, + -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, + -1.00000000e+02, -2.05233000e+01, -0.00000000e+00, -9.58435547e-02, -0.00000000e+00, + -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, + -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, + -1.00000000e+02, -0.00000000e+00, -1.14900102e+01, -7.73085905e+01, -1.00000000e+02, + -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, + -0.00000000e+00, -8.64770605e+01, -1.00000000e+02, -1.18090663e-01, -1.00000000e+02, + -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, + -0.00000000e+00, -6.27523608e+01, -0.00000000e+00, -4.38003436e+01, -0.00000000e+00, + -0.00000000e+00, -5.36807440e-02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, + -0.00000000e+00, -1.51862509e-01, -2.23505792e+01, -0.00000000e+00, -1.71549400e+00, + -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, + -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, + -6.48908553e+01, -5.45079781e+01, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, + -1.00000000e+02, -4.15526000e+01, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, + -3.97322757e+01, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, + -1.00000000e+02, -8.51452564e+01, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, + -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, + -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, + -1.00000000e+02, -1.36707150e+01, -2.28944671e+00, -1.00000000e+02, -1.00000000e+02, + -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -9.70237576e+01, + -0.00000000e+00, -1.00000000e+02, -8.98901380e+00, -1.00000000e+02, -0.00000000e+00, + -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, + -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -2.31872364e+00, -0.00000000e+00, + -1.00000000e+02, -1.00000000e+02, -6.81207558e+01, -0.00000000e+00, -1.00000000e+02, + -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, + -1.25804913e+01, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, + -0.00000000e+00, -0.00000000e+00, -5.79636185e+01, -0.00000000e+00, -3.60349193e+01, + -1.00000000e+02, -1.00000000e+02], + [1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, + 1.00000000e+02, 0.00000000e+00, 1.22133880e+01, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, + 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 5.45699567e+01, 0.00000000e+00, + 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 0.00000000e+00, + 1.00000000e+02, 1.00000000e+02, 5.30198194e+01, 1.00000000e+02, 0.00000000e+00, + 8.10028022e+01, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 8.57299348e+01, + 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, + 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 6.98226850e+00, 1.00000000e+02, + 1.00000000e+02, 2.28942244e+00, 1.00000000e+02, 0.00000000e+00, 3.10951756e+00, + 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 2.43965458e+01, + 5.54247795e+01, 4.89715327e+01, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 8.77648862e-01, + 1.41352297e+00, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 5.87399500e+01, + 1.00000000e+02, 7.89673831e+01, 7.17216921e-01, 7.08622898e+01, 1.00000000e+02, + 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 0.00000000e+00, 7.08652210e+01, + 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 2.28740165e+00, 1.00000000e+02, + 1.00000000e+02, 1.00000000e+02, 6.26644343e+01, 1.51915932e+01, 9.33156003e+01, + 1.00000000e+02, 5.73480226e-01, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, + 6.51947143e+01, 0.00000000e+00, 1.00000000e+02, 3.61854680e+01, 1.50700439e+00, + 3.93114839e+01, 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00, + 1.00000000e+02, 1.62942145e+01, 1.00000000e+02, 1.00000000e+02, 3.65697187e+01, + 3.32328741e+01, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, + 1.00000000e+02, 0.00000000e+00, 3.84017861e-02, 3.27497129e+00, 1.00000000e+02, + 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, -1.00000000e+02, -1.00000000e+02, + -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -7.87287696e+01, + -1.00000000e+02, -2.17133274e+01, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, + -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, + -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, + -1.00000000e+02, -4.03561653e+01, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, + -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -8.73885349e+01, -1.00000000e+02, + -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, + -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, + -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, + -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, + -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, + -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -7.69821289e+01, -0.00000000e+00, + -1.00000000e+02, -8.28241499e+01, -1.00000000e+02, -6.27852100e+00, -8.74723914e+01, + -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, + -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, + -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -2.15412985e+01, -0.00000000e+00, + -2.97074994e+01, -9.62658735e+01, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, + -1.00000000e+02, -0.00000000e+00]]) + + # intercept = rho + intercept = np.array([-0.62674907, 1.31994877, 0.67252991]) + + # support vectors = x_i + support_vec = np.array([[5.49570019e-07, -2.58632551e-07, 3.16229206e-02, 0.00000000e+00, 0.00000000e+00, 7.07110000e-02, 0.00000000e+00, 0.00000000e+00, 5.49570019e-07,-1.79132036e-08], + [0.01061208, -0.00799413, 0.00572262, 0.025, 0.02809756,0.016263, 0.058, 0.00866667, 0.01061208, 0.03799132], + [0.0005069, -0.0005069, 0.0031833, 0.007, 0.00060976,0.0070711, 0.014, 0.00087179, 0.00039282, 0.00024191], + [0.02659884, -0.02236652, 0.01818914, 0.054, 3.49731707,0.062225, 0.063, 0.01923077, 0.02659884, 3.53815367], + [0.1959552, -0.19377234, 0.49935644, 2.567, 0.212,1.2473, 4.086, 0.27730769, 0.1959552, 0.21128449], + [0.01296803, -0.01070662, 0.01579683, 0.055, 2.52643902,0.043134, 0.057, 0.0114359, 0.01296803, 2.54031008], + [0.04634941, -0.03616377, 0.03342396, 0.285, 1.25278049,0.11031, 0.285, 0.05482051, 0.04634941, 1.24439126], + [0.01161685, -0.01161685, 0.01472225, 0.061, 0.00495122,0.036062, 0.131, 0.00758974, 0.00936955, 0.00494698], + [3.36047450e-17, -2.42947453e-17, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.36047450e-17,-2.74622599e-18], + [0.05403825, -0.04130143, 0.01255584, 0.27, 3.89758537,0.047376, 0.27, 0.06717949, 0.05403825, 3.87923565], + [0.06322635, -0.0450853, 0.0069893, 0.107, 0.52617073,0.019799, 0.166, 0.01469231, 0.06322635, 0.51547654], + [0.02740927, -0.02740927, 0.01595008, 0.106, 0.93326829,0.043841, 0.106, 0.01169231, 0.01663183, 0.92669874], + [0.02181645, -0.02181645, 0.00031623, 0.045, 0.04685366,0.00070711, 0.045, 0.00605128, 0.01964941, 0.05939633], + [0.03168223, -0.03168223, 0.03369301, 0.13, 4.64253659,0.10182, 0.13, 0.03661538, 0.02589546, 4.63806066], + [0.00648365, -0.00648365, 0.00126494, 0.039, 0.29312195,0.0056569, 0.039, 0.00682051, 0.0043318, 0.28575076], + [0.02260403, -0.02260403, 0.01570345, 0.123, 3.08187805,0.053033, 0.123, 0.03435897, 0.01139526, 3.11862292], + [7.19856842e-05, -7.19856842e-05, 0.00000000e+00, 1.00000000e-03, 2.43902439e-05, 0.00000000e+00, 2.00000000e-03, 1.02564103e-04, 5.59639155e-05, 2.43769719e-05], + [5.77955577e-09, -2.77557303e-09, 2.88676449e-04, 0.00000000e+00, 0.00000000e+00, 7.07110000e-04, 0.00000000e+00, 0.00000000e+00, 5.77955577e-09,-4.05052739e-10], + [0.0168393, -0.0168393, 0.04987211, 0.172, 0.83290244,0.13011, 0.287, 0.02610256, 0.01063438, 0.82408369], + [0.00670974, -0.00670974, 0.00420523, 0.009, 0.92597561,0.012021, 0.012, 0.00369231, 0.00394705, 0.92583814], + [0.04881422, -0.04881422, 0.06039519, 0.128, 0.84173171,0.15274, 0.166, 0.0145641, 0.02705203, 0.83114509], + [5.56411248e-04, -5.56411248e-04, 4.25246049e-03, 1.00000000e-03, 2.43902439e-05, 9.19240000e-03, 1.00000000e-03, 2.56410256e-05, 4.16114259e-04, 2.18000765e-04], + [0.00461828, -0.00346772, 0.00394135, 0.022, 0.88760976,0.011314, 0.022, 0.00489744, 0.00461828, 0.88799505], + [0.02181738, -0.02181738, 0.06135461, 0.472, 9.21263415,0.16688, 0.472, 0.0695641, 0.01361679, 9.19153391], + [0.18064104, -0.18064104, 0.02243327, 0.141, 1.74753659,0.065761, 0.141, 0.02587179, 0.08614869, 1.89288442], + [0.04502684, -0.04502684, 0.03092595, 0.075, 0.66726829,0.070004, 0.075, 0.01507692, 0.02703687, 0.6524218 ], + [0.00350864, -0.00337375, 0.00472601, 0.026, 0.48378049,0.016971, 0.038, 0.00794872, 0.00350864, 0.48333175], + [4.22507597e-120, -2.00921455e-120, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 4.22507597e-120, 9.65026603e-122], + [0.0511954, -0.0511954, 0.00917865, 0.032, 1.50741463,0.028284, 0.056, 0.00958974, 0.01612377, 1.43087637], + [0.0504243, -0.03682241, 0.03693438, 0.147, 3.4434878,0.086267, 0.147, 0.02051282, 0.0504243, 3.49633275], + [0.00556735, -0.00442938, 0.00870258, 0.033, 0.10897561,0.025456, 0.033, 0.00553846, 0.00556735, 0.12761266], + [2.34438020e-08, -2.34438020e-08, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.15475168e-08,-1.38812548e-09], + [0.01361869, -0.01361869, 0.00208565, 0.05, 0.66663415,0.0049497, 0.05, 0.00510256, 0.0087315, 0.66182973], + [0.00526982, -0.00287197, 0.016589, 0.06, 0.02595122,0.042426, 0.06, 0.00515385, 0.00526982, 0.02530658], + [1.65194036e-02, -1.65194036e-02, 3.87787571e-04, 7.00000000e-02, 3.74146341e+00, 2.33350000e-02, 1.06000000e-01, 2.41025641e-02, 1.58008422e-02, 3.72790310e+00], + [7.19856850e-05, -7.19856850e-05, 2.88676449e-04, 1.00000000e-03, 2.43902439e-05, 7.07110000e-04, 2.00000000e-03, 1.02564103e-04, 5.59639159e-05, 2.44149661e-05], + [4.35202063e-02, -4.35202063e-02, 5.32593935e-03, 3.24000000e-01, 1.14713659e+01, 1.20920000e-01, 3.24000000e-01, 8.83589744e-02, 3.31111507e-02, 1.13977278e+01], + [0.0767267, -0.0767267, 0.07121201, 0.446, 8.87180488,0.24324, 0.446, 0.10620513, 0.0720187, 8.83162683], + [0.02927458, -0.02914746, 0.03271076, 0.138, 1.63631707,0.074953, 0.138, 0.02410256, 0.02927458, 1.65473267], + [0.02124579, -0.00660226, 0.05683001, 0.218, 0.16541463,0.13718, 0.241, 0.04171795, 0.02124579, 0.07039812], + [2.71490067e-40, -2.71490067e-40, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.26462710e-40, 6.01460518e-42], + [0.0147575, -0.0147575, 0.016589, 0.06, 0.03368293,0.042426, 0.06, 0.00917949, 0.01001548, 0.03172037], + [0.05563514, -0.05003055, 0.0377493, 0.474, 3.34209756,0.10819, 0.474, 0.04441026, 0.05563514, 3.34310614], + [0.01066405, -0.01066405, 0.00912966, 0.03, 0.52358537,0.026163, 0.042, 0.00769231, 0.00753237, 0.52319376], + [0.02506229, -0.01686662, 0.06327203, 0.407, 0.10931707,0.1492, 0.407, 0.04461538, 0.02506229, 0.13188551], + [0.05540528, -0.05540528, 0.05916798, 0.36, 5.93456098,0.18526, 0.36, 0.14474359, 0.03879351, 5.91696978], + [0.05114493, -0.04906722, 0.03169166, 0.444, 1.59946341,0.089803, 0.444, 0.051, 0.05114493, 1.39984371], + [1.87455177e-04, -8.82323678e-05, 3.60629601e-02, 0.00000000e+00, 0.00000000e+00, 7.63680000e-02, 0.00000000e+00, 0.00000000e+00, 1.87455177e-04,-1.21670239e-05], + [0.00573919, -0.00573919, 0.00418135, 0.04, 0.0444878,0.013435, 0.04, 0.00984615, 0.00463094, 0.03577326], + [0.02921642, -0.02921642, 0.06455675, 0.145, 1.28114634,0.15486, 0.145, 0.0314359, 0.00907076, 1.30595106], + [0.08101569, -0.05496349, 0.03591274, 0.557, 5.79265854,0.1294, 0.557, 0.10274359, 0.08101569, 5.7521422 ], + [5.37317466e-07, -2.56175141e-07, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.37317466e-07, 1.03127202e-08], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [7.79437718e-04, -7.79437718e-04, 1.24131975e-02, 0.00000000e+00, 0.00000000e+00, 3.04060000e-02, 0.00000000e+00, 0.00000000e+00, 3.70210952e-04, 3.27917545e-05], + [7.19856850e-05, -7.19856850e-05, 2.88676449e-04, 1.00000000e-03, 2.43902439e-05, 7.07110000e-04, 2.00000000e-03, 1.02564103e-04, 4.31188798e-05, 2.60691650e-05], + [0.00866364, -0.00866364, 0.01214903, 0.03, 0.46446341,0.033941, 0.048, 0.00933333, 0.00584264, 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0.00890233, 0.068, 0.26421951,0.025456, 0.068, 0.00902564, 0.00546137, 0.2587529 ], + [8.19684883e-03, -8.19684883e-03, 2.09202670e-02, 6.30000000e-02, 6.51109756e+00, 4.45480000e-02, 6.30000000e-02, 1.66153846e-02, 5.80672162e-03, 6.50965345e+00], + [0.00023369, -0.00023369, 0.00246646, 0.007, 0.16326829,0.006364, 0.007, 0.00079487, 0.00019522, 0.1627248 ], + [0.00490015, -0.00322078, 0.00861112, 0.016, 1.29187805,0.025456, 0.016, 0.00689744, 0.00490015, 1.27916244], + [0.0134886, -0.00766548, 0.0189313, 0.109, 6.21553659,0.041012, 0.109, 0.03533333, 0.0134886, 6.22416714], + [0.02813757, -0.01747513, 0.0254467, 0.127, 6.28670732,0.084146, 0.127, 0.03102564, 0.02813757, 6.35501978], + [0.02044022, -0.02044022, 0.03488198, 0.176, 8.10602439,0.065761, 0.176, 0.04466667, 0.01777938, 8.12759058], + [0.01947497, -0.01946271, 0.05739752, 0.284,10.64578049, 0.10889, 0.296, 0.04564103, 0.01947497, 10.64447668], + [0.01922614, -0.01922614, 0.02849813, 0.097, 4.29178049,0.08061, 0.097, 0.02720513, 0.01556252, 4.30987745], + [0.00027514, -0.00027514, 0.00073029, 0.004, 0.07358537,0.0014142, 0.004, 0.00153846, 0.0002546, 0.0735262]]) + + num_support_vec = [61, 97, 89] + + return {'dual_coef': dual_coef, + 'support_vec': support_vec, + 'intercept': intercept, + 'gamma': gamma, + 'num_support_vec': num_support_vec} + + +if __name__ == '__main__': + + binary_test_data = np.array([ + [0., 0., 0., 0., 0., 0., 0., 0.14212846, 0.02368808, 0.0580237, 0.06960633, 0.04921911, 0.], + [0.21073171, 0.794, 0.922, 0.14076923, 0.20974742, -0.32312654, 0.32312654, 0.88741901, 0.17300546, 0.3544437, 0.39891235, 0.5271785, 0.0076014], + [0.04058537, 0.009, 0.008, 0.00225641, 0.03362015, -0.00420592, 0.00420592, 0.00565685, 0.00235702, 0.00193218, 0.00581951, 0.00861878, 0.00671751], + [0.07887805, 0.0035, 0.007, 0.00179487, 0.07598638, -0.00096018, 0.00113304, 0.00494975, 0.00235702, 0.00177012, 0.00098742, 0.00128452, 0.00335876], + [0.10126829, 0.0015, 0.004, 0.00174359, 0.09954269, -0.00034342, 0.00034342, 0.00212132, 0.00153206, 0.00028868, 0.00075829, 0.00064872, 0.00212132] + ]) + binary_predictions = predict_binary_classifier(binary_test_data) + + import pickle + f2 = open('/Users/sarataylor/Dev/eda-explorer-public/SVMBinary.p', 'rb') + s2 = f2.read() + clf = pickle.loads(s2) + + assert(len([1 for i in range(5) if binary_predictions[i] != clf.predict(binary_test_data)[i]]) == 0) + + # Test multiclass + test_data = np.array([[3.11141105e-02, -3.11136868e-02, 2.42079822e-02, 7.49220000e-02, 1.15335178e+01, 8.37681119e-02, 7.49220000e-02, 1.03606795e-02, 3.11141105e-02, 1.15205823e+01], + [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], + [0.10255266, -0.10255266, 0.03827904, 0.328471, 6.61645195, 0.26352986, 0.406695, 0.02494941, 0.05696297, 7.64941098], + [0.1095642, -0.1095642, 0.08589464, 0.113983, 11.49373772, 0.26352986, 0.113983, 0.01375942, 0.03753318, 11.51816541], + [0.15404637, -0.08878016, 0.1020834, 0.768917, 11.40673696, 0.28606288, 0.768917, 0.08697605, 0.15404637, 11.46339086]]) + + multi_predictions = predict_multiclass_classifier(test_data) + + f2 = open('/Users/sarataylor/Dev/eda-explorer-public/SVMMulticlass.p', 'rb') + s2 = f2.read() + clf = pickle.loads(s2) + + assert (len([1 for i in range(5) if multi_predictions[i] != clf.predict(test_data)[i]]) == 0) + + + diff --git a/calculatingfeatures/eda_explorer/EDA-Artifact-Detection-Script.py b/calculatingfeatures/eda_explorer/EDA-Artifact-Detection-Script.py new file mode 100644 index 00000000..13f0432e --- /dev/null +++ b/calculatingfeatures/eda_explorer/EDA-Artifact-Detection-Script.py @@ -0,0 +1,411 @@ +import pandas as pd +import numpy as np +import matplotlib +import matplotlib.pyplot as plt +import pywt +import os +import datetime + +from load_files import getInputLoadFile, get_user_input +from ArtifactClassifiers import predict_binary_classifier, predict_multiclass_classifier + +matplotlib.rcParams['ps.useafm'] = True +matplotlib.rcParams['pdf.use14corefonts'] = True +matplotlib.rcParams['text.usetex'] = True + + +def getWaveletData(data): + ''' + This function computes the wavelet coefficients + + INPUT: + data: DataFrame, index is a list of timestamps at 8Hz, columns include EDA, filtered_eda + + OUTPUT: + wave1Second: DateFrame, index is a list of timestamps at 1Hz, columns include OneSecond_feature1, OneSecond_feature2, OneSecond_feature3 + waveHalfSecond: DateFrame, index is a list of timestamps at 2Hz, columns include HalfSecond_feature1, HalfSecond_feature2 + ''' + startTime = data.index[0] + + # Create wavelet dataframes + oneSecond = pd.date_range(start=startTime, periods=len(data), freq='1s') + halfSecond = pd.date_range(start=startTime, periods=len(data), freq='500L') + + # Compute wavelets + cA_n, cD_3, cD_2, cD_1 = pywt.wavedec(data['EDA'], 'Haar', level=3) #3 = 1Hz, 2 = 2Hz, 1=4Hz + + # Wavelet 1 second window + N = int(len(data)/8) + coeff1 = np.max(abs(np.reshape(cD_1[0:4*N],(N,4))), axis=1) + coeff2 = np.max(abs(np.reshape(cD_2[0:2*N],(N,2))), axis=1) + coeff3 = abs(cD_3[0:N]) + wave1Second = pd.DataFrame({'OneSecond_feature1':coeff1,'OneSecond_feature2':coeff2,'OneSecond_feature3':coeff3}) + wave1Second.index = oneSecond[:len(wave1Second)] + + # Wavelet Half second window + N = int(np.floor((len(data)/8.0)*2)) + coeff1 = np.max(abs(np.reshape(cD_1[0:2*N],(N,2))),axis=1) + coeff2 = abs(cD_2[0:N]) + waveHalfSecond = pd.DataFrame({'HalfSecond_feature1':coeff1,'HalfSecond_feature2':coeff2}) + waveHalfSecond.index = halfSecond[:len(waveHalfSecond)] + + return wave1Second,waveHalfSecond + + +def getDerivatives(eda): + deriv = (eda[1:-1] + eda[2:])/ 2. - (eda[1:-1] + eda[:-2])/ 2. + second_deriv = eda[2:] - 2*eda[1:-1] + eda[:-2] + return deriv,second_deriv + + +def getDerivStats(eda): + deriv, second_deriv = getDerivatives(eda) + maxd = max(deriv) + mind = min(deriv) + maxabsd = max(abs(deriv)) + avgabsd = np.mean(abs(deriv)) + max2d = max(second_deriv) + min2d = min(second_deriv) + maxabs2d = max(abs(second_deriv)) + avgabs2d = np.mean(abs(second_deriv)) + + return maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d + + +def getStats(data): + eda = data['EDA'].values + filt = data['filtered_eda'].values + maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d = getDerivStats(eda) + maxd_f,mind_f,maxabsd_f,avgabsd_f,max2d_f,min2d_f,maxabs2d_f,avgabs2d_f = getDerivStats(filt) + amp = np.mean(eda) + amp_f = np.mean(filt) + return amp, maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d,amp_f,maxd_f,mind_f,maxabsd_f,avgabsd_f,max2d_f,min2d_f,maxabs2d_f,avgabs2d_f + + +def computeWaveletFeatures(waveDF): + maxList = waveDF.max().tolist() + meanList = waveDF.mean().tolist() + stdList = waveDF.std().tolist() + medianList = waveDF.median().tolist() + aboveZeroList = (waveDF[waveDF>0]).count().tolist() + + return maxList,meanList,stdList,medianList,aboveZeroList + + +def getWavelet(wave1Second,waveHalfSecond): + max_1,mean_1,std_1,median_1,aboveZero_1 = computeWaveletFeatures(wave1Second) + max_H,mean_H,std_H,median_H,aboveZero_H = computeWaveletFeatures(waveHalfSecond) + return max_1,mean_1,std_1,median_1,aboveZero_1,max_H,mean_H,std_H,median_H,aboveZero_H + + +def getFeatures(data,w1,wH): + # Get DerivStats + amp,maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d,amp_f,maxd_f,mind_f,maxabsd_f,avgabsd_f,max2d_f,min2d_f,maxabs2d_f,avgabs2d_f = getStats(data) + statFeat = np.hstack([amp,maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d,amp_f,maxd_f,mind_f,maxabsd_f,avgabsd_f,max2d_f,min2d_f,maxabs2d_f,avgabs2d_f]) + + # Get Wavelet Features + max_1,mean_1,std_1,median_1,aboveZero_1,max_H,mean_H,std_H,median_H,aboveZero_H = getWavelet(w1,wH) + waveletFeat = np.hstack([max_1,mean_1,std_1,median_1,aboveZero_1,max_H,mean_H,std_H,median_H,aboveZero_H]) + + all_feat = np.hstack([statFeat,waveletFeat]) + + if np.Inf in all_feat: + print("Inf") + + if np.NaN in all_feat: + print("NaN") + + return list(all_feat) + + +def createFeatureDF(data): + ''' + INPUTS: + filepath: string, path to input file + OUTPUTS: + features: DataFrame, index is a list of timestamps for each 5 seconds, contains all the features + data: DataFrame, index is a list of timestamps at 8Hz, columns include AccelZ, AccelY, AccelX, Temp, EDA, filtered_eda + ''' + # Load data from q sensor + wave1sec,waveHalf = getWaveletData(data) + + # Create 5 second timestamp list + timestampList = data.index.tolist()[0::40] + + # feature names for DataFrame columns + allFeatureNames = ['raw_amp','raw_maxd','raw_mind','raw_maxabsd','raw_avgabsd','raw_max2d','raw_min2d','raw_maxabs2d','raw_avgabs2d','filt_amp','filt_maxd','filt_mind', + 'filt_maxabsd','filt_avgabsd','filt_max2d','filt_min2d','filt_maxabs2d','filt_avgabs2d','max_1s_1','max_1s_2','max_1s_3','mean_1s_1','mean_1s_2','mean_1s_3', + 'std_1s_1','std_1s_2','std_1s_3','median_1s_1','median_1s_2','median_1s_3','aboveZero_1s_1','aboveZero_1s_2','aboveZero_1s_3','max_Hs_1','max_Hs_2','mean_Hs_1', + 'mean_Hs_2','std_Hs_1','std_Hs_2','median_Hs_1','median_Hs_2','aboveZero_Hs_1','aboveZero_Hs_2'] + + # Initialize Feature Data Frame + features = pd.DataFrame(np.zeros((len(timestampList),len(allFeatureNames))),columns=allFeatureNames,index=timestampList) + + # Compute features for each 5 second epoch + for i in range(len(features)-1): + start = features.index[i] + end = features.index[i+1] + this_data = data[start:end] + this_w1 = wave1sec[start:end] + this_w2 = waveHalf[start:end] + features.iloc[i] = getFeatures(this_data,this_w1,this_w2) + return features + + +def classifyEpochs(features,featureNames,classifierName): + ''' + This function takes the full features DataFrame and classifies each 5 second epoch into artifact, questionable, or clean + + INPUTS: + features: DataFrame, index is a list of timestamps for each 5 seconds, contains all the features + featureNames: list of Strings, subset of feature names needed for classification + classifierName: string, type of SVM (binary or multiclass) + + OUTPUTS: + labels: Series, index is a list of timestamps for each 5 seconds, values of -1, 0, or 1 for artifact, questionable, or clean + ''' + # Only get relevant features + features = features[featureNames] + X = features[featureNames].values + + # Classify each 5 second epoch and put into DataFrame + if 'Binary' in classifierName: + featuresLabels = predict_binary_classifier(X) + elif 'Multi' in classifierName: + featuresLabels = predict_multiclass_classifier(X) + + return featuresLabels + + +def getSVMFeatures(key): + ''' + This returns the list of relevant features + + INPUT: + key: string, either "Binary" or "Multiclass" + + OUTPUT: + featureList: list of Strings, subset of feature names needed for classification + ''' + if key == "Binary": + return ['raw_amp','raw_maxabsd','raw_max2d','raw_avgabs2d','filt_amp','filt_min2d','filt_maxabs2d','max_1s_1', + 'mean_1s_1','std_1s_1','std_1s_2','std_1s_3','median_1s_3'] + elif key == "Multiclass": + return ['filt_maxabs2d','filt_min2d','std_1s_1','raw_max2d','raw_amp','max_1s_1','raw_maxabs2d','raw_avgabs2d', + 'filt_max2d','filt_amp'] + else: + print('Error!! Invalid key, choose "Binary" or "Multiclass"\n\n') + return + + +def classify(classifierList): + ''' + This function wraps other functions in order to load, classify, and return the label for each 5 second epoch of Q sensor data. + + INPUT: + classifierList: list of strings, either "Binary" or "Multiclass" + OUTPUT: + featureLabels: Series, index is a list of timestamps for each 5 seconds, values of -1, 0, or 1 for artifact, questionable, or clean + data: DataFrame, only output if fullFeatureOutput=1, index is a list of timestamps at 8Hz, columns include AccelZ, AccelY, AccelX, Temp, EDA, filtered_eda + ''' + # Constants + oneHour = 8*60*60 # 8(samp/s)*60(s/min)*60(min/hour) = samp/hour + fiveSec = 8*5 + + # Load data + data, _ = getInputLoadFile() + + # Get pickle List and featureNames list + featureNameList = [[]]*len(classifierList) + for i in range(len(classifierList)): + featureNames = getSVMFeatures(classifierList[i]) + featureNameList[i]=featureNames + + # Get the number of data points, hours, and labels + rows = len(data) + num_labels = int(np.ceil(float(rows)/fiveSec)) + hours = int(np.ceil(float(rows)/oneHour)) + + # Initialize labels array + labels = -1*np.ones((num_labels,len(classifierList))) + + for h in range(hours): + # Get a data slice that is at most 1 hour long + start = h*oneHour + end = min((h+1)*oneHour,rows) + cur_data = data[start:end] + + features = createFeatureDF(cur_data) + + for i in range(len(classifierList)): + # Get correct feature names for classifier + classifierName = classifierList[i] + featureNames = featureNameList[i] + + # Label each 5 second epoch + temp_labels = classifyEpochs(features, featureNames, classifierName) + labels[(h*12*60):(h*12*60+temp_labels.shape[0]),i] = temp_labels + + return labels,data + + +def plotData(data,labels,classifierList,filteredPlot=0,secondsPlot=0): + ''' + This function plots the Q sensor EDA data with shading for artifact (red) and questionable data (grey). + Note that questionable data will only appear if you choose a multiclass classifier + + INPUT: + data: DataFrame, indexed by timestamps at 8Hz, columns include EDA and filtered_eda + labels: array, each row is a 5 second period and each column is a different classifier + filteredPlot: binary, 1 for including filtered EDA in plot, 0 for only raw EDA on the plot, defaults to 0 + secondsPlot: binary, 1 for x-axis in seconds, 0 for x-axis in minutes, defaults to 0 + + OUTPUT: + [plot] the resulting plot has N subplots (where N is the length of classifierList) that have linked x and y axes + and have shading for artifact (red) and questionable data (grey) + + ''' + + # Initialize x axis + if secondsPlot: + scale = 1.0 + else: + scale = 60.0 + time_m = np.arange(0,len(data))/(8.0*scale) + + # Initialize Figure + plt.figure(figsize=(10,5)) + + # For each classifier, label each epoch and plot + for k in range(np.shape(labels)[1]): + key = classifierList[k] + + # Initialize Subplots + if k==0: + ax = plt.subplot(len(classifierList),1,k+1) + else: + ax = plt.subplot(len(classifierList),1,k+1,sharex=ax,sharey=ax) + + # Plot EDA + ax.plot(time_m,data['EDA']) + + # For each epoch, shade if necessary + for i in range(0,len(labels)-1): + if labels[i,k]==-1: + # artifact + start = i*40/(8.0*scale) + end = start+5.0/scale + ax.axvspan(start, end, facecolor='red', alpha=0.7, edgecolor ='none') + elif labels[i,k]==0: + # Questionable + start = i*40/(8.0*scale) + end = start+5.0/scale + ax.axvspan(start, end, facecolor='.5', alpha=0.5,edgecolor ='none') + + # Plot filtered data if requested + if filteredPlot: + ax.plot(time_m-.625/scale,data['filtered_eda'], c='g') + plt.legend(['Raw SC','Filtered SC'],loc=0) + + # Label and Title each subplot + plt.ylabel('$\mu$S') + plt.title(key) + + # Only include x axis label on final subplot + if secondsPlot: + plt.xlabel('Time (s)') + else: + plt.xlabel('Time (min)') + + # Display the plot + plt.subplots_adjust(hspace=.3) + plt.show() + return + + +if __name__ == "__main__": + numClassifiers = int(get_user_input('Would you like 1 classifier (Binary or Multiclass) or both (enter 1 or 2): ')) + + # Create list of classifiers + if numClassifiers==1: + temp_clf = int(get_user_input("Select a classifier:\n1: Binary\n2: Multiclass\n:")) + while temp_clf != 1 and temp_clf !=2: + temp_clf = get_user_input("Something went wrong. Enter the number 1 or 2.\n Select a classifier:\n1: Binary\n2: Multiclass):") + if temp_clf == 1: + print('Binary Classifier selected') + classifierList = ['Binary'] + elif temp_clf == 2: + print('Multiclass Classifier selected') + classifierList = ['Multiclass'] + else: + classifierList = ['Binary', 'Multiclass'] + + # Classify the data + labels, data = classify(classifierList) + + # Plotting the data + plotDataInput = get_user_input('Do you want to plot the labels? (y/n): ') + + if plotDataInput=='y': + # Include filter plot? + filteredPlot = get_user_input('Would you like to include filtered data in your plot? (y/n): ') + if filteredPlot=='y': + filteredPlot=1 + else: + filteredPlot=0 + + # X axis in seconds? + secondsPlot = get_user_input('Would you like the x-axis to be in seconds or minutes? (sec/min): ') + if secondsPlot=='sec': + secondsPlot=1 + else: + secondsPlot=0 + + # Plot Data + plotData(data,labels,classifierList,filteredPlot,secondsPlot) + + print("Remember! Red is for epochs with artifact, grey is for epochs that are questionable, and no shading is for clean epochs") + + + # Saving the data + saveDataInput = get_user_input('Do you want to save the labels? (y/n): ') + + if saveDataInput=='y': + outputPath = get_user_input('Output directory: ') + outputLabelFilename= get_user_input('Output filename: ') + + # Save labels + fullOutputPath = os.path.join(outputPath,outputLabelFilename) + if fullOutputPath[-4:] != '.csv': + fullOutputPath = fullOutputPath+'.csv' + + featureLabels = pd.DataFrame(labels, index=pd.date_range(start=data.index[0], periods=len(labels), freq='5s'), + columns=classifierList) + + featureLabels.reset_index(inplace=True) + featureLabels.rename(columns={'index':'StartTime'}, inplace=True) + featureLabels['EndTime'] = featureLabels['StartTime']+datetime.timedelta(seconds=5) + featureLabels.index.name = 'EpochNum' + + cols = ['StartTime', 'EndTime'] + cols.extend(classifierList) + + featureLabels = featureLabels[cols] + featureLabels.rename(columns={'Binary': 'BinaryLabels', 'Multiclass': 'MulticlassLabels'}, + inplace=True) + + featureLabels.to_csv(fullOutputPath) + + print("Labels saved to " + fullOutputPath) + print("Remember! The first column is timestamps and the second column is the labels (-1 for artifact, 0 for questionable, 1 for clean)") + + print('--------------------------------') + print("Please also cite this project:") + print("Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. Automatic identification of artifacts in electrodermal activity data. In Engineering in Medicine and Biology Conference. 2015") + print('--------------------------------') + + + + + + diff --git a/calculatingfeatures/eda_explorer/EDA_Peak_Detection_Script.py b/calculatingfeatures/eda_explorer/EDA_Peak_Detection_Script.py new file mode 100644 index 00000000..acfec93c --- /dev/null +++ b/calculatingfeatures/eda_explorer/EDA_Peak_Detection_Script.py @@ -0,0 +1,250 @@ +import pandas as pd +import numpy as np +import os +import matplotlib.pyplot as plt +import pprint + +from .load_files import getInputLoadFile, get_user_input, getOutputPath + +SAMPLE_RATE = 8 + +def findPeaks(data, offset, start_WT, end_WT, thres=0, sampleRate=SAMPLE_RATE): + ''' + This function finds the peaks of an EDA signal and returns basic properties. + Also, peak_end is assumed to be no later than the start of the next peak. (Is this okay??) + + ********* INPUTS ********** + data: DataFrame with EDA as one of the columns and indexed by a datetimeIndex + offset: the number of rising samples and falling samples after a peak needed to be counted as a peak + start_WT: maximum number of seconds before the apex of a peak that is the "start" of the peak + end_WT: maximum number of seconds after the apex of a peak that is the "rec.t/2" of the peak, 50% of amp + thres: the minimum uS change required to register as a peak, defaults as 0 (i.e. all peaks count) + sampleRate: number of samples per second, default=8 + + ********* OUTPUTS ********** + peaks: list of binary, 1 if apex of SCR + peak_start: list of binary, 1 if start of SCR + peak_start_times: list of strings, if this index is the apex of an SCR, it contains datetime of start of peak + peak_end: list of binary, 1 if rec.t/2 of SCR + peak_end_times: list of strings, if this index is the apex of an SCR, it contains datetime of rec.t/2 + amplitude: list of floats, value of EDA at apex - value of EDA at start + max_deriv: list of floats, max derivative within 1 second of apex of SCR + + ''' + EDA_deriv = data['filtered_eda'][1:].values - data['filtered_eda'][:-1].values + peaks = np.zeros(len(EDA_deriv)) + peak_sign = np.sign(EDA_deriv) + for i in range(int(offset), int(len(EDA_deriv) - offset)): + if peak_sign[i] == 1 and peak_sign[i + 1] < 1: + peaks[i] = 1 + for j in range(1, int(offset)): + if peak_sign[i - j] < 1 or peak_sign[i + j] > -1: + #if peak_sign[i-j]==-1 or peak_sign[i+j]==1: + peaks[i] = 0 + break + + # Finding start of peaks + peak_start = np.zeros(len(EDA_deriv)) + peak_start_times = [''] * len(data) + max_deriv = np.zeros(len(data)) + rise_time = np.zeros(len(data)) + + for i in range(0, len(peaks)): + if peaks[i] == 1: + temp_start = max(0, i - sampleRate) + max_deriv[i] = max(EDA_deriv[temp_start:i]) + start_deriv = .01 * max_deriv[i] + + found = False + find_start = i + # has to peak within start_WT seconds + while found == False and find_start > (i - start_WT * sampleRate): + if EDA_deriv[find_start] < start_deriv: + found = True + peak_start[find_start] = 1 + peak_start_times[i] = data.index[find_start] + rise_time[i] = get_seconds_and_microseconds(data.index[i] - pd.to_datetime(peak_start_times[i])) + + find_start = find_start - 1 + + # If we didn't find a start + if found == False: + peak_start[i - start_WT * sampleRate] = 1 + peak_start_times[i] = data.index[i - start_WT * sampleRate] + rise_time[i] = start_WT + + # Check if amplitude is too small + if thres > 0 and (data['EDA'].iloc[i] - data['EDA'][peak_start_times[i]]) < thres: + peaks[i] = 0 + peak_start[i] = 0 + peak_start_times[i] = '' + max_deriv[i] = 0 + rise_time[i] = 0 + + # Finding the end of the peak, amplitude of peak + peak_end = np.zeros(len(data)) + peak_end_times = [''] * len(data) + amplitude = np.zeros(len(data)) + decay_time = np.zeros(len(data)) + half_rise = [''] * len(data) + SCR_width = np.zeros(len(data)) + + for i in range(0, len(peaks)): + if peaks[i] == 1: + peak_amp = data['EDA'].iloc[i] + start_amp = data['EDA'][peak_start_times[i]] + amplitude[i] = peak_amp - start_amp + + half_amp = amplitude[i] * .5 + start_amp + + found = False + find_end = i + # has to decay within end_WT seconds + while found == False and find_end < (i + end_WT * sampleRate) and find_end < len(peaks): + if data['EDA'].iloc[find_end] < half_amp: + found = True + peak_end[find_end] = 1 + peak_end_times[i] = data.index[find_end] + decay_time[i] = get_seconds_and_microseconds(pd.to_datetime(peak_end_times[i]) - data.index[i]) + + # Find width + find_rise = i + found_rise = False + while found_rise == False: + if data['EDA'].iloc[find_rise] < half_amp: + found_rise = True + half_rise[i] = data.index[find_rise] + SCR_width[i] = get_seconds_and_microseconds(pd.to_datetime(peak_end_times[i]) - data.index[find_rise]) + find_rise = find_rise - 1 + + elif peak_start[find_end] == 1: + found = True + peak_end[find_end] = 1 + peak_end_times[i] = data.index[find_end] + find_end = find_end + 1 + + # If we didn't find an end + if found == False: + min_index = np.argmin(data['EDA'].iloc[i:(i + end_WT * sampleRate)].tolist()) + peak_end[i + min_index] = 1 + peak_end_times[i] = data.index[i + min_index] + + peaks = np.concatenate((peaks, np.array([0]))) + peak_start = np.concatenate((peak_start, np.array([0]))) + max_deriv = max_deriv * sampleRate # now in change in amplitude over change in time form (uS/second) + + return peaks, peak_start, peak_start_times, peak_end, peak_end_times, amplitude, max_deriv, rise_time, decay_time, SCR_width, half_rise + +def get_seconds_and_microseconds(pandas_time): + return pandas_time.seconds + pandas_time.microseconds * 1e-6 + +def calcPeakFeatures(data,offset,thresh,start_WT,end_WT, sampleRate): + returnedPeakData = findPeaks(data, offset*sampleRate, start_WT, end_WT, thresh, sampleRate) + data['peaks'] = returnedPeakData[0] + data['peak_start'] = returnedPeakData[1] + data['peak_end'] = returnedPeakData[3] + + data['peak_start_times'] = returnedPeakData[2] + data['peak_end_times'] = returnedPeakData[4] + data['half_rise'] = returnedPeakData[10] + # Note: If an SCR doesn't decrease to 50% of amplitude, then the peak_end = min(the next peak's start, 15 seconds after peak) + data['amp'] = returnedPeakData[5] + data['max_deriv'] = returnedPeakData[6] + data['rise_time'] = returnedPeakData[7] + data['decay_time'] = returnedPeakData[8] + data['SCR_width'] = returnedPeakData[9] + + # To keep all filtered data remove this line + # featureData = data[data.peaks==1][['EDA','rise_time','max_deriv','amp','decay_time','SCR_width']] + + # Replace 0s with NaN, this is where the 50% of the peak was not found, too close to the next peak + # featureData[['SCR_width','decay_time']]=featureData[['SCR_width','decay_time']].replace(0, np.nan) + # featureData['AUC']=featureData['amp']*featureData['SCR_width'] + # if outfile is not None: + # featureData.to_csv(outfile) + + return data + +# draws a graph of the data with the peaks marked on it +# assumes that 'data' dataframe already contains the 'peaks' column +def plotPeaks(data, x_seconds, sampleRate = SAMPLE_RATE): + if x_seconds: + time_m = np.arange(0,len(data))/float(sampleRate) + else: + time_m = np.arange(0,len(data))/(sampleRate*60.) + + data_min = min(data['EDA']) + data_max = max(data['EDA']) + + #Plot the data with the Peaks marked + plt.figure(1,figsize=(20, 5)) + peak_height = data_max * 1.15 + data['peaks_plot'] = data['peaks'] * peak_height + plt.plot(time_m,data['peaks_plot'],'#4DBD33') + #plt.plot(time_m,data['EDA']) + plt.plot(time_m,data['filtered_eda']) + plt.xlim([0,time_m[-1]]) + y_min = min(0, data_min) - (data_max - data_min) * 0.1 + plt.ylim([min(y_min, data_min),peak_height]) + plt.title('EDA with Peaks marked') + plt.ylabel('$\mu$S') + if x_seconds: + plt.xlabel('Time (s)') + else: + plt.xlabel('Time (min)') + + plt.show() + +def chooseValueOrDefault(str_input, default): + if str_input == "": + return default + else: + return float(str_input) + +if __name__ == "__main__": + + data, filepath_confirm = getInputLoadFile() + + fullOutputPath = getOutputPath() + + print("") + print("Please choose settings for the peak detection algorithm. For default values press return") + thresh_str = get_user_input('\tMinimum peak amplitude (default = .02):') + thresh = chooseValueOrDefault(thresh_str,.02) + offset_str = get_user_input('\tOffset (default = 1): ') + offset = chooseValueOrDefault(offset_str,1) + start_WT_str = get_user_input('\tMax rise time (s) (default = 4): ') + start_WT = chooseValueOrDefault(start_WT_str,4) + end_WT_str = get_user_input('\tMax decay time (s) (default = 4): ') + end_WT = chooseValueOrDefault(end_WT_str,4) + + settings_dict = {'threshold':thresh, + 'offset':offset, + 'rise time':start_WT, + 'decay time':end_WT} + + print("") + print("Okay, finding peaks in file "+ filepath_confirm + " using the following parameters") + pprint.pprint(settings_dict) + peakData = calcPeakFeatures(data,fullOutputPath,offset,thresh,start_WT,end_WT) + print("Features computed and saved to " + fullOutputPath) + + # Plotting the data + print("") + plot_ans = get_user_input("Do you want to plot the detected peaks? (y/n): ") + if 'y' in plot_ans: + secs_ans = get_user_input("Would you like the x-axis to be in seconds or minutes? (sec/min): ") + if 'sec' in secs_ans: + x_seconds=True + else: + x_seconds=False + plotPeaks(peakData,x_seconds) + else: + print("\tOkay, script will not produce a plot") + + print("") + print('--------------------------------') + print("Please also cite this project:") + print("Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. Automatic identification of artifacts in electrodermal activity data. In Engineering in Medicine and Biology Conference. 2015") + print('--------------------------------') + diff --git a/calculatingfeatures/eda_explorer/LICENSE.txt b/calculatingfeatures/eda_explorer/LICENSE.txt new file mode 100644 index 00000000..b46824a7 --- /dev/null +++ b/calculatingfeatures/eda_explorer/LICENSE.txt @@ -0,0 +1,21 @@ +EDA-Explorer is an open-source project under the MIT License + +Copyright (c) 2016 Sara Taylor and Natasha Jaques + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/calculatingfeatures/eda_explorer/README.md b/calculatingfeatures/eda_explorer/README.md new file mode 100644 index 00000000..89af8bba --- /dev/null +++ b/calculatingfeatures/eda_explorer/README.md @@ -0,0 +1,39 @@ +eda-explorer +============ + +Scripts to detect artifacts and in electrodermal activity (EDA) data. Note that these scripts are written for Python 2.7 and Python 3.7 + + +Version 1.0 + +Please also cite this project: +Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. Automatic identification of artifacts in electrodermal activity data. In Engineering in Medicine and Biology Conference. 2015. + + +Required python packages can be found in requirements.txt + +To run artifact detection from the command line: +== +python EDA-Artifact-Detection-Script.py + +Currently there are only 2 classifiers to choose from: Binary or Multiclass + +To run peak detection: +== +python EDA-Peak-Detection-Script.py + +Descriptions of the algorithm settings can be found at http://eda-explorer.media.mit.edu/info/ + +To run accelerometer feature extraction: +== +python AccelerometerFeatureExtractionScript.py + +This file works slightly differently than the others in that it gives summary information over periods of time. + +Notes: +=== + +1. Currently, these files are written with the assumption that the sample rate is an integer power of 2. + +2. Please visit [eda-explorer.media.mit.edu](https://eda-explorer.media.mit.edu) + to use the web-based version diff --git a/calculatingfeatures/eda_explorer/__init__.py b/calculatingfeatures/eda_explorer/__init__.py new file mode 100644 index 00000000..31b10d24 --- /dev/null +++ b/calculatingfeatures/eda_explorer/__init__.py @@ -0,0 +1 @@ +from .load_files import * \ No newline at end of file diff --git a/calculatingfeatures/eda_explorer/classify.py b/calculatingfeatures/eda_explorer/classify.py new file mode 100644 index 00000000..a1947321 --- /dev/null +++ b/calculatingfeatures/eda_explorer/classify.py @@ -0,0 +1,90 @@ +from sklearn.svm import SVC +import pickle + + +#docs: http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC +class SVM: + def __init__(self, C=1.0,beta=0.0, kernel='linear', poly_degree=3, max_iter=-1, tol=0.001): + #possible kernels: linear, 'poly', 'rbf', 'sigmoid', 'precomputed' or a callable + + #data features + self.n_features = None + self.train_X = [] + self.train_Y = [] + self.val_X = [] + self.val_Y = [] + self.test_X = [] + self.test_Y = [] + + #classifier features + self.C = C + self.beta = beta + self.kernel = kernel + self.poly_degree = poly_degree + self.max_iter = max_iter + self.tolerance = tol + + self.classifier = None + + + #SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, + # gamma=0.0, kernel='rbf', max_iter=-1, probability=False, + # random_state=None, shrinking=True, tol=0.001, verbose=False) + + def setTrainData(self, X, Y): + self.train_X = X + self.train_Y = Y + + self.n_features = self.train_X.shape[1] + + def setTestData(self, X, Y): + self.test_X = X + self.test_Y = Y + + def setC(self, c): + self.C = c + + def setBeta(self, beta): + if beta is None: + self.beta = 0.0 + else: + self.beta=beta + + def setKernel(self, kernel, poly_degree=3): + self.kernel = kernel + self.poly_degree = poly_degree + + def setValData(self, X, Y): + self.val_X = X + self.val_Y = Y + + def train(self): + self.classifier = SVC(C=self.C, kernel=self.kernel, gamma=self.beta, degree=self.poly_degree, max_iter=self.max_iter, + tol=self.tolerance) + self.classifier.fit(self.train_X, self.train_Y) + + def predict(self, X): + return self.classifier.predict(X) + + def getScore(self, X, Y): + #returns accuracy + return self.classifier.score(X, Y) + + def getNumSupportVectors(self): + return self.classifier.n_support_ + + def getHingeLoss(self,X,Y): + preds = self.predict(X) + hinge_inner_func = 1.0 - preds*Y + hinge_inner_func = [max(0,x) for x in hinge_inner_func] + return sum(hinge_inner_func) + + def saveClassifierToFile(self, filepath): + s = pickle.dumps(self.classifier) + f = open(filepath, 'wb') + f.write(s) + + def loadClassifierFromFile(self, filepath): + f2 = open(filepath, 'rb') + s2 = f2.read() + self.classifier = pickle.loads(s2) diff --git a/calculatingfeatures/eda_explorer/load_files.py b/calculatingfeatures/eda_explorer/load_files.py new file mode 100644 index 00000000..ea8be670 --- /dev/null +++ b/calculatingfeatures/eda_explorer/load_files.py @@ -0,0 +1,264 @@ +import pandas as pd +import scipy.signal as scisig +import os +import numpy as np + + +def get_user_input(prompt): + try: + return raw_input(prompt) + except NameError: + return input(prompt) + + +def getInputLoadFile(): + '''Asks user for type of file and file path. Loads corresponding data. + + OUTPUT: + data: DataFrame, index is a list of timestamps at 8Hz, columns include + AccelZ, AccelY, AccelX, Temp, EDA, filtered_eda + ''' + print("Please enter information about your EDA file... ") + dataType = get_user_input("\tData Type (e4, q, shimmer, or misc): ") + if dataType=='q': + filepath = get_user_input("\tFile path: ") + filepath_confirm = filepath + data = loadData_Qsensor(filepath) + elif dataType=='e4': + filepath = get_user_input("\tPath to E4 directory: ") + filepath_confirm = os.path.join(filepath,"EDA.csv") + data = loadData_E4(filepath) + elif dataType=='shimmer': + filepath = get_user_input("\tFile path: ") + filepath_confirm = filepath + data = loadData_shimmer(filepath) + elif dataType=="misc": + filepath = get_user_input("\tFile path: ") + filepath_confirm = filepath + data = loadData_misc(filepath) + else: + print("Error: not a valid file choice") + + return data, filepath_confirm + +def getOutputPath(): + print("") + print("Where would you like to save the computed output file?") + outfile = get_user_input('\tFile name: ') + outputPath = get_user_input('\tFile directory (./ for this directory): ') + fullOutputPath = os.path.join(outputPath,outfile) + if fullOutputPath[-4:] != '.csv': + fullOutputPath = fullOutputPath+'.csv' + return fullOutputPath + +def loadData_Qsensor(filepath): + ''' + This function loads the Q sensor data, uses a lowpass butterworth filter on the EDA signal + Note: currently assumes sampling rate of 8hz, 16hz, 32hz; if sampling rate is 16hz or 32hz the signal is downsampled + + INPUT: + filepath: string, path to input file + + OUTPUT: + data: DataFrame, index is a list of timestamps at 8Hz, columns include AccelZ, AccelY, AccelX, Temp, EDA, filtered_eda + ''' + # Get header info + try: + header_info = pd.io.parsers.read_csv(filepath, nrows=5) + except IOError: + print("Error!! Couldn't load file, make sure the filepath is correct and you are using a csv from the q sensor software\n\n") + return + + # Get sample rate + sampleRate = int((header_info.iloc[3,0]).split(":")[1].strip()) + + # Get the raw data + data = pd.io.parsers.read_csv(filepath, skiprows=7) + data = data.reset_index() + + # Reset the index to be a time and reset the column headers + data.columns = ['AccelZ','AccelY','AccelX','Battery','Temp','EDA'] + + # Get Start Time + startTime = pd.to_datetime(header_info.iloc[4,0][12:-10]) + + # Make sure data has a sample rate of 8Hz + data = interpolateDataTo8Hz(data,sampleRate,startTime) + + # Remove Battery Column + data = data[['AccelZ','AccelY','AccelX','Temp','EDA']] + + # Get the filtered data using a low-pass butterworth filter (cutoff:1hz, fs:8hz, order:6) + data['filtered_eda'] = butter_lowpass_filter(data['EDA'], 1.0, 8, 6) + + return data + +def _loadSingleFile_E4(filepath,list_of_columns, expected_sample_rate,freq): + # Load data + data = pd.read_csv(filepath) + + # Get the startTime and sample rate + startTime = pd.to_datetime(float(data.columns.values[0]),unit="s") + sampleRate = float(data.iloc[0][0]) + data = data[data.index!=0] + data.index = data.index-1 + + # Reset the data frame assuming expected_sample_rate + data.columns = list_of_columns + if sampleRate != expected_sample_rate: + print('ERROR, NOT SAMPLED AT {0}HZ. PROBLEMS WILL OCCUR\n'.format(expected_sample_rate)) + + # Make sure data has a sample rate of 8Hz + data = interpolateDataTo8Hz(data,sampleRate,startTime) + + return data + + +def loadData_E4(filepath): + # Load EDA data + eda_data = _loadSingleFile_E4(os.path.join(filepath,'EDA.csv'),["EDA"],4,"250L") + # Get the filtered data using a low-pass butterworth filter (cutoff:1hz, fs:8hz, order:6) + eda_data['filtered_eda'] = butter_lowpass_filter(eda_data['EDA'], 1.0, 8, 6) + + # Load ACC data + acc_data = _loadSingleFile_E4(os.path.join(filepath,'ACC.csv'),["AccelX","AccelY","AccelZ"],32,"31250U") + # Scale the accelometer to +-2g + acc_data[["AccelX","AccelY","AccelZ"]] = acc_data[["AccelX","AccelY","AccelZ"]]/64.0 + + # Load Temperature data + temperature_data = _loadSingleFile_E4(os.path.join(filepath,'TEMP.csv'),["Temp"],4,"250L") + + data = eda_data.join(acc_data, how='outer') + data = data.join(temperature_data, how='outer') + + # E4 sometimes records different length files - adjust as necessary + min_length = min(len(acc_data), len(eda_data), len(temperature_data)) + + return data[:min_length] + +def loadData_shimmer(filepath): + data = pd.read_csv(filepath, sep='\t', skiprows=(0,1)) + + orig_cols = data.columns + rename_cols = {} + + for search, new_col in [['Timestamp','Timestamp'], + ['Accel_LN_X', 'AccelX'], ['Accel_LN_Y', 'AccelY'], ['Accel_LN_Z', 'AccelZ'], + ['Skin_Conductance', 'EDA']]: + orig = [c for c in orig_cols if search in c] + if len(orig) == 0: + continue + rename_cols[orig[0]] = new_col + + data.rename(columns=rename_cols, inplace=True) + + # TODO: Assuming no temperature is recorded + data['Temp'] = 0 + + # Drop the units row and unnecessary columns + data = data[data['Timestamp'] != 'ms'] + data.index = pd.to_datetime(data['Timestamp'], unit='ms') + data = data[['AccelZ', 'AccelY', 'AccelX', 'Temp', 'EDA']] + + for c in ['AccelZ', 'AccelY', 'AccelX', 'Temp', 'EDA']: + data[c] = pd.to_numeric(data[c]) + + # Convert to 8Hz + data = data.resample("125L").mean() + data.interpolate(inplace=True) + + # Get the filtered data using a low-pass butterworth filter (cutoff:1hz, fs:8hz, order:6) + data['filtered_eda'] = butter_lowpass_filter(data['EDA'], 1.0, 8, 6) + + return data + + +def loadData_getColNames(data_columns): + print("Here are the data columns of your file: ") + print(data_columns) + + # Find the column names for each of the 5 data streams + colnames = ['EDA data','Temperature data','Acceleration X','Acceleration Y','Acceleration Z'] + new_colnames = ['','','','',''] + + for i in range(len(new_colnames)): + new_colnames[i] = get_user_input("Column name that contains "+colnames[i]+": ") + while (new_colnames[i] not in data_columns): + print("Column not found. Please try again") + print("Here are the data columns of your file: ") + print(data_columns) + + new_colnames[i] = get_user_input("Column name that contains "+colnames[i]+": ") + + # Get user input on sample rate + sampleRate = get_user_input("Enter sample rate (must be an integer power of 2): ") + while (sampleRate.isdigit()==False) or (np.log(int(sampleRate))/np.log(2) != np.floor(np.log(int(sampleRate))/np.log(2))): + print("Not an integer power of two") + sampleRate = get_user_input("Enter sample rate (must be a integer power of 2): ") + sampleRate = int(sampleRate) + + # Get user input on start time + startTime = pd.to_datetime(get_user_input("Enter a start time (format: YYYY-MM-DD HH:MM:SS): ")) + while type(startTime)==str: + print("Not a valid date/time") + startTime = pd.to_datetime(get_user_input("Enter a start time (format: YYYY-MM-DD HH:MM:SS): ")) + + + return sampleRate, startTime, new_colnames + + +def loadData_misc(filepath): + # Load data + data = pd.read_csv(filepath) + + # Get the correct colnames + sampleRate, startTime, new_colnames = loadData_getColNames(data.columns.values) + + data.rename(columns=dict(zip(new_colnames,['EDA','Temp','AccelX','AccelY','AccelZ'])), inplace=True) + data = data[['AccelZ','AccelY','AccelX','Temp','EDA']] + + # Make sure data has a sample rate of 8Hz + data = interpolateDataTo8Hz(data,sampleRate,startTime) + + # Get the filtered data using a low-pass butterworth filter (cutoff:1hz, fs:8hz, order:6) + data['filtered_eda'] = butter_lowpass_filter(data['EDA'], 1.0, 8, 6) + + return data + +def interpolateDataTo8Hz(data,sample_rate,startTime): + if sample_rate<8: + # Upsample by linear interpolation + if sample_rate==2: + data.index = pd.date_range(start=startTime, periods=len(data), freq='500L') + elif sample_rate==4: + data.index = pd.date_range(start=startTime, periods=len(data), freq='250L') + data = data.resample("125L").mean() + else: + if sample_rate>8: + # Downsample + idx_range = list(range(0,len(data))) # TODO: double check this one + data = data.iloc[idx_range[0::int(int(sample_rate)/8)]] + # Set the index to be 8Hz + data.index = pd.date_range(start=startTime, periods=len(data), freq='125L') + + # Interpolate all empty values + data = interpolateEmptyValues(data) + return data + +def interpolateEmptyValues(data): + cols = data.columns.values + for c in cols: + data.loc[:, c] = data[c].interpolate() + + return data + +def butter_lowpass(cutoff, fs, order=5): + # Filtering Helper functions + sos = scisig.butter(order, cutoff, btype='low', analog=False, output='sos', fs=fs) + return sos + +def butter_lowpass_filter(data, cutoff, fs, order=5): + # Filtering Helper functions + sos = butter_lowpass(cutoff, fs, order=order) + y = scisig.sosfilt(sos, data) + return y \ No newline at end of file diff --git a/calculatingfeatures/eda_explorer/requirements.txt b/calculatingfeatures/eda_explorer/requirements.txt new file mode 100644 index 00000000..8ba778ee --- /dev/null +++ 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a/calculatingfeatures/example_data/S2_E4_Data/info.txt b/calculatingfeatures/example_data/S2_E4_Data/info.txt new file mode 100644 index 00000000..fe6220ac --- /dev/null +++ b/calculatingfeatures/example_data/S2_E4_Data/info.txt @@ -0,0 +1,32 @@ +.csv files in this archive are in the following format: +The first row is the initial time of the session expressed as unix timestamp in UTC. +The second row is the sample rate expressed in Hz. + +TEMP.csv +Data from temperature sensor expressed degrees on the Celsius (°C) scale. + +EDA.csv +Data from the electrodermal activity sensor expressed as microsiemens (μS). + +BVP.csv +Data from photoplethysmograph. + +ACC.csv +Data from 3-axis accelerometer sensor. The accelerometer is configured to measure acceleration in the range [-2g, 2g]. Therefore the unit in this file is 1/64g. +Data from x, y, and z axis are respectively in first, second, and third column. + +IBI.csv +Time between individuals heart beats extracted from the BVP signal. +No sample rate is needed for this file. +The first column is the time (respect to the initial time) of the detected inter-beat interval expressed in seconds (s). +The second column is the duration in seconds (s) of the detected inter-beat interval (i.e., the distance in seconds from the previous beat). + +HR.csv +Average heart rate extracted from the BVP signal.The first row is the initial time of the session expressed as unix timestamp in UTC. +The second row is the sample rate expressed in Hz. + + +tags.csv +Event mark times. +Each row corresponds to a physical button press on the device; the same time as the status LED is first illuminated. +The time is expressed as a unix timestamp in UTC and it is synchronized with initial time of the session indicated in the related data files from the corresponding session. diff --git a/calculatingfeatures/example_data/S2_E4_Data/tags.csv b/calculatingfeatures/example_data/S2_E4_Data/tags.csv new file mode 100644 index 00000000..e69de29b diff --git a/calculatingfeatures/pyproject.toml b/calculatingfeatures/pyproject.toml new file mode 100644 index 00000000..07de284a --- /dev/null +++ b/calculatingfeatures/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools", "wheel"] +build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/calculatingfeatures/setup.cfg b/calculatingfeatures/setup.cfg new file mode 100644 index 00000000..1f342cb0 --- /dev/null +++ b/calculatingfeatures/setup.cfg @@ -0,0 +1,16 @@ +[metadata] +name = CalculatingFeatures +version = 0.1.0 +description = 'Library for calculating features' + +[options] +packages = CalculatingFeatures +install_requires = + numpy + pandas + scipy + tqdm + matplotlib + scikit-learn + PeakUtils + pywt \ No newline at end of file diff --git a/calculatingfeatures/usage_examples.ipynb b/calculatingfeatures/usage_examples.ipynb new file mode 100644 index 00000000..4628c273 --- /dev/null +++ b/calculatingfeatures/usage_examples.ipynb @@ -0,0 +1,6112 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Usage examples" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from calculatingfeatures.CalculatingFeatures.calculate_features import calculateFeatures, calculateFeatures, convertInputInto2d, convertInputInto2dTime\n", + "from calculatingfeatures.CalculatingFeatures.helper_functions import gsrFeatureNames, frequencyFeatureNames, hrvFeatureNames, genericFeatureNames, accelerometerFeatureNames, gyroscopeFeatureNames\n", + "import CalculatingFeatures.feature_functions as functions\n", + "import random\n", + "import numpy as np\n", + "import time as t\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import pickle\n", + "from calculatingfeatures.CalculatingFeatures.calculate_hrv_peaks import peak_detector\n", + "%matplotlib notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate HRV and frequency features with calculateFeatures() from empatica file" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1008/1008 [02:07<00:00, 7.89it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ibi_X meanHr_X numRR_X pnn20_X pnn50_X rmssd_X \\\n", + "0 0.863281 69.502262 4.0 66.666667 66.666667 0.170449 \n", + "1 0.720313 83.297180 10.0 66.666667 33.333333 0.050765 \n", + "2 0.679688 88.275862 8.0 85.714286 28.571429 0.045363 \n", + "3 0.723437 82.937365 10.0 77.777778 55.555556 0.103382 \n", + "4 0.800347 74.967462 9.0 62.500000 37.500000 0.057675 \n", + "... ... ... ... ... ... ... \n", + "1003 1.097656 54.661922 4.0 33.333333 33.333333 0.045999 \n", + "1004 0.805804 74.459834 7.0 83.333333 83.333333 0.137701 \n", + "1005 0.911458 65.828571 6.0 100.000000 80.000000 0.098326 \n", + "1006 NaN NaN 3.0 NaN NaN NaN \n", + "1007 1.515625 39.587629 4.0 100.000000 100.000000 0.138878 \n", + "\n", + " sd1/sd2_X sd2_X sd_X sdnn_X sdsd_X \n", + "0 0.523417 0.120684 0.063168 0.089333 0.096319 \n", + "1 1.405853 0.025695 0.036124 0.051087 0.031346 \n", + "2 NaN NaN 0.030758 0.043498 0.016096 \n", + "3 0.668476 0.077188 0.051598 0.072971 0.065652 \n", + "4 0.684488 0.042214 0.028895 0.040863 0.036172 \n", + "... ... ... ... ... ... \n", + "1003 0.496161 0.042761 0.021216 0.030004 0.033754 \n", + "1004 1.001993 0.057706 0.057821 0.081771 0.057763 \n", + "1005 1.544265 0.034477 0.053242 0.075296 0.044852 \n", + "1006 NaN NaN NaN NaN NaN \n", + "1007 0.968519 0.049066 0.047522 0.067206 0.048300 \n", + "\n", + "[1008 rows x 11 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from calculatingfeatures.CalculatingFeatures.helper_functions import convert1DEmpaticaToArray\n", + "\n", + "pathToHrvCsv = \"example_data/S2_E4_Data/BVP.csv\"\n", + "windowLength = 500\n", + "\n", + "#np.seterr(all='raise')\n", + "\n", + "# get an array of values from HRV empatica file\n", + "hrv_data, startTimeStamp, sampleRate = convert1DEmpaticaToArray(pathToHrvCsv)\n", + "\n", + "# Convert the HRV data into 2D array\n", + "hrv_data_2D = convertInputInto2d(hrv_data, windowLength)\n", + "\n", + "# Create a list with feature names\n", + "featureNames = []\n", + "featureNames.extend(hrvFeatureNames)\n", + "featureNames.extend(frequencyFeatureNames)\n", + "\n", + "pd.set_option('display.max_columns', None)\n", + "#pd.set_option('display.max_rows', None)\n", + "\n", + "# Calculate features\n", + "calculatedFeatures = calculateFeatures(hrv_data_2D, fs=int(sampleRate), featureNames=featureNames)\n", + "print(calculatedFeatures)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate ACC features with calculateFeatures() from empatica file" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 504/504 [03:46<00:00, 2.23it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " absoluteAreaAllBand absoluteArea_XBand absoluteArea_YBand \\\n", + "0 312.592555 72.500968 -16.538178 \n", + "1 864.715960 72.220023 -21.781158 \n", + "2 3616.215615 74.042112 -77.602245 \n", + "3 4165.495456 105.191655 -79.146730 \n", + "4 8033.196394 95.132686 -124.754418 \n", + ".. ... ... ... \n", + "499 384.671068 88.880824 -121.088645 \n", + "500 611.899981 90.148744 -125.571337 \n", + "501 1307.327021 98.014717 -123.718204 \n", + "502 778.281403 102.766032 -118.941814 \n", + "503 898.820043 37.005843 -49.001318 \n", + "\n", + " absoluteArea_ZBand absoluteMean_XBand absoluteMean_YBand \\\n", + "0 137.499998 0.145002 -0.033076 \n", + "1 137.184763 0.144440 -0.043562 \n", + "2 32.381332 0.148084 -0.155204 \n", + "3 27.225527 0.210383 -0.158293 \n", + "4 2.235905 0.190265 -0.249509 \n", + ".. ... ... ... \n", + "499 42.304659 0.177762 -0.242177 \n", + "500 41.328719 0.180297 -0.251143 \n", + "501 8.405056 0.196029 -0.247436 \n", + "502 -0.946992 0.205532 -0.237884 \n", + "503 0.000397 0.074012 -0.098003 \n", + "\n", + " absoluteMean_ZBand amplitude_XBand amplitude_YBand amplitude_ZBand \\\n", + "0 2.750000e-01 29.284433 6.386696 55.229196 \n", + "1 2.743695e-01 44.658625 15.583131 62.157114 \n", + "2 6.476266e-02 45.327413 26.308157 71.081348 \n", + "3 5.445105e-02 42.572756 23.106671 20.137936 \n", + "4 4.471811e-03 72.576962 40.377516 55.010798 \n", + ".. ... ... ... ... \n", + "499 8.460932e-02 35.718442 49.655967 17.649243 \n", + "500 8.265744e-02 39.161026 50.804928 19.970953 \n", + "501 1.681011e-02 42.835779 62.312165 17.508028 \n", + "502 -1.893985e-03 50.837576 51.201160 14.531671 \n", + "503 7.939179e-07 51.448326 54.050552 9.273581 \n", + "\n", + " areaUnderAccelerationMagnitude area_XLow area_YLow area_ZLow \\\n", + "0 5330.383914 14420.999440 -3350.641321 27557.000001 \n", + "1 5357.677382 10865.253056 -3033.252188 27902.323548 \n", + "2 5430.496677 15722.297711 -14156.396220 15642.774418 \n", + "3 4335.662961 22389.054964 -13963.007308 2435.697567 \n", + "4 5395.581186 19409.122939 -14484.885912 180.145873 \n", + ".. ... ... ... ... \n", + "499 5350.702140 17583.763049 -24380.273741 8308.160445 \n", + "500 5337.171768 17261.897541 -24409.269350 8664.655729 \n", + "501 5471.304292 18922.657077 -23201.084064 5627.731332 \n", + "502 5391.568437 20860.878551 -23277.757290 -715.703219 \n", + "503 5236.681184 19686.330067 -20534.332597 3487.999778 \n", + "\n", + " averageVectorLength averageVectorLengthPower \\\n", + "0 62.571636 3915.209631 \n", + "1 62.828208 3947.383684 \n", + "2 65.670379 4312.598665 \n", + "3 66.911456 4477.142892 \n", + "4 73.431020 5392.114734 \n", + ".. ... ... \n", + "499 62.381792 3891.487998 \n", + "500 62.447365 3899.673343 \n", + "501 62.910131 3957.684586 \n", + "502 62.841575 3949.063506 \n", + "503 58.941575 3474.109225 \n", + "\n", + " coefficientOfVariation_XBand coefficientOfVariation_YBand \\\n", + "0 1.541025 0.771037 \n", + "1 0.708974 0.585220 \n", + "2 0.386066 0.391320 \n", + "3 0.347936 0.349965 \n", + "4 0.330193 0.341132 \n", + ".. ... ... \n", + "499 1.519349 1.736730 \n", + "500 0.951674 1.209954 \n", + "501 0.505862 0.831018 \n", + "502 0.804625 1.001523 \n", + "503 0.716975 0.902984 \n", + "\n", + " coefficientOfVariation_ZBand correlation_X_YBand correlation_X_ZBand \\\n", + "0 1.915226 0.999748 0.999992 \n", + "1 0.995279 0.999936 0.999982 \n", + "2 0.443334 0.999991 0.999988 \n", + "3 0.333548 0.999992 0.999992 \n", + "4 0.326846 0.999993 0.999992 \n", + ".. ... ... ... \n", + "499 1.007314 0.999484 0.999985 \n", + "500 0.699102 0.999787 0.999988 \n", + "501 0.408757 0.999932 0.999987 \n", + "502 0.576986 0.999861 0.999947 \n", + "503 0.482851 0.999889 0.999968 \n", + "\n", + " correlation_Y_ZBand dominantFrequencyEnergy_XBand \\\n", + "0 0.999657 0.008964 \n", + "1 0.999909 0.018750 \n", + "2 0.999984 0.020694 \n", + "3 0.999992 0.015335 \n", + "4 0.999991 0.027059 \n", + ".. ... ... \n", + "499 0.999638 0.009092 \n", + "500 0.999841 0.012191 \n", + "501 0.999948 0.015881 \n", + "502 0.999944 0.012228 \n", + "503 0.999951 0.016684 \n", + "\n", + " dominantFrequencyEnergy_YBand dominantFrequencyEnergy_ZBand \\\n", + "0 0.009608 0.009000 \n", + "1 0.024015 0.011299 \n", + "2 0.029685 0.019629 \n", + "3 0.029545 0.019664 \n", + "4 0.038101 0.021032 \n", + ".. ... ... \n", + "499 0.008966 0.009465 \n", + "500 0.011666 0.015069 \n", + "501 0.012706 0.022372 \n", + "502 0.011949 0.014030 \n", + "503 0.013070 0.024085 \n", + "\n", + " entropy_XBand entropy_YBand entropy_ZBand fqAbsMean_X fqAbsMean_Y \\\n", + "0 7.803281 7.764458 7.798979 28.842 6.706 \n", + "1 7.592033 7.170297 7.763982 26.618 9.336 \n", + "2 7.428787 7.183324 7.549577 32.040 29.518 \n", + "3 7.531214 7.127566 7.475685 45.778 34.176 \n", + "4 7.416095 7.166612 7.373007 43.710 34.224 \n", + ".. ... ... ... ... ... \n", + "499 7.796856 7.799046 7.791035 35.172 48.756 \n", + "500 7.748183 7.773281 7.696858 34.530 48.842 \n", + "501 7.643179 7.758942 7.449880 37.878 46.368 \n", + "502 7.717640 7.756327 7.483432 41.712 46.562 \n", + "503 7.629066 7.769570 6.972002 39.200 40.840 \n", + "\n", + " fqAbsMean_Z fqEnergyFeat_X fqEnergyFeat_Y fqEnergyFeat_Z \\\n", + "0 55.114 416147.0 22687.0 1518857.0 \n", + "1 55.804 370169.0 47400.0 1564792.0 \n", + "2 36.648 662796.0 660597.0 923012.0 \n", + "3 18.330 1304769.0 815064.0 254347.0 \n", + "4 31.546 1358605.0 862102.0 748213.0 \n", + ".. ... ... ... ... \n", + "499 16.616 618810.0 1188766.0 138268.0 \n", + "500 17.328 601149.0 1195095.0 154722.0 \n", + "501 11.712 785687.0 1115182.0 91322.0 \n", + "502 2.338 884896.0 1091337.0 8025.0 \n", + "503 7.004 848456.0 965204.0 38318.0 \n", + "\n", + " fqEntropyFeat_X fqEntropyFeat_Y fqEntropyFeat_Z fqHighestPeak1_X \\\n", + "0 2.056371 4.227074 1.091003 0.659914 \n", + "1 4.954203 5.151731 3.852122 1586.457564 \n", + "2 5.650713 5.174951 5.356747 190.676281 \n", + "3 5.673421 5.136240 5.765772 613.304285 \n", + "4 5.783132 5.565740 5.760390 878.499446 \n", + ".. ... ... ... ... \n", + "499 2.155353 1.477249 3.158663 0.075611 \n", + "500 3.715911 2.901126 4.330129 0.062029 \n", + "501 5.028585 4.208921 5.484010 299.897572 \n", + "502 4.861145 4.051993 5.942058 0.365282 \n", + "503 4.721612 4.266797 5.192156 0.837968 \n", + "\n", + " fqHighestPeak1_Y fqHighestPeak1_Z fqHighestPeak2_X fqHighestPeak2_Y \\\n", + "0 0.329848 0.062427 0.214743 0.142434 \n", + "1 244.301350 21.022571 844.302348 125.356487 \n", + "2 605.220870 660.676570 176.340483 559.722940 \n", + "3 3404.162602 1654.679747 511.048051 2426.512293 \n", + "4 617.961281 2966.643517 696.645541 510.218120 \n", + ".. ... ... ... ... \n", + "499 0.066151 0.152542 0.064141 0.045393 \n", + "500 0.109477 0.294831 0.059480 0.071158 \n", + "501 217.027649 59.428941 158.318483 105.919777 \n", + "502 0.393256 0.331209 0.185996 0.225281 \n", + "503 0.537947 3.181930 0.836692 0.526419 \n", + "\n", + " fqHighestPeak2_Z fqHighestPeak3_X fqHighestPeak3_Y fqHighestPeak3_Z \\\n", + "0 0.056354 0.201488 0.121684 0.044537 \n", + "1 10.221185 239.346016 47.646533 9.525602 \n", + "2 408.824249 159.845333 323.797473 388.611398 \n", + "3 282.492840 345.468368 953.173254 237.514909 \n", + "4 1258.914443 690.811103 347.967190 581.366504 \n", + ".. ... ... ... ... \n", + "499 0.146578 0.059236 0.040802 0.143887 \n", + "500 0.152252 0.048268 0.053454 0.089385 \n", + "501 58.678460 55.928570 35.348018 40.291161 \n", + "502 0.163962 0.136052 0.136601 0.156347 \n", + "503 3.038781 0.786312 0.431989 0.911122 \n", + "\n", + " fqHighestPeakFreq1_X fqHighestPeakFreq1_Y fqHighestPeakFreq1_Z \\\n", + "0 0.125 0.125 0.125 \n", + "1 0.125 0.125 0.125 \n", + "2 0.125 0.125 0.125 \n", + "3 0.250 0.250 0.125 \n", + "4 0.875 0.125 0.125 \n", + ".. ... ... ... \n", + "499 4.625 4.250 0.250 \n", + "500 1.875 0.125 0.125 \n", + "501 0.125 0.125 0.250 \n", + "502 0.125 0.125 0.125 \n", + "503 0.250 1.000 0.125 \n", + "\n", + " fqHighestPeakFreq2_X fqHighestPeakFreq2_Y fqHighestPeakFreq2_Z \\\n", + "0 0.000 7.625 5.750 \n", + "1 0.250 0.250 2.000 \n", + "2 0.750 0.250 0.250 \n", + "3 0.125 0.125 0.250 \n", + "4 1.500 0.500 0.250 \n", + ".. ... ... ... \n", + "499 4.250 4.375 3.625 \n", + "500 3.000 1.875 3.500 \n", + "501 0.250 0.250 0.125 \n", + "502 0.000 0.000 0.250 \n", + "503 1.000 1.250 0.250 \n", + "\n", + " fqHighestPeakFreq3_X fqHighestPeakFreq3_Y fqHighestPeakFreq3_Z \\\n", + "0 0.250 0.250 5.875 \n", + "1 0.375 0.375 1.875 \n", + "2 3.500 0.375 0.375 \n", + "3 0.375 0.375 0.500 \n", + "4 0.750 1.500 1.250 \n", + ".. ... ... ... \n", + "499 4.500 3.875 4.000 \n", + "500 2.875 0.250 0.250 \n", + "501 0.375 0.375 0.375 \n", + "502 0.250 1.125 0.750 \n", + "503 1.750 1.375 0.375 \n", + "\n", + " fqHistogramBin10_X fqHistogramBin10_Y fqHistogramBin10_Z \\\n", + "0 0.059330 0.389456 0.753217 \n", + "1 0.000061 0.000015 0.051873 \n", + "2 0.025240 0.000251 0.005582 \n", + "3 0.018200 0.000005 0.000702 \n", + "4 0.049420 0.005158 0.002619 \n", + ".. ... ... ... \n", + "499 0.185922 0.043815 0.065751 \n", + "500 0.170029 0.096525 0.052775 \n", + "501 0.000928 0.000214 0.006905 \n", + "502 0.033254 0.015588 0.042798 \n", + "503 0.158842 0.020134 0.002435 \n", + "\n", + " fqHistogramBin1_X fqHistogramBin1_Y fqHistogramBin1_Z \\\n", + "0 0.917365 0.501879 0.145201 \n", + "1 0.996411 0.960550 0.354704 \n", + "2 0.343415 0.920326 0.845095 \n", + "3 0.716864 0.992726 0.989875 \n", + "4 0.194959 0.712702 0.878294 \n", + ".. ... ... ... \n", + "499 0.048028 0.141320 0.193154 \n", + "500 0.115791 0.465422 0.576209 \n", + "501 0.980711 0.968692 0.874908 \n", + "502 0.832916 0.823695 0.627951 \n", + "503 0.248169 0.072066 0.966204 \n", + "\n", + " fqHistogramBin2_X fqHistogramBin2_Y fqHistogramBin2_Z \\\n", + "0 0.000904 0.007043 0.041231 \n", + "1 0.002688 0.025178 0.043965 \n", + "2 0.187601 0.013045 0.076190 \n", + "3 0.074381 0.006033 0.006555 \n", + "4 0.455763 0.079752 0.031283 \n", + ".. ... ... ... \n", + "499 0.015525 0.069480 0.036488 \n", + "500 0.012550 0.014091 0.008415 \n", + "501 0.003834 0.017005 0.002335 \n", + "502 0.053437 0.093162 0.268378 \n", + "503 0.220826 0.303537 0.018221 \n", + "\n", + " fqHistogramBin3_X fqHistogramBin3_Y fqHistogramBin3_Z \\\n", + "0 0.012913 0.003186 0.028656 \n", + "1 0.000209 0.007173 0.172133 \n", + "2 0.038983 0.046723 0.022851 \n", + "3 0.123416 0.000264 0.000817 \n", + "4 0.190181 0.146581 0.060788 \n", + ".. ... ... ... \n", + "499 0.061261 0.021545 0.015547 \n", + "500 0.069835 0.080216 0.027630 \n", + "501 0.005995 0.003567 0.008486 \n", + "502 0.036650 0.050618 0.017483 \n", + "503 0.139805 0.432839 0.004041 \n", + "\n", + " fqHistogramBin4_X fqHistogramBin4_Y fqHistogramBin4_Z \\\n", + "0 0.001133 0.004548 0.001605 \n", + "1 0.000137 0.004941 0.209779 \n", + "2 0.059610 0.007699 0.019253 \n", + "3 0.039071 0.000808 0.001340 \n", + "4 0.023405 0.013695 0.005697 \n", + ".. ... ... ... \n", + "499 0.018228 0.115281 0.030084 \n", + "500 0.192815 0.165997 0.024964 \n", + "501 0.000197 0.007786 0.097574 \n", + "502 0.012061 0.011319 0.008113 \n", + "503 0.083092 0.057813 0.002771 \n", + "\n", + " fqHistogramBin5_X fqHistogramBin5_Y fqHistogramBin5_Z \\\n", + "0 0.000133 0.004992 0.008487 \n", + "1 0.000304 0.001234 0.087198 \n", + "2 0.109452 0.009152 0.015953 \n", + "3 0.014320 0.000140 0.000244 \n", + "4 0.053001 0.030390 0.005559 \n", + ".. ... ... ... \n", + "499 0.030837 0.012466 0.042601 \n", + "500 0.213722 0.010250 0.016697 \n", + "501 0.000435 0.002103 0.007861 \n", + "502 0.003046 0.000400 0.004838 \n", + "503 0.003842 0.042695 0.000124 \n", + "\n", + " fqHistogramBin6_X fqHistogramBin6_Y fqHistogramBin6_Z \\\n", + "0 0.002641 0.002711 0.004863 \n", + "1 0.000101 0.000490 0.041666 \n", + "2 0.201867 0.001369 0.005122 \n", + "3 0.005846 0.000012 0.000312 \n", + "4 0.010443 0.008340 0.010087 \n", + ".. ... ... ... \n", + "499 0.057460 0.021705 0.165463 \n", + "500 0.103490 0.103651 0.203756 \n", + "501 0.005911 0.000510 0.000915 \n", + "502 0.008047 0.002567 0.006263 \n", + "503 0.012732 0.036009 0.000879 \n", + "\n", + " fqHistogramBin7_X fqHistogramBin7_Y fqHistogramBin7_Z \\\n", + "0 0.001861 0.007453 0.002807 \n", + "1 0.000028 0.000260 0.015986 \n", + "2 0.021811 0.000078 0.007235 \n", + "3 0.002830 0.000002 0.000080 \n", + "4 0.009787 0.000881 0.001366 \n", + ".. ... ... ... \n", + "499 0.197672 0.376762 0.276174 \n", + "500 0.057769 0.033303 0.054540 \n", + "501 0.000476 0.000048 0.000353 \n", + "502 0.013900 0.000432 0.010065 \n", + "503 0.025386 0.023334 0.003946 \n", + "\n", + " fqHistogramBin8_X fqHistogramBin8_Y fqHistogramBin8_Z \\\n", + "0 0.001955 0.064559 0.007588 \n", + "1 0.000034 0.000109 0.011734 \n", + "2 0.010835 0.000937 0.001323 \n", + "3 0.002280 0.000002 0.000067 \n", + "4 0.010018 0.000904 0.003826 \n", + ".. ... ... ... \n", + "499 0.291858 0.129554 0.073230 \n", + "500 0.008201 0.018185 0.004613 \n", + "501 0.001449 0.000075 0.000293 \n", + "502 0.002615 0.001125 0.010125 \n", + "503 0.103507 0.008222 0.001323 \n", + "\n", + " fqHistogramBin9_X fqHistogramBin9_Y fqHistogramBin9_Z fqInterquart_X \\\n", + "0 0.001764 1.417175e-02 0.006344 1.0 \n", + "1 0.000028 4.913791e-05 0.010962 2.0 \n", + "2 0.001186 4.205692e-04 0.001395 19.0 \n", + "3 0.002791 6.951576e-06 0.000008 33.0 \n", + "4 0.003023 1.596709e-03 0.000482 45.0 \n", + ".. ... ... ... ... \n", + "499 0.093209 6.807324e-02 0.101508 1.0 \n", + "500 0.055798 1.235906e-02 0.030399 0.0 \n", + "501 0.000064 2.846989e-07 0.000369 17.0 \n", + "502 0.004074 1.095070e-03 0.003987 0.0 \n", + "503 0.003799 3.350736e-03 0.000056 3.0 \n", + "\n", + " fqInterquart_Y fqInterquart_Z fqKurtosis_X fqKurtosis_Y fqKurtosis_Z \\\n", + "0 1.00 0.0 82.921957 37.277378 6.172148 \n", + "1 3.00 1.0 76.273672 71.165185 15.416382 \n", + "2 35.00 45.0 6.878412 37.918868 31.087918 \n", + "3 44.25 29.0 22.193253 59.206577 109.082455 \n", + "4 38.50 55.0 14.428181 21.579397 68.114033 \n", + ".. ... ... ... ... ... \n", + "499 1.00 1.0 3.493950 6.700808 7.553704 \n", + "500 0.25 1.0 6.446087 22.380162 45.749892 \n", + "501 14.25 8.0 73.117153 75.619776 29.411194 \n", + "502 1.00 1.0 55.401241 51.909021 31.568406 \n", + "503 4.25 3.0 3.812462 8.022810 52.037962 \n", + "\n", + " fqSkewness_X fqSkewness_Y fqSkewness_Z \\\n", + "0 8.685933 5.237774 2.194513 \n", + "1 8.554455 8.103869 3.253087 \n", + "2 2.634778 6.003941 5.233903 \n", + "3 4.341981 7.604222 10.282016 \n", + "4 3.633590 4.341104 7.689533 \n", + ".. ... ... ... \n", + "499 1.894942 2.466336 2.691462 \n", + "500 2.245002 4.119326 6.033663 \n", + "501 8.293539 8.386964 5.246583 \n", + "502 6.920554 6.770191 5.179224 \n", + "503 2.074293 2.809057 7.181060 \n", + "\n", + " interQuartileRangeMagnitudesBand interQuartileRange_XBand \\\n", + "0 0.116283 0.108442 \n", + "1 0.515822 0.245962 \n", + "2 7.383329 2.079383 \n", + "3 4.620823 4.435474 \n", + "4 7.758987 9.680704 \n", + ".. ... ... \n", + "499 0.140573 0.070322 \n", + "500 0.356938 0.083357 \n", + "501 1.881589 0.645332 \n", + "502 0.720664 0.200399 \n", + "503 1.160543 0.326100 \n", + "\n", + " interQuartileRange_YBand interQuartileRange_ZBand kurtosis_XBand \\\n", + "0 0.081722 0.065099 230.852565 \n", + "1 0.251985 0.266026 60.605795 \n", + "2 1.559643 1.743397 2.674175 \n", + "3 3.394423 3.348774 0.775528 \n", + "4 6.326923 8.676731 0.356543 \n", + ".. ... ... ... \n", + "499 0.074261 0.176276 225.023923 \n", + "500 0.123523 0.143614 177.722773 \n", + "501 0.435514 0.552009 55.645718 \n", + "502 0.154690 0.196822 120.080582 \n", + "503 0.281286 0.224567 78.167856 \n", + "\n", + " kurtosis_YBand kurtosis_ZBand manipulationLow meanCrossingRate_XBand \\\n", + "0 164.957757 229.058361 0.018765 13.0 \n", + "1 18.028431 181.277262 0.018541 23.0 \n", + "2 0.970036 19.627766 0.023965 41.0 \n", + "3 0.244360 0.024724 0.026066 59.0 \n", + "4 1.039344 -0.283146 0.026051 80.0 \n", + ".. ... ... ... ... \n", + "499 222.308079 211.547373 0.028112 19.0 \n", + "500 200.121968 120.912006 0.027962 16.0 \n", + "501 162.283176 5.298307 0.030719 49.0 \n", + "502 170.359071 41.987689 0.034514 31.0 \n", + "503 180.353197 6.524585 16.298247 27.0 \n", + "\n", + " meanCrossingRate_YBand meanCrossingRate_ZBand meanKineticEnergy_XBand \\\n", + "0 26.0 1.0 25623.466885 \n", + "1 28.0 17.0 25623.457823 \n", + "2 35.0 31.0 25623.470198 \n", + "3 42.0 80.0 19042.431778 \n", + "4 46.0 61.0 67303.477598 \n", + ".. ... ... ... \n", + "499 10.0 37.0 37323.124131 \n", + "500 8.0 24.0 39486.339334 \n", + "501 22.0 39.0 37323.128222 \n", + "502 24.0 34.0 53745.297878 \n", + "503 14.0 24.0 41710.499213 \n", + "\n", + " meanKineticEnergy_YBand meanKineticEnergy_ZBand mean_XLow mean_YLow \\\n", + "0 1096.843008 9.216527e+04 28.841999 -6.701283 \n", + "1 1492.924665 9.216527e+04 21.730506 -6.066504 \n", + "2 3046.793625 9.216521e+04 31.444595 -28.312792 \n", + "3 2467.903796 1.949942e+03 44.778110 -27.926015 \n", + "4 20596.274067 3.317945e+04 38.818246 -28.969772 \n", + ".. ... ... ... ... \n", + "499 73153.322291 8.805210e+03 35.167526 -48.760547 \n", + "500 70197.942449 8.805211e+03 34.523795 -48.818539 \n", + "501 88844.250780 3.046786e+03 37.845314 -46.402168 \n", + "502 67303.493481 1.218714e+02 41.721757 -46.555515 \n", + "503 73153.306037 7.324061e-08 39.372660 -41.068665 \n", + "\n", + " mean_ZLow peaksAmplitudeAvgLow peaksCountLow peaksPeakAvgLow \\\n", + "0 55.114000 0.000000 0.0 0.000000 \n", + "1 55.804647 0.025651 14.0 66.347089 \n", + "2 31.285549 0.003297 40.0 71.749715 \n", + "3 4.871395 0.195948 81.0 68.373870 \n", + "4 0.360292 0.014893 76.0 74.287445 \n", + ".. ... ... ... ... \n", + "499 16.616321 0.052488 4.0 63.068346 \n", + "500 17.329311 0.348241 12.0 63.974253 \n", + "501 11.255463 0.003551 35.0 65.307378 \n", + "502 -1.431406 0.029812 16.0 65.606093 \n", + "503 6.976000 0.047245 20.0 65.067478 \n", + "\n", + " peaksSumLow pitchAvgLow pitchPeak0 pitchPeak1 pitchPeak2 pitchPeak3 \\\n", + "0 0.000000 0.000938 0.0 0.000000 0.000000 0.000000 \n", + "1 928.859251 0.015892 0.0 0.000000 0.000000 0.000000 \n", + "2 2869.988603 0.132808 0.0 0.000000 0.000000 0.000000 \n", + "3 5538.283465 1.441479 0.0 0.000000 0.000000 0.000000 \n", + "4 5645.845839 1.611968 4.0 -0.489273 -0.122318 0.012606 \n", + ".. ... ... ... ... ... ... \n", + "499 252.273386 0.002021 0.0 0.000000 0.000000 0.000000 \n", + "500 767.691038 0.011532 0.0 0.000000 0.000000 0.000000 \n", + "501 2285.758227 0.059133 0.0 0.000000 0.000000 0.000000 \n", + "502 1049.697496 0.052483 0.0 0.000000 0.000000 0.000000 \n", + "503 1301.349551 1.560049 0.0 0.000000 0.000000 0.000000 \n", + "\n", + " pitchStdDevLow postureDistance_XLow postureDistance_YLow \\\n", + "0 0.000217 35.543282 -26.272001 \n", + "1 0.004891 27.797010 -34.074141 \n", + "2 0.016102 59.757388 0.159047 \n", + "3 0.103448 72.704125 39.906715 \n", + "4 0.206093 67.788018 38.457954 \n", + ".. ... ... ... \n", + "499 0.000332 83.928074 18.551205 \n", + "500 0.001512 83.342334 17.194484 \n", + "501 0.014748 84.247482 26.589851 \n", + "502 0.004246 88.277272 43.153164 \n", + "503 0.336867 80.441325 32.396661 \n", + "\n", + " postureDistance_ZLow quartilesMagnitudes_XBand \\\n", + "0 -61.815283 0.061738 \n", + "1 -61.871151 0.145435 \n", + "2 -59.598341 0.281532 \n", + "3 -32.797410 2.869744 \n", + "4 -29.330064 6.280481 \n", + ".. ... ... \n", + "499 -65.376868 0.086797 \n", + "500 -66.147850 0.054171 \n", + "501 -57.657631 0.188673 \n", + "502 -45.124108 0.103420 \n", + "503 -48.044665 0.103100 \n", + "\n", + " quartilesMagnitudes_YBand quartilesMagnitudes_ZBand quartilesQ1_XBand \\\n", + "0 0.118949 0.178021 -0.054443 \n", + "1 0.260237 0.661256 -0.086167 \n", + "2 3.402929 7.664861 -1.268853 \n", + "3 4.702810 7.490567 -2.200263 \n", + "4 9.396958 14.039468 -4.611741 \n", + ".. ... ... ... \n", + "499 0.145225 0.227369 -0.017104 \n", + "500 0.152417 0.411110 -0.035056 \n", + "501 0.795689 2.070261 -0.347386 \n", + "502 0.191260 0.824083 -0.074998 \n", + "503 0.332578 1.263642 -0.120617 \n", + "\n", + " quartilesQ1_YBand quartilesQ1_ZBand quartilesQ2_XBand \\\n", + "0 -0.032033 -0.039487 -0.009518 \n", + "1 -0.135954 -0.136618 0.013514 \n", + "2 -1.243138 -1.259616 -0.007098 \n", + "3 -2.005430 -1.505724 0.084118 \n", + "4 -3.234598 -4.618432 0.041283 \n", + ".. ... ... ... \n", + "499 -0.035340 -0.078979 0.003914 \n", + "500 -0.090815 -0.069541 0.000288 \n", + "501 -0.245273 -0.317599 -0.000426 \n", + "502 -0.084686 -0.089356 0.000005 \n", + "503 -0.173414 -0.101217 -0.000454 \n", + "\n", + " quartilesQ2_YBand quartilesQ2_ZBand quartilesQ3_XBand \\\n", + "0 1.454335e-02 2.479075e-07 0.053999 \n", + "1 4.007931e-04 2.578741e-03 0.159795 \n", + "2 -5.021189e-02 3.661978e-06 0.810530 \n", + "3 -1.789399e-01 2.803538e-02 2.235211 \n", + "4 3.167752e-01 -6.475688e-02 5.068963 \n", + ".. ... ... ... \n", + "499 -2.528229e-06 -8.867480e-03 0.053218 \n", + "500 -1.020861e-04 7.748469e-12 0.048301 \n", + "501 -1.606221e-04 -3.735316e-03 0.297946 \n", + "502 3.517538e-04 2.042814e-04 0.125401 \n", + "503 -1.027010e-09 -1.098812e-03 0.205484 \n", + "\n", + " quartilesQ3_YBand quartilesQ3_ZBand rollAvgLow rollMotionAmountLow \\\n", + "0 0.049688 0.025612 0.029061 0.008021 \n", + "1 0.116031 0.129407 0.541524 0.085935 \n", + "2 0.316505 0.483781 6.280891 1.447569 \n", + "3 1.388993 1.843050 6.271116 2.250457 \n", + "4 3.092325 4.058300 6.265128 2.342195 \n", + ".. ... ... ... ... \n", + "499 0.038921 0.097297 0.071320 0.007932 \n", + "500 0.032708 0.074073 0.372438 0.031162 \n", + "501 0.190241 0.234410 6.278076 0.962455 \n", + "502 0.070004 0.107466 6.281475 1.129232 \n", + "503 0.107873 0.123350 1.133865 0.133845 \n", + "\n", + " rollMotionRegularityLow rollPeak0 rollPeak1 rollPeak2 rollPeak3 \\\n", + "0 1.000 0.0 0.000000 0.000000 0.000000 \n", + "1 1.000 0.0 0.000000 0.000000 0.000000 \n", + "2 1.000 9.0 21.707206 2.411912 0.002484 \n", + "3 0.988 11.0 28.398787 2.581708 0.025052 \n", + "4 0.998 27.0 57.342009 2.123778 0.000035 \n", + ".. ... ... ... ... ... \n", + "499 1.000 0.0 0.000000 0.000000 0.000000 \n", + "500 1.000 0.0 0.000000 0.000000 0.000000 \n", + "501 1.000 4.0 12.485143 3.121286 0.003238 \n", + "502 1.000 3.0 9.145478 3.048493 2.092086 \n", + "503 1.000 0.0 0.000000 0.000000 0.000000 \n", + "\n", + " rollPitchCorrelation rollStdDevLow skewness_XBand skewness_YBand \\\n", + "0 1.000000 0.008562 14.436898 -11.494984 \n", + "1 0.999999 0.123100 2.807118 -1.100507 \n", + "2 0.999997 1.958079 -0.633833 -1.197336 \n", + "3 0.999995 2.397779 -0.833540 -1.348253 \n", + "4 0.999985 2.419125 -1.132133 -1.665492 \n", + ".. ... ... ... ... \n", + "499 1.000000 0.009974 14.222786 -14.169953 \n", + "500 1.000000 0.054444 11.853592 -13.132488 \n", + "501 0.999998 1.701608 4.491024 -11.201286 \n", + "502 0.999999 1.866030 8.739616 -11.817088 \n", + "503 0.999986 0.215263 4.777859 -12.139106 \n", + "\n", + " skewness_ZBand squareSumOfComponent squareSumOfComponent_X \\\n", + "0 14.424527 1.619926e+07 867.287775 \n", + "1 12.046715 1.630370e+07 865.242225 \n", + "2 1.684239 1.654691e+07 870.233406 \n", + "3 -1.359373 6.736924e+05 661.235510 \n", + "4 -1.256109 1.680410e+07 2273.525442 \n", + ".. ... ... ... \n", + "499 13.535020 1.630266e+07 1263.693630 \n", + "500 8.691259 1.608824e+07 1335.055131 \n", + "501 -0.771061 1.905301e+07 1266.701486 \n", + "502 2.300305 1.681807e+07 1819.059819 \n", + "503 -0.432671 1.504212e+07 1414.613179 \n", + "\n", + " squareSumOfComponent_Y squareSumOfComponent_Z sumOfSquareComponents \\\n", + "0 37.266065 3120.276758 1.048970e+07 \n", + "1 51.060403 3121.481343 1.049490e+07 \n", + "2 109.430560 3088.125621 1.030580e+07 \n", + "3 90.891793 68.660385 4.502080e+05 \n", + "4 704.049839 1121.705691 6.922828e+06 \n", + ".. ... ... ... \n", + "499 2476.085590 297.876317 7.816652e+06 \n", + "500 2377.699120 298.260456 7.524785e+06 \n", + "501 2994.586208 103.687125 1.058283e+07 \n", + "502 2277.777042 4.147205 8.497264e+06 \n", + "503 2463.791439 0.011977 8.071399e+06 \n", + "\n", + " sumPerComponent_XBand sumPerComponent_YBand sumPerComponent_ZBand \\\n", + "0 29.002355 6.001318 55.003283 \n", + "1 29.009274 7.006492 55.008412 \n", + "2 29.038915 10.034777 55.036378 \n", + "3 25.052536 9.039004 8.037320 \n", + "4 47.093516 26.066646 33.087563 \n", + ".. ... ... ... \n", + "499 35.002929 49.003488 17.002448 \n", + "500 36.005837 48.005767 17.004361 \n", + "501 35.015515 54.010908 10.011338 \n", + "502 42.009258 47.007507 2.004713 \n", + "503 37.010612 49.008707 0.006081 \n", + "\n", + " totalAbsoluteAreaBand totalEnergy_XBand totalEnergy_YBand \\\n", + "0 312.592555 1290.394581 60.611492 \n", + "1 864.715960 2537.180818 677.832377 \n", + "2 3616.215615 9218.952240 6770.834825 \n", + "3 4165.495456 12493.455174 6106.239879 \n", + "4 8033.196394 33021.472511 16683.117998 \n", + ".. ... ... ... \n", + "499 384.671068 1908.638778 3729.334512 \n", + "500 611.899981 2211.472549 3700.438879 \n", + "501 1307.327021 3237.943851 5051.325838 \n", + "502 778.281403 3591.558470 3805.375198 \n", + "503 898.820043 3413.563386 4090.414372 \n", + "\n", + " totalEnergy_ZBand totalKineticEnergyBand totalMagnitudeBand \\\n", + "0 4694.902256 3.782695e+09 78.088845 \n", + "1 5132.819023 3.795297e+09 91.657431 \n", + "2 11785.651118 3.844737e+09 166.747125 \n", + "3 5277.603869 7.464573e+08 154.645146 \n", + "4 26879.848539 3.852492e+09 276.840101 \n", + ".. ... ... ... \n", + "499 455.208874 3.795297e+09 78.389786 \n", + "500 572.202692 3.770092e+09 80.859842 \n", + "501 895.466940 4.111329e+09 96.114795 \n", + "502 341.436881 3.855401e+09 88.266159 \n", + "503 365.728609 3.654731e+09 88.770283 \n", + "\n", + " totalMeanLow variance_XBand variance_YBand variance_ZBand \\\n", + "0 12875.786040 2.586383 0.137999 9.396457 \n", + "1 11911.441472 5.334184 1.579538 10.411239 \n", + "2 5736.225303 24.202143 19.306809 29.209739 \n", + "3 3620.581741 35.239450 19.355604 16.080099 \n", + "4 1701.460966 100.726696 54.227260 85.860586 \n", + ".. ... ... ... ... \n", + "499 503.883251 3.825507 7.779741 0.922262 \n", + "500 505.761307 4.495299 7.917488 1.197929 \n", + "501 449.768115 7.228331 11.218333 2.322136 \n", + "502 -1044.193986 7.407112 8.274669 0.777455 \n", + "503 879.999083 7.290436 8.752011 0.882940 \n", + "\n", + " velocity_XBand velocity_YBand velocity_ZBand \n", + "0 29.001359 -6.000329 5.500258e+01 \n", + "1 29.001351 -7.000462 5.500257e+01 \n", + "2 29.001408 -10.002113 5.499929e+01 \n", + "3 25.002506 -9.002192 8.000601e+00 \n", + "4 47.001504 -26.003086 3.299904e+01 \n", + ".. ... ... ... \n", + "499 35.001684 -49.002253 1.700079e+01 \n", + "500 36.001692 -48.002424 1.700076e+01 \n", + "501 35.001969 -54.002179 9.999950e+00 \n", + "502 42.001899 -47.002248 -1.999967e+00 \n", + "503 37.000000 -49.000000 1.240497e-08 \n", + "\n", + "[504 rows x 169 columns]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from calculatingfeatures.CalculatingFeatures.helper_functions import convert3DEmpaticaToArray\n", + "\n", + "pathToAccCsv = \"example_data/S2_E4_Data/ACC.csv\"\n", + "windowLength = 500\n", + "\n", + "#np.seterr(all='raise')\n", + "\n", + "# get an array of values from ACC empatica file\n", + "data, startTimeStamp, sampleRate = convert3DEmpaticaToArray(pathToAccCsv)\n", + "\n", + "# Convert the ACC data into 2D array\n", + "x_2D = convertInputInto2d(data[0], windowLength)\n", + "y_2D = convertInputInto2d(data[1], windowLength)\n", + "z_2D = convertInputInto2d(data[2], windowLength)\n", + "\n", + "# Create a list with feature names\n", + "featureNames = []\n", + "featureNames.extend(accelerometerFeatureNames)\n", + "featureNames.extend(frequencyFeatureNames)\n", + "\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "# Calculate features\n", + "calculatedFeatures = calculateFeatures(x_2D, y_2D, z_2D, fs=int(sampleRate), featureNames=featureNames)\n", + "print(calculatedFeatures)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## convertInputInto2dTime" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[10, 150, 1100, 1150], [1200, 1300], [1400, 1500], [1600]]\n", + "[[0, 50, 100, 150], [200, 300], [400, 500], [600]]\n" + ] + } + ], + "source": [ + "df = pd.DataFrame()\n", + "df[\"data\"] = [10, 150, 1100, 1150, 1200, 1300, 1400, 1500, 1600]\n", + "df[\"time\"] = [0, 50, 100, 150, 200, 300, 400, 500, 600]\n", + "\n", + "outData, outTime = convertInputInto2dTime(df, 200)\n", + "print(outData)\n", + "print(outTime)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate all features" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 24%|██▎ | 4/17 [00:03<00:11, 1.12it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bad signal. Too little RRs detected. 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 29%|██▉ | 5/17 [00:04<00:11, 1.08it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bad signal. Too little RRs detected. 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 35%|███▌ | 6/17 [00:05<00:10, 1.09it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bad signal. Too little RRs detected. 1\n", + "Bad signal. Too little RRs detected. 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 53%|█████▎ | 9/17 [00:08<00:07, 1.06it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bad signal. Too little RRs detected. 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 59%|█████▉ | 10/17 [00:09<00:06, 1.07it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bad signal. Too little RRs detected. 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 71%|███████ | 12/17 [00:11<00:04, 1.12it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bad signal. Too little RRs detected. 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 88%|████████▊ | 15/17 [00:13<00:01, 1.06it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bad signal. Too little RRs detected. 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 17/17 [00:15<00:00, 1.09it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bad signal. Too little RRs detected. 1\n", + "Bad signal. Too little RRs detected. 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " absEnergy_X absEnergy_Y absEnergy_Z absoluteAreaAllBand \\\n", + "0 9757.282156 10849.028553 9811.741631 930.324637 \n", + "1 9448.079674 8899.975693 9956.680101 880.255964 \n", + "2 9669.760737 9184.529987 9121.402956 889.394563 \n", + "3 10026.356290 9801.236188 9982.377726 920.462589 \n", + "4 9709.603922 10691.230186 9662.734156 906.331468 \n", + "5 10233.693555 10371.886729 10563.612304 922.999063 \n", + "6 9482.841316 10326.075806 10154.052192 891.241288 \n", + "7 9520.121266 10241.641412 10387.903719 889.958466 \n", + "8 10395.792313 10179.629166 10107.379246 899.898502 \n", + "9 11026.931493 9834.741763 10120.339487 937.539507 \n", + "10 9574.581294 8891.577695 9480.013849 853.098151 \n", + "11 10266.304113 9524.407888 9519.015587 894.370062 \n", + "12 9639.580580 9683.493055 10251.534492 912.690717 \n", + "13 10149.017500 9632.893918 9966.713340 896.978822 \n", + "14 9190.474376 10330.498873 10536.313374 904.039012 \n", + "15 10890.487664 10640.928635 10044.509683 949.133312 \n", + "16 6484.122156 6140.316953 7010.804341 618.770314 \n", + "\n", + " absoluteArea_XBand absoluteArea_YBand absoluteArea_ZBand \\\n", + "0 9.802377 -3.995068 4.135917 \n", + "1 11.552164 -11.852706 6.764970 \n", + "2 -6.075127 -14.974184 -3.550690 \n", + "3 13.940072 -1.208619 -5.439318 \n", + "4 -0.778795 -8.573990 9.118902 \n", + "5 -5.432606 -2.345853 -5.366276 \n", + "6 -10.268284 1.608141 -2.508280 \n", + "7 7.573499 -3.973162 9.895818 \n", + "8 2.323463 -7.297003 5.956317 \n", + "9 0.066851 11.319004 4.828713 \n", + "10 -5.535852 -3.323174 9.748395 \n", + "11 -7.277916 -9.262661 6.302632 \n", + "12 -4.434544 -4.278268 6.222900 \n", + "13 -7.436684 3.024444 -5.008479 \n", + "14 4.340700 2.523212 7.274647 \n", + "15 -0.108792 -12.711790 11.993602 \n", + "16 6.800276 8.102356 9.725532 \n", + "\n", + " absoluteMean_XBand absoluteMean_YBand absoluteMean_ZBand \\\n", + "0 0.032675 -0.013317 0.013786 \n", + "1 0.038507 -0.039509 0.022550 \n", + "2 -0.020250 -0.049914 -0.011836 \n", + "3 0.046467 -0.004029 -0.018131 \n", + "4 -0.002596 -0.028580 0.030396 \n", + "5 -0.018109 -0.007820 -0.017888 \n", + "6 -0.034228 0.005360 -0.008361 \n", + "7 0.025245 -0.013244 0.032986 \n", + "8 0.007745 -0.024323 0.019854 \n", + "9 0.000223 0.037730 0.016096 \n", + "10 -0.018453 -0.011077 0.032495 \n", + "11 -0.024260 -0.030876 0.021009 \n", + "12 -0.014782 -0.014261 0.020743 \n", + "13 -0.024789 0.010081 -0.016695 \n", + "14 0.014469 0.008411 0.024249 \n", + "15 -0.000363 -0.042373 0.039979 \n", + "16 0.022668 0.027008 0.032418 \n", + "\n", + " amplitude_XBand amplitude_XLow amplitude_YBand amplitude_YLow \\\n", + "0 11.840441 15.367655 5.561830 13.599581 \n", + "1 10.842231 13.185562 11.954154 15.408985 \n", + "2 12.065636 15.300944 12.504229 15.425589 \n", + "3 9.885092 13.416908 5.636100 12.551434 \n", + "4 5.721227 12.760709 10.656748 13.420126 \n", + "5 6.394455 12.454305 7.781983 13.642673 \n", + "6 11.251708 14.216014 8.006252 12.116988 \n", + "7 9.572914 12.906963 5.469677 12.083638 \n", + "8 5.521984 12.295475 10.685971 14.293234 \n", + "9 5.739659 12.753636 12.351271 15.905907 \n", + "10 9.259486 12.892579 7.553088 11.666330 \n", + "11 12.682163 15.907374 10.232092 12.641317 \n", + "12 10.885035 13.532587 7.599297 11.406162 \n", + "13 8.409818 13.167735 6.321747 11.584058 \n", + "14 5.417743 12.668153 7.214473 12.111088 \n", + "15 5.565133 13.389627 12.269357 15.438568 \n", + "16 9.572596 11.662286 10.803180 14.518034 \n", + "\n", + " amplitude_ZBand amplitude_ZLow areaAutocorr_X areaAutocorr_Y \\\n", + "0 6.120984 12.077082 1.251150 0.346077 \n", + "1 7.338200 13.034990 0.613081 -0.337795 \n", + "2 8.157018 11.770679 1.320552 0.285729 \n", + "3 5.816986 11.504766 -0.714717 0.104095 \n", + "4 10.494853 13.742355 -0.177515 0.744384 \n", + "5 8.948602 12.838789 -0.284261 0.190317 \n", + "6 5.537172 12.112158 0.403123 2.862261 \n", + "7 12.102983 14.971049 1.163917 0.491810 \n", + "8 4.930710 12.311160 1.211632 0.166704 \n", + "9 8.926564 12.662584 0.403719 0.886899 \n", + "10 12.090767 15.875714 0.957135 1.056713 \n", + "11 5.467044 11.685198 0.034285 0.114891 \n", + "12 11.504623 14.014334 0.083044 -0.309026 \n", + "13 7.422933 12.495047 2.116489 -0.061512 \n", + "14 6.803331 10.741192 1.100854 0.394110 \n", + "15 10.905015 14.203833 1.739354 0.889813 \n", + "16 12.463576 15.348119 NaN NaN \n", + "\n", + " areaAutocorr_Z areaUnderAccelerationMagnitude areaUnderMagnitude \\\n", + "0 0.489267 817.023838 817.023838 \n", + "1 1.201937 746.603640 746.603640 \n", + "2 0.731113 863.282811 863.282811 \n", + "3 0.980113 797.080198 797.080198 \n", + "4 0.461838 627.711913 627.711913 \n", + "5 0.885326 793.145688 793.145688 \n", + "6 0.046986 751.758387 751.758387 \n", + "7 0.285232 804.852128 804.852128 \n", + "8 1.022880 737.318718 737.318718 \n", + "9 0.418596 748.433836 748.433836 \n", + "10 1.295665 729.288649 729.288649 \n", + "11 0.433735 832.523937 832.523937 \n", + "12 0.114227 874.454264 874.454264 \n", + "13 0.895080 684.783664 684.783664 \n", + "14 0.864031 684.304828 684.304828 \n", + "15 0.615599 845.185357 845.185357 \n", + "16 NaN 889.558912 889.558912 \n", + "\n", + " area_XLow area_YLow area_ZLow autocorrelation_100_X \\\n", + "0 8.507300 17.448677 -93.586973 -0.033598 \n", + "1 195.204955 -2.151729 -22.210498 -0.128406 \n", + "2 14.886643 -72.277090 3.763140 -0.045989 \n", + "3 -6.580937 -57.815404 64.301458 -0.114063 \n", + "4 -33.226356 -169.940579 -44.658588 -0.023989 \n", + "5 92.264979 54.311395 114.608368 -0.006580 \n", + "6 146.122043 106.458752 -12.069510 0.071640 \n", + "7 81.070954 9.782555 101.605517 0.114433 \n", + "8 76.638693 57.580025 -135.876734 0.025191 \n", + "9 19.624899 43.201275 151.705470 -0.047697 \n", + "10 -16.976897 2.086521 3.798669 0.081849 \n", + "11 4.496054 17.036321 -78.608625 0.073983 \n", + "12 2.403524 -47.309146 161.572935 0.137246 \n", + "13 -156.226880 19.395747 7.455774 0.020696 \n", + "14 -25.123373 -32.135156 -54.622977 0.048791 \n", + "15 -3.221847 -77.477830 -75.762919 0.186899 \n", + "16 120.936148 -69.355449 158.385979 -0.076146 \n", + "\n", + " autocorrelation_100_Y autocorrelation_100_Z autocorrelation_10_X \\\n", + "0 0.059537 0.053420 0.106405 \n", + "1 0.103122 0.007333 0.045905 \n", + "2 0.039841 0.040569 -0.054911 \n", + "3 0.034858 0.000401 -0.008231 \n", + "4 0.090927 -0.158701 0.071754 \n", + "5 0.012849 0.006175 0.009983 \n", + "6 -0.050546 0.037803 0.045039 \n", + "7 -0.154497 0.117893 -0.042434 \n", + "8 0.078231 -0.106049 0.074367 \n", + "9 -0.033887 0.048270 0.089410 \n", + "10 -0.043212 0.116065 0.060132 \n", + "11 0.008015 -0.043320 -0.017004 \n", + "12 0.061625 -0.075384 0.034301 \n", + "13 -0.025759 -0.025377 0.031711 \n", + "14 0.009157 0.018478 -0.044737 \n", + "15 0.075965 -0.007770 0.038175 \n", + "16 -0.065110 0.043484 -0.065927 \n", + "\n", + " autocorrelation_10_Y autocorrelation_10_Z autocorrelation_20_X \\\n", + "0 -0.100977 -0.038760 0.040176 \n", + "1 0.052315 -0.034063 0.127281 \n", + "2 0.001645 -0.038531 -0.017747 \n", + "3 0.023636 0.033845 0.054339 \n", + "4 -0.067678 -0.037247 0.064662 \n", + "5 -0.047529 0.088462 0.088562 \n", + "6 -0.033754 0.004394 0.035105 \n", + "7 0.025099 -0.017073 0.014068 \n", + "8 0.043763 0.123542 -0.107926 \n", + "9 -0.065737 -0.075726 0.050419 \n", + "10 -0.093968 0.025957 0.023002 \n", + "11 -0.007979 -0.011119 -0.061107 \n", + "12 0.006941 -0.190285 -0.003771 \n", + "13 0.052174 0.014034 0.033961 \n", + "14 -0.014764 -0.070400 -0.046461 \n", + "15 0.033185 0.053601 -0.006713 \n", + "16 0.028038 0.073310 -0.056683 \n", + "\n", + " autocorrelation_20_Y autocorrelation_20_Z autocorrelation_30_X \\\n", + "0 0.003765 0.002543 -0.049990 \n", + "1 -0.129480 -0.014282 0.027596 \n", + "2 0.011977 -0.110984 0.038068 \n", + "3 -0.058947 -0.038399 0.000622 \n", + "4 0.067429 0.004266 -0.057145 \n", + "5 0.031023 -0.001941 0.034667 \n", + "6 0.012584 0.030843 -0.046854 \n", + "7 -0.006981 -0.007008 -0.096810 \n", + "8 0.066055 0.003499 -0.025921 \n", + "9 0.046704 0.051978 0.054563 \n", + "10 -0.039985 -0.062853 0.002230 \n", + "11 0.063928 -0.108339 0.071981 \n", + "12 -0.056253 -0.000450 -0.026721 \n", + "13 -0.010468 0.001997 -0.019577 \n", + "14 -0.068659 0.079928 0.065463 \n", + "15 0.001135 -0.026500 -0.135474 \n", + "16 0.074539 -0.126566 0.061670 \n", + "\n", + " autocorrelation_30_Y autocorrelation_30_Z autocorrelation_50_X \\\n", + "0 -0.040294 -0.011024 0.060141 \n", + "1 0.040854 0.093325 -0.071681 \n", + "2 0.007768 -0.104029 0.077159 \n", + "3 -0.037307 -0.083809 0.031726 \n", + "4 -0.085651 -0.033945 -0.019312 \n", + "5 0.031843 0.010472 0.007438 \n", + "6 -0.049723 -0.104720 0.027879 \n", + "7 0.058257 -0.034894 -0.007670 \n", + "8 -0.022534 0.069451 0.088884 \n", + "9 0.021820 -0.040629 -0.026695 \n", + "10 0.024232 -0.013382 -0.052934 \n", + "11 0.014563 -0.048561 -0.064959 \n", + "12 -0.115923 0.132645 0.045606 \n", + "13 0.023235 0.017712 -0.000441 \n", + "14 -0.017772 0.114692 -0.061390 \n", + "15 -0.083997 0.049844 -0.052004 \n", + "16 0.040640 -0.130493 -0.014951 \n", + "\n", + " autocorrelation_50_Y autocorrelation_50_Z autocorrelation_5_X \\\n", + "0 -0.035205 -0.055939 -0.009348 \n", + "1 -0.062680 -0.055987 -0.069317 \n", + "2 0.002311 0.111358 -0.004053 \n", + "3 -0.017752 -0.016835 0.005310 \n", + "4 -0.035473 -0.033905 0.031233 \n", + "5 -0.072231 -0.044880 -0.042741 \n", + "6 0.035824 0.096863 -0.060521 \n", + "7 0.087412 -0.149575 -0.082264 \n", + "8 0.031052 0.071567 0.002081 \n", + "9 0.028157 0.055367 0.040055 \n", + "10 0.020769 0.027237 -0.052585 \n", + "11 0.009232 0.083537 0.037802 \n", + "12 0.077260 0.037070 -0.009196 \n", + "13 -0.125566 -0.028065 0.091008 \n", + "14 0.058202 0.018044 -0.084337 \n", + "15 -0.063941 -0.123819 -0.024515 \n", + "16 -0.034196 -0.061111 -0.092936 \n", + "\n", + " autocorrelation_5_Y autocorrelation_5_Z autocorrelation_75_X \\\n", + "0 0.036256 -0.059414 -0.064041 \n", + "1 -0.016056 0.066141 -0.001367 \n", + "2 -0.084868 -0.058238 -0.029358 \n", + "3 -0.087827 -0.099613 -0.051004 \n", + "4 -0.013456 0.012132 0.008428 \n", + "5 0.023408 0.109833 0.023188 \n", + "6 -0.090370 0.013848 -0.054948 \n", + "7 -0.024935 0.033933 -0.104112 \n", + "8 0.039163 0.068791 -0.156985 \n", + "9 -0.085966 -0.018881 -0.035873 \n", + "10 0.155348 0.089262 -0.018731 \n", + "11 -0.055070 -0.053334 0.088817 \n", + "12 -0.062619 0.039355 -0.028028 \n", + "13 0.025877 0.003278 0.002593 \n", + "14 0.077899 -0.037150 -0.053984 \n", + "15 -0.028301 -0.017459 0.105301 \n", + "16 0.116500 0.077768 -0.030928 \n", + "\n", + " autocorrelation_75_Y autocorrelation_75_Z averageVectorLength \\\n", + "0 0.022417 0.117357 9.683036 \n", + "1 0.023094 0.006500 9.222364 \n", + "2 0.073042 0.013775 9.235800 \n", + "3 0.086656 -0.026402 9.612871 \n", + "4 -0.010023 0.062667 9.645595 \n", + "5 -0.058372 0.022381 9.861227 \n", + "6 -0.012912 0.057195 9.574246 \n", + "7 0.022179 -0.087485 9.665710 \n", + "8 0.051851 0.009877 9.744560 \n", + "9 0.001869 0.020469 9.764734 \n", + "10 -0.060570 -0.032457 9.275388 \n", + "11 0.029491 0.056988 9.519998 \n", + "12 0.021713 0.100584 9.425750 \n", + "13 0.039316 0.153231 9.580493 \n", + "14 0.034723 -0.056405 9.668018 \n", + "15 0.009760 0.016339 9.944224 \n", + "16 0.030622 0.073927 6.330216 \n", + "\n", + " averageVectorLengthLow averageVectorLengthPower \\\n", + "0 3.954539 93.761196 \n", + "1 3.875639 85.051992 \n", + "2 3.639579 85.300010 \n", + "3 3.991453 92.407281 \n", + "4 3.881851 93.037501 \n", + "5 4.133362 97.243807 \n", + "6 3.935599 91.666188 \n", + "7 3.991640 93.425941 \n", + "8 4.194873 94.956458 \n", + "9 3.979599 95.350039 \n", + "10 3.938795 86.032830 \n", + "11 3.852962 90.630358 \n", + "12 4.085098 88.844765 \n", + "13 3.968863 91.785846 \n", + "14 3.958077 93.470567 \n", + "15 4.074935 98.887582 \n", + "16 2.623497 40.071633 \n", + "\n", + " averageVectorLengthPowerLow avgPeakAmplitudeChangeAfter_X \\\n", + "0 15.638376 NaN \n", + "1 15.020578 NaN \n", + "2 13.246539 NaN \n", + "3 15.931694 NaN \n", + "4 15.068770 NaN \n", + "5 17.084679 NaN \n", + "6 15.488938 NaN \n", + "7 15.933187 NaN \n", + "8 17.596959 NaN \n", + "9 15.837206 NaN \n", + "10 15.514102 NaN \n", + "11 14.845319 NaN \n", + "12 16.688024 NaN \n", + "13 15.751873 NaN \n", + "14 15.666371 NaN \n", + "15 16.605092 NaN \n", + "16 6.882736 NaN \n", + "\n", + " avgPeakAmplitudeChangeAfter_Y avgPeakAmplitudeChangeAfter_Z \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "\n", + " avgPeakAmplitudeChangeBefore_X avgPeakAmplitudeChangeBefore_Y \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "\n", + " avgPeakAmplitudeChangeBefore_Z avgPeakChangeRatio_X \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "\n", + " avgPeakChangeRatio_Y avgPeakChangeRatio_Z avgPeakDecreaseTime_X \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "\n", + " avgPeakDecreaseTime_Y avgPeakDecreaseTime_Z avgPeakDuration_X \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "\n", + " avgPeakDuration_Y avgPeakDuration_Z avgPeakIncreaseTime_X \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "\n", + " avgPeakIncreaseTime_Y avgPeakIncreaseTime_Z binnedEntropy_X \\\n", + "0 NaN NaN 2.298323 \n", + "1 NaN NaN 2.289073 \n", + "2 NaN NaN 2.280011 \n", + "3 NaN NaN 2.286551 \n", + "4 NaN NaN 2.292404 \n", + "5 NaN NaN 2.288386 \n", + "6 NaN NaN 2.282082 \n", + "7 NaN NaN 2.291969 \n", + "8 NaN NaN 2.290071 \n", + "9 NaN NaN 2.282979 \n", + "10 NaN NaN 2.291118 \n", + "11 NaN NaN 2.287470 \n", + "12 NaN NaN 2.298981 \n", + "13 NaN NaN 2.294519 \n", + "14 NaN NaN 2.290551 \n", + "15 NaN NaN 2.284167 \n", + "16 NaN NaN 2.000757 \n", + "\n", + " binnedEntropy_Y binnedEntropy_Z calcMeanCrossingRateAutocorr_X \\\n", + "0 2.278283 2.287630 65.0 \n", + "1 2.272205 2.290649 67.0 \n", + "2 2.268384 2.285813 71.0 \n", + "3 2.278670 2.299186 57.0 \n", + "4 2.286274 2.297375 65.0 \n", + "5 2.290115 2.285257 63.0 \n", + "6 2.294149 2.283044 53.0 \n", + "7 2.288501 2.283649 71.0 \n", + "8 2.293779 2.284550 61.0 \n", + "9 2.294618 2.296906 65.0 \n", + "10 2.288205 2.280010 65.0 \n", + "11 2.287591 2.296889 67.0 \n", + "12 2.291509 2.288529 61.0 \n", + "13 2.273759 2.296630 61.0 \n", + "14 2.295320 2.288277 63.0 \n", + "15 2.295281 2.287458 50.0 \n", + "16 1.978263 1.988834 0.0 \n", + "\n", + " calcMeanCrossingRateAutocorr_Y calcMeanCrossingRateAutocorr_Z \\\n", + "0 59.0 65.0 \n", + "1 61.0 62.0 \n", + "2 71.0 69.0 \n", + "3 57.0 65.0 \n", + "4 67.0 63.0 \n", + "5 59.0 60.0 \n", + "6 77.0 57.0 \n", + "7 65.0 60.0 \n", + "8 54.0 64.0 \n", + "9 57.0 51.0 \n", + "10 75.0 58.0 \n", + "11 56.0 69.0 \n", + "12 53.0 55.0 \n", + "13 54.0 75.0 \n", + "14 65.0 69.0 \n", + "15 64.0 61.0 \n", + "16 0.0 0.0 \n", + "\n", + " changeDuration_X changeDuration_Y changeDuration_Z changeRate_X \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "5 NaN NaN NaN NaN \n", + "6 NaN NaN NaN NaN \n", + "7 NaN NaN NaN NaN \n", + "8 NaN NaN NaN NaN \n", + "9 NaN NaN NaN NaN \n", + "10 NaN NaN NaN NaN \n", + "11 NaN NaN NaN NaN \n", + "12 NaN NaN NaN NaN \n", + "13 NaN NaN NaN NaN \n", + "14 NaN NaN NaN NaN \n", + "15 NaN NaN NaN NaN \n", + "16 NaN NaN NaN NaN \n", + "\n", + " changeRate_Y changeRate_Z coefficientOfVariation_XBand \\\n", + "0 NaN NaN 0.533236 \n", + "1 NaN NaN 0.527096 \n", + "2 NaN NaN 0.533909 \n", + "3 NaN NaN 0.515671 \n", + "4 NaN NaN 0.522352 \n", + "5 NaN NaN 0.528759 \n", + "6 NaN NaN 0.547856 \n", + "7 NaN NaN 0.530163 \n", + "8 NaN NaN 0.517942 \n", + "9 NaN NaN 0.518977 \n", + "10 NaN NaN 0.542770 \n", + "11 NaN NaN 0.539878 \n", + "12 NaN NaN 0.534670 \n", + "13 NaN NaN 0.533739 \n", + "14 NaN NaN 0.521455 \n", + "15 NaN NaN 0.516158 \n", + "16 NaN NaN 0.599106 \n", + "\n", + " coefficientOfVariation_XLow coefficientOfVariation_YBand \\\n", + "0 0.527017 0.514151 \n", + "1 0.452063 0.554167 \n", + "2 0.526474 0.556140 \n", + "3 0.526374 0.520226 \n", + "4 0.540857 0.534679 \n", + "5 0.493230 0.527201 \n", + "6 0.477819 0.525871 \n", + "7 0.495349 0.527140 \n", + "8 0.494374 0.537649 \n", + "9 0.524269 0.532100 \n", + "10 0.536975 0.525607 \n", + "11 0.537260 0.542834 \n", + "12 0.532577 0.526495 \n", + "13 0.570930 0.511987 \n", + "14 0.536704 0.522882 \n", + "15 0.525543 0.546662 \n", + "16 0.543337 0.624214 \n", + "\n", + " coefficientOfVariation_YLow coefficientOfVariation_ZBand \\\n", + "0 0.527270 0.516443 \n", + "1 0.525025 0.519009 \n", + "2 0.557286 0.529653 \n", + "3 0.548729 0.521221 \n", + "4 0.589041 0.527130 \n", + "5 0.513757 0.525058 \n", + "6 0.483711 0.525506 \n", + "7 0.522798 0.535721 \n", + "8 0.505772 0.514093 \n", + "9 0.518419 0.527682 \n", + "10 0.529116 0.542240 \n", + "11 0.516687 0.515561 \n", + "12 0.549653 0.529763 \n", + "13 0.524731 0.520360 \n", + "14 0.536917 0.515216 \n", + "15 0.557694 0.526219 \n", + "16 0.630624 0.619395 \n", + "\n", + " coefficientOfVariation_ZLow correlation_X_YBand correlation_X_YLow \\\n", + "0 0.555990 0.999983 0.999981 \n", + "1 0.537604 0.999979 0.999984 \n", + "2 0.529651 0.999985 0.999983 \n", + "3 0.500520 0.999982 0.999981 \n", + "4 0.539844 0.999982 0.999983 \n", + "5 0.485686 0.999986 0.999986 \n", + "6 0.532170 0.999981 0.999984 \n", + "7 0.491897 0.999983 0.999983 \n", + "8 0.570792 0.999981 0.999984 \n", + "9 0.473817 0.999983 0.999983 \n", + "10 0.521041 0.999982 0.999983 \n", + "11 0.556969 0.999985 0.999986 \n", + "12 0.479618 0.999985 0.999982 \n", + "13 0.527940 0.999982 0.999981 \n", + "14 0.546964 0.999986 0.999984 \n", + "15 0.563018 0.999982 0.999982 \n", + "16 0.540351 0.999979 0.999978 \n", + "\n", + " correlation_X_ZBand correlation_X_ZLow correlation_Y_ZBand \\\n", + "0 0.999984 0.999984 0.999983 \n", + "1 0.999984 0.999984 0.999981 \n", + "2 0.999986 0.999984 0.999986 \n", + "3 0.999983 0.999984 0.999985 \n", + "4 0.999983 0.999982 0.999980 \n", + "5 0.999984 0.999985 0.999987 \n", + "6 0.999984 0.999985 0.999984 \n", + "7 0.999985 0.999986 0.999982 \n", + "8 0.999984 0.999984 0.999982 \n", + "9 0.999984 0.999984 0.999987 \n", + "10 0.999979 0.999983 0.999983 \n", + "11 0.999981 0.999978 0.999982 \n", + "12 0.999980 0.999981 0.999980 \n", + "13 0.999986 0.999984 0.999985 \n", + "14 0.999984 0.999981 0.999981 \n", + "15 0.999983 0.999984 0.999979 \n", + "16 0.999980 0.999978 0.999978 \n", + "\n", + " correlation_Y_ZLow countAboveMeanAutocorr_X countAboveMeanAutocorr_Y \\\n", + "0 0.999981 100.0 105.0 \n", + "1 0.999984 96.0 103.0 \n", + "2 0.999986 97.0 98.0 \n", + "3 0.999984 106.0 107.0 \n", + "4 0.999982 100.0 96.0 \n", + "5 0.999984 101.0 108.0 \n", + "6 0.999984 98.0 97.0 \n", + "7 0.999985 110.0 101.0 \n", + "8 0.999985 104.0 106.0 \n", + "9 0.999987 92.0 102.0 \n", + "10 0.999983 90.0 92.0 \n", + "11 0.999983 91.0 103.0 \n", + "12 0.999982 106.0 103.0 \n", + "13 0.999982 97.0 107.0 \n", + "14 0.999980 98.0 98.0 \n", + "15 0.999981 98.0 101.0 \n", + "16 0.999975 0.0 0.0 \n", + "\n", + " countAboveMeanAutocorr_Z countAboveMean_X countAboveMean_Y \\\n", + "0 98.0 153.0 151.0 \n", + "1 99.0 155.0 145.0 \n", + "2 103.0 146.0 149.0 \n", + "3 96.0 148.0 148.0 \n", + "4 99.0 158.0 142.0 \n", + "5 100.0 158.0 156.0 \n", + "6 102.0 154.0 151.0 \n", + "7 94.0 157.0 148.0 \n", + "8 103.0 156.0 154.0 \n", + "9 102.0 147.0 150.0 \n", + "10 98.0 149.0 144.0 \n", + "11 98.0 149.0 153.0 \n", + "12 104.0 149.0 145.0 \n", + "13 105.0 143.0 163.0 \n", + "14 103.0 143.0 152.0 \n", + "15 104.0 153.0 152.0 \n", + "16 0.0 107.0 194.0 \n", + "\n", + " countAboveMean_Z countBelowMean_X countBelowMean_Y countBelowMean_Z \\\n", + "0 151.0 147.0 149.0 149.0 \n", + "1 145.0 145.0 155.0 155.0 \n", + "2 153.0 154.0 151.0 147.0 \n", + "3 153.0 152.0 152.0 147.0 \n", + "4 147.0 142.0 158.0 153.0 \n", + "5 161.0 142.0 144.0 139.0 \n", + "6 141.0 146.0 149.0 159.0 \n", + "7 152.0 143.0 152.0 148.0 \n", + "8 148.0 144.0 146.0 152.0 \n", + "9 156.0 153.0 150.0 144.0 \n", + "10 160.0 151.0 156.0 140.0 \n", + "11 147.0 151.0 147.0 153.0 \n", + "12 148.0 151.0 155.0 152.0 \n", + "13 147.0 157.0 137.0 153.0 \n", + "14 143.0 157.0 148.0 157.0 \n", + "15 143.0 147.0 148.0 157.0 \n", + "16 107.0 193.0 106.0 193.0 \n", + "\n", + " derivTonic_X derivTonic_Y derivTonic_Z deriv_X deriv_Y deriv_Z \\\n", + "0 2.264066 1.663697 -1.978076 -29.613465 9.554828 15.833924 \n", + "1 3.378476 0.002513 -2.840698 2.282190 9.914019 -17.346159 \n", + "2 9.343808 0.982711 0.262335 13.724742 8.435181 19.735082 \n", + "3 0.811307 1.112904 7.486793 -21.061162 0.162531 10.641201 \n", + "4 -4.935032 0.429249 4.470062 13.630978 28.242010 -17.852050 \n", + "5 0.615650 9.011439 1.019376 11.908970 12.286923 7.905318 \n", + "6 -0.419359 0.649203 -7.831564 4.122110 8.160791 9.625673 \n", + "7 -8.606199 0.611211 -9.516828 4.243979 -8.139702 -18.404318 \n", + "8 2.839395 -2.098615 3.895081 -14.428611 20.572239 0.857868 \n", + "9 -7.724864 -6.195070 -5.434508 2.497424 -15.977319 -18.537690 \n", + "10 4.473552 -8.085741 1.447796 6.607903 -12.228337 -11.535852 \n", + "11 -0.316638 -3.037811 6.594929 17.468651 1.330721 8.141867 \n", + "12 4.142621 3.497675 -13.110317 23.332381 -2.861054 -1.739394 \n", + "13 -0.286484 2.316337 1.315161 25.572897 5.733510 16.532478 \n", + "14 -4.116657 -1.928712 6.173228 -12.050769 -19.877969 -9.532184 \n", + "15 -9.825086 4.237792 10.268698 2.794769 12.292966 -3.724351 \n", + "16 2.010568 4.794947 5.181957 -7.922947 -7.804784 -16.465154 \n", + "\n", + " dominantFrequencyEnergy_XBand dominantFrequencyEnergy_XLow \\\n", + "0 0.021613 0.032114 \n", + "1 0.028958 0.040884 \n", + "2 0.021711 0.033403 \n", + "3 0.022758 0.049079 \n", + "4 0.021350 0.031001 \n", + "5 0.020972 0.033817 \n", + "6 0.025502 0.033085 \n", + "7 0.022359 0.033667 \n", + "8 0.026543 0.037157 \n", + "9 0.020597 0.036296 \n", + "10 0.018570 0.030853 \n", + "11 0.018851 0.030482 \n", + "12 0.023323 0.034106 \n", + "13 0.021933 0.051276 \n", + "14 0.023552 0.035077 \n", + "15 0.026252 0.036015 \n", + "16 0.018765 0.030059 \n", + "\n", + " dominantFrequencyEnergy_YBand dominantFrequencyEnergy_YLow \\\n", + "0 0.019957 0.031307 \n", + "1 0.021265 0.043713 \n", + "2 0.024045 0.036826 \n", + "3 0.029402 0.030302 \n", + "4 0.022344 0.032830 \n", + "5 0.018170 0.025395 \n", + "6 0.021805 0.033030 \n", + "7 0.023081 0.039157 \n", + "8 0.019678 0.036312 \n", + "9 0.022386 0.031697 \n", + "10 0.022396 0.031159 \n", + "11 0.021019 0.029694 \n", + "12 0.025142 0.032953 \n", + "13 0.024782 0.037983 \n", + "14 0.021930 0.030912 \n", + "15 0.028484 0.037540 \n", + "16 0.024735 0.035134 \n", + "\n", + " dominantFrequencyEnergy_ZBand dominantFrequencyEnergy_ZLow \\\n", + "0 0.029077 0.027159 \n", + "1 0.022946 0.031538 \n", + "2 0.019791 0.027571 \n", + "3 0.025449 0.032896 \n", + "4 0.021159 0.035992 \n", + "5 0.019549 0.032218 \n", + "6 0.024637 0.032953 \n", + "7 0.023016 0.046724 \n", + "8 0.024761 0.043462 \n", + "9 0.025724 0.035699 \n", + "10 0.019494 0.042561 \n", + "11 0.019693 0.035747 \n", + "12 0.026942 0.032238 \n", + "13 0.019526 0.029579 \n", + "14 0.020186 0.023121 \n", + "15 0.020404 0.039396 \n", + "16 0.019362 0.037625 \n", + "\n", + " entropy_XBand entropy_XLow entropy_YBand entropy_YLow entropy_ZBand \\\n", + "0 6.939601 6.788092 6.948577 6.798214 6.935590 \n", + "1 6.962013 6.745530 6.941184 6.684952 6.970926 \n", + "2 6.951091 6.847274 6.915013 6.731275 6.971465 \n", + "3 7.007258 6.748955 6.907122 6.793629 6.937713 \n", + "4 6.946219 6.798508 6.949631 6.798677 6.988088 \n", + "5 6.951435 6.781081 6.987060 6.826218 6.953750 \n", + "6 6.966593 6.808914 6.895219 6.701539 6.942418 \n", + "7 6.909865 6.738131 6.950536 6.776491 6.999050 \n", + "8 6.897070 6.683523 6.989430 6.804307 7.001734 \n", + "9 6.889445 6.743457 6.915464 6.744615 6.945432 \n", + "10 6.985462 6.808788 6.934208 6.785896 6.954452 \n", + "11 6.976421 6.813671 6.958526 6.779792 6.989724 \n", + "12 6.968936 6.811086 6.979781 6.796927 6.899934 \n", + "13 7.007956 6.743319 6.947040 6.797169 6.959353 \n", + "14 6.926548 6.710982 6.930923 6.774075 6.936753 \n", + "15 6.906153 6.698576 6.936426 6.763655 6.976713 \n", + "16 6.964619 6.772995 6.937821 6.765930 6.985357 \n", + "\n", + " entropy_ZLow fp01_X fp01_Y fp01_Z fp02_X fp02_Y fp02_Z fp03_X \\\n", + "0 6.765376 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "1 6.836181 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "2 6.858678 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 6.779053 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 6.808467 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "5 6.727309 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "6 6.825859 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "7 6.803815 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "8 6.753551 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "9 6.781219 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "10 6.703950 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "11 6.792300 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "12 6.652299 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "13 6.838618 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "14 6.824054 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "15 6.826027 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "16 6.781932 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " fp03_Y fp03_Z fp04_X fp04_Y fp04_Z fp05_X fp05_Y fp05_Z fp06_X \\\n", + "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " fp06_Y fp06_Z fqAbsMean_X fqAbsMean_Y fqAbsMean_Z fqEnergyFeat_X \\\n", + "0 0.0 0.0 4.904584 5.246425 4.990305 9757.282156 \n", + "1 0.0 0.0 4.822180 4.645956 4.976632 9448.079674 \n", + "2 0.0 0.0 4.844398 4.809809 4.760420 9669.760737 \n", + "3 0.0 0.0 5.019139 4.967566 4.990291 10026.356290 \n", + "4 0.0 0.0 4.857150 5.237531 4.879719 9709.603922 \n", + "5 0.0 0.0 5.073285 5.102289 5.181881 10233.693555 \n", + "6 0.0 0.0 4.754901 5.170028 4.966644 9482.841316 \n", + "7 0.0 0.0 4.854265 5.078081 5.121374 9520.121266 \n", + "8 0.0 0.0 5.121016 5.118333 5.043765 10395.792313 \n", + "9 0.0 0.0 5.365118 4.954334 5.007938 11026.931493 \n", + "10 0.0 0.0 4.861183 4.696802 4.795042 9574.581294 \n", + "11 0.0 0.0 5.151702 4.821758 4.838656 10266.304113 \n", + "12 0.0 0.0 4.902073 4.841616 5.048460 9639.580580 \n", + "13 0.0 0.0 5.063369 4.971973 4.946686 10149.017500 \n", + "14 0.0 0.0 4.728647 5.075870 5.210589 9190.474376 \n", + "15 0.0 0.0 5.250907 5.215007 5.076110 10890.487664 \n", + "16 0.0 0.0 3.311168 3.172689 3.480565 6484.122156 \n", + "\n", + " fqEnergyFeat_Y fqEnergyFeat_Z fqEntropyFeat_X fqEntropyFeat_Y \\\n", + "0 10849.028553 9811.741631 5.555442 5.557260 \n", + "1 8899.975693 9956.680101 5.560929 5.536202 \n", + "2 9184.529987 9121.402956 5.575139 5.549254 \n", + "3 9801.236188 9982.377726 5.567258 5.570079 \n", + "4 10691.230186 9662.734156 5.562654 5.588084 \n", + "5 10371.886729 10563.612304 5.576252 5.583882 \n", + "6 10326.075806 10154.052192 5.603480 5.554958 \n", + "7 10241.641412 10387.903719 5.544296 5.567887 \n", + "8 10179.629166 10107.379246 5.548230 5.580267 \n", + "9 9834.741763 10120.339487 5.529802 5.567041 \n", + "10 8891.577695 9480.013849 5.576667 5.544810 \n", + "11 9524.407888 9519.015587 5.586517 5.589838 \n", + "12 9683.493055 10251.534492 5.581777 5.585898 \n", + "13 9632.893918 9966.713340 5.576922 5.570770 \n", + "14 10330.498873 10536.313374 5.560282 5.559407 \n", + "15 10640.928635 10044.509683 5.559758 5.560199 \n", + "16 6140.316953 7010.804341 5.584447 5.553468 \n", + "\n", + " fqEntropyFeat_Z fqHighestPeak1_X fqHighestPeak1_Y fqHighestPeak1_Z \\\n", + "0 5.574111 3.754916 5.607029 2.822416 \n", + "1 5.567919 2.929343 3.188250 3.588912 \n", + "2 5.581274 3.844362 2.215684 2.556580 \n", + "3 5.565787 2.664748 3.548463 3.840508 \n", + "4 5.572350 2.547426 4.058459 2.667875 \n", + "5 5.557310 3.489921 3.000982 3.052319 \n", + "6 5.554691 2.736799 3.462840 4.595469 \n", + "7 5.553808 4.006190 2.889108 3.063053 \n", + "8 5.572265 4.038741 2.673385 3.050807 \n", + "9 5.582309 3.495014 3.126081 3.176471 \n", + "10 5.542950 3.755804 2.893660 2.494761 \n", + "11 5.562584 3.657855 3.397249 2.557454 \n", + "12 5.538797 2.844807 2.782663 7.491650 \n", + "13 5.551558 3.582364 3.078669 2.436009 \n", + "14 5.540887 3.341951 2.905688 4.448141 \n", + "15 5.571962 3.659974 3.506205 3.095609 \n", + "16 5.549691 3.987941 2.775496 3.371644 \n", + "\n", + " fqHighestPeak2_X fqHighestPeak2_Y fqHighestPeak2_Z fqHighestPeak3_X \\\n", + "0 3.440065 4.450443 2.816086 3.141781 \n", + "1 2.272656 3.099145 3.375868 2.083292 \n", + "2 2.908314 2.072371 2.216813 2.889407 \n", + "3 2.558650 3.289128 2.740128 2.240007 \n", + "4 2.487128 3.198084 1.896326 2.064078 \n", + "5 2.793373 2.508526 2.849588 2.728617 \n", + "6 2.505620 2.829232 3.680274 2.496508 \n", + "7 3.474055 2.680011 2.961508 3.065422 \n", + "8 3.466653 2.333558 2.735938 2.543124 \n", + "9 3.241806 2.928799 2.562433 3.110792 \n", + "10 3.664174 2.613525 2.154152 2.815367 \n", + "11 2.584176 2.583424 2.526804 2.073441 \n", + "12 2.292539 2.606928 4.118490 2.035305 \n", + "13 2.653953 2.853552 2.430396 2.152986 \n", + "14 2.379974 2.528655 3.186949 2.361937 \n", + "15 3.228197 3.144964 3.065074 2.935554 \n", + "16 2.998196 2.765137 3.367243 2.373744 \n", + "\n", + " fqHighestPeak3_Y fqHighestPeak3_Z fqHighestPeakFreq1_X \\\n", + "0 3.806449 2.524760 20.312500 \n", + "1 2.937218 2.573928 43.359375 \n", + "2 1.933323 2.057448 17.578125 \n", + "3 2.928341 2.130777 18.359375 \n", + "4 2.634893 1.783069 21.093750 \n", + "5 2.076394 2.233292 44.531250 \n", + "6 2.630907 3.549613 10.546875 \n", + "7 2.337003 2.418840 44.531250 \n", + "8 2.187545 2.541616 7.421875 \n", + "9 2.693042 2.411896 3.125000 \n", + "10 2.063317 1.976259 33.203125 \n", + "11 2.275117 2.521100 46.875000 \n", + "12 1.701413 4.086219 19.921875 \n", + "13 2.583418 2.361895 40.234375 \n", + "14 2.265739 2.817797 7.031250 \n", + "15 2.798654 3.025101 41.796875 \n", + "16 2.469993 2.899311 47.265625 \n", + "\n", + " fqHighestPeakFreq1_Y fqHighestPeakFreq1_Z fqHighestPeakFreq2_X \\\n", + "0 35.546875 6.250000 8.593750 \n", + "1 27.734375 32.812500 8.593750 \n", + "2 39.062500 48.437500 21.875000 \n", + "3 7.812500 44.140625 24.609375 \n", + "4 45.312500 23.437500 20.703125 \n", + "5 47.265625 22.656250 17.187500 \n", + "6 36.718750 25.781250 28.515625 \n", + "7 26.171875 1.562500 23.046875 \n", + "8 9.375000 49.609375 7.812500 \n", + "9 13.671875 23.046875 24.218750 \n", + "10 42.187500 21.484375 30.859375 \n", + "11 39.062500 27.734375 3.515625 \n", + "12 5.468750 6.640625 20.312500 \n", + "13 18.750000 46.484375 40.625000 \n", + "14 22.265625 25.781250 32.812500 \n", + "15 22.656250 12.500000 2.343750 \n", + "16 35.546875 41.015625 12.500000 \n", + "\n", + " fqHighestPeakFreq2_Y fqHighestPeakFreq2_Z fqHighestPeakFreq3_X \\\n", + "0 35.937500 6.640625 48.046875 \n", + "1 12.890625 33.203125 23.828125 \n", + "2 1.171875 10.546875 15.234375 \n", + "3 7.421875 39.062500 38.281250 \n", + "4 34.765625 23.046875 11.718750 \n", + "5 5.078125 48.828125 35.937500 \n", + "6 39.843750 8.593750 41.796875 \n", + "7 40.234375 47.265625 23.437500 \n", + "8 14.843750 33.203125 8.203125 \n", + "9 9.765625 32.812500 24.609375 \n", + "10 23.828125 21.875000 31.250000 \n", + "11 38.671875 2.734375 40.625000 \n", + "12 7.812500 7.031250 24.218750 \n", + "13 1.562500 24.218750 21.093750 \n", + "14 37.890625 44.531250 6.640625 \n", + "15 38.281250 16.796875 12.109375 \n", + "16 35.937500 41.406250 12.109375 \n", + "\n", + " fqHighestPeakFreq3_Y fqHighestPeakFreq3_Z fqHistogramBin10_X \\\n", + "0 35.156250 18.359375 0.663673 \n", + "1 37.109375 49.609375 0.598443 \n", + "2 29.296875 27.734375 0.700968 \n", + "3 7.031250 44.531250 0.757265 \n", + "4 3.906250 15.625000 0.706994 \n", + "5 24.609375 40.234375 0.657617 \n", + "6 37.109375 20.312500 0.580507 \n", + "7 14.453125 13.281250 0.768002 \n", + "8 26.562500 42.187500 0.454272 \n", + "9 13.281250 22.656250 0.639002 \n", + "10 42.578125 32.031250 0.814090 \n", + "11 9.375000 44.531250 0.667374 \n", + "12 49.218750 6.250000 0.749569 \n", + "13 20.312500 21.484375 0.782994 \n", + "14 46.875000 21.093750 0.370842 \n", + "15 9.375000 16.406250 0.621563 \n", + "16 36.328125 31.640625 0.652595 \n", + "\n", + " fqHistogramBin10_Y fqHistogramBin10_Z fqHistogramBin1_X \\\n", + "0 0.877304 0.609371 0.011435 \n", + "1 0.682931 0.738879 0.009578 \n", + "2 0.639824 0.639717 0.009419 \n", + "3 0.380102 0.687712 0.036663 \n", + "4 0.716680 0.757831 0.009164 \n", + "5 0.611367 0.768926 0.009183 \n", + "6 0.750736 0.747705 0.003853 \n", + "7 0.623185 0.631193 0.009826 \n", + "8 0.679706 0.704537 0.005385 \n", + "9 0.490088 0.789989 0.021200 \n", + "10 0.697826 0.717765 0.006414 \n", + "11 0.504953 0.793269 0.002902 \n", + "12 0.562516 0.313177 0.018680 \n", + "13 0.640973 0.789066 0.063078 \n", + "14 0.599062 0.831171 0.074244 \n", + "15 0.697624 0.548579 0.012678 \n", + "16 0.777193 0.768463 0.012546 \n", + "\n", + " fqHistogramBin1_Y fqHistogramBin1_Z fqHistogramBin2_X \\\n", + "0 0.002534 0.034084 0.044164 \n", + "1 0.008954 0.047458 0.038388 \n", + "2 0.175215 0.005396 0.016583 \n", + "3 0.009890 0.034134 0.015763 \n", + "4 0.017415 0.001091 0.057432 \n", + "5 0.015252 0.069538 0.003456 \n", + "6 0.011466 0.009114 0.065513 \n", + "7 0.014012 0.128513 0.010067 \n", + "8 0.009760 0.121179 0.016688 \n", + "9 0.005246 0.004605 0.128821 \n", + "10 0.020237 0.022206 0.049923 \n", + "11 0.055101 0.002404 0.139587 \n", + "12 0.012117 0.029844 0.068914 \n", + "13 0.127296 0.001244 0.004839 \n", + "14 0.010998 0.007489 0.059668 \n", + "15 0.023385 0.014032 0.123925 \n", + "16 0.006016 0.001267 0.024574 \n", + "\n", + " fqHistogramBin2_Y fqHistogramBin2_Z fqHistogramBin3_X \\\n", + "0 0.033360 0.007048 0.015473 \n", + "1 0.037731 0.013756 0.089662 \n", + "2 0.015225 0.070362 0.019016 \n", + "3 0.019602 0.038623 0.000672 \n", + "4 0.015294 0.020563 0.009633 \n", + "5 0.081691 0.007247 0.072019 \n", + "6 0.011883 0.008837 0.045029 \n", + "7 0.018238 0.012506 0.065859 \n", + "8 0.018410 0.002731 0.011055 \n", + "9 0.021401 0.001020 0.029574 \n", + "10 0.018052 0.072599 0.001866 \n", + "11 0.020906 0.080207 0.077911 \n", + "12 0.006486 0.000533 0.042862 \n", + "13 0.039558 0.067846 0.003214 \n", + "14 0.045980 0.017778 0.009217 \n", + "15 0.011245 0.023402 0.050375 \n", + "16 0.038144 0.008480 0.010846 \n", + "\n", + " fqHistogramBin3_Y fqHistogramBin3_Z fqHistogramBin4_X \\\n", + "0 0.008985 0.017334 0.037594 \n", + "1 0.013746 0.005594 0.007540 \n", + "2 0.017045 0.061946 0.017200 \n", + "3 0.032866 0.025035 0.073223 \n", + "4 0.074446 0.042059 0.015283 \n", + "5 0.124216 0.016943 0.106799 \n", + "6 0.084053 0.006094 0.065454 \n", + "7 0.014187 0.063587 0.073324 \n", + "8 0.004976 0.011629 0.204065 \n", + "9 0.010850 0.066517 0.027461 \n", + "10 0.049761 0.012516 0.020644 \n", + "11 0.109207 0.027671 0.044941 \n", + "12 0.128897 0.013828 0.013930 \n", + "13 0.001674 0.007445 0.029504 \n", + "14 0.127170 0.025692 0.250933 \n", + "15 0.066975 0.017625 0.061256 \n", + "16 0.012055 0.008057 0.002157 \n", + "\n", + " fqHistogramBin4_Y fqHistogramBin4_Z fqHistogramBin5_X \\\n", + "0 0.007280 0.230446 0.148221 \n", + "1 0.013747 0.003758 0.101322 \n", + "2 0.059446 0.008613 0.012498 \n", + "3 0.207596 0.040753 0.013068 \n", + "4 0.009695 0.018577 0.003700 \n", + "5 0.014965 0.016717 0.035826 \n", + "6 0.026560 0.001329 0.052150 \n", + "7 0.019774 0.028393 0.029322 \n", + "8 0.028927 0.003415 0.231494 \n", + "9 0.001615 0.045945 0.051472 \n", + "10 0.028766 0.044691 0.023635 \n", + "11 0.004299 0.016184 0.013258 \n", + "12 0.035805 0.506606 0.005249 \n", + "13 0.001400 0.022177 0.020778 \n", + "14 0.068129 0.026048 0.059393 \n", + "15 0.010769 0.004830 0.027219 \n", + "16 0.000727 0.024769 0.004323 \n", + "\n", + " fqHistogramBin5_Y fqHistogramBin5_Z fqHistogramBin6_X \\\n", + "0 0.008045 0.020751 0.029557 \n", + "1 0.056060 0.012401 0.109483 \n", + "2 0.006489 0.010081 0.040686 \n", + "3 0.192067 0.069238 0.002349 \n", + "4 0.008621 0.012083 0.037449 \n", + "5 0.008543 0.021928 0.010102 \n", + "6 0.032678 0.113572 0.107633 \n", + "7 0.030802 0.001284 0.008414 \n", + "8 0.082748 0.018476 0.038918 \n", + "9 0.060710 0.010148 0.034336 \n", + "10 0.050349 0.043929 0.035819 \n", + "11 0.084466 0.004698 0.021953 \n", + "12 0.114383 0.005456 0.042467 \n", + "13 0.028269 0.034825 0.006944 \n", + "14 0.030626 0.002667 0.048076 \n", + "15 0.079745 0.004426 0.003690 \n", + "16 0.025724 0.010074 0.037145 \n", + "\n", + " fqHistogramBin6_Y fqHistogramBin6_Z fqHistogramBin7_X \\\n", + "0 0.006872 0.015075 0.004131 \n", + "1 0.017294 0.057771 0.021135 \n", + "2 0.012852 0.081111 0.017768 \n", + "3 0.036326 0.035171 0.015247 \n", + "4 0.003971 0.027636 0.084530 \n", + "5 0.037657 0.011643 0.006608 \n", + "6 0.012021 0.009564 0.004735 \n", + "7 0.049353 0.012791 0.006654 \n", + "8 0.037622 0.007045 0.007279 \n", + "9 0.151886 0.038589 0.015723 \n", + "10 0.028725 0.010863 0.026443 \n", + "11 0.032166 0.039215 0.006532 \n", + "12 0.067280 0.001306 0.031381 \n", + "13 0.082380 0.025548 0.025241 \n", + "14 0.011153 0.009987 0.029182 \n", + "15 0.005518 0.036627 0.081907 \n", + "16 0.064808 0.022580 0.181095 \n", + "\n", + " fqHistogramBin7_Y fqHistogramBin7_Z fqHistogramBin8_X \\\n", + "0 0.037588 0.050086 0.031146 \n", + "1 0.153668 0.029589 0.002795 \n", + "2 0.037881 0.024510 0.118228 \n", + "3 0.081061 0.055085 0.025833 \n", + "4 0.040810 0.031324 0.038717 \n", + "5 0.013062 0.039543 0.010482 \n", + "6 0.028420 0.004242 0.030659 \n", + "7 0.078465 0.083887 0.022828 \n", + "8 0.021692 0.047647 0.003704 \n", + "9 0.086268 0.021053 0.015700 \n", + "10 0.006769 0.008092 0.015450 \n", + "11 0.019611 0.022611 0.008412 \n", + "12 0.017028 0.026175 0.011397 \n", + "13 0.054565 0.025450 0.047621 \n", + "14 0.021420 0.050353 0.035187 \n", + "15 0.051670 0.160352 0.007104 \n", + "16 0.024693 0.061235 0.031480 \n", + "\n", + " fqHistogramBin8_Y fqHistogramBin8_Z fqHistogramBin9_X \\\n", + "0 0.009261 0.006448 0.014607 \n", + "1 0.011119 0.048257 0.021653 \n", + "2 0.027623 0.053150 0.047635 \n", + "3 0.029350 0.010179 0.059918 \n", + "4 0.063144 0.020308 0.037099 \n", + "5 0.030682 0.008196 0.087907 \n", + "6 0.036800 0.023401 0.044468 \n", + "7 0.136048 0.030321 0.005704 \n", + "8 0.079730 0.070270 0.027140 \n", + "9 0.161211 0.005097 0.036711 \n", + "10 0.020497 0.001989 0.005717 \n", + "11 0.009008 0.008274 0.017131 \n", + "12 0.048368 0.083072 0.015551 \n", + "13 0.014041 0.002945 0.015788 \n", + "14 0.071365 0.020033 0.063259 \n", + "15 0.006093 0.005710 0.010283 \n", + "16 0.007054 0.081870 0.043239 \n", + "\n", + " fqHistogramBin9_Y fqHistogramBin9_Z fqInterquart_X fqInterquart_Y \\\n", + "0 0.008771 0.009357 9.901367 10.769594 \n", + "1 0.004750 0.042537 9.211392 8.740737 \n", + "2 0.008400 0.045114 8.916415 9.052568 \n", + "3 0.011141 0.004070 10.241726 9.943358 \n", + "4 0.049923 0.068527 9.850650 10.473927 \n", + "5 0.062564 0.039319 9.978477 10.357262 \n", + "6 0.005384 0.076140 9.370658 10.321082 \n", + "7 0.015936 0.007526 9.819547 10.452315 \n", + "8 0.036429 0.013070 10.341732 10.685950 \n", + "9 0.010726 0.017037 11.158979 10.258023 \n", + "10 0.079018 0.065350 9.521979 9.357901 \n", + "11 0.160284 0.005465 10.044118 9.042127 \n", + "12 0.007119 0.020003 9.998723 9.539069 \n", + "13 0.009845 0.023455 9.922438 9.869460 \n", + "14 0.014097 0.008781 9.165367 10.443672 \n", + "15 0.046976 0.184416 10.540761 10.997395 \n", + "16 0.043587 0.013205 5.042390 4.702765 \n", + "\n", + " fqInterquart_Z fqKurtosis_X fqKurtosis_Y fqKurtosis_Z fqSkewness_X \\\n", + "0 10.056605 5.389523 12.118564 3.566577 2.148555 \n", + "1 9.630549 2.185909 3.320473 3.964078 1.409120 \n", + "2 9.614408 5.062124 1.258535 0.811035 2.169789 \n", + "3 10.256184 1.619553 5.728227 6.772829 1.300120 \n", + "4 9.184260 1.481245 4.313905 1.015344 1.432945 \n", + "5 10.217679 2.459255 1.131431 1.561264 1.639629 \n", + "6 10.029209 0.986987 2.772263 5.296333 1.184096 \n", + "7 10.394492 5.616668 1.330291 2.351409 2.247110 \n", + "8 9.735894 6.472586 0.920492 1.486168 2.203444 \n", + "9 10.045014 3.805614 2.370727 2.802290 1.722104 \n", + "10 8.904051 3.718599 3.614872 1.258294 1.749833 \n", + "11 9.411591 6.415278 3.023737 0.643165 2.042124 \n", + "12 10.280947 1.124096 2.295497 20.791269 1.177845 \n", + "13 9.738362 3.921990 2.156045 1.162370 1.628852 \n", + "14 10.618324 2.814428 2.047687 6.540693 1.597015 \n", + "15 10.931453 2.446653 3.344342 2.292431 1.580833 \n", + "16 5.345191 6.708391 1.408064 2.552483 2.063941 \n", + "\n", + " fqSkewness_Y fqSkewness_Z ibi_X ibi_Y ibi_Z \\\n", + "0 3.024553 1.834466 0.960000 1.500000 1.190000 \n", + "1 1.753868 1.839541 0.623333 0.750000 1.000000 \n", + "2 1.284222 1.166554 0.726667 0.700000 0.745000 \n", + "3 2.170687 2.179557 0.816250 NaN 0.680000 \n", + "4 1.904344 1.053234 0.478750 0.680000 NaN \n", + "5 1.201932 1.321003 NaN NaN 0.736250 \n", + "6 1.662902 2.250038 0.611250 1.246667 1.010000 \n", + "7 1.323276 1.405953 1.350000 1.220000 0.686667 \n", + "8 1.190934 1.425740 0.740000 NaN 0.796250 \n", + "9 1.569910 1.561782 NaN 0.430000 0.800000 \n", + "10 1.637232 1.182328 0.990000 0.540000 1.440000 \n", + "11 1.694750 1.113059 0.785000 1.590000 NaN \n", + "12 1.361880 3.825978 0.695000 0.887500 0.446667 \n", + "13 1.555360 1.304668 1.680000 0.755000 0.800000 \n", + "14 1.485917 2.161342 0.810000 NaN 1.330000 \n", + "15 1.767959 1.601817 0.470000 2.020000 1.095000 \n", + "16 1.442438 1.673701 0.350000 NaN NaN \n", + "\n", + " interQuartileRangeMagnitudesBand interQuartileRangeMagnitudesLow \\\n", + "0 0.920676 1.973073 \n", + "1 1.016984 1.942406 \n", + "2 0.858312 1.950954 \n", + "3 0.927504 2.134564 \n", + "4 0.928132 2.370860 \n", + "5 0.951660 2.180499 \n", + "6 1.093823 1.981298 \n", + "7 0.950979 1.944341 \n", + "8 0.959495 1.856825 \n", + "9 0.969892 2.417664 \n", + "10 0.918003 2.108845 \n", + "11 0.901527 2.156514 \n", + "12 1.076304 2.230688 \n", + "13 0.879532 1.978442 \n", + "14 0.975241 2.003503 \n", + "15 0.859382 2.671705 \n", + "16 2.127381 4.303851 \n", + "\n", + " interQuartileRange_XBand interQuartileRange_XLow \\\n", + "0 1.757454 3.465240 \n", + "1 1.889158 3.490192 \n", + "2 1.983132 3.253496 \n", + "3 1.901486 3.577908 \n", + "4 1.760314 3.333432 \n", + "5 1.897415 3.580993 \n", + "6 1.701012 3.476000 \n", + "7 1.785110 3.441714 \n", + "8 2.014451 3.847754 \n", + "9 1.941076 3.456800 \n", + "10 1.714848 3.444731 \n", + "11 1.936398 3.372739 \n", + "12 1.798283 3.162916 \n", + "13 1.683782 3.680219 \n", + "14 1.755337 3.764849 \n", + "15 1.964076 4.005708 \n", + "16 0.967114 1.754752 \n", + "\n", + " interQuartileRange_YBand interQuartileRange_YLow \\\n", + "0 1.984149 3.308310 \n", + "1 1.718109 3.810787 \n", + "2 1.741294 3.310818 \n", + "3 1.856279 3.431322 \n", + "4 2.026612 3.352234 \n", + "5 1.881604 3.403631 \n", + "6 1.826520 4.152857 \n", + "7 1.832994 3.463992 \n", + "8 1.852815 3.812084 \n", + "9 2.007690 3.334020 \n", + "10 1.685229 3.120421 \n", + "11 1.635660 3.727290 \n", + "12 1.779794 3.617491 \n", + "13 1.876858 3.111813 \n", + "14 1.911297 3.594541 \n", + "15 2.062958 3.761337 \n", + "16 0.728493 1.848640 \n", + "\n", + " interQuartileRange_ZBand interQuartileRange_ZLow kurtosis_XBand \\\n", + "0 1.903667 3.708505 1.140452 \n", + "1 1.831989 3.167890 0.539754 \n", + "2 1.778376 3.062305 3.774831 \n", + "3 1.995422 3.335374 -0.083848 \n", + "4 1.777317 3.523871 -0.494826 \n", + "5 1.967905 3.828525 -0.496284 \n", + "6 1.700507 3.052828 2.532450 \n", + "7 1.731774 3.385692 -0.114128 \n", + "8 1.689307 3.609902 -0.593102 \n", + "9 1.738103 3.286394 -0.666559 \n", + "10 1.597126 4.093169 1.209637 \n", + "11 1.789724 3.246647 3.741451 \n", + "12 1.893762 3.725728 2.714485 \n", + "13 1.877308 3.165189 -0.070193 \n", + "14 1.895194 3.394883 -0.696165 \n", + "15 1.707837 3.198155 -0.644687 \n", + "16 1.047990 1.582356 1.724068 \n", + "\n", + " kurtosis_XLow kurtosis_YBand kurtosis_YLow kurtosis_ZBand \\\n", + "0 -0.433486 -0.656973 -0.165213 -0.555322 \n", + "1 -0.344966 4.522720 -0.410874 -0.510377 \n", + "2 0.025646 4.674121 -0.217201 0.376264 \n", + "3 -0.831627 -0.428645 -0.588536 -0.534416 \n", + "4 -0.629886 1.011833 -0.530130 0.296548 \n", + "5 -0.255814 -0.187485 -0.135181 0.791489 \n", + "6 0.043343 -0.515236 -0.512346 -0.540677 \n", + "7 -0.495910 -0.644373 -0.478376 1.593402 \n", + "8 -0.482052 1.723593 -0.397926 -0.696604 \n", + "9 -0.427513 1.107252 -0.528508 -0.395877 \n", + "10 -0.329674 -0.533438 -0.416344 1.893127 \n", + "11 0.288675 1.247625 -0.083694 -0.678479 \n", + "12 -0.084646 -0.221705 -0.183027 0.392623 \n", + "13 -0.876383 -0.519862 -0.721379 -0.187303 \n", + "14 -0.203898 -0.597615 -0.685827 -0.755930 \n", + "15 -0.622968 3.219826 -0.130987 0.365543 \n", + "16 -0.200486 3.750956 0.309812 6.243062 \n", + "\n", + " kurtosis_ZLow linearTrendSlope_X linearTrendSlope_Y linearTrendSlope_Z \\\n", + "0 -0.535814 0.006313 -0.000514 0.003657 \n", + "1 -0.687367 -0.003839 0.002532 0.004240 \n", + "2 -0.411320 0.004571 0.002077 -0.000739 \n", + "3 -0.177124 0.005734 0.002063 0.001625 \n", + "4 -0.544198 -0.001512 -0.003387 -0.000964 \n", + "5 -0.245533 0.001101 0.000232 0.004975 \n", + "6 -0.570700 -0.000640 -0.009724 -0.001061 \n", + "7 -0.428218 0.003242 -0.007163 -0.002162 \n", + "8 -0.748547 -0.002593 0.002881 -0.001886 \n", + "9 -0.276559 0.002354 -0.001473 -0.002418 \n", + "10 -0.570432 0.005904 -0.006322 -0.004904 \n", + "11 -0.533622 0.002094 0.000147 -0.000672 \n", + "12 -0.597233 -0.001648 -0.000326 -0.001772 \n", + "13 -0.338392 -0.008854 0.000951 0.002029 \n", + "14 -0.572608 -0.003999 0.003651 0.003445 \n", + "15 -0.567454 -0.008439 -0.003843 -0.001975 \n", + "16 0.798154 -0.003043 0.001917 -0.001591 \n", + "\n", + " longestStrikeAboveMean_X longestStrikeAboveMean_Y \\\n", + "0 5.0 6.0 \n", + "1 9.0 7.0 \n", + "2 5.0 6.0 \n", + "3 7.0 9.0 \n", + "4 9.0 6.0 \n", + "5 6.0 7.0 \n", + "6 6.0 10.0 \n", + "7 11.0 6.0 \n", + "8 9.0 6.0 \n", + "9 7.0 5.0 \n", + "10 6.0 5.0 \n", + "11 11.0 7.0 \n", + "12 6.0 5.0 \n", + "13 6.0 8.0 \n", + "14 6.0 9.0 \n", + "15 9.0 6.0 \n", + "16 8.0 103.0 \n", + "\n", + " longestStrikeAboveMean_Z longestStrikeBelowMean_X \\\n", + "0 7.0 6.0 \n", + "1 8.0 7.0 \n", + "2 6.0 6.0 \n", + "3 7.0 6.0 \n", + "4 5.0 5.0 \n", + "5 11.0 6.0 \n", + "6 5.0 6.0 \n", + "7 10.0 6.0 \n", + "8 8.0 9.0 \n", + "9 8.0 11.0 \n", + "10 13.0 9.0 \n", + "11 6.0 8.0 \n", + "12 8.0 6.0 \n", + "13 5.0 11.0 \n", + "14 7.0 14.0 \n", + "15 8.0 6.0 \n", + "16 7.0 102.0 \n", + "\n", + " longestStrikeBelowMean_Y longestStrikeBelowMean_Z manipulationLow \\\n", + "0 13.0 11.0 3.951277e-01 \n", + "1 9.0 7.0 4.038103e-01 \n", + "2 7.0 6.0 4.809599e-01 \n", + "3 6.0 10.0 4.220357e-01 \n", + "4 8.0 7.0 4.508842e-01 \n", + "5 7.0 7.0 3.929400e-01 \n", + "6 8.0 9.0 3.645748e-01 \n", + "7 6.0 8.0 3.754912e-01 \n", + "8 7.0 10.0 3.534541e-01 \n", + "9 7.0 9.0 4.310468e-01 \n", + "10 9.0 7.0 4.364014e-01 \n", + "11 11.0 11.0 4.579393e-01 \n", + "12 8.0 8.0 4.263630e-01 \n", + "13 6.0 5.0 3.871373e-01 \n", + "14 7.0 7.0 4.354654e-01 \n", + "15 8.0 7.0 4.154386e-01 \n", + "16 9.0 100.0 1.041319e+13 \n", + "\n", + " maxPeakAmplitudeChangeAfter_X maxPeakAmplitudeChangeAfter_Y \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "\n", + " maxPeakAmplitudeChangeAfter_Z maxPeakAmplitudeChangeBefore_X \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "\n", + " maxPeakAmplitudeChangeBefore_Y maxPeakAmplitudeChangeBefore_Z \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "\n", + " maxPeakChangeRatio_X maxPeakChangeRatio_Y maxPeakChangeRatio_Z \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "\n", + " maxPeakDecreaseTime_X maxPeakDecreaseTime_Y maxPeakDecreaseTime_Z \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "\n", + " maxPeakDuration_X maxPeakDuration_Y maxPeakDuration_Z \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "\n", + " maxPeakIncreaseTime_X maxPeakIncreaseTime_Y maxPeakIncreaseTime_Z \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "\n", + " maxPeakResponseSlopeAfter_X maxPeakResponseSlopeAfter_Y \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "\n", + " maxPeakResponseSlopeAfter_Z maxPeakResponseSlopeBefore_X \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 NaN NaN \n", + "6 NaN NaN \n", + "7 NaN NaN \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 NaN NaN \n", + "11 NaN NaN \n", + "12 NaN NaN \n", + "13 NaN NaN \n", + "14 NaN NaN \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "\n", + " maxPeakResponseSlopeBefore_Y maxPeakResponseSlopeBefore_Z maximum_X \\\n", + "0 NaN NaN 9.913559 \n", + "1 NaN NaN 9.791841 \n", + "2 NaN NaN 9.999467 \n", + "3 NaN NaN 9.929909 \n", + "4 NaN NaN 9.955434 \n", + "5 NaN NaN 9.827071 \n", + "6 NaN NaN 9.938884 \n", + "7 NaN NaN 9.908534 \n", + "8 NaN NaN 9.987894 \n", + "9 NaN NaN 9.841638 \n", + "10 NaN NaN 9.959363 \n", + "11 NaN NaN 9.963883 \n", + "12 NaN NaN 9.885265 \n", + "13 NaN NaN 9.907724 \n", + "14 NaN NaN 9.957088 \n", + "15 NaN NaN 9.888184 \n", + "16 NaN NaN 9.870713 \n", + "\n", + " maximum_Y maximum_Z meanAbsChange_X meanAbsChange_Y meanAbsChange_Z \\\n", + "0 9.879872 9.994753 6.708199 6.973540 6.387339 \n", + "1 9.995226 9.721326 6.352467 6.104119 6.843040 \n", + "2 9.932527 9.550520 6.448388 6.381125 6.585151 \n", + "3 9.886429 9.988993 6.510313 6.232186 6.635215 \n", + "4 9.987530 9.930801 6.646537 6.833095 6.771830 \n", + "5 9.917932 9.945268 6.754050 6.989046 6.821789 \n", + "6 9.920647 9.934602 6.347022 6.686862 6.783886 \n", + "7 9.932158 9.916075 6.423501 6.652570 7.042471 \n", + "8 9.909668 9.962329 6.406909 6.557554 6.863164 \n", + "9 9.928470 9.984929 6.851138 6.503910 6.678991 \n", + "10 9.730016 9.952821 6.649205 6.473480 6.272612 \n", + "11 9.904416 9.970920 6.993114 6.571131 6.624015 \n", + "12 9.979279 9.902320 6.472765 6.775512 6.540266 \n", + "13 9.996517 9.991721 6.500397 6.241242 6.967983 \n", + "14 9.967348 9.999424 5.997819 6.654779 6.906320 \n", + "15 9.921541 9.921249 6.617277 6.761452 6.751033 \n", + "16 9.777902 9.878466 4.545531 4.240018 4.510443 \n", + "\n", + " meanChange_X meanChange_Y meanChange_Z meanCrossingRate_XBand \\\n", + "0 -0.061334 0.006416 0.016333 79.0 \n", + "1 0.001574 0.008047 -0.041014 78.0 \n", + "2 0.030453 0.001773 0.045463 90.0 \n", + "3 -0.034580 0.002982 0.033159 79.0 \n", + "4 0.023233 0.056371 -0.030075 92.0 \n", + "5 0.034817 0.043220 0.010260 77.0 \n", + "6 0.003560 0.011067 0.002166 82.0 \n", + "7 -0.010176 -0.022255 -0.028938 79.0 \n", + "8 -0.012925 0.029457 -0.000918 85.0 \n", + "9 0.005239 -0.033076 -0.042385 86.0 \n", + "10 0.026809 -0.032378 -0.029518 86.0 \n", + "11 0.048897 -0.008182 0.020187 79.0 \n", + "12 0.050265 -0.002427 -0.018213 79.0 \n", + "13 0.043435 0.017885 0.036188 73.0 \n", + "14 -0.036082 -0.044090 -0.005177 75.0 \n", + "15 0.000130 0.022171 -0.000606 88.0 \n", + "16 -0.022743 -0.027098 -0.032527 71.0 \n", + "\n", + " meanCrossingRate_XLow meanCrossingRate_YBand meanCrossingRate_YLow \\\n", + "0 55.0 93.0 68.0 \n", + "1 64.0 72.0 56.0 \n", + "2 72.0 78.0 54.0 \n", + "3 42.0 76.0 46.0 \n", + "4 46.0 75.0 36.0 \n", + "5 62.0 87.0 63.0 \n", + "6 62.0 72.0 53.0 \n", + "7 51.0 84.0 60.0 \n", + "8 64.0 74.0 62.0 \n", + "9 62.0 77.0 57.0 \n", + "10 52.0 93.0 53.0 \n", + "11 65.0 72.0 74.0 \n", + "12 53.0 78.0 70.0 \n", + "13 38.0 83.0 47.0 \n", + "14 63.0 79.0 47.0 \n", + "15 58.0 76.0 64.0 \n", + "16 47.0 71.0 41.0 \n", + "\n", + " meanCrossingRate_ZBand meanCrossingRate_ZLow meanHr_X meanHr_Y \\\n", + "0 77.0 55.0 62.500000 40.000000 \n", + "1 91.0 61.0 96.256684 80.000000 \n", + "2 91.0 65.0 82.568807 85.714286 \n", + "3 89.0 78.0 73.506891 NaN \n", + "4 83.0 53.0 125.326371 88.235294 \n", + "5 90.0 58.0 NaN NaN \n", + "6 92.0 70.0 98.159509 48.128342 \n", + "7 89.0 59.0 44.444444 49.180328 \n", + "8 89.0 46.0 81.081081 NaN \n", + "9 81.0 63.0 NaN 139.534884 \n", + "10 69.0 61.0 60.606061 111.111111 \n", + "11 82.0 68.0 76.433121 37.735849 \n", + "12 77.0 55.0 86.330935 67.605634 \n", + "13 88.0 64.0 35.714286 79.470199 \n", + "14 91.0 54.0 74.074074 NaN \n", + "15 86.0 54.0 127.659574 29.702970 \n", + "16 63.0 61.0 171.428571 NaN \n", + "\n", + " meanHr_Z meanKineticEnergy_XBand meanKineticEnergy_YBand \\\n", + "0 50.420168 2618.753542 15.119339 \n", + "1 60.000000 2062.783810 2577.595421 \n", + "2 80.536913 2632.893555 2732.565173 \n", + "3 88.235294 1565.539599 267.909763 \n", + "4 NaN 102.001874 1690.707679 \n", + "5 81.494058 423.119096 734.081168 \n", + "6 59.405941 2118.507995 862.367879 \n", + "7 87.378641 1510.951610 21.975499 \n", + "8 75.353218 62.166538 1824.410823 \n", + "9 75.000000 19.485687 2832.060761 \n", + "10 41.666667 1317.479334 686.057248 \n", + "11 NaN 2717.538775 1568.039863 \n", + "12 134.328358 2067.344519 658.309984 \n", + "13 75.000000 900.033642 0.456851 \n", + "14 45.112782 206.529944 617.725595 \n", + "15 54.794521 2.180843 2742.296041 \n", + "16 NaN 1320.759448 1875.202062 \n", + "\n", + " meanKineticEnergy_ZBand mean_X mean_XLow mean_Y mean_YLow \\\n", + "0 285.854378 -0.053815 0.028358 0.037484 0.058162 \n", + "1 608.639737 0.612934 0.650683 0.038049 -0.007172 \n", + "2 954.255185 0.136521 0.049622 -0.210952 -0.240924 \n", + "3 304.747050 -0.051505 -0.021936 -0.168607 -0.192718 \n", + "4 1898.523547 -0.123231 -0.110755 -0.505054 -0.566469 \n", + "5 1293.340920 0.339549 0.307550 0.247265 0.181038 \n", + "6 52.863608 0.532152 0.487073 0.307206 0.354863 \n", + "7 2704.499361 0.215323 0.270237 0.008370 0.032609 \n", + "8 139.741126 0.249445 0.255462 0.245851 0.191933 \n", + "9 1098.466155 0.057833 0.065416 0.082462 0.144004 \n", + "10 2635.436742 0.003605 -0.056590 -0.074096 0.006955 \n", + "11 230.299711 0.112133 0.014987 0.085015 0.056788 \n", + "12 2036.611335 0.104627 0.008012 -0.129541 -0.157697 \n", + "13 692.888968 -0.475879 -0.520756 0.088602 0.064652 \n", + "14 528.401837 -0.109499 -0.083745 -0.170524 -0.107117 \n", + "15 1883.843543 -0.013173 -0.010739 -0.203450 -0.258259 \n", + "16 2701.182626 0.350229 0.403120 -0.294203 -0.231185 \n", + "\n", + " mean_Z mean_ZLow median_X median_Y median_Z minimum_X minimum_Y \\\n", + "0 -0.324680 -0.311957 0.023734 0.171356 -0.085728 -9.965873 -9.985840 \n", + "1 -0.108151 -0.074035 0.902051 -0.326440 -0.220649 -9.825098 -9.824381 \n", + "2 0.080433 0.012544 -0.159980 -0.481939 0.288308 -9.995123 -9.967720 \n", + "3 0.238372 0.214338 -0.444864 -0.381945 0.327812 -9.969508 -9.946186 \n", + "4 -0.201574 -0.148862 0.054146 -1.271820 -0.581734 -9.995572 -9.919872 \n", + "5 0.435037 0.382028 0.639785 0.584743 1.007224 -9.980317 -9.948741 \n", + "6 -0.041299 -0.040232 0.726784 0.518219 -0.300821 -9.850364 -9.892071 \n", + "7 0.270523 0.338685 0.583696 -0.228860 0.377563 -9.846594 -9.971076 \n", + "8 -0.447767 -0.452922 0.742909 0.639441 -0.748193 -9.949239 -9.961308 \n", + "9 0.447544 0.505685 -0.208261 0.034027 0.820417 -9.988752 -9.918648 \n", + "10 -0.063634 0.012662 -0.042287 -0.317458 0.433556 -9.919072 -9.951992 \n", + "11 -0.249747 -0.262029 0.089504 0.325710 -0.285281 -9.918629 -9.884742 \n", + "12 0.466454 0.538576 -0.059035 -0.317737 0.380071 -9.923152 -9.961030 \n", + "13 0.067747 0.024853 -0.750726 0.700494 -0.061674 -9.960061 -9.790094 \n", + "14 -0.196882 -0.182077 -0.275986 -0.137257 -0.601903 -9.978229 -9.975827 \n", + "15 -0.284220 -0.252543 0.315943 -0.119290 -0.772663 -9.969270 -9.910271 \n", + "16 0.452310 0.527953 0.000000 0.000000 0.000000 -9.921719 -9.957280 \n", + "\n", + " minimum_Z numOfPeaksAutocorr_X numOfPeaksAutocorr_Y \\\n", + "0 -9.989369 0.0 0.0 \n", + "1 -9.921596 0.0 0.0 \n", + "2 -9.904902 0.0 0.0 \n", + "3 -9.968657 0.0 0.0 \n", + "4 -9.966824 0.0 0.0 \n", + "5 -9.746075 0.0 0.0 \n", + "6 -9.968426 0.0 0.0 \n", + "7 -9.991425 0.0 0.0 \n", + "8 -9.945887 0.0 0.0 \n", + "9 -9.943115 0.0 0.0 \n", + "10 -9.995130 0.0 0.0 \n", + "11 -9.911025 0.0 0.0 \n", + "12 -9.942037 0.0 0.0 \n", + "13 -9.996525 0.0 0.0 \n", + "14 -9.968281 0.0 0.0 \n", + "15 -9.942458 0.0 0.0 \n", + "16 -9.944961 0.0 0.0 \n", + "\n", + " numOfPeaksAutocorr_Z numPeaks_X numPeaks_Y numPeaks_Z \\\n", + "0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "10 0.0 0.0 0.0 0.0 \n", + "11 0.0 0.0 0.0 0.0 \n", + "12 0.0 0.0 0.0 0.0 \n", + "13 0.0 0.0 0.0 0.0 \n", + "14 0.0 0.0 0.0 0.0 \n", + "15 0.0 0.0 0.0 0.0 \n", + "16 0.0 0.0 0.0 0.0 \n", + "\n", + " numberOfZeroCrossingsAutocorr_X numberOfZeroCrossingsAutocorr_Y \\\n", + "0 113.0 117.0 \n", + "1 106.0 106.0 \n", + "2 104.0 107.0 \n", + "3 98.0 99.0 \n", + "4 100.0 123.0 \n", + "5 111.0 129.0 \n", + "6 99.0 108.0 \n", + "7 121.0 84.0 \n", + "8 86.0 96.0 \n", + "9 105.0 91.0 \n", + "10 103.0 113.0 \n", + "11 111.0 100.0 \n", + "12 91.0 110.0 \n", + "13 81.0 90.0 \n", + "14 76.0 105.0 \n", + "15 90.0 102.0 \n", + "16 125.0 101.0 \n", + "\n", + " numberOfZeroCrossingsAutocorr_Z numberOfZeroCrossings_X \\\n", + "0 91.0 151.0 \n", + "1 100.0 144.0 \n", + "2 97.0 142.0 \n", + "3 105.0 145.0 \n", + "4 113.0 158.0 \n", + "5 108.0 149.0 \n", + "6 105.0 142.0 \n", + "7 108.0 142.0 \n", + "8 110.0 137.0 \n", + "9 102.0 148.0 \n", + "10 96.0 155.0 \n", + "11 121.0 155.0 \n", + "12 114.0 145.0 \n", + "13 117.0 149.0 \n", + "14 118.0 143.0 \n", + "15 111.0 152.0 \n", + "16 117.0 95.0 \n", + "\n", + " numberOfZeroCrossings_Y numberOfZeroCrossings_Z peaksAmplitudeAvgLow \\\n", + "0 144.0 138.0 0.004347 \n", + "1 154.0 155.0 0.042207 \n", + "2 150.0 147.0 0.020205 \n", + "3 140.0 153.0 0.101105 \n", + "4 151.0 155.0 0.038745 \n", + "5 159.0 146.0 0.044786 \n", + "6 144.0 156.0 0.052742 \n", + "7 154.0 154.0 0.017061 \n", + "8 147.0 156.0 0.007580 \n", + "9 151.0 147.0 0.025005 \n", + "10 161.0 146.0 0.004923 \n", + "11 148.0 150.0 0.007717 \n", + "12 154.0 144.0 0.018046 \n", + "13 140.0 163.0 0.030769 \n", + "14 143.0 154.0 0.021353 \n", + "15 146.0 148.0 0.024445 \n", + "16 97.0 101.0 0.078129 \n", + "\n", + " peaksCountLow peaksCountLow_Q1 peaksCountLow_Q2 peaksCountLow_Q3 \\\n", + "0 72.0 57.0 309.361480 57.0 \n", + "1 68.0 51.0 271.726393 51.0 \n", + "2 69.0 48.0 254.965233 48.0 \n", + "3 65.0 50.0 289.440791 50.0 \n", + "4 69.0 49.0 284.170123 49.0 \n", + "5 77.0 54.0 305.670505 54.0 \n", + "6 64.0 55.0 296.210557 55.0 \n", + "7 71.0 59.0 324.927240 59.0 \n", + "8 67.0 54.0 300.957699 54.0 \n", + "9 69.0 53.0 306.728286 53.0 \n", + "10 73.0 55.0 297.733790 55.0 \n", + "11 72.0 51.0 274.507960 51.0 \n", + "12 66.0 59.0 323.652760 59.0 \n", + "13 67.0 52.0 282.127707 52.0 \n", + "14 64.0 54.0 293.794128 54.0 \n", + "15 72.0 48.0 290.775130 48.0 \n", + "16 43.0 29.0 167.727864 29.0 \n", + "\n", + " peaksCountLow_Q4 peaksPeakAvgLow peaksSumLow pitchAvgLow pitchPeak0 \\\n", + "0 309.361480 5.040374 362.906957 3.040772 17.0 \n", + "1 271.726393 4.909723 333.861168 2.989347 20.0 \n", + "2 254.965233 4.824639 332.900101 2.961778 20.0 \n", + "3 289.440791 5.353140 347.954088 3.121756 23.0 \n", + "4 284.170123 5.124634 353.599737 2.883563 17.0 \n", + "5 305.670505 5.250856 404.315911 3.111643 18.0 \n", + "6 296.210557 5.090897 325.817395 3.018990 14.0 \n", + "7 324.927240 5.214193 370.207730 3.013429 25.0 \n", + "8 300.957699 5.242828 351.269476 2.952927 20.0 \n", + "9 306.728286 5.210245 359.506912 2.839403 21.0 \n", + "10 297.733790 5.073142 370.339333 3.065137 21.0 \n", + "11 274.507960 4.841892 348.616221 3.079928 23.0 \n", + "12 323.652760 5.102014 336.732929 3.024905 18.0 \n", + "13 282.127707 5.138125 344.254404 3.112154 25.0 \n", + "14 293.794128 5.144971 329.278152 2.999553 31.0 \n", + "15 290.775130 5.408422 389.406373 2.988340 17.0 \n", + "16 167.727864 5.017205 215.739794 3.131790 18.0 \n", + "\n", + " pitchPeak1 pitchPeak2 pitchPeak3 pitchStdDevLow pnn20_X pnn20_Y \\\n", + "0 13.579794 0.798811 0.007989 0.486736 100.000000 0.0 \n", + "1 10.988615 0.549431 0.012838 0.469389 50.000000 0.0 \n", + "2 13.188341 0.659417 0.009117 0.486348 50.000000 0.0 \n", + "3 19.352167 0.841399 0.042834 0.528436 100.000000 NaN \n", + "4 11.280661 0.663568 0.057515 0.474275 100.000000 0.0 \n", + "5 13.520449 0.751136 0.082269 0.468915 NaN NaN \n", + "6 9.893896 0.706707 0.016822 0.428226 33.333333 50.0 \n", + "7 17.965807 0.718632 0.048602 0.480323 0.000000 0.0 \n", + "8 12.073847 0.603692 0.014102 0.485176 100.000000 NaN \n", + "9 16.691937 0.794854 0.010707 0.462277 NaN 100.0 \n", + "10 11.844907 0.564043 0.003436 0.495768 0.000000 0.0 \n", + "11 15.343869 0.667125 0.035624 0.516570 100.000000 0.0 \n", + "12 11.012970 0.611832 0.051925 0.487811 100.000000 100.0 \n", + "13 21.396988 0.855880 0.032036 0.545359 0.000000 100.0 \n", + "14 20.769242 0.669976 0.000129 0.550696 0.000000 NaN \n", + "15 8.187062 0.481592 0.059789 0.520021 0.000000 0.0 \n", + "16 10.739782 0.596655 0.001056 0.879333 0.000000 NaN \n", + "\n", + " pnn20_Z pnn50_X pnn50_Y pnn50_Z postureDistance_XLow \\\n", + "0 0.000000 0.000000 0.000000 0.000000 -0.029805 \n", + "1 0.000000 50.000000 0.000000 0.000000 0.657856 \n", + "2 0.000000 50.000000 0.000000 0.000000 0.290546 \n", + "3 100.000000 100.000000 NaN 50.000000 0.170782 \n", + "4 NaN 66.666667 0.000000 NaN 0.455714 \n", + "5 100.000000 NaN NaN 100.000000 0.126512 \n", + "6 0.000000 33.333333 50.000000 0.000000 0.132211 \n", + "7 50.000000 0.000000 0.000000 50.000000 0.237628 \n", + "8 66.666667 0.000000 NaN 66.666667 0.063529 \n", + "9 50.000000 NaN 100.000000 50.000000 -0.078588 \n", + "10 0.000000 0.000000 0.000000 0.000000 -0.063545 \n", + "11 NaN 0.000000 0.000000 NaN -0.041801 \n", + "12 50.000000 66.666667 100.000000 50.000000 0.165709 \n", + "13 0.000000 0.000000 33.333333 0.000000 -0.585409 \n", + "14 0.000000 0.000000 NaN 0.000000 0.023373 \n", + "15 100.000000 0.000000 0.000000 100.000000 0.247520 \n", + "16 NaN 0.000000 NaN NaN 0.634305 \n", + "\n", + " postureDistance_YLow postureDistance_ZLow powerPeaks_X powerPeaks_Y \\\n", + "0 0.340314 0.370119 NaN NaN \n", + "1 0.724718 0.066863 NaN NaN \n", + "2 0.037078 -0.253467 NaN NaN \n", + "3 -0.236275 -0.407056 NaN NaN \n", + "4 0.038107 -0.417607 NaN NaN \n", + "5 -0.074478 -0.200990 NaN NaN \n", + "6 0.527305 0.395094 NaN NaN \n", + "7 -0.068449 -0.306077 NaN NaN \n", + "8 0.708385 0.644856 NaN NaN \n", + "9 -0.440269 -0.361681 NaN NaN \n", + "10 -0.069252 -0.005707 NaN NaN \n", + "11 0.277016 0.318816 NaN NaN \n", + "12 -0.530565 -0.696274 NaN NaN \n", + "13 -0.545609 0.039800 NaN NaN \n", + "14 0.098332 0.074959 NaN NaN \n", + "15 0.241804 -0.005716 NaN NaN \n", + "16 -0.124833 -0.759138 NaN NaN \n", + "\n", + " powerPeaks_Z power_X power_Y power_Z propPosDeriv_X \\\n", + "0 NaN 32.524274 36.163429 32.705805 0.470000 \n", + "1 NaN 31.493599 29.666586 33.188934 0.480000 \n", + "2 NaN 32.232536 30.615100 30.404677 0.530000 \n", + "3 NaN 33.421188 32.670787 33.274592 0.506667 \n", + "4 NaN 32.365346 35.637434 32.209114 0.490000 \n", + "5 NaN 34.112312 34.572956 35.212041 0.486667 \n", + "6 NaN 31.609471 34.420253 33.846841 0.523333 \n", + "7 NaN 31.733738 34.138805 34.626346 0.496667 \n", + "8 NaN 34.652641 33.932097 33.691264 0.463333 \n", + "9 NaN 36.756438 32.782473 33.734465 0.523333 \n", + "10 NaN 31.915271 29.638592 31.600046 0.490000 \n", + "11 NaN 34.221014 31.748026 31.730052 0.526667 \n", + "12 NaN 32.131935 32.278310 34.171782 0.506667 \n", + "13 NaN 33.830058 32.109646 33.222378 0.496667 \n", + "14 NaN 30.634915 34.434996 35.121045 0.540000 \n", + "15 NaN 36.301626 35.469762 33.481699 0.493333 \n", + "16 NaN 21.613741 20.467723 23.369348 0.323333 \n", + "\n", + " propPosDeriv_Y propPosDeriv_Z q25_X q25_Y q25_Z q75_X \\\n", + "0 0.503333 0.503333 -9.904968 -9.913460 -9.891769 -9.815603 \n", + "1 0.513333 0.490000 -9.823503 -9.623845 -9.895991 -9.804560 \n", + "2 0.503333 0.506667 -9.867619 -9.803923 -9.852809 -9.732812 \n", + "3 0.476667 0.540000 -9.783592 -9.889890 -9.927404 -9.641721 \n", + "4 0.493333 0.490000 -9.810951 -9.886232 -9.954297 -9.675613 \n", + "5 0.503333 0.500000 -9.856497 -9.917740 -9.743170 -9.808626 \n", + "6 0.526667 0.503333 -9.800427 -9.848262 -9.900453 -9.709491 \n", + "7 0.496667 0.490000 -9.807134 -9.948366 -9.990132 -9.624504 \n", + "8 0.513333 0.526667 -9.938917 -9.922598 -9.720563 -9.920205 \n", + "9 0.470000 0.486667 -9.983522 -9.842103 -9.795918 -9.902970 \n", + "10 0.523333 0.490000 -9.886542 -9.883881 -9.924314 -9.794184 \n", + "11 0.476667 0.496667 -9.858026 -9.878642 -9.880814 -9.808943 \n", + "12 0.506667 0.490000 -9.911604 -9.951319 -9.910403 -9.881491 \n", + "13 0.536667 0.526667 -9.828445 -9.739559 -9.984165 -9.759210 \n", + "14 0.490000 0.496667 -9.842490 -9.926872 -9.867218 -9.731634 \n", + "15 0.473333 0.506667 -9.950656 -9.780268 -9.796500 -9.886783 \n", + "16 0.333333 0.350000 -9.864598 -9.840362 -9.910587 -9.808965 \n", + "\n", + " q75_Y q75_Z qd_X qd_Y qd_Z \\\n", + "0 -9.722151 -9.784468 0.089365 0.191309 0.107300 \n", + "1 -9.457585 -9.749080 0.018943 0.166260 0.146912 \n", + "2 -9.489301 -9.749357 0.134807 0.314622 0.103452 \n", + "3 -9.826188 -9.778254 0.141871 0.063702 0.149151 \n", + "4 -9.799702 -9.897261 0.135337 0.086530 0.057035 \n", + "5 -9.895604 -9.719051 0.047870 0.022136 0.024119 \n", + "6 -9.815993 -9.736544 0.090936 0.032269 0.163909 \n", + "7 -9.845109 -9.902559 0.182630 0.103257 0.087573 \n", + "8 -9.695187 -9.633150 0.018712 0.227412 0.087413 \n", + "9 -9.701007 -9.673999 0.080552 0.141096 0.121919 \n", + "10 -9.758558 -9.875219 0.092358 0.125324 0.049096 \n", + "11 -9.870160 -9.830770 0.049083 0.008482 0.050044 \n", + "12 -9.863729 -9.856373 0.030113 0.087590 0.054030 \n", + "13 -9.663949 -9.804782 0.069235 0.075611 0.179384 \n", + "14 -9.753898 -9.823848 0.110856 0.172974 0.043369 \n", + "15 -9.527091 -9.561305 0.063874 0.253177 0.235195 \n", + "16 -9.755245 -9.704994 0.055633 0.085117 0.205592 \n", + "\n", + " quartilesMagnitudeLow_Q1 quartilesMagnitudeLow_Q2 \\\n", + "0 2.997074 3.831943 \n", + "1 2.843185 3.823473 \n", + "2 2.603842 3.455643 \n", + "3 2.858406 3.899183 \n", + "4 2.668560 3.745257 \n", + "5 2.981484 4.133815 \n", + "6 2.944049 3.830869 \n", + "7 3.014263 3.936224 \n", + "8 3.154731 4.072560 \n", + "9 2.685077 3.947851 \n", + "10 2.744576 3.936554 \n", + "11 2.646841 3.830938 \n", + "12 2.864009 4.011649 \n", + "13 2.879925 3.937298 \n", + "14 2.967667 3.971088 \n", + "15 2.667819 3.939721 \n", + "16 0.000506 2.642343 \n", + "\n", + " quartilesMagnitudeLow_Q3 quartilesMagnitudes_XBand \\\n", + "0 4.970148 1.529648 \n", + "1 4.785591 1.354417 \n", + "2 4.554796 1.429635 \n", + "3 4.992970 1.503415 \n", + "4 5.039420 1.423320 \n", + "5 5.161984 1.505583 \n", + "6 4.925347 1.370036 \n", + "7 4.958604 1.424857 \n", + "8 5.011556 1.459846 \n", + "9 5.102741 1.519479 \n", + "10 4.853421 1.381178 \n", + "11 4.803355 1.419972 \n", + "12 5.094697 1.348596 \n", + "13 4.858366 1.505410 \n", + "14 4.971170 1.463376 \n", + "15 5.339524 1.607608 \n", + "16 4.304358 0.000888 \n", + "\n", + " quartilesMagnitudes_YBand quartilesMagnitudes_ZBand quartilesQ1_XBand \\\n", + "0 1.953035 2.450324 -0.857281 \n", + "1 1.823645 2.371402 -0.890517 \n", + "2 1.857566 2.287946 -0.908937 \n", + "3 1.927286 2.430919 -0.936330 \n", + "4 1.943362 2.351452 -0.848642 \n", + "5 2.044482 2.457244 -0.964691 \n", + "6 1.911373 2.463859 -0.853602 \n", + "7 1.923635 2.375836 -0.933618 \n", + "8 1.894052 2.419341 -0.989153 \n", + "9 1.983375 2.489371 -0.981082 \n", + "10 1.803099 2.299181 -0.839424 \n", + "11 1.892468 2.321498 -0.947796 \n", + "12 1.920256 2.424900 -0.885908 \n", + "13 1.886238 2.384941 -0.905751 \n", + "14 1.953468 2.438616 -0.829282 \n", + "15 2.024101 2.466989 -0.915346 \n", + "16 1.447118 2.128269 -0.416022 \n", + "\n", + " quartilesQ1_YBand quartilesQ1_ZBand quartilesQ2_XBand \\\n", + "0 -1.066341 -0.943389 1.734648e-03 \n", + "1 -0.896180 -0.890692 5.305092e-02 \n", + "2 -0.905143 -0.904948 -1.316958e-01 \n", + "3 -0.895335 -1.012306 7.424161e-02 \n", + "4 -1.009354 -0.870719 9.819226e-03 \n", + "5 -0.898707 -0.974285 -5.631767e-02 \n", + "6 -0.919344 -0.833187 -2.319636e-02 \n", + "7 -0.909365 -0.856205 3.927045e-03 \n", + "8 -0.990565 -0.815172 5.484881e-02 \n", + "9 -1.022765 -0.921673 4.270924e-02 \n", + "10 -0.860238 -0.793160 -6.855676e-03 \n", + "11 -0.910512 -0.870465 -5.014919e-02 \n", + "12 -0.910620 -0.927338 2.410547e-02 \n", + "13 -0.898025 -0.906175 -8.665132e-02 \n", + "14 -0.974343 -0.955520 -4.738416e-02 \n", + "15 -0.984985 -0.811973 -3.887618e-02 \n", + "16 -0.440029 -0.540883 3.331106e-07 \n", + "\n", + " quartilesQ2_YBand quartilesQ2_ZBand quartilesQ3_XBand \\\n", + "0 1.474204e-01 2.570732e-02 0.900173 \n", + "1 -1.393668e-01 6.543044e-02 0.998641 \n", + "2 -2.592307e-02 3.705648e-02 1.074195 \n", + "3 7.881847e-02 3.799249e-02 0.965155 \n", + "4 -6.889970e-02 4.224540e-03 0.911672 \n", + "5 -8.779827e-02 8.764006e-02 0.932725 \n", + "6 -9.019255e-03 -3.461384e-03 0.847410 \n", + "7 -1.005830e-02 3.384215e-03 0.851492 \n", + "8 3.164539e-02 -2.583106e-02 1.025297 \n", + "9 6.089817e-02 5.860674e-02 0.959995 \n", + "10 -4.994243e-03 -2.613465e-02 0.875425 \n", + "11 -3.398671e-02 -1.037207e-02 0.988602 \n", + "12 -2.678631e-02 1.237609e-01 0.912376 \n", + "13 3.114818e-02 -3.300223e-02 0.778031 \n", + "14 1.429302e-02 1.719155e-02 0.926055 \n", + "15 -4.416076e-02 -2.242612e-02 1.048730 \n", + "16 -2.639121e-07 -4.257538e-09 0.551091 \n", + "\n", + " quartilesQ3_YBand quartilesQ3_ZBand quartiles_Q1_XLow \\\n", + "0 0.917809 0.960279 -1.692628 \n", + "1 0.821929 0.941297 -1.075061 \n", + "2 0.836151 0.873429 -1.413513 \n", + "3 0.960944 0.983116 -1.860919 \n", + "4 1.017258 0.906597 -1.853105 \n", + "5 0.982897 0.993620 -1.426029 \n", + "6 0.907176 0.867320 -1.164096 \n", + "7 0.923628 0.875569 -1.476751 \n", + "8 0.862250 0.874136 -1.723314 \n", + "9 0.984925 0.816430 -1.718565 \n", + "10 0.824992 0.803966 -1.691706 \n", + "11 0.725148 0.919259 -1.536187 \n", + "12 0.869175 0.966424 -1.586190 \n", + "13 0.978833 0.971133 -2.410799 \n", + "14 0.936954 0.939674 -1.731873 \n", + "15 1.077973 0.895863 -1.958784 \n", + "16 0.288465 0.507107 -0.354829 \n", + "\n", + " quartiles_Q1_YLow quartiles_Q1_ZLow quartiles_Q2_XLow \\\n", + "0 -1.541206 -2.190574 7.106834e-02 \n", + "1 -1.881130 -1.707743 7.121445e-01 \n", + "2 -1.861473 -1.407143 -1.932959e-02 \n", + "3 -1.922915 -1.264425 -2.975076e-01 \n", + "4 -2.190908 -1.998139 -6.881272e-02 \n", + "5 -1.490952 -1.543161 3.411664e-01 \n", + "6 -1.796971 -1.543134 4.847145e-01 \n", + "7 -1.732299 -1.375862 3.162880e-01 \n", + "8 -1.752244 -2.346500 3.795663e-01 \n", + "9 -1.588920 -1.023154 8.254056e-02 \n", + "10 -1.424364 -2.000485 -1.249822e-01 \n", + "11 -1.844722 -1.895595 4.847813e-03 \n", + "12 -1.785255 -1.375454 1.907984e-01 \n", + "13 -1.576045 -1.598559 -8.241570e-01 \n", + "14 -1.924112 -1.910202 -1.233831e-01 \n", + "15 -2.041210 -1.911564 -4.371645e-02 \n", + "16 -1.282318 -0.007787 -1.034646e-10 \n", + "\n", + " quartiles_Q2_YLow quartiles_Q2_ZLow quartiles_Q3_XLow \\\n", + "0 1.925838e-01 -0.244365 1.772612 \n", + "1 4.735999e-03 -0.227438 2.415131 \n", + "2 -1.750099e-01 -0.007410 1.839983 \n", + "3 -1.634460e-01 0.273415 1.716989 \n", + "4 -6.622510e-01 -0.047323 1.480328 \n", + "5 2.301154e-01 0.542243 2.154964 \n", + "6 5.439990e-01 -0.094862 2.311904 \n", + "7 5.966718e-02 0.301915 1.964963 \n", + "8 2.255246e-01 -0.316655 2.124440 \n", + "9 1.560492e-01 0.474546 1.738235 \n", + "10 -1.370148e-01 -0.053503 1.753026 \n", + "11 3.170505e-01 -0.285082 1.836553 \n", + "12 5.473598e-03 0.528322 1.576726 \n", + "13 -7.963359e-02 0.064714 1.269420 \n", + "14 -1.492296e-01 -0.151915 2.032976 \n", + "15 -4.362463e-02 -0.219009 2.046924 \n", + "16 1.421830e-09 0.000036 1.399923 \n", + "\n", + " quartiles_Q3_YLow quartiles_Q3_ZLow ratePeaks_X ratePeaks_Y \\\n", + "0 1.767104 1.517931 0.0 0.0 \n", + "1 1.929656 1.460147 0.0 0.0 \n", + "2 1.449344 1.655161 0.0 0.0 \n", + "3 1.508407 2.070949 0.0 0.0 \n", + "4 1.161326 1.525732 0.0 0.0 \n", + "5 1.912678 2.285364 0.0 0.0 \n", + "6 2.355886 1.509694 0.0 0.0 \n", + "7 1.731693 2.009830 0.0 0.0 \n", + "8 2.059840 1.263401 0.0 0.0 \n", + "9 1.745099 2.263241 0.0 0.0 \n", + "10 1.696057 2.092684 0.0 0.0 \n", + "11 1.882568 1.351052 0.0 0.0 \n", + "12 1.832236 2.350274 0.0 0.0 \n", + "13 1.535768 1.566630 0.0 0.0 \n", + "14 1.670429 1.484681 0.0 0.0 \n", + "15 1.720127 1.286591 0.0 0.0 \n", + "16 0.566322 1.574568 0.0 0.0 \n", + "\n", + " ratePeaks_Z ratioBeyondRSigma_X ratioBeyondRSigma_Y \\\n", + "0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "10 0.0 0.0 0.0 \n", + "11 0.0 0.0 0.0 \n", + "12 0.0 0.0 0.0 \n", + "13 0.0 0.0 0.0 \n", + "14 0.0 0.0 0.0 \n", + "15 0.0 0.0 0.0 \n", + "16 0.0 0.0 0.0 \n", + "\n", + " ratioBeyondRSigma_Z rmssd_X rmssd_Y rmssd_Z rollAvgLow \\\n", + "0 0.0 0.040000 0.00000 0.000000 6.267041 \n", + "1 0.0 0.070711 0.00000 0.000000 6.229705 \n", + "2 0.0 0.056569 0.00000 0.010000 6.246037 \n", + "3 0.0 0.176564 NaN 0.079057 6.263200 \n", + "4 0.0 0.073314 0.00000 NaN 6.257153 \n", + "5 0.0 NaN NaN 0.143788 6.235513 \n", + "6 0.0 0.055227 0.13435 0.000000 6.256733 \n", + "7 0.0 0.000000 0.00000 0.099247 6.249577 \n", + "8 0.0 0.020000 NaN 0.084113 6.257396 \n", + "9 0.0 NaN 0.16000 0.106066 6.229289 \n", + "10 0.0 0.000000 0.00000 0.000000 6.197414 \n", + "11 0.0 0.030000 0.00000 NaN 6.245839 \n", + "12 0.0 0.152315 0.19500 0.049497 6.191870 \n", + "13 0.0 0.000000 0.06364 0.000000 6.216458 \n", + "14 0.0 0.000000 NaN 0.000000 6.250970 \n", + "15 0.0 0.000000 0.00000 0.090000 6.252043 \n", + "16 0.0 0.015811 NaN NaN 6.168358 \n", + "\n", + " rollMotionAmountLow rollMotionRegularityLow rollPeak0 rollPeak1 \\\n", + "0 1.542201 0.960000 58.0 81.579155 \n", + "1 1.572021 0.946667 50.0 81.415426 \n", + "2 1.644758 0.920000 57.0 85.111788 \n", + "3 1.628594 0.960000 53.0 95.000403 \n", + "4 1.751518 0.986667 60.0 101.781375 \n", + "5 1.479838 0.943333 50.0 97.621402 \n", + "6 1.454305 0.913333 54.0 89.027514 \n", + "7 1.514670 0.936667 52.0 89.122608 \n", + "8 1.486984 0.940000 55.0 79.561599 \n", + "9 1.543290 0.943333 53.0 95.600485 \n", + "10 1.593219 0.953333 52.0 78.927944 \n", + "11 1.516778 0.953333 59.0 77.765390 \n", + "12 1.565020 0.960000 46.0 75.389760 \n", + "13 1.549493 0.943333 51.0 92.043536 \n", + "14 1.644497 0.946667 63.0 118.478766 \n", + "15 1.605553 0.930000 64.0 97.084014 \n", + "16 1.106250 0.966667 36.0 67.487914 \n", + "\n", + " rollPeak2 rollPeak3 rollPitchCorrelation rollStdDevLow sd1/sd2_X \\\n", + "0 1.406537 9.519295e-02 0.999979 1.781788 NaN \n", + "1 1.628309 6.604003e-02 0.999981 1.828565 0.534522 \n", + "2 1.493189 9.489318e-02 0.999979 1.882802 0.534522 \n", + "3 1.792460 1.092292e-02 0.999981 1.853306 1.379034 \n", + "4 1.696356 7.270613e-02 0.999981 1.957519 0.995458 \n", + "5 1.952428 3.119174e-02 0.999980 1.714472 NaN \n", + "6 1.648658 1.089759e-01 0.999980 1.744573 0.577154 \n", + "7 1.713896 3.651673e-02 0.999978 1.774453 NaN \n", + "8 1.446575 4.361178e-02 0.999979 1.745401 NaN \n", + "9 1.803783 5.295251e-02 0.999981 1.797112 NaN \n", + "10 1.517845 4.367994e-03 0.999978 1.798309 NaN \n", + "11 1.318057 3.999860e-02 0.999980 1.785573 NaN \n", + "12 1.638908 5.001221e-02 0.999981 1.788564 0.638044 \n", + "13 1.804775 1.027405e-01 0.999984 1.809506 NaN \n", + "14 1.880615 8.600262e-02 0.999982 1.883839 NaN \n", + "15 1.516938 7.589902e-03 0.999981 1.863443 NaN \n", + "16 1.874664 7.709882e-19 0.999983 1.500198 1.414214 \n", + "\n", + " sd1/sd2_Y sd1/sd2_Z sd2_X sd2_Y sd2_Z sd_X sd_Y \\\n", + "0 NaN NaN NaN NaN NaN 0.014142 0.000000 \n", + "1 NaN NaN 0.062361 NaN NaN 0.033333 0.000000 \n", + "2 NaN NaN 0.049889 NaN NaN 0.026667 0.000000 \n", + "3 NaN 1.414214 0.044234 NaN 0.020412 0.061001 NaN \n", + "4 NaN NaN 0.026623 NaN NaN 0.026502 0.000000 \n", + "5 NaN 1.831731 NaN NaN 0.026584 NaN NaN \n", + "6 0.534522 NaN 0.050584 0.118486 NaN 0.029195 0.063333 \n", + "7 NaN 0.619666 NaN NaN 0.078138 0.000000 0.000000 \n", + "8 NaN 0.700467 NaN NaN 0.056811 0.007071 NaN \n", + "9 NaN 0.534522 NaN NaN 0.093541 NaN 0.056569 \n", + "10 NaN NaN NaN NaN NaN 0.000000 0.000000 \n", + "11 NaN NaN NaN NaN NaN 0.010607 0.000000 \n", + "12 NaN 0.534522 0.087789 NaN 0.043653 0.056013 0.068943 \n", + "13 1.133893 NaN NaN 0.019843 NaN 0.000000 0.022500 \n", + "14 NaN NaN NaN NaN NaN 0.000000 NaN \n", + "15 NaN NaN NaN NaN NaN 0.000000 0.000000 \n", + "16 NaN NaN 0.004082 NaN NaN 0.005774 NaN \n", + "\n", + " sd_Z sdnn_X sdnn_Y sdnn_Z sdsd_X sdsd_Y sdsd_Z \\\n", + "0 0.000000 0.020000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1 0.000000 0.047140 0.000000 0.000000 0.050000 0.000000 0.000000 \n", + "2 0.003536 0.037712 0.000000 0.005000 0.040000 0.000000 0.000000 \n", + "3 0.028868 0.086268 NaN 0.040825 0.053281 NaN 0.025000 \n", + "4 NaN 0.037479 0.000000 NaN 0.026562 0.000000 NaN \n", + "5 0.048694 NaN NaN 0.068864 NaN NaN 0.039229 \n", + "6 0.000000 0.041288 0.089567 0.000000 0.041298 0.095000 0.000000 \n", + "7 0.048419 0.000000 0.000000 0.068475 0.000000 0.000000 0.065000 \n", + "8 0.039794 0.010000 NaN 0.056278 0.000000 NaN 0.049046 \n", + "9 0.050000 NaN 0.080000 0.070711 NaN 0.000000 0.075000 \n", + "10 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "11 NaN 0.015000 0.000000 NaN 0.000000 0.000000 NaN \n", + "12 0.023333 0.079215 0.097500 0.032998 0.073636 0.000000 0.035000 \n", + "13 0.000000 0.000000 0.031820 0.000000 0.000000 0.021213 0.000000 \n", + "14 0.000000 0.000000 NaN 0.000000 0.000000 NaN 0.000000 \n", + "15 0.031820 0.000000 0.000000 0.045000 0.000000 0.000000 0.000000 \n", + "16 NaN 0.008165 NaN NaN 0.005000 NaN NaN \n", + "\n", + " sigTonicDifference_X sigTonicDifference_Y sigTonicDifference_Z \\\n", + "0 9.199656 9.338675 8.899684 \n", + "1 9.616484 8.656152 8.719506 \n", + "2 9.254964 8.942960 9.371260 \n", + "3 8.953227 9.256710 9.405749 \n", + "4 9.227503 8.427440 8.868092 \n", + "5 9.610274 9.664016 9.881796 \n", + "6 9.707548 9.728117 9.286315 \n", + "7 8.843457 9.474760 9.450369 \n", + "8 9.430278 9.303699 8.659857 \n", + "9 9.505284 8.649510 9.470890 \n", + "10 8.655102 8.230653 9.430806 \n", + "11 8.710545 9.404535 8.934167 \n", + "12 8.777491 9.328002 9.314613 \n", + "13 8.489345 9.333659 9.356877 \n", + "14 7.818805 8.840335 9.259486 \n", + "15 9.371048 9.028195 8.869437 \n", + "16 7.249286 5.986623 7.562215 \n", + "\n", + " signalOverallChange_X signalOverallChange_Y signalOverallChange_Z \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 NaN NaN NaN \n", + "10 NaN NaN NaN \n", + "11 NaN NaN NaN \n", + "12 NaN NaN NaN \n", + "13 NaN NaN NaN \n", + "14 NaN NaN NaN \n", + "15 NaN NaN NaN \n", + "16 NaN NaN NaN \n", + "\n", + " significantDecreaseChange_X significantDecreaseChange_Y \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantDecreaseChange_Z significantDecreaseDuration_X \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantDecreaseDuration_Y significantDecreaseDuration_Z \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantDecreaseIntensity_X significantDecreaseIntensity_Y \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantDecreaseIntensity_Z significantDecreaseMean_X \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantDecreaseMean_Y significantDecreaseMean_Z \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantDecreaseSpeed_X significantDecreaseSpeed_Y \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantDecreaseSpeed_Z significantIncreaseChange_X \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantIncreaseChange_Y significantIncreaseChange_Z \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantIncreaseDuration_X significantIncreaseDuration_Y \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantIncreaseDuration_Z significantIncreaseIntensity_X \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantIncreaseIntensity_Y significantIncreaseIntensity_Z \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantIncreaseMean_X significantIncreaseMean_Y \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantIncreaseMean_Z significantIncreaseSpeed_X \\\n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "\n", + " significantIncreaseSpeed_Y significantIncreaseSpeed_Z skewness_XBand \\\n", + "0 0.0 0.0 -0.789241 \n", + "1 0.0 0.0 -0.964118 \n", + "2 0.0 0.0 -1.947521 \n", + "3 0.0 0.0 -1.074669 \n", + "4 0.0 0.0 -1.429767 \n", + "5 0.0 0.0 -1.423178 \n", + "6 0.0 0.0 -1.810281 \n", + "7 0.0 0.0 -1.029154 \n", + "8 0.0 0.0 -1.399832 \n", + "9 0.0 0.0 -1.368791 \n", + "10 0.0 0.0 -1.711224 \n", + "11 0.0 0.0 -1.930326 \n", + "12 0.0 0.0 -1.829400 \n", + "13 0.0 0.0 -1.433780 \n", + "14 0.0 0.0 -1.331531 \n", + "15 0.0 0.0 -1.393066 \n", + "16 0.0 0.0 -0.841880 \n", + "\n", + " skewness_XLow skewness_YBand skewness_YLow skewness_ZBand \\\n", + "0 -1.336122 -1.408522 -1.448100 -1.400358 \n", + "1 -1.261287 -1.989833 -1.397036 -1.338255 \n", + "2 -1.475923 -2.027313 -1.467638 -1.562003 \n", + "3 -1.266773 -1.453350 -1.383511 -1.442892 \n", + "4 -1.339143 -1.615808 -1.422805 -0.943698 \n", + "5 -1.416528 -1.459715 -1.448963 -1.675927 \n", + "6 -1.386903 -1.294143 -1.357113 -1.412690 \n", + "7 -1.330033 -1.368968 -1.402020 -0.702916 \n", + "8 -1.384544 -1.713550 -1.379397 -1.356883 \n", + "9 -1.372940 -0.716725 -1.210997 -1.201617 \n", + "10 -1.425541 -1.311673 -1.400647 -0.616432 \n", + "11 -1.533702 -1.614584 -1.548574 -1.364344 \n", + "12 -1.478368 -1.469157 -1.519127 -1.050517 \n", + "13 -1.329548 -1.421468 -1.253525 -1.496642 \n", + "14 -1.497803 -1.339151 -1.344117 -1.300268 \n", + "15 -1.368357 -1.891049 -1.525716 -1.012493 \n", + "16 -0.784251 -0.129888 -1.336934 0.048994 \n", + "\n", + " skewness_ZLow squareSumOfComponent squareSumOfComponent_X \\\n", + "0 -1.445621 9491.043380 87.304565 \n", + "1 -1.277417 30651.148332 68.792877 \n", + "2 -1.393616 44408.530623 87.771841 \n", + "3 -1.483391 5106.736870 52.245492 \n", + "4 -1.392960 15176.836847 3.443728 \n", + "5 -1.425731 6709.160654 14.176021 \n", + "6 -1.363114 10254.961649 70.644597 \n", + "7 -1.265042 19979.281586 50.420508 \n", + "8 -1.365171 4581.997895 2.110400 \n", + "9 -1.319741 17362.935975 0.673737 \n", + "10 -1.352344 23952.457725 43.977395 \n", + "11 -1.425509 22698.768662 90.606767 \n", + "12 -1.140978 25247.727112 68.948690 \n", + "13 -1.399283 2837.286157 30.077470 \n", + "14 -1.411368 2051.843171 6.938203 \n", + "15 -1.344404 23828.015922 0.081206 \n", + "16 -0.643958 42835.132332 46.378025 \n", + "\n", + " squareSumOfComponent_Y squareSumOfComponent_Z stdDev_X stdDev_Y \\\n", + "0 0.525027 9.592395 5.702752 6.013487 \n", + "1 85.920194 20.361622 5.578343 5.446571 \n", + "2 91.087375 31.874100 5.675729 5.529069 \n", + "3 8.992683 10.223261 5.780877 5.713349 \n", + "4 56.423599 63.326978 5.687720 5.948307 \n", + "5 24.547381 43.186064 5.830696 5.874676 \n", + "6 28.825635 1.796552 5.596989 5.858829 \n", + "7 0.756588 90.170990 5.629154 5.842836 \n", + "8 60.870553 4.709504 5.881362 5.819936 \n", + "9 94.407329 36.687427 6.062433 5.725004 \n", + "10 22.934706 87.853715 5.649359 5.443630 \n", + "11 52.320191 7.734147 5.848798 5.633897 \n", + "12 22.013900 67.932744 5.667538 5.679923 \n", + "13 0.019081 23.169631 5.796861 5.665845 \n", + "14 20.667281 17.691791 5.533798 5.865656 \n", + "15 91.434499 62.847554 6.025069 5.952174 \n", + "16 65.801181 94.787294 4.635847 4.514551 \n", + "\n", + " stdDev_Z std_X std_Y std_Z sumOfSquareComponents \\\n", + "0 5.709675 5.702752 6.013487 5.709675 7714.376715 \n", + "1 5.759969 5.578343 5.446571 5.759969 12529.335362 \n", + "2 5.513457 5.675729 5.529069 5.513457 17016.764285 \n", + "3 5.763486 5.780877 5.713349 5.763486 2914.974871 \n", + "4 5.671727 5.687720 5.948307 5.671727 7205.787985 \n", + "5 5.918005 5.830696 5.874676 5.918005 2668.569628 \n", + "6 5.817657 5.596989 5.858829 5.817657 5824.803953 \n", + "7 5.878194 5.629154 5.842836 5.878194 10673.607545 \n", + "8 5.787121 5.881362 5.819936 5.787121 3731.857444 \n", + "9 5.790869 6.062433 5.725004 5.790869 10259.165086 \n", + "10 5.621032 5.649359 5.443630 5.621032 10178.287252 \n", + "11 5.627404 5.848798 5.633897 5.627404 11006.805647 \n", + "12 5.827023 5.667538 5.679923 5.827023 9853.391350 \n", + "13 5.763488 5.796861 5.665845 5.763488 1441.486376 \n", + "14 5.923030 5.533798 5.865656 5.923030 788.274651 \n", + "15 5.779353 6.025069 5.952174 5.779353 12310.089283 \n", + "16 4.812979 4.635847 4.514551 4.812979 15465.347834 \n", + "\n", + " sumPerComponent_XBand sumPerComponent_YBand sumPerComponent_ZBand \\\n", + "0 9.332071 0.711978 3.085267 \n", + "1 8.282718 9.258236 4.500362 \n", + "2 9.357266 9.532453 5.634279 \n", + "3 7.216070 2.987004 3.185468 \n", + "4 1.844006 7.499087 7.946165 \n", + "5 3.752978 4.942276 6.559151 \n", + "6 8.393765 5.356485 1.328328 \n", + "7 7.089141 0.857534 9.483464 \n", + "8 1.440467 7.789667 2.157835 \n", + "9 0.807834 9.704695 6.045037 \n", + "10 6.619876 4.777700 9.361491 \n", + "11 9.506484 7.221729 2.769357 \n", + "12 8.291813 4.680328 8.230175 \n", + "13 5.472029 0.126213 4.801692 \n", + "14 2.622796 4.534010 4.193525 \n", + "15 0.272450 9.549740 7.915426 \n", + "16 6.800411 8.092254 9.720961 \n", + "\n", + " sumPer_X sumPer_Y sumPer_Z sumPosDeriv_X sumPosDeriv_Y sumPosDeriv_Z \\\n", + "0 9.343691 0.724587 3.097159 1.533655 1.811178 1.681572 \n", + "1 8.294147 9.269315 4.512385 1.591732 1.580072 1.700955 \n", + "2 9.368663 9.543971 5.645715 1.755798 1.650120 1.671424 \n", + "3 7.228104 2.998780 3.197383 1.697559 1.655703 1.668006 \n", + "4 1.855728 7.511564 7.957825 1.714760 1.769990 1.689834 \n", + "5 3.765106 4.954531 6.571610 1.601735 1.663261 1.676063 \n", + "6 8.405034 5.368951 1.340355 1.608356 1.653202 1.825924 \n", + "7 7.100740 0.869821 9.495841 1.701632 1.756226 1.709203 \n", + "8 1.452721 7.801958 2.170139 1.653067 1.827721 1.536427 \n", + "9 0.820815 9.716343 6.057015 1.824568 1.587335 1.700598 \n", + "10 6.631545 4.789019 9.373031 1.624215 1.513528 1.636635 \n", + "11 9.518759 7.233270 2.781033 1.650110 1.606799 1.702796 \n", + "12 8.303535 4.691897 8.242132 1.706478 1.541407 1.577032 \n", + "13 5.484293 0.138134 4.813484 1.771312 1.704668 1.763609 \n", + "14 2.634047 4.546128 4.206161 1.551998 1.620187 1.795793 \n", + "15 0.284968 9.562139 7.927645 1.618430 1.746358 1.796606 \n", + "16 6.810141 8.111793 9.735877 1.009023 1.086773 1.161188 \n", + "\n", + " sumSquared_X sumSquared_Y sumSquared_Z totalAbsoluteAreaBand \\\n", + "0 87.304565 0.525027 9.592395 930.324637 \n", + "1 68.792877 85.920194 20.361622 880.255964 \n", + "2 87.771841 91.087375 31.874100 889.394563 \n", + "3 52.245492 8.992683 10.223261 920.462589 \n", + "4 3.443728 56.423599 63.326978 906.331468 \n", + "5 14.176021 24.547381 43.186064 922.999063 \n", + "6 70.644597 28.825635 1.796552 891.241288 \n", + "7 50.420508 0.756588 90.170990 889.958466 \n", + "8 2.110400 60.870553 4.709504 899.898502 \n", + "9 0.673737 94.407329 36.687427 937.539507 \n", + "10 43.977395 22.934706 87.853715 853.098151 \n", + "11 90.606767 52.320191 7.734147 894.370062 \n", + "12 68.948690 22.013900 67.932744 912.690717 \n", + "13 30.077470 0.019081 23.169631 896.978822 \n", + "14 6.938203 20.667281 17.691791 904.039012 \n", + "15 0.081206 91.434499 62.847554 949.133312 \n", + "16 46.378025 65.801181 94.787294 618.770314 \n", + "\n", + " totalAbsoluteAreaLow totalEnergy_XBand totalEnergy_XLow \\\n", + "0 1780.575497 517.364214 1788.802647 \n", + "1 1764.960003 484.409520 1660.514544 \n", + "2 1660.479036 500.176502 1608.410552 \n", + "3 1820.669589 451.010824 1992.791606 \n", + "4 1768.462902 392.463100 1611.102684 \n", + "5 1875.393901 471.891573 1857.190163 \n", + "6 1787.154967 486.437759 1675.123945 \n", + "7 1812.332972 462.825167 1787.230831 \n", + "8 1914.346191 460.461739 2145.783460 \n", + "9 1820.621790 459.708676 1891.234146 \n", + "10 1788.896569 456.655747 1775.297410 \n", + "11 1754.867700 530.355391 1909.625501 \n", + "12 1844.751328 457.213609 1748.235049 \n", + "13 1774.809989 431.661941 2061.651867 \n", + "14 1785.109858 422.788778 1908.060229 \n", + "15 1849.464050 487.266003 2234.828172 \n", + "16 1206.745729 325.226513 1173.958591 \n", + "\n", + " totalEnergy_YBand totalEnergy_YLow totalEnergy_ZBand totalEnergy_ZLow \\\n", + "0 439.584822 1739.615369 450.831393 1794.973308 \n", + "1 457.269815 1856.515911 413.902079 1532.911953 \n", + "2 472.406450 1718.034506 403.722870 1392.356015 \n", + "3 464.921005 1690.931963 438.332549 1759.580678 \n", + "4 529.857943 1861.075738 461.368239 1706.093090 \n", + "5 456.903709 1860.241368 459.162105 2076.012958 \n", + "6 462.875255 2038.645919 403.746970 1497.384032 \n", + "7 426.235740 1833.567302 490.346279 1776.759610 \n", + "8 485.681060 1887.933048 377.333528 1856.973429 \n", + "9 556.100152 1919.097714 474.084271 1774.462002 \n", + "10 374.230201 1527.449086 461.128464 2062.255419 \n", + "11 450.140820 1765.227258 385.755339 1529.079555 \n", + "12 425.974207 1747.207222 529.734045 2313.928055 \n", + "13 412.340960 1682.121184 434.772069 1493.757008 \n", + "14 475.704726 1914.740172 429.391914 1481.573747 \n", + "15 563.461483 1932.475692 474.052209 1633.787069 \n", + "16 340.504512 1173.019368 386.447472 1226.625881 \n", + "\n", + " totalKineticEnergyBand totalMagnitudeBand totalMagnitudeLow \\\n", + "0 2.925126e+08 37.561417 73.200325 \n", + "1 5.258724e+08 36.836583 71.964761 \n", + "2 6.331399e+08 37.155660 68.858981 \n", + "3 2.142150e+08 36.827968 73.961246 \n", + "4 3.698058e+08 37.211833 72.700856 \n", + "5 2.455072e+08 37.317283 76.700344 \n", + "6 3.039349e+08 36.815042 72.961288 \n", + "7 4.245261e+08 37.156183 73.861435 \n", + "8 2.030065e+08 36.400458 77.362336 \n", + "9 3.957316e+08 38.649692 75.327253 \n", + "10 4.647551e+08 35.970652 73.266806 \n", + "11 4.524228e+08 37.022559 72.336756 \n", + "12 4.771071e+08 37.613472 76.851962 \n", + "13 1.596326e+08 35.767026 72.943244 \n", + "14 1.355158e+08 36.471281 72.961260 \n", + "15 4.636878e+08 39.090631 76.441830 \n", + "16 -3.812113e+07 32.463418 61.018083 \n", + "\n", + " totalMeanLow variance_XBand variance_XLow variance_YBand \\\n", + "0 -22.543665 2.721968 9.754062 2.590389 \n", + "1 56.947576 2.563355 7.355288 2.510291 \n", + "2 -17.875769 2.804377 8.647055 2.622988 \n", + "3 -0.031628 2.429697 11.250914 2.645239 \n", + "4 -82.608508 2.215412 9.352735 2.991478 \n", + "5 87.061581 2.673239 9.243265 2.587991 \n", + "6 80.170428 2.693489 7.684352 2.562234 \n", + "7 64.153009 2.469863 9.024566 2.414425 \n", + "8 -0.552672 2.593119 11.128252 2.712446 \n", + "9 71.510548 2.611981 10.151689 2.926317 \n", + "10 -3.697235 2.502221 9.994805 2.122154 \n", + "11 -19.025417 2.948125 10.263177 2.519518 \n", + "12 38.889104 2.545405 9.535645 2.420954 \n", + "13 -43.125120 2.439554 14.330087 2.351737 \n", + "14 -37.293835 2.341786 10.937580 2.644027 \n", + "15 -52.154199 2.788693 12.546204 3.149414 \n", + "16 69.988893 1.527052 4.799571 1.541689 \n", + "\n", + " variance_YLow variance_ZBand variance_ZLow velocity_XBand \\\n", + "0 9.313834 2.528331 11.471023 9.329075 \n", + "1 10.417940 2.274827 8.751125 8.279796 \n", + "2 10.472756 2.271032 7.621608 -9.354190 \n", + "3 10.178643 2.536343 9.079495 7.213186 \n", + "4 12.998558 2.465946 10.177490 1.841149 \n", + "5 9.554210 2.626139 10.193015 -3.749940 \n", + "6 10.207149 2.279214 8.458739 -8.390872 \n", + "7 10.084753 2.561608 8.665412 7.086253 \n", + "8 9.794086 2.111711 12.556467 1.437382 \n", + "9 9.953002 2.584094 8.199316 0.804720 \n", + "10 8.401283 2.379874 11.553055 -6.617017 \n", + "11 9.721455 2.142709 9.559985 -9.503376 \n", + "12 10.224227 2.851741 10.736961 -8.288873 \n", + "13 8.996186 2.515854 8.132504 -5.469177 \n", + "14 11.128877 2.382600 8.970394 2.619904 \n", + "15 11.788846 2.508219 9.934570 0.269214 \n", + "16 6.359343 1.750283 4.757309 6.798417 \n", + "\n", + " velocity_YBand velocity_ZBand \n", + "0 0.708808 3.082230 \n", + "1 -9.255495 4.497523 \n", + "2 -9.529691 -5.631469 \n", + "3 -2.983890 -3.182467 \n", + "4 -7.495938 7.943277 \n", + "5 -4.939248 -6.556118 \n", + "6 5.353457 -1.325481 \n", + "7 -0.854626 9.480576 \n", + "8 -7.786676 2.155067 \n", + "9 9.701594 6.042039 \n", + "10 4.774896 9.358744 \n", + "11 -7.218897 2.766581 \n", + "12 -4.677415 8.227049 \n", + "13 0.123248 -4.798694 \n", + "14 4.530926 4.190602 \n", + "15 -9.546621 7.912537 \n", + "16 8.090398 9.732142 \n" + ] + } + ], + "source": [ + "INPUT_LEN = 5000\n", + "random.seed(1234)\n", + "np.random.seed(1234)\n", + "\n", + "x = np.array([random.random() * 20 - 10 for _ in range(INPUT_LEN)])\n", + "y = np.array([random.random() * 20 - 10 for _ in range(INPUT_LEN)])\n", + "z = np.array([random.random() * 20 - 10 for _ in range(INPUT_LEN)])\n", + "pd.set_option('display.max_rows', 500)\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.width', None)\n", + "\n", + "time = [i*0.01 for i in range(INPUT_LEN)]\n", + "\n", + "numCols = 300\n", + "\n", + "ax = convertInputInto2d(x, numCols)\n", + "ay = convertInputInto2d(y, numCols)\n", + "az = convertInputInto2d(z, numCols)\n", + "\n", + "time = convertInputInto2d(time, numCols)\n", + "\n", + "df = calculateFeatures(ax, ay, az, time)\n", + "\n", + "print(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate execution times" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_LEN = 100000\n", + "PRINT_OUTPUT = False\n", + "\n", + "\n", + "def timeFunc(fName, f, *args):\n", + " start_time = t.time()\n", + " out = f(*args)\n", + " print(fName, \" \" * (40-len(fName)), t.time() - start_time, \"seconds\")\n", + " if PRINT_OUTPUT:\n", + " print(out)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "compute_features 0.06318068504333496 seconds\n", + "calcAreaUnderAccelerationMagnitude 0.1833968162536621 seconds\n", + "calcAverageVectorLength 0.001512765884399414 seconds\n", + "calcAverageVectorLengthPower 0.0005660057067871094 seconds\n", + "calcMeanKineticEnergy 0.9430029392242432 seconds\n", + "calcPeaks 0.8460264205932617 seconds\n", + "calcTotalKineticEnergy 1.1924221515655518 seconds\n", + "calcAcAbsoluteArea 0.0012974739074707031 seconds\n", + "calcAbsoluteMean 0.0006303787231445312 seconds\n", + "calcAmplitude 0.0005743503570556641 seconds\n", + "calcCoefficientOfVariation 17.25027561187744 seconds\n", + "calcCorrelation 0.3744330406188965 seconds\n", + "calcEntropy 1.2423591613769531 seconds\n", + "calcInterQuartileRange 5.245208740234375e-06 seconds\n", + "calcKurtosis 1.1491007804870605 seconds\n", + "calcMeanCrossingRate 0.12923097610473633 seconds\n", + "calcQuartiles 0.10563778877258301 seconds\n", + "calcSkewness 1.1148443222045898 seconds\n", + "calcTotalAbsoluteArea 0.0001842975616455078 seconds\n", + "calcTotalEnergy 0.8747704029083252 seconds\n", + "calcDominantFrequencyEnergy 0.9095582962036133 seconds\n", + "calcTotalMagnitude 1.2974116802215576 seconds\n", + "calcVariance 15.527474403381348 seconds\n", + "calcArea 0.0006725788116455078 seconds\n", + "calcMean 0.00042319297790527344 seconds\n", + "calcPostureDistance 1.4543533325195312e-05 seconds\n", + "calcTotalMean 0.0002124309539794922 seconds\n", + "calcPeakCountBoza 0.0006649494171142578 seconds\n", + "calcSumPerComponent 1.1699285507202148 seconds\n", + "computeACVelocity 0.768822193145752 seconds\n", + "lowPassFilter 0.1188957691192627 seconds\n", + "bandPassFilter 0.24991559982299805 seconds\n", + "highPassFilter 0.12767314910888672 seconds\n", + "magnitudeVector 1.7064223289489746 seconds\n", + "covariance 0.13443350791931152 seconds\n", + "calcRoll 0.6018388271331787 seconds\n", + "calcPitch 0.7513015270233154 seconds\n", + "stdDev 0.13022685050964355 seconds\n", + "rollMotionAmount 0.4034900665283203 seconds\n", + "rollMotionRegularity 0.47931718826293945 seconds\n", + "manipulation 1.9478199481964111 seconds\n", + "min 0.0003123283386230469 seconds\n", + "min 0.0002841949462890625 seconds\n", + "max 0.00025963783264160156 seconds\n", + "max 0.00019931793212890625 seconds\n", + "avg 0.0003573894500732422 seconds\n", + "avg 0.000213623046875 seconds\n" + ] + } + ], + "source": [ + "magnitudes = np.array([random.random() * 100 for _ in range(DATA_LEN)])\n", + "time = np.array([random.randint(1499719594, 1599719594) for _ in range(DATA_LEN)])\n", + "\n", + "x = np.array([random.random() * 100 for _ in range(DATA_LEN)])\n", + "y = np.array([random.random() * 100 for _ in range(DATA_LEN)])\n", + "z = np.array([random.random() * 100 for _ in range(DATA_LEN)])\n", + "\n", + "timeFunc(\"compute_features\", functions.computeFreqFeatures, magnitudes)\n", + "\n", + "timeFunc(\"calcAreaUnderAccelerationMagnitude\", functions.calcAreaUnderAccelerationMagnitude, magnitudes, time)\n", + "timeFunc(\"calcAverageVectorLength\", functions.calcAverageVectorLength, magnitudes)\n", + "timeFunc(\"calcAverageVectorLengthPower\", functions.calcAverageVectorLengthPower, magnitudes)\n", + "timeFunc(\"calcMeanKineticEnergy\", functions.calcMeanKineticEnergy, magnitudes, time)\n", + "timeFunc(\"calcPeaks\", functions.calcPeaks, magnitudes)\n", + "timeFunc(\"calcTotalKineticEnergy\", functions.calcTotalKineticEnergy, x, y, z, time)\n", + "timeFunc(\"calcAcAbsoluteArea\", functions.calcAcAbsoluteArea, x, y, z)\n", + "timeFunc(\"calcAbsoluteMean\", functions.calcAbsoluteMean, magnitudes)\n", + "timeFunc(\"calcAmplitude\", functions.calcAmplitude, magnitudes)\n", + "timeFunc(\"calcCoefficientOfVariation\", functions.calcCoefficientOfVariation, magnitudes)\n", + "timeFunc(\"calcCorrelation\", functions.calcCorrelation, magnitudes, magnitudes[::-1])\n", + "timeFunc(\"calcEntropy\", functions.calcEntropy, magnitudes)\n", + "timeFunc(\"calcInterQuartileRange\", functions.calcInterQuartileRange, magnitudes)\n", + "timeFunc(\"calcKurtosis\", functions.calcKurtosis, magnitudes)\n", + "timeFunc(\"calcMeanCrossingRate\", functions.calcMeanCrossingRate, magnitudes)\n", + "timeFunc(\"calcQuartiles\", functions.calcQuartiles, magnitudes)\n", + "timeFunc(\"calcSkewness\", functions.calcSkewness, magnitudes)\n", + "timeFunc(\"calcTotalAbsoluteArea\", functions.calcTotalAbsoluteArea, magnitudes)\n", + "timeFunc(\"calcTotalEnergy\", functions.calcTotalEnergy, magnitudes)\n", + "timeFunc(\"calcDominantFrequencyEnergy\", functions.calcDominantFrequencyEnergy, magnitudes)\n", + "timeFunc(\"calcTotalMagnitude\", functions.calcTotalMagnitude, x, y, z)\n", + "timeFunc(\"calcVariance\", functions.calcVariance, magnitudes)\n", + "timeFunc(\"calcArea\", functions.calcArea, magnitudes)\n", + "timeFunc(\"calcMean\", functions.calcMean, magnitudes)\n", + "timeFunc(\"calcPostureDistance\", functions.calcPostureDistance, functions.calcMean(x), functions.calcMean(y),\n", + " functions.calcMean(z))\n", + "timeFunc(\"calcTotalMean\", functions.calcTotalMean, magnitudes, magnitudes[::-1])\n", + "timeFunc(\"calcPeakCountBoza\", functions.calcPeakCount,\n", + " np.array([1.0, 4.0, 60.0, 4.5, 1.0, 4.0, 60.0, 4.5, 1.0, 4.0, 60.0, 4.5]))\n", + "timeFunc(\"calcSumPerComponent\", functions.calcSumPerComponent, magnitudes, time)\n", + "timeFunc(\"computeACVelocity\", functions.computeACVelocity, magnitudes, time)\n", + "timeFunc(\"lowPassFilter\", functions.lowPassFilter, magnitudes, 0.5)\n", + "timeFunc(\"bandPassFilter\", functions.bandPassFilter, magnitudes, 0.5, 0.7)\n", + "timeFunc(\"highPassFilter\", functions.highPassFilter, magnitudes, 0.5)\n", + "timeFunc(\"magnitudeVector\", functions.magnitudeVector, x, y, z)\n", + "timeFunc(\"covariance\", functions.covariance, magnitudes, magnitudes[::-1])\n", + "\n", + "timeFunc(\"calcRoll\", functions.calcRoll, magnitudes, magnitudes[::-1])\n", + "timeFunc(\"calcPitch\", functions.calcPitch, x, y, z)\n", + "timeFunc(\"stdDev\", functions.stdDev, magnitudes)\n", + "timeFunc(\"rollMotionAmount\", functions.rollMotionAmount, magnitudes)\n", + "timeFunc(\"rollMotionRegularity\", functions.rollMotionRegularity, magnitudes)\n", + "timeFunc(\"manipulation\", functions.manipulation, x, y, z, magnitudes, magnitudes[::-1])\n", + "\n", + "intArray = time\n", + "doubleArray = magnitudes\n", + "timeFunc(\"min\", functions.min, intArray)\n", + "timeFunc(\"min\", functions.min, doubleArray)\n", + "timeFunc(\"max\", functions.max, intArray)\n", + "timeFunc(\"max\", functions.max, doubleArray)\n", + "timeFunc(\"avg\", functions.avg, intArray)\n", + "timeFunc(\"avg\", functions.avg, doubleArray)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate EDA features with calculateFeatures" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Cannot change to a different GUI toolkit: widget. Using notebook instead.\n" + ] + } + ], + "source": [ + "import CalculatingFeatures.gsr\n", + "from eda_explorer.load_files import butter_lowpass_filter\n", + "from eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures\n", + "%matplotlib widget" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "pathToEDACsv = \"example_data/EDA.csv\"\n", + "\n", + "# get an array of values from EDA empatica file\n", + "eda_data, startTimeStamp_EDA, sampleRate_EDA = convert1DEmpaticaToArray(pathToEDACsv)\n", + "\n", + "windowLength_EDA = int(sampleRate_EDA*120)\n", + "\n", + "# Convert the HRV data into 2D array\n", + "eda_data_2D = convertInputInto2d(eda_data, windowLength_EDA)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "df_EDA = pd.DataFrame()\n", + "for row in eda_data_2D:\n", + " current_result = {}\n", + " current_result.update(CalculatingFeatures.gsr.extractGsrFeatures(row, sampleRate=int(sampleRate_EDA),featureNames=gsrFeatureNames))\n", + " df_EDA = df_EDA.append(current_result, ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In `extractGsrFeatures`, the EDA signal is first filtered using a Butterworth filter (`scipy.signal.butter`) from the SciPy library (`scipy.signal.lfilter`).\n", + "Its order ($N = 6$) and the cutoff frequency ($W_n = 1$ Hz) are hard-coded." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "no_interest = 131\n", + "current_row = eda_data_2D[no_interest,]\n", + "filtered_EDA = butter_lowpass_filter(current_row, 1.0, int(sampleRate_EDA), 6)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.'\n", + " );\n", + " }\n", + "};\n", + "\n", + "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = document.createElement('div');\n", + " titlebar.classList =\n", + " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", + " var titletext = document.createElement('div');\n", + " titletext.classList = 'ui-dialog-title';\n", + " titletext.setAttribute(\n", + " 'style',\n", + " 'width: 100%; text-align: center; padding: 3px;'\n", + " );\n", + " titlebar.appendChild(titletext);\n", + " this.root.appendChild(titlebar);\n", + " this.header = titletext;\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute(\n", + " 'style',\n", + " 'border: 1px solid #ddd;' +\n", + " 'box-sizing: content-box;' +\n", + " 'clear: both;' +\n", + " 'min-height: 1px;' +\n", + " 'min-width: 1px;' +\n", + " 'outline: 0;' +\n", + " 'overflow: hidden;' +\n", + " 'position: relative;' +\n", + " 'resize: both;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'keydown',\n", + " on_keyboard_event_closure('key_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'keyup',\n", + " on_keyboard_event_closure('key_release')\n", + " );\n", + "\n", + " this._canvas_extra_style(canvas_div);\n", + " this.root.appendChild(canvas_div);\n", + "\n", + " var canvas = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute('style', 'box-sizing: content-box;');\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\n", + " var backingStore =\n", + " this.context.backingStorePixelRatio ||\n", + " this.context.webkitBackingStorePixelRatio ||\n", + " this.context.mozBackingStorePixelRatio ||\n", + " this.context.msBackingStorePixelRatio ||\n", + " this.context.oBackingStorePixelRatio ||\n", + " this.context.backingStorePixelRatio ||\n", + " 1;\n", + "\n", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + " if (this.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n", + " }\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", + " );\n", + "\n", + " var resizeObserver = new ResizeObserver(function (entries) {\n", + " var nentries = entries.length;\n", + " for (var i = 0; i < nentries; i++) {\n", + " var entry = entries[i];\n", + " var width, height;\n", + " if (entry.contentBoxSize) {\n", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\n", + " width = entry.contentRect.width;\n", + " height = entry.contentRect.height;\n", + " }\n", + "\n", + " // Keep the size of the canvas and rubber band canvas in sync with\n", + " // the canvas container.\n", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'width: ' + width + 'px; height: ' + height + 'px;'\n", + " );\n", + "\n", + " rubberband_canvas.setAttribute('width', width);\n", + " rubberband_canvas.setAttribute('height', height);\n", + "\n", + " // And update the size in Python. We ignore the initial 0/0 size\n", + " // that occurs as the element is placed into the DOM, which should\n", + " // otherwise not happen due to the minimum size styling.\n", + " if (width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " resizeObserver.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband_canvas.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\n", + " this._resize_canvas = function (width, height, forward) {\n", + " if (forward) {\n", + " canvas_div.style.width = width + 'px';\n", + " canvas_div.style.height = height + 'px';\n", + " }\n", + " };\n", + "\n", + " // Disable right mouse context menu.\n", + " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " continue;\n", + " }\n", + "\n", + " var button = (fig.buttons[name] = document.createElement('button'));\n", + " button.classList = 'mpl-widget';\n", + " button.setAttribute('role', 'button');\n", + " button.setAttribute('aria-disabled', 'false');\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"

\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.one(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(current_row, color='blue')\n", + "plt.plot(filtered_EDA, color='red')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, the peaks are detected using the EDA Explorer.\n", + "Here, the parameters can be set in `extractGsrFeatures`, but their default values are also set.\n", + "\n", + "The explanations and illustrations of these parameters can be found at the [EDA Explorer FAQ](https://eda-explorer.media.mit.edu/info/), but in brief, they are:\n", + "* threshold: the minimum amplitude a potential skin conductance response (SCR) must reach in order to be counted as an SCR (in µS); \n", + "* offset: the number of seconds for which the *derivative must be positive before a peak* and the derivative must be negative after a peak; \n", + "* max rise time: allowed time in seconds from the \"start\" of the peak to the apex of a peak;\n", + "* max decay time: allowed time in seconds after the apex of a peak to the 50 % of maximum amplitude of the peak.\n", + "\n", + "Their default values are: `(threshold=.02, offset=1, riseTime=4, decayTime=4,)`" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "gsr_data = pd.DataFrame(current_row, columns=[\"EDA\"])\n", + "startTime = pd.to_datetime(0, unit=\"s\")\n", + "gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000/sampleRate_EDA) + 'L')\n", + "gsr_data['filtered_eda'] = filtered_EDA\n", + "peakData = calcPeakFeatures(gsr_data, offset=1, thresh=.02, start_WT=4, end_WT=4, sampleRate=int(sampleRate_EDA))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This function returns peaks (i.e., the time of the peaks) and their characteristics.\n", + "These include their start and end time (when the amplitude falls by 50 %), their amplitude, their duration (or width) and more.\n", + "\n", + "Some peaks are visualized below, along with their start (>) and end (■) times." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 46 66 81 107 159 293 313 328 376 397 452]\n", + "[ 17 30 58 73 101 152 280 304 321 365 393 444]\n", + "[ 58 73 89 112 168 304 318 334 386 400 460]\n" + ] + } + ], + "source": [ + "peaks = np.where(peakData.peaks == 1.0)[0]\n", + "peak_starts = np.where(peakData.peak_start == 1.0)[0]\n", + "peak_ends = np.where(peakData.peak_end == 1.0)[0]\n", + "print(peaks)\n", + "print(peak_starts)\n", + "print(peak_ends)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "/* global mpl */\n", + "window.mpl = {};\n", + "\n", + "mpl.get_websocket_type = function () {\n", + " if (typeof WebSocket !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof MozWebSocket !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert(\n", + " 'Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.'\n", + " );\n", + " }\n", + "};\n", + "\n", + "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = this.ws.binaryType !== undefined;\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById('mpl-warnings');\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent =\n", + " 'This browser does not support binary websocket messages. ' +\n", + " 'Performance may be slow.';\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = document.createElement('div');\n", + " this.root.setAttribute('style', 'display: inline-block');\n", + " this._root_extra_style(this.root);\n", + "\n", + " parent_element.appendChild(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message('supports_binary', { value: fig.supports_binary });\n", + " fig.send_message('send_image_mode', {});\n", + " if (fig.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", + " }\n", + " fig.send_message('refresh', {});\n", + " };\n", + "\n", + " this.imageObj.onload = function () {\n", + " if (fig.image_mode === 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function () {\n", + " fig.ws.close();\n", + " };\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "};\n", + "\n", + "mpl.figure.prototype._init_header = function () {\n", + " var titlebar = document.createElement('div');\n", + " titlebar.classList =\n", + " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", + " var titletext = document.createElement('div');\n", + " titletext.classList = 'ui-dialog-title';\n", + " titletext.setAttribute(\n", + " 'style',\n", + " 'width: 100%; text-align: center; padding: 3px;'\n", + " );\n", + " titlebar.appendChild(titletext);\n", + " this.root.appendChild(titlebar);\n", + " this.header = titletext;\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", + "\n", + "mpl.figure.prototype._init_canvas = function () {\n", + " var fig = this;\n", + "\n", + " var canvas_div = (this.canvas_div = document.createElement('div'));\n", + " canvas_div.setAttribute(\n", + " 'style',\n", + " 'border: 1px solid #ddd;' +\n", + " 'box-sizing: content-box;' +\n", + " 'clear: both;' +\n", + " 'min-height: 1px;' +\n", + " 'min-width: 1px;' +\n", + " 'outline: 0;' +\n", + " 'overflow: hidden;' +\n", + " 'position: relative;' +\n", + " 'resize: both;'\n", + " );\n", + "\n", + " function on_keyboard_event_closure(name) {\n", + " return function (event) {\n", + " return fig.key_event(event, name);\n", + " };\n", + " }\n", + "\n", + " canvas_div.addEventListener(\n", + " 'keydown',\n", + " on_keyboard_event_closure('key_press')\n", + " );\n", + " canvas_div.addEventListener(\n", + " 'keyup',\n", + " on_keyboard_event_closure('key_release')\n", + " );\n", + "\n", + " this._canvas_extra_style(canvas_div);\n", + " this.root.appendChild(canvas_div);\n", + "\n", + " var canvas = (this.canvas = document.createElement('canvas'));\n", + " canvas.classList.add('mpl-canvas');\n", + " canvas.setAttribute('style', 'box-sizing: content-box;');\n", + "\n", + " this.context = canvas.getContext('2d');\n", + "\n", + " var backingStore =\n", + " this.context.backingStorePixelRatio ||\n", + " this.context.webkitBackingStorePixelRatio ||\n", + " this.context.mozBackingStorePixelRatio ||\n", + " this.context.msBackingStorePixelRatio ||\n", + " this.context.oBackingStorePixelRatio ||\n", + " this.context.backingStorePixelRatio ||\n", + " 1;\n", + "\n", + " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + " if (this.ratio !== 1) {\n", + " fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n", + " }\n", + "\n", + " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", + " 'canvas'\n", + " ));\n", + " rubberband_canvas.setAttribute(\n", + " 'style',\n", + " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", + " );\n", + "\n", + " var resizeObserver = new ResizeObserver(function (entries) {\n", + " var nentries = entries.length;\n", + " for (var i = 0; i < nentries; i++) {\n", + " var entry = entries[i];\n", + " var width, height;\n", + " if (entry.contentBoxSize) {\n", + " if (entry.contentBoxSize instanceof Array) {\n", + " // Chrome 84 implements new version of spec.\n", + " width = entry.contentBoxSize[0].inlineSize;\n", + " height = entry.contentBoxSize[0].blockSize;\n", + " } else {\n", + " // Firefox implements old version of spec.\n", + " width = entry.contentBoxSize.inlineSize;\n", + " height = entry.contentBoxSize.blockSize;\n", + " }\n", + " } else {\n", + " // Chrome <84 implements even older version of spec.\n", + " width = entry.contentRect.width;\n", + " height = entry.contentRect.height;\n", + " }\n", + "\n", + " // Keep the size of the canvas and rubber band canvas in sync with\n", + " // the canvas container.\n", + " if (entry.devicePixelContentBoxSize) {\n", + " // Chrome 84 implements new version of spec.\n", + " canvas.setAttribute(\n", + " 'width',\n", + " entry.devicePixelContentBoxSize[0].inlineSize\n", + " );\n", + " canvas.setAttribute(\n", + " 'height',\n", + " entry.devicePixelContentBoxSize[0].blockSize\n", + " );\n", + " } else {\n", + " canvas.setAttribute('width', width * fig.ratio);\n", + " canvas.setAttribute('height', height * fig.ratio);\n", + " }\n", + " canvas.setAttribute(\n", + " 'style',\n", + " 'width: ' + width + 'px; height: ' + height + 'px;'\n", + " );\n", + "\n", + " rubberband_canvas.setAttribute('width', width);\n", + " rubberband_canvas.setAttribute('height', height);\n", + "\n", + " // And update the size in Python. We ignore the initial 0/0 size\n", + " // that occurs as the element is placed into the DOM, which should\n", + " // otherwise not happen due to the minimum size styling.\n", + " if (width != 0 && height != 0) {\n", + " fig.request_resize(width, height);\n", + " }\n", + " }\n", + " });\n", + " resizeObserver.observe(canvas_div);\n", + "\n", + " function on_mouse_event_closure(name) {\n", + " return function (event) {\n", + " return fig.mouse_event(event, name);\n", + " };\n", + " }\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mousedown',\n", + " on_mouse_event_closure('button_press')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseup',\n", + " on_mouse_event_closure('button_release')\n", + " );\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband_canvas.addEventListener(\n", + " 'mousemove',\n", + " on_mouse_event_closure('motion_notify')\n", + " );\n", + "\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseenter',\n", + " on_mouse_event_closure('figure_enter')\n", + " );\n", + " rubberband_canvas.addEventListener(\n", + " 'mouseleave',\n", + " on_mouse_event_closure('figure_leave')\n", + " );\n", + "\n", + " canvas_div.addEventListener('wheel', function (event) {\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " on_mouse_event_closure('scroll')(event);\n", + " });\n", + "\n", + " canvas_div.appendChild(canvas);\n", + " canvas_div.appendChild(rubberband_canvas);\n", + "\n", + " this.rubberband_context = rubberband_canvas.getContext('2d');\n", + " this.rubberband_context.strokeStyle = '#000000';\n", + "\n", + " this._resize_canvas = function (width, height, forward) {\n", + " if (forward) {\n", + " canvas_div.style.width = width + 'px';\n", + " canvas_div.style.height = height + 'px';\n", + " }\n", + " };\n", + "\n", + " // Disable right mouse context menu.\n", + " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", + " event.preventDefault();\n", + " return false;\n", + " });\n", + "\n", + " function set_focus() {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'mpl-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'mpl-button-group';\n", + " continue;\n", + " }\n", + "\n", + " var button = (fig.buttons[name] = document.createElement('button'));\n", + " button.classList = 'mpl-widget';\n", + " button.setAttribute('role', 'button');\n", + " button.setAttribute('aria-disabled', 'false');\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + "\n", + " var icon_img = document.createElement('img');\n", + " icon_img.src = '_images/' + image + '.png';\n", + " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", + " icon_img.alt = tooltip;\n", + " button.appendChild(icon_img);\n", + "\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " var fmt_picker = document.createElement('select');\n", + " fmt_picker.classList = 'mpl-widget';\n", + " toolbar.appendChild(fmt_picker);\n", + " this.format_dropdown = fmt_picker;\n", + "\n", + " for (var ind in mpl.extensions) {\n", + " var fmt = mpl.extensions[ind];\n", + " var option = document.createElement('option');\n", + " option.selected = fmt === mpl.default_extension;\n", + " option.innerHTML = fmt;\n", + " fmt_picker.appendChild(option);\n", + " }\n", + "\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "};\n", + "\n", + "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", + " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", + " // which will in turn request a refresh of the image.\n", + " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", + "};\n", + "\n", + "mpl.figure.prototype.send_message = function (type, properties) {\n", + " properties['type'] = type;\n", + " properties['figure_id'] = this.id;\n", + " this.ws.send(JSON.stringify(properties));\n", + "};\n", + "\n", + "mpl.figure.prototype.send_draw_message = function () {\n", + " if (!this.waiting) {\n", + " this.waiting = true;\n", + " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " var format_dropdown = fig.format_dropdown;\n", + " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", + " fig.ondownload(fig, format);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", + " var size = msg['size'];\n", + " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", + " fig._resize_canvas(size[0], size[1], msg['forward']);\n", + " fig.send_message('refresh', {});\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", + " var x0 = msg['x0'] / fig.ratio;\n", + " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", + " var x1 = msg['x1'] / fig.ratio;\n", + " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", + " x0 = Math.floor(x0) + 0.5;\n", + " y0 = Math.floor(y0) + 0.5;\n", + " x1 = Math.floor(x1) + 0.5;\n", + " y1 = Math.floor(y1) + 0.5;\n", + " var min_x = Math.min(x0, x1);\n", + " var min_y = Math.min(y0, y1);\n", + " var width = Math.abs(x1 - x0);\n", + " var height = Math.abs(y1 - y0);\n", + "\n", + " fig.rubberband_context.clearRect(\n", + " 0,\n", + " 0,\n", + " fig.canvas.width / fig.ratio,\n", + " fig.canvas.height / fig.ratio\n", + " );\n", + "\n", + " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", + " // Updates the figure title.\n", + " fig.header.textContent = msg['label'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", + " var cursor = msg['cursor'];\n", + " switch (cursor) {\n", + " case 0:\n", + " cursor = 'pointer';\n", + " break;\n", + " case 1:\n", + " cursor = 'default';\n", + " break;\n", + " case 2:\n", + " cursor = 'crosshair';\n", + " break;\n", + " case 3:\n", + " cursor = 'move';\n", + " break;\n", + " }\n", + " fig.rubberband_canvas.style.cursor = cursor;\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_message = function (fig, msg) {\n", + " fig.message.textContent = msg['message'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", + " // Request the server to send over a new figure.\n", + " fig.send_draw_message();\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", + " fig.image_mode = msg['mode'];\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", + " for (var key in msg) {\n", + " if (!(key in fig.buttons)) {\n", + " continue;\n", + " }\n", + " fig.buttons[key].disabled = !msg[key];\n", + " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", + " if (msg['mode'] === 'PAN') {\n", + " fig.buttons['Pan'].classList.add('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " } else if (msg['mode'] === 'ZOOM') {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.add('active');\n", + " } else {\n", + " fig.buttons['Pan'].classList.remove('active');\n", + " fig.buttons['Zoom'].classList.remove('active');\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Called whenever the canvas gets updated.\n", + " this.send_message('ack', {});\n", + "};\n", + "\n", + "// A function to construct a web socket function for onmessage handling.\n", + "// Called in the figure constructor.\n", + "mpl.figure.prototype._make_on_message_function = function (fig) {\n", + " return function socket_on_message(evt) {\n", + " if (evt.data instanceof Blob) {\n", + " /* FIXME: We get \"Resource interpreted as Image but\n", + " * transferred with MIME type text/plain:\" errors on\n", + " * Chrome. But how to set the MIME type? It doesn't seem\n", + " * to be part of the websocket stream */\n", + " evt.data.type = 'image/png';\n", + "\n", + " /* Free the memory for the previous frames */\n", + " if (fig.imageObj.src) {\n", + " (window.URL || window.webkitURL).revokeObjectURL(\n", + " fig.imageObj.src\n", + " );\n", + " }\n", + "\n", + " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", + " evt.data\n", + " );\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " } else if (\n", + " typeof evt.data === 'string' &&\n", + " evt.data.slice(0, 21) === 'data:image/png;base64'\n", + " ) {\n", + " fig.imageObj.src = evt.data;\n", + " fig.updated_canvas_event();\n", + " fig.waiting = false;\n", + " return;\n", + " }\n", + "\n", + " var msg = JSON.parse(evt.data);\n", + " var msg_type = msg['type'];\n", + "\n", + " // Call the \"handle_{type}\" callback, which takes\n", + " // the figure and JSON message as its only arguments.\n", + " try {\n", + " var callback = fig['handle_' + msg_type];\n", + " } catch (e) {\n", + " console.log(\n", + " \"No handler for the '\" + msg_type + \"' message type: \",\n", + " msg\n", + " );\n", + " return;\n", + " }\n", + "\n", + " if (callback) {\n", + " try {\n", + " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", + " callback(fig, msg);\n", + " } catch (e) {\n", + " console.log(\n", + " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", + " e,\n", + " e.stack,\n", + " msg\n", + " );\n", + " }\n", + " }\n", + " };\n", + "};\n", + "\n", + "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", + "mpl.findpos = function (e) {\n", + " //this section is from http://www.quirksmode.org/js/events_properties.html\n", + " var targ;\n", + " if (!e) {\n", + " e = window.event;\n", + " }\n", + " if (e.target) {\n", + " targ = e.target;\n", + " } else if (e.srcElement) {\n", + " targ = e.srcElement;\n", + " }\n", + " if (targ.nodeType === 3) {\n", + " // defeat Safari bug\n", + " targ = targ.parentNode;\n", + " }\n", + "\n", + " // pageX,Y are the mouse positions relative to the document\n", + " var boundingRect = targ.getBoundingClientRect();\n", + " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", + " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", + "\n", + " return { x: x, y: y };\n", + "};\n", + "\n", + "/*\n", + " * return a copy of an object with only non-object keys\n", + " * we need this to avoid circular references\n", + " * http://stackoverflow.com/a/24161582/3208463\n", + " */\n", + "function simpleKeys(original) {\n", + " return Object.keys(original).reduce(function (obj, key) {\n", + " if (typeof original[key] !== 'object') {\n", + " obj[key] = original[key];\n", + " }\n", + " return obj;\n", + " }, {});\n", + "}\n", + "\n", + "mpl.figure.prototype.mouse_event = function (event, name) {\n", + " var canvas_pos = mpl.findpos(event);\n", + "\n", + " if (name === 'button_press') {\n", + " this.canvas.focus();\n", + " this.canvas_div.focus();\n", + " }\n", + "\n", + " var x = canvas_pos.x * this.ratio;\n", + " var y = canvas_pos.y * this.ratio;\n", + "\n", + " this.send_message(name, {\n", + " x: x,\n", + " y: y,\n", + " button: event.button,\n", + " step: event.step,\n", + " guiEvent: simpleKeys(event),\n", + " });\n", + "\n", + " /* This prevents the web browser from automatically changing to\n", + " * the text insertion cursor when the button is pressed. We want\n", + " * to control all of the cursor setting manually through the\n", + " * 'cursor' event from matplotlib */\n", + " event.preventDefault();\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", + " // Handle any extra behaviour associated with a key event\n", + "};\n", + "\n", + "mpl.figure.prototype.key_event = function (event, name) {\n", + " // Prevent repeat events\n", + " if (name === 'key_press') {\n", + " if (event.which === this._key) {\n", + " return;\n", + " } else {\n", + " this._key = event.which;\n", + " }\n", + " }\n", + " if (name === 'key_release') {\n", + " this._key = null;\n", + " }\n", + "\n", + " var value = '';\n", + " if (event.ctrlKey && event.which !== 17) {\n", + " value += 'ctrl+';\n", + " }\n", + " if (event.altKey && event.which !== 18) {\n", + " value += 'alt+';\n", + " }\n", + " if (event.shiftKey && event.which !== 16) {\n", + " value += 'shift+';\n", + " }\n", + "\n", + " value += 'k';\n", + " value += event.which.toString();\n", + "\n", + " this._key_event_extra(event, name);\n", + "\n", + " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", + " return false;\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", + " if (name === 'download') {\n", + " this.handle_save(this, null);\n", + " } else {\n", + " this.send_message('toolbar_button', { name: name });\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", + " this.message.textContent = tooltip;\n", + "};\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "\n", + "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", + "\n", + "mpl.default_extension = \"png\";/* global mpl */\n", + "\n", + "var comm_websocket_adapter = function (comm) {\n", + " // Create a \"websocket\"-like object which calls the given IPython comm\n", + " // object with the appropriate methods. Currently this is a non binary\n", + " // socket, so there is still some room for performance tuning.\n", + " var ws = {};\n", + "\n", + " ws.close = function () {\n", + " comm.close();\n", + " };\n", + " ws.send = function (m) {\n", + " //console.log('sending', m);\n", + " comm.send(m);\n", + " };\n", + " // Register the callback with on_msg.\n", + " comm.on_msg(function (msg) {\n", + " //console.log('receiving', msg['content']['data'], msg);\n", + " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", + " ws.onmessage(msg['content']['data']);\n", + " });\n", + " return ws;\n", + "};\n", + "\n", + "mpl.mpl_figure_comm = function (comm, msg) {\n", + " // This is the function which gets called when the mpl process\n", + " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", + "\n", + " var id = msg.content.data.id;\n", + " // Get hold of the div created by the display call when the Comm\n", + " // socket was opened in Python.\n", + " var element = document.getElementById(id);\n", + " var ws_proxy = comm_websocket_adapter(comm);\n", + "\n", + " function ondownload(figure, _format) {\n", + " window.open(figure.canvas.toDataURL());\n", + " }\n", + "\n", + " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", + "\n", + " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", + " // web socket which is closed, not our websocket->open comm proxy.\n", + " ws_proxy.onopen();\n", + "\n", + " fig.parent_element = element;\n", + " fig.cell_info = mpl.find_output_cell(\"
\");\n", + " if (!fig.cell_info) {\n", + " console.error('Failed to find cell for figure', id, fig);\n", + " return;\n", + " }\n", + " fig.cell_info[0].output_area.element.one(\n", + " 'cleared',\n", + " { fig: fig },\n", + " fig._remove_fig_handler\n", + " );\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_close = function (fig, msg) {\n", + " var width = fig.canvas.width / fig.ratio;\n", + " fig.cell_info[0].output_area.element.off(\n", + " 'cleared',\n", + " fig._remove_fig_handler\n", + " );\n", + "\n", + " // Update the output cell to use the data from the current canvas.\n", + " fig.push_to_output();\n", + " var dataURL = fig.canvas.toDataURL();\n", + " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", + " // the notebook keyboard shortcuts fail.\n", + " IPython.keyboard_manager.enable();\n", + " fig.parent_element.innerHTML =\n", + " '';\n", + " fig.close_ws(fig, msg);\n", + "};\n", + "\n", + "mpl.figure.prototype.close_ws = function (fig, msg) {\n", + " fig.send_message('closing', msg);\n", + " // fig.ws.close()\n", + "};\n", + "\n", + "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", + " // Turn the data on the canvas into data in the output cell.\n", + " var width = this.canvas.width / this.ratio;\n", + " var dataURL = this.canvas.toDataURL();\n", + " this.cell_info[1]['text/html'] =\n", + " '';\n", + "};\n", + "\n", + "mpl.figure.prototype.updated_canvas_event = function () {\n", + " // Tell IPython that the notebook contents must change.\n", + " IPython.notebook.set_dirty(true);\n", + " this.send_message('ack', {});\n", + " var fig = this;\n", + " // Wait a second, then push the new image to the DOM so\n", + " // that it is saved nicely (might be nice to debounce this).\n", + " setTimeout(function () {\n", + " fig.push_to_output();\n", + " }, 1000);\n", + "};\n", + "\n", + "mpl.figure.prototype._init_toolbar = function () {\n", + " var fig = this;\n", + "\n", + " var toolbar = document.createElement('div');\n", + " toolbar.classList = 'btn-toolbar';\n", + " this.root.appendChild(toolbar);\n", + "\n", + " function on_click_closure(name) {\n", + " return function (_event) {\n", + " return fig.toolbar_button_onclick(name);\n", + " };\n", + " }\n", + "\n", + " function on_mouseover_closure(tooltip) {\n", + " return function (event) {\n", + " if (!event.currentTarget.disabled) {\n", + " return fig.toolbar_button_onmouseover(tooltip);\n", + " }\n", + " };\n", + " }\n", + "\n", + " fig.buttons = {};\n", + " var buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " var button;\n", + " for (var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " /* Instead of a spacer, we start a new button group. */\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + " buttonGroup = document.createElement('div');\n", + " buttonGroup.classList = 'btn-group';\n", + " continue;\n", + " }\n", + "\n", + " button = fig.buttons[name] = document.createElement('button');\n", + " button.classList = 'btn btn-default';\n", + " button.href = '#';\n", + " button.title = name;\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', on_click_closure(method_name));\n", + " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", + " buttonGroup.appendChild(button);\n", + " }\n", + "\n", + " if (buttonGroup.hasChildNodes()) {\n", + " toolbar.appendChild(buttonGroup);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = document.createElement('span');\n", + " status_bar.classList = 'mpl-message pull-right';\n", + " toolbar.appendChild(status_bar);\n", + " this.message = status_bar;\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = document.createElement('div');\n", + " buttongrp.classList = 'btn-group inline pull-right';\n", + " button = document.createElement('button');\n", + " button.classList = 'btn btn-mini btn-primary';\n", + " button.href = '#';\n", + " button.title = 'Stop Interaction';\n", + " button.innerHTML = '';\n", + " button.addEventListener('click', function (_evt) {\n", + " fig.handle_close(fig, {});\n", + " });\n", + " button.addEventListener(\n", + " 'mouseover',\n", + " on_mouseover_closure('Stop Interaction')\n", + " );\n", + " buttongrp.appendChild(button);\n", + " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", + " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", + "};\n", + "\n", + "mpl.figure.prototype._remove_fig_handler = function (event) {\n", + " var fig = event.data.fig;\n", + " fig.close_ws(fig, {});\n", + "};\n", + "\n", + "mpl.figure.prototype._root_extra_style = function (el) {\n", + " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", + "};\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function (el) {\n", + " // this is important to make the div 'focusable\n", + " el.setAttribute('tabindex', 0);\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " } else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager) {\n", + " manager = IPython.keyboard_manager;\n", + " }\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which === 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "};\n", + "\n", + "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", + " fig.ondownload(fig, null);\n", + "};\n", + "\n", + "mpl.find_output_cell = function (html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i = 0; i < ncells; i++) {\n", + " var cell = cells[i];\n", + " if (cell.cell_type === 'code') {\n", + " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", + " var data = cell.output_area.outputs[j];\n", + " if (data.data) {\n", + " // IPython >= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] === html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "};\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel !== null) {\n", + " IPython.notebook.kernel.comm_manager.register_target(\n", + " 'matplotlib',\n", + " mpl.mpl_figure_comm\n", + " );\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.plot(filtered_EDA, color='red')\n", + "plt.scatter(peaks, filtered_EDA[peaks], color=\"green\")\n", + "plt.scatter(peak_starts, filtered_EDA[peak_starts], color=\"green\", marker=\">\", alpha=0.5)\n", + "plt.scatter(peak_ends, filtered_EDA[peak_ends], color=\"green\", marker=\"s\", alpha=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Features, related to the peaks, are then calculated.\n", + "These include the number and rate (peaks per second) of the peaks, as well as the average and maximum of their characteristics (duration, increase and decrease time etc.)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "avgPeakAmplitudeChangeAfter 0.564829\n", + "avgPeakAmplitudeChangeBefore 0.000000\n", + "avgPeakChangeRatio 0.096774\n", + "avgPeakDecreaseTime 2.818182\n", + "avgPeakDuration 49.454545\n", + "avgPeakIncreaseTime 0.272727\n", + "maxPeakAmplitudeChangeAfter 1.660329\n", + "maxPeakAmplitudeChangeBefore 1.400329\n", + "maxPeakChangeRatio 0.259259\n", + "maxPeakDecreaseTime 6.750000\n", + "maxPeakDuration 8.500000\n", + "maxPeakIncreaseTime 1.750000\n", + "maxPeakResponseSlopeAfter -0.065041\n", + "maxPeakResponseSlopeBefore 0.233815\n", + "numPeaks 11.000000\n", + "powerPeaks 3.643448\n", + "ratePeaks 0.091667\n", + "Name: 131, dtype: float64\n" + ] + } + ], + "source": [ + "print(df_EDA.loc[no_interest, df_EDA.columns.str.contains('Peak')])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 239/239 [01:04<00:00, 3.69it/s]\n" + ] + } + ], + "source": [ + "calculatedFeatures_EDA = calculateFeatures(eda_data_2D, fs=int(sampleRate_EDA), featureNames=gsrFeatureNames)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/environment.yml b/environment.yml index 98fe3c80..1bdd2760 100644 --- a/environment.yml +++ b/environment.yml @@ -3,114 +3,164 @@ channels: - conda-forge - defaults dependencies: + - _libgcc_mutex=0.1 + - _openmp_mutex=4.5 - _py-xgboost-mutex=2.0 - - appdirs=1.4.* + - appdirs=1.4.4 - arrow=0.16.0 - - asn1crypto=1.4.* - - astropy=4.2.* - - attrs=20.3.* - - binaryornot=0.4.* + - asn1crypto=1.4.0 + - astropy=4.2.1 + - attrs=20.3.0 + - binaryornot=0.4.4 - blas=1.0 - - brotlipy=0.7.* - - bzip2=1.0.* - - ca-certificates - - certifi + - brotli=1.0.9 + - brotlipy=0.7.0 + - bzip2=1.0.8 + - ca-certificates=2022.2.1 + - certifi=2021.10.8 - cffi=1.14.4 - - chardet=3.0.* - - click=7.1.* - - cookiecutter=1.6.* - - cryptography=3.3.* - - datrie=0.8.* + - chardet=3.0.4 + - click=7.1.2 + - colorama=0.4.4 + - cookiecutter=1.6.0 + - cryptography=3.3.1 + - cycler=0.11.0 + - datrie=0.8.2 + - dbus=1.13.18 - docutils=0.16 + - expat=2.4.4 + - fontconfig=2.13.1 + - fonttools=4.25.0 + - freetype=2.11.0 - future=0.18.2 - - gitdb=4.0.* - - gitdb2=4.0.* - - gitpython=3.1.* + - giflib=5.2.1 + - gitdb=4.0.5 + - gitdb2=4.0.2 + - gitpython=3.1.11 + - glib=2.69.1 + - gst-plugins-base=1.14.0 + - gstreamer=1.14.0 + - icu=58.2 - idna=2.10 - - imbalanced-learn=0.6.* - - importlib-metadata=2.0.* - - importlib_metadata=2.0.* + - imbalanced-learn=0.6.2 + - importlib-metadata=2.0.0 + - importlib_metadata=2.0.0 - intel-openmp=2019.4 - jinja2=2.11.2 - - jinja2-time=0.2.* - - joblib=1.0.* - - jsonschema=3.2.* - - libblas=3.8.* - - libcblas=3.8.* - - libcxx=10.0.* - - libedit=3.1.* + - jinja2-time=0.2.0 + - joblib=1.0.0 + - jpeg=9d + - jsonschema=3.2.0 + - kiwisolver=1.3.2 + - lcms2=2.12 + - ld_impl_linux-64=2.36.1 + - libblas=3.8.0 + - libcblas=3.8.0 + - libcxx=10.0.0 + - libcxxabi=10.0.0 + - libedit=3.1.20191231 - libffi=3.3 + - libgcc-ng=11.2.0 - libgfortran - - liblapack=3.8.* - - libopenblas=0.3.* + - libgfortran + - libgfortran + - liblapack=3.8.0 + - libopenblas=0.3.10 + - libpng=1.6.37 + - libstdcxx-ng=11.2.0 + - libtiff=4.2.0 + - libuuid=1.0.3 + - libwebp=1.2.2 + - libwebp-base=1.2.2 + - libxcb=1.14 - libxgboost=0.90 - - lightgbm=3.1.* - - llvm-openmp=10.0.* - - markupsafe=1.1.* + - libxml2=2.9.12 + - libzlib=1.2.11 + - lightgbm=3.1.1 + - llvm-openmp=10.0.0 + - lz4-c=1.9.3 + - markupsafe=1.1.1 + - matplotlib=3.5.1 + - matplotlib-base=3.5.1 - mkl - - mkl-service=2.3.* - - mkl_fft=1.2.* - - mkl_random=1.1.* - - more-itertools=8.6.* + - mkl-service=2.3.0 + - mkl_fft=1.2.0 + - mkl_random=1.1.1 + - more-itertools=8.6.0 + - munkres=1.1.4 - ncurses=6.2 - numpy=1.19.2 - numpy-base=1.19.2 - - openblas=0.3.* - - openssl - - pandas=1.1.* - - pbr=5.5.* - - pip=20.3.* + - openblas=0.3.4 + - openssl=1.1.1m + - packaging=21.3 + - pandas=1.1.5 + - pbr=5.5.1 + - pcre=8.45 + - pillow=9.0.1 + - pip=20.3.3 - plotly=4.14.1 - - poyo=0.5.* - - psutil=5.7.* - - psycopg2 + - poyo=0.5.0 + - psutil=5.7.2 - py-xgboost=0.90 - pycparser=2.20 - - pyerfa=1.7.* - - pyopenssl=20.0.* - - pyprojroot - - pysocks=1.7.* - - python=3.7.* - - python-dateutil=2.8.* - - python-dotenv + - pyerfa=1.7.1.1 + - pyopenssl=20.0.1 + - pyqt=5.9.2 + - pysocks=1.7.1 + - python=3.7.9 + - python-dateutil=2.8.1 - python_abi=3.7 - pytz=2020.4 - - pyyaml=5.3.* + - pyyaml=5.3.1 + - qt=5.9.7 - readline=8.0 - requests=2.25.0 - - retrying=1.3.* + - retrying=1.3.3 - scikit-learn=0.23.2 - - scipy=1.5.* - - setuptools=51.0.* + - scipy=1.5.2 + - setuptools=51.0.0 + - sip=4.19.8 - six=1.15.0 - - smmap=3.0.* - - smmap2=3.0.* - - sqlalchemy + - smmap=3.0.4 + - smmap2=3.0.1 - sqlite=3.33.0 - - threadpoolctl=2.1.* - - tk=8.6.* + - threadpoolctl=2.1.0 + - tk=8.6.10 + - tornado=6.1 - tqdm=4.62.0 - urllib3=1.25.11 - wheel=0.36.2 - - whichcraft=0.6.* + - whichcraft=0.6.1 - wrapt=1.12.1 - xgboost=0.90 - - xz=5.2.* - - yaml=0.2.* - - zipp=3.4.* - - zlib=1.2.* + - xz=5.2.5 + - yaml=0.2.5 + - zipp=3.4.0 + - zlib=1.2.11 + - zstd=1.4.9 - pip: - - amply==0.1.* + - amply==0.1.4 + - bidict==0.21.4 + - biosppy==0.8.0 + - build==0.7.0 + - cached-property==1.5.2 - configargparse==0.15.1 - - decorator==4.4.* - - ipython-genutils==0.2.* - - jupyter-core==4.6.* - - nbformat==5.0.* + - decorator==4.4.2 + - h5py==3.6.0 + - ipython-genutils==0.2.0 + - jupyter-core==4.6.3 + - nbformat==5.0.7 + - opencv-python==4.5.5.64 + - peakutils==1.3.3 + - pep517==0.12.0 - pulp==2.4 - - pyparsing==2.4.* + - pyparsing==2.4.7 - pyrsistent==0.15.5 - - ratelimiter==1.2.* + - ratelimiter==1.2.0.post0 + - shortuuid==1.0.8 - snakemake==5.30.2 + - tomli==2.0.1 - toposort==1.5 - - traitlets==4.3.* -prefix: /usr/local/Caskroom/miniconda/base/envs/rapids202108 + - traitlets==4.3.3 +prefix: /opt/conda/envs/rapids From a80f7c0cc418a8b261f08de77323f01418165a30 Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 21 Mar 2022 10:38:15 +0000 Subject: [PATCH 06/57] Change of the relative import statements. --- .../calculate_acc_gyro_common_features.py | 2 +- .../CalculatingFeatures/calculate_features.py | 8 ++++---- .../CalculatingFeatures/feature_functions.py | 2 +- calculatingfeatures/CalculatingFeatures/gsr.py | 6 +++--- calculatingfeatures/CalculatingFeatures/hrv.py | 4 ++-- 5 files changed, 11 insertions(+), 11 deletions(-) diff --git a/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py b/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py index 796cf25d..e5bcee35 100644 --- a/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py +++ b/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py @@ -1,4 +1,4 @@ -from calculatingfeatures.CalculatingFeatures import feature_functions as f +from CalculatingFeatures import feature_functions as f import numpy as np diff --git a/calculatingfeatures/CalculatingFeatures/calculate_features.py b/calculatingfeatures/CalculatingFeatures/calculate_features.py index 975441aa..b22eeb07 100644 --- a/calculatingfeatures/CalculatingFeatures/calculate_features.py +++ b/calculatingfeatures/CalculatingFeatures/calculate_features.py @@ -1,8 +1,8 @@ from tqdm import tqdm -from calculatingfeatures.CalculatingFeatures.helper_functions import * -import calculatingfeatures.CalculatingFeatures.gsr as gsr -import calculatingfeatures.CalculatingFeatures.hrv as hrv -from calculatingfeatures.CalculatingFeatures.calculate_acc_gyro_common_features import * +from CalculatingFeatures.helper_functions import * +import CalculatingFeatures.gsr as gsr +import CalculatingFeatures.hrv as hrv +from CalculatingFeatures.calculate_acc_gyro_common_features import * ALPHA_LOW = 0.3 ALPHA_BAND_LOW = 0.3 diff --git a/calculatingfeatures/CalculatingFeatures/feature_functions.py b/calculatingfeatures/CalculatingFeatures/feature_functions.py index e336703d..4ca108ad 100644 --- a/calculatingfeatures/CalculatingFeatures/feature_functions.py +++ b/calculatingfeatures/CalculatingFeatures/feature_functions.py @@ -3,7 +3,7 @@ from scipy.signal import welch from scipy.stats import entropy, skew, kurtosis, iqr, linregress import pandas as pd import itertools -from calculatingfeatures.CalculatingFeatures.helper_functions import checkForFeature +from CalculatingFeatures.helper_functions import checkForFeature def computeFreqFeatures(data, featureNames=None, Fs=50): diff --git a/calculatingfeatures/CalculatingFeatures/gsr.py b/calculatingfeatures/CalculatingFeatures/gsr.py index 54b87c37..92174b66 100644 --- a/calculatingfeatures/CalculatingFeatures/gsr.py +++ b/calculatingfeatures/CalculatingFeatures/gsr.py @@ -1,11 +1,11 @@ -from calculatingfeatures.eda_explorer.load_files import butter_lowpass_filter -from calculatingfeatures.eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures +from eda_explorer.load_files import butter_lowpass_filter +from eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures import numpy as np import peakutils import matplotlib.pyplot as plt import scipy.signal as signal import biosppy.signals.tools as st -from calculatingfeatures.CalculatingFeatures.helper_functions import checkForFeature +from CalculatingFeatures.helper_functions import checkForFeature def extractGsrFeatures(signal, startTimestampSeconds=0, sampleRate=4, threshold=.02, offset=1, riseTime=4, decayTime=4, diff --git a/calculatingfeatures/CalculatingFeatures/hrv.py b/calculatingfeatures/CalculatingFeatures/hrv.py index af807da8..080a08fe 100644 --- a/calculatingfeatures/CalculatingFeatures/hrv.py +++ b/calculatingfeatures/CalculatingFeatures/hrv.py @@ -2,8 +2,8 @@ import pandas as pd import numpy as np import scipy.signal as _signal -from calculatingfeatures.CalculatingFeatures.helper_functions import checkForFeature, hrvFeatureNames -from calculatingfeatures.CalculatingFeatures.calculate_hrv_peaks import peak_detector +from CalculatingFeatures.helper_functions import checkForFeature, hrvFeatureNames +from CalculatingFeatures.calculate_hrv_peaks import peak_detector def extractHrvFeatures(_sample, ma=False, detrend=False, m_deternd=False, low_pass=False, winsorize=False, From d1c59de2e9663434586880392dc835c070884396 Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 21 Mar 2022 10:40:18 +0000 Subject: [PATCH 07/57] Add folder structure for CF testing and EDA test. --- __init__.py | 0 calculatingfeatures/.gitignore | 3 + calculatingfeatures/cf_tests/__init__.py | 0 .../cf_tests/calculate_EDA_features_test.py | 63 +++++++++++++++++++ .../cf_tests/calculate_HR_features_test.py | 10 ++- 5 files changed, 73 insertions(+), 3 deletions(-) create mode 100644 __init__.py create mode 100644 calculatingfeatures/cf_tests/__init__.py create mode 100644 calculatingfeatures/cf_tests/calculate_EDA_features_test.py rename calculate_HR_features_test.py => calculatingfeatures/cf_tests/calculate_HR_features_test.py (65%) diff --git a/__init__.py b/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/calculatingfeatures/.gitignore b/calculatingfeatures/.gitignore index 779580fa..fd077a7a 100644 --- a/calculatingfeatures/.gitignore +++ b/calculatingfeatures/.gitignore @@ -219,3 +219,6 @@ fabric.properties # Android studio 3.1+ serialized cache file .idea/caches/build_file_checksums.ser +# Output images +cf_tests/output_images/ + diff --git a/calculatingfeatures/cf_tests/__init__.py b/calculatingfeatures/cf_tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/calculatingfeatures/cf_tests/calculate_EDA_features_test.py b/calculatingfeatures/cf_tests/calculate_EDA_features_test.py new file mode 100644 index 00000000..1ad52838 --- /dev/null +++ b/calculatingfeatures/cf_tests/calculate_EDA_features_test.py @@ -0,0 +1,63 @@ +import sys +sys.path.append("..") +from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames +from CalculatingFeatures.calculate_features import calculateFeatures +from CalculatingFeatures import gsr +from eda_explorer.load_files import butter_lowpass_filter +from eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + + +pathToEDACsv = "../example_data/EDA.csv" + +# get an array of values from EDA empatica file +eda_data, startTimeStamp_EDA, sampleRate_EDA = convert1DEmpaticaToArray(pathToEDACsv) + +windowLength_EDA = int(sampleRate_EDA*120) + +# Convert the HRV data into 2D array +eda_data_2D = convertInputInto2d(eda_data, windowLength_EDA) + +df_EDA = pd.DataFrame() +for row in eda_data_2D: + current_result = {} + current_result.update(gsr.extractGsrFeatures(row, sampleRate=int(sampleRate_EDA),featureNames=gsrFeatureNames)) + df_EDA = df_EDA.append(current_result, ignore_index=True) + +no_interest = 131 +current_row = eda_data_2D[no_interest,] +filtered_EDA = butter_lowpass_filter(current_row, 1.0, int(sampleRate_EDA), 6) + +plt.figure() +plt.plot(current_row, color='blue') +plt.plot(filtered_EDA, color='red') +plt.savefig('output_images/EDA_exa1.png') + +gsr_data = pd.DataFrame(current_row, columns=["EDA"]) +startTime = pd.to_datetime(0, unit="s") +gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000/sampleRate_EDA) + 'L') +gsr_data['filtered_eda'] = filtered_EDA +peakData = calcPeakFeatures(gsr_data, offset=1, thresh=.02, start_WT=4, end_WT=4, sampleRate=int(sampleRate_EDA)) + +peaks = np.where(peakData.peaks == 1.0)[0] +peak_starts = np.where(peakData.peak_start == 1.0)[0] +peak_ends = np.where(peakData.peak_end == 1.0)[0] +print(peaks) +print(peak_starts) +print(peak_ends) + +plt.figure() +plt.plot(filtered_EDA, color='red') +plt.scatter(peaks, filtered_EDA[peaks], color="green") +plt.scatter(peak_starts, filtered_EDA[peak_starts], color="green", marker=">", alpha=0.5) +plt.scatter(peak_ends, filtered_EDA[peak_ends], color="green", marker="s", alpha=0.5) +plt.savefig('output_images/EDA_exa2.png') + +print(df_EDA.loc[no_interest, df_EDA.columns.str.contains('Peak')]) + +# calculatedFeatures_EDA = calculateFeatures(eda_data_2D, fs=int(sampleRate_EDA), featureNames=gsrFeatureNames) +# print(calculatedFeatures_EDA) + + diff --git a/calculate_HR_features_test.py b/calculatingfeatures/cf_tests/calculate_HR_features_test.py similarity index 65% rename from calculate_HR_features_test.py rename to calculatingfeatures/cf_tests/calculate_HR_features_test.py index 5fd91bce..17ac815c 100644 --- a/calculate_HR_features_test.py +++ b/calculatingfeatures/cf_tests/calculate_HR_features_test.py @@ -1,8 +1,12 @@ -from calculatingfeatures.CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, frequencyFeatureNames, hrvFeatureNames -from calculatingfeatures.CalculatingFeatures.calculate_features import calculateFeatures +import sys +sys.path.append("..") +from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, frequencyFeatureNames, hrvFeatureNames +from CalculatingFeatures.calculate_features import calculateFeatures import pandas as pd -pathToHrvCsv = "calculatingfeatures/example_data/S2_E4_Data/BVP.csv" + + +pathToHrvCsv = "../example_data/S2_E4_Data/BVP.csv" windowLength = 500 # get an array of values from HRV empatica file From bd5a81125627cae9e2074a724ff84126c0539e48 Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 21 Mar 2022 12:00:57 +0000 Subject: [PATCH 08/57] Shortening input CSVs' and added test for ACC. --- .../cf_tests/calculate_ACC_features_test.py | 32 +++++++++++++++++++ .../cf_tests/calculate_EDA_features_test.py | 4 +-- .../cf_tests/calculate_HR_features_test.py | 1 + .../cf_tests/calculate_TEMP_features_test.py | 1 + 4 files changed, 36 insertions(+), 2 deletions(-) create mode 100644 calculatingfeatures/cf_tests/calculate_ACC_features_test.py create mode 100644 calculatingfeatures/cf_tests/calculate_TEMP_features_test.py diff --git a/calculatingfeatures/cf_tests/calculate_ACC_features_test.py b/calculatingfeatures/cf_tests/calculate_ACC_features_test.py new file mode 100644 index 00000000..fcd62181 --- /dev/null +++ b/calculatingfeatures/cf_tests/calculate_ACC_features_test.py @@ -0,0 +1,32 @@ +import sys +sys.path.append("..") +from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames, frequencyFeatureNames +from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray +from CalculatingFeatures.calculate_features import calculateFeatures + +import pandas as pd + +pathToAccCsv = "../example_data/S2_E4_Data_shortened/ACC.csv" +windowLength = 500 + +#np.seterr(all='raise') + +# get an array of values from ACC empatica file +acc_data, startTimeStamp, sampleRate = convert3DEmpaticaToArray(pathToAccCsv) +acc_data = acc_data[:, :int(300000//sampleRate)] + +# Convert the ACC data into 2D array +x_2D = convertInputInto2d(acc_data[0], windowLength) +y_2D = convertInputInto2d(acc_data[1], windowLength) +z_2D = convertInputInto2d(acc_data[2], windowLength) + +# Create a list with feature names +featureNames = [] +featureNames.extend(accelerometerFeatureNames) +featureNames.extend(frequencyFeatureNames) + +pd.set_option('display.max_columns', None) + +# Calculate features +calculatedFeatures = calculateFeatures(x_2D, y_2D, z_2D, fs=int(sampleRate), featureNames=featureNames) +print(calculatedFeatures) \ No newline at end of file diff --git a/calculatingfeatures/cf_tests/calculate_EDA_features_test.py b/calculatingfeatures/cf_tests/calculate_EDA_features_test.py index 1ad52838..7a1ba32c 100644 --- a/calculatingfeatures/cf_tests/calculate_EDA_features_test.py +++ b/calculatingfeatures/cf_tests/calculate_EDA_features_test.py @@ -57,7 +57,7 @@ plt.savefig('output_images/EDA_exa2.png') print(df_EDA.loc[no_interest, df_EDA.columns.str.contains('Peak')]) -# calculatedFeatures_EDA = calculateFeatures(eda_data_2D, fs=int(sampleRate_EDA), featureNames=gsrFeatureNames) -# print(calculatedFeatures_EDA) +calculatedFeatures_EDA = calculateFeatures(eda_data_2D, fs=int(sampleRate_EDA), featureNames=gsrFeatureNames) +print(calculatedFeatures_EDA) diff --git a/calculatingfeatures/cf_tests/calculate_HR_features_test.py b/calculatingfeatures/cf_tests/calculate_HR_features_test.py index 17ac815c..2b4b5134 100644 --- a/calculatingfeatures/cf_tests/calculate_HR_features_test.py +++ b/calculatingfeatures/cf_tests/calculate_HR_features_test.py @@ -11,6 +11,7 @@ windowLength = 500 # get an array of values from HRV empatica file hrv_data, startTimeStamp, sampleRate = convert1DEmpaticaToArray(pathToHrvCsv) +hrv_data = hrv_data[:int(300000//sampleRate)] # Convert the HRV data into 2D array hrv_data_2D = convertInputInto2d(hrv_data, windowLength) diff --git a/calculatingfeatures/cf_tests/calculate_TEMP_features_test.py b/calculatingfeatures/cf_tests/calculate_TEMP_features_test.py new file mode 100644 index 00000000..3fc1378e --- /dev/null +++ b/calculatingfeatures/cf_tests/calculate_TEMP_features_test.py @@ -0,0 +1 @@ +# Temparature features do not exist - should be used from the RAPIDS defaults? From 2da0911d4c5bf872de67df75e319dcc27c8f9f13 Mon Sep 17 00:00:00 2001 From: Primoz Date: Tue, 22 Mar 2022 12:48:43 +0000 Subject: [PATCH 09/57] Skeleton file main.py for EDA CalcFt. integration. --- config.yaml | 18 +++-- .../cf/main.py | 76 +++++++++++++++++++ 2 files changed, 87 insertions(+), 7 deletions(-) create mode 100644 src/features/empatica_electrodermal_activity/cf/main.py diff --git a/config.yaml b/config.yaml index d82b2f8e..67aeb299 100644 --- a/config.yaml +++ b/config.yaml @@ -180,7 +180,7 @@ PHONE_CALLS: CONTAINER: calls.csv PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES_TYPE: EPISODES # EVENTS or EPISODES CALL_TYPES: [missed, incoming, outgoing] FEATURES: @@ -477,7 +477,7 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py @@ -486,7 +486,7 @@ EMPATICA_HEARTRATE: CONTAINER: HR PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py @@ -495,7 +495,7 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py @@ -504,16 +504,20 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py + CF: + COMPUTE: True + FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] + SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py @@ -522,7 +526,7 @@ EMPATICA_INTER_BEAT_INTERVAL: CONTAINER: IBI PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cf/main.py new file mode 100644 index 00000000..524ebd99 --- /dev/null +++ b/src/features/empatica_electrodermal_activity/cf/main.py @@ -0,0 +1,76 @@ +import pandas as pd +from scipy.stats import entropy + + +def statsFeatures(eda_data, features, eda_features): + col_name = "electrodermal_activity" + if "sumeda" in features: + eda_features["sumeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].sum() + if "maxeda" in features: + eda_features["maxeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].max() + if "mineda" in features: + eda_features["mineda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].min() + if "avgeda" in features: + eda_features["avgeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].mean() + if "medianeda" in features: + eda_features["medianeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].median() + if "modeeda" in features: + eda_features["modeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "stdeda" in features: + eda_features["stdeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].std() + if "diffmaxmodeeda" in features: + eda_features["diffmaxmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].max() - \ + eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "diffminmodeeda" in features: + eda_features["diffminmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].agg(lambda x: pd.Series.mode(x)[0]) - \ + eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].min() + if "entropyeda" in features: + eda_features["entropyeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ + col_name].agg(entropy) + + return eda_features + + +def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment): + eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features) + if not eda_intraday_data.empty: + eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment) + + if not eda_intraday_data.empty: + eda_intraday_features = pd.DataFrame() + + # get stats of eda + eda_intraday_features = statsFeatures(eda_intraday_data, features, eda_intraday_features) + + eda_intraday_features.reset_index(inplace=True) + + return eda_intraday_features + + +def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_intraday_features = provider["FEATURES"] + # name of the features this function can compute + base_intraday_features_names = ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", + "diffminmodeeda", "entropyeda"] + # the subset of requested features this function can compute + intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) + + # extract features from intraday data + eda_intraday_features = extractEDAFeaturesFromIntradayData(eda_intraday_data, + intraday_features_to_compute, time_segment, + filter_data_by_segment) + + return eda_intraday_features \ No newline at end of file From d3a3f01f2953cf0c260722327d66ca9af3355a37 Mon Sep 17 00:00:00 2001 From: Primoz Date: Tue, 22 Mar 2022 15:36:52 +0000 Subject: [PATCH 10/57] Preparation for the EDA features integration from CF. --- .../cf_tests/calculate_EDA_features_test.py | 80 ++++++++++--------- config.yaml | 4 +- 2 files changed, 44 insertions(+), 40 deletions(-) diff --git a/calculatingfeatures/cf_tests/calculate_EDA_features_test.py b/calculatingfeatures/cf_tests/calculate_EDA_features_test.py index 7a1ba32c..58c9fc64 100644 --- a/calculatingfeatures/cf_tests/calculate_EDA_features_test.py +++ b/calculatingfeatures/cf_tests/calculate_EDA_features_test.py @@ -9,55 +9,59 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +pd.set_option('display.max_rows', None) +pd.set_option('display.max_columns', None) -pathToEDACsv = "../example_data/EDA.csv" + +pathToEDACsv = "../example_data/S2_E4_Data/EDA.csv" # get an array of values from EDA empatica file eda_data, startTimeStamp_EDA, sampleRate_EDA = convert1DEmpaticaToArray(pathToEDACsv) +eda_data = eda_data[:int(300000//sampleRate_EDA)] windowLength_EDA = int(sampleRate_EDA*120) # Convert the HRV data into 2D array eda_data_2D = convertInputInto2d(eda_data, windowLength_EDA) -df_EDA = pd.DataFrame() -for row in eda_data_2D: - current_result = {} - current_result.update(gsr.extractGsrFeatures(row, sampleRate=int(sampleRate_EDA),featureNames=gsrFeatureNames)) - df_EDA = df_EDA.append(current_result, ignore_index=True) - -no_interest = 131 -current_row = eda_data_2D[no_interest,] -filtered_EDA = butter_lowpass_filter(current_row, 1.0, int(sampleRate_EDA), 6) - -plt.figure() -plt.plot(current_row, color='blue') -plt.plot(filtered_EDA, color='red') -plt.savefig('output_images/EDA_exa1.png') - -gsr_data = pd.DataFrame(current_row, columns=["EDA"]) -startTime = pd.to_datetime(0, unit="s") -gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000/sampleRate_EDA) + 'L') -gsr_data['filtered_eda'] = filtered_EDA -peakData = calcPeakFeatures(gsr_data, offset=1, thresh=.02, start_WT=4, end_WT=4, sampleRate=int(sampleRate_EDA)) - -peaks = np.where(peakData.peaks == 1.0)[0] -peak_starts = np.where(peakData.peak_start == 1.0)[0] -peak_ends = np.where(peakData.peak_end == 1.0)[0] -print(peaks) -print(peak_starts) -print(peak_ends) - -plt.figure() -plt.plot(filtered_EDA, color='red') -plt.scatter(peaks, filtered_EDA[peaks], color="green") -plt.scatter(peak_starts, filtered_EDA[peak_starts], color="green", marker=">", alpha=0.5) -plt.scatter(peak_ends, filtered_EDA[peak_ends], color="green", marker="s", alpha=0.5) -plt.savefig('output_images/EDA_exa2.png') - -print(df_EDA.loc[no_interest, df_EDA.columns.str.contains('Peak')]) - calculatedFeatures_EDA = calculateFeatures(eda_data_2D, fs=int(sampleRate_EDA), featureNames=gsrFeatureNames) print(calculatedFeatures_EDA) +# df_EDA = pd.DataFrame() +# for row in eda_data_2D: +# current_result = {} +# current_result.update(gsr.extractGsrFeatures(row, sampleRate=int(sampleRate_EDA),featureNames=gsrFeatureNames)) +# df_EDA = df_EDA.append(current_result, ignore_index=True) + +# no_interest = 131 +# current_row = eda_data_2D[no_interest,] +# filtered_EDA = butter_lowpass_filter(current_row, 1.0, int(sampleRate_EDA), 6) + +# plt.figure() +# plt.plot(current_row, color='blue') +# plt.plot(filtered_EDA, color='red') +# plt.savefig('output_images/EDA_exa1.png') + +# gsr_data = pd.DataFrame(current_row, columns=["EDA"]) +# startTime = pd.to_datetime(0, unit="s") +# gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000/sampleRate_EDA) + 'L') +# gsr_data['filtered_eda'] = filtered_EDA +# peakData = calcPeakFeatures(gsr_data, offset=1, thresh=.02, start_WT=4, end_WT=4, sampleRate=int(sampleRate_EDA)) + +# peaks = np.where(peakData.peaks == 1.0)[0] +# peak_starts = np.where(peakData.peak_start == 1.0)[0] +# peak_ends = np.where(peakData.peak_end == 1.0)[0] +# print(peaks) +# print(peak_starts) +# print(peak_ends) + +# plt.figure() +# plt.plot(filtered_EDA, color='red') +# plt.scatter(peaks, filtered_EDA[peaks], color="green") +# plt.scatter(peak_starts, filtered_EDA[peak_starts], color="green", marker=">", alpha=0.5) +# plt.scatter(peak_ends, filtered_EDA[peak_ends], color="green", marker="s", alpha=0.5) +# plt.savefig('output_images/EDA_exa2.png') + +# print(df_EDA.loc[no_interest, df_EDA.columns.str.contains('Peak')]) + diff --git a/config.yaml b/config.yaml index 67aeb299..381e5cb0 100644 --- a/config.yaml +++ b/config.yaml @@ -504,12 +504,12 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CF: COMPUTE: True - FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] + FEATURES: ['mean', 'std', 'q25', 'q75', 'qd'] # To-Do add remaining features from CF helper file. SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ From 191e53e54332e766828a9adc3c58ebdc34fa5cdd Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 23 Mar 2022 15:13:53 +0000 Subject: [PATCH 11/57] Added cf provider for EDA feature processing. --- .gitignore | 1 + config.yaml | 9 ++- data/external/empatica/empatica1/E4 Data.zip | Bin 1678743 -> 1678743 bytes .../cf/main.py | 55 +++++------------- 4 files changed, 22 insertions(+), 43 deletions(-) diff --git a/.gitignore b/.gitignore index 4ac47683..6cf83e29 100644 --- a/.gitignore +++ b/.gitignore @@ -93,6 +93,7 @@ packrat/* # exclude data from source control by default data/external/* +!/data/external/empatica/empatica1/E4 Data.zip !/data/external/.gitkeep !/data/external/stachl_application_genre_catalogue.csv !/data/external/timesegments*.csv diff --git a/config.yaml b/config.yaml index 381e5cb0..290b8b89 100644 --- a/config.yaml +++ b/config.yaml @@ -509,7 +509,14 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CF: COMPUTE: True - FEATURES: ['mean', 'std', 'q25', 'q75', 'qd'] # To-Do add remaining features from CF helper file. + FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', + 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats', + 'maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', + 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', + 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', + 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', + 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', + 'significantIncrease', 'significantDecrease'] SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ diff --git a/data/external/empatica/empatica1/E4 Data.zip b/data/external/empatica/empatica1/E4 Data.zip index 40691f5f799c3917f192e03ecaf8500bc511525c..bb1e36fe7f3420a0072bb7889cec4e6c8d0cc1a6 100644 GIT binary patch delta 388 zcmYk$ze_?<6bJD0e)yiLnY~x$(SkQP1c5XJ5t3x);LsqtM-&#VMnMD-A<`Pef$kF? zO|>)xA|ip*P=A7LLJ|Z)Lu**a2SOJCDSUn2-5cfQ_;s z8)G3h&J1R<2{y^Xjb?A^Eva7a9u}*P6=^sY;?b#Gk)?{vyHb(Wy6kE8$PhTI001ou zoYt=O>dkG6HUx$5Vyk@Ma}NL{h``U|eBv(E zk=ma|;XXwn8>2M$5eZUPU_jYcP5KfT8~(%bg1*0GLT)ujX@QY{L6pw8tZ2d{$Ho~= z-8&zQ_QVx$yJo`v=1$q|(1VR(vTXFxVhyAEJBP*WI;{xAVauK4`HIti-Gp=|<2L;l Q-4lMZBmFvYI<8^t4+2z=!~g&Q delta 307 zcmbQfJ8k;zwDx~ng@ITEh(&=|42Z?I|Jy3D`$;2HD-&ZY6Od$XWnyV%Vr^w&Yh_|@ zW#VXM;%sH&YGvYXW#VaN;%#N(Yh~hZWfEv*65Prp^w65c+g~xb{kgT!_UG2Zl2y~S z=1OX?GXR03v-7mMk`e;KTZGN+EpIUXU!Y^9d`1Q`q$Xb0j4dE-CPv zfn?m6)xjzPycwC~8AO0)a4>*OpRO=lQjEI+$e*Mj!QkrR2r_s2zB!T#(<^67O0mhl zlwj}(LI_?23Nq`;Nlt$^TT%w1PHPTOXn(!rboV)uvLGR`=~Z(iRhTQAB&RQ&BN@&- QAISI(l@prYJXcZ#02-@gQ2+n{ diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cf/main.py index 524ebd99..54cb417d 100644 --- a/src/features/empatica_electrodermal_activity/cf/main.py +++ b/src/features/empatica_electrodermal_activity/cf/main.py @@ -1,57 +1,29 @@ import pandas as pd from scipy.stats import entropy +import sys +sys.path.insert(1, '/workspaces/rapids/calculatingfeatures') +from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames +from CalculatingFeatures.calculate_features import calculateFeatures -def statsFeatures(eda_data, features, eda_features): - col_name = "electrodermal_activity" - if "sumeda" in features: - eda_features["sumeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].sum() - if "maxeda" in features: - eda_features["maxeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].max() - if "mineda" in features: - eda_features["mineda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].min() - if "avgeda" in features: - eda_features["avgeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].mean() - if "medianeda" in features: - eda_features["medianeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].median() - if "modeeda" in features: - eda_features["modeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].agg(lambda x: pd.Series.mode(x)[0]) - if "stdeda" in features: - eda_features["stdeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].std() - if "diffmaxmodeeda" in features: - eda_features["diffmaxmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].max() - \ - eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].agg(lambda x: pd.Series.mode(x)[0]) - if "diffminmodeeda" in features: - eda_features["diffminmodeeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].agg(lambda x: pd.Series.mode(x)[0]) - \ - eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].min() - if "entropyeda" in features: - eda_features["entropyeda"] = eda_data[["local_segment", col_name]].groupby(["local_segment"])[ - col_name].agg(entropy) +pd.set_option('display.max_columns', None) - return eda_features def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment): eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features) - if not eda_intraday_data.empty: + + if not eda_intraday_data.empty: eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment) if not eda_intraday_data.empty: + eda_intraday_features = pd.DataFrame() - # get stats of eda - eda_intraday_features = statsFeatures(eda_intraday_data, features, eda_intraday_features) + # apply a method from calculate features module + eda_intraday_features = \ + eda_intraday_data.groupby('local_segment').apply(\ + lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=4, featureNames=features)) eda_intraday_features.reset_index(inplace=True) @@ -63,8 +35,7 @@ def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_intraday_features = provider["FEATURES"] # name of the features this function can compute - base_intraday_features_names = ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", - "diffminmodeeda", "entropyeda"] + base_intraday_features_names = gsrFeatureNames # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) From f389ac9d89d31876536661b86cf0220e3e30a730 Mon Sep 17 00:00:00 2001 From: Primoz Date: Fri, 25 Mar 2022 16:24:52 +0000 Subject: [PATCH 12/57] Delete CF features folder --- calculatingfeatures/.gitignore | 224 - .../CalculatingFeatures/__init__.py | 0 .../calculate_acc_gyro_common_features.py | 610 - .../CalculatingFeatures/calculate_features.py | 138 - .../calculate_hrv_peaks.py | 90 - .../CalculatingFeatures/feature_functions.py | 986 - .../CalculatingFeatures/gsr.py | 669 - .../CalculatingFeatures/helper_functions.py | 131 - .../CalculatingFeatures/hrv.py | 304 - calculatingfeatures/README.md | 207 - calculatingfeatures/__init__.py | 0 calculatingfeatures/cf_tests/__init__.py | 0 .../cf_tests/calculate_ACC_features_test.py | 32 - .../cf_tests/calculate_EDA_features_test.py | 67 - .../cf_tests/calculate_HR_features_test.py | 28 - .../cf_tests/calculate_TEMP_features_test.py | 1 - calculatingfeatures/eda_explorer/.csv | 175 - calculatingfeatures/eda_explorer/.gitignore | 5 - .../AccelerometerFeatureExtractionScript.py | 309 - .../eda_explorer/ArtifactClassifiers.py | 601 - .../EDA-Artifact-Detection-Script.py | 411 - .../eda_explorer/EDA_Peak_Detection_Script.py | 250 - calculatingfeatures/eda_explorer/LICENSE.txt | 21 - calculatingfeatures/eda_explorer/README.md | 39 - calculatingfeatures/eda_explorer/__init__.py | 1 - calculatingfeatures/eda_explorer/classify.py | 90 - .../eda_explorer/load_files.py | 264 - .../eda_explorer/requirements.txt | 6 - calculatingfeatures/example_data/EDA.csv | 114320 ---- .../example_data/S2_E4_Data/ACC.csv | 251972 ------- .../example_data/S2_E4_Data/BVP.csv | 503945 --------------- .../example_data/S2_E4_Data/EDA.csv | 31496 - .../example_data/S2_E4_Data/HR.csv | 7867 - .../example_data/S2_E4_Data/IBI.csv | 3459 - .../example_data/S2_E4_Data/TEMP.csv | 31498 - .../example_data/S2_E4_Data/info.txt | 32 - .../example_data/S2_E4_Data/tags.csv | 0 calculatingfeatures/pyproject.toml | 3 - calculatingfeatures/setup.cfg | 16 - calculatingfeatures/usage_examples.ipynb | 6112 - .../cf/main.py | 2 - 41 files changed, 956381 deletions(-) delete mode 100644 calculatingfeatures/.gitignore delete mode 100644 calculatingfeatures/CalculatingFeatures/__init__.py delete mode 100644 calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py delete mode 100644 calculatingfeatures/CalculatingFeatures/calculate_features.py delete mode 100644 calculatingfeatures/CalculatingFeatures/calculate_hrv_peaks.py delete mode 100644 calculatingfeatures/CalculatingFeatures/feature_functions.py delete mode 100644 calculatingfeatures/CalculatingFeatures/gsr.py delete mode 100644 calculatingfeatures/CalculatingFeatures/helper_functions.py delete mode 100644 calculatingfeatures/CalculatingFeatures/hrv.py delete mode 100644 calculatingfeatures/README.md delete mode 100644 calculatingfeatures/__init__.py delete mode 100644 calculatingfeatures/cf_tests/__init__.py delete mode 100644 calculatingfeatures/cf_tests/calculate_ACC_features_test.py delete mode 100644 calculatingfeatures/cf_tests/calculate_EDA_features_test.py delete mode 100644 calculatingfeatures/cf_tests/calculate_HR_features_test.py delete mode 100644 calculatingfeatures/cf_tests/calculate_TEMP_features_test.py delete mode 100644 calculatingfeatures/eda_explorer/.csv delete mode 100644 calculatingfeatures/eda_explorer/.gitignore delete mode 100644 calculatingfeatures/eda_explorer/AccelerometerFeatureExtractionScript.py delete mode 100644 calculatingfeatures/eda_explorer/ArtifactClassifiers.py delete mode 100644 calculatingfeatures/eda_explorer/EDA-Artifact-Detection-Script.py delete mode 100644 calculatingfeatures/eda_explorer/EDA_Peak_Detection_Script.py delete mode 100644 calculatingfeatures/eda_explorer/LICENSE.txt delete mode 100644 calculatingfeatures/eda_explorer/README.md delete mode 100644 calculatingfeatures/eda_explorer/__init__.py delete mode 100644 calculatingfeatures/eda_explorer/classify.py delete mode 100644 calculatingfeatures/eda_explorer/load_files.py delete mode 100644 calculatingfeatures/eda_explorer/requirements.txt delete mode 100644 calculatingfeatures/example_data/EDA.csv delete mode 100644 calculatingfeatures/example_data/S2_E4_Data/ACC.csv delete mode 100644 calculatingfeatures/example_data/S2_E4_Data/BVP.csv delete mode 100644 calculatingfeatures/example_data/S2_E4_Data/EDA.csv delete mode 100644 calculatingfeatures/example_data/S2_E4_Data/HR.csv delete mode 100644 calculatingfeatures/example_data/S2_E4_Data/IBI.csv delete mode 100644 calculatingfeatures/example_data/S2_E4_Data/TEMP.csv delete mode 100644 calculatingfeatures/example_data/S2_E4_Data/info.txt delete mode 100644 calculatingfeatures/example_data/S2_E4_Data/tags.csv delete mode 100644 calculatingfeatures/pyproject.toml delete mode 100644 calculatingfeatures/setup.cfg delete mode 100644 calculatingfeatures/usage_examples.ipynb diff --git a/calculatingfeatures/.gitignore b/calculatingfeatures/.gitignore deleted file mode 100644 index fd077a7a..00000000 --- a/calculatingfeatures/.gitignore +++ /dev/null @@ -1,224 +0,0 @@ -.ipynb_checkpoints -build -CalculatingFeatures.egg-info -dist -venv - -# From the website: https://github.com/github/gitignore/blob/master/Python.gitignore -# Also from the website: https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore - - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider -# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 - -# User-specific stuff -.idea/**/workspace.xml -.idea/**/tasks.xml -.idea/**/usage.statistics.xml -.idea/**/dictionaries -.idea/**/shelf - -# Generated files -.idea/**/contentModel.xml - -# Sensitive or high-churn files -.idea/**/dataSources/ -.idea/**/dataSources.ids -.idea/**/dataSources.local.xml -.idea/**/sqlDataSources.xml -.idea/**/dynamic.xml -.idea/**/uiDesigner.xml -.idea/**/dbnavigator.xml - -# Gradle -.idea/**/gradle.xml -.idea/**/libraries - -# Gradle and Maven with auto-import -# When using Gradle or Maven with auto-import, you should exclude module files, -# since they will be recreated, and may cause churn. Uncomment if using -# auto-import. -# .idea/artifacts -# .idea/compiler.xml -# .idea/jarRepositories.xml -# .idea/modules.xml -# .idea/*.iml -# .idea/modules -# *.iml -# *.ipr - -# CMake -cmake-build-*/ - -# Mongo Explorer plugin -.idea/**/mongoSettings.xml - -# File-based project format -*.iws - -# IntelliJ -out/ - -# mpeltonen/sbt-idea plugin -.idea_modules/ - -# JIRA plugin -atlassian-ide-plugin.xml - -# Cursive Clojure plugin -.idea/replstate.xml - -# Crashlytics plugin (for Android Studio and IntelliJ) -com_crashlytics_export_strings.xml -crashlytics.properties -crashlytics-build.properties -fabric.properties - -# Editor-based Rest Client -.idea/httpRequests - -# Android studio 3.1+ serialized cache file -.idea/caches/build_file_checksums.ser - -# Output images -cf_tests/output_images/ - diff --git a/calculatingfeatures/CalculatingFeatures/__init__.py b/calculatingfeatures/CalculatingFeatures/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py b/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py deleted file mode 100644 index e5bcee35..00000000 --- a/calculatingfeatures/CalculatingFeatures/calculate_acc_gyro_common_features.py +++ /dev/null @@ -1,610 +0,0 @@ -from CalculatingFeatures import feature_functions as f -import numpy as np - - -def calculateFeaturesAcc(ax, ay, az, time, axBand, ayBand, azBand, axLow, ayLow, azLow, featureNames=None): - """ Calculate features for the accelerometer - - :param ax2d: 2D array including the X axis - :param ay2d: 2D array including the Y axis - :param az2d: 2D array including the Z axis - :param time2d: 2D array of times, denoting when each measurement occurred - :param afs: sampling frequency of the accelerometer - :param prefix: prefix for column names in returned dataframe - :return: features of the accelerometer - """ - if ay is not None: - magnitudes = f.magnitudeVector(ax, ay, az) - magnitudesLow = f.magnitudeVector(axLow, ayLow, azLow) - magnitudesBand = f.magnitudeVector(axBand, ayBand, azBand) - else: - magnitudes = ax - magnitudesLow = axLow - magnitudesBand = axBand - - ACsumPerXBand = f.calcSumPerComponent(axBand, time) - if ay is not None: - ACsumPerYBand = f.calcSumPerComponent(ayBand, time) - ACsumPerZBand = f.calcSumPerComponent(azBand, time) - - meanKineticEnergyX = f.calcMeanKineticEnergy(axBand, time) - if ay is not None: - meanKineticEnergyY = f.calcMeanKineticEnergy(ayBand, time) - meanKineticEnergyZ = f.calcMeanKineticEnergy(azBand, time) - totalKineticEnergy = f.calcTotalKineticEnergy(axBand, ayBand, azBand, time) - - roll = f.calcRoll(ayLow, azLow) - pitch = f.calcPitch(axLow, ayLow, azLow) - - acAbsoluteArea = f.calcAcAbsoluteArea(axBand, ayBand, azBand) - - row = {} - if f.checkForFeature("meanLow", featureNames): - row["mean_XLow"] = f.calcMean(axLow) - if ay is not None: - row["mean_YLow"] = f.calcMean(ayLow) - row["mean_ZLow"] = f.calcMean(azLow) - row["totalMeanLow"] = (row["mean_XLow"] + row["mean_YLow"] + row["mean_ZLow"]) * len(ax) / 3 - - if f.checkForFeature("areaLow", featureNames): - row["area_XLow"] = f.calcArea(axLow) - if ay is not None: - row["area_YLow"] = f.calcArea(ayLow) - row["area_ZLow"] = f.calcArea(azLow) - - if ay is not None: - if f.checkForFeature("totalAbsoluteAreaBand", featureNames): - row["totalAbsoluteAreaBand"] = f.calcTotalAbsoluteArea(f.calcAcAbsoluteArea(axBand, ayBand, azBand)) - - if f.checkForFeature("totalMagnitudeBand", featureNames): - row["totalMagnitudeBand"] = f.calcTotalMagnitude(axBand, ayBand, azBand) - - # Measures of body posture - if f.checkForFeature("postureDistanceLow", featureNames): - postureDistance = f.calcPostureDistance(row["mean_XLow"], row["mean_YLow"], - row["mean_ZLow"]) if "mean_XLow" in row \ - else f.calcPostureDistance(f.calcMean(axLow), f.calcMean(ayLow), f.calcMean(azLow)) - row["postureDistance_XLow"] = postureDistance[0] - row["postureDistance_YLow"] = postureDistance[1] - row["postureDistance_ZLow"] = postureDistance[2] - - if f.checkForFeature("entropyBand", featureNames): - row["entropy_XBand"] = f.calcEntropy(axBand) - if ay is not None: - row["entropy_YBand"] = f.calcEntropy(ayBand) - row["entropy_ZBand"] = f.calcEntropy(azBand) - - if f.checkForFeature("skewnessBand", featureNames): - row["skewness_XBand"] = f.calcSkewness(axBand) - if ay is not None: - row["skewness_YBand"] = f.calcSkewness(ayBand) - row["skewness_ZBand"] = f.calcSkewness(azBand) - - if f.checkForFeature("kurtosisBand", featureNames): - row["kurtosis_XBand"] = f.calcKurtosis(axBand) - if ay is not None: - row["kurtosis_YBand"] = f.calcKurtosis(ayBand) - row["kurtosis_ZBand"] = f.calcKurtosis(azBand) - - # Measures of motion shape - if f.checkForFeature("absoluteMeanBand", featureNames): - row["absoluteMean_XBand"] = f.calcMean(axBand) - if ay is not None: - row["absoluteMean_YBand"] = f.calcMean(ayBand) - row["absoluteMean_ZBand"] = f.calcMean(azBand) - - if f.checkForFeature("absoluteAreaBand", featureNames): - row["absoluteArea_XBand"] = f.calcArea(axBand) - if ay is not None: - row["absoluteArea_YBand"] = f.calcArea(ayBand) - row["absoluteArea_ZBand"] = f.calcArea(azBand) - row["absoluteAreaAllBand"] = f.calcTotalAbsoluteArea(acAbsoluteArea) - - acQuartilesX = f.calcQuartiles(axBand) - if ay is not None: - acQuartilesY = f.calcQuartiles(ayBand) - acQuartilesZ = f.calcQuartiles(azBand) - - if f.checkForFeature("quartilesBand", featureNames): - row["quartilesQ1_XBand"] = acQuartilesX[0] - row["quartilesQ2_XBand"] = acQuartilesX[1] - row["quartilesQ3_XBand"] = acQuartilesX[2] - if ay is not None: - row["quartilesQ1_YBand"] = acQuartilesY[0] - row["quartilesQ2_YBand"] = acQuartilesY[1] - row["quartilesQ3_YBand"] = acQuartilesY[2] - - row["quartilesQ1_ZBand"] = acQuartilesZ[0] - row["quartilesQ2_ZBand"] = acQuartilesZ[1] - row["quartilesQ3_ZBand"] = acQuartilesZ[2] - - if f.checkForFeature("interQuartileRangeBand", featureNames): - row["interQuartileRange_XBand"] = f.calcInterQuartileRange(acQuartilesX) - if ay is not None: - row["interQuartileRange_YBand"] = f.calcInterQuartileRange(acQuartilesY) - row["interQuartileRange_ZBand"] = f.calcInterQuartileRange(acQuartilesZ) - - # Measures of motion variation - if f.checkForFeature("varianceBand", featureNames): - row["variance_XBand"] = f.calcVariance(axBand) - if ay is not None: - row["variance_YBand"] = f.calcVariance(ayBand) - row["variance_ZBand"] = f.calcVariance(azBand) - - if f.checkForFeature("coefficientOfVariationBand", featureNames): - row["coefficientOfVariation_XBand"] = f.calcCoefficientOfVariation(axBand) - if ay is not None: - row["coefficientOfVariation_YBand"] = f.calcCoefficientOfVariation(ayBand) - row["coefficientOfVariation_ZBand"] = f.calcCoefficientOfVariation(azBand) - - if f.checkForFeature("amplitudeBand", featureNames): - row["amplitude_XBand"] = f.calcAmplitude(axBand) - if ay is not None: - row["amplitude_YBand"] = f.calcAmplitude(ayBand) - row["amplitude_ZBand"] = f.calcAmplitude(azBand) - - if f.checkForFeature("totalEnergyBand", featureNames): - row["totalEnergy_XBand"] = f.calcTotalEnergy(axBand) - if ay is not None: - row["totalEnergy_YBand"] = f.calcTotalEnergy(ayBand) - row["totalEnergy_ZBand"] = f.calcTotalEnergy(azBand) - - if f.checkForFeature("dominantFrequencyEnergyBand", featureNames): - row["dominantFrequencyEnergy_XBand"] = f.calcDominantFrequencyEnergy(axBand) - if ay is not None: - row["dominantFrequencyEnergy_YBand"] = f.calcDominantFrequencyEnergy(ayBand) - row["dominantFrequencyEnergy_ZBand"] = f.calcDominantFrequencyEnergy(azBand) - - if f.checkForFeature("meanCrossingRateBand", featureNames): - row["meanCrossingRate_XBand"] = f.calcMeanCrossingRate(axBand) - if ay is not None: - row["meanCrossingRate_YBand"] = f.calcMeanCrossingRate(ayBand) - row["meanCrossingRate_ZBand"] = f.calcMeanCrossingRate(azBand) - - if ay is not None: - if f.checkForFeature("correlationBand", featureNames): - row["correlation_X_YBand"] = f.calcCorrelation(axBand, ayBand) - row["correlation_X_ZBand"] = f.calcCorrelation(axBand, azBand) - row["correlation_Y_ZBand"] = f.calcCorrelation(ayBand, azBand) - - acQuartilesMagnitude = f.calcQuartiles(magnitudesBand) - if f.checkForFeature("quartilesMagnitudesBand", featureNames): - row["quartilesMagnitudes_XBand"] = acQuartilesMagnitude[0] - row["quartilesMagnitudes_YBand"] = acQuartilesMagnitude[1] - row["quartilesMagnitudes_ZBand"] = acQuartilesMagnitude[2] - - if f.checkForFeature("interQuartileRangeMagnitudesBand", featureNames): - row["interQuartileRangeMagnitudesBand"] = f.calcInterQuartileRange(acQuartilesMagnitude) - - if f.checkForFeature("areaUnderAccelerationMagnitude", featureNames): - row["areaUnderAccelerationMagnitude"] = f.calcAreaUnderAccelerationMagnitude(magnitudes, time) - - if f.checkForFeature("peaksDataLow", featureNames): - peakCount = f.calcPeakCount(magnitudesLow) - row["peaksCountLow"] = peakCount[0] - row["peaksSumLow"] = peakCount[1] - row["peaksAmplitudeAvgLow"] = peakCount[3] - row["peaksPeakAvgLow"] = peakCount[2] - - if f.checkForFeature("sumPerComponentBand", featureNames): - row["sumPerComponent_XBand"] = ACsumPerXBand - if ay is not None: - row["sumPerComponent_YBand"] = ACsumPerYBand - row["sumPerComponent_ZBand"] = ACsumPerZBand - - if f.checkForFeature("velocityBand", featureNames): - row["velocity_XBand"] = f.computeACVelocity(axBand, time) - if ay is not None: - row["velocity_YBand"] = f.computeACVelocity(ayBand, time) - row["velocity_ZBand"] = f.computeACVelocity(azBand, time) - - if f.checkForFeature("meanKineticEnergyBand", featureNames): - row["meanKineticEnergy_XBand"] = meanKineticEnergyX - if ay is not None: - row["meanKineticEnergy_YBand"] = meanKineticEnergyY - row["meanKineticEnergy_ZBand"] = meanKineticEnergyZ - - if ay is not None: - if f.checkForFeature("totalKineticEnergyBand", featureNames): - row["totalKineticEnergyBand"] = totalKineticEnergy - - ACsumPerX = f.calcSumPerComponent(ax, time) - acSumSquaredX = pow(ACsumPerX, 2) - if ay is not None: - ACsumPerY = f.calcSumPerComponent(ay, time) - ACsumPerZ = f.calcSumPerComponent(az, time) - acSumSquaredY = pow(ACsumPerY, 2) - acSumSquaredZ = pow(ACsumPerZ, 2) - - if f.checkForFeature("squareSumOfComponent", featureNames): - row["squareSumOfComponent_X"] = acSumSquaredX - if ay is not None: - row["squareSumOfComponent_Y"] = acSumSquaredY - row["squareSumOfComponent_Z"] = acSumSquaredZ - - if f.checkForFeature("averageVectorLength", featureNames): - row["averageVectorLength"] = f.calcAverageVectorLength(magnitudes) - - if f.checkForFeature("averageVectorLengthPower", featureNames): - row["averageVectorLengthPower"] = f.calcAverageVectorLengthPower(magnitudes) - - if ay is not None: - if f.checkForFeature("rollAvgLow", featureNames): - row["rollAvgLow"] = (f.max(roll) - f.min(roll)) - - if f.checkForFeature("pitchAvgLow", featureNames): - row["pitchAvgLow"] = (f.max(pitch) - f.min(pitch)) - - if f.checkForFeature("rollStdDevLow", featureNames): - row["rollStdDevLow"] = f.stdDev(roll) - - if f.checkForFeature("pitchStdDevLow", featureNames): - row["pitchStdDevLow"] = f.stdDev(pitch) - - if f.checkForFeature("rollMotionAmountLow", featureNames): - row["rollMotionAmountLow"] = f.rollMotionAmount(roll) - - if f.checkForFeature("rollMotionRegularityLow", featureNames): - row["rollMotionRegularityLow"] = f.rollMotionRegularity(roll) - - if f.checkForFeature("manipulationLow", featureNames): - row["manipulationLow"] = f.manipulation(axLow, ayLow, azLow, roll, pitch) - - if f.checkForFeature("rollPeaks", featureNames): - roll_peaks = f.calcPeakCount(roll) - row["rollPeak0"] = roll_peaks[0] - row["rollPeak1"] = roll_peaks[1] - row["rollPeak2"] = roll_peaks[2] - row["rollPeak3"] = roll_peaks[3] - - if f.checkForFeature("pitchPeaks", featureNames): - pitch_peaks = f.calcPeakCount(pitch) - row["pitchPeak0"] = pitch_peaks[0] - row["pitchPeak1"] = pitch_peaks[1] - row["pitchPeak2"] = pitch_peaks[2] - row["pitchPeak3"] = pitch_peaks[3] - - if f.checkForFeature("rollPitchCorrelation", featureNames): - row["rollPitchCorrelation"] = f.calcCorrelation(roll, pitch) - return row - - -def calcCommonFeatures(x, y, z, time, featureNames=None): - """ Calculate common features of accelerometer and gyroscope - - :param prefix: prefix of all feature names - :param x: array including the X axis - :param y: array including the Y axis - :param z: array including the Z axis - :return: pandas dataframe with the calculated features - """ - row = {} - if f.checkForFeature("autocorrelations", featureNames): - row.update(calcAutocorrelations(x, "_X")) - if y is not None: - row.update(calcAutocorrelations(y, "_Y")) - row.update(calcAutocorrelations(z, "_Z")) - - if f.checkForFeature("countAboveMean", featureNames): - row["countAboveMean_X"] = f.countAboveMean(x) - if y is not None: - row["countAboveMean_Y"] = f.countAboveMean(y) - row["countAboveMean_Z"] = f.countAboveMean(z) - - if f.checkForFeature("countBelowMean", featureNames): - row["countBelowMean_X"] = f.countBelowMean(x) - if y is not None: - row["countBelowMean_Y"] = f.countBelowMean(y) - row["countBelowMean_Z"] = f.countBelowMean(z) - - if f.checkForFeature("maximum", featureNames): - row["maximum_X"] = f.max(x) - if y is not None: - row["maximum_Y"] = f.max(y) - row["maximum_Z"] = f.max(z) - - if f.checkForFeature("minimum", featureNames): - row["minimum_X"] = f.min(x) - if y is not None: - row["minimum_Y"] = f.min(y) - row["minimum_Z"] = f.min(z) - - if f.checkForFeature("meanAbsChange", featureNames): - row["meanAbsChange_X"] = f.meanAbsChange(x) - if y is not None: - row["meanAbsChange_Y"] = f.meanAbsChange(y) - row["meanAbsChange_Z"] = f.meanAbsChange(z) - - if f.checkForFeature("longestStrikeAboveMean", featureNames): - row["longestStrikeAboveMean_X"] = f._calcMaxLengthOfSequenceTrueOrOne(x > np.mean(x)) - if y is not None: - row["longestStrikeAboveMean_Y"] = f._calcMaxLengthOfSequenceTrueOrOne(y > np.mean(y)) - row["longestStrikeAboveMean_Z"] = f._calcMaxLengthOfSequenceTrueOrOne(z > np.mean(z)) - - if f.checkForFeature("longestStrikeBelowMean", featureNames): - row["longestStrikeBelowMean_X"] = f._calcMaxLengthOfSequenceTrueOrOne(x < np.mean(x)) - if y is not None: - row["longestStrikeBelowMean_Y"] = f._calcMaxLengthOfSequenceTrueOrOne(y < np.mean(y)) - row["longestStrikeBelowMean_Z"] = f._calcMaxLengthOfSequenceTrueOrOne(z < np.mean(z)) - - if f.checkForFeature("stdDev", featureNames): - row["stdDev_X"] = f.stdDev(x) - if y is not None: - row["stdDev_Y"] = f.stdDev(y) - row["stdDev_Z"] = f.stdDev(z) - - if f.checkForFeature("median", featureNames): - row["median_X"] = np.median(x) - if y is not None: - row["median_Y"] = np.median(y) - row["median_Z"] = np.median(z) - - if f.checkForFeature("meanChange", featureNames): - row["meanChange_X"] = f.meanChange(x) - if y is not None: - row["meanChange_Y"] = f.meanChange(y) - row["meanChange_Z"] = f.meanChange(z) - - if f.checkForFeature("numberOfZeroCrossings", featureNames): - row["numberOfZeroCrossings_X"] = f.numberOfZeroCrossings(x) - if y is not None: - row["numberOfZeroCrossings_Y"] = f.numberOfZeroCrossings(y) - row["numberOfZeroCrossings_Z"] = f.numberOfZeroCrossings(z) - - if f.checkForFeature("absEnergy", featureNames): - row["absEnergy_X"] = f.absEnergy(x) - if y is not None: - row["absEnergy_Y"] = f.absEnergy(y) - row["absEnergy_Z"] = f.absEnergy(z) - - if f.checkForFeature("linearTrendSlope", featureNames): - row["linearTrendSlope_X"] = f.linearTrendSlope(x) - if y is not None: - row["linearTrendSlope_Y"] = f.linearTrendSlope(y) - row["linearTrendSlope_Z"] = f.linearTrendSlope(z) - - if f.checkForFeature("ratioBeyondRSigma", featureNames): - r = 2.5 - row["ratioBeyondRSigma_X"] = f.ratioBeyondRSigma(x, r) - if y is not None: - row["ratioBeyondRSigma_Y"] = f.ratioBeyondRSigma(y, r) - row["ratioBeyondRSigma_Z"] = f.ratioBeyondRSigma(z, r) - - if f.checkForFeature("binnedEntropy", featureNames): - max_bins = 10 - row["binnedEntropy_X"] = f.binnedEntropy(x, max_bins) - if y is not None: - row["binnedEntropy_Y"] = f.binnedEntropy(y, max_bins) - row["binnedEntropy_Z"] = f.binnedEntropy(z, max_bins) - - autocorrelationsX = f.autocorrelations(x) - if y is not None: - autocorrelationsY = f.autocorrelations(y) - autocorrelationsZ = f.autocorrelations(z) - - if f.checkForFeature("numOfPeaksAutocorr", featureNames): - row["numOfPeaksAutocorr_X"] = f.calcPeakCount(autocorrelationsX)[0] - if y is not None: - row["numOfPeaksAutocorr_Y"] = f.calcPeakCount(autocorrelationsY)[0] - row["numOfPeaksAutocorr_Z"] = f.calcPeakCount(autocorrelationsZ)[0] - - if f.checkForFeature("numberOfZeroCrossingsAutocorr", featureNames): - row["numberOfZeroCrossingsAutocorr_X"] = f.numberOfZeroCrossings(autocorrelationsX) - if y is not None: - row["numberOfZeroCrossingsAutocorr_Y"] = f.numberOfZeroCrossings(autocorrelationsY) - row["numberOfZeroCrossingsAutocorr_Z"] = f.numberOfZeroCrossings(autocorrelationsZ) - - if f.checkForFeature("areaAutocorr", featureNames): - row["areaAutocorr_X"] = f.calcArea(autocorrelationsX) - if y is not None: - row["areaAutocorr_Y"] = f.calcArea(autocorrelationsY) - row["areaAutocorr_Z"] = f.calcArea(autocorrelationsZ) - - if f.checkForFeature("calcMeanCrossingRateAutocorr", featureNames): - row["calcMeanCrossingRateAutocorr_X"] = f.calcMeanCrossingRate(autocorrelationsX) - if y is not None: - row["calcMeanCrossingRateAutocorr_Y"] = f.calcMeanCrossingRate(autocorrelationsY) - row["calcMeanCrossingRateAutocorr_Z"] = f.calcMeanCrossingRate(autocorrelationsZ) - - if f.checkForFeature("countAboveMeanAutocorr", featureNames): - row["countAboveMeanAutocorr_X"] = f.countAboveMean(autocorrelationsX) - if y is not None: - row["countAboveMeanAutocorr_Y"] = f.countAboveMean(autocorrelationsY) - row["countAboveMeanAutocorr_Z"] = f.countAboveMean(autocorrelationsZ) - - GCsumPerX_gyro = f.calcSumPerComponent(x, time) - if y is not None: - GCsumPerY_gyro = f.calcSumPerComponent(y, time) - GCsumPerZ_gyro = f.calcSumPerComponent(z, time) - - if f.checkForFeature("sumPer", featureNames): - row["sumPer_X"] = GCsumPerX_gyro - if y is not None: - row["sumPer_Y"] = GCsumPerY_gyro - row["sumPer_Z"] = GCsumPerZ_gyro - - GCsumSquaredX = pow(GCsumPerX_gyro, 2) - if y is not None: - GCsumSquaredY = pow(GCsumPerY_gyro, 2) - GCsumSquaredZ = pow(GCsumPerZ_gyro, 2) - - if f.checkForFeature("sumSquared", featureNames): - row["sumSquared_X"] = GCsumSquaredX - if y is not None: - row["sumSquared_Y"] = GCsumSquaredY - row["sumSquared_Z"] = GCsumSquaredZ - - if y is not None: - if f.checkForFeature("squareSumOfComponent", featureNames): - row["squareSumOfComponent"] = pow((GCsumSquaredX + GCsumSquaredY + GCsumSquaredZ), 2) - - if f.checkForFeature("sumOfSquareComponents", featureNames): - row["sumOfSquareComponents"] = pow(GCsumSquaredX, 2) + pow(GCsumSquaredY, 2) + pow(GCsumSquaredZ, 2) - - return row - - -def calcAutocorrelations(signal, suffix): - """ Calculate autocorrelations of the given signal with lags 5, 10, 20, 30, 50, 75 and 100 - - :param signal: signal on which to calculate the autocorrelations - :param suffix: suffix of the feature name - :return: dict with calculated autocorrelations - """ - row = {} - for i in [5, 10, 20, 30, 50, 75, 100]: - row["autocorrelation_" + str(i) + suffix] = f.autocorrelation(signal, i) - return row - - -def calculateFeaturesGyro(gx, gy, gz, time, gxLow, gyLow, gzLow, featureNames=None): - """ - - :param gx2d: 2D array including the X axis of the gyroscope - :param gy2d: 2D array including the Y axis of the gyroscope - :param gz2d: 2D array including the Z axis of the gyroscope - :param gtime2d: 2D array of times, denoting when each measurement of the gyroscope occurred - :param gFs: sampling frequency of the gyroscope - :return: pandas dataframe including the calculated features - """ - if gy is not None: - magnitudesLow_gyro = f.magnitudeVector(gxLow, gyLow, gzLow) - else: - magnitudesLow_gyro = gxLow - - row = {} - - if f.checkForFeature("meanLow", featureNames): - row["mean_XLow"] = f.calcMean(gxLow) - if gy is not None: - row["mean_YLow"] = f.calcMean(gyLow) - row["mean_ZLow"] = f.calcMean(gzLow) - row["totalMeanLow"] = (row["mean_XLow"] + row["mean_YLow"] + row["mean_ZLow"]) * len(gx) / 3 - - if f.checkForFeature("areaLow", featureNames): - row["area_XLow"] = f.calcArea(gxLow) - if gy is not None: - row["area_YLow"] = f.calcArea(gyLow) - row["area_ZLow"] = f.calcArea(gzLow) - - if gy is not None: - if f.checkForFeature("totalAbsoluteAreaLow", featureNames): - row["totalAbsoluteAreaLow"] = f.calcTotalAbsoluteArea(f.calcAcAbsoluteArea(gxLow, gyLow, gzLow)) - - if f.checkForFeature("totalMagnitudeLow", featureNames): - row["totalMagnitudeLow"] = f.calcTotalMagnitude(gxLow, gyLow, gzLow) - - if f.checkForFeature("entropyLow", featureNames): - row["entropy_XLow"] = f.calcEntropy(gxLow) - if gy is not None: - row["entropy_YLow"] = f.calcEntropy(gyLow) - row["entropy_ZLow"] = f.calcEntropy(gzLow) - - if f.checkForFeature("skewnessLow", featureNames): - row["skewness_XLow"] = f.calcSkewness(gxLow) - if gy is not None: - row["skewness_YLow"] = f.calcSkewness(gyLow) - row["skewness_ZLow"] = f.calcSkewness(gzLow) - - if f.checkForFeature("kurtosisLow", featureNames): - row["kurtosis_XLow"] = f.calcKurtosis(gxLow) - if gy is not None: - row["kurtosis_YLow"] = f.calcKurtosis(gyLow) - row["kurtosis_ZLow"] = f.calcKurtosis(gzLow) - - gcQuartilesX = f.calcQuartiles(gxLow) - if gy is not None: - gcQuartilesY = f.calcQuartiles(gyLow) - gcQuartilesZ = f.calcQuartiles(gzLow) - - if f.checkForFeature("quartilesLow", featureNames): - row["quartiles_Q1_XLow"] = gcQuartilesX[0] - row["quartiles_Q2_XLow"] = gcQuartilesX[1] - row["quartiles_Q3_XLow"] = gcQuartilesX[2] - - if gy is not None: - row["quartiles_Q1_YLow"] = gcQuartilesY[0] - row["quartiles_Q2_YLow"] = gcQuartilesY[1] - row["quartiles_Q3_YLow"] = gcQuartilesY[2] - - row["quartiles_Q1_ZLow"] = gcQuartilesZ[0] - row["quartiles_Q2_ZLow"] = gcQuartilesZ[1] - row["quartiles_Q3_ZLow"] = gcQuartilesZ[2] - - if f.checkForFeature("interQuartileRangeLow", featureNames): - row["interQuartileRange_XLow"] = f.calcInterQuartileRange(gcQuartilesX) - if gy is not None: - row["interQuartileRange_YLow"] = f.calcInterQuartileRange(gcQuartilesY) - row["interQuartileRange_ZLow"] = f.calcInterQuartileRange(gcQuartilesZ) - - # Measures of motion variation - if f.checkForFeature("varianceLow", featureNames): - row["variance_XLow"] = f.calcVariance(gxLow) - if gy is not None: - row["variance_YLow"] = f.calcVariance(gyLow) - row["variance_ZLow"] = f.calcVariance(gzLow) - - if f.checkForFeature("coefficientOfVariationLow", featureNames): - row["coefficientOfVariation_XLow"] = f.calcCoefficientOfVariation(gxLow) - if gy is not None: - row["coefficientOfVariation_YLow"] = f.calcCoefficientOfVariation(gyLow) - row["coefficientOfVariation_ZLow"] = f.calcCoefficientOfVariation(gzLow) - - if f.checkForFeature("amplitudeLow", featureNames): - row["amplitude_XLow"] = f.calcAmplitude(gxLow) - if gy is not None: - row["amplitude_YLow"] = f.calcAmplitude(gyLow) - row["amplitude_ZLow"] = f.calcAmplitude(gzLow) - - if f.checkForFeature("totalEnergyLow", featureNames): - row["totalEnergy_XLow"] = f.calcTotalEnergy(gxLow) - if gy is not None: - row["totalEnergy_YLow"] = f.calcTotalEnergy(gyLow) - row["totalEnergy_ZLow"] = f.calcTotalEnergy(gzLow) - - if f.checkForFeature("dominantFrequencyEnergyLow", featureNames): - row["dominantFrequencyEnergy_XLow"] = f.calcDominantFrequencyEnergy(gxLow) - if gy is not None: - row["dominantFrequencyEnergy_YLow"] = f.calcDominantFrequencyEnergy(gyLow) - row["dominantFrequencyEnergy_ZLow"] = f.calcDominantFrequencyEnergy(gzLow) - - if f.checkForFeature("meanCrossingRateLow", featureNames): - row["meanCrossingRate_XLow"] = f.calcMeanCrossingRate(gxLow) - if gy is not None: - row["meanCrossingRate_YLow"] = f.calcMeanCrossingRate(gyLow) - row["meanCrossingRate_ZLow"] = f.calcMeanCrossingRate(gzLow) - - if gy is not None: - if f.checkForFeature("correlationLow", featureNames): - row["correlation_X_YLow"] = f.calcCorrelation(gxLow, gyLow) - row["correlation_X_ZLow"] = f.calcCorrelation(gxLow, gzLow) - row["correlation_Y_ZLow"] = f.calcCorrelation(gyLow, gzLow) - - gcQuartilesMagnitude = f.calcQuartiles(magnitudesLow_gyro) - - if f.checkForFeature("quartilesMagnitudeLow", featureNames): - row["quartilesMagnitudeLow_Q1"] = gcQuartilesMagnitude[0] - row["quartilesMagnitudeLow_Q2"] = gcQuartilesMagnitude[1] - row["quartilesMagnitudeLow_Q3"] = gcQuartilesMagnitude[2] - - if f.checkForFeature("interQuartileRangeMagnitudesLow", featureNames): - row["interQuartileRangeMagnitudesLow"] = f.calcInterQuartileRange(gcQuartilesMagnitude) - - if gy is not None: - if f.checkForFeature("areaUnderMagnitude", featureNames): - row["areaUnderMagnitude"] = f.calcAreaUnderAccelerationMagnitude(f.magnitudeVector(gx, gy, gz), time) - - if f.checkForFeature("peaksCountLow", featureNames): - peaksCount_gyro = f.calcPeaks(magnitudesLow_gyro) - row["peaksCountLow_Q1"] = peaksCount_gyro[0] - row["peaksCountLow_Q2"] = peaksCount_gyro[1] - row["peaksCountLow_Q3"] = peaksCount_gyro[0] - row["peaksCountLow_Q4"] = peaksCount_gyro[1] - - if f.checkForFeature("averageVectorLengthLow", featureNames): - row["averageVectorLengthLow"] = f.calcAverageVectorLength(magnitudesLow_gyro) - - if f.checkForFeature("averageVectorLengthPowerLow", featureNames): - row["averageVectorLengthPowerLow"] = f.calcAverageVectorLengthPower(magnitudesLow_gyro) - - return row diff --git a/calculatingfeatures/CalculatingFeatures/calculate_features.py b/calculatingfeatures/CalculatingFeatures/calculate_features.py deleted file mode 100644 index b22eeb07..00000000 --- a/calculatingfeatures/CalculatingFeatures/calculate_features.py +++ /dev/null @@ -1,138 +0,0 @@ -from tqdm import tqdm -from CalculatingFeatures.helper_functions import * -import CalculatingFeatures.gsr as gsr -import CalculatingFeatures.hrv as hrv -from CalculatingFeatures.calculate_acc_gyro_common_features import * - -ALPHA_LOW = 0.3 -ALPHA_BAND_LOW = 0.3 -ALPHA_BAND_HIGH = 0.6 - -DEFAULT_DELTA_TIME = 0.02 - -MIN_INPUT_ARRAY_WIDTH = 256 - - -def calculateFeatures(x2d, y2d=None, z2d=None, time2d=None, fs=None, prefix=None, featureNames=None): - """ Calculate features for the given data - Feature names are stored at the end of the "helper_functions.py" file in variables frequencyFeatureNames, genericFeatureNames, - accelerometerFeatureNames, gyroscopeFeatureNames, edaFeatureNames and bvpFeatureNames. For information about features - read the README.md file - - For calculation of features with 1D input shape (one axis), input only x2d parameter. Examples of inputs with 1D input shape - are BVP and EDA signals. - For calculation of features with 3D input shape (three axes), input also y2d and z2d parameter. Examples of inputs with 3D input - shape are gyroscope and accelerometer signals. - - Each individual input axis has to be in a 2D shape. This means that input signals have to be split into rows. - If BVP features are being calculated, width of window has to be at least 256. The conversion from 1D to 2D can be - made with "convertInputInto2d()" function, located in the helper_functions.py file. - - If sampling frequency (fs) is not given, it will be calculated from time2d parameter. - If array of times (time2d) is not given, it will be calculated from fs parameter. - If none of them is given, fs will be calculated as 1/DEFAULT_DELTA_TIME - - :param x2d: 2D array including the X axis - :param y2d: 2D array including the Y axis - :param z2d: 2D array including the Z axis - :param time2d: 2D array of times, denoting when each measurement occurred - :param fs: sampling frequency - :param prefix: prefix to append before each column name - :param featureNames: list of features to calculate. If it is None, all features will be calculated - :return: pandas DataFrame of the calculated features - """ - - if len(x2d[0]) < MIN_INPUT_ARRAY_WIDTH: - raise Exception("Input 2D array width has to be at least " + str(MIN_INPUT_ARRAY_WIDTH)) - - if type(x2d) is list: - x2d = np.asarray(x2d) - - if type(y2d) is list: - y2d = np.asarray(y2d) - - if type(z2d) is list: - z2d = np.asarray(z2d) - - if type(time2d) is list: - time2d = np.asarray(time2d) - - if (x2d is not None and y2d is not None and z2d is not None) or (x2d is not None and y2d is None and z2d is None): - if y2d is not None and not (x2d.shape == y2d.shape and y2d.shape == z2d.shape): - raise Exception("x2d, y2d, z2d shapes have to be the same!") - # Verify fs and time array - if time2d is not None and fs is not None and fs != 1 / (time2d[0, 1] - time2d[0, 0]): - raise Exception("sampling frequency of the given time2D matrix and fs do not match!") - if time2d is None: - deltaTime = 1 / fs if fs is not None else DEFAULT_DELTA_TIME - time2d = np.asarray(convertInputInto2d([i * deltaTime for i in range(x2d.size)], x2d.shape[1])) - if fs is None: - fs = 1 / (time2d[0][1] - time2d[0][0]) - - else: - raise Exception("Incorrect input! Either x2d, y2d and z2d are given, or only x2d is given!") - - fs = int(fs) - - if y2d is None: - y2d = z2d = [None] * len(x2d) - - df = pd.DataFrame() - for x, y, z, time in tqdm(zip(x2d, y2d, z2d, time2d), total=len(x2d)): - xBand = f.bandPassFilter(x, ALPHA_BAND_LOW, ALPHA_BAND_HIGH) - if y is not None: - yBand = f.bandPassFilter(y, ALPHA_BAND_LOW, ALPHA_BAND_HIGH) - zBand = f.bandPassFilter(z, ALPHA_BAND_LOW, ALPHA_BAND_HIGH) - - xLow = f.lowPassFilter(x, ALPHA_LOW) - if y is not None: - yLow = f.lowPassFilter(y, ALPHA_LOW) - zLow = f.lowPassFilter(z, ALPHA_LOW) - - row = {} - - if y is not None: - row.update(calculateFeaturesAcc(x, y, z, time, xBand, yBand, zBand, xLow, yLow, zLow, featureNames)) - else: - row.update(calculateFeaturesAcc(x, y, z, time, xBand, None, None, xLow, None, None, featureNames)) - - if y is not None: - row.update(calculateFeaturesGyro(x, y, z, time, xLow, yLow, zLow, featureNames)) - else: - row.update(calculateFeaturesGyro(x, y, z, time, xLow, None, None, featureNames)) - - row.update(calcCommonFeatures(x, y, z, time, featureNames)) - - # Add frequency features - row.update({str(key) + "_X": val for key, val in f.computeFreqFeatures(x, featureNames, fs).items()}) - if y is not None: - row.update({str(key) + "_Y": val for key, val in f.computeFreqFeatures(y, featureNames, fs).items()}) - row.update({str(key) + "_Z": val for key, val in f.computeFreqFeatures(z, featureNames, fs).items()}) - - # EDA features - row.update({str(key) + "_X": val for key, val in - gsr.extractGsrFeatures(x, sampleRate=fs, featureNames=featureNames).items()}) - if y is not None: - row.update({str(key) + "_Y": val for key, val in - gsr.extractGsrFeatures(y, sampleRate=fs, featureNames=featureNames).items()}) - row.update({str(key) + "_Z": val for key, val in - gsr.extractGsrFeatures(z, sampleRate=fs, featureNames=featureNames).items()}) - - # BVP features - row.update({str(key) + "_X": val for key, val in - hrv.extractHrvFeatures(x, sampling=fs, featureNames=featureNames).items()}) - if y is not None: - row.update({str(key) + "_Y": val for key, val in - hrv.extractHrvFeatures(y, sampling=fs, featureNames=featureNames).items()}) - row.update({str(key) + "_Z": val for key, val in - hrv.extractHrvFeatures(z, sampling=fs, featureNames=featureNames).items()}) - - df = df.append(row, ignore_index=True) - - if prefix is not None: - dfNewCols = [] - for col in df.columns: - dfNewCols.append(prefix + "_" + col) - df.columns = dfNewCols - - return df diff --git a/calculatingfeatures/CalculatingFeatures/calculate_hrv_peaks.py b/calculatingfeatures/CalculatingFeatures/calculate_hrv_peaks.py deleted file mode 100644 index 9be80d8b..00000000 --- a/calculatingfeatures/CalculatingFeatures/calculate_hrv_peaks.py +++ /dev/null @@ -1,90 +0,0 @@ -import numpy as np -import math - - -def peak_detector(ppg, Fs): - """ - Peak detector written by Gasper Slapnicar. Optimized for detecting peaks in PPG signal. - :param ppg: Signal where peaks are to be detected. 1-D array like - :param Fs: Sampling frequency - :return: Peaks and valleys in PPG. Two arrays of indices in ppg input - """ - peak_duration = int(math.floor((1.0 / 9) * Fs)) - aver_pulse_rate = int(math.floor((2.0 / 3) * Fs)) - aver_level_window = int(math.floor(2 * Fs)) - - mean_coef_for_threshold = 0.02 - signal_length = len(ppg) - - ppg_squared = np.square(ppg) - ppg_squared[ppg < 0] = 0 - - mean_peak_level = np.convolve(ppg_squared, np.ones((peak_duration,)) / peak_duration, mode='same') - mean_beat_level = np.convolve(ppg_squared, np.ones((aver_pulse_rate,)) / aver_pulse_rate, mode='same') - - thresh1 = np.add(mean_beat_level, - mean_coef_for_threshold * np.convolve(ppg_squared, - np.ones((aver_level_window,)) / aver_level_window, - mode='same')) - block_of_interest = np.zeros(signal_length) - block_of_interest[mean_peak_level > thresh1] = 1 - - block_edges = np.diff(block_of_interest) - block_start = np.add(np.where(block_edges == 1), 1)[0] - if block_start.size == 0: - return np.array([]), np.array([]) - else: - block_end = np.where(block_edges == -1)[0] - - if block_end.size == 0: - return np.array([]), np.array([]) - - if block_start[0] > block_end[0]: - block_start = np.insert(block_start, 0, 1, axis=0) - - if block_start[-1] > block_end[-1]: - block_end = np.append(block_end, signal_length) - - if len(block_start) != len(block_end): - return np.array([]), np.array([]) - - length_block = np.subtract(block_end, block_start) - correct_blocks = np.where(length_block > peak_duration) - - peak_pos = np.zeros(len(correct_blocks[0])) - i_peak = 0 - for iBlock in correct_blocks[0]: - block_of_interest = ppg_squared[block_start[iBlock]:block_end[iBlock]] - peak_pos[i_peak] = max(range(len(block_of_interest)), key=block_of_interest.__getitem__) - peak_pos[i_peak] = peak_pos[i_peak] + (block_start[iBlock] - 1) - i_peak += 1 - - interpeak_threshold_coeff = 0.65 - max_over_average = 1.15 - need_check = True - while need_check: - interpeak = np.diff(peak_pos) - - if interpeak.size == 0: - return np.array([]), np.array([]) - - mean_interpeak = np.mean(interpeak) - interpeak_double = np.insert(np.add(interpeak[0:-1], interpeak[1:]), 0, 2 * interpeak[0], axis=0) - interpeak_thresh = np.insert(interpeak_double[0:-2], 0, [2 * mean_interpeak, 2 * mean_interpeak], axis=0) - interpeak_thresh[interpeak_thresh > 2 * max_over_average * mean_interpeak] = 2 * max_over_average \ - * mean_interpeak - interpeak_thresh = interpeak_thresh * interpeak_threshold_coeff - index_short_interval = np.where(interpeak_double < interpeak_thresh)[0] - - if index_short_interval.size != 0: - peak_pos = np.delete(peak_pos, index_short_interval.tolist()) - else: - need_check = False - - # Add valley (simple detection) - valley_pos = [] - for start, end in zip(peak_pos[0:-1].astype('int'), peak_pos[1:].astype('int')): - valley_pos.append(min(range(len(ppg[start:end])), key=ppg[start:end].__getitem__) + start) - - # Addition because I missed it somewhere in the code...but i cant find where - return np.add(peak_pos, 1).astype('int'), np.array(valley_pos) diff --git a/calculatingfeatures/CalculatingFeatures/feature_functions.py b/calculatingfeatures/CalculatingFeatures/feature_functions.py deleted file mode 100644 index 4ca108ad..00000000 --- a/calculatingfeatures/CalculatingFeatures/feature_functions.py +++ /dev/null @@ -1,986 +0,0 @@ -import numpy as np -from scipy.signal import welch -from scipy.stats import entropy, skew, kurtosis, iqr, linregress -import pandas as pd -import itertools -from CalculatingFeatures.helper_functions import checkForFeature - - -def computeFreqFeatures(data, featureNames=None, Fs=50): - """ Computes frequency features of the given array. The signal is converted to power spectral density signal and - features are calculated on that signal - - :param data: data on which to calculate the features - :param featureNames: names of features to calculate - :param Fs: sampling rate of the given data - :return: the calculated features (names of the features are given in array "freqFeatureNames") - """ - features = {} - - ## 3 highest peaks and corresponding freqs - f, Pxx_den = welch(data, Fs) # f: array of sample frequencies, Pxx_den: power spectrum of data - - # arr.argsort() returns an array of indices of the same shape as arr that that would sort the array - # arr[arr.argsort()] returns the sorted array arr (if one-dimensional) - indices_of_max = Pxx_den.argsort()[-3:][::-1] # last three values (=highest peaks), reversed (?) - - if checkForFeature("fqHighestPeakFreqs", featureNames): - highestPeakFreqs = f[indices_of_max] # three frequencies corresponding to the largest peaks added to features - features["fqHighestPeakFreq1"] = highestPeakFreqs[0] - features["fqHighestPeakFreq2"] = highestPeakFreqs[1] - features["fqHighestPeakFreq3"] = highestPeakFreqs[2] - - if checkForFeature("fqHighestPeaks", featureNames): - highestPeaks = Pxx_den[indices_of_max] # three largest peaks added to features - features["fqHighestPeak1"] = highestPeaks[0] - features["fqHighestPeak2"] = highestPeaks[1] - features["fqHighestPeak3"] = highestPeaks[2] - - ## Energy and Entropy - # np.fft.fft() computes the one-dimensional n-point discrete Fourier Transform (DFT) with the efficient FFT algorithm - Y = np.fft.fft(data) - # energy calculated as the sum of the squared FFT component magnitudes, and normalized - Y_abs = np.abs(Y) - energy_feat = np.sum(np.square(Y_abs)) / len(data) # np.abs = absolute value - - entropy_feat = entropy(np.abs(Y)) if Y_abs.any() else np.NaN - - if checkForFeature("fqEnergyFeat", featureNames): - features["fqEnergyFeat"] = energy_feat - - if checkForFeature("fqEntropyFeat", featureNames): - features["fqEntropyFeat"] = entropy_feat - - # Binned distribution (histogram) - # First, the PSD is split into 10 equal-sized bins ranging from 0 Hz to 25 Hz. - # Then, the fraction of magnitudes falling into each bin is calculated. - if checkForFeature("fqHistogramBins", featureNames): - total_fft_sum = np.sum(np.square(Pxx_den)) - - def getBin(start, end): - return np.nan if total_fft_sum == 0 else np.sum(np.square(Pxx_den[start:end])) / total_fft_sum - - features["fqHistogramBin1"] = getBin(0, 5) - features["fqHistogramBin2"] = getBin(5, 10) - features["fqHistogramBin3"] = getBin(10, 15) - features["fqHistogramBin4"] = getBin(15, 20) - features["fqHistogramBin5"] = getBin(20, 25) - features["fqHistogramBin6"] = getBin(25, 30) - features["fqHistogramBin7"] = getBin(30, 35) - features["fqHistogramBin8"] = getBin(35, 40) - features["fqHistogramBin9"] = getBin(40, 45) - features["fqHistogramBin10"] = getBin(45, len(Pxx_den)) - - # Statistical features - if checkForFeature("fqAbsMean", featureNames): - features["fqAbsMean"] = np.mean(np.abs(data)) # this on raw signal - if checkForFeature("fqAbsMean", featureNames): - features["fqSkewness"] = skew(Pxx_den) # this on "distribution-like" periodogram - if checkForFeature("fqKurtosis", featureNames): - features["fqKurtosis"] = kurtosis(Pxx_den) # this on "distribution-like" periodogram - if checkForFeature("fqInterquart", featureNames): - features["fqInterquart"] = iqr(data) # this on raw signal - - return features - - -def calcAreaUnderAccelerationMagnitude(magnitudes, time): - """ - Calculates AreaUnderAccelerationMagnitude feature - :param magnitudes: vector of magnitudes - :param time: array of timestamps - :return: AreaUnderAccelerationMagnitude for the selected axis - """ - - eeArea = 0.0 - dT = 35 - - for i in range(len(magnitudes)): - eeArea += magnitudes[i] * dT # - gravity - if i > 0: - dT = time[i] - time[i - 1] - else: - dT = 35 - return eeArea - - -def calcAverageVectorLength(magnitudes): - """ - Calculates mean of magnitude vector - :param magnitudes: vector of magnitudes - :return: mean of magnitude vector - """ - return np.sum(magnitudes) / (len(magnitudes)) - - -def calcAverageVectorLengthPower(magnitudes): - """ - Calculates square mean of magnitude vector - :param magnitudes: vector of magnitudes - :return: mean of magnitude vector squared - """ - return np.square(calcAverageVectorLength(magnitudes)) - - -def calcMeanKineticEnergy(data, time): - """ - Calculates mean kinetic energy for the selected axis - :param data: data from accelerometer for selected axis (band-pass filtered) - :param time: array of timestamps - :return: mean kinetic energy 1/2*mV^2 - """ - weight = 60.0 - dT = 1.0 - velocity = 0.0 - kinetic = 0.0 - - for i in range(len(data)): - velocity += data[i] * dT - kinetic += 0.5 * weight * velocity * velocity * dT - if i < len(time) - 1: - dT = (time[i + 1] - time[i]) / 1000.0 - - return kinetic - - -def calcPeaks(magnitudes): - """ - Calculates number of peaks and sum of values - :param magnitudes: vector of magnitudes - :return: array of double - [0] number of peaks, [1] sum of peak values - """ - maxValue = 500.0 - previous = -200.0 - threshold = 3 - peaks = np.empty(0) - sumOfPeakValues = 0.0 - peak = False - - for curMagnitude in magnitudes: - if curMagnitude > threshold: - if curMagnitude >= maxValue: - maxValue = curMagnitude - peak = True - elif curMagnitude < maxValue: - if peak and previous > curMagnitude: - peaks = np.append(peaks, maxValue) - peak = False - sumOfPeakValues += maxValue - - if curMagnitude > previous and not peak: - peak = True - maxValue = -200.0 - previous = curMagnitude - - return np.array([float(len(peaks)), sumOfPeakValues]) - - -def calcTotalKineticEnergy(x, y, z, t): - """ - Calculates total kinetic energy for all axes - :param x: data from accelerometer for X axis (band-pass filtered) - :param y: data from accelerometer for Y axis (band-pass filtered) - :param z: data from accelerometer for Z axis (band-pass filtered) - :param t: array of timestamps - :return: total kinetic energy 1/2*mV^2 - """ - weight = 60.0 - totaltime = (t[-1] - t[0]) / 1000.0 - - dT = 1.0 - velocityX = 0.0 - velocityY = 0.0 - velocityZ = 0.0 - kineticX = 0.0 - kineticY = 0.0 - kineticZ = 0.0 - totalEnergy = 0.0 - - for i in range(len(x)): - velocityX += x[i] * dT - velocityY += y[i] * dT - velocityZ += z[i] * dT - - kineticX += 0.5 * weight * velocityX * velocityX * dT - kineticY += 0.5 * weight * velocityY * velocityY * dT - kineticZ += 0.5 * weight * velocityZ * velocityZ * dT - - totalEnergy += kineticX + kineticY + kineticZ - if i < t.size - 1: - dT = (t[i + 1] - t[i]) / 1000.0 - - return totalEnergy / totaltime - - -def calcAcAbsoluteArea(x, y, z): - """ - Calculates a vector with sums of absolute values for the given sensor - :param x: x component (band-pass filtered) - :param y: y component (band-pass filtered) - :param z: z component (band-pass filtered) - :return: [sumX, sumY, sumZ] - """ - return np.array([np.sum(np.absolute(x)), np.sum(np.absolute(y)), np.sum(np.absolute(z))]) - - -def calcAbsoluteMean(data): - """ - - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: mean (sum)/N - """ - return np.sum(np.absolute(data)) / len(data) - - -def calcAmplitude(data): - """ - Calculates dispersion for a given vector component - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: dispersion - """ - return np.max(data) - np.min(data) - - -def calcCoefficientOfVariation(data): - """ - Calculates dispersion for a given vector component - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: dispersion - """ - s = np.sum(np.absolute(data)) - if s == 0: - return np.NaN - return np.sqrt(calcVariance(data)) / s * 100 - - -# -def calcCorrelation(a, b): - """ - Calculates Pearson's correlation between sensor axis - :param a: first component (band-pass filtered) - :param b: second component (band-pass filtered) - :return: correlation between a and b - """ - selfCovarianceA = covariance(a, a) - selfCovarianceB = covariance(b, b) - s = np.sqrt(selfCovarianceA * selfCovarianceB) - if s == 0: - return np.NaN - return covariance(a, b) / s - - -def calcEntropy(data): - """ - Calculates the degree of disorder - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: entropy for the selected axis - """ - acc = 0 - for d in normalize(fftMagnitude(fft(data))): - if d == 0: - return np.NaN - acc += d * np.log(d) / np.log(2.0) - - return -acc - - -# -def calcInterQuartileRange(qData): - """ - Calculates interquartile range - :param qData: quartiles vector - :return: range for the selected axis - """ - return qData[2] - qData[0] - - -def calcKurtosis(data): - """ - Calculates Kurtosis for given vector - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: kurtosis for selected vector - """ - mean = calcAbsoluteMean(data) - - acc = 0 - for d in data: - acc += np.power(d - mean, 4.0) - pow4 = acc - - acc = 0 - for d in data: - acc += np.power(d - mean, 2.0) - pow2 = acc - if pow2 == 0: - return np.NaN - return len(data) * pow4 / np.square(pow2) - 3 - - -def calcMeanCrossingRate(data): - """ - Calculates the number of signal crossings with mean - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: number of mean crossings - """ - mean = np.sum(np.abs(data)) / len(data) - - crossings = 0 - last = data[0] - mean - - for i in range(len(data)): - current = data[i] - mean - if last * current < 0: - crossings += 1 - last = current - - return crossings - - -def calcQuartiles(data): - """ - Quartiles at 25%, 50% and 75% per signal - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: [accQ25, accQ50, accQ75] - """ - sorted1 = sorted(data) - size = len(data) - return np.array([sorted1[int(size / 4)], sorted1[int(size / 2)], sorted1[int(size * 3 / 4)]]) - - -def calcSkewness(data): - """ - Calculates skewness for given vector - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: skewness for selected vector - """ - mean = calcAbsoluteMean(data) - - acc = 0 - for d in data: - acc += np.power(d - mean, 3.0) - pow3 = acc - - acc = 0 - for d in data: - acc += np.power(d - mean, 2.0) - pow2 = acc - if pow2 == 0: - return np.NaN - return np.sqrt(float(len(data))) * pow3 / np.power(pow2, 1.5) - - -# -def calcTotalAbsoluteArea(area): - """ - Calculates sum of component areas for selected sensor (@see calcAcAbsoluteArea) - :param area: [sumX, sumY, sumZ] for given device (band-pass filtered) (@see calcAcAbsoluteArea) - :return: sum of component sums - """ - return np.sum(area) - - -def calcTotalEnergy(data): - """ - Calculates total magnitude of AC signal for the given sensor - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: total energy for selected axis - """ - fftMagnitudeTmp = fftMagnitude(fft(data)) - - return np.sum(np.square(fftMagnitudeTmp[1:])) / len(fftMagnitudeTmp) - - -def calcDominantFrequencyEnergy(data): - """ - Calculates ratio of energy in dominant frequency - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: energy ratio for selected axis - """ - fftMagnitudeTmp = fftMagnitude(fft(data)) - sortedTmp = sorted(fftMagnitudeTmp) - s = np.sum(sortedTmp) - if s == 0: - return np.NaN - return sortedTmp[-1] / s - - -def calcTotalMagnitude(x, y, z): - """ - Calculates total magnitude of AC signal for the given sensor - :param x: x component (band-pass filtered) - :param y: y component (band-pass filtered) - :param z: z component (band-pass filtered) - :return: sqrt(sum(x^2+y^2+z^2)) - """ - return np.sqrt(magnitudes(x) + magnitudes(y) + magnitudes(z)) - - -def calcVariance(data): - """ - Calculates variance for given vector - :param data: data from accelerometer for selected axis (band-pass filtered) - :return: variance - """ - acc = 0 - for d in data: - acc = acc + np.square(d - calcAbsoluteMean(data)) / len(data) - return acc - - -def calcArea(data): - """ - Calculates sum of component - :param data: data from accelerometer for selected axis (low-pass filtered) - :return: area - """ - return np.sum(data) - - -def calcMean(data): - """ - Calculates mean value for a given vector - :param data: data from accelerometer for selected axis (low-pass filtered) - :return: mean (sum)/N - """ - return np.sum(data) / len(data) - - -def calcPostureDistance(meanX, meanY, meanZ): - """ - Calculates difference between mean values for a given sensor (low-pass filtered) - :param meanX: mean for X components - :param meanY: mean for Y components - :param meanZ: mean for Z components - :return: [X-Y, X-Z, Y-Z] - """ - return np.array([meanX - meanY, meanX - meanZ, meanY - meanZ]) - - -def calcTotalMean(sumPhone, sumBand): - """ - Calculates mean of all sensors (low-pass filtered) - :param sumPhone: meanX + meanY + meanZ - :param sumBand: meanX + meanY + meanZ - :return: mean of all means - """ - return (sumPhone.sum() + sumBand.sum()) / 6 - - -def calcPeakCount(magnitudes): - """ - Calculates PeakCount feature - :param magnitudes: vector of magnitudes - :return: [numberOfPeaks, sumOfPeakValues, peakAvg, amplitudeAvg] - """ - previous = 0.0 - eExpenditurePeaks = 0.0 - eExpenditureAmplitude = 0.0 - - state = np.zeros(0) - peaks = np.zeros(0) - low = -1.0 - - for currentmagnitude in magnitudes: - if currentmagnitude > previous: - state = np.append(state, True) - else: - state = np.append(state, False) - - if len(state) > 2: - state = np.delete(state, 0) - if state[0] and not state[1]: - if low != -1.0: - eExpenditureAmplitude = previous - low - else: - low = previous - if previous - low > 1.0: - peaks = np.append(peaks, currentmagnitude) - eExpenditurePeaks += previous - - if not state[0] and state[1]: - low = previous - - previous = currentmagnitude - - peaksReturn0 = len(peaks) - peaksReturn1 = eExpenditurePeaks - peaksReturn2 = 0.0 - peaksReturn3 = 0.0 - if len(peaks) > 0: - peaksReturn2 = eExpenditurePeaks / len(peaks) - peaksReturn3 = eExpenditureAmplitude / len(peaks) - - return np.array([float(peaksReturn0), peaksReturn1, peaksReturn2, peaksReturn3]) - - -def calcSumPerComponent(data, time): - """ - Calculates calcSumPerComponent feature - :param data: data from accelerometer for selected axis - :param time: array of timestamps - :return: sum of Xi*dT - """ - calc = 0.0 - dT = 1.0 - - for i in range(len(data)): - calc += np.abs(data[i]) * dT - if i < len(data) - 1: - dT = (time[i + 1] - time[i]) / 1000.0 - - return calc - - -def computeACVelocity(data, time): - """ - Calculates velocity feature - :param data: data from accelerometer for selected axis (band-pass filtered) - :param time: array of timestamps - :return: velocity for selected axis - """ - calc = 0.0 - dT = 1.0 - - for i in range(len(data)): - calc += data[i] * dT - if i < data.size - 1: - dT = (time[i + 1] - time[i]) / 1000.0 - - return calc - - -def lowPassFilter(input, alpha=0.2): - """ Low-pass filter implemented in discrete time - - :param input: input signal to be filtered - :param alpha: smoothing factor - :return: filtered signal - """ - output = np.zeros(input.size) - output[0] = input[0] - for i in range(1, output.size): - output[i] = output[i - 1] + alpha * (input[i] - output[i - 1]) - return output - - -def bandPassFilter(input, alphaLpf=0.2, alphaHpf=0.6): - """ Band-pass filer implemented in discrete time - - :param input: input signal to be filtered - :param alphaLpf: smoothing factor for LPF - :param alphaHpf: smoothing factor for HPF - :return: filtered signal - """ - output = lowPassFilter(input, alphaLpf) - output = highPassFilter(output, alphaHpf) - return output - - -def highPassFilter(input, alpha=0.6): - """ High-pass filter implemented in discrete time - - :param input: input signal to be filtered - :param alpha: smoothing factor - :return: filtered signal - """ - output = np.zeros(input.size) - output[0] = input[0] - for i in range(1, output.size): - output[i] = alpha * (output[i - 1] + input[i] - input[i - 1]) - return output - - -def magnitudeVector(x, y, z): - """ Calculates magnitudes of vectors x, y and z - - :param x: x axis - :param y: y axis - :param z: z axis - :return: numpy array of magnitudes - """ - acc = np.zeros(x.size) - for i in range(acc.size): - acc[i] = np.sqrt(np.square(x[i]) + np.square(y[i]) + np.square(z[i])) - return acc - - -def sum(data): - """ Sums up the given array - - :param data: array to sum up - :return: summed value - """ - return np.sum(data) - - -def absoluteSum(data): - """ Sums up absolute values of the given array - - :param data: array to sum up - :return: summed value - """ - return np.sum(np.abs(data)) - - -def fft(data): - """ Performs fast fourier transform on the given array - - :param data: the array on which fft should be performed - :return: processed array - """ - tmpArray = [None] * (len(data) * 2) - for i in range(len(data)): - tmpArray[i] = data[i] - tmpArray = np.fft.fft(tmpArray, len(data)) - - tmpArray2 = [] - for t in tmpArray: - tmpArray2.append(t.real) - tmpArray2.append(t.imag) - - if len(data) % 2 == 0: - ret = np.zeros(len(data)) - else: - ret = np.zeros(len(data) + 1) - - for i, _ in enumerate(ret): - ret[i] = tmpArray2[i] - return ret - - -def fftMagnitude(data): - """ - - :param data: - :return: - """ - ret = np.zeros(int(len(data) / 2)) - for i, _ in enumerate(ret): - ret[i] = np.sqrt(np.square(data[2 * i]) + np.square(data[2 * i + 1])) - return ret - - -def normalize(data): - """ Normalize the given array - - :param data: the array to normalize - :return: normalized array - """ - ret = np.zeros(len(data)) - sum = np.sum(data) - for i, _ in enumerate(data): - if sum == 0: - ret[i] = np.NaN - else: - ret[i] = data[i] / sum - return ret - - -def covariance(array1, array2): - """ - Covariance between two arrays - :param array1: first array of values - :param array2: second array of values - :return: covariance(array1, array2) - """ - cov = 0.0 - - m1 = np.sum(np.abs(array1)) - m2 = np.sum(np.abs(array2)) - - for i, _ in enumerate(array1): - cov += (array1[i] - m1) * (array2[i] - m2) - - return cov - - -def magnitudes(data): - """ Calculates the sum of squares of given array - - :param data: given array - :return: sum of squares - """ - acc = 0 - for d in data: - acc += np.square(d) - return acc - - -def calcRoll(arrayAy, arrayAz): - """ Calculate the roll value of y and z axis - - :param arrayAy: array of y values - :param arrayAz: array of z values - :return: array of calculated roll values - """ - roll = np.zeros(arrayAy.size) - - for i, _ in enumerate(arrayAy): - roll[i] = np.arctan2(arrayAz[i], arrayAy[i]) - - return roll - - -def calcPitch(arrayAx, arrayAy, arrayAz): - """Calculate the pitch - - :param arrayAx: array of x values - :param arrayAy: array of y values - :param arrayAz: array of z values - :return: array of calculated pitch values - """ - pitch = np.zeros(arrayAx.size) - - for i, _ in enumerate(arrayAy): - pitch[i] = np.arctan2(-arrayAx[i], (arrayAy[i] * arrayAy[i] + arrayAz[i] * arrayAz[i])) - - return pitch - - -def stdDev(arrayX): - """ Calculates the standard deviation of the given array - - :param arrayX: the array on which to calculate standard deviation - :return: standard deviation of the given array - """ - std = 0.0 - - mean = calcMean(arrayX) - for cnt, _ in enumerate(arrayX): - std += (arrayX[cnt] - mean) * (arrayX[cnt] - mean); - - return np.sqrt(std / arrayX.size) - - -def rollMotionAmount(roll): - """ - amount of wrist roll motion - Improving the Recognition of Eating Gestures Using Intergesture Sequential Dependencies [R.I. Ramos-Garcia] - """ - meanRoll = calcMean(roll) - rollMot_mean = np.zeros(roll.size) - - for i in range(roll.size): - rollMot_mean[i] = np.abs(roll[i] - meanRoll) # - gravity; - - return calcMean(rollMot_mean) - - -def rollMotionRegularity(roll): - """ - regularity of wrist roll motion - represents the percentage of time that the wrist is in roll motion - Improving the Recognition of Eating Gestures Using Intergesture Sequential Dependencies [R.I. Ramos-Garcia] - """ - rollBoundary = 10 * (np.pi / 180) - instance_number = 0.0 - - for i in range(roll.size): - if np.abs(roll[i]) > rollBoundary: - instance_number += 1.0 - - return instance_number / roll.size - - -def manipulation(axLow, ayLow, azLow, roll, pitch): - """ - - :param axLow: low-pass filtered accelerometer's x axis - :param ayLow: low-pass filtered accelerometer's y axis - :param azLow: low-pass filtered accelerometer's z axis - :param roll: roll value - :param pitch: pitch value - :return: manipulation array - """ - man_velocity = np.zeros(roll.size) - - for i in range(roll.size): - man_velocity[i] = \ - (np.abs(roll[i]) + np.abs(pitch[i])) / (np.abs(axLow[i]) + np.abs(ayLow[i]) + np.abs(azLow[i])) - return calcMean(man_velocity) - - -def min(list): - """ Return the minimum value of the given list. - - :param list: the given list - :return: the minimum value of the list - """ - return np.min(list) - - -def max(list): - """ Return the maximum value of the given list. - - :param list: the given list - :return: the maximum value of the list - """ - return np.max(list) - - -def avg(list): - """ Return the average value of the given list. - - :param list: the given list - :return: the average value of the list - """ - return np.average(list) - - -def countAboveMean(signal): - """ Returns the number of values in x that are higher than the mean of x - - :param signal: the time series to calculate the feature of - :return: the value of this feature - """ - mean = np.mean(signal) - return np.where(signal > mean)[0].size - - -def countBelowMean(signal): - """ Returns the number of values in x that are lower than the mean of x - - :param signal: the time series to calculate the feature of - :return: the value of this feature - """ - mean = np.mean(signal) - return np.where(signal < mean)[0].size - - -def meanAbsChange(signal): - """ Returns the mean of absolute differences between subsequent time series values - - :param signal: input signal - :return: mean of absolute differences - """ - return np.mean(np.abs(np.diff(signal))) - - -def autocorrelation(signal, lag): - """Compute the lag-N autocorrelation. - - This method computes the Pearson correlation between - the Series and its shifted self. - :param signal: the signal to preform the autocorrelation on - :param lag: Number of lags to apply before performing autocorrelation. - :return: - """ - signal = pd.Series(signal) - return signal.autocorr(lag) - - -def autocorrelations(signal): - """ This method computes autocorrelations for each lag from 0 to len(signal2D[0]) * 0.7 - - :param signal: input signal on which to calculate autocorrelations - :return: array of autocorrelations - """ - nlags = int(len(signal) * 0.7) - autocorrs = np.empty(nlags) - for lag in range(nlags): - autocorrs[lag] = autocorrelation(signal, lag) - return autocorrs - - -def _calcMaxLengthOfSequenceTrueOrOne(signal): - if len(signal) == 0: - return 0 - else: - res = [len(list(group)) for value, group in itertools.groupby(signal) if value == 1] - return max(res) if len(res) > 0 else 0 - - -def meanChange(x): - """ Returns the mean over the differences between subsequent time series values - - :param x: the time series to calculate the feature of - :return: the value of this feature - """ - x = np.asarray(x) - return (x[-1] - x[0]) / (len(x) - 1) if len(x) > 1 else np.NaN - - -def numberOfZeroCrossings(x): - """ Calculates the number of crossings of x on 0. A crossing is defined as two sequential values where the first - value is lower than 0 and the next is greater, or vice-versa. - - :param x: the time series to calculate the feature of - :return: the value of this feature - """ - x = np.asarray(x) - positive = x > 0 - return np.where(np.diff(positive))[0].size - - -def ratioBeyondRSigma(x, r): - """ Ratio of values that are more than r*std(x) (so r sigma) away from the mean of x. - - :param x: the time series to calculate the feature of - :param r: the ratio to compare with - :return: the value of this feature - """ - return np.sum(np.abs(x - np.mean(x)) > r * np.std(x)) / x.size - - -def binnedEntropy(x, max_bins): - """ - First bins the values of x into max_bins equidistant bins. - Then calculates the value of - - .. math:: - - - \\sum_{k=0}^{min(max\\_bins, len(x))} p_k log(p_k) \\cdot \\mathbf{1}_{(p_k > 0)} - - where :math:`p_k` is the percentage of samples in bin :math:`k`. - - :param x: the time series to calculate the feature of - :type x: numpy.ndarray - :param max_bins: the maximal number of bins - :type max_bins: int - :return: the value of this feature - :return type: float - """ - if not isinstance(x, (np.ndarray, pd.Series)): - x = np.asarray(x) - - # nan makes no sense here - if np.isnan(x).any(): - return np.nan - - hist, bin_edges = np.histogram(x, bins=max_bins) - probs = hist / x.size - probs[probs == 0] = 1.0 - return - np.sum(probs * np.log(probs)) - - -def absEnergy(x): - """ - Returns the absolute energy of the time series which is the sum over the squared values - - .. math:: - - E = \\sum_{i=1,\\ldots, n} x_i^2 - - :param x: the time series to calculate the feature of - :type x: numpy.ndarray - :return: the value of this feature - :return type: float - """ - if not isinstance(x, (np.ndarray, pd.Series)): - x = np.asarray(x) - return np.dot(x, x) - - -def linearTrendSlope(x): - """ - Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to - length of the time series minus one. - This feature assumes the signal to be uniformly sampled. It will not use the time stamps to fit the model. - The parameters control which of the characteristics are returned. - - :param x: the time series to calculate the feature of - :return: slope of the model - """ - slope, intercept, r_value, p_value, std_err = linregress(range(len(x)), x) - - return slope diff --git a/calculatingfeatures/CalculatingFeatures/gsr.py b/calculatingfeatures/CalculatingFeatures/gsr.py deleted file mode 100644 index 92174b66..00000000 --- a/calculatingfeatures/CalculatingFeatures/gsr.py +++ /dev/null @@ -1,669 +0,0 @@ -from eda_explorer.load_files import butter_lowpass_filter -from eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures -import numpy as np -import peakutils -import matplotlib.pyplot as plt -import scipy.signal as signal -import biosppy.signals.tools as st -from CalculatingFeatures.helper_functions import checkForFeature - - -def extractGsrFeatures(signal, startTimestampSeconds=0, sampleRate=4, threshold=.02, offset=1, riseTime=4, decayTime=4, - featureNames=None): - """ Extract Martin's GSR features with eda-explorer peak detection - - :param signal: numpy array containing the signal - :param startTimestampSeconds: seconds from epoch when the signal stared - :param sampleRate: sampling rate of the input signal - :param threshold: threshold for detected peaks - :param offset: - :param riseTime: rise time of detected peaks - :param decayTime: decay time of detected peaks - :return: calculated GSR features - """ - filteredSignal = butter_lowpass_filter(signal, 1.0, sampleRate, 6) - - gsr_data = pd.DataFrame(signal, columns=["EDA"]) - - startTime = pd.to_datetime(startTimestampSeconds, unit="s") - gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000 / sampleRate) + 'L') - - # Filter the signal - gsr_data['filtered_eda'] = filteredSignal - # Calculate peak data with eda-explorer - peakData = calcPeakFeatures(gsr_data, offset, threshold, - riseTime, decayTime, sampleRate) - - peaks = np.where(peakData.peaks == 1.0)[0] - - if np.any(signal): - tonic = peakutils.baseline(signal, 10) - else: - tonic = signal - - # Calculate features with Martin's library - feats = calculate_GSR_features(signal, peaks, tonic, sampleRate, featureNames=featureNames) - freq_feats = GSR_freq(signal, sampleRate, False, print_flag=False, featureNames=featureNames) - peaks, ends, starts = get_peak_intervals(signal, peaks, sampleRate, False) - peak_features = get_peak_intervals_features(signal, peaks, starts, ends, sampleRate, featureNames=featureNames) - significant_change_features = significant_change(signal, sampleRate, False, False, featureNames=featureNames) - - return {**feats, **freq_feats, **peak_features, **significant_change_features} - - -def extractGsrFeatures2D(signal2D, startTimestampSeconds=0, sampleRate=4, threshold=.02, offset=1, riseTime=4, - decayTime=4): - """ Extract Martin's GSR features with eda-explorer peak detection - - :param signal2D: 2 dimensional numpy array containing the signal (each row is processed seperately) - :param startTimestampSeconds: seconds from epoch when the signal stared - :param sampleRate: sampling rate of the input signal - :param threshold: threshold for detected peaks - :param offset: - :param riseTime: rise time of detected peaks - :param decayTime: decay time of detected peaks - :return: pandas dataframe of calculated GSR features, each row corresponds with each input row - """ - data = pd.DataFrame() - - for signal in signal2D: - features = extractGsrFeatures(signal, startTimestampSeconds, sampleRate, threshold, offset, riseTime, decayTime) - data = data.append(features, ignore_index=True) - - return data - - -def filter_FIR(signal, sampling_rate, plt_flag=True, ): - filtered = st.filter_signal(signal=signal, - ftype="FIR", - band="bandpass", - frequency=(0.01, 1), - order=20, - sampling_rate=sampling_rate) - - signal_f = filtered['signal'] - if (plt_flag): - plt.plot(signal, label='raw', c="blue") - plt.plot(signal_f, label='filtered', c="red") - plt.xlabel("Sample") - plt.ylabel("GSR value") - plt.legend() - plt.show() - - return signal_f - - -def find_peaks(signal, sampling_rate, plt_flag=True): - tonic = peakutils.baseline(signal, 10) - singal_bf = signal - tonic - indexes = peakutils.indexes(singal_bf, thres=0.3, min_dist=sampling_rate) - if (plt_flag): - plt.figure(figsize=(30, 3)) - plt.plot(singal_bf, alpha=0.5, color='blue') - plt.scatter(indexes, singal_bf[indexes], color='red') # Plot detected peaks - plt.title("GSR with removed tonic") - plt.show() - plt.figure(figsize=(30, 3)) - plt.plot(signal, alpha=0.5, color='blue', label="GSR signal") - plt.scatter(indexes, signal[indexes], color='red') # Plot detected peaks - plt.plot(tonic, alpha=0.5, color='green', label="GSR tonic driver") - plt.legend() - plt.show() - return indexes, tonic - - -def find_peaks_heght_filter(signal, sampling_rate, height_threshold=.1, plt_flag=True): - tonic = peakutils.baseline(signal, 10) - singal_bf = signal - tonic - indexes = peakutils.indexes(singal_bf, thres=0.1, min_dist=sampling_rate) - - all_indexes = np.copy(indexes) - good_hights = [] - bad_indexes = [] - - good_hights = np.argwhere(singal_bf[indexes] > height_threshold) - bad_hights = np.argwhere(singal_bf[indexes] <= height_threshold) - if (len(good_hights) > 0): - indexes = np.concatenate(indexes[good_hights]) - else: - indexes = [] # all are bad - if (len(bad_hights) > 0): - bad_indexes = np.concatenate(all_indexes[bad_hights]) - # print(singal_bf[indexes]) - - if (plt_flag): - plt.figure(figsize=(30, 3)) - plt.plot(singal_bf, alpha=0.5, color='blue', label='GSR-tonic') - plt.scatter(indexes, singal_bf[indexes], color='red') # Plot detected peaks - plt.legend() - plt.show() - plt.figure(figsize=(30, 3)) - plt.plot(signal, alpha=0.5, color='blue', label="GSR signal") - plt.scatter(indexes, signal[indexes], color='red', label='Good Detected peaks') - plt.scatter(bad_indexes, signal[bad_indexes], color='purple', label='Bad detected peaks') - plt.plot(tonic, alpha=0.5, color='green', label="GSR tonic driver") - plt.legend() - plt.show() - - return indexes, tonic - - -import pandas as pd - - -def find_peaks_sliding(sig, sampling_rate, height_threshold=.1, plt_flag=True): - window_size = 60 * sampling_rate - window_count = 1 - - # detrending using sliding window. For signals in which the trend is not linear - singal_bf = np.copy(sig) - tonic_sliding = [] - while ((window_count * window_size) <= len(sig)): - start = (window_count - 1) * window_size - end = window_count * window_size - if ((len(singal_bf) - end) < window_size): - end = end + window_size - tonic_sliding.extend(peakutils.baseline(sig[start:end], 3)) - window_count = window_count + 1 - sig_df = pd.DataFrame(tonic_sliding) - tonic_sliding = sig_df.iloc[:, 0].rolling(window=(3 * sampling_rate), center=True).mean().values - tonic_sliding[np.isnan(tonic_sliding)] = np.reshape(sig_df[np.isnan(tonic_sliding)].values, - len(sig_df[np.isnan(tonic_sliding)].values)) - tonic_sliding = np.reshape(tonic_sliding, len(tonic_sliding)) - - tonic = peakutils.baseline(sig, 3) - - if (len(tonic_sliding) > 0): - singal_bf = singal_bf - tonic_sliding - else: - singal_bf = singal_bf - tonic - indexes = peakutils.indexes(singal_bf, thres=0.3, min_dist=sampling_rate) - all_indexes = np.copy(indexes) - good_hights = [] - bad_indexes = [] - good_hights = np.argwhere(singal_bf[indexes] > height_threshold) - bad_hights = np.argwhere(singal_bf[indexes] <= height_threshold) - if (len(good_hights) > 0): - indexes = np.concatenate(indexes[good_hights]) - if (len(bad_hights) > 0): - bad_indexes = np.concatenate(all_indexes[bad_hights]) - - if (plt_flag): - plt.figure(figsize=(30, 3)) - plt.plot(singal_bf, alpha=0.5, color='blue') - plt.scatter(indexes, singal_bf[indexes], color='red') # Plot detected peaks - plt.title("GSR with removed tonic") - plt.show() - plt.figure(figsize=(30, 3)) - plt.plot(sig, alpha=0.5, color='blue', label="GSR signal") - plt.scatter(indexes, sig[indexes], color='red') - plt.scatter(bad_indexes, sig[bad_indexes], color='yellow') - plt.plot(tonic, alpha=0.5, color='green', label="GSR tonic driver") # Plot semi-transparent HR - plt.plot(tonic_sliding, alpha=0.5, color='purple', - label="GSR tonic driver - sliding") # Plot semi-transparent HR - plt.legend() - plt.show() - return indexes, tonic - - -def calculate_GSR_features(signal, peaks, tonic, sampling_rate, featureNames=None): - q25 = np.percentile(signal, 0.25) - q75 = np.percentile(signal, 0.75) - derivative = np.gradient(signal) - pos_idx = np.where(derivative > 0)[0] - - out = {} - if checkForFeature('mean', featureNames): - out['mean'] = np.mean(signal) - - if checkForFeature('std', featureNames): - out['std'] = np.std(signal) - - if checkForFeature('q25', featureNames): - out['q25'] = q25 - - if checkForFeature('q75', featureNames): - out['q75'] = q75 - - if checkForFeature('qd', featureNames): - out['qd'] = q75 - q25 - - if checkForFeature('deriv', featureNames): - out['deriv'] = np.sum(np.gradient(signal)) - - if checkForFeature('power', featureNames): - out['power'] = np.mean(signal * signal) - - if checkForFeature('numPeaks', featureNames): - out['numPeaks'] = len(peaks) - - if checkForFeature('ratePeaks', featureNames): - out['ratePeaks'] = len(peaks) / (len(signal) / sampling_rate) - - if checkForFeature('powerPeaks', featureNames): - if len(signal[peaks]) == 0: - out['powerPeaks'] = np.nan - else: - out['powerPeaks'] = np.mean(signal[peaks]) - - if checkForFeature('sumPosDeriv', featureNames): - out['sumPosDeriv'] = np.sum(derivative[pos_idx]) / len(derivative) - - if checkForFeature('propPosDeriv', featureNames): - out['propPosDeriv'] = len(pos_idx) / len(derivative) - - if checkForFeature('derivTonic', featureNames): - out['derivTonic'] = np.sum(np.gradient(tonic)) - - if checkForFeature('sigTonicDifference', featureNames): - out['sigTonicDifference'] = np.mean(signal - tonic) - - return out - - -# In[7]: - -def get_GSR_features(signal, sampling_rate, height_threshold=.1, plt_flag=True): - # signal_f =filter_FIR(signal,sampling_rate,plt_flag) - # signal_f = mean_filter(signal,3*sampling_rate,1,sampling_rate,plt_flag) - signal_f = signal - peaks, tonic = find_peaks_heght_filter(signal_f, sampling_rate, height_threshold, plt_flag) - - feats = calculate_GSR_features(signal_f, peaks, tonic, sampling_rate) - freq_feats = GSR_freq(signal_f, sampling_rate, plt_flag, print_flag=plt_flag) - - peaks, ends, starts = get_peak_intervals(signal_f, peaks, sampling_rate, plt_flag) - peak_features = get_peak_intervals_features(signal_f, peaks, starts, ends, sampling_rate) - significant_change_features = significant_change(signal, sampling_rate, plt_flag, plt_flag) - # print('significant_change_features',significant_change_features) - - return np.concatenate((feats, freq_feats, peak_features, significant_change_features)) - - -def get_GSR_features_old(signal, sampling_rate, plt_flag=True): - signal_f = filter_FIR(signal, sampling_rate, plt_flag) - peaks, tonic = find_peaks(signal_f, sampling_rate, plt_flag) - feats = calculate_GSR_features(signal_f, peaks, tonic, sampling_rate) - # freq_feats = GSR_freq(signal_f,sampling_rate,plt_flag,print_flag=plt_flag) - - return feats - - -def GSR_freq(s, fs, plot_flag, print_flag, featureNames=None): - if not checkForFeature('freqFeats', featureNames): - return dict() - - ff, Pxx_spec = signal.periodogram(s, fs, 'flattop', scaling='spectrum') - if (plot_flag): - # plt.plot(s,label="Signal freq") - # plt.legend() - # plt.show() - plt.semilogy(ff, Pxx_spec) - plt.xlabel('frequency [Hz]') - plt.ylabel('PSD [V**2/Hz]') - plt.xlim(0, fs // 2) - plt.show() - # get the power in the band [0-0.5] - current_f = 0.0 - increment = 0.1 - feats = [] - while (current_f < 0.6): - feat = np.trapz(abs(Pxx_spec[(ff >= current_f) & (ff <= current_f + increment)])) - feats.append(feat) - # if(print_flag): - # print(current_f,"-",current_f+increment, feat) - current_f = current_f + increment - - return dict(zip(['fp01', 'fp02', 'fp03', 'fp04', 'fp05', 'fp06'], feats)) - - -def significant_increase(sig, fs, print_flag): - # 5 seconds - win_size = 5 * fs - sig_change_threshold = 1.05 # 5% - sig_counter = 0 - sig_duration_threshold = 15 * fs # 10% change should be sustained for a duration of 15 seconds - sig_duration = 0 - sig_windows = [] - sig_windows_all = [] - - for idx in range(len(sig) // win_size - 1): - # print('inside') - win_prev = sig[idx * win_size] - win_next = sig[(idx + 1) * win_size] - if win_prev == 0: - win_prev = win_prev + 0.00001 - if (win_next / win_prev) > sig_change_threshold: - sig_counter = sig_counter + 1 - # print("Sig increase") - sig_windows.append(win_prev) - else: - if sig_counter * win_size >= sig_duration_threshold: # foe how manu windows there was a sig change? - sig_duration = sig_duration + (sig_counter * win_size) - # if(print_flag): - # print("Significant increase ended") - sig_windows_all.extend(sig_windows) - sig_counter = 0 - sig_windows = [] - # if(print_flag): - # print(idx*win_size,(idx+1)*win_size,win_next/win_prev) - if (sig_counter * win_size >= sig_duration_threshold): - sig_duration = sig_duration + (sig_counter * win_size) - - # how many seconds there has been a significant increase - mean = 0 - intensity = 0 - change = 0 - speed = 0 - if len(sig_windows_all) > 0: - mean = np.mean(sig_windows_all) - intensity = np.mean(sig_windows_all) * sig_duration - change = max(sig_windows_all) - min(sig_windows_all) - speed = change / sig_duration - return [sig_duration, mean, intensity, change, speed] - - -def significant_decrease(sig, fs, print_flag): - # 5 seconds - win_size = 5 * fs - sig_change_threshold = 1.05 - sig_counter = 0 - sig_duration_threshold = 15 * fs # 10% change should be sustained for a duration of 15 seconds - sig_duration = 0 - - sig_windows = [] - sig_windows_all = [] - - for idx in range(len(sig) // win_size - 1): - win_prev = sig[idx * win_size] - win_next = sig[(idx + 1) * win_size] - if win_next == 0: - win_next = win_prev + 0.00001 - if (win_prev / win_next) > sig_change_threshold: - sig_counter = sig_counter + 1 - sig_windows.append(win_prev) - else: - if (sig_counter * win_size) >= sig_duration_threshold: - sig_duration = sig_duration + (sig_counter * win_size) - # if(print_flag): - # print("Significant decrease ended") - sig_windows_all.extend(sig_windows) - sig_counter = 0 - sig_windows = [] - # if(print_flag): - # print(idx*win_size,(idx+1)*win_size,win_prev/win_next) - if (sig_counter * win_size >= sig_duration_threshold): - sig_duration = sig_duration + (sig_counter * win_size) - - # how many seconds there has been a significant decrease - mean = 0 - intensity = 0 - change = 0 - speed = 0 - if len(sig_windows_all) > 0: - mean = np.mean(sig_windows_all) - intensity = np.mean(sig_windows_all) * sig_duration - change = min(sig_windows_all) - max(sig_windows_all) - speed = change / sig_duration - return [sig_duration, mean, intensity, change, speed] - - -def significant_change(sig, fs, plt_flag, print_flag, featureNames=None): - out = {} - - if checkForFeature('significantIncrease', featureNames): - a = significant_increase(sig, fs, print_flag) - out['significantIncreaseDuration'] = a[0] - out['significantIncreaseMean'] = a[1] - out['significantIncreaseIntensity'] = a[2] - out['significantIncreaseChange'] = a[3] - out['significantIncreaseSpeed'] = a[4] - - if checkForFeature('significantDecrease', featureNames): - b = significant_decrease(sig, fs, print_flag) - out['significantDecreaseDuration'] = b[0] - out['significantDecreaseMean'] = b[1] - out['significantDecreaseIntensity'] = b[2] - out['significantDecreaseChange'] = b[3] - out['significantDecreaseSpeed'] = b[4] - - return out - - -def get_peak_intervals(sig, peak_indexes, sampling_frequency, plt_flag): - window_size = 4 - window_slide = 1 - inertion = .01 - ends = [] - starts = [] - for start_idx in peak_indexes: - # go backwards - mean_prev = np.mean(sig[start_idx:(start_idx + window_size)]) - window_start = start_idx - window_size - window_end = start_idx - mean_current = np.mean(sig[window_start:window_end]) - while (window_start >= 0 and (mean_current + inertion) <= mean_prev): - window_end = window_end - window_slide - window_start = window_start - window_slide - mean_prev = mean_current - mean_current = np.mean(sig[window_start:window_end]) - if (window_end < 0): - window_end = 0 - value = window_end - if (value > start_idx): - value = start_idx - window_size - if (value < 0): - value = 0 - starts.append(value) - - # go forward - mean_prev = np.mean(sig[start_idx:(start_idx + window_size)]) - window_start = start_idx + window_slide - window_end = window_start + window_size - mean_current = np.mean(sig[window_start:window_end]) - while (window_end <= len(sig) and (mean_current + inertion) <= mean_prev): - window_start = window_start + window_slide - window_end = window_end + window_slide - mean_prev = mean_current - mean_current = np.mean(sig[window_start:window_end]) - if (window_start >= len(sig)): - window_start = len(sig) - 1 - - value = window_start - if (value <= start_idx): - value = start_idx + window_size - if (value >= len(sig)): - value = len(sig) - 1 - - ends.append(value) - - # #filter bad-short peaks - # inc_duration_threshold = 1 - # dec_duration_threshold = 1 - # inc_amplitude_threshold = .1 - # dec_amplitude_threshold = .1 - good_indexes = [] - bad_indexes = [] - for i in range(len(peak_indexes)): - good_indexes.append(i) - # inc_duration = (peak_indexes[i]-starts[i])/sampling_frequency - # dec_duration = (ends[i]-peak_indexes[i])/sampling_frequency - # inc_amplitude = (sig[peak_indexes[i]]-sig[starts[i]]) - # dec_amplitude = (sig[peak_indexes[i]]-sig[ends[i]]) - # # print(i,inc_duration,dec_duration,inc_amplitude,dec_amplitude) - # if (inc_duration>=inc_duration_threshold and - # dec_duration>=dec_duration_threshold and - # inc_amplitude>=inc_amplitude_threshold and - # dec_amplitude>=dec_amplitude_threshold): - # good_indexes.append(i) - # else: - # bad_indexes.append(i) - peak_indexes = np.array(peak_indexes) - bad_peak_indexes = peak_indexes[bad_indexes] - peak_indexes = peak_indexes[good_indexes] - ends = np.array(ends) - starts = np.array(starts) - ends = ends[good_indexes] - starts = starts[good_indexes] - - if (plt_flag and len(peak_indexes) > 0): - plt.figure(figsize=(30, 3)) - plt.plot(sig, label='GSR') - plt.scatter(peak_indexes, sig[peak_indexes], color='red', label='Good Detected peaks') # Plot detected peaks - plt.scatter(bad_peak_indexes, sig[bad_peak_indexes], color='purple', - label='Bad Detected peaks') # Plot detected peaks - - plt.scatter(ends, .001 + sig[ends], color='orange', label='Peak end') # Plot detected peaks - plt.scatter(starts, .001 + sig[starts], color='green', label='Peak start') # Plot detected peaks - plt.legend() - plt.show() - - return peak_indexes, np.array(ends), np.array(starts) - - -def get_peak_intervals_features(sig, peak_indexes, starts, ends, sampling_frequency, featureNames=None): - if (len(peak_indexes) > 0): - - max_peak_idx = np.argmax(sig[peak_indexes]) - - max_peak_start = starts[max_peak_idx] - max_peak_end = ends[max_peak_idx] - - max_peak_amlitude_change_before = sig[peak_indexes[max_peak_idx]] - sig[max_peak_start] - max_peak_amlitude_change_after = sig[peak_indexes[max_peak_idx]] - sig[max_peak_end] - - # max_peak_change_ratio = max_peak_amlitude_change_before/max_peak_amlitude_change_after - - avg_peak_amlitude_change_before = np.median(sig[peak_indexes] - sig[starts]) - avg_peak_amlitude_change_after = np.median(sig[peak_indexes] - sig[ends]) - - # avg_peak_change_ratio=0 - # if avg_peak_amlitude_change_after!=0: - # avg_peak_change_ratio = avg_peak_amlitude_change_before/avg_peak_amlitude_change_after - - max_peak_increase_time = (peak_indexes[max_peak_idx] - max_peak_start) / sampling_frequency - max_peak_decrease_time = (max_peak_end - peak_indexes[max_peak_idx]) / sampling_frequency - - max_peak_duration = (max_peak_end - max_peak_start) / sampling_frequency - - max_peak_change_ratio = 0 - if max_peak_decrease_time != 0: - max_peak_change_ratio = max_peak_increase_time / max_peak_decrease_time - - avg_peak_increase_time = np.mean(peak_indexes - starts) / sampling_frequency - avg_peak_decrease_time = np.mean(ends - peak_indexes) / sampling_frequency - avg_peak_duration = np.mean(ends - starts) * sampling_frequency - avg_peak_change_ratio = 0 - if (avg_peak_decrease_time != 0): - avg_peak_change_ratio = avg_peak_increase_time / avg_peak_decrease_time - - dif = np.diff(sig[max_peak_start:peak_indexes[max_peak_idx]]) - # prevent "Mean of empty slice" warning - if len(dif) == 0: - max_peak_response_slope_before = np.nan - else: - max_peak_response_slope_before = np.mean(dif) - - # if np.isnan(max_peak_response_slope_before): - # max_peak_response_slope_before = 0 - - dif = np.diff(sig[peak_indexes[max_peak_idx]:max_peak_end]) - - # prevent "Mean of empty slice" warning - if len(dif) == 0: - max_peak_response_slope_after = np.nan - else: - max_peak_response_slope_after = np.mean(dif) - - # if np.isnan(max_peak_response_slope_after): - # max_peak_response_slope_after = 0 - - signal_overall_change = np.max(sig) - np.min(sig) - change_duration = np.abs((np.argmax(sig) - np.argmin(sig))) / sampling_frequency - if (signal_overall_change != 0): - change_rate = change_duration / signal_overall_change - gsr_peak_features = [max_peak_amlitude_change_before, max_peak_amlitude_change_after, - avg_peak_amlitude_change_before, avg_peak_amlitude_change_after, - avg_peak_change_ratio, max_peak_increase_time, max_peak_decrease_time, - max_peak_duration, max_peak_change_ratio, - avg_peak_increase_time, avg_peak_decrease_time, avg_peak_duration, - max_peak_response_slope_before, max_peak_response_slope_after, signal_overall_change, - change_duration, change_rate] - else: - num_features = 17 - gsr_peak_features = np.array([np.nan] * num_features) - else: - num_features = 17 - gsr_peak_features = np.array([np.nan] * num_features) - # print('bad features',gsr_peak_features) - - out = {} - if checkForFeature('maxPeakAmplitudeChangeBefore', featureNames): - out['maxPeakAmplitudeChangeBefore'] = gsr_peak_features[0] - - if checkForFeature('maxPeakAmplitudeChangeAfter', featureNames): - out['maxPeakAmplitudeChangeAfter'] = gsr_peak_features[1] - - if checkForFeature('avgPeakAmplitudeChangeBefore', featureNames): - out['avgPeakAmplitudeChangeBefore'] = gsr_peak_features[2] - - if checkForFeature('avgPeakAmplitudeChangeAfter', featureNames): - out['avgPeakAmplitudeChangeAfter'] = gsr_peak_features[3] - - if checkForFeature('avgPeakChangeRatio', featureNames): - out['avgPeakChangeRatio'] = gsr_peak_features[4] - - if checkForFeature('maxPeakIncreaseTime', featureNames): - out['maxPeakIncreaseTime'] = gsr_peak_features[5] - - if checkForFeature('maxPeakDecreaseTime', featureNames): - out['maxPeakDecreaseTime'] = gsr_peak_features[6] - - if checkForFeature('maxPeakDuration', featureNames): - out['maxPeakDuration'] = gsr_peak_features[7] - - if checkForFeature('maxPeakChangeRatio', featureNames): - out['maxPeakChangeRatio'] = gsr_peak_features[8] - - if checkForFeature('avgPeakIncreaseTime', featureNames): - out['avgPeakIncreaseTime'] = gsr_peak_features[9] - - if checkForFeature('avgPeakDecreaseTime', featureNames): - out['avgPeakDecreaseTime'] = gsr_peak_features[10] - - if checkForFeature('avgPeakDuration', featureNames): - out['avgPeakDuration'] = gsr_peak_features[11] - - if checkForFeature('maxPeakResponseSlopeBefore', featureNames): - out['maxPeakResponseSlopeBefore'] = gsr_peak_features[12] - - if checkForFeature('maxPeakResponseSlopeAfter', featureNames): - out['maxPeakResponseSlopeAfter'] = gsr_peak_features[13] - - if checkForFeature('signalOverallChange', featureNames): - out['signalOverallChange'] = gsr_peak_features[14] - - if checkForFeature('changeDuration', featureNames): - out['changeDuration'] = gsr_peak_features[15] - - if checkForFeature('changeRate', featureNames): - out['changeRate'] = gsr_peak_features[16] - - return out - - -def mean_filter(s, windows_size, window_slide, sampling_rate, plt_flag=True): - mean_s = [] - start = 0 - end = windows_size - while (end <= len(s)): - mean_s.append(np.mean(s[start:end])) - start = start + window_slide - end = start + windows_size - if (plt_flag): - plt.plot(s, label='original') - plt.plot(mean_s, label='mean_filter') - plt.legend() - plt.show() - - return np.array(mean_s) diff --git a/calculatingfeatures/CalculatingFeatures/helper_functions.py b/calculatingfeatures/CalculatingFeatures/helper_functions.py deleted file mode 100644 index bb2be020..00000000 --- a/calculatingfeatures/CalculatingFeatures/helper_functions.py +++ /dev/null @@ -1,131 +0,0 @@ -import numpy as np -import pandas as pd - - -def convertInputInto2d(input, windowLength, overlap=0): - """Convert input into 2d matrix with width = numCols. The last row is padded with zeros to match the other rows. - - Overlap has to be smaller than window length - - :param input: the one dimensional array - :param windowLength: window length, expressed in number of samples - :param overlap: Amount of overlap - :return: 2D matrix - """ - - if windowLength <= overlap: - raise Exception("Overlap has to be smaller than window length") - - inputWasList = True - if type(input) != list: - inputWasList = False - input = input.tolist() - - out = [input[i: i + windowLength] for i in range(0, len(input), windowLength - overlap)] - out[-1].extend([0] * (windowLength - len(out[-1]))) - return out if inputWasList else np.asarray(out) - - -def convertInputInto2dTime(input, timeThreshold): - """ Convert input array into 2D matrix by time interval. When the timeThreshold is reached in each row, - the process continues in the next row. - - :param input: the pandas dataframe with rows "time" and "data" - :param timeThreshold: the threshold with which the row width is defined - :return: 2D matrix - """ - outData = [[]] - outTime = [[]] - startTime = 0 - - for index, row in input.iterrows(): - t = row["time"] - data = row["data"] - - if t - startTime >= timeThreshold: - startTime = t - outData.append([]) - outTime.append([]) - - outData[-1].append(data) - outTime[-1].append(t) - - return outData, outTime - - -def convert1DEmpaticaToArray(pathToEmpaticaCsvFile): - """ Convert 1D empatica file to array - - :param pathToEmpaticaCsvFile: path to Empatica csv file - :return: array of data, starting timestamp of data, sample rate of data - """ - df = pd.read_csv(pathToEmpaticaCsvFile, names=["name"]) - startTimeStamp = df.name[0] - sampleRate = df.name[1] - df.drop([0, 1], inplace=True) - data = df.name.ravel() - return data, startTimeStamp, sampleRate - - -def convert3DEmpaticaToArray(pathToEmpaticaCsvFile): - """ Convert 3D empatica file to array - - :param pathToEmpaticaCsvFile: path to Empatica csv file - :return: array of data, starting timestamp of data, sample rate of data - """ - df = pd.read_csv(pathToEmpaticaCsvFile, names=["x", "y", "z"]) - startTimeStamp = df.x[0] - sampleRate = df.x[1] - df.drop([0, 1], inplace=True) - data = np.vstack((df.x.ravel(), df.y.ravel(), df.z.ravel())) - return data, startTimeStamp, sampleRate - - -def checkForFeature(featureName, featureNames): - return featureNames is None or featureName in featureNames - - -frequencyFeatureNames = ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins", - "fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart"] - -genericFeatureNames = ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", - "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", - "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", "ratioBeyondRSigma", "binnedEntropy", - "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", - "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", - "squareSumOfComponent", "sumOfSquareComponents"] - -accelerometerFeatureNames = ["meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", - "skewnessBand", "kurtosisBand", "postureDistanceLow", "absoluteMeanBand", - "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", - "varianceBand", "coefficientOfVariationBand", "amplitudeBand", "totalEnergyBand", - "dominantFrequencyEnergyBand", "meanCrossingRateBand", "correlationBand", - "quartilesMagnitudesBand", - "interQuartileRangeMagnitudesBand", "areaUnderAccelerationMagnitude", "peaksDataLow", - "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", - "totalKineticEnergyBand", "squareSumOfComponent", "sumOfSquareComponents", - "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", - "rollStdDevLow", "pitchStdDevLow", - "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", - "pitchPeaks", - "rollPitchCorrelation"] - -gyroscopeFeatureNames = ["meanLow", "areaLow", "totalAbsoluteAreaLow", "totalMagnitudeLow", "entropyLow", "skewnessLow", - "kurtosisLow", - "quartilesLow", "interQuartileRangeLow", "varianceLow", "coefficientOfVariationLow", - "amplitudeLow", - "totalEnergyLow", "dominantFrequencyEnergyLow", "meanCrossingRateLow", "correlationLow", - "quartilesMagnitudeLow", "interQuartileRangeMagnitudesLow", "areaUnderMagnitude", - "peaksCountLow", - "averageVectorLengthLow", "averageVectorLengthPowerLow"] - -gsrFeatureNames = ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', - 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats', - 'maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', - 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', - 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', - 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', - 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', - 'significantIncrease', 'significantDecrease'] - -hrvFeatureNames = ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR'] diff --git a/calculatingfeatures/CalculatingFeatures/hrv.py b/calculatingfeatures/CalculatingFeatures/hrv.py deleted file mode 100644 index 080a08fe..00000000 --- a/calculatingfeatures/CalculatingFeatures/hrv.py +++ /dev/null @@ -1,304 +0,0 @@ -import pandas as pd -import numpy as np -import scipy.signal as _signal - -from CalculatingFeatures.helper_functions import checkForFeature, hrvFeatureNames -from CalculatingFeatures.calculate_hrv_peaks import peak_detector - - -def extractHrvFeatures(_sample, ma=False, detrend=False, m_deternd=False, low_pass=False, winsorize=False, - winsorize_value=25, hampel_fiter=True, sampling=64, featureNames=None): - """ Extract Martin's HRV features - - Warning: Input sample length has to be at least 256! - - :param _sample: array containing the HRV signal - :param ma: should moving average filter be used prior the calculation - :param detrend: should overall detrending be used prior the calculation - :param m_deternd: should moving detrending be used prior the calculation - :param low_pass: should low pass filter be used prior the calculation - :param winsorize: should winsorize filter be used prior the calculation - :param winsorize_value: winsorize value - :param hampel_fiter: hould winsorize filter be used after the calculation - :param sampling: the sampling frequency of the signal - :param featureNames: - :return: HRV features - """ - - hrv_time_features, sample, rr, timings, peak_indx = get_HRV_features(_sample, ma, detrend, m_deternd, low_pass, - winsorize, winsorize_value, - hampel_fiter, sampling, - featureNames=featureNames) - return hrv_time_features - - -def extractHrvFeatures2D(signal2D, ma=False, detrend=False, m_deternd=False, low_pass=False, winsorize=False, - winsorize_value=25, hampel_fiter=True, sampling=64): - """ Extract Martin's HRV features - - Warning: Input 2D array width (column count) has to be at least 200! - - :param signal2D: array containing the HRV signal in 2D (each row is processed seperately) - :param ma: should moving average filter be used prior the calculation - :param detrend: should overall detrending be used prior the calculation - :param m_deternd: should moving detrending be used prior the calculation - :param low_pass: should low pass filter be used prior the calculation - :param winsorize: should winsorize filter be used prior the calculation - :param winsorize_value: winsorize value - :param hampel_fiter: hould winsorize filter be used after the calculation - :param sampling: the sampling frequency of the signal - :return: pandas dataframe of calculated HRV features, each row corresponds with each input row - """ - outData = pd.DataFrame() - - for sample in signal2D: - features = extractHrvFeatures(sample, ma, detrend, m_deternd, low_pass, - winsorize, winsorize_value, - hampel_fiter, sampling) - - outData = outData.append(features, ignore_index=True) - - return outData - - -# filter signala and calculate HRV features in time and in frequency domain -def get_HRV_features(_sample, ma=False, detrend=False, m_deternd=False, low_pass=False, winsorize=True, - winsorize_value=25, hampel_fiter=True, sampling=1000, featureNames=None): - if featureNames is not None and len(set(featureNames).intersection(set(hrvFeatureNames))) == 0: - return dict(), 0, 0, 0, 0 - - sample = _sample.copy() - - if low_pass: # lowpass filter - sample = butter_lowpass_filter(sample) - if m_deternd: # moving detrending - sample = moving_detrending(sample, sampling) - if detrend: # overall detrending - sample = _signal.detrend(sample) - if ma: # moving average - sample = moving_average(sample) - - # winsorize the signal - if winsorize: - sample = winsorize_signal(sample, winsorize_value) - # if dynamic_threshold: #find the median of the min-max normalized signal - # thres = dynamic_threshold_value*np.median((sample - sample.min())/(sample.max() - sample.min())) - - rr, timings, peak_indx = detect_RR(sample, sampling) - - if hampel_fiter: - rr, outlier_indeces = hampel_filtering(rr) - - timings, rr = medianFilter(timings, rr) - - bad_signal = False - - if len(rr) < len(sample) / (2 * sampling): # check whether HR is>30 - #print("Bad signal. Too little RRs detected.") - bad_signal = True - elif len(rr) > len(sample) / (sampling / 4): # check whether HR is<240 - #print("Bad signal. Too much RRs detected.") - bad_signal = True - hrv_time_features = HRV_time(rr, print_flag=False, badSignal=bad_signal, featureNames=featureNames) - - return hrv_time_features, sample, rr, timings, peak_indx - - -def butter_lowpass(cutoff, fs, order): - nyq = 0.5 * fs - normal_cutoff = cutoff / nyq - b, a = _signal.butter(order, normal_cutoff, btype='low', analog=False) - return b, a - - -def butter_lowpass_filter(data, cutoff=5, fs=64, order=3): - b, a = butter_lowpass(cutoff, fs, order=order) - y = _signal.lfilter(b, a, data) - return pd.Series(y[1000:]) - - -# perfrom detrending using sliding window -def moving_detrending(sig_input, sampling_rate=64): - sig = np.copy(sig_input) - window_size = 1 * sampling_rate - window_count = 1 - start = (window_count - 1) * window_size - end = window_count * window_size - while (end <= len(sig)): - if ((len(sig) - end) < window_size): - end = end + window_size - sig[start:end] = _signal.detrend(sig[start:end]) - window_count = window_count + 1 - start = (window_count - 1) * window_size - end = window_count * window_size - return sig - - -# perform moving average -def moving_average(sample, ma_size=10): - sample = pd.Series(sample) - sample_ma = sample.rolling(ma_size).mean() - sample_ma = sample_ma.iloc[ma_size:].values - return sample_ma - - -def winsorize_signal(sample, winsorize_value): - p_min = np.percentile(sample, winsorize_value) - p_max = np.percentile(sample, 100 - winsorize_value) - - sample[sample > p_max] = p_max - sample[sample < p_min] = p_min - - return sample - - -# https://www.mathworks.com/help/signal/ref/hampel.html -# compute median and standard deviation -# of a window composed of the sample and its six surrounding samples -# If a sample differs from the median by more than three standard deviations, -# it is replaced with the median. -# reutn fistered RRs and outlier indices -def hampel_filtering(sample_rr): - outlier_indicies = [] - filtered_rr = [] - for i in range(len(sample_rr)): - start = i - 3 - end = i + 3 - if start < 0: # for the first 3 samples calculate median and std using the closest 6 samples - start = 0 - end = end + 3 - i - if end > len(sample_rr) - 1: # for the last 3 samples calculate median and std using the first 6 samples - start = len(sample_rr) - 7 - end = len(sample_rr) - 1 - - sample_rr_part = sample_rr[start:end] - - # Prevent "Mean of empty slice" warning - if len(sample_rr_part) == 0: - sample_med = np.nan - sample_std = np.nan - else: - sample_med = np.median(sample_rr_part) - sample_std = np.std(sample_rr_part) - - if abs(sample_rr[i] - sample_med) > 3 * sample_std: - outlier_indicies.append(i) - filtered_rr.append(sample_med) - # print('outlier') - filtered_rr.append(sample_rr[i]) - return np.array(filtered_rr), outlier_indicies - - -def medianFilter(time, rr): - percentageBorder = 0.8 - if len(rr) == 0: - median = np.nan - else: - median = np.median(rr) - idx = (rr / median >= percentageBorder) & (rr / median <= (2 - percentageBorder)) - # f_rr = rr[(rr/median>=percentageBorder) & (rr/median<=(2-percentageBorder))] - f_rr = np.copy(rr) - # f_rr[~idx]=median - f_rr = f_rr[idx] - f_time = timestamps_from_RR(f_rr) - # f_time = time[(rr/median>=percentageBorder) & (rr/median<=(2-percentageBorder))] - return f_time, f_rr - - -def detect_RR(sig, sampling_rate): - # peak_indx = peakutils.indexes(sig, thres=thres, min_dist=sampling_rate/2.5) - peak_indx, _ = peak_detector(sig, sampling_rate) - if len(peak_indx) == 0: - return [], [], [] - time = np.arange(len(sig)) - tmp = time[peak_indx] - timings1 = tmp[0:] - timings = tmp[1:] - RR_intervals = timings - timings1[:len(timings1) - 1] - - return RR_intervals / sampling_rate, timings / sampling_rate, peak_indx - - -# extract HRV features in time domain -def HRV_time(RR_intervals, print_flag, badSignal=False, featureNames=None): - if not badSignal: - ibi = np.mean(RR_intervals) # Take the mean of RR_list to get the mean Inter Beat Interval - mean_hr = 60 / ibi - sdnn = np.std(RR_intervals) # Take standard deviation of all R-R intervals - # find successive/neighbouring RRs (i.e., filter noise) - RR_diff = [] - RR_sqdiff = [] - for i in range(len(RR_intervals) - 1): - RR_diff.append(np.absolute(RR_intervals[i + 1] - RR_intervals[i])) - RR_sqdiff.append(np.power(np.absolute(RR_intervals[i + 1] - RR_intervals[i]), 2)) - RR_diff = np.array(RR_diff) - RR_sqdiff = np.array(RR_sqdiff) - sdsd = np.std(RR_diff) # Take standard deviation of the differences between all subsequent R-R intervals - rmssd = np.sqrt(np.mean(RR_sqdiff)) # Take root of the mean of the list of squared differences - nn20 = [x for x in RR_diff if (x > 0.02)] # First create a list of all values over 20, 50 - nn50 = [x for x in RR_diff if (x > 0.05)] - pnn20 = 100 * float(len(nn20)) / float(len(RR_diff)) if len( - RR_diff) > 0 else np.nan # Calculate the proportion of NN20, NN50 intervals to all intervals - pnn50 = 100 * float(len(nn50)) / float(len(RR_diff)) if len(RR_diff) > 0 else np.nan - sd1 = np.sqrt(0.5 * sdnn * sdnn) - sd2 = np.nan - tmp = 2.0 * sdsd * sdsd - 0.5 * sdnn * sdnn - if tmp > 0: # avoid sqrt of negative values - sd2 = np.sqrt(2.0 * sdsd * sdsd - 0.5 * sdnn * sdnn) - - if (print_flag): - print("menHR:", mean_hr) - print("IBI:", ibi) - print("SDNN:", sdnn) - print("sdsd", sdsd) - print("RMSSD:", rmssd) - print("pNN20:", pnn20) - print("pNN50:", pnn50) - print("sd1:", sd1) - print("sd2:", sd2) - print("sd1/sd2:", sd1 / sd2) - - out = {} - if checkForFeature("meanHr", featureNames): - out['meanHr'] = mean_hr if not badSignal else np.NaN - - if checkForFeature("ibi", featureNames): - out['ibi'] = ibi if not badSignal else np.NaN - - if checkForFeature("sdnn", featureNames): - out['sdnn'] = sdnn if not badSignal else np.NaN - - if checkForFeature("sdsd", featureNames): - out['sdsd'] = sdsd if not badSignal else np.NaN - - if checkForFeature("rmssd", featureNames): - out['rmssd'] = rmssd if not badSignal else np.NaN - - if checkForFeature("pnn20", featureNames): - out['pnn20'] = pnn20 if not badSignal else np.NaN - - if checkForFeature("pnn50", featureNames): - out['pnn50'] = pnn50 if not badSignal else np.NaN - - if checkForFeature("sd", featureNames): - out['sd'] = sd1 if not badSignal else np.NaN - - if checkForFeature("sd2", featureNames): - out['sd2'] = sd2 if not badSignal else np.NaN - - if checkForFeature("sd1/sd2", featureNames): - out['sd1/sd2'] = sd1 / sd2 if not badSignal else np.NaN - - if checkForFeature("numRR", featureNames): - out['numRR'] = len(RR_intervals) - - return out - - -def timestamps_from_RR(rr_intervals): - time = [] - current_time = 0.0 - for rr in rr_intervals: - current_time = current_time + rr - time.append(current_time) - return np.array(time) diff --git a/calculatingfeatures/README.md b/calculatingfeatures/README.md deleted file mode 100644 index 8fd333e3..00000000 --- a/calculatingfeatures/README.md +++ /dev/null @@ -1,207 +0,0 @@ -# Calculating features - -## Usage -- Install the library with: - ``` - pip install pep517 - python -m pep517.build . - - Alternative: - pip install build - python -m build - ``` -- Basic usage is: - ``` - from calculatingfeatures.CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, frequencyFeatureNames, hrvFeatureNames - from calculatingfeatures.CalculatingFeatures.calculate_features import calculateFeatures - import pandas as pd - - pathToHrvCsv = "example_data/S2_E4_Data/BVP.csv" - windowLength = 500 - - # get an array of values from HRV empatica file - hrv_data, startTimeStamp, sampleRate = convert1DEmpaticaToArray(pathToHrvCsv) - - # Convert the HRV data into 2D array - hrv_data_2D = convertInputInto2d(hrv_data, windowLength) - - # Create a list with feature names - featureNames = [] - featureNames.extend(hrvFeatureNames) - featureNames.extend(frequencyFeatureNames) - - pd.set_option('display.max_columns', None) - - # Calculate features - calculatedFeatures = calculateFeatures(hrv_data_2D, fs=int(sampleRate), featureNames=featureNames) - ``` -- More usage examples are located in **usage_examples.ipynb** file - -## Features -- Features are returned (from calculateFeatures() function) in a Pandas DataFrame object. -- In the case if a feature couldn't be calculated (for example, if input signal is invalid), NaN value is returned. -- Further in this section, the list with descriptions of all possible features is presented. - -### GSR features: -These features are useful for 1D GSR(EDA) signals -- `mean`: mean of the signal -- `std`: standard deviation of signal -- `q25`: 0.25 quantile -- `q75`: 0.75 quantile -- `qd`: q75 - q25 -- `deriv`: sum of gradients of the signal -- `power`: power of the signal (mean of squared signal) -- `numPeaks`: number of EDA peaks -- `ratePeaks`: average number of peaks per second -- `powerPeaks`: power of peaks (mean of signal at indexes of peaks) -- `sumPosDeriv`: sum of positive derivatives divided by number of all derivatives -- `propPosDeriv`: proportion of positive derivatives per all derivatives -- `derivTonic`: sum of gradients of the tonic -- `sigTonicDifference`: mean of tonic subtracted from signal -- `freqFeats`: -- `maxPeakAmplitudeChangeBefore`: maximum peak amplitude change before peak -- `maxPeakAmplitudeChangeAfter`: maximum peak amplitude change after peak -- `avgPeakAmplitudeChangeBefore`: average peak amplitude change before peak -- `avgPeakAmplitudeChangeAfter`: average peak amplitude change after peak -- `avgPeakChangeRatio`: avg_peak_increase_time / avg_peak_decrease_time -- `maxPeakIncreaseTime`: maximum peak increase time -- `maxPeakDecreaseTime`: maximum peak decrease time -- `maxPeakDuration`: maximum peak duration -- `maxPeakChangeRatio`: max_peak_increase_time / max_peak_decrease_time -- `avgPeakIncreaseTime`: average peak increase time -- `avgPeakDecreaseTime`: average peak decreade time -- `avgPeakDuration`: average peak duration -- `maxPeakResponseSlopeBefore`: maximum peak response slope before peak -- `maxPeakResponseSlopeAfter`: maximum peak response slope after peak -- `signalOverallChange`: maximum difference between samples (max(sig)-min(sig)) -- `changeDuration`: duration between maximum and minimum values -- `changeRate`: change_duration / signal_overall_change -- `significantIncrease`: -- `significantDecrease`: - -### HRV features: -These features are useful for 1D HRV(BVP) signals. - -If number of RR intervals (numRR) is less than `length of sample / (2 * sampling rate)` (30 BPM) or greater than `length of sample / (sampling rate / 4)` (240 BPM), BPM value is incorrect and thus, all other HRV features are set to NaN. - -- `meanHr`: mean heart rate -- `ibi`: mean interbeat interval -- `sdnn`: standard deviation of the ibi -- `sdsd`: standard deviation of the differences between all subsequent R-R intervals -- `rmssd`: root of the mean of the list of squared differences -- `pnn20`: the proportion of NN20 intervals to all intervals -- `pnn50`: the proportion of NN50 intervals to all intervals -- `sd`: -- `sd2`: -- `sd1/sd2`: sd / sd2 ratio -- `numRR`: number of RR intervals - -### Accelerometer features: -These features are useful for 3D signals from accelerometer -- `meanLow`: mean of low-pass filtered signal -- `areaLow`: area under the low-pass filtered signal -- `totalAbsoluteAreaBand`: sum of absolute areas under the band-pass filtered x, y and z signal -- `totalMagnitudeBand`: square root of sum of squared band-pass filtered x, y and z components -- `entropyBand`: entropy of band-pass filtered signal -- `skewnessBand`: skewness of band-pass filtered signal -- `kurtosisBand`: kurtosis of band-pass filtered signal -- `postureDistanceLow`: calculates difference between mean values for a given sensor (low-pass filtered) -- `absoluteMeanBand`: mean of band-pass filtered signal -- `absoluteAreaBand`: area under the band-pass filtered signal -- `quartilesBand`: quartiles of band-pass filtered signal -- `interQuartileRangeBand`: inter quartile range of band-pass filtered signal -- `varianceBand`: variance of band-pass filtered signal -- `coefficientOfVariationBand`: dispersion of band-pass filtered signal -- `amplitudeBand`: difference between maximum and minimum sample of band-pass filtered signal -- `totalEnergyBand`: total magnitude of band-pass filtered signal -- `dominantFrequencyEnergyBand`: ratio of energy in dominant frequency -- `meanCrossingRateBand`: the number of signal crossings with mean of band-pass filtered signal -- `correlationBand`: Pearson's correlation between band-pass filtered axis -- `quartilesMagnitudesBand`: quartiles at 25%, 50% and 75% per band-pass filtered signal -- `interQuartileRangeMagnitudesBand`: interquartile range of band-pass filtered signal -- `areaUnderAccelerationMagnitude`: area under acceleration magnitude -- `peaksDataLow`: number of peaks, sum of peak values, peak avg, amplitude avg -- `sumPerComponentBand`: sum per component of band-pass filtered signal -- `velocityBand`: velocity of the band-pass filtered signal -- `meanKineticEnergyBand`: mean kinetic energy 1/2*mV^2 of band-pass filtered signal -- `totalKineticEnergyBand`: total kinetic energy 1/2*mV^2 for all axes (band-pass filtered) -- `squareSumOfComponent`: squared sum of component -- `sumOfSquareComponents`: sum of squared components -- `averageVectorLength`: mean of magnitude vector -- `averageVectorLengthPower`: square mean of magnitude vector -- `rollAvgLow`: maximum difference of low-pass filtered roll samples -- `pitchAvgLow`: maximum difference of low-pass filtered pitch samples -- `rollStdDevLow`: standard deviation of roll (calculated from low-pass filtered signal) -- `pitchStdDevLow`: standard deviation of pitch (calculated from low-pass filtered signal) -- `rollMotionAmountLow`: amount of wrist roll (from low-pass filtered signal) motion -- `rollMotionRegularityLow`: regularity of wrist roll motion -- `manipulationLow`: manipulation of low-pass filtered signals -- `rollPeaks`: number of roll peaks, sum of roll peak values, roll peak avg, roll amplitude avg -- `pitchPeaks`: number of pitch peaks, sum of pitch peak values, pitch peak avg, pitch amplitude avg -- `rollPitchCorrelation`: correlation between roll and peak (obtained from low-pass filtered signal) - -### Gyroscope features: -These features are useful for 3D signals from gyroscope -- `meanLow`: mean of low-pass filtered signal -- `areaLow`: area under the low-pass filtered signal -- `totalAbsoluteAreaLow`: sum of absolute areas under the low-pass filtered x, y and z signal -- `totalMagnitudeLow`: square root of sum of squared band-pass filtered x, y and z components -- `entropyLow`: entropy of low-pass filtered signal -- `skewnessLow`: skewness of low-pass filtered signal -- `kurtosisLow`: kurtosis of low-pass filtered signal -- `quartilesLow`: quartiles of low-pass filtered signal -- `interQuartileRangeLow`: inter quartile range of low-pass filtered signal -- `varianceLow`: variance of low-pass filtered signal -- `coefficientOfVariationLow`: dispersion of low-pass filtered signal -- `amplitudeLow`: difference between maximum and minimum sample of low-pass filtered signal -- `totalEnergyLow`: total magnitude of low-pass filtered signal -- `dominantFrequencyEnergyLow`: ratio of energy in dominant frequency -- `meanCrossingRateLow`: the number of signal crossings with mean of low-pass filtered signal -- `correlationLow`: Pearson's correlation between low-pass filtered axis -- `quartilesMagnitudeLow`: quartiles at 25%, 50% and 75% per low-pass filtered signal -- `interQuartileRangeMagnitudesLow`: interquartile range of band-pass filtered signal -- `areaUnderMagnitude`: area under magnitude -- `peaksCountLow`: number of peaks in low-pass filtered signal -- `averageVectorLengthLow`: mean of low-pass filtered magnitude vector -- `averageVectorLengthPowerLow`: square mean of low-pass filtered magnitude vector - - -### Generic features: -These are generic features, useful for many different types of signals -- `autocorrelations`: autocorrelations of the given signal with lags 5, 10, 20, 30, 50, 75 and 100 -- `countAboveMean`: number of values in signal that are higher than the mean of signal -- `countBelowMean`: number of values in signal that are lower than the mean of signal -- `maximum`: maximum value of the signal -- `minimum`: minimum value of the signal -- `meanAbsChange`: the mean of absolute differences between subsequent time series values -- `longestStrikeAboveMean`: longest part of signal above mean -- `longestStrikeBelowMean`: longest part of signal below mean -- `stdDev`: standard deviation of the signal -- `median`: median of the signal -- `meanChange`: the mean over the differences between subsequent time series values -- `numberOfZeroCrossings`: number of crossings of signal on 0 -- `absEnergy`: the absolute energy of the time series which is the sum over the squared values -- `linearTrendSlope`: a linear least-squares regression for the values of the time series versus the sequence from 0 to length of the time series minus one -- `ratioBeyondRSigma`: ratio of values that are more than r*std(x) (so r sigma) away from the mean of signal. r in this case is 2.5 -- `binnedEntropy`: entropy of binned values -- `numOfPeaksAutocorr`: number of peaks of autocorrelations -- `numberOfZeroCrossingsAutocorr`: number of crossings of autocorrelations on 0 -- `areaAutocorr`: area under autocorrelations -- `calcMeanCrossingRateAutocorr`: the number of autocorrelation crossings with mean -- `countAboveMeanAutocorr`: umber of values in signal that are higher than the mean of autocorrelation -- `sumPer`: sum per component -- `sumSquared`: squared sum per component -- `squareSumOfComponent`: square sum of component -- `sumOfSquareComponents`:sum of square components - -### Frequency features: -These are frequency features, useful for many different types of signals. The signal is converted to power spectral density signal and features are calculated on this signal -- `fqHighestPeakFreqs`: three frequencies corresponding to the largest peaks added to features -- `fqHighestPeaks`: three largest peaks added to features -- `fqEnergyFeat`: energy calculated as the sum of the squared FFT component magnitudes, and normalized -- `fqEntropyFeat`: entropy of the FFT of the signal -- `fqHistogramBins`: Binned distribution (histogram) -- `fqAbsMean`: absolute mean of the raw signal -- `fqSkewness`: skewness of the power spectrum of the data -- `fqKurtosis`: kurtosis of the power spectrum of the data -- `fqInterquart`: inter quartile range of the raw signal \ No newline at end of file diff --git a/calculatingfeatures/__init__.py b/calculatingfeatures/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/calculatingfeatures/cf_tests/__init__.py b/calculatingfeatures/cf_tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/calculatingfeatures/cf_tests/calculate_ACC_features_test.py b/calculatingfeatures/cf_tests/calculate_ACC_features_test.py deleted file mode 100644 index fcd62181..00000000 --- a/calculatingfeatures/cf_tests/calculate_ACC_features_test.py +++ /dev/null @@ -1,32 +0,0 @@ -import sys -sys.path.append("..") -from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames, frequencyFeatureNames -from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray -from CalculatingFeatures.calculate_features import calculateFeatures - -import pandas as pd - -pathToAccCsv = "../example_data/S2_E4_Data_shortened/ACC.csv" -windowLength = 500 - -#np.seterr(all='raise') - -# get an array of values from ACC empatica file -acc_data, startTimeStamp, sampleRate = convert3DEmpaticaToArray(pathToAccCsv) -acc_data = acc_data[:, :int(300000//sampleRate)] - -# Convert the ACC data into 2D array -x_2D = convertInputInto2d(acc_data[0], windowLength) -y_2D = convertInputInto2d(acc_data[1], windowLength) -z_2D = convertInputInto2d(acc_data[2], windowLength) - -# Create a list with feature names -featureNames = [] -featureNames.extend(accelerometerFeatureNames) -featureNames.extend(frequencyFeatureNames) - -pd.set_option('display.max_columns', None) - -# Calculate features -calculatedFeatures = calculateFeatures(x_2D, y_2D, z_2D, fs=int(sampleRate), featureNames=featureNames) -print(calculatedFeatures) \ No newline at end of file diff --git a/calculatingfeatures/cf_tests/calculate_EDA_features_test.py b/calculatingfeatures/cf_tests/calculate_EDA_features_test.py deleted file mode 100644 index 58c9fc64..00000000 --- a/calculatingfeatures/cf_tests/calculate_EDA_features_test.py +++ /dev/null @@ -1,67 +0,0 @@ -import sys -sys.path.append("..") -from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames -from CalculatingFeatures.calculate_features import calculateFeatures -from CalculatingFeatures import gsr -from eda_explorer.load_files import butter_lowpass_filter -from eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd - -pd.set_option('display.max_rows', None) -pd.set_option('display.max_columns', None) - - -pathToEDACsv = "../example_data/S2_E4_Data/EDA.csv" - -# get an array of values from EDA empatica file -eda_data, startTimeStamp_EDA, sampleRate_EDA = convert1DEmpaticaToArray(pathToEDACsv) -eda_data = eda_data[:int(300000//sampleRate_EDA)] - -windowLength_EDA = int(sampleRate_EDA*120) - -# Convert the HRV data into 2D array -eda_data_2D = convertInputInto2d(eda_data, windowLength_EDA) - -calculatedFeatures_EDA = calculateFeatures(eda_data_2D, fs=int(sampleRate_EDA), featureNames=gsrFeatureNames) -print(calculatedFeatures_EDA) - -# df_EDA = pd.DataFrame() -# for row in eda_data_2D: -# current_result = {} -# current_result.update(gsr.extractGsrFeatures(row, sampleRate=int(sampleRate_EDA),featureNames=gsrFeatureNames)) -# df_EDA = df_EDA.append(current_result, ignore_index=True) - -# no_interest = 131 -# current_row = eda_data_2D[no_interest,] -# filtered_EDA = butter_lowpass_filter(current_row, 1.0, int(sampleRate_EDA), 6) - -# plt.figure() -# plt.plot(current_row, color='blue') -# plt.plot(filtered_EDA, color='red') -# plt.savefig('output_images/EDA_exa1.png') - -# gsr_data = pd.DataFrame(current_row, columns=["EDA"]) -# startTime = pd.to_datetime(0, unit="s") -# gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000/sampleRate_EDA) + 'L') -# gsr_data['filtered_eda'] = filtered_EDA -# peakData = calcPeakFeatures(gsr_data, offset=1, thresh=.02, start_WT=4, end_WT=4, sampleRate=int(sampleRate_EDA)) - -# peaks = np.where(peakData.peaks == 1.0)[0] -# peak_starts = np.where(peakData.peak_start == 1.0)[0] -# peak_ends = np.where(peakData.peak_end == 1.0)[0] -# print(peaks) -# print(peak_starts) -# print(peak_ends) - -# plt.figure() -# plt.plot(filtered_EDA, color='red') -# plt.scatter(peaks, filtered_EDA[peaks], color="green") -# plt.scatter(peak_starts, filtered_EDA[peak_starts], color="green", marker=">", alpha=0.5) -# plt.scatter(peak_ends, filtered_EDA[peak_ends], color="green", marker="s", alpha=0.5) -# plt.savefig('output_images/EDA_exa2.png') - -# print(df_EDA.loc[no_interest, df_EDA.columns.str.contains('Peak')]) - - diff --git a/calculatingfeatures/cf_tests/calculate_HR_features_test.py b/calculatingfeatures/cf_tests/calculate_HR_features_test.py deleted file mode 100644 index 2b4b5134..00000000 --- a/calculatingfeatures/cf_tests/calculate_HR_features_test.py +++ /dev/null @@ -1,28 +0,0 @@ -import sys -sys.path.append("..") -from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, frequencyFeatureNames, hrvFeatureNames -from CalculatingFeatures.calculate_features import calculateFeatures -import pandas as pd - - - -pathToHrvCsv = "../example_data/S2_E4_Data/BVP.csv" -windowLength = 500 - -# get an array of values from HRV empatica file -hrv_data, startTimeStamp, sampleRate = convert1DEmpaticaToArray(pathToHrvCsv) -hrv_data = hrv_data[:int(300000//sampleRate)] - -# Convert the HRV data into 2D array -hrv_data_2D = convertInputInto2d(hrv_data, windowLength) - -# Create a list with feature names -featureNames = [] -featureNames.extend(hrvFeatureNames) -featureNames.extend(frequencyFeatureNames) - -pd.set_option('display.max_columns', None) - -# Calculate features -calculatedFeatures = calculateFeatures(hrv_data_2D, fs=int(sampleRate), featureNames=featureNames) -print(calculatedFeatures) diff --git a/calculatingfeatures/cf_tests/calculate_TEMP_features_test.py b/calculatingfeatures/cf_tests/calculate_TEMP_features_test.py deleted file mode 100644 index 3fc1378e..00000000 --- a/calculatingfeatures/cf_tests/calculate_TEMP_features_test.py +++ /dev/null @@ -1 +0,0 @@ -# Temparature features do not exist - should be used from the RAPIDS defaults? diff --git a/calculatingfeatures/eda_explorer/.csv b/calculatingfeatures/eda_explorer/.csv deleted file mode 100644 index 6fd23aef..00000000 --- a/calculatingfeatures/eda_explorer/.csv +++ /dev/null @@ -1,175 +0,0 @@ -,EDA,rise_time,max_deriv,amp,decay_time,SCR_width,AUC -2017-05-22 07:15:34.750,0.503563,2.375,0.014828457554093788,0.023063999999999973,3.375,5.125,0.11820299999999986 -2017-05-22 07:16:18.500,0.669122,1.25,0.11153807317588882,0.03459599999999996,0.375,1.5,0.05189399999999994 -2017-05-22 07:16:29.250,0.808787,1.625,0.15124313794040223,0.057659999999999934,0.375,1.75,0.10090499999999988 -2017-05-22 07:16:43.250,1.3176430000000001,2.125,0.3137904553094124,0.1410450000000001,0.5,2.375,0.3349818750000002 -2017-05-22 07:16:49.625,1.436807,1.5,0.2754308494258524,0.09930299999999992,0.125,1.5,0.14895449999999988 -2017-05-22 07:17:09.250,1.425176,1.0,0.27652972963920597,0.07688000000000006,0.125,1.0,0.07688000000000006 -2017-05-22 07:17:20.250,1.732795,2.0,0.3374972909832241,0.32299500000000014,1.125,2.125,0.6863643750000004 -2017-05-22 07:17:31.625,1.1331315,1.375,0.22530763506209084,0.06406650000000003,0.5,1.75,0.11211637500000005 -2017-05-22 07:17:38.500,1.403394,1.5,0.34871084578724343,0.09225600000000012,0.625,2.0,0.18451200000000023 -2017-05-22 07:17:45.125,1.4354269999999998,2.0,0.17189871106182686,0.15632299999999977,2.875,4.375,0.683913124999999 -2017-05-22 07:18:07.125,1.655274,2.125,0.26828106990271827,0.24153099999999994,1.5,3.25,0.7849757499999999 -2017-05-22 07:18:15.000,1.59313,1.625,0.15518636240276962,0.10378849999999984,1.25,2.5,0.2594712499999996 -2017-05-22 07:18:28.375,1.5700655,1.75,0.061967970875677736,0.04100250000000005,1.0,2.375,0.09738093750000013 -2017-05-22 07:18:34.500,1.92115,2.0,0.3612223590696164,0.36646,2.625,4.0,1.46584 -2017-05-22 07:18:40.375,1.8026270000000002,1.625,0.10289846560438853,0.07623900000000017,1.0,2.25,0.17153775000000038 -2017-05-22 07:18:48.375,1.8154405000000002,1.875,0.1396967735464596,0.11852350000000045,1.25,2.75,0.32593962500000123 -2017-05-22 07:19:09.000,1.663603,1.75,0.05329105073014517,0.039722000000000035,1.25,2.625,0.10427025000000009 -2017-05-22 07:19:20.375,1.6591179999999999,1.75,0.08783426805671546,0.06598799999999994,1.25,2.625,0.17321849999999983 -2017-05-22 07:19:26.125,1.6450235000000002,2.25,0.044012205524090575,0.043565000000000076,2.125,3.625,0.15792312500000028 -2017-05-22 07:21:03.625,1.171473,4.0,0.11766914755295232,0.22102999999999995,,, -2017-05-22 07:21:11.375,1.1048434999999999,1.875,0.026811067104887343,0.02050149999999995,0.625,2.0,0.0410029999999999 -2017-05-22 07:21:27.625,1.031167,2.125,0.07750606429624973,0.07752100000000006,2.0,3.625,0.2810136250000002 -2017-05-22 07:21:48.375,1.0177125,2.75,0.061221624333410496,0.100584,3.0,5.0,0.50292 -2017-05-22 07:21:58.625,1.1054840000000001,3.75,0.09361829423393608,0.15440050000000016,2.0,4.5,0.6948022500000007 -2017-05-22 07:23:26.375,0.6320325,1.75,0.047011122388191495,0.03844000000000003,1.125,2.375,0.09129500000000007 -2017-05-22 07:24:26.000,0.534582,2.0,0.0323970421729749,0.025626999999999955,1.875,3.5,0.08969449999999984 -2017-05-22 07:25:42.500,0.724219,4.0,0.11106882694184428,0.29086199999999995,,, -2017-05-22 07:26:03.125,1.3380455,3.125,0.2512871921254973,0.34852150000000004,2.125,3.875,1.3505208125000001 -2017-05-22 07:26:14.250,1.34189,2.25,0.2918536338935702,0.254985,3.125,4.875,1.2430518750000001 -2017-05-22 07:26:20.500,1.321388,1.5,0.1539927116847526,0.09481799999999985,0.75,2.0,0.1896359999999997 -2017-05-22 07:26:29.250,1.425176,2.125,0.25289703835694866,0.21270100000000003,2.375,4.0,0.8508040000000001 -2017-05-22 07:26:35.750,1.3944239999999999,1.875,0.12879051845700573,0.09674000000000005,1.125,2.625,0.2539425000000001 -2017-05-22 07:26:41.000,1.480372,1.875,0.20847826783929513,0.15898349999999994,1.125,2.375,0.3775858124999999 -2017-05-22 07:26:57.250,1.336863,1.875,0.13441189501376805,0.112757,1.625,3.125,0.352365625 -2017-05-22 07:27:31.375,1.1786189999999999,2.25,0.08202871886284768,0.08328649999999982,2.625,4.25,0.35396762499999923 -2017-05-22 07:27:55.375,1.029344,2.125,0.05728986633907596,0.052535,1.5,3.125,0.164171875 -2017-05-22 07:28:11.125,1.3579065000000001,2.625,0.24722820810617208,0.3466005000000001,,, -2017-05-22 07:28:17.750,1.860928,4.0,0.31268337765616927,0.47290999999999994,2.375,4.375,2.0689812499999998 -2017-05-22 07:28:38.375,1.7680315000000002,2.25,0.29119838550697885,0.32353650000000034,3.125,4.875,1.5772404375000018 -2017-05-22 07:29:04.375,1.5643,2.125,0.1404294931600223,0.13390000000000013,,, -2017-05-22 07:29:26.375,1.7776414999999999,2.125,0.2393192190774318,0.23448349999999984,2.0,3.625,0.8500026874999994 -2017-05-22 07:30:05.125,1.399008,1.5,0.05270208243056018,0.03203350000000005,0.75,1.875,0.06006281250000009 -2017-05-22 07:30:31.375,1.653352,3.375,0.2871077101193489,0.429246,,, -2017-05-22 07:30:34.625,1.8404265,1.875,0.23727048901370473,0.18835550000000012,0.875,2.375,0.4473443125000003 -2017-05-22 07:30:47.375,1.7411235,2.0,0.14199373618878397,0.11980500000000016,2.0,3.625,0.4342931250000006 -2017-05-22 07:30:53.000,1.731513,1.75,0.06045060566822791,0.030752000000000113,0.625,2.0,0.061504000000000225 -2017-05-22 07:31:15.500,1.525219,2.375,0.03471297956604147,0.03395550000000003,0.875,2.625,0.08913318750000007 -2017-05-22 07:32:17.875,2.08536,2.375,0.45106857195387384,0.502481,,, -2017-05-22 07:32:23.000,2.114831,1.375,0.15153710180561575,0.05445650000000013,0.375,1.625,0.08849181250000021 -2017-05-22 07:32:40.750,2.410332,1.875,0.7084231020390668,0.5767534999999997,3.0,4.5,2.5953907499999986 -2017-05-22 07:33:12.375,1.870096,1.0,0.12952756935894172,0.035236000000000045,0.5,1.5,0.05285400000000007 -2017-05-22 07:33:31.500,1.647145,1.75,0.28035533686811753,0.1255710000000001,0.375,1.375,0.17266012500000014 -2017-05-22 07:33:48.875,1.6398979999999999,2.5,0.22399495229998578,0.29086199999999995,3.5,5.375,1.5633832499999998 -2017-05-22 07:33:58.875,1.6597585000000001,2.0,0.23193033054720047,0.23832700000000018,1.75,3.25,0.7745627500000005 -2017-05-22 07:34:20.125,1.5976145000000002,3.0,0.19560839697922994,0.26843950000000016,,, -2017-05-22 07:34:49.875,1.3932419999999999,2.375,0.028080802357340673,0.032032999999999756,0.75,2.375,0.07607837499999942 -2017-05-22 07:35:19.750,1.143382,2.75,0.032085826213871016,0.03075199999999989,1.0,2.75,0.0845679999999997 -2017-05-22 07:35:30.500,1.186948,1.75,0.20425108909752332,0.12557099999999988,0.25,1.75,0.21974924999999979 -2017-05-22 07:35:53.125,1.251014,1.625,0.11746281156048255,0.0320330000000002,0.5,1.875,0.060061875000000375 -2017-05-22 07:36:05.625,1.306752,2.0,0.10508940062988081,0.09866249999999988,0.625,2.25,0.22199062499999972 -2017-05-22 07:36:26.625,1.3002465,2.5,0.21642722813322202,0.22103000000000006,0.375,2.125,0.4696887500000001 -2017-05-22 07:36:42.750,1.176598,1.75,0.14901857025425969,0.11147600000000013,1.5,2.875,0.3204935000000004 -2017-05-22 07:36:57.375,1.0888265,1.875,0.09062315859340409,0.0762385000000001,1.375,2.875,0.21918568750000028 -2017-05-22 07:37:19.375,1.0817795000000001,1.75,0.03859067965386842,0.026908500000000224,,, -2017-05-22 07:37:22.250,1.224007,2.0,0.14184516802949076,0.12813300000000005,1.625,3.125,0.40041562500000016 -2017-05-22 07:37:31.500,1.158659,2.0,0.05539320099511258,0.04869000000000012,1.625,3.25,0.1582425000000004 -2017-05-22 07:37:53.500,1.4290200000000002,2.25,0.24063567651439755,0.24985900000000005,,, -2017-05-22 07:38:02.250,1.375204,1.75,0.0511188487860057,0.03972100000000012,0.375,1.75,0.0695117500000002 -2017-05-22 07:38:13.375,1.298965,2.0,0.048745168416420626,0.049331000000000014,2.375,3.75,0.18499125000000005 -2017-05-22 07:38:22.500,1.288074,2.75,0.019866865669309064,0.028189999999999937,1.625,3.625,0.10218874999999977 -2017-05-22 07:38:42.500,1.250915,2.0,0.04330280907170092,0.03459599999999985,1.75,3.375,0.11676149999999949 -2017-05-22 07:38:57.875,1.2336174999999998,1.375,0.13929210080343957,0.07367649999999992,0.375,1.5,0.11051474999999988 -2017-05-22 07:39:24.875,1.2291325,2.625,0.07696330743284285,0.07047349999999986,2.625,4.5,0.31713074999999935 -2017-05-22 07:39:58.000,1.386736,2.25,0.1950892271550373,0.22807699999999986,,, -2017-05-22 07:40:07.750,1.431682,3.625,0.10232995613186091,0.1551404999999999,1.5,4.125,0.6399545624999996 -2017-05-22 07:40:13.875,1.336223,1.5,0.03691823367415381,0.021782999999999886,0.625,1.875,0.040843124999999786 -2017-05-22 07:40:31.750,1.7097310000000001,2.125,0.29895999294932096,0.299191,2.125,3.75,1.1219662499999998 -2017-05-22 07:41:02.125,1.4476985,3.5,0.12358274335508845,0.026908000000000154,0.125,3.125,0.08408750000000048 -2017-05-22 07:41:30.250,1.3060120000000002,4.0,0.018481394048414757,0.044846000000000164,,, -2017-05-22 07:41:48.500,1.311138,3.125,0.026024648999197098,0.02050149999999973,0.75,2.5,0.05125374999999932 -2017-05-22 07:42:05.875,1.2926575,2.625,0.10473285642778052,0.1274925,1.0,2.625,0.3346678125 -2017-05-22 07:42:18.250,1.158659,2.375,0.2680741258669528,0.20757550000000013,,, -2017-05-22 07:44:54.875,1.07281,2.25,0.2296469005371069,0.14222750000000006,1.0,2.5,0.35556875000000016 -2017-05-22 07:45:24.250,1.209913,4.0,0.12521915371023162,0.21013900000000008,3.5,5.25,1.1032297500000003 -2017-05-22 07:45:33.125,1.1778795,3.625,0.019288312412006903,0.08584950000000013,1.0,3.375,0.28974206250000045 -2017-05-22 07:45:37.000,1.536751,2.125,0.30655783735363684,0.382576,1.25,2.75,1.052084 -2017-05-22 07:45:51.125,1.6725720000000002,2.625,0.3439185714022184,0.420277,,, -2017-05-22 07:46:39.250,1.36249,1.875,0.06455079234815564,0.06726999999999994,0.625,2.0,0.13453999999999988 -2017-05-22 07:46:43.375,1.234357,1.125,0.36833145242077237,0.09738099999999994,0.375,1.5,0.1460714999999999 -2017-05-22 07:46:54.625,1.300345,2.125,0.302864053208582,0.23256100000000002,,, -2017-05-22 08:05:41.125,0.227703,1.75,0.0699073493119271,0.033954999999999985,2.875,4.5,0.15279749999999992 -2017-05-22 08:07:34.625,0.20976450000000002,1.875,0.06528087959205453,0.04164350000000003,,, -2017-05-22 08:14:31.375,0.42438750000000003,4.0,0.04608763734814758,0.13774350000000002,2.125,5.0,0.6887175000000001 -2017-05-22 08:16:05.625,0.43848200000000004,1.875,0.022908910049771247,0.02050099999999999,0.875,2.25,0.04612724999999998 -2017-05-22 08:16:31.750,0.421825,2.375,0.020235005129130013,0.0262675,1.375,3.0,0.0788025 -2017-05-22 08:17:08.375,0.442967,4.0,0.027462685910427886,0.05637900000000001,,, -2017-05-22 08:17:23.625,0.48589150000000003,1.875,0.022108622083991225,0.021782500000000038,1.0,2.375,0.05173343750000009 -2017-05-22 08:17:41.375,0.47371850000000004,1.875,0.020662833646469814,0.02114149999999998,1.25,2.625,0.05549643749999995 -2017-05-22 08:17:57.000,0.570459,3.75,0.09028796294871899,0.12172600000000006,,, -2017-05-22 08:18:15.000,0.752408,4.0,0.09074689036959427,0.20373099999999988,,, -2017-05-22 08:18:18.125,0.776113,1.375,0.045155390005493956,0.022423000000000082,0.5,1.625,0.03643737500000013 -2017-05-22 08:18:41.375,0.7703470000000001,1.25,0.15855556460387632,0.05830050000000009,0.375,1.5,0.08745075000000013 -2017-05-22 08:20:34.750,0.6652779999999999,2.125,0.05843900549537295,0.06598899999999996,1.75,3.375,0.22271287499999987 -2017-05-22 08:20:47.000,0.628119,2.625,0.028209886616290092,0.02818949999999998,1.0,2.75,0.07752112499999994 -2017-05-22 08:21:05.125,0.633885,2.0,0.04517072457773352,0.05125350000000006,3.125,4.5,0.23064075000000028 -2017-05-22 08:21:16.500,0.6434949999999999,2.125,0.03412071629378932,0.030111499999999847,1.25,3.0,0.09033449999999954 -2017-05-22 08:22:17.250,0.674247,2.5,0.10312026697879695,0.11531999999999998,,, -2017-05-22 08:22:35.000,0.690904,2.125,0.027065625284675043,0.021141999999999994,0.625,2.25,0.04756949999999999 -2017-05-22 08:22:48.000,0.697311,2.625,0.03720895432743454,0.049331000000000014,,, -2017-05-22 08:22:53.250,0.708843,2.25,0.035330474875114426,0.03459599999999996,,, -2017-05-22 08:23:02.000,0.748564,2.0,0.03098284779636007,0.02434500000000006,1.125,2.625,0.06390562500000016 -2017-05-22 08:23:37.000,0.761378,1.625,0.02820908045797399,0.02050149999999995,0.875,2.125,0.043565687499999894 -2017-05-22 08:23:45.375,0.9170595,2.5,0.10900167444209874,0.16785450000000002,2.125,4.0,0.6714180000000001 -2017-05-22 08:23:52.125,0.877979,2.25,0.046180062567243496,0.04869099999999993,0.875,2.25,0.10955474999999984 -2017-05-22 08:23:58.000,0.902324,2.0,0.0451624964223063,0.049972000000000016,1.0,2.375,0.11868350000000004 -2017-05-22 08:24:01.875,0.9817665,1.875,0.12067163327367236,0.10378749999999992,1.875,3.25,0.33730937499999974 -2017-05-22 08:24:27.250,1.0369329999999999,1.875,0.10883694564521296,0.07047349999999997,1.375,2.875,0.20261131249999992 -2017-05-22 08:24:48.875,1.015791,3.25,0.05283368025782487,0.10250700000000013,2.375,4.625,0.4740948750000006 -2017-05-22 08:24:54.125,1.0061805000000001,2.5,0.034086601379224035,0.03972100000000023,,, -2017-05-22 08:24:57.250,1.142002,2.25,0.14365102798932483,0.14222800000000002,2.625,4.25,0.6044690000000001 -2017-05-22 08:25:19.375,1.1118905,2.75,0.05343529253432422,0.05894149999999998,0.875,2.5,0.14735374999999995 -2017-05-22 08:25:36.375,1.146487,3.125,0.06381041996704617,0.14799399999999996,2.0,4.25,0.6289744999999998 -2017-05-22 08:26:00.875,1.089467,2.375,0.10804115398923564,0.12428899999999987,1.625,3.25,0.4039392499999996 -2017-05-22 08:26:23.875,1.058075,1.875,0.04895165908758514,0.03267400000000009,0.375,1.75,0.05717950000000016 -2017-05-22 08:26:29.750,1.07281,1.625,0.03252146988782734,0.02434549999999991,0.75,2.125,0.051734187499999806 -2017-05-22 08:26:46.750,1.062559,1.875,0.04287899744724122,0.04420550000000012,1.375,2.75,0.12156512500000033 -2017-05-22 08:26:53.750,1.033089,2.5,0.02030497122408903,0.026907999999999932,3.375,5.125,0.13790349999999965 -2017-05-22 08:27:10.625,1.109328,4.0,0.08325412668487431,0.08777150000000011,1.375,2.875,0.2523430625000003 -2017-05-22 08:27:22.125,1.1714725000000001,3.5,0.1102682429606805,0.1313365000000002,3.5,5.125,0.6730995625000009 -2017-05-22 08:27:34.500,1.106125,2.125,0.049404017703517766,0.04484700000000008,1.5,3.0,0.13454100000000024 -2017-05-22 08:28:11.000,1.175317,2.375,0.0767191386014403,0.08649050000000003,1.625,3.375,0.2919054375000001 -2017-05-22 08:28:15.750,1.161222,2.125,0.023469816031331803,0.031392499999999934,1.625,3.125,0.0981015624999998 -2017-05-22 08:28:40.000,1.131751,2.125,0.021426131696090422,0.026907999999999932,2.5,4.0,0.10763199999999973 -2017-05-22 08:28:45.625,1.231695,2.375,0.10967664002624034,0.11147599999999991,,, -2017-05-22 08:29:05.250,1.254759,1.75,0.11844124124046118,0.10763199999999995,3.375,4.625,0.49779799999999974 -2017-05-22 08:29:16.500,1.2252889999999999,2.75,0.04090415106637302,0.06406699999999987,2.0,3.75,0.24025124999999953 -2017-05-22 08:29:47.875,1.2560405,2.25,0.06433835805311716,0.06662899999999983,2.25,4.0,0.2665159999999993 -2017-05-22 08:29:55.000,1.317544,2.0,0.11189364771460575,0.09353699999999998,,, -2017-05-22 08:30:06.250,1.231695,2.5,0.0815369875816696,0.047409000000000034,0.125,2.0,0.09481800000000007 -2017-05-22 08:30:33.500,0.8972680000000001,1.5,0.20104111662310498,0.05894200000000005,1.125,2.375,0.13998725000000012 -2017-05-22 08:30:38.750,0.9997739999999999,2.25,0.46054202442366776,0.29086199999999995,,, -2017-05-22 08:32:36.750,0.656308,2.625,0.054126330081166074,0.07431699999999997,2.25,4.125,0.30655762499999983 -2017-05-22 08:33:43.500,0.594804,1.875,0.07805246554195655,0.051253000000000104,1.25,2.5,0.12813250000000026 -2017-05-22 08:36:47.625,0.319959,3.125,0.037886110588743804,0.045487,0.625,2.5,0.1137175 -2017-05-22 08:36:51.500,0.402605,1.25,0.0822805602175376,0.02947099999999997,0.375,1.375,0.04052262499999996 -2017-05-22 08:38:24.125,0.6838569999999999,3.125,0.011308014179868486,0.03908099999999992,,, -2017-05-22 08:38:46.125,0.636448,1.0,0.261456682334547,0.02434550000000002,0.125,1.125,0.027388687500000022 -2017-05-22 08:39:22.625,0.553802,1.375,0.22335943852213358,0.02050099999999999,0.125,1.25,0.02562624999999999 -2017-05-22 08:41:21.750,0.424387,4.0,0.004364841866654867,0.021782000000000024,,, -2017-05-22 08:45:52.875,0.2539705,2.125,0.03486080607122055,0.024345499999999992,0.25,1.625,0.03956143749999999 -2017-05-22 08:49:09.875,0.5993580000000001,1.625,1.5722707914359493,0.34026200000000006,0.125,1.25,0.4253275000000001 -2017-05-22 08:57:45.625,0.1143055,1.875,0.05009238869002808,0.026908500000000002,0.75,1.875,0.050453437500000003 -2017-05-22 08:58:12.750,0.124556,2.25,0.03482288095641184,0.042284,,, -2017-05-22 08:59:58.250,0.123275,1.5,0.04808163407584454,0.03203399999999999,0.625,1.875,0.060063749999999985 -2017-05-22 09:00:08.625,0.1732465,1.0,0.1155253863671517,0.02370449999999999,1.25,2.25,0.053335124999999976 -2017-05-22 09:00:31.000,0.251408,3.25,0.043829239927442254,0.07047300000000004,2.5,4.375,0.3083193750000002 -2017-05-22 09:00:58.000,0.221937,4.0,0.01431226024740595,0.032033000000000006,,, -2017-05-22 09:01:11.750,0.232188,2.5,0.02680488117904911,0.02306400000000003,3.125,4.625,0.10667100000000013 -2017-05-22 09:02:07.250,0.21424899999999997,1.875,0.021082079263371023,0.02242299999999997,0.875,2.25,0.050451749999999934 -2017-05-22 09:02:16.750,0.230906,2.375,0.03262723948795654,0.02882949999999998,0.75,2.5,0.07207374999999995 -2017-05-22 09:02:56.125,0.240516,2.125,0.03661649007111234,0.033954999999999985,1.125,2.75,0.09337624999999997 -2017-05-22 09:04:57.125,0.3609615,4.0,0.05488506385313352,0.1845115,,, 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09:24:08.875,0.439123,2.875,0.05135528153867819,0.059582000000000024,3.375,5.25,0.3128055000000001 -2017-05-22 09:26:05.625,0.7614465,1.375,1.1177070991499694,0.44469050000000004,2.125,3.25,1.445244125 -2017-05-22 09:26:27.125,0.473078,1.875,0.03726277291054325,0.023063999999999973,2.0,3.125,0.07207499999999992 diff --git a/calculatingfeatures/eda_explorer/.gitignore b/calculatingfeatures/eda_explorer/.gitignore deleted file mode 100644 index 1d6e4473..00000000 --- a/calculatingfeatures/eda_explorer/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -*.pyc -.idea -env-python2 -env-python3 -tests \ No newline at end of file diff --git a/calculatingfeatures/eda_explorer/AccelerometerFeatureExtractionScript.py b/calculatingfeatures/eda_explorer/AccelerometerFeatureExtractionScript.py deleted file mode 100644 index 6412ae1c..00000000 --- a/calculatingfeatures/eda_explorer/AccelerometerFeatureExtractionScript.py +++ /dev/null @@ -1,309 +0,0 @@ -import numpy as np -import pandas as pd -import scipy.signal as scisig -import os -import matplotlib.pyplot as plt - -from load_files import getInputLoadFile, getOutputPath, get_user_input - -DEBUG = True - -SAMPLING_RATE = 8 - -ONE_MINUTE_S = 60 -THIRTY_MIN_S = ONE_MINUTE_S*30 -SECONDS_IN_DAY = 24*60*60 - -STILLNESS_MOTION_THRESHOLD = .1 -PERCENT_STILLNESS_THRESHOLD = .95 - -STEP_DIFFERENCE_THRESHOLD = 0.3 - - - -def computeAllAccelerometerFeatures(data, time_frames): - if DEBUG: print("\t\tcomputing motion...") - motion = computeMotion(data['AccelX'], data['AccelY'], data['AccelZ']) - - if DEBUG: print("\t\tcomputing steps...") - steps = computeSteps(motion) - - if DEBUG: print("\t\tcomputing stillness...") - stillness = computeStillness(motion) - - features = [] - - for time_frame in time_frames: - start = time_frame[0] - end = time_frame[1] - start1Hz = int(start / SAMPLING_RATE) - end1Hz = end if end == -1 else int(end / SAMPLING_RATE) - if DEBUG: print("\t\tcomputing features for time frame. Start index: "+ str(start)+ " end index: "+ str(end)) - - time_frame_feats = computeAccelerometerFeaturesOverOneTimeFrame(motion[start:end], - steps[start:end], - stillness[start1Hz:end1Hz]) - features.append(time_frame_feats) - - return features, steps, motion - -def computeMotion(acc1, acc2, acc3): - '''Aggregates 3-axis accelerometer signal into a single motion signal''' - return np.sqrt(np.array(acc1)**2 + np.array(acc2)**2 + np.array(acc3)**2) - -def computeSteps(motion): - '''Determines the location of steps from the aggregated accelerometer signal. - Signal is low-pass filtered, then minimums are located in the signal. For each - min, if the max absolute derivative (first difference) immediately surrounding - it is greater than a threshold, it is counted as a step. - - Args: - motion: root mean squared 3 axis acceleration - Returns: - steps: binary array at 8Hz which is 1 everywhere there is a step''' - - filtered_signal = filterSignalFIR(motion, 2, 256) - diff = filtered_signal[1:]-filtered_signal[:-1] - - mins = scisig.argrelextrema(filtered_signal, np.less)[0] - - steps = [0] * len(filtered_signal) - for m in mins: - if m <= 4 or m >= len(diff) - 4: - continue - if max(abs(diff[m-4:m+4])) > STEP_DIFFERENCE_THRESHOLD: - steps[m] = 1.0 - - return steps - -def filterSignalFIR(eda, cutoff=0.4, numtaps=64): - f = cutoff/(SAMPLING_RATE/2.0) - FIR_coeff = scisig.firwin(numtaps,f) - - return scisig.lfilter(FIR_coeff,1,eda) - -def computeStillness(motion): - '''Locates periods in which the person is still or motionless. - Total acceleration must be less than a threshold for 95 percent of one - minute in order for that minute to count as still - - Args: - motion: an array containing the root mean squared acceleration - Returns: - A 1Hz array that is 1 for each second belonging to a still period, 0 otherwise - ''' - diff = motion[1:]-motion[:-1] - momentary_stillness = diff < STILLNESS_MOTION_THRESHOLD - np.append(momentary_stillness,0) # to ensure list is the same size as the full day signal - num_minutes_in_day = 24*60 - - #create array indicating whether person was still or not for each second of the day - #to be still the momentary_stillness signal must be true for more than 95% of the minute - #containing that second - second_stillness = [0]*SECONDS_IN_DAY - - for i in range(num_minutes_in_day): - hours_start = int(i / 60) - mins_start = i % 60 - hours_end = int((i+1) / 60) - mins_end = (i+1) % 60 - - start_idx = getIndexFromTimestamp(hours_start, mins_start) - end_idx = getIndexFromTimestamp(hours_end, mins_end) - - this_minute = momentary_stillness[start_idx:end_idx] - minute_stillness = sum(this_minute) > PERCENT_STILLNESS_THRESHOLD*(60*SAMPLING_RATE) - - second_idx = int(start_idx/8) - for si in range(second_idx,second_idx+60): - second_stillness[si] = float(minute_stillness) - - return second_stillness - -def computeAccelerometerFeaturesOverOneTimeFrame(motion, steps, stillness): - ''' Computes all available features for a time period. Incoming signals are assumed to be from - only that time period. - - Args: - motion: 8Hz root mean squared 3 axis acceleration - steps: 8Hz binary signal that is 1 if there is a step - stillness: 1Hz 1 if the person was still during this second, 0 otherwise - Returns: - A list of features containing (in order): - -Step count number of steps detected - -mean step time during movement average number of samples between two steps (aggregated first to 1 minute, - then we take the mean of only the parts of this signal occuring during movement) - -percent stillness percentage of time the person spent nearly motionless - ''' - - features = [] - - features.extend(computeStepFeatures(steps,stillness)) - features.append(countStillness(stillness)) - - return features - -def computeStepFeatures(steps,stillness): - '''Counts the total number of steps over a given period, - as well as the average time between steps (meant to approximate walking speed) - - Args: - steps: an binary array at 8 Hz that is 1 every time there is a step - Returns: - sum: the number of steps in a period - median time: average number of samples between two steps''' - - sum_steps = float(sum(steps)) - - step_indices = np.nonzero(steps)[0] - diff = step_indices[1:]-step_indices[:-1] - - #ensure length of step difference array is the same so we can get the actual locations of step differences - timed_step_diff = np.empty(len(steps)) * np.nan - timed_step_diff[step_indices[:len(diff)]] = diff - - signal_length_1s = len(stillness) - signal_length_1min = int(signal_length_1s / 60) - - # if there aren't enough steps during this period, cannot accurately compute mean step diff - if len(timed_step_diff) < signal_length_1min: - return [sum_steps, np.nan] - - agg_stillness = aggregateSignal(stillness, signal_length_1min, 'max') - agg_step_diff = aggregateSignal(timed_step_diff, signal_length_1min, 'mean') - - movement_indices = [i for i in range(len(agg_stillness)) if agg_stillness[i] == 0.0] - step_diff_during_movement = agg_step_diff[movement_indices] - - return [sum_steps,round(np.nanmean(step_diff_during_movement),10)] - -def countStillness(stillness): - '''Counts the total percentage of time spent still over a period - - Args: - stillness: an binary array at 1Hz that is 1 if that second is part of a still period - Returns: - the percentage time spent still over a period''' - - return float(sum(stillness)) / float(len(stillness)) - -def aggregateSignal(signal, new_signal_length, agg_method='sum'): - new_signal = np.zeros(new_signal_length) - samples_per_bucket = int(len(signal) / new_signal_length) - - #the new signal length must be large enough that there is at least 1 sample per bucket - assert(samples_per_bucket > 0) - - for i in range(new_signal_length): - if agg_method == 'sum': - new_signal[i] = np.nansum(signal[i*samples_per_bucket:(i+1)*samples_per_bucket]) - elif agg_method == 'percent': - new_signal[i] = np.nansum(signal[i*samples_per_bucket:(i+1)*samples_per_bucket]) / samples_per_bucket - elif agg_method == 'mean': - new_signal[i] = np.nanmean(signal[i*samples_per_bucket:(i+1)*samples_per_bucket]) - elif agg_method == 'max': - new_signal[i] = np.nanmax(signal[i*samples_per_bucket:(i+1)*samples_per_bucket]) - return new_signal - -def getIndexFromTimestamp(hours, mins=0): - return ((hours * 60) + mins) * 60 * SAMPLING_RATE - -def inputTimeFrames(): - '''Allows user to choose the time frames over which they compute accelerometer features.''' - - time_frames = [] - print("Accelerometer features can be extracted over different time periods.") - cont = get_user_input("If you would like to enter a time period over which to compute features, enter 'y', or press enter to compute features over the entire file.") - while cont == 'y' or cont == 'Y': - start = int(get_user_input("Enter the starting hour of the time period (hour 0 is when the file starts):")) - end = int(get_user_input("Enter the ending hour of the time period (hour 0 is when the file starts; use -1 for the end of the file):")) - start = getIndexFromTimestamp(int(start)) - if end != -1: - end = getIndexFromTimestamp(int(end)) - time_frames.append([start,end]) - print("Great! Now computing features for the following time periods:"+ str(time_frames)) - cont = get_user_input("To add another time period, enter 'y'. To finish, press enter.") - - if len(time_frames) == 0: - time_frames = [[0,-1]] # the whole file - - return time_frames - -def saveFeaturesToFile(features, time_frames, output_file): - of = open(output_file, 'w') - of.write("Time period start hour, Time period end hour, Step count, Mean step time during movement, Percent stillness\n") - tf_i = 0 - for tf in time_frames: - output_str = str(tf[0]) + ' , ' + str(tf[1]) - for feat in features[tf_i]: - output_str += ' , ' + str(feat) - tf_i += 1 - of.write(output_str + '\n') - of.close() - print("Saved features to file"+ output_file) - -# draws a graph of the data with the peaks marked on it -# assumes that 'data' dataframe already contains the 'peaks' column -def plotSteps(data, x_seconds, sampleRate = SAMPLING_RATE): - if x_seconds: - time_m = np.arange(0,len(data))/float(sampleRate) - realign = 128/(sampleRate) - else: - time_m = np.arange(0,len(data))/(sampleRate*60.) - realign = 128/(sampleRate*60.) - - data_min = data['motion'].min() - data_max = data['motion'].max() - - #Plot the data with the Peaks marked - plt.figure(1,figsize=(20, 5)) - - plt.plot(time_m,data['motion']) - - for i in range(len(data)): - if data.iloc[i]["steps"]==1: - x_loc = time_m[i] - realign - plt.plot([x_loc,x_loc],[data_min,data_max],"k") - step_height = data_max * 1.15 - #data['steps_plot'] = data['steps'] * step_height - #plt.plot(time_m,data['steps_plot'],'k') - - plt.xlim([0,time_m[-1]]) - plt.ylim([data_min-.1,data_max+.1]) - plt.title('Motion with Detected "Steps" marked') - plt.ylabel('g') - if x_seconds: - plt.xlabel('Time (s)') - else: - plt.xlabel('Time (min)') - - plt.show() - -if __name__ == "__main__": - print("This script will extract features related to accelerometer data.") - - data, filepath_confirm = getInputLoadFile() - - output_path = getOutputPath() - - time_frames = inputTimeFrames() - - features, steps, motion = computeAllAccelerometerFeatures(data, time_frames) - - data["steps"] = steps - data["motion"] = motion - - saveFeaturesToFile(features, time_frames, output_path) - - print("") - plot_ans = get_user_input("Do you want to plot the detected steps? (y/n): ") - if 'y' in plot_ans: - secs_ans = get_user_input("Would you like the x-axis to be in seconds or minutes? (sec/min): ") - if 'sec' in secs_ans: - x_seconds=True - else: - x_seconds=False - plotSteps(data, x_seconds) - else: - print("\tOkay, script will not produce a plot") - diff --git a/calculatingfeatures/eda_explorer/ArtifactClassifiers.py b/calculatingfeatures/eda_explorer/ArtifactClassifiers.py deleted file mode 100644 index 045f4f59..00000000 --- a/calculatingfeatures/eda_explorer/ArtifactClassifiers.py +++ /dev/null @@ -1,601 +0,0 @@ -import numpy as np -from sklearn.metrics.pairwise import rbf_kernel - - -def predict_binary_classifier(X): - '''' - X: num test data by 13 features - ''' - - # Get params - params = binary_classifier() - - # compute kernel for all data points - K = rbf_kernel(params['support_vec'], X, gamma=params['gamma']) - - # Prediction = sign((sum_{i=1}^n y_i*alpha*K(x_i,x)) + rho) - predictions = np.zeros(X.shape[0]) - for i in range(X.shape[0]): - predictions[i] = np.sign(np.sum(params['dual_coef']*K[:, i]) + params['intercept']) - - return predictions - - -def predict_multiclass_classifier(X): - ''' - X: num test data by 10 features - ''' - # Get params - params = multiclass_classifier() - - K = rbf_kernel(params['support_vec'], X, gamma=params['gamma']) - - # define the start and end index for support vectors for each class - nv = params['num_support_vec'] - start = [sum(nv[:i]) for i in range(len(nv))] - end = [start[i] + nv[i] for i in range(len(nv))] - - # calculate: sum(a_p * k(x_p, x)) between every 2 classes - dual_coef = params['dual_coef'].T - predictions_0_1 = np.zeros(X.shape[0]) - for i in range(X.shape[0]): - temp_prediction = np.sum(dual_coef[start[0]:end[0], 0] * K[start[0]:end[0], i]) + \ - np.sum(dual_coef[start[1]:end[1], 0] * K[start[1]:end[1], i]) + params['intercept'][0] - predictions_0_1[i] = 0 if temp_prediction > 0 else 1 - - predictions_0_2 = np.zeros(X.shape[0]) - for i in range(X.shape[0]): - temp_prediction = np.sum(dual_coef[start[0]:end[0], 1] * K[start[0]:end[0], i]) + \ - np.sum(dual_coef[start[2]:end[2], 0] * K[start[2]:end[2], i]) + params['intercept'][1] - predictions_0_2[i] = 0 if temp_prediction > 0 else 2 - - predictions_1_2 = np.zeros(X.shape[0]) - for i in range(X.shape[0]): - temp_prediction = np.sum(dual_coef[start[1]:end[1], 1] * K[start[1]:end[1], i]) + \ - np.sum(dual_coef[start[2]:end[2], 1] * K[start[2]:end[2], i]) + params['intercept'][2] - predictions_1_2[i] = 1 if temp_prediction > 0 else 2 - - decision_function = np.vstack([predictions_0_1, predictions_0_2, predictions_1_2]).T - - # Majority Vote to find the best class - predictions = np.zeros(X.shape[0]) - for i in range(X.shape[0]): - lst = decision_function[i,:].tolist() - predictions[i] = max(set(lst), key=lst.count)-1 - - return predictions - - - -def binary_classifier(): - gamma = 0.1 - - # dual coef = y_i*alpha_i - dual_coef = np.array([[-1.12775599e+02, -1.00000000e+03, -1.00000000e+03, - -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, - -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, - -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, - -4.65947457e+02, -1.00000000e+03, -1.00000000e+03, - -1.00000000e+03, -1.17935400e+02, -1.00000000e+03, - -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, - -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, - -1.00000000e+03, -2.92534132e+02, -1.00000000e+03, - -1.00000000e+03, -3.69965631e+01, -1.00000000e+03, - -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, - -1.00000000e+03, -1.00000000e+03, -1.00000000e+03, - -1.00000000e+03, -1.00000000e+03, 1.00000000e+03, - 1.00000000e+03, 1.00000000e+03, 1.00000000e+03, - 7.92366387e+02, 3.00553142e+02, 2.22950860e-01, - 1.00000000e+03, 1.00000000e+03, 5.58636056e+02, - 1.21751544e+02, 1.00000000e+03, 1.00000000e+03, - 2.61920652e+00, 9.96570403e+02, 1.00000000e+03, - 1.00000000e+03, 1.00000000e+03, 1.00000000e+03, - 1.00000000e+03, 1.00000000e+03, 1.02270060e+02, - 5.41288840e+01, 1.91650287e+02, 1.00000000e+03, - 1.00000000e+03, 1.00000000e+03, 1.00000000e+03, - 1.00000000e+03, 2.45152637e+02, 7.53766346e+02, - 1.00000000e+03, 1.00000000e+03, 3.63211198e+00, - 1.00000000e+03, 3.31675798e+01, 5.64620367e+02, - 1.00000000e+03, 1.00000000e+03, 1.00000000e+03, - 2.66900636e+02, 1.00000000e+03, 6.54763900e+02, - 3.38216549e+02, 6.86434772e+01, 2.78998678e+02, - 6.97557950e+02, 1.00000000e+03]]) - - # intercept = rho - intercept = np.array([-2.63232929]) - - # support vectors = x_i - support_vec = np.array([[0.02809756, 0.0455, 0.025, 0.00866667, 0.03799132, -0.00799413, 0.01061208, 0.016263, 0.00671743, 0.00572262, 0.00578504, 0.00542415, 0.00318195], - [0.00060976, 0.0035, 0.007, 0.00087179, 0.00024191, -0.0005069, 0.0005069, 0.0070711, 0.00306413, 0.0031833, 0.0107827, 0.0066959, 0.0022981], - [3.49731707, 0.092, 0.054, 0.01923077, 3.53815367, -0.02236652, 0.02659884, 0.062225, 0.0316782, 0.01818914, 0.06607571, 0.03342241, 0.099702], - [2.52643902, 0.058, 0.055, 0.0114359, 2.54031008, -0.01070662, 0.01296803, 0.043134, 0.01649923, 0.01579683, 0.03326171, 0.05004163, 0.013965], - [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -2.74622599e-18, -2.42947453e-17, 3.36047450e-17, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], - [3.89758537, 0.167, 0.27, 0.06717949, 3.87923565, -0.04130143, 0.05403825, 0.047376, 0.0328098, 0.01255584, 0.03676955, 0.14237773, 0.11031], - [0.93326829, 0.0855, 0.106, 0.01169231, 0.92669874, -0.02740927, 0.02740927, 0.043841, 0.01131377, 0.01595008, 0.0231871, 0.02414775, 0.0139655], - [4.64253659, 0.106, 0.13, 0.03661538, 4.63806066, -0.03168223, 0.03168223, 0.10182, 0.0559785, 0.03369301, 0.06341563, 0.08583294, 0.0251025], - [0.29312195, 0.028, 0.039, 0.00682051, 0.28575076, -0.00648365, 0.00648365, 0.0056569, 0.00367694, 0.00126494, 0.00364005, 0.01814984, 0.006364], - [3.08187805, 0.0615, 0.123, 0.03435897, 3.11862292, -0.02260403, 0.02260403, 0.053033, 0.0397394, 0.01570345, 0.0338851, 0.10069204, 0.16652], - [2.43902439e-05, 5.00000000e-04, 1.00000000e-03, 1.02564103e-04, 2.43769719e-05, -7.19856842e-05, 7.19856842e-05, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], - [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -4.05052739e-10, -2.77557303e-09, 5.77955577e-09, 7.07110000e-04, 1.17851667e-04, 2.88676449e-04, 2.04124145e-04, 1.44336183e-04, 0.00000000e+00], - [0.83290244, 0.099, 0.172, 0.02610256, 0.82408369, -0.0168393, 0.0168393, 0.13011, 0.02875613, 0.04987211, 0.03786379, 0.02684837, 0.0155565], - [0.92597561, 0.017, 0.009, 0.00369231, 0.92583814, -0.00670974, 0.00670974, 0.012021, 0.00506763, 0.00420523, 0.01259266, 0.0115391, 0.00265165], - [2.43902439e-05, 5.00000000e-04, 1.00000000e-03, 2.56410256e-05, 2.18000765e-04, -5.56411248e-04, 5.56411248e-04, 9.19240000e-03, 2.71058333e-03, 4.25246049e-03, 2.49833278e-03, 7.64311464e-03, 0.00000000e+00], - [0.88760976, 0.0205, 0.022, 0.00489744, 0.88799505, -0.00346772, 0.00461828, 0.011314, 0.00447838, 0.00394135, 0.01327278, 0.01434142, 0.00406585], - [9.21263415, 0.118, 0.472, 0.0695641, 9.19153391, -0.02181738, 0.02181738, 0.16688, 0.07130037, 0.06135461, 0.04328934, 0.04277416, 0.0829085], - [0.48378049, 0.017, 0.026, 0.00794872, 0.48333175, -0.00337375, 0.00350864, 0.016971, 0.0089568, 0.00472601, 0.01168189, 0.01629524, 0.0226275], - [0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 9.65026603e-122, -2.00921455e-120, 4.22507597e-120, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000], - [0.10897561, 0.03, 0.033, 0.00553846, 0.12761266, -0.00442938, 0.00556735, 0.025456, 0.00872107, 0.00870258, 0.01130487, 0.01554551, 0.0123745], - [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -1.38812548e-09, -2.34438020e-08, 2.34438020e-08, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], - [0.66663415, 0.052, 0.05, 0.00510256, 0.66182973, -0.01361869, 0.01361869, 0.0049497, 0.00296982, 0.00208565, 0.00424264, 0.00961131, 0.012374], - [3.74146341e+00, 6.60000000e-02, 7.00000000e-02, 2.41025641e-02, 3.72790310e+00, -1.65194036e-02, 1.65194036e-02, 2.33350000e-02, 2.29102000e-02, 3.87787571e-04, 7.25086202e-03, 8.04828002e-03, 2.26270000e-02], - [2.43902439e-05, 5.00000000e-04, 1.00000000e-03, 1.02564103e-04, 2.44149661e-05, -7.19856850e-05, 7.19856850e-05, 7.07110000e-04, 1.17851667e-04, 2.88676449e-04, 2.04124145e-04, 1.44336183e-04, 0.00000000e+00], - [1.14713659e+01, 1.68000000e-01, 3.24000000e-01, 8.83589744e-02, 1.13977278e+01, -4.35202063e-02, 4.35202063e-02, 1.20920000e-01, 1.15826000e-01, 5.32593935e-03, 4.29825546e-02, 1.11681949e-01, 1.82080000e-01], - [1.63631707, 0.0825, 0.138, 0.02410256, 1.65473267, -0.02914746, 0.02927458, 0.074953, 0.02899134, 0.03271076, 0.02718317, 0.09610564, 0.012728], - [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 6.01460518e-42, -2.71490067e-40, 2.71490067e-40, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], - [0.52358537, 0.038, 0.03, 0.00769231, 0.52319376, -0.01066405, 0.01066405, 0.026163, 0.01025307, 0.00912966, 0.02678697, 0.04011893, 0.00866185], - [0.10931707, 0.103, 0.407, 0.04461538, 0.13188551, -0.01686662, 0.02506229, 0.1492, 0.0384195, 0.06327203, 0.06411448, 0.05508901, 0], - [0.0444878, 0.0245, 0.04, 0.00984615, 0.03577326, -0.00573919, 0.00573919, 0.013435, 0.0078961, 0.00418135, 0.01136515, 0.01291603, 0.0134352], - [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.03127202e-08, -2.56175141e-07, 5.37317466e-07, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], - [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.27917545e-05, -7.79437718e-04, 7.79437718e-04, 3.04060000e-02, 5.06766667e-03, 1.24131975e-02, 1.34721936e-02, 5.34029589e-02, 0.00000000e+00], - [2.43902439e-05, 5.00000000e-04, 1.00000000e-03, 1.02564103e-04, 2.60691650e-05, -7.19856850e-05, 7.19856850e-05, 7.07110000e-04, 1.17851667e-04, 2.88676449e-04, 2.04124145e-04, 1.44336183e-04, 0.00000000e+00], - [0.46446341, 0.033, 0.03, 0.00933333, 0.46299034, -0.00866364, 0.00866364, 0.033941, 0.01357644, 0.01214903, 0.02164486, 0.02701617, 0.012374], - [5.89978049, 0.117, 0.112, 0.04453846, 5.88525247, -0.02253416, 0.02253416, 0.084146, 0.0492146, 0.01985341, 0.06802812, 0.09041259, 0.045255], - [0.01317073, 0.0195, 0.015, 0.00538462, 0.00829287, -0.00622806, 0.00622806, 0.026163, 0.01145514, 0.00926554, 0.00690652, 0.02540613, 0.018031], - [1.16509756, 0.028, 0.02, 0.01051282, 1.16338281, -0.01379371, 0.01379371, 0.020506, 0.01461345, 0.00563317, 0.01416569, 0.01971055, 0.0281075], - [3.67914634, 0.1235, 0.126, 0.02676923, 3.67052968, -0.04266586, 0.04266586, 0.041719, 0.0233342, 0.0106888, 0.03232337, 0.07260248, 0.050912], - [0.11331707, 0.0015, 0.004, 0.0014359, 0.11329803, -0.00042144, 0.00042144, 0.0021213, 0.0014142, 0.00109543, 0.00124164, 0.00053231, 0.00070713], - [1.11256098, 0.026, 0.016, 0.00561538, 1.09093248, -0.00174647, 0.00490015, 0.02192, 0.01272782, 0.00816993, 0.02111102, 0.04921207, 0.012021], - [0.06846341, 0.007, 0.01, 0.00307692, 0.06774886, -0.00179795, 0.00190969, 0.0056569, 0.00311126, 0.00162791, 0.00195576, 0.00721732, 0.01096], - [1.16454634e+01, 1.78500000e-01, 3.20000000e-01, 8.94615385e-02, 1.15869935e+01, -1.15451745e-02, 1.59897956e-02, 1.37890000e-01, 1.23393333e-01, 1.01170444e-02, 3.66151153e-02, 1.46607419e-01, 1.94455000e-01], - [3.45158537, 0.1375, 0.052, 0.01676923, 3.44594643, -0.03141983, 0.03141983, 0.038184, 0.0272946, 0.00958649, 0.01698014, 0.06290749, 0.1393], - [3.12563415, 0.0535, 0.111, 0.02897436, 3.17337638, -0.02835417, 0.02835417, 0.054447, 0.0278601, 0.0188188, 0.00755315, 0.03628251, 0.055154], - [8.50975610e-02, 1.00000000e-03, 4.00000000e-03, 8.20512821e-04, 8.50491997e-02, -1.84870042e-04, 2.35933619e-04, 1.41420000e-03, 1.41420000e-03, 2.60312573e-11, 4.08248290e-04, 2.88668284e-04, 7.07110000e-04], - [0.82373171, 0.048, 0.121, 0.01853846, 0.82149219, -0.0053288, 0.00684639, 0.041012, 0.0208598, 0.01423898, 0.02609294, 0.02676908, 0.01078335], - [4.39680488, 0.223, 0.354, 0.09258974, 4.35973108, -0.03206468, 0.03450864, 0.20506, 0.0971572, 0.07235446, 0.13713059, 0.23019854, 0.32138], - [5.66058537, 0.0285, 0.093, 0.01282051, 5.66682734, -0.00633008, 0.00633008, 0.040305, 0.01513214, 0.01889847, 0.01503912, 0.03383458, 0], - [0.13329268, 0.011, 0.021, 0.00338462, 0.13419267, -0.00262455, 0.00262455, 0.0035355, 0.00226272, 0.00092195, 0.00772172, 0.00411547, 0.0038891], - [0.15463415, 0.0325, 0.065, 0.01617949, 0.15422134, -0.00766504, 0.00766504, 0.067882, 0.02286322, 0.02270081, 0.02939288, 0.0224428, 0.017501], - [1.47902439e-01, 1.50000000e-03, 2.00000000e-03, 3.84615385e-04, 1.48269290e-01, -1.36058722e-04, 1.36058722e-04, 2.12130000e-03, 8.24950000e-04, 9.39849132e-04, 5.16397779e-04, 5.91603500e-04, 0.00000000e+00], - [2.76797561, 0.071, 0.17, 0.03212821, 2.84223399, -0.01692731, 0.01692731, 0.04879, 0.03441267, 0.00934515, 0.03221283, 0.05768286, 0.092806], - [1.30939024, 0.044, 0.066, 0.0165641, 1.2967273, -0.01727205, 0.01727205, 0.03182, 0.01456652, 0.01056655, 0.00732632, 0.02987207, 0.038891], - [0.0914878, 0.038, 0.028, 0.00364103, 0.08295897, -0.00877545, 0.00877545, 0.032527, 0.00648182, 0.01277828, 0.01289089, 0.01040763, 0.0042426], - [0.13621951, 0.0015, 0.006, 0.00174359, 0.13689296, -0.00036169, 0.00040731, 0.0021213, 0.00153205, 0.00082663, 0.00058452, 0.00069522, 0.00088391], - [0.05692683, 0.007, 0.006, 0.00189744, 0.05532006, -0.00145672, 0.00145672, 0.0056569, 0.00311126, 0.00184393, 0.00420714, 0.00465287, 0.0070711], - [0.07460976, 0.002, 0.006, 0.00097436, 0.07430141, -0.00035004, 0.00038011, 0.0028284, 0.00113136, 0.0011832, 0.00070711, 0.0005916, 0.00070711], - [0.04782927, 0.006, 0.011, 0.00353846, 0.04406202, -0.00232859, 0.00232859, 0.012021, 0.00438408, 0.00442728, 0.00363318, 0.00540593, 0.0091924], - [4.443, 0.141, 0.076, 0.02310256, 4.40858239, -0.03710778, 0.03710778, 0.03182, 0.0271528, 0.00465324, 0.03506173, 0.07970664, 0.11278], - [8.79678049, 0.057, 0.208, 0.04194872, 8.784878, -0.01132933, 0.01132933, 0.08061, 0.04695182, 0.039817, 0.0405623, 0.01937402, 0.033234], - [2.58236585, 0.063, 0.128, 0.02112821, 2.5705713, -0.0079298, 0.01979542, 0.062225, 0.0309712, 0.02172778, 0.02949491, 0.02741888, 0.02687], - [0.08992683, 0.0015, 0.006, 0.00030769, 0.09000535, -0.00020308, 0.00020308, 0.0021213, 0.00106065, 0.00116188, 0.0007746, 0.00086603, 0.00053035], - [0.09085366, 0.0175, 0.037, 0.00694872, 0.09607742, -0.00456388, 0.00456388, 0.0098995, 0.00523258, 0.00310646, 0.01357571, 0.0133944, 0.0056569], - [1.34473171, 0.0255, 0.022, 0.00953846, 1.37010789, -0.00558419, 0.00558419, 0.030406, 0.0134351, 0.00877511, 0.00929516, 0.03188089, 0.0265165], - [0.14253659, 0.001, 0.004, 0.00097436, 0.14237889, -0.0002998, 0.0002998, 0.0014142, 0.0011785, 0.00057734, 0.0005164, 0.00069521, 0.00106066], - [0.07617073, 0.001, 0.004, 0.00179487, 0.07597272, -0.00025949, 0.00025949, 0.0014142, 0.0011785, 0.00057734, 0.0005164, 0.00063245, 0.00070711], - [0.28502439, 0.0025, 0.01, 0.00241026, 0.28596915, -0.000355, 0.000355, 0.12869, 0.02333393, 0.05162999, 0.0313152, 0.13233722, 0.0044194], - [5.97658537, 0.0645, 0.106, 0.02925641, 5.95365623, -0.01454886, 0.01454886, 0.045962, 0.02913296, 0.02145587, 0.04602717, 0.06410626, 0.053033], - [4.19787805, 0.0405, 0.072, 0.02764103, 4.21230508, -0.01456906, 0.01468492, 0.030406, 0.02206174, 0.01003006, 0.02031748, 0.03873656, 0.034295], - [0.06904878, 0.0025, 0.005, 0.00117949, 0.06819891, -0.00023428, 0.00033805, 0.0035355, 0.00098994, 0.00154918, 0.001, 0.0007071, 0.00070711], - [2.07410488e+01, 1.10000000e-02, 4.40000000e-02, 1.24102564e-02, 2.07288498e+01, -5.11402880e-02, 5.11402880e-02, 1.55560000e-02, 1.55560000e-02, 0.00000000e+00, 5.68037557e-03, 3.17543685e-03, 7.77820000e-03], - [0.15141463, 0.0025, 0.008, 0.00161538, 0.15286961, -0.00066236, 0.00066236, 0.0049497, 0.0021213, 0.00180276, 0.00235584, 0.01268589, 0.0021213], - [1.07970732, 0.0275, 0.046, 0.00725641, 1.0819483, -0.0025949, 0.00261392, 0.026163, 0.00754248, 0.00945165, 0.01400506, 0.00566908, 0.011137], - [1.45278049e+00, 2.50000000e-02, 3.40000000e-02, 8.23076923e-03, 1.46401853e+00, -5.22375992e-03, 7.56803574e-03, 8.48530000e-03, 6.71755000e-03, 1.39641061e-03, 4.14024959e-03, 1.47976972e-02, 2.03295000e-02], - [1.18829268e-01, 1.00000000e-03, 4.00000000e-03, 1.17948718e-03, 1.18657803e-01, -3.33958979e-04, 3.55599268e-04, 1.41420000e-03, 1.41420000e-03, 2.60312573e-11, 6.32455532e-04, 5.32284214e-04, 7.07110000e-04], - [0.09217073, 0.0085, 0.007, 0.00258974, 0.07952256, -0.00104703, 0.00138337, 0.006364, 0.00466692, 0.00203719, 0.00509166, 0.01307342, 0.021213], - [0.06936585, 0.0095, 0.015, 0.00394872, 0.06837444, -0.00205373, 0.00205373, 0.0084853, 0.00296984, 0.0030984, 0.00234521, 0.00419839, 0.0017678], - [5.05807317, 0.049, 0.082, 0.02402564, 5.06327737, -0.01120311, 0.01120311, 0.031113, 0.0239, 0.01338272, 0.01117139, 0.04351642, 0.020506], - [0.26421951, 0.04, 0.068, 0.00902564, 0.2587529, -0.01040894, 0.01040894, 0.025456, 0.01060666, 0.00890233, 0.01111643, 0.04563416, 0.011314], - [3.59336585, 0.0575, 0.054, 0.02094872, 3.58195886, -0.01804095, 0.01838506, 0.043134, 0.0336584, 0.01240579, 0.01683523, 0.04717173, 0.038184], - [1.29187805, 0.026, 0.016, 0.00689744, 1.27916244, -0.00322078, 0.00490015, 0.025456, 0.01032378, 0.00861112, 0.01863263, 0.0636921, 0.038537], - [6.28670732, 0.1245, 0.127, 0.03102564, 6.35501978, -0.01747513, 0.02813757, 0.084146, 0.04690465, 0.0254467, 0.06541464, 0.18275149, 0.15008], - [10.64578049, 0.079, 0.284, 0.04564103, 10.64447668, -0.01946271, 0.01947497, 0.10889, 0.04186, 0.05739752, 0.06891299, 0.05417812, 0.050205], - [3.32470732, 0.092, 0.046, 0.01687179, 3.32977984, -0.02794509, 0.02794509, 0.072125, 0.0288498, 0.02428699, 0.06277798, 0.10343739, 0.061518], - [0.07358537, 0.001, 0.004, 0.00153846, 0.0735262, -0.00027514, 0.00027514, 0.0014142, 0.0009428, 0.00073029, 0.00075277, 0.00053228, 0.00070711]]) - - return {'dual_coef': dual_coef, - 'support_vec': support_vec, - 'intercept': intercept, - 'gamma': gamma} - - -def multiclass_classifier(): - gamma = 0.1 - - # dual coef = y_i*alpha_i - dual_coef = np.array([[1.00000000e+02, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, 2.19164051e-01, - 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, - 2.73972798e+00, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 5.78184818e+01, - 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, - 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 4.43824790e+01, - 0.00000000e+00, 0.00000000e+00, 8.90021137e+01, 1.00000000e+02, 3.38829336e+01, - 1.00000000e+02, 7.35308055e+01, 5.00832282e+01, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 9.04295253e+01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, - 1.00000000e+02, 1.00000000e+02, 7.37255035e+01, 1.00000000e+02, 0.00000000e+00, - 1.00000000e+02, -1.00000000e+02, -4.59726588e+01, -9.10060871e+01, -0.00000000e+00, - -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, - -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, - -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, - -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, - -1.00000000e+02, -1.00000000e+02, -2.32473120e-01, -1.00000000e+02, -0.00000000e+00, - -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, - -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, - -2.01478019e-01, -1.00000000e+02, -5.32795432e+01, -0.00000000e+00, -0.00000000e+00, - -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, - -1.00000000e+02, -2.05233000e+01, -0.00000000e+00, -9.58435547e-02, -0.00000000e+00, - -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, - -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, - -1.00000000e+02, -0.00000000e+00, -1.14900102e+01, -7.73085905e+01, -1.00000000e+02, - -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, - -0.00000000e+00, -8.64770605e+01, -1.00000000e+02, -1.18090663e-01, -1.00000000e+02, - -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, - -0.00000000e+00, -6.27523608e+01, -0.00000000e+00, -4.38003436e+01, -0.00000000e+00, - -0.00000000e+00, -5.36807440e-02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, - -0.00000000e+00, -1.51862509e-01, -2.23505792e+01, -0.00000000e+00, -1.71549400e+00, - -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, - -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, - -6.48908553e+01, -5.45079781e+01, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, - -1.00000000e+02, -4.15526000e+01, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, - -3.97322757e+01, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, - -1.00000000e+02, -8.51452564e+01, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, - -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, - -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, - -1.00000000e+02, -1.36707150e+01, -2.28944671e+00, -1.00000000e+02, -1.00000000e+02, - -0.00000000e+00, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -9.70237576e+01, - -0.00000000e+00, -1.00000000e+02, -8.98901380e+00, -1.00000000e+02, -0.00000000e+00, - -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, - -0.00000000e+00, -0.00000000e+00, -0.00000000e+00, -2.31872364e+00, -0.00000000e+00, - -1.00000000e+02, -1.00000000e+02, -6.81207558e+01, -0.00000000e+00, -1.00000000e+02, - -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, - -1.25804913e+01, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, - -0.00000000e+00, -0.00000000e+00, -5.79636185e+01, -0.00000000e+00, -3.60349193e+01, - -1.00000000e+02, -1.00000000e+02], - [1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, - 1.00000000e+02, 0.00000000e+00, 1.22133880e+01, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, - 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 5.45699567e+01, 0.00000000e+00, - 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 0.00000000e+00, - 1.00000000e+02, 1.00000000e+02, 5.30198194e+01, 1.00000000e+02, 0.00000000e+00, - 8.10028022e+01, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 8.57299348e+01, - 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, - 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 6.98226850e+00, 1.00000000e+02, - 1.00000000e+02, 2.28942244e+00, 1.00000000e+02, 0.00000000e+00, 3.10951756e+00, - 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 2.43965458e+01, - 5.54247795e+01, 4.89715327e+01, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 8.77648862e-01, - 1.41352297e+00, 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 5.87399500e+01, - 1.00000000e+02, 7.89673831e+01, 7.17216921e-01, 7.08622898e+01, 1.00000000e+02, - 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, 0.00000000e+00, 7.08652210e+01, - 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 2.28740165e+00, 1.00000000e+02, - 1.00000000e+02, 1.00000000e+02, 6.26644343e+01, 1.51915932e+01, 9.33156003e+01, - 1.00000000e+02, 5.73480226e-01, 0.00000000e+00, 0.00000000e+00, 1.00000000e+02, - 6.51947143e+01, 0.00000000e+00, 1.00000000e+02, 3.61854680e+01, 1.50700439e+00, - 3.93114839e+01, 1.00000000e+02, 1.00000000e+02, 0.00000000e+00, 0.00000000e+00, - 1.00000000e+02, 1.62942145e+01, 1.00000000e+02, 1.00000000e+02, 3.65697187e+01, - 3.32328741e+01, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, 1.00000000e+02, - 1.00000000e+02, 0.00000000e+00, 3.84017861e-02, 3.27497129e+00, 1.00000000e+02, - 1.00000000e+02, 0.00000000e+00, 1.00000000e+02, -1.00000000e+02, -1.00000000e+02, - -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -7.87287696e+01, - -1.00000000e+02, -2.17133274e+01, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, - -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, - -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, - -1.00000000e+02, -4.03561653e+01, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, - -1.00000000e+02, -0.00000000e+00, -0.00000000e+00, -8.73885349e+01, -1.00000000e+02, - -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, - -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, - -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, - -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, - -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, -1.00000000e+02, - -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -7.69821289e+01, -0.00000000e+00, - -1.00000000e+02, -8.28241499e+01, -1.00000000e+02, -6.27852100e+00, -8.74723914e+01, - -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, - -0.00000000e+00, -0.00000000e+00, -1.00000000e+02, -1.00000000e+02, -0.00000000e+00, - -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, -2.15412985e+01, -0.00000000e+00, - -2.97074994e+01, -9.62658735e+01, -1.00000000e+02, -1.00000000e+02, -1.00000000e+02, - -1.00000000e+02, -0.00000000e+00]]) - - # intercept = rho - intercept = np.array([-0.62674907, 1.31994877, 0.67252991]) - - # support vectors = x_i - support_vec = np.array([[5.49570019e-07, -2.58632551e-07, 3.16229206e-02, 0.00000000e+00, 0.00000000e+00, 7.07110000e-02, 0.00000000e+00, 0.00000000e+00, 5.49570019e-07,-1.79132036e-08], - [0.01061208, -0.00799413, 0.00572262, 0.025, 0.02809756,0.016263, 0.058, 0.00866667, 0.01061208, 0.03799132], - [0.0005069, -0.0005069, 0.0031833, 0.007, 0.00060976,0.0070711, 0.014, 0.00087179, 0.00039282, 0.00024191], - [0.02659884, -0.02236652, 0.01818914, 0.054, 3.49731707,0.062225, 0.063, 0.01923077, 0.02659884, 3.53815367], - [0.1959552, -0.19377234, 0.49935644, 2.567, 0.212,1.2473, 4.086, 0.27730769, 0.1959552, 0.21128449], - [0.01296803, -0.01070662, 0.01579683, 0.055, 2.52643902,0.043134, 0.057, 0.0114359, 0.01296803, 2.54031008], - [0.04634941, -0.03616377, 0.03342396, 0.285, 1.25278049,0.11031, 0.285, 0.05482051, 0.04634941, 1.24439126], - [0.01161685, -0.01161685, 0.01472225, 0.061, 0.00495122,0.036062, 0.131, 0.00758974, 0.00936955, 0.00494698], - [3.36047450e-17, -2.42947453e-17, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 3.36047450e-17,-2.74622599e-18], - [0.05403825, -0.04130143, 0.01255584, 0.27, 3.89758537,0.047376, 0.27, 0.06717949, 0.05403825, 3.87923565], - [0.06322635, -0.0450853, 0.0069893, 0.107, 0.52617073,0.019799, 0.166, 0.01469231, 0.06322635, 0.51547654], - [0.02740927, -0.02740927, 0.01595008, 0.106, 0.93326829,0.043841, 0.106, 0.01169231, 0.01663183, 0.92669874], - [0.02181645, -0.02181645, 0.00031623, 0.045, 0.04685366,0.00070711, 0.045, 0.00605128, 0.01964941, 0.05939633], - [0.03168223, -0.03168223, 0.03369301, 0.13, 4.64253659,0.10182, 0.13, 0.03661538, 0.02589546, 4.63806066], - [0.00648365, -0.00648365, 0.00126494, 0.039, 0.29312195,0.0056569, 0.039, 0.00682051, 0.0043318, 0.28575076], - [0.02260403, -0.02260403, 0.01570345, 0.123, 3.08187805,0.053033, 0.123, 0.03435897, 0.01139526, 3.11862292], - [7.19856842e-05, -7.19856842e-05, 0.00000000e+00, 1.00000000e-03, 2.43902439e-05, 0.00000000e+00, 2.00000000e-03, 1.02564103e-04, 5.59639155e-05, 2.43769719e-05], - [5.77955577e-09, -2.77557303e-09, 2.88676449e-04, 0.00000000e+00, 0.00000000e+00, 7.07110000e-04, 0.00000000e+00, 0.00000000e+00, 5.77955577e-09,-4.05052739e-10], - [0.0168393, -0.0168393, 0.04987211, 0.172, 0.83290244,0.13011, 0.287, 0.02610256, 0.01063438, 0.82408369], - [0.00670974, -0.00670974, 0.00420523, 0.009, 0.92597561,0.012021, 0.012, 0.00369231, 0.00394705, 0.92583814], - [0.04881422, -0.04881422, 0.06039519, 0.128, 0.84173171,0.15274, 0.166, 0.0145641, 0.02705203, 0.83114509], - [5.56411248e-04, -5.56411248e-04, 4.25246049e-03, 1.00000000e-03, 2.43902439e-05, 9.19240000e-03, 1.00000000e-03, 2.56410256e-05, 4.16114259e-04, 2.18000765e-04], - [0.00461828, -0.00346772, 0.00394135, 0.022, 0.88760976,0.011314, 0.022, 0.00489744, 0.00461828, 0.88799505], - [0.02181738, -0.02181738, 0.06135461, 0.472, 9.21263415,0.16688, 0.472, 0.0695641, 0.01361679, 9.19153391], - [0.18064104, -0.18064104, 0.02243327, 0.141, 1.74753659,0.065761, 0.141, 0.02587179, 0.08614869, 1.89288442], - [0.04502684, -0.04502684, 0.03092595, 0.075, 0.66726829,0.070004, 0.075, 0.01507692, 0.02703687, 0.6524218 ], - [0.00350864, -0.00337375, 0.00472601, 0.026, 0.48378049,0.016971, 0.038, 0.00794872, 0.00350864, 0.48333175], - [4.22507597e-120, -2.00921455e-120, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 4.22507597e-120, 9.65026603e-122], - [0.0511954, -0.0511954, 0.00917865, 0.032, 1.50741463,0.028284, 0.056, 0.00958974, 0.01612377, 1.43087637], - [0.0504243, -0.03682241, 0.03693438, 0.147, 3.4434878,0.086267, 0.147, 0.02051282, 0.0504243, 3.49633275], - [0.00556735, -0.00442938, 0.00870258, 0.033, 0.10897561,0.025456, 0.033, 0.00553846, 0.00556735, 0.12761266], - [2.34438020e-08, -2.34438020e-08, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.15475168e-08,-1.38812548e-09], - 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[0.07891577, -0.05286848, 0.10496868, 0.385,17.73741463, 0.2588, 0.751, 0.13687179, 0.07891577, 17.7611277 ], - [2.57691246e-03, -1.65339484e-03, 1.37839166e-03, 1.00000000e-02, 1.53980488e+00, 9.19240000e-03, 1.00000000e-02, 4.35897436e-03, 2.57691246e-03, 1.52310545e+00], - [1.65446780e-02, -1.65446780e-02, 5.91533656e-02, 1.30000000e-02, 1.14603171e+01, 1.14550000e-01, 1.30000000e-02, 1.33333333e-03, 1.26380521e-02, 1.14674074e+01], - [4.11400031e-02, -3.79141831e-02, 6.84964646e-03, 5.12000000e-01, 1.48432683e+01, 1.97990000e-01, 5.28000000e-01, 1.59692308e-01, 4.11400031e-02, 1.47993898e+01], - [0.02155868, -0.01887666, 0.07962665, 0.238,13.24478049, 0.15344, 0.238, 0.04825641, 0.02155868, 13.22587027], - [0.00599973, -0.00599973, 0.00697133, 0.034, 0.63663415,0.019799, 0.034, 0.00594872, 0.00480282, 0.61555439], - [0.04266586, -0.04266586, 0.0106888, 0.126, 3.67914634,0.041719, 0.132, 0.02676923, 0.03469959, 3.67052968], - [0.00042144, -0.00042144, 0.00109543, 0.004, 0.11331707,0.0021213, 0.006, 0.0014359, 0.00027545, 0.11329803], - [0.02663118, -0.02639755, 0.01336405, 0.07, 4.36412195,0.054447, 0.079, 0.025, 0.02663118, 4.359373 ], - [0.00730426, -0.00730426, 0.00435895, 0.072, 0.61409756,0.012728, 0.096, 0.00930769, 0.00603133, 0.60116405], - [0.01877031, -0.00838677, 0.02480503, 0.068, 2.56031707,0.077075, 0.068, 0.02294872, 0.01877031, 2.4790144 ], - [0.01901328, -0.01785507, 0.0129973, 0.155, 2.71221951,0.047376, 0.155, 0.03697436, 0.01901328, 2.68282596], - [0.00190969, -0.00179795, 0.00162791, 0.01, 0.06846341,0.0056569, 0.01, 0.00307692, 0.00190969, 0.06774886], - [0.02343739, -0.02343739, 0.06043373, 0.17,10.8854878, 0.11172, 0.17, 0.07266667, 0.01923799, 10.87016693], - [1.59897956e-02, -1.15451745e-02, 1.01170444e-02, 3.20000000e-01, 1.16454634e+01, 1.37890000e-01, 3.47000000e-01, 8.94615385e-02, 1.59897956e-02, 1.15869935e+01], - [0.01774385, -0.01578744, 0.03231943, 0.085, 2.70382927,0.12233, 0.133, 0.03161538, 0.01774385, 2.68325119], - [0.02835417, -0.02835417, 0.0188188, 0.111, 3.12563415,0.054447, 0.111, 0.02897436, 0.01213407, 3.17337638], - [2.35933619e-04, -1.84870042e-04, 2.60312573e-11, 4.00000000e-03, 8.50975610e-02, 1.41420000e-03, 4.00000000e-03, 8.20512821e-04, 2.35933619e-04, 8.50491997e-02], - [0.00684639, -0.0053288, 0.01423898, 0.121, 0.82373171,0.041012, 0.121, 0.01853846, 0.00684639, 0.82149219], - [0.00656202, -0.00298516, 0.00525832, 0.04, 0.32917073,0.013435, 0.04, 0.00435897, 0.00656202, 0.32469572], - [0.03061193, -0.01830022, 0.02278918, 0.083, 3.852,0.07566, 0.083, 0.02366667, 0.03061193, 3.82389036], - [0.03450864, -0.03206468, 0.07235446, 0.354, 4.39680488,0.20506, 0.354, 0.09258974, 0.03450864, 4.35973108], - [1.33757941e-03, -1.33757941e-03, 1.81681257e-02, 1.60000000e-02, 6.81234146e+00, 4.73760000e-02, 1.60000000e-02, 5.74358974e-03, 7.88662159e-04, 6.81237822e+00], - [0.05408959, -0.05408959, 0.07739115, 0.232,13.12702439, 0.16263, 0.46, 0.12551282, 0.02715891, 13.09196866], - [0.01414166, -0.01414166, 0, 0.214, 9.05543902,0, 0.214, 0.01707692, 0.01284695, 9.04209973], - [0.00262455, -0.00262455, 0.00092195, 0.021, 0.13329268,0.0035355, 0.021, 0.00338462, 0.00176235, 0.13419267], - [0.00766504, -0.00766504, 0.02270081, 0.065, 0.15463415,0.067882, 0.065, 0.01617949, 0.00760269, 0.15422134], - [0.01601326, -0.01601326, 0.00953142, 0.044, 4.34658537,0.029698, 0.082, 0.02784615, 0.0152717, 4.34824852], - [1.36058722e-04, -1.36058722e-04, 9.39849132e-04, 2.00000000e-03, 1.47902439e-01, 2.12130000e-03, 4.00000000e-03, 3.84615385e-04, 1.20873351e-04, 1.48269290e-01], - [0.01692731, -0.01692731, 0.00934515, 0.17, 2.76797561,0.04879, 0.17, 0.03212821, 0.00804809, 2.84223399], - [0.00021792, -0.00016886, 0.00077459, 0.002, 0.16504878,0.0014142, 0.004, 0.00020513, 0.00021792, 0.16495962], - [0.0025624, -0.0017206, 0.00242901, 0.013, 0.22082927,0.0084853, 0.015, 0.00476923, 0.0025624, 0.21293887], - [0.00825234, -0.00825234, 0.00915876, 0.068, 4.30339024,0.028284, 0.073, 0.02058974, 0.00762434, 4.32659455], - [0.03004306, -0.02691667, 0.04972124, 0.152,10.16087805, 0.10324, 0.158, 0.07189744, 0.03004306, 10.1129346 ], - [0.02155975, -0.02155975, 0.04474389, 0.129, 8.92531707,0.084146, 0.236, 0.07838462, 0.02002646, 8.88551601], - [1.14268607e-02, -9.73097720e-03, 7.75027419e-04, 6.40000000e-02, 3.79953659e+00, 2.40420000e-02, 6.40000000e-02, 1.87692308e-02, 1.14268607e-02, 3.80236594e+00], - [0.02707888, -0.02707888, 0.01880556, 0.076, 4.34395122,0.053033, 0.076, 0.02764103, 0.02459437, 4.34208839], - [0.01727205, -0.01727205, 0.01056655, 0.066, 1.30939024,0.03182, 0.066, 0.0165641, 0.01633014, 1.2967273 ], - [0.00877545, -0.00877545, 0.01277828, 0.028, 0.0914878,0.032527, 0.037, 0.00364103, 0.00682708, 0.08295897], - [0.00040731, -0.00036169, 0.00082663, 0.006, 0.13621951,0.0021213, 0.006, 0.00174359, 0.00040731, 0.13689296], - [0.00145672, -0.00145672, 0.00184393, 0.006, 0.05692683,0.0056569, 0.008, 0.00189744, 0.00103611, 0.05532006], - [0.00539013, -0.00539013, 0.01083511, 0.025, 0.60539024,0.02687, 0.052, 0.00476923, 0.00387435, 0.60237517], - [0.00576748, -0.00576748, 0.01658765, 0.082, 2.201,0.045962, 0.082, 0.01251282, 0.00554347, 2.2021353 ], - [0.00038011, -0.00035004, 0.0011832, 0.006, 0.07460976,0.0028284, 0.006, 0.00097436, 0.00038011, 0.07430141], - [0.00232859, -0.00232859, 0.00442728, 0.011, 0.04782927,0.012021, 0.012, 0.00353846, 0.00200706, 0.04406202], - [0.03710778, -0.03710778, 0.00465324, 0.076, 4.443,0.03182, 0.086, 0.02310256, 0.03407041, 4.40858239], - [1.13293306e-02, -1.13293306e-02, 3.98170020e-02, 2.08000000e-01, 8.79678049e+00, 8.06100000e-02, 2.08000000e-01, 4.19487179e-02, 8.02841512e-03, 8.78487800e+00], - [0.00241135, -0.00186458, 0.0089488, 0.059, 1.06817073,0.02192, 0.059, 0.00302564, 0.00241135, 1.06816635], - [0.01979542, -0.0079298, 0.02172778, 0.128, 2.58236585,0.062225, 0.128, 0.02112821, 0.01979542, 2.5705713 ], - [0.02311331, -0.02311331, 0.01137534, 0.062, 4.10326829,0.048083, 0.076, 0.02253846, 0.02280474, 4.08634796], - [0.00020308, -0.00020308, 0.00116188, 0.006, 0.08992683,0.0021213, 0.006, 0.00030769, 0.0001032, 0.09000535], - [0.02430024, -0.01198259, 0.05125066, 0.284,10.61465854, 0.10041, 0.284, 0.04492308, 0.02430024, 10.68180335], - [0.00456388, -0.00456388, 0.00310646, 0.037, 0.09085366,0.0098995, 0.037, 0.00694872, 0.00379939, 0.09607742], - [0.0258999, -0.02057017, 0.03178882, 0.123, 5.97997561,0.086974, 0.123, 0.02774359, 0.0258999, 5.91008859], - [0.02995729, -0.02995729, 0.02248653, 0.065, 3.89078049,0.067175, 0.072, 0.02235897, 0.02419343, 3.84855841], - [0.03168889, -0.01073342, 0.02388286, 0.091, 4.27865854,0.08061, 0.091, 0.02253846, 0.03168889, 4.27815091], - [0.00558419, -0.00558419, 0.00877511, 0.022, 1.34473171,0.030406, 0.026, 0.00953846, 0.00182773, 1.37010789], - [0.0002998, -0.0002998, 0.00057734, 0.004, 0.14253659,0.0014142, 0.004, 0.00097436, 0.00028829, 0.14237889], - [0.00219221, -0.00094528, 0.01264908, 0.057, 0.64868293,0.027577, 0.057, 0.00864103, 0.00219221, 0.63404688], - [0.00025949, -0.00025949, 0.00057734, 0.004, 0.07617073,0.0014142, 0.004, 0.00179487, 0.00015403, 0.07597272], - [3.54995192e-04, -3.54995192e-04, 5.16299928e-02, 1.00000000e-02, 2.85024390e-01, 1.28690000e-01, 1.00000000e-02, 2.41025641e-03, 2.75611646e-04, 2.85969148e-01], - [0.01468492, -0.01456906, 0.01003006, 0.072, 4.19787805,0.030406, 0.072, 0.02764103, 0.01468492, 4.21230508], - [0.02925993, -0.02564169, 0.0243944, 0.069, 3.981,0.081317, 0.069, 0.01961538, 0.02925993, 3.88386281], - [0.00033805, -0.00023428, 0.00154918, 0.005, 0.06904878,0.0035355, 0.005, 0.00117949, 0.00033805, 0.06819891], - [0.02167887, -0.01716068, 0.03000235, 0.167, 7.68443902,0.068589, 0.176, 0.05779487, 0.02167887, 7.62067289], - [0.00860053, -0.00687122, 0.00984125, 0.028, 1.9362439,0.032527, 0.028, 0.01158974, 0.00860053, 1.85928806], - [1.62829923e-02, -1.27782612e-02, 5.03488484e-03, 2.93000000e-01, 1.10292927e+01, 9.19240000e-03, 2.93000000e-01, 4.98717949e-02, 1.62829923e-02, 1.10624738e+01], - [5.11402880e-02, -5.11402880e-02, 0.00000000e+00, 4.40000000e-02, 2.07410488e+01, 1.55560000e-02, 4.40000000e-02, 1.24102564e-02, 2.17468552e-02, 2.07288498e+01], - [0.02048881, -0.01983837, 0.01785776, 0.068, 3.90980488,0.045255, 0.068, 0.02412821, 0.02048881, 3.92262256], - [0.00066236, -0.00066236, 0.00180276, 0.008, 0.15141463,0.0049497, 0.008, 0.00161538, 0.00045688, 0.15286961], - [0.00060837, -0.00053966, 0.00036514, 0.006, 0.15390244,0.0021213, 0.006, 0.00184615, 0.00060837, 0.1543969 ], - [0.00261392, -0.0025949, 0.00945165, 0.046, 1.07970732,0.026163, 0.046, 0.00725641, 0.00261392, 1.0819483 ], - [2.21525541e-02, -2.09299114e-02, 4.55533610e-03, 1.75000000e-01, 7.59114634e+00, 6.50540000e-02, 1.75000000e-01, 4.90256410e-02, 2.21525541e-02, 7.58764892e+00], - [7.56803574e-03, -5.22375992e-03, 1.39641061e-03, 3.40000000e-02, 1.45278049e+00, 8.48530000e-03, 3.90000000e-02, 8.23076923e-03, 7.56803574e-03, 1.46401853e+00], - [0.00042417, -0.00042417, 0.00109543, 0.003, 0.16736585,0.0021213, 0.006, 0.00123077, 0.00031221, 0.16745186], - [3.55599268e-04, -3.33958979e-04, 2.60312573e-11, 4.00000000e-03, 1.18829268e-01, 1.41420000e-03, 4.00000000e-03, 1.17948718e-03, 3.55599268e-04, 1.18657803e-01], - [8.49163246e-03, -7.36645573e-03, 1.26472005e-03, 4.00000000e-02, 4.19334146e+00, 2.82840000e-02, 8.00000000e-02, 2.64615385e-02, 8.49163246e-03, 4.21478989e+00], - [0.04132153, -0.04132153, 0.08233984, 0.491,13.82968293, 0.1789, 0.491, 0.09758974, 0.02614823, 13.93101321], - [0.00138337, -0.00104703, 0.00203719, 0.007, 0.09217073,0.006364, 0.007, 0.00258974, 0.00138337, 0.07952256], - [0.02013966, -0.02013966, 0.06794402, 0.35,11.46958537, 0.12728, 0.35, 0.09010256, 0.01988961, 11.45768721], - [0.00205373, -0.00205373, 0.0030984, 0.015, 0.06936585,0.0084853, 0.017, 0.00394872, 0.00152146, 0.06837444], - [0.01040894, -0.01040894, 0.00890233, 0.068, 0.26421951,0.025456, 0.068, 0.00902564, 0.00546137, 0.2587529 ], - [8.19684883e-03, -8.19684883e-03, 2.09202670e-02, 6.30000000e-02, 6.51109756e+00, 4.45480000e-02, 6.30000000e-02, 1.66153846e-02, 5.80672162e-03, 6.50965345e+00], - [0.00023369, -0.00023369, 0.00246646, 0.007, 0.16326829,0.006364, 0.007, 0.00079487, 0.00019522, 0.1627248 ], - [0.00490015, -0.00322078, 0.00861112, 0.016, 1.29187805,0.025456, 0.016, 0.00689744, 0.00490015, 1.27916244], - [0.0134886, -0.00766548, 0.0189313, 0.109, 6.21553659,0.041012, 0.109, 0.03533333, 0.0134886, 6.22416714], - [0.02813757, -0.01747513, 0.0254467, 0.127, 6.28670732,0.084146, 0.127, 0.03102564, 0.02813757, 6.35501978], - [0.02044022, -0.02044022, 0.03488198, 0.176, 8.10602439,0.065761, 0.176, 0.04466667, 0.01777938, 8.12759058], - [0.01947497, -0.01946271, 0.05739752, 0.284,10.64578049, 0.10889, 0.296, 0.04564103, 0.01947497, 10.64447668], - [0.01922614, -0.01922614, 0.02849813, 0.097, 4.29178049,0.08061, 0.097, 0.02720513, 0.01556252, 4.30987745], - [0.00027514, -0.00027514, 0.00073029, 0.004, 0.07358537,0.0014142, 0.004, 0.00153846, 0.0002546, 0.0735262]]) - - num_support_vec = [61, 97, 89] - - return {'dual_coef': dual_coef, - 'support_vec': support_vec, - 'intercept': intercept, - 'gamma': gamma, - 'num_support_vec': num_support_vec} - - -if __name__ == '__main__': - - binary_test_data = np.array([ - [0., 0., 0., 0., 0., 0., 0., 0.14212846, 0.02368808, 0.0580237, 0.06960633, 0.04921911, 0.], - [0.21073171, 0.794, 0.922, 0.14076923, 0.20974742, -0.32312654, 0.32312654, 0.88741901, 0.17300546, 0.3544437, 0.39891235, 0.5271785, 0.0076014], - [0.04058537, 0.009, 0.008, 0.00225641, 0.03362015, -0.00420592, 0.00420592, 0.00565685, 0.00235702, 0.00193218, 0.00581951, 0.00861878, 0.00671751], - [0.07887805, 0.0035, 0.007, 0.00179487, 0.07598638, -0.00096018, 0.00113304, 0.00494975, 0.00235702, 0.00177012, 0.00098742, 0.00128452, 0.00335876], - [0.10126829, 0.0015, 0.004, 0.00174359, 0.09954269, -0.00034342, 0.00034342, 0.00212132, 0.00153206, 0.00028868, 0.00075829, 0.00064872, 0.00212132] - ]) - binary_predictions = predict_binary_classifier(binary_test_data) - - import pickle - f2 = open('/Users/sarataylor/Dev/eda-explorer-public/SVMBinary.p', 'rb') - s2 = f2.read() - clf = pickle.loads(s2) - - assert(len([1 for i in range(5) if binary_predictions[i] != clf.predict(binary_test_data)[i]]) == 0) - - # Test multiclass - test_data = np.array([[3.11141105e-02, -3.11136868e-02, 2.42079822e-02, 7.49220000e-02, 1.15335178e+01, 8.37681119e-02, 7.49220000e-02, 1.03606795e-02, 3.11141105e-02, 1.15205823e+01], - [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], - [0.10255266, -0.10255266, 0.03827904, 0.328471, 6.61645195, 0.26352986, 0.406695, 0.02494941, 0.05696297, 7.64941098], - [0.1095642, -0.1095642, 0.08589464, 0.113983, 11.49373772, 0.26352986, 0.113983, 0.01375942, 0.03753318, 11.51816541], - [0.15404637, -0.08878016, 0.1020834, 0.768917, 11.40673696, 0.28606288, 0.768917, 0.08697605, 0.15404637, 11.46339086]]) - - multi_predictions = predict_multiclass_classifier(test_data) - - f2 = open('/Users/sarataylor/Dev/eda-explorer-public/SVMMulticlass.p', 'rb') - s2 = f2.read() - clf = pickle.loads(s2) - - assert (len([1 for i in range(5) if multi_predictions[i] != clf.predict(test_data)[i]]) == 0) - - - diff --git a/calculatingfeatures/eda_explorer/EDA-Artifact-Detection-Script.py b/calculatingfeatures/eda_explorer/EDA-Artifact-Detection-Script.py deleted file mode 100644 index 13f0432e..00000000 --- a/calculatingfeatures/eda_explorer/EDA-Artifact-Detection-Script.py +++ /dev/null @@ -1,411 +0,0 @@ -import pandas as pd -import numpy as np -import matplotlib -import matplotlib.pyplot as plt -import pywt -import os -import datetime - -from load_files import getInputLoadFile, get_user_input -from ArtifactClassifiers import predict_binary_classifier, predict_multiclass_classifier - -matplotlib.rcParams['ps.useafm'] = True -matplotlib.rcParams['pdf.use14corefonts'] = True -matplotlib.rcParams['text.usetex'] = True - - -def getWaveletData(data): - ''' - This function computes the wavelet coefficients - - INPUT: - data: DataFrame, index is a list of timestamps at 8Hz, columns include EDA, filtered_eda - - OUTPUT: - wave1Second: DateFrame, index is a list of timestamps at 1Hz, columns include OneSecond_feature1, OneSecond_feature2, OneSecond_feature3 - waveHalfSecond: DateFrame, index is a list of timestamps at 2Hz, columns include HalfSecond_feature1, HalfSecond_feature2 - ''' - startTime = data.index[0] - - # Create wavelet dataframes - oneSecond = pd.date_range(start=startTime, periods=len(data), freq='1s') - halfSecond = pd.date_range(start=startTime, periods=len(data), freq='500L') - - # Compute wavelets - cA_n, cD_3, cD_2, cD_1 = pywt.wavedec(data['EDA'], 'Haar', level=3) #3 = 1Hz, 2 = 2Hz, 1=4Hz - - # Wavelet 1 second window - N = int(len(data)/8) - coeff1 = np.max(abs(np.reshape(cD_1[0:4*N],(N,4))), axis=1) - coeff2 = np.max(abs(np.reshape(cD_2[0:2*N],(N,2))), axis=1) - coeff3 = abs(cD_3[0:N]) - wave1Second = pd.DataFrame({'OneSecond_feature1':coeff1,'OneSecond_feature2':coeff2,'OneSecond_feature3':coeff3}) - wave1Second.index = oneSecond[:len(wave1Second)] - - # Wavelet Half second window - N = int(np.floor((len(data)/8.0)*2)) - coeff1 = np.max(abs(np.reshape(cD_1[0:2*N],(N,2))),axis=1) - coeff2 = abs(cD_2[0:N]) - waveHalfSecond = pd.DataFrame({'HalfSecond_feature1':coeff1,'HalfSecond_feature2':coeff2}) - waveHalfSecond.index = halfSecond[:len(waveHalfSecond)] - - return wave1Second,waveHalfSecond - - -def getDerivatives(eda): - deriv = (eda[1:-1] + eda[2:])/ 2. - (eda[1:-1] + eda[:-2])/ 2. - second_deriv = eda[2:] - 2*eda[1:-1] + eda[:-2] - return deriv,second_deriv - - -def getDerivStats(eda): - deriv, second_deriv = getDerivatives(eda) - maxd = max(deriv) - mind = min(deriv) - maxabsd = max(abs(deriv)) - avgabsd = np.mean(abs(deriv)) - max2d = max(second_deriv) - min2d = min(second_deriv) - maxabs2d = max(abs(second_deriv)) - avgabs2d = np.mean(abs(second_deriv)) - - return maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d - - -def getStats(data): - eda = data['EDA'].values - filt = data['filtered_eda'].values - maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d = getDerivStats(eda) - maxd_f,mind_f,maxabsd_f,avgabsd_f,max2d_f,min2d_f,maxabs2d_f,avgabs2d_f = getDerivStats(filt) - amp = np.mean(eda) - amp_f = np.mean(filt) - return amp, maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d,amp_f,maxd_f,mind_f,maxabsd_f,avgabsd_f,max2d_f,min2d_f,maxabs2d_f,avgabs2d_f - - -def computeWaveletFeatures(waveDF): - maxList = waveDF.max().tolist() - meanList = waveDF.mean().tolist() - stdList = waveDF.std().tolist() - medianList = waveDF.median().tolist() - aboveZeroList = (waveDF[waveDF>0]).count().tolist() - - return maxList,meanList,stdList,medianList,aboveZeroList - - -def getWavelet(wave1Second,waveHalfSecond): - max_1,mean_1,std_1,median_1,aboveZero_1 = computeWaveletFeatures(wave1Second) - max_H,mean_H,std_H,median_H,aboveZero_H = computeWaveletFeatures(waveHalfSecond) - return max_1,mean_1,std_1,median_1,aboveZero_1,max_H,mean_H,std_H,median_H,aboveZero_H - - -def getFeatures(data,w1,wH): - # Get DerivStats - amp,maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d,amp_f,maxd_f,mind_f,maxabsd_f,avgabsd_f,max2d_f,min2d_f,maxabs2d_f,avgabs2d_f = getStats(data) - statFeat = np.hstack([amp,maxd,mind,maxabsd,avgabsd,max2d,min2d,maxabs2d,avgabs2d,amp_f,maxd_f,mind_f,maxabsd_f,avgabsd_f,max2d_f,min2d_f,maxabs2d_f,avgabs2d_f]) - - # Get Wavelet Features - max_1,mean_1,std_1,median_1,aboveZero_1,max_H,mean_H,std_H,median_H,aboveZero_H = getWavelet(w1,wH) - waveletFeat = np.hstack([max_1,mean_1,std_1,median_1,aboveZero_1,max_H,mean_H,std_H,median_H,aboveZero_H]) - - all_feat = np.hstack([statFeat,waveletFeat]) - - if np.Inf in all_feat: - print("Inf") - - if np.NaN in all_feat: - print("NaN") - - return list(all_feat) - - -def createFeatureDF(data): - ''' - INPUTS: - filepath: string, path to input file - OUTPUTS: - features: DataFrame, index is a list of timestamps for each 5 seconds, contains all the features - data: DataFrame, index is a list of timestamps at 8Hz, columns include AccelZ, AccelY, AccelX, Temp, EDA, filtered_eda - ''' - # Load data from q sensor - wave1sec,waveHalf = getWaveletData(data) - - # Create 5 second timestamp list - timestampList = data.index.tolist()[0::40] - - # feature names for DataFrame columns - allFeatureNames = ['raw_amp','raw_maxd','raw_mind','raw_maxabsd','raw_avgabsd','raw_max2d','raw_min2d','raw_maxabs2d','raw_avgabs2d','filt_amp','filt_maxd','filt_mind', - 'filt_maxabsd','filt_avgabsd','filt_max2d','filt_min2d','filt_maxabs2d','filt_avgabs2d','max_1s_1','max_1s_2','max_1s_3','mean_1s_1','mean_1s_2','mean_1s_3', - 'std_1s_1','std_1s_2','std_1s_3','median_1s_1','median_1s_2','median_1s_3','aboveZero_1s_1','aboveZero_1s_2','aboveZero_1s_3','max_Hs_1','max_Hs_2','mean_Hs_1', - 'mean_Hs_2','std_Hs_1','std_Hs_2','median_Hs_1','median_Hs_2','aboveZero_Hs_1','aboveZero_Hs_2'] - - # Initialize Feature Data Frame - features = pd.DataFrame(np.zeros((len(timestampList),len(allFeatureNames))),columns=allFeatureNames,index=timestampList) - - # Compute features for each 5 second epoch - for i in range(len(features)-1): - start = features.index[i] - end = features.index[i+1] - this_data = data[start:end] - this_w1 = wave1sec[start:end] - this_w2 = waveHalf[start:end] - features.iloc[i] = getFeatures(this_data,this_w1,this_w2) - return features - - -def classifyEpochs(features,featureNames,classifierName): - ''' - This function takes the full features DataFrame and classifies each 5 second epoch into artifact, questionable, or clean - - INPUTS: - features: DataFrame, index is a list of timestamps for each 5 seconds, contains all the features - featureNames: list of Strings, subset of feature names needed for classification - classifierName: string, type of SVM (binary or multiclass) - - OUTPUTS: - labels: Series, index is a list of timestamps for each 5 seconds, values of -1, 0, or 1 for artifact, questionable, or clean - ''' - # Only get relevant features - features = features[featureNames] - X = features[featureNames].values - - # Classify each 5 second epoch and put into DataFrame - if 'Binary' in classifierName: - featuresLabels = predict_binary_classifier(X) - elif 'Multi' in classifierName: - featuresLabels = predict_multiclass_classifier(X) - - return featuresLabels - - -def getSVMFeatures(key): - ''' - This returns the list of relevant features - - INPUT: - key: string, either "Binary" or "Multiclass" - - OUTPUT: - featureList: list of Strings, subset of feature names needed for classification - ''' - if key == "Binary": - return ['raw_amp','raw_maxabsd','raw_max2d','raw_avgabs2d','filt_amp','filt_min2d','filt_maxabs2d','max_1s_1', - 'mean_1s_1','std_1s_1','std_1s_2','std_1s_3','median_1s_3'] - elif key == "Multiclass": - return ['filt_maxabs2d','filt_min2d','std_1s_1','raw_max2d','raw_amp','max_1s_1','raw_maxabs2d','raw_avgabs2d', - 'filt_max2d','filt_amp'] - else: - print('Error!! Invalid key, choose "Binary" or "Multiclass"\n\n') - return - - -def classify(classifierList): - ''' - This function wraps other functions in order to load, classify, and return the label for each 5 second epoch of Q sensor data. - - INPUT: - classifierList: list of strings, either "Binary" or "Multiclass" - OUTPUT: - featureLabels: Series, index is a list of timestamps for each 5 seconds, values of -1, 0, or 1 for artifact, questionable, or clean - data: DataFrame, only output if fullFeatureOutput=1, index is a list of timestamps at 8Hz, columns include AccelZ, AccelY, AccelX, Temp, EDA, filtered_eda - ''' - # Constants - oneHour = 8*60*60 # 8(samp/s)*60(s/min)*60(min/hour) = samp/hour - fiveSec = 8*5 - - # Load data - data, _ = getInputLoadFile() - - # Get pickle List and featureNames list - featureNameList = [[]]*len(classifierList) - for i in range(len(classifierList)): - featureNames = getSVMFeatures(classifierList[i]) - featureNameList[i]=featureNames - - # Get the number of data points, hours, and labels - rows = len(data) - num_labels = int(np.ceil(float(rows)/fiveSec)) - hours = int(np.ceil(float(rows)/oneHour)) - - # Initialize labels array - labels = -1*np.ones((num_labels,len(classifierList))) - - for h in range(hours): - # Get a data slice that is at most 1 hour long - start = h*oneHour - end = min((h+1)*oneHour,rows) - cur_data = data[start:end] - - features = createFeatureDF(cur_data) - - for i in range(len(classifierList)): - # Get correct feature names for classifier - classifierName = classifierList[i] - featureNames = featureNameList[i] - - # Label each 5 second epoch - temp_labels = classifyEpochs(features, featureNames, classifierName) - labels[(h*12*60):(h*12*60+temp_labels.shape[0]),i] = temp_labels - - return labels,data - - -def plotData(data,labels,classifierList,filteredPlot=0,secondsPlot=0): - ''' - This function plots the Q sensor EDA data with shading for artifact (red) and questionable data (grey). - Note that questionable data will only appear if you choose a multiclass classifier - - INPUT: - data: DataFrame, indexed by timestamps at 8Hz, columns include EDA and filtered_eda - labels: array, each row is a 5 second period and each column is a different classifier - filteredPlot: binary, 1 for including filtered EDA in plot, 0 for only raw EDA on the plot, defaults to 0 - secondsPlot: binary, 1 for x-axis in seconds, 0 for x-axis in minutes, defaults to 0 - - OUTPUT: - [plot] the resulting plot has N subplots (where N is the length of classifierList) that have linked x and y axes - and have shading for artifact (red) and questionable data (grey) - - ''' - - # Initialize x axis - if secondsPlot: - scale = 1.0 - else: - scale = 60.0 - time_m = np.arange(0,len(data))/(8.0*scale) - - # Initialize Figure - plt.figure(figsize=(10,5)) - - # For each classifier, label each epoch and plot - for k in range(np.shape(labels)[1]): - key = classifierList[k] - - # Initialize Subplots - if k==0: - ax = plt.subplot(len(classifierList),1,k+1) - else: - ax = plt.subplot(len(classifierList),1,k+1,sharex=ax,sharey=ax) - - # Plot EDA - ax.plot(time_m,data['EDA']) - - # For each epoch, shade if necessary - for i in range(0,len(labels)-1): - if labels[i,k]==-1: - # artifact - start = i*40/(8.0*scale) - end = start+5.0/scale - ax.axvspan(start, end, facecolor='red', alpha=0.7, edgecolor ='none') - elif labels[i,k]==0: - # Questionable - start = i*40/(8.0*scale) - end = start+5.0/scale - ax.axvspan(start, end, facecolor='.5', alpha=0.5,edgecolor ='none') - - # Plot filtered data if requested - if filteredPlot: - ax.plot(time_m-.625/scale,data['filtered_eda'], c='g') - plt.legend(['Raw SC','Filtered SC'],loc=0) - - # Label and Title each subplot - plt.ylabel('$\mu$S') - plt.title(key) - - # Only include x axis label on final subplot - if secondsPlot: - plt.xlabel('Time (s)') - else: - plt.xlabel('Time (min)') - - # Display the plot - plt.subplots_adjust(hspace=.3) - plt.show() - return - - -if __name__ == "__main__": - numClassifiers = int(get_user_input('Would you like 1 classifier (Binary or Multiclass) or both (enter 1 or 2): ')) - - # Create list of classifiers - if numClassifiers==1: - temp_clf = int(get_user_input("Select a classifier:\n1: Binary\n2: Multiclass\n:")) - while temp_clf != 1 and temp_clf !=2: - temp_clf = get_user_input("Something went wrong. Enter the number 1 or 2.\n Select a classifier:\n1: Binary\n2: Multiclass):") - if temp_clf == 1: - print('Binary Classifier selected') - classifierList = ['Binary'] - elif temp_clf == 2: - print('Multiclass Classifier selected') - classifierList = ['Multiclass'] - else: - classifierList = ['Binary', 'Multiclass'] - - # Classify the data - labels, data = classify(classifierList) - - # Plotting the data - plotDataInput = get_user_input('Do you want to plot the labels? (y/n): ') - - if plotDataInput=='y': - # Include filter plot? - filteredPlot = get_user_input('Would you like to include filtered data in your plot? (y/n): ') - if filteredPlot=='y': - filteredPlot=1 - else: - filteredPlot=0 - - # X axis in seconds? - secondsPlot = get_user_input('Would you like the x-axis to be in seconds or minutes? (sec/min): ') - if secondsPlot=='sec': - secondsPlot=1 - else: - secondsPlot=0 - - # Plot Data - plotData(data,labels,classifierList,filteredPlot,secondsPlot) - - print("Remember! Red is for epochs with artifact, grey is for epochs that are questionable, and no shading is for clean epochs") - - - # Saving the data - saveDataInput = get_user_input('Do you want to save the labels? (y/n): ') - - if saveDataInput=='y': - outputPath = get_user_input('Output directory: ') - outputLabelFilename= get_user_input('Output filename: ') - - # Save labels - fullOutputPath = os.path.join(outputPath,outputLabelFilename) - if fullOutputPath[-4:] != '.csv': - fullOutputPath = fullOutputPath+'.csv' - - featureLabels = pd.DataFrame(labels, index=pd.date_range(start=data.index[0], periods=len(labels), freq='5s'), - columns=classifierList) - - featureLabels.reset_index(inplace=True) - featureLabels.rename(columns={'index':'StartTime'}, inplace=True) - featureLabels['EndTime'] = featureLabels['StartTime']+datetime.timedelta(seconds=5) - featureLabels.index.name = 'EpochNum' - - cols = ['StartTime', 'EndTime'] - cols.extend(classifierList) - - featureLabels = featureLabels[cols] - featureLabels.rename(columns={'Binary': 'BinaryLabels', 'Multiclass': 'MulticlassLabels'}, - inplace=True) - - featureLabels.to_csv(fullOutputPath) - - print("Labels saved to " + fullOutputPath) - print("Remember! The first column is timestamps and the second column is the labels (-1 for artifact, 0 for questionable, 1 for clean)") - - print('--------------------------------') - print("Please also cite this project:") - print("Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. Automatic identification of artifacts in electrodermal activity data. In Engineering in Medicine and Biology Conference. 2015") - print('--------------------------------') - - - - - - diff --git a/calculatingfeatures/eda_explorer/EDA_Peak_Detection_Script.py b/calculatingfeatures/eda_explorer/EDA_Peak_Detection_Script.py deleted file mode 100644 index acfec93c..00000000 --- a/calculatingfeatures/eda_explorer/EDA_Peak_Detection_Script.py +++ /dev/null @@ -1,250 +0,0 @@ -import pandas as pd -import numpy as np -import os -import matplotlib.pyplot as plt -import pprint - -from .load_files import getInputLoadFile, get_user_input, getOutputPath - -SAMPLE_RATE = 8 - -def findPeaks(data, offset, start_WT, end_WT, thres=0, sampleRate=SAMPLE_RATE): - ''' - This function finds the peaks of an EDA signal and returns basic properties. - Also, peak_end is assumed to be no later than the start of the next peak. (Is this okay??) - - ********* INPUTS ********** - data: DataFrame with EDA as one of the columns and indexed by a datetimeIndex - offset: the number of rising samples and falling samples after a peak needed to be counted as a peak - start_WT: maximum number of seconds before the apex of a peak that is the "start" of the peak - end_WT: maximum number of seconds after the apex of a peak that is the "rec.t/2" of the peak, 50% of amp - thres: the minimum uS change required to register as a peak, defaults as 0 (i.e. all peaks count) - sampleRate: number of samples per second, default=8 - - ********* OUTPUTS ********** - peaks: list of binary, 1 if apex of SCR - peak_start: list of binary, 1 if start of SCR - peak_start_times: list of strings, if this index is the apex of an SCR, it contains datetime of start of peak - peak_end: list of binary, 1 if rec.t/2 of SCR - peak_end_times: list of strings, if this index is the apex of an SCR, it contains datetime of rec.t/2 - amplitude: list of floats, value of EDA at apex - value of EDA at start - max_deriv: list of floats, max derivative within 1 second of apex of SCR - - ''' - EDA_deriv = data['filtered_eda'][1:].values - data['filtered_eda'][:-1].values - peaks = np.zeros(len(EDA_deriv)) - peak_sign = np.sign(EDA_deriv) - for i in range(int(offset), int(len(EDA_deriv) - offset)): - if peak_sign[i] == 1 and peak_sign[i + 1] < 1: - peaks[i] = 1 - for j in range(1, int(offset)): - if peak_sign[i - j] < 1 or peak_sign[i + j] > -1: - #if peak_sign[i-j]==-1 or peak_sign[i+j]==1: - peaks[i] = 0 - break - - # Finding start of peaks - peak_start = np.zeros(len(EDA_deriv)) - peak_start_times = [''] * len(data) - max_deriv = np.zeros(len(data)) - rise_time = np.zeros(len(data)) - - for i in range(0, len(peaks)): - if peaks[i] == 1: - temp_start = max(0, i - sampleRate) - max_deriv[i] = max(EDA_deriv[temp_start:i]) - start_deriv = .01 * max_deriv[i] - - found = False - find_start = i - # has to peak within start_WT seconds - while found == False and find_start > (i - start_WT * sampleRate): - if EDA_deriv[find_start] < start_deriv: - found = True - peak_start[find_start] = 1 - peak_start_times[i] = data.index[find_start] - rise_time[i] = get_seconds_and_microseconds(data.index[i] - pd.to_datetime(peak_start_times[i])) - - find_start = find_start - 1 - - # If we didn't find a start - if found == False: - peak_start[i - start_WT * sampleRate] = 1 - peak_start_times[i] = data.index[i - start_WT * sampleRate] - rise_time[i] = start_WT - - # Check if amplitude is too small - if thres > 0 and (data['EDA'].iloc[i] - data['EDA'][peak_start_times[i]]) < thres: - peaks[i] = 0 - peak_start[i] = 0 - peak_start_times[i] = '' - max_deriv[i] = 0 - rise_time[i] = 0 - - # Finding the end of the peak, amplitude of peak - peak_end = np.zeros(len(data)) - peak_end_times = [''] * len(data) - amplitude = np.zeros(len(data)) - decay_time = np.zeros(len(data)) - half_rise = [''] * len(data) - SCR_width = np.zeros(len(data)) - - for i in range(0, len(peaks)): - if peaks[i] == 1: - peak_amp = data['EDA'].iloc[i] - start_amp = data['EDA'][peak_start_times[i]] - amplitude[i] = peak_amp - start_amp - - half_amp = amplitude[i] * .5 + start_amp - - found = False - find_end = i - # has to decay within end_WT seconds - while found == False and find_end < (i + end_WT * sampleRate) and find_end < len(peaks): - if data['EDA'].iloc[find_end] < half_amp: - found = True - peak_end[find_end] = 1 - peak_end_times[i] = data.index[find_end] - decay_time[i] = get_seconds_and_microseconds(pd.to_datetime(peak_end_times[i]) - data.index[i]) - - # Find width - find_rise = i - found_rise = False - while found_rise == False: - if data['EDA'].iloc[find_rise] < half_amp: - found_rise = True - half_rise[i] = data.index[find_rise] - SCR_width[i] = get_seconds_and_microseconds(pd.to_datetime(peak_end_times[i]) - data.index[find_rise]) - find_rise = find_rise - 1 - - elif peak_start[find_end] == 1: - found = True - peak_end[find_end] = 1 - peak_end_times[i] = data.index[find_end] - find_end = find_end + 1 - - # If we didn't find an end - if found == False: - min_index = np.argmin(data['EDA'].iloc[i:(i + end_WT * sampleRate)].tolist()) - peak_end[i + min_index] = 1 - peak_end_times[i] = data.index[i + min_index] - - peaks = np.concatenate((peaks, np.array([0]))) - peak_start = np.concatenate((peak_start, np.array([0]))) - max_deriv = max_deriv * sampleRate # now in change in amplitude over change in time form (uS/second) - - return peaks, peak_start, peak_start_times, peak_end, peak_end_times, amplitude, max_deriv, rise_time, decay_time, SCR_width, half_rise - -def get_seconds_and_microseconds(pandas_time): - return pandas_time.seconds + pandas_time.microseconds * 1e-6 - -def calcPeakFeatures(data,offset,thresh,start_WT,end_WT, sampleRate): - returnedPeakData = findPeaks(data, offset*sampleRate, start_WT, end_WT, thresh, sampleRate) - data['peaks'] = returnedPeakData[0] - data['peak_start'] = returnedPeakData[1] - data['peak_end'] = returnedPeakData[3] - - data['peak_start_times'] = returnedPeakData[2] - data['peak_end_times'] = returnedPeakData[4] - data['half_rise'] = returnedPeakData[10] - # Note: If an SCR doesn't decrease to 50% of amplitude, then the peak_end = min(the next peak's start, 15 seconds after peak) - data['amp'] = returnedPeakData[5] - data['max_deriv'] = returnedPeakData[6] - data['rise_time'] = returnedPeakData[7] - data['decay_time'] = returnedPeakData[8] - data['SCR_width'] = returnedPeakData[9] - - # To keep all filtered data remove this line - # featureData = data[data.peaks==1][['EDA','rise_time','max_deriv','amp','decay_time','SCR_width']] - - # Replace 0s with NaN, this is where the 50% of the peak was not found, too close to the next peak - # featureData[['SCR_width','decay_time']]=featureData[['SCR_width','decay_time']].replace(0, np.nan) - # featureData['AUC']=featureData['amp']*featureData['SCR_width'] - # if outfile is not None: - # featureData.to_csv(outfile) - - return data - -# draws a graph of the data with the peaks marked on it -# assumes that 'data' dataframe already contains the 'peaks' column -def plotPeaks(data, x_seconds, sampleRate = SAMPLE_RATE): - if x_seconds: - time_m = np.arange(0,len(data))/float(sampleRate) - else: - time_m = np.arange(0,len(data))/(sampleRate*60.) - - data_min = min(data['EDA']) - data_max = max(data['EDA']) - - #Plot the data with the Peaks marked - plt.figure(1,figsize=(20, 5)) - peak_height = data_max * 1.15 - data['peaks_plot'] = data['peaks'] * peak_height - plt.plot(time_m,data['peaks_plot'],'#4DBD33') - #plt.plot(time_m,data['EDA']) - plt.plot(time_m,data['filtered_eda']) - plt.xlim([0,time_m[-1]]) - y_min = min(0, data_min) - (data_max - data_min) * 0.1 - plt.ylim([min(y_min, data_min),peak_height]) - plt.title('EDA with Peaks marked') - plt.ylabel('$\mu$S') - if x_seconds: - plt.xlabel('Time (s)') - else: - plt.xlabel('Time (min)') - - plt.show() - -def chooseValueOrDefault(str_input, default): - if str_input == "": - return default - else: - return float(str_input) - -if __name__ == "__main__": - - data, filepath_confirm = getInputLoadFile() - - fullOutputPath = getOutputPath() - - print("") - print("Please choose settings for the peak detection algorithm. For default values press return") - thresh_str = get_user_input('\tMinimum peak amplitude (default = .02):') - thresh = chooseValueOrDefault(thresh_str,.02) - offset_str = get_user_input('\tOffset (default = 1): ') - offset = chooseValueOrDefault(offset_str,1) - start_WT_str = get_user_input('\tMax rise time (s) (default = 4): ') - start_WT = chooseValueOrDefault(start_WT_str,4) - end_WT_str = get_user_input('\tMax decay time (s) (default = 4): ') - end_WT = chooseValueOrDefault(end_WT_str,4) - - settings_dict = {'threshold':thresh, - 'offset':offset, - 'rise time':start_WT, - 'decay time':end_WT} - - print("") - print("Okay, finding peaks in file "+ filepath_confirm + " using the following parameters") - pprint.pprint(settings_dict) - peakData = calcPeakFeatures(data,fullOutputPath,offset,thresh,start_WT,end_WT) - print("Features computed and saved to " + fullOutputPath) - - # Plotting the data - print("") - plot_ans = get_user_input("Do you want to plot the detected peaks? (y/n): ") - if 'y' in plot_ans: - secs_ans = get_user_input("Would you like the x-axis to be in seconds or minutes? (sec/min): ") - if 'sec' in secs_ans: - x_seconds=True - else: - x_seconds=False - plotPeaks(peakData,x_seconds) - else: - print("\tOkay, script will not produce a plot") - - print("") - print('--------------------------------') - print("Please also cite this project:") - print("Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. Automatic identification of artifacts in electrodermal activity data. In Engineering in Medicine and Biology Conference. 2015") - print('--------------------------------') - diff --git a/calculatingfeatures/eda_explorer/LICENSE.txt b/calculatingfeatures/eda_explorer/LICENSE.txt deleted file mode 100644 index b46824a7..00000000 --- a/calculatingfeatures/eda_explorer/LICENSE.txt +++ /dev/null @@ -1,21 +0,0 @@ -EDA-Explorer is an open-source project under the MIT License - -Copyright (c) 2016 Sara Taylor and Natasha Jaques - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/calculatingfeatures/eda_explorer/README.md b/calculatingfeatures/eda_explorer/README.md deleted file mode 100644 index 89af8bba..00000000 --- a/calculatingfeatures/eda_explorer/README.md +++ /dev/null @@ -1,39 +0,0 @@ -eda-explorer -============ - -Scripts to detect artifacts and in electrodermal activity (EDA) data. Note that these scripts are written for Python 2.7 and Python 3.7 - - -Version 1.0 - -Please also cite this project: -Taylor, S., Jaques, N., Chen, W., Fedor, S., Sano, A., & Picard, R. Automatic identification of artifacts in electrodermal activity data. In Engineering in Medicine and Biology Conference. 2015. - - -Required python packages can be found in requirements.txt - -To run artifact detection from the command line: -== -python EDA-Artifact-Detection-Script.py - -Currently there are only 2 classifiers to choose from: Binary or Multiclass - -To run peak detection: -== -python EDA-Peak-Detection-Script.py - -Descriptions of the algorithm settings can be found at http://eda-explorer.media.mit.edu/info/ - -To run accelerometer feature extraction: -== -python AccelerometerFeatureExtractionScript.py - -This file works slightly differently than the others in that it gives summary information over periods of time. - -Notes: -=== - -1. Currently, these files are written with the assumption that the sample rate is an integer power of 2. - -2. Please visit [eda-explorer.media.mit.edu](https://eda-explorer.media.mit.edu) - to use the web-based version diff --git a/calculatingfeatures/eda_explorer/__init__.py b/calculatingfeatures/eda_explorer/__init__.py deleted file mode 100644 index 31b10d24..00000000 --- a/calculatingfeatures/eda_explorer/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .load_files import * \ No newline at end of file diff --git a/calculatingfeatures/eda_explorer/classify.py b/calculatingfeatures/eda_explorer/classify.py deleted file mode 100644 index a1947321..00000000 --- a/calculatingfeatures/eda_explorer/classify.py +++ /dev/null @@ -1,90 +0,0 @@ -from sklearn.svm import SVC -import pickle - - -#docs: http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC -class SVM: - def __init__(self, C=1.0,beta=0.0, kernel='linear', poly_degree=3, max_iter=-1, tol=0.001): - #possible kernels: linear, 'poly', 'rbf', 'sigmoid', 'precomputed' or a callable - - #data features - self.n_features = None - self.train_X = [] - self.train_Y = [] - self.val_X = [] - self.val_Y = [] - self.test_X = [] - self.test_Y = [] - - #classifier features - self.C = C - self.beta = beta - self.kernel = kernel - self.poly_degree = poly_degree - self.max_iter = max_iter - self.tolerance = tol - - self.classifier = None - - - #SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, - # gamma=0.0, kernel='rbf', max_iter=-1, probability=False, - # random_state=None, shrinking=True, tol=0.001, verbose=False) - - def setTrainData(self, X, Y): - self.train_X = X - self.train_Y = Y - - self.n_features = self.train_X.shape[1] - - def setTestData(self, X, Y): - self.test_X = X - self.test_Y = Y - - def setC(self, c): - self.C = c - - def setBeta(self, beta): - if beta is None: - self.beta = 0.0 - else: - self.beta=beta - - def setKernel(self, kernel, poly_degree=3): - self.kernel = kernel - self.poly_degree = poly_degree - - def setValData(self, X, Y): - self.val_X = X - self.val_Y = Y - - def train(self): - self.classifier = SVC(C=self.C, kernel=self.kernel, gamma=self.beta, degree=self.poly_degree, max_iter=self.max_iter, - tol=self.tolerance) - self.classifier.fit(self.train_X, self.train_Y) - - def predict(self, X): - return self.classifier.predict(X) - - def getScore(self, X, Y): - #returns accuracy - return self.classifier.score(X, Y) - - def getNumSupportVectors(self): - return self.classifier.n_support_ - - def getHingeLoss(self,X,Y): - preds = self.predict(X) - hinge_inner_func = 1.0 - preds*Y - hinge_inner_func = [max(0,x) for x in hinge_inner_func] - return sum(hinge_inner_func) - - def saveClassifierToFile(self, filepath): - s = pickle.dumps(self.classifier) - f = open(filepath, 'wb') - f.write(s) - - def loadClassifierFromFile(self, filepath): - f2 = open(filepath, 'rb') - s2 = f2.read() - self.classifier = pickle.loads(s2) diff --git a/calculatingfeatures/eda_explorer/load_files.py b/calculatingfeatures/eda_explorer/load_files.py deleted file mode 100644 index ea8be670..00000000 --- a/calculatingfeatures/eda_explorer/load_files.py +++ /dev/null @@ -1,264 +0,0 @@ -import pandas as pd -import scipy.signal as scisig -import os -import numpy as np - - -def get_user_input(prompt): - try: - return raw_input(prompt) - except NameError: - return input(prompt) - - -def getInputLoadFile(): - '''Asks user for type of file and file path. Loads corresponding data. - - OUTPUT: - data: DataFrame, index is a list of timestamps at 8Hz, columns include - AccelZ, AccelY, AccelX, Temp, EDA, filtered_eda - ''' - print("Please enter information about your EDA file... ") - dataType = get_user_input("\tData Type (e4, q, shimmer, or misc): ") - if dataType=='q': - filepath = get_user_input("\tFile path: ") - filepath_confirm = filepath - data = loadData_Qsensor(filepath) - elif dataType=='e4': - filepath = get_user_input("\tPath to E4 directory: ") - filepath_confirm = os.path.join(filepath,"EDA.csv") - data = loadData_E4(filepath) - elif dataType=='shimmer': - filepath = get_user_input("\tFile path: ") - filepath_confirm = filepath - data = loadData_shimmer(filepath) - elif dataType=="misc": - filepath = get_user_input("\tFile path: ") - filepath_confirm = filepath - data = loadData_misc(filepath) - else: - print("Error: not a valid file choice") - - return data, filepath_confirm - -def getOutputPath(): - print("") - print("Where would you like to save the computed output file?") - outfile = get_user_input('\tFile name: ') - outputPath = get_user_input('\tFile directory (./ for this directory): ') - fullOutputPath = os.path.join(outputPath,outfile) - if fullOutputPath[-4:] != '.csv': - fullOutputPath = fullOutputPath+'.csv' - return fullOutputPath - -def loadData_Qsensor(filepath): - ''' - This function loads the Q sensor data, uses a lowpass butterworth filter on the EDA signal - Note: currently assumes sampling rate of 8hz, 16hz, 32hz; if sampling rate is 16hz or 32hz the signal is downsampled - - INPUT: - filepath: string, path to input file - - OUTPUT: - data: DataFrame, index is a list of timestamps at 8Hz, columns include AccelZ, AccelY, AccelX, Temp, EDA, filtered_eda - ''' - # Get header info - try: - header_info = pd.io.parsers.read_csv(filepath, nrows=5) - except IOError: - print("Error!! Couldn't load file, make sure the filepath is correct and you are using a csv from the q sensor software\n\n") - return - - # Get sample rate - sampleRate = int((header_info.iloc[3,0]).split(":")[1].strip()) - - # Get the raw data - data = pd.io.parsers.read_csv(filepath, skiprows=7) - data = data.reset_index() - - # Reset the index to be a time and reset the column headers - data.columns = ['AccelZ','AccelY','AccelX','Battery','Temp','EDA'] - - # Get Start Time - startTime = pd.to_datetime(header_info.iloc[4,0][12:-10]) - - # Make sure data has a sample rate of 8Hz - data = interpolateDataTo8Hz(data,sampleRate,startTime) - - # Remove Battery Column - data = data[['AccelZ','AccelY','AccelX','Temp','EDA']] - - # Get the filtered data using a low-pass butterworth filter (cutoff:1hz, fs:8hz, order:6) - data['filtered_eda'] = butter_lowpass_filter(data['EDA'], 1.0, 8, 6) - - return data - -def _loadSingleFile_E4(filepath,list_of_columns, expected_sample_rate,freq): - # Load data - data = pd.read_csv(filepath) - - # Get the startTime and sample rate - startTime = pd.to_datetime(float(data.columns.values[0]),unit="s") - sampleRate = float(data.iloc[0][0]) - data = data[data.index!=0] - data.index = data.index-1 - - # Reset the data frame assuming expected_sample_rate - data.columns = list_of_columns - if sampleRate != expected_sample_rate: - print('ERROR, NOT SAMPLED AT {0}HZ. PROBLEMS WILL OCCUR\n'.format(expected_sample_rate)) - - # Make sure data has a sample rate of 8Hz - data = interpolateDataTo8Hz(data,sampleRate,startTime) - - return data - - -def loadData_E4(filepath): - # Load EDA data - eda_data = _loadSingleFile_E4(os.path.join(filepath,'EDA.csv'),["EDA"],4,"250L") - # Get the filtered data using a low-pass butterworth filter (cutoff:1hz, fs:8hz, order:6) - eda_data['filtered_eda'] = butter_lowpass_filter(eda_data['EDA'], 1.0, 8, 6) - - # Load ACC data - acc_data = _loadSingleFile_E4(os.path.join(filepath,'ACC.csv'),["AccelX","AccelY","AccelZ"],32,"31250U") - # Scale the accelometer to +-2g - acc_data[["AccelX","AccelY","AccelZ"]] = acc_data[["AccelX","AccelY","AccelZ"]]/64.0 - - # Load Temperature data - temperature_data = _loadSingleFile_E4(os.path.join(filepath,'TEMP.csv'),["Temp"],4,"250L") - - data = eda_data.join(acc_data, how='outer') - data = data.join(temperature_data, how='outer') - - # E4 sometimes records different length files - adjust as necessary - min_length = min(len(acc_data), len(eda_data), len(temperature_data)) - - return data[:min_length] - -def loadData_shimmer(filepath): - data = pd.read_csv(filepath, sep='\t', skiprows=(0,1)) - - orig_cols = data.columns - rename_cols = {} - - for search, new_col in [['Timestamp','Timestamp'], - ['Accel_LN_X', 'AccelX'], ['Accel_LN_Y', 'AccelY'], ['Accel_LN_Z', 'AccelZ'], - ['Skin_Conductance', 'EDA']]: - orig = [c for c in orig_cols if search in c] - if len(orig) == 0: - continue - rename_cols[orig[0]] = new_col - - data.rename(columns=rename_cols, inplace=True) - - # TODO: Assuming no temperature is recorded - data['Temp'] = 0 - - # Drop the units row and unnecessary columns - data = data[data['Timestamp'] != 'ms'] - data.index = pd.to_datetime(data['Timestamp'], unit='ms') - data = data[['AccelZ', 'AccelY', 'AccelX', 'Temp', 'EDA']] - - for c in ['AccelZ', 'AccelY', 'AccelX', 'Temp', 'EDA']: - data[c] = pd.to_numeric(data[c]) - - # Convert to 8Hz - data = data.resample("125L").mean() - data.interpolate(inplace=True) - - # Get the filtered data using a low-pass butterworth filter (cutoff:1hz, fs:8hz, order:6) - data['filtered_eda'] = butter_lowpass_filter(data['EDA'], 1.0, 8, 6) - - return data - - -def loadData_getColNames(data_columns): - print("Here are the data columns of your file: ") - print(data_columns) - - # Find the column names for each of the 5 data streams - colnames = ['EDA data','Temperature data','Acceleration X','Acceleration Y','Acceleration Z'] - new_colnames = ['','','','',''] - - for i in range(len(new_colnames)): - new_colnames[i] = get_user_input("Column name that contains "+colnames[i]+": ") - while (new_colnames[i] not in data_columns): - print("Column not found. Please try again") - print("Here are the data columns of your file: ") - print(data_columns) - - new_colnames[i] = get_user_input("Column name that contains "+colnames[i]+": ") - - # Get user input on sample rate - sampleRate = get_user_input("Enter sample rate (must be an integer power of 2): ") - while (sampleRate.isdigit()==False) or (np.log(int(sampleRate))/np.log(2) != np.floor(np.log(int(sampleRate))/np.log(2))): - print("Not an integer power of two") - sampleRate = get_user_input("Enter sample rate (must be a integer power of 2): ") - sampleRate = int(sampleRate) - - # Get user input on start time - startTime = pd.to_datetime(get_user_input("Enter a start time (format: YYYY-MM-DD HH:MM:SS): ")) - while type(startTime)==str: - print("Not a valid date/time") - startTime = pd.to_datetime(get_user_input("Enter a start time (format: YYYY-MM-DD HH:MM:SS): ")) - - - return sampleRate, startTime, new_colnames - - -def loadData_misc(filepath): - # Load data - data = pd.read_csv(filepath) - - # Get the correct colnames - sampleRate, startTime, new_colnames = loadData_getColNames(data.columns.values) - - data.rename(columns=dict(zip(new_colnames,['EDA','Temp','AccelX','AccelY','AccelZ'])), inplace=True) - data = data[['AccelZ','AccelY','AccelX','Temp','EDA']] - - # Make sure data has a sample rate of 8Hz - data = interpolateDataTo8Hz(data,sampleRate,startTime) - - # Get the filtered data using a low-pass butterworth filter (cutoff:1hz, fs:8hz, order:6) - data['filtered_eda'] = butter_lowpass_filter(data['EDA'], 1.0, 8, 6) - - return data - -def interpolateDataTo8Hz(data,sample_rate,startTime): - if sample_rate<8: - # Upsample by linear interpolation - if sample_rate==2: - data.index = pd.date_range(start=startTime, periods=len(data), freq='500L') - elif sample_rate==4: - data.index = pd.date_range(start=startTime, periods=len(data), freq='250L') - data = data.resample("125L").mean() - else: - if sample_rate>8: - # Downsample - idx_range = list(range(0,len(data))) # TODO: double check this one - data = data.iloc[idx_range[0::int(int(sample_rate)/8)]] - # Set the index to be 8Hz - data.index = pd.date_range(start=startTime, periods=len(data), freq='125L') - - # Interpolate all empty values - data = interpolateEmptyValues(data) - return data - -def interpolateEmptyValues(data): - cols = data.columns.values - for c in cols: - data.loc[:, c] = data[c].interpolate() - - return data - -def butter_lowpass(cutoff, fs, order=5): - # Filtering Helper functions - sos = scisig.butter(order, cutoff, btype='low', analog=False, output='sos', fs=fs) - return sos - -def butter_lowpass_filter(data, cutoff, fs, order=5): - # Filtering Helper functions - sos = butter_lowpass(cutoff, fs, order=order) - y = scisig.sosfilt(sos, data) - return y \ No newline at end of file diff --git a/calculatingfeatures/eda_explorer/requirements.txt b/calculatingfeatures/eda_explorer/requirements.txt deleted file mode 100644 index 8ba778ee..00000000 --- a/calculatingfeatures/eda_explorer/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -numpy==1.16.2 -scipy==1.2.1 -pandas==0.24.1 -scikit-learn==0.20.3 -matplotlib>=2.1.2 -PyWavelets==1.0.2 \ No newline at end of file diff --git a/calculatingfeatures/example_data/EDA.csv b/calculatingfeatures/example_data/EDA.csv deleted file mode 100644 index a9e51ba6..00000000 --- a/calculatingfeatures/example_data/EDA.csv +++ /dev/null @@ -1,114320 +0,0 @@ -1468820057.000000 -4.000000 -0.000000 -0.011527 -0.046108 -0.030739 -0.017931 -0.011527 -0.011527 -0.014089 -0.025616 -0.019212 -0.034581 -0.015369 -0.015369 -0.015369 -0.046108 -0.049951 -0.037143 -0.065320 -0.014089 -0.043547 -0.039704 -0.015369 -0.015369 -0.020493 -0.019212 -0.017931 -0.043547 -0.053793 -0.070443 -0.021773 -0.058916 -0.057636 -0.053793 -0.055074 -0.014089 -0.039704 -0.030739 -0.014089 -0.012808 -0.014089 -0.021773 -0.020493 -0.044828 -0.016650 -0.015369 -0.097340 -0.017931 -0.010246 -0.076847 -0.038424 -0.067882 -0.044828 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-15,2,63 -7,4,59 -13,2,68 --4,5,58 -6,2,61 -6,3,62 --1,3,61 --9,0,62 -7,-2,60 -4,2,59 -28,4,60 -16,10,54 -28,13,34 -57,9,45 -57,11,42 -53,13,25 -67,13,27 -73,14,20 -60,17,4 -66,15,12 -66,16,14 -49,15,9 -59,16,19 -58,12,19 -68,10,22 -59,7,23 -63,6,28 -76,8,39 -44,17,30 -3,26,42 -38,10,45 -43,15,33 -33,15,38 -40,11,48 -30,17,38 -32,14,57 -37,13,52 -51,16,63 -18,14,50 -8,11,43 -29,12,54 -29,8,72 -17,8,59 -6,2,59 -0,0,65 -21,7,54 -38,3,50 -27,8,38 -58,6,41 -62,4,43 -63,1,47 -50,7,43 -50,9,52 -19,7,24 -38,2,16 -75,0,37 -43,6,24 -55,3,28 -53,15,20 -64,8,38 -54,5,25 -63,11,14 -67,4,17 -67,2,24 -64,3,29 -51,3,14 -58,8,25 -57,8,23 -55,14,21 -54,17,16 -56,13,12 -60,7,15 -67,1,9 -70,-2,20 -60,1,8 -59,2,9 -69,8,15 -60,16,11 -68,18,20 -65,22,18 -55,30,10 -54,32,0 -54,35,-2 -60,38,2 -46,42,-3 -48,46,-10 -46,50,-11 -41,53,-18 -59,53,-16 -52,52,-12 -41,55,-24 -42,49,-10 -35,50,-5 -26,50,-19 -35,50,-8 -30,49,-7 -45,50,-2 -41,48,-1 -27,45,-9 -38,43,1 -28,39,1 -34,34,14 -40,34,21 -37,34,10 -47,36,20 -51,35,16 -58,31,25 -53,34,17 -55,31,9 -69,28,24 -70,30,18 -61,29,6 -68,30,17 -63,31,9 -57,29,-3 -54,28,4 -57,28,9 -57,27,12 -56,26,6 -58,25,3 -60,19,9 -62,20,9 -64,23,3 -66,19,12 -69,20,19 -68,21,19 -65,21,24 -75,21,35 -57,22,27 -12,16,59 -19,10,75 -20,35,59 -34,30,58 -36,4,28 --35,-7,38 --29,-2,72 --16,2,70 --25,2,66 --8,1,60 --2,1,57 --3,2,59 --10,2,62 --20,2,63 --1,-2,59 -19,4,50 -27,9,49 --26,24,72 --35,22,76 -55,18,71 -28,22,19 -17,19,-28 -127,8,-3 -115,-16,24 -40,19,5 -61,12,37 -88,21,54 -81,22,24 -75,24,34 -40,28,14 -51,38,27 -53,41,30 -35,47,30 -32,52,20 -23,51,16 -17,52,13 -13,54,14 -16,51,10 -20,48,6 -17,45,1 -27,39,-4 -34,36,-4 -29,25,-15 -62,20,-12 -60,18,-11 -51,6,-30 -72,1,-21 -64,7,-10 -68,-1,-17 -78,1,-6 -66,4,4 -65,6,-4 -81,3,10 -76,0,17 -69,-1,12 -77,0,23 -84,-1,30 -69,-2,22 -92,-11,43 -78,-6,30 --9,3,27 --30,17,25 -0,11,69 --5,33,58 --6,40,55 -39,19,54 -31,-4,50 -4,-3,54 -4,2,63 -13,-4,61 --48,-16,51 --9,-13,71 --6,-17,70 --19,-2,44 --15,0,50 --18,-1,48 --18,-5,56 --31,-5,48 --55,-3,61 --49,-2,54 -14,-5,64 --20,1,66 --9,5,61 --1,5,67 -1,4,69 -16,8,66 -20,10,63 -34,15,61 -43,14,48 -42,16,43 -42,16,39 -52,18,42 -51,21,29 -61,21,29 -63,23,20 -61,23,26 -66,25,22 -69,27,26 -62,29,19 -63,28,18 -63,27,10 -64,27,8 -63,31,10 -60,32,13 -56,31,4 -53,33,-7 -50,36,-3 -41,46,4 -35,47,9 -26,51,6 -14,56,7 -8,57,11 --1,56,11 --9,55,10 --15,54,10 --15,54,5 --17,52,9 --20,53,11 --14,47,13 --14,49,22 --15,47,11 --7,45,32 --8,43,26 --10,41,31 --5,38,41 --9,35,24 --7,35,54 --13,32,39 -1,33,65 -1,32,65 --5,32,66 --13,27,76 --12,24,57 --32,18,60 --34,13,63 --37,10,68 --37,9,74 --30,11,72 --17,16,64 --19,16,59 --24,10,64 --31,7,59 --24,6,58 --19,10,68 --20,9,70 --19,12,64 --15,10,59 --15,7,61 --12,0,59 --8,-1,62 --24,-1,53 --5,-10,56 -18,-13,67 -16,-14,63 -32,-11,55 -34,-10,66 -26,-14,77 -46,-16,74 -49,7,30 -78,8,16 -94,4,8 -57,20,13 -61,12,8 -59,8,2 -56,5,3 -68,5,8 -61,5,4 -55,3,0 -45,4,1 -45,1,5 -64,-8,18 -58,-10,22 -51,-7,24 -67,-11,13 -71,-11,9 -57,-9,3 -59,-13,10 -61,-13,11 -54,-15,8 -55,-14,9 -64,-12,16 -59,-12,13 -62,-12,15 -67,-10,15 -65,-11,16 -56,-13,13 -63,-11,13 -61,-12,15 -59,-9,15 -63,-8,11 -55,-15,10 -64,-7,17 -57,-15,8 -56,-17,15 -60,-11,24 -62,-11,20 -59,-7,15 -59,-9,15 -59,-7,14 -68,-10,31 -63,-8,34 -53,-4,27 -58,-1,24 -51,7,13 -57,10,2 -65,13,1 -66,15,-3 -66,19,-4 -73,16,1 -57,9,2 -59,10,2 -60,-1,17 -49,-7,36 -52,-10,29 -46,-18,29 -48,-18,28 -40,-22,22 -44,-24,17 -66,-24,16 -54,-20,11 -58,-13,15 -69,-7,4 -94,-4,6 -104,3,12 -58,0,14 -57,-3,6 -63,-7,4 -71,-6,8 -64,-8,5 -57,-11,6 -62,-11,5 -70,-8,8 -74,-6,7 -52,-7,-1 -68,-3,-2 -82,1,1 -63,-9,2 -58,-5,3 -63,-3,20 -63,-2,23 -57,-1,25 -51,-1,22 -57,-5,15 -55,-6,10 -52,-9,6 -56,-12,6 -67,-7,12 -65,-7,8 -72,-5,10 -73,-5,10 -69,-3,7 -69,-6,5 -68,-10,7 -68,-14,5 -63,-15,3 -63,-15,5 -56,-14,4 -54,-14,3 -54,-12,3 -57,-12,1 -53,-15,-1 -52,-14,-4 -57,-15,-4 -63,-14,-1 -69,-10,2 -65,-7,1 -62,-4,4 -60,-2,5 -62,-3,4 -65,-5,6 -60,-3,6 -63,-8,6 -73,-11,8 -70,-7,3 -66,-6,-1 -61,-2,10 -59,0,10 -63,3,5 -72,2,6 -81,-2,1 -83,-2,-2 -76,0,2 -67,-3,5 -65,-4,3 -60,0,1 -52,5,-3 -49,6,3 -49,13,7 -53,14,5 -58,15,1 -60,18,6 -57,18,4 -62,21,3 -58,21,3 -54,25,2 -59,27,3 -62,25,4 -55,28,2 -59,26,4 -63,25,6 -61,24,6 -64,23,10 -62,23,11 -55,22,8 -61,19,9 -62,19,13 -55,20,10 -62,18,12 -52,20,6 -58,19,5 -62,16,3 -61,15,-3 -62,15,1 -63,15,4 -60,16,5 -61,16,5 -57,16,5 -56,17,9 -50,17,5 -57,20,5 -61,19,6 -58,19,1 -63,21,2 -67,22,3 -65,25,0 -61,25,-2 -63,27,-4 -62,23,-2 -64,24,1 -63,19,5 -64,16,4 -61,14,2 -60,9,2 -65,6,4 -61,4,4 -65,1,6 -73,2,10 -65,4,13 -60,5,12 -61,5,12 -68,5,19 -61,6,19 -60,5,20 -64,3,23 -59,4,22 -46,5,30 -45,6,28 -53,8,28 -54,8,34 -51,15,35 -46,17,36 -40,18,34 -49,19,46 -59,20,46 -47,21,43 -28,20,55 -44,19,64 -25,18,59 -26,17,58 -26,14,54 -14,11,59 -19,8,58 -12,5,58 --4,2,64 -0,-1,68 -5,1,61 --2,2,55 -7,3,60 -11,8,64 -4,13,61 -18,12,60 -14,13,62 -8,18,57 -10,13,56 --1,10,62 -6,6,69 -1,9,64 -9,5,64 -7,7,64 -31,7,61 -17,10,50 -21,8,47 -38,13,61 -34,15,53 -24,16,42 -43,18,52 -50,22,50 -35,20,42 -31,20,36 -42,20,43 -38,21,47 -37,21,45 -62,19,38 -59,18,38 -52,16,28 -64,14,25 -55,13,25 -49,12,26 -58,11,30 -60,13,23 -60,13,20 -61,13,18 -61,14,19 -58,14,19 -59,14,21 -61,14,19 -64,14,18 -58,16,15 -55,12,24 -55,12,27 -56,12,23 -58,11,22 -57,11,24 -57,11,23 -57,11,23 -57,12,25 -56,12,25 -57,12,23 -60,12,23 -56,13,26 -56,14,24 -60,14,24 -65,14,27 -65,14,31 -63,14,32 -60,12,44 -47,13,43 -42,11,55 -34,22,54 -18,20,54 -17,21,58 -26,20,55 --1,20,48 -5,16,55 -5,13,57 -0,10,60 -3,8,58 -3,8,60 --3,5,60 --2,4,59 --4,3,58 --8,-1,60 --13,-1,59 --13,-4,57 --18,-4,60 --20,-5,62 --22,-3,59 --13,-4,64 --14,-2,61 --12,0,60 --12,1,63 --11,1,61 --10,2,62 --13,2,63 --15,3,63 --12,2,64 --12,4,62 --12,3,62 --9,1,63 --9,2,62 --9,3,62 --10,1,63 --13,2,63 --13,2,63 --14,2,62 --11,1,64 --9,1,63 --8,1,62 --8,0,63 --9,1,63 --10,1,61 --9,2,61 --10,1,62 --9,1,62 --11,1,63 --12,1,63 --12,1,63 --10,1,62 --7,2,62 --9,0,63 --13,1,62 --13,1,62 --13,1,62 --12,1,62 --11,1,62 --11,1,62 --11,1,61 --12,0,63 --12,1,63 --14,1,62 --11,1,62 --9,1,63 --12,1,63 --13,1,62 --14,1,61 --11,1,62 --13,1,61 --12,1,61 --10,1,62 --11,1,62 --13,1,63 --12,1,62 --11,2,61 --11,1,63 --11,1,63 --11,1,62 --12,1,61 --12,1,62 --10,1,63 --10,1,62 --12,1,62 --11,1,63 --11,1,62 --12,1,62 --11,1,62 --11,1,62 --11,1,63 --11,1,62 --11,1,63 --11,1,63 --11,0,62 --11,1,62 --11,1,63 --11,1,63 --11,0,62 --11,1,62 --11,1,63 --11,0,63 --11,0,62 --11,1,63 --11,1,62 --11,1,62 --11,0,62 --11,1,62 --10,0,62 --11,0,62 --11,0,62 --11,1,63 --11,1,63 --11,0,62 --10,0,63 --10,1,63 --10,0,63 --10,1,62 --10,0,62 --10,1,62 --11,0,62 --11,0,62 --8,1,63 --11,0,62 --11,0,63 --9,1,63 --10,0,63 --11,1,62 --10,1,63 --10,1,63 --11,1,63 --10,1,63 --9,1,63 --10,1,63 --10,1,63 --10,1,63 --10,1,62 --10,1,62 --10,0,62 --9,0,62 --9,0,62 --9,1,62 --10,0,62 --9,1,63 --9,1,63 --9,1,63 --9,1,63 --8,1,63 --8,1,62 --9,1,62 --9,1,63 --9,0,63 --8,1,63 --8,1,63 --8,1,63 --8,0,63 --8,1,63 --8,1,63 --8,1,63 --8,1,63 --9,1,62 --8,1,63 --8,0,63 --8,1,63 --8,1,63 --8,0,63 --7,0,63 --8,1,63 --8,1,63 --8,1,63 --7,1,63 --8,0,63 --8,1,63 --8,1,63 --8,1,62 --8,1,62 --8,0,62 --7,0,63 --6,0,63 --5,1,64 --4,1,63 --5,1,63 --6,0,64 --4,1,64 --4,0,64 --4,1,62 --6,1,63 --9,0,63 --7,2,62 --7,0,64 --10,0,63 --8,0,62 --8,0,63 --8,0,62 --7,0,62 --8,0,63 --7,0,63 --7,0,63 --8,-1,63 --7,0,62 --7,0,63 --8,0,63 --7,0,63 --6,0,63 --7,0,63 --7,0,63 --6,0,63 --6,0,63 --6,0,63 --6,0,63 --7,-1,63 --6,0,63 --6,0,63 --7,-1,62 --7,-1,62 --7,-1,61 --10,-2,62 --8,-2,62 --2,-1,65 --4,0,66 --10,1,60 --8,-1,63 --5,-2,62 --4,-2,64 --10,-3,66 --1,-4,70 --2,-2,60 -14,-1,68 -17,3,65 -7,7,59 -21,10,60 -27,13,58 -26,16,53 -25,19,50 -30,22,44 -34,22,44 -40,25,43 -37,27,34 -34,28,38 -47,30,36 -38,32,31 -35,39,25 -41,39,30 -50,38,33 -54,35,27 -60,30,21 -67,23,26 -62,24,18 -58,18,10 -56,14,11 -59,17,12 -68,12,19 -59,12,19 -59,11,15 -66,12,20 -63,14,19 -61,14,23 -60,15,24 -62,15,25 -60,15,23 -57,14,18 -58,14,24 -56,15,22 -54,15,20 -59,16,25 -55,16,26 -51,15,17 -61,15,21 -60,16,22 -55,15,22 -57,14,25 -57,14,28 -54,14,28 -53,14,31 -55,14,27 -55,14,26 -55,14,27 -55,15,25 -57,16,24 -57,16,22 -58,17,20 -60,18,17 -65,18,21 -59,20,20 -60,18,22 -59,18,23 -55,18,24 -52,17,23 -54,17,19 -57,18,19 -56,19,20 -54,17,20 -59,18,23 -57,20,23 -54,19,21 -56,17,24 -56,18,24 -58,18,27 -53,20,26 -54,20,27 -57,20,28 -50,19,29 -48,19,30 -46,19,30 -50,19,32 -50,20,33 -52,20,33 -52,21,34 -51,21,37 -46,20,32 -47,18,31 -55,19,37 -54,20,38 -45,20,36 -46,21,34 -50,20,40 -38,20,36 -43,20,36 -48,20,39 -43,20,41 -42,20,41 -43,20,41 -45,19,43 -42,19,42 -43,18,40 -45,17,41 -43,16,39 -44,17,37 -47,17,40 -44,16,34 -43,15,31 -52,15,35 -56,14,37 -60,14,34 -63,11,36 -57,12,33 -49,13,38 -51,8,40 -54,9,50 -41,18,52 -37,20,53 -23,20,56 -20,23,54 -22,20,54 -13,15,52 -7,13,48 -5,9,58 -12,7,70 -10,7,64 -5,6,55 -15,5,64 -17,8,62 -17,8,57 -17,10,58 -10,9,64 -17,9,61 -16,10,60 -13,10,59 -17,10,62 -23,10,61 -20,11,61 -19,11,60 -24,13,57 -26,11,57 -23,12,55 -22,12,53 -25,12,56 -27,13,60 -28,13,60 -40,13,54 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-8,25,46 -3,26,46 -6,21,53 --7,17,58 -7,24,71 -5,32,59 -14,33,62 -16,22,75 -18,24,75 --11,32,68 -10,13,69 -18,20,70 -3,20,61 -2,21,58 -3,18,48 -30,14,38 -68,18,46 -38,10,33 -22,18,46 -31,17,31 -29,16,41 -15,15,56 -24,15,50 -34,17,44 -34,20,52 -54,22,49 -50,23,47 -41,21,43 -45,21,50 -51,19,54 -47,18,48 -45,17,46 -52,13,48 -53,14,47 -51,14,41 -43,12,37 -45,13,41 -43,9,39 -44,11,34 -48,11,34 -46,13,29 -51,12,26 -53,11,33 -51,10,20 -47,9,26 -69,5,39 -61,5,30 -59,7,27 -63,3,29 -49,7,27 -60,8,26 -59,9,17 -61,17,14 -64,25,10 -66,26,1 -73,27,-3 -59,26,-19 -67,23,-21 -65,17,-12 -57,11,-7 -59,10,-28 -62,10,-14 -61,6,-23 -51,-8,-3 -39,2,-22 -54,10,-36 -60,7,-34 -38,17,-44 -37,21,-37 -49,22,-33 -35,23,-35 -41,23,-24 -62,24,-32 -49,23,-35 -55,21,-19 -60,20,-25 -60,21,-34 -59,23,-22 -77,17,-8 -89,12,-20 -94,13,-14 -120,14,-18 -94,7,-28 --20,-1,-4 -39,0,39 -48,4,63 -8,8,45 -18,-1,48 -7,4,43 -6,9,50 -5,14,42 -9,15,42 -26,20,52 -43,29,55 -49,35,54 -23,33,52 -19,28,50 -29,30,25 --3,22,45 --8,18,51 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--45.24 --40.39 --35.23 --30.61 --27.42 --26.25 --27.13 --29.59 --32.83 --36.03 --38.52 --39.86 --39.81 --38.36 --35.79 --32.78 --30.20 --28.90 --29.16 --30.25 --30.64 --28.56 --22.83 --13.51 --2.07 -9.30 -18.61 -25.01 -29.46 -34.08 -41.15 -51.96 -66.10 -81.40 -95.09 -104.85 -109.59 -110.12 -108.14 -105.37 -102.90 -101.02 -99.18 -97.07 -94.68 -92.19 -90.59 -90.86 -93.99 -101.08 -113.25 -130.89 -154.02 -181.30 -209.64 -235.40 -254.44 -263.24 -260.07 -245.58 -221.88 -193.01 -163.19 -130.09 -92.36 -49.31 -1.27 --50.46 --100.56 --148.66 --194.35 --235.18 --266.52 --282.10 --276.89 --250.03 --205.55 --150.78 --93.75 --38.96 -12.84 -62.85 -112.16 -159.20 -200.06 -230.51 -248.29 -253.49 -248.51 -235.85 -216.80 -192.10 -162.53 -129.89 -96.97 -66.59 -40.02 -16.90 --4.60 --27.03 --52.74 --83.48 --120.10 --162.16 --207.42 --251.99 --290.46 --317.29 --328.32 --322.03 --300.10 --266.81 --227.48 --186.69 --147.44 --110.94 --77.71 --48.97 --27.35 --16.19 --17.72 --31.43 --53.41 --77.40 --96.79 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--13.09 --14.43 --15.91 --17.36 --18.47 --18.78 --17.80 --15.22 --11.01 --5.56 -0.45 -6.28 -11.28 -15.08 -17.55 -18.77 -18.88 -18.02 -16.34 -14.05 -11.41 -8.77 -6.45 -4.69 -3.54 -2.90 -2.61 -2.56 -2.76 -3.26 -4.09 -5.16 -6.20 -6.88 -6.94 -6.29 -5.06 -3.53 -1.96 -0.49 --0.85 --2.14 --3.47 --4.88 --6.31 --7.66 --8.85 --9.85 --10.71 --11.55 --12.53 --13.76 --15.28 --17.02 --18.73 --20.01 --20.35 --19.29 --16.55 --12.19 --6.57 --0.31 -5.85 -11.30 -15.57 -18.42 -19.82 -19.88 -18.83 -16.95 -14.50 -11.75 -8.95 -6.29 -3.98 -2.18 -1.03 -0.56 -0.72 -1.38 -2.31 -3.29 -4.09 -4.53 -4.51 -4.04 -3.21 -2.24 -1.30 -0.51 --0.16 --0.81 --1.56 --2.49 --3.58 --4.80 --6.12 --7.52 --9.00 --10.50 --11.94 --13.30 --14.57 --15.81 --16.99 --17.95 --18.34 --17.73 --15.77 --12.39 --7.80 --2.45 -3.11 -8.36 -12.86 -16.28 -18.35 -18.94 -18.11 -16.10 -13.33 -10.30 -7.46 -5.11 -3.39 -2.29 -1.76 -1.74 -2.17 -2.98 -4.01 -5.07 -5.95 -6.52 -6.76 -6.70 -6.39 -5.82 -4.94 -3.75 -2.27 -0.61 --1.06 --2.66 --4.12 --5.47 --6.74 --7.99 --9.31 --10.78 --12.50 --14.39 --16.22 --17.51 --17.73 --16.40 --13.37 --8.81 --3.22 -2.76 -8.48 -13.41 -17.19 -19.63 -20.68 -20.44 -19.10 -16.95 -14.32 -11.52 -8.82 -6.41 -4.42 -2.93 -1.97 -1.56 -1.61 -1.98 -2.45 -2.78 -2.76 -2.35 -1.63 -0.77 --0.07 --0.84 --1.56 --2.31 --3.14 --4.04 --4.99 --6.00 --7.10 --8.34 --9.72 --11.18 --12.64 --14.08 --15.49 --16.91 --18.26 --19.29 --19.56 --18.62 --16.15 --12.10 --6.75 --0.66 -5.50 -11.11 -15.68 -18.90 -20.61 -20.84 -19.76 -17.65 -14.90 -11.91 -9.01 -6.43 -4.35 -2.84 -1.93 -1.59 -1.73 -2.17 -2.74 -3.27 -3.68 -3.92 -3.98 -3.85 -3.48 -2.84 -1.92 -0.70 --0.76 --2.39 --4.12 --5.82 --7.36 --8.66 --9.72 --10.60 --11.45 --12.38 --13.49 --14.79 --16.27 --17.85 --19.36 --20.46 --20.69 --19.56 --16.76 --12.30 --6.59 --0.32 -5.71 -10.84 -14.66 -17.05 -18.07 -17.91 -16.76 -14.83 -12.39 -9.70 -7.07 -4.77 -2.99 -1.81 -1.23 -1.19 -1.59 -2.30 -3.22 -4.25 -5.26 -6.16 -6.79 -7.02 -6.73 -5.85 -4.44 -2.63 -0.63 --1.37 --3.21 --4.83 --6.21 --7.40 --8.43 --9.33 --10.10 --10.74 --11.31 --11.93 --12.74 --13.84 --15.18 --16.45 --17.13 --16.65 --14.59 --10.85 --5.74 -0.12 -6.03 -11.38 -15.79 -19.04 -21.06 -21.82 -21.37 -19.85 -17.48 -14.64 -11.67 -8.89 -6.53 -4.70 -3.38 -2.58 -2.24 -2.29 -2.66 -3.23 -3.87 -4.45 -4.86 -5.04 -4.94 -4.53 -3.79 -2.69 -1.19 --0.64 --2.67 --4.71 --6.55 --8.04 --9.12 --9.87 --10.51 --11.26 --12.33 --13.84 --15.76 --17.89 --19.89 --21.28 --21.57 --20.36 --17.46 --13.03 --7.47 --1.41 -4.47 -9.55 -13.39 -15.80 -16.79 -16.56 -15.38 -13.57 -11.50 -9.48 -7.76 -6.51 -5.78 -5.53 -5.70 -6.23 -7.06 -8.11 -9.26 -10.32 -11.08 -11.39 -11.20 -10.56 -9.58 -8.31 -6.75 -4.88 -2.75 -0.54 --1.46 --3.02 --4.03 --4.62 --5.07 --5.72 --6.84 --8.52 --10.71 --13.17 --15.59 --17.59 --18.78 --18.86 --17.63 --15.16 --11.71 --7.74 --3.75 --0.14 -2.83 -5.09 -6.70 -7.75 -8.35 -8.56 -8.41 -7.93 -7.12 -6.05 -4.77 -3.40 -2.07 -0.91 --0.02 --0.71 --1.24 --1.67 --2.04 --2.37 --2.64 --2.91 --3.26 --3.75 --4.38 --5.04 --5.55 --5.73 --5.53 --5.03 --4.38 --3.77 --3.33 --3.11 --3.12 --3.41 --4.06 --5.16 --6.70 --8.55 --10.43 --11.92 --12.56 --11.99 --10.05 --6.83 --2.73 -1.74 -5.99 -9.55 -12.10 -13.47 -13.64 -12.66 -10.66 -7.89 -4.67 -1.39 --1.50 --3.66 --4.89 --5.20 --4.77 --3.86 --2.76 --1.72 --0.87 --0.25 -0.21 -0.61 -1.01 -1.46 -1.88 -2.22 -2.39 -2.41 -2.28 -2.04 -1.71 -1.22 -0.48 --0.61 --2.12 --4.06 --6.37 --8.90 --11.35 --13.25 --14.02 --13.16 --10.39 --5.88 --0.20 -5.81 -11.27 -15.52 -18.21 -19.35 -19.19 -18.04 -16.23 -13.99 -11.54 -9.06 -6.78 -4.90 -3.55 -2.78 -2.55 -2.73 -3.19 -3.78 -4.34 -4.72 -4.76 -4.38 -3.62 -2.63 -1.58 -0.60 --0.33 --1.32 --2.47 --3.79 --5.16 --6.35 --7.20 --7.70 --8.06 --8.59 --9.64 --11.43 --13.98 --17.12 --20.43 --23.36 --25.28 --25.61 --23.96 --20.30 --14.95 --8.52 --1.75 -4.68 -10.27 -14.75 -18.01 -20.05 -20.94 -20.83 -19.93 -18.53 -16.90 -15.26 -13.76 -12.49 -11.50 -10.85 -10.53 -10.52 -10.64 -10.71 -10.50 -9.86 -8.76 -7.23 -5.36 -3.24 -1.00 --1.24 --3.31 --5.13 --6.67 --8.05 --9.36 --10.66 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--4.60 --5.45 --6.42 --7.48 --8.55 --9.53 --10.32 --10.89 --11.29 --11.70 --12.35 --13.40 --14.90 --16.68 --18.39 --19.51 --19.54 --18.07 --14.99 --10.45 --4.96 -0.78 -6.03 -10.24 -13.12 -14.70 -15.20 -14.93 -14.15 -13.02 -11.69 -10.29 -8.98 -7.89 -7.13 -6.68 -6.47 -6.41 -6.45 -6.58 -6.83 -7.18 -7.59 -7.95 -8.15 -8.09 -7.73 -7.07 -6.21 -5.22 -4.19 -3.14 -2.10 -1.03 --0.04 --1.10 --2.12 --3.08 --3.99 --4.85 --5.67 --6.45 --7.19 --7.93 --8.71 --9.53 --10.37 --11.16 --11.84 --12.43 --13.06 --13.82 --14.71 --15.52 --15.80 --15.06 --12.95 --9.44 --4.88 -0.11 -4.82 -8.65 -11.25 -12.58 -12.80 -12.19 -11.04 -9.58 -7.96 -6.23 -4.46 -2.75 -1.26 -0.18 --0.35 --0.33 -0.08 -0.65 -1.17 -1.58 -1.92 -2.35 -2.92 -3.57 -4.11 -4.34 -4.16 -3.63 -2.94 -2.27 -1.71 -1.24 -0.74 -0.13 --0.57 --1.28 --1.89 --2.42 --2.93 --3.56 --4.37 --5.34 --6.36 --7.40 --8.49 --9.79 --11.39 --13.22 --14.89 --15.85 --15.54 --13.67 --10.33 --5.99 --1.29 -3.19 -7.04 -10.10 -12.32 -13.72 -14.30 -14.11 -13.22 -11.84 -10.20 -8.54 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--1.16 --1.31 --1.59 --1.94 --2.34 --2.71 --2.94 --2.88 --2.39 --1.52 --0.56 --0.00 --0.30 --1.68 --3.97 --6.62 --8.90 --10.15 --9.98 --8.38 --5.65 --2.27 -1.31 -4.71 -7.72 -10.20 -12.07 -13.21 -13.54 -13.02 -11.80 -10.19 -8.54 -7.12 -5.98 -4.95 -3.78 -2.29 -0.50 --1.39 --3.06 --4.28 --4.98 --5.30 --5.52 --5.91 --6.62 --7.70 --9.06 --10.55 --11.97 --13.14 --13.86 --14.02 --13.66 --12.98 --12.28 --11.88 --11.89 --12.12 --12.07 --11.19 --9.08 --5.69 --1.40 -3.16 -7.38 -10.81 -13.32 -14.96 -15.89 -16.21 -15.97 -15.15 -13.79 -12.04 -10.19 -8.52 -7.27 -6.46 -5.97 -5.63 -5.30 -4.93 -4.57 -4.28 -4.07 -3.85 -3.50 -2.92 -2.06 -0.92 --0.47 --2.06 --3.74 --5.42 --7.00 --8.42 --9.74 --11.05 --12.41 --13.74 --14.74 --14.93 --13.85 --11.23 --7.18 --2.21 -2.92 -7.47 -10.97 -13.25 -14.45 -14.81 -14.55 -13.79 -12.63 -11.15 -9.47 -7.74 -6.05 -4.43 -2.83 -1.27 --0.20 --1.44 --2.33 --2.84 --3.03 --3.05 --3.04 --3.08 --3.25 --3.60 --4.19 --5.09 --6.30 --7.73 --9.24 --10.72 --12.14 --13.55 --15.04 --16.62 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--5.29 --6.86 --8.44 --10.15 --12.02 --13.99 --15.82 --17.19 --17.69 --16.93 --14.63 --10.77 --5.65 -0.15 -5.85 -10.74 -14.33 -16.46 -17.27 -17.04 -16.05 -14.46 -12.39 -9.93 -7.25 -4.64 -2.41 -0.80 --0.10 --0.43 --0.41 --0.33 --0.33 --0.44 --0.56 --0.59 --0.56 --0.62 --1.00 --1.88 --3.29 --5.05 --6.89 --8.49 --9.69 --10.42 --10.76 --10.85 --10.82 --10.81 --10.90 --11.17 --11.62 --12.08 --12.25 --11.70 --10.06 --7.18 --3.14 -1.68 -6.77 -11.59 -15.71 -18.84 -20.82 -21.69 -21.52 -20.49 -18.80 -16.65 -14.28 -11.95 -9.91 -8.38 -7.48 -7.12 -7.07 -7.02 -6.71 -6.06 -5.16 -4.15 -3.17 -2.23 -1.25 -0.13 --1.19 --2.68 --4.29 --5.92 --7.54 --9.08 --10.47 --11.61 --12.45 --13.04 --13.58 --14.30 --15.40 --16.85 --18.27 --19.08 --18.63 --16.49 --12.66 --7.55 --1.86 -3.68 -8.51 -12.31 -15.02 -16.69 -17.41 -17.26 -16.33 -14.78 -12.80 -10.67 -8.66 -6.99 -5.77 -5.01 -4.61 -4.44 -4.36 -4.29 -4.17 -3.99 -3.75 -3.41 -2.95 -2.29 -1.38 -0.18 --1.32 --3.09 --5.00 --6.89 --8.54 --9.80 --10.63 --11.15 --11.59 --12.22 --13.27 --14.75 --16.50 --18.15 --19.28 --19.46 --18.36 --15.80 --11.84 --6.82 --1.31 -4.00 -8.50 -11.79 -13.81 -14.73 -14.89 -14.57 -13.93 -13.05 -11.94 -10.69 -9.39 -8.16 -7.06 -6.11 -5.32 -4.73 -4.34 -4.19 -4.23 -4.36 -4.43 -4.29 -3.79 -2.88 -1.59 -0.00 --1.76 --3.58 --5.37 --7.07 --8.60 --9.89 --10.90 --11.64 --12.22 --12.82 --13.60 --14.66 --15.94 --17.26 --18.29 --18.62 --17.88 --15.82 --12.42 --7.94 --2.86 -2.23 -6.83 -10.63 -13.50 -15.43 -16.44 -16.54 -15.81 -14.39 -12.57 -10.67 -8.95 -7.55 -6.49 -5.73 -5.19 -4.85 -4.65 -4.51 -4.32 -4.00 -3.55 -3.04 -2.54 -2.07 -1.55 -0.87 --0.06 --1.20 --2.45 --3.67 --4.77 --5.76 --6.71 --7.66 --8.64 --9.67 --10.74 --11.90 --13.15 --14.42 --15.49 --16.02 --15.65 --14.11 --11.34 --7.49 --2.90 -2.01 -6.82 -11.14 -14.65 -17.14 -18.49 -18.75 -18.08 -16.74 -15.01 -13.15 -11.35 -9.74 -8.42 -7.44 -6.78 -6.35 -6.00 -5.60 -5.08 -4.55 -4.14 -3.97 -4.01 -4.08 -3.92 -3.25 -1.98 -0.22 --1.78 --3.74 --5.50 --7.05 --8.47 --9.91 --11.47 --13.18 --15.07 --17.08 --19.06 --20.71 --21.57 --21.17 --19.17 --15.60 --10.93 --5.89 --1.25 -2.50 -5.22 -7.10 -8.41 -9.37 -10.10 -10.71 -11.24 -11.69 -11.97 -11.87 -11.11 -9.60 -7.52 -5.33 -3.56 -2.62 -2.50 -2.93 -3.45 -3.72 -3.57 -3.03 -2.22 -1.22 -0.09 --1.15 --2.42 --3.65 --4.74 --5.66 --6.45 --7.22 --8.03 --8.96 --10.02 --11.20 --12.52 --13.92 --15.29 --16.38 --16.87 --16.43 --14.79 --11.92 --8.00 --3.44 -1.26 -5.60 -9.20 -11.85 -13.47 -14.14 -13.98 -13.15 -11.85 -10.28 -8.67 -7.22 -6.08 -5.29 -4.75 -4.27 -3.68 -2.94 -2.16 -1.59 -1.47 -1.87 -2.66 -3.50 -3.99 -3.84 -2.96 -1.52 --0.17 --1.75 --2.96 --3.76 --4.28 --4.77 --5.48 --6.56 --8.06 --9.86 --11.77 --13.55 --14.90 --15.48 --14.96 --13.11 --9.91 --5.57 --0.57 -4.52 -9.15 -12.93 -15.67 -17.34 -18.00 -17.71 -16.53 -14.58 -12.04 -9.18 -6.28 -3.60 -1.26 --0.75 --2.52 --4.10 --5.49 --6.57 --7.15 --7.11 --6.46 --5.43 --4.40 --3.71 --3.58 --3.97 --4.62 --5.22 --5.52 --5.50 --5.30 --5.17 --5.28 --5.74 --6.53 --7.59 --8.84 --10.16 --11.32 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--19.41 --13.01 --5.48 -2.31 -9.48 -15.37 -19.64 -22.24 -23.31 -23.08 -21.79 -19.66 -16.91 -13.88 -10.93 -8.43 -6.70 -5.82 -5.70 -6.01 -6.41 -6.58 -6.41 -5.94 -5.29 -4.58 -3.83 -3.01 -2.10 -1.08 --0.01 --1.19 --2.52 --4.13 --6.12 --8.47 --11.04 --13.57 --15.79 --17.55 --18.83 --19.79 --20.67 --21.70 --22.95 --24.27 --25.25 --25.27 --23.75 --20.32 --15.01 --8.25 --0.78 -6.58 -13.10 -18.29 -21.88 -23.80 -24.12 -23.02 -20.79 -17.83 -14.57 -11.46 -8.83 -6.91 -5.83 -5.58 -6.03 -6.95 -8.03 -8.94 -9.45 -9.41 -8.83 -7.84 -6.58 -5.15 -3.61 -1.93 -0.10 --1.85 --3.87 --5.81 --7.58 --9.12 --10.47 --11.76 --13.15 --14.77 --16.64 --18.64 --20.52 --21.87 --22.21 --21.07 --18.14 --13.39 --7.16 --0.08 -7.03 -13.37 -18.31 -21.50 -22.84 -22.45 -20.60 -17.68 -14.18 -10.61 -7.43 -4.97 -3.38 -2.65 -2.68 -3.31 -4.31 -5.44 -6.42 -7.01 -7.07 -6.60 -5.70 -4.51 -3.14 -1.65 -0.03 --1.77 --3.76 --5.94 --8.24 --10.55 --12.77 --14.78 --16.52 --18.03 --19.39 --20.76 --22.29 --23.95 --25.55 --26.65 --26.65 --25.04 --21.49 --16.09 --9.32 --1.91 -5.32 -11.69 -16.75 -20.29 -22.29 -22.85 -22.14 -20.40 -17.95 -15.15 -12.37 -9.90 -7.94 -6.58 -5.82 -5.61 -5.84 -6.35 -6.97 -7.44 -7.55 -7.18 -6.33 -5.10 -3.68 -2.22 -0.75 --0.77 --2.41 --4.14 --5.83 --7.30 --8.43 --9.23 --9.87 --10.58 --11.55 --12.87 --14.49 --16.29 --18.02 --19.30 --19.68 --18.65 --15.86 --11.19 --4.95 -2.25 -9.61 -16.30 -21.70 -25.38 -27.20 -27.20 -25.60 -22.74 -19.03 -14.92 -10.88 -7.31 -4.59 -2.93 -2.38 -2.75 -3.72 -4.88 -5.87 -6.49 -6.67 -6.51 -6.10 -5.53 -4.84 -3.97 -2.87 -1.47 --0.21 --2.15 --4.21 --6.26 --8.18 --9.90 --11.45 --12.91 --14.42 --16.09 --17.93 --19.87 --21.64 --22.82 --22.91 --21.45 --18.16 --13.07 --6.60 -0.55 -7.56 -13.68 -18.38 -21.41 -22.76 -22.58 -21.11 -18.64 -15.50 -12.11 -8.91 -6.32 -4.62 -3.95 -4.23 -5.19 -6.51 -7.82 -8.82 -9.28 -9.08 -8.19 -6.72 -4.87 -2.89 -1.01 --0.68 --2.16 --3.56 --4.97 --6.49 --8.08 --9.69 --11.25 --12.70 --14.05 --15.32 --16.54 --17.76 --19.05 --20.49 --22.08 --23.64 --24.75 --24.82 --23.25 --19.69 --14.21 --7.33 -0.12 -7.23 -13.28 -17.82 -20.71 -22.01 -21.86 -20.49 -18.17 -15.24 -12.14 -9.31 -7.15 -5.90 -5.56 -5.93 -6.64 -7.32 -7.71 -7.75 -7.52 -7.16 -6.76 -6.33 -5.80 -5.11 -4.27 -3.31 -2.23 -0.96 --0.60 --2.45 --4.50 --6.50 --8.26 --9.64 --10.72 --11.73 --12.92 --14.49 --16.42 --18.50 --20.38 --21.55 --21.50 --19.81 --16.25 --10.88 --4.14 -3.26 -10.43 -16.56 -21.02 -23.56 -24.24 -23.34 -21.26 -18.38 -15.11 -11.77 -8.70 -6.17 -4.37 -3.32 -2.93 -3.05 -3.51 -4.16 -4.85 -5.40 -5.64 -5.41 -4.71 -3.61 -2.27 -0.83 --0.63 --2.10 --3.59 --5.12 --6.63 --8.06 --9.39 --10.63 --11.83 --13.06 --14.32 --15.53 --16.68 --17.78 --18.93 --20.27 --21.85 --23.47 --24.68 --24.82 --23.25 --19.58 --13.89 --6.73 -1.04 -8.47 -14.75 -19.35 -22.09 -23.03 -22.42 -20.65 -18.13 -15.27 -12.43 -9.93 -7.97 -6.69 -6.10 -6.07 -6.42 -6.92 -7.38 -7.71 -7.95 -8.16 -8.37 -8.51 -8.40 -7.88 -6.84 -5.33 -3.52 -1.61 --0.25 --2.02 --3.75 --5.50 --7.29 --9.08 --10.77 --12.28 --13.53 --14.57 --15.48 --16.41 --17.51 --18.91 --20.66 --22.61 --24.44 --25.61 --25.53 --23.72 --19.93 --14.35 --7.49 --0.12 -6.93 -12.97 -17.56 -20.50 -21.84 -21.73 -20.46 -18.35 -15.79 -13.13 -10.65 -8.50 -6.74 -5.37 -4.44 -4.00 -4.13 -4.80 -5.81 -6.87 -7.65 -7.93 -7.65 -6.94 -5.98 -4.91 -3.80 -2.62 -1.35 --0.04 --1.49 --2.96 --4.42 --5.90 --7.42 --8.99 --10.61 --12.23 --13.85 --15.53 --17.36 --19.35 --21.33 --22.82 --23.15 --21.65 --17.93 --12.08 --4.75 -3.08 -10.37 -16.33 -20.54 -22.96 -23.74 -23.14 -21.41 -18.84 -15.76 -12.57 -9.65 -7.26 -5.52 -4.39 -3.78 -3.60 -3.76 -4.17 -4.67 -5.08 -5.23 -5.03 -4.51 -3.72 -2.76 -1.65 -0.39 --1.03 --2.56 --4.15 --5.74 --7.29 --8.80 --10.27 --11.63 --12.84 --13.86 --14.75 --15.67 --16.87 --18.49 --20.51 --22.61 --24.22 --24.63 --23.20 --19.55 --13.75 --6.31 -1.95 -10.09 -17.22 -22.68 -26.14 -27.59 -27.28 -25.59 -22.96 -19.81 -16.46 -13.18 -10.19 -7.65 -5.71 -4.40 -3.69 -3.43 -3.38 -3.36 -3.23 -2.96 -2.57 -2.15 -1.76 -1.40 -1.00 -0.42 --0.52 --1.92 --3.74 --5.85 --7.97 --9.84 --11.32 --12.39 --13.22 --14.07 --15.22 --16.86 --19.01 --21.48 --23.84 --25.47 --25.70 --23.97 --20.07 --14.21 --7.03 -0.62 -7.84 -13.91 -18.41 -21.21 -22.36 -22.02 -20.41 -17.77 -14.44 -10.80 -7.26 -4.24 -2.05 -0.81 -0.43 -0.66 -1.16 -1.64 -1.97 -2.17 -2.36 -2.65 -3.00 -3.23 -3.09 -2.40 -1.14 --0.50 --2.26 --3.88 --5.25 --6.41 --7.51 --8.70 --10.09 --11.70 --13.49 --15.36 --17.17 --18.67 --19.44 --18.97 --16.82 --12.80 --7.11 --0.35 -6.64 -12.99 -18.08 -21.57 -23.42 -23.73 -22.69 -20.47 -17.30 -13.49 -9.44 -5.66 -2.59 -0.52 --0.50 --0.63 --0.15 -0.64 -1.50 -2.29 -2.88 -3.17 -3.03 -2.39 -1.23 --0.35 --2.19 --4.09 --5.94 --7.66 --9.29 --10.89 --12.54 --14.30 --16.21 --18.23 --20.30 --22.24 --23.79 --24.51 --23.93 --21.58 --17.25 --11.07 --3.60 -4.31 -11.72 -17.86 -22.27 -24.82 -25.65 -24.94 -22.95 -19.94 -16.24 -12.33 -8.74 -5.90 -4.08 -3.21 -3.06 -3.26 -3.54 -3.73 -3.79 -3.72 -3.51 -3.13 -2.52 -1.65 -0.52 --0.85 --2.46 --4.27 --6.23 --8.21 --10.05 --11.65 --13.00 --14.27 --15.75 --17.71 --20.19 --22.96 --25.44 --26.83 --26.38 --23.63 --18.57 --11.66 --3.69 -4.48 -12.10 -18.59 -23.59 -26.83 -28.19 -27.66 -25.43 -21.94 -17.76 -13.59 -10.01 -7.45 -6.07 -5.85 -6.58 -7.90 -9.37 -10.53 -11.06 -10.83 -9.94 -8.60 -7.05 -5.45 -3.78 -2.00 -0.03 --2.12 --4.37 --6.60 --8.69 --10.61 --12.38 --14.05 --15.68 --17.31 --18.89 --20.30 --21.31 --21.60 --20.87 --18.83 --15.38 --10.60 --4.82 -1.53 -7.93 -13.95 -19.24 -23.52 -26.57 -28.13 -28.08 -26.43 -23.39 -19.40 -15.04 -10.85 -7.34 -4.79 -3.35 -2.93 -3.32 -4.19 -5.17 -5.95 -6.34 -6.27 -5.70 -4.64 -3.10 -1.13 --1.09 --3.34 --5.38 --7.07 --8.45 --9.68 --10.93 --12.29 --13.72 --15.10 --16.34 --17.50 --18.76 --20.31 --22.23 --24.31 --26.01 --26.57 --25.24 --21.54 --15.48 --7.58 -1.19 -9.73 -17.08 -22.65 -26.24 -27.99 -28.18 -27.06 -24.87 -21.86 -18.36 -14.81 -11.71 -9.40 -8.05 -7.56 -7.68 -8.14 -8.73 -9.36 -9.95 -10.41 -10.57 -10.27 -9.40 -8.00 -6.23 -4.27 -2.26 -0.25 --1.78 --3.88 --6.09 --8.38 --10.67 --12.81 --14.65 --16.03 --16.83 --17.05 --16.80 --16.39 --16.20 --16.62 --17.96 --20.23 --23.17 --26.14 --28.28 --28.68 --26.68 --22.02 --15.01 --6.41 -2.73 -11.34 -18.55 -23.79 -26.83 -27.72 -26.74 -24.32 -20.97 -17.23 -13.60 -10.49 -8.17 -6.79 -6.33 -6.67 -7.53 -8.61 -9.59 -10.22 -10.36 -9.97 -9.09 -7.82 -6.28 -4.58 -2.83 -1.09 --0.58 --2.15 --3.67 --5.20 --6.84 --8.62 --10.48 --12.29 --13.82 --14.92 --15.53 --15.75 --15.77 --15.89 --16.36 --17.31 --18.66 --20.07 --21.00 --20.80 --18.93 --15.09 --9.35 --2.19 -5.64 -13.25 -19.81 -24.66 -27.40 -28.00 -26.75 -24.13 -20.72 -17.04 -13.50 -10.43 -8.07 -6.61 -6.11 -6.47 -7.38 -8.47 -9.43 -10.06 -10.30 -10.17 -9.64 -8.69 -7.27 -5.46 -3.46 -1.50 --0.31 --1.95 --3.56 --5.25 --7.04 --8.80 --10.36 --11.58 --12.50 --13.33 --14.29 --15.57 --17.18 --19.01 --20.79 --22.17 --22.78 --22.22 --20.11 --16.23 --10.58 --3.52 -4.30 -11.98 -18.58 -23.34 -25.83 -26.05 -24.38 -21.44 -17.92 -14.41 -11.27 -8.62 -6.41 -4.51 -2.84 -1.39 -0.17 --0.77 --1.41 --1.72 --1.59 --0.93 -0.31 -2.03 -3.92 -5.53 -6.43 -6.34 -5.25 -3.35 -0.91 --1.90 --5.02 --8.46 --12.24 --16.25 --20.23 --23.87 --26.85 --29.03 --30.49 --31.54 --32.66 --34.29 --36.56 --39.13 --41.09 --41.27 --38.60 --32.57 --23.45 --12.31 --0.63 -10.12 -18.86 -25.14 -29.02 -30.85 -31.09 -30.09 -28.11 -25.36 -22.10 -18.66 -15.42 -12.68 -10.66 -9.45 -8.99 -9.20 -9.92 -11.01 -12.25 -13.36 -14.00 -13.85 -12.71 -10.56 -7.64 -4.33 -1.12 --1.59 --3.60 --4.94 --5.79 --6.37 --6.84 --7.25 --7.63 --8.02 --8.57 --9.52 --11.07 --13.30 --16.01 --18.79 --21.11 --22.35 --21.99 --19.65 --15.26 --9.04 --1.58 -6.28 -13.60 -19.62 -23.83 -26.10 -26.53 -25.41 -23.08 -19.91 -16.30 -12.67 -9.41 -6.78 -4.88 -3.69 -3.10 -2.99 -3.28 -3.83 -4.45 -4.88 -4.90 -4.41 -3.46 -2.30 -1.17 -0.25 --0.44 --1.01 --1.67 --2.61 --3.94 --5.62 --7.50 --9.34 --10.92 --12.06 --12.74 --13.08 --13.30 --13.67 --14.40 --15.61 --17.23 --18.94 --20.21 --20.39 --18.91 --15.47 --10.21 --3.66 -3.37 -10.07 -15.78 -20.15 -23.05 -24.50 -24.61 -23.51 -21.39 -18.52 -15.23 -11.89 -8.84 -6.38 -4.68 -3.77 -3.52 -3.67 -3.93 -4.08 -4.03 -3.89 -3.81 -3.92 -4.18 -4.37 -4.21 -3.48 -2.13 -0.31 --1.72 --3.64 --5.31 --6.68 --7.88 --9.05 --10.29 --11.62 --12.94 --14.14 --15.18 --16.11 --17.07 --18.28 --19.89 --21.91 --24.15 --26.17 --27.44 --27.37 --25.54 --21.78 --16.24 --9.37 --1.81 -5.67 -12.38 -17.72 -21.33 -23.15 -23.40 -22.46 -20.77 -18.71 -16.52 -14.35 -12.29 -10.46 -9.02 -8.08 -7.67 -7.68 -7.89 -8.10 -8.14 -8.01 -7.74 -7.43 -7.09 -6.65 -5.99 -4.94 -3.39 -1.38 --0.95 --3.37 --5.63 --7.59 --9.21 --10.52 --11.65 --12.70 --13.76 --14.96 --16.41 --18.19 --20.22 --22.15 --23.38 --23.21 --21.04 --16.65 --10.34 --2.82 -4.97 -12.12 -17.97 -22.16 -24.58 -25.31 -24.56 -22.62 -19.90 -16.83 -13.83 -11.22 -9.16 -7.67 -6.65 -6.00 -5.62 -5.44 -5.39 -5.40 -5.38 -5.21 -4.79 -4.07 -3.09 -1.94 -0.72 --0.52 --1.83 --3.29 --4.97 --6.86 --8.82 --10.69 --12.33 --13.73 --14.94 --16.09 --17.27 --18.56 --19.99 --21.57 --23.25 --24.83 --25.91 --25.88 --24.08 --20.10 --13.94 --6.16 -2.31 -10.39 -17.21 -22.25 -25.38 -26.73 -26.53 -25.07 -22.65 -19.68 -16.63 -13.91 -11.79 -10.26 -9.11 -8.10 -7.08 -6.11 -5.35 -4.97 -5.00 -5.30 -5.63 -5.75 -5.49 -4.81 -3.73 -2.29 -0.57 --1.38 --3.46 --5.55 --7.54 --9.35 --10.93 --12.32 --13.60 --14.89 --16.31 --17.97 --19.93 --22.09 --24.25 --26.03 --26.94 --26.47 --24.26 --20.14 --14.27 --7.12 -0.61 -8.11 -14.67 -19.74 -23.06 -24.60 -24.53 -23.14 -20.77 -17.81 -14.64 -11.64 -9.13 -7.32 -6.27 -5.94 -6.15 -6.71 -7.43 -8.17 -8.82 -9.32 -9.55 -9.40 -8.74 -7.48 -5.66 -3.40 -0.88 --1.70 --4.17 --6.47 --8.56 --10.48 --12.28 --14.01 --15.78 --17.71 --19.90 --22.28 --24.59 --26.29 --26.69 --25.16 --21.37 --15.46 --8.04 --0.04 -7.60 -14.13 -19.09 -22.33 -23.87 -23.85 -22.47 -19.99 -16.76 -13.19 -9.71 -6.70 -4.43 -3.08 -2.63 -2.93 -3.62 -4.33 -4.75 -4.75 -4.39 -3.86 -3.31 -2.74 -2.04 -1.07 --0.24 --1.83 --3.57 --5.32 --7.03 --8.70 --10.36 --12.02 --13.69 --15.41 --17.29 --19.46 --21.91 --24.35 --26.25 --26.89 --25.65 --22.23 --16.79 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-252.57 -226.62 -199.16 -168.98 -134.39 -94.71 -50.77 -4.46 --42.14 --87.66 --131.69 --174.55 --216.99 --259.94 --304.58 --352.13 --403.61 --459.10 --516.77 --572.20 --618.75 --648.57 --654.56 --632.73 --583.98 --514.89 --436.75 --363.18 --306.88 --276.43 --274.23 --295.98 --332.18 --370.83 --400.73 --414.15 --408.32 --385.18 --349.93 --308.75 --266.81 --227.22 --190.79 --156.67 --123.24 --89.01 --52.85 --14.30 -26.57 -69.00 -111.66 -153.21 -193.14 -232.62 -274.19 -320.55 -372.11 -425.15 -471.79 -502.10 -507.51 -483.60 -431.73 -358.64 -274.58 -191.29 -120.08 -70.16 -47.07 -51.81 -80.68 -126.44 -179.98 -232.31 -275.64 -303.74 -312.03 -297.80 -260.81 -203.23 -129.32 -44.76 --43.95 --129.76 --205.26 --263.23 --298.40 --308.77 --296.29 --266.02 --224.14 --176.04 --125.39 --74.97 --27.57 -13.72 -46.29 -69.47 -85.18 -97.26 -109.48 -123.62 -138.67 -151.67 -159.20 -159.29 -152.23 -140.22 -126.26 -112.74 -100.66 -89.39 -77.29 -62.56 -44.11 -21.85 --3.27 --29.55 --54.90 --77.25 --94.90 --106.78 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-2.47 -1.28 -0.24 --0.75 --1.88 --3.26 --4.85 --6.48 --7.95 --9.17 --10.21 --11.26 --12.51 --14.02 --15.77 --17.63 --19.52 --21.36 --23.05 --24.38 --25.02 --24.63 --23.00 --20.12 --16.28 --11.84 --7.25 --2.84 -1.13 -4.48 -7.05 -8.73 -9.50 -9.45 -8.78 -7.74 -6.60 -5.57 -4.76 -4.22 -3.90 -3.71 -3.56 -3.38 -3.21 -3.09 -3.12 -3.32 -3.67 -4.08 -4.40 -4.50 -4.25 -3.59 -2.54 -1.18 --0.34 --1.92 --3.52 --5.14 --6.86 --8.71 --10.70 --12.79 --14.95 --17.14 --19.22 --20.96 --21.98 --21.83 --20.21 --17.05 --12.63 --7.49 --2.26 -2.50 -6.42 -9.36 -11.36 -12.51 -12.96 -12.82 -12.25 -11.37 -10.34 -9.29 -8.29 -7.36 -6.49 -5.65 -4.81 -3.98 -3.19 -2.51 -1.98 -1.64 -1.48 -1.39 -1.25 -0.90 -0.25 --0.71 --1.92 --3.23 --4.57 --5.91 --7.30 --8.87 --10.74 --12.97 --15.50 --18.12 --20.48 --22.11 --22.55 --21.40 --18.49 --13.95 --8.18 --1.83 -4.39 -9.84 -14.08 -16.95 -18.54 -19.13 -19.05 -18.58 -17.90 -17.08 -16.12 -15.02 -13.84 -12.68 -11.67 -10.90 -10.36 -9.99 -9.64 -9.19 -8.56 -7.75 -6.85 -5.94 -5.08 -4.24 -3.36 -2.30 -1.00 --0.56 --2.35 --4.32 --6.43 --8.63 --10.86 --13.02 --14.99 --16.71 --18.22 --19.72 --21.45 --23.60 --26.11 --28.57 --30.30 --30.57 --28.78 --24.76 --18.78 --11.47 --3.69 -3.76 -10.26 -15.42 -19.16 -21.55 -22.78 -23.07 -22.65 -21.74 -20.54 -19.22 -17.91 -16.73 -15.73 -14.94 -14.38 -14.08 -14.04 -14.28 -14.71 -15.20 -15.56 -15.64 -15.35 -14.73 -13.87 -12.89 -11.87 -10.82 -9.68 -8.40 -6.95 -5.37 -3.73 -2.09 -0.45 --1.22 --2.99 --4.88 --6.82 --8.71 --10.46 --12.06 --13.56 --15.04 --16.54 --18.08 --19.61 --21.15 --22.79 --24.72 --27.06 --29.77 --32.51 --34.68 --35.54 --34.43 --31.01 --25.36 --17.99 --9.69 --1.33 -6.33 -12.71 -17.53 -20.68 -22.27 -22.52 -21.71 -20.21 -18.37 -16.52 -14.88 -13.57 -12.60 -11.88 -11.31 -10.83 -10.49 -10.41 -10.74 -11.53 -12.68 -13.93 -14.98 -15.54 -15.49 -14.85 -13.76 -12.36 -10.73 -8.91 -6.94 -4.94 -3.03 -1.36 -0.01 --1.07 --2.02 --2.96 --4.00 --5.12 --6.27 --7.45 --8.75 --10.29 --12.17 --14.35 --16.64 --18.83 --20.76 --22.43 --23.97 --25.51 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--7.39 --1.22 -3.98 -7.93 -10.56 -11.96 -12.32 -11.90 -10.98 -9.85 -8.73 -7.77 -6.99 -6.36 -5.80 -5.27 -4.87 -4.73 -4.97 -5.60 -6.47 -7.32 -7.88 -7.94 -7.44 -6.48 -5.23 -3.84 -2.43 -1.06 --0.30 --1.73 --3.32 --5.16 --7.29 --9.68 --12.23 --14.79 --17.23 --19.52 --21.68 --23.83 --26.13 --28.69 --31.54 --34.49 --37.15 --38.90 --39.01 --36.90 --32.30 --25.43 --16.95 --7.83 -0.92 -8.52 -14.53 -18.83 -21.56 -22.99 -23.41 -23.11 -22.35 -21.34 -20.24 -19.18 -18.21 -17.39 -16.74 -16.28 -16.06 -16.11 -16.42 -16.95 -17.55 -18.02 -18.13 -17.72 -16.70 -15.09 -13.03 -10.75 -8.46 -6.34 -4.42 -2.61 -0.67 --1.67 --4.54 --7.87 --11.37 --14.66 --17.43 --19.58 --21.30 --22.97 --24.97 --27.61 --30.90 --34.59 --38.12 --40.72 --41.56 --39.98 --35.64 --28.68 --19.72 --9.69 -0.34 -9.43 -16.92 -22.48 -26.12 -28.05 -28.62 -28.23 -27.23 -25.90 -24.45 -22.98 -21.57 -20.25 -19.07 -18.05 -17.27 -16.80 -16.75 -17.13 -17.87 -18.77 -19.56 -19.97 -19.80 -18.98 -17.62 -15.89 -13.97 -11.95 -9.84 -7.54 -4.94 -1.96 --1.33 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--13.40 --16.64 --19.53 --22.12 --24.53 --26.91 --29.51 --32.55 --36.06 --39.72 --42.79 --44.23 --43.07 --38.73 --31.28 --21.48 --10.49 -0.46 -10.40 -18.78 -25.40 -30.27 -33.46 -35.06 -35.23 -34.22 -32.38 -30.19 -28.08 -26.37 -25.21 -24.60 -24.42 -24.52 -24.78 -25.15 -25.62 -26.15 -26.70 -27.19 -27.48 -27.39 -26.82 -25.78 -24.43 -23.04 -21.85 -21.00 -20.50 -20.25 -20.12 -20.07 -20.23 -20.80 -21.98 -23.68 -25.67 -27.54 -28.82 -29.07 -28.20 -26.53 -24.89 -24.29 -25.32 -28.02 -31.71 -34.99 -36.33 -34.82 -30.84 -25.97 -22.08 -19.95 -19.25 -18.51 -15.44 -7.86 --5.30 --23.49 --44.60 --65.75 --84.65 --98.95 --106.98 --108.76 --106.94 --106.64 --113.80 --132.66 --163.43 --201.56 --236.79 --256.64 --251.34 --217.57 --159.55 --86.64 --9.88 -66.03 -132.70 -182.02 -209.36 -214.77 -203.24 -179.50 -147.15 -110.14 -71.37 -31.67 --8.11 --47.03 --84.39 --119.70 --152.56 --181.68 --204.55 --217.94 --219.32 --206.48 --178.63 --137.47 --87.40 --34.84 -13.56 -52.56 -79.30 -93.54 -98.12 -97.52 -95.97 -95.77 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--10.62 --10.19 --8.79 --6.85 --5.04 --3.89 --3.63 --4.05 --4.70 --4.99 --4.36 --2.38 -1.14 -6.01 -11.61 -16.98 -21.10 -23.30 -23.52 -22.33 -20.58 -18.95 -17.59 -16.13 -13.92 -10.50 -5.96 -0.91 --3.70 --7.06 --8.74 --8.88 --8.01 --6.87 --6.10 --6.02 --6.63 --7.60 --8.53 --9.09 --9.17 --8.91 --8.57 --8.38 --8.43 --8.67 --9.00 --9.37 --9.83 --10.45 --11.21 --12.00 --12.61 --12.93 --12.97 --12.84 --12.62 --12.19 --11.23 --9.36 --6.33 --2.27 -2.39 -7.00 -11.02 -14.20 -16.57 -18.33 -19.63 -20.42 -20.52 -19.80 -18.33 -16.42 -14.50 -12.86 -11.56 -10.34 -8.89 -7.05 -4.96 -3.00 -1.57 -0.87 -0.78 -0.93 -0.93 -0.50 --0.37 --1.51 --2.73 --3.95 --5.21 --6.56 --8.02 --9.47 --10.73 --11.64 --12.21 --12.58 --12.98 --13.62 --14.60 --15.85 --17.13 --18.07 --18.20 --17.11 --14.61 --10.82 --6.17 --1.23 -3.46 -7.57 -10.99 -13.71 -15.65 -16.70 -16.71 -15.68 -13.87 -11.68 -9.54 -7.70 -6.19 -4.83 -3.47 -2.04 -0.60 --0.67 --1.64 --2.24 --2.49 --2.48 --2.29 --2.01 --1.74 --1.61 --1.70 --2.09 --2.73 --3.49 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--18.09 --15.88 --11.98 --6.83 --1.09 -4.45 -9.14 -12.57 -14.62 -15.40 -15.19 -14.35 -13.21 -12.04 -11.00 -10.11 -9.34 -8.60 -7.82 -6.97 -6.10 -5.30 -4.64 -4.11 -3.65 -3.19 -2.67 -2.08 -1.49 -0.95 -0.52 -0.14 --0.27 --0.81 --1.56 --2.54 --3.68 --4.92 --6.18 --7.41 --8.59 --9.71 --10.75 --11.77 --12.84 --14.05 --15.40 --16.70 --17.56 --17.40 --15.75 --12.44 --7.73 --2.29 -3.09 -7.75 -11.37 -13.89 -15.44 -16.12 -15.99 -15.06 -13.45 -11.33 -8.98 -6.63 -4.45 -2.50 -0.87 --0.36 --1.07 --1.24 --0.89 --0.20 -0.65 -1.44 -2.04 -2.39 -2.44 -2.23 -1.80 -1.20 -0.50 --0.24 --1.00 --1.79 --2.63 --3.52 --4.44 --5.38 --6.39 --7.52 --8.87 --10.48 --12.28 --14.04 --15.42 --15.96 --15.27 --13.16 --9.71 --5.30 --0.54 -3.99 -7.78 -10.56 -12.26 -12.96 -12.79 -11.95 -10.63 -9.01 -7.28 -5.57 -3.98 -2.61 -1.52 -0.79 -0.46 -0.51 -0.82 -1.26 -1.66 -1.96 -2.07 -1.95 -1.50 -0.66 --0.59 --2.14 --3.77 --5.25 --6.43 --7.31 --8.05 --8.88 --10.01 --11.51 --13.28 --15.00 --16.20 --16.37 --15.13 --12.33 --8.15 --3.04 -2.41 -7.61 -12.14 -15.71 -18.20 -19.56 -19.85 -19.20 -17.80 -15.96 -13.97 -12.10 -10.54 -9.34 -8.44 -7.74 -7.07 -6.31 -5.40 -4.34 -3.21 -2.14 -1.25 -0.61 -0.19 --0.14 --0.59 --1.32 --2.42 --3.85 --5.49 --7.17 --8.79 --10.32 --11.76 --13.14 --14.49 --15.83 --17.17 --18.51 --19.75 --20.61 --20.69 --19.54 --16.95 --13.01 --8.16 --3.05 -1.70 -5.65 -8.66 -10.77 -12.06 -12.61 -12.42 -11.55 -10.15 -8.45 -6.74 -5.23 -4.06 -3.25 -2.72 -2.41 -2.23 -2.14 -2.11 -2.09 -2.08 -2.07 -2.07 -2.05 -1.93 -1.57 -0.85 --0.23 --1.51 --2.79 --3.88 --4.76 --5.57 --6.53 --7.79 --9.34 --11.04 --12.70 --14.16 --15.31 --16.06 --16.22 --15.53 --13.77 --10.87 --6.97 --2.43 -2.30 -6.78 -10.71 -13.85 -16.14 -17.58 -18.25 -18.27 -17.76 -16.84 -15.59 -14.11 -12.52 -10.97 -9.61 -8.55 -7.79 -7.23 -6.71 -6.10 -5.30 -4.34 -3.28 -2.25 -1.31 -0.47 --0.36 --1.31 --2.50 --3.97 --5.65 --7.41 --9.05 --10.41 --11.43 --12.14 --12.66 --13.11 --13.64 --14.38 --15.42 --16.78 --18.28 --19.57 --20.12 --19.42 --17.15 --13.36 --8.44 --3.03 -2.21 -6.76 -10.34 -12.94 -14.65 -15.66 -16.15 -16.25 -16.00 -15.40 -14.41 -13.08 -11.51 -9.91 -8.49 -7.39 -6.61 -6.05 -5.59 -5.16 -4.75 -4.41 -4.18 -4.04 -3.89 -3.64 -3.21 -2.58 -1.73 -0.68 --0.54 --1.88 --3.27 --4.61 --5.83 --6.92 --7.91 --8.86 --9.87 --11.01 --12.33 --13.83 --15.47 --17.09 --18.42 --19.06 --18.59 --16.69 --13.28 --8.56 --3.01 -2.73 -8.05 -12.42 -15.55 -17.33 -17.86 -17.32 -16.04 -14.35 -12.56 -10.85 -9.31 -7.91 -6.60 -5.34 -4.16 -3.11 -2.25 -1.65 -1.35 -1.31 -1.47 -1.69 -1.77 -1.53 -0.90 --0.09 --1.21 --2.25 --3.06 --3.62 --4.09 --4.72 --5.66 --6.94 --8.46 --9.98 --11.32 --12.36 --13.14 --13.87 --14.82 --16.18 --17.91 --19.67 --20.91 --21.02 --19.55 --16.43 --11.91 --6.52 --0.88 -4.45 -9.08 -12.74 -15.33 -16.82 -17.30 -16.91 -15.82 -14.22 -12.26 -10.17 -8.15 -6.44 -5.18 -4.39 -3.95 -3.67 -3.39 -3.06 -2.75 -2.56 -2.55 -2.66 -2.75 -2.64 -2.25 -1.60 -0.79 --0.11 --1.09 --2.19 --3.45 --4.86 --6.29 --7.62 --8.82 --9.98 --11.31 --12.96 --14.89 --16.75 --18.00 --18.06 --16.55 --13.43 --9.01 --3.85 -1.44 -6.35 -10.55 -13.89 -16.32 -17.82 -18.37 -17.98 -16.75 -14.89 -12.71 -10.57 -8.72 -7.28 -6.27 -5.63 -5.30 -5.25 -5.42 -5.69 -5.91 -5.91 -5.63 -5.10 -4.42 -3.62 -2.67 -1.47 --0.06 --1.87 --3.80 --5.66 --7.30 --8.71 --9.98 --11.25 --12.61 --14.08 --15.64 --17.24 --18.73 --19.87 --20.30 --19.60 --17.42 --13.68 --8.56 --2.55 -3.73 -9.64 -14.67 -18.47 -20.92 -22.05 -22.06 -21.19 -19.70 -17.82 -15.68 -13.44 -11.23 -9.25 -7.69 -6.65 -6.14 -6.05 -6.18 -6.33 -6.31 -5.97 -5.23 -4.11 -2.73 -1.33 -0.10 --0.85 --1.61 --2.39 --3.40 --4.79 --6.54 --8.49 --10.40 --12.10 --13.49 --14.60 --15.51 --16.38 --17.34 --18.54 --20.00 --21.66 --23.21 --24.21 --24.09 --22.42 --18.97 --13.89 --7.71 --1.19 -4.85 -9.75 -13.16 -15.08 -15.73 -15.48 -14.63 -13.43 -11.98 -10.39 -8.73 -7.11 -5.68 -4.58 -3.90 -3.67 -3.83 -4.27 -4.81 -5.28 -5.52 -5.43 -4.93 -4.03 -2.76 -1.23 --0.46 --2.17 --3.77 --5.16 --6.27 --7.09 --7.69 --8.21 --8.84 --9.75 --11.02 --12.58 --14.20 --15.51 --16.05 --15.39 --13.26 --9.65 --4.79 -0.83 -6.59 -11.90 -16.27 -19.45 -21.36 -22.03 -21.60 -20.25 -18.21 -15.82 -13.41 -11.30 -9.69 -8.61 -7.95 -7.55 -7.26 -6.99 -6.72 -6.43 -6.07 -5.57 -4.88 -4.01 -3.01 -1.99 -1.01 -0.11 --0.77 --1.71 --2.81 --4.12 --5.62 --7.23 --8.83 --10.31 --11.59 --12.67 --13.60 --14.48 --15.44 --16.61 --18.02 --19.58 --20.96 --21.64 --21.04 --18.72 --14.56 --8.88 --2.37 -4.09 -9.71 -13.90 -16.45 -17.47 -17.33 -16.54 -15.53 -14.59 -13.81 -13.11 -12.39 -11.61 -10.87 -10.30 -9.95 -9.80 -9.69 -9.47 -9.08 -8.56 -8.01 -7.53 -7.08 -6.53 -5.76 -4.68 -3.37 -1.98 -0.70 --0.32 --1.03 --1.51 --1.92 --2.45 --3.24 --4.34 --5.71 --7.21 --8.67 --9.92 --10.84 --11.39 --11.68 --11.88 --12.26 --13.05 --14.40 --16.26 --18.36 --20.22 --21.27 --20.93 --18.89 --15.12 --10.01 --4.19 -1.57 -6.64 -10.62 -13.38 -15.04 -15.77 -15.74 -15.07 -13.86 -12.27 -10.57 -9.07 -7.99 -7.45 -7.36 -7.48 -7.57 -7.46 -7.15 -6.77 -6.49 -6.36 -6.32 -6.20 -5.83 -5.12 -4.10 -2.90 -1.66 -0.46 --0.68 --1.76 --2.83 --3.88 --4.89 --5.87 --6.87 --7.89 --8.95 --9.96 --10.79 --11.34 --11.56 --11.55 --11.48 --11.57 --12.01 --12.92 --14.23 --15.69 --16.85 --17.14 --16.07 --13.39 --9.28 --4.26 -0.89 -5.47 -9.07 -11.65 -13.43 -14.69 -15.61 -16.18 -16.28 -15.82 -14.82 -13.45 -11.93 -10.48 -9.26 -8.35 -7.77 -7.50 -7.45 -7.51 -7.55 -7.45 -7.16 -6.68 -5.99 -5.05 -3.79 -2.19 -0.34 --1.59 --3.29 --4.52 --5.12 --5.16 --4.89 --4.68 --4.83 --5.52 --6.70 --8.14 --9.57 --10.73 --11.55 --12.13 --12.66 --13.36 --14.37 --15.69 --17.18 --18.61 --19.63 --19.86 --18.92 --16.55 --12.71 --7.69 --2.02 -3.59 -8.51 -12.30 -14.79 -16.04 -16.28 -15.80 -14.89 -13.81 -12.76 -11.88 -11.21 -10.75 -10.40 -10.07 -9.73 -9.37 -9.05 -8.78 -8.57 -8.37 -8.07 -7.58 -6.86 -5.96 -4.95 -3.97 -3.08 -2.28 -1.55 -0.85 -0.15 --0.58 --1.36 --2.23 --3.17 --4.16 --5.16 --6.10 --6.95 --7.71 --8.43 --9.14 --9.84 --10.52 --11.13 --11.67 --12.21 --12.90 --13.87 --15.17 --16.69 --18.12 --18.99 --18.83 --17.30 --14.30 --10.03 --4.96 -0.30 -5.14 -9.06 -11.83 -13.42 -14.03 -13.92 -13.36 -12.52 -11.51 -10.39 -9.25 -8.16 -7.22 -6.45 -5.83 -5.32 -4.87 -4.48 -4.18 -4.01 -3.95 -3.94 -3.87 -3.60 -3.05 -2.22 -1.20 -0.14 --0.79 --1.50 --1.97 --2.28 --2.57 --2.95 --3.48 --4.16 --4.94 --5.79 --6.67 --7.61 --8.59 --9.59 --10.60 --11.58 --12.61 --13.78 --15.20 --16.79 --18.31 --19.31 --19.26 --17.80 --14.85 --10.71 --5.92 --1.12 -3.18 -6.72 -9.47 -11.56 -13.12 -14.23 -14.89 -15.05 -14.75 -14.06 -13.16 -12.23 -11.44 -10.87 -10.56 -10.51 -10.68 -11.03 -11.45 -11.83 -12.02 -11.88 -11.37 -10.52 -9.41 -8.15 -6.83 -5.48 -4.13 -2.80 -1.50 -0.26 --0.93 --2.09 --3.30 --4.57 --5.92 --7.33 --8.74 --10.10 --11.41 --12.68 --13.98 --15.38 --16.95 --18.68 --20.46 --22.07 --23.15 --23.31 --22.28 --19.92 --16.33 --11.82 --6.81 --1.76 -2.93 -6.99 -10.23 -12.57 -14.00 -14.57 -14.43 -13.76 -12.74 -11.56 -10.35 -9.22 -8.27 -7.58 -7.23 -7.24 -7.52 -7.96 -8.38 -8.70 -8.90 -9.01 -9.03 -8.91 -8.51 -7.74 -6.56 -5.12 -3.65 -2.43 -1.64 -1.23 -0.98 -0.57 --0.28 --1.68 --3.46 --5.31 --6.83 --7.80 --8.23 --8.41 --8.78 --9.74 --11.45 --13.80 --16.37 --18.63 --20.08 --20.35 --19.26 --16.78 --13.07 --8.45 --3.36 -1.66 -6.05 -9.40 -11.52 -12.48 -12.52 -11.93 -10.99 -9.90 -8.77 -7.70 -6.76 -5.98 -5.34 -4.77 -4.23 -3.71 -3.28 -3.04 -3.08 -3.37 -3.77 -4.08 -4.07 -3.64 -2.81 -1.70 -0.50 --0.65 --1.63 --2.45 --3.11 --3.68 --4.22 --4.79 --5.43 --6.15 --6.89 --7.57 --8.12 --8.55 --8.97 --9.57 --10.56 --12.00 --13.80 --15.60 --16.92 --17.23 --16.15 --13.59 --9.71 --4.96 -0.12 -4.99 -9.21 -12.50 -14.72 -15.88 -16.04 -15.40 -14.19 -12.64 -10.93 -9.21 -7.52 -5.89 -4.37 -3.01 -1.92 -1.20 -0.92 -1.05 -1.47 -1.96 -2.30 -2.33 -2.00 -1.44 -0.84 -0.41 -0.23 -0.25 -0.28 -0.12 --0.40 --1.30 --2.46 --3.70 --4.79 --5.63 --6.23 --6.71 --7.30 --8.22 --9.57 --11.29 --13.09 --14.55 --15.16 --14.51 --12.37 --8.82 --4.23 -0.79 -5.64 -9.79 -12.93 -14.96 -15.91 -15.91 -15.12 -13.73 -11.98 -10.11 -8.36 -6.89 -5.77 -4.96 -4.41 -4.05 -3.88 -3.87 -4.01 -4.25 -4.54 -4.86 -5.19 -5.47 -5.60 -5.44 -4.90 -4.00 -2.82 -1.55 -0.26 --1.03 --2.41 --3.97 --5.73 --7.56 --9.26 --10.64 --11.62 --12.30 --12.92 --13.76 --15.01 --16.68 --18.55 --20.18 --20.97 --20.38 --18.05 --13.94 --8.37 --1.99 -4.40 -10.04 -14.34 -17.01 -18.09 -17.85 -16.73 -15.18 -13.58 -12.20 -11.13 -10.37 -9.83 -9.42 -9.08 -8.76 -8.46 -8.20 -8.00 -7.87 -7.79 -7.71 -7.49 -7.00 -6.15 -4.94 -3.52 -2.10 -0.94 -0.17 --0.20 --0.38 --0.64 --1.29 --2.48 --4.20 --6.28 --8.42 --10.39 --12.01 --13.26 --14.21 --14.98 --15.74 --16.65 --17.80 --19.19 --20.59 --21.59 --21.69 --20.42 --17.56 --13.25 --7.93 --2.23 -3.23 -7.95 -11.64 -14.19 -15.62 -16.07 -15.73 -14.85 -13.66 -12.34 -11.02 -9.75 -8.54 -7.43 -6.47 -5.74 -5.31 -5.16 -5.23 -5.36 -5.41 -5.25 -4.81 -4.05 -3.01 -1.77 -0.39 --1.04 --2.41 --3.63 --4.62 --5.37 --5.93 --6.45 --7.09 --7.94 --8.95 --9.96 --10.76 --11.22 --11.41 --11.58 --12.03 --12.97 --14.32 --15.68 --16.46 --16.04 --14.04 --10.42 --5.47 -0.25 -6.07 -11.39 -15.77 -19.00 -21.06 -22.08 -22.21 -21.60 -20.36 -18.63 -16.58 -14.45 -12.50 -10.91 -9.75 -8.92 -8.25 -7.59 -6.90 -6.23 -5.69 -5.35 -5.17 -5.01 -4.67 -3.97 -2.83 -1.32 --0.39 --2.03 --3.34 --4.15 --4.45 --4.38 --4.21 --4.27 --4.83 --5.95 --7.51 --9.22 --10.74 --11.88 --12.62 --13.10 --13.53 --14.08 --14.88 --15.93 --17.17 --18.44 --19.37 --19.49 --18.29 --15.46 --11.03 --5.42 -0.62 -6.31 -11.04 -14.53 -16.79 -18.02 -18.45 -18.30 -17.74 -16.86 -15.79 -14.61 -13.38 -12.17 -11.04 -10.03 -9.19 -8.56 -8.17 -7.98 -7.90 -7.81 -7.62 -7.23 -6.66 -5.95 -5.18 -4.40 -3.62 -2.86 -2.10 -1.34 -0.57 --0.26 --1.20 --2.33 --3.70 --5.33 --7.22 --9.34 --11.62 --14.00 --16.36 --18.54 --20.33 --21.55 --22.07 --21.93 --21.37 --20.73 --20.42 --20.64 --21.35 --22.25 --22.85 --22.72 --21.64 --19.63 --16.95 --13.94 --10.93 --8.17 --5.78 --3.77 --2.04 --0.39 -1.41 -3.51 -5.91 -8.49 -11.00 -13.27 -15.24 -16.95 -18.57 -20.28 -22.30 -24.75 -27.61 -30.62 -33.28 -35.02 -35.42 -34.47 -32.62 -30.51 -28.65 -27.19 -25.78 -23.85 -20.91 -16.85 -11.92 -6.62 -1.45 --3.27 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--10.29 --12.14 --13.83 --15.42 --17.04 --18.80 --20.81 --23.09 --25.47 --27.62 --29.03 --29.12 --27.41 --23.62 --17.86 --10.56 --2.44 -5.65 -12.87 -18.62 -22.54 -24.60 -25.02 -24.16 -22.39 -20.07 -17.50 -14.89 -12.51 -10.57 -9.25 -8.62 -8.54 -8.70 -8.76 -8.49 -7.89 -7.12 -6.41 -5.87 -5.38 -4.68 -3.48 -1.66 --0.64 --3.11 --5.43 --7.40 --9.01 --10.38 --11.67 --13.05 --14.67 --16.65 --19.05 --21.69 --24.13 --25.65 --25.45 --22.93 --17.92 --10.85 --2.57 -5.85 -13.45 -19.58 -23.92 -26.45 -27.30 -26.69 -24.90 -22.25 -19.12 -15.91 -13.00 -10.66 -9.04 -8.10 -7.67 -7.49 -7.29 -6.91 -6.28 -5.48 -4.66 -3.88 -3.12 -2.20 -0.91 --0.89 --3.21 --5.86 --8.59 --11.10 --13.22 --14.93 --16.37 --17.80 --19.52 --21.77 --24.54 --27.59 --30.38 --32.20 --32.32 --30.19 --25.61 --18.79 --10.36 --1.21 -7.63 -15.26 -21.07 -24.82 -26.60 -26.76 -25.67 -23.69 -21.15 -18.33 -15.55 -13.16 -11.43 -10.46 -10.10 -10.03 -9.91 -9.47 -8.71 -7.77 -6.87 -6.10 -5.44 -4.65 -3.49 -1.79 --0.44 --3.04 --5.77 --8.36 --10.67 --12.65 --14.37 --15.98 --17.68 --19.68 --22.06 --24.72 --27.36 --29.44 --30.35 --29.48 --26.41 --21.08 --13.80 --5.31 -3.41 -11.34 -17.63 -21.82 -23.90 -24.21 -23.25 -21.48 -19.26 -16.87 -14.52 -12.49 -11.01 -10.21 -10.02 -10.17 -10.34 -10.26 -9.88 -9.29 -8.68 -8.15 -7.65 -6.98 -5.85 -4.08 -1.61 --1.36 --4.48 --7.38 --9.73 --11.43 --12.61 --13.57 --14.69 --16.28 --18.43 --21.00 --23.55 --25.44 --25.92 --24.37 --20.44 --14.26 --6.41 -2.16 -10.41 -17.44 -22.73 -26.15 -27.82 -28.02 -26.99 -24.93 -22.08 -18.79 -15.49 -12.60 -10.42 -9.03 -8.30 -8.01 -7.93 -7.89 -7.78 -7.51 -7.00 -6.18 -5.07 -3.76 -2.38 -0.98 --0.47 --2.12 --4.12 --6.48 --9.09 --11.69 --14.03 --15.98 --17.59 --19.03 --20.52 --22.20 --24.16 --26.34 --28.59 --30.59 --31.89 --31.91 --30.09 --26.07 --19.88 --12.00 --3.28 -5.28 -12.77 -18.63 -22.62 -24.83 -25.52 -25.01 -23.61 -21.64 -19.42 -17.27 -15.44 -14.13 -13.40 -13.21 -13.41 -13.79 -14.13 -14.25 -14.05 -13.58 -12.93 -12.24 -11.53 -10.73 -9.71 -8.32 -6.52 -4.39 -2.11 --0.10 --2.07 --3.73 --5.12 --6.38 --7.72 --9.27 --11.08 --13.05 --15.04 --16.87 --18.44 --19.74 --20.82 --21.81 --22.86 --24.15 --25.84 --28.02 --30.50 --32.81 --34.15 --33.68 --30.79 --25.34 --17.77 --8.99 --0.05 -8.09 -14.83 -19.91 -23.32 -25.15 -25.53 -24.61 -22.63 -20.00 -17.24 -14.85 -13.18 -12.31 -12.03 -12.03 -12.02 -11.85 -11.56 -11.31 -11.23 -11.34 -11.52 -11.55 -11.17 -10.22 -8.67 -6.64 -4.38 -2.21 -0.37 --1.04 --2.06 --2.89 --3.73 --4.74 --5.95 --7.30 --8.70 --10.08 --11.43 --12.78 --14.14 --15.47 --16.74 --18.00 --19.39 --21.05 --23.06 --25.21 --27.01 --27.73 --26.61 --23.16 --17.38 --9.81 --1.40 -6.76 -13.75 -19.05 -22.55 -24.44 -25.00 -24.47 -23.00 -20.72 -17.93 -15.05 -12.64 -11.08 -10.54 -10.82 -11.52 -12.21 -12.61 -12.69 -12.54 -12.31 -12.03 -11.60 -10.86 -9.70 -8.12 -6.27 -4.37 -2.62 -1.09 --0.28 --1.63 --3.14 --4.92 --7.01 --9.32 --11.71 --13.98 --15.94 --17.45 --18.52 --19.35 --20.27 --21.62 --23.61 --26.16 --28.86 --31.05 --31.97 --31.03 --27.87 --22.51 --15.36 --7.08 -1.46 -9.41 -16.07 -21.00 -24.08 -25.42 -25.33 -24.13 -22.18 -19.80 -17.29 -14.95 -13.09 -11.89 -11.38 -11.42 -11.69 -11.88 -11.78 -11.39 -10.86 -10.37 -10.02 -9.71 -9.22 -8.30 -6.90 -5.14 -3.26 -1.52 -0.04 --1.26 --2.56 --4.09 --5.96 --8.18 --10.57 --12.91 --14.97 --16.63 --17.86 --18.81 --19.76 --20.99 --22.74 --24.99 --27.47 --29.71 --31.07 --30.90 --28.70 --24.27 --17.78 --9.80 --1.22 -6.98 -13.93 -19.11 -22.41 -24.03 -24.29 -23.51 -21.88 -19.59 -16.88 -14.09 -11.61 -9.72 -8.51 -7.88 -7.66 -7.66 -7.81 -8.04 -8.30 -8.51 -8.50 -8.17 -7.41 -6.25 -4.78 -3.14 -1.45 --0.25 --1.97 --3.76 --5.64 --7.57 --9.45 --11.14 --12.54 --13.63 --14.55 --15.49 --16.65 --18.18 --20.11 --22.36 --24.71 --26.75 --27.94 --27.62 --25.25 --20.61 --13.93 --5.88 -2.56 -10.38 -16.81 -21.45 -24.26 -25.41 -25.17 -23.83 -21.66 -19.01 -16.22 -13.65 -11.60 -10.25 -9.58 -9.47 -9.72 -10.14 -10.63 -11.14 -11.64 -12.05 -12.23 -12.05 -11.40 -10.29 -8.81 -7.14 -5.41 -3.70 -2.03 -0.35 --1.35 --3.07 --4.78 --6.49 --8.24 --10.05 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--9.07 --11.61 --13.63 --15.22 --16.66 --18.35 --20.61 --23.57 --27.08 --30.61 --33.37 --34.43 --32.97 --28.57 --21.36 --12.04 --1.74 -8.29 -16.95 -23.50 -27.65 -29.50 -29.40 -27.83 -25.28 -22.20 -18.95 -15.84 -13.12 -10.96 -9.44 -8.54 -8.09 -7.89 -7.72 -7.45 -7.09 -6.67 -6.25 -5.75 -5.00 -3.78 -1.94 --0.52 --3.40 --6.39 --9.16 --11.50 --13.37 --14.94 --16.51 --18.43 --20.97 --24.11 --27.42 --30.07 --30.97 --29.15 --24.11 --16.08 --5.98 -4.79 -14.85 -23.10 -28.97 -32.39 -33.59 -32.94 -30.88 -27.81 -24.16 -20.39 -16.90 -14.00 -11.90 -10.63 -10.10 -10.12 -10.41 -10.65 -10.58 -10.02 -8.95 -7.50 -5.86 -4.16 -2.44 -0.59 --1.58 --4.20 --7.18 --10.33 --13.31 --15.86 --17.87 --19.44 --20.80 --22.23 --23.94 --26.13 --28.81 --31.83 --34.78 --36.92 --37.32 --35.06 --29.63 --21.15 --10.44 -1.14 -12.08 -21.14 -27.64 -31.48 -33.04 -32.90 -31.60 -29.52 -26.87 -23.85 -20.68 -17.71 -15.26 -13.58 -12.68 -12.39 -12.47 -12.66 -12.84 -12.90 -12.73 -12.17 -11.06 -9.31 -7.05 -4.54 -2.04 --0.27 --2.43 --4.59 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-20.70 -17.46 -14.46 -11.85 -9.69 -7.96 -6.63 -5.75 -5.32 -5.33 -5.70 -6.21 -6.62 -6.68 -6.22 -5.16 -3.47 -1.17 --1.71 --5.07 --8.73 --12.47 --16.02 --19.15 --21.67 --23.52 --24.76 --25.60 --26.30 --27.14 --28.31 --29.78 --31.30 --32.33 --32.22 --30.31 --26.17 --19.75 --11.47 --2.09 -7.40 -16.06 -23.15 -28.28 -31.42 -32.76 -32.63 -31.32 -29.08 -26.08 -22.53 -18.68 -14.86 -11.41 -8.63 -6.71 -5.69 -5.46 -5.83 -6.56 -7.44 -8.21 -8.71 -8.74 -8.18 -6.97 -5.15 -2.90 -0.47 --1.85 --3.90 --5.70 --7.46 --9.46 --11.87 --14.60 --17.31 --19.60 --21.20 --22.14 --22.80 --23.70 --25.27 --27.56 --30.22 --32.52 --33.59 --32.69 --29.36 --23.62 --15.88 --6.86 -2.56 -11.48 -19.16 -25.13 -29.17 -31.38 -32.04 -31.50 -30.10 -28.10 -25.67 -22.98 -20.17 -17.40 -14.82 -12.55 -10.68 -9.25 -8.33 -8.01 -8.34 -9.34 -10.84 -12.49 -13.78 -14.17 -13.28 -11.06 -7.80 -4.06 -0.46 --2.58 --4.98 --6.95 --8.84 --10.91 --13.22 --15.60 --17.78 --19.53 --20.74 --21.40 --21.65 --21.67 --21.73 --22.13 --23.13 --24.80 --26.87 --28.71 --29.43 --28.19 --24.48 --18.33 --10.29 --1.22 -7.90 -16.27 -23.29 -28.62 -32.07 -33.62 -33.40 -31.71 -28.94 -25.51 -21.81 -18.13 -14.71 -11.75 -9.39 -7.74 -6.80 -6.50 -6.65 -7.03 -7.43 -7.65 -7.57 -7.05 -6.03 -4.49 -2.44 -0.00 --2.71 --5.55 --8.43 --11.25 --13.92 --16.32 --18.31 --19.80 --20.79 --21.45 --22.08 --23.03 --24.53 --26.57 --28.92 --31.18 --32.83 --33.39 --32.45 --29.74 --25.19 --19.02 --11.80 --4.34 -2.53 -8.17 -12.35 -15.19 -17.00 -18.08 -18.55 -18.38 -17.51 -15.99 -14.06 -12.03 -10.18 -8.70 -7.58 -6.76 -6.15 -5.70 -5.39 -5.20 -5.08 -4.93 -4.57 -3.83 -2.56 -0.72 --1.62 --4.28 --7.09 --9.89 --12.57 --15.03 --17.18 --18.96 --20.43 --21.73 --23.15 --24.86 --26.82 --28.65 --29.67 --29.10 --26.34 --21.12 --13.65 --4.48 -5.58 -15.62 -24.77 -32.27 -37.64 -40.69 -41.58 -40.75 -38.71 -35.93 -32.70 -29.16 -25.36 -21.35 -17.32 -13.55 -10.40 -8.19 -7.06 -6.96 -7.59 -8.55 -9.44 -9.98 -10.08 -9.79 -9.19 -8.35 -7.26 -5.82 -3.93 -1.53 --1.32 --4.48 --7.74 --10.88 --13.72 --16.16 --18.24 --20.08 --21.90 --23.90 --26.22 --28.87 --31.61 --33.91 --35.00 --34.01 --30.26 --23.52 --14.28 --3.61 -7.11 -16.55 -23.81 -28.61 -31.13 -31.85 -31.26 -29.74 -27.52 -24.76 -21.70 -18.61 -15.77 -13.39 -11.56 -10.21 -9.21 -8.44 -7.83 -7.40 -7.17 -7.17 -7.33 -7.51 -7.53 -7.18 -6.29 -4.72 -2.37 --0.72 --4.42 --8.39 --12.22 --15.51 --18.05 --19.80 --20.98 --21.92 --22.91 --24.17 --25.69 --27.34 --28.74 --29.43 --28.81 --26.33 --21.64 --14.75 --6.16 -3.26 -12.47 -20.55 -26.91 -31.40 -34.16 -35.44 -35.45 -34.24 -31.83 -28.28 -23.85 -18.96 -14.08 -9.67 -6.02 -3.31 -1.58 -0.78 -0.78 -1.38 -2.27 -3.14 -3.70 -3.68 -2.97 -1.52 --0.57 --3.12 --5.91 --8.70 --11.31 --13.66 --15.72 --17.57 --19.27 --20.95 --22.72 --24.67 --26.80 --28.92 --30.68 --31.49 --30.74 --27.92 --22.89 --15.88 --7.50 -1.37 -9.92 -17.48 -23.64 -28.27 -31.33 -32.84 -32.80 -31.25 -28.37 -24.47 -20.06 -15.67 -11.73 -8.54 -6.19 -4.62 -3.66 -3.18 -3.05 -3.21 -3.62 -4.22 -4.86 -5.31 -5.27 -4.47 -2.75 -0.17 --2.99 --6.31 --9.40 --12.01 --14.14 --16.04 --18.00 --20.22 --22.69 --25.05 --26.72 --27.03 --25.34 --21.32 --15.02 --6.95 -2.05 -11.01 -19.04 -25.48 -29.98 -32.46 -33.09 -32.13 -29.95 -26.96 -23.54 -20.04 -16.71 -13.68 -11.05 -8.80 -6.94 -5.46 -4.34 -3.61 -3.31 -3.43 -3.86 -4.35 -4.52 -3.96 -2.34 --0.53 --4.56 --9.49 --14.82 --19.98 --24.39 --27.55 --29.22 --29.40 --28.31 --26.32 --23.74 --20.74 --17.35 --13.50 --9.07 --4.00 -1.71 -8.01 -14.71 -21.44 -27.63 -32.52 -35.30 -35.31 -32.20 -26.11 -17.69 -7.97 --1.84 --10.53 --17.21 --21.37 --22.99 --22.47 --20.47 --17.75 --14.93 --12.38 --10.13 --8.02 --5.79 --3.34 --0.73 -1.72 -3.62 -4.61 -4.44 -3.08 -0.65 --2.65 --6.53 --10.75 --15.07 --19.31 --23.30 --26.87 --29.75 --31.55 --31.81 --30.10 --26.23 --20.32 --12.84 --4.54 -3.74 -11.25 -17.46 -22.08 -25.13 -26.77 -27.28 -26.98 -26.04 -24.56 -22.57 -20.09 -17.26 -14.27 -11.41 -8.90 -6.86 -5.28 -4.05 -3.04 -2.12 -1.21 -0.29 --0.65 --1.55 --2.39 --3.20 --4.09 --5.22 --6.76 --8.82 --11.41 --14.42 --17.67 --20.89 --23.83 --26.20 --27.77 --28.45 --28.36 --27.80 --27.13 --26.55 --25.98 --24.98 --22.94 --19.31 --13.85 --6.77 -1.34 -9.68 -17.45 -23.99 -28.91 -32.10 -33.65 -33.77 -32.70 -30.67 -27.92 -24.64 -21.09 -17.51 -14.13 -11.15 -8.70 -6.86 -5.71 -5.31 -5.70 -6.79 -8.38 -10.08 -11.52 -12.38 -12.48 -11.83 -10.58 -8.90 -6.90 -4.63 -2.04 --0.95 --4.39 --8.29 --12.54 --16.91 --21.11 --24.80 --27.72 --29.74 --30.96 --31.64 --32.08 --32.53 --32.94 --32.90 --31.71 --28.58 --22.98 --14.85 --4.70 -6.53 -17.67 -27.65 -35.67 -41.20 -44.02 -44.17 -41.91 -37.73 -32.28 -26.30 -20.39 -15.01 -10.39 -6.63 -3.67 -1.45 --0.14 --1.25 --1.98 --2.39 --2.53 --2.46 --2.25 --2.05 --1.95 --1.96 --2.01 --1.95 --1.74 --1.53 --1.60 --2.37 --4.13 --6.93 --10.60 --14.74 --18.88 --22.58 --25.49 --27.39 --28.25 --28.21 --27.62 --26.91 --26.43 --26.27 --26.12 --25.34 --23.09 --18.70 --11.93 --3.14 -6.78 -16.65 -25.36 -32.15 -36.74 -39.25 -39.99 -39.34 -37.54 -34.81 -31.32 -27.29 -23.01 -18.73 -14.66 -10.98 -7.82 -5.32 -3.53 -2.46 -2.02 -2.04 -2.37 -2.85 -3.36 -3.76 -3.90 -3.59 -2.73 -1.36 --0.35 --2.15 --3.83 --5.32 --6.69 --8.09 --9.64 --11.36 --13.18 --15.00 --16.79 --18.55 --20.33 --22.16 --23.96 --25.55 --26.65 --26.90 --25.87 --23.22 --18.78 --12.66 --5.39 -2.24 -9.37 -15.31 -19.75 -22.67 -24.26 -24.68 -24.01 -22.29 -19.66 -16.42 -13.03 -9.97 -7.55 -5.88 -4.83 -4.21 -3.80 -3.46 -3.11 -2.72 -2.27 -1.74 -1.12 -0.38 --0.51 --1.57 --2.86 --4.40 --6.25 --8.42 --10.87 --13.45 --15.94 --18.06 --19.61 --20.50 --20.91 --21.15 --21.63 --22.61 --24.14 --25.94 --27.46 --28.03 --27.03 --24.06 --19.09 --12.40 --4.47 -4.06 -12.53 -20.31 -26.82 -31.58 -34.31 -34.99 -33.91 -31.48 -28.19 -24.40 -20.36 -16.26 -12.28 -8.72 -5.84 -3.84 -2.74 -2.42 -2.63 -3.12 -3.66 -4.08 -4.25 -4.11 -3.68 -3.05 -2.31 -1.50 -0.60 --0.50 --1.94 --3.78 --6.04 --8.61 --11.33 --14.01 --16.51 --18.73 --20.66 --22.38 --23.98 --25.60 --27.29 --28.93 --30.17 --30.43 --29.08 --25.63 --19.92 --12.29 --3.45 -5.62 -14.02 -21.14 -26.68 -30.59 -32.91 -33.66 -32.87 -30.65 -27.23 -23.06 -18.63 -14.41 -10.76 -7.90 -5.90 -4.76 -4.41 -4.70 -5.44 -6.38 -7.28 -7.93 -8.14 -7.77 -6.79 -5.30 -3.45 -1.46 --0.52 --2.46 --4.45 --6.62 --9.05 --11.68 --14.36 --16.89 --19.11 --20.98 --22.56 --23.99 --25.40 --26.91 --28.54 --30.20 --31.62 --32.39 --32.01 --29.97 --25.94 --19.88 --12.16 --3.44 -5.42 -13.60 -20.45 -25.54 -28.70 -29.90 -29.21 -26.82 -23.10 -18.55 -13.77 -9.35 -5.70 -3.03 -1.35 -0.55 -0.48 -1.03 -2.07 -3.44 -4.95 -6.35 -7.46 -8.18 -8.51 -8.55 -8.41 -8.12 -7.66 -6.97 -5.96 -4.59 -2.85 -0.72 --1.83 --4.88 --8.51 --12.75 --17.54 --22.71 --27.93 --32.74 --36.63 --39.04 --39.54 --37.89 --34.10 --28.58 --21.94 --14.96 --8.21 --1.92 -4.15 -10.55 -17.85 -26.26 -35.34 -44.04 -50.92 -54.65 -54.43 -50.31 -43.18 -34.58 -26.25 -19.69 -15.85 -14.93 -16.42 -19.38 -22.69 -25.36 -26.68 -26.30 -24.21 -20.67 -16.14 -11.17 -6.33 -2.03 --1.57 --4.62 --7.54 --10.81 --14.78 --19.48 --24.56 --29.41 --33.36 --35.93 --37.01 --36.87 --36.08 --35.33 --35.18 --35.90 --37.25 --38.53 --38.68 --36.62 --31.61 --23.55 --13.06 --1.37 -10.05 -19.86 -27.19 -31.77 -33.90 -34.22 -33.43 -32.07 -30.42 -28.49 -26.18 -23.43 -20.29 -16.94 -13.60 -10.47 -7.71 -5.39 -3.65 -2.57 -2.23 -2.58 -3.44 -4.50 -5.37 -5.71 -5.30 -4.09 -2.20 --0.08 --2.43 --4.56 --6.35 --7.85 --9.23 --10.74 --12.52 --14.59 --16.79 --18.89 --20.63 --21.86 --22.62 --23.11 --23.61 --24.39 --25.51 --26.79 --27.70 --27.55 --25.62 --21.51 --15.24 --7.34 -1.36 -9.88 -17.39 -23.30 -27.32 -29.41 -29.68 -28.40 -25.91 -22.63 -18.95 -15.27 -11.91 -9.14 -7.17 -6.10 -5.92 -6.42 -7.27 -8.10 -8.62 -8.70 -8.38 -7.76 -6.92 -5.81 -4.27 -2.09 --0.88 --4.67 --9.11 --13.88 --18.54 --22.63 --25.77 --27.80 --28.83 --29.27 --29.67 --30.57 --32.18 --34.31 --36.24 --36.98 --35.47 --31.00 --23.47 --13.46 --2.15 -8.96 -18.46 -25.34 -29.29 -30.64 -30.19 -28.94 -27.69 -26.89 -26.54 -26.26 -25.49 -23.74 -20.77 -16.74 -12.19 -7.88 -4.60 -2.90 -2.90 -4.30 -6.47 -8.59 -9.94 -10.03 -8.75 -6.31 -3.21 -0.05 --2.70 --4.80 --6.31 --7.52 --8.85 --10.65 --13.11 --16.12 --19.32 --22.24 --24.47 --25.85 --26.60 --27.18 --28.13 --29.69 --31.60 --33.04 --32.92 --30.23 --24.44 --15.73 --4.94 -6.65 -17.68 -26.97 -33.78 -37.83 -39.24 -38.42 -35.87 -32.07 -27.44 -22.36 -17.23 -12.50 -8.63 -6.03 -4.91 -5.22 -6.62 -8.63 -10.67 -12.23 -13.00 -12.88 -11.98 -10.56 -8.90 -7.22 -5.62 -4.06 -2.44 -0.66 --1.37 --3.68 --6.25 --9.01 --11.82 --14.47 --16.77 --18.57 --19.90 --20.96 --22.06 --23.54 --25.54 --28.03 --30.67 --32.83 --33.71 --32.49 --28.60 --21.90 --12.90 --2.59 -7.77 -17.04 -24.45 -29.70 -32.81 -33.92 -33.22 -30.96 -27.45 -23.16 -18.66 -14.50 -11.03 -8.40 -6.57 -5.44 -4.92 -4.95 -5.47 -6.40 -7.53 -8.62 -9.41 -9.68 -9.36 -8.48 -7.18 -5.64 -3.98 -2.25 -0.37 --1.77 --4.28 --7.23 --10.52 --13.94 --17.21 --20.08 --22.41 --24.20 --25.62 --26.94 --28.44 --30.30 --32.47 --34.66 --36.22 --36.35 --34.23 --29.33 --21.58 --11.52 --0.18 -11.19 -21.39 -29.51 -35.06 -37.96 -38.48 -37.05 -34.25 -30.68 -26.89 -23.29 -20.15 -17.55 -15.44 -13.70 -12.26 -11.09 -10.25 -9.82 -9.84 -10.22 -10.79 -11.27 -11.37 -10.88 -9.72 -7.91 -5.56 -2.84 --0.10 --3.13 --6.13 --9.03 --11.77 --14.30 --16.59 --18.60 --20.31 --21.73 --22.96 --24.24 --25.79 --27.78 --30.12 --32.39 --33.86 --33.72 --31.29 --26.28 --18.93 --9.90 --0.13 -9.42 -18.01 -25.10 -30.40 -33.78 -35.20 -34.70 -32.50 -28.94 -24.51 -19.76 -15.19 -11.19 -8.04 -5.86 -4.68 -4.35 -4.61 -5.11 -5.58 -5.87 -5.94 -5.91 -5.81 -5.64 -5.28 -4.60 -3.52 -2.01 -0.09 --2.23 --4.97 --8.12 --11.56 --15.10 --18.48 --21.40 --23.69 --25.24 --26.13 --26.57 --26.91 --27.53 --28.68 --30.42 --32.45 --34.12 --34.61 --33.08 --28.98 --22.19 --13.16 --2.77 -7.85 -17.61 -25.67 -31.54 -35.10 -36.51 -36.06 -34.11 -31.09 -27.43 -23.51 -19.68 -16.12 -12.94 -10.21 -8.02 -6.52 -5.85 -6.02 -6.86 -8.03 -9.09 -9.68 -9.61 -8.84 -7.47 -5.63 -3.40 -0.81 --2.12 --5.35 --8.76 --12.12 --15.18 --17.70 --19.60 --20.96 --22.05 --23.24 --24.82 --26.92 --29.38 --31.83 --33.76 --34.55 --33.62 --30.51 --24.97 --17.13 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--3.75 --5.09 --6.36 --7.68 --9.13 --10.70 --12.33 --13.92 --15.39 --16.72 --17.96 --19.24 --20.66 --22.22 --23.82 --25.28 --26.37 --26.82 --26.34 --24.66 --21.63 --17.27 --11.92 --6.11 --0.49 -4.40 -8.24 -11.00 -12.84 -14.02 -14.73 -15.12 -15.22 -15.04 -14.60 -13.90 -13.01 -12.07 -11.21 -10.57 -10.22 -10.15 -10.30 -10.63 -11.06 -11.54 -11.99 -12.24 -12.12 -11.47 -10.25 -8.52 -6.45 -4.28 -2.17 -0.24 --1.51 --3.15 --4.78 --6.53 --8.44 --10.45 --12.43 --14.22 --15.74 --17.05 --18.35 --19.88 --21.75 --23.84 --25.82 --27.22 --27.58 --26.60 --24.19 --20.45 --15.61 --10.10 --4.38 -1.09 -5.95 -9.95 -13.04 -15.26 -16.68 -17.40 -17.46 -16.95 -15.97 -14.69 -13.31 -12.05 -11.08 -10.52 -10.41 -10.64 -11.10 -11.61 -12.07 -12.43 -12.69 -12.81 -12.66 -12.09 -11.01 -9.50 -7.74 -5.96 -4.31 -2.78 -1.27 --0.37 --2.21 --4.23 --6.32 --8.34 --10.25 --12.02 --13.65 --15.16 --16.61 --18.04 --19.54 --21.23 --23.12 --25.15 --27.16 --28.86 --29.91 --29.96 --28.68 --25.85 --21.45 --15.66 --8.94 --1.93 -4.70 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--14.77 --16.16 --17.68 --19.51 --21.73 --24.29 --26.94 --29.30 --30.93 --31.39 --30.30 --27.40 --22.65 --16.29 --8.87 --1.13 -6.18 -12.40 -17.22 -20.57 -22.59 -23.49 -23.49 -22.74 -21.38 -19.54 -17.37 -15.08 -12.92 -11.10 -9.81 -9.11 -8.92 -9.09 -9.42 -9.72 -9.93 -9.98 -9.84 -9.47 -8.83 -7.89 -6.71 -5.39 -4.02 -2.65 -1.24 --0.32 --2.10 --4.12 --6.29 --8.46 --10.50 --12.31 --13.90 --15.36 --16.85 --18.51 --20.45 --22.69 --25.09 --27.36 --29.11 --29.79 --28.97 --26.34 --21.84 --15.83 --8.86 --1.71 -4.88 -10.31 -14.30 -16.82 -18.07 -18.38 -18.07 -17.45 -16.73 -15.99 -15.23 -14.37 -13.30 -11.99 -10.51 -9.01 -7.73 -6.85 -6.49 -6.62 -7.13 -7.85 -8.55 -9.02 -9.06 -8.62 -7.70 -6.40 -4.82 -3.07 -1.16 --0.86 --2.93 --4.96 --6.88 --8.70 --10.50 --12.38 --14.35 --16.34 --18.24 --19.96 --21.44 --22.65 --23.48 --23.69 --22.90 --20.74 --17.04 --11.93 --5.81 -0.68 -6.90 -12.32 -16.58 -19.52 -21.13 -21.56 -21.04 -19.90 -18.42 -16.86 -15.39 -14.08 -12.90 -11.83 -10.81 -9.85 -9.03 -8.40 -8.05 -8.01 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--15.14 --20.22 --23.28 --24.66 --24.85 --24.39 --23.70 --23.04 --22.48 --22.03 --21.61 --21.12 --20.44 --19.49 --18.38 --17.37 --16.85 --17.09 --18.02 --19.25 --20.14 --20.24 --19.50 --18.45 --17.90 --18.56 --20.69 --23.93 --27.54 --30.71 --32.90 --33.99 --34.24 --34.07 --33.86 --33.84 --34.09 --34.60 --35.34 --36.22 --37.15 --38.03 --38.74 --39.20 --39.38 --39.24 --38.73 --37.83 --36.47 --34.61 --32.20 --29.19 --25.59 --21.42 --16.77 --11.85 --6.94 --2.33 -1.75 -5.18 -7.94 -10.04 -11.52 -12.41 -12.81 -12.84 -12.68 -12.44 -12.25 -12.14 -12.13 -12.24 -12.51 -12.91 -13.38 -13.79 -13.99 -13.86 -13.33 -12.46 -11.32 -9.97 -8.45 -6.76 -4.94 -3.10 -1.35 --0.25 --1.65 --2.85 --3.93 --4.96 --6.04 --7.24 --8.63 --10.23 --12.04 --14.00 --16.03 --17.98 --19.67 --20.87 --21.28 --20.56 --18.43 --14.83 --9.99 --4.43 -1.20 -6.34 -10.64 -13.98 -16.39 -17.96 -18.69 -18.59 -17.70 -16.21 -14.43 -12.72 -11.35 -10.46 -10.05 -10.04 -10.31 -10.71 -11.14 -11.49 -11.68 -11.67 -11.44 -11.07 -10.60 -10.11 -9.63 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--1.59 --4.37 --6.44 --8.19 --9.99 --12.07 --14.45 --17.03 --19.69 --22.33 --24.89 --27.25 --29.27 --30.87 --32.07 --33.09 --34.18 --35.53 --37.02 --38.27 --38.57 --37.27 --33.89 --28.33 --20.96 --12.48 --3.77 -4.34 -11.21 -16.49 -20.12 -22.21 -23.03 -22.81 -21.83 -20.19 -17.99 -15.37 -12.59 -10.02 -8.06 -6.98 -6.83 -7.44 -8.42 -9.38 -10.08 -10.43 -10.50 -10.29 -9.80 -8.90 -7.57 -5.81 -3.72 -1.40 --1.09 --3.74 --6.55 --9.43 --12.21 --14.69 --16.78 --18.60 --20.35 --22.28 --24.41 --26.53 --28.22 --28.91 --28.16 --25.72 --21.56 --15.91 --9.21 --2.02 -5.06 -11.46 -16.77 -20.75 -23.35 -24.66 -24.83 -24.01 -22.35 -20.01 -17.20 -14.21 -11.41 -9.16 -7.81 -7.47 -8.03 -9.15 -10.40 -11.38 -11.91 -11.98 -11.69 -11.12 -10.30 -9.17 -7.66 -5.81 -3.72 -1.57 --0.47 --2.36 --4.16 --6.01 --8.03 --10.30 --12.73 --15.15 --17.31 --18.99 --20.10 --20.74 --21.19 --21.80 --22.87 --24.54 --26.76 --29.26 --31.58 --33.14 --33.31 --31.56 --27.56 --21.35 --13.32 --4.24 -5.01 -13.52 -20.60 -25.84 -29.14 -30.62 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--0.37 --4.04 --7.37 --10.46 --13.59 --17.08 --21.23 --26.21 --31.91 --37.91 --43.54 --48.04 --50.72 --51.14 --49.15 --44.86 --38.60 --30.81 --21.98 --12.59 --3.12 -6.02 -14.45 -21.88 -28.02 -32.64 -35.60 -36.81 -36.35 -34.48 -31.67 -28.55 -25.69 -23.50 -22.06 -21.14 -20.43 -19.70 -18.96 -18.34 -18.04 -18.08 -18.30 -18.42 -18.10 -17.10 -15.26 -12.60 -9.22 -5.37 -1.42 --2.30 --5.58 --8.51 --11.37 --14.51 --18.12 --22.16 --26.31 --30.23 --33.77 --36.97 --40.11 --43.51 --47.39 --51.82 --56.59 --61.30 --65.38 --68.21 --69.07 --67.32 --62.45 --54.22 --42.80 --28.86 --13.53 -1.73 -15.47 -26.58 -34.47 -39.15 -41.07 -40.91 -39.37 -37.04 -34.41 -31.81 -29.46 -27.46 -25.81 -24.44 -23.17 -21.83 -20.31 -18.69 -17.25 -16.33 -16.26 -17.06 -18.41 -19.73 -20.41 -19.99 -18.37 -15.67 -12.18 -8.23 -4.12 -0.16 --3.31 --6.01 --7.86 --9.01 --9.85 --10.82 --12.27 --14.31 --16.79 --19.41 --21.85 --23.88 --25.53 --27.06 --28.80 --31.02 --33.80 --36.82 --39.51 --41.12 --40.90 --38.29 --33.11 --25.53 --16.05 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--22.24 --24.02 --25.91 --28.04 --30.51 --33.25 --35.95 --38.01 --38.65 --37.12 --33.00 --26.34 --17.65 --7.80 -2.26 -11.64 -19.64 -25.83 -30.02 -32.25 -32.75 -31.92 -30.20 -28.09 -26.01 -24.24 -22.89 -21.90 -21.11 -20.32 -19.44 -18.48 -17.63 -17.10 -16.97 -17.09 -17.13 -16.71 -15.58 -13.66 -11.04 -7.95 -4.62 -1.24 --1.99 --4.87 --7.23 --9.05 --10.50 --11.96 --13.80 --16.21 --19.12 --22.23 --25.19 --27.81 --30.18 --32.56 --35.12 --37.92 --40.78 --43.52 --46.12 --48.72 --51.52 --54.48 --57.19 --58.91 --58.86 --56.44 --51.50 --44.24 --35.05 --24.26 --12.05 -1.39 -15.63 -29.83 -42.85 -53.61 -61.62 -67.33 -72.02 -77.24 -84.01 -92.34 -101.17 -109.06 -114.92 -118.52 -120.33 -120.89 -120.08 -116.82 -109.54 -97.04 -79.44 -58.47 -36.85 -17.22 -0.99 --12.16 --23.78 --35.56 --48.22 --60.89 --71.47 --77.71 --78.32 --73.64 --65.47 --56.17 --47.68 --40.87 --35.59 --31.11 --26.73 --22.19 --17.67 --13.65 --10.71 --9.34 --9.99 --13.03 --18.67 --26.88 --37.19 --48.64 --59.96 --69.79 --77.01 --81.00 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--7.11 --14.54 --20.61 --24.86 --27.41 --29.24 --31.96 --37.09 --45.15 --55.08 --64.48 --70.53 --71.18 --66.09 --56.58 --44.92 --33.25 --22.84 --14.05 --6.75 --0.94 -2.99 -4.49 -3.31 --0.07 --4.55 --8.77 --11.66 --12.84 --12.67 --11.92 --11.38 --11.37 --11.70 --11.75 --10.80 --8.43 --4.73 --0.29 -4.04 -7.45 -9.53 -10.32 -10.19 -9.55 -8.54 -7.04 -4.77 -1.65 --2.04 --5.74 --8.85 --11.07 --12.50 --13.64 --15.06 --17.23 --20.32 --24.17 --28.27 --31.84 --33.96 --33.76 --30.81 --25.24 --17.88 --9.95 --2.54 -3.78 -9.09 -13.83 -18.40 -22.82 -26.65 -29.36 -30.65 -30.72 -30.08 -29.41 -29.06 -28.98 -28.84 -28.26 -27.14 -25.76 -24.62 -24.18 -24.60 -25.65 -26.75 -27.32 -26.94 -25.55 -23.38 -20.83 -18.23 -15.74 -13.31 -10.78 -8.05 -5.17 -2.36 --0.12 --2.14 --3.75 --5.18 --6.72 --8.57 --10.77 --13.25 --15.86 --18.39 --20.57 --21.98 --22.07 --20.35 --16.62 --11.11 --4.42 -2.66 -9.48 -15.68 -21.19 -26.02 -30.09 -33.19 -35.16 -35.96 -35.80 -35.02 -33.94 -32.75 -31.50 -30.14 -28.74 -27.45 -26.51 -26.09 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--38.90 --64.11 --98.97 --132.26 --152.63 --152.53 --131.31 --95.13 --54.11 --18.19 -6.08 -16.56 -14.27 -0.35 --23.32 --54.47 --90.38 --128.08 --164.29 --195.48 --218.09 --229.42 --230.28 --222.84 --209.57 --192.27 --171.99 --148.56 --120.84 --87.42 --38.95 -22.98 -86.78 -137.99 -168.89 -180.84 -181.44 -178.00 -174.78 -173.31 -171.68 -168.22 -162.10 -154.34 -145.97 -137.25 -127.60 -116.29 -102.82 -85.96 -65.89 -44.41 -24.14 -7.08 --6.08 --15.77 --22.75 --27.78 --32.20 --36.82 --41.62 --46.05 --49.71 --52.50 --54.60 --56.11 --57.01 --57.47 --57.39 --56.31 --53.70 --49.55 --44.50 --39.58 --35.70 --33.23 --32.01 --31.35 --30.38 --28.51 --25.63 --22.13 --18.47 --14.98 --11.80 --8.87 --6.23 --4.07 --2.69 --2.32 --2.90 --3.95 --4.74 --4.54 --2.90 -0.05 -3.69 -7.12 -9.44 -10.08 -8.99 -6.55 -3.56 -0.96 --0.42 --0.09 -1.91 -4.99 -8.22 -10.64 -11.64 -11.13 -9.56 -7.58 -5.80 -4.51 -3.64 -2.97 -2.35 -1.87 -1.95 -3.13 -5.77 -9.78 -14.52 -19.01 -22.29 -23.78 -23.50 -21.85 -19.30 -15.97 -11.55 -5.51 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--15.28 --16.99 --18.34 --19.51 --20.83 --22.62 --25.04 --27.96 --31.03 --33.65 --35.16 --34.97 --32.70 --28.29 --22.00 --14.39 --6.23 -1.68 -8.66 -14.28 -18.43 -21.29 -23.17 -24.36 -24.97 -24.91 -24.06 -22.39 -20.10 -17.59 -15.39 -13.88 -13.23 -13.33 -13.89 -14.59 -15.26 -15.81 -16.15 -16.12 -15.48 -14.03 -11.74 -8.82 -5.65 -2.62 --0.04 --2.36 --4.50 --6.64 --8.87 --11.15 --13.38 --15.53 --17.68 --19.99 --22.55 --25.25 --27.72 --29.37 --29.60 --27.99 --24.43 --19.22 --12.88 --5.98 -0.98 -7.59 -13.54 -18.53 -22.27 -24.61 -25.51 -25.13 -23.79 -21.84 -19.63 -17.44 -15.53 -14.11 -13.29 -13.03 -13.09 -13.16 -12.97 -12.47 -11.82 -11.31 -11.16 -11.37 -11.66 -11.68 -11.17 -10.08 -8.60 -6.93 -5.23 -3.49 -1.57 --0.68 --3.33 --6.30 --9.45 --12.62 --15.63 --18.33 --20.61 --22.35 --23.62 --24.58 --25.52 --26.77 --28.54 --30.93 --33.80 --36.85 --39.61 --41.44 --41.69 --39.81 --35.59 --29.25 --21.44 --13.03 --4.82 -2.60 -8.97 -14.18 -18.19 -20.96 -22.47 -22.84 -22.32 -21.22 -19.86 -18.44 -17.04 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-28.07 -19.97 -6.91 --10.27 --29.06 --45.82 --56.74 --59.17 --52.72 --39.51 --23.33 --8.34 -2.48 -8.07 -9.11 -7.16 -3.80 -0.09 --3.36 --6.05 --7.46 --7.31 --5.94 --4.47 --4.46 --7.07 --12.44 --19.39 --25.86 --29.89 --30.35 --27.28 --21.67 --14.82 --7.84 --1.46 -4.02 -8.50 -12.03 -14.83 -17.29 -19.92 -23.13 -27.01 -31.22 -35.18 -38.27 -40.11 -40.59 -39.79 -37.87 -34.95 -31.22 -26.96 -22.58 -18.59 -15.29 -12.71 -10.56 -8.39 -5.93 -3.17 -0.44 --1.80 --3.22 --3.82 --3.83 --3.65 --3.61 --3.84 --4.31 --4.95 --5.76 --6.82 --8.20 --9.79 --11.36 --12.62 --13.42 --13.84 --14.11 --14.53 --15.21 --16.01 --16.58 --16.62 --16.06 --15.22 --14.76 --15.35 --17.39 --20.70 --24.48 --27.60 --28.95 --27.81 --24.10 --18.31 --11.27 --3.88 -3.19 -9.54 -14.94 -19.23 -22.23 -23.76 -23.70 -22.18 -19.52 -16.29 -13.14 -10.64 -9.13 -8.62 -8.80 -9.19 -9.31 -8.93 -8.12 -7.20 -6.56 -6.47 -6.93 -7.69 -8.40 -8.76 -8.64 -8.13 -7.40 -6.60 -5.80 -4.92 -3.88 -2.62 -1.12 --0.56 --2.32 --4.04 --5.63 --7.07 --8.40 --9.69 --10.98 --12.27 --13.52 --14.69 --15.77 --16.82 --17.92 --19.11 --20.36 --21.45 --22.06 --21.78 --20.23 --17.16 --12.61 --6.87 --0.48 -5.93 -11.76 -16.50 -19.78 -21.37 -21.24 -19.59 -16.93 -13.91 -11.09 -8.76 -6.84 -5.00 -2.96 -0.73 --1.35 --2.82 --3.42 --3.22 --2.62 --2.02 --1.63 --1.43 --1.24 --0.97 --0.68 --0.56 --0.72 --1.07 --1.40 --1.53 --1.47 --1.40 --1.56 --2.00 --2.59 --3.00 --3.05 --2.84 --2.75 --3.20 --4.40 --6.16 --7.95 --9.25 --9.76 --9.68 --9.44 --9.49 --10.06 --11.03 --12.21 --13.43 --14.74 --16.24 --17.93 --19.47 --20.28 --19.70 --17.33 --13.22 --7.87 --2.03 -3.55 -8.32 -11.95 -14.29 -15.29 -14.93 -13.29 -10.63 -7.40 -4.18 -1.56 --0.15 --0.99 --1.36 --1.90 --3.19 --5.49 --8.61 --11.98 --14.92 --16.93 --17.87 --17.98 --17.66 --17.29 --16.93 --16.44 --15.53 --14.04 --12.04 --9.79 --7.68 --5.95 --4.67 --3.68 --2.82 --2.04 --1.43 --1.19 --1.54 --2.59 --4.27 --6.40 --8.65 --10.63 --11.92 --12.10 --10.87 --8.15 --4.10 -1.00 -6.74 -12.85 -19.07 -25.13 -30.58 -34.96 -37.88 -39.28 -39.53 -39.21 -38.89 -38.80 -38.78 -38.38 -37.26 -35.36 -33.04 -30.87 -29.23 -28.24 -27.68 -27.22 -26.71 -26.15 -25.67 -25.27 -24.80 -23.92 -22.46 -20.61 -19.00 -18.43 -19.42 -21.96 -25.42 -28.95 -31.80 -33.73 -34.90 -35.63 -35.99 -35.63 -33.84 -29.94 -23.57 -14.85 -4.39 --6.93 --18.21 --28.51 --37.02 --43.07 --46.20 --46.31 --43.68 --38.96 --33.03 --26.79 --20.78 --15.04 --9.02 --1.81 -7.33 -18.44 -30.43 -41.31 -48.91 -51.82 -49.95 -44.60 -37.70 -31.04 -25.69 -21.91 -19.47 -18.03 -17.24 -16.76 -16.20 -15.23 -13.71 -11.82 -9.93 -8.44 -7.60 -7.54 -8.36 -10.15 -12.78 -15.69 -17.84 -17.94 -15.11 -9.30 -1.52 --6.54 --13.24 --17.64 --19.82 --20.71 --21.62 --23.62 --27.15 --31.92 --37.07 --41.73 --45.43 --48.32 --50.97 --53.79 --56.59 --58.49 --58.34 --55.53 --50.40 --44.10 --37.91 --32.30 --26.73 --20.03 --11.31 --0.78 -10.15 -19.40 -25.22 -26.92 -25.15 -21.46 -17.71 -15.35 -15.06 -16.61 -19.07 -21.26 -22.15 -21.28 -18.90 -15.77 -12.69 -10.17 -8.13 -6.13 -3.63 -0.23 --4.16 --9.37 --14.96 --20.33 --24.82 --27.99 --29.75 --30.45 --30.74 --31.22 --32.10 --33.24 --34.30 --35.06 --35.65 --36.51 --38.06 --40.49 --43.63 --47.17 --50.83 --54.47 --58.04 --61.29 --63.80 --65.10 --65.02 --63.79 --62.03 --60.28 --58.74 --57.06 --54.55 --50.72 --45.61 --39.93 --34.62 --30.28 --26.78 --23.22 --18.47 --11.89 --3.78 -4.82 -12.77 -19.58 -25.65 -31.95 -39.16 -47.11 -54.64 -60.16 -62.53 -61.56 -58.01 -53.07 -47.73 -42.49 -37.42 -32.53 -27.86 -23.60 -19.79 -16.41 -13.39 -10.85 -9.00 -7.97 -7.50 -6.90 -5.42 -2.70 --0.91 --4.46 --7.02 --8.22 --8.45 --8.51 --9.01 --10.00 --11.02 --11.52 --11.29 --10.54 --9.67 --8.86 --7.90 --6.42 --4.16 --1.29 -1.75 -4.38 -6.27 -7.39 -7.93 -8.17 -8.28 -8.38 -8.57 -8.99 -9.73 -10.83 -12.23 -13.82 -15.46 -17.08 -18.64 -20.22 -21.90 -23.76 -25.77 -27.70 -29.21 -29.95 -29.74 -28.71 -27.33 -26.15 -25.56 -25.48 -25.45 -24.84 -23.23 -20.68 -17.72 -15.07 -13.36 -12.92 -13.73 -15.55 -17.98 -20.65 -23.18 -25.14 -26.09 -25.61 -23.45 -19.63 -14.42 -8.31 -1.86 --4.38 --9.97 --14.65 --18.35 --21.12 --23.07 --24.24 --24.51 --23.74 --21.88 --19.17 --16.21 --13.74 --12.37 --12.37 --13.60 --15.70 --18.40 --21.58 --25.10 --28.64 --31.56 --33.15 --32.93 --30.93 --27.72 --24.12 --20.71 --17.68 --14.88 --12.09 --9.30 --6.80 --4.93 --3.83 --3.36 --3.08 --2.63 --1.93 --1.32 --1.38 --2.70 --5.61 --10.11 --15.93 --22.70 --30.04 --37.47 --44.30 --49.64 --52.63 --52.69 --49.89 --44.86 --38.57 --31.91 --25.41 --19.20 --13.23 --7.54 --2.42 -1.75 -4.67 -6.40 -7.26 -7.75 -8.27 -8.96 -9.77 -10.65 -11.75 -13.37 -15.91 -19.50 -23.88 -28.45 -32.52 -35.65 -37.86 -39.52 -41.06 -42.72 -44.43 -45.94 -47.09 -47.86 -48.38 -48.78 -49.05 -48.92 -48.09 -46.32 -43.52 -39.82 -35.42 -30.56 -25.42 -20.14 -14.81 -9.51 -4.28 --0.81 --5.68 --10.17 --13.99 --16.76 --18.08 --17.68 --15.49 --11.70 --6.74 --1.15 -4.41 -9.34 -13.10 -15.37 -16.05 -15.22 -13.19 -10.36 -7.23 -4.22 -1.75 -0.08 --0.68 --0.65 --0.14 -0.49 -0.92 -0.97 -0.64 -0.04 --0.72 --1.54 --2.32 --2.97 --3.41 --3.64 --3.74 --3.92 --4.41 --5.37 --6.87 --8.84 --11.15 --13.56 --15.87 --17.89 --19.56 --20.98 --22.44 --24.23 --26.60 --29.57 --32.89 --36.17 --38.92 --40.73 --41.24 --40.18 --37.33 --32.63 --26.27 --18.63 --10.38 --2.19 -5.30 -11.68 -16.79 -20.69 -23.59 -25.70 -27.14 -27.90 -27.87 -27.00 -25.33 -23.12 -20.64 -18.22 -16.03 -14.17 -12.60 -11.26 -10.08 -8.92 -7.74 -6.48 -5.19 -3.96 -2.84 -1.75 -0.47 --1.27 --3.71 --6.81 --10.34 --13.92 --17.22 --20.08 --22.45 --24.36 --25.82 --26.78 --27.21 --27.19 --26.93 --26.70 --26.82 --27.43 --28.55 --30.01 --31.44 --32.33 --32.21 --30.68 --27.57 --23.06 --17.62 --11.94 --6.75 --2.53 -0.55 -2.69 -4.34 -5.98 -7.92 -10.20 -12.62 -14.81 -16.47 -17.41 -17.67 -17.44 -16.97 -16.49 -16.06 -15.64 -15.06 -14.17 -12.89 -11.35 -9.86 -8.81 -8.62 -9.53 -11.48 -14.13 -16.93 -19.26 -20.67 -21.03 -20.54 -19.59 -18.57 -17.68 -16.87 -15.99 -14.89 -13.57 -12.08 -10.38 -8.23 -5.25 -1.04 --4.44 --10.75 --17.04 --22.39 --26.09 --27.94 --28.05 --26.73 --24.14 --20.29 --15.24 --9.27 --3.06 -2.57 -6.88 -9.56 -10.79 -11.11 -11.08 -11.05 -11.07 -10.99 -10.71 -10.24 -9.80 -9.67 -10.01 -10.76 -11.69 -12.48 -12.92 -13.02 -13.00 -13.14 -13.69 -14.66 -15.80 -16.71 -16.96 -16.30 -14.73 -12.48 -9.91 -7.32 -4.81 -2.28 --0.48 --3.58 --6.97 --10.39 --13.54 --16.26 --18.54 --20.54 --22.46 --24.38 --26.21 --27.75 --28.80 --29.21 --28.93 --27.96 --26.30 --23.92 --20.79 --16.92 --12.42 --7.51 --2.53 -2.11 -6.07 -9.08 -11.08 -12.17 -12.55 -12.42 -11.88 -10.94 -9.62 -7.98 -6.19 -4.57 -3.49 -3.17 -3.66 -4.78 -6.19 -7.47 -8.32 -8.58 -8.25 -7.44 -6.23 -4.78 -3.20 -1.63 -0.15 --1.19 --2.45 --3.72 --5.16 --6.87 --8.89 --11.13 --13.47 --15.72 --17.71 --19.31 --20.57 --21.65 --22.91 --24.65 --26.97 --29.62 --32.01 --33.37 --33.04 --30.67 --26.32 --20.38 --13.46 --6.20 -0.82 -7.09 -12.25 -16.15 -18.79 -20.37 -21.21 -21.64 -21.86 -21.90 -21.67 -20.99 -19.77 -18.07 -16.17 -14.42 -13.15 -12.50 -12.39 -12.61 -12.89 -13.04 -12.99 -12.74 -12.28 -11.64 -10.82 -9.84 -8.76 -7.61 -6.41 -5.16 -3.84 -2.46 -1.03 --0.47 --2.12 --4.00 --6.14 --8.41 --10.57 --12.37 --13.69 --14.56 --15.21 --15.90 --16.85 --18.17 --19.86 --21.76 --23.71 --25.40 --26.43 --26.34 --24.80 --21.63 --16.97 --11.20 --4.83 -1.57 -7.58 -12.90 -17.36 -20.89 -23.46 -25.05 -25.69 -25.51 -24.72 -23.57 -22.34 -21.23 -20.31 -19.53 -18.78 -17.95 -17.02 -16.07 -15.22 -14.62 -14.28 -14.24 -14.40 -14.67 -14.87 -14.83 -14.31 -13.12 -11.16 -8.51 -5.46 -2.37 --0.42 --2.72 --4.55 --6.07 --7.51 --9.03 --10.71 --12.51 --14.30 --15.98 --17.51 --18.91 --20.29 --21.71 --23.23 --24.85 --26.45 --27.80 --28.58 --28.35 --26.72 --23.42 --18.47 --12.19 --5.21 -1.74 -7.99 -13.14 -17.06 -19.79 -21.46 -22.17 -22.05 -21.25 -19.97 -18.50 -17.07 -15.86 -14.89 -14.09 -13.37 -12.68 -12.05 -11.55 -11.25 -11.18 -11.30 -11.52 -11.71 -11.76 -11.53 -10.93 -9.89 -8.45 -6.75 -5.05 -3.60 -2.51 -1.69 -0.86 --0.31 --2.03 --4.24 --6.71 --9.11 --11.18 --12.87 --14.30 --15.62 --17.00 --18.55 --20.30 --22.20 --24.11 --25.69 --26.47 --25.93 --23.65 --19.47 --13.59 --6.56 -0.81 -7.71 -13.46 -17.73 -20.48 -21.94 -22.45 -22.33 -21.83 -21.07 -20.07 -18.84 -17.37 -15.68 -13.83 -11.97 -10.32 -9.18 -8.77 -9.18 -10.23 -11.56 -12.77 -13.53 -13.71 -13.38 -12.70 -11.80 -10.75 -9.50 -8.03 -6.35 -4.58 -2.88 -1.34 --0.07 --1.51 --3.19 --5.22 --7.57 --10.04 --12.41 --14.48 --16.23 --17.71 --19.02 --20.29 --21.60 --23.02 --24.56 --26.11 --27.40 --28.05 --27.59 --25.62 --21.99 --16.82 --10.52 --3.70 -3.01 -9.04 -14.00 -17.71 -20.16 -21.45 -21.77 -21.34 -20.39 -19.12 -17.63 -15.99 -14.19 -12.27 -10.33 -8.58 -7.23 -6.42 -6.19 -6.39 -6.78 -7.17 -7.45 -7.60 -7.62 -7.49 -7.14 -6.51 -5.57 -4.38 -3.05 -1.71 -0.48 --0.62 --1.67 --2.77 --4.04 --5.57 --7.36 --9.34 --11.37 --13.27 --14.92 --16.28 --17.41 --18.48 --19.70 --21.24 --23.06 --24.96 --26.54 --27.31 --26.89 --25.01 --21.68 --17.12 --11.69 --5.88 --0.17 -5.03 -9.41 -12.79 -15.13 -16.50 -17.04 -16.91 -16.28 -15.28 -14.02 -12.59 -11.06 -9.52 -8.12 -7.00 -6.28 -6.00 -6.14 -6.56 -7.11 -7.62 -7.90 -7.80 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--10.40 --14.00 --17.37 --20.60 --23.89 --27.38 --31.10 --34.96 --38.72 --42.08 --44.70 --46.16 --45.99 --43.71 --38.99 --31.88 --22.88 --12.88 --2.95 -5.99 -13.36 -19.04 -23.18 -26.03 -27.80 -28.57 -28.37 -27.36 -25.81 -24.11 -22.59 -21.44 -20.68 -20.19 -19.77 -19.29 -18.70 -18.05 -17.42 -16.87 -16.37 -15.79 -14.97 -13.73 -11.95 -9.57 -6.61 -3.16 --0.63 --4.54 --8.37 --12.01 --15.49 --18.91 --22.38 --25.95 --29.56 --33.16 --36.73 --40.27 --43.70 --46.71 --48.72 --48.90 --46.58 --41.44 --33.75 --24.28 --14.07 --4.09 -5.02 -12.93 -19.54 -24.81 -28.70 -31.16 -32.25 -32.23 -31.52 -30.57 -29.66 -28.83 -27.95 -26.94 -25.82 -24.83 -24.25 -24.18 -24.48 -24.84 -24.88 -24.37 -23.27 -21.70 -19.78 -17.59 -15.10 -12.29 -9.18 -5.89 -2.55 --0.78 --4.13 --7.66 --11.47 --15.59 --19.89 --24.12 --28.07 --31.66 --34.96 --38.18 --41.46 --44.74 --47.64 --49.51 --49.57 --47.24 --42.26 --34.83 --25.58 --15.34 --4.98 -4.80 -13.46 -20.66 -26.14 -29.72 -31.37 -31.27 -29.85 -27.76 -25.56 -23.65 -22.15 -21.00 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--2.09 --5.89 --10.23 --14.95 --19.72 --24.15 --28.00 --31.28 --34.16 --36.95 --39.94 --43.35 --47.28 --51.55 --55.67 --58.83 --60.00 --58.25 --52.99 --44.24 --32.69 --19.49 --6.02 -6.50 -17.21 -25.69 -31.92 -36.09 -38.48 -39.31 -38.83 -37.31 -35.09 -32.62 -30.31 -28.47 -27.23 -26.53 -26.21 -26.08 -26.04 -26.04 -26.11 -26.23 -26.31 -26.21 -25.68 -24.50 -22.63 -20.18 -17.42 -14.64 -11.94 -9.19 -6.06 -2.24 --2.40 --7.62 --12.99 --17.98 --22.27 --25.83 --28.92 --31.92 --35.14 --38.72 --42.62 --46.65 --50.51 --53.71 --55.67 --55.65 --52.97 --47.26 --38.59 --27.61 --15.39 --3.11 -8.21 -17.87 -25.56 -31.25 -34.99 -36.90 -37.14 -35.94 -33.60 -30.55 -27.24 -24.10 -21.51 -19.68 -18.67 -18.32 -18.32 -18.34 -18.13 -17.65 -16.94 -16.16 -15.27 -14.15 -12.61 -10.54 -7.98 -5.09 -1.97 --1.35 --4.98 --9.05 --13.57 --18.41 --23.29 --27.96 --32.30 --36.36 --40.31 --44.34 --48.61 --53.10 --57.60 --61.67 --64.60 --65.56 --63.79 --58.83 --50.72 --40.05 --27.76 --14.87 --2.37 -9.04 -18.83 -26.64 -32.27 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--4.08 --9.04 --14.07 --18.84 --23.18 --27.07 --30.68 --34.24 --38.03 --42.17 --46.55 --50.77 --54.20 --56.02 --55.39 --51.66 --44.59 --34.51 --22.32 --9.32 -3.15 -14.05 -22.83 -29.39 -33.93 -36.70 -37.91 -37.71 -36.28 -33.92 -31.03 -28.06 -25.41 -23.42 -22.26 -21.96 -22.37 -23.12 -23.76 -23.85 -23.11 -21.49 -19.14 -16.25 -12.98 -9.40 -5.52 -1.41 --2.80 --6.95 --10.93 --14.72 --18.44 --22.20 --26.10 --30.17 --34.44 --39.01 --43.96 --49.30 --54.72 --59.63 --63.07 --63.96 --61.37 --54.84 --44.57 --31.46 --16.90 --2.47 -10.47 -21.06 -28.97 -34.36 -37.63 -39.27 -39.70 -39.33 -38.48 -37.50 -36.66 -36.15 -36.06 -36.37 -36.94 -37.66 -38.39 -39.02 -39.50 -39.78 -39.72 -39.16 -37.86 -35.61 -32.35 -28.17 -23.36 -18.25 -13.19 -8.40 -3.97 --0.13 --4.02 --7.86 --11.80 --15.99 --20.44 --25.04 --29.60 --33.89 --37.83 --41.54 --45.36 --49.68 --54.74 --60.33 --65.75 --69.84 --71.23 --68.75 --61.84 --50.73 --36.50 --20.76 --5.26 -8.58 -19.87 -28.42 -34.45 -38.41 -40.74 -41.77 -41.75 -40.95 -39.65 -38.12 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--27.31 --28.97 --30.38 --30.71 --28.85 --23.70 --14.75 --2.41 -12.11 -27.24 -41.62 -54.58 -66.06 -76.18 -84.78 -91.30 -95.06 -95.89 -94.53 -92.42 -91.11 -91.33 -92.44 -92.78 -90.51 -84.64 -75.54 -64.67 -53.71 -43.75 -34.80 -26.08 -16.68 -6.08 --5.44 --17.02 --27.59 --36.20 --42.29 --45.72 --46.70 --45.66 --43.17 --39.78 --35.90 --31.81 --27.67 --23.66 --20.06 --17.26 --15.59 --15.30 --16.28 --18.09 --20.07 --21.45 --21.63 --20.31 --17.53 --13.52 --8.62 --3.19 -2.42 -7.82 -12.55 -16.14 -18.21 -18.55 -17.28 -14.73 -11.35 -7.53 -3.51 --0.56 --4.57 --8.38 --11.81 --14.72 --17.03 --18.77 --19.99 --20.81 --21.35 --21.77 --22.27 --23.04 --24.26 --26.00 --28.21 --30.75 --33.55 --36.63 --40.07 --43.88 --47.87 --51.62 --54.72 --56.95 --58.47 --59.77 --61.50 --64.17 --67.94 --72.48 --77.08 --80.68 --82.18 --80.59 --75.27 --66.18 --53.86 --39.49 --24.52 --10.54 -1.26 -10.16 -16.03 -19.24 -20.44 -20.37 -19.59 -18.50 -17.23 -15.87 -14.49 -13.21 -12.17 -11.47 -11.17 -11.27 -11.75 -12.59 -13.67 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--16.12 --11.73 --6.28 -0.82 -9.58 -19.18 -28.19 -35.23 -39.49 -41.04 -40.70 -39.52 -38.28 -37.37 -36.76 -36.36 -36.11 -35.98 -35.92 -35.74 -35.18 -34.06 -32.46 -30.72 -29.34 -28.66 -28.66 -28.98 -28.99 -28.21 -26.44 -23.86 -20.90 -18.00 -15.40 -13.04 -10.58 -7.62 -3.90 --0.54 --5.35 --10.00 --13.96 --16.94 --18.94 --20.19 --21.03 --21.71 --22.42 --23.22 --24.17 --25.22 --26.28 --27.12 --27.37 --26.66 --24.64 --21.09 --15.91 --9.05 --0.54 -9.49 -20.66 -32.31 -43.52 -53.26 -60.61 -64.96 -65.99 -63.71 -58.45 -50.92 -42.26 -33.87 -27.22 -23.25 -22.03 -22.43 -22.52 -20.15 -13.85 -3.39 --10.18 --24.90 --38.72 --50.08 --58.27 --63.35 --65.87 --66.58 --66.22 --65.50 --65.18 --65.99 --68.58 --73.25 --79.72 --87.08 --93.88 --98.67 --100.45 --99.04 --94.82 --88.42 --80.10 --69.62 --56.51 --40.74 --23.09 --5.26 -10.73 -23.32 -31.90 -36.89 -39.28 -40.06 -39.89 -39.11 -38.05 -37.24 -37.44 -39.32 -43.21 -48.88 -55.62 -62.53 -68.76 -73.65 -76.84 -78.15 -77.50 -74.95 -70.65 -64.97 -58.58 -52.30 -46.87 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-48.83 -45.84 -39.45 -30.43 -19.99 -9.46 -0.16 --6.96 --11.51 --13.64 --13.87 --12.78 --10.78 --8.08 --4.80 --1.15 -2.44 -5.41 -7.17 -7.34 -5.78 -2.62 --1.87 --7.26 --13.02 --18.57 --23.33 --26.87 --29.12 --30.34 --31.04 --31.68 --32.43 --33.03 --32.98 --31.73 --28.95 --24.76 --19.57 --14.00 --8.59 --3.83 -0.01 -2.76 -4.44 -5.25 -5.56 -5.85 -6.49 -7.66 -9.24 -10.92 -12.33 -13.24 -13.55 -13.31 -12.59 -11.48 -10.11 -8.69 -7.48 -6.68 -6.39 -6.48 -6.69 -6.75 -6.51 -5.94 -5.19 -4.36 -3.46 -2.38 -0.96 --0.98 --3.51 --6.57 --9.85 --12.93 --15.28 --16.43 --16.06 --14.10 --10.76 --6.44 --1.66 -3.06 -7.28 -10.70 -13.17 -14.70 -15.36 -15.27 -14.56 -13.37 -11.87 -10.21 -8.54 -6.96 -5.51 -4.18 -2.94 -1.82 -0.84 -0.04 --0.58 --1.06 --1.48 --1.87 --2.30 --2.83 --3.56 --4.62 --6.08 --7.88 --9.85 --11.74 --13.35 --14.61 --15.59 --16.49 --17.50 --18.76 --20.28 --21.86 --23.18 --23.84 --23.38 --21.49 --18.06 --13.29 --7.64 --1.72 -3.84 -8.57 -12.17 -14.62 -16.04 -16.68 -16.80 -16.61 -16.25 -15.79 -15.23 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--20.48 --24.45 --28.36 --31.85 --34.67 --36.55 --37.28 --36.54 --33.97 --29.24 --22.23 --13.27 --3.14 -6.98 -15.80 -22.19 -25.42 -25.34 -22.40 -17.61 -12.33 -8.04 -5.95 -6.69 -10.14 -15.59 -21.88 -27.80 -32.33 -34.78 -34.78 -32.29 -27.54 -21.05 -13.67 -6.39 -0.14 --4.41 --7.01 --7.81 --7.25 --5.83 --3.93 --1.88 -0.12 -1.88 -3.26 -4.13 -4.47 -4.31 -3.72 -2.71 -1.19 --1.01 --4.02 --7.75 --11.88 --15.75 --18.55 --19.44 --17.84 --13.53 --6.78 -1.70 -10.83 -19.38 -26.19 -30.49 -31.98 -30.95 -28.11 -24.40 -20.65 -17.40 -14.86 -12.87 -11.13 -9.26 -6.97 -4.12 -0.80 --2.72 --6.09 --8.90 --10.84 --11.75 --11.67 --10.76 --9.35 --7.79 --6.39 --5.43 --5.04 --5.19 --5.68 --6.22 --6.56 --6.57 --6.35 --6.23 --6.53 --7.43 --8.75 --9.96 --10.31 --9.22 --6.47 --2.35 -2.47 -7.31 -11.66 -15.31 -18.28 -20.66 -22.41 -23.45 -23.62 -22.88 -21.32 -19.15 -16.62 -13.94 -11.26 -8.72 -6.45 -4.57 -3.18 -2.33 -1.97 -1.98 -2.21 -2.47 -2.54 -2.26 -1.50 -0.28 --1.31 --3.07 --4.79 --6.40 --7.93 --9.53 --11.33 --13.33 --15.39 --17.28 --18.76 --19.65 --19.86 --19.35 --18.13 --16.23 --13.75 --10.85 --7.76 --4.72 --1.83 -0.88 -3.41 -5.71 -7.64 -9.05 -9.82 -10.01 -9.78 -9.39 -9.00 -8.70 -8.39 -7.94 -7.28 -6.45 -5.56 -4.75 -4.08 -3.47 -2.82 -1.97 -0.83 --0.56 --2.16 --3.83 --5.46 --6.92 --8.16 --9.15 --9.95 --10.63 --11.34 --12.17 --13.14 --14.13 --14.91 --15.12 --14.41 --12.50 --9.34 --5.16 --0.44 -4.21 -8.24 -11.34 -13.52 -15.00 -16.05 -16.86 -17.39 -17.49 -17.00 -15.93 -14.47 -12.95 -11.67 -10.80 -10.28 -9.91 -9.48 -8.94 -8.37 -7.96 -7.95 -8.50 -9.63 -11.17 -12.81 -14.12 -14.77 -14.69 -14.18 -13.93 -14.72 -17.06 -20.81 -25.05 -28.36 -29.51 -28.08 -24.90 -21.78 -20.71 -22.86 -27.93 -34.19 -39.47 -42.26 -42.70 -42.49 -43.99 -48.76 -56.34 -63.75 -66.24 -58.81 -37.84 -2.52 --44.30 --96.06 --143.43 --175.74 --183.40 --160.47 --106.73 --28.70 -61.07 -146.63 -213.03 -250.74 -258.47 -242.77 -214.34 -183.03 -153.71 -125.53 -94.27 -56.69 -13.98 --27.73 --60.09 --76.92 --77.39 --66.39 --51.77 --40.65 --36.42 --38.23 --42.78 --46.96 --49.93 --53.37 --60.36 --72.99 --90.85 --110.74 --127.98 --138.58 --140.92 --136.23 --127.77 --119.12 --112.51 --108.02 --103.90 --97.77 --88.10 --75.01 --60.35 --46.75 --36.58 --30.90 --29.21 --29.80 --30.66 --30.30 --28.26 --25.09 --21.82 --19.35 --17.90 --16.97 --15.66 --13.20 --9.40 --4.68 -0.04 -3.85 -6.19 -7.12 -7.19 -7.10 -7.45 -8.43 -9.85 -11.30 -12.35 -12.78 -12.59 -11.94 -10.98 -9.82 -8.45 -6.77 -4.77 -2.54 -0.29 --1.62 --2.84 --3.10 --2.30 --0.58 -1.78 -4.43 -7.06 -9.46 -11.51 -13.09 -14.03 -14.17 -13.45 -11.96 -9.99 -7.97 -6.33 -5.34 -5.09 -5.42 -6.07 -6.71 -7.07 -6.98 -6.36 -5.23 -3.73 -2.11 -0.63 --0.51 --1.25 --1.68 --1.99 --2.44 --3.19 --4.22 --5.31 --6.14 --6.45 --6.03 --4.89 --3.24 --1.38 -0.26 -1.30 -1.46 -0.64 --1.03 --3.15 --5.21 --6.70 --7.21 --6.49 --4.55 --1.57 -2.09 -6.01 -9.76 -12.98 -15.48 -17.23 -18.28 -18.71 -18.53 -17.76 -16.42 -14.68 -12.72 -10.73 -8.83 -7.03 -5.35 -3.80 -2.49 -1.55 -1.13 -1.22 -1.67 -2.20 -2.46 -2.17 -1.18 --0.48 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--6.71 --7.04 --7.35 --7.72 --8.22 --8.86 --9.61 --10.42 --11.17 --11.67 --11.72 --11.17 --10.03 --8.40 --6.50 --4.58 --2.75 --1.09 -0.42 -1.82 -3.14 -4.35 -5.44 -6.37 -7.10 -7.56 -7.69 -7.42 -6.75 -5.79 -4.75 -3.88 -3.40 -3.38 -3.72 -4.17 -4.44 -4.37 -3.91 -3.16 -2.32 -1.59 -1.20 -1.39 -2.25 -3.67 -5.29 -6.49 -6.63 -5.33 -2.65 --0.83 --4.24 --6.74 --7.78 --7.23 --5.43 --2.98 --0.64 -0.83 -0.91 --0.54 --3.25 --6.55 --9.60 --11.65 --12.26 --11.43 --9.61 --7.42 --5.46 --4.16 --3.62 --3.66 --3.98 --4.23 --4.14 --3.59 --2.51 --0.91 -1.20 -3.72 -6.41 -8.93 -10.87 -11.90 -11.89 -10.99 -9.50 -7.82 -6.31 -5.13 -4.31 -3.77 -3.39 -3.10 -2.91 -2.81 -2.78 -2.75 -2.60 -2.22 -1.64 -0.93 -0.27 --0.20 --0.39 --0.31 --0.04 -0.32 -0.65 -0.86 -0.90 -0.76 -0.45 --0.01 --0.55 --1.16 --1.84 --2.59 --3.43 --4.32 --5.22 --6.03 --6.72 --7.23 --7.56 --7.72 --7.78 --7.83 --7.99 --8.36 --8.93 --9.64 --10.25 --10.52 --10.23 --9.31 --7.80 --5.87 --3.72 --1.52 -0.58 -2.51 -4.22 -5.67 -6.80 -7.58 -8.01 -8.11 -7.97 -7.67 -7.31 -6.91 -6.44 -5.85 -5.13 -4.37 -3.75 -3.44 -3.52 -3.92 -4.49 -5.03 -5.46 -5.75 -5.92 -5.95 -5.77 -5.27 -4.37 -3.19 -1.88 -0.72 --0.11 --0.60 --0.85 --1.08 --1.50 --2.19 --3.12 --4.19 --5.22 --6.11 --6.83 --7.37 --7.78 --8.07 --8.29 --8.55 --8.95 --9.58 --10.44 --11.33 --11.90 --11.78 --10.70 --8.63 --5.78 --2.51 -0.76 -3.72 -6.17 -8.04 -9.31 -10.04 -10.26 -10.02 -9.43 -8.61 -7.75 -7.02 -6.50 -6.17 -5.96 -5.72 -5.38 -4.94 -4.44 -3.97 -3.65 -3.51 -3.58 -3.80 -4.10 -4.35 -4.46 -4.30 -3.82 -3.03 -1.97 -0.75 --0.52 --1.68 --2.60 --3.17 --3.43 --3.55 --3.79 --4.35 --5.27 --6.43 --7.60 --8.56 --9.24 --9.69 --10.05 --10.43 --10.91 --11.46 --12.10 --12.84 --13.60 --14.18 --14.22 --13.31 --11.22 --8.04 --4.17 --0.24 -3.18 -5.70 -7.24 -7.94 -8.06 -7.85 -7.53 -7.25 -7.06 -6.95 -6.80 -6.48 -5.85 -4.87 -3.67 -2.44 -1.46 -0.92 -0.92 -1.39 -2.19 -3.07 -3.83 -4.30 -4.40 -4.11 -3.49 -2.65 -1.76 -0.97 -0.36 --0.08 --0.44 --0.85 --1.35 --1.90 --2.45 --2.96 --3.48 --4.09 --4.87 --5.79 --6.72 --7.47 --7.89 --7.99 --7.93 --7.90 --8.08 --8.54 --9.21 --9.87 --10.25 --10.08 --9.15 --7.38 --4.88 --1.90 -1.22 -4.11 -6.53 -8.35 -9.60 -10.38 -10.83 -11.08 -11.20 -11.26 -11.27 -11.28 -11.25 -11.14 -10.78 -10.00 -8.69 -6.94 -5.03 -3.38 -2.32 -1.91 -1.97 -2.13 -2.11 -1.93 -1.93 -2.53 -3.99 -6.15 -8.43 -9.97 -9.96 -7.86 -3.68 --1.99 --8.10 --13.39 --16.68 --17.25 --15.06 --10.83 --5.86 --1.62 -0.67 -0.47 --1.98 --5.74 --9.48 --11.85 --11.90 --9.33 --4.55 -1.44 -7.31 -11.81 -14.11 -14.01 -11.92 -8.69 -5.26 -2.47 -0.85 -0.61 -1.67 -3.74 -6.34 -8.83 -10.58 -11.09 -10.17 -8.03 -5.22 -2.40 -0.18 --1.09 --1.34 --0.76 -0.34 -1.57 -2.58 -3.13 -3.13 -2.61 -1.69 -0.49 --0.88 --2.38 --3.97 --5.56 --7.00 --8.12 --8.83 --9.14 --9.18 --9.13 --9.15 --9.34 --9.71 --10.22 --10.77 --11.20 --11.29 --10.80 --9.52 --7.37 --4.53 --1.36 -1.72 -4.30 -6.21 -7.42 -8.00 -8.09 -7.76 -7.07 -6.11 -5.02 -3.93 -2.97 -2.17 -1.49 -0.92 -0.48 -0.23 -0.29 -0.69 -1.37 -2.15 -2.76 -3.00 -2.74 -1.98 -0.86 --0.40 --1.64 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-4.37 -4.66 -4.96 -5.18 -5.29 -5.24 -4.98 -4.51 -3.81 -2.95 -2.03 -1.19 -0.50 --0.02 --0.44 --0.86 --1.37 --2.05 --2.89 --3.83 --4.77 --5.64 --6.37 --6.95 --7.41 --7.78 --8.12 --8.49 --8.96 --9.59 --10.36 --11.22 --12.00 --12.52 --12.56 --11.96 --10.65 --8.67 --6.15 --3.25 --0.18 -2.82 -5.49 -7.60 -9.04 -9.81 -10.00 -9.78 -9.29 -8.61 -7.83 -7.03 -6.33 -5.84 -5.63 -5.64 -5.77 -5.94 -6.08 -6.26 -6.50 -6.80 -7.09 -7.22 -7.13 -6.81 -6.38 -5.94 -5.58 -5.24 -4.82 -4.25 -3.52 -2.72 -1.96 -1.29 -0.67 -0.03 --0.75 --1.67 --2.64 --3.51 --4.13 --4.47 --4.61 --4.73 --5.01 --5.56 --6.44 --7.57 --8.82 --9.95 --10.69 --10.80 --10.11 --8.63 --6.55 --4.14 --1.70 -0.58 -2.61 -4.39 -5.94 -7.25 -8.26 -8.90 -9.13 -8.99 -8.59 -8.06 -7.52 -7.00 -6.50 -5.98 -5.47 -5.02 -4.76 -4.80 -5.17 -5.74 -6.32 -6.74 -6.86 -6.69 -6.29 -5.78 -5.26 -4.73 -4.16 -3.47 -2.58 -1.50 -0.33 --0.78 --1.67 --2.23 --2.42 --2.33 --2.10 --1.94 --2.06 --2.58 --3.52 --4.79 --6.19 --7.51 --8.59 --9.36 --9.83 --10.16 --10.43 --10.73 --11.03 --11.28 --11.38 --11.20 --10.66 --9.71 --8.36 --6.69 --4.82 --2.86 --0.86 -1.15 -3.21 -5.33 -7.42 -9.31 -10.74 -11.49 -11.44 -10.66 -9.33 -7.69 -5.97 -4.31 -2.83 -1.69 -1.10 -1.24 -2.16 -3.69 -5.42 -6.81 -7.42 -7.03 -5.74 -3.91 -1.92 -0.13 --1.25 --2.10 --2.47 --2.46 --2.19 --1.88 --1.69 --1.78 --2.18 --2.82 --3.53 --4.16 --4.62 --4.92 --5.16 --5.49 --5.97 --6.61 --7.34 --8.04 --8.67 --9.24 --9.80 --10.41 --11.02 --11.44 --11.40 --10.64 --9.01 --6.61 --3.67 --0.61 -2.21 -4.54 -6.32 -7.61 -8.53 -9.13 -9.40 -9.29 -8.77 -7.89 -6.78 -5.62 -4.59 -3.83 -3.41 -3.37 -3.70 -4.35 -5.21 -6.10 -6.85 -7.31 -7.43 -7.20 -6.72 -6.08 -5.36 -4.64 -3.96 -3.34 -2.77 -2.24 -1.72 -1.17 -0.56 --0.17 --1.04 --2.05 --3.19 --4.41 --5.61 --6.72 --7.62 --8.23 --8.50 --8.46 --8.26 --8.12 --8.26 --8.81 --9.69 --10.61 --11.17 --10.99 --9.87 --7.84 --5.17 --2.23 -0.61 -3.10 -5.06 -6.47 -7.35 -7.82 -8.00 -8.01 -7.91 -7.73 -7.42 -6.97 -6.41 -5.84 -5.34 -5.01 -4.89 -4.93 -5.11 -5.40 -5.74 -6.10 -6.33 -6.28 -5.87 -5.12 -4.18 -3.28 -2.58 -2.07 -1.66 -1.16 -0.46 --0.45 --1.48 --2.52 --3.48 --4.33 --5.08 --5.74 --6.30 --6.74 --7.10 --7.44 --7.82 --8.29 --8.83 --9.37 --9.89 --10.40 --11.01 --11.82 --12.88 --14.08 --15.14 --15.72 --15.52 --14.36 --12.27 --9.42 --6.05 --2.45 -1.09 -4.28 -6.86 -8.64 -9.58 -9.78 -9.47 -8.86 -8.18 -7.53 -6.97 -6.53 -6.26 -6.16 -6.23 -6.36 -6.41 -6.31 -6.06 -5.74 -5.47 -5.31 -5.22 -5.10 -4.86 -4.45 -3.92 -3.36 -2.79 -2.18 -1.46 -0.60 --0.38 --1.38 --2.31 --3.14 --3.89 --4.60 --5.31 --5.95 --6.43 --6.65 --6.62 --6.43 --6.24 --6.22 --6.49 --7.06 --7.85 --8.68 --9.31 --9.51 --9.08 --7.91 --6.05 --3.65 --0.95 -1.78 -4.36 -6.66 -8.61 -10.14 -11.18 -11.65 -11.59 -11.12 -10.46 -9.83 -9.34 -8.99 -8.67 -8.27 -7.77 -7.26 -6.85 -6.62 -6.50 -6.38 -6.18 -5.88 -5.51 -5.15 -4.82 -4.47 -4.01 -3.36 -2.55 -1.67 -0.86 -0.25 --0.15 --0.43 --0.76 --1.32 --2.22 --3.44 --4.87 --6.36 --7.75 --9.00 --10.12 --11.12 --12.06 --12.92 --13.67 --14.28 --14.66 --14.72 --14.28 --13.16 --11.23 --8.50 --5.16 --1.59 -1.78 -4.58 -6.62 -7.89 -8.58 -8.89 -9.04 -9.12 -9.13 -8.96 -8.46 -7.59 -6.37 -4.97 -3.62 -2.50 -1.76 -1.40 -1.37 -1.57 -1.91 -2.28 -2.58 -2.70 -2.58 -2.20 -1.59 -0.87 -0.15 --0.44 --0.86 --1.07 --1.09 --1.00 --0.88 --0.89 --1.15 --1.74 --2.64 --3.74 --4.87 --5.89 --6.72 --7.36 --7.84 --8.23 --8.56 --8.85 --9.15 --9.52 --10.01 --10.54 --10.92 --10.90 --10.17 --8.62 --6.32 --3.51 --0.54 -2.24 -4.60 -6.44 -7.72 -8.52 -8.90 -8.93 -8.66 -8.16 -7.53 -6.87 -6.31 -5.95 -5.84 -5.95 -6.21 -6.47 -6.63 -6.64 -6.52 -6.35 -6.19 -6.11 -6.03 -5.83 -5.37 -4.55 -3.40 -2.03 -0.62 --0.67 --1.76 --2.71 --3.60 --4.46 --5.28 --5.92 --6.28 --6.28 --5.93 --5.38 --4.81 --4.40 --4.33 --4.69 --5.49 --6.62 --7.91 --9.08 --9.86 --10.06 --9.55 --8.32 --6.48 --4.19 --1.62 -1.01 -3.49 -5.69 -7.48 -8.83 -9.77 -10.31 -10.49 -10.33 -9.91 -9.30 -8.65 -8.07 -7.63 -7.32 -7.08 -6.89 -6.70 -6.55 -6.47 -6.49 -6.58 -6.69 -6.74 -6.67 -6.41 -5.90 -5.14 -4.14 -2.97 -1.74 -0.52 --0.64 --1.70 --2.65 --3.45 --4.13 --4.73 --5.32 --5.98 --6.73 --7.55 --8.37 --9.13 --9.84 --10.57 --11.39 --12.28 --13.12 --13.65 --13.60 --12.78 --11.16 --8.89 --6.22 --3.44 --0.75 -1.70 -3.83 -5.59 -6.97 -7.93 -8.46 -8.58 -8.35 -7.86 -7.24 -6.60 -6.08 -5.74 -5.62 -5.66 -5.79 -5.94 -6.03 -6.04 -6.00 -5.94 -5.86 -5.72 -5.44 -4.95 -4.25 -3.40 -2.50 -1.65 -0.90 -0.22 --0.41 --1.05 --1.67 --2.25 --2.77 --3.25 --3.69 --4.12 --4.54 --4.95 --5.39 --5.87 --6.41 --7.01 --7.63 --8.29 --9.06 --9.99 --11.12 --12.38 --13.57 --14.37 --14.49 --13.70 --11.98 --9.50 --6.54 --3.45 --0.58 -1.84 -3.76 -5.16 -6.13 -6.75 -7.09 -7.18 -7.10 -6.89 -6.63 -6.35 -6.04 -5.68 -5.25 -4.86 -4.63 -4.68 -4.98 -5.37 -5.56 -5.31 -4.56 -3.44 -2.25 -1.26 -0.58 -0.18 --0.11 --0.42 --0.81 --1.21 --1.54 --1.78 --2.00 --2.30 --2.80 --3.51 --4.38 --5.32 --6.19 --6.92 --7.47 --7.85 --8.10 --8.28 --8.45 --8.65 --8.84 --8.98 --9.02 --8.95 --8.80 --8.55 --8.14 --7.43 --6.28 --4.59 --2.39 -0.13 -2.74 -5.18 -7.30 -9.04 -10.35 -11.21 -11.51 -11.21 -10.34 -9.09 -7.72 -6.48 -5.50 -4.84 -4.44 -4.27 -4.33 -4.67 -5.30 -6.14 -6.98 -7.60 -7.82 -7.58 -6.96 -6.12 -5.25 -4.48 -3.86 -3.35 -2.84 -2.24 -1.46 -0.49 --0.71 --2.08 --3.56 --5.02 --6.33 --7.38 --8.15 --8.67 --9.07 --9.50 --10.02 --10.64 --11.29 --11.85 --12.20 --12.22 --11.78 --10.74 --9.04 --6.73 --4.02 --1.19 -1.48 -3.81 -5.73 -7.26 -8.41 -9.15 -9.41 -9.18 -8.61 -7.94 -7.43 -7.19 -7.15 -7.11 -6.85 -6.28 -5.45 -4.54 -3.73 -3.13 -2.78 -2.63 -2.66 -2.85 -3.19 -3.60 -3.96 -4.12 -3.90 -3.29 -2.33 -1.21 -0.13 --0.70 --1.19 --1.39 --1.46 --1.60 --1.98 --2.71 --3.72 --4.89 --6.00 --6.88 --7.52 --8.02 --8.57 --9.34 --10.43 --11.77 --13.19 --14.42 --15.17 --15.20 --14.30 --12.42 --9.62 --6.18 --2.47 -1.10 -4.19 -6.64 -8.46 -9.77 -10.65 -11.15 -11.24 -10.93 -10.30 -9.51 -8.77 -8.20 -7.84 -7.61 -7.40 -7.19 -6.99 -6.87 -6.83 -6.85 -6.81 -6.61 -6.20 -5.60 -4.89 -4.13 -3.41 -2.77 -2.25 -1.86 -1.55 -1.22 -0.75 -0.07 --0.83 --1.85 --2.84 --3.70 --4.43 --5.10 --5.86 --6.75 --7.73 --8.63 --9.31 --9.70 --9.86 --9.95 --10.14 --10.51 --11.07 --11.72 --12.33 --12.80 --13.04 --12.98 --12.50 --11.47 --9.80 --7.52 --4.75 --1.74 -1.26 -4.05 -6.49 -8.53 -10.18 -11.37 -12.09 -12.30 -12.03 -11.37 -10.48 -9.52 -8.63 -7.90 -7.35 -6.97 -6.72 -6.58 -6.52 -6.49 -6.39 -6.11 -5.61 -4.87 -4.03 -3.31 -2.89 -2.87 -3.18 -3.56 -3.63 -3.11 -1.83 --0.11 --2.45 --4.84 --6.95 --8.51 --9.37 --9.54 --9.11 --8.30 --7.35 --6.49 --5.85 --5.48 --5.31 --5.23 --5.10 --4.72 --3.93 --2.61 --0.75 -1.52 -3.96 -6.25 -8.14 -9.45 -10.14 -10.22 -9.80 -9.00 -7.95 -6.90 -6.03 -5.48 -5.23 -5.17 -5.13 -5.03 -4.87 -4.76 -4.79 -4.94 -5.09 -5.07 -4.80 -4.32 -3.81 -3.45 -3.26 -3.14 -2.85 -2.19 -1.15 --0.11 --1.34 --2.30 --2.87 --3.10 --3.12 --3.11 --3.19 --3.46 --3.94 --4.65 --5.50 --6.40 --7.20 --7.81 --8.21 --8.42 --8.58 --8.82 --9.28 --10.02 --11.04 --12.23 --13.32 --14.02 --14.00 --13.08 --11.25 --8.71 --5.75 --2.70 -0.19 -2.79 -5.03 -6.86 -8.18 -8.89 -8.96 -8.43 -7.52 -6.49 -5.63 -5.06 -4.79 -4.72 -4.73 -4.80 -4.97 -5.33 -5.91 -6.57 -7.08 -7.19 -6.76 -5.80 -4.52 -3.12 -1.83 -0.77 --0.01 --0.54 --0.83 --0.94 --0.93 --0.86 --0.83 --0.93 --1.21 --1.73 --2.45 --3.34 --4.35 --5.42 --6.50 --7.55 --8.53 --9.39 --10.05 --10.34 --10.13 --9.33 --7.95 --6.12 --4.08 --2.05 --0.17 -1.48 -2.93 -4.22 -5.32 -6.18 -6.76 -7.01 -6.99 -6.78 -6.46 -6.14 -5.85 -5.57 -5.25 -4.86 -4.39 -3.90 -3.48 -3.22 -3.16 -3.29 -3.52 -3.73 -3.81 -3.64 -3.19 -2.44 -1.46 -0.37 --0.67 --1.54 --2.17 --2.54 --2.76 --2.91 --3.06 --3.23 --3.40 --3.56 --3.75 --4.04 --4.56 --5.37 --6.48 --7.81 --9.25 --10.65 --11.89 --12.88 --13.56 --13.83 --13.61 --12.82 --11.35 --9.23 --6.53 --3.41 --0.14 -2.97 -5.65 -7.68 -8.96 -9.49 -9.40 -8.83 -7.90 -6.73 -5.45 -4.24 -3.33 -2.94 -3.17 -3.96 -5.09 -6.22 -7.04 -7.33 -7.06 -6.31 -5.22 -3.93 -2.59 -1.33 -0.33 --0.22 --0.18 -0.43 -1.41 -2.41 -3.02 -2.96 -2.12 -0.64 --1.27 --3.31 --5.27 --7.02 --8.50 --9.65 --10.43 --10.80 --10.73 --10.28 --9.62 --9.00 --8.67 --8.80 --9.37 --10.16 --10.77 --10.79 --9.94 --8.20 --5.77 --2.93 -0.02 -2.91 -5.65 -8.16 -10.30 -11.89 -12.73 -12.76 -12.01 -10.70 -9.08 -7.43 -5.95 -4.76 -3.95 -3.50 -3.38 -3.53 -3.90 -4.39 -4.96 -5.58 -6.21 -6.83 -7.36 -7.73 -7.83 -7.59 -6.96 -5.96 -4.63 -3.07 -1.40 --0.27 --1.86 --3.26 --4.42 --5.31 --5.95 --6.39 --6.75 --7.14 --7.67 --8.41 --9.34 --10.32 --11.09 --11.40 --11.02 --9.87 --7.98 --5.54 --2.86 --0.22 -2.11 -3.97 -5.33 -6.24 -6.76 -7.02 -7.05 -6.89 -6.57 -6.16 -5.73 -5.31 -4.90 -4.44 -3.88 -3.22 -2.55 -2.01 -1.77 -1.88 -2.32 -2.94 -3.54 -3.97 -4.12 -4.03 -3.78 -3.50 -3.27 -3.10 -2.91 -2.60 -2.02 -1.09 --0.22 --1.83 --3.61 --5.41 --7.02 --8.29 --9.09 --9.37 --9.16 --8.61 --7.94 --7.41 --7.25 --7.52 --8.14 --8.87 --9.39 --9.44 --8.87 --7.65 --5.88 --3.66 --1.13 -1.60 -4.35 -6.91 -9.02 -10.43 -10.98 -10.65 -9.54 -7.87 -5.95 -4.05 -2.45 -1.34 -0.80 -0.84 -1.35 -2.19 -3.11 -3.90 -4.37 -4.42 -4.05 -3.37 -2.51 -1.59 -0.71 --0.14 --0.98 --1.82 --2.64 --3.39 --4.06 --4.65 --5.23 --5.86 --6.56 --7.26 --7.85 --8.25 --8.46 --8.60 --8.86 --9.36 --10.06 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--10.24 --9.25 --8.87 --9.46 --11.00 --13.05 --14.91 --15.82 --15.32 --13.31 --10.12 --6.32 --2.53 -0.76 -3.27 -4.97 -6.02 -6.59 -6.84 -6.88 -6.75 -6.49 -6.11 -5.65 -5.18 -4.86 -4.83 -5.19 -5.86 -6.63 -7.22 -7.44 -7.26 -6.83 -6.37 -6.06 -5.96 -5.94 -5.78 -5.33 -4.56 -3.56 -2.49 -1.52 -0.69 --0.03 --0.74 --1.55 --2.47 --3.38 --4.15 --4.68 --4.96 --5.09 --5.16 --5.25 --5.41 --5.75 --6.46 --7.72 --9.61 --11.89 --14.01 --15.23 --14.92 --12.89 --9.45 --5.31 --1.25 -2.18 -4.78 -6.62 -7.88 -8.65 -8.91 -8.70 -8.13 -7.49 -7.10 -7.21 -7.82 -8.75 -9.73 -10.48 -10.83 -10.69 -10.06 -9.06 -7.90 -6.86 -6.15 -5.81 -5.68 -5.52 -5.10 -4.42 -3.69 -3.19 -3.11 -3.37 -3.69 -3.67 -2.98 -1.53 --0.53 --2.83 --4.99 --6.65 --7.68 --8.13 --8.23 --8.32 --8.72 --9.61 --10.97 --12.60 --14.13 --15.15 --15.31 --14.41 --12.42 --9.54 --6.10 --2.56 -0.64 -3.19 -4.92 -5.92 -6.40 -6.61 -6.66 -6.51 -6.00 -4.99 -3.55 -1.96 -0.69 -0.17 -0.60 -1.86 -3.54 -5.20 -6.55 -7.55 -8.35 -9.10 -9.80 -10.25 -10.16 -9.33 -7.83 -5.93 -4.03 -2.46 -1.28 -0.42 --0.37 --1.25 --2.30 --3.41 --4.46 --5.32 --5.95 --6.40 --6.79 --7.29 --7.96 --8.85 --9.97 --11.31 --12.83 --14.35 --15.54 --15.94 --15.15 --12.94 --9.49 --5.33 --1.19 -2.27 -4.73 -6.23 -7.07 -7.63 -8.11 -8.49 -8.57 -8.21 -7.39 -6.37 -5.56 -5.39 -6.03 -7.39 -9.04 -10.48 -11.39 -11.68 -11.52 -11.13 -10.68 -10.08 -9.12 -7.59 -5.44 -2.92 -0.35 --1.86 --3.47 --4.46 --5.01 --5.38 --5.83 --6.47 --7.36 --8.45 --9.59 --10.65 --11.49 --12.01 --12.19 --12.10 --11.92 --11.85 --12.18 --13.05 --14.42 --16.03 --17.40 --17.94 --17.15 --14.80 --11.02 --6.38 --1.59 -2.66 -5.87 -7.97 -9.14 -9.68 -9.87 -9.86 -9.71 -9.40 -9.00 -8.66 -8.57 -8.85 -9.45 -10.16 -10.70 -10.89 -10.69 -10.25 -9.82 -9.61 -9.59 -9.51 -8.95 -7.61 -5.44 -2.75 -0.06 --2.05 --3.24 --3.50 --3.17 --2.76 --2.75 --3.39 --4.62 --6.12 --7.46 --8.24 --8.27 --7.67 --6.79 --6.15 --6.23 --7.36 --9.54 --12.45 --15.47 --17.88 --19.06 --18.63 --16.58 --13.25 --9.17 --4.89 --0.87 -2.61 -5.42 -7.52 -8.98 -9.88 -10.41 -10.81 -11.29 -12.04 -13.08 -14.28 -15.45 -16.39 -16.88 -16.80 -16.04 -14.65 -12.80 -10.76 -8.81 -7.16 -5.85 -4.72 -3.55 -2.19 -0.58 --1.13 --2.80 --4.27 --5.59 --6.91 --8.35 --10.01 --11.78 --13.53 --15.02 --16.10 --16.75 --17.17 --17.68 --18.64 --20.26 --22.50 --25.00 --27.07 --27.92 --26.93 --23.87 --19.12 --13.50 --8.12 --4.04 --1.92 --1.76 --2.87 --3.94 --3.37 -0.28 -7.83 -19.06 -32.64 -46.39 -57.78 -64.80 -66.48 -63.14 -56.04 -46.84 -36.93 -27.09 -17.58 -8.44 --0.11 --7.60 --13.46 --17.38 --19.54 --20.60 --21.35 --22.35 --23.76 --25.34 --26.82 --28.11 --29.38 --30.93 --32.95 --35.32 --37.58 --39.22 --39.86 --39.44 --38.09 --36.06 --33.43 --30.08 --25.81 --20.46 --14.12 --7.14 --0.17 -6.13 -11.22 -14.89 -17.27 -18.69 -19.53 -20.01 -20.20 -20.01 -19.37 -18.38 -17.27 -16.44 -16.18 -16.54 -17.26 -17.89 -17.99 -17.29 -15.81 -13.81 -11.61 -9.52 -7.69 -6.27 -5.31 -4.94 -5.19 -5.87 -6.52 -6.44 -4.95 -1.75 --2.89 --8.19 --13.08 --16.70 --18.76 --19.52 --19.61 --19.50 --19.18 --18.10 --15.47 --10.79 --4.13 -3.75 -11.68 -18.52 -23.49 -26.40 -27.49 -27.28 -26.21 -24.70 -22.93 -21.02 -19.04 -17.01 -15.01 -13.13 -11.54 -10.40 -9.79 -9.58 -9.44 -8.89 -7.50 -5.11 -1.88 --1.66 --4.90 --7.45 --9.22 --10.53 --11.88 --13.70 --16.09 --18.79 --21.18 --22.60 --22.54 --20.84 --17.77 --13.86 --9.86 --6.39 --3.96 --2.78 --2.90 --4.11 --6.06 --8.21 --9.88 --10.39 --9.27 --6.48 --2.52 -1.66 -5.05 -6.89 -7.00 -5.83 -4.22 -3.07 -3.05 -4.38 -6.84 -9.91 -12.81 -14.83 -15.48 -14.55 -12.19 -8.90 -5.37 -2.30 -0.25 --0.55 --0.13 -1.33 -3.55 -6.20 -8.92 -11.22 -12.49 -12.09 -9.49 -4.61 --2.11 --9.63 --16.61 --21.75 --24.28 --24.19 --22.27 --19.83 --18.19 --18.11 --19.54 --21.55 --22.84 --22.24 --19.21 --13.96 --7.40 --0.59 -5.59 -10.59 -14.22 -16.41 -17.12 -16.33 -14.18 -10.98 -7.36 -4.11 -1.97 -1.49 -2.82 -5.66 -9.34 -12.96 -15.62 -16.72 -16.11 -14.12 -11.49 -9.04 -7.39 -6.83 -7.31 -8.59 -10.48 -12.94 -15.93 -19.28 -22.41 -24.44 -24.46 -21.89 -16.86 -10.13 -2.84 --3.97 --9.69 --14.23 --17.94 --21.32 --24.79 --28.57 --32.53 --36.24 --38.96 --39.90 --38.42 --34.33 --27.97 --20.14 --11.85 --4.05 -2.58 -7.67 -11.02 -12.65 -12.82 -12.02 -10.99 -10.50 -11.14 -13.10 -16.14 -19.65 -22.91 -25.27 -26.29 -25.76 -23.70 -20.42 -16.42 -12.49 -9.39 -7.65 -7.34 -8.04 -9.04 -9.58 -9.18 -7.71 -5.37 -2.50 --0.56 --3.54 --6.25 --8.49 --10.09 --10.95 --11.14 --10.90 --10.59 --10.61 --11.28 --12.73 --14.83 --17.25 --19.43 --20.69 --20.43 --18.29 --14.40 --9.31 --3.84 -1.24 -5.46 -8.71 -11.16 -12.97 -14.21 -14.79 -14.72 -14.13 -13.42 -12.98 -13.13 -13.79 -14.64 -15.21 -15.17 -14.44 -13.20 -11.76 -10.35 -9.15 -8.15 -7.25 -6.29 -5.05 -3.38 -1.18 --1.41 --4.10 --6.48 --8.21 --9.16 --9.41 --9.35 --9.33 --9.70 --10.61 --12.03 --13.80 --15.72 --17.61 --19.31 --20.78 --21.99 --22.87 --23.28 --22.94 --21.51 --18.84 --14.99 --10.39 --5.65 --1.42 -1.89 -4.22 -5.90 -7.42 -9.26 -11.62 -14.28 -16.68 -18.13 -18.11 -16.54 -13.88 -10.91 -8.46 -7.03 -6.59 -6.66 -6.64 -6.11 -5.07 -3.92 -3.12 -2.93 -3.27 -3.78 -4.00 -3.72 -2.97 -1.98 -0.97 --0.01 --1.10 --2.48 --4.22 --6.25 --8.38 --10.36 --12.07 --13.44 --14.51 --15.41 --16.30 --17.40 --18.82 --20.50 --22.04 --22.86 --22.31 --19.98 --15.96 --10.75 --5.15 -0.22 -5.04 -9.42 -13.57 -17.72 -21.79 -25.43 -28.22 -29.80 -30.11 -29.40 -28.11 -26.64 -25.32 -24.17 -22.92 -21.21 -18.73 -15.51 -11.87 -8.41 -5.69 -3.96 -3.03 -2.29 -1.06 --1.19 --4.60 --8.85 --13.32 --17.28 --20.19 --21.81 --22.25 --21.90 --21.33 --21.08 --21.53 --22.71 --24.36 --25.93 --26.73 --26.15 --23.89 --20.03 --15.04 --9.63 --4.52 --0.24 -2.94 -5.06 -6.23 -6.60 -6.29 -5.33 -3.70 -1.43 --1.37 --4.47 --7.45 --9.88 --11.42 --11.96 --11.75 --11.26 --10.93 --11.00 --11.35 --11.52 --10.92 --9.13 --6.05 --1.88 -2.90 -7.73 -12.00 -15.19 -16.90 -16.91 -15.28 -12.37 -8.69 -4.73 -0.73 --3.48 --8.38 --14.33 --21.18 --27.91 --32.81 --34.04 --30.44 --22.11 --10.42 -2.50 -14.74 -25.28 -34.20 -42.15 -49.61 -56.32 -61.30 -63.44 -62.21 -58.07 -52.17 -45.74 -39.41 -33.14 -26.48 -19.30 -12.07 -5.68 -0.92 --1.99 --3.52 --4.54 --5.74 --7.40 --9.35 --11.29 --13.09 --14.78 --16.43 --17.88 --18.79 --18.78 --17.79 --16.21 --14.80 --14.41 --15.65 --18.69 --23.23 --28.55 --33.71 --37.70 --39.67 --39.18 --36.20 --31.20 --24.97 --18.42 --12.37 --7.41 --3.79 --1.37 -0.29 -1.90 -4.20 -7.67 -12.42 -18.02 -23.83 -29.24 -34.04 -38.19 -41.74 -44.66 -46.64 -47.44 -46.86 -44.94 -41.89 -37.91 -33.17 -27.81 -22.04 -16.12 -10.29 -4.60 --1.06 --6.90 --12.80 --18.07 --21.41 --21.25 --16.36 --6.37 -7.64 -23.15 -36.75 -45.02 -45.75 -38.85 -26.31 -11.40 --2.80 --14.66 --24.35 --33.19 --42.39 --52.03 --60.78 --66.75 --68.46 --65.77 --59.81 --52.22 --44.39 --36.89 --29.64 --22.43 --15.35 --8.77 --3.10 -1.56 -5.38 -8.52 -10.92 -12.20 -11.99 -10.18 -7.18 -3.82 -0.89 --1.09 --2.17 --2.66 --3.08 --3.78 --4.86 --6.16 --7.30 --7.86 --7.43 --5.91 --3.54 --0.89 -1.34 -2.62 -2.80 -2.07 -0.88 --0.33 --1.33 --2.10 --2.72 --3.26 --3.70 --3.96 --4.01 --3.95 --4.00 --4.47 --5.56 --7.21 --9.12 --10.79 --11.85 --12.18 --12.04 --12.04 --12.73 --14.41 --16.95 --19.85 --22.50 --24.43 --25.50 --25.83 --25.79 --25.66 --25.63 --25.71 --25.71 --25.36 --24.17 --21.51 --16.64 --9.05 -1.42 -14.31 -28.72 -43.64 -58.23 -72.02 -84.72 -95.94 -104.88 -110.45 -111.52 -107.50 -98.71 -86.44 -72.61 -59.15 -47.46 -37.94 -30.17 -23.04 -15.24 -5.78 --5.76 --18.96 --32.61 --45.06 --54.59 --60.00 --60.94 --58.14 --53.23 --48.32 --45.43 --45.84 --49.74 --56.10 --62.98 --68.08 --69.53 --66.41 --59.08 --48.89 --37.62 --26.83 --17.31 --9.11 --1.81 -5.01 -11.50 -17.43 -22.48 -26.39 -29.09 -30.77 -31.67 -31.98 -31.80 -31.12 -29.88 -28.11 -25.95 -23.66 -21.57 -19.88 -18.59 -17.45 -16.09 -14.17 -11.61 -8.68 -5.93 -3.98 -3.23 -3.64 -4.74 -5.76 -5.98 -5.01 -2.94 -0.25 --2.51 --5.08 --7.57 --10.40 --14.01 --18.68 --24.50 --31.40 --39.27 --47.79 --56.37 --63.95 --69.19 --70.84 --68.06 --60.79 --49.64 --35.86 --21.00 --6.75 -5.27 -13.79 -18.10 -18.27 -15.09 -9.82 -3.80 --1.76 --5.99 --8.43 --8.97 --7.94 --6.02 --3.95 --2.30 --1.23 --0.43 -0.52 -1.88 -3.61 -5.42 -6.97 -8.18 -9.22 -10.36 -11.79 -13.50 -15.28 -16.93 -18.33 -19.64 -21.25 -23.71 -27.45 -32.49 -38.33 -43.91 -47.90 -49.25 -47.67 -43.90 -39.44 -36.08 -35.32 -37.88 -43.51 -51.08 -58.90 -65.12 -68.12 -66.94 -61.32 -51.86 -39.63 -26.00 -12.23 --0.62 --11.79 --20.80 --27.45 --32.08 --35.60 --39.46 --45.10 --53.31 --63.70 --74.59 --83.54 --88.19 --87.19 --80.62 --69.75 --56.36 --42.11 --28.09 --14.89 --2.86 -7.72 -16.54 -23.44 -28.43 -31.62 -33.06 -32.60 -30.02 -25.20 -18.45 -10.39 -1.89 --6.22 --13.31 --18.97 --22.87 --24.64 --24.08 --21.23 --16.59 --11.05 --5.56 --0.79 -3.00 -5.99 -8.42 -10.33 -11.51 -11.56 -10.22 -7.64 -4.38 -1.19 --1.34 --2.98 --3.91 --4.50 --5.07 --5.67 --6.13 --6.17 --5.56 --4.28 --2.50 --0.54 -1.34 -2.91 -4.03 -4.59 -4.45 -3.45 -1.59 --0.80 --3.06 --4.24 --3.47 --0.32 -4.77 -10.63 -15.57 -18.05 -17.24 -13.31 -7.34 -0.86 --4.67 --8.29 --9.63 --8.79 --6.00 --1.46 -4.62 -11.88 -19.56 -26.48 -31.33 -33.01 -31.05 -25.81 -18.34 -10.04 -2.31 --3.79 --7.59 --8.90 --7.85 --4.92 --0.83 -3.55 -7.33 -9.85 -10.70 -9.90 -7.76 -4.81 -1.65 --1.14 --3.19 --4.34 --4.72 --4.74 --4.89 --5.65 --7.25 --9.60 --12.34 --15.05 --17.38 --19.20 --20.60 --21.71 --22.57 --23.00 --22.62 --21.01 --17.89 --13.32 --7.59 --1.25 -5.12 -10.89 -15.47 -18.39 -19.31 -18.30 -15.82 -12.69 -9.79 -7.85 -7.10 -7.35 -8.10 -8.86 -9.32 -9.42 -9.27 -8.99 -8.63 -8.14 -7.41 -6.36 -4.93 -3.19 -1.23 --0.79 --2.68 --4.28 --5.54 --6.48 --7.24 --7.91 --8.53 --9.03 --9.33 --9.36 --9.17 --8.95 --8.99 --9.56 --10.87 --12.98 --15.74 --18.71 --21.29 --22.78 --22.65 --20.70 --17.14 --12.52 --7.53 --2.77 -1.38 -4.80 -7.55 -9.77 -11.57 -13.04 -14.15 -14.94 -15.43 -15.67 -15.75 -15.72 -15.53 -15.09 -14.26 -12.99 -11.42 -9.83 -8.54 -7.78 -7.52 -7.52 -7.42 -6.88 -5.74 -4.00 -1.83 --0.59 --3.08 --5.58 --8.04 --10.35 --12.34 --13.81 --14.61 --14.76 --14.55 --14.43 --14.82 --15.89 --17.38 --18.70 --19.08 --17.97 --15.28 --11.39 --6.95 --2.63 -1.11 -4.14 -6.45 -8.11 -9.13 -9.48 -9.21 -8.45 -7.41 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--62.84 --70.02 --75.83 --79.91 --81.77 --80.92 --77.02 --70.19 --61.07 --50.71 --40.18 --30.25 --21.12 --12.52 --4.00 -4.83 -14.08 -23.54 -32.78 -41.36 -48.90 -55.20 -60.20 -64.03 -66.82 -68.67 -69.48 -69.00 -66.92 -63.08 -57.58 -50.86 -43.57 -36.39 -29.91 -24.46 -20.16 -16.83 -14.11 -11.51 -8.53 -4.85 -0.45 --4.36 --9.06 --13.02 --15.68 --16.76 --16.23 --14.40 --11.81 --9.14 --7.09 --6.21 --6.86 --9.02 --12.30 --15.94 --18.94 --20.33 --19.44 --16.17 --10.98 --4.77 -1.35 -6.46 -9.95 -11.64 -11.76 -10.84 -9.56 -8.65 -8.68 -9.82 -11.93 -14.42 -16.60 -17.82 -17.69 -16.06 -12.98 -8.56 -2.98 --3.51 --10.42 --17.02 --22.40 --25.72 --26.60 --25.20 --22.33 --19.07 --16.33 --14.47 --13.17 --11.67 --9.18 --5.32 --0.26 -5.35 -10.69 -15.10 -18.28 -20.22 -21.08 -21.06 -20.29 -18.82 -16.78 -14.31 -11.65 -9.06 -6.80 -5.00 -3.62 -2.45 -1.15 --0.61 --3.03 --6.10 --9.55 --13.01 --16.09 --18.53 --20.23 --21.16 --21.33 --20.71 --19.27 --17.02 --14.08 --10.69 --7.23 --4.08 --1.57 -0.12 -0.97 -1.12 -0.82 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-3.60 -2.46 -1.12 --0.19 --1.18 --1.57 --1.21 --0.13 -1.46 -3.24 -4.87 -6.03 -6.52 -6.28 -5.43 -4.16 -2.67 -1.06 --0.73 --2.80 --5.20 --7.86 --10.54 --12.95 --14.94 --16.50 --17.69 --18.45 --18.50 --17.37 --14.66 --10.32 --4.85 -0.80 -5.52 -8.43 -9.21 -8.12 -5.79 -2.94 -0.09 --2.52 --4.85 --6.88 --8.55 --9.66 --10.03 --9.50 --8.06 --5.75 --2.74 -0.76 -4.45 -7.93 -10.72 -12.42 -12.85 -12.06 -10.39 -8.25 -6.02 -3.87 -1.88 -0.01 --1.65 --2.94 --3.60 --3.45 --2.54 --1.16 -0.24 -1.24 -1.63 -1.44 -0.85 -0.14 --0.55 --1.20 --1.90 --2.71 --3.65 --4.64 --5.50 --6.07 --6.26 --6.11 --5.84 --5.86 --6.54 --8.21 --10.80 --13.85 --16.59 --18.13 --17.79 --15.42 --11.38 --6.43 --1.50 -2.69 -5.76 -7.64 -8.50 -8.62 -8.30 -7.85 -7.56 -7.61 -8.08 -8.87 -9.70 -10.28 -10.42 -10.09 -9.46 -8.77 -8.19 -7.82 -7.67 -7.62 -7.59 -7.48 -7.19 -6.72 -6.21 -5.90 -6.11 -7.03 -8.55 -10.21 -11.33 -11.19 -9.46 -6.30 -2.39 --1.38 --4.27 --6.02 --6.91 --7.56 --8.62 --10.45 --12.97 --15.72 --18.11 --19.70 --20.31 --20.03 --19.13 --17.88 --16.44 --14.83 --13.08 --11.16 --9.19 --7.31 --5.65 --4.27 --3.09 --2.02 --0.95 -0.08 -1.01 -1.71 -2.12 -2.32 -2.42 -2.60 -2.93 -3.39 -3.86 -4.16 -4.10 -3.51 -2.26 -0.35 --2.02 --4.46 --6.38 --7.20 --6.61 --4.66 --1.81 -1.30 -4.06 -6.12 -7.47 -8.29 -8.86 -9.38 -9.97 -10.60 -11.28 -11.93 -12.53 -13.03 -13.41 -13.69 -13.89 -14.03 -14.03 -13.76 -13.10 -12.02 -10.59 -8.99 -7.40 -5.90 -4.47 -3.04 -1.55 -0.06 --1.30 --2.48 --3.51 --4.52 --5.65 --6.97 --8.41 --9.86 --11.16 --12.22 --13.03 --13.71 --14.43 --15.40 --16.72 --18.37 --20.08 --21.43 --21.94 --21.25 --19.28 --16.37 --13.04 --9.84 --7.12 --4.92 --3.02 --1.10 -1.02 -3.28 -5.39 -7.06 -8.18 -8.94 -9.72 -10.94 -12.78 -15.06 -17.35 -19.17 -20.19 -20.42 -20.14 -19.76 -19.57 -19.54 -19.31 -18.33 -16.16 -12.72 -8.35 -3.70 --0.51 --3.88 --6.37 --8.25 --9.82 --11.18 --12.17 --12.53 --12.08 --10.95 --9.52 --8.32 --7.68 --7.67 --8.19 --9.09 --10.37 --12.17 --14.59 --17.44 --20.18 --22.00 --22.17 --20.37 --16.87 --12.37 --7.72 --3.53 -0.04 -3.11 -5.89 -8.46 -10.68 -12.32 -13.19 -13.32 -12.95 -12.42 -12.08 -12.09 -12.40 -12.83 -13.10 -12.99 -12.42 -11.45 -10.29 -9.17 -8.28 -7.62 -7.09 -6.51 -5.76 -4.87 -4.00 -3.32 -2.91 -2.75 -2.66 -2.44 -1.97 -1.24 -0.40 --0.40 --1.03 --1.51 --1.96 --2.56 --3.47 --4.69 --6.14 --7.61 --8.98 --10.20 --11.38 --12.69 --14.21 --15.87 --17.34 --18.14 --17.86 --16.30 --13.54 --10.02 --6.30 --2.90 --0.13 -1.94 -3.49 -4.77 -5.96 -7.12 -8.10 -8.71 -8.86 -8.62 -8.25 -8.05 -8.26 -8.88 -9.71 -10.43 -10.76 -10.54 -9.79 -8.63 -7.29 -5.92 -4.66 -3.60 -2.73 -2.03 -1.45 -0.94 -0.47 -0.11 --0.11 --0.13 --0.01 -0.06 --0.11 --0.70 --1.74 --3.11 --4.56 --5.89 --7.01 --7.96 --8.88 --9.87 --11.02 --12.38 --13.92 --15.63 --17.34 --18.73 --19.32 --18.65 --16.47 --12.95 --8.58 --4.05 -0.05 -3.36 -5.81 -7.49 -8.57 -9.20 -9.52 -9.64 -9.64 -9.59 -9.53 -9.47 -9.46 -9.55 -9.84 -10.35 -11.00 -11.63 -12.04 -12.09 -11.79 -11.22 -10.58 -10.01 -9.57 -9.18 -8.68 -7.85 -6.60 -4.94 -3.04 -1.10 --0.64 --2.01 --2.93 --3.39 --3.52 --3.48 --3.50 --3.79 --4.50 --5.66 --7.18 --8.84 --10.35 --11.50 --12.22 --12.59 --12.89 --13.48 --14.57 --16.22 --18.14 --19.72 --20.31 --19.38 --16.75 --12.68 --7.79 --2.78 -1.69 -5.22 -7.69 -9.18 -9.88 -9.99 -9.69 -9.09 -8.29 -7.34 -6.32 -5.38 -4.69 -4.43 -4.69 -5.42 -6.44 -7.45 -8.25 -8.70 -8.82 -8.60 -8.09 -7.30 -6.24 -5.01 -3.75 -2.63 -1.77 -1.25 -1.01 -0.98 -0.99 -0.95 -0.73 -0.25 --0.47 --1.42 --2.50 --3.63 --4.73 --5.76 --6.73 --7.58 --8.22 --8.60 --8.75 --8.83 --9.11 --9.91 --11.39 --13.41 --15.48 --16.89 --16.97 --15.39 --12.27 --8.17 --3.80 -0.19 -3.44 -5.90 -7.70 -9.05 -10.07 -10.75 -11.00 -10.77 -10.08 -9.08 -8.01 -7.11 -6.60 -6.61 -7.16 -8.12 -9.34 -10.59 -11.69 -12.44 -12.72 -12.49 -11.79 -10.75 -9.52 -8.26 -7.08 -6.08 -5.28 -4.66 -4.15 -3.66 -3.00 -2.07 -0.79 --0.71 --2.19 --3.37 --4.11 --4.48 --4.75 --5.31 --6.48 --8.42 --11.07 --14.06 --16.85 --18.85 --19.56 --18.74 --16.55 --13.40 --9.90 --6.62 --3.90 --1.85 --0.33 -0.84 -1.76 -2.45 -2.92 -3.11 -3.10 -3.00 -2.94 -3.10 -3.57 -4.35 -5.35 -6.37 -7.17 -7.55 -7.44 -6.92 -6.24 -5.68 -5.40 -5.42 -5.57 -5.63 -5.44 -4.95 -4.22 -3.28 -2.15 -0.75 --0.93 --2.89 --4.96 --6.86 --8.35 --9.35 --9.98 --10.56 --11.44 --12.75 --14.34 --15.73 --16.31 --15.69 --13.77 --10.84 --7.39 --3.94 --0.82 -1.88 -4.15 -6.02 -7.37 -8.08 -8.06 -7.36 -6.15 -4.72 -3.38 -2.33 -1.73 -1.61 -1.95 -2.65 -3.56 -4.44 -5.06 -5.21 -4.81 -3.94 -2.80 -1.57 -0.41 --0.63 --1.57 --2.51 --3.56 --4.77 --6.20 --7.79 --9.44 --10.95 --12.10 --12.74 --12.89 --12.78 --12.77 --13.22 --14.29 --15.87 --17.56 --18.75 --18.92 --17.68 --14.98 --11.09 --6.46 --1.69 -2.70 -6.35 -9.14 -11.19 -12.76 -14.09 -15.32 -16.38 -17.06 -17.18 -16.71 -15.82 -14.81 -14.04 -13.59 -13.36 -13.06 -12.43 -11.35 -9.92 -8.35 -6.82 -5.53 -4.45 -3.53 -2.73 -2.03 -1.44 -0.92 -0.31 --0.58 --1.90 --3.78 --6.16 --8.78 --11.24 --13.17 --14.38 --14.93 --15.16 --15.49 --16.24 --17.37 --18.49 --18.97 --18.17 --15.76 --11.79 --6.75 --1.34 -3.74 -8.00 -11.22 -13.35 -14.61 -15.22 -15.40 -15.28 -14.94 -14.39 -13.73 -13.06 -12.54 -12.33 -12.43 -12.74 -13.03 -13.10 -12.83 -12.27 -11.54 -10.81 -10.14 -9.49 -8.77 -7.87 -6.73 -5.36 -3.82 -2.21 -0.59 --1.01 --2.67 --4.40 --6.20 --8.02 --9.74 --11.30 --12.71 --14.13 --15.74 --17.66 --19.70 --21.41 --22.19 --21.48 --18.99 --14.84 --9.49 --3.59 -2.17 -7.27 -11.32 -14.18 -15.91 -16.70 -16.84 -16.60 -16.16 -15.62 -14.93 -14.10 -13.14 -12.21 -11.45 -11.02 -10.92 -11.09 -11.38 -11.59 -11.57 -11.23 -10.50 -9.32 -7.71 -5.77 -3.72 -1.85 -0.37 --0.66 --1.42 --2.15 --3.10 --4.33 --5.68 --6.94 --7.86 --8.41 --8.71 --9.00 --9.56 --10.59 --12.24 --14.42 --16.84 --18.89 --19.85 --19.08 --16.36 --12.02 --6.84 --1.76 -2.46 -5.48 -7.34 -8.30 -8.65 -8.52 -7.98 -7.03 -5.77 -4.38 -3.16 -2.39 -2.35 -3.10 -4.54 -6.33 -8.09 -9.51 -10.41 -10.78 -10.69 -10.18 -9.18 -7.65 -5.62 -3.35 -1.18 --0.48 --1.47 --1.90 --2.08 --2.39 --3.00 --3.88 --4.80 --5.56 --6.08 --6.52 --7.22 --8.50 --10.49 --13.03 --15.64 --17.69 --18.59 --17.99 --15.82 --12.40 --8.22 --3.87 -0.18 -3.58 -6.18 -7.95 -8.98 -9.44 -9.52 -9.43 -9.25 -9.05 -8.81 -8.58 -8.48 -8.67 -9.21 -10.05 -11.01 -11.83 -12.39 -12.70 -12.83 -12.86 -12.69 -12.11 -10.90 -9.06 -6.77 -4.38 -2.25 -0.55 --0.76 --1.94 --3.26 --4.94 --7.04 --9.40 --11.70 --13.66 --15.04 --15.82 --16.17 --16.39 --16.78 --17.57 --18.80 --20.31 --21.79 --22.78 --22.79 --21.46 --18.63 --14.47 --9.40 --4.08 -0.91 -5.12 -8.42 -10.90 -12.79 -14.30 -15.53 -16.45 -16.99 -17.10 -16.85 -16.43 -16.06 -15.89 -15.91 -15.96 -15.77 -15.13 -14.01 -12.54 -11.00 -9.59 -8.41 -7.35 -6.25 -4.90 -3.18 -1.15 --1.05 --3.21 --5.17 --6.90 --8.45 --9.91 --11.34 --12.75 --14.03 --15.09 --15.86 --16.41 --16.91 --17.55 --18.41 --19.33 --20.02 --20.05 --19.12 --17.12 --14.21 --10.75 --7.11 --3.66 --0.57 -2.10 -4.49 -6.70 -8.84 -10.94 -12.92 -14.61 -15.85 -16.57 -16.85 -16.93 -16.99 -17.07 -17.09 -16.81 -16.00 -14.58 -12.63 -10.38 -8.08 -5.94 -4.06 -2.53 -1.42 -0.76 -0.50 -0.43 -0.17 --0.63 --2.16 --4.35 --6.89 --9.36 --11.39 --12.81 --13.67 --14.21 --14.72 --15.47 --16.61 --18.12 --19.75 --21.06 --21.50 --20.67 --18.36 --14.68 --10.05 --5.03 --0.23 -3.88 -7.05 -9.25 -10.65 -11.45 -11.86 -11.94 -11.68 -11.04 -10.06 -8.87 -7.73 -6.85 -6.38 -6.29 -6.45 -6.66 -6.74 -6.52 -5.89 -4.85 -3.47 -1.93 -0.46 --0.75 --1.57 --2.02 --2.26 --2.56 --3.16 --4.26 --5.90 --7.90 --9.91 --11.47 --12.17 --11.87 --10.70 --9.04 --7.32 --5.92 --5.04 --4.64 --4.43 --4.03 --3.01 --1.16 -1.47 -4.47 -7.25 -9.28 -10.28 -10.35 -9.90 -9.41 -9.17 -9.24 -9.44 -9.60 -9.62 -9.49 -9.30 -9.12 -8.99 -8.88 -8.77 -8.59 -8.29 -7.82 -7.12 -6.16 -4.95 -3.54 -2.02 -0.49 --0.93 --2.12 --2.96 --3.41 --3.52 --3.48 --3.63 --4.29 --5.67 --7.71 --10.00 --12.06 --13.43 --14.03 --14.14 --14.27 --14.92 --16.31 --18.22 --20.04 --21.05 --20.68 --18.68 --15.21 --10.72 --5.75 --0.86 -3.55 -7.22 -10.01 -11.90 -12.94 -13.26 -13.01 -12.34 -11.38 -10.28 -9.23 -8.42 -8.04 -8.16 -8.68 -9.38 -10.00 -10.35 -10.44 -10.37 -10.26 -10.10 -9.74 -8.95 -7.59 -5.72 -3.51 -1.28 --0.71 --2.35 --3.63 --4.69 --5.70 --6.81 --8.11 --9.63 --11.34 --13.14 --14.95 --16.67 --18.22 --19.39 --19.88 --19.31 --17.47 --14.32 --10.19 --5.65 --1.35 -2.19 -4.72 -6.25 -6.98 -7.17 -7.08 -6.88 -6.66 -6.42 -6.10 -5.58 -4.82 -3.88 -2.90 -2.16 -1.85 -2.04 -2.72 -3.74 -4.88 -5.96 -6.77 -7.10 -6.79 -5.79 -4.22 -2.36 -0.56 --0.96 --2.08 --2.94 --3.78 --4.75 --5.92 --7.22 --8.50 --9.61 --10.49 --11.14 --11.64 --12.14 --12.77 --13.65 --14.82 --16.08 --17.01 --17.08 --15.81 --13.00 --8.85 --3.92 -1.02 -5.22 -8.20 -9.82 -10.37 -10.31 -10.09 -9.97 -9.85 -9.46 -8.52 -7.03 -5.32 -4.02 -3.69 -4.51 -6.17 -7.94 -9.05 -9.01 -7.81 -5.84 -3.70 -1.86 -0.52 --0.38 --1.02 --1.53 --1.99 --2.37 --2.69 --3.01 --3.49 --4.30 --5.47 --6.97 --8.50 --9.81 --10.71 --11.23 --11.60 --12.13 --12.99 --14.01 --14.79 --14.72 --13.34 --10.50 --6.48 --1.89 -2.64 -6.62 -9.85 -12.38 -14.32 -15.73 -16.50 -16.45 -15.40 -13.41 -10.74 -7.91 -5.45 -3.77 -3.05 -3.15 -3.81 -4.63 -5.39 -6.00 -6.49 -6.89 -7.19 -7.28 -7.01 -6.33 -5.22 -3.79 -2.19 -0.59 --0.89 --2.18 --3.23 --4.07 --4.77 --5.47 --6.31 --7.34 --8.49 --9.60 --10.35 --10.48 --9.78 --8.19 --5.81 --2.88 -0.23 -3.11 -5.37 -6.76 -7.19 -6.74 -5.63 -4.10 -2.39 -0.68 --0.86 --2.06 --2.81 --2.99 --2.65 --1.93 --1.08 --0.36 -0.01 -0.00 --0.33 --0.86 --1.52 --2.32 --3.30 --4.49 --5.81 --7.05 --7.94 --8.27 --8.02 --7.35 --6.57 --5.98 --5.78 --6.03 --6.69 --7.62 --8.58 --9.18 --9.03 --7.84 --5.52 --2.34 -1.18 -4.51 -7.29 -9.40 -10.99 -12.25 -13.28 -14.12 -14.62 -14.70 -14.34 -13.64 -12.72 -11.68 -10.60 -9.45 -8.23 -6.85 -5.28 -3.58 -1.92 -0.51 --0.47 --0.99 --1.24 --1.48 --2.04 --3.03 --4.43 --6.02 --7.56 --8.81 --9.64 --10.07 --10.18 --10.23 --10.49 --11.28 --12.76 --14.83 --17.04 --18.71 --19.20 --18.09 --15.40 --11.55 --7.20 --3.04 -0.46 -3.07 -4.91 -6.16 -7.08 -7.77 -8.27 -8.56 -8.61 -8.47 -8.26 -8.11 -8.10 -8.25 -8.47 -8.63 -8.64 -8.44 -8.08 -7.64 -7.21 -6.76 -6.22 -5.41 -4.22 -2.66 -0.83 --1.05 --2.86 --4.61 --6.41 --8.34 --10.34 --12.15 --13.39 --13.83 --13.50 --12.69 --11.80 --11.13 --10.62 --10.01 --8.91 --7.06 --4.52 --1.51 -1.65 -4.73 -7.62 -10.40 -13.05 -15.47 -17.45 -18.75 -19.21 -18.88 -17.94 -16.61 -15.09 -13.43 -11.58 -9.49 -7.18 -4.81 -2.66 -1.00 --0.01 --0.33 --0.10 -0.49 -1.19 -1.70 -1.76 -1.16 --0.20 --2.23 --4.68 --7.15 --9.20 --10.48 --10.82 --10.30 --9.24 --8.03 --7.04 --6.47 --6.30 --6.30 --6.08 --5.24 --3.45 --0.58 -3.19 -7.39 -11.34 -14.29 -15.64 -15.13 -12.85 -9.26 -4.91 -0.33 --4.10 --8.13 --11.58 --14.34 --16.26 --17.23 --17.26 --16.47 --15.20 --13.80 --12.65 --11.97 --11.81 --12.09 --12.59 --13.13 --13.54 --13.67 --13.34 --12.35 --10.38 --7.23 --2.87 -2.38 -7.93 -13.09 -17.27 -20.18 -21.74 -22.09 -21.36 -19.64 -16.98 -13.60 -9.93 -6.61 -4.34 -3.59 -4.43 -6.50 -9.15 -11.69 -13.60 -14.71 -15.09 -14.95 -14.43 -13.48 -11.94 -9.66 -6.73 -3.53 -0.66 --1.24 --1.74 --0.86 -0.98 -3.04 -4.57 -5.05 -4.41 -2.91 -1.04 --0.74 --2.16 --3.18 --3.94 --4.67 --5.50 --6.47 --7.54 --8.73 --10.05 --11.54 --13.15 --14.74 --16.09 --16.96 --17.24 --16.98 --16.46 --15.96 --15.68 --15.54 --15.22 --14.33 --12.57 --9.98 --6.87 --3.76 --1.15 -0.62 -1.50 -1.70 -1.59 -1.54 -1.77 -2.36 -3.19 -4.09 -4.91 -5.59 -6.26 -7.08 -8.14 -9.34 -10.45 -11.19 -11.37 -11.01 -10.25 -9.33 -8.39 -7.47 -6.55 -5.58 -4.60 -3.73 -3.14 -2.93 -3.11 -3.58 -4.14 -4.55 -4.61 -4.19 -3.20 -1.62 --0.59 --3.48 --6.98 --10.79 --14.35 --16.92 --17.76 --16.50 --13.26 --8.67 --3.63 -1.03 -4.80 -7.54 -9.37 -10.47 -10.96 -10.89 -10.27 -9.22 -7.88 -6.46 -5.19 -4.23 -3.74 -3.74 -4.19 -4.88 -5.53 -5.90 -5.87 -5.51 -4.97 -4.47 -4.06 -3.70 -3.24 -2.45 -1.22 --0.45 --2.42 --4.49 --6.42 --8.06 --9.36 --10.35 --11.16 --11.87 --12.54 --13.24 --13.96 --14.67 --15.21 --15.29 --14.52 --12.62 --9.46 --5.24 --0.45 -4.25 -8.25 -11.14 -12.83 -13.52 -13.53 -13.15 -12.55 -11.74 -10.67 -9.35 -7.90 -6.61 -5.74 -5.48 -5.73 -6.26 -6.70 -6.77 -6.34 -5.49 -4.38 -3.20 -2.04 -0.90 --0.32 --1.77 --3.50 --5.46 --7.51 --9.42 --11.01 --12.19 --12.95 --13.40 --13.71 --14.04 --14.52 --15.21 --16.02 --16.74 --17.03 --16.52 --14.97 --12.33 --8.82 --4.86 --0.97 -2.45 -5.17 -7.17 -8.65 -9.85 -10.92 -11.86 -12.49 -12.58 -12.04 -11.03 -9.90 -9.05 -8.77 -8.97 -9.32 -9.36 -8.78 -7.54 -5.88 -4.17 -2.67 -1.45 -0.38 --0.70 --1.90 --3.12 --4.28 --5.29 --6.22 --7.19 --8.33 --9.63 --10.89 --11.89 --12.45 --12.58 --12.49 --12.48 --12.83 --13.54 --14.36 --14.77 --14.23 --12.37 --9.17 --5.04 --0.63 -3.33 -6.34 -8.25 -9.22 -9.59 -9.67 -9.71 -9.87 -10.21 -10.74 -11.39 -12.03 -12.47 -12.60 -12.42 -12.07 -11.67 -11.29 -10.88 -10.34 -9.64 -8.84 -8.05 -7.32 -6.60 -5.76 -4.67 -3.31 -1.82 -0.33 --1.08 --2.50 --4.08 --5.92 --7.92 --9.88 --11.54 --12.77 --13.61 --14.24 --14.88 --15.68 --16.64 --17.58 --18.22 --18.24 --17.41 --15.61 --12.91 --9.55 --5.87 --2.25 -1.04 -3.82 -6.08 -7.87 -9.24 -10.23 -10.83 -11.09 -11.08 -11.02 -11.17 -11.84 -13.25 -15.43 -18.24 -21.33 -24.33 -26.88 -28.78 -29.94 -30.30 -29.80 -28.32 -25.79 -22.28 -18.01 -13.35 -8.72 -4.40 -0.49 --3.08 --6.49 --9.82 --12.99 --15.80 --18.04 --19.59 --20.54 --21.16 --21.77 --22.66 --23.94 --25.47 --26.87 --27.62 --27.20 --25.29 --21.82 --17.11 --11.69 --6.22 --1.29 -2.69 -5.57 -7.37 -8.19 -8.16 -7.36 -5.91 -3.99 -1.93 -0.16 --0.85 --0.75 -0.54 -2.77 -5.49 -8.12 -10.25 -11.69 -12.42 -12.51 -12.03 -10.97 -9.37 -7.35 -5.08 -2.82 -0.72 --1.16 --2.88 --4.48 --5.95 --7.12 --7.75 --7.73 --7.11 --6.15 --5.26 --4.86 --5.25 --6.49 --8.33 --10.23 --11.59 --11.86 --10.81 --8.58 --5.66 --2.67 --0.19 -1.48 -2.30 -2.41 -1.99 -1.14 --0.18 --2.01 --4.37 --7.15 --10.04 --12.61 --14.51 --15.47 --15.52 --14.94 --14.18 --13.68 --13.71 --14.29 --15.20 --16.12 --16.79 --17.12 --17.12 --16.88 --16.48 --15.87 --14.94 --13.31 --10.41 --5.46 -2.25 -13.02 -26.47 -41.40 -56.10 -68.73 -77.86 -82.72 -83.12 -79.25 -71.47 -60.45 -47.26 -33.51 -21.15 -11.98 -7.10 -6.45 -8.91 -12.65 -15.83 -17.13 -16.06 -12.89 -8.40 -3.46 --1.27 --5.38 --8.72 --11.22 --12.77 --13.31 --12.84 --11.69 --10.45 --9.92 --10.81 --13.59 --18.22 --24.27 --31.04 --37.73 --43.64 --48.20 --51.01 --51.88 --50.85 --48.13 --44.02 --38.92 --33.15 --27.03 --20.96 --15.37 --10.74 --7.47 --5.78 --5.60 --6.61 --8.28 --9.93 --10.97 --10.83 --9.16 --5.87 --1.23 -4.21 -9.71 -14.56 -18.26 -20.57 -21.59 -21.57 -20.78 -19.48 -17.86 -16.08 -14.33 -12.80 -11.60 -10.79 -10.30 -9.99 -9.69 -9.30 -8.70 -7.90 -6.92 -5.87 -4.87 -4.01 -3.31 -2.67 -1.93 -0.93 --0.33 --1.77 --3.12 --4.19 --4.88 --5.29 --5.67 --6.24 --7.11 --8.17 --9.19 --9.92 --10.15 --9.76 --8.75 --7.15 --5.05 --2.67 --0.21 -2.09 -4.02 -5.49 -6.51 -7.19 -7.61 -7.85 -7.89 -7.76 -7.51 -7.25 -7.13 -7.20 -7.43 -7.66 -7.64 -7.10 -5.91 -4.13 -1.99 --0.11 --1.80 --2.87 --3.39 --3.65 --4.06 --4.85 --5.97 --7.13 --7.88 --7.95 --7.34 --6.36 --5.51 --5.23 --5.79 --7.16 --9.12 --11.25 --13.09 --14.17 --14.08 --12.59 --9.72 --5.83 --1.43 -2.81 -6.40 -9.06 -10.84 -12.03 -12.90 -13.64 -14.19 -14.34 -13.92 -12.90 -11.51 -10.08 -8.93 -8.13 -7.46 -6.62 -5.41 -3.87 -2.31 -1.08 -0.33 --0.04 --0.37 --0.98 --2.01 --3.39 --4.87 --6.22 --7.35 --8.32 --9.23 --10.18 --11.10 --11.86 --12.32 --12.53 --12.71 --13.18 --14.15 --15.51 --16.82 --17.39 --16.63 --14.31 --10.72 --6.50 --2.37 -1.16 -3.92 -6.05 -7.80 -9.33 -10.64 -11.59 -12.06 -12.07 -11.74 -11.33 -11.10 -11.21 -11.66 -12.23 -12.61 -12.50 -11.80 -10.60 -9.18 -7.83 -6.73 -5.88 -5.14 -4.41 -3.58 -2.64 -1.61 -0.43 --0.97 --2.59 --4.35 --6.00 --7.25 --7.95 --8.17 --8.22 --8.53 --9.45 --11.11 --13.26 --15.41 --16.88 --17.14 --15.91 --13.29 --9.73 --5.79 --2.02 -1.25 -3.88 -5.96 -7.60 -8.94 -10.02 -10.75 -11.07 -10.96 -10.54 -10.04 -9.77 -9.90 -10.51 -11.46 -12.44 -13.10 -13.19 -12.62 -11.48 -10.01 -8.42 -6.90 -5.46 -4.05 -2.56 -0.92 --0.81 --2.50 --3.99 --5.17 --6.03 --6.65 --7.11 --7.50 --7.91 --8.46 --9.30 --10.50 --12.06 --13.74 --15.11 --15.67 --15.04 --13.08 --9.89 --5.86 --1.51 -2.60 -6.04 -8.54 -10.07 -10.78 -10.98 -10.92 -10.73 -10.40 -9.88 -9.21 -8.57 -8.20 -8.27 -8.77 -9.50 -10.18 -10.60 -10.65 -10.33 -9.61 -8.45 -6.81 -4.83 -2.77 -0.94 --0.46 --1.44 --2.26 --3.17 --4.30 --5.59 --6.85 --7.92 --8.78 --9.50 --10.18 --10.89 --11.59 --12.18 --12.69 --13.22 --13.93 --14.88 --15.93 --16.73 --16.84 --15.88 --13.70 --10.50 --6.74 --3.02 -0.13 -2.39 -3.70 -4.20 -4.19 -3.96 -3.73 -3.64 -3.73 -3.89 -4.03 -4.01 -3.80 -3.39 -2.79 -2.03 -1.14 -0.27 --0.34 --0.25 -0.94 -3.42 -7.04 -11.26 -15.41 -18.94 -21.68 -23.75 -25.44 -26.90 -27.96 -28.22 -27.19 -24.62 -20.54 -15.35 -9.64 -4.00 --1.08 --5.26 --8.35 --10.29 --11.16 --11.26 --11.04 --11.07 --11.80 --13.45 --15.92 --18.87 --21.88 --24.52 --26.53 --27.67 --27.76 --26.61 --24.17 --20.55 --16.07 --11.22 --6.54 --2.54 -0.46 -2.31 -3.10 -3.18 -2.95 -2.79 -2.86 -3.07 -3.15 -2.88 -2.31 -1.65 -1.26 -1.27 -1.55 -1.81 -1.79 -1.45 -0.99 -0.65 -0.52 -0.44 -0.08 --0.80 --2.23 --3.93 --5.47 --6.48 --6.67 --5.94 --4.32 --1.96 -0.90 -4.00 -6.98 -9.68 -12.04 -14.06 -15.75 -17.00 -17.61 -17.46 -16.62 -15.36 -14.10 -13.22 -12.81 -12.80 -12.96 -13.02 -12.72 -11.86 -10.30 -8.02 -5.21 -2.26 --0.36 --2.26 --3.33 --3.75 --3.80 --3.68 --3.41 --2.79 --1.66 -0.08 -2.31 -4.80 -7.33 -9.70 -11.72 -13.12 -13.55 -12.64 -10.16 -6.18 -0.97 --4.98 --11.26 --17.51 --23.45 --28.85 --33.42 --36.79 --38.59 --38.52 --36.43 --32.44 --26.92 --20.49 --13.80 --7.46 --1.82 -2.98 -6.91 -10.05 -12.43 -14.03 -14.85 -14.96 -14.42 -13.36 -11.93 -10.31 -8.74 -7.48 -6.74 -6.61 -6.97 -7.61 -8.21 -8.59 -8.61 -8.23 -7.48 -6.40 -5.07 -3.59 -2.06 -0.53 --0.94 --2.41 --3.92 --5.44 --6.88 --8.10 --8.94 --9.34 --9.33 --9.06 --8.87 --9.03 --9.76 --11.08 --12.75 --14.32 --15.28 --15.19 --13.90 --11.54 --8.52 --5.30 --2.32 -0.18 -2.19 -3.82 -5.26 -6.56 -7.67 -8.42 -8.59 -8.05 -6.90 -5.48 -4.24 -3.67 -4.03 -5.33 -7.30 -9.53 -11.60 -13.30 -14.56 -15.41 -15.89 -15.97 -15.51 -14.36 -12.48 -9.97 -7.08 -4.08 -1.18 --1.49 --3.89 --5.99 --7.66 --8.81 --9.34 --9.36 --9.17 --9.23 --9.96 --11.67 --14.29 --17.37 --20.27 --22.17 --22.40 --20.66 --17.05 --12.11 --6.74 --1.89 -1.72 -3.64 -3.87 -2.70 -0.56 --2.07 --4.83 --7.40 --9.53 --11.07 --12.00 --12.41 --12.53 --12.66 --12.94 --13.34 --13.60 --13.43 --12.77 --11.96 --11.72 --12.88 --16.02 --21.03 --27.19 --33.39 --38.49 --41.72 --42.69 --41.28 --37.40 --31.03 --22.26 --11.62 --0.11 -10.98 -20.48 -27.73 -32.76 -36.06 -38.34 -40.11 -41.54 -42.62 -43.34 -43.79 -44.24 -44.95 -45.95 -46.97 -47.53 -47.14 -45.52 -42.72 -38.94 -34.45 -29.42 -23.86 -17.84 -11.71 -6.16 -2.14 -0.47 -1.46 -4.73 -9.20 -13.52 -16.41 -17.05 -15.14 -10.86 -4.75 --2.44 --9.74 --16.16 --20.81 --23.01 --22.52 --19.60 --14.97 --9.68 --5.04 --2.22 --2.07 --4.71 --9.47 --15.01 --19.82 --22.76 --23.45 --22.26 --19.91 --16.98 --13.60 --9.55 --4.54 -1.39 -7.77 -13.78 -18.60 -21.74 -23.12 -23.08 -22.16 -20.90 -19.76 -19.00 -18.63 -18.50 -18.23 -17.32 -15.24 -11.51 -5.88 --1.57 --10.24 --19.14 --27.12 --33.25 --37.18 --39.28 --40.43 --41.61 --43.45 --45.96 --48.60 --50.51 --50.92 --49.25 --45.23 --38.88 --30.41 --20.32 --9.45 -1.08 -9.97 -16.09 -18.82 -18.26 -15.21 -10.96 -6.79 -3.62 -1.72 -0.80 -0.24 --0.52 --1.82 --3.62 --5.63 --7.48 --8.76 --9.09 --8.16 --5.76 --1.98 -2.76 -7.63 -11.72 -14.25 -14.87 -13.73 -11.33 -8.35 -5.34 -2.67 -0.51 --1.11 --2.36 --3.46 --4.63 --5.96 --7.29 --8.22 --8.25 --6.97 --4.19 --0.09 -4.91 -10.23 -15.26 -19.43 -22.27 -23.45 -22.86 -20.68 -17.36 -13.52 -9.81 -6.73 -4.47 -3.00 -2.08 -1.49 -1.08 -0.86 -0.87 -1.13 -1.47 -1.61 -1.25 -0.22 --1.41 --3.36 --5.26 --6.76 --7.66 --7.92 --7.58 --6.86 --6.01 --5.40 --5.38 --6.14 --7.62 --9.47 --11.13 --12.00 --11.66 --9.93 --6.92 --2.89 -1.84 -6.89 -11.91 -16.54 -20.46 -23.52 -25.77 -27.44 -28.88 -30.32 -31.78 -33.04 -33.82 -33.92 -33.40 -32.51 -31.56 -30.71 -29.93 -28.95 -27.48 -25.31 -22.40 -18.89 -15.02 -11.10 -7.38 -4.08 -1.36 --0.76 --2.46 --4.12 --6.19 --9.13 --13.18 --18.22 --23.80 --29.16 --33.53 --36.34 --37.39 --36.86 --35.17 --32.89 --30.36 --27.80 --25.22 --22.62 --20.12 --17.96 --16.47 --15.79 --15.80 --16.02 --15.86 --14.86 --12.94 --10.42 --7.88 --5.83 --4.48 --3.75 --3.36 --3.05 --2.71 --2.26 --1.69 --0.91 -0.16 -1.50 -2.95 -4.29 -5.35 -6.14 -6.82 -7.56 -8.32 -8.81 -8.56 -7.23 -4.81 -1.81 --0.95 --2.55 --2.42 --0.50 -2.81 -6.81 -10.95 -14.91 -18.57 -21.84 -24.51 -26.26 -26.76 -25.93 -23.98 -21.39 -18.75 -16.51 -14.89 -13.91 -13.38 -13.06 -12.69 -12.06 -11.11 -9.96 -8.81 -7.93 -7.44 -7.27 -7.17 -6.86 -6.11 -4.93 -3.43 -1.86 -0.44 --0.68 --1.39 --1.64 --1.44 --0.95 --0.44 --0.28 --0.76 --1.93 --3.53 --5.06 --5.96 --5.85 --4.66 --2.57 -0.01 -2.69 -5.09 -6.87 -7.62 -6.90 -4.31 --0.28 --6.67 --14.23 --22.09 --29.29 --35.07 --39.02 --41.17 --41.84 --41.56 --40.87 --40.15 --39.63 --39.35 --39.12 --38.69 --37.78 --36.13 --33.60 --30.17 --25.93 --21.14 --16.13 --11.14 --6.27 --1.40 -3.73 -9.29 -15.16 -20.79 -25.35 -28.02 -28.52 -27.24 -25.11 -23.25 -22.36 -22.53 -23.34 -24.20 -24.65 -24.60 -24.13 -23.29 -22.04 -20.23 -17.87 -15.29 -13.19 -12.38 -13.47 -16.55 -21.13 -26.38 -31.43 -35.79 -39.49 -42.86 -46.03 -48.60 -49.64 -48.02 -43.12 -35.21 -25.53 -15.85 -7.98 -3.35 -2.90 -7.04 -15.44 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--13.64 --16.36 --18.54 --19.83 --20.09 --19.61 --19.02 --19.10 --20.31 --22.61 --25.36 --27.67 --28.72 --27.94 --25.16 --20.49 --14.25 --6.91 -0.92 -8.65 -16.05 -23.37 -31.39 -40.99 -52.53 -65.51 -78.51 -89.77 -97.76 -101.80 -101.92 -98.56 -92.20 -83.34 -72.68 -61.31 -50.59 -41.73 -35.41 -31.44 -29.01 -27.14 -25.10 -22.58 -19.54 -15.88 -11.24 -5.09 --3.15 --13.84 --26.80 --41.26 --55.78 --68.41 --77.16 --80.55 --78.16 --70.87 --60.66 --49.89 --40.68 --34.32 --31.14 --30.80 --32.56 --35.64 --39.19 --42.44 --44.57 --44.96 --43.22 --39.33 --33.76 --27.30 --20.95 --15.66 --12.16 --10.67 --10.86 --11.94 --12.83 --12.61 --10.79 --7.59 --3.83 --0.58 -1.32 -1.69 -1.11 -0.70 -1.49 -3.94 -7.68 -11.63 -14.67 -16.20 -16.49 -16.68 -18.44 -23.57 -33.67 -49.92 -72.58 -100.59 -130.89 -158.58 -177.80 -183.52 -173.47 -149.16 -115.48 -78.93 -45.38 -18.46 --0.85 --13.38 --20.62 --23.83 --24.00 --22.16 --19.82 --18.72 --20.41 --25.55 --33.65 --43.41 --53.29 --62.23 --69.90 --76.69 --83.25 --90.12 --97.31 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--3.92 --4.82 --5.81 --6.93 --8.18 --9.50 --10.85 --12.17 --13.41 --14.62 --15.83 --17.10 --18.51 --20.08 --21.77 --23.47 --24.97 --26.02 --26.32 --25.58 --23.61 --20.40 --16.09 --11.03 --5.65 --0.43 -4.24 -8.12 -11.12 -13.26 -14.63 -15.31 -15.38 -14.93 -14.08 -13.03 -12.02 -11.28 -10.94 -11.03 -11.45 -12.05 -12.66 -13.15 -13.50 -13.72 -13.86 -13.92 -13.86 -13.64 -13.19 -12.53 -11.76 -10.95 -10.15 -9.28 -8.23 -6.90 -5.28 -3.51 -1.76 -0.18 --1.23 --2.57 --4.01 --5.65 --7.49 --9.44 --11.43 --13.38 --15.31 --17.29 --19.34 --21.42 --23.36 --24.96 --26.01 --26.38 --25.96 --24.67 --22.40 --19.04 --14.56 --9.13 --3.15 -2.77 -7.94 -11.81 -14.10 -14.88 -14.50 -13.45 -12.15 -10.93 -9.95 -9.27 -8.88 -8.77 -8.90 -9.19 -9.55 -9.87 -10.07 -10.14 -10.10 -9.99 -9.85 -9.68 -9.46 -9.09 -8.52 -7.62 -6.33 -4.63 -2.60 -0.38 --1.86 --4.04 --6.15 --8.20 --10.19 --12.00 --13.51 --14.66 --15.54 --16.37 --17.44 --18.95 --20.93 --23.22 --25.54 --27.55 --28.91 --29.28 --28.39 --26.09 --22.43 --17.66 --12.24 --6.62 --1.23 -3.68 -7.99 -11.65 -14.62 -16.83 -18.26 -18.92 -18.93 -18.48 -17.80 -17.10 -16.54 -16.25 -16.25 -16.51 -16.91 -17.32 -17.56 -17.52 -17.12 -16.31 -15.11 -13.55 -11.70 -9.71 -7.72 -5.92 -4.36 -3.02 -1.79 -0.54 --0.81 --2.25 --3.76 --5.28 --6.79 --8.28 --9.77 --11.29 --12.83 --14.42 --16.12 --17.95 --19.93 --21.97 --23.89 --25.38 --26.06 --25.57 --23.69 --20.40 --15.95 --10.79 --5.41 --0.26 -4.39 -8.42 -11.78 -14.43 -16.30 -17.32 -17.50 -16.99 -16.07 -15.08 -14.27 -13.76 -13.47 -13.29 -13.08 -12.79 -12.50 -12.29 -12.23 -12.30 -12.37 -12.33 -12.05 -11.48 -10.61 -9.49 -8.22 -6.89 -5.62 -4.39 -3.18 -1.84 -0.24 --1.71 --3.95 --6.34 --8.62 --10.56 --12.01 --13.00 --13.72 --14.46 --15.49 --16.96 --18.87 --21.00 --22.97 --24.33 --24.65 --23.61 --21.10 --17.25 --12.40 --7.00 --1.54 -3.58 -8.08 -11.76 -14.53 -16.40 -17.42 -17.76 -17.63 -17.18 -16.52 -15.69 -14.73 -13.73 -12.86 -12.26 -12.03 -12.09 -12.25 -12.31 -12.11 -11.65 -11.02 -10.30 -9.52 -8.69 -7.77 -6.75 -5.71 -4.67 -3.69 -2.75 -1.75 -0.62 --0.70 --2.19 --3.76 --5.32 --6.76 --8.05 --9.19 --10.24 --11.28 --12.41 --13.68 --15.14 --16.77 --18.48 --20.08 --21.30 --21.79 --21.22 --19.36 --16.20 --11.95 --7.05 --2.02 -2.64 -6.58 -9.65 -11.89 -13.40 -14.27 -14.59 -14.43 -13.92 -13.21 -12.46 -11.82 -11.33 -11.02 -10.86 -10.81 -10.83 -10.91 -11.03 -11.18 -11.33 -11.45 -11.47 -11.31 -10.89 -10.18 -9.22 -8.15 -7.09 -6.12 -5.19 -4.25 -3.27 -2.31 -1.47 -0.81 -0.27 --0.35 --1.27 --2.63 --4.39 --6.32 --8.15 --9.71 --11.00 --12.19 --13.52 --15.22 --17.40 --19.99 --22.74 --25.22 --26.94 --27.44 --26.42 --23.82 --19.82 --14.82 --9.36 --3.96 -0.92 -4.99 -8.11 -10.34 -11.82 -12.78 -13.41 -13.86 -14.13 -14.21 -14.04 -13.67 -13.17 -12.68 -12.27 -11.94 -11.64 -11.32 -11.02 -10.84 -10.89 -11.22 -11.69 -12.00 -11.85 -11.03 -9.57 -7.80 -6.11 -4.80 -3.91 -3.17 -2.23 -0.84 --0.99 --3.00 --4.82 --6.21 --7.13 --7.73 --8.22 --8.73 --9.31 --9.92 --10.55 --11.23 --11.96 --12.76 --13.57 --14.36 --15.21 --16.26 --17.67 --19.47 --21.44 --23.12 --23.88 --23.19 --20.83 --16.98 --12.15 --7.04 --2.22 -2.00 -5.65 -8.90 -11.93 -14.74 -17.06 -18.56 -19.05 -18.67 -17.96 -17.63 -18.27 -20.11 -22.82 -25.72 -28.04 -29.31 -29.59 -29.45 -29.71 -31.03 -33.60 -36.99 -40.28 -42.51 -43.10 -42.15 -40.57 -39.63 -40.56 -44.13 -50.41 -58.91 -68.59 -77.82 -84.33 -85.25 -77.49 -58.37 -26.35 --18.51 --74.65 --139.70 --211.18 --286.72 --363.50 --437.33 --502.23 --551.22 --578.17 --579.93 --557.66 --516.44 --463.36 --405.12 --346.22 --288.67 --232.91 --179.21 --128.51 --82.21 --41.35 --5.64 -26.74 -58.75 -93.43 -132.69 -176.33 -221.74 -264.44 -299.42 -322.64 -332.32 -329.48 -317.51 -301.03 -284.64 -271.84 -264.64 -263.42 -267.20 -273.97 -281.18 -286.34 -287.68 -284.51 -277.28 -267.21 -255.82 -244.42 -233.97 -224.88 -216.99 -209.56 -201.62 -192.38 -181.58 -169.86 -158.30 -147.96 -139.20 -131.37 -123.04 -112.51 -98.37 -79.78 -56.53 -28.79 --2.46 --35.11 --65.87 --90.73 --106.02 --109.54 --101.36 --83.65 --60.05 --33.94 --7.87 -16.24 -36.21 -48.75 -50.03 -37.11 -9.73 --29.26 --73.83 --116.81 --152.03 --176.04 --188.58 --191.70 --188.54 --179.60 --166.09 --150.47 --136.06 --124.70 --115.79 --107.06 --95.79 --79.82 --58.32 --33.65 --6.91 -19.93 -44.33 -63.78 -76.43 -81.67 -80.19 -73.63 -63.65 -52.01 -40.53 -30.51 -22.26 -15.00 -7.37 --1.87 --13.02 --25.43 --37.25 --46.50 --51.79 --52.89 --50.78 --47.22 --44.02 --42.45 --42.97 --45.03 --47.53 --49.14 --48.61 --45.06 --38.15 --28.22 --16.34 --4.21 -6.29 -13.48 -16.41 -15.14 -10.71 -4.65 --1.48 --6.63 --10.33 --12.57 --13.49 --13.29 --12.19 --10.48 --8.59 --6.92 --5.50 --3.85 --0.94 -4.28 -12.36 -23.02 -35.39 -48.61 -62.48 -77.57 -94.60 -113.57 -133.02 -150.25 -162.25 -166.82 -163.24 -151.90 -133.30 -107.20 -72.56 -28.26 --25.73 --87.86 --155.29 --224.80 --293.61 --359.47 --420.05 --472.32 --512.31 --535.98 --540.43 --525.32 --493.34 --449.59 --400.22 --350.84 --305.47 --266.30 --234.06 --208.57 --189.22 --174.98 --164.23 --154.77 --144.07 --129.92 --111.13 --87.88 --61.57 --34.21 --7.65 -16.90 -39.05 -58.96 -77.06 -93.72 -109.13 -123.18 -135.51 -145.55 -152.66 -156.35 -156.47 -153.37 -147.81 -140.88 -133.70 -127.20 -121.97 -118.09 -115.19 -112.52 -109.16 -104.21 -97.18 -88.13 -77.78 -67.37 -58.36 -52.13 -49.57 -50.94 -55.80 -63.14 -71.53 -79.53 -85.92 -89.93 -91.27 -90.00 -86.27 -80.31 -72.34 -62.70 -51.93 -40.84 -30.29 -21.02 -13.41 -7.34 -2.31 --2.35 --6.99 --11.38 --14.65 --15.72 --13.86 --9.12 --2.40 -5.11 -12.52 -19.51 -26.05 -31.92 -36.33 -38.21 -36.85 -32.64 -27.05 -21.92 -18.42 -16.46 -14.87 -12.30 -8.08 -2.58 --3.30 --8.87 --14.07 --19.35 --25.01 --30.69 --35.28 --37.44 --36.18 --31.40 --23.83 --14.72 --5.26 -3.79 -12.11 -19.74 -26.86 -33.73 -40.53 -47.47 -54.70 -62.29 -70.17 -78.02 -85.31 -91.33 -95.43 -97.13 -96.23 -92.83 -87.30 -80.11 -71.80 -62.87 -53.79 -44.94 -36.56 -28.81 -21.75 -15.48 -10.08 -5.63 -2.10 --0.68 --3.07 --5.45 --8.10 --11.05 --14.07 --16.85 --19.16 --20.92 --22.25 --23.31 --24.14 --24.56 --24.18 --22.48 --19.05 --13.61 --6.16 -3.10 -13.78 -25.37 -37.21 -48.54 -58.57 -66.65 -72.42 -75.84 -77.15 -76.69 -74.85 -71.88 -67.88 -62.89 -56.80 -49.56 -41.15 -31.77 -21.90 -12.34 -4.01 --2.32 --6.29 --8.12 --8.52 --8.41 --8.60 --9.56 --11.34 --13.69 --16.21 --18.57 --20.56 --22.14 --23.50 --24.96 --26.93 --29.64 --32.94 --36.14 --38.22 --38.17 --35.34 --29.75 --22.06 --13.30 --4.53 -3.41 -9.95 -14.76 -17.71 -18.81 -18.26 -16.40 -13.62 -10.23 -6.46 -2.46 --1.58 --5.42 --8.83 --11.71 --14.09 --16.09 --17.76 --18.92 --19.21 --18.30 --16.09 --12.83 --9.08 --5.42 --2.19 -0.64 -3.45 -6.71 -10.77 -15.66 -21.07 -26.44 -31.17 -34.85 -37.42 -39.16 -40.56 -42.04 -43.72 -45.32 -46.34 -46.37 -45.26 -43.28 -40.82 -38.23 -35.58 -32.70 -29.37 -25.52 -21.30 -17.01 -12.94 -9.24 -5.87 -2.63 --0.67 --4.06 --7.42 --10.50 --13.05 --14.94 --16.17 --16.88 --17.16 --17.00 --16.25 --14.74 --12.43 --9.52 --6.41 --3.59 --1.43 --0.07 -0.58 -0.76 -0.71 -0.59 -0.47 -0.39 -0.39 -0.52 -0.78 -1.15 -1.52 -1.80 -1.89 -1.83 -1.71 -1.65 -1.72 -1.89 -2.01 -1.89 -1.30 -0.11 --1.69 --3.88 --6.04 --7.62 --8.17 --7.57 --6.06 --4.19 --2.47 --1.18 --0.21 -0.81 -2.15 -3.84 -5.56 -6.90 -7.56 -7.58 -7.20 -6.68 -6.07 -5.16 -3.59 -1.08 --2.40 --6.63 --11.18 --15.60 --19.53 --22.77 --25.30 --27.19 --28.56 --29.53 --30.20 --30.62 --30.80 --30.74 --30.37 --29.56 --28.21 --26.24 --23.78 --21.06 --18.46 --16.34 --14.93 --14.23 --14.00 --13.83 --13.27 --12.03 --10.07 --7.68 --5.32 --3.44 --2.31 --1.88 --1.88 --1.96 --1.88 --1.63 --1.32 --1.12 --1.14 --1.35 --1.65 --1.96 --2.20 --2.39 --2.56 --2.76 --3.02 --3.37 --3.82 --4.36 --4.95 --5.61 --6.35 --7.17 --7.99 --8.66 --8.96 --8.71 --7.85 --6.51 --4.96 --3.47 --2.22 --1.27 --0.53 -0.12 -0.76 -1.40 -2.02 -2.61 -3.19 -3.79 -4.39 -4.90 -5.16 -5.07 -4.60 -3.84 -3.01 -2.33 -1.94 -1.89 -2.10 -2.44 -2.83 -3.19 -3.48 -3.62 -3.56 -3.21 -2.54 -1.63 -0.66 --0.16 --0.61 --0.60 --0.21 -0.36 -0.83 -0.99 -0.75 -0.17 --0.52 --1.01 --1.06 --0.55 -0.45 -1.75 -3.11 -4.32 -5.23 -5.82 -6.14 -6.26 -6.23 -6.08 -5.78 -5.34 -4.79 -4.19 -3.66 -3.31 -3.19 -3.26 -3.36 -3.27 -2.73 -1.62 --0.06 --2.14 --4.35 --6.35 --7.89 --8.78 --9.01 --8.72 --8.23 --7.92 --8.16 --9.23 --11.13 --13.59 --16.10 --18.03 --18.93 --18.71 --17.65 --16.28 --15.09 --14.32 --13.83 --13.31 --12.47 --11.19 --9.55 --7.65 --5.42 --2.60 -1.00 -5.28 -9.63 -13.15 -15.07 -15.25 -14.20 -12.89 -12.13 -12.13 -12.47 -12.42 -11.56 -10.05 -8.65 -8.16 -8.98 -10.72 -12.48 -13.25 -12.47 -10.24 -7.17 -4.07 -1.49 --0.45 --2.02 --3.61 --5.45 --7.50 --9.43 --10.77 --11.12 --10.31 --8.42 --5.79 --2.84 --0.05 -2.21 -3.69 -4.24 -3.89 -2.76 -1.08 --0.86 --2.71 --4.23 --5.24 --5.71 --5.67 --5.24 --4.50 --3.54 --2.45 --1.32 --0.21 -0.85 -1.86 -2.85 -3.88 -4.96 -6.06 -7.11 -8.07 -8.88 -9.57 -10.18 -10.76 -11.37 -12.04 -12.75 -13.42 -13.99 -14.38 -14.63 -14.88 -15.34 -16.20 -17.53 -19.17 -20.83 -22.12 -22.73 -22.59 -21.87 -20.91 -20.04 -19.44 -19.07 -18.72 -18.02 -16.50 -13.55 -8.47 -0.59 --10.39 --24.12 --39.49 --54.80 --68.24 --78.35 --84.41 --86.53 --85.37 --81.84 --76.79 --70.93 --64.81 --58.96 --53.89 --49.98 --47.33 --45.61 --44.09 --41.84 --38.15 --32.72 --25.87 --18.27 --10.54 --3.02 -4.39 -11.99 -19.97 -28.18 -36.06 -42.91 -48.19 -51.68 -53.55 -54.14 -53.78 -52.60 -50.50 -47.34 -43.01 -37.66 -31.73 -25.89 -20.78 -16.81 -13.95 -11.76 -9.62 -7.10 -4.11 -0.96 --1.85 --3.95 --5.26 --6.02 --6.59 --7.28 --8.15 --9.11 --9.98 --10.66 --11.17 --11.58 --11.98 --12.42 --12.87 --13.34 --13.84 --14.37 --14.93 --15.48 --15.92 --16.18 --16.23 --16.07 --15.75 --15.33 --14.80 --14.15 --13.38 --12.48 --11.46 --10.23 --8.64 --6.47 --3.54 -0.17 -4.49 -9.14 -13.83 -18.34 -22.58 -26.42 -29.62 -31.84 -32.83 -32.61 -31.59 -30.51 -30.05 -30.52 -31.73 -33.04 -33.72 -33.26 -31.65 -29.20 -26.43 -23.69 -21.03 -18.25 -15.09 -11.44 -7.38 -3.26 --0.55 --3.79 --6.35 --8.30 --9.72 --10.68 --11.18 --11.24 --10.91 --10.32 --9.64 --9.04 --8.60 --8.33 --8.18 --8.11 --8.11 --8.12 --8.03 --7.72 --7.11 --6.19 --5.14 --4.23 --3.63 --3.44 --3.53 --3.71 --3.83 --3.89 --4.05 --4.47 --5.16 --5.92 --6.42 --6.36 --5.61 --4.30 --2.73 --1.24 --0.07 -0.71 -1.21 -1.64 -2.20 -2.94 -3.81 -4.62 -5.22 -5.56 -5.73 -5.92 -6.36 -7.13 -8.15 -9.18 -9.90 -10.01 -9.36 -7.94 -5.91 -3.50 -1.01 --1.29 --3.23 --4.74 --5.83 --6.55 --6.92 --6.94 --6.65 --6.13 --5.58 --5.19 --5.04 --5.04 --4.98 --4.64 --4.05 --3.49 --3.44 --4.26 --5.98 --8.23 --10.41 --11.97 --12.68 --12.66 --12.24 --11.70 --11.15 --10.52 --9.74 --8.85 --8.05 --7.54 --7.43 --7.63 --7.93 --8.12 --8.07 --7.77 --7.27 --6.66 --6.11 --5.90 --6.35 --7.72 --10.02 --12.86 --15.55 --17.34 --17.78 --16.95 --15.33 --13.59 --12.21 --11.32 --10.75 --10.25 --9.67 --9.08 --8.62 --8.33 --8.14 --7.84 --7.32 --6.61 --5.94 --5.56 --5.62 --6.06 --6.63 --7.02 --6.95 --6.27 --4.91 --2.95 --0.53 -2.16 -4.91 -7.49 -9.70 -11.38 -12.46 -12.94 -12.94 -12.65 -12.33 -12.26 -12.64 -13.58 -15.02 -16.72 -18.40 -19.75 -20.55 -20.70 -20.18 -19.11 -17.60 -15.80 -13.80 -11.69 -9.61 -7.70 -6.14 -4.98 -4.16 -3.44 -2.52 -1.22 --0.39 --1.95 --2.94 --2.92 --1.75 -0.22 -2.32 -3.77 -3.93 -2.56 --0.20 --3.90 --7.92 --11.65 --14.55 --16.31 --16.81 --16.26 --15.05 --13.65 --12.27 --10.78 --8.59 --5.11 --0.05 -6.26 -12.80 -18.25 -21.45 -21.83 -19.55 -15.24 -9.74 -3.83 --1.78 --6.40 --9.35 --10.12 --8.68 --5.62 --2.12 -0.42 -0.86 --1.28 --5.76 --11.76 --18.26 --24.32 --29.32 --32.89 --34.94 --35.54 --34.86 --33.16 --30.59 --27.22 --23.04 --18.06 --12.46 --6.59 --0.90 -4.15 -8.21 -11.07 -12.65 -12.91 -11.96 -10.13 -7.97 -6.16 -5.23 -5.39 -6.37 -7.58 -8.42 -8.57 -8.15 -7.63 -7.61 -8.53 -10.50 -13.30 -16.49 -19.62 -22.39 -24.73 -26.72 -28.53 -30.26 -31.86 -33.17 -34.05 -34.44 -34.35 -33.75 -32.47 -30.30 -27.05 -22.80 -17.95 -13.15 -9.02 -5.95 -4.01 -2.97 -2.45 -2.13 -1.65 -0.64 --1.42 --5.01 --10.51 --17.91 --26.82 --36.58 --46.44 --55.73 --63.99 --70.98 --76.57 --80.71 --83.38 --84.46 --83.87 --81.56 --77.55 --72.01 --65.23 --57.50 --49.01 --39.84 --29.91 --19.29 --8.25 -2.57 -12.44 -20.75 -27.22 -32.00 -35.46 -38.04 -40.01 -41.53 -42.60 -43.22 -43.36 -42.87 -41.57 -39.34 -36.33 -33.07 -30.49 -29.61 -31.05 -34.68 -39.56 -44.21 -47.20 -47.75 -45.91 -42.53 -38.78 -35.66 -33.58 -32.23 -30.70 -27.75 -22.12 -12.98 -0.16 --15.70 --33.17 --50.30 --65.04 --75.67 --81.19 --81.48 --77.27 --69.99 --61.51 --53.70 --48.05 --45.32 --45.41 --47.47 --50.22 --52.36 --52.84 --51.20 --47.46 --42.13 --36.02 --30.02 --24.80 --20.52 --16.57 --11.69 --4.42 -6.11 -19.49 -33.91 -46.82 -55.99 -60.34 -60.32 -57.35 -52.80 -47.20 -40.02 -30.28 -17.41 -2.01 --14.16 --28.69 --39.45 --45.37 --46.70 --44.76 --41.22 --37.22 --32.98 --27.60 --19.83 --8.99 -4.32 -18.07 -29.66 -37.01 -39.62 -38.75 -36.69 -35.59 -36.31 -38.31 -40.23 -40.90 -40.21 -39.07 -38.85 -40.46 -43.80 -47.85 -51.27 -53.12 -53.29 -52.43 -51.38 -50.66 -50.20 -49.43 -47.68 -44.50 -39.84 -33.90 -26.93 -19.16 -10.82 -2.29 --5.87 --13.00 --18.58 --22.46 --24.87 --26.39 --27.60 --28.84 --30.21 --31.54 --32.61 --33.34 --33.76 --34.04 --34.28 --34.42 --34.21 --33.40 --31.84 --29.71 --27.44 --25.48 --24.15 --23.38 --22.82 --22.00 --20.66 --18.75 --16.50 --14.14 --11.73 --9.19 --6.34 --3.14 -0.27 -3.52 -6.20 -7.89 -8.25 -7.10 -4.42 -0.41 --4.50 --9.61 --14.19 --17.62 --19.66 --20.51 --20.71 --20.79 --21.04 --21.34 --21.27 --20.42 --18.66 --16.29 --13.90 --12.07 --11.16 --11.09 --11.51 --11.95 --12.05 --11.63 --10.66 --9.12 --6.94 --4.07 --0.58 -3.29 -7.11 -10.38 -12.73 -14.08 -14.63 -14.74 -14.71 -14.72 -14.77 -14.85 -14.99 -15.34 -16.06 -17.25 -18.83 -20.58 -22.20 -23.45 -24.23 -24.55 -24.53 -24.26 -23.81 -23.20 -22.40 -21.43 -20.31 -19.06 -17.66 -16.09 -14.31 -12.33 -10.27 -8.30 -6.63 -5.23 -3.89 -2.17 --0.34 --3.85 --8.24 --13.10 --17.90 --22.15 --25.57 --28.07 --29.63 --30.29 --30.06 --28.95 --27.05 --24.54 --21.65 --18.65 --15.82 --13.33 --11.34 --9.85 --8.81 --8.06 --7.40 --6.62 --5.59 --4.26 --2.74 --1.23 -0.05 -0.93 -1.38 -1.51 -1.52 -1.64 -2.05 -2.80 -3.83 -4.98 -6.03 -6.78 -7.11 -7.00 -6.57 -6.01 -5.53 -5.27 -5.26 -5.42 -5.63 -5.73 -5.65 -5.34 -4.85 -4.24 -3.62 -3.07 -2.60 -2.15 -1.66 -1.09 -0.45 --0.13 --0.53 --0.61 --0.33 -0.28 -1.15 -2.20 -3.35 -4.55 -5.73 -6.80 -7.61 -8.06 -8.10 -7.82 -7.38 -6.99 -6.81 -6.82 -6.92 -7.01 -7.01 -6.97 -7.02 -7.22 -7.52 -7.83 -7.99 -7.94 -7.71 -7.40 -7.11 -6.83 -6.49 -6.00 -5.35 -4.61 -3.95 -3.50 -3.32 -3.28 -3.22 -2.96 -2.41 -1.65 -0.82 -0.10 --0.38 --0.65 --0.80 --0.93 --1.13 --1.43 --1.78 --2.16 --2.55 --2.90 --3.15 --3.16 --2.79 --1.94 --0.66 -0.87 -2.35 -3.50 -4.17 -4.36 -4.23 -3.94 -3.59 -3.16 -2.66 -2.08 -1.52 -1.10 -0.92 -0.97 -1.18 -1.44 -1.65 -1.79 -1.85 -1.84 -1.78 -1.65 -1.48 -1.31 -1.18 -1.13 -1.12 -1.11 -1.06 -0.94 -0.72 -0.39 --0.09 --0.69 --1.35 --1.95 --2.39 --2.63 --2.73 --2.79 --2.90 --3.09 --3.33 --3.56 --3.74 --3.85 --3.89 --3.80 --3.51 --2.94 --2.08 --1.03 -0.04 -0.98 -1.69 -2.17 -2.47 -2.65 -2.73 -2.69 -2.51 -2.21 -1.86 -1.58 -1.46 -1.53 -1.70 -1.87 -1.91 -1.81 -1.60 -1.41 -1.35 -1.44 -1.69 -2.01 -2.34 -2.59 -2.74 -2.76 -2.60 -2.27 -1.74 -1.09 -0.38 --0.28 --0.80 --1.12 --1.25 --1.29 --1.35 --1.57 --2.00 --2.63 --3.34 --3.98 --4.39 --4.46 --4.14 --3.52 --2.76 --2.10 --1.74 --1.79 --2.18 --2.72 --3.12 --3.16 --2.79 --2.20 --1.73 --1.79 --2.71 --4.66 --7.62 --11.39 --15.57 --19.71 --23.33 --26.12 --27.93 --28.74 --28.48 --26.85 --23.29 --17.20 --8.21 -3.43 -16.89 -30.90 -44.08 -55.26 -63.64 -68.69 -70.11 -67.85 -62.18 -53.82 -43.83 -33.39 -23.56 -15.03 -8.07 -2.58 --1.67 --4.95 --7.40 --9.03 --9.78 --9.58 --8.46 --6.57 --4.17 --1.56 -0.94 -2.99 -4.26 -4.49 -3.69 -2.21 -0.76 -0.24 -1.33 -4.17 -8.26 -12.67 -16.50 -19.21 -20.91 -22.09 -23.31 -24.79 -26.25 -27.10 -26.70 -24.78 -21.48 -17.39 -13.19 -9.44 -6.36 -3.80 -1.41 --1.12 --3.91 --6.88 --9.75 --12.26 --14.18 --15.34 --15.72 --15.49 --15.05 --14.94 --15.62 --17.21 --19.40 --21.59 --23.21 --24.08 --24.48 --24.76 --25.11 --25.28 --24.88 --23.86 --22.78 --22.65 --24.42 --28.44 --34.21 --40.81 --47.40 --53.62 --59.52 --65.19 --70.35 --74.43 --76.93 --77.72 --77.15 --75.62 --73.15 --69.25 --63.33 --55.31 --46.00 --36.92 --29.47 --24.28 --21.02 --18.72 --16.67 --14.82 --13.73 --13.88 --15.09 --16.41 --16.67 --15.19 --12.07 --8.06 --3.81 -0.54 -5.27 -10.60 -16.16 -20.96 -23.83 -24.30 -22.96 -21.40 -21.38 -23.93 -28.92 -35.15 -41.17 -46.14 -50.39 -55.19 -61.94 -71.19 -81.96 -92.01 -98.72 -100.40 -97.03 -90.23 -82.38 -75.36 -69.79 -64.97 -59.69 -53.14 -45.44 -37.58 -30.78 -25.74 -22.31 -19.66 -16.82 -13.19 -8.87 -4.50 -0.76 --2.14 --4.67 --7.83 --12.61 --19.42 --27.82 --36.57 --44.17 --49.31 --51.38 --50.55 --47.54 --43.28 --38.62 --34.21 --30.41 --27.40 --25.24 --23.86 --23.08 --22.65 --22.24 --21.53 --20.31 --18.52 --16.25 --13.72 --11.17 --8.82 --6.83 --5.25 --4.05 --3.12 --2.28 --1.39 --0.38 -0.69 -1.68 -2.45 -2.92 -3.18 -3.41 -3.74 -4.26 -4.84 -5.32 -5.49 -5.26 -4.69 -3.92 -3.16 -2.56 -2.21 -2.12 -2.28 -2.65 -3.24 -4.02 -4.95 -5.90 -6.71 -7.19 -7.21 -6.74 -5.86 -4.73 -3.52 -2.35 -1.28 -0.36 --0.38 --0.92 --1.26 --1.51 --1.81 --2.29 --2.96 --3.69 --4.19 --4.21 --3.66 --2.68 --1.62 --0.88 --0.74 --1.24 --2.21 --3.33 --4.29 --4.91 --5.16 --5.21 --5.33 --5.76 --6.63 --7.90 --9.33 --10.56 --11.29 --11.34 --10.79 --9.85 --8.78 --7.64 --6.15 --3.68 -0.62 -7.60 -17.78 -31.00 -46.22 -61.55 -74.57 -82.88 -84.88 -80.32 -70.39 -57.33 -43.51 -30.58 -19.11 -8.81 --0.80 --9.75 --17.40 --22.84 --25.48 --25.52 --23.87 --21.74 --20.17 --19.68 --20.34 --21.97 --24.27 --26.83 --29.09 --30.38 --30.09 --27.95 --24.20 --19.60 --15.18 --11.91 --10.33 --10.45 --11.73 --13.38 --14.59 --14.84 --13.99 --12.24 --9.94 --7.45 --5.03 --2.89 --1.18 --0.06 -0.48 -0.68 -1.00 -2.05 -4.29 -7.85 -12.53 -17.82 -23.16 -28.15 -32.61 -36.54 -39.95 -42.73 -44.54 -44.86 -43.18 -39.29 -33.50 -26.66 -19.98 -14.61 -11.17 -9.54 -8.91 -8.14 -6.25 -2.84 --1.85 --7.11 --12.16 --16.53 --20.14 --23.35 --26.57 --30.14 --34.12 --38.23 --41.95 --44.71 --45.98 --45.44 --43.05 --39.02 --33.81 --28.00 --22.24 --17.18 --13.34 --10.92 --9.60 --8.57 --6.73 --3.14 -2.52 -9.84 -17.88 -25.72 -32.93 -39.69 -46.49 -53.59 -60.65 -66.81 -71.13 -73.09 -72.81 -70.82 -67.66 -63.62 -58.69 -52.80 -46.07 -38.92 -31.91 -25.58 -20.21 -15.74 -11.89 -8.26 -4.50 -0.37 --4.25 --9.50 --15.66 --23.31 --33.26 --46.26 --62.56 --81.58 --101.91 --121.58 --138.59 --151.38 --158.87 --160.52 --156.23 --146.37 --132.01 --114.90 --97.13 --80.45 --65.48 --51.47 --36.75 --19.72 -0.01 -21.17 -41.14 -56.90 -66.16 -68.09 -63.43 -54.08 -42.53 -31.25 -22.32 -17.14 -16.28 -19.39 -25.35 -32.63 -39.69 -45.41 -49.14 -50.77 -50.57 -48.97 -46.64 -44.29 -42.66 -42.27 -43.29 -45.43 -47.94 -49.95 -50.73 -50.04 -48.21 -46.02 -44.39 -44.21 -46.10 -50.29 -56.38 -63.11 -68.55 -70.57 -67.70 -59.88 -48.68 -36.65 -26.23 -18.80 -14.16 -11.10 -8.14 -4.35 --0.41 --5.70 --10.90 --15.50 --19.19 --21.96 --24.32 --27.60 --33.89 --45.60 --64.63 --91.41 --124.42 --160.42 --195.14 --224.31 --244.29 --252.49 --247.54 --229.49 --199.96 --162.10 --120.07 --78.13 --39.64 --6.48 -20.81 -42.36 -58.20 -67.99 -71.34 -68.52 -60.88 -50.78 -40.91 -33.38 -29.20 -28.18 -29.43 -31.94 -35.01 -38.39 -42.01 -45.74 -49.05 -51.08 -50.90 -48.02 -42.79 -36.53 -31.02 -27.76 -27.20 -28.53 -30.07 -30.23 -28.33 -24.95 -21.66 -20.10 -21.13 -24.40 -28.56 -32.03 -33.74 -33.65 -32.58 -31.70 -31.85 -33.19 -35.16 -36.82 -37.37 -36.49 -34.42 -31.78 -29.24 -27.30 -26.15 -25.72 -25.83 -26.24 -26.77 -27.16 -27.20 -26.81 -26.16 -25.66 -25.91 -27.26 -29.67 -32.57 -35.10 -36.50 -36.48 -35.33 -33.67 -32.03 -30.58 -29.11 -27.36 -25.35 -23.33 -21.55 -19.92 -17.85 -14.47 -9.16 -1.93 --6.37 --14.50 --21.33 --26.41 --30.15 --33.45 --37.25 --41.97 --47.35 --52.69 --57.22 --60.42 --62.23 --62.94 --62.92 --62.42 --61.50 --60.16 --58.47 --56.72 --55.36 --54.86 --55.59 --57.69 --61.08 --65.47 --70.45 --75.62 --80.61 --85.20 --89.27 --92.70 --95.29 --96.65 --96.24 --93.53 --88.17 --80.22 --70.09 --58.44 --45.90 --32.90 --19.66 --6.32 -6.88 -19.43 -30.65 -39.88 -46.68 -50.87 -52.41 -51.27 -47.45 -41.18 -33.09 -24.21 -15.65 -8.22 -2.06 --3.12 --7.50 --10.74 --11.96 --10.43 --6.35 --1.28 -2.39 -2.75 --0.49 --5.65 --10.06 --11.64 --10.09 --6.94 --4.46 --4.10 --5.71 --7.77 --8.60 --7.43 --4.70 --1.41 -1.75 -5.08 -9.53 -15.88 -24.04 -32.93 -41.04 -47.34 -51.70 -54.76 -57.25 -59.30 -60.16 -58.52 -53.22 -43.85 -30.99 -15.99 -0.38 --14.56 --27.98 --39.30 --47.90 --53.08 --54.12 --50.73 --43.33 --33.04 --21.32 --9.52 -1.49 -11.29 -19.62 -26.19 -30.72 -33.18 -34.01 -34.14 -34.49 -35.57 -37.13 -38.46 -38.88 -38.31 -37.32 -36.68 -36.74 -37.16 -37.13 -35.97 -33.71 -31.18 -29.51 -29.48 -31.00 -33.05 -34.06 -32.51 -27.48 -18.92 -7.67 --4.73 --16.53 --26.19 --32.89 --36.61 --38.02 --37.93 --36.84 --34.71 --31.05 --25.36 --17.60 --8.43 -0.93 -9.09 -15.08 -18.75 -20.78 -22.42 -24.87 -28.64 -33.31 -37.68 -40.42 -40.75 -38.97 -36.30 -34.26 -33.93 -35.33 -37.47 -38.86 -38.22 -35.14 -30.18 -24.55 -19.50 -15.73 -13.15 -10.99 -8.26 -4.17 --1.74 --9.51 --18.79 --28.81 --38.23 --45.21 --47.79 --44.63 --35.79 --23.12 --9.86 -0.41 -5.10 -3.58 --2.84 --11.62 --20.23 --27.14 --32.12 --35.86 --39.25 --42.74 --46.09 --48.61 --49.58 --48.66 --45.99 --42.01 --37.17 --31.76 --25.80 --19.36 --12.67 --6.31 --1.01 -2.62 -4.34 -4.46 -3.70 -2.92 -2.72 -3.24 -4.20 -5.07 -5.38 -4.96 -3.92 -2.60 -1.44 -0.74 -0.71 -1.36 -2.56 -4.13 -5.80 -7.39 -8.78 -9.98 -11.13 -12.34 -13.65 -14.94 -16.00 -16.63 -16.77 -16.54 -16.18 -15.92 -15.83 -15.85 -15.76 -15.40 -14.70 -13.74 -12.70 -11.74 -10.97 -10.32 -9.65 -8.76 -7.54 -6.03 -4.50 -3.33 -2.83 -3.14 -4.04 -5.03 -5.49 -4.91 -3.08 -0.11 --3.63 --7.71 --11.71 --15.36 --18.51 --21.12 --23.23 --24.83 --25.91 --26.44 --26.36 --25.63 --24.24 --22.14 --19.26 --15.59 --11.30 --6.85 --2.97 --0.40 -0.50 -0.07 --0.71 --0.60 -1.23 -4.79 -9.25 -13.50 -16.75 -18.97 -20.83 -23.13 -26.40 -30.64 -35.55 -40.77 -46.14 -51.50 -56.50 -60.47 -62.59 -62.36 -59.88 -55.92 -51.57 -47.74 -44.79 -42.50 -40.36 -37.88 -34.87 -31.45 -27.88 -24.46 -21.46 -19.02 -17.16 -15.71 -14.24 -12.16 -8.76 -3.47 --3.95 --13.34 --24.10 --35.40 --46.26 --55.79 --63.24 --68.14 --70.33 --70.01 --67.66 --63.98 --59.67 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--18.39 --17.58 --15.76 --12.93 --9.24 --4.98 --0.57 -3.59 -7.13 -9.88 -11.84 -13.17 -14.08 -14.74 -15.24 -15.60 -15.78 -15.77 -15.57 -15.22 -14.77 -14.26 -13.70 -13.11 -12.51 -11.91 -11.30 -10.63 -9.88 -9.05 -8.20 -7.46 -6.90 -6.53 -6.21 -5.72 -4.87 -3.61 -2.02 -0.33 --1.26 --2.63 --3.76 --4.69 --5.53 --6.35 --7.26 --8.30 --9.54 --10.93 --12.36 --13.69 --14.79 --15.59 --16.12 --16.43 --16.52 --16.24 --15.38 --13.72 --11.19 --7.90 --4.20 --0.48 -2.86 -5.63 -7.76 -9.29 -10.28 -10.76 -10.74 -10.30 -9.61 -8.87 -8.29 -7.91 -7.71 -7.60 -7.54 -7.56 -7.72 -8.02 -8.36 -8.58 -8.51 -8.09 -7.38 -6.49 -5.50 -4.49 -3.45 -2.40 -1.38 -0.42 --0.47 --1.27 --2.04 --2.82 --3.68 --4.65 --5.76 --7.04 --8.51 --10.18 --12.01 --13.93 --15.84 --17.63 --19.16 --20.34 --21.08 --21.31 --20.94 --19.87 --18.01 --15.34 --12.00 --8.24 --4.36 --0.67 -2.57 -5.22 -7.19 -8.42 -8.93 -8.76 -8.00 -6.85 -5.58 -4.44 -3.60 -3.12 -2.94 -2.95 -3.05 -3.15 -3.22 -3.21 -3.08 -2.74 -2.21 -1.61 -1.17 -1.08 -1.35 -1.68 -1.59 -0.59 --1.58 --4.83 --8.77 --12.88 --16.74 --20.12 --23.02 --25.62 --28.18 --30.87 --33.78 --36.79 --39.73 --42.38 --44.63 --46.47 --48.01 --49.36 --50.52 --51.38 --51.71 --51.27 --49.85 --47.31 --43.58 --38.68 --32.92 --26.92 --21.61 --17.95 --16.56 --17.38 --19.58 --21.90 --23.15 --22.78 --21.04 --18.76 --16.80 --15.51 --14.56 --13.10 --10.32 --5.82 -0.08 -6.63 -12.88 -18.10 -21.96 -24.56 -26.29 -27.64 -29.05 -30.76 -32.78 -34.88 -36.78 -38.22 -39.07 -39.31 -38.96 -38.00 -36.41 -34.24 -31.72 -29.28 -27.40 -26.44 -26.52 -27.47 -29.02 -30.90 -33.02 -35.31 -37.68 -39.84 -41.33 -41.74 -40.85 -38.76 -35.82 -32.51 -29.23 -26.25 -23.66 -21.52 -19.91 -18.85 -18.25 -17.86 -17.25 -15.88 -13.24 -9.02 -3.28 --3.49 --10.46 --16.59 --20.90 --22.70 --21.79 --18.47 --13.40 --7.42 --1.37 -4.11 -8.57 -11.77 -13.58 -13.99 -13.13 -11.32 -8.97 -6.50 -4.19 -2.09 -0.02 --2.24 --4.80 --7.56 --10.19 --12.34 --13.67 --14.05 --13.47 --12.03 --9.88 --7.13 --3.94 --0.49 -3.04 -6.46 -9.68 -12.57 -14.99 -16.75 -17.71 -17.85 -17.43 -16.95 -16.97 -17.83 -19.47 -21.42 -23.03 -23.83 -23.74 -23.02 -22.11 -21.26 -20.46 -19.46 -18.06 -16.29 -14.42 -12.82 -11.62 -10.66 -9.55 -7.92 -5.71 -3.24 -1.08 --0.29 --0.73 --0.55 --0.29 --0.43 --1.14 --2.23 --3.42 --4.44 --5.29 --6.09 --6.92 --7.66 --7.88 --7.04 --4.74 --0.91 -4.04 -9.40 -14.32 -18.16 -20.54 -21.36 -20.73 -18.87 -16.15 -13.03 -10.12 -7.98 -6.95 -6.99 -7.70 -8.42 -8.56 -7.90 -6.53 -4.85 -3.24 -1.88 -0.70 --0.54 --2.07 --4.00 --6.25 --8.69 --11.12 --13.44 --15.65 --17.85 --20.15 --22.54 --24.88 --26.90 --28.37 --29.15 --29.32 --29.16 --28.95 --28.86 --28.84 --28.71 --28.19 --27.11 --25.41 --23.13 --20.42 --17.44 --14.47 --11.78 --9.62 --8.10 --7.10 --6.32 --5.49 --4.46 --3.28 --2.12 --1.06 -0.01 -1.35 -3.24 -5.80 -8.87 -12.17 -15.46 -18.69 -21.92 -25.21 -28.45 -31.32 -33.44 -34.57 -34.71 -34.07 -32.90 -31.37 -29.51 -27.29 -24.76 -22.11 -19.65 -17.62 -16.09 -15.01 -14.17 -13.37 -12.44 -11.26 -9.72 -7.79 -5.52 -3.05 -0.60 --1.63 --3.50 --4.95 --5.96 --6.50 --6.48 --5.82 --4.57 --2.89 --1.01 -0.87 -2.63 -4.25 -5.59 -6.43 -6.48 -5.60 -4.00 -2.31 -1.29 -1.54 -3.14 -5.53 -7.77 -8.86 -8.12 -5.41 -1.01 --4.50 --10.45 --16.20 --21.22 --25.01 --27.21 --27.68 --26.60 --24.44 --21.92 --19.70 --18.20 --17.42 --16.94 --16.15 --14.57 --12.10 --9.12 --6.27 --4.15 --2.97 --2.52 --2.34 --2.07 --1.61 --1.12 --0.85 --0.82 --0.75 --0.27 -0.87 -2.57 -4.39 -5.88 -6.77 -7.19 -7.48 -8.03 -8.95 -10.08 -11.05 -11.52 -11.35 -10.59 -9.41 -8.02 -6.60 -5.29 -4.18 -3.32 -2.78 -2.51 -2.40 -2.28 -1.97 -1.41 -0.71 -0.12 --0.10 -0.20 -0.93 -1.72 -2.09 -1.65 -0.25 --2.01 --4.79 --7.75 --10.63 --13.32 --15.83 --18.16 --20.30 --22.24 --24.05 --25.77 --27.42 --28.90 --29.98 --30.36 --29.82 --28.24 --25.71 --22.44 --18.67 --14.69 --10.73 --6.97 --3.52 --0.35 -2.62 -5.48 -8.27 -10.85 -12.98 -14.43 -15.10 -15.11 -14.78 -14.48 -14.50 -14.94 -15.64 -16.34 -16.75 -16.65 -15.92 -14.48 -12.26 -9.17 -5.21 -0.64 --4.09 --8.35 --11.59 --13.59 --14.50 --14.80 --14.99 --15.43 --16.12 --16.79 --17.03 --16.46 --14.90 --12.44 --9.34 --6.00 --2.76 -0.15 -2.63 -4.71 -6.43 -7.84 -8.96 -9.80 -10.37 -10.75 -11.06 -11.35 -11.69 -12.03 -12.29 -12.34 -12.14 -11.75 -11.35 -11.16 -11.29 -11.70 -12.13 -12.23 -11.78 -10.74 -9.32 -7.84 -6.51 -5.36 -4.26 -3.04 -1.63 -0.16 --1.16 --2.19 --2.97 --3.71 --4.64 --5.86 --7.20 --8.28 --8.69 --8.22 --6.92 --5.14 --3.29 --1.73 --0.58 -0.17 -0.60 -0.72 -0.45 --0.29 --1.53 --3.10 --4.67 --5.88 --6.44 --6.24 --5.34 --3.90 --2.15 --0.28 -1.53 -3.14 -4.46 -5.39 -5.95 -6.15 -6.04 -5.69 -5.19 -4.68 -4.27 -4.08 -4.12 -4.29 -4.39 -4.22 -3.70 -2.85 -1.84 -0.83 --0.06 --0.82 --1.52 --2.24 --3.01 --3.77 --4.41 --4.82 --4.94 --4.75 --4.27 --3.55 --2.64 --1.59 --0.46 -0.66 -1.68 -2.54 -3.25 -3.86 -4.42 -4.99 -5.51 -5.87 -5.95 -5.68 -5.15 -4.54 -4.09 -3.93 -4.03 -4.27 -4.47 -4.52 -4.44 -4.36 -4.39 -4.55 -4.78 -5.00 -5.12 -5.14 -5.13 -5.15 -5.24 -5.36 -5.42 -5.41 -5.31 -5.19 -5.08 -4.99 -4.87 -4.71 -4.52 -4.37 -4.43 -4.78 -5.38 -6.06 -6.53 -6.45 -5.57 -3.81 -1.33 --1.46 --4.03 --5.84 --6.56 --6.21 --5.19 --4.08 --3.44 --3.57 --4.42 --5.68 --6.95 --8.03 --8.91 --9.73 --10.60 --11.48 --12.14 --12.35 --12.01 --11.20 --10.23 --9.44 --9.09 --9.18 --9.50 --9.77 --9.68 --9.10 --8.04 --6.63 --5.13 --3.77 --2.77 --2.30 --2.45 --3.27 --4.76 --6.84 --9.27 --11.66 --13.45 --14.04 --13.01 --10.27 --6.21 --1.58 -2.68 -5.85 -7.54 -7.81 -6.99 -5.48 -3.61 -1.60 --0.36 --2.04 --3.18 --3.57 --3.19 --2.28 --1.26 --0.59 --0.55 --1.17 --2.24 --3.42 --4.48 --5.33 --6.04 --6.79 --7.66 --8.59 --9.42 --9.91 --9.90 --9.30 --8.16 --6.57 --4.62 --2.40 --0.00 -2.43 -4.72 -6.67 -8.19 -9.23 -9.86 -10.17 -10.22 -10.15 -10.06 -10.13 -10.45 -10.96 -11.41 -11.44 -10.82 -9.56 -8.06 -6.89 -6.56 -7.26 -8.74 -10.47 -11.89 -12.59 -12.41 -11.38 -9.56 -7.02 -3.80 -0.10 --3.72 --7.19 --9.85 --11.47 --12.14 --12.21 --12.15 --12.27 --12.66 --13.16 --13.47 --13.42 --13.02 --12.47 --12.06 --11.98 --12.21 --12.47 --12.36 --11.53 --9.88 --7.65 --5.29 --3.23 --1.66 --0.38 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--9.28 --7.05 --4.73 --2.07 -0.86 -3.67 -5.75 -6.55 -5.91 -4.16 -2.03 -0.39 --0.14 -0.58 -2.13 -3.85 -5.06 -5.38 -4.76 -3.39 -1.58 --0.28 --1.83 --2.68 --2.44 --0.81 -2.28 -6.58 -11.42 -15.84 -18.86 -19.91 -19.02 -16.92 -14.66 -13.19 -12.95 -13.77 -15.16 -16.69 -18.42 -20.81 -24.30 -28.86 -33.76 -37.79 -39.80 -39.27 -36.45 -32.21 -27.50 -22.99 -18.90 -15.17 -11.73 -8.61 -5.91 -3.61 -1.49 --0.78 --3.48 --6.74 --10.43 --14.32 --18.26 --22.12 --25.79 --29.05 --31.57 --32.99 --33.05 --31.81 --29.58 --26.80 --23.87 --21.02 --18.33 --15.81 --13.57 --11.79 --10.71 --10.47 --10.99 --11.92 --12.76 --12.99 --12.33 --10.76 --8.56 --6.12 --3.84 --2.01 --0.71 -0.12 -0.63 -1.03 -1.52 -2.23 -3.23 -4.44 -5.69 -6.73 -7.38 -7.56 -7.40 -7.08 -6.82 -6.74 -6.84 -7.07 -7.29 -7.43 -7.44 -7.32 -7.04 -6.59 -5.95 -5.15 -4.28 -3.45 -2.77 -2.33 -2.10 -2.01 -1.93 -1.78 -1.51 -1.17 -0.87 -0.68 -0.64 -0.68 -0.69 -0.55 -0.17 --0.48 --1.34 --2.32 --3.29 --4.11 --4.63 --4.74 --4.40 --3.62 --2.51 --1.21 -0.17 -1.54 -2.83 -3.98 -4.89 -5.47 -5.70 -5.64 -5.47 -5.38 -5.55 -6.01 -6.72 -7.50 -8.19 -8.65 -8.76 -8.50 -7.89 -6.99 -5.89 -4.73 -3.63 -2.69 -1.94 -1.34 -0.82 -0.29 --0.26 --0.76 --1.02 --0.82 --0.06 -1.18 -2.53 -3.49 -3.60 -2.61 -0.54 --2.36 --5.71 --9.16 --12.36 --15.06 --16.99 --17.93 --17.72 --16.44 --14.37 --11.94 --9.58 --7.54 --5.90 --4.52 --3.20 --1.83 --0.41 -0.91 -1.96 -2.61 -2.86 -2.80 -2.58 -2.35 -2.19 -2.17 -2.29 -2.59 -3.08 -3.74 -4.49 -5.25 -5.93 -6.45 -6.78 -6.86 -6.62 -5.92 -4.62 -2.62 --0.04 --3.13 --6.30 --9.10 --11.14 --12.20 --12.22 --11.28 --9.46 --6.82 --3.48 -0.42 -4.60 -8.66 -12.18 -14.79 -16.27 -16.58 -15.86 -14.42 -12.62 -10.88 -9.51 -8.63 -8.24 -8.19 -8.35 -8.64 -9.14 -9.94 -11.07 -12.47 -13.91 -15.11 -15.79 -15.68 -14.62 -12.61 -9.78 -6.46 -3.09 -0.10 --2.20 --3.67 --4.37 --4.43 --3.96 --3.05 --1.76 --0.24 -1.24 -2.34 -2.79 -2.54 -1.79 -0.96 -0.45 -0.52 -1.10 -1.83 -2.13 -1.39 --0.90 --5.06 --11.02 --18.30 --25.98 --32.90 --38.03 --40.81 --41.36 --40.27 --38.30 --35.97 --33.38 --30.34 --26.80 --23.03 --19.59 --17.05 --15.56 --14.69 --13.65 --11.67 --8.42 --4.06 -0.95 -6.19 -11.54 -17.12 -23.05 -29.17 -34.85 -39.17 -41.23 -40.51 -37.14 -31.72 -25.22 -18.70 -13.08 -8.95 -6.48 -5.36 -4.82 -3.97 -2.02 --1.22 --5.22 --8.86 --10.82 --10.13 --6.57 --0.75 -6.07 -12.41 -16.94 -18.78 -17.57 -13.55 -7.45 -0.42 --6.23 --11.26 --13.88 --13.96 --12.04 --9.18 --6.63 --5.48 --6.36 --9.33 --13.95 --19.61 --25.67 --31.56 --36.78 --40.85 --43.30 --43.69 --41.73 --37.38 --31.03 --23.31 --14.81 --6.01 -2.75 -11.07 -18.46 -24.35 -28.30 -29.71 -28.43 -25.02 -20.77 -17.36 -16.37 -18.78 -24.74 -33.52 -42.81 -50.19 -54.39 -55.83 -56.24 -57.60 -61.23 -67.29 -75.17 -82.04 -85.10 -82.76 -75.16 -63.52 -49.22 -33.35 -16.64 -0.03 --17.87 --38.44 --62.10 --87.86 --114.15 --139.87 --164.85 --189.78 --205.11 --203.94 --184.86 --153.41 --117.45 --82.21 --49.67 --20.26 -2.71 -21.52 -36.19 -46.43 -52.72 -56.43 -58.79 -59.90 -59.53 -56.40 -50.21 -39.68 -24.41 -5.23 --16.10 --37.63 --57.85 --75.64 --90.09 --99.83 --105.13 --106.78 --105.64 --102.34 --97.22 --90.58 --82.65 --73.70 --63.50 --52.47 --41.02 --29.43 --17.89 --6.64 -4.02 -13.82 -22.66 -30.78 -38.11 -44.38 -49.11 -51.92 -52.69 -51.89 -50.42 -49.37 -49.60 -51.29 -53.92 -56.51 -58.13 -58.25 -56.94 -54.72 -52.21 -49.87 -47.82 -45.98 -44.18 -42.39 -40.64 -38.97 -37.29 -35.39 -33.06 -30.31 -27.42 -24.79 -22.80 -21.55 -20.79 -20.04 -18.84 -17.00 -14.73 -12.56 -11.18 -11.13 -12.58 -15.25 -18.53 -21.64 -23.84 -24.51 -23.22 -19.67 -13.61 -4.87 --6.59 --20.51 --36.20 --52.32 --67.08 --78.53 --85.03 --85.86 --81.50 --73.51 --63.91 --54.55 --46.48 --39.83 --34.22 --29.19 --24.68 --20.97 --18.37 --16.90 --16.12 --15.25 --13.61 --10.94 --7.39 --3.44 -0.45 -4.01 -7.21 -10.14 -12.79 -15.03 -16.64 -17.44 -17.42 -16.77 -15.84 -15.06 -14.75 -15.18 -16.44 -18.45 -20.89 -23.26 -24.98 -25.55 -24.75 -22.76 -20.14 -17.60 -15.77 -15.04 -15.46 -16.78 -18.55 -20.14 -20.76 -19.65 -16.34 -10.98 -4.41 --2.08 --7.31 --10.71 --12.57 --13.74 --15.08 --16.89 --18.75 --19.83 --19.35 --16.95 --12.83 --7.55 --1.86 -3.45 -7.57 -9.80 -9.73 -7.43 -3.65 --0.34 --3.05 --3.10 -0.43 -7.80 -18.52 -31.48 -45.07 -57.49 -67.10 -72.78 -74.19 -71.87 -67.07 -61.49 -56.87 -54.71 -55.95 -60.76 -68.44 -77.52 -86.12 -92.53 -95.67 -95.42 -92.38 -87.50 -81.51 -74.63 -66.71 -57.55 -47.28 -36.33 -25.19 -13.95 -2.19 --10.76 --25.28 --40.98 --56.41 --69.50 --78.29 --81.67 --79.87 --74.18 --66.50 --58.50 --51.19 --44.80 --39.07 --33.68 --28.57 --23.96 --20.19 --17.39 --15.37 --13.74 --12.10 --10.26 --8.36 --6.69 --5.56 --5.08 --5.09 --5.27 --5.22 --4.71 --3.73 --2.54 --1.51 --0.97 --1.10 --1.80 --2.79 --3.76 --4.51 --5.08 --5.63 --6.33 --7.23 --8.25 --9.23 --10.10 --10.91 --11.80 --12.87 --14.13 --15.40 --16.43 --16.97 --16.86 --16.08 --14.70 --12.87 --10.73 --8.45 --6.12 --3.84 --1.67 -0.27 -1.84 -2.91 -3.43 -3.46 -3.20 -2.85 -2.51 -2.15 -1.63 -0.86 --0.12 --1.09 --1.81 --2.13 --2.08 --1.89 --1.80 --2.03 --2.66 --3.58 --4.63 --5.59 --6.24 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--13.10 -8.75 -31.10 -51.51 -67.43 -76.88 -79.18 -75.17 -67.05 -57.60 -49.29 -43.54 -40.56 -39.64 -39.81 -40.45 -41.42 -42.94 -45.25 -48.34 -51.90 -55.53 -58.98 -62.26 -65.58 -69.12 -72.82 -76.24 -78.77 -79.83 -79.13 -76.76 -73.03 -68.26 -62.64 -56.12 -48.62 -40.18 -31.08 -21.83 -12.99 -4.98 --2.03 --8.03 --13.09 --17.20 --20.34 --22.54 --24.02 --25.20 --26.49 --28.19 --30.25 --32.33 --33.92 --34.57 --34.11 --32.70 --30.81 --29.11 --28.38 --29.43 --33.02 --39.65 --49.34 --61.47 --74.71 --87.32 --97.65 --104.59 --107.96 --108.45 --107.14 --105.03 --102.55 --99.56 --95.65 --90.46 --84.05 --76.76 --69.00 --60.91 --52.26 --42.52 --31.20 --18.18 --3.80 -11.16 -25.69 -38.86 -49.89 -58.17 -63.30 -65.05 -63.47 -59.09 -53.02 -46.78 -41.96 -39.81 -40.78 -44.45 -49.73 -55.31 -60.18 -63.81 -66.22 -67.68 -68.49 -68.70 -68.27 -67.15 -65.54 -63.92 -62.91 -62.94 -63.96 -65.33 -66.00 -64.95 -61.70 -56.51 -50.37 -44.41 -39.33 -35.08 -30.89 -25.86 -19.59 -12.58 -5.89 -0.63 --2.75 --4.67 --6.10 --8.03 --10.88 --14.44 --18.12 --21.38 --23.97 --25.89 --27.17 --27.70 --27.25 --25.85 --23.89 --22.38 --22.67 --26.21 --33.98 --46.26 --62.40 --80.91 --99.79 --116.98 --130.76 --140.15 --144.88 --145.28 --142.01 --135.76 --127.21 --116.96 --105.58 --93.45 --80.62 --66.62 --50.58 --31.74 --10.07 -13.20 -35.60 -54.14 -66.39 -71.47 -70.39 -65.63 -60.04 -55.72 -53.45 -52.84 -52.97 -53.21 -53.52 -54.32 -56.02 -58.63 -61.69 -64.50 -66.57 -67.86 -68.88 -70.39 -73.12 -77.31 -82.57 -87.89 -91.83 -93.02 -90.69 -85.12 -77.57 -69.74 -62.93 -57.40 -52.35 -46.45 -38.67 -28.84 -17.68 -6.29 --4.41 --13.94 --22.19 --29.02 --34.26 --37.99 --41.04 --45.14 --52.56 --65.02 --82.76 --104.14 --126.04 --145.00 --158.36 --165.01 --165.29 --160.48 --152.00 --140.99 --128.17 --113.99 --98.72 --82.37 --64.57 --44.58 --21.73 -3.88 -30.78 -56.09 -76.27 -88.32 -91.00 -85.34 -74.21 -61.19 -49.30 -40.26 -34.43 -31.32 -30.27 -30.84 -32.82 -35.96 -39.86 -44.01 -47.98 -51.73 -55.61 -60.16 -65.85 -72.67 -79.98 -86.55 -90.79 -91.27 -87.24 -78.97 -67.80 -55.76 -44.87 -36.44 -30.70 -26.94 -24.04 -21.10 -17.86 -14.64 -11.83 -9.53 -7.33 -4.57 -0.74 --4.20 --9.77 --15.31 --20.38 --24.91 --29.19 --33.56 --38.06 --42.34 --45.78 --47.92 --48.72 --48.63 --48.43 --48.74 --49.71 --51.05 --52.28 --53.26 --54.44 --56.78 --61.18 --67.84 --75.92 --83.80 --89.80 --92.94 --93.29 --91.62 --88.70 --84.79 --79.59 --72.81 --64.84 --56.91 --50.51 --46.56 --44.61 --42.90 --39.11 --31.51 --19.82 --5.31 -9.83 -23.44 -34.14 -41.56 -46.12 -48.55 -49.59 -49.90 -50.13 -51.01 -53.24 -57.25 -62.93 -69.53 -75.82 -80.45 -82.44 -81.40 -77.59 -71.59 -64.19 -56.06 -47.76 -39.74 -32.32 -25.71 -19.99 -15.19 -11.34 -8.48 -6.50 -5.03 -3.42 -0.94 --2.88 --7.86 --13.23 --17.93 --21.15 --22.68 --22.99 --22.82 --22.74 --22.95 --23.40 --24.09 --25.28 --27.32 --30.38 --34.10 --37.65 --40.13 --41.13 --40.90 --40.16 --39.61 --39.36 --38.93 --37.53 --34.54 --29.76 --23.47 --16.13 --8.17 -0.10 -8.41 -16.47 -23.87 -30.24 -35.29 -38.96 -41.26 -42.28 -42.18 -41.14 -39.50 -37.68 -36.08 -34.93 -34.24 -33.78 -33.23 -32.30 -30.87 -28.94 -26.63 -24.08 -21.42 -18.74 -16.04 -13.28 -10.32 -7.16 -3.92 -0.99 --1.14 --2.10 --1.85 --0.88 -0.06 -0.16 --0.94 --3.11 --5.83 --8.52 --10.82 --12.63 --13.96 --14.73 --14.78 --14.01 --12.55 --10.86 --9.53 --8.98 --9.28 --10.07 --10.83 --11.11 --10.78 --9.99 --9.16 --8.66 --8.67 --9.12 --9.68 --9.94 --9.62 --8.79 --7.89 --7.60 --8.53 --10.88 --14.27 --17.89 --20.75 --22.05 --21.44 --19.12 --15.75 --12.24 --9.53 --8.25 --8.56 --10.03 --11.74 --12.59 --11.73 --8.93 --4.64 -0.17 -4.44 -7.43 -8.91 -9.09 -8.31 -6.81 -4.69 -2.04 --0.90 --3.54 --5.16 --5.20 --3.55 --0.62 -2.84 -6.18 -9.21 -12.15 -15.44 -19.37 -23.77 -28.12 -31.84 -34.69 -36.98 -39.41 -42.65 -46.95 -51.85 -56.36 -59.41 -60.31 -59.03 -56.16 -52.50 -48.62 -44.67 -40.38 -35.40 -29.66 -23.49 -17.43 -11.89 -6.79 -1.48 --4.99 --13.41 --23.94 --35.94 --48.04 --58.58 --66.00 --69.32 --68.29 --63.50 --56.18 --47.84 --39.85 --33.11 --27.87 --23.86 --20.56 --17.52 --14.47 --11.39 --8.30 --5.18 --1.87 -1.88 -6.34 -11.70 -18.06 -25.38 -33.54 -42.28 -51.17 -59.76 -67.55 -74.09 -79.01 -81.96 -82.63 -80.74 -76.13 -68.84 -59.29 -48.17 -36.38 -24.72 -13.66 -3.24 --6.81 --16.75 --26.54 --35.74 --43.69 --49.77 --53.71 --55.69 --56.13 --55.44 --53.83 --51.27 --47.77 --43.54 --39.05 --34.88 --31.51 --29.05 --27.18 --25.37 --23.10 --20.22 --16.99 --13.91 --11.42 --9.74 --8.71 --8.05 --7.55 --7.26 --7.44 --8.37 --10.09 --12.26 --14.44 --16.21 --17.52 --18.59 --19.69 --20.92 --22.09 --22.86 --23.00 --22.56 --21.84 --21.22 --20.86 --20.62 --20.11 --18.86 --16.54 --13.08 --8.62 --3.55 -1.66 -6.50 -10.52 -13.42 -15.12 -15.80 -15.88 -15.94 -16.61 -18.29 -21.04 -24.48 -27.82 -30.13 -30.57 -28.71 -24.76 -19.50 -14.09 -9.57 -6.52 -4.80 -3.59 -1.76 --1.61 --6.84 --13.43 --20.29 --26.18 --30.16 --31.89 --31.60 --29.93 --27.73 --25.88 --25.17 --26.12 --28.73 --32.34 --35.69 --37.32 --36.02 --31.35 --23.70 --13.99 --3.31 -7.55 -18.16 -28.35 -37.94 -46.70 -54.25 -60.09 -63.70 -64.58 -62.44 -57.43 -50.22 -41.95 -33.87 -26.95 -21.55 -17.46 -14.18 -11.23 -8.36 -5.53 -2.75 -0.07 --2.43 --4.53 --5.90 --6.21 --5.39 --3.80 --2.17 --1.45 --2.41 --5.40 --10.23 --16.24 --22.51 --28.10 --32.24 --34.51 --34.87 --33.64 --31.34 --28.54 --25.65 --22.83 --20.02 --17.10 --14.01 --10.92 --8.11 --5.83 --4.14 --2.85 --1.66 --0.28 -1.35 -3.12 -4.89 -6.64 -8.58 -11.10 -14.58 -19.15 -24.67 -30.63 -36.33 -40.99 -43.82 -44.04 -41.06 -34.56 -24.65 -12.10 --1.69 --14.91 --25.77 --33.10 --36.63 --37.10 --35.85 --34.34 --33.66 --34.33 --36.19 --38.73 --41.31 --43.31 --44.22 --43.64 --41.36 --37.48 --32.50 --27.18 --22.33 --18.44 --15.33 --12.33 --8.62 --3.71 -2.13 -8.03 -12.91 -16.12 -17.86 -19.04 -20.75 -23.77 -28.23 -33.71 -39.57 -45.22 -50.16 -53.95 -56.05 -55.93 -53.31 -48.41 -41.92 -34.86 -28.13 -22.30 -17.48 -13.41 -9.63 -5.74 -1.53 --3.05 --7.79 --12.40 --16.52 --19.88 --22.33 --23.82 --24.38 --24.03 --22.91 --21.24 --19.41 --17.93 --17.14 --17.14 --17.60 --18.06 --17.99 --17.15 --15.56 --13.33 --10.53 --7.11 --3.02 -1.57 -6.12 -9.98 -12.62 -14.02 -14.62 -15.13 -16.11 -17.64 -19.44 -21.09 -22.37 -23.44 -24.63 -26.16 -27.86 -29.28 -29.79 -28.87 -26.28 -22.01 -16.21 -9.16 -1.17 --7.46 --16.56 --26.13 --36.30 --47.11 --58.11 --68.30 --76.27 --80.70 --80.96 --77.28 --70.60 --62.00 --52.27 --41.75 --30.65 --19.39 --8.79 -0.16 -6.68 -10.65 -12.64 -13.61 -14.36 -15.36 -16.64 -18.13 -19.88 -22.12 -25.04 -28.61 -32.42 -35.77 -38.01 -38.78 -38.16 -36.64 -34.84 -33.38 -32.70 -33.13 -34.82 -37.88 -42.24 -47.71 -53.91 -60.21 -65.85 -69.97 -71.93 -71.47 -68.90 -64.89 -60.23 -55.44 -50.57 -45.32 -39.32 -32.42 -24.67 -16.25 -7.19 --2.58 --13.13 --24.26 --35.37 --45.52 --53.78 --59.42 --62.14 --62.06 --59.73 --56.00 --52.01 --48.92 --47.73 --48.93 --52.43 --57.60 --63.57 --69.60 --75.25 --80.51 --85.78 --91.70 --98.88 --107.59 --117.38 --126.98 --134.50 --138.07 --136.68 --130.68 --121.78 --112.29 --104.10 --97.86 --92.94 --87.92 --81.40 --72.62 --61.42 --48.06 --32.87 --16.17 -1.45 -19.02 -35.18 -48.67 -58.73 -65.50 -69.84 -72.84 -75.43 -78.15 -81.26 -84.98 -89.52 -94.96 -101.05 -107.10 -112.17 -115.41 -116.40 -115.29 -112.58 -108.77 -104.08 -98.43 -91.63 -83.78 -75.47 -67.69 -61.44 -57.34 -55.30 -54.50 -53.76 -51.87 -48.06 -42.22 -34.92 -27.16 -19.99 -14.10 -9.55 -5.82 -2.04 --2.57 --8.40 --15.32 --22.93 --30.81 --38.74 --46.65 --54.39 --61.55 --67.49 --71.51 --73.23 --72.75 --70.58 --67.42 --63.90 --60.37 --56.93 --53.45 --49.75 --45.68 --41.19 --36.41 --31.62 --27.23 --23.56 --20.76 --18.69 --16.93 --15.00 --12.50 --9.31 --5.63 --1.83 -1.69 -4.65 -7.02 -8.91 -10.52 -11.96 -13.23 -14.23 -14.79 -14.83 -14.44 -13.85 -13.37 -13.26 -13.61 -14.35 -15.27 -16.08 -16.59 -16.67 -16.36 -15.74 -14.95 -14.08 -13.22 -12.44 -11.80 -11.32 -10.95 -10.60 -10.15 -9.47 -8.49 -7.22 -5.74 -4.11 -2.45 -0.83 --0.65 --1.93 --2.96 --3.74 --4.33 --4.81 --5.28 --5.71 --6.00 --5.98 --5.46 --4.39 --2.85 --1.04 -0.78 -2.39 -3.65 -4.55 -5.19 -5.73 -6.32 -7.08 -8.01 -9.00 -9.83 -10.31 -10.29 -9.78 -8.94 -8.01 -7.20 -6.64 -6.32 -6.19 -6.17 -6.18 -6.15 -6.00 -5.70 -5.21 -4.58 -3.93 -3.38 -3.03 -2.89 -2.86 -2.80 -2.59 -2.19 -1.60 -0.87 -0.02 --0.91 --1.85 --2.74 --3.48 --4.02 --4.38 --4.69 --5.07 --5.59 --6.14 --6.52 --6.52 --6.08 --5.35 --4.65 --4.23 --4.15 --4.19 --4.03 --3.42 --2.27 --0.59 -1.65 -4.62 -8.65 -13.87 -20.11 -26.75 -32.94 -37.98 -41.61 -44.04 -45.76 -47.15 -48.25 -48.79 -48.56 -47.78 -47.41 -48.91 -53.75 -62.66 -75.08 -89.25 -102.60 -112.53 -117.15 -115.70 -108.46 -96.47 -81.11 -63.81 -45.95 -28.73 -13.15 --0.17 --11.06 --19.81 --27.01 --33.31 --39.20 --44.85 --50.09 --54.55 --57.83 --59.61 --59.88 --58.81 --56.83 --54.50 --52.43 --51.08 --50.70 --51.18 --52.05 --52.68 --52.51 --51.25 --49.08 --46.50 --44.13 --42.38 --41.25 --40.38 --39.23 --37.42 --34.87 --31.84 --28.74 --25.95 --23.55 --21.39 --19.21 --16.86 --14.44 --12.25 --10.66 --9.83 --9.67 --9.83 --9.92 --9.72 --9.21 --8.54 --7.86 --7.27 --6.77 --6.38 --6.20 --6.30 --6.72 --7.30 --7.80 --8.03 --7.88 --7.49 --6.98 --6.44 --5.76 --4.78 --3.36 --1.54 -0.48 -2.52 -4.42 -6.16 -7.70 -8.94 -9.67 -9.76 -9.25 -8.45 -7.85 -7.82 -8.42 -9.37 -10.22 -10.57 -10.29 -9.62 -8.91 -8.45 -8.26 -8.13 -7.81 -7.14 -6.28 -5.51 -5.07 -4.94 -4.87 -4.50 -3.61 -2.24 -0.58 --1.13 --2.84 --4.63 --6.57 --8.53 --10.16 --11.01 --10.83 --9.79 --8.42 --7.42 --7.29 --8.08 --9.32 --10.30 --10.35 --9.19 --7.01 --4.30 --1.61 -0.75 -2.79 -4.73 -6.75 -8.77 -10.39 -11.05 -10.34 -8.16 -4.85 -0.99 --2.76 --5.85 --7.96 --8.97 --8.95 --8.18 --7.04 --5.96 --5.19 --4.80 --4.69 --4.73 --4.93 --5.50 --6.72 --8.78 --11.57 --14.71 --17.75 --20.28 --22.07 --23.02 --22.99 --21.89 --19.68 --16.49 --12.65 --8.58 --4.53 --0.55 -3.50 -7.79 -12.31 -16.79 -20.88 -24.33 -27.14 -29.57 -31.95 -34.51 -37.21 -39.78 -41.79 -42.89 -42.85 -41.59 -39.15 -35.64 -31.25 -26.26 -20.95 -15.59 -10.36 -5.33 -0.47 --4.23 --8.71 --12.85 --16.52 --19.60 --22.06 --23.89 --25.08 --25.60 --25.45 --24.71 --23.53 --22.15 --20.85 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--37.82 --51.21 --51.88 --45.10 --36.53 --30.85 --30.83 --36.67 --47.10 --60.06 --74.15 --87.81 --99.95 --107.56 --108.74 --103.71 --94.33 --83.42 --73.70 --67.17 --64.18 --63.09 --61.21 --56.44 --48.20 --36.79 --22.74 --6.81 -9.69 -25.97 -42.40 -60.27 -81.07 -102.78 -123.43 -141.83 -156.05 -162.09 -155.38 -133.25 -96.99 -52.81 -10.53 --19.24 --29.22 --17.47 -6.99 -28.52 -35.20 -25.26 -5.89 --10.94 --19.79 --17.52 --3.76 -19.81 -51.61 -91.47 -135.67 -178.72 -212.88 -220.99 -193.92 -135.87 -62.67 --6.45 --57.33 --84.79 --91.42 --84.70 --73.40 --62.60 --54.58 --50.47 --48.85 --47.02 --42.23 --32.74 --19.11 --4.62 -7.64 -14.36 -12.42 --1.24 --25.69 --56.73 --88.99 --117.66 --139.14 --149.56 --147.32 --134.44 --115.61 --95.93 --79.11 --66.63 --58.23 --53.08 --50.45 --50.16 --53.80 --62.63 --75.72 --90.47 --104.06 --114.92 --123.08 --127.36 --126.97 --121.05 --108.18 --86.38 --54.31 --11.78 -40.74 -100.21 -163.50 -224.60 -275.17 -306.97 -315.01 -299.56 -266.09 -223.10 -166.38 -100.45 -33.64 --26.93 --78.03 --118.65 --148.07 --166.36 --174.56 --173.80 --163.13 --143.00 --115.61 --83.91 --51.00 --19.96 -8.36 -36.22 -67.06 -104.07 -145.09 -182.74 -208.19 -215.85 -206.22 -185.25 -161.13 -140.18 -125.56 -116.05 -108.19 -99.27 -89.00 -79.21 -72.28 -69.31 -69.23 -69.80 -68.37 -63.24 -54.53 -43.80 -32.85 -22.84 -13.95 -5.78 --1.83 --8.40 --12.82 --13.53 --8.91 -2.09 -19.74 -43.35 -71.43 -102.00 -132.96 -162.06 -187.04 -205.63 -215.53 -214.65 -201.39 -175.03 -135.95 -85.58 -26.33 --38.49 --104.60 --167.04 --220.82 --262.05 --289.02 --302.58 --305.60 --301.65 --293.45 --282.02 --266.83 --246.49 --219.97 --187.55 --151.33 --114.84 --82.03 --56.14 --38.78 --29.79 --27.23 --27.94 --28.35 --25.07 --15.58 -1.29 -24.98 -53.18 -82.00 -107.44 -126.84 -139.90 -148.31 -154.39 -159.41 -162.25 -159.61 -146.58 -119.23 -76.31 -19.35 --48.32 --122.34 --197.36 --267.45 --326.36 --369.94 --395.30 --400.28 --383.81 --346.94 --293.65 --229.97 --163.95 --103.76 --58.44 --33.32 --27.55 --35.20 --48.71 --61.95 --70.63 --73.30 --70.02 --65.39 --64.86 --71.90 --87.78 --112.43 --144.68 --180.91 --216.68 --227.52 --199.61 --140.71 --74.89 --23.46 -5.54 -12.19 -8.68 -4.46 -5.20 -9.96 -15.94 -24.29 -37.43 -57.49 -85.16 -121.29 -164.23 -210.93 -255.86 -295.32 -331.15 -367.47 -406.27 -444.65 -477.06 -494.27 -486.98 -448.44 -379.20 -290.27 -197.26 -114.86 -53.47 -20.94 -19.03 -43.64 -85.73 -134.01 -180.10 -216.39 -237.04 -223.51 -172.18 -99.60 -31.51 --10.03 --32.41 --46.83 --62.39 --85.37 --116.08 --148.23 --175.21 --194.23 --206.94 --217.51 --227.00 --235.15 --240.65 --241.51 --234.20 --217.27 --192.75 --165.11 --139.31 --116.93 --98.35 --83.39 --71.20 --59.49 --46.59 --32.74 --19.72 --10.02 --4.15 --1.55 --0.86 --0.11 -2.98 -9.46 -18.48 -27.87 -35.31 -39.97 -41.97 -41.99 -40.93 -39.82 -39.49 -40.41 -42.60 -45.56 -48.41 -49.62 -47.41 -40.24 -27.32 -8.66 --15.02 --42.53 --72.21 --101.72 --127.96 --147.48 --157.29 --155.91 --143.93 --123.76 --98.77 --72.20 --46.43 --22.89 --2.33 -14.73 -27.84 -36.86 -42.21 -44.95 -46.51 -48.17 -50.68 -53.97 -57.37 -60.03 -61.40 -61.55 -61.16 -61.23 -62.58 -65.49 -69.65 -74.40 -79.00 -82.74 -84.86 -84.34 -80.01 -71.04 -57.59 -41.21 -24.60 -10.71 -1.61 --2.25 --1.99 -0.46 -3.30 -5.54 -6.99 -7.92 -8.60 -9.04 -9.21 -9.28 -9.72 -11.20 -14.21 -18.80 -24.31 -29.66 -33.63 -35.40 -34.89 -32.72 -29.95 -27.51 -25.76 -24.33 -22.34 -18.89 -13.64 -7.06 -0.42 --4.71 --7.03 --6.03 --2.15 -3.43 -9.23 -13.84 -16.16 -15.43 -11.15 -3.06 --8.77 --23.76 --40.80 --58.54 --75.62 --90.99 --103.78 --113.06 --117.54 --115.62 --105.78 --87.30 --60.93 --29.01 -4.92 -36.95 -63.59 -82.47 -92.49 -93.75 -87.31 -74.94 -58.86 -41.60 -25.70 -13.32 -5.85 -3.56 -5.46 -9.63 -13.87 -16.37 -16.32 -13.98 -10.40 -6.93 -4.61 -3.89 -4.72 -6.70 -9.35 -12.21 -14.87 -17.03 -18.57 -19.62 -20.63 -22.12 -24.40 -27.12 -29.19 -28.91 -24.55 -15.04 -0.54 --17.26 --35.50 --50.81 --60.28 --62.34 --57.23 --46.83 --34.01 --21.68 --11.95 --5.62 --2.19 --0.31 -1.59 -4.80 -9.88 -16.69 -24.48 -32.29 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--35.71 --28.56 --20.76 --12.67 --4.69 -2.68 -9.04 -14.16 -18.03 -20.77 -22.56 -23.51 -23.74 -23.37 -22.58 -21.54 -20.43 -19.40 -18.66 -18.47 -19.06 -20.43 -22.20 -23.63 -23.87 -22.29 -18.85 -14.09 -8.94 -4.29 -0.68 --1.89 --3.89 --5.98 --8.77 --12.66 --17.80 --24.15 --31.53 --39.64 --47.96 --55.65 --61.55 --64.55 --64.08 --60.78 --56.61 --54.52 --57.39 --66.84 --82.56 --102.32 --122.86 --141.16 --155.44 --165.48 --172.19 --176.75 --179.90 --181.72 --182.09 --181.28 --180.37 --181.09 --185.04 --192.61 --202.21 --210.42 --212.90 --205.78 --186.66 --154.95 --111.52 --58.41 -1.28 -63.28 -122.32 -172.83 -210.54 -233.78 -244.06 -245.31 -242.31 -239.00 -237.61 -238.60 -241.27 -244.42 -246.74 -246.94 -244.00 -237.55 -228.07 -216.84 -205.36 -194.68 -185.07 -176.03 -166.74 -156.45 -144.63 -131.05 -115.71 -98.92 -81.31 -63.85 -47.37 -32.30 -18.64 -6.08 --5.44 --15.46 --23.05 --27.36 --28.19 --26.28 --23.00 --19.75 --17.34 --15.86 --14.85 --13.65 --11.77 --8.90 --4.81 -0.77 -8.16 -17.45 -28.29 -39.97 -51.70 -63.00 -73.86 -84.52 -95.08 -105.09 -113.53 -119.20 -121.45 -120.64 -118.07 -115.31 -113.40 -112.30 -111.16 -109.04 -105.89 -102.88 -102.07 -105.39 -113.61 -125.73 -139.12 -150.47 -156.82 -156.42 -149.05 -135.93 -119.25 -101.64 -85.59 -72.43 -61.64 -50.43 -34.55 -10.54 --21.90 --58.07 --89.34 --106.34 --103.58 --82.59 --52.10 --24.88 --12.48 --21.16 --50.34 --93.75 --141.98 --185.46 --216.20 --229.38 --224.81 --207.42 --186.05 --169.89 --164.27 --168.25 --175.82 --180.08 --178.02 --172.36 --169.13 --172.96 --183.01 --192.74 --193.66 --180.52 --154.74 --123.47 --95.97 --78.59 --72.06 --71.66 --69.81 --59.67 --37.90 --5.69 -31.46 -65.73 -89.60 -98.09 -90.20 -69.02 -40.72 -12.74 --7.90 --16.05 --9.48 -10.57 -39.48 -70.24 -95.07 -107.24 -102.56 -80.31 -43.07 --3.85 --53.52 --98.61 --132.78 --151.86 --154.85 --144.16 --124.84 --103.03 --84.15 --71.35 --65.01 --63.27 --63.20 --62.12 --58.48 --52.12 --44.08 --35.98 --29.26 --24.78 --22.59 --22.09 --22.31 --22.36 --21.77 --20.63 --19.53 --19.24 --20.32 --22.73 --25.75 --28.17 --28.80 --27.01 --23.16 --18.53 --14.99 --14.26 --17.25 --23.60 --31.72 --39.27 --44.03 --44.80 --41.96 --37.54 --34.56 --35.80 --42.57 --53.95 --66.79 --76.67 --79.48 --72.75 --56.48 --33.29 --7.71 -14.78 -29.21 -32.58 -25.04 -10.47 --4.28 --11.53 --5.66 -14.33 -44.00 -74.99 -98.16 -106.96 -99.64 -79.37 -52.35 -25.46 -4.07 --8.85 --12.50 --7.30 -5.86 -25.73 -50.29 -75.89 -97.45 -109.60 -108.62 -94.45 -71.16 -45.62 -24.71 -12.57 -9.32 -11.92 -16.31 -19.72 -21.68 -23.40 -26.31 -30.77 -35.75 -39.45 -40.29 -37.61 -31.66 -23.27 -13.39 -2.92 --7.29 --16.37 --23.54 --28.23 --30.38 --30.48 --29.52 --28.65 --28.82 --30.57 --33.87 --38.32 --43.18 --47.68 --51.00 --52.46 --51.61 --48.43 --43.43 --37.62 --32.26 --28.44 --26.63 --26.54 --27.21 --27.48 --26.54 --24.29 --21.31 --18.51 --16.66 --16.02 --16.24 --16.61 --16.37 --15.04 --12.55 --9.16 --5.25 --1.20 -2.73 -6.41 -9.78 -12.79 -15.34 -17.22 -18.11 -17.56 -15.23 -10.96 -5.13 --1.35 --7.31 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-1.77 --1.29 --4.80 --8.02 --10.28 --11.39 --11.79 --12.31 --13.58 --15.72 --18.30 --20.63 --22.27 --23.18 --23.67 --24.17 --25.06 --26.57 --28.87 --32.13 --36.21 --40.46 --43.66 --44.39 --41.55 --34.98 --25.62 --15.11 --5.10 -3.43 -10.40 -16.46 -22.29 -28.18 -33.73 -38.06 -40.30 -40.14 -37.99 -34.89 -32.03 -30.22 -29.49 -29.21 -28.47 -26.63 -23.75 -20.55 -18.01 -16.83 -17.01 -17.87 -18.34 -17.51 -14.98 -10.91 -5.96 -0.94 --3.39 --6.59 --8.67 --10.06 --11.51 --13.73 --17.14 --21.61 --26.61 --31.42 --35.41 --38.30 --40.11 --41.07 --41.56 --41.97 --42.68 --43.92 --45.62 --47.34 --48.36 --47.88 --45.45 --41.08 --35.20 --28.46 --21.40 --14.42 --7.78 --1.71 -3.57 -7.86 -11.05 -13.21 -14.48 -15.05 -15.10 -14.79 -14.24 -13.54 -12.76 -11.98 -11.36 -11.11 -11.42 -12.36 -13.80 -15.32 -16.38 -16.46 -15.22 -12.63 -8.98 -4.77 -0.66 --2.73 --4.98 --6.03 --6.26 --6.41 --7.28 --9.44 --12.97 --17.38 --21.90 --25.80 --28.69 --30.70 --32.38 --34.36 --37.04 --40.26 --43.24 --44.81 --43.83 --39.65 --32.44 --23.11 --12.92 --3.03 -5.94 -13.90 -21.02 -27.47 -33.17 -37.74 -40.70 -41.69 -40.68 -37.99 -34.29 -30.47 -27.37 -25.51 -24.93 -25.26 -25.90 -26.37 -26.54 -26.55 -26.61 -26.69 -26.46 -25.41 -23.15 -19.72 -15.57 -11.40 -7.80 -5.00 -2.83 -0.86 --1.39 --4.23 --7.79 --11.98 --16.60 --21.33 --25.79 --29.53 --32.16 --33.44 --33.53 --32.91 --32.25 --32.00 --32.13 --32.06 --30.93 --28.05 --23.22 --16.81 --9.53 --2.06 -5.19 -12.13 -18.74 -24.83 -29.98 -33.64 -35.54 -35.83 -35.18 -34.41 -34.12 -34.42 -34.94 -35.17 -34.82 -34.06 -33.37 -33.31 -34.13 -35.67 -37.42 -38.81 -39.40 -38.88 -37.09 -33.94 -29.44 -23.90 -17.95 -12.45 -8.09 -5.13 -3.27 -1.75 --0.31 --3.55 --8.16 --13.78 --19.73 --25.27 --29.83 --33.33 --36.08 --38.65 --41.60 --45.07 --48.67 --51.54 --52.66 --51.33 --47.43 --41.47 --34.32 --26.89 --19.78 --13.21 --7.14 --1.49 -3.70 -8.21 -11.83 -14.41 -16.02 -16.87 -17.11 -16.78 -15.77 -13.97 -11.44 -8.49 -5.61 -3.35 -2.06 -1.81 -2.38 -3.33 -4.11 -4.15 -3.00 -0.45 --3.32 --7.80 --12.19 --15.77 --18.11 --19.31 --19.99 --21.07 --23.48 --27.71 --33.51 --39.92 --45.52 --49.18 --50.59 --50.55 --50.63 --52.41 --56.66 --62.76 --68.89 --72.76 --72.55 --67.63 --58.74 --47.56 --36.13 --26.18 --18.75 --13.94 --11.08 --8.92 --6.21 --2.31 -2.22 -5.42 -4.66 --2.14 --15.00 --30.91 --44.29 --48.55 --38.03 --9.53 -36.92 -98.06 -167.81 -237.99 -299.42 -343.60 -364.74 -361.38 -336.82 -297.85 -252.36 -206.86 -165.02 -127.90 -95.05 -65.75 -39.33 -14.91 --9.47 --37.14 --72.13 --117.33 --172.47 --232.83 --289.47 --331.63 --350.32 --341.59 --307.75 --256.32 --197.03 --138.72 --87.38 --45.87 --14.76 -6.82 -21.12 -32.00 -44.61 -63.98 -92.60 -128.71 -166.37 -197.84 -216.99 -221.98 -215.77 -204.14 -192.66 -184.36 -179.19 -174.98 -169.06 -159.28 -144.15 -122.43 -92.91 -54.99 -9.39 --41.55 --94.38 --145.93 --194.76 --241.91 --290.32 --342.90 --400.24 --458.94 --511.62 --549.01 --563.30 --551.26 --515.65 --463.98 --405.46 --347.68 --294.60 --246.73 --202.78 --161.66 --123.30 --88.29 --56.70 --27.34 -2.04 -33.71 -68.82 -106.89 -146.22 -185.09 -222.89 -260.27 -298.32 -337.12 -374.68 -406.96 -428.95 -436.35 -426.87 -400.52 -358.99 -304.70 -240.52 -170.01 -98.07 -30.75 --25.81 --67.20 --92.18 --102.99 --104.14 --100.50 --95.59 --91.02 --87.02 --83.65 --81.66 --82.49 --87.30 --95.96 --106.69 --116.94 --124.85 --130.60 --136.52 --145.94 --161.28 --182.30 --205.72 --226.36 --239.36 --242.26 --236.03 --224.53 --212.71 --204.39 --200.73 --199.88 --198.01 --191.01 --176.29 --153.56 --124.75 --92.88 --60.71 --29.65 -0.41 -30.63 -62.30 -95.94 -130.87 -165.36 -197.21 -224.43 -245.76 -260.96 -270.88 -277.16 -281.75 -286.25 -291.24 -296.00 -298.89 -298.28 -293.60 -285.96 -278.01 -273.11 -274.22 -283.07 -299.70 -322.50 -348.56 -374.04 -394.54 -405.85 -404.67 -389.34 -360.18 -319.25 -270.05 -216.95 -164.75 -117.99 -80.14 -52.60 -34.25 -21.98 -12.03 -1.64 --10.02 --22.02 --32.71 --40.89 --46.54 --50.42 --53.41 --55.80 --57.29 --57.43 --56.16 --53.99 --51.70 --49.95 --48.88 --48.16 --47.20 --45.51 --42.83 --39.28 --35.26 --31.60 --29.38 --29.64 --32.86 --38.79 --46.58 --55.52 --65.69 --78.11 --94.01 --113.83 --136.55 --160.09 --182.55 --203.22 --222.79 --242.01 --260.25 --274.75 --281.76 --278.77 --266.52 --249.21 --232.44 --220.30 --213.28 --208.52 --202.10 --191.66 --177.92 --164.01 --153.28 --147.35 --145.37 --145.00 --143.96 --141.27 --137.14 --132.12 --125.99 --117.66 --105.82 --90.27 --72.52 --55.50 --42.20 --34.17 --30.80 --29.73 --28.25 --24.60 --18.64 --11.42 --4.33 -1.69 -6.29 -9.34 -10.74 -10.57 -9.66 -9.90 -13.84 -23.42 -38.71 -57.29 -74.93 -87.34 -92.05 -89.53 -82.99 -76.93 -75.42 -80.64 -92.45 -108.84 -126.98 -144.23 -158.80 -169.85 -177.42 -182.33 -185.99 -190.13 -196.31 -205.30 -216.52 -228.09 -237.46 -242.53 -242.72 -239.36 -235.00 -232.03 -231.35 -231.54 -229.31 -220.75 -202.87 -174.81 -138.09 -96.02 -52.61 -11.48 --24.91 --55.42 --80.07 --99.68 --115.39 --128.39 --139.58 --149.55 --158.73 --167.53 --176.51 --186.26 --197.13 --208.89 --220.29 --229.07 --232.15 --226.32 --209.34 --181.05 --144.11 --103.77 --66.48 --37.60 --19.59 --11.30 --9.14 --9.32 --9.79 --10.87 --14.13 --20.55 --29.10 --37.14 --41.86 --42.04 --38.84 --34.88 --32.39 --31.86 --31.90 --30.36 --25.95 --19.01 --11.22 --4.53 -0.11 -3.13 -5.80 -9.28 -14.03 -19.63 -25.45 -31.17 -36.90 -42.79 -48.64 -53.85 -57.84 -60.71 -63.49 -67.91 -75.49 -86.59 -99.98 -113.02 -122.73 -126.92 -125.11 -118.38 -108.68 -97.52 -85.12 -70.36 -51.69 -28.50 -2.04 --24.77 --48.53 --67.02 --80.11 --89.51 --97.49 --105.55 --113.54 --119.84 --122.22 --118.88 --109.28 --94.40 --76.61 --58.97 --44.38 --34.65 --30.02 --29.32 --30.72 --32.55 --33.84 --34.13 --32.92 --29.32 --22.36 --11.73 -1.61 -15.47 -27.30 -35.30 -39.08 -39.47 -37.80 -34.98 -31.15 -25.87 -18.85 -10.42 -1.77 --5.55 --10.37 --12.45 --12.50 --11.59 --10.35 --8.50 --5.00 -1.14 -10.08 -20.91 -32.31 -43.49 -54.80 -67.57 -82.88 -100.23 -116.97 -128.92 -131.82 -122.93 -101.91 -70.67 -32.58 --8.67 --49.52 --86.63 --116.87 --137.68 --147.70 --147.53 --139.90 --128.92 --118.46 --110.50 --104.55 --98.26 --88.82 --74.63 --56.12 --35.56 --16.07 --0.25 -11.12 -19.28 -26.70 -35.65 -46.99 -59.81 -72.30 -83.25 -93.05 -103.58 -116.72 -132.87 -150.17 -165.25 -174.83 -177.53 -174.38 -167.67 -159.46 -150.50 -140.73 -130.47 -121.21 -115.42 -115.18 -120.73 -129.88 -138.71 -143.23 -141.01 -131.92 -117.72 -100.86 -83.48 -66.94 -51.93 -38.87 -27.76 -17.93 -7.73 --4.97 --21.67 --42.09 --63.76 --82.84 --95.73 --100.75 --98.88 --93.15 --87.41 --84.92 --87.92 --97.43 --113.25 --133.81 --155.87 --174.79 --185.86 --186.02 --175.36 --157.18 --136.64 --118.49 --105.20 --96.49 --90.35 --84.68 --78.41 --71.55 --64.48 --57.02 --48.37 --37.73 --25.03 --11.18 -2.06 -12.84 -19.94 -22.89 -22.09 -18.38 -12.57 -5.15 --3.76 --13.99 --25.05 --36.10 --46.28 --55.23 --63.26 --70.75 --77.23 --80.80 --78.53 --68.16 --49.98 --27.84 --8.03 -3.41 -3.67 --5.50 --19.03 --31.29 --38.83 --41.36 --40.66 --38.43 --35.10 --29.82 --21.51 --10.42 -1.69 -12.34 -19.72 -23.58 -25.29 -26.79 -29.59 -34.18 -40.17 -46.91 -53.99 -61.35 -69.08 -77.20 -85.72 -94.78 -104.64 -115.39 -126.43 -136.23 -142.58 -143.56 -138.54 -128.65 -116.55 -105.22 -96.81 -92.09 -90.48 -90.82 -92.07 -93.60 -95.00 -95.80 -95.48 -93.67 -90.60 -87.13 -84.41 -83.24 -83.53 -84.20 -83.53 -79.78 -71.79 -59.36 -43.04 -23.81 -2.72 --19.20 --40.85 --61.07 --79.04 --94.91 --109.97 --126.23 --145.20 --166.67 --188.35 --206.84 --219.45 --225.71 --227.47 --227.51 --227.55 --227.09 --223.75 --215.11 --200.46 --181.40 --160.80 --140.99 --122.44 --103.73 --82.80 --58.47 --31.40 --3.82 -21.49 -42.34 -57.86 -68.40 -75.07 -79.13 -81.57 -83.05 -84.12 -85.42 -87.73 -91.70 -97.58 -104.90 -112.59 -119.31 -124.05 -126.60 -127.63 -128.31 -129.71 -132.22 -135.42 -138.47 -140.62 -141.60 -141.63 -141.01 -139.70 -137.14 -132.36 -124.14 -111.29 -92.51 -66.23 -30.92 --13.96 --66.80 --123.24 --176.55 --219.63 --247.29 --258.02 --254.07 --240.08 --221.05 --200.73 --181.00 --162.38 --144.75 --127.69 --110.57 --92.39 --72.00 --48.96 --24.27 --0.45 -19.34 -32.82 -39.71 -41.86 -42.22 -43.40 -46.33 -50.34 -53.93 -56.05 -56.77 -57.21 -58.56 -61.11 -63.96 -65.36 -63.81 -58.82 -51.15 -42.41 -34.20 -27.43 -22.14 -17.91 -14.34 -11.57 -10.02 -9.83 -10.20 -9.27 -4.66 --5.31 --20.52 --38.67 --56.00 --68.74 --74.60 --73.49 --67.23 --58.38 --49.08 --40.42 --32.59 --25.36 --18.67 --12.64 --7.32 --2.43 -2.64 -8.44 -14.98 -21.51 -26.66 -28.93 -27.17 -21.00 -10.88 --1.93 --15.74 --28.85 --39.98 --48.61 --54.98 --59.63 --62.99 --65.03 --65.52 --64.58 --63.08 --62.50 --64.33 --69.31 --77.15 --86.67 --96.26 --104.04 --107.77 --104.76 --92.34 --68.83 --34.82 -6.39 -49.46 -88.72 -119.94 -141.22 -152.79 -155.83 -151.66 -141.49 -126.85 -109.90 -93.29 -79.35 -69.26 -62.71 -58.28 -54.29 -49.61 -44.04 -38.04 -32.30 -27.42 -23.71 -21.24 -19.78 -18.81 -17.62 -15.55 -12.35 -8.40 -4.55 -1.70 -0.23 --0.14 --0.21 --0.73 --2.14 --4.47 --7.69 --12.04 --17.98 --25.86 --35.40 --45.37 --53.81 --58.69 --58.61 --53.36 --43.97 --32.27 --20.42 --10.29 --3.09 -0.80 -1.75 -0.77 --0.80 --1.63 --0.67 -2.46 -7.39 -13.13 -18.39 -22.18 -24.08 -24.41 -23.84 -23.07 -22.39 -21.76 -20.95 -19.91 -18.71 -17.43 -15.89 -13.61 -10.07 -5.14 --0.59 --5.97 --9.78 --11.27 --10.59 --8.67 --6.82 --6.18 --7.28 --9.99 --13.72 --17.74 --21.46 --24.52 --26.69 --27.68 --27.16 --24.87 --20.83 --15.45 --9.47 --3.69 -1.30 -5.33 -8.57 -11.34 -13.81 -15.94 -17.51 -18.28 -18.14 -17.15 -15.61 -13.95 -12.66 -12.14 -12.57 -13.83 -15.47 -16.97 -17.89 -18.03 -17.46 -16.34 -14.84 -13.02 -10.91 -8.60 -6.26 -4.12 -2.22 -0.40 --1.69 --4.42 --7.95 --12.14 --16.64 --21.02 --25.08 --28.80 --32.31 --35.73 --39.05 --42.26 --45.47 --49.06 --53.54 --59.24 --66.00 --72.97 --78.84 --82.30 --82.54 --79.68 --74.57 --68.52 --62.81 --58.37 --55.64 --54.76 --55.46 --56.82 --57.03 --53.42 --43.31 --25.17 -0.24 -29.66 -58.31 -81.60 -96.83 -103.82 -104.45 -101.21 -96.04 -89.72 -82.26 -73.64 -64.41 -55.57 -48.19 -42.81 -39.30 -37.17 -36.20 -36.77 -39.96 -47.00 -58.44 -73.40 -89.54 -103.74 -113.42 -117.69 -117.70 -115.85 -114.48 -114.63 -115.66 -115.97 -114.19 -110.11 -104.76 -99.61 -95.60 -92.70 -90.03 -86.57 -81.70 -75.31 -67.66 -58.91 -49.09 -38.27 -26.88 -15.71 -5.76 --2.32 --8.70 --14.40 --20.84 --29.16 --39.52 --51.02 --62.19 --71.63 --78.58 --83.18 --85.99 --87.56 --88.02 --87.20 --85.11 --82.40 --80.28 --79.96 --81.92 --85.40 --88.69 --90.00 --88.47 --84.66 --80.32 --77.50 --77.56 --80.63 --85.73 --91.37 --96.37 --100.21 --103.06 --105.48 --108.10 --111.41 --115.77 --121.30 --127.73 --134.32 --139.72 --142.07 --139.33 --129.69 --112.16 --87.04 --56.13 --22.63 -9.59 -37.03 -57.59 -70.95 -78.29 -81.39 -81.67 -79.74 -75.61 -69.36 -61.79 -54.43 -49.03 -46.69 -47.36 -49.88 -52.59 -54.11 -53.91 -52.33 -50.21 -48.26 -46.76 -45.46 -43.85 -41.53 -38.43 -34.86 -31.38 -28.54 -26.70 -25.81 -25.40 -24.69 -22.93 -19.69 -14.93 -8.93 -2.00 --5.71 --13.91 --21.87 --28.17 --30.95 --28.65 --20.97 --9.28 -3.79 -15.42 -23.59 -27.63 -27.91 -25.23 -20.24 -13.44 -5.37 --3.10 --10.88 --17.00 --20.94 --22.86 --23.28 --22.80 --21.75 --20.20 --18.03 --15.11 --11.50 --7.53 --3.93 --1.73 --2.02 --5.37 --11.45 --18.97 --26.09 --31.17 --33.49 --33.45 --32.27 --31.28 --31.37 --32.80 --35.42 --39.01 --43.45 --48.70 --54.60 --60.83 --66.93 --72.43 --76.93 --80.02 --81.24 --80.14 --76.51 --70.53 --62.93 --54.58 --46.09 --37.28 --27.21 --14.55 -1.69 -21.55 -44.01 -67.16 -88.99 -108.06 -123.80 -136.49 -146.77 -155.13 -161.73 -166.50 -169.35 -170.19 -168.81 -164.57 -156.34 -142.93 -123.54 -98.23 -68.05 -34.63 --0.38 --35.85 --71.15 --105.82 --139.07 --169.47 --195.11 --214.05 --225.01 --227.67 --222.79 --211.77 --196.17 --177.14 --155.01 --129.26 --99.07 --64.23 --25.94 -12.81 -47.84 -75.17 -92.57 -100.51 -102.09 -101.58 -102.74 -107.55 -116.10 -127.29 -139.60 -151.38 -160.61 -164.70 -160.93 -147.53 -124.73 -94.82 -61.07 -26.12 --9.14 --45.36 --83.36 --122.44 --159.64 --190.46 --210.78 --218.61 --214.73 --201.91 --183.35 --161.23 --136.38 --108.75 --78.42 --46.46 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-5.43 -4.20 -2.94 -1.60 -0.19 --1.23 --2.63 --4.01 --5.38 --6.73 --8.02 --9.14 --10.00 --10.56 --10.87 --11.00 --11.07 --11.18 --11.36 --11.65 --12.06 --12.59 --13.27 --14.09 --14.99 --15.88 --16.57 --16.87 --16.53 --15.40 --13.38 --10.56 --7.11 --3.30 -0.63 -4.45 -7.94 -10.95 -13.32 -14.93 -15.69 -15.64 -14.89 -13.69 -12.34 -11.17 -10.37 -10.02 -9.98 -10.02 -9.92 -9.56 -9.00 -8.41 -7.97 -7.79 -7.80 -7.80 -7.55 -6.85 -5.63 -3.95 -1.99 --0.05 --1.94 --3.54 --4.83 --5.91 --6.95 --8.10 --9.42 --10.80 --12.04 --12.98 --13.54 --13.84 --14.18 --14.86 --16.05 --17.67 --19.38 --20.69 --21.14 --20.43 --18.55 --15.67 --12.09 --8.07 --3.88 -0.29 -4.21 -7.64 -10.38 -12.36 -13.67 -14.54 -15.20 -15.76 -16.25 -16.56 -16.67 -16.59 -16.45 -16.33 -16.28 -16.21 -16.02 -15.62 -15.01 -14.24 -13.35 -12.38 -11.27 -9.98 -8.49 -6.80 -4.93 -2.92 -0.78 --1.40 --3.51 --5.42 --6.99 --8.19 --9.09 --9.84 --10.60 --11.50 --12.56 --13.77 --15.06 --16.39 --17.73 --19.11 --20.54 --22.01 --23.49 --24.88 --25.97 --26.50 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--19.74 --20.46 --21.00 --21.57 --22.35 --23.47 --24.88 --26.28 --27.28 --27.43 --26.44 --24.22 --20.95 --16.96 --12.59 --8.12 --3.69 -0.59 -4.64 -8.24 -11.17 -13.22 -14.34 -14.71 -14.61 -14.34 -14.12 -13.99 -13.87 -13.66 -13.31 -12.81 -12.21 -11.55 -10.87 -10.22 -9.63 -9.10 -8.58 -7.94 -7.03 -5.75 -4.20 -2.55 -1.13 -0.28 -0.22 -1.02 -2.62 -4.77 -7.12 -9.36 -11.26 -12.73 -13.88 -14.81 -15.59 -16.14 -16.31 -15.97 -15.19 -14.19 -13.27 -12.61 -12.19 -11.78 -11.10 -9.98 -8.34 -6.22 -3.58 -0.33 --3.63 --8.14 --12.71 --16.54 --18.74 --18.71 --16.34 --12.14 --6.99 --1.85 -2.51 -5.65 -7.41 -7.84 -7.07 -5.34 -2.91 -0.11 --2.76 --5.55 --8.23 --10.91 --13.70 --16.64 --19.61 --22.40 --24.71 --26.27 --26.86 --26.35 --24.77 --22.28 --19.19 --15.88 --12.74 --10.07 --8.00 --6.50 --5.45 --4.68 --4.06 --3.49 --2.94 --2.44 --2.08 --1.93 --2.01 --2.27 --2.61 --2.97 --3.31 --3.63 --3.99 --4.40 --4.80 --5.15 --5.45 --5.77 --6.28 --7.11 --8.35 --9.94 --11.71 --13.39 --14.68 --15.27 --14.88 --13.40 --10.84 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--62.48 --70.49 --77.20 --82.78 --87.47 --91.49 --94.86 --97.34 --98.55 --98.01 --95.29 --90.08 --82.30 --72.17 --60.26 --47.39 --34.46 --22.18 --10.93 --0.81 -8.24 -16.17 -22.79 -27.83 -31.16 -32.95 -33.60 -33.56 -33.14 -32.43 -31.37 -29.95 -28.30 -26.69 -25.29 -24.04 -22.52 -20.17 -16.49 -11.37 -5.16 --1.32 --7.00 --10.79 --11.90 --9.96 --5.17 -1.87 -10.35 -19.41 -28.24 -36.07 -42.23 -46.15 -47.56 -46.60 -43.95 -40.69 -38.00 -36.75 -37.10 -38.41 -39.53 -39.32 -37.27 -33.78 -30.01 -27.32 -26.52 -27.49 -29.16 -30.13 -29.24 -25.97 -20.37 -12.65 -2.89 --9.01 --23.10 --38.95 --55.57 --71.58 --85.63 --96.67 --104.04 --107.20 --105.71 --99.22 --87.93 --72.83 --55.79 --39.02 --24.30 --12.47 --3.29 -4.04 -10.20 -15.40 -19.30 -21.50 -21.98 -21.10 -19.39 -17.09 -14.04 -9.78 -3.96 --3.36 --11.69 --20.20 --28.07 --34.58 --39.20 --41.52 --41.22 --38.20 --32.58 --24.78 --15.47 --5.49 -4.32 -13.32 -21.16 -27.75 -33.14 -37.37 -40.38 -42.09 -42.50 -41.88 -40.75 -39.68 -39.04 -38.87 -38.90 -38.76 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--36.76 --20.68 --5.50 -7.25 -16.56 -22.06 -24.06 -23.39 -21.11 -18.16 -15.11 -12.11 -9.02 -5.68 -2.09 --1.62 --5.26 --8.74 --12.10 --15.37 --18.31 --20.35 --20.65 --18.53 --13.75 --6.69 -1.72 -10.36 -18.21 -24.64 -29.42 -32.64 -34.59 -35.61 -35.94 -35.73 -34.89 -33.18 -30.27 -25.88 -20.07 -13.20 -5.92 --1.09 --7.37 --12.82 --17.64 --22.10 --26.37 --30.38 --33.85 --36.47 --37.95 --38.07 --36.64 --33.60 --28.99 --23.16 --16.68 --10.33 --4.75 --0.30 -3.11 -5.87 -8.43 -11.06 -13.78 -16.45 -19.01 -21.65 -24.90 -29.43 -35.70 -43.66 -52.57 -61.17 -68.08 -72.21 -73.07 -70.81 -66.01 -59.36 -51.42 -42.48 -32.76 -22.44 -11.80 -1.17 --9.22 --19.25 --28.90 --38.16 --46.89 --54.85 --61.69 --67.08 --70.82 --72.95 --73.79 --73.84 --73.72 --74.01 --75.07 --76.89 --78.99 --80.37 --79.77 --76.13 --69.04 --59.00 --47.29 --35.42 --24.57 --15.07 --6.50 -1.87 -10.48 -19.16 -27.22 -33.85 -38.62 -41.60 -43.21 -43.94 -44.04 -43.58 -42.57 -41.20 -39.86 -39.00 -38.92 -39.56 -40.53 -41.30 -41.49 -41.08 -40.40 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--34.34 --31.08 --27.39 --23.30 --18.93 --14.38 --9.61 --4.54 -0.79 -6.09 -10.88 -14.70 -17.33 -18.94 -19.91 -20.64 -21.28 -21.69 -21.69 -21.34 -21.00 -21.35 -22.98 -26.11 -30.52 -35.71 -41.14 -46.41 -51.21 -55.15 -57.66 -58.06 -55.75 -50.51 -42.56 -32.43 -20.84 -8.58 --3.50 --14.60 --24.08 --31.63 --37.39 --41.77 --45.08 --47.29 --48.01 --46.82 --43.72 --39.24 --34.17 --29.04 --23.77 --17.76 --10.34 --1.37 -8.48 -17.82 -25.13 -29.34 -30.21 -28.20 -24.06 -18.34 -11.19 -2.48 --7.76 --19.02 --30.28 --40.31 --48.14 --53.42 --56.49 --58.05 --58.74 --58.83 --58.17 --56.45 --53.37 --48.80 --42.74 --35.35 --26.95 --18.12 --9.57 --2.01 -4.14 -8.93 -12.78 -16.23 -19.56 -22.68 -25.12 -26.42 -26.33 -25.00 -22.82 -20.24 -17.53 -14.89 -12.53 -10.88 -10.52 -11.91 -15.11 -19.66 -24.66 -29.09 -32.24 -33.86 -34.20 -33.71 -32.84 -31.88 -31.03 -30.41 -30.13 -30.13 -30.13 -29.58 -27.89 -24.65 -19.92 -14.20 -8.26 -2.84 --1.68 --5.38 --8.72 --12.27 --16.47 --21.49 --27.23 --33.48 --40.01 --46.75 --53.79 --61.35 --69.58 --78.43 --87.38 --95.37 --101.11 --103.39 --101.62 --96.15 --88.21 --79.39 --70.93 --63.12 --55.10 --45.33 --32.36 --15.69 -3.87 -24.35 -43.38 -58.89 -69.83 -76.18 -78.72 -78.64 -77.16 -75.39 -74.20 -74.08 -75.03 -76.52 -77.64 -77.48 -75.51 -71.83 -67.06 -62.05 -57.37 -53.06 -48.55 -42.99 -35.69 -26.52 -16.15 -5.81 --3.21 --10.08 --14.88 --18.42 --21.86 --25.95 --30.73 --35.54 --39.50 --41.94 --42.71 --42.03 --40.15 --37.16 --32.97 --27.72 --22.01 --16.97 --14.07 --14.64 --19.54 --28.98 --42.53 --59.17 --77.34 --95.01 --109.78 --119.20 --121.21 --114.67 --99.77 --78.12 --52.42 --25.88 --1.44 -18.86 -34.19 -44.86 -51.89 -56.40 -59.09 -60.14 -59.40 -56.77 -52.54 -47.36 -42.05 -37.15 -32.80 -28.90 -25.40 -22.65 -21.26 -21.82 -24.38 -28.26 -32.17 -34.83 -35.47 -34.10 -31.34 -28.08 -25.06 -22.76 -21.36 -20.80 -20.78 -20.77 -19.95 -17.57 -13.24 -7.19 -0.27 --6.38 --11.80 --15.60 --17.97 --19.59 --21.12 --22.96 --25.14 --27.55 --30.16 --33.27 --37.32 --42.67 --49.20 --56.14 --62.30 --66.57 --68.35 --67.76 --65.45 --62.12 --58.17 --53.64 --48.43 --42.64 --36.77 --31.54 --27.51 --24.75 --22.64 --20.11 --15.99 --9.52 --0.49 -10.71 -23.25 -36.12 -48.18 -58.30 -65.41 -68.85 -68.65 -65.68 -61.48 -57.63 -55.14 -53.92 -52.97 -51.02 -47.34 -42.30 -37.12 -33.19 -31.22 -30.87 -30.93 -30.06 -27.46 -23.13 -17.61 -11.44 -4.79 --2.57 --10.95 --20.40 --30.55 --40.77 --50.50 --59.44 --67.53 --74.66 --80.47 --84.35 --85.80 --84.76 --81.76 --77.70 --73.30 --68.74 --63.60 --57.24 --49.24 --39.75 --29.34 --18.73 --8.37 -1.58 -11.22 -20.66 -30.02 -39.41 -48.99 -58.71 -68.08 -76.02 -81.16 -82.52 -80.10 -75.11 -69.51 -65.04 -62.42 -61.06 -59.49 -56.18 -50.46 -42.70 -34.11 -26.03 -19.30 -13.99 -9.58 -5.44 -1.32 --2.45 --5.28 --6.71 --6.81 --6.25 --5.98 --6.75 --8.85 --12.06 --15.90 --19.86 --23.58 --26.70 --28.75 --29.08 --27.05 --22.31 --14.91 --5.34 -5.66 -17.25 -28.71 -39.33 -48.52 -55.80 -60.93 -64.00 -65.51 -66.15 -66.54 -66.84 -66.45 -64.09 -58.18 -47.51 -31.98 -12.84 --7.51 --26.21 --40.83 --50.12 --54.19 --54.19 --51.75 --48.32 --44.77 --41.41 --38.10 --34.70 --31.26 --28.11 --25.70 --24.40 --24.18 --24.60 --25.01 --24.89 --24.15 --23.20 --22.71 --23.31 --25.26 --28.39 --32.28 --36.36 --40.09 --42.90 --44.16 --43.27 --39.89 --34.26 --27.20 --19.81 --12.99 --7.03 --1.48 -4.41 -11.14 -18.50 -25.67 -31.63 -35.74 -38.00 -38.93 -39.07 -38.63 -37.44 -35.20 -31.79 -27.48 -22.74 -18.09 -13.86 -10.20 -7.22 -5.03 -3.73 -3.19 -2.95 -2.18 --0.06 --4.47 --11.16 --19.53 --28.40 --36.34 --42.19 --45.35 --45.99 --44.86 --42.95 --40.99 --39.22 --37.35 --34.82 --31.16 --26.25 --20.40 --14.14 --7.88 --1.73 -4.62 -11.65 -19.63 -28.35 -37.11 -44.95 -51.03 -54.89 -56.47 -55.95 -53.49 -49.13 -43.05 -35.76 -28.28 -21.95 -17.93 -16.62 -17.39 -18.68 -18.60 -15.74 -9.79 -1.58 --7.33 --15.35 --21.44 --25.23 --26.74 --25.96 --22.76 --17.09 --9.45 --1.10 -6.07 -10.18 -10.20 -6.43 -0.44 --5.66 --10.02 --11.75 --11.18 --9.42 --7.79 --7.11 --7.45 --8.17 --8.39 --7.34 --4.72 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--482.84 --488.68 --475.80 --443.52 --396.80 --343.57 --291.19 --244.08 --203.52 --169.21 --140.80 --118.48 --102.44 --91.86 --84.65 --77.84 --68.90 --56.78 --42.16 --26.97 --13.25 --2.14 -6.68 -14.73 -24.08 -36.28 -51.90 -70.29 -90.04 -109.60 -127.97 -145.08 -161.94 -180.42 -202.65 -229.94 -261.70 -294.93 -324.80 -346.20 -355.75 -352.96 -340.23 -321.55 -300.66 -280.02 -260.63 -242.81 -226.79 -212.83 -200.69 -189.17 -176.23 -159.91 -139.52 -116.34 -93.55 -74.98 -63.92 -61.78 -67.73 -79.00 -91.88 -103.08 -110.46 -113.38 -112.35 -108.42 -102.90 -97.43 -94.23 -95.79 -103.94 -118.59 -137.10 -155.08 -168.20 -174.42 -174.84 -172.83 -171.87 -173.31 -175.52 -174.77 -167.11 -150.07 -123.28 -88.13 -47.10 -3.35 --39.09 --75.57 --101.56 --114.16 --113.53 --103.12 --88.67 --75.95 --68.70 --67.71 --71.43 --77.38 --83.53 --88.83 --92.93 --95.42 --95.59 --92.58 --85.95 --75.98 --63.59 --49.99 --36.15 --22.60 --9.43 -3.39 -15.92 -28.12 -39.98 -51.39 -61.87 -70.32 -74.98 -73.67 -64.50 -46.57 -20.50 --11.61 --46.61 --81.06 --112.10 --137.85 --157.63 --171.83 --181.62 --188.88 --196.43 --208.00 --227.63 --258.13 --299.14 --346.03 --390.37 --422.48 --434.93 --425.60 --398.31 --361.11 --322.68 --289.24 --262.97 --242.71 --225.82 --210.09 --194.41 --178.49 --162.10 --144.56 --124.97 --102.70 --77.66 --50.43 --22.02 -6.67 -35.38 -64.70 -96.05 -130.86 -169.44 -209.93 -248.07 -278.45 -296.65 -301.33 -294.99 -282.79 -270.11 -260.08 -252.58 -245.16 -235.26 -222.13 -207.45 -194.12 -184.41 -178.52 -174.54 -169.64 -161.82 -150.99 -138.89 -127.99 -119.93 -114.57 -110.37 -105.58 -99.59 -93.61 -90.01 -90.82 -96.30 -104.42 -111.83 -115.73 -115.36 -112.31 -109.48 -109.17 -111.83 -116.05 -119.63 -121.15 -120.75 -119.68 -119.08 -118.69 -116.33 -108.58 -92.28 -65.87 -30.08 --12.35 --57.85 --103.25 --146.75 --188.14 --228.17 --267.38 --305.06 --339.03 --366.48 --385.25 --394.76 --395.89 --390.01 --377.80 --358.73 --331.82 --296.81 --255.09 --209.23 --161.86 --114.26 --66.14 --16.71 -33.41 -80.83 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--28.28 --20.31 --14.24 --10.16 --7.98 --7.40 --7.82 --8.52 --8.76 --8.06 --6.41 --4.16 --1.85 -0.10 -1.53 -2.53 -3.26 -3.83 -4.25 -4.46 -4.43 -4.23 -3.98 -3.78 -3.63 -3.36 -2.82 -2.04 -1.29 -1.06 -1.74 -3.41 -5.70 -7.87 -9.14 -8.97 -7.25 -4.32 -0.81 --2.58 --5.26 --6.84 --7.30 --6.97 --6.42 --6.35 --7.24 --9.18 --11.76 --14.20 --15.68 --15.63 --13.91 --10.78 --6.77 --2.47 -1.60 -4.99 -7.42 -8.75 -9.09 -8.68 -7.92 -7.19 -6.72 -6.59 -6.74 -7.06 -7.47 -7.96 -8.49 -9.04 -9.56 -10.00 -10.34 -10.57 -10.66 -10.55 -10.19 -9.53 -8.64 -7.66 -6.72 -5.87 -5.08 -4.20 -3.11 -1.77 -0.28 --1.18 --2.36 --3.16 --3.54 --3.63 --3.61 --3.69 --4.10 --5.03 --6.55 --8.52 --10.56 --12.11 --12.56 --11.50 --8.93 --5.25 --1.18 -2.57 -5.50 -7.46 -8.61 -9.24 -9.59 -9.73 -9.59 -9.06 -8.18 -7.08 -6.00 -5.16 -4.67 -4.53 -4.64 -4.89 -5.23 -5.66 -6.22 -6.88 -7.54 -8.03 -8.19 -7.90 -7.19 -6.16 -5.02 -3.95 -3.07 -2.46 -2.01 -1.63 -1.10 -0.28 --0.98 --2.70 --4.75 --6.85 --8.67 --9.92 --10.45 --10.33 --9.76 --9.07 --8.53 --8.30 --8.41 --8.75 --9.23 --9.74 --10.21 --10.57 --10.78 --10.86 --10.92 --11.16 --11.81 --13.03 --14.73 --16.52 --17.77 --17.82 --16.32 --13.35 --9.43 --5.32 --1.74 -0.91 -2.57 -3.47 -3.95 -4.27 -4.57 -4.84 -5.00 -5.03 -4.93 -4.80 -4.68 -4.57 -4.45 -4.27 -4.08 -4.05 -4.38 -5.17 -6.32 -7.50 -8.35 -8.59 -8.24 -7.53 -6.75 -6.09 -5.53 -4.96 -4.22 -3.23 -2.06 -0.81 --0.43 --1.63 --2.82 --4.02 --5.20 --6.28 --7.16 --7.79 --8.23 --8.62 --9.11 --9.78 --10.54 --11.16 --11.44 --11.33 --11.05 --11.02 --11.68 --13.19 --15.30 --17.37 --18.57 --18.26 --16.18 --12.57 --8.02 --3.28 -1.10 -4.76 -7.66 -9.85 -11.45 -12.50 -12.97 -12.81 -12.08 -10.94 -9.64 -8.49 -7.70 -7.36 -7.34 -7.48 -7.60 -7.64 -7.66 -7.78 -8.08 -8.58 -9.12 -9.49 -9.44 -8.79 -7.51 -5.71 -3.63 -1.53 --0.36 --1.94 --3.20 --4.19 --5.01 --5.68 --6.27 --6.83 --7.40 --8.02 --8.65 --9.22 --9.68 --10.08 --10.61 --11.56 --13.10 --15.17 --17.39 --19.12 --19.68 --18.64 --15.89 --11.82 --7.07 --2.40 -1.63 -4.73 -6.87 -8.27 -9.14 -9.67 -9.92 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--8.30 --9.55 --8.35 --4.93 --0.11 -4.89 -8.99 -11.56 -12.58 -12.62 -12.30 -12.08 -12.06 -12.04 -11.79 -11.26 -10.58 -10.01 -9.79 -9.87 -10.06 -10.01 -9.48 -8.51 -7.39 -6.61 -6.57 -7.37 -8.73 -10.11 -10.93 -10.78 -9.65 -7.71 -5.34 -2.86 -0.52 --1.53 --3.16 --4.32 --5.05 --5.52 --6.00 --6.76 --7.92 --9.44 --11.15 --12.81 --14.26 --15.47 --16.46 --17.30 --18.09 --18.92 --19.83 --20.75 --21.47 --21.70 --21.12 --19.49 --16.80 --13.21 --9.02 --4.63 --0.49 -3.01 -5.54 -6.93 -7.32 -6.99 -6.38 -5.84 -5.57 -5.54 -5.63 -5.76 -5.90 -6.14 -6.53 -7.03 -7.57 -8.03 -8.38 -8.67 -8.99 -9.44 -10.01 -10.61 -11.10 -11.37 -11.34 -10.99 -10.35 -9.37 -7.97 -6.12 -3.88 -1.44 --0.96 --3.09 --4.87 --6.40 --7.81 --9.20 --10.50 --11.57 --12.30 --12.72 --13.05 --13.54 --14.40 --15.74 --17.53 --19.64 --21.81 --23.62 --24.53 --23.97 --21.56 --17.28 --11.54 --5.03 -1.46 -7.31 -12.18 -15.97 -18.71 -20.46 -21.26 -21.12 -20.14 -18.52 -16.65 -14.95 -13.80 -13.31 -13.36 -13.62 -13.80 -13.77 -13.62 -13.48 -13.43 -13.35 -12.97 -12.02 -10.38 -8.19 -5.73 -3.41 -1.56 -0.38 --0.12 --0.12 -0.05 -0.01 --0.56 --1.78 --3.53 --5.49 --7.29 --8.67 --9.61 --10.25 --10.84 --11.57 --12.55 --13.78 --15.21 --16.72 --18.11 --19.07 --19.19 --18.05 --15.40 --11.24 --5.93 --0.08 -5.60 -10.51 -14.28 -16.81 -18.25 -18.86 -18.83 -18.33 -17.42 -16.16 -14.63 -12.96 -11.33 -9.88 -8.68 -7.74 -7.08 -6.63 -6.36 -6.23 -6.17 -6.14 -6.04 -5.79 -5.22 -4.20 -2.61 -0.47 --2.04 --4.64 --7.05 --9.12 --10.85 --12.31 --13.57 --14.67 --15.63 --16.50 --17.46 --18.69 --20.15 --21.55 --22.30 --21.77 --19.53 --15.61 --10.45 --4.74 -0.83 -5.75 -9.80 -12.91 -15.13 -16.47 -16.94 -16.59 -15.53 -13.94 -12.08 -10.20 -8.46 -6.96 -5.66 -4.47 -3.39 -2.48 -1.95 -1.97 -2.57 -3.56 -4.57 -5.21 -5.20 -4.53 -3.34 -1.93 -0.52 --0.73 --1.80 --2.73 --3.56 --4.37 --5.30 --6.59 --8.48 --11.05 --14.12 --17.17 --19.49 --20.43 --19.58 --16.96 --12.96 --8.18 --3.27 -1.29 -5.23 -8.41 -10.78 -12.26 -12.81 -12.48 -11.42 -9.93 -8.36 -7.00 -5.98 -5.24 -4.58 -3.91 -3.25 -2.81 -2.82 -3.40 -4.43 -5.61 -6.52 -6.82 -6.34 -5.14 -3.42 -1.48 --0.42 --2.06 --3.35 --4.33 --5.22 --6.32 --7.89 --10.04 --12.62 --15.20 --17.19 --17.99 --17.21 --14.77 --10.97 --6.32 --1.42 -3.16 -7.03 -9.96 -11.89 -12.91 -13.16 -12.81 -11.98 -10.82 -9.42 -7.93 -6.48 -5.21 -4.16 -3.34 -2.69 -2.17 -1.83 -1.72 -1.91 -2.34 -2.86 -3.19 -3.11 -2.50 -1.37 --0.08 --1.63 --3.04 --4.15 --4.99 --5.79 --6.88 --8.64 --11.23 --14.45 --17.71 --20.20 --21.15 --20.06 --16.98 --12.33 --6.88 --1.36 -3.64 -7.78 -10.90 -12.95 -13.98 -14.11 -13.54 -12.50 -11.24 -9.89 -8.56 -7.28 -6.06 -4.94 -3.99 -3.23 -2.68 -2.34 -2.21 -2.27 -2.49 -2.76 -2.92 -2.74 -2.01 -0.67 --1.24 --3.44 --5.57 --7.25 --8.28 --8.70 --8.91 --9.46 --10.90 --13.38 --16.58 --19.67 --21.63 --21.65 --19.43 --15.24 --9.79 --3.95 -1.55 -6.20 -9.77 -12.21 -13.55 -13.98 -13.75 -13.17 -12.49 -11.85 -11.18 -10.27 -8.99 -7.36 -5.61 -4.07 -3.11 -2.85 -3.23 -3.96 -4.70 -5.22 -5.45 -5.45 -5.32 -5.14 -4.87 -4.40 -3.62 -2.46 -0.95 --0.79 --2.65 --4.50 --6.25 --7.83 --9.23 --10.50 --11.83 --13.47 --15.59 --18.18 --20.88 --23.02 --23.81 --22.66 --19.40 --14.40 --8.41 --2.27 -3.31 -7.96 -11.54 -14.13 -15.80 -16.59 -16.60 -15.96 -14.91 -13.71 -12.60 -11.74 -11.14 -10.76 -10.53 -10.43 -10.41 -10.54 -10.87 -11.46 -12.33 -13.41 -14.48 -15.21 -15.31 -14.54 -12.97 -10.89 -8.74 -6.90 -5.52 -4.46 -3.36 -1.85 --0.27 --2.90 --5.70 --8.26 --10.27 --11.60 --12.36 --12.84 --13.43 --14.54 --16.46 --19.21 --22.45 --25.46 --27.31 --27.19 --24.72 --20.06 --13.89 --7.07 --0.41 -5.50 -10.40 -14.20 -16.90 -18.52 -19.09 -18.76 -17.84 -16.69 -15.70 -15.12 -15.02 -15.24 -15.53 -15.67 -15.54 -15.20 -14.78 -14.41 -14.10 -13.77 -13.27 -12.51 -11.45 -10.13 -8.64 -7.04 -5.36 -3.58 -1.75 --0.12 --1.94 --3.65 --5.26 --6.84 --8.44 --10.12 --11.87 --13.62 --15.33 --16.94 --18.48 --19.95 --21.39 --22.78 --24.16 --25.57 --27.09 --28.68 --30.15 --31.03 --30.79 --28.90 --25.16 --19.76 --13.31 --6.56 --0.25 -5.13 -9.39 -12.56 -14.75 -16.13 -16.77 -16.75 -16.20 -15.29 -14.28 -13.41 -12.86 -12.68 -12.81 -13.11 -13.47 -13.81 -14.14 -14.47 -14.81 -15.12 -15.29 -15.23 -14.85 -14.12 -13.07 -11.73 -10.14 -8.32 -6.33 -4.26 -2.19 -0.21 --1.59 --3.20 --4.61 --5.88 --7.09 --8.32 --9.64 --11.04 --12.48 --13.85 --15.08 --16.15 --17.14 --18.26 --19.74 --21.72 --24.11 --26.52 --28.32 --28.81 --27.48 --24.18 --19.19 --13.09 --6.64 --0.55 -4.70 -8.85 -11.89 -13.97 -15.28 -16.02 -16.35 -16.31 -15.92 -15.23 -14.31 -13.29 -12.33 -11.55 -11.02 -10.77 -10.84 -11.25 -12.00 -13.01 -14.08 -14.90 -15.21 -14.89 -14.02 -12.82 -11.57 -10.43 -9.40 -8.33 -7.01 -5.36 -3.40 -1.31 --0.69 --2.43 --3.84 --5.03 --6.14 --7.37 --8.81 --10.48 --12.28 --14.07 --15.68 --17.03 --18.14 --19.07 --20.04 --21.23 --22.78 --24.67 --26.62 --28.17 --28.80 --28.04 --25.66 --21.73 --16.56 --10.65 --4.53 -1.28 -6.34 -10.39 -13.33 -15.23 -16.27 -16.65 -16.50 -15.92 -14.97 -13.79 -12.56 -11.47 -10.67 -10.18 -9.97 -10.03 -10.34 -10.94 -11.81 -12.81 -13.74 -14.36 -14.47 -14.02 -13.07 -11.76 -10.28 -8.76 -7.33 -6.05 -4.93 -3.90 -2.89 -1.82 -0.64 --0.60 --1.80 --2.86 --3.71 --4.38 --5.02 --5.83 --7.00 --8.59 --10.42 --12.18 --13.55 --14.35 --14.71 --15.04 --15.83 --17.40 --19.70 --22.24 --24.30 --25.18 --24.43 --21.94 --17.94 --12.86 --7.25 --1.65 -3.45 -7.66 -10.76 -12.67 -13.46 -13.31 -12.43 -11.04 -9.43 -7.88 -6.66 -5.94 -5.79 -6.07 -6.58 -7.12 -7.58 -7.98 -8.40 -8.91 -9.48 -9.92 -10.00 -9.50 -8.41 -6.90 -5.26 -3.83 -2.80 -2.18 -1.78 -1.33 -0.54 --0.71 --2.36 --4.18 --5.92 --7.32 --8.26 --8.78 --9.04 --9.26 --9.72 --10.64 --12.17 --14.29 --16.73 --19.00 --20.52 --20.70 --19.21 --16.09 --11.70 --6.68 --1.63 -2.92 -6.68 -9.54 -11.56 -12.83 -13.46 -13.51 -13.02 -12.08 -10.81 -9.35 -7.93 -6.76 -5.98 -5.66 -5.79 -6.29 -7.01 -7.83 -8.59 -9.12 -9.29 -9.01 -8.28 -7.23 -6.05 -4.91 -3.86 -2.81 -1.57 --0.04 --2.10 --4.44 --6.77 --8.79 --10.27 --11.20 --11.79 --12.41 --13.40 --15.01 --17.21 --19.70 --21.90 --23.20 --23.06 --21.25 --17.88 --13.35 --8.27 --3.24 -1.28 -5.03 -7.90 -9.92 -11.11 -11.56 -11.35 -10.68 -9.76 -8.83 -8.06 -7.46 -6.93 -6.34 -5.67 -5.01 -4.57 -4.57 -5.05 -5.90 -6.88 -7.70 -8.18 -8.24 -7.90 -7.24 -6.30 -5.10 -3.66 -2.03 -0.22 --1.69 --3.69 --5.74 --7.82 --9.85 --11.73 --13.35 --14.66 --15.83 --17.15 --18.99 --21.54 --24.62 --27.62 --29.65 --29.89 --27.90 --23.75 --18.02 --11.50 --5.00 -0.90 -5.91 -9.88 -12.81 -14.71 -15.65 -15.80 -15.40 -14.76 -14.17 -13.74 -13.42 -13.05 -12.46 -11.59 -10.61 -9.79 -9.41 -9.58 -10.25 -11.17 -12.03 -12.58 -12.65 -12.17 -11.16 -9.73 -8.01 -6.17 -4.25 -2.24 -0.10 --2.19 --4.60 --7.02 --9.27 --11.26 --12.92 --14.30 --15.46 --16.54 --17.73 --19.24 --21.26 --23.70 --26.12 --27.84 --28.06 --26.29 --22.50 --17.15 --10.95 --4.61 -1.41 -6.85 -11.59 -15.51 -18.47 -20.34 -21.13 -20.99 -20.18 -19.00 -17.64 -16.25 -14.95 -13.83 -12.99 -12.52 -12.43 -12.69 -13.23 -13.93 -14.67 -15.33 -15.80 -15.97 -15.81 -15.26 -14.37 -13.19 -11.81 -10.29 -8.72 -7.12 -5.49 -3.85 -2.23 -0.73 --0.60 --1.74 --2.76 --3.83 --5.17 --6.90 --8.98 --11.20 --13.27 --14.91 --16.02 --16.69 --17.24 --18.08 --19.57 --21.92 --25.02 --28.41 --31.32 --32.87 --32.34 --29.36 --24.12 --17.25 --9.67 --2.26 -4.33 -9.76 -13.97 -17.03 -19.07 -20.23 -20.69 -20.60 -20.19 -19.63 -19.07 -18.57 -18.11 -17.63 -17.11 -16.58 -16.14 -15.91 -16.01 -16.44 -17.13 -17.85 -18.43 -18.69 -18.60 -18.15 -17.45 -16.56 -15.53 -14.40 -13.19 -11.97 -10.77 -9.64 -8.58 -7.55 -6.50 -5.36 -4.15 -2.88 -1.60 -0.35 --0.88 --2.07 --3.27 --4.52 --5.96 --7.69 --9.84 --12.39 --15.18 --17.91 --20.23 --21.92 --23.00 --23.73 --24.51 --25.67 --27.36 --29.37 --31.23 --32.29 --31.99 --29.97 --26.17 --20.87 --14.57 --7.91 --1.45 -4.31 -9.09 -12.77 -15.33 -16.88 -17.57 -17.59 -17.11 -16.35 -15.50 -14.74 -14.19 -13.87 -13.75 -13.76 -13.85 -14.04 -14.37 -14.85 -15.41 -15.95 -16.28 -16.31 -16.00 -15.40 -14.60 -13.65 -12.55 -11.25 -9.76 -8.12 -6.45 -4.88 -3.48 -2.31 -1.29 -0.38 --0.50 --1.40 --2.33 --3.31 --4.28 --5.20 --6.00 --6.67 --7.29 --7.99 --8.93 --10.19 --11.72 --13.41 --15.12 --16.77 --18.33 --19.79 --21.14 --22.38 --23.62 --25.01 --26.73 --28.78 --30.94 --32.65 --33.23 --32.06 --28.90 --23.94 --17.79 --11.20 --4.91 -0.61 -5.17 -8.79 -11.59 -13.67 -15.09 -15.85 -16.00 -15.65 -14.95 -14.11 -13.32 -12.74 -12.47 -12.56 -13.00 -13.72 -14.61 -15.50 -16.28 -16.83 -17.07 -16.94 -16.44 -15.63 -14.65 -13.67 -12.82 -12.12 -11.45 -10.59 -9.36 -7.73 -5.77 -3.65 -1.53 --0.47 --2.29 --3.84 --4.99 --5.64 --5.80 --5.64 --5.49 --5.72 --6.60 --8.19 --10.35 --12.78 --15.10 --17.00 --18.30 --19.06 --19.63 --20.51 --22.19 --24.90 --28.35 --31.79 --34.21 --34.77 --33.00 --29.05 --23.43 --16.90 --10.15 --3.75 -1.91 -6.58 -10.10 -12.45 -13.76 -14.26 -14.19 -13.75 -13.07 -12.25 -11.40 -10.71 -10.38 -10.51 -11.12 -12.06 -13.12 -14.08 -14.77 -15.12 -15.13 -14.83 -14.24 -13.43 -12.46 -11.42 -10.38 -9.35 -8.29 -7.11 -5.77 -4.28 -2.74 -1.26 --0.03 --1.05 --1.79 --2.28 --2.64 --2.96 --3.40 --4.05 --4.98 --6.16 --7.47 --8.72 --9.76 --10.49 --10.98 --11.47 --12.23 --13.52 --15.42 --17.75 --20.13 --22.05 --23.02 --22.69 --20.94 --17.86 --13.72 --8.95 --4.02 -0.64 -4.73 -8.07 -10.60 -12.37 -13.45 -13.92 -13.88 -13.45 -12.75 -11.90 -11.02 -10.21 -9.55 -9.09 -8.86 -8.83 -8.98 -9.30 -9.78 -10.44 -11.21 -11.91 -12.28 -12.09 -11.24 -9.84 -8.18 -6.58 -5.23 -4.16 -3.21 -2.21 -0.96 --0.59 --2.46 --4.56 --6.73 --8.73 --10.30 --11.25 --11.59 --11.61 --11.79 --12.63 --14.43 --17.11 --20.19 --23.01 --24.86 --25.21 --23.81 --20.71 --16.16 --10.60 --4.60 -1.29 -6.51 -10.67 -13.60 -15.39 -16.27 -16.56 -16.46 -16.09 -15.48 -14.68 -13.81 -13.02 -12.43 -12.04 -11.75 -11.46 -11.14 -10.88 -10.85 -11.18 -11.79 -12.40 -12.66 -12.25 -11.05 -9.16 -6.84 -4.40 -2.11 -0.15 --1.46 --2.77 --3.88 --4.94 --6.04 --7.26 --8.61 --10.04 --11.51 --12.93 --14.29 --15.64 --17.10 --18.85 --21.02 --23.61 --26.40 --28.97 --30.76 --31.19 --29.87 --26.70 --21.97 --16.20 --10.05 --4.04 -1.39 -6.02 -9.74 -12.49 -14.27 -15.14 -15.28 -14.93 -14.33 -13.72 -13.21 -12.90 -12.85 -13.14 -13.83 -14.88 -16.15 -17.41 -18.49 -19.26 -19.67 -19.70 -19.31 -18.47 -17.17 -15.49 -13.61 -11.71 -9.95 -8.35 -6.85 -5.34 -3.74 -2.04 -0.31 --1.39 --2.94 --4.33 --5.56 --6.73 --7.95 --9.35 --10.94 --12.68 --14.43 --16.00 --17.30 --18.39 --19.47 --20.77 --22.53 --24.69 --26.95 --28.76 --29.47 --28.61 --25.97 --21.78 --16.52 --10.81 --5.19 --0.02 -4.47 -8.21 -11.18 -13.38 -14.90 -15.84 -16.37 -16.63 -16.69 -16.56 -16.23 -15.71 -15.06 -14.40 -13.87 -13.59 -13.64 -14.05 -14.72 -15.49 -16.08 -16.24 -15.81 -14.75 -13.18 -11.33 -9.47 -7.74 -6.23 -4.85 -3.49 -2.08 -0.61 --0.86 --2.21 --3.36 --4.30 --5.09 --5.85 --6.65 --7.54 --8.55 --9.68 --10.92 --12.24 --13.59 --14.85 --15.94 --16.82 --17.60 --18.51 --19.81 --21.66 --23.89 --26.12 --27.69 --27.96 --26.51 --23.26 --18.49 --12.73 --6.58 --0.62 -4.67 -8.98 -12.15 -14.16 -15.16 -15.41 -15.20 -14.74 -14.18 -13.52 -12.74 -11.87 -10.95 -10.16 -9.66 -9.59 -9.98 -10.72 -11.54 -12.16 -12.32 -11.92 -11.03 -9.80 -8.42 -7.06 -5.78 -4.57 -3.40 -2.26 -1.16 -0.19 --0.65 --1.35 --1.93 --2.45 --2.99 --3.60 --4.31 --5.17 --6.19 --7.40 --8.76 --10.16 --11.47 --12.54 --13.29 --13.82 --14.36 --15.25 --16.77 --19.01 --21.67 --24.17 --25.74 --25.72 --23.77 --20.01 --14.93 --9.27 --3.73 -1.19 -5.21 -8.28 -10.40 -11.62 -11.95 -11.48 -10.39 -8.95 -7.53 -6.40 -5.72 -5.41 -5.33 -5.31 -5.35 -5.61 -6.23 -7.34 -8.78 -10.26 -11.47 -12.18 -12.36 -12.15 -11.72 -11.11 -10.29 -9.14 -7.56 -5.61 -3.41 -1.13 --1.11 --3.22 --5.13 --6.77 --8.03 --8.85 --9.23 --9.34 --9.43 --9.86 --10.89 --12.64 --15.05 --17.83 --20.53 --22.55 --23.37 --22.57 --20.02 --15.91 --10.69 --5.02 -0.50 -5.35 -9.26 -12.15 -14.09 -15.21 -15.60 -15.36 -14.53 -13.27 -11.73 -10.17 -8.79 -7.75 -7.07 -6.69 -6.53 -6.50 -6.59 -6.72 -6.84 -6.88 -6.74 -6.35 -5.71 -4.85 -3.82 -2.65 -1.35 --0.11 --1.76 --3.56 --5.46 --7.32 --8.97 --10.31 --11.34 --12.20 --13.19 --14.62 --16.60 --19.01 --21.41 --23.19 --23.76 --22.72 --20.03 --15.98 --11.03 --5.75 --0.60 -4.07 -8.05 -11.20 -13.43 -14.70 -15.05 -14.63 -13.68 -12.50 -11.34 -10.34 -9.56 -8.97 -8.51 -8.21 -8.13 -8.36 -8.93 -9.75 -10.61 -11.27 -11.49 -11.16 -10.31 -9.04 -7.52 -5.84 -4.05 -2.09 --0.12 --2.59 --5.29 --8.10 --10.85 --13.39 --15.63 --17.54 --19.24 --20.88 --22.67 --24.76 --27.16 --29.69 --31.93 --33.34 --33.37 --31.62 --28.04 --22.86 --16.62 --9.94 --3.41 -2.55 -7.65 -11.78 -14.90 -17.07 -18.38 -18.98 -18.99 -18.62 -18.00 -17.34 -16.82 -16.54 -16.52 -16.69 -16.95 -17.18 -17.39 -17.61 -17.93 -18.33 -18.71 -18.88 -18.64 -17.93 -16.80 -15.45 -14.06 -12.72 -11.37 -9.89 -8.16 -6.19 -4.10 -2.13 -0.45 --0.88 --1.98 --3.03 --4.26 --5.79 --7.62 --9.67 --11.83 --13.95 --15.92 --17.68 --19.21 --20.60 --22.07 --23.89 --26.31 --29.38 --32.80 --35.98 --38.11 --38.41 --36.40 --32.03 --25.66 --17.99 --9.83 --1.91 -5.21 -11.17 -15.84 -19.21 -21.41 -22.69 -23.29 -23.50 -23.48 -23.33 -23.04 -22.57 -21.92 -21.17 -20.50 -20.09 -20.06 -20.38 -20.96 -21.62 -22.18 -22.53 -22.55 -22.18 -21.38 -20.11 -18.40 -16.32 -14.04 -11.71 -9.49 -7.46 -5.68 -4.10 -2.65 -1.29 -0.01 --1.17 --2.24 --3.19 --4.10 --5.08 --6.21 --7.48 --8.85 --10.18 --11.41 --12.60 --13.81 --15.17 --16.69 --18.29 --19.90 --21.50 --23.25 --25.38 --28.05 --31.14 --34.18 --36.40 --36.99 --35.40 --31.48 --25.57 --18.34 --10.56 --2.96 -3.96 -9.87 -14.60 -18.04 -20.21 -21.23 -21.34 -20.86 -20.08 -19.20 -18.32 -17.46 -16.65 -15.98 -15.57 -15.56 -16.00 -16.83 -17.90 -19.02 -19.97 -20.58 -20.75 -20.40 -19.53 -18.19 -16.51 -14.60 -12.60 -10.62 -8.69 -6.84 -5.08 -3.46 -2.04 -0.88 -0.01 --0.56 --0.87 --1.04 --1.22 --1.61 --2.39 --3.58 --5.12 --6.82 --8.43 --9.76 --10.77 --11.54 --12.30 --13.30 --14.74 --16.72 --19.17 --21.88 --24.46 --26.46 --27.34 --26.67 --24.25 --20.19 --14.88 --8.95 --3.07 -2.23 -6.63 -10.12 -12.79 -14.77 -16.07 -16.61 -16.28 -15.12 -13.37 -11.41 -9.66 -8.42 -7.77 -7.64 -7.91 -8.45 -9.20 -10.15 -11.18 -12.17 -12.91 -13.24 -13.11 -12.57 -11.77 -10.84 -9.88 -8.89 -7.81 -6.58 -5.13 -3.51 -1.85 -0.32 --0.90 --1.78 --2.43 --3.10 --4.05 --5.46 --7.29 --9.34 --11.33 --12.96 --14.12 --14.86 --15.43 --16.17 --17.40 --19.34 --21.95 --24.91 --27.63 --29.39 --29.55 --27.73 --23.98 --18.70 --12.57 --6.31 --0.52 -4.42 -8.39 -11.38 -13.48 -14.78 -15.39 -15.41 -14.97 -14.24 -13.39 -12.61 -12.06 -11.83 -11.96 -12.42 -13.14 -14.01 -14.92 -15.73 -16.33 -16.63 -16.58 -16.24 -15.67 -14.97 -14.21 -13.33 -12.19 -10.66 -8.62 -6.14 -3.43 -0.80 --1.48 --3.29 --4.66 --5.79 --6.86 --8.01 --9.28 --10.64 --12.10 --13.72 --15.53 --17.53 --19.59 --21.48 --23.08 --24.40 --25.65 --27.15 --29.11 --31.44 --33.69 --35.19 --35.25 --33.41 --29.67 --24.38 --18.14 --11.58 --5.17 -0.77 -6.00 -10.36 -13.65 -15.77 -16.81 -17.04 -16.86 -16.63 -16.56 -16.61 -16.62 -16.45 -16.11 -15.76 -15.58 -15.71 -16.08 -16.54 -16.91 -17.07 -17.01 -16.82 -16.56 -16.23 -15.78 -15.06 -14.00 -12.57 -10.89 -9.09 -7.34 -5.71 -4.22 -2.82 -1.44 --0.02 --1.57 --3.21 --4.86 --6.41 --7.78 --8.94 --9.92 --10.84 --11.82 --12.89 --14.02 --15.12 --16.10 --16.95 --17.73 --18.57 --19.58 --20.93 --22.73 --25.04 --27.74 --30.47 --32.59 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--22.72 --24.83 --26.70 --28.52 --30.56 --33.03 --36.03 --39.38 --42.56 --44.83 --45.41 --43.71 --39.50 --32.97 --24.68 --15.37 --5.86 -3.16 -11.20 -17.95 -23.32 -27.38 -30.28 -32.19 -33.25 -33.59 -33.30 -32.49 -31.30 -29.92 -28.57 -27.41 -26.56 -26.03 -25.76 -25.60 -25.40 -25.03 -24.39 -23.41 -22.14 -20.63 -18.98 -17.24 -15.42 -13.48 -11.37 -9.11 -6.76 -4.44 -2.26 -0.29 --1.47 --3.10 --4.66 --6.24 --7.86 --9.54 --11.32 --13.22 --15.25 --17.36 --19.44 --21.39 --23.20 --24.97 --26.94 --29.36 --32.33 --35.66 --38.90 --41.41 --42.50 --41.65 --38.57 --33.28 --26.08 --17.54 --8.39 -0.58 -8.74 -15.65 -21.16 -25.31 -28.19 -29.85 -30.29 -29.53 -27.76 -25.36 -22.85 -20.75 -19.39 -18.80 -18.80 -19.10 -19.43 -19.67 -19.80 -19.82 -19.72 -19.37 -18.66 -17.52 -15.96 -14.06 -11.95 -9.75 -7.55 -5.47 -3.62 -2.08 -0.85 --0.24 --1.46 --3.05 --5.21 --7.87 --10.82 --13.68 --16.14 --17.96 --19.18 --20.04 --20.90 --22.14 --23.99 --26.45 --29.30 --32.06 --34.11 --34.86 --33.78 --30.60 --25.34 --18.36 --10.24 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--9.90 --12.92 --15.92 --18.78 --21.36 --23.56 --25.38 --26.94 --28.42 --30.13 --32.33 --35.12 --38.36 --41.60 --44.16 --45.31 --44.46 --41.33 --35.99 --28.78 --20.24 --10.96 --1.52 -7.54 -15.70 -22.54 -27.75 -31.23 -33.07 -33.53 -32.91 -31.53 -29.64 -27.47 -25.25 -23.20 -21.46 -20.15 -19.25 -18.65 -18.31 -18.16 -18.19 -18.42 -18.71 -18.84 -18.55 -17.57 -15.77 -13.29 -10.37 -7.38 -4.59 -2.08 --0.24 --2.60 --5.15 --7.97 --10.97 --13.99 --16.85 --19.42 --21.63 --23.46 --24.98 --26.36 --27.88 --29.85 --32.51 --35.83 --39.37 --42.44 --44.18 --43.83 --41.00 --35.67 --28.24 --19.32 --9.70 --0.12 -8.73 -16.35 -22.41 -26.87 -29.82 -31.48 -32.10 -31.88 -30.94 -29.41 -27.46 -25.30 -23.24 -21.51 -20.31 -19.70 -19.62 -19.91 -20.36 -20.79 -21.01 -20.92 -20.43 -19.54 -18.25 -16.52 -14.36 -11.80 -8.93 -5.91 -2.96 -0.19 --2.33 --4.68 --7.04 --9.55 --12.29 --15.23 --18.22 --21.06 --23.53 --25.57 --27.22 --28.77 --30.60 --33.02 --36.22 --40.04 --44.03 --47.52 --49.80 --50.17 --48.13 --43.39 --36.02 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--6.38 --7.89 --9.68 --11.78 --14.07 --16.37 --18.55 --20.53 --22.38 --24.22 --26.20 --28.47 --31.10 --33.97 --36.80 --39.08 --40.15 --39.37 --36.32 --30.87 --23.30 --14.27 --4.60 -4.83 -13.31 -20.36 -25.75 -29.46 -31.55 -32.12 -31.34 -29.43 -26.72 -23.64 -20.66 -18.13 -16.31 -15.25 -14.86 -15.04 -15.62 -16.44 -17.27 -17.90 -18.12 -17.79 -16.85 -15.32 -13.28 -10.80 -8.03 -5.13 -2.26 --0.49 --3.11 --5.69 --8.35 --11.15 --14.10 --17.05 --19.87 --22.46 --24.77 --26.85 --28.83 --30.83 --33.02 --35.54 --38.35 --41.30 --43.98 --45.77 --45.98 --44.07 --39.75 --33.10 --24.57 --14.84 --4.70 -5.05 -13.82 -21.17 -26.95 -31.15 -33.89 -35.30 -35.52 -34.70 -33.07 -30.93 -28.55 -26.25 -24.21 -22.54 -21.32 -20.57 -20.28 -20.35 -20.57 -20.67 -20.41 -19.64 -18.37 -16.73 -14.87 -12.88 -10.81 -8.63 -6.32 -3.87 -1.32 --1.29 --3.90 --6.54 --9.22 --12.00 --14.86 --17.72 --20.41 --22.80 --24.79 --26.44 --27.96 --29.58 --31.51 --33.86 --36.59 --39.54 --42.45 --44.87 --46.32 --46.24 --44.15 --39.79 --33.22 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--29.67 --31.92 --34.16 --36.73 --39.82 --43.41 --47.16 --50.42 --52.34 --52.16 --49.38 --43.85 --35.83 --25.91 --14.91 --3.71 -6.85 -16.15 -23.80 -29.67 -33.86 -36.58 -38.10 -38.66 -38.47 -37.70 -36.49 -35.00 -33.40 -31.83 -30.43 -29.25 -28.31 -27.56 -26.96 -26.43 -25.89 -25.22 -24.27 -22.88 -20.97 -18.61 -15.96 -13.30 -10.79 -8.49 -6.30 -4.09 -1.76 --0.69 --3.11 --5.34 --7.24 --8.88 --10.38 --11.99 --13.92 --16.26 --18.98 --21.90 --24.81 --27.54 --29.99 --32.25 --34.53 --37.12 --40.20 --43.78 --47.53 --50.78 --52.65 --52.28 --49.08 --42.93 --34.24 --23.82 --12.67 --1.76 -8.14 -16.55 -23.29 -28.42 -32.10 -34.50 -35.80 -36.11 -35.63 -34.56 -33.16 -31.64 -30.14 -28.69 -27.33 -26.09 -25.10 -24.52 -24.38 -24.55 -24.74 -24.60 -23.89 -22.54 -20.67 -18.47 -16.05 -13.48 -10.76 -7.92 -5.09 -2.40 --0.03 --2.16 --4.09 --5.98 --7.98 --10.12 --12.37 --14.59 --16.64 --18.46 --20.04 --21.40 --22.59 --23.69 --24.80 --26.11 --27.84 --30.16 --33.11 --36.42 --39.56 --41.75 --42.20 --40.33 --35.95 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--2.14 --3.04 --4.47 --6.55 --9.23 --12.23 --15.21 --17.86 --20.00 --21.65 --22.98 --24.26 --25.76 --27.78 --30.47 --33.76 --37.27 --40.29 --41.94 --41.45 --38.35 --32.72 --25.08 --16.28 --7.22 -1.30 -8.72 -14.75 -19.31 -22.51 -24.58 -25.78 -26.33 -26.35 -25.82 -24.74 -23.15 -21.24 -19.31 -17.68 -16.55 -16.04 -16.09 -16.58 -17.40 -18.45 -19.59 -20.66 -21.42 -21.63 -21.15 -19.98 -18.25 -16.22 -14.07 -11.92 -9.77 -7.55 -5.21 -2.79 -0.41 --1.80 --3.72 --5.30 --6.62 --7.80 --8.96 --10.20 --11.54 --12.92 --14.26 --15.48 --16.66 --17.93 --19.50 --21.56 --24.11 --26.88 --29.37 --30.92 --30.90 --28.97 --25.12 --19.71 --13.31 --6.56 -0.04 -6.13 -11.47 -15.91 -19.32 -21.60 -22.75 -22.88 -22.18 -20.96 -19.42 -17.71 -15.84 -13.80 -11.66 -9.62 -8.02 -7.20 -7.35 -8.41 -10.08 -11.92 -13.50 -14.57 -15.01 -14.79 -13.98 -12.65 -10.91 -8.93 -6.86 -4.79 -2.70 -0.44 --2.08 --4.87 --7.72 --10.36 --12.49 --14.01 --15.02 --15.75 --16.52 --17.61 --19.20 --21.38 --24.07 --27.04 --29.83 --31.83 --32.41 --31.07 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--10.62 --11.96 --13.45 --15.12 --17.00 --19.09 --21.32 --23.58 --25.76 --27.78 --29.66 --31.49 --33.43 --35.63 --38.16 --40.93 --43.64 --45.80 --46.77 --45.93 --42.86 --37.49 --30.13 --21.38 --12.01 --2.74 -5.78 -13.12 -19.01 -23.38 -26.34 -28.12 -29.00 -29.18 -28.83 -28.08 -27.03 -25.79 -24.50 -23.26 -22.13 -21.20 -20.52 -20.18 -20.24 -20.69 -21.41 -22.15 -22.64 -22.64 -22.01 -20.76 -19.01 -16.97 -14.79 -12.69 -10.73 -8.94 -7.30 -5.73 -4.17 -2.58 -0.95 --0.69 --2.33 --3.98 --5.70 --7.54 --9.52 --11.62 --13.81 --16.02 --18.18 --20.25 --22.19 --23.98 --25.62 --27.20 --28.90 --30.90 --33.35 --36.16 --38.97 --41.15 --41.95 --40.75 --37.23 --31.49 --23.96 --15.35 --6.39 -2.25 -10.05 -16.63 -21.81 -25.50 -27.78 -28.85 -28.96 -28.37 -27.29 -25.89 -24.30 -22.62 -20.98 -19.51 -18.33 -17.53 -17.19 -17.29 -17.77 -18.48 -19.20 -19.73 -19.91 -19.62 -18.83 -17.60 -16.02 -14.24 -12.42 -10.69 -9.08 -7.51 -5.85 -3.99 -1.95 --0.11 --1.98 --3.45 --4.51 --5.28 --5.97 --6.81 --7.99 --9.56 --11.53 --13.81 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-1.65 --1.14 --3.73 --6.19 --8.58 --10.97 --13.40 --15.82 --18.12 --20.15 --21.81 --23.12 --24.22 --25.40 --26.94 --29.04 --31.65 --34.47 --36.89 --38.18 --37.71 --35.11 --30.43 --24.00 --16.44 --8.36 --0.36 -7.11 -13.65 -18.92 -22.66 -24.73 -25.16 -24.16 -22.10 -19.44 -16.69 -14.35 -12.78 -12.19 -12.54 -13.59 -14.96 -16.29 -17.32 -17.93 -18.11 -17.85 -17.10 -15.81 -14.08 -12.19 -10.63 -9.96 -10.57 -12.43 -15.08 -17.71 -19.39 -19.46 -17.67 -14.21 -9.56 -4.24 --1.26 --6.60 --11.51 --15.83 --19.49 --22.60 --25.43 --28.33 --31.56 --35.16 --38.81 --41.85 --43.51 --43.11 --40.25 --34.91 --27.46 --18.61 --9.22 --0.16 -7.86 -14.41 -19.39 -22.91 -25.19 -26.44 -26.80 -26.37 -25.31 -23.83 -22.17 -20.59 -19.26 -18.23 -17.49 -17.01 -16.74 -16.68 -16.73 -16.77 -16.59 -15.98 -14.82 -13.10 -10.93 -8.49 -5.99 -3.57 -1.27 --0.96 --3.19 --5.50 --7.87 --10.25 --12.46 --14.36 --15.85 --16.94 --17.76 --18.58 --19.69 --21.33 --23.58 --26.31 --29.13 --31.53 --32.93 --32.81 --30.83 --26.82 --20.89 --13.44 --5.11 -3.31 -11.03 -17.46 -22.31 -25.54 -27.33 -27.95 -27.65 -26.69 -25.34 -23.85 -22.38 -21.04 -19.84 -18.76 -17.81 -17.04 -16.54 -16.33 -16.34 -16.39 -16.22 -15.59 -14.35 -12.45 -10.02 -7.32 -4.61 -2.14 -0.02 --1.80 --3.50 --5.25 --7.20 --9.35 --11.62 --13.86 --15.89 --17.62 --19.08 --20.41 --21.90 --23.83 --26.46 --29.83 --33.71 --37.55 --40.56 --41.92 --41.02 --37.61 --31.84 --24.25 --15.55 --6.53 -2.12 -9.85 -16.30 -21.29 -24.81 -26.98 -28.02 -28.19 -27.70 -26.77 -25.62 -24.41 -23.27 -22.33 -21.63 -21.21 -21.07 -21.19 -21.49 -21.83 -22.01 -21.79 -21.00 -19.56 -17.54 -15.13 -12.61 -10.23 -8.18 -6.50 -5.10 -3.83 -2.53 -1.09 --0.52 --2.29 --4.18 --6.19 --8.30 --10.51 --12.79 --15.06 --17.26 --19.31 --21.16 --22.79 --24.19 --25.33 --26.27 --27.12 --28.10 --29.50 --31.56 --34.31 --37.46 --40.41 --42.38 --42.64 --40.75 --36.62 --30.57 --23.12 --14.89 --6.48 -1.55 -8.81 -14.97 -19.90 -23.60 -26.13 -27.62 -28.15 -27.85 -26.90 -25.58 -24.18 -23.00 -22.20 -21.75 -21.57 -21.56 -21.68 -21.91 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-12.12 -10.10 -8.02 -6.04 -4.24 -2.59 -0.99 --0.66 --2.40 --4.18 --5.91 --7.56 --9.14 --10.76 --12.49 --14.31 --16.11 --17.73 --19.07 --20.19 --21.30 --22.71 --24.68 --27.32 --30.49 --33.79 --36.65 --38.37 --38.33 --36.13 --31.71 --25.37 --17.74 --9.61 --1.73 -5.37 -11.39 -16.28 -20.08 -22.85 -24.65 -25.53 -25.62 -25.13 -24.33 -23.40 -22.45 -21.46 -20.37 -19.21 -18.06 -17.10 -16.48 -16.25 -16.37 -16.71 -17.10 -17.37 -17.37 -17.00 -16.16 -14.86 -13.17 -11.26 -9.29 -7.39 -5.56 -3.75 -1.95 -0.17 --1.53 --3.05 --4.43 --5.72 --7.01 --8.36 --9.77 --11.20 --12.63 --14.15 --15.85 --17.82 --20.02 --22.26 --24.34 --26.07 --27.45 --28.68 --30.10 --32.02 --34.57 --37.63 --40.72 --43.18 --44.22 --43.25 --39.98 --34.52 --27.28 --18.92 --10.13 --1.49 -6.48 -13.44 -19.10 -23.34 -26.17 -27.79 -28.50 -28.62 -28.36 -27.81 -26.97 -25.85 -24.53 -23.18 -22.02 -21.24 -20.93 -21.09 -21.55 -22.13 -22.58 -22.74 -22.55 -22.04 -21.33 -20.52 -19.62 -18.51 -17.10 -15.30 -13.17 -10.87 -8.58 -6.39 -4.35 -2.46 -0.75 --0.73 --1.93 --2.89 --3.76 --4.73 --5.95 --7.39 --8.88 --10.20 --11.16 --11.78 --12.23 --12.83 --13.86 --15.47 --17.55 --19.84 --22.03 --23.88 --25.36 --26.65 --28.10 --30.09 --32.80 --36.09 --39.46 --42.15 --43.38 --42.58 --39.50 --34.30 --27.41 --19.44 --11.02 --2.78 -4.75 -11.14 -16.13 -19.70 -22.01 -23.38 -24.10 -24.38 -24.30 -23.89 -23.16 -22.16 -21.01 -19.85 -18.87 -18.25 -18.13 -18.59 -19.53 -20.73 -21.83 -22.53 -22.58 -21.96 -20.78 -19.26 -17.59 -15.87 -14.17 -12.43 -10.62 -8.70 -6.65 -4.50 -2.30 -0.18 --1.71 --3.26 --4.40 --5.22 --5.90 --6.69 --7.78 --9.26 --11.12 --13.20 --15.31 --17.31 --19.10 --20.70 --22.21 --23.77 --25.51 --27.41 --29.26 --30.64 --31.03 --29.93 --27.11 --22.65 --16.95 --10.59 --4.14 -2.00 -7.61 -12.62 -17.00 -20.67 -23.45 -25.13 -25.59 -24.87 -23.18 -20.89 -18.34 -15.85 -13.65 -11.90 -10.74 -10.25 -10.45 -11.25 -12.41 -13.63 -14.60 -15.14 -15.13 -14.64 -13.75 -12.55 -11.14 -9.53 -7.77 -5.88 -3.92 -1.95 -0.05 --1.74 --3.43 --5.10 --6.90 --8.96 --11.35 --14.07 --16.97 --19.82 --22.41 --24.60 --26.44 --28.18 --30.16 --32.66 --35.64 --38.73 --41.22 --42.33 --41.49 --38.44 --33.39 --26.74 --19.10 --10.98 --2.92 -4.64 -11.25 -16.56 -20.46 -23.00 -24.45 -25.12 -25.26 -24.97 -24.26 -23.14 -21.75 -20.33 -19.16 -18.43 -18.18 -18.31 -18.65 -19.08 -19.54 -20.02 -20.48 -20.80 -20.77 -20.23 -19.10 -17.46 -15.44 -13.19 -10.71 -7.97 -4.88 -1.53 --1.83 --4.85 --7.15 --8.57 --9.14 --9.14 --8.95 --8.92 --9.29 --10.11 --11.34 --12.85 --14.53 --16.30 --18.11 --19.93 --21.83 --23.87 --26.18 --28.88 --31.98 --35.33 --38.51 --40.91 --41.86 --40.80 --37.49 --32.06 --24.96 --16.86 --8.47 --0.38 -6.94 -13.17 -18.15 -21.84 -24.30 -25.68 -26.21 -26.11 -25.55 -24.68 -23.61 -22.44 -21.31 -20.35 -19.60 -19.11 -18.85 -18.84 -19.08 -19.52 -20.10 -20.60 -20.81 -20.53 -19.69 -18.36 -16.73 -15.01 -13.34 -11.80 -10.39 -9.06 -7.75 -6.43 -5.02 -3.52 -1.95 -0.38 --1.08 --2.34 --3.40 --4.31 --5.20 --6.25 --7.56 --9.16 --10.98 --12.82 --14.52 --15.93 --17.04 --17.98 --19.00 --20.37 --22.29 --24.78 --27.64 --30.55 --33.02 --34.54 --34.62 --32.93 --29.34 --23.98 --17.25 --9.71 --2.01 -5.27 -11.68 -16.96 -20.96 -23.66 -25.08 -25.34 -24.60 -23.12 -21.21 -19.15 -17.15 -15.36 -13.90 -12.87 -12.38 -12.52 -13.27 -14.48 -15.89 -17.15 -18.00 -18.27 -17.90 -16.96 -15.55 -13.77 -11.73 -9.54 -7.25 -4.96 -2.73 -0.61 --1.30 --2.94 --4.30 --5.48 --6.66 --8.02 --9.71 --11.78 --14.11 --16.53 --18.85 --20.89 --22.63 --24.13 --25.60 --27.30 --29.50 --32.30 --35.57 --38.88 --41.54 --42.80 --42.03 --38.98 --33.74 --26.74 --18.62 --10.07 --1.71 -5.93 -12.47 -17.69 -21.57 -24.23 -25.95 -27.01 -27.57 -27.72 -27.45 -26.75 -25.71 -24.47 -23.17 -21.90 -20.68 -19.55 -18.59 -17.96 -17.82 -18.22 -19.04 -19.95 -20.60 -20.70 -20.17 -19.06 -17.53 -15.74 -13.76 -11.68 -9.52 -7.37 -5.30 -3.39 -1.67 -0.08 --1.45 --3.02 --4.63 --6.24 --7.75 --9.08 --10.25 --11.39 --12.65 --14.16 --15.95 --17.94 --19.98 --21.91 --23.69 --25.35 --27.05 --28.97 --31.26 --34.01 --37.13 --40.35 --43.17 --44.93 --44.96 --42.72 --38.03 --31.08 --22.45 --12.93 --3.39 -5.39 -12.88 -18.82 -23.19 -26.17 -27.99 -28.91 -29.10 -28.71 -27.82 -26.55 -25.05 -23.49 -22.07 -20.94 -20.20 -19.90 -20.00 -20.42 -21.00 -21.52 -21.75 -21.57 -20.95 -19.98 -18.79 -17.50 -16.13 -14.68 -13.12 -11.47 -9.84 -8.27 -6.81 -5.46 -4.23 -3.09 -2.04 -1.04 -0.02 --1.09 --2.32 --3.63 --4.92 --6.11 --7.23 --8.41 --9.84 --11.66 --13.88 --16.37 --18.89 --21.23 --23.27 --25.00 --26.52 --27.99 --29.64 --31.71 --34.34 --37.47 --40.75 --43.55 --45.10 --44.74 --42.08 --37.14 --30.24 --21.97 --12.97 --3.96 -4.46 -11.79 -17.71 -22.10 -25.07 -26.85 -27.70 -27.83 -27.41 -26.61 -25.58 -24.50 -23.45 -22.50 -21.62 -20.81 -20.13 -19.66 -19.48 -19.60 -19.90 -20.21 -20.34 -20.18 -19.69 -18.86 -17.74 -16.37 -14.84 -13.27 -11.77 -10.39 -9.07 -7.66 -6.00 -4.02 -1.79 --0.53 --2.77 --4.85 --6.82 --8.77 --10.79 --12.82 --14.75 --16.42 --17.74 --18.71 --19.43 --20.03 --20.65 --21.44 --22.53 --24.01 --25.95 --28.33 --30.95 --33.44 --35.26 --35.80 --34.54 --31.23 --25.93 --19.08 --11.31 --3.33 -4.25 -11.06 -16.90 -21.69 -25.37 -27.87 -29.11 -29.06 -27.85 -25.75 -23.12 -20.34 -17.71 -15.46 -13.72 -12.60 -12.18 -12.44 -13.21 -14.21 -15.13 -15.68 -15.79 -15.50 -14.94 -14.22 -13.40 -12.41 -11.22 -9.75 -8.01 -6.06 -4.01 -2.00 -0.16 --1.45 --2.89 --4.29 --5.81 --7.60 --9.65 --11.87 --14.02 --15.90 --17.39 --18.51 --19.46 --20.51 --21.98 --24.12 --26.97 --30.36 --33.82 --36.69 --38.23 --37.81 --35.10 --30.12 --23.32 --15.37 --7.08 -0.81 -7.74 -13.41 -17.76 -20.88 -22.93 -24.10 -24.47 -24.11 -23.09 -21.51 -19.62 -17.70 -16.06 -14.94 -14.45 -14.52 -15.01 -15.73 -16.49 -17.14 -17.58 -17.76 -17.65 -17.26 -16.61 -15.69 -14.49 -13.06 -11.45 -9.73 -7.95 -6.10 -4.13 -1.98 --0.33 --2.69 --4.92 --6.83 --8.40 --9.77 --11.21 --12.97 --15.17 --17.71 --20.32 --22.68 --24.56 --25.90 --26.87 --27.75 --28.92 --30.67 --33.13 --36.15 --39.32 --42.01 --43.47 --43.08 --40.46 --35.59 --28.78 --20.60 --11.79 --3.04 -5.02 -12.01 -17.73 -22.15 -25.39 -27.55 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--0.03 -0.65 -1.01 -1.05 -0.85 -0.49 -0.11 --0.18 --0.33 --0.33 --0.23 --0.06 -0.13 -0.34 -0.57 -0.77 -0.93 -1.02 -1.03 -1.00 -0.93 -0.84 -0.75 -0.68 -0.68 -0.77 -0.94 -1.13 -1.25 -1.22 -1.02 -0.70 -0.36 -0.08 --0.12 --0.28 --0.51 --0.88 --1.40 --2.04 --2.71 --3.30 --3.73 --3.97 --4.04 --3.99 --3.93 --3.99 --4.27 --4.83 --5.65 --6.58 --7.35 --7.67 --7.34 --6.33 --4.79 --3.01 --1.27 -0.27 -1.54 -2.56 -3.39 -4.03 -4.44 -4.59 -4.52 -4.29 -4.05 -3.93 -4.02 -4.35 -4.81 -5.28 -5.64 -5.84 -5.93 -5.99 -6.11 -6.25 -6.27 -6.03 -5.47 -4.66 -3.77 -3.00 -2.45 -2.13 -1.97 -1.87 -1.78 -1.66 -1.52 -1.34 -1.13 -0.86 -0.53 -0.12 --0.39 --1.03 --1.78 --2.56 --3.23 --3.68 --3.90 --3.99 --4.14 --4.56 --5.33 --6.40 --7.55 --8.52 --9.04 --8.95 --8.23 --6.96 --5.31 --3.50 --1.70 --0.03 -1.41 -2.59 -3.49 -4.11 -4.44 -4.51 -4.32 -3.96 -3.49 -2.99 -2.52 -2.11 -1.76 -1.47 -1.26 -1.18 -1.27 -1.52 -1.89 -2.29 -2.63 -2.85 -2.92 -2.86 -2.72 -2.51 -2.24 -1.93 -1.60 -1.28 -0.97 -0.63 -0.20 --0.40 --1.14 --1.96 --2.70 --3.21 --3.40 --3.31 --3.11 --2.95 --2.97 --3.13 --3.38 --3.60 --3.76 --3.89 --4.08 --4.43 --4.96 --5.66 --6.46 --7.26 --7.97 --8.43 --8.49 --8.07 --7.15 --5.85 --4.37 --2.92 --1.63 --0.53 -0.38 -1.13 -1.71 -2.10 -2.29 -2.33 -2.31 -2.32 -2.40 -2.50 -2.53 -2.45 -2.29 -2.16 -2.17 -2.42 -2.90 -3.50 -4.02 -4.31 -4.28 -3.95 -3.41 -2.80 -2.25 -1.82 -1.51 -1.30 -1.13 -0.96 -0.78 -0.59 -0.37 -0.11 --0.17 --0.41 --0.53 --0.48 --0.30 --0.11 --0.06 --0.27 --0.79 --1.57 --2.51 --3.52 --4.57 --5.62 --6.64 --7.58 --8.38 --9.03 --9.62 --10.22 --10.91 --11.65 --12.23 --12.39 --11.91 --10.72 --8.97 --7.00 --5.16 --3.73 --2.75 --2.10 --1.57 --0.95 --0.16 -0.75 -1.66 -2.40 -2.85 -2.95 -2.78 -2.49 -2.27 -2.24 -2.43 -2.78 -3.20 -3.61 -4.02 -4.46 -4.96 -5.48 -5.89 -6.04 -5.82 -5.27 -4.52 -3.74 -3.07 -2.56 -2.16 -1.81 -1.43 -1.00 -0.57 -0.19 --0.11 --0.36 --0.60 --0.88 --1.22 --1.59 --1.96 --2.33 --2.71 --3.12 --3.60 --4.09 --4.55 --4.96 --5.37 --5.87 --6.57 --7.49 --8.57 --9.66 --10.59 --11.25 --11.57 --11.55 --11.14 --10.32 --9.06 --7.41 --5.51 --3.58 --1.80 --0.33 -0.82 -1.69 -2.33 -2.77 -3.02 -3.07 -2.99 -2.89 -2.86 -2.98 -3.22 -3.48 -3.68 -3.80 -3.92 -4.14 -4.52 -5.03 -5.57 -6.01 -6.27 -6.37 -6.39 -6.40 -6.43 -6.42 -6.29 -5.96 -5.41 -4.67 -3.81 -2.90 -2.00 -1.17 -0.46 --0.08 --0.48 --0.75 --1.00 --1.28 --1.64 --2.07 --2.54 --3.00 --3.40 --3.72 --4.00 --4.33 --4.84 --5.64 --6.78 --8.19 --9.64 --10.87 --11.59 --11.64 --10.98 --9.73 --8.06 --6.17 --4.20 --2.28 --0.52 -1.02 -2.27 -3.24 -3.91 -4.31 -4.47 -4.43 -4.23 -3.92 -3.58 -3.32 -3.22 -3.38 -3.82 -4.51 -5.35 -6.20 -6.90 -7.35 -7.53 -7.48 -7.28 -7.02 -6.75 -6.46 -6.15 -5.81 -5.51 -5.26 -5.07 -4.87 -4.55 -4.02 -3.29 -2.47 -1.76 -1.28 -1.05 -0.95 -0.76 -0.33 --0.39 --1.36 --2.42 --3.44 --4.30 --4.93 --5.30 --5.43 --5.46 --5.54 --5.85 --6.44 --7.27 --8.13 --8.82 --9.17 --9.07 --8.54 --7.62 --6.38 --4.94 --3.46 --2.12 --1.07 --0.32 -0.19 -0.61 -1.04 -1.56 -2.12 -2.64 -3.07 -3.39 -3.66 -3.96 -4.34 -4.84 -5.45 -6.12 -6.81 -7.47 -8.09 -8.71 -9.30 -9.83 -10.18 -10.32 -10.27 -10.14 -10.10 -10.25 -10.59 -10.99 -11.26 -11.25 -11.13 -11.14 -11.55 -12.43 -13.70 -15.13 -16.47 -17.53 -18.22 -18.76 -19.37 -20.17 -21.17 -22.36 -23.72 -25.33 -27.18 -29.16 -31.46 -34.04 -36.73 -39.24 -41.21 -42.37 -42.64 -42.11 -41.00 -40.19 -40.42 -42.01 -44.68 -47.70 -50.29 -52.14 -53.67 -52.17 -46.44 -36.45 -23.36 -9.10 --4.62 --16.76 --26.98 --35.34 --42.05 --46.79 --49.42 --50.11 --49.38 --47.96 --46.52 --45.48 --45.01 --45.09 --45.23 --45.08 --44.45 --43.34 --41.88 --40.11 --38.06 --35.79 --33.43 --30.95 --28.34 --25.64 --22.88 --20.19 --17.64 --15.30 --13.19 --11.32 --9.57 --7.87 --6.16 --4.40 --2.60 --0.60 -1.71 -4.37 -7.31 -10.31 -13.14 -15.54 -17.67 -19.14 -19.77 -19.46 -18.14 -15.77 -12.43 -8.29 -3.62 --1.25 --6.04 --10.38 --14.02 --16.83 --18.79 --19.94 --20.38 --20.26 --19.70 --18.92 --18.02 --17.09 --16.19 --15.28 --14.30 --13.16 --11.87 --10.49 --9.24 --8.24 --7.58 --7.22 --7.05 --6.93 --6.74 --6.47 --6.12 --5.78 --5.45 --5.16 --4.89 --4.62 --4.35 --4.06 --3.73 --3.38 --3.07 --2.87 --2.84 --3.04 --3.42 --3.94 --4.47 --4.94 --5.31 --5.60 --5.86 --6.13 --6.48 --6.88 --7.34 --7.79 --8.13 --8.19 --7.82 --6.89 --5.34 --3.26 --0.86 -1.57 -3.73 -5.42 -6.55 -7.17 -7.40 -7.36 -7.15 -6.86 -6.55 -6.33 -6.24 -6.32 -6.52 -6.72 -6.81 -6.74 -6.51 -6.21 -5.92 -5.69 -5.54 -5.44 -5.38 -5.30 -5.18 -4.94 -4.55 -3.98 -3.29 -2.56 -1.85 -1.20 -0.61 -0.03 --0.56 --1.13 --1.64 --2.02 --2.25 --2.35 --2.39 --2.48 --2.71 --3.14 --3.76 --4.50 --5.26 --5.96 --6.56 --7.06 --7.50 --7.93 --8.45 --9.12 --10.01 --11.07 --12.11 --12.82 --12.87 --12.03 --10.28 --7.84 --5.08 --2.40 --0.07 -1.79 -3.21 -4.31 -5.23 -6.04 -6.75 -7.26 -7.44 -7.19 -6.52 -5.60 -4.71 -4.13 -4.05 -4.41 -5.01 -5.60 -5.98 -6.10 -6.03 -5.84 -5.61 -5.32 -4.94 -4.46 -3.96 -3.52 -3.21 -3.02 -2.84 -2.55 -2.08 -1.44 -0.76 -0.12 --0.44 --0.94 --1.48 --2.11 --2.82 --3.54 --4.14 --4.54 --4.72 --4.72 --4.67 --4.68 --4.81 --5.07 --5.40 --5.73 --6.03 --6.34 --6.71 --7.19 --7.72 --8.14 --8.26 --7.94 --7.10 --5.81 --4.22 --2.44 --0.59 -1.25 -3.04 -4.68 -6.08 -7.11 -7.72 -7.88 -7.66 -7.18 -6.64 -6.22 -6.06 -6.21 -6.58 -7.04 -7.42 -7.64 -7.70 -7.67 -7.66 -7.65 -7.59 -7.37 -6.89 -6.16 -5.30 -4.49 -3.83 -3.36 -2.98 -2.56 -2.03 -1.41 -0.79 -0.21 --0.29 --0.78 --1.31 --1.91 --2.52 --3.06 --3.49 --3.83 --4.11 --4.42 --4.75 --5.10 --5.44 --5.80 --6.26 --6.87 --7.60 --8.35 --8.97 --9.34 --9.43 --9.25 --8.85 --8.20 --7.23 --5.87 --4.13 --2.15 --0.15 -1.63 -3.04 -4.07 -4.82 -5.42 -5.93 -6.32 -6.48 -6.35 -5.94 -5.37 -4.82 -4.48 -4.45 -4.72 -5.15 -5.57 -5.85 -5.92 -5.84 -5.69 -5.58 -5.53 -5.51 -5.41 -5.16 -4.76 -4.25 -3.75 -3.36 -3.12 -2.99 -2.86 -2.63 -2.22 -1.62 -0.90 -0.16 --0.50 --1.02 --1.42 --1.72 --1.98 --2.19 --2.35 --2.47 --2.57 --2.73 --3.01 --3.45 --4.00 --4.55 --5.03 --5.38 --5.62 --5.81 --6.05 --6.40 --6.88 --7.49 --8.18 --8.88 --9.44 --9.72 --9.63 --9.15 --8.32 --7.24 --6.00 --4.62 --3.14 --1.59 --0.03 -1.47 -2.79 -3.87 -4.66 -5.14 -5.35 -5.35 -5.21 -5.03 -4.88 -4.83 -4.92 -5.10 -5.32 -5.45 -5.40 -5.09 -4.52 -3.78 -3.05 -2.49 -2.19 -2.17 -2.34 -2.56 -2.72 -2.73 -2.56 -2.20 -1.72 -1.17 -0.65 -0.25 -0.03 --0.06 --0.10 --0.20 --0.44 --0.85 --1.35 --1.86 --2.31 --2.65 --2.90 --3.10 --3.33 --3.61 --3.98 --4.46 --5.03 --5.67 --6.32 --6.94 --7.50 --7.98 --8.42 --8.90 --9.40 --9.87 --10.19 --10.23 --9.84 --8.95 --7.56 --5.75 --3.63 --1.42 -0.67 -2.42 -3.69 -4.45 -4.75 -4.75 -4.59 -4.36 -4.12 -3.89 -3.70 -3.61 -3.68 -3.92 -4.25 -4.55 -4.70 -4.68 -4.54 -4.41 -4.43 -4.62 -4.93 -5.24 -5.41 -5.36 -5.07 -4.61 -4.06 -3.48 -2.89 -2.29 -1.72 -1.20 -0.82 -0.61 -0.55 -0.53 -0.40 -0.05 --0.55 --1.33 --2.12 --2.79 --3.29 --3.71 --4.14 --4.69 --5.35 --6.06 --6.69 --7.17 --7.42 --7.41 --7.11 --6.49 --5.54 --4.31 --2.90 --1.39 -0.11 -1.56 -2.94 -4.18 -5.20 -5.89 -6.14 -5.97 -5.49 -4.90 -4.35 -3.96 -3.70 -3.54 -3.43 -3.39 -3.47 -3.70 -4.02 -4.29 -4.39 -4.24 -3.89 -3.48 -3.13 -2.90 -2.73 -2.50 -2.12 -1.57 -0.87 -0.10 --0.64 --1.29 --1.80 --2.16 --2.38 --2.56 --2.81 --3.19 --3.71 --4.26 --4.71 --4.98 --5.11 --5.22 --5.50 --6.06 --6.91 --8.00 --9.14 --10.16 --10.84 --10.99 --10.46 --9.20 --7.29 --4.96 --2.50 --0.22 -1.65 -2.97 -3.75 -4.10 -4.19 -4.21 -4.33 -4.60 -5.01 -5.44 -5.76 -5.84 -5.67 -5.29 -4.83 -4.44 -4.27 -4.39 -4.78 -5.34 -5.92 -6.37 -6.60 -6.59 -6.39 -6.07 -5.65 -5.14 -4.54 -3.86 -3.16 -2.51 -1.93 -1.42 -0.93 -0.39 --0.15 --0.67 --1.10 --1.45 --1.77 --2.14 --2.62 --3.19 --3.76 --4.23 --4.56 --4.81 --5.10 --5.58 --6.31 --7.22 --8.17 --8.97 --9.53 --9.84 --9.98 --10.08 --10.17 --10.22 --10.09 --9.61 --8.68 --7.29 --5.56 --3.67 --1.83 --0.17 -1.24 -2.42 -3.44 -4.31 -5.06 -5.61 -5.88 -5.86 -5.59 -5.20 -4.84 -4.58 -4.46 -4.39 -4.30 -4.14 -3.91 -3.67 -3.46 -3.32 -3.25 -3.21 -3.17 -3.09 -2.94 -2.70 -2.36 -1.92 -1.42 -0.92 -0.45 -0.05 --0.31 --0.68 --1.10 --1.55 --2.01 --2.43 --2.77 --3.04 --3.28 --3.48 --3.66 --3.80 --3.93 --4.12 --4.44 --4.94 --5.57 --6.21 --6.70 --6.95 --6.94 --6.70 --6.27 --5.61 --4.62 --3.22 --1.42 -0.62 -2.65 -4.41 -5.73 -6.60 -7.13 -7.44 -7.61 -7.67 -7.55 -7.23 -6.77 -6.32 -6.03 -6.00 -6.21 -6.51 -6.73 -6.77 -6.62 -6.37 -6.10 -5.84 -5.53 -5.06 -4.41 -3.61 -2.82 -2.16 -1.70 -1.39 -1.09 -0.67 -0.06 --0.67 --1.39 --1.95 --2.31 --2.54 --2.77 --3.10 --3.54 --4.05 --4.57 --5.10 --5.72 --6.49 --7.42 --8.36 --9.08 --9.33 --8.95 --7.98 --6.58 --4.97 --3.35 --1.82 --0.40 -0.96 -2.32 -3.68 -4.99 -6.11 -6.87 -7.15 -6.91 -6.26 -5.41 -4.59 -4.02 -3.75 -3.76 -3.91 -4.06 -4.09 -3.95 -3.72 -3.48 -3.32 -3.26 -3.25 -3.19 -3.00 -2.66 -2.20 -1.65 -1.05 -0.40 --0.27 --0.92 --1.47 --1.86 --2.06 --2.13 --2.20 --2.38 --2.74 --3.25 --3.81 --4.34 --4.77 --5.08 --5.35 --5.65 --6.04 --6.56 --7.16 --7.70 --8.03 --8.00 --7.51 --6.59 --5.36 --3.96 --2.57 --1.25 --0.03 -1.11 -2.19 -3.14 -3.87 -4.27 -4.32 -4.04 -3.60 -3.17 -2.84 -2.64 -2.51 -2.37 -2.21 -2.10 -2.09 -2.26 -2.57 -2.92 -3.19 -3.30 -3.25 -3.11 -2.93 -2.72 -2.43 -2.02 -1.45 -0.78 -0.09 --0.50 --0.95 --1.26 --1.48 --1.69 --1.94 --2.30 --2.79 --3.40 --4.08 --4.73 --5.26 --5.59 --5.74 --5.80 --5.90 --6.10 --6.45 --6.87 --7.26 --7.48 --7.43 --7.01 --6.17 --4.90 --3.29 --1.49 -0.32 -1.97 -3.36 -4.45 -5.26 -5.79 -6.10 -6.19 -6.10 -5.89 -5.61 -5.30 -5.01 -4.81 -4.80 -5.07 -5.66 -6.47 -7.28 -7.80 -7.81 -7.24 -6.19 -4.91 -3.68 -2.68 -2.01 -1.61 -1.35 -1.07 -0.65 -0.02 --0.77 --1.63 --2.41 --3.00 --3.35 --3.52 --3.61 --3.73 --3.95 --4.33 --4.84 --5.41 --5.88 --6.10 --5.90 --5.20 --4.02 --2.52 --0.90 -0.66 -2.04 -3.19 -4.16 -4.96 -5.59 -5.98 -6.06 -5.79 -5.25 -4.56 -3.96 -3.60 -3.56 -3.77 -4.09 -4.37 -4.51 -4.51 -4.42 -4.30 -4.14 -3.91 -3.60 -3.22 -2.82 -2.43 -2.05 -1.63 -1.14 -0.60 -0.12 --0.18 --0.24 --0.14 --0.05 --0.18 --0.60 --1.22 --1.82 --2.21 --2.28 --2.17 --2.11 --2.34 --2.95 --3.85 --4.80 --5.59 --6.10 --6.36 --6.53 --6.70 --6.97 --7.35 --7.83 --8.43 --9.14 --9.93 --10.63 --11.00 --10.79 --9.85 --8.24 --6.17 --3.93 --1.81 -0.08 -1.72 -3.12 -4.30 -5.19 -5.71 -5.81 -5.52 -4.91 -4.13 -3.32 -2.57 -1.99 -1.61 -1.48 -1.59 -1.92 -2.36 -2.75 -2.93 -2.81 -2.43 -1.92 -1.49 -1.29 -1.33 -1.48 -1.59 -1.52 -1.27 -0.91 -0.53 -0.18 --0.18 --0.59 --1.07 --1.57 --2.01 --2.31 --2.49 --2.60 --2.72 --2.96 --3.32 --3.79 --4.30 --4.77 --5.14 --5.36 --5.43 --5.40 --5.36 --5.46 --5.79 --6.37 --7.11 --7.80 --8.21 --8.11 --7.41 --6.15 --4.48 --2.62 --0.81 -0.80 -2.12 -3.17 -4.04 -4.83 -5.56 -6.14 -6.43 -6.29 -5.73 -4.87 -3.97 -3.30 -3.03 -3.14 -3.49 -3.91 -4.25 -4.48 -4.62 -4.73 -4.80 -4.80 -4.69 -4.44 -4.10 -3.70 -3.29 -2.87 -2.39 -1.81 -1.13 -0.46 --0.09 --0.40 --0.43 --0.21 -0.09 -0.30 -0.24 --0.15 --0.87 --1.78 --2.73 --3.55 --4.16 --4.58 --4.89 --5.20 --5.58 --6.00 --6.34 --6.46 --6.23 --5.63 --4.72 --3.61 --2.42 --1.20 -0.07 -1.41 -2.80 -4.10 -5.11 -5.67 -5.71 -5.34 -4.75 -4.20 -3.84 -3.67 -3.61 -3.52 -3.33 -3.08 -2.89 -2.90 -3.17 -3.68 -4.26 -4.75 -5.02 -5.01 -4.78 -4.42 -3.98 -3.49 -2.92 -2.25 -1.55 -0.92 -0.48 -0.27 -0.27 -0.34 -0.32 -0.09 --0.37 --0.99 --1.66 --2.27 --2.77 --3.16 --3.49 --3.84 --4.26 --4.78 --5.42 --6.17 --7.00 --7.86 --8.68 --9.33 --9.65 --9.49 --8.76 --7.50 --5.86 --4.05 --2.26 --0.59 -0.93 -2.33 -3.62 -4.75 -5.63 -6.17 -6.35 -6.18 -5.71 -5.04 -4.27 -3.54 -2.99 -2.73 -2.74 -2.93 -3.12 -3.17 -3.04 -2.80 -2.57 -2.45 -2.42 -2.37 -2.20 -1.88 -1.43 -0.96 -0.53 -0.11 --0.32 --0.79 --1.26 --1.62 --1.80 --1.81 --1.76 --1.84 --2.16 --2.73 --3.43 --4.09 --4.55 --4.75 --4.72 --4.56 --4.44 --4.53 --4.95 --5.76 --6.85 --8.05 --9.07 --9.67 --9.67 --9.03 --7.83 --6.18 --4.24 --2.19 --0.23 -1.45 -2.69 -3.46 -3.84 -4.02 -4.18 -4.38 -4.56 -4.55 -4.20 -3.51 -2.65 -1.89 -1.47 -1.54 -2.05 -2.83 -3.71 -4.53 -5.25 -5.85 -6.33 -6.63 -6.64 -6.29 -5.59 -4.68 -3.77 -3.09 -2.74 -2.67 -2.73 -2.75 -2.63 -2.36 -1.97 -1.47 -0.88 -0.20 --0.50 --1.13 --1.61 --1.91 --2.11 --2.33 --2.70 --3.30 --4.10 --5.00 --5.85 --6.48 --6.73 --6.47 --5.66 --4.34 --2.66 --0.84 -0.95 -2.59 -3.98 -5.13 -5.97 -6.43 -6.47 -6.05 -5.23 -4.16 -3.10 -2.26 -1.83 -1.84 -2.20 -2.75 -3.35 -3.89 -4.34 -4.66 -4.82 -4.78 -4.50 -3.99 -3.35 -2.68 -2.08 -1.58 -1.19 -0.85 -0.50 -0.10 --0.40 --1.00 --1.66 --2.29 --2.77 --3.03 --3.11 --3.13 --3.25 --3.63 --4.31 --5.19 --6.11 --6.91 --7.48 --7.79 --7.85 --7.65 --7.12 --6.19 --4.84 --3.17 --1.32 -0.47 -2.03 -3.26 -4.12 -4.65 -4.91 -4.95 -4.84 -4.64 -4.42 -4.23 -4.10 -4.06 -4.10 -4.20 -4.32 -4.38 -4.32 -4.10 -3.75 -3.37 -3.07 -2.90 -2.78 -2.60 -2.24 -1.66 -0.95 -0.28 --0.21 --0.46 --0.53 --0.54 --0.65 --0.95 --1.47 --2.19 --3.05 --3.95 --4.76 --5.39 --5.85 --6.22 --6.62 --7.16 --7.87 --8.65 --9.36 --9.82 --9.88 --9.49 --8.66 --7.45 --5.98 --4.37 --2.75 --1.23 -0.12 -1.29 -2.29 -3.11 -3.75 -4.20 -4.39 -4.33 -4.04 -3.60 -3.16 -2.88 -2.86 -3.14 -3.63 -4.15 -4.59 -4.83 -4.88 -4.80 -4.64 -4.41 -4.11 -3.72 -3.28 -2.86 -2.52 -2.29 -2.17 -2.07 -1.91 -1.61 -1.16 -0.59 -0.04 --0.38 --0.57 --0.53 --0.37 --0.27 --0.40 --0.78 --1.35 --1.94 --2.37 --2.55 --2.51 --2.34 --2.23 --2.35 --2.82 --3.69 --4.87 --6.16 --7.29 --7.97 --8.01 --7.37 --6.10 --4.36 --2.31 --0.17 -1.88 -3.62 -4.90 -5.67 -6.01 -6.05 -5.97 -5.85 -5.73 -5.56 -5.30 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-2.24 -1.79 -1.36 -0.89 -0.31 --0.34 --1.02 --1.65 --2.23 --2.79 --3.36 --3.94 --4.49 --4.95 --5.30 --5.60 --5.98 --6.53 --7.25 --8.09 --8.89 --9.56 --10.01 --10.25 --10.26 --10.01 --9.45 --8.50 --7.18 --5.55 --3.75 --1.94 --0.29 -1.15 -2.33 -3.25 -3.90 -4.23 -4.27 -4.06 -3.75 -3.49 -3.41 -3.50 -3.69 -3.87 -4.00 -4.11 -4.27 -4.49 -4.72 -4.81 -4.66 -4.27 -3.74 -3.24 -2.90 -2.77 -2.80 -2.91 -3.01 -3.04 -2.95 -2.70 -2.26 -1.68 -1.03 -0.42 --0.07 --0.43 --0.69 --0.92 --1.14 --1.38 --1.61 --1.83 --2.07 --2.35 --2.69 --3.08 --3.51 --3.99 --4.59 --5.35 --6.24 --7.22 --8.13 --8.83 --9.18 --9.05 --8.33 --6.97 --5.03 --2.70 --0.28 -1.95 -3.74 -5.03 -5.84 -6.26 -6.38 -6.28 -5.99 -5.57 -5.13 -4.73 -4.42 -4.22 -4.10 -4.09 -4.18 -4.37 -4.64 -4.93 -5.14 -5.22 -5.15 -4.94 -4.63 -4.28 -3.98 -3.75 -3.61 -3.50 -3.33 -2.99 -2.42 -1.68 -0.88 -0.17 --0.36 --0.68 --0.87 --1.01 --1.20 --1.46 --1.80 --2.17 --2.56 --2.98 --3.46 --3.99 --4.56 --5.11 --5.59 --6.02 --6.46 --7.00 --7.66 --8.33 --8.78 --8.77 --8.09 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--1.85 --2.10 --2.51 --3.04 --3.65 --4.28 --4.88 --5.43 --5.95 --6.47 --7.09 --7.90 --8.98 --10.30 --11.68 --12.82 --13.39 --13.16 --12.10 --10.36 --8.20 --5.88 --3.58 --1.38 -0.67 -2.52 -4.14 -5.46 -6.47 -7.17 -7.58 -7.73 -7.63 -7.34 -6.94 -6.57 -6.33 -6.24 -6.23 -6.21 -6.12 -5.95 -5.75 -5.56 -5.38 -5.17 -4.89 -4.53 -4.14 -3.78 -3.48 -3.28 -3.12 -2.92 -2.60 -2.06 -1.30 -0.37 --0.56 --1.31 --1.74 --1.81 --1.64 --1.47 --1.52 --1.91 --2.59 --3.41 --4.20 --4.83 --5.27 --5.58 --5.81 --6.03 --6.28 --6.58 --7.01 --7.62 --8.41 --9.26 --9.92 --10.08 --9.49 --8.07 --5.96 --3.49 --1.00 -1.22 -3.04 -4.43 -5.44 -6.12 -6.53 -6.68 -6.62 -6.40 -6.07 -5.69 -5.32 -5.00 -4.75 -4.62 -4.61 -4.76 -5.07 -5.53 -6.06 -6.54 -6.83 -6.84 -6.51 -5.92 -5.18 -4.41 -3.71 -3.13 -2.64 -2.23 -1.83 -1.35 -0.75 -0.03 --0.79 --1.60 --2.32 --2.91 --3.40 --3.84 --4.29 --4.78 --5.31 --5.84 --6.36 --6.88 --7.38 --7.84 --8.26 --8.67 --9.15 --9.79 --10.59 --11.39 --11.92 --11.80 --10.78 --8.79 --6.04 --2.96 --0.00 -2.46 -4.28 -5.48 -6.23 -6.72 -7.06 -7.29 -7.35 -7.23 -6.90 -6.42 -5.90 -5.41 -5.01 -4.71 -4.50 -4.37 -4.35 -4.47 -4.73 -5.07 -5.39 -5.55 -5.48 -5.17 -4.69 -4.13 -3.56 -3.03 -2.56 -2.15 -1.76 -1.35 -0.84 -0.15 --0.72 --1.69 --2.58 --3.19 --3.38 --3.19 --2.82 --2.56 --2.65 --3.15 --3.99 --4.98 --5.93 --6.75 --7.43 --8.04 --8.66 --9.38 --10.20 --11.08 --11.87 --12.40 --12.43 --11.77 --10.31 --8.14 --5.47 --2.59 -0.18 -2.60 -4.52 -5.90 -6.76 -7.19 -7.26 -7.05 -6.63 -6.07 -5.46 -4.95 -4.63 -4.56 -4.69 -4.91 -5.06 -5.07 -4.97 -4.86 -4.87 -5.08 -5.41 -5.77 -5.98 -5.93 -5.59 -5.02 -4.30 -3.52 -2.71 -1.91 -1.14 -0.50 -0.07 --0.10 --0.08 --0.04 --0.19 --0.63 --1.30 --2.02 --2.56 --2.83 --2.92 --3.02 --3.33 --3.95 --4.78 --5.66 --6.43 --7.05 --7.57 --8.08 --8.66 --9.31 --9.96 --10.58 --11.11 --11.51 --11.70 --11.55 --10.91 --9.65 --7.79 --5.49 --3.06 --0.89 -0.74 -1.67 -1.95 -1.73 -1.20 -0.50 --0.34 --1.42 --2.86 --4.76 --7.11 --9.80 --12.73 --15.72 --18.62 --21.24 --23.34 --24.59 --24.66 --23.27 --20.29 --15.80 --10.07 --3.54 -3.22 -9.67 -15.34 -19.93 -23.28 -25.39 -26.32 -26.19 -25.15 -23.44 -21.39 -19.30 -17.45 -15.97 -14.81 -13.82 -12.84 -11.74 -10.45 -9.00 -7.41 -5.74 -4.03 -2.32 -0.68 --0.84 --2.24 --3.46 --4.44 --5.09 --5.30 --5.00 --4.18 --2.92 --1.34 -0.41 -2.18 -3.85 -5.30 -6.48 -7.38 -8.00 -8.37 -8.45 -8.17 -7.52 -6.56 -5.47 -4.52 -3.92 -3.75 -3.91 -4.18 -4.37 -4.38 -4.29 -4.23 -4.32 -4.55 -4.81 -4.93 -4.77 -4.28 -3.49 -2.54 -1.57 -0.69 --0.06 --0.70 --1.26 --1.78 --2.20 --2.46 --2.51 --2.37 --2.17 --2.06 --2.18 --2.56 --3.14 --3.83 --4.51 --5.17 --5.77 --6.33 --6.79 --7.10 --7.27 --7.37 --7.57 --8.02 --8.77 --9.68 --10.46 --10.71 --10.16 --8.71 --6.55 --4.04 --1.61 -0.43 -1.96 -3.04 -3.84 -4.50 -5.11 -5.63 -6.00 -6.14 -6.04 -5.73 -5.29 -4.84 -4.51 -4.38 -4.45 -4.65 -4.85 -4.96 -4.95 -4.83 -4.66 -4.49 -4.31 -4.10 -3.82 -3.42 -2.92 -2.36 -1.76 -1.19 -0.72 -0.37 -0.14 -0.01 --0.08 --0.16 --0.24 --0.36 --0.56 --0.88 --1.36 --1.95 --2.55 --3.07 --3.43 --3.68 --3.95 --4.37 --5.00 --5.76 --6.53 --7.19 --7.70 --8.13 --8.60 --9.20 --9.89 --10.52 --10.88 --10.73 --9.95 --8.59 --6.84 --4.99 --3.24 --1.69 --0.28 -1.07 -2.41 -3.66 -4.61 -5.11 -5.10 -4.68 -4.08 -3.53 -3.15 -2.97 -2.89 -2.83 -2.75 -2.66 -2.65 -2.80 -3.13 -3.59 -4.06 -4.42 -4.61 -4.61 -4.52 -4.40 -4.27 -4.09 -3.80 -3.33 -2.69 -1.90 -1.02 -0.13 --0.70 --1.35 --1.73 --1.82 --1.67 --1.47 --1.39 --1.55 --1.97 --2.59 --3.28 --3.97 --4.61 --5.16 --5.57 --5.84 --5.99 --6.11 --6.34 --6.75 --7.32 --7.90 --8.27 --8.20 --7.54 --6.31 --4.62 --2.64 --0.56 -1.50 -3.44 -5.23 -6.79 -8.03 -8.79 -8.99 -8.63 -7.87 -6.94 -6.08 -5.48 -5.15 -5.01 -4.94 -4.86 -4.73 -4.56 -4.39 -4.23 -4.08 -3.90 -3.68 -3.40 -3.10 -2.79 -2.50 -2.24 -1.97 -1.66 -1.24 -0.71 -0.14 --0.35 --0.65 --0.73 --0.69 --0.69 --0.95 --1.56 --2.51 --3.65 --4.74 --5.61 --6.11 --6.26 --6.17 --6.05 --6.09 --6.44 --7.10 --7.93 --8.73 --9.25 --9.31 --8.79 --7.67 --6.02 --3.96 --1.69 -0.52 -2.43 -3.86 -4.76 -5.18 -5.26 -5.14 -4.94 -4.73 -4.55 -4.46 -4.46 -4.56 -4.75 -5.00 -5.28 -5.51 -5.64 -5.59 -5.35 -5.00 -4.67 -4.51 -4.58 -4.83 -5.09 -5.17 -5.02 -4.64 -4.14 -3.58 -2.99 -2.36 -1.69 -1.07 -0.59 -0.33 -0.29 -0.31 -0.21 --0.16 --0.84 --1.76 --2.79 --3.77 --4.61 --5.24 --5.65 --5.83 --5.86 --5.89 --6.11 --6.68 --7.68 --9.01 --10.42 --11.62 --12.27 --12.15 --11.21 --9.50 --7.24 --4.73 --2.29 --0.17 -1.48 -2.67 -3.52 -4.13 -4.56 -4.77 -4.71 -4.38 -3.87 -3.35 -2.99 -2.82 -2.79 -2.77 -2.71 -2.67 -2.79 -3.14 -3.63 -4.07 -4.23 -4.01 -3.53 -3.00 -2.60 -2.42 -2.39 -2.40 -2.37 -2.29 -2.15 -1.96 -1.63 -1.10 -0.34 --0.57 --1.50 --2.31 --2.91 --3.31 --3.58 --3.79 --4.04 --4.36 --4.79 --5.29 --5.83 --6.37 --6.91 --7.50 --8.14 --8.79 --9.32 --9.53 --9.22 --8.27 --6.72 --4.71 --2.50 --0.30 -1.67 -3.28 -4.46 -5.21 -5.59 -5.69 -5.61 -5.39 -5.07 -4.66 -4.22 -3.82 -3.53 -3.41 -3.51 -3.79 -4.21 -4.65 -5.03 -5.29 -5.41 -5.44 -5.38 -5.27 -5.07 -4.72 -4.18 -3.47 -2.66 -1.90 -1.28 -0.83 -0.52 -0.20 --0.23 --0.87 --1.71 --2.68 --3.59 --4.29 --4.65 --4.67 --4.49 --4.31 --4.34 --4.74 --5.50 --6.50 --7.53 --8.34 --8.68 --8.39 --7.38 --5.67 --3.41 --0.82 -1.81 -4.19 -6.10 -7.39 -8.02 -8.02 -7.49 -6.59 -5.54 -4.53 -3.76 -3.31 -3.21 -3.36 -3.64 -3.88 -3.97 -3.87 -3.62 -3.33 -3.09 -2.98 -2.98 -3.04 -3.06 -2.92 -2.61 -2.12 -1.53 -0.92 -0.36 --0.12 --0.54 --0.92 --1.31 --1.73 --2.18 --2.63 --3.06 --3.48 --3.90 --4.36 --4.88 --5.42 --5.94 --6.38 --6.76 --7.17 --7.75 --8.59 --9.68 --10.81 --11.67 --11.96 --11.51 --10.34 --8.63 --6.62 --4.52 --2.44 --0.41 -1.52 -3.26 -4.65 -5.54 -5.89 -5.83 -5.57 -5.32 -5.18 -5.14 -5.11 -5.04 -4.92 -4.84 -4.86 -4.98 -5.14 -5.25 -5.22 -5.10 -4.94 -4.80 -4.64 -4.38 -3.90 -3.16 -2.19 -1.13 -0.17 --0.55 --0.98 --1.16 --1.19 --1.19 --1.24 --1.35 --1.52 --1.74 --2.06 --2.51 --3.15 --3.95 --4.82 --5.61 --6.17 --6.47 --6.55 --6.59 --6.76 --7.19 --7.88 --8.72 --9.53 --10.11 --10.33 --10.11 --9.40 --8.21 --6.57 --4.55 --2.26 -0.14 -2.44 -4.44 -6.01 -7.12 -7.80 -8.17 -8.33 -8.34 -8.20 -7.87 -7.30 -6.53 -5.63 -4.71 -3.92 -3.37 -3.08 -3.06 -3.26 -3.61 -4.03 -4.42 -4.68 -4.75 -4.60 -4.26 -3.81 -3.29 -2.70 -2.03 -1.23 -0.38 --0.41 --0.97 --1.19 --1.12 --0.90 --0.78 --0.90 --1.29 --1.84 --2.39 --2.83 --3.11 --3.27 --3.41 --3.60 --3.86 --4.23 --4.69 --5.25 --5.92 --6.68 --7.47 --8.15 --8.53 --8.36 --7.49 --5.86 --3.62 --1.06 -1.45 -3.59 -5.16 -6.13 -6.60 -6.69 -6.53 -6.20 -5.74 -5.17 -4.52 -3.85 -3.25 -2.83 -2.72 -2.97 -3.56 -4.32 -5.02 -5.48 -5.61 -5.48 -5.26 -5.09 -4.99 -4.85 -4.52 -3.90 -3.04 -2.08 -1.20 -0.48 --0.07 --0.51 --0.90 --1.26 --1.60 --1.93 --2.31 --2.77 --3.34 --3.95 --4.50 --4.92 --5.22 --5.50 --5.92 --6.63 --7.65 --8.86 --10.02 --10.89 --11.26 --10.99 --10.05 --8.50 --6.44 --4.05 --1.57 -0.74 -2.67 -4.10 -5.08 -5.71 -6.15 -6.47 -6.66 -6.64 -6.36 -5.87 -5.29 -4.81 -4.52 -4.47 -4.57 -4.69 -4.73 -4.66 -4.54 -4.47 -4.51 -4.65 -4.78 -4.78 -4.51 -3.98 -3.24 -2.42 -1.65 -0.97 -0.36 --0.24 --0.90 --1.60 --2.25 --2.70 --2.90 --2.85 --2.68 --2.56 --2.63 --2.96 --3.51 --4.17 --4.82 --5.35 --5.74 --6.07 --6.47 --7.07 --7.93 --8.97 --9.92 --10.44 --10.26 --9.29 --7.67 --5.70 --3.67 --1.78 --0.06 -1.52 -3.01 -4.38 -5.57 -6.47 -7.04 -7.29 -7.22 -6.85 -6.24 -5.46 -4.63 -3.89 -3.38 -3.16 -3.24 -3.59 -4.11 -4.69 -5.24 -5.66 -5.92 -6.00 -5.88 -5.59 -5.10 -4.46 -3.72 -2.96 -2.28 -1.76 -1.43 -1.24 -1.12 -0.99 -0.80 -0.52 -0.12 --0.41 --1.07 --1.86 --2.70 --3.51 --4.22 --4.77 --5.19 --5.55 --5.99 --6.63 --7.48 --8.45 --9.32 --9.78 --9.61 --8.72 --7.20 --5.26 --3.16 --1.12 -0.74 -2.35 -3.71 -4.83 -5.70 -6.33 -6.70 -6.82 -6.69 -6.35 -5.89 -5.42 -5.06 -4.91 -5.02 -5.37 -5.87 -6.44 -6.97 -7.35 -7.50 -7.40 -7.08 -6.64 -6.21 -5.91 -5.78 -5.78 -5.76 -5.53 -4.97 -4.02 -2.72 -1.21 --0.35 --1.77 --2.88 --3.57 --3.76 --3.53 --3.09 --2.68 --2.56 --2.90 --3.70 --4.89 --6.40 --8.22 --10.40 --12.93 --15.66 --18.26 --20.24 --21.13 --20.65 --18.82 --15.97 --12.66 --9.52 --7.10 --5.69 --5.30 --5.69 --6.41 --6.91 --6.69 --5.34 --2.76 -0.86 -5.01 -9.00 -12.22 -14.32 -15.34 -15.68 -15.82 -16.16 -16.82 -17.72 -18.64 -19.30 -19.46 -18.91 -17.42 -14.90 -11.38 -7.18 -2.84 --1.00 --3.81 --5.34 --5.73 --5.44 --4.98 --4.79 --4.93 --5.11 --4.85 --3.74 --1.72 -0.87 -3.33 -4.87 -4.90 -3.14 --0.30 --5.01 --10.43 --16.05 --21.41 --26.18 --30.22 --33.61 --36.60 --39.49 --42.48 --45.44 --47.88 --49.10 --48.47 --45.84 --41.65 --36.97 --33.17 --31.52 --32.62 --36.16 --40.83 --44.76 --46.04 --43.40 --36.63 --26.59 --14.84 --2.96 -7.93 -17.43 -25.56 -32.48 -38.18 -42.42 -45.04 -46.15 -46.22 -45.85 -45.42 -44.88 -43.80 -41.78 -38.78 -35.42 -32.67 -31.37 -31.60 -32.49 -32.48 -29.98 -24.12 -15.08 -4.11 --7.01 --16.59 --23.46 --27.11 --27.53 --25.07 --20.27 --13.88 --6.83 --0.21 -4.95 -7.96 -8.71 -7.69 -5.83 -4.15 -3.39 -3.79 -5.01 -6.36 -7.14 -6.92 -5.74 -4.05 -2.46 -1.52 -1.47 -2.19 -3.38 -4.64 -5.67 -6.35 -6.67 -6.72 -6.59 -6.40 -6.25 -6.26 -6.48 -6.94 -7.53 -8.09 -8.40 -8.28 -7.64 -6.47 -4.85 -2.91 -0.79 --1.36 --3.40 --5.21 --6.68 --7.73 --8.23 --8.07 --7.13 --5.36 --2.87 -0.10 -3.18 -6.00 -8.29 -9.98 -11.13 -11.86 -12.24 -12.25 -11.85 -11.00 -9.75 -8.28 -6.81 -5.55 -4.65 -4.12 -3.87 -3.72 -3.55 -3.25 -2.80 -2.20 -1.50 -0.75 --0.03 --0.80 --1.56 --2.32 --3.06 --3.77 --4.43 --4.98 --5.41 --5.70 --5.94 --6.20 --6.58 --7.10 --7.73 --8.40 --9.02 --9.54 --9.86 --9.91 --9.61 --8.91 --7.76 --6.18 --4.27 --2.19 --0.14 -1.68 -3.14 -4.23 -5.05 -5.72 -6.32 -6.84 -7.17 -7.17 -6.77 -6.05 -5.11 -4.07 -2.98 -1.84 -0.64 --0.53 --1.56 --2.31 --2.71 --2.79 --2.61 --2.23 --1.71 --1.07 --0.40 -0.13 -0.38 -0.25 --0.20 --0.83 --1.44 --1.90 --2.19 --2.38 --2.54 --2.74 --2.97 --3.20 --3.40 --3.47 --3.31 --2.76 --1.68 --0.01 -2.13 -4.46 -6.61 -8.21 -9.02 -9.00 -8.31 -7.28 -6.32 -5.80 -5.98 -6.89 -8.36 -10.07 -11.69 -12.96 -13.78 -14.16 -14.23 -14.05 -13.65 -12.98 -11.97 -10.55 -8.71 -6.51 -4.11 -1.74 --0.33 --1.88 --2.75 --2.89 --2.36 --1.30 -0.08 -1.58 -3.04 -4.34 -5.43 -6.29 -6.86 -7.02 -6.56 -5.23 -2.89 --0.41 --4.30 --8.16 --11.27 --13.07 --13.32 --12.21 --10.23 --8.04 --6.17 --4.88 --4.13 --3.64 --3.12 --2.34 --1.27 --0.00 -1.31 -2.49 -3.42 -4.02 -4.28 -4.18 -3.75 -3.03 -2.12 -1.13 -0.23 --0.44 --0.79 --0.81 --0.56 --0.21 -0.06 -0.09 --0.17 --0.69 --1.34 --1.97 --2.43 --2.66 --2.69 --2.64 --2.64 --2.80 --3.18 --3.74 --4.43 --5.19 --6.01 --6.89 --7.81 --8.65 --9.22 --9.30 --8.73 --7.47 --5.64 --3.48 --1.30 -0.64 -2.15 -3.19 -3.82 -4.17 -4.36 -4.46 -4.50 -4.44 -4.27 -4.02 -3.74 -3.50 -3.34 -3.23 -3.11 -2.94 -2.67 -2.30 -1.84 -1.34 -0.82 -0.30 --0.16 --0.53 --0.77 --0.87 --0.85 --0.76 --0.69 --0.70 --0.83 --1.06 --1.36 --1.70 --2.09 --2.58 --3.24 --4.12 --5.18 --6.30 --7.31 --8.09 --8.58 --8.76 --8.63 --8.16 --7.30 --6.00 --4.29 --2.27 --0.15 -1.85 -3.56 -4.84 -5.68 -6.09 -6.14 -5.91 -5.48 -4.89 -4.21 -3.51 -2.84 -2.26 -1.75 -1.22 -0.59 --0.18 --1.03 --1.79 --2.22 --2.17 --1.60 --0.65 -0.42 -1.33 -1.86 -1.89 -1.45 -0.64 --0.39 --1.46 --2.44 --3.21 --3.74 --4.04 --4.18 --4.27 --4.44 --4.73 --5.15 --5.64 --6.10 --6.48 --6.74 --6.94 --7.14 --7.39 --7.66 --7.85 --7.82 --7.38 --6.43 --4.95 --3.03 --0.90 -1.16 -2.88 -4.12 -4.91 -5.40 -5.76 -6.09 -6.35 -6.43 -6.21 -5.70 -5.03 -4.43 -4.06 -3.99 -4.13 -4.31 -4.36 -4.25 -4.02 -3.76 -3.52 -3.32 -3.09 -2.77 -2.30 -1.70 -1.05 -0.44 --0.00 --0.26 --0.39 --0.50 --0.73 --1.12 --1.64 --2.19 --2.70 --3.17 --3.66 --4.25 --4.94 --5.63 --6.19 --6.53 --6.65 --6.69 --6.76 --6.91 --7.07 --7.11 --6.89 --6.35 --5.51 --4.41 --3.15 --1.79 --0.40 -0.91 -2.06 -2.97 -3.57 -3.87 -3.93 -3.85 -3.73 -3.67 -3.71 -3.81 -3.91 -3.91 -3.79 -3.58 -3.35 -3.16 -3.06 -3.02 -2.98 -2.87 -2.62 -2.19 -1.58 -0.85 -0.09 --0.60 --1.17 --1.65 --2.07 --2.50 --2.94 --3.35 --3.66 --3.84 --3.90 --3.88 --3.83 --3.77 --3.76 --3.84 --4.05 --4.41 --4.89 --5.38 --5.72 --5.79 --5.49 --4.78 --3.70 --2.31 --0.71 -0.96 -2.54 -3.90 -4.94 -5.63 -6.01 -6.17 -6.20 -6.13 -5.96 -5.66 -5.18 -4.55 -3.89 -3.35 -3.10 -3.21 -3.64 -4.18 -4.61 -4.73 -4.50 -4.00 -3.37 -2.74 -2.16 -1.60 -1.02 -0.43 --0.14 --0.60 --0.91 --1.10 --1.26 --1.54 --2.04 --2.76 --3.60 --4.42 --5.05 --5.44 --5.64 --5.80 --6.06 --6.50 --7.08 --7.62 --7.91 --7.77 --7.09 --5.91 --4.33 --2.50 --0.61 -1.14 -2.59 -3.60 -4.17 -4.37 -4.37 -4.33 -4.29 -4.24 -4.08 -3.74 -3.27 -2.81 -2.50 -2.40 -2.40 -2.34 -2.07 -1.59 -1.07 -0.73 -0.74 -1.08 -1.58 -2.01 -2.19 -2.06 -1.69 -1.19 -0.64 -0.07 --0.52 --1.13 --1.74 --2.31 --2.79 --3.17 --3.44 --3.62 --3.77 --3.95 --4.22 --4.62 --5.15 --5.77 --6.41 --7.01 --7.56 --8.01 --8.31 --8.40 --8.17 --7.57 --6.54 --5.09 --3.25 --1.13 -1.12 -3.30 -5.16 -6.51 -7.25 -7.40 -7.08 -6.47 -5.73 -4.97 -4.28 -3.70 -3.29 -3.05 -2.94 -2.85 -2.67 -2.29 -1.73 -1.10 -0.51 -0.07 --0.25 --0.51 --0.80 --1.10 --1.34 --1.41 --1.27 --0.95 --0.62 --0.46 --0.63 --1.13 --1.88 --2.71 --3.48 --4.10 --4.55 --4.86 --5.08 --5.18 --5.10 --4.75 --4.07 --3.12 --2.08 --1.16 --0.52 --0.17 -0.05 -0.39 -1.10 -2.28 -3.80 -5.41 -6.79 -7.73 -8.19 -8.29 -8.22 -8.12 -8.04 -7.89 -7.60 -7.16 -6.64 -6.17 -5.82 -5.55 -5.24 -4.79 -4.18 -3.52 -2.96 -2.59 -2.43 -2.42 -2.45 -2.52 -2.63 -2.78 -2.94 -3.03 -3.01 -2.87 -2.67 -2.49 -2.33 -2.13 -1.79 -1.19 -0.36 --0.60 --1.54 --2.33 --2.95 --3.48 --4.02 --4.68 --5.50 --6.41 --7.25 --7.79 --7.81 --7.14 --5.73 --3.73 --1.43 -0.78 -2.55 -3.68 -4.19 -4.27 -4.18 -4.09 -4.03 -3.90 -3.57 -3.00 -2.33 -1.77 -1.55 -1.72 -2.18 -2.71 -3.11 -3.32 -3.37 -3.38 -3.41 -3.41 -3.25 -2.84 -2.20 -1.45 -0.77 -0.29 -0.01 --0.17 --0.33 --0.54 --0.80 --1.09 --1.44 --1.92 --2.61 --3.49 --4.47 --5.39 --6.16 --6.75 --7.29 --7.87 --8.55 --9.28 --9.92 --10.33 --10.47 --10.36 --10.03 --9.43 --8.49 --7.10 --5.31 --3.25 --1.17 -0.69 -2.20 -3.35 -4.20 -4.86 -5.41 -5.87 -6.22 -6.44 -6.49 -6.39 -6.13 -5.78 -5.41 -5.11 -4.96 -4.99 -5.17 -5.40 -5.58 -5.60 -5.45 -5.16 -4.82 -4.51 -4.23 -3.96 -3.59 -3.07 -2.38 -1.60 -0.88 -0.33 -0.03 --0.03 -0.05 -0.13 -0.06 --0.23 --0.76 --1.50 --2.42 --3.48 --4.64 --5.82 --6.97 --8.06 --9.12 --10.24 --11.47 --12.74 --13.84 --14.40 --14.07 --12.71 --10.39 --7.41 --4.14 --0.84 -2.30 -5.13 -7.45 -9.00 -9.54 -8.97 -7.44 -5.35 -3.18 -1.39 -0.23 --0.22 --0.09 -0.44 -1.15 -1.82 -2.33 -2.62 -2.71 -2.70 -2.71 -2.83 -3.03 -3.26 -3.40 -3.39 -3.24 -2.98 -2.65 -2.28 -1.85 -1.33 -0.71 --0.03 --0.86 --1.76 --2.67 --3.50 --4.14 --4.46 --4.45 --4.17 --3.75 --3.38 --3.22 --3.37 --3.84 --4.55 --5.35 --6.07 --6.52 --6.56 --6.11 --5.15 --3.75 --2.02 --0.16 -1.59 -3.01 -3.94 -4.29 -4.13 -3.63 -3.03 -2.53 -2.28 -2.34 -2.63 -3.01 -3.31 -3.40 -3.20 -2.79 -2.28 -1.82 -1.49 -1.27 -1.05 -0.70 -0.09 --0.81 --1.94 --3.17 --4.32 --5.24 --5.84 --6.09 --6.07 --5.92 --5.82 --5.91 --6.24 --6.79 --7.42 --7.98 --8.38 --8.60 --8.79 --9.09 --9.60 --10.29 --10.96 --11.37 --11.30 --10.68 --9.56 --8.03 --6.21 --4.17 --1.99 -0.24 -2.38 -4.30 -5.85 -6.95 -7.60 -7.80 -7.53 -6.78 -5.58 -4.09 -2.55 -1.31 -0.76 -1.09 -2.31 -4.14 -6.14 -7.80 -8.74 -8.83 -8.14 -6.97 -5.62 -4.30 -3.14 -2.16 -1.39 -0.93 -0.90 -1.36 -2.21 -3.14 -3.74 -3.70 -2.96 -1.77 -0.60 --0.17 --0.39 --0.20 -0.13 -0.35 -0.42 -0.46 -0.59 -0.79 -0.91 -0.75 -0.27 --0.36 --0.81 --0.76 --0.10 -1.05 -2.39 -3.67 -4.74 -5.58 -6.21 -6.60 -6.64 -6.20 -5.31 -4.11 -2.90 -1.96 -1.50 -1.56 -2.04 -2.76 -3.45 -3.80 -3.57 -2.64 -1.15 --0.48 --1.65 --1.84 --0.84 -1.11 -3.36 -5.09 -5.58 -4.50 -1.88 --1.86 --6.15 --10.38 --13.99 --16.53 --17.65 --17.16 --15.10 --11.76 --7.78 --3.93 --1.05 -0.29 --0.13 --2.07 --4.91 --7.88 --10.28 --11.64 --11.83 --10.98 --9.36 --7.28 --4.93 --2.43 -0.09 -2.45 -4.39 -5.71 -6.36 -6.48 -6.40 -6.38 -6.54 -6.77 -6.85 -6.66 -6.23 -5.85 -5.78 -6.19 -7.00 -8.02 -9.04 -9.97 -10.82 -11.58 -12.14 -12.32 -11.90 -10.86 -9.39 -7.82 -6.49 -5.62 -5.17 -4.98 -4.85 -4.65 -4.32 -3.89 -3.34 -2.60 -1.61 -0.31 --1.33 --3.32 --5.62 --8.18 --10.76 --13.04 --14.50 --14.69 --13.31 --10.40 --6.43 --2.13 -1.60 -4.02 -4.67 -3.50 -0.78 --2.98 --7.16 --11.18 --14.50 --16.63 --17.18 --15.90 --12.85 --8.43 --3.42 -1.22 -4.66 -6.40 -6.43 -5.16 -3.24 -1.32 --0.11 --0.84 --0.84 --0.27 -0.67 -1.76 -2.79 -3.53 -3.80 -3.48 -2.58 -1.25 --0.23 --1.53 --2.34 --2.50 --2.04 --1.15 --0.09 -0.86 -1.47 -1.62 -1.27 -0.49 --0.59 --1.76 --2.80 --3.46 --3.57 --3.03 --1.87 --0.24 -1.65 -3.55 -5.23 -6.55 -7.41 -7.87 -8.00 -7.89 -7.60 -7.19 -6.71 -6.24 -5.88 -5.68 -5.63 -5.66 -5.67 -5.57 -5.34 -5.06 -4.81 -4.67 -4.61 -4.55 -4.37 -4.01 -3.48 -2.85 -2.22 -1.67 -1.24 -0.93 -0.72 -0.57 -0.43 -0.24 --0.05 --0.47 --1.02 --1.69 --2.45 --3.26 --4.10 --4.95 --5.75 --6.42 --6.91 --7.25 --7.54 --7.99 --8.75 --9.84 --11.10 --12.22 --12.86 --12.77 --11.89 --10.34 --8.36 --6.18 --3.97 --1.84 -0.14 -1.91 -3.35 -4.41 -5.03 -5.26 -5.18 -4.94 -4.63 -4.31 -3.95 -3.50 -2.94 -2.31 -1.76 -1.50 -1.66 -2.27 -3.20 -4.21 -5.06 -5.56 -5.69 -5.53 -5.20 -4.83 -4.50 -4.21 -3.91 -3.55 -3.07 -2.43 -1.68 -0.89 -0.19 --0.36 --0.70 --0.88 --0.97 --1.03 --1.06 --1.05 --0.97 --0.89 --0.92 --1.20 --1.83 --2.75 --3.80 --4.74 --5.39 --5.71 --5.79 --5.78 --5.79 --5.84 --5.91 --6.00 --6.11 --6.32 --6.64 --6.97 --7.10 --6.77 --5.83 --4.29 --2.36 --0.38 -1.33 -2.54 -3.20 -3.41 -3.35 -3.18 -3.00 -2.87 -2.81 -2.79 -2.84 -2.96 -3.15 -3.35 -3.48 -3.47 -3.30 -3.05 -2.84 -2.80 -2.97 -3.29 -3.62 -3.82 -3.80 -3.57 -3.19 -2.74 -2.26 -1.80 -1.34 -0.89 -0.47 -0.08 --0.27 --0.59 --0.87 --1.08 --1.24 --1.36 --1.50 --1.76 --2.20 --2.79 --3.45 --4.03 --4.40 --4.52 --4.45 --4.34 --4.31 --4.43 --4.70 --5.06 --5.45 --5.83 --6.18 --6.41 --6.36 --5.91 --4.96 --3.58 --1.95 --0.31 -1.12 -2.25 -3.12 -3.81 -4.40 -4.90 -5.29 -5.54 -5.66 -5.71 -5.74 -5.78 -5.72 -5.46 -4.91 -4.14 -3.32 -2.73 -2.58 -2.88 -3.45 -3.99 -4.20 -3.98 -3.36 -2.58 -1.84 -1.27 -0.87 -0.56 -0.28 --0.01 --0.28 --0.50 --0.70 --0.94 --1.29 --1.76 --2.28 --2.72 --3.02 --3.19 --3.32 --3.52 --3.86 --4.28 --4.67 --4.92 --5.01 --5.00 --4.99 --5.06 --5.25 --5.49 --5.75 --5.99 --6.26 --6.62 --7.11 --7.68 --8.16 --8.35 --8.08 --7.30 --6.09 --4.61 --3.01 --1.40 -0.22 -1.83 -3.38 -4.76 -5.79 -6.34 -6.36 -5.95 -5.30 -4.63 -4.15 -3.95 -4.05 -4.39 -4.87 -5.38 -5.79 -6.04 -6.11 -6.04 -5.84 -5.57 -5.22 -4.82 -4.36 -3.88 -3.41 -2.96 -2.52 -2.06 -1.56 -1.06 -0.60 -0.27 -0.11 -0.08 -0.09 -0.04 --0.13 --0.43 --0.75 --1.00 --1.13 --1.16 --1.19 --1.38 --1.83 --2.53 --3.38 --4.19 --4.81 --5.16 --5.28 --5.32 --5.45 --5.81 --6.42 --7.25 --8.17 --9.02 --9.61 --9.78 --9.37 --8.35 --6.75 --4.72 --2.52 --0.43 -1.30 -2.54 -3.29 -3.69 -3.93 -4.14 -4.38 -4.61 -4.73 -4.63 -4.29 -3.76 -3.17 -2.66 -2.31 -2.20 -2.32 -2.59 -2.95 -3.36 -3.78 -4.22 -4.66 -4.99 -5.13 -4.99 -4.58 -4.04 -3.50 -3.05 -2.69 -2.30 -1.75 -1.06 -0.28 --0.44 --1.03 --1.49 --1.89 --2.32 --2.75 --3.14 --3.41 --3.57 --3.70 --3.93 --4.29 --4.73 --5.08 --5.21 --5.10 --4.84 --4.67 --4.79 --5.32 --6.20 --7.22 --8.09 --8.56 --8.47 --7.82 --6.70 --5.22 --3.50 --1.64 -0.28 -2.18 -3.89 -5.23 -6.06 -6.32 -6.15 -5.77 -5.43 -5.30 -5.38 -5.55 -5.65 -5.58 -5.33 -4.96 -4.55 -4.15 -3.80 -3.50 -3.30 -3.28 -3.50 -3.94 -4.47 -4.93 -5.17 -5.10 -4.71 -4.06 -3.25 -2.41 -1.66 -1.10 -0.81 -0.76 -0.86 -0.99 -0.98 -0.74 -0.25 --0.44 --1.21 --1.92 --2.48 --2.82 --2.95 --2.97 --3.01 --3.26 --3.83 --4.74 --5.88 --7.04 --7.93 --8.28 --7.91 --6.83 --5.18 --3.25 --1.34 -0.30 -1.57 -2.49 -3.19 -3.80 -4.35 -4.79 -5.00 -4.85 -4.33 -3.52 -2.59 -1.79 -1.28 -1.13 -1.25 -1.49 -1.69 -1.79 -1.82 -1.89 -2.06 -2.30 -2.50 -2.55 -2.39 -2.08 -1.76 -1.53 -1.42 -1.35 -1.18 -0.81 -0.22 --0.51 --1.30 --2.04 --2.67 --3.20 --3.63 --4.01 --4.38 --4.79 --5.24 --5.67 --6.01 --6.19 --6.18 --6.08 --6.08 --6.31 --6.79 --7.35 --7.64 --7.31 --6.18 --4.32 --2.02 -0.30 -2.30 -3.86 -5.01 -5.86 -6.46 -6.79 -6.76 -6.40 -5.82 -5.21 -4.74 -4.47 -4.35 -4.26 -4.13 -3.95 -3.81 -3.82 -4.03 -4.40 -4.82 -5.14 -5.24 -5.02 -4.47 -3.64 -2.63 -1.63 -0.79 -0.22 --0.06 --0.12 --0.14 --0.32 --0.79 --1.60 --2.69 --3.87 --4.97 --5.81 --6.34 --6.56 --6.57 --6.54 --6.64 --7.03 --7.76 --8.76 --9.78 --10.49 --10.57 --9.82 --8.27 --6.15 --3.81 --1.53 -0.44 -2.04 -3.26 -4.11 -4.63 -4.89 -4.97 -5.00 -5.05 -5.13 -5.19 -5.12 -4.87 -4.50 -4.11 -3.85 -3.81 -3.99 -4.34 -4.75 -5.13 -5.37 -5.41 -5.20 -4.77 -4.16 -3.45 -2.71 -1.97 -1.24 -0.55 --0.02 --0.40 --0.54 --0.48 --0.37 --0.39 --0.65 --1.18 --1.87 --2.58 --3.21 --3.71 --4.11 --4.47 --4.87 --5.36 --5.95 --6.57 --7.11 --7.37 --7.18 --6.41 --5.09 --3.42 --1.65 -0.01 -1.42 -2.61 -3.66 -4.61 -5.43 -5.99 -6.19 -6.00 -5.48 -4.78 -4.08 -3.51 -3.16 -3.04 -3.16 -3.49 -4.00 -4.60 -5.16 -5.52 -5.56 -5.25 -4.67 -3.99 -3.42 -3.11 -3.08 -3.20 -3.27 -3.03 -2.39 -1.37 -0.14 --1.05 --1.99 --2.57 --2.84 --2.88 --2.80 --2.67 --2.56 --2.59 --2.90 --3.60 --4.71 --6.06 --7.31 --8.07 --8.02 --7.04 --5.23 --2.86 --0.27 -2.18 -4.23 -5.72 -6.62 -7.00 -7.03 -6.83 -6.49 -6.03 -5.44 -4.74 -4.04 -3.44 -3.04 -2.89 -2.94 -3.07 -3.18 -3.18 -3.07 -2.86 -2.61 -2.34 -2.01 -1.57 -0.98 -0.22 --0.62 --1.39 --1.92 --2.11 --1.97 --1.70 --1.53 --1.74 --2.44 --3.64 --5.14 --6.64 --7.84 --8.54 --8.70 --8.49 --8.20 --8.11 --8.43 --9.13 --10.02 --10.79 --11.08 --10.65 --9.36 --7.27 --4.55 --1.52 -1.48 -4.15 -6.30 -7.79 -8.58 -8.68 -8.17 -7.21 -6.03 -4.89 -4.02 -3.55 -3.46 -3.61 -3.82 -3.95 -3.95 -3.83 -3.65 -3.47 -3.31 -3.18 -3.03 -2.76 -2.32 -1.63 -0.73 --0.27 --1.21 --1.91 --2.28 --2.33 --2.19 --2.06 --2.10 --2.41 --2.98 --3.74 --4.58 --5.42 --6.24 --7.06 --7.94 --8.83 --9.61 --10.07 --9.95 --9.06 --7.35 --4.92 --2.03 -0.98 -3.75 -5.99 -7.51 -8.28 -8.43 -8.14 -7.62 -7.03 -6.44 -5.89 -5.37 -4.92 -4.58 -4.41 -4.40 -4.51 -4.62 -4.61 -4.41 -3.98 -3.39 -2.70 -1.99 -1.27 -0.54 --0.20 --0.94 --1.64 --2.27 --2.82 --3.32 --3.82 --4.37 --4.92 --5.33 --5.41 --4.96 --3.98 --2.64 --1.36 --0.57 --0.63 --1.64 --3.43 --5.63 --7.82 --9.65 --10.93 --11.57 --11.55 --10.87 --9.46 --7.31 --4.45 --1.02 -2.63 -6.09 -8.90 -10.67 -11.24 -10.73 -9.45 -7.83 -6.26 -5.01 -4.18 -3.78 -3.75 -4.00 -4.42 -4.83 -5.06 -4.97 -4.54 -3.87 -3.18 -2.68 -2.49 -2.61 -2.91 -3.20 -3.33 -3.22 -2.88 -2.38 -1.80 -1.19 -0.52 --0.26 --1.20 --2.25 --3.33 --4.29 --4.99 --5.41 --5.58 --5.64 --5.77 --6.08 --6.62 --7.30 --7.97 --8.44 --8.51 --8.05 --7.01 --5.46 --3.51 --1.35 -0.80 -2.74 -4.32 -5.46 -6.14 -6.45 -6.47 -6.31 -6.00 -5.58 -5.06 -4.53 -4.14 -3.98 -4.10 -4.39 -4.64 -4.65 -4.33 -3.77 -3.19 -2.81 -2.72 -2.80 -2.81 -2.50 -1.78 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-6.59 -6.47 -6.38 -6.36 -6.41 -6.45 -6.35 -6.04 -5.56 -5.03 -4.66 -4.61 -4.88 -5.37 -5.84 -6.10 -5.99 -5.49 -4.68 -3.71 -2.78 -2.02 -1.50 -1.16 -0.88 -0.53 -0.05 --0.60 --1.33 --2.07 --2.77 --3.46 --4.19 --5.01 --5.94 --6.91 --7.82 --8.50 --8.87 --8.90 --8.61 --8.07 --7.28 --6.21 --4.83 --3.14 --1.21 -0.83 -2.78 -4.49 -5.81 -6.66 -7.04 -7.02 -6.72 -6.33 -5.97 -5.73 -5.60 -5.49 -5.31 -5.03 -4.75 -4.58 -4.62 -4.85 -5.14 -5.34 -5.36 -5.22 -4.99 -4.75 -4.49 -4.10 -3.48 -2.57 -1.44 -0.28 --0.73 --1.49 --2.03 --2.52 --3.09 --3.83 --4.66 --5.42 --5.93 --6.12 --6.07 --5.99 --6.08 --6.48 --7.19 --8.07 --8.97 --9.77 --10.40 --10.79 --10.87 --10.51 --9.57 --8.03 --5.95 --3.58 --1.22 -0.85 -2.43 -3.45 -3.98 -4.16 -4.11 -3.95 -3.75 -3.55 -3.36 -3.16 -2.96 -2.76 -2.58 -2.43 -2.29 -2.14 -1.98 -1.82 -1.70 -1.68 -1.81 -2.07 -2.36 -2.54 -2.51 -2.19 -1.64 -0.94 -0.23 --0.40 --0.95 --1.46 --2.01 --2.63 --3.31 --3.93 --4.36 --4.52 --4.44 --4.27 --4.24 --4.51 --5.13 --5.99 --6.80 --7.28 --7.19 --6.44 --5.08 --3.30 --1.34 -0.59 -2.31 -3.76 -4.95 -5.90 -6.59 -7.00 -7.05 -6.75 -6.16 -5.43 -4.73 -4.18 -3.82 -3.58 -3.43 -3.35 -3.39 -3.61 -4.02 -4.52 -4.93 -5.12 -5.01 -4.68 -4.26 -3.87 -3.52 -3.17 -2.73 -2.12 -1.36 -0.52 --0.35 --1.22 --2.07 --2.87 --3.54 --3.96 --4.07 --3.91 --3.65 --3.52 --3.65 --4.09 --4.74 --5.46 --6.16 --6.77 --7.23 --7.43 --7.18 --6.31 --4.76 --2.67 --0.37 -1.75 -3.40 -4.53 -5.31 -5.98 -6.70 -7.48 -8.17 -8.62 -8.74 -8.62 -8.39 -8.22 -8.18 -8.20 -8.14 -7.83 -7.17 -6.17 -4.96 -3.74 -2.72 -2.03 -1.66 -1.51 -1.38 -1.13 -0.69 -0.13 --0.42 --0.89 --1.28 --1.65 --2.04 --2.43 --2.76 --2.95 --2.99 --2.88 --2.61 --2.06 --1.05 -0.58 -2.80 -5.32 -7.68 -9.35 -10.08 -10.01 -9.66 -9.82 -11.26 -14.48 -19.58 -26.27 -34.03 -42.32 -50.74 -59.07 -67.28 -75.32 -83.08 -90.38 -96.95 -102.46 -106.64 -109.19 -109.80 -108.08 -103.68 -96.20 -85.25 -70.42 -51.26 -27.46 --0.92 --33.10 --67.22 --100.38 --128.86 --149.11 --158.74 --157.47 --147.28 --131.86 --115.27 --100.60 --89.15 --80.60 --73.86 --68.16 --63.73 --61.63 --62.98 --68.01 --75.65 --83.83 --90.24 --93.22 --92.20 --87.62 --80.58 --72.32 --63.88 --55.78 --47.93 --39.64 --29.70 --16.80 --0.08 -20.29 -42.89 -65.29 -84.44 -97.40 -101.98 -97.23 -84.01 -65.40 -46.66 -34.01 -32.34 -42.74 -61.38 -81.09 -95.09 -100.93 -102.18 -106.45 -120.80 -147.25 -181.21 -213.78 -236.31 -244.50 -239.42 -225.58 -207.61 -187.85 -166.08 -141.23 -113.14 -83.12 -53.17 -24.66 --2.63 --30.26 --60.00 --92.49 --126.38 --158.40 --184.45 --201.03 --206.69 --202.71 --192.69 --181.16 --171.90 --166.51 --164.29 --162.93 --159.80 --153.19 --142.81 --129.59 --115.03 --100.52 --86.95 --74.75 --64.06 --54.97 --47.51 --41.69 --37.33 --34.03 --31.32 --28.83 --26.32 --23.73 --21.08 --18.41 --15.77 --13.15 --10.53 --7.84 --4.94 --1.62 -2.29 -6.75 -11.44 -15.78 -19.10 -20.93 -21.24 -20.49 -19.40 -18.69 -18.75 -19.59 -20.94 -22.51 -24.11 -25.74 -27.47 -29.22 -30.67 -31.30 -30.47 -27.62 -22.52 -15.40 -6.92 --1.97 --10.27 --17.18 --22.41 --26.19 --29.11 --31.76 --34.30 --36.30 --36.88 --35.20 --31.07 --25.09 --18.57 --12.89 --8.93 --6.81 --6.12 --6.33 --7.18 --8.68 --10.73 --12.66 --13.14 --10.64 --4.14 -6.33 -19.68 -34.31 -48.77 -62.24 -74.47 -85.34 -94.53 -101.35 -105.09 -105.33 -102.27 -96.74 -89.87 -82.76 -76.29 -71.02 -67.29 -65.27 -64.93 -66.16 -68.73 -72.38 -76.85 -81.84 -86.96 -91.79 -96.04 -99.81 -103.66 -108.49 -115.08 -123.72 -133.97 -144.80 -154.96 -163.47 -169.81 -173.77 -175.19 -173.69 -168.88 -160.71 -149.88 -137.86 -126.36 -116.65 -108.85 -101.85 -93.90 -83.42 -69.86 -53.81 -36.64 -19.66 -3.48 --12.01 --27.13 --41.74 --54.86 --64.99 --70.81 --71.99 --69.44 --64.92 --60.26 --56.62 --54.20 --52.60 --51.39 --50.52 --50.26 --50.90 --52.27 --53.67 --54.13 --52.97 --50.12 --46.23 --42.29 --39.15 --37.15 --36.01 --35.15 --34.10 --32.84 --31.88 --32.04 --34.07 --38.33 --44.67 --52.44 --60.67 --68.39 --74.80 --79.35 --81.82 --82.27 --81.17 --79.34 --78.03 --78.64 --82.47 --90.24 --101.75 --115.72 --130.06 --142.52 --151.53 --156.81 --159.47 --161.29 --163.80 --167.41 --171.36 --174.28 --175.15 --173.95 --171.61 --169.42 --168.18 --167.84 --167.70 --167.08 --165.80 --164.21 --162.68 --161.18 --158.95 --154.64 --146.75 --134.09 --116.25 --93.83 --68.47 --42.53 --18.54 -1.56 -16.85 -27.65 -35.04 -40.27 -44.28 -47.42 -49.74 -51.11 -51.39 -50.36 -47.71 -43.26 -37.26 -30.46 -23.80 -17.92 -12.80 -7.92 -2.68 --2.91 --8.12 --11.87 --13.32 --12.35 --9.49 --5.42 --0.38 -6.00 -14.47 -25.41 -38.14 -50.75 -60.55 -65.04 -62.94 -54.75 -42.51 -29.05 -16.84 -7.32 -0.75 --3.35 --5.52 --6.12 --5.31 --3.43 --1.12 -0.85 -2.01 -2.43 -2.72 -3.55 -5.27 -7.62 -9.88 -11.31 -11.62 -11.26 -11.25 -12.77 -16.63 -22.85 -30.66 -38.79 -46.08 -51.86 -56.15 -59.46 -62.44 -65.66 -69.46 -74.08 -79.87 -87.17 -96.18 -106.50 -117.01 -125.90 -131.23 -131.58 -126.51 -116.55 -102.69 -85.88 -66.88 -46.71 -27.15 -10.87 -0.74 --1.64 -2.78 -10.04 -14.33 -10.13 --5.75 --33.03 --68.43 --107.24 --144.91 --178.25 --205.53 --226.09 --239.43 --244.58 --239.91 --223.44 --193.87 --151.86 --100.71 --45.98 -6.00 -50.44 -85.79 -113.99 -139.13 -165.03 -193.23 -222.09 -247.48 -264.11 -267.13 -253.44 -222.72 -178.15 -126.41 -76.57 -37.89 -16.81 -14.51 -26.15 -42.61 -54.15 -54.47 -42.73 -23.05 -1.40 --18.14 --35.39 --53.51 --76.25 --104.90 --136.75 --166.32 --188.32 --200.51 --204.98 --206.69 --210.54 --218.68 --229.43 --238.34 --240.57 --233.16 --216.23 --192.64 --166.52 --141.61 --120.16 --102.68 --88.44 --76.45 --66.11 --57.41 --50.74 --46.29 --43.82 --42.64 --41.96 --41.31 --40.64 --40.10 --39.58 --38.42 --35.43 --29.34 --19.39 --5.76 -10.32 -26.87 -41.82 -53.43 -60.77 -63.83 -63.35 -60.54 -56.74 -53.03 -50.00 -47.67 -45.58 -43.02 -39.43 -34.56 -28.57 -22.06 -15.73 -10.34 -6.37 -3.96 -2.88 -2.64 -2.68 -2.52 -1.85 -0.54 --1.38 --3.77 --6.50 --9.39 --12.33 --15.22 --18.01 --20.55 --22.69 --24.12 --24.46 --23.27 --20.22 --15.23 --8.62 --1.07 -6.49 -13.16 -18.37 -22.00 -24.38 -26.03 -27.40 -28.67 -29.72 -30.25 -29.97 -28.81 -26.98 -24.86 -22.95 -21.58 -20.91 -20.90 -21.40 -22.24 -23.36 -24.71 -26.22 -27.68 -28.75 -29.14 -28.72 -27.64 -26.29 -25.08 -24.32 -24.09 -24.26 -24.63 -25.06 -25.40 -25.53 -25.32 -24.63 -23.41 -21.76 -19.92 -18.17 -16.65 -15.37 -14.11 -12.65 -10.77 -8.43 -5.82 -3.24 -1.09 --0.30 --0.71 --0.02 -1.73 -4.36 -7.61 -11.09 -14.37 -17.06 -18.87 -19.71 -19.70 -19.08 -18.10 -16.94 -15.66 -14.27 -12.80 -11.38 -10.19 -9.44 -9.26 -9.73 -10.80 -12.35 -14.26 -16.38 -18.57 -20.63 -22.34 -23.48 -23.89 -23.56 -22.59 -21.20 -19.71 -18.35 -17.28 -16.49 -15.87 -15.21 -14.29 -12.98 -11.27 -9.26 -7.15 -5.17 -3.43 -1.97 -0.70 --0.52 --1.87 --3.46 --5.35 --7.42 --9.35 --10.71 --11.06 --10.11 --7.84 --4.49 --0.48 -3.77 -7.90 -11.70 -15.03 -17.79 -19.88 -21.28 -22.02 -22.29 -22.33 -22.32 -22.38 -22.49 -22.60 -22.68 -22.78 -22.99 -23.39 -24.00 -24.69 -25.27 -25.55 -25.43 -24.93 -24.22 -23.44 -22.72 -22.08 -21.46 -20.75 -19.90 -18.89 -17.75 -16.53 -15.26 -13.90 -12.39 -10.62 -8.61 -6.45 -4.35 -2.54 -1.16 -0.16 --0.64 --1.50 --2.62 --4.07 --5.77 --7.49 --9.00 --10.05 --10.46 --10.06 --8.77 --6.57 --3.57 -0.00 -3.80 -7.54 -10.96 -13.93 -16.40 -18.34 -19.67 -20.31 -20.21 -19.40 -18.04 -16.37 -14.67 -13.15 -11.92 -11.02 -10.40 -10.01 -9.75 -9.54 -9.30 -8.97 -8.52 -7.94 -7.23 -6.39 -5.38 -4.22 -2.90 -1.47 -0.01 --1.42 --2.76 --3.92 --4.84 --5.44 --5.67 --5.49 --4.97 --4.18 --3.30 --2.46 --1.83 --1.50 --1.54 --1.97 --2.75 --3.79 --4.89 --5.78 --6.15 --5.70 --4.22 --1.68 -1.77 -5.81 -10.04 -14.10 -17.69 -20.68 -23.01 -24.69 -25.71 -26.08 -25.80 -24.91 -23.54 -21.87 -20.14 -18.56 -17.29 -16.38 -15.82 -15.59 -15.65 -15.98 -16.55 -17.27 -17.98 -18.43 -18.34 -17.52 -15.98 -13.94 -11.77 -9.87 -8.45 -7.54 -6.98 -6.52 -6.00 -5.37 -4.68 -4.02 -3.41 -2.79 -2.03 -1.04 --0.20 --1.65 --3.24 --4.87 --6.40 --7.66 --8.40 --8.34 --7.30 --5.24 --2.35 -0.99 -4.31 -7.24 -9.51 -11.06 -11.96 -12.36 -12.44 -12.34 -12.18 -12.05 -11.99 -11.98 -12.02 -12.08 -12.15 -12.26 -12.38 -12.51 -12.60 -12.62 -12.59 -12.53 -12.46 -12.41 -12.33 -12.19 -11.97 -11.66 -11.27 -10.78 -10.14 -9.35 -8.42 -7.43 -6.44 -5.47 -4.48 -3.37 -2.08 -0.63 --0.86 --2.25 --3.43 --4.43 --5.39 --6.51 --7.94 --9.69 --11.60 --13.36 --14.60 --15.00 --14.37 --12.68 --10.08 --6.81 --3.20 -0.39 -3.67 -6.40 -8.47 -9.90 -10.85 -11.46 -11.88 -12.15 -12.22 -12.07 -11.75 -11.41 -11.24 -11.39 -11.87 -12.54 -13.21 -13.72 -13.98 -13.98 -13.77 -13.33 -12.67 -11.74 -10.60 -9.36 -8.15 -7.09 -6.18 -5.40 -4.70 -4.04 -3.44 -2.89 -2.37 -1.82 -1.22 -0.54 --0.19 --0.97 --1.78 --2.63 --3.53 --4.47 --5.42 --6.35 --7.23 --8.09 --8.97 --9.92 --10.94 --11.95 --12.76 --13.15 --12.90 --11.87 --10.06 --7.60 --4.75 --1.84 -0.79 -2.90 -4.32 -5.05 -5.18 -4.87 -4.29 -3.61 -2.88 -2.14 -1.38 -0.67 -0.06 --0.32 --0.41 --0.21 -0.20 -0.72 -1.23 -1.66 -2.00 -2.23 -2.36 -2.39 -2.34 -2.26 -2.23 -2.30 -2.48 -2.74 -3.04 -3.33 -3.58 -3.76 -3.79 -3.65 -3.32 -2.86 -2.37 -1.88 -1.41 -0.92 -0.32 --0.38 --1.11 --1.77 --2.30 --2.73 --3.17 --3.76 --4.54 --5.45 --6.30 --6.84 --6.86 --6.17 --4.69 --2.46 -0.32 -3.31 -6.07 -8.24 -9.61 -10.17 -10.09 -9.64 -9.02 -8.33 -7.58 -6.76 -5.92 -5.11 -4.39 -3.81 -3.36 -3.04 -2.85 -2.81 -2.87 -2.93 -2.84 -2.46 -1.72 -0.71 --0.40 --1.41 --2.20 --2.70 --2.96 --3.10 --3.24 --3.48 --3.91 --4.50 --5.21 --5.94 --6.59 --7.14 --7.58 --7.98 --8.41 --8.90 --9.44 --9.95 --10.37 --10.58 --10.58 --10.40 --10.15 --9.98 --10.00 --10.27 --10.68 --11.07 --11.18 --10.80 --9.78 --8.10 --5.87 --3.30 --0.67 -1.71 -3.62 -4.91 -5.56 -5.63 -5.26 -4.59 -3.75 -2.90 -2.19 -1.75 -1.65 -1.92 -2.48 -3.23 -4.05 -4.80 -5.39 -5.77 -5.91 -5.84 -5.59 -5.24 -4.81 -4.33 -3.72 -2.92 -1.85 -0.56 --0.83 --2.12 --3.15 --3.83 --4.20 --4.40 --4.59 --4.89 --5.31 --5.80 --6.27 --6.66 --6.98 --7.26 --7.50 --7.69 --7.80 --7.84 --7.87 --7.99 --8.27 --8.73 --9.29 --9.83 --10.25 --10.49 --10.57 --10.53 --10.38 --10.08 --9.49 --8.48 --6.96 --4.97 --2.65 --0.28 -1.88 -3.56 -4.65 -5.16 -5.18 -4.88 -4.39 -3.83 -3.28 -2.74 -2.22 -1.68 -1.13 -0.57 -0.05 --0.38 --0.72 --0.97 --1.23 --1.60 --2.18 --2.97 --3.92 --4.91 --5.84 --6.66 --7.42 --8.16 --8.97 --9.86 --10.84 --11.93 --13.14 --14.41 --15.54 --16.17 --15.92 --14.58 --12.28 --9.51 --6.93 --5.08 --4.09 --3.72 --3.46 --2.88 --1.83 --0.51 -0.70 -1.46 -1.64 -1.33 -0.73 -0.01 --0.78 --1.66 --2.61 --3.49 --4.01 --3.83 --2.78 --0.95 -1.27 -3.31 -4.69 -5.14 -4.68 -3.53 -2.02 -0.45 --1.00 --2.27 --3.39 --4.45 --5.59 --6.87 --8.29 --9.75 --11.09 --12.13 --12.80 --13.12 --13.24 --13.27 --13.26 --13.14 --12.74 --11.85 --10.23 --7.76 --4.42 --0.43 -3.75 -7.52 -10.27 -11.61 -11.50 -10.24 -8.31 -6.14 -4.03 -2.09 -0.43 --0.82 --1.52 --1.65 --1.37 --1.13 --1.41 --2.57 --4.62 --7.20 --9.77 --11.83 --13.12 --13.70 --13.91 --14.16 --14.74 --15.65 --16.58 --16.93 --15.94 --12.95 --7.58 -0.05 -9.22 -18.70 -27.05 -32.91 -35.44 -34.45 -30.47 -24.50 -17.74 -11.23 -5.71 -1.44 --1.67 --4.02 --6.08 --8.26 --10.80 --13.67 --16.69 --19.58 --22.08 --23.99 --25.14 --25.40 --24.73 --23.34 --21.73 --20.66 --20.88 --22.77 --25.95 --29.19 --30.67 --28.56 --21.77 --10.40 -4.16 -19.73 -33.96 -45.09 -52.34 -55.78 -55.98 -53.58 -48.96 -42.36 -34.10 -24.87 -15.79 -8.22 -3.24 -1.38 -2.33 -5.14 -8.87 -13.19 -18.69 -26.54 -37.71 -52.12 -68.43 -84.44 -97.97 -107.60 -112.83 -113.82 -110.86 -104.17 -94.23 -82.12 -69.67 -58.75 -50.43 -44.28 -38.71 -32.12 -24.23 -16.63 -11.98 -12.41 -18.09 -26.90 -35.46 -41.05 -43.10 -43.23 -44.10 -47.45 -52.89 -58.05 -59.98 -57.06 -50.26 -42.96 -39.19 -41.40 -48.89 -58.01 -64.15 -64.28 -58.57 -49.79 -40.75 -31.93 -21.13 -5.81 --13.39 --30.94 --38.22 --28.35 --0.71 -37.54 -74.77 -100.91 -112.42 -113.25 -111.44 -113.59 -120.90 -129.13 -132.08 -126.28 -113.40 -99.21 -89.61 -86.35 -85.01 -76.54 -51.97 -7.48 --52.67 --117.27 --173.07 --210.55 --227.68 --229.83 --225.96 --223.70 --226.12 --231.36 --235.10 --233.98 --227.54 --218.34 --210.15 --205.50 --204.28 --203.77 --200.16 --190.41 --173.74 --151.89 --128.19 --106.15 --88.03 --74.31 --64.07 --55.88 --48.73 --42.44 --37.35 --33.84 --31.89 --30.96 --30.48 --30.07 --29.67 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a/calculatingfeatures/example_data/S2_E4_Data/info.txt b/calculatingfeatures/example_data/S2_E4_Data/info.txt deleted file mode 100644 index fe6220ac..00000000 --- a/calculatingfeatures/example_data/S2_E4_Data/info.txt +++ /dev/null @@ -1,32 +0,0 @@ -.csv files in this archive are in the following format: -The first row is the initial time of the session expressed as unix timestamp in UTC. -The second row is the sample rate expressed in Hz. - -TEMP.csv -Data from temperature sensor expressed degrees on the Celsius (°C) scale. - -EDA.csv -Data from the electrodermal activity sensor expressed as microsiemens (μS). - -BVP.csv -Data from photoplethysmograph. - -ACC.csv -Data from 3-axis accelerometer sensor. The accelerometer is configured to measure acceleration in the range [-2g, 2g]. Therefore the unit in this file is 1/64g. -Data from x, y, and z axis are respectively in first, second, and third column. - -IBI.csv -Time between individuals heart beats extracted from the BVP signal. -No sample rate is needed for this file. -The first column is the time (respect to the initial time) of the detected inter-beat interval expressed in seconds (s). -The second column is the duration in seconds (s) of the detected inter-beat interval (i.e., the distance in seconds from the previous beat). - -HR.csv -Average heart rate extracted from the BVP signal.The first row is the initial time of the session expressed as unix timestamp in UTC. -The second row is the sample rate expressed in Hz. - - -tags.csv -Event mark times. -Each row corresponds to a physical button press on the device; the same time as the status LED is first illuminated. -The time is expressed as a unix timestamp in UTC and it is synchronized with initial time of the session indicated in the related data files from the corresponding session. diff --git a/calculatingfeatures/example_data/S2_E4_Data/tags.csv b/calculatingfeatures/example_data/S2_E4_Data/tags.csv deleted file mode 100644 index e69de29b..00000000 diff --git a/calculatingfeatures/pyproject.toml b/calculatingfeatures/pyproject.toml deleted file mode 100644 index 07de284a..00000000 --- a/calculatingfeatures/pyproject.toml +++ /dev/null @@ -1,3 +0,0 @@ -[build-system] -requires = ["setuptools", "wheel"] -build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/calculatingfeatures/setup.cfg b/calculatingfeatures/setup.cfg deleted file mode 100644 index 1f342cb0..00000000 --- a/calculatingfeatures/setup.cfg +++ /dev/null @@ -1,16 +0,0 @@ -[metadata] -name = CalculatingFeatures -version = 0.1.0 -description = 'Library for calculating features' - -[options] -packages = CalculatingFeatures -install_requires = - numpy - pandas - scipy - tqdm - matplotlib - scikit-learn - PeakUtils - pywt \ No newline at end of file diff --git a/calculatingfeatures/usage_examples.ipynb b/calculatingfeatures/usage_examples.ipynb deleted file mode 100644 index 4628c273..00000000 --- a/calculatingfeatures/usage_examples.ipynb +++ /dev/null @@ -1,6112 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Usage examples" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from calculatingfeatures.CalculatingFeatures.calculate_features import calculateFeatures, calculateFeatures, convertInputInto2d, convertInputInto2dTime\n", - "from calculatingfeatures.CalculatingFeatures.helper_functions import gsrFeatureNames, frequencyFeatureNames, hrvFeatureNames, genericFeatureNames, accelerometerFeatureNames, gyroscopeFeatureNames\n", - "import CalculatingFeatures.feature_functions as functions\n", - "import random\n", - "import numpy as np\n", - "import time as t\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import pickle\n", - "from calculatingfeatures.CalculatingFeatures.calculate_hrv_peaks import peak_detector\n", - "%matplotlib notebook" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate HRV and frequency features with calculateFeatures() from empatica file" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1008/1008 [02:07<00:00, 7.89it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " ibi_X meanHr_X numRR_X pnn20_X pnn50_X rmssd_X \\\n", - "0 0.863281 69.502262 4.0 66.666667 66.666667 0.170449 \n", - "1 0.720313 83.297180 10.0 66.666667 33.333333 0.050765 \n", - "2 0.679688 88.275862 8.0 85.714286 28.571429 0.045363 \n", - "3 0.723437 82.937365 10.0 77.777778 55.555556 0.103382 \n", - "4 0.800347 74.967462 9.0 62.500000 37.500000 0.057675 \n", - "... ... ... ... ... ... ... \n", - "1003 1.097656 54.661922 4.0 33.333333 33.333333 0.045999 \n", - "1004 0.805804 74.459834 7.0 83.333333 83.333333 0.137701 \n", - "1005 0.911458 65.828571 6.0 100.000000 80.000000 0.098326 \n", - "1006 NaN NaN 3.0 NaN NaN NaN \n", - "1007 1.515625 39.587629 4.0 100.000000 100.000000 0.138878 \n", - "\n", - " sd1/sd2_X sd2_X sd_X sdnn_X sdsd_X \n", - "0 0.523417 0.120684 0.063168 0.089333 0.096319 \n", - "1 1.405853 0.025695 0.036124 0.051087 0.031346 \n", - "2 NaN NaN 0.030758 0.043498 0.016096 \n", - "3 0.668476 0.077188 0.051598 0.072971 0.065652 \n", - "4 0.684488 0.042214 0.028895 0.040863 0.036172 \n", - "... ... ... ... ... ... \n", - "1003 0.496161 0.042761 0.021216 0.030004 0.033754 \n", - "1004 1.001993 0.057706 0.057821 0.081771 0.057763 \n", - "1005 1.544265 0.034477 0.053242 0.075296 0.044852 \n", - "1006 NaN NaN NaN NaN NaN \n", - "1007 0.968519 0.049066 0.047522 0.067206 0.048300 \n", - "\n", - "[1008 rows x 11 columns]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from calculatingfeatures.CalculatingFeatures.helper_functions import convert1DEmpaticaToArray\n", - "\n", - "pathToHrvCsv = \"example_data/S2_E4_Data/BVP.csv\"\n", - "windowLength = 500\n", - "\n", - "#np.seterr(all='raise')\n", - "\n", - "# get an array of values from HRV empatica file\n", - "hrv_data, startTimeStamp, sampleRate = convert1DEmpaticaToArray(pathToHrvCsv)\n", - "\n", - "# Convert the HRV data into 2D array\n", - "hrv_data_2D = convertInputInto2d(hrv_data, windowLength)\n", - "\n", - "# Create a list with feature names\n", - "featureNames = []\n", - "featureNames.extend(hrvFeatureNames)\n", - "featureNames.extend(frequencyFeatureNames)\n", - "\n", - "pd.set_option('display.max_columns', None)\n", - "#pd.set_option('display.max_rows', None)\n", - "\n", - "# Calculate features\n", - "calculatedFeatures = calculateFeatures(hrv_data_2D, fs=int(sampleRate), featureNames=featureNames)\n", - "print(calculatedFeatures)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate ACC features with calculateFeatures() from empatica file" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 504/504 [03:46<00:00, 2.23it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " absoluteAreaAllBand absoluteArea_XBand absoluteArea_YBand \\\n", - "0 312.592555 72.500968 -16.538178 \n", - "1 864.715960 72.220023 -21.781158 \n", - "2 3616.215615 74.042112 -77.602245 \n", - "3 4165.495456 105.191655 -79.146730 \n", - "4 8033.196394 95.132686 -124.754418 \n", - ".. ... ... ... \n", - "499 384.671068 88.880824 -121.088645 \n", - "500 611.899981 90.148744 -125.571337 \n", - "501 1307.327021 98.014717 -123.718204 \n", - "502 778.281403 102.766032 -118.941814 \n", - "503 898.820043 37.005843 -49.001318 \n", - "\n", - " absoluteArea_ZBand absoluteMean_XBand absoluteMean_YBand \\\n", - "0 137.499998 0.145002 -0.033076 \n", - "1 137.184763 0.144440 -0.043562 \n", - "2 32.381332 0.148084 -0.155204 \n", - "3 27.225527 0.210383 -0.158293 \n", - "4 2.235905 0.190265 -0.249509 \n", - ".. ... ... ... \n", - "499 42.304659 0.177762 -0.242177 \n", - "500 41.328719 0.180297 -0.251143 \n", - "501 8.405056 0.196029 -0.247436 \n", - "502 -0.946992 0.205532 -0.237884 \n", - "503 0.000397 0.074012 -0.098003 \n", - "\n", - " absoluteMean_ZBand amplitude_XBand amplitude_YBand amplitude_ZBand \\\n", - "0 2.750000e-01 29.284433 6.386696 55.229196 \n", - "1 2.743695e-01 44.658625 15.583131 62.157114 \n", - "2 6.476266e-02 45.327413 26.308157 71.081348 \n", - "3 5.445105e-02 42.572756 23.106671 20.137936 \n", - "4 4.471811e-03 72.576962 40.377516 55.010798 \n", - ".. ... ... ... ... \n", - "499 8.460932e-02 35.718442 49.655967 17.649243 \n", - "500 8.265744e-02 39.161026 50.804928 19.970953 \n", - "501 1.681011e-02 42.835779 62.312165 17.508028 \n", - "502 -1.893985e-03 50.837576 51.201160 14.531671 \n", - "503 7.939179e-07 51.448326 54.050552 9.273581 \n", - "\n", - " areaUnderAccelerationMagnitude area_XLow area_YLow area_ZLow \\\n", - "0 5330.383914 14420.999440 -3350.641321 27557.000001 \n", - "1 5357.677382 10865.253056 -3033.252188 27902.323548 \n", - "2 5430.496677 15722.297711 -14156.396220 15642.774418 \n", - "3 4335.662961 22389.054964 -13963.007308 2435.697567 \n", - "4 5395.581186 19409.122939 -14484.885912 180.145873 \n", - ".. ... ... ... ... \n", - "499 5350.702140 17583.763049 -24380.273741 8308.160445 \n", - "500 5337.171768 17261.897541 -24409.269350 8664.655729 \n", - "501 5471.304292 18922.657077 -23201.084064 5627.731332 \n", - "502 5391.568437 20860.878551 -23277.757290 -715.703219 \n", - "503 5236.681184 19686.330067 -20534.332597 3487.999778 \n", - "\n", - " averageVectorLength averageVectorLengthPower \\\n", - "0 62.571636 3915.209631 \n", - "1 62.828208 3947.383684 \n", - "2 65.670379 4312.598665 \n", - "3 66.911456 4477.142892 \n", - "4 73.431020 5392.114734 \n", - ".. ... ... \n", - "499 62.381792 3891.487998 \n", - "500 62.447365 3899.673343 \n", - "501 62.910131 3957.684586 \n", - "502 62.841575 3949.063506 \n", - "503 58.941575 3474.109225 \n", - "\n", - " coefficientOfVariation_XBand coefficientOfVariation_YBand \\\n", - "0 1.541025 0.771037 \n", - "1 0.708974 0.585220 \n", - "2 0.386066 0.391320 \n", - "3 0.347936 0.349965 \n", - "4 0.330193 0.341132 \n", - ".. ... ... \n", - "499 1.519349 1.736730 \n", - "500 0.951674 1.209954 \n", - "501 0.505862 0.831018 \n", - "502 0.804625 1.001523 \n", - "503 0.716975 0.902984 \n", - "\n", - " coefficientOfVariation_ZBand correlation_X_YBand correlation_X_ZBand \\\n", - "0 1.915226 0.999748 0.999992 \n", - "1 0.995279 0.999936 0.999982 \n", - "2 0.443334 0.999991 0.999988 \n", - "3 0.333548 0.999992 0.999992 \n", - "4 0.326846 0.999993 0.999992 \n", - ".. ... ... ... \n", - "499 1.007314 0.999484 0.999985 \n", - "500 0.699102 0.999787 0.999988 \n", - "501 0.408757 0.999932 0.999987 \n", - "502 0.576986 0.999861 0.999947 \n", - "503 0.482851 0.999889 0.999968 \n", - "\n", - " correlation_Y_ZBand dominantFrequencyEnergy_XBand \\\n", - "0 0.999657 0.008964 \n", - "1 0.999909 0.018750 \n", - "2 0.999984 0.020694 \n", - "3 0.999992 0.015335 \n", - "4 0.999991 0.027059 \n", - ".. ... ... \n", - "499 0.999638 0.009092 \n", - "500 0.999841 0.012191 \n", - "501 0.999948 0.015881 \n", - "502 0.999944 0.012228 \n", - "503 0.999951 0.016684 \n", - "\n", - " dominantFrequencyEnergy_YBand dominantFrequencyEnergy_ZBand \\\n", - "0 0.009608 0.009000 \n", - "1 0.024015 0.011299 \n", - "2 0.029685 0.019629 \n", - "3 0.029545 0.019664 \n", - "4 0.038101 0.021032 \n", - ".. ... ... \n", - "499 0.008966 0.009465 \n", - "500 0.011666 0.015069 \n", - "501 0.012706 0.022372 \n", - "502 0.011949 0.014030 \n", - "503 0.013070 0.024085 \n", - "\n", - " entropy_XBand entropy_YBand entropy_ZBand fqAbsMean_X fqAbsMean_Y \\\n", - "0 7.803281 7.764458 7.798979 28.842 6.706 \n", - "1 7.592033 7.170297 7.763982 26.618 9.336 \n", - "2 7.428787 7.183324 7.549577 32.040 29.518 \n", - "3 7.531214 7.127566 7.475685 45.778 34.176 \n", - "4 7.416095 7.166612 7.373007 43.710 34.224 \n", - ".. ... ... ... ... ... \n", - "499 7.796856 7.799046 7.791035 35.172 48.756 \n", - "500 7.748183 7.773281 7.696858 34.530 48.842 \n", - "501 7.643179 7.758942 7.449880 37.878 46.368 \n", - "502 7.717640 7.756327 7.483432 41.712 46.562 \n", - "503 7.629066 7.769570 6.972002 39.200 40.840 \n", - "\n", - " fqAbsMean_Z fqEnergyFeat_X fqEnergyFeat_Y fqEnergyFeat_Z \\\n", - "0 55.114 416147.0 22687.0 1518857.0 \n", - "1 55.804 370169.0 47400.0 1564792.0 \n", - "2 36.648 662796.0 660597.0 923012.0 \n", - "3 18.330 1304769.0 815064.0 254347.0 \n", - "4 31.546 1358605.0 862102.0 748213.0 \n", - ".. ... ... ... ... \n", - "499 16.616 618810.0 1188766.0 138268.0 \n", - "500 17.328 601149.0 1195095.0 154722.0 \n", - "501 11.712 785687.0 1115182.0 91322.0 \n", - "502 2.338 884896.0 1091337.0 8025.0 \n", - "503 7.004 848456.0 965204.0 38318.0 \n", - "\n", - " fqEntropyFeat_X fqEntropyFeat_Y fqEntropyFeat_Z fqHighestPeak1_X \\\n", - "0 2.056371 4.227074 1.091003 0.659914 \n", - "1 4.954203 5.151731 3.852122 1586.457564 \n", - "2 5.650713 5.174951 5.356747 190.676281 \n", - "3 5.673421 5.136240 5.765772 613.304285 \n", - "4 5.783132 5.565740 5.760390 878.499446 \n", - ".. ... ... ... ... \n", - "499 2.155353 1.477249 3.158663 0.075611 \n", - "500 3.715911 2.901126 4.330129 0.062029 \n", - "501 5.028585 4.208921 5.484010 299.897572 \n", - "502 4.861145 4.051993 5.942058 0.365282 \n", - "503 4.721612 4.266797 5.192156 0.837968 \n", - "\n", - " fqHighestPeak1_Y fqHighestPeak1_Z fqHighestPeak2_X fqHighestPeak2_Y \\\n", - "0 0.329848 0.062427 0.214743 0.142434 \n", - "1 244.301350 21.022571 844.302348 125.356487 \n", - "2 605.220870 660.676570 176.340483 559.722940 \n", - "3 3404.162602 1654.679747 511.048051 2426.512293 \n", - "4 617.961281 2966.643517 696.645541 510.218120 \n", - ".. ... ... ... ... \n", - "499 0.066151 0.152542 0.064141 0.045393 \n", - "500 0.109477 0.294831 0.059480 0.071158 \n", - "501 217.027649 59.428941 158.318483 105.919777 \n", - "502 0.393256 0.331209 0.185996 0.225281 \n", - "503 0.537947 3.181930 0.836692 0.526419 \n", - "\n", - " fqHighestPeak2_Z fqHighestPeak3_X fqHighestPeak3_Y fqHighestPeak3_Z \\\n", - "0 0.056354 0.201488 0.121684 0.044537 \n", - "1 10.221185 239.346016 47.646533 9.525602 \n", - "2 408.824249 159.845333 323.797473 388.611398 \n", - "3 282.492840 345.468368 953.173254 237.514909 \n", - "4 1258.914443 690.811103 347.967190 581.366504 \n", - ".. ... ... ... ... \n", - "499 0.146578 0.059236 0.040802 0.143887 \n", - "500 0.152252 0.048268 0.053454 0.089385 \n", - "501 58.678460 55.928570 35.348018 40.291161 \n", - "502 0.163962 0.136052 0.136601 0.156347 \n", - "503 3.038781 0.786312 0.431989 0.911122 \n", - "\n", - " fqHighestPeakFreq1_X fqHighestPeakFreq1_Y fqHighestPeakFreq1_Z \\\n", - "0 0.125 0.125 0.125 \n", - "1 0.125 0.125 0.125 \n", - "2 0.125 0.125 0.125 \n", - "3 0.250 0.250 0.125 \n", - "4 0.875 0.125 0.125 \n", - ".. ... ... ... \n", - "499 4.625 4.250 0.250 \n", - "500 1.875 0.125 0.125 \n", - "501 0.125 0.125 0.250 \n", - "502 0.125 0.125 0.125 \n", - "503 0.250 1.000 0.125 \n", - "\n", - " fqHighestPeakFreq2_X fqHighestPeakFreq2_Y fqHighestPeakFreq2_Z \\\n", - "0 0.000 7.625 5.750 \n", - "1 0.250 0.250 2.000 \n", - "2 0.750 0.250 0.250 \n", - "3 0.125 0.125 0.250 \n", - "4 1.500 0.500 0.250 \n", - ".. ... ... ... \n", - "499 4.250 4.375 3.625 \n", - "500 3.000 1.875 3.500 \n", - "501 0.250 0.250 0.125 \n", - "502 0.000 0.000 0.250 \n", - "503 1.000 1.250 0.250 \n", - "\n", - " fqHighestPeakFreq3_X fqHighestPeakFreq3_Y fqHighestPeakFreq3_Z \\\n", - "0 0.250 0.250 5.875 \n", - "1 0.375 0.375 1.875 \n", - "2 3.500 0.375 0.375 \n", - "3 0.375 0.375 0.500 \n", - "4 0.750 1.500 1.250 \n", - ".. ... ... ... \n", - "499 4.500 3.875 4.000 \n", - "500 2.875 0.250 0.250 \n", - "501 0.375 0.375 0.375 \n", - "502 0.250 1.125 0.750 \n", - "503 1.750 1.375 0.375 \n", - "\n", - " fqHistogramBin10_X fqHistogramBin10_Y fqHistogramBin10_Z \\\n", - "0 0.059330 0.389456 0.753217 \n", - "1 0.000061 0.000015 0.051873 \n", - "2 0.025240 0.000251 0.005582 \n", - "3 0.018200 0.000005 0.000702 \n", - "4 0.049420 0.005158 0.002619 \n", - ".. ... ... ... \n", - "499 0.185922 0.043815 0.065751 \n", - "500 0.170029 0.096525 0.052775 \n", - "501 0.000928 0.000214 0.006905 \n", - "502 0.033254 0.015588 0.042798 \n", - "503 0.158842 0.020134 0.002435 \n", - "\n", - " fqHistogramBin1_X fqHistogramBin1_Y fqHistogramBin1_Z \\\n", - "0 0.917365 0.501879 0.145201 \n", - "1 0.996411 0.960550 0.354704 \n", - "2 0.343415 0.920326 0.845095 \n", - "3 0.716864 0.992726 0.989875 \n", - "4 0.194959 0.712702 0.878294 \n", - ".. ... ... ... \n", - "499 0.048028 0.141320 0.193154 \n", - "500 0.115791 0.465422 0.576209 \n", - "501 0.980711 0.968692 0.874908 \n", - "502 0.832916 0.823695 0.627951 \n", - "503 0.248169 0.072066 0.966204 \n", - "\n", - " fqHistogramBin2_X fqHistogramBin2_Y fqHistogramBin2_Z \\\n", - "0 0.000904 0.007043 0.041231 \n", - "1 0.002688 0.025178 0.043965 \n", - "2 0.187601 0.013045 0.076190 \n", - "3 0.074381 0.006033 0.006555 \n", - "4 0.455763 0.079752 0.031283 \n", - ".. ... ... ... \n", - "499 0.015525 0.069480 0.036488 \n", - "500 0.012550 0.014091 0.008415 \n", - "501 0.003834 0.017005 0.002335 \n", - "502 0.053437 0.093162 0.268378 \n", - "503 0.220826 0.303537 0.018221 \n", - "\n", - " fqHistogramBin3_X fqHistogramBin3_Y fqHistogramBin3_Z \\\n", - "0 0.012913 0.003186 0.028656 \n", - "1 0.000209 0.007173 0.172133 \n", - "2 0.038983 0.046723 0.022851 \n", - "3 0.123416 0.000264 0.000817 \n", - "4 0.190181 0.146581 0.060788 \n", - ".. ... ... ... \n", - "499 0.061261 0.021545 0.015547 \n", - "500 0.069835 0.080216 0.027630 \n", - "501 0.005995 0.003567 0.008486 \n", - "502 0.036650 0.050618 0.017483 \n", - "503 0.139805 0.432839 0.004041 \n", - "\n", - " fqHistogramBin4_X fqHistogramBin4_Y fqHistogramBin4_Z \\\n", - "0 0.001133 0.004548 0.001605 \n", - "1 0.000137 0.004941 0.209779 \n", - "2 0.059610 0.007699 0.019253 \n", - "3 0.039071 0.000808 0.001340 \n", - "4 0.023405 0.013695 0.005697 \n", - ".. ... ... ... \n", - "499 0.018228 0.115281 0.030084 \n", - "500 0.192815 0.165997 0.024964 \n", - "501 0.000197 0.007786 0.097574 \n", - "502 0.012061 0.011319 0.008113 \n", - "503 0.083092 0.057813 0.002771 \n", - "\n", - " fqHistogramBin5_X fqHistogramBin5_Y fqHistogramBin5_Z \\\n", - "0 0.000133 0.004992 0.008487 \n", - "1 0.000304 0.001234 0.087198 \n", - "2 0.109452 0.009152 0.015953 \n", - "3 0.014320 0.000140 0.000244 \n", - "4 0.053001 0.030390 0.005559 \n", - ".. ... ... ... \n", - "499 0.030837 0.012466 0.042601 \n", - "500 0.213722 0.010250 0.016697 \n", - "501 0.000435 0.002103 0.007861 \n", - "502 0.003046 0.000400 0.004838 \n", - "503 0.003842 0.042695 0.000124 \n", - "\n", - " fqHistogramBin6_X fqHistogramBin6_Y fqHistogramBin6_Z \\\n", - "0 0.002641 0.002711 0.004863 \n", - "1 0.000101 0.000490 0.041666 \n", - "2 0.201867 0.001369 0.005122 \n", - "3 0.005846 0.000012 0.000312 \n", - "4 0.010443 0.008340 0.010087 \n", - ".. ... ... ... \n", - "499 0.057460 0.021705 0.165463 \n", - "500 0.103490 0.103651 0.203756 \n", - "501 0.005911 0.000510 0.000915 \n", - "502 0.008047 0.002567 0.006263 \n", - "503 0.012732 0.036009 0.000879 \n", - "\n", - " fqHistogramBin7_X fqHistogramBin7_Y fqHistogramBin7_Z \\\n", - "0 0.001861 0.007453 0.002807 \n", - "1 0.000028 0.000260 0.015986 \n", - "2 0.021811 0.000078 0.007235 \n", - "3 0.002830 0.000002 0.000080 \n", - "4 0.009787 0.000881 0.001366 \n", - ".. ... ... ... \n", - "499 0.197672 0.376762 0.276174 \n", - "500 0.057769 0.033303 0.054540 \n", - "501 0.000476 0.000048 0.000353 \n", - "502 0.013900 0.000432 0.010065 \n", - "503 0.025386 0.023334 0.003946 \n", - "\n", - " fqHistogramBin8_X fqHistogramBin8_Y fqHistogramBin8_Z \\\n", - "0 0.001955 0.064559 0.007588 \n", - "1 0.000034 0.000109 0.011734 \n", - "2 0.010835 0.000937 0.001323 \n", - "3 0.002280 0.000002 0.000067 \n", - "4 0.010018 0.000904 0.003826 \n", - ".. ... ... ... \n", - "499 0.291858 0.129554 0.073230 \n", - "500 0.008201 0.018185 0.004613 \n", - "501 0.001449 0.000075 0.000293 \n", - "502 0.002615 0.001125 0.010125 \n", - "503 0.103507 0.008222 0.001323 \n", - "\n", - " fqHistogramBin9_X fqHistogramBin9_Y fqHistogramBin9_Z fqInterquart_X \\\n", - "0 0.001764 1.417175e-02 0.006344 1.0 \n", - "1 0.000028 4.913791e-05 0.010962 2.0 \n", - "2 0.001186 4.205692e-04 0.001395 19.0 \n", - "3 0.002791 6.951576e-06 0.000008 33.0 \n", - "4 0.003023 1.596709e-03 0.000482 45.0 \n", - ".. ... ... ... ... \n", - "499 0.093209 6.807324e-02 0.101508 1.0 \n", - "500 0.055798 1.235906e-02 0.030399 0.0 \n", - "501 0.000064 2.846989e-07 0.000369 17.0 \n", - "502 0.004074 1.095070e-03 0.003987 0.0 \n", - "503 0.003799 3.350736e-03 0.000056 3.0 \n", - "\n", - " fqInterquart_Y fqInterquart_Z fqKurtosis_X fqKurtosis_Y fqKurtosis_Z \\\n", - "0 1.00 0.0 82.921957 37.277378 6.172148 \n", - "1 3.00 1.0 76.273672 71.165185 15.416382 \n", - "2 35.00 45.0 6.878412 37.918868 31.087918 \n", - "3 44.25 29.0 22.193253 59.206577 109.082455 \n", - "4 38.50 55.0 14.428181 21.579397 68.114033 \n", - ".. ... ... ... ... ... \n", - "499 1.00 1.0 3.493950 6.700808 7.553704 \n", - "500 0.25 1.0 6.446087 22.380162 45.749892 \n", - "501 14.25 8.0 73.117153 75.619776 29.411194 \n", - "502 1.00 1.0 55.401241 51.909021 31.568406 \n", - "503 4.25 3.0 3.812462 8.022810 52.037962 \n", - "\n", - " fqSkewness_X fqSkewness_Y fqSkewness_Z \\\n", - "0 8.685933 5.237774 2.194513 \n", - "1 8.554455 8.103869 3.253087 \n", - "2 2.634778 6.003941 5.233903 \n", - "3 4.341981 7.604222 10.282016 \n", - "4 3.633590 4.341104 7.689533 \n", - ".. ... ... ... \n", - "499 1.894942 2.466336 2.691462 \n", - "500 2.245002 4.119326 6.033663 \n", - "501 8.293539 8.386964 5.246583 \n", - "502 6.920554 6.770191 5.179224 \n", - "503 2.074293 2.809057 7.181060 \n", - "\n", - " interQuartileRangeMagnitudesBand interQuartileRange_XBand \\\n", - "0 0.116283 0.108442 \n", - "1 0.515822 0.245962 \n", - "2 7.383329 2.079383 \n", - "3 4.620823 4.435474 \n", - "4 7.758987 9.680704 \n", - ".. ... ... \n", - "499 0.140573 0.070322 \n", - "500 0.356938 0.083357 \n", - "501 1.881589 0.645332 \n", - "502 0.720664 0.200399 \n", - "503 1.160543 0.326100 \n", - "\n", - " interQuartileRange_YBand interQuartileRange_ZBand kurtosis_XBand \\\n", - "0 0.081722 0.065099 230.852565 \n", - "1 0.251985 0.266026 60.605795 \n", - "2 1.559643 1.743397 2.674175 \n", - "3 3.394423 3.348774 0.775528 \n", - "4 6.326923 8.676731 0.356543 \n", - ".. ... ... ... \n", - "499 0.074261 0.176276 225.023923 \n", - "500 0.123523 0.143614 177.722773 \n", - "501 0.435514 0.552009 55.645718 \n", - "502 0.154690 0.196822 120.080582 \n", - "503 0.281286 0.224567 78.167856 \n", - "\n", - " kurtosis_YBand kurtosis_ZBand manipulationLow meanCrossingRate_XBand \\\n", - "0 164.957757 229.058361 0.018765 13.0 \n", - "1 18.028431 181.277262 0.018541 23.0 \n", - "2 0.970036 19.627766 0.023965 41.0 \n", - "3 0.244360 0.024724 0.026066 59.0 \n", - "4 1.039344 -0.283146 0.026051 80.0 \n", - ".. ... ... ... ... \n", - "499 222.308079 211.547373 0.028112 19.0 \n", - "500 200.121968 120.912006 0.027962 16.0 \n", - "501 162.283176 5.298307 0.030719 49.0 \n", - "502 170.359071 41.987689 0.034514 31.0 \n", - "503 180.353197 6.524585 16.298247 27.0 \n", - "\n", - " meanCrossingRate_YBand meanCrossingRate_ZBand meanKineticEnergy_XBand \\\n", - "0 26.0 1.0 25623.466885 \n", - "1 28.0 17.0 25623.457823 \n", - "2 35.0 31.0 25623.470198 \n", - "3 42.0 80.0 19042.431778 \n", - "4 46.0 61.0 67303.477598 \n", - ".. ... ... ... \n", - "499 10.0 37.0 37323.124131 \n", - "500 8.0 24.0 39486.339334 \n", - "501 22.0 39.0 37323.128222 \n", - "502 24.0 34.0 53745.297878 \n", - "503 14.0 24.0 41710.499213 \n", - "\n", - " meanKineticEnergy_YBand meanKineticEnergy_ZBand mean_XLow mean_YLow \\\n", - "0 1096.843008 9.216527e+04 28.841999 -6.701283 \n", - "1 1492.924665 9.216527e+04 21.730506 -6.066504 \n", - "2 3046.793625 9.216521e+04 31.444595 -28.312792 \n", - "3 2467.903796 1.949942e+03 44.778110 -27.926015 \n", - "4 20596.274067 3.317945e+04 38.818246 -28.969772 \n", - ".. ... ... ... ... \n", - "499 73153.322291 8.805210e+03 35.167526 -48.760547 \n", - "500 70197.942449 8.805211e+03 34.523795 -48.818539 \n", - "501 88844.250780 3.046786e+03 37.845314 -46.402168 \n", - "502 67303.493481 1.218714e+02 41.721757 -46.555515 \n", - "503 73153.306037 7.324061e-08 39.372660 -41.068665 \n", - "\n", - " mean_ZLow peaksAmplitudeAvgLow peaksCountLow peaksPeakAvgLow \\\n", - "0 55.114000 0.000000 0.0 0.000000 \n", - "1 55.804647 0.025651 14.0 66.347089 \n", - "2 31.285549 0.003297 40.0 71.749715 \n", - "3 4.871395 0.195948 81.0 68.373870 \n", - "4 0.360292 0.014893 76.0 74.287445 \n", - ".. ... ... ... ... \n", - "499 16.616321 0.052488 4.0 63.068346 \n", - "500 17.329311 0.348241 12.0 63.974253 \n", - "501 11.255463 0.003551 35.0 65.307378 \n", - "502 -1.431406 0.029812 16.0 65.606093 \n", - "503 6.976000 0.047245 20.0 65.067478 \n", - "\n", - " peaksSumLow pitchAvgLow pitchPeak0 pitchPeak1 pitchPeak2 pitchPeak3 \\\n", - "0 0.000000 0.000938 0.0 0.000000 0.000000 0.000000 \n", - "1 928.859251 0.015892 0.0 0.000000 0.000000 0.000000 \n", - "2 2869.988603 0.132808 0.0 0.000000 0.000000 0.000000 \n", - "3 5538.283465 1.441479 0.0 0.000000 0.000000 0.000000 \n", - "4 5645.845839 1.611968 4.0 -0.489273 -0.122318 0.012606 \n", - ".. ... ... ... ... ... ... \n", - "499 252.273386 0.002021 0.0 0.000000 0.000000 0.000000 \n", - "500 767.691038 0.011532 0.0 0.000000 0.000000 0.000000 \n", - "501 2285.758227 0.059133 0.0 0.000000 0.000000 0.000000 \n", - "502 1049.697496 0.052483 0.0 0.000000 0.000000 0.000000 \n", - "503 1301.349551 1.560049 0.0 0.000000 0.000000 0.000000 \n", - "\n", - " pitchStdDevLow postureDistance_XLow postureDistance_YLow \\\n", - "0 0.000217 35.543282 -26.272001 \n", - "1 0.004891 27.797010 -34.074141 \n", - "2 0.016102 59.757388 0.159047 \n", - "3 0.103448 72.704125 39.906715 \n", - "4 0.206093 67.788018 38.457954 \n", - ".. ... ... ... \n", - "499 0.000332 83.928074 18.551205 \n", - "500 0.001512 83.342334 17.194484 \n", - "501 0.014748 84.247482 26.589851 \n", - "502 0.004246 88.277272 43.153164 \n", - "503 0.336867 80.441325 32.396661 \n", - "\n", - " postureDistance_ZLow quartilesMagnitudes_XBand \\\n", - "0 -61.815283 0.061738 \n", - "1 -61.871151 0.145435 \n", - "2 -59.598341 0.281532 \n", - "3 -32.797410 2.869744 \n", - "4 -29.330064 6.280481 \n", - ".. ... ... \n", - "499 -65.376868 0.086797 \n", - "500 -66.147850 0.054171 \n", - "501 -57.657631 0.188673 \n", - "502 -45.124108 0.103420 \n", - "503 -48.044665 0.103100 \n", - "\n", - " quartilesMagnitudes_YBand quartilesMagnitudes_ZBand quartilesQ1_XBand \\\n", - "0 0.118949 0.178021 -0.054443 \n", - "1 0.260237 0.661256 -0.086167 \n", - "2 3.402929 7.664861 -1.268853 \n", - "3 4.702810 7.490567 -2.200263 \n", - "4 9.396958 14.039468 -4.611741 \n", - ".. ... ... ... \n", - "499 0.145225 0.227369 -0.017104 \n", - "500 0.152417 0.411110 -0.035056 \n", - "501 0.795689 2.070261 -0.347386 \n", - "502 0.191260 0.824083 -0.074998 \n", - "503 0.332578 1.263642 -0.120617 \n", - "\n", - " quartilesQ1_YBand quartilesQ1_ZBand quartilesQ2_XBand \\\n", - "0 -0.032033 -0.039487 -0.009518 \n", - "1 -0.135954 -0.136618 0.013514 \n", - "2 -1.243138 -1.259616 -0.007098 \n", - "3 -2.005430 -1.505724 0.084118 \n", - "4 -3.234598 -4.618432 0.041283 \n", - ".. ... ... ... \n", - "499 -0.035340 -0.078979 0.003914 \n", - "500 -0.090815 -0.069541 0.000288 \n", - "501 -0.245273 -0.317599 -0.000426 \n", - "502 -0.084686 -0.089356 0.000005 \n", - "503 -0.173414 -0.101217 -0.000454 \n", - "\n", - " quartilesQ2_YBand quartilesQ2_ZBand quartilesQ3_XBand \\\n", - "0 1.454335e-02 2.479075e-07 0.053999 \n", - "1 4.007931e-04 2.578741e-03 0.159795 \n", - "2 -5.021189e-02 3.661978e-06 0.810530 \n", - "3 -1.789399e-01 2.803538e-02 2.235211 \n", - "4 3.167752e-01 -6.475688e-02 5.068963 \n", - ".. ... ... ... \n", - "499 -2.528229e-06 -8.867480e-03 0.053218 \n", - "500 -1.020861e-04 7.748469e-12 0.048301 \n", - "501 -1.606221e-04 -3.735316e-03 0.297946 \n", - "502 3.517538e-04 2.042814e-04 0.125401 \n", - "503 -1.027010e-09 -1.098812e-03 0.205484 \n", - "\n", - " quartilesQ3_YBand quartilesQ3_ZBand rollAvgLow rollMotionAmountLow \\\n", - "0 0.049688 0.025612 0.029061 0.008021 \n", - "1 0.116031 0.129407 0.541524 0.085935 \n", - "2 0.316505 0.483781 6.280891 1.447569 \n", - "3 1.388993 1.843050 6.271116 2.250457 \n", - "4 3.092325 4.058300 6.265128 2.342195 \n", - ".. ... ... ... ... \n", - "499 0.038921 0.097297 0.071320 0.007932 \n", - "500 0.032708 0.074073 0.372438 0.031162 \n", - "501 0.190241 0.234410 6.278076 0.962455 \n", - "502 0.070004 0.107466 6.281475 1.129232 \n", - "503 0.107873 0.123350 1.133865 0.133845 \n", - "\n", - " rollMotionRegularityLow rollPeak0 rollPeak1 rollPeak2 rollPeak3 \\\n", - "0 1.000 0.0 0.000000 0.000000 0.000000 \n", - "1 1.000 0.0 0.000000 0.000000 0.000000 \n", - "2 1.000 9.0 21.707206 2.411912 0.002484 \n", - "3 0.988 11.0 28.398787 2.581708 0.025052 \n", - "4 0.998 27.0 57.342009 2.123778 0.000035 \n", - ".. ... ... ... ... ... \n", - "499 1.000 0.0 0.000000 0.000000 0.000000 \n", - "500 1.000 0.0 0.000000 0.000000 0.000000 \n", - "501 1.000 4.0 12.485143 3.121286 0.003238 \n", - "502 1.000 3.0 9.145478 3.048493 2.092086 \n", - "503 1.000 0.0 0.000000 0.000000 0.000000 \n", - "\n", - " rollPitchCorrelation rollStdDevLow skewness_XBand skewness_YBand \\\n", - "0 1.000000 0.008562 14.436898 -11.494984 \n", - "1 0.999999 0.123100 2.807118 -1.100507 \n", - "2 0.999997 1.958079 -0.633833 -1.197336 \n", - "3 0.999995 2.397779 -0.833540 -1.348253 \n", - "4 0.999985 2.419125 -1.132133 -1.665492 \n", - ".. ... ... ... ... \n", - "499 1.000000 0.009974 14.222786 -14.169953 \n", - "500 1.000000 0.054444 11.853592 -13.132488 \n", - "501 0.999998 1.701608 4.491024 -11.201286 \n", - "502 0.999999 1.866030 8.739616 -11.817088 \n", - "503 0.999986 0.215263 4.777859 -12.139106 \n", - "\n", - " skewness_ZBand squareSumOfComponent squareSumOfComponent_X \\\n", - "0 14.424527 1.619926e+07 867.287775 \n", - "1 12.046715 1.630370e+07 865.242225 \n", - "2 1.684239 1.654691e+07 870.233406 \n", - "3 -1.359373 6.736924e+05 661.235510 \n", - "4 -1.256109 1.680410e+07 2273.525442 \n", - ".. ... ... ... \n", - "499 13.535020 1.630266e+07 1263.693630 \n", - "500 8.691259 1.608824e+07 1335.055131 \n", - "501 -0.771061 1.905301e+07 1266.701486 \n", - "502 2.300305 1.681807e+07 1819.059819 \n", - "503 -0.432671 1.504212e+07 1414.613179 \n", - "\n", - " squareSumOfComponent_Y squareSumOfComponent_Z sumOfSquareComponents \\\n", - "0 37.266065 3120.276758 1.048970e+07 \n", - "1 51.060403 3121.481343 1.049490e+07 \n", - "2 109.430560 3088.125621 1.030580e+07 \n", - "3 90.891793 68.660385 4.502080e+05 \n", - "4 704.049839 1121.705691 6.922828e+06 \n", - ".. ... ... ... \n", - "499 2476.085590 297.876317 7.816652e+06 \n", - "500 2377.699120 298.260456 7.524785e+06 \n", - "501 2994.586208 103.687125 1.058283e+07 \n", - "502 2277.777042 4.147205 8.497264e+06 \n", - "503 2463.791439 0.011977 8.071399e+06 \n", - "\n", - " sumPerComponent_XBand sumPerComponent_YBand sumPerComponent_ZBand \\\n", - "0 29.002355 6.001318 55.003283 \n", - "1 29.009274 7.006492 55.008412 \n", - "2 29.038915 10.034777 55.036378 \n", - "3 25.052536 9.039004 8.037320 \n", - "4 47.093516 26.066646 33.087563 \n", - ".. ... ... ... \n", - "499 35.002929 49.003488 17.002448 \n", - "500 36.005837 48.005767 17.004361 \n", - "501 35.015515 54.010908 10.011338 \n", - "502 42.009258 47.007507 2.004713 \n", - "503 37.010612 49.008707 0.006081 \n", - "\n", - " totalAbsoluteAreaBand totalEnergy_XBand totalEnergy_YBand \\\n", - "0 312.592555 1290.394581 60.611492 \n", - "1 864.715960 2537.180818 677.832377 \n", - "2 3616.215615 9218.952240 6770.834825 \n", - "3 4165.495456 12493.455174 6106.239879 \n", - "4 8033.196394 33021.472511 16683.117998 \n", - ".. ... ... ... \n", - "499 384.671068 1908.638778 3729.334512 \n", - "500 611.899981 2211.472549 3700.438879 \n", - "501 1307.327021 3237.943851 5051.325838 \n", - "502 778.281403 3591.558470 3805.375198 \n", - "503 898.820043 3413.563386 4090.414372 \n", - "\n", - " totalEnergy_ZBand totalKineticEnergyBand totalMagnitudeBand \\\n", - "0 4694.902256 3.782695e+09 78.088845 \n", - "1 5132.819023 3.795297e+09 91.657431 \n", - "2 11785.651118 3.844737e+09 166.747125 \n", - "3 5277.603869 7.464573e+08 154.645146 \n", - "4 26879.848539 3.852492e+09 276.840101 \n", - ".. ... ... ... \n", - "499 455.208874 3.795297e+09 78.389786 \n", - "500 572.202692 3.770092e+09 80.859842 \n", - "501 895.466940 4.111329e+09 96.114795 \n", - "502 341.436881 3.855401e+09 88.266159 \n", - "503 365.728609 3.654731e+09 88.770283 \n", - "\n", - " totalMeanLow variance_XBand variance_YBand variance_ZBand \\\n", - "0 12875.786040 2.586383 0.137999 9.396457 \n", - "1 11911.441472 5.334184 1.579538 10.411239 \n", - "2 5736.225303 24.202143 19.306809 29.209739 \n", - "3 3620.581741 35.239450 19.355604 16.080099 \n", - "4 1701.460966 100.726696 54.227260 85.860586 \n", - ".. ... ... ... ... \n", - "499 503.883251 3.825507 7.779741 0.922262 \n", - "500 505.761307 4.495299 7.917488 1.197929 \n", - "501 449.768115 7.228331 11.218333 2.322136 \n", - "502 -1044.193986 7.407112 8.274669 0.777455 \n", - "503 879.999083 7.290436 8.752011 0.882940 \n", - "\n", - " velocity_XBand velocity_YBand velocity_ZBand \n", - "0 29.001359 -6.000329 5.500258e+01 \n", - "1 29.001351 -7.000462 5.500257e+01 \n", - "2 29.001408 -10.002113 5.499929e+01 \n", - "3 25.002506 -9.002192 8.000601e+00 \n", - "4 47.001504 -26.003086 3.299904e+01 \n", - ".. ... ... ... \n", - "499 35.001684 -49.002253 1.700079e+01 \n", - "500 36.001692 -48.002424 1.700076e+01 \n", - "501 35.001969 -54.002179 9.999950e+00 \n", - "502 42.001899 -47.002248 -1.999967e+00 \n", - "503 37.000000 -49.000000 1.240497e-08 \n", - "\n", - "[504 rows x 169 columns]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "from calculatingfeatures.CalculatingFeatures.helper_functions import convert3DEmpaticaToArray\n", - "\n", - "pathToAccCsv = \"example_data/S2_E4_Data/ACC.csv\"\n", - "windowLength = 500\n", - "\n", - "#np.seterr(all='raise')\n", - "\n", - "# get an array of values from ACC empatica file\n", - "data, startTimeStamp, sampleRate = convert3DEmpaticaToArray(pathToAccCsv)\n", - "\n", - "# Convert the ACC data into 2D array\n", - "x_2D = convertInputInto2d(data[0], windowLength)\n", - "y_2D = convertInputInto2d(data[1], windowLength)\n", - "z_2D = convertInputInto2d(data[2], windowLength)\n", - "\n", - "# Create a list with feature names\n", - "featureNames = []\n", - "featureNames.extend(accelerometerFeatureNames)\n", - "featureNames.extend(frequencyFeatureNames)\n", - "\n", - "pd.set_option('display.max_columns', None)\n", - "\n", - "# Calculate features\n", - "calculatedFeatures = calculateFeatures(x_2D, y_2D, z_2D, fs=int(sampleRate), featureNames=featureNames)\n", - "print(calculatedFeatures)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## convertInputInto2dTime" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[10, 150, 1100, 1150], [1200, 1300], [1400, 1500], [1600]]\n", - "[[0, 50, 100, 150], [200, 300], [400, 500], [600]]\n" - ] - } - ], - "source": [ - "df = pd.DataFrame()\n", - "df[\"data\"] = [10, 150, 1100, 1150, 1200, 1300, 1400, 1500, 1600]\n", - "df[\"time\"] = [0, 50, 100, 150, 200, 300, 400, 500, 600]\n", - "\n", - "outData, outTime = convertInputInto2dTime(df, 200)\n", - "print(outData)\n", - "print(outTime)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate all features" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 24%|██▎ | 4/17 [00:03<00:11, 1.12it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad signal. Too little RRs detected. 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 29%|██▉ | 5/17 [00:04<00:11, 1.08it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad signal. Too little RRs detected. 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 35%|███▌ | 6/17 [00:05<00:10, 1.09it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad signal. Too little RRs detected. 1\n", - "Bad signal. Too little RRs detected. 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 53%|█████▎ | 9/17 [00:08<00:07, 1.06it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad signal. Too little RRs detected. 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 59%|█████▉ | 10/17 [00:09<00:06, 1.07it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad signal. Too little RRs detected. 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 71%|███████ | 12/17 [00:11<00:04, 1.12it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad signal. Too little RRs detected. 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 88%|████████▊ | 15/17 [00:13<00:01, 1.06it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad signal. Too little RRs detected. 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 17/17 [00:15<00:00, 1.09it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bad signal. Too little RRs detected. 1\n", - "Bad signal. Too little RRs detected. 1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " absEnergy_X absEnergy_Y absEnergy_Z absoluteAreaAllBand \\\n", - "0 9757.282156 10849.028553 9811.741631 930.324637 \n", - "1 9448.079674 8899.975693 9956.680101 880.255964 \n", - "2 9669.760737 9184.529987 9121.402956 889.394563 \n", - "3 10026.356290 9801.236188 9982.377726 920.462589 \n", - "4 9709.603922 10691.230186 9662.734156 906.331468 \n", - "5 10233.693555 10371.886729 10563.612304 922.999063 \n", - "6 9482.841316 10326.075806 10154.052192 891.241288 \n", - "7 9520.121266 10241.641412 10387.903719 889.958466 \n", - "8 10395.792313 10179.629166 10107.379246 899.898502 \n", - "9 11026.931493 9834.741763 10120.339487 937.539507 \n", - "10 9574.581294 8891.577695 9480.013849 853.098151 \n", - "11 10266.304113 9524.407888 9519.015587 894.370062 \n", - "12 9639.580580 9683.493055 10251.534492 912.690717 \n", - "13 10149.017500 9632.893918 9966.713340 896.978822 \n", - "14 9190.474376 10330.498873 10536.313374 904.039012 \n", - "15 10890.487664 10640.928635 10044.509683 949.133312 \n", - "16 6484.122156 6140.316953 7010.804341 618.770314 \n", - "\n", - " absoluteArea_XBand absoluteArea_YBand absoluteArea_ZBand \\\n", - "0 9.802377 -3.995068 4.135917 \n", - "1 11.552164 -11.852706 6.764970 \n", - "2 -6.075127 -14.974184 -3.550690 \n", - "3 13.940072 -1.208619 -5.439318 \n", - "4 -0.778795 -8.573990 9.118902 \n", - "5 -5.432606 -2.345853 -5.366276 \n", - "6 -10.268284 1.608141 -2.508280 \n", - "7 7.573499 -3.973162 9.895818 \n", - "8 2.323463 -7.297003 5.956317 \n", - "9 0.066851 11.319004 4.828713 \n", - "10 -5.535852 -3.323174 9.748395 \n", - "11 -7.277916 -9.262661 6.302632 \n", - "12 -4.434544 -4.278268 6.222900 \n", - "13 -7.436684 3.024444 -5.008479 \n", - "14 4.340700 2.523212 7.274647 \n", - "15 -0.108792 -12.711790 11.993602 \n", - "16 6.800276 8.102356 9.725532 \n", - "\n", - " absoluteMean_XBand absoluteMean_YBand absoluteMean_ZBand \\\n", - "0 0.032675 -0.013317 0.013786 \n", - "1 0.038507 -0.039509 0.022550 \n", - "2 -0.020250 -0.049914 -0.011836 \n", - "3 0.046467 -0.004029 -0.018131 \n", - "4 -0.002596 -0.028580 0.030396 \n", - "5 -0.018109 -0.007820 -0.017888 \n", - "6 -0.034228 0.005360 -0.008361 \n", - "7 0.025245 -0.013244 0.032986 \n", - "8 0.007745 -0.024323 0.019854 \n", - "9 0.000223 0.037730 0.016096 \n", - "10 -0.018453 -0.011077 0.032495 \n", - "11 -0.024260 -0.030876 0.021009 \n", - "12 -0.014782 -0.014261 0.020743 \n", - "13 -0.024789 0.010081 -0.016695 \n", - "14 0.014469 0.008411 0.024249 \n", - "15 -0.000363 -0.042373 0.039979 \n", - "16 0.022668 0.027008 0.032418 \n", - "\n", - " amplitude_XBand amplitude_XLow amplitude_YBand amplitude_YLow \\\n", - "0 11.840441 15.367655 5.561830 13.599581 \n", - "1 10.842231 13.185562 11.954154 15.408985 \n", - "2 12.065636 15.300944 12.504229 15.425589 \n", - "3 9.885092 13.416908 5.636100 12.551434 \n", - "4 5.721227 12.760709 10.656748 13.420126 \n", - "5 6.394455 12.454305 7.781983 13.642673 \n", - "6 11.251708 14.216014 8.006252 12.116988 \n", - "7 9.572914 12.906963 5.469677 12.083638 \n", - "8 5.521984 12.295475 10.685971 14.293234 \n", - "9 5.739659 12.753636 12.351271 15.905907 \n", - "10 9.259486 12.892579 7.553088 11.666330 \n", - "11 12.682163 15.907374 10.232092 12.641317 \n", - "12 10.885035 13.532587 7.599297 11.406162 \n", - "13 8.409818 13.167735 6.321747 11.584058 \n", - "14 5.417743 12.668153 7.214473 12.111088 \n", - "15 5.565133 13.389627 12.269357 15.438568 \n", - "16 9.572596 11.662286 10.803180 14.518034 \n", - "\n", - " amplitude_ZBand amplitude_ZLow areaAutocorr_X areaAutocorr_Y \\\n", - "0 6.120984 12.077082 1.251150 0.346077 \n", - "1 7.338200 13.034990 0.613081 -0.337795 \n", - "2 8.157018 11.770679 1.320552 0.285729 \n", - "3 5.816986 11.504766 -0.714717 0.104095 \n", - "4 10.494853 13.742355 -0.177515 0.744384 \n", - "5 8.948602 12.838789 -0.284261 0.190317 \n", - "6 5.537172 12.112158 0.403123 2.862261 \n", - "7 12.102983 14.971049 1.163917 0.491810 \n", - "8 4.930710 12.311160 1.211632 0.166704 \n", - "9 8.926564 12.662584 0.403719 0.886899 \n", - "10 12.090767 15.875714 0.957135 1.056713 \n", - "11 5.467044 11.685198 0.034285 0.114891 \n", - "12 11.504623 14.014334 0.083044 -0.309026 \n", - "13 7.422933 12.495047 2.116489 -0.061512 \n", - "14 6.803331 10.741192 1.100854 0.394110 \n", - "15 10.905015 14.203833 1.739354 0.889813 \n", - "16 12.463576 15.348119 NaN NaN \n", - "\n", - " areaAutocorr_Z areaUnderAccelerationMagnitude areaUnderMagnitude \\\n", - "0 0.489267 817.023838 817.023838 \n", - "1 1.201937 746.603640 746.603640 \n", - "2 0.731113 863.282811 863.282811 \n", - "3 0.980113 797.080198 797.080198 \n", - "4 0.461838 627.711913 627.711913 \n", - "5 0.885326 793.145688 793.145688 \n", - "6 0.046986 751.758387 751.758387 \n", - "7 0.285232 804.852128 804.852128 \n", - "8 1.022880 737.318718 737.318718 \n", - "9 0.418596 748.433836 748.433836 \n", - "10 1.295665 729.288649 729.288649 \n", - "11 0.433735 832.523937 832.523937 \n", - "12 0.114227 874.454264 874.454264 \n", - "13 0.895080 684.783664 684.783664 \n", - "14 0.864031 684.304828 684.304828 \n", - "15 0.615599 845.185357 845.185357 \n", - "16 NaN 889.558912 889.558912 \n", - "\n", - " area_XLow area_YLow area_ZLow autocorrelation_100_X \\\n", - "0 8.507300 17.448677 -93.586973 -0.033598 \n", - "1 195.204955 -2.151729 -22.210498 -0.128406 \n", - "2 14.886643 -72.277090 3.763140 -0.045989 \n", - "3 -6.580937 -57.815404 64.301458 -0.114063 \n", - "4 -33.226356 -169.940579 -44.658588 -0.023989 \n", - "5 92.264979 54.311395 114.608368 -0.006580 \n", - "6 146.122043 106.458752 -12.069510 0.071640 \n", - "7 81.070954 9.782555 101.605517 0.114433 \n", - "8 76.638693 57.580025 -135.876734 0.025191 \n", - "9 19.624899 43.201275 151.705470 -0.047697 \n", - "10 -16.976897 2.086521 3.798669 0.081849 \n", - "11 4.496054 17.036321 -78.608625 0.073983 \n", - "12 2.403524 -47.309146 161.572935 0.137246 \n", - "13 -156.226880 19.395747 7.455774 0.020696 \n", - "14 -25.123373 -32.135156 -54.622977 0.048791 \n", - "15 -3.221847 -77.477830 -75.762919 0.186899 \n", - "16 120.936148 -69.355449 158.385979 -0.076146 \n", - "\n", - " autocorrelation_100_Y autocorrelation_100_Z autocorrelation_10_X \\\n", - "0 0.059537 0.053420 0.106405 \n", - "1 0.103122 0.007333 0.045905 \n", - "2 0.039841 0.040569 -0.054911 \n", - "3 0.034858 0.000401 -0.008231 \n", - "4 0.090927 -0.158701 0.071754 \n", - "5 0.012849 0.006175 0.009983 \n", - "6 -0.050546 0.037803 0.045039 \n", - "7 -0.154497 0.117893 -0.042434 \n", - "8 0.078231 -0.106049 0.074367 \n", - "9 -0.033887 0.048270 0.089410 \n", - "10 -0.043212 0.116065 0.060132 \n", - "11 0.008015 -0.043320 -0.017004 \n", - "12 0.061625 -0.075384 0.034301 \n", - "13 -0.025759 -0.025377 0.031711 \n", - "14 0.009157 0.018478 -0.044737 \n", - "15 0.075965 -0.007770 0.038175 \n", - "16 -0.065110 0.043484 -0.065927 \n", - "\n", - " autocorrelation_10_Y autocorrelation_10_Z autocorrelation_20_X \\\n", - "0 -0.100977 -0.038760 0.040176 \n", - "1 0.052315 -0.034063 0.127281 \n", - "2 0.001645 -0.038531 -0.017747 \n", - "3 0.023636 0.033845 0.054339 \n", - "4 -0.067678 -0.037247 0.064662 \n", - "5 -0.047529 0.088462 0.088562 \n", - "6 -0.033754 0.004394 0.035105 \n", - "7 0.025099 -0.017073 0.014068 \n", - "8 0.043763 0.123542 -0.107926 \n", - "9 -0.065737 -0.075726 0.050419 \n", - "10 -0.093968 0.025957 0.023002 \n", - "11 -0.007979 -0.011119 -0.061107 \n", - "12 0.006941 -0.190285 -0.003771 \n", - "13 0.052174 0.014034 0.033961 \n", - "14 -0.014764 -0.070400 -0.046461 \n", - "15 0.033185 0.053601 -0.006713 \n", - "16 0.028038 0.073310 -0.056683 \n", - "\n", - " autocorrelation_20_Y autocorrelation_20_Z autocorrelation_30_X \\\n", - "0 0.003765 0.002543 -0.049990 \n", - "1 -0.129480 -0.014282 0.027596 \n", - "2 0.011977 -0.110984 0.038068 \n", - "3 -0.058947 -0.038399 0.000622 \n", - "4 0.067429 0.004266 -0.057145 \n", - "5 0.031023 -0.001941 0.034667 \n", - "6 0.012584 0.030843 -0.046854 \n", - "7 -0.006981 -0.007008 -0.096810 \n", - "8 0.066055 0.003499 -0.025921 \n", - "9 0.046704 0.051978 0.054563 \n", - "10 -0.039985 -0.062853 0.002230 \n", - "11 0.063928 -0.108339 0.071981 \n", - "12 -0.056253 -0.000450 -0.026721 \n", - "13 -0.010468 0.001997 -0.019577 \n", - "14 -0.068659 0.079928 0.065463 \n", - "15 0.001135 -0.026500 -0.135474 \n", - "16 0.074539 -0.126566 0.061670 \n", - "\n", - " autocorrelation_30_Y autocorrelation_30_Z autocorrelation_50_X \\\n", - "0 -0.040294 -0.011024 0.060141 \n", - "1 0.040854 0.093325 -0.071681 \n", - "2 0.007768 -0.104029 0.077159 \n", - "3 -0.037307 -0.083809 0.031726 \n", - "4 -0.085651 -0.033945 -0.019312 \n", - "5 0.031843 0.010472 0.007438 \n", - "6 -0.049723 -0.104720 0.027879 \n", - "7 0.058257 -0.034894 -0.007670 \n", - "8 -0.022534 0.069451 0.088884 \n", - "9 0.021820 -0.040629 -0.026695 \n", - "10 0.024232 -0.013382 -0.052934 \n", - "11 0.014563 -0.048561 -0.064959 \n", - "12 -0.115923 0.132645 0.045606 \n", - "13 0.023235 0.017712 -0.000441 \n", - "14 -0.017772 0.114692 -0.061390 \n", - "15 -0.083997 0.049844 -0.052004 \n", - "16 0.040640 -0.130493 -0.014951 \n", - "\n", - " autocorrelation_50_Y autocorrelation_50_Z autocorrelation_5_X \\\n", - "0 -0.035205 -0.055939 -0.009348 \n", - "1 -0.062680 -0.055987 -0.069317 \n", - "2 0.002311 0.111358 -0.004053 \n", - "3 -0.017752 -0.016835 0.005310 \n", - "4 -0.035473 -0.033905 0.031233 \n", - "5 -0.072231 -0.044880 -0.042741 \n", - "6 0.035824 0.096863 -0.060521 \n", - "7 0.087412 -0.149575 -0.082264 \n", - "8 0.031052 0.071567 0.002081 \n", - "9 0.028157 0.055367 0.040055 \n", - "10 0.020769 0.027237 -0.052585 \n", - "11 0.009232 0.083537 0.037802 \n", - "12 0.077260 0.037070 -0.009196 \n", - "13 -0.125566 -0.028065 0.091008 \n", - "14 0.058202 0.018044 -0.084337 \n", - "15 -0.063941 -0.123819 -0.024515 \n", - "16 -0.034196 -0.061111 -0.092936 \n", - "\n", - " autocorrelation_5_Y autocorrelation_5_Z autocorrelation_75_X \\\n", - "0 0.036256 -0.059414 -0.064041 \n", - "1 -0.016056 0.066141 -0.001367 \n", - "2 -0.084868 -0.058238 -0.029358 \n", - "3 -0.087827 -0.099613 -0.051004 \n", - "4 -0.013456 0.012132 0.008428 \n", - "5 0.023408 0.109833 0.023188 \n", - "6 -0.090370 0.013848 -0.054948 \n", - "7 -0.024935 0.033933 -0.104112 \n", - "8 0.039163 0.068791 -0.156985 \n", - "9 -0.085966 -0.018881 -0.035873 \n", - "10 0.155348 0.089262 -0.018731 \n", - "11 -0.055070 -0.053334 0.088817 \n", - "12 -0.062619 0.039355 -0.028028 \n", - "13 0.025877 0.003278 0.002593 \n", - "14 0.077899 -0.037150 -0.053984 \n", - "15 -0.028301 -0.017459 0.105301 \n", - "16 0.116500 0.077768 -0.030928 \n", - "\n", - " autocorrelation_75_Y autocorrelation_75_Z averageVectorLength \\\n", - "0 0.022417 0.117357 9.683036 \n", - "1 0.023094 0.006500 9.222364 \n", - "2 0.073042 0.013775 9.235800 \n", - "3 0.086656 -0.026402 9.612871 \n", - "4 -0.010023 0.062667 9.645595 \n", - "5 -0.058372 0.022381 9.861227 \n", - "6 -0.012912 0.057195 9.574246 \n", - "7 0.022179 -0.087485 9.665710 \n", - "8 0.051851 0.009877 9.744560 \n", - "9 0.001869 0.020469 9.764734 \n", - "10 -0.060570 -0.032457 9.275388 \n", - "11 0.029491 0.056988 9.519998 \n", - "12 0.021713 0.100584 9.425750 \n", - "13 0.039316 0.153231 9.580493 \n", - "14 0.034723 -0.056405 9.668018 \n", - "15 0.009760 0.016339 9.944224 \n", - "16 0.030622 0.073927 6.330216 \n", - "\n", - " averageVectorLengthLow averageVectorLengthPower \\\n", - "0 3.954539 93.761196 \n", - "1 3.875639 85.051992 \n", - "2 3.639579 85.300010 \n", - "3 3.991453 92.407281 \n", - "4 3.881851 93.037501 \n", - "5 4.133362 97.243807 \n", - "6 3.935599 91.666188 \n", - "7 3.991640 93.425941 \n", - "8 4.194873 94.956458 \n", - "9 3.979599 95.350039 \n", - "10 3.938795 86.032830 \n", - "11 3.852962 90.630358 \n", - "12 4.085098 88.844765 \n", - "13 3.968863 91.785846 \n", - "14 3.958077 93.470567 \n", - "15 4.074935 98.887582 \n", - "16 2.623497 40.071633 \n", - "\n", - " averageVectorLengthPowerLow avgPeakAmplitudeChangeAfter_X \\\n", - "0 15.638376 NaN \n", - "1 15.020578 NaN \n", - "2 13.246539 NaN \n", - "3 15.931694 NaN \n", - "4 15.068770 NaN \n", - "5 17.084679 NaN \n", - "6 15.488938 NaN \n", - "7 15.933187 NaN \n", - "8 17.596959 NaN \n", - "9 15.837206 NaN \n", - "10 15.514102 NaN \n", - "11 14.845319 NaN \n", - "12 16.688024 NaN \n", - "13 15.751873 NaN \n", - "14 15.666371 NaN \n", - "15 16.605092 NaN \n", - "16 6.882736 NaN \n", - "\n", - " avgPeakAmplitudeChangeAfter_Y avgPeakAmplitudeChangeAfter_Z \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "\n", - " avgPeakAmplitudeChangeBefore_X avgPeakAmplitudeChangeBefore_Y \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "\n", - " avgPeakAmplitudeChangeBefore_Z avgPeakChangeRatio_X \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "\n", - " avgPeakChangeRatio_Y avgPeakChangeRatio_Z avgPeakDecreaseTime_X \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "\n", - " avgPeakDecreaseTime_Y avgPeakDecreaseTime_Z avgPeakDuration_X \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "\n", - " avgPeakDuration_Y avgPeakDuration_Z avgPeakIncreaseTime_X \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "\n", - " avgPeakIncreaseTime_Y avgPeakIncreaseTime_Z binnedEntropy_X \\\n", - "0 NaN NaN 2.298323 \n", - "1 NaN NaN 2.289073 \n", - "2 NaN NaN 2.280011 \n", - "3 NaN NaN 2.286551 \n", - "4 NaN NaN 2.292404 \n", - "5 NaN NaN 2.288386 \n", - "6 NaN NaN 2.282082 \n", - "7 NaN NaN 2.291969 \n", - "8 NaN NaN 2.290071 \n", - "9 NaN NaN 2.282979 \n", - "10 NaN NaN 2.291118 \n", - "11 NaN NaN 2.287470 \n", - "12 NaN NaN 2.298981 \n", - "13 NaN NaN 2.294519 \n", - "14 NaN NaN 2.290551 \n", - "15 NaN NaN 2.284167 \n", - "16 NaN NaN 2.000757 \n", - "\n", - " binnedEntropy_Y binnedEntropy_Z calcMeanCrossingRateAutocorr_X \\\n", - "0 2.278283 2.287630 65.0 \n", - "1 2.272205 2.290649 67.0 \n", - "2 2.268384 2.285813 71.0 \n", - "3 2.278670 2.299186 57.0 \n", - "4 2.286274 2.297375 65.0 \n", - "5 2.290115 2.285257 63.0 \n", - "6 2.294149 2.283044 53.0 \n", - "7 2.288501 2.283649 71.0 \n", - "8 2.293779 2.284550 61.0 \n", - "9 2.294618 2.296906 65.0 \n", - "10 2.288205 2.280010 65.0 \n", - "11 2.287591 2.296889 67.0 \n", - "12 2.291509 2.288529 61.0 \n", - "13 2.273759 2.296630 61.0 \n", - "14 2.295320 2.288277 63.0 \n", - "15 2.295281 2.287458 50.0 \n", - "16 1.978263 1.988834 0.0 \n", - "\n", - " calcMeanCrossingRateAutocorr_Y calcMeanCrossingRateAutocorr_Z \\\n", - "0 59.0 65.0 \n", - "1 61.0 62.0 \n", - "2 71.0 69.0 \n", - "3 57.0 65.0 \n", - "4 67.0 63.0 \n", - "5 59.0 60.0 \n", - "6 77.0 57.0 \n", - "7 65.0 60.0 \n", - "8 54.0 64.0 \n", - "9 57.0 51.0 \n", - "10 75.0 58.0 \n", - "11 56.0 69.0 \n", - "12 53.0 55.0 \n", - "13 54.0 75.0 \n", - "14 65.0 69.0 \n", - "15 64.0 61.0 \n", - "16 0.0 0.0 \n", - "\n", - " changeDuration_X changeDuration_Y changeDuration_Z changeRate_X \\\n", - "0 NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN \n", - "5 NaN NaN NaN NaN \n", - "6 NaN NaN NaN NaN \n", - "7 NaN NaN NaN NaN \n", - "8 NaN NaN NaN NaN \n", - "9 NaN NaN NaN NaN \n", - "10 NaN NaN NaN NaN \n", - "11 NaN NaN NaN NaN \n", - "12 NaN NaN NaN NaN \n", - "13 NaN NaN NaN NaN \n", - "14 NaN NaN NaN NaN \n", - "15 NaN NaN NaN NaN \n", - "16 NaN NaN NaN NaN \n", - "\n", - " changeRate_Y changeRate_Z coefficientOfVariation_XBand \\\n", - "0 NaN NaN 0.533236 \n", - "1 NaN NaN 0.527096 \n", - "2 NaN NaN 0.533909 \n", - "3 NaN NaN 0.515671 \n", - "4 NaN NaN 0.522352 \n", - "5 NaN NaN 0.528759 \n", - "6 NaN NaN 0.547856 \n", - "7 NaN NaN 0.530163 \n", - "8 NaN NaN 0.517942 \n", - "9 NaN NaN 0.518977 \n", - "10 NaN NaN 0.542770 \n", - "11 NaN NaN 0.539878 \n", - "12 NaN NaN 0.534670 \n", - "13 NaN NaN 0.533739 \n", - "14 NaN NaN 0.521455 \n", - "15 NaN NaN 0.516158 \n", - "16 NaN NaN 0.599106 \n", - "\n", - " coefficientOfVariation_XLow coefficientOfVariation_YBand \\\n", - "0 0.527017 0.514151 \n", - "1 0.452063 0.554167 \n", - "2 0.526474 0.556140 \n", - "3 0.526374 0.520226 \n", - "4 0.540857 0.534679 \n", - "5 0.493230 0.527201 \n", - "6 0.477819 0.525871 \n", - "7 0.495349 0.527140 \n", - "8 0.494374 0.537649 \n", - "9 0.524269 0.532100 \n", - "10 0.536975 0.525607 \n", - "11 0.537260 0.542834 \n", - "12 0.532577 0.526495 \n", - "13 0.570930 0.511987 \n", - "14 0.536704 0.522882 \n", - "15 0.525543 0.546662 \n", - "16 0.543337 0.624214 \n", - "\n", - " coefficientOfVariation_YLow coefficientOfVariation_ZBand \\\n", - "0 0.527270 0.516443 \n", - "1 0.525025 0.519009 \n", - "2 0.557286 0.529653 \n", - "3 0.548729 0.521221 \n", - "4 0.589041 0.527130 \n", - "5 0.513757 0.525058 \n", - "6 0.483711 0.525506 \n", - "7 0.522798 0.535721 \n", - "8 0.505772 0.514093 \n", - "9 0.518419 0.527682 \n", - "10 0.529116 0.542240 \n", - "11 0.516687 0.515561 \n", - "12 0.549653 0.529763 \n", - "13 0.524731 0.520360 \n", - "14 0.536917 0.515216 \n", - "15 0.557694 0.526219 \n", - "16 0.630624 0.619395 \n", - "\n", - " coefficientOfVariation_ZLow correlation_X_YBand correlation_X_YLow \\\n", - "0 0.555990 0.999983 0.999981 \n", - "1 0.537604 0.999979 0.999984 \n", - "2 0.529651 0.999985 0.999983 \n", - "3 0.500520 0.999982 0.999981 \n", - "4 0.539844 0.999982 0.999983 \n", - "5 0.485686 0.999986 0.999986 \n", - "6 0.532170 0.999981 0.999984 \n", - "7 0.491897 0.999983 0.999983 \n", - "8 0.570792 0.999981 0.999984 \n", - "9 0.473817 0.999983 0.999983 \n", - "10 0.521041 0.999982 0.999983 \n", - "11 0.556969 0.999985 0.999986 \n", - "12 0.479618 0.999985 0.999982 \n", - "13 0.527940 0.999982 0.999981 \n", - "14 0.546964 0.999986 0.999984 \n", - "15 0.563018 0.999982 0.999982 \n", - "16 0.540351 0.999979 0.999978 \n", - "\n", - " correlation_X_ZBand correlation_X_ZLow correlation_Y_ZBand \\\n", - "0 0.999984 0.999984 0.999983 \n", - "1 0.999984 0.999984 0.999981 \n", - "2 0.999986 0.999984 0.999986 \n", - "3 0.999983 0.999984 0.999985 \n", - "4 0.999983 0.999982 0.999980 \n", - "5 0.999984 0.999985 0.999987 \n", - "6 0.999984 0.999985 0.999984 \n", - "7 0.999985 0.999986 0.999982 \n", - "8 0.999984 0.999984 0.999982 \n", - "9 0.999984 0.999984 0.999987 \n", - "10 0.999979 0.999983 0.999983 \n", - "11 0.999981 0.999978 0.999982 \n", - "12 0.999980 0.999981 0.999980 \n", - "13 0.999986 0.999984 0.999985 \n", - "14 0.999984 0.999981 0.999981 \n", - "15 0.999983 0.999984 0.999979 \n", - "16 0.999980 0.999978 0.999978 \n", - "\n", - " correlation_Y_ZLow countAboveMeanAutocorr_X countAboveMeanAutocorr_Y \\\n", - "0 0.999981 100.0 105.0 \n", - "1 0.999984 96.0 103.0 \n", - "2 0.999986 97.0 98.0 \n", - "3 0.999984 106.0 107.0 \n", - "4 0.999982 100.0 96.0 \n", - "5 0.999984 101.0 108.0 \n", - "6 0.999984 98.0 97.0 \n", - "7 0.999985 110.0 101.0 \n", - "8 0.999985 104.0 106.0 \n", - "9 0.999987 92.0 102.0 \n", - "10 0.999983 90.0 92.0 \n", - "11 0.999983 91.0 103.0 \n", - "12 0.999982 106.0 103.0 \n", - "13 0.999982 97.0 107.0 \n", - "14 0.999980 98.0 98.0 \n", - "15 0.999981 98.0 101.0 \n", - "16 0.999975 0.0 0.0 \n", - "\n", - " countAboveMeanAutocorr_Z countAboveMean_X countAboveMean_Y \\\n", - "0 98.0 153.0 151.0 \n", - "1 99.0 155.0 145.0 \n", - "2 103.0 146.0 149.0 \n", - "3 96.0 148.0 148.0 \n", - "4 99.0 158.0 142.0 \n", - "5 100.0 158.0 156.0 \n", - "6 102.0 154.0 151.0 \n", - "7 94.0 157.0 148.0 \n", - "8 103.0 156.0 154.0 \n", - "9 102.0 147.0 150.0 \n", - "10 98.0 149.0 144.0 \n", - "11 98.0 149.0 153.0 \n", - "12 104.0 149.0 145.0 \n", - "13 105.0 143.0 163.0 \n", - "14 103.0 143.0 152.0 \n", - "15 104.0 153.0 152.0 \n", - "16 0.0 107.0 194.0 \n", - "\n", - " countAboveMean_Z countBelowMean_X countBelowMean_Y countBelowMean_Z \\\n", - "0 151.0 147.0 149.0 149.0 \n", - "1 145.0 145.0 155.0 155.0 \n", - "2 153.0 154.0 151.0 147.0 \n", - "3 153.0 152.0 152.0 147.0 \n", - "4 147.0 142.0 158.0 153.0 \n", - "5 161.0 142.0 144.0 139.0 \n", - "6 141.0 146.0 149.0 159.0 \n", - "7 152.0 143.0 152.0 148.0 \n", - "8 148.0 144.0 146.0 152.0 \n", - "9 156.0 153.0 150.0 144.0 \n", - "10 160.0 151.0 156.0 140.0 \n", - "11 147.0 151.0 147.0 153.0 \n", - "12 148.0 151.0 155.0 152.0 \n", - "13 147.0 157.0 137.0 153.0 \n", - "14 143.0 157.0 148.0 157.0 \n", - "15 143.0 147.0 148.0 157.0 \n", - "16 107.0 193.0 106.0 193.0 \n", - "\n", - " derivTonic_X derivTonic_Y derivTonic_Z deriv_X deriv_Y deriv_Z \\\n", - "0 2.264066 1.663697 -1.978076 -29.613465 9.554828 15.833924 \n", - "1 3.378476 0.002513 -2.840698 2.282190 9.914019 -17.346159 \n", - "2 9.343808 0.982711 0.262335 13.724742 8.435181 19.735082 \n", - "3 0.811307 1.112904 7.486793 -21.061162 0.162531 10.641201 \n", - "4 -4.935032 0.429249 4.470062 13.630978 28.242010 -17.852050 \n", - "5 0.615650 9.011439 1.019376 11.908970 12.286923 7.905318 \n", - "6 -0.419359 0.649203 -7.831564 4.122110 8.160791 9.625673 \n", - "7 -8.606199 0.611211 -9.516828 4.243979 -8.139702 -18.404318 \n", - "8 2.839395 -2.098615 3.895081 -14.428611 20.572239 0.857868 \n", - "9 -7.724864 -6.195070 -5.434508 2.497424 -15.977319 -18.537690 \n", - "10 4.473552 -8.085741 1.447796 6.607903 -12.228337 -11.535852 \n", - "11 -0.316638 -3.037811 6.594929 17.468651 1.330721 8.141867 \n", - "12 4.142621 3.497675 -13.110317 23.332381 -2.861054 -1.739394 \n", - "13 -0.286484 2.316337 1.315161 25.572897 5.733510 16.532478 \n", - "14 -4.116657 -1.928712 6.173228 -12.050769 -19.877969 -9.532184 \n", - "15 -9.825086 4.237792 10.268698 2.794769 12.292966 -3.724351 \n", - "16 2.010568 4.794947 5.181957 -7.922947 -7.804784 -16.465154 \n", - "\n", - " dominantFrequencyEnergy_XBand dominantFrequencyEnergy_XLow \\\n", - "0 0.021613 0.032114 \n", - "1 0.028958 0.040884 \n", - "2 0.021711 0.033403 \n", - "3 0.022758 0.049079 \n", - "4 0.021350 0.031001 \n", - "5 0.020972 0.033817 \n", - "6 0.025502 0.033085 \n", - "7 0.022359 0.033667 \n", - "8 0.026543 0.037157 \n", - "9 0.020597 0.036296 \n", - "10 0.018570 0.030853 \n", - "11 0.018851 0.030482 \n", - "12 0.023323 0.034106 \n", - "13 0.021933 0.051276 \n", - "14 0.023552 0.035077 \n", - "15 0.026252 0.036015 \n", - "16 0.018765 0.030059 \n", - "\n", - " dominantFrequencyEnergy_YBand dominantFrequencyEnergy_YLow \\\n", - "0 0.019957 0.031307 \n", - "1 0.021265 0.043713 \n", - "2 0.024045 0.036826 \n", - "3 0.029402 0.030302 \n", - "4 0.022344 0.032830 \n", - "5 0.018170 0.025395 \n", - "6 0.021805 0.033030 \n", - "7 0.023081 0.039157 \n", - "8 0.019678 0.036312 \n", - "9 0.022386 0.031697 \n", - "10 0.022396 0.031159 \n", - "11 0.021019 0.029694 \n", - "12 0.025142 0.032953 \n", - "13 0.024782 0.037983 \n", - "14 0.021930 0.030912 \n", - "15 0.028484 0.037540 \n", - "16 0.024735 0.035134 \n", - "\n", - " dominantFrequencyEnergy_ZBand dominantFrequencyEnergy_ZLow \\\n", - "0 0.029077 0.027159 \n", - "1 0.022946 0.031538 \n", - "2 0.019791 0.027571 \n", - "3 0.025449 0.032896 \n", - "4 0.021159 0.035992 \n", - "5 0.019549 0.032218 \n", - "6 0.024637 0.032953 \n", - "7 0.023016 0.046724 \n", - "8 0.024761 0.043462 \n", - "9 0.025724 0.035699 \n", - "10 0.019494 0.042561 \n", - "11 0.019693 0.035747 \n", - "12 0.026942 0.032238 \n", - "13 0.019526 0.029579 \n", - "14 0.020186 0.023121 \n", - "15 0.020404 0.039396 \n", - "16 0.019362 0.037625 \n", - "\n", - " entropy_XBand entropy_XLow entropy_YBand entropy_YLow entropy_ZBand \\\n", - "0 6.939601 6.788092 6.948577 6.798214 6.935590 \n", - "1 6.962013 6.745530 6.941184 6.684952 6.970926 \n", - "2 6.951091 6.847274 6.915013 6.731275 6.971465 \n", - "3 7.007258 6.748955 6.907122 6.793629 6.937713 \n", - "4 6.946219 6.798508 6.949631 6.798677 6.988088 \n", - "5 6.951435 6.781081 6.987060 6.826218 6.953750 \n", - "6 6.966593 6.808914 6.895219 6.701539 6.942418 \n", - "7 6.909865 6.738131 6.950536 6.776491 6.999050 \n", - "8 6.897070 6.683523 6.989430 6.804307 7.001734 \n", - "9 6.889445 6.743457 6.915464 6.744615 6.945432 \n", - "10 6.985462 6.808788 6.934208 6.785896 6.954452 \n", - "11 6.976421 6.813671 6.958526 6.779792 6.989724 \n", - "12 6.968936 6.811086 6.979781 6.796927 6.899934 \n", - "13 7.007956 6.743319 6.947040 6.797169 6.959353 \n", - "14 6.926548 6.710982 6.930923 6.774075 6.936753 \n", - "15 6.906153 6.698576 6.936426 6.763655 6.976713 \n", - "16 6.964619 6.772995 6.937821 6.765930 6.985357 \n", - "\n", - " entropy_ZLow fp01_X fp01_Y fp01_Z fp02_X fp02_Y fp02_Z fp03_X \\\n", - "0 6.765376 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "1 6.836181 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "2 6.858678 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "3 6.779053 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "4 6.808467 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "5 6.727309 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "6 6.825859 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "7 6.803815 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "8 6.753551 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "9 6.781219 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "10 6.703950 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "11 6.792300 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "12 6.652299 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "13 6.838618 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "14 6.824054 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "15 6.826027 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "16 6.781932 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "\n", - " fp03_Y fp03_Z fp04_X fp04_Y fp04_Z fp05_X fp05_Y fp05_Z fp06_X \\\n", - "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", - "\n", - " fp06_Y fp06_Z fqAbsMean_X fqAbsMean_Y fqAbsMean_Z fqEnergyFeat_X \\\n", - "0 0.0 0.0 4.904584 5.246425 4.990305 9757.282156 \n", - "1 0.0 0.0 4.822180 4.645956 4.976632 9448.079674 \n", - "2 0.0 0.0 4.844398 4.809809 4.760420 9669.760737 \n", - "3 0.0 0.0 5.019139 4.967566 4.990291 10026.356290 \n", - "4 0.0 0.0 4.857150 5.237531 4.879719 9709.603922 \n", - "5 0.0 0.0 5.073285 5.102289 5.181881 10233.693555 \n", - "6 0.0 0.0 4.754901 5.170028 4.966644 9482.841316 \n", - "7 0.0 0.0 4.854265 5.078081 5.121374 9520.121266 \n", - "8 0.0 0.0 5.121016 5.118333 5.043765 10395.792313 \n", - "9 0.0 0.0 5.365118 4.954334 5.007938 11026.931493 \n", - "10 0.0 0.0 4.861183 4.696802 4.795042 9574.581294 \n", - "11 0.0 0.0 5.151702 4.821758 4.838656 10266.304113 \n", - "12 0.0 0.0 4.902073 4.841616 5.048460 9639.580580 \n", - "13 0.0 0.0 5.063369 4.971973 4.946686 10149.017500 \n", - "14 0.0 0.0 4.728647 5.075870 5.210589 9190.474376 \n", - "15 0.0 0.0 5.250907 5.215007 5.076110 10890.487664 \n", - "16 0.0 0.0 3.311168 3.172689 3.480565 6484.122156 \n", - "\n", - " fqEnergyFeat_Y fqEnergyFeat_Z fqEntropyFeat_X fqEntropyFeat_Y \\\n", - "0 10849.028553 9811.741631 5.555442 5.557260 \n", - "1 8899.975693 9956.680101 5.560929 5.536202 \n", - "2 9184.529987 9121.402956 5.575139 5.549254 \n", - "3 9801.236188 9982.377726 5.567258 5.570079 \n", - "4 10691.230186 9662.734156 5.562654 5.588084 \n", - "5 10371.886729 10563.612304 5.576252 5.583882 \n", - "6 10326.075806 10154.052192 5.603480 5.554958 \n", - "7 10241.641412 10387.903719 5.544296 5.567887 \n", - "8 10179.629166 10107.379246 5.548230 5.580267 \n", - "9 9834.741763 10120.339487 5.529802 5.567041 \n", - "10 8891.577695 9480.013849 5.576667 5.544810 \n", - "11 9524.407888 9519.015587 5.586517 5.589838 \n", - "12 9683.493055 10251.534492 5.581777 5.585898 \n", - "13 9632.893918 9966.713340 5.576922 5.570770 \n", - "14 10330.498873 10536.313374 5.560282 5.559407 \n", - "15 10640.928635 10044.509683 5.559758 5.560199 \n", - "16 6140.316953 7010.804341 5.584447 5.553468 \n", - "\n", - " fqEntropyFeat_Z fqHighestPeak1_X fqHighestPeak1_Y fqHighestPeak1_Z \\\n", - "0 5.574111 3.754916 5.607029 2.822416 \n", - "1 5.567919 2.929343 3.188250 3.588912 \n", - "2 5.581274 3.844362 2.215684 2.556580 \n", - "3 5.565787 2.664748 3.548463 3.840508 \n", - "4 5.572350 2.547426 4.058459 2.667875 \n", - "5 5.557310 3.489921 3.000982 3.052319 \n", - "6 5.554691 2.736799 3.462840 4.595469 \n", - "7 5.553808 4.006190 2.889108 3.063053 \n", - "8 5.572265 4.038741 2.673385 3.050807 \n", - "9 5.582309 3.495014 3.126081 3.176471 \n", - "10 5.542950 3.755804 2.893660 2.494761 \n", - "11 5.562584 3.657855 3.397249 2.557454 \n", - "12 5.538797 2.844807 2.782663 7.491650 \n", - "13 5.551558 3.582364 3.078669 2.436009 \n", - "14 5.540887 3.341951 2.905688 4.448141 \n", - "15 5.571962 3.659974 3.506205 3.095609 \n", - "16 5.549691 3.987941 2.775496 3.371644 \n", - "\n", - " fqHighestPeak2_X fqHighestPeak2_Y fqHighestPeak2_Z fqHighestPeak3_X \\\n", - "0 3.440065 4.450443 2.816086 3.141781 \n", - "1 2.272656 3.099145 3.375868 2.083292 \n", - "2 2.908314 2.072371 2.216813 2.889407 \n", - "3 2.558650 3.289128 2.740128 2.240007 \n", - "4 2.487128 3.198084 1.896326 2.064078 \n", - "5 2.793373 2.508526 2.849588 2.728617 \n", - "6 2.505620 2.829232 3.680274 2.496508 \n", - "7 3.474055 2.680011 2.961508 3.065422 \n", - "8 3.466653 2.333558 2.735938 2.543124 \n", - "9 3.241806 2.928799 2.562433 3.110792 \n", - "10 3.664174 2.613525 2.154152 2.815367 \n", - "11 2.584176 2.583424 2.526804 2.073441 \n", - "12 2.292539 2.606928 4.118490 2.035305 \n", - "13 2.653953 2.853552 2.430396 2.152986 \n", - "14 2.379974 2.528655 3.186949 2.361937 \n", - "15 3.228197 3.144964 3.065074 2.935554 \n", - "16 2.998196 2.765137 3.367243 2.373744 \n", - "\n", - " fqHighestPeak3_Y fqHighestPeak3_Z fqHighestPeakFreq1_X \\\n", - "0 3.806449 2.524760 20.312500 \n", - "1 2.937218 2.573928 43.359375 \n", - "2 1.933323 2.057448 17.578125 \n", - "3 2.928341 2.130777 18.359375 \n", - "4 2.634893 1.783069 21.093750 \n", - "5 2.076394 2.233292 44.531250 \n", - "6 2.630907 3.549613 10.546875 \n", - "7 2.337003 2.418840 44.531250 \n", - "8 2.187545 2.541616 7.421875 \n", - "9 2.693042 2.411896 3.125000 \n", - "10 2.063317 1.976259 33.203125 \n", - "11 2.275117 2.521100 46.875000 \n", - "12 1.701413 4.086219 19.921875 \n", - "13 2.583418 2.361895 40.234375 \n", - "14 2.265739 2.817797 7.031250 \n", - "15 2.798654 3.025101 41.796875 \n", - "16 2.469993 2.899311 47.265625 \n", - "\n", - " fqHighestPeakFreq1_Y fqHighestPeakFreq1_Z fqHighestPeakFreq2_X \\\n", - "0 35.546875 6.250000 8.593750 \n", - "1 27.734375 32.812500 8.593750 \n", - "2 39.062500 48.437500 21.875000 \n", - "3 7.812500 44.140625 24.609375 \n", - "4 45.312500 23.437500 20.703125 \n", - "5 47.265625 22.656250 17.187500 \n", - "6 36.718750 25.781250 28.515625 \n", - "7 26.171875 1.562500 23.046875 \n", - "8 9.375000 49.609375 7.812500 \n", - "9 13.671875 23.046875 24.218750 \n", - "10 42.187500 21.484375 30.859375 \n", - "11 39.062500 27.734375 3.515625 \n", - "12 5.468750 6.640625 20.312500 \n", - "13 18.750000 46.484375 40.625000 \n", - "14 22.265625 25.781250 32.812500 \n", - "15 22.656250 12.500000 2.343750 \n", - "16 35.546875 41.015625 12.500000 \n", - "\n", - " fqHighestPeakFreq2_Y fqHighestPeakFreq2_Z fqHighestPeakFreq3_X \\\n", - "0 35.937500 6.640625 48.046875 \n", - "1 12.890625 33.203125 23.828125 \n", - "2 1.171875 10.546875 15.234375 \n", - "3 7.421875 39.062500 38.281250 \n", - "4 34.765625 23.046875 11.718750 \n", - "5 5.078125 48.828125 35.937500 \n", - "6 39.843750 8.593750 41.796875 \n", - "7 40.234375 47.265625 23.437500 \n", - "8 14.843750 33.203125 8.203125 \n", - "9 9.765625 32.812500 24.609375 \n", - "10 23.828125 21.875000 31.250000 \n", - "11 38.671875 2.734375 40.625000 \n", - "12 7.812500 7.031250 24.218750 \n", - "13 1.562500 24.218750 21.093750 \n", - "14 37.890625 44.531250 6.640625 \n", - "15 38.281250 16.796875 12.109375 \n", - "16 35.937500 41.406250 12.109375 \n", - "\n", - " fqHighestPeakFreq3_Y fqHighestPeakFreq3_Z fqHistogramBin10_X \\\n", - "0 35.156250 18.359375 0.663673 \n", - "1 37.109375 49.609375 0.598443 \n", - "2 29.296875 27.734375 0.700968 \n", - "3 7.031250 44.531250 0.757265 \n", - "4 3.906250 15.625000 0.706994 \n", - "5 24.609375 40.234375 0.657617 \n", - "6 37.109375 20.312500 0.580507 \n", - "7 14.453125 13.281250 0.768002 \n", - "8 26.562500 42.187500 0.454272 \n", - "9 13.281250 22.656250 0.639002 \n", - "10 42.578125 32.031250 0.814090 \n", - "11 9.375000 44.531250 0.667374 \n", - "12 49.218750 6.250000 0.749569 \n", - "13 20.312500 21.484375 0.782994 \n", - "14 46.875000 21.093750 0.370842 \n", - "15 9.375000 16.406250 0.621563 \n", - "16 36.328125 31.640625 0.652595 \n", - "\n", - " fqHistogramBin10_Y fqHistogramBin10_Z fqHistogramBin1_X \\\n", - "0 0.877304 0.609371 0.011435 \n", - "1 0.682931 0.738879 0.009578 \n", - "2 0.639824 0.639717 0.009419 \n", - "3 0.380102 0.687712 0.036663 \n", - "4 0.716680 0.757831 0.009164 \n", - "5 0.611367 0.768926 0.009183 \n", - "6 0.750736 0.747705 0.003853 \n", - "7 0.623185 0.631193 0.009826 \n", - "8 0.679706 0.704537 0.005385 \n", - "9 0.490088 0.789989 0.021200 \n", - "10 0.697826 0.717765 0.006414 \n", - "11 0.504953 0.793269 0.002902 \n", - "12 0.562516 0.313177 0.018680 \n", - "13 0.640973 0.789066 0.063078 \n", - "14 0.599062 0.831171 0.074244 \n", - "15 0.697624 0.548579 0.012678 \n", - "16 0.777193 0.768463 0.012546 \n", - "\n", - " fqHistogramBin1_Y fqHistogramBin1_Z fqHistogramBin2_X \\\n", - "0 0.002534 0.034084 0.044164 \n", - "1 0.008954 0.047458 0.038388 \n", - "2 0.175215 0.005396 0.016583 \n", - "3 0.009890 0.034134 0.015763 \n", - "4 0.017415 0.001091 0.057432 \n", - "5 0.015252 0.069538 0.003456 \n", - "6 0.011466 0.009114 0.065513 \n", - "7 0.014012 0.128513 0.010067 \n", - "8 0.009760 0.121179 0.016688 \n", - "9 0.005246 0.004605 0.128821 \n", - "10 0.020237 0.022206 0.049923 \n", - "11 0.055101 0.002404 0.139587 \n", - "12 0.012117 0.029844 0.068914 \n", - "13 0.127296 0.001244 0.004839 \n", - "14 0.010998 0.007489 0.059668 \n", - "15 0.023385 0.014032 0.123925 \n", - "16 0.006016 0.001267 0.024574 \n", - "\n", - " fqHistogramBin2_Y fqHistogramBin2_Z fqHistogramBin3_X \\\n", - "0 0.033360 0.007048 0.015473 \n", - "1 0.037731 0.013756 0.089662 \n", - "2 0.015225 0.070362 0.019016 \n", - "3 0.019602 0.038623 0.000672 \n", - "4 0.015294 0.020563 0.009633 \n", - "5 0.081691 0.007247 0.072019 \n", - "6 0.011883 0.008837 0.045029 \n", - "7 0.018238 0.012506 0.065859 \n", - "8 0.018410 0.002731 0.011055 \n", - "9 0.021401 0.001020 0.029574 \n", - "10 0.018052 0.072599 0.001866 \n", - "11 0.020906 0.080207 0.077911 \n", - "12 0.006486 0.000533 0.042862 \n", - "13 0.039558 0.067846 0.003214 \n", - "14 0.045980 0.017778 0.009217 \n", - "15 0.011245 0.023402 0.050375 \n", - "16 0.038144 0.008480 0.010846 \n", - "\n", - " fqHistogramBin3_Y fqHistogramBin3_Z fqHistogramBin4_X \\\n", - "0 0.008985 0.017334 0.037594 \n", - "1 0.013746 0.005594 0.007540 \n", - "2 0.017045 0.061946 0.017200 \n", - "3 0.032866 0.025035 0.073223 \n", - "4 0.074446 0.042059 0.015283 \n", - "5 0.124216 0.016943 0.106799 \n", - "6 0.084053 0.006094 0.065454 \n", - "7 0.014187 0.063587 0.073324 \n", - "8 0.004976 0.011629 0.204065 \n", - "9 0.010850 0.066517 0.027461 \n", - "10 0.049761 0.012516 0.020644 \n", - "11 0.109207 0.027671 0.044941 \n", - "12 0.128897 0.013828 0.013930 \n", - "13 0.001674 0.007445 0.029504 \n", - "14 0.127170 0.025692 0.250933 \n", - "15 0.066975 0.017625 0.061256 \n", - "16 0.012055 0.008057 0.002157 \n", - "\n", - " fqHistogramBin4_Y fqHistogramBin4_Z fqHistogramBin5_X \\\n", - "0 0.007280 0.230446 0.148221 \n", - "1 0.013747 0.003758 0.101322 \n", - "2 0.059446 0.008613 0.012498 \n", - "3 0.207596 0.040753 0.013068 \n", - "4 0.009695 0.018577 0.003700 \n", - "5 0.014965 0.016717 0.035826 \n", - "6 0.026560 0.001329 0.052150 \n", - "7 0.019774 0.028393 0.029322 \n", - "8 0.028927 0.003415 0.231494 \n", - "9 0.001615 0.045945 0.051472 \n", - "10 0.028766 0.044691 0.023635 \n", - "11 0.004299 0.016184 0.013258 \n", - "12 0.035805 0.506606 0.005249 \n", - "13 0.001400 0.022177 0.020778 \n", - "14 0.068129 0.026048 0.059393 \n", - "15 0.010769 0.004830 0.027219 \n", - "16 0.000727 0.024769 0.004323 \n", - "\n", - " fqHistogramBin5_Y fqHistogramBin5_Z fqHistogramBin6_X \\\n", - "0 0.008045 0.020751 0.029557 \n", - "1 0.056060 0.012401 0.109483 \n", - "2 0.006489 0.010081 0.040686 \n", - "3 0.192067 0.069238 0.002349 \n", - "4 0.008621 0.012083 0.037449 \n", - "5 0.008543 0.021928 0.010102 \n", - "6 0.032678 0.113572 0.107633 \n", - "7 0.030802 0.001284 0.008414 \n", - "8 0.082748 0.018476 0.038918 \n", - "9 0.060710 0.010148 0.034336 \n", - "10 0.050349 0.043929 0.035819 \n", - "11 0.084466 0.004698 0.021953 \n", - "12 0.114383 0.005456 0.042467 \n", - "13 0.028269 0.034825 0.006944 \n", - "14 0.030626 0.002667 0.048076 \n", - "15 0.079745 0.004426 0.003690 \n", - "16 0.025724 0.010074 0.037145 \n", - "\n", - " fqHistogramBin6_Y fqHistogramBin6_Z fqHistogramBin7_X \\\n", - "0 0.006872 0.015075 0.004131 \n", - "1 0.017294 0.057771 0.021135 \n", - "2 0.012852 0.081111 0.017768 \n", - "3 0.036326 0.035171 0.015247 \n", - "4 0.003971 0.027636 0.084530 \n", - "5 0.037657 0.011643 0.006608 \n", - "6 0.012021 0.009564 0.004735 \n", - "7 0.049353 0.012791 0.006654 \n", - "8 0.037622 0.007045 0.007279 \n", - "9 0.151886 0.038589 0.015723 \n", - "10 0.028725 0.010863 0.026443 \n", - "11 0.032166 0.039215 0.006532 \n", - "12 0.067280 0.001306 0.031381 \n", - "13 0.082380 0.025548 0.025241 \n", - "14 0.011153 0.009987 0.029182 \n", - "15 0.005518 0.036627 0.081907 \n", - "16 0.064808 0.022580 0.181095 \n", - "\n", - " fqHistogramBin7_Y fqHistogramBin7_Z fqHistogramBin8_X \\\n", - "0 0.037588 0.050086 0.031146 \n", - "1 0.153668 0.029589 0.002795 \n", - "2 0.037881 0.024510 0.118228 \n", - "3 0.081061 0.055085 0.025833 \n", - "4 0.040810 0.031324 0.038717 \n", - "5 0.013062 0.039543 0.010482 \n", - "6 0.028420 0.004242 0.030659 \n", - "7 0.078465 0.083887 0.022828 \n", - "8 0.021692 0.047647 0.003704 \n", - "9 0.086268 0.021053 0.015700 \n", - "10 0.006769 0.008092 0.015450 \n", - "11 0.019611 0.022611 0.008412 \n", - "12 0.017028 0.026175 0.011397 \n", - "13 0.054565 0.025450 0.047621 \n", - "14 0.021420 0.050353 0.035187 \n", - "15 0.051670 0.160352 0.007104 \n", - "16 0.024693 0.061235 0.031480 \n", - "\n", - " fqHistogramBin8_Y fqHistogramBin8_Z fqHistogramBin9_X \\\n", - "0 0.009261 0.006448 0.014607 \n", - "1 0.011119 0.048257 0.021653 \n", - "2 0.027623 0.053150 0.047635 \n", - "3 0.029350 0.010179 0.059918 \n", - "4 0.063144 0.020308 0.037099 \n", - "5 0.030682 0.008196 0.087907 \n", - "6 0.036800 0.023401 0.044468 \n", - "7 0.136048 0.030321 0.005704 \n", - "8 0.079730 0.070270 0.027140 \n", - "9 0.161211 0.005097 0.036711 \n", - "10 0.020497 0.001989 0.005717 \n", - "11 0.009008 0.008274 0.017131 \n", - "12 0.048368 0.083072 0.015551 \n", - "13 0.014041 0.002945 0.015788 \n", - "14 0.071365 0.020033 0.063259 \n", - "15 0.006093 0.005710 0.010283 \n", - "16 0.007054 0.081870 0.043239 \n", - "\n", - " fqHistogramBin9_Y fqHistogramBin9_Z fqInterquart_X fqInterquart_Y \\\n", - "0 0.008771 0.009357 9.901367 10.769594 \n", - "1 0.004750 0.042537 9.211392 8.740737 \n", - "2 0.008400 0.045114 8.916415 9.052568 \n", - "3 0.011141 0.004070 10.241726 9.943358 \n", - "4 0.049923 0.068527 9.850650 10.473927 \n", - "5 0.062564 0.039319 9.978477 10.357262 \n", - "6 0.005384 0.076140 9.370658 10.321082 \n", - "7 0.015936 0.007526 9.819547 10.452315 \n", - "8 0.036429 0.013070 10.341732 10.685950 \n", - "9 0.010726 0.017037 11.158979 10.258023 \n", - "10 0.079018 0.065350 9.521979 9.357901 \n", - "11 0.160284 0.005465 10.044118 9.042127 \n", - "12 0.007119 0.020003 9.998723 9.539069 \n", - "13 0.009845 0.023455 9.922438 9.869460 \n", - "14 0.014097 0.008781 9.165367 10.443672 \n", - "15 0.046976 0.184416 10.540761 10.997395 \n", - "16 0.043587 0.013205 5.042390 4.702765 \n", - "\n", - " fqInterquart_Z fqKurtosis_X fqKurtosis_Y fqKurtosis_Z fqSkewness_X \\\n", - "0 10.056605 5.389523 12.118564 3.566577 2.148555 \n", - "1 9.630549 2.185909 3.320473 3.964078 1.409120 \n", - "2 9.614408 5.062124 1.258535 0.811035 2.169789 \n", - "3 10.256184 1.619553 5.728227 6.772829 1.300120 \n", - "4 9.184260 1.481245 4.313905 1.015344 1.432945 \n", - "5 10.217679 2.459255 1.131431 1.561264 1.639629 \n", - "6 10.029209 0.986987 2.772263 5.296333 1.184096 \n", - "7 10.394492 5.616668 1.330291 2.351409 2.247110 \n", - "8 9.735894 6.472586 0.920492 1.486168 2.203444 \n", - "9 10.045014 3.805614 2.370727 2.802290 1.722104 \n", - "10 8.904051 3.718599 3.614872 1.258294 1.749833 \n", - "11 9.411591 6.415278 3.023737 0.643165 2.042124 \n", - "12 10.280947 1.124096 2.295497 20.791269 1.177845 \n", - "13 9.738362 3.921990 2.156045 1.162370 1.628852 \n", - "14 10.618324 2.814428 2.047687 6.540693 1.597015 \n", - "15 10.931453 2.446653 3.344342 2.292431 1.580833 \n", - "16 5.345191 6.708391 1.408064 2.552483 2.063941 \n", - "\n", - " fqSkewness_Y fqSkewness_Z ibi_X ibi_Y ibi_Z \\\n", - "0 3.024553 1.834466 0.960000 1.500000 1.190000 \n", - "1 1.753868 1.839541 0.623333 0.750000 1.000000 \n", - "2 1.284222 1.166554 0.726667 0.700000 0.745000 \n", - "3 2.170687 2.179557 0.816250 NaN 0.680000 \n", - "4 1.904344 1.053234 0.478750 0.680000 NaN \n", - "5 1.201932 1.321003 NaN NaN 0.736250 \n", - "6 1.662902 2.250038 0.611250 1.246667 1.010000 \n", - "7 1.323276 1.405953 1.350000 1.220000 0.686667 \n", - "8 1.190934 1.425740 0.740000 NaN 0.796250 \n", - "9 1.569910 1.561782 NaN 0.430000 0.800000 \n", - "10 1.637232 1.182328 0.990000 0.540000 1.440000 \n", - "11 1.694750 1.113059 0.785000 1.590000 NaN \n", - "12 1.361880 3.825978 0.695000 0.887500 0.446667 \n", - "13 1.555360 1.304668 1.680000 0.755000 0.800000 \n", - "14 1.485917 2.161342 0.810000 NaN 1.330000 \n", - "15 1.767959 1.601817 0.470000 2.020000 1.095000 \n", - "16 1.442438 1.673701 0.350000 NaN NaN \n", - "\n", - " interQuartileRangeMagnitudesBand interQuartileRangeMagnitudesLow \\\n", - "0 0.920676 1.973073 \n", - "1 1.016984 1.942406 \n", - "2 0.858312 1.950954 \n", - "3 0.927504 2.134564 \n", - "4 0.928132 2.370860 \n", - "5 0.951660 2.180499 \n", - "6 1.093823 1.981298 \n", - "7 0.950979 1.944341 \n", - "8 0.959495 1.856825 \n", - "9 0.969892 2.417664 \n", - "10 0.918003 2.108845 \n", - "11 0.901527 2.156514 \n", - "12 1.076304 2.230688 \n", - "13 0.879532 1.978442 \n", - "14 0.975241 2.003503 \n", - "15 0.859382 2.671705 \n", - "16 2.127381 4.303851 \n", - "\n", - " interQuartileRange_XBand interQuartileRange_XLow \\\n", - "0 1.757454 3.465240 \n", - "1 1.889158 3.490192 \n", - "2 1.983132 3.253496 \n", - "3 1.901486 3.577908 \n", - "4 1.760314 3.333432 \n", - "5 1.897415 3.580993 \n", - "6 1.701012 3.476000 \n", - "7 1.785110 3.441714 \n", - "8 2.014451 3.847754 \n", - "9 1.941076 3.456800 \n", - "10 1.714848 3.444731 \n", - "11 1.936398 3.372739 \n", - "12 1.798283 3.162916 \n", - "13 1.683782 3.680219 \n", - "14 1.755337 3.764849 \n", - "15 1.964076 4.005708 \n", - "16 0.967114 1.754752 \n", - "\n", - " interQuartileRange_YBand interQuartileRange_YLow \\\n", - "0 1.984149 3.308310 \n", - "1 1.718109 3.810787 \n", - "2 1.741294 3.310818 \n", - "3 1.856279 3.431322 \n", - "4 2.026612 3.352234 \n", - "5 1.881604 3.403631 \n", - "6 1.826520 4.152857 \n", - "7 1.832994 3.463992 \n", - "8 1.852815 3.812084 \n", - "9 2.007690 3.334020 \n", - "10 1.685229 3.120421 \n", - "11 1.635660 3.727290 \n", - "12 1.779794 3.617491 \n", - "13 1.876858 3.111813 \n", - "14 1.911297 3.594541 \n", - "15 2.062958 3.761337 \n", - "16 0.728493 1.848640 \n", - "\n", - " interQuartileRange_ZBand interQuartileRange_ZLow kurtosis_XBand \\\n", - "0 1.903667 3.708505 1.140452 \n", - "1 1.831989 3.167890 0.539754 \n", - "2 1.778376 3.062305 3.774831 \n", - "3 1.995422 3.335374 -0.083848 \n", - "4 1.777317 3.523871 -0.494826 \n", - "5 1.967905 3.828525 -0.496284 \n", - "6 1.700507 3.052828 2.532450 \n", - "7 1.731774 3.385692 -0.114128 \n", - "8 1.689307 3.609902 -0.593102 \n", - "9 1.738103 3.286394 -0.666559 \n", - "10 1.597126 4.093169 1.209637 \n", - "11 1.789724 3.246647 3.741451 \n", - "12 1.893762 3.725728 2.714485 \n", - "13 1.877308 3.165189 -0.070193 \n", - "14 1.895194 3.394883 -0.696165 \n", - "15 1.707837 3.198155 -0.644687 \n", - "16 1.047990 1.582356 1.724068 \n", - "\n", - " kurtosis_XLow kurtosis_YBand kurtosis_YLow kurtosis_ZBand \\\n", - "0 -0.433486 -0.656973 -0.165213 -0.555322 \n", - "1 -0.344966 4.522720 -0.410874 -0.510377 \n", - "2 0.025646 4.674121 -0.217201 0.376264 \n", - "3 -0.831627 -0.428645 -0.588536 -0.534416 \n", - "4 -0.629886 1.011833 -0.530130 0.296548 \n", - "5 -0.255814 -0.187485 -0.135181 0.791489 \n", - "6 0.043343 -0.515236 -0.512346 -0.540677 \n", - "7 -0.495910 -0.644373 -0.478376 1.593402 \n", - "8 -0.482052 1.723593 -0.397926 -0.696604 \n", - "9 -0.427513 1.107252 -0.528508 -0.395877 \n", - "10 -0.329674 -0.533438 -0.416344 1.893127 \n", - "11 0.288675 1.247625 -0.083694 -0.678479 \n", - "12 -0.084646 -0.221705 -0.183027 0.392623 \n", - "13 -0.876383 -0.519862 -0.721379 -0.187303 \n", - "14 -0.203898 -0.597615 -0.685827 -0.755930 \n", - "15 -0.622968 3.219826 -0.130987 0.365543 \n", - "16 -0.200486 3.750956 0.309812 6.243062 \n", - "\n", - " kurtosis_ZLow linearTrendSlope_X linearTrendSlope_Y linearTrendSlope_Z \\\n", - "0 -0.535814 0.006313 -0.000514 0.003657 \n", - "1 -0.687367 -0.003839 0.002532 0.004240 \n", - "2 -0.411320 0.004571 0.002077 -0.000739 \n", - "3 -0.177124 0.005734 0.002063 0.001625 \n", - "4 -0.544198 -0.001512 -0.003387 -0.000964 \n", - "5 -0.245533 0.001101 0.000232 0.004975 \n", - "6 -0.570700 -0.000640 -0.009724 -0.001061 \n", - "7 -0.428218 0.003242 -0.007163 -0.002162 \n", - "8 -0.748547 -0.002593 0.002881 -0.001886 \n", - "9 -0.276559 0.002354 -0.001473 -0.002418 \n", - "10 -0.570432 0.005904 -0.006322 -0.004904 \n", - "11 -0.533622 0.002094 0.000147 -0.000672 \n", - "12 -0.597233 -0.001648 -0.000326 -0.001772 \n", - "13 -0.338392 -0.008854 0.000951 0.002029 \n", - "14 -0.572608 -0.003999 0.003651 0.003445 \n", - "15 -0.567454 -0.008439 -0.003843 -0.001975 \n", - "16 0.798154 -0.003043 0.001917 -0.001591 \n", - "\n", - " longestStrikeAboveMean_X longestStrikeAboveMean_Y \\\n", - "0 5.0 6.0 \n", - "1 9.0 7.0 \n", - "2 5.0 6.0 \n", - "3 7.0 9.0 \n", - "4 9.0 6.0 \n", - "5 6.0 7.0 \n", - "6 6.0 10.0 \n", - "7 11.0 6.0 \n", - "8 9.0 6.0 \n", - "9 7.0 5.0 \n", - "10 6.0 5.0 \n", - "11 11.0 7.0 \n", - "12 6.0 5.0 \n", - "13 6.0 8.0 \n", - "14 6.0 9.0 \n", - "15 9.0 6.0 \n", - "16 8.0 103.0 \n", - "\n", - " longestStrikeAboveMean_Z longestStrikeBelowMean_X \\\n", - "0 7.0 6.0 \n", - "1 8.0 7.0 \n", - "2 6.0 6.0 \n", - "3 7.0 6.0 \n", - "4 5.0 5.0 \n", - "5 11.0 6.0 \n", - "6 5.0 6.0 \n", - "7 10.0 6.0 \n", - "8 8.0 9.0 \n", - "9 8.0 11.0 \n", - "10 13.0 9.0 \n", - "11 6.0 8.0 \n", - "12 8.0 6.0 \n", - "13 5.0 11.0 \n", - "14 7.0 14.0 \n", - "15 8.0 6.0 \n", - "16 7.0 102.0 \n", - "\n", - " longestStrikeBelowMean_Y longestStrikeBelowMean_Z manipulationLow \\\n", - "0 13.0 11.0 3.951277e-01 \n", - "1 9.0 7.0 4.038103e-01 \n", - "2 7.0 6.0 4.809599e-01 \n", - "3 6.0 10.0 4.220357e-01 \n", - "4 8.0 7.0 4.508842e-01 \n", - "5 7.0 7.0 3.929400e-01 \n", - "6 8.0 9.0 3.645748e-01 \n", - "7 6.0 8.0 3.754912e-01 \n", - "8 7.0 10.0 3.534541e-01 \n", - "9 7.0 9.0 4.310468e-01 \n", - "10 9.0 7.0 4.364014e-01 \n", - "11 11.0 11.0 4.579393e-01 \n", - "12 8.0 8.0 4.263630e-01 \n", - "13 6.0 5.0 3.871373e-01 \n", - "14 7.0 7.0 4.354654e-01 \n", - "15 8.0 7.0 4.154386e-01 \n", - "16 9.0 100.0 1.041319e+13 \n", - "\n", - " maxPeakAmplitudeChangeAfter_X maxPeakAmplitudeChangeAfter_Y \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "\n", - " maxPeakAmplitudeChangeAfter_Z maxPeakAmplitudeChangeBefore_X \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "\n", - " maxPeakAmplitudeChangeBefore_Y maxPeakAmplitudeChangeBefore_Z \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "\n", - " maxPeakChangeRatio_X maxPeakChangeRatio_Y maxPeakChangeRatio_Z \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "\n", - " maxPeakDecreaseTime_X maxPeakDecreaseTime_Y maxPeakDecreaseTime_Z \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "\n", - " maxPeakDuration_X maxPeakDuration_Y maxPeakDuration_Z \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "\n", - " maxPeakIncreaseTime_X maxPeakIncreaseTime_Y maxPeakIncreaseTime_Z \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "\n", - " maxPeakResponseSlopeAfter_X maxPeakResponseSlopeAfter_Y \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "\n", - " maxPeakResponseSlopeAfter_Z maxPeakResponseSlopeBefore_X \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 NaN NaN \n", - "10 NaN NaN \n", - "11 NaN NaN \n", - "12 NaN NaN \n", - "13 NaN NaN \n", - "14 NaN NaN \n", - "15 NaN NaN \n", - "16 NaN NaN \n", - "\n", - " maxPeakResponseSlopeBefore_Y maxPeakResponseSlopeBefore_Z maximum_X \\\n", - "0 NaN NaN 9.913559 \n", - "1 NaN NaN 9.791841 \n", - "2 NaN NaN 9.999467 \n", - "3 NaN NaN 9.929909 \n", - "4 NaN NaN 9.955434 \n", - "5 NaN NaN 9.827071 \n", - "6 NaN NaN 9.938884 \n", - "7 NaN NaN 9.908534 \n", - "8 NaN NaN 9.987894 \n", - "9 NaN NaN 9.841638 \n", - "10 NaN NaN 9.959363 \n", - "11 NaN NaN 9.963883 \n", - "12 NaN NaN 9.885265 \n", - "13 NaN NaN 9.907724 \n", - "14 NaN NaN 9.957088 \n", - "15 NaN NaN 9.888184 \n", - "16 NaN NaN 9.870713 \n", - "\n", - " maximum_Y maximum_Z meanAbsChange_X meanAbsChange_Y meanAbsChange_Z \\\n", - "0 9.879872 9.994753 6.708199 6.973540 6.387339 \n", - "1 9.995226 9.721326 6.352467 6.104119 6.843040 \n", - "2 9.932527 9.550520 6.448388 6.381125 6.585151 \n", - "3 9.886429 9.988993 6.510313 6.232186 6.635215 \n", - "4 9.987530 9.930801 6.646537 6.833095 6.771830 \n", - "5 9.917932 9.945268 6.754050 6.989046 6.821789 \n", - "6 9.920647 9.934602 6.347022 6.686862 6.783886 \n", - "7 9.932158 9.916075 6.423501 6.652570 7.042471 \n", - "8 9.909668 9.962329 6.406909 6.557554 6.863164 \n", - "9 9.928470 9.984929 6.851138 6.503910 6.678991 \n", - "10 9.730016 9.952821 6.649205 6.473480 6.272612 \n", - "11 9.904416 9.970920 6.993114 6.571131 6.624015 \n", - "12 9.979279 9.902320 6.472765 6.775512 6.540266 \n", - "13 9.996517 9.991721 6.500397 6.241242 6.967983 \n", - "14 9.967348 9.999424 5.997819 6.654779 6.906320 \n", - "15 9.921541 9.921249 6.617277 6.761452 6.751033 \n", - "16 9.777902 9.878466 4.545531 4.240018 4.510443 \n", - "\n", - " meanChange_X meanChange_Y meanChange_Z meanCrossingRate_XBand \\\n", - "0 -0.061334 0.006416 0.016333 79.0 \n", - "1 0.001574 0.008047 -0.041014 78.0 \n", - "2 0.030453 0.001773 0.045463 90.0 \n", - "3 -0.034580 0.002982 0.033159 79.0 \n", - "4 0.023233 0.056371 -0.030075 92.0 \n", - "5 0.034817 0.043220 0.010260 77.0 \n", - "6 0.003560 0.011067 0.002166 82.0 \n", - "7 -0.010176 -0.022255 -0.028938 79.0 \n", - "8 -0.012925 0.029457 -0.000918 85.0 \n", - "9 0.005239 -0.033076 -0.042385 86.0 \n", - "10 0.026809 -0.032378 -0.029518 86.0 \n", - "11 0.048897 -0.008182 0.020187 79.0 \n", - "12 0.050265 -0.002427 -0.018213 79.0 \n", - "13 0.043435 0.017885 0.036188 73.0 \n", - "14 -0.036082 -0.044090 -0.005177 75.0 \n", - "15 0.000130 0.022171 -0.000606 88.0 \n", - "16 -0.022743 -0.027098 -0.032527 71.0 \n", - "\n", - " meanCrossingRate_XLow meanCrossingRate_YBand meanCrossingRate_YLow \\\n", - "0 55.0 93.0 68.0 \n", - "1 64.0 72.0 56.0 \n", - "2 72.0 78.0 54.0 \n", - "3 42.0 76.0 46.0 \n", - "4 46.0 75.0 36.0 \n", - "5 62.0 87.0 63.0 \n", - "6 62.0 72.0 53.0 \n", - "7 51.0 84.0 60.0 \n", - "8 64.0 74.0 62.0 \n", - "9 62.0 77.0 57.0 \n", - "10 52.0 93.0 53.0 \n", - "11 65.0 72.0 74.0 \n", - "12 53.0 78.0 70.0 \n", - "13 38.0 83.0 47.0 \n", - "14 63.0 79.0 47.0 \n", - "15 58.0 76.0 64.0 \n", - "16 47.0 71.0 41.0 \n", - "\n", - " meanCrossingRate_ZBand meanCrossingRate_ZLow meanHr_X meanHr_Y \\\n", - "0 77.0 55.0 62.500000 40.000000 \n", - "1 91.0 61.0 96.256684 80.000000 \n", - "2 91.0 65.0 82.568807 85.714286 \n", - "3 89.0 78.0 73.506891 NaN \n", - "4 83.0 53.0 125.326371 88.235294 \n", - "5 90.0 58.0 NaN NaN \n", - "6 92.0 70.0 98.159509 48.128342 \n", - "7 89.0 59.0 44.444444 49.180328 \n", - "8 89.0 46.0 81.081081 NaN \n", - "9 81.0 63.0 NaN 139.534884 \n", - "10 69.0 61.0 60.606061 111.111111 \n", - "11 82.0 68.0 76.433121 37.735849 \n", - "12 77.0 55.0 86.330935 67.605634 \n", - "13 88.0 64.0 35.714286 79.470199 \n", - "14 91.0 54.0 74.074074 NaN \n", - "15 86.0 54.0 127.659574 29.702970 \n", - "16 63.0 61.0 171.428571 NaN \n", - "\n", - " meanHr_Z meanKineticEnergy_XBand meanKineticEnergy_YBand \\\n", - "0 50.420168 2618.753542 15.119339 \n", - "1 60.000000 2062.783810 2577.595421 \n", - "2 80.536913 2632.893555 2732.565173 \n", - "3 88.235294 1565.539599 267.909763 \n", - "4 NaN 102.001874 1690.707679 \n", - "5 81.494058 423.119096 734.081168 \n", - "6 59.405941 2118.507995 862.367879 \n", - "7 87.378641 1510.951610 21.975499 \n", - "8 75.353218 62.166538 1824.410823 \n", - "9 75.000000 19.485687 2832.060761 \n", - "10 41.666667 1317.479334 686.057248 \n", - "11 NaN 2717.538775 1568.039863 \n", - "12 134.328358 2067.344519 658.309984 \n", - "13 75.000000 900.033642 0.456851 \n", - "14 45.112782 206.529944 617.725595 \n", - "15 54.794521 2.180843 2742.296041 \n", - "16 NaN 1320.759448 1875.202062 \n", - "\n", - " meanKineticEnergy_ZBand mean_X mean_XLow mean_Y mean_YLow \\\n", - "0 285.854378 -0.053815 0.028358 0.037484 0.058162 \n", - "1 608.639737 0.612934 0.650683 0.038049 -0.007172 \n", - "2 954.255185 0.136521 0.049622 -0.210952 -0.240924 \n", - "3 304.747050 -0.051505 -0.021936 -0.168607 -0.192718 \n", - "4 1898.523547 -0.123231 -0.110755 -0.505054 -0.566469 \n", - "5 1293.340920 0.339549 0.307550 0.247265 0.181038 \n", - "6 52.863608 0.532152 0.487073 0.307206 0.354863 \n", - "7 2704.499361 0.215323 0.270237 0.008370 0.032609 \n", - "8 139.741126 0.249445 0.255462 0.245851 0.191933 \n", - "9 1098.466155 0.057833 0.065416 0.082462 0.144004 \n", - "10 2635.436742 0.003605 -0.056590 -0.074096 0.006955 \n", - "11 230.299711 0.112133 0.014987 0.085015 0.056788 \n", - "12 2036.611335 0.104627 0.008012 -0.129541 -0.157697 \n", - "13 692.888968 -0.475879 -0.520756 0.088602 0.064652 \n", - "14 528.401837 -0.109499 -0.083745 -0.170524 -0.107117 \n", - "15 1883.843543 -0.013173 -0.010739 -0.203450 -0.258259 \n", - "16 2701.182626 0.350229 0.403120 -0.294203 -0.231185 \n", - "\n", - " mean_Z mean_ZLow median_X median_Y median_Z minimum_X minimum_Y \\\n", - "0 -0.324680 -0.311957 0.023734 0.171356 -0.085728 -9.965873 -9.985840 \n", - "1 -0.108151 -0.074035 0.902051 -0.326440 -0.220649 -9.825098 -9.824381 \n", - "2 0.080433 0.012544 -0.159980 -0.481939 0.288308 -9.995123 -9.967720 \n", - "3 0.238372 0.214338 -0.444864 -0.381945 0.327812 -9.969508 -9.946186 \n", - "4 -0.201574 -0.148862 0.054146 -1.271820 -0.581734 -9.995572 -9.919872 \n", - "5 0.435037 0.382028 0.639785 0.584743 1.007224 -9.980317 -9.948741 \n", - "6 -0.041299 -0.040232 0.726784 0.518219 -0.300821 -9.850364 -9.892071 \n", - "7 0.270523 0.338685 0.583696 -0.228860 0.377563 -9.846594 -9.971076 \n", - "8 -0.447767 -0.452922 0.742909 0.639441 -0.748193 -9.949239 -9.961308 \n", - "9 0.447544 0.505685 -0.208261 0.034027 0.820417 -9.988752 -9.918648 \n", - "10 -0.063634 0.012662 -0.042287 -0.317458 0.433556 -9.919072 -9.951992 \n", - "11 -0.249747 -0.262029 0.089504 0.325710 -0.285281 -9.918629 -9.884742 \n", - "12 0.466454 0.538576 -0.059035 -0.317737 0.380071 -9.923152 -9.961030 \n", - "13 0.067747 0.024853 -0.750726 0.700494 -0.061674 -9.960061 -9.790094 \n", - "14 -0.196882 -0.182077 -0.275986 -0.137257 -0.601903 -9.978229 -9.975827 \n", - "15 -0.284220 -0.252543 0.315943 -0.119290 -0.772663 -9.969270 -9.910271 \n", - "16 0.452310 0.527953 0.000000 0.000000 0.000000 -9.921719 -9.957280 \n", - "\n", - " minimum_Z numOfPeaksAutocorr_X numOfPeaksAutocorr_Y \\\n", - "0 -9.989369 0.0 0.0 \n", - "1 -9.921596 0.0 0.0 \n", - "2 -9.904902 0.0 0.0 \n", - "3 -9.968657 0.0 0.0 \n", - "4 -9.966824 0.0 0.0 \n", - "5 -9.746075 0.0 0.0 \n", - "6 -9.968426 0.0 0.0 \n", - "7 -9.991425 0.0 0.0 \n", - "8 -9.945887 0.0 0.0 \n", - "9 -9.943115 0.0 0.0 \n", - "10 -9.995130 0.0 0.0 \n", - "11 -9.911025 0.0 0.0 \n", - "12 -9.942037 0.0 0.0 \n", - "13 -9.996525 0.0 0.0 \n", - "14 -9.968281 0.0 0.0 \n", - "15 -9.942458 0.0 0.0 \n", - "16 -9.944961 0.0 0.0 \n", - "\n", - " numOfPeaksAutocorr_Z numPeaks_X numPeaks_Y numPeaks_Z \\\n", - "0 0.0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 0.0 \n", - "5 0.0 0.0 0.0 0.0 \n", - "6 0.0 0.0 0.0 0.0 \n", - "7 0.0 0.0 0.0 0.0 \n", - "8 0.0 0.0 0.0 0.0 \n", - "9 0.0 0.0 0.0 0.0 \n", - "10 0.0 0.0 0.0 0.0 \n", - "11 0.0 0.0 0.0 0.0 \n", - "12 0.0 0.0 0.0 0.0 \n", - "13 0.0 0.0 0.0 0.0 \n", - "14 0.0 0.0 0.0 0.0 \n", - "15 0.0 0.0 0.0 0.0 \n", - "16 0.0 0.0 0.0 0.0 \n", - "\n", - " numberOfZeroCrossingsAutocorr_X numberOfZeroCrossingsAutocorr_Y \\\n", - "0 113.0 117.0 \n", - "1 106.0 106.0 \n", - "2 104.0 107.0 \n", - "3 98.0 99.0 \n", - "4 100.0 123.0 \n", - "5 111.0 129.0 \n", - "6 99.0 108.0 \n", - "7 121.0 84.0 \n", - "8 86.0 96.0 \n", - "9 105.0 91.0 \n", - "10 103.0 113.0 \n", - "11 111.0 100.0 \n", - "12 91.0 110.0 \n", - "13 81.0 90.0 \n", - "14 76.0 105.0 \n", - "15 90.0 102.0 \n", - "16 125.0 101.0 \n", - "\n", - " numberOfZeroCrossingsAutocorr_Z numberOfZeroCrossings_X \\\n", - "0 91.0 151.0 \n", - "1 100.0 144.0 \n", - "2 97.0 142.0 \n", - "3 105.0 145.0 \n", - "4 113.0 158.0 \n", - "5 108.0 149.0 \n", - "6 105.0 142.0 \n", - "7 108.0 142.0 \n", - "8 110.0 137.0 \n", - "9 102.0 148.0 \n", - "10 96.0 155.0 \n", - "11 121.0 155.0 \n", - "12 114.0 145.0 \n", - "13 117.0 149.0 \n", - "14 118.0 143.0 \n", - "15 111.0 152.0 \n", - "16 117.0 95.0 \n", - "\n", - " numberOfZeroCrossings_Y numberOfZeroCrossings_Z peaksAmplitudeAvgLow \\\n", - "0 144.0 138.0 0.004347 \n", - "1 154.0 155.0 0.042207 \n", - "2 150.0 147.0 0.020205 \n", - "3 140.0 153.0 0.101105 \n", - "4 151.0 155.0 0.038745 \n", - "5 159.0 146.0 0.044786 \n", - "6 144.0 156.0 0.052742 \n", - "7 154.0 154.0 0.017061 \n", - "8 147.0 156.0 0.007580 \n", - "9 151.0 147.0 0.025005 \n", - "10 161.0 146.0 0.004923 \n", - "11 148.0 150.0 0.007717 \n", - "12 154.0 144.0 0.018046 \n", - "13 140.0 163.0 0.030769 \n", - "14 143.0 154.0 0.021353 \n", - "15 146.0 148.0 0.024445 \n", - "16 97.0 101.0 0.078129 \n", - "\n", - " peaksCountLow peaksCountLow_Q1 peaksCountLow_Q2 peaksCountLow_Q3 \\\n", - "0 72.0 57.0 309.361480 57.0 \n", - "1 68.0 51.0 271.726393 51.0 \n", - "2 69.0 48.0 254.965233 48.0 \n", - "3 65.0 50.0 289.440791 50.0 \n", - "4 69.0 49.0 284.170123 49.0 \n", - "5 77.0 54.0 305.670505 54.0 \n", - "6 64.0 55.0 296.210557 55.0 \n", - "7 71.0 59.0 324.927240 59.0 \n", - "8 67.0 54.0 300.957699 54.0 \n", - "9 69.0 53.0 306.728286 53.0 \n", - "10 73.0 55.0 297.733790 55.0 \n", - "11 72.0 51.0 274.507960 51.0 \n", - "12 66.0 59.0 323.652760 59.0 \n", - "13 67.0 52.0 282.127707 52.0 \n", - "14 64.0 54.0 293.794128 54.0 \n", - "15 72.0 48.0 290.775130 48.0 \n", - "16 43.0 29.0 167.727864 29.0 \n", - "\n", - " peaksCountLow_Q4 peaksPeakAvgLow peaksSumLow pitchAvgLow pitchPeak0 \\\n", - "0 309.361480 5.040374 362.906957 3.040772 17.0 \n", - "1 271.726393 4.909723 333.861168 2.989347 20.0 \n", - "2 254.965233 4.824639 332.900101 2.961778 20.0 \n", - "3 289.440791 5.353140 347.954088 3.121756 23.0 \n", - "4 284.170123 5.124634 353.599737 2.883563 17.0 \n", - "5 305.670505 5.250856 404.315911 3.111643 18.0 \n", - "6 296.210557 5.090897 325.817395 3.018990 14.0 \n", - "7 324.927240 5.214193 370.207730 3.013429 25.0 \n", - "8 300.957699 5.242828 351.269476 2.952927 20.0 \n", - "9 306.728286 5.210245 359.506912 2.839403 21.0 \n", - "10 297.733790 5.073142 370.339333 3.065137 21.0 \n", - "11 274.507960 4.841892 348.616221 3.079928 23.0 \n", - "12 323.652760 5.102014 336.732929 3.024905 18.0 \n", - "13 282.127707 5.138125 344.254404 3.112154 25.0 \n", - "14 293.794128 5.144971 329.278152 2.999553 31.0 \n", - "15 290.775130 5.408422 389.406373 2.988340 17.0 \n", - "16 167.727864 5.017205 215.739794 3.131790 18.0 \n", - "\n", - " pitchPeak1 pitchPeak2 pitchPeak3 pitchStdDevLow pnn20_X pnn20_Y \\\n", - "0 13.579794 0.798811 0.007989 0.486736 100.000000 0.0 \n", - "1 10.988615 0.549431 0.012838 0.469389 50.000000 0.0 \n", - "2 13.188341 0.659417 0.009117 0.486348 50.000000 0.0 \n", - "3 19.352167 0.841399 0.042834 0.528436 100.000000 NaN \n", - "4 11.280661 0.663568 0.057515 0.474275 100.000000 0.0 \n", - "5 13.520449 0.751136 0.082269 0.468915 NaN NaN \n", - "6 9.893896 0.706707 0.016822 0.428226 33.333333 50.0 \n", - "7 17.965807 0.718632 0.048602 0.480323 0.000000 0.0 \n", - "8 12.073847 0.603692 0.014102 0.485176 100.000000 NaN \n", - "9 16.691937 0.794854 0.010707 0.462277 NaN 100.0 \n", - "10 11.844907 0.564043 0.003436 0.495768 0.000000 0.0 \n", - "11 15.343869 0.667125 0.035624 0.516570 100.000000 0.0 \n", - "12 11.012970 0.611832 0.051925 0.487811 100.000000 100.0 \n", - "13 21.396988 0.855880 0.032036 0.545359 0.000000 100.0 \n", - "14 20.769242 0.669976 0.000129 0.550696 0.000000 NaN \n", - "15 8.187062 0.481592 0.059789 0.520021 0.000000 0.0 \n", - "16 10.739782 0.596655 0.001056 0.879333 0.000000 NaN \n", - "\n", - " pnn20_Z pnn50_X pnn50_Y pnn50_Z postureDistance_XLow \\\n", - "0 0.000000 0.000000 0.000000 0.000000 -0.029805 \n", - "1 0.000000 50.000000 0.000000 0.000000 0.657856 \n", - "2 0.000000 50.000000 0.000000 0.000000 0.290546 \n", - "3 100.000000 100.000000 NaN 50.000000 0.170782 \n", - "4 NaN 66.666667 0.000000 NaN 0.455714 \n", - "5 100.000000 NaN NaN 100.000000 0.126512 \n", - "6 0.000000 33.333333 50.000000 0.000000 0.132211 \n", - "7 50.000000 0.000000 0.000000 50.000000 0.237628 \n", - "8 66.666667 0.000000 NaN 66.666667 0.063529 \n", - "9 50.000000 NaN 100.000000 50.000000 -0.078588 \n", - "10 0.000000 0.000000 0.000000 0.000000 -0.063545 \n", - "11 NaN 0.000000 0.000000 NaN -0.041801 \n", - "12 50.000000 66.666667 100.000000 50.000000 0.165709 \n", - "13 0.000000 0.000000 33.333333 0.000000 -0.585409 \n", - "14 0.000000 0.000000 NaN 0.000000 0.023373 \n", - "15 100.000000 0.000000 0.000000 100.000000 0.247520 \n", - "16 NaN 0.000000 NaN NaN 0.634305 \n", - "\n", - " postureDistance_YLow postureDistance_ZLow powerPeaks_X powerPeaks_Y \\\n", - "0 0.340314 0.370119 NaN NaN \n", - "1 0.724718 0.066863 NaN NaN \n", - "2 0.037078 -0.253467 NaN NaN \n", - "3 -0.236275 -0.407056 NaN NaN \n", - "4 0.038107 -0.417607 NaN NaN \n", - "5 -0.074478 -0.200990 NaN NaN \n", - "6 0.527305 0.395094 NaN NaN \n", - "7 -0.068449 -0.306077 NaN NaN \n", - "8 0.708385 0.644856 NaN NaN \n", - "9 -0.440269 -0.361681 NaN NaN \n", - "10 -0.069252 -0.005707 NaN NaN \n", - "11 0.277016 0.318816 NaN NaN \n", - "12 -0.530565 -0.696274 NaN NaN \n", - "13 -0.545609 0.039800 NaN NaN \n", - "14 0.098332 0.074959 NaN NaN \n", - "15 0.241804 -0.005716 NaN NaN \n", - "16 -0.124833 -0.759138 NaN NaN \n", - "\n", - " powerPeaks_Z power_X power_Y power_Z propPosDeriv_X \\\n", - "0 NaN 32.524274 36.163429 32.705805 0.470000 \n", - "1 NaN 31.493599 29.666586 33.188934 0.480000 \n", - "2 NaN 32.232536 30.615100 30.404677 0.530000 \n", - "3 NaN 33.421188 32.670787 33.274592 0.506667 \n", - "4 NaN 32.365346 35.637434 32.209114 0.490000 \n", - "5 NaN 34.112312 34.572956 35.212041 0.486667 \n", - "6 NaN 31.609471 34.420253 33.846841 0.523333 \n", - "7 NaN 31.733738 34.138805 34.626346 0.496667 \n", - "8 NaN 34.652641 33.932097 33.691264 0.463333 \n", - "9 NaN 36.756438 32.782473 33.734465 0.523333 \n", - "10 NaN 31.915271 29.638592 31.600046 0.490000 \n", - "11 NaN 34.221014 31.748026 31.730052 0.526667 \n", - "12 NaN 32.131935 32.278310 34.171782 0.506667 \n", - "13 NaN 33.830058 32.109646 33.222378 0.496667 \n", - "14 NaN 30.634915 34.434996 35.121045 0.540000 \n", - "15 NaN 36.301626 35.469762 33.481699 0.493333 \n", - "16 NaN 21.613741 20.467723 23.369348 0.323333 \n", - "\n", - " propPosDeriv_Y propPosDeriv_Z q25_X q25_Y q25_Z q75_X \\\n", - "0 0.503333 0.503333 -9.904968 -9.913460 -9.891769 -9.815603 \n", - "1 0.513333 0.490000 -9.823503 -9.623845 -9.895991 -9.804560 \n", - "2 0.503333 0.506667 -9.867619 -9.803923 -9.852809 -9.732812 \n", - "3 0.476667 0.540000 -9.783592 -9.889890 -9.927404 -9.641721 \n", - "4 0.493333 0.490000 -9.810951 -9.886232 -9.954297 -9.675613 \n", - "5 0.503333 0.500000 -9.856497 -9.917740 -9.743170 -9.808626 \n", - "6 0.526667 0.503333 -9.800427 -9.848262 -9.900453 -9.709491 \n", - "7 0.496667 0.490000 -9.807134 -9.948366 -9.990132 -9.624504 \n", - "8 0.513333 0.526667 -9.938917 -9.922598 -9.720563 -9.920205 \n", - "9 0.470000 0.486667 -9.983522 -9.842103 -9.795918 -9.902970 \n", - "10 0.523333 0.490000 -9.886542 -9.883881 -9.924314 -9.794184 \n", - "11 0.476667 0.496667 -9.858026 -9.878642 -9.880814 -9.808943 \n", - "12 0.506667 0.490000 -9.911604 -9.951319 -9.910403 -9.881491 \n", - "13 0.536667 0.526667 -9.828445 -9.739559 -9.984165 -9.759210 \n", - "14 0.490000 0.496667 -9.842490 -9.926872 -9.867218 -9.731634 \n", - "15 0.473333 0.506667 -9.950656 -9.780268 -9.796500 -9.886783 \n", - "16 0.333333 0.350000 -9.864598 -9.840362 -9.910587 -9.808965 \n", - "\n", - " q75_Y q75_Z qd_X qd_Y qd_Z \\\n", - "0 -9.722151 -9.784468 0.089365 0.191309 0.107300 \n", - "1 -9.457585 -9.749080 0.018943 0.166260 0.146912 \n", - "2 -9.489301 -9.749357 0.134807 0.314622 0.103452 \n", - "3 -9.826188 -9.778254 0.141871 0.063702 0.149151 \n", - "4 -9.799702 -9.897261 0.135337 0.086530 0.057035 \n", - "5 -9.895604 -9.719051 0.047870 0.022136 0.024119 \n", - "6 -9.815993 -9.736544 0.090936 0.032269 0.163909 \n", - "7 -9.845109 -9.902559 0.182630 0.103257 0.087573 \n", - "8 -9.695187 -9.633150 0.018712 0.227412 0.087413 \n", - "9 -9.701007 -9.673999 0.080552 0.141096 0.121919 \n", - "10 -9.758558 -9.875219 0.092358 0.125324 0.049096 \n", - "11 -9.870160 -9.830770 0.049083 0.008482 0.050044 \n", - "12 -9.863729 -9.856373 0.030113 0.087590 0.054030 \n", - "13 -9.663949 -9.804782 0.069235 0.075611 0.179384 \n", - "14 -9.753898 -9.823848 0.110856 0.172974 0.043369 \n", - "15 -9.527091 -9.561305 0.063874 0.253177 0.235195 \n", - "16 -9.755245 -9.704994 0.055633 0.085117 0.205592 \n", - "\n", - " quartilesMagnitudeLow_Q1 quartilesMagnitudeLow_Q2 \\\n", - "0 2.997074 3.831943 \n", - "1 2.843185 3.823473 \n", - "2 2.603842 3.455643 \n", - "3 2.858406 3.899183 \n", - "4 2.668560 3.745257 \n", - "5 2.981484 4.133815 \n", - "6 2.944049 3.830869 \n", - "7 3.014263 3.936224 \n", - "8 3.154731 4.072560 \n", - "9 2.685077 3.947851 \n", - "10 2.744576 3.936554 \n", - "11 2.646841 3.830938 \n", - "12 2.864009 4.011649 \n", - "13 2.879925 3.937298 \n", - "14 2.967667 3.971088 \n", - "15 2.667819 3.939721 \n", - "16 0.000506 2.642343 \n", - "\n", - " quartilesMagnitudeLow_Q3 quartilesMagnitudes_XBand \\\n", - "0 4.970148 1.529648 \n", - "1 4.785591 1.354417 \n", - "2 4.554796 1.429635 \n", - "3 4.992970 1.503415 \n", - "4 5.039420 1.423320 \n", - "5 5.161984 1.505583 \n", - "6 4.925347 1.370036 \n", - "7 4.958604 1.424857 \n", - "8 5.011556 1.459846 \n", - "9 5.102741 1.519479 \n", - "10 4.853421 1.381178 \n", - "11 4.803355 1.419972 \n", - "12 5.094697 1.348596 \n", - "13 4.858366 1.505410 \n", - "14 4.971170 1.463376 \n", - "15 5.339524 1.607608 \n", - "16 4.304358 0.000888 \n", - "\n", - " quartilesMagnitudes_YBand quartilesMagnitudes_ZBand quartilesQ1_XBand \\\n", - "0 1.953035 2.450324 -0.857281 \n", - "1 1.823645 2.371402 -0.890517 \n", - "2 1.857566 2.287946 -0.908937 \n", - "3 1.927286 2.430919 -0.936330 \n", - "4 1.943362 2.351452 -0.848642 \n", - "5 2.044482 2.457244 -0.964691 \n", - "6 1.911373 2.463859 -0.853602 \n", - "7 1.923635 2.375836 -0.933618 \n", - "8 1.894052 2.419341 -0.989153 \n", - "9 1.983375 2.489371 -0.981082 \n", - "10 1.803099 2.299181 -0.839424 \n", - "11 1.892468 2.321498 -0.947796 \n", - "12 1.920256 2.424900 -0.885908 \n", - "13 1.886238 2.384941 -0.905751 \n", - "14 1.953468 2.438616 -0.829282 \n", - "15 2.024101 2.466989 -0.915346 \n", - "16 1.447118 2.128269 -0.416022 \n", - "\n", - " quartilesQ1_YBand quartilesQ1_ZBand quartilesQ2_XBand \\\n", - "0 -1.066341 -0.943389 1.734648e-03 \n", - "1 -0.896180 -0.890692 5.305092e-02 \n", - "2 -0.905143 -0.904948 -1.316958e-01 \n", - "3 -0.895335 -1.012306 7.424161e-02 \n", - "4 -1.009354 -0.870719 9.819226e-03 \n", - "5 -0.898707 -0.974285 -5.631767e-02 \n", - "6 -0.919344 -0.833187 -2.319636e-02 \n", - "7 -0.909365 -0.856205 3.927045e-03 \n", - "8 -0.990565 -0.815172 5.484881e-02 \n", - "9 -1.022765 -0.921673 4.270924e-02 \n", - "10 -0.860238 -0.793160 -6.855676e-03 \n", - "11 -0.910512 -0.870465 -5.014919e-02 \n", - "12 -0.910620 -0.927338 2.410547e-02 \n", - "13 -0.898025 -0.906175 -8.665132e-02 \n", - "14 -0.974343 -0.955520 -4.738416e-02 \n", - "15 -0.984985 -0.811973 -3.887618e-02 \n", - "16 -0.440029 -0.540883 3.331106e-07 \n", - "\n", - " quartilesQ2_YBand quartilesQ2_ZBand quartilesQ3_XBand \\\n", - "0 1.474204e-01 2.570732e-02 0.900173 \n", - "1 -1.393668e-01 6.543044e-02 0.998641 \n", - "2 -2.592307e-02 3.705648e-02 1.074195 \n", - "3 7.881847e-02 3.799249e-02 0.965155 \n", - "4 -6.889970e-02 4.224540e-03 0.911672 \n", - "5 -8.779827e-02 8.764006e-02 0.932725 \n", - "6 -9.019255e-03 -3.461384e-03 0.847410 \n", - "7 -1.005830e-02 3.384215e-03 0.851492 \n", - "8 3.164539e-02 -2.583106e-02 1.025297 \n", - "9 6.089817e-02 5.860674e-02 0.959995 \n", - "10 -4.994243e-03 -2.613465e-02 0.875425 \n", - "11 -3.398671e-02 -1.037207e-02 0.988602 \n", - "12 -2.678631e-02 1.237609e-01 0.912376 \n", - "13 3.114818e-02 -3.300223e-02 0.778031 \n", - "14 1.429302e-02 1.719155e-02 0.926055 \n", - "15 -4.416076e-02 -2.242612e-02 1.048730 \n", - "16 -2.639121e-07 -4.257538e-09 0.551091 \n", - "\n", - " quartilesQ3_YBand quartilesQ3_ZBand quartiles_Q1_XLow \\\n", - "0 0.917809 0.960279 -1.692628 \n", - "1 0.821929 0.941297 -1.075061 \n", - "2 0.836151 0.873429 -1.413513 \n", - "3 0.960944 0.983116 -1.860919 \n", - "4 1.017258 0.906597 -1.853105 \n", - "5 0.982897 0.993620 -1.426029 \n", - "6 0.907176 0.867320 -1.164096 \n", - "7 0.923628 0.875569 -1.476751 \n", - "8 0.862250 0.874136 -1.723314 \n", - "9 0.984925 0.816430 -1.718565 \n", - "10 0.824992 0.803966 -1.691706 \n", - "11 0.725148 0.919259 -1.536187 \n", - "12 0.869175 0.966424 -1.586190 \n", - "13 0.978833 0.971133 -2.410799 \n", - "14 0.936954 0.939674 -1.731873 \n", - "15 1.077973 0.895863 -1.958784 \n", - "16 0.288465 0.507107 -0.354829 \n", - "\n", - " quartiles_Q1_YLow quartiles_Q1_ZLow quartiles_Q2_XLow \\\n", - "0 -1.541206 -2.190574 7.106834e-02 \n", - "1 -1.881130 -1.707743 7.121445e-01 \n", - "2 -1.861473 -1.407143 -1.932959e-02 \n", - "3 -1.922915 -1.264425 -2.975076e-01 \n", - "4 -2.190908 -1.998139 -6.881272e-02 \n", - "5 -1.490952 -1.543161 3.411664e-01 \n", - "6 -1.796971 -1.543134 4.847145e-01 \n", - "7 -1.732299 -1.375862 3.162880e-01 \n", - "8 -1.752244 -2.346500 3.795663e-01 \n", - "9 -1.588920 -1.023154 8.254056e-02 \n", - "10 -1.424364 -2.000485 -1.249822e-01 \n", - "11 -1.844722 -1.895595 4.847813e-03 \n", - "12 -1.785255 -1.375454 1.907984e-01 \n", - "13 -1.576045 -1.598559 -8.241570e-01 \n", - "14 -1.924112 -1.910202 -1.233831e-01 \n", - "15 -2.041210 -1.911564 -4.371645e-02 \n", - "16 -1.282318 -0.007787 -1.034646e-10 \n", - "\n", - " quartiles_Q2_YLow quartiles_Q2_ZLow quartiles_Q3_XLow \\\n", - "0 1.925838e-01 -0.244365 1.772612 \n", - "1 4.735999e-03 -0.227438 2.415131 \n", - "2 -1.750099e-01 -0.007410 1.839983 \n", - "3 -1.634460e-01 0.273415 1.716989 \n", - "4 -6.622510e-01 -0.047323 1.480328 \n", - "5 2.301154e-01 0.542243 2.154964 \n", - "6 5.439990e-01 -0.094862 2.311904 \n", - "7 5.966718e-02 0.301915 1.964963 \n", - "8 2.255246e-01 -0.316655 2.124440 \n", - "9 1.560492e-01 0.474546 1.738235 \n", - "10 -1.370148e-01 -0.053503 1.753026 \n", - "11 3.170505e-01 -0.285082 1.836553 \n", - "12 5.473598e-03 0.528322 1.576726 \n", - "13 -7.963359e-02 0.064714 1.269420 \n", - "14 -1.492296e-01 -0.151915 2.032976 \n", - "15 -4.362463e-02 -0.219009 2.046924 \n", - "16 1.421830e-09 0.000036 1.399923 \n", - "\n", - " quartiles_Q3_YLow quartiles_Q3_ZLow ratePeaks_X ratePeaks_Y \\\n", - "0 1.767104 1.517931 0.0 0.0 \n", - "1 1.929656 1.460147 0.0 0.0 \n", - "2 1.449344 1.655161 0.0 0.0 \n", - "3 1.508407 2.070949 0.0 0.0 \n", - "4 1.161326 1.525732 0.0 0.0 \n", - "5 1.912678 2.285364 0.0 0.0 \n", - "6 2.355886 1.509694 0.0 0.0 \n", - "7 1.731693 2.009830 0.0 0.0 \n", - "8 2.059840 1.263401 0.0 0.0 \n", - "9 1.745099 2.263241 0.0 0.0 \n", - "10 1.696057 2.092684 0.0 0.0 \n", - "11 1.882568 1.351052 0.0 0.0 \n", - "12 1.832236 2.350274 0.0 0.0 \n", - "13 1.535768 1.566630 0.0 0.0 \n", - "14 1.670429 1.484681 0.0 0.0 \n", - "15 1.720127 1.286591 0.0 0.0 \n", - "16 0.566322 1.574568 0.0 0.0 \n", - "\n", - " ratePeaks_Z ratioBeyondRSigma_X ratioBeyondRSigma_Y \\\n", - "0 0.0 0.0 0.0 \n", - "1 0.0 0.0 0.0 \n", - "2 0.0 0.0 0.0 \n", - "3 0.0 0.0 0.0 \n", - "4 0.0 0.0 0.0 \n", - "5 0.0 0.0 0.0 \n", - "6 0.0 0.0 0.0 \n", - "7 0.0 0.0 0.0 \n", - "8 0.0 0.0 0.0 \n", - "9 0.0 0.0 0.0 \n", - "10 0.0 0.0 0.0 \n", - "11 0.0 0.0 0.0 \n", - "12 0.0 0.0 0.0 \n", - "13 0.0 0.0 0.0 \n", - "14 0.0 0.0 0.0 \n", - "15 0.0 0.0 0.0 \n", - "16 0.0 0.0 0.0 \n", - "\n", - " ratioBeyondRSigma_Z rmssd_X rmssd_Y rmssd_Z rollAvgLow \\\n", - "0 0.0 0.040000 0.00000 0.000000 6.267041 \n", - "1 0.0 0.070711 0.00000 0.000000 6.229705 \n", - "2 0.0 0.056569 0.00000 0.010000 6.246037 \n", - "3 0.0 0.176564 NaN 0.079057 6.263200 \n", - "4 0.0 0.073314 0.00000 NaN 6.257153 \n", - "5 0.0 NaN NaN 0.143788 6.235513 \n", - "6 0.0 0.055227 0.13435 0.000000 6.256733 \n", - "7 0.0 0.000000 0.00000 0.099247 6.249577 \n", - "8 0.0 0.020000 NaN 0.084113 6.257396 \n", - "9 0.0 NaN 0.16000 0.106066 6.229289 \n", - "10 0.0 0.000000 0.00000 0.000000 6.197414 \n", - "11 0.0 0.030000 0.00000 NaN 6.245839 \n", - "12 0.0 0.152315 0.19500 0.049497 6.191870 \n", - "13 0.0 0.000000 0.06364 0.000000 6.216458 \n", - "14 0.0 0.000000 NaN 0.000000 6.250970 \n", - "15 0.0 0.000000 0.00000 0.090000 6.252043 \n", - "16 0.0 0.015811 NaN NaN 6.168358 \n", - "\n", - " rollMotionAmountLow rollMotionRegularityLow rollPeak0 rollPeak1 \\\n", - "0 1.542201 0.960000 58.0 81.579155 \n", - "1 1.572021 0.946667 50.0 81.415426 \n", - "2 1.644758 0.920000 57.0 85.111788 \n", - "3 1.628594 0.960000 53.0 95.000403 \n", - "4 1.751518 0.986667 60.0 101.781375 \n", - "5 1.479838 0.943333 50.0 97.621402 \n", - "6 1.454305 0.913333 54.0 89.027514 \n", - "7 1.514670 0.936667 52.0 89.122608 \n", - "8 1.486984 0.940000 55.0 79.561599 \n", - "9 1.543290 0.943333 53.0 95.600485 \n", - "10 1.593219 0.953333 52.0 78.927944 \n", - "11 1.516778 0.953333 59.0 77.765390 \n", - "12 1.565020 0.960000 46.0 75.389760 \n", - "13 1.549493 0.943333 51.0 92.043536 \n", - "14 1.644497 0.946667 63.0 118.478766 \n", - "15 1.605553 0.930000 64.0 97.084014 \n", - "16 1.106250 0.966667 36.0 67.487914 \n", - "\n", - " rollPeak2 rollPeak3 rollPitchCorrelation rollStdDevLow sd1/sd2_X \\\n", - "0 1.406537 9.519295e-02 0.999979 1.781788 NaN \n", - "1 1.628309 6.604003e-02 0.999981 1.828565 0.534522 \n", - "2 1.493189 9.489318e-02 0.999979 1.882802 0.534522 \n", - "3 1.792460 1.092292e-02 0.999981 1.853306 1.379034 \n", - "4 1.696356 7.270613e-02 0.999981 1.957519 0.995458 \n", - "5 1.952428 3.119174e-02 0.999980 1.714472 NaN \n", - "6 1.648658 1.089759e-01 0.999980 1.744573 0.577154 \n", - "7 1.713896 3.651673e-02 0.999978 1.774453 NaN \n", - "8 1.446575 4.361178e-02 0.999979 1.745401 NaN \n", - "9 1.803783 5.295251e-02 0.999981 1.797112 NaN \n", - "10 1.517845 4.367994e-03 0.999978 1.798309 NaN \n", - "11 1.318057 3.999860e-02 0.999980 1.785573 NaN \n", - "12 1.638908 5.001221e-02 0.999981 1.788564 0.638044 \n", - "13 1.804775 1.027405e-01 0.999984 1.809506 NaN \n", - "14 1.880615 8.600262e-02 0.999982 1.883839 NaN \n", - "15 1.516938 7.589902e-03 0.999981 1.863443 NaN \n", - "16 1.874664 7.709882e-19 0.999983 1.500198 1.414214 \n", - "\n", - " sd1/sd2_Y sd1/sd2_Z sd2_X sd2_Y sd2_Z sd_X sd_Y \\\n", - "0 NaN NaN NaN NaN NaN 0.014142 0.000000 \n", - "1 NaN NaN 0.062361 NaN NaN 0.033333 0.000000 \n", - "2 NaN NaN 0.049889 NaN NaN 0.026667 0.000000 \n", - "3 NaN 1.414214 0.044234 NaN 0.020412 0.061001 NaN \n", - "4 NaN NaN 0.026623 NaN NaN 0.026502 0.000000 \n", - "5 NaN 1.831731 NaN NaN 0.026584 NaN NaN \n", - "6 0.534522 NaN 0.050584 0.118486 NaN 0.029195 0.063333 \n", - "7 NaN 0.619666 NaN NaN 0.078138 0.000000 0.000000 \n", - "8 NaN 0.700467 NaN NaN 0.056811 0.007071 NaN \n", - "9 NaN 0.534522 NaN NaN 0.093541 NaN 0.056569 \n", - "10 NaN NaN NaN NaN NaN 0.000000 0.000000 \n", - "11 NaN NaN NaN NaN NaN 0.010607 0.000000 \n", - "12 NaN 0.534522 0.087789 NaN 0.043653 0.056013 0.068943 \n", - "13 1.133893 NaN NaN 0.019843 NaN 0.000000 0.022500 \n", - "14 NaN NaN NaN NaN NaN 0.000000 NaN \n", - "15 NaN NaN NaN NaN NaN 0.000000 0.000000 \n", - "16 NaN NaN 0.004082 NaN NaN 0.005774 NaN \n", - "\n", - " sd_Z sdnn_X sdnn_Y sdnn_Z sdsd_X sdsd_Y sdsd_Z \\\n", - "0 0.000000 0.020000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.000000 0.047140 0.000000 0.000000 0.050000 0.000000 0.000000 \n", - "2 0.003536 0.037712 0.000000 0.005000 0.040000 0.000000 0.000000 \n", - "3 0.028868 0.086268 NaN 0.040825 0.053281 NaN 0.025000 \n", - "4 NaN 0.037479 0.000000 NaN 0.026562 0.000000 NaN \n", - "5 0.048694 NaN NaN 0.068864 NaN NaN 0.039229 \n", - "6 0.000000 0.041288 0.089567 0.000000 0.041298 0.095000 0.000000 \n", - "7 0.048419 0.000000 0.000000 0.068475 0.000000 0.000000 0.065000 \n", - "8 0.039794 0.010000 NaN 0.056278 0.000000 NaN 0.049046 \n", - "9 0.050000 NaN 0.080000 0.070711 NaN 0.000000 0.075000 \n", - "10 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "11 NaN 0.015000 0.000000 NaN 0.000000 0.000000 NaN \n", - "12 0.023333 0.079215 0.097500 0.032998 0.073636 0.000000 0.035000 \n", - "13 0.000000 0.000000 0.031820 0.000000 0.000000 0.021213 0.000000 \n", - "14 0.000000 0.000000 NaN 0.000000 0.000000 NaN 0.000000 \n", - "15 0.031820 0.000000 0.000000 0.045000 0.000000 0.000000 0.000000 \n", - "16 NaN 0.008165 NaN NaN 0.005000 NaN NaN \n", - "\n", - " sigTonicDifference_X sigTonicDifference_Y sigTonicDifference_Z \\\n", - "0 9.199656 9.338675 8.899684 \n", - "1 9.616484 8.656152 8.719506 \n", - "2 9.254964 8.942960 9.371260 \n", - "3 8.953227 9.256710 9.405749 \n", - "4 9.227503 8.427440 8.868092 \n", - "5 9.610274 9.664016 9.881796 \n", - "6 9.707548 9.728117 9.286315 \n", - "7 8.843457 9.474760 9.450369 \n", - "8 9.430278 9.303699 8.659857 \n", - "9 9.505284 8.649510 9.470890 \n", - "10 8.655102 8.230653 9.430806 \n", - "11 8.710545 9.404535 8.934167 \n", - "12 8.777491 9.328002 9.314613 \n", - "13 8.489345 9.333659 9.356877 \n", - "14 7.818805 8.840335 9.259486 \n", - "15 9.371048 9.028195 8.869437 \n", - "16 7.249286 5.986623 7.562215 \n", - "\n", - " signalOverallChange_X signalOverallChange_Y signalOverallChange_Z \\\n", - "0 NaN NaN NaN \n", - "1 NaN NaN NaN \n", - "2 NaN NaN NaN \n", - "3 NaN NaN NaN \n", - "4 NaN NaN NaN \n", - "5 NaN NaN NaN \n", - "6 NaN NaN NaN \n", - "7 NaN NaN NaN \n", - "8 NaN NaN NaN \n", - "9 NaN NaN NaN \n", - "10 NaN NaN NaN \n", - "11 NaN NaN NaN \n", - "12 NaN NaN NaN \n", - "13 NaN NaN NaN \n", - "14 NaN NaN NaN \n", - "15 NaN NaN NaN \n", - "16 NaN NaN NaN \n", - "\n", - " significantDecreaseChange_X significantDecreaseChange_Y \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantDecreaseChange_Z significantDecreaseDuration_X \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantDecreaseDuration_Y significantDecreaseDuration_Z \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantDecreaseIntensity_X significantDecreaseIntensity_Y \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantDecreaseIntensity_Z significantDecreaseMean_X \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantDecreaseMean_Y significantDecreaseMean_Z \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantDecreaseSpeed_X significantDecreaseSpeed_Y \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantDecreaseSpeed_Z significantIncreaseChange_X \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantIncreaseChange_Y significantIncreaseChange_Z \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantIncreaseDuration_X significantIncreaseDuration_Y \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantIncreaseDuration_Z significantIncreaseIntensity_X \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantIncreaseIntensity_Y significantIncreaseIntensity_Z \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantIncreaseMean_X significantIncreaseMean_Y \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantIncreaseMean_Z significantIncreaseSpeed_X \\\n", - "0 0.0 0.0 \n", - "1 0.0 0.0 \n", - "2 0.0 0.0 \n", - "3 0.0 0.0 \n", - "4 0.0 0.0 \n", - "5 0.0 0.0 \n", - "6 0.0 0.0 \n", - "7 0.0 0.0 \n", - "8 0.0 0.0 \n", - "9 0.0 0.0 \n", - "10 0.0 0.0 \n", - "11 0.0 0.0 \n", - "12 0.0 0.0 \n", - "13 0.0 0.0 \n", - "14 0.0 0.0 \n", - "15 0.0 0.0 \n", - "16 0.0 0.0 \n", - "\n", - " significantIncreaseSpeed_Y significantIncreaseSpeed_Z skewness_XBand \\\n", - "0 0.0 0.0 -0.789241 \n", - "1 0.0 0.0 -0.964118 \n", - "2 0.0 0.0 -1.947521 \n", - "3 0.0 0.0 -1.074669 \n", - "4 0.0 0.0 -1.429767 \n", - "5 0.0 0.0 -1.423178 \n", - "6 0.0 0.0 -1.810281 \n", - "7 0.0 0.0 -1.029154 \n", - "8 0.0 0.0 -1.399832 \n", - "9 0.0 0.0 -1.368791 \n", - "10 0.0 0.0 -1.711224 \n", - "11 0.0 0.0 -1.930326 \n", - "12 0.0 0.0 -1.829400 \n", - "13 0.0 0.0 -1.433780 \n", - "14 0.0 0.0 -1.331531 \n", - "15 0.0 0.0 -1.393066 \n", - "16 0.0 0.0 -0.841880 \n", - "\n", - " skewness_XLow skewness_YBand skewness_YLow skewness_ZBand \\\n", - "0 -1.336122 -1.408522 -1.448100 -1.400358 \n", - "1 -1.261287 -1.989833 -1.397036 -1.338255 \n", - "2 -1.475923 -2.027313 -1.467638 -1.562003 \n", - "3 -1.266773 -1.453350 -1.383511 -1.442892 \n", - "4 -1.339143 -1.615808 -1.422805 -0.943698 \n", - "5 -1.416528 -1.459715 -1.448963 -1.675927 \n", - "6 -1.386903 -1.294143 -1.357113 -1.412690 \n", - "7 -1.330033 -1.368968 -1.402020 -0.702916 \n", - "8 -1.384544 -1.713550 -1.379397 -1.356883 \n", - "9 -1.372940 -0.716725 -1.210997 -1.201617 \n", - "10 -1.425541 -1.311673 -1.400647 -0.616432 \n", - "11 -1.533702 -1.614584 -1.548574 -1.364344 \n", - "12 -1.478368 -1.469157 -1.519127 -1.050517 \n", - "13 -1.329548 -1.421468 -1.253525 -1.496642 \n", - "14 -1.497803 -1.339151 -1.344117 -1.300268 \n", - "15 -1.368357 -1.891049 -1.525716 -1.012493 \n", - "16 -0.784251 -0.129888 -1.336934 0.048994 \n", - "\n", - " skewness_ZLow squareSumOfComponent squareSumOfComponent_X \\\n", - "0 -1.445621 9491.043380 87.304565 \n", - "1 -1.277417 30651.148332 68.792877 \n", - "2 -1.393616 44408.530623 87.771841 \n", - "3 -1.483391 5106.736870 52.245492 \n", - "4 -1.392960 15176.836847 3.443728 \n", - "5 -1.425731 6709.160654 14.176021 \n", - "6 -1.363114 10254.961649 70.644597 \n", - "7 -1.265042 19979.281586 50.420508 \n", - "8 -1.365171 4581.997895 2.110400 \n", - "9 -1.319741 17362.935975 0.673737 \n", - "10 -1.352344 23952.457725 43.977395 \n", - "11 -1.425509 22698.768662 90.606767 \n", - "12 -1.140978 25247.727112 68.948690 \n", - "13 -1.399283 2837.286157 30.077470 \n", - "14 -1.411368 2051.843171 6.938203 \n", - "15 -1.344404 23828.015922 0.081206 \n", - "16 -0.643958 42835.132332 46.378025 \n", - "\n", - " squareSumOfComponent_Y squareSumOfComponent_Z stdDev_X stdDev_Y \\\n", - "0 0.525027 9.592395 5.702752 6.013487 \n", - "1 85.920194 20.361622 5.578343 5.446571 \n", - "2 91.087375 31.874100 5.675729 5.529069 \n", - "3 8.992683 10.223261 5.780877 5.713349 \n", - "4 56.423599 63.326978 5.687720 5.948307 \n", - "5 24.547381 43.186064 5.830696 5.874676 \n", - "6 28.825635 1.796552 5.596989 5.858829 \n", - "7 0.756588 90.170990 5.629154 5.842836 \n", - "8 60.870553 4.709504 5.881362 5.819936 \n", - "9 94.407329 36.687427 6.062433 5.725004 \n", - "10 22.934706 87.853715 5.649359 5.443630 \n", - "11 52.320191 7.734147 5.848798 5.633897 \n", - "12 22.013900 67.932744 5.667538 5.679923 \n", - "13 0.019081 23.169631 5.796861 5.665845 \n", - "14 20.667281 17.691791 5.533798 5.865656 \n", - "15 91.434499 62.847554 6.025069 5.952174 \n", - "16 65.801181 94.787294 4.635847 4.514551 \n", - "\n", - " stdDev_Z std_X std_Y std_Z sumOfSquareComponents \\\n", - "0 5.709675 5.702752 6.013487 5.709675 7714.376715 \n", - "1 5.759969 5.578343 5.446571 5.759969 12529.335362 \n", - "2 5.513457 5.675729 5.529069 5.513457 17016.764285 \n", - "3 5.763486 5.780877 5.713349 5.763486 2914.974871 \n", - "4 5.671727 5.687720 5.948307 5.671727 7205.787985 \n", - "5 5.918005 5.830696 5.874676 5.918005 2668.569628 \n", - "6 5.817657 5.596989 5.858829 5.817657 5824.803953 \n", - "7 5.878194 5.629154 5.842836 5.878194 10673.607545 \n", - "8 5.787121 5.881362 5.819936 5.787121 3731.857444 \n", - "9 5.790869 6.062433 5.725004 5.790869 10259.165086 \n", - "10 5.621032 5.649359 5.443630 5.621032 10178.287252 \n", - "11 5.627404 5.848798 5.633897 5.627404 11006.805647 \n", - "12 5.827023 5.667538 5.679923 5.827023 9853.391350 \n", - "13 5.763488 5.796861 5.665845 5.763488 1441.486376 \n", - "14 5.923030 5.533798 5.865656 5.923030 788.274651 \n", - "15 5.779353 6.025069 5.952174 5.779353 12310.089283 \n", - "16 4.812979 4.635847 4.514551 4.812979 15465.347834 \n", - "\n", - " sumPerComponent_XBand sumPerComponent_YBand sumPerComponent_ZBand \\\n", - "0 9.332071 0.711978 3.085267 \n", - "1 8.282718 9.258236 4.500362 \n", - "2 9.357266 9.532453 5.634279 \n", - "3 7.216070 2.987004 3.185468 \n", - "4 1.844006 7.499087 7.946165 \n", - "5 3.752978 4.942276 6.559151 \n", - "6 8.393765 5.356485 1.328328 \n", - "7 7.089141 0.857534 9.483464 \n", - "8 1.440467 7.789667 2.157835 \n", - "9 0.807834 9.704695 6.045037 \n", - "10 6.619876 4.777700 9.361491 \n", - "11 9.506484 7.221729 2.769357 \n", - "12 8.291813 4.680328 8.230175 \n", - "13 5.472029 0.126213 4.801692 \n", - "14 2.622796 4.534010 4.193525 \n", - "15 0.272450 9.549740 7.915426 \n", - "16 6.800411 8.092254 9.720961 \n", - "\n", - " sumPer_X sumPer_Y sumPer_Z sumPosDeriv_X sumPosDeriv_Y sumPosDeriv_Z \\\n", - "0 9.343691 0.724587 3.097159 1.533655 1.811178 1.681572 \n", - "1 8.294147 9.269315 4.512385 1.591732 1.580072 1.700955 \n", - "2 9.368663 9.543971 5.645715 1.755798 1.650120 1.671424 \n", - "3 7.228104 2.998780 3.197383 1.697559 1.655703 1.668006 \n", - "4 1.855728 7.511564 7.957825 1.714760 1.769990 1.689834 \n", - "5 3.765106 4.954531 6.571610 1.601735 1.663261 1.676063 \n", - "6 8.405034 5.368951 1.340355 1.608356 1.653202 1.825924 \n", - "7 7.100740 0.869821 9.495841 1.701632 1.756226 1.709203 \n", - "8 1.452721 7.801958 2.170139 1.653067 1.827721 1.536427 \n", - "9 0.820815 9.716343 6.057015 1.824568 1.587335 1.700598 \n", - "10 6.631545 4.789019 9.373031 1.624215 1.513528 1.636635 \n", - "11 9.518759 7.233270 2.781033 1.650110 1.606799 1.702796 \n", - "12 8.303535 4.691897 8.242132 1.706478 1.541407 1.577032 \n", - "13 5.484293 0.138134 4.813484 1.771312 1.704668 1.763609 \n", - "14 2.634047 4.546128 4.206161 1.551998 1.620187 1.795793 \n", - "15 0.284968 9.562139 7.927645 1.618430 1.746358 1.796606 \n", - "16 6.810141 8.111793 9.735877 1.009023 1.086773 1.161188 \n", - "\n", - " sumSquared_X sumSquared_Y sumSquared_Z totalAbsoluteAreaBand \\\n", - "0 87.304565 0.525027 9.592395 930.324637 \n", - "1 68.792877 85.920194 20.361622 880.255964 \n", - "2 87.771841 91.087375 31.874100 889.394563 \n", - "3 52.245492 8.992683 10.223261 920.462589 \n", - "4 3.443728 56.423599 63.326978 906.331468 \n", - "5 14.176021 24.547381 43.186064 922.999063 \n", - "6 70.644597 28.825635 1.796552 891.241288 \n", - "7 50.420508 0.756588 90.170990 889.958466 \n", - "8 2.110400 60.870553 4.709504 899.898502 \n", - "9 0.673737 94.407329 36.687427 937.539507 \n", - "10 43.977395 22.934706 87.853715 853.098151 \n", - "11 90.606767 52.320191 7.734147 894.370062 \n", - "12 68.948690 22.013900 67.932744 912.690717 \n", - "13 30.077470 0.019081 23.169631 896.978822 \n", - "14 6.938203 20.667281 17.691791 904.039012 \n", - "15 0.081206 91.434499 62.847554 949.133312 \n", - "16 46.378025 65.801181 94.787294 618.770314 \n", - "\n", - " totalAbsoluteAreaLow totalEnergy_XBand totalEnergy_XLow \\\n", - "0 1780.575497 517.364214 1788.802647 \n", - "1 1764.960003 484.409520 1660.514544 \n", - "2 1660.479036 500.176502 1608.410552 \n", - "3 1820.669589 451.010824 1992.791606 \n", - "4 1768.462902 392.463100 1611.102684 \n", - "5 1875.393901 471.891573 1857.190163 \n", - "6 1787.154967 486.437759 1675.123945 \n", - "7 1812.332972 462.825167 1787.230831 \n", - "8 1914.346191 460.461739 2145.783460 \n", - "9 1820.621790 459.708676 1891.234146 \n", - "10 1788.896569 456.655747 1775.297410 \n", - "11 1754.867700 530.355391 1909.625501 \n", - "12 1844.751328 457.213609 1748.235049 \n", - "13 1774.809989 431.661941 2061.651867 \n", - "14 1785.109858 422.788778 1908.060229 \n", - "15 1849.464050 487.266003 2234.828172 \n", - "16 1206.745729 325.226513 1173.958591 \n", - "\n", - " totalEnergy_YBand totalEnergy_YLow totalEnergy_ZBand totalEnergy_ZLow \\\n", - "0 439.584822 1739.615369 450.831393 1794.973308 \n", - "1 457.269815 1856.515911 413.902079 1532.911953 \n", - "2 472.406450 1718.034506 403.722870 1392.356015 \n", - "3 464.921005 1690.931963 438.332549 1759.580678 \n", - "4 529.857943 1861.075738 461.368239 1706.093090 \n", - "5 456.903709 1860.241368 459.162105 2076.012958 \n", - "6 462.875255 2038.645919 403.746970 1497.384032 \n", - "7 426.235740 1833.567302 490.346279 1776.759610 \n", - "8 485.681060 1887.933048 377.333528 1856.973429 \n", - "9 556.100152 1919.097714 474.084271 1774.462002 \n", - "10 374.230201 1527.449086 461.128464 2062.255419 \n", - "11 450.140820 1765.227258 385.755339 1529.079555 \n", - "12 425.974207 1747.207222 529.734045 2313.928055 \n", - "13 412.340960 1682.121184 434.772069 1493.757008 \n", - "14 475.704726 1914.740172 429.391914 1481.573747 \n", - "15 563.461483 1932.475692 474.052209 1633.787069 \n", - "16 340.504512 1173.019368 386.447472 1226.625881 \n", - "\n", - " totalKineticEnergyBand totalMagnitudeBand totalMagnitudeLow \\\n", - "0 2.925126e+08 37.561417 73.200325 \n", - "1 5.258724e+08 36.836583 71.964761 \n", - "2 6.331399e+08 37.155660 68.858981 \n", - "3 2.142150e+08 36.827968 73.961246 \n", - "4 3.698058e+08 37.211833 72.700856 \n", - "5 2.455072e+08 37.317283 76.700344 \n", - "6 3.039349e+08 36.815042 72.961288 \n", - "7 4.245261e+08 37.156183 73.861435 \n", - "8 2.030065e+08 36.400458 77.362336 \n", - "9 3.957316e+08 38.649692 75.327253 \n", - "10 4.647551e+08 35.970652 73.266806 \n", - "11 4.524228e+08 37.022559 72.336756 \n", - "12 4.771071e+08 37.613472 76.851962 \n", - "13 1.596326e+08 35.767026 72.943244 \n", - "14 1.355158e+08 36.471281 72.961260 \n", - "15 4.636878e+08 39.090631 76.441830 \n", - "16 -3.812113e+07 32.463418 61.018083 \n", - "\n", - " totalMeanLow variance_XBand variance_XLow variance_YBand \\\n", - "0 -22.543665 2.721968 9.754062 2.590389 \n", - "1 56.947576 2.563355 7.355288 2.510291 \n", - "2 -17.875769 2.804377 8.647055 2.622988 \n", - "3 -0.031628 2.429697 11.250914 2.645239 \n", - "4 -82.608508 2.215412 9.352735 2.991478 \n", - "5 87.061581 2.673239 9.243265 2.587991 \n", - "6 80.170428 2.693489 7.684352 2.562234 \n", - "7 64.153009 2.469863 9.024566 2.414425 \n", - "8 -0.552672 2.593119 11.128252 2.712446 \n", - "9 71.510548 2.611981 10.151689 2.926317 \n", - "10 -3.697235 2.502221 9.994805 2.122154 \n", - "11 -19.025417 2.948125 10.263177 2.519518 \n", - "12 38.889104 2.545405 9.535645 2.420954 \n", - "13 -43.125120 2.439554 14.330087 2.351737 \n", - "14 -37.293835 2.341786 10.937580 2.644027 \n", - "15 -52.154199 2.788693 12.546204 3.149414 \n", - "16 69.988893 1.527052 4.799571 1.541689 \n", - "\n", - " variance_YLow variance_ZBand variance_ZLow velocity_XBand \\\n", - "0 9.313834 2.528331 11.471023 9.329075 \n", - "1 10.417940 2.274827 8.751125 8.279796 \n", - "2 10.472756 2.271032 7.621608 -9.354190 \n", - "3 10.178643 2.536343 9.079495 7.213186 \n", - "4 12.998558 2.465946 10.177490 1.841149 \n", - "5 9.554210 2.626139 10.193015 -3.749940 \n", - "6 10.207149 2.279214 8.458739 -8.390872 \n", - "7 10.084753 2.561608 8.665412 7.086253 \n", - "8 9.794086 2.111711 12.556467 1.437382 \n", - "9 9.953002 2.584094 8.199316 0.804720 \n", - "10 8.401283 2.379874 11.553055 -6.617017 \n", - "11 9.721455 2.142709 9.559985 -9.503376 \n", - "12 10.224227 2.851741 10.736961 -8.288873 \n", - "13 8.996186 2.515854 8.132504 -5.469177 \n", - "14 11.128877 2.382600 8.970394 2.619904 \n", - "15 11.788846 2.508219 9.934570 0.269214 \n", - "16 6.359343 1.750283 4.757309 6.798417 \n", - "\n", - " velocity_YBand velocity_ZBand \n", - "0 0.708808 3.082230 \n", - "1 -9.255495 4.497523 \n", - "2 -9.529691 -5.631469 \n", - "3 -2.983890 -3.182467 \n", - "4 -7.495938 7.943277 \n", - "5 -4.939248 -6.556118 \n", - "6 5.353457 -1.325481 \n", - "7 -0.854626 9.480576 \n", - "8 -7.786676 2.155067 \n", - "9 9.701594 6.042039 \n", - "10 4.774896 9.358744 \n", - "11 -7.218897 2.766581 \n", - "12 -4.677415 8.227049 \n", - "13 0.123248 -4.798694 \n", - "14 4.530926 4.190602 \n", - "15 -9.546621 7.912537 \n", - "16 8.090398 9.732142 \n" - ] - } - ], - "source": [ - "INPUT_LEN = 5000\n", - "random.seed(1234)\n", - "np.random.seed(1234)\n", - "\n", - "x = np.array([random.random() * 20 - 10 for _ in range(INPUT_LEN)])\n", - "y = np.array([random.random() * 20 - 10 for _ in range(INPUT_LEN)])\n", - "z = np.array([random.random() * 20 - 10 for _ in range(INPUT_LEN)])\n", - "pd.set_option('display.max_rows', 500)\n", - "pd.set_option('display.max_columns', None)\n", - "pd.set_option('display.width', None)\n", - "\n", - "time = [i*0.01 for i in range(INPUT_LEN)]\n", - "\n", - "numCols = 300\n", - "\n", - "ax = convertInputInto2d(x, numCols)\n", - "ay = convertInputInto2d(y, numCols)\n", - "az = convertInputInto2d(z, numCols)\n", - "\n", - "time = convertInputInto2d(time, numCols)\n", - "\n", - "df = calculateFeatures(ax, ay, az, time)\n", - "\n", - "print(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate execution times" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "DATA_LEN = 100000\n", - "PRINT_OUTPUT = False\n", - "\n", - "\n", - "def timeFunc(fName, f, *args):\n", - " start_time = t.time()\n", - " out = f(*args)\n", - " print(fName, \" \" * (40-len(fName)), t.time() - start_time, \"seconds\")\n", - " if PRINT_OUTPUT:\n", - " print(out)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "compute_features 0.06318068504333496 seconds\n", - "calcAreaUnderAccelerationMagnitude 0.1833968162536621 seconds\n", - "calcAverageVectorLength 0.001512765884399414 seconds\n", - "calcAverageVectorLengthPower 0.0005660057067871094 seconds\n", - "calcMeanKineticEnergy 0.9430029392242432 seconds\n", - "calcPeaks 0.8460264205932617 seconds\n", - "calcTotalKineticEnergy 1.1924221515655518 seconds\n", - "calcAcAbsoluteArea 0.0012974739074707031 seconds\n", - "calcAbsoluteMean 0.0006303787231445312 seconds\n", - "calcAmplitude 0.0005743503570556641 seconds\n", - "calcCoefficientOfVariation 17.25027561187744 seconds\n", - "calcCorrelation 0.3744330406188965 seconds\n", - "calcEntropy 1.2423591613769531 seconds\n", - "calcInterQuartileRange 5.245208740234375e-06 seconds\n", - "calcKurtosis 1.1491007804870605 seconds\n", - "calcMeanCrossingRate 0.12923097610473633 seconds\n", - "calcQuartiles 0.10563778877258301 seconds\n", - "calcSkewness 1.1148443222045898 seconds\n", - "calcTotalAbsoluteArea 0.0001842975616455078 seconds\n", - "calcTotalEnergy 0.8747704029083252 seconds\n", - "calcDominantFrequencyEnergy 0.9095582962036133 seconds\n", - "calcTotalMagnitude 1.2974116802215576 seconds\n", - "calcVariance 15.527474403381348 seconds\n", - "calcArea 0.0006725788116455078 seconds\n", - "calcMean 0.00042319297790527344 seconds\n", - "calcPostureDistance 1.4543533325195312e-05 seconds\n", - "calcTotalMean 0.0002124309539794922 seconds\n", - "calcPeakCountBoza 0.0006649494171142578 seconds\n", - "calcSumPerComponent 1.1699285507202148 seconds\n", - "computeACVelocity 0.768822193145752 seconds\n", - "lowPassFilter 0.1188957691192627 seconds\n", - "bandPassFilter 0.24991559982299805 seconds\n", - "highPassFilter 0.12767314910888672 seconds\n", - "magnitudeVector 1.7064223289489746 seconds\n", - "covariance 0.13443350791931152 seconds\n", - "calcRoll 0.6018388271331787 seconds\n", - "calcPitch 0.7513015270233154 seconds\n", - "stdDev 0.13022685050964355 seconds\n", - "rollMotionAmount 0.4034900665283203 seconds\n", - "rollMotionRegularity 0.47931718826293945 seconds\n", - "manipulation 1.9478199481964111 seconds\n", - "min 0.0003123283386230469 seconds\n", - "min 0.0002841949462890625 seconds\n", - "max 0.00025963783264160156 seconds\n", - "max 0.00019931793212890625 seconds\n", - "avg 0.0003573894500732422 seconds\n", - "avg 0.000213623046875 seconds\n" - ] - } - ], - "source": [ - "magnitudes = np.array([random.random() * 100 for _ in range(DATA_LEN)])\n", - "time = np.array([random.randint(1499719594, 1599719594) for _ in range(DATA_LEN)])\n", - "\n", - "x = np.array([random.random() * 100 for _ in range(DATA_LEN)])\n", - "y = np.array([random.random() * 100 for _ in range(DATA_LEN)])\n", - "z = np.array([random.random() * 100 for _ in range(DATA_LEN)])\n", - "\n", - "timeFunc(\"compute_features\", functions.computeFreqFeatures, magnitudes)\n", - "\n", - "timeFunc(\"calcAreaUnderAccelerationMagnitude\", functions.calcAreaUnderAccelerationMagnitude, magnitudes, time)\n", - "timeFunc(\"calcAverageVectorLength\", functions.calcAverageVectorLength, magnitudes)\n", - "timeFunc(\"calcAverageVectorLengthPower\", functions.calcAverageVectorLengthPower, magnitudes)\n", - "timeFunc(\"calcMeanKineticEnergy\", functions.calcMeanKineticEnergy, magnitudes, time)\n", - "timeFunc(\"calcPeaks\", functions.calcPeaks, magnitudes)\n", - "timeFunc(\"calcTotalKineticEnergy\", functions.calcTotalKineticEnergy, x, y, z, time)\n", - "timeFunc(\"calcAcAbsoluteArea\", functions.calcAcAbsoluteArea, x, y, z)\n", - "timeFunc(\"calcAbsoluteMean\", functions.calcAbsoluteMean, magnitudes)\n", - "timeFunc(\"calcAmplitude\", functions.calcAmplitude, magnitudes)\n", - "timeFunc(\"calcCoefficientOfVariation\", functions.calcCoefficientOfVariation, magnitudes)\n", - "timeFunc(\"calcCorrelation\", functions.calcCorrelation, magnitudes, magnitudes[::-1])\n", - "timeFunc(\"calcEntropy\", functions.calcEntropy, magnitudes)\n", - "timeFunc(\"calcInterQuartileRange\", functions.calcInterQuartileRange, magnitudes)\n", - "timeFunc(\"calcKurtosis\", functions.calcKurtosis, magnitudes)\n", - "timeFunc(\"calcMeanCrossingRate\", functions.calcMeanCrossingRate, magnitudes)\n", - "timeFunc(\"calcQuartiles\", functions.calcQuartiles, magnitudes)\n", - "timeFunc(\"calcSkewness\", functions.calcSkewness, magnitudes)\n", - "timeFunc(\"calcTotalAbsoluteArea\", functions.calcTotalAbsoluteArea, magnitudes)\n", - "timeFunc(\"calcTotalEnergy\", functions.calcTotalEnergy, magnitudes)\n", - "timeFunc(\"calcDominantFrequencyEnergy\", functions.calcDominantFrequencyEnergy, magnitudes)\n", - "timeFunc(\"calcTotalMagnitude\", functions.calcTotalMagnitude, x, y, z)\n", - "timeFunc(\"calcVariance\", functions.calcVariance, magnitudes)\n", - "timeFunc(\"calcArea\", functions.calcArea, magnitudes)\n", - "timeFunc(\"calcMean\", functions.calcMean, magnitudes)\n", - "timeFunc(\"calcPostureDistance\", functions.calcPostureDistance, functions.calcMean(x), functions.calcMean(y),\n", - " functions.calcMean(z))\n", - "timeFunc(\"calcTotalMean\", functions.calcTotalMean, magnitudes, magnitudes[::-1])\n", - "timeFunc(\"calcPeakCountBoza\", functions.calcPeakCount,\n", - " np.array([1.0, 4.0, 60.0, 4.5, 1.0, 4.0, 60.0, 4.5, 1.0, 4.0, 60.0, 4.5]))\n", - "timeFunc(\"calcSumPerComponent\", functions.calcSumPerComponent, magnitudes, time)\n", - "timeFunc(\"computeACVelocity\", functions.computeACVelocity, magnitudes, time)\n", - "timeFunc(\"lowPassFilter\", functions.lowPassFilter, magnitudes, 0.5)\n", - "timeFunc(\"bandPassFilter\", functions.bandPassFilter, magnitudes, 0.5, 0.7)\n", - "timeFunc(\"highPassFilter\", functions.highPassFilter, magnitudes, 0.5)\n", - "timeFunc(\"magnitudeVector\", functions.magnitudeVector, x, y, z)\n", - "timeFunc(\"covariance\", functions.covariance, magnitudes, magnitudes[::-1])\n", - "\n", - "timeFunc(\"calcRoll\", functions.calcRoll, magnitudes, magnitudes[::-1])\n", - "timeFunc(\"calcPitch\", functions.calcPitch, x, y, z)\n", - "timeFunc(\"stdDev\", functions.stdDev, magnitudes)\n", - "timeFunc(\"rollMotionAmount\", functions.rollMotionAmount, magnitudes)\n", - "timeFunc(\"rollMotionRegularity\", functions.rollMotionRegularity, magnitudes)\n", - "timeFunc(\"manipulation\", functions.manipulation, x, y, z, magnitudes, magnitudes[::-1])\n", - "\n", - "intArray = time\n", - "doubleArray = magnitudes\n", - "timeFunc(\"min\", functions.min, intArray)\n", - "timeFunc(\"min\", functions.min, doubleArray)\n", - "timeFunc(\"max\", functions.max, intArray)\n", - "timeFunc(\"max\", functions.max, doubleArray)\n", - "timeFunc(\"avg\", functions.avg, intArray)\n", - "timeFunc(\"avg\", functions.avg, doubleArray)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate EDA features with calculateFeatures" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Cannot change to a different GUI toolkit: widget. Using notebook instead.\n" - ] - } - ], - "source": [ - "import CalculatingFeatures.gsr\n", - "from eda_explorer.load_files import butter_lowpass_filter\n", - "from eda_explorer.EDA_Peak_Detection_Script import calcPeakFeatures\n", - "%matplotlib widget" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "pathToEDACsv = \"example_data/EDA.csv\"\n", - "\n", - "# get an array of values from EDA empatica file\n", - "eda_data, startTimeStamp_EDA, sampleRate_EDA = convert1DEmpaticaToArray(pathToEDACsv)\n", - "\n", - "windowLength_EDA = int(sampleRate_EDA*120)\n", - "\n", - "# Convert the HRV data into 2D array\n", - "eda_data_2D = convertInputInto2d(eda_data, windowLength_EDA)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "df_EDA = pd.DataFrame()\n", - "for row in eda_data_2D:\n", - " current_result = {}\n", - " current_result.update(CalculatingFeatures.gsr.extractGsrFeatures(row, sampleRate=int(sampleRate_EDA),featureNames=gsrFeatureNames))\n", - " df_EDA = df_EDA.append(current_result, ignore_index=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In `extractGsrFeatures`, the EDA signal is first filtered using a Butterworth filter (`scipy.signal.butter`) from the SciPy library (`scipy.signal.lfilter`).\n", - "Its order ($N = 6$) and the cutoff frequency ($W_n = 1$ Hz) are hard-coded." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "no_interest = 131\n", - "current_row = eda_data_2D[no_interest,]\n", - "filtered_EDA = butter_lowpass_filter(current_row, 1.0, int(sampleRate_EDA), 6)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - " if (this.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n", - " }\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " var resizeObserver = new ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " resizeObserver.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - 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" event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.one(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(current_row, color='blue')\n", - "plt.plot(filtered_EDA, color='red')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, the peaks are detected using the EDA Explorer.\n", - "Here, the parameters can be set in `extractGsrFeatures`, but their default values are also set.\n", - "\n", - "The explanations and illustrations of these parameters can be found at the [EDA Explorer FAQ](https://eda-explorer.media.mit.edu/info/), but in brief, they are:\n", - "* threshold: the minimum amplitude a potential skin conductance response (SCR) must reach in order to be counted as an SCR (in µS); \n", - "* offset: the number of seconds for which the *derivative must be positive before a peak* and the derivative must be negative after a peak; \n", - "* max rise time: allowed time in seconds from the \"start\" of the peak to the apex of a peak;\n", - "* max decay time: allowed time in seconds after the apex of a peak to the 50 % of maximum amplitude of the peak.\n", - "\n", - "Their default values are: `(threshold=.02, offset=1, riseTime=4, decayTime=4,)`" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "gsr_data = pd.DataFrame(current_row, columns=[\"EDA\"])\n", - "startTime = pd.to_datetime(0, unit=\"s\")\n", - "gsr_data.index = pd.date_range(start=startTime, periods=len(gsr_data), freq=str(1000/sampleRate_EDA) + 'L')\n", - "gsr_data['filtered_eda'] = filtered_EDA\n", - "peakData = calcPeakFeatures(gsr_data, offset=1, thresh=.02, start_WT=4, end_WT=4, sampleRate=int(sampleRate_EDA))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function returns peaks (i.e., the time of the peaks) and their characteristics.\n", - "These include their start and end time (when the amplitude falls by 50 %), their amplitude, their duration (or width) and more.\n", - "\n", - "Some peaks are visualized below, along with their start (>) and end (■) times." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 46 66 81 107 159 293 313 328 376 397 452]\n", - "[ 17 30 58 73 101 152 280 304 321 365 393 444]\n", - "[ 58 73 89 112 168 304 318 334 386 400 460]\n" - ] - } - ], - "source": [ - "peaks = np.where(peakData.peaks == 1.0)[0]\n", - "peak_starts = np.where(peakData.peak_start == 1.0)[0]\n", - "peak_ends = np.where(peakData.peak_end == 1.0)[0]\n", - "print(peaks)\n", - "print(peak_starts)\n", - "print(peak_ends)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "/* global mpl */\n", - "window.mpl = {};\n", - "\n", - "mpl.get_websocket_type = function () {\n", - " if (typeof WebSocket !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof MozWebSocket !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert(\n", - " 'Your browser does not have WebSocket support. ' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.'\n", - " );\n", - " }\n", - "};\n", - "\n", - "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = this.ws.binaryType !== undefined;\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById('mpl-warnings');\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent =\n", - " 'This browser does not support binary websocket messages. ' +\n", - " 'Performance may be slow.';\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = document.createElement('div');\n", - " this.root.setAttribute('style', 'display: inline-block');\n", - " this._root_extra_style(this.root);\n", - "\n", - " parent_element.appendChild(this.root);\n", - "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", - "\n", - " var fig = this;\n", - "\n", - " this.waiting = false;\n", - "\n", - " this.ws.onopen = function () {\n", - " fig.send_message('supports_binary', { value: fig.supports_binary });\n", - " fig.send_message('send_image_mode', {});\n", - " if (fig.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", - " }\n", - " fig.send_message('refresh', {});\n", - " };\n", - "\n", - " this.imageObj.onload = function () {\n", - " if (fig.image_mode === 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", - "\n", - " this.imageObj.onunload = function () {\n", - " fig.ws.close();\n", - " };\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "};\n", - "\n", - "mpl.figure.prototype._init_header = function () {\n", - " var titlebar = document.createElement('div');\n", - " titlebar.classList =\n", - " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", - " var titletext = document.createElement('div');\n", - " titletext.classList = 'ui-dialog-title';\n", - " titletext.setAttribute(\n", - " 'style',\n", - " 'width: 100%; text-align: center; padding: 3px;'\n", - " );\n", - " titlebar.appendChild(titletext);\n", - " this.root.appendChild(titlebar);\n", - " this.header = titletext;\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", - "\n", - "mpl.figure.prototype._init_canvas = function () {\n", - " var fig = this;\n", - "\n", - " var canvas_div = (this.canvas_div = document.createElement('div'));\n", - " canvas_div.setAttribute(\n", - " 'style',\n", - " 'border: 1px solid #ddd;' +\n", - " 'box-sizing: content-box;' +\n", - " 'clear: both;' +\n", - " 'min-height: 1px;' +\n", - " 'min-width: 1px;' +\n", - " 'outline: 0;' +\n", - " 'overflow: hidden;' +\n", - " 'position: relative;' +\n", - " 'resize: both;'\n", - " );\n", - "\n", - " function on_keyboard_event_closure(name) {\n", - " return function (event) {\n", - " return fig.key_event(event, name);\n", - " };\n", - " }\n", - "\n", - " canvas_div.addEventListener(\n", - " 'keydown',\n", - " on_keyboard_event_closure('key_press')\n", - " );\n", - " canvas_div.addEventListener(\n", - " 'keyup',\n", - " on_keyboard_event_closure('key_release')\n", - " );\n", - "\n", - " this._canvas_extra_style(canvas_div);\n", - " this.root.appendChild(canvas_div);\n", - "\n", - " var canvas = (this.canvas = document.createElement('canvas'));\n", - " canvas.classList.add('mpl-canvas');\n", - " canvas.setAttribute('style', 'box-sizing: content-box;');\n", - "\n", - " this.context = canvas.getContext('2d');\n", - "\n", - " var backingStore =\n", - " this.context.backingStorePixelRatio ||\n", - " this.context.webkitBackingStorePixelRatio ||\n", - " this.context.mozBackingStorePixelRatio ||\n", - " this.context.msBackingStorePixelRatio ||\n", - " this.context.oBackingStorePixelRatio ||\n", - " this.context.backingStorePixelRatio ||\n", - " 1;\n", - "\n", - " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - " if (this.ratio !== 1) {\n", - " fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n", - " }\n", - "\n", - " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", - " 'canvas'\n", - " ));\n", - " rubberband_canvas.setAttribute(\n", - " 'style',\n", - " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", - " );\n", - "\n", - " var resizeObserver = new ResizeObserver(function (entries) {\n", - " var nentries = entries.length;\n", - " for (var i = 0; i < nentries; i++) {\n", - " var entry = entries[i];\n", - " var width, height;\n", - " if (entry.contentBoxSize) {\n", - " if (entry.contentBoxSize instanceof Array) {\n", - " // Chrome 84 implements new version of spec.\n", - " width = entry.contentBoxSize[0].inlineSize;\n", - " height = entry.contentBoxSize[0].blockSize;\n", - " } else {\n", - " // Firefox implements old version of spec.\n", - " width = entry.contentBoxSize.inlineSize;\n", - " height = entry.contentBoxSize.blockSize;\n", - " }\n", - " } else {\n", - " // Chrome <84 implements even older version of spec.\n", - " width = entry.contentRect.width;\n", - " height = entry.contentRect.height;\n", - " }\n", - "\n", - " // Keep the size of the canvas and rubber band canvas in sync with\n", - " // the canvas container.\n", - " if (entry.devicePixelContentBoxSize) {\n", - " // Chrome 84 implements new version of spec.\n", - " canvas.setAttribute(\n", - " 'width',\n", - " entry.devicePixelContentBoxSize[0].inlineSize\n", - " );\n", - " canvas.setAttribute(\n", - " 'height',\n", - " entry.devicePixelContentBoxSize[0].blockSize\n", - " );\n", - " } else {\n", - " canvas.setAttribute('width', width * fig.ratio);\n", - " canvas.setAttribute('height', height * fig.ratio);\n", - " }\n", - " canvas.setAttribute(\n", - " 'style',\n", - " 'width: ' + width + 'px; height: ' + height + 'px;'\n", - " );\n", - "\n", - " rubberband_canvas.setAttribute('width', width);\n", - " rubberband_canvas.setAttribute('height', height);\n", - "\n", - " // And update the size in Python. We ignore the initial 0/0 size\n", - " // that occurs as the element is placed into the DOM, which should\n", - " // otherwise not happen due to the minimum size styling.\n", - " if (width != 0 && height != 0) {\n", - " fig.request_resize(width, height);\n", - " }\n", - " }\n", - " });\n", - " resizeObserver.observe(canvas_div);\n", - "\n", - " function on_mouse_event_closure(name) {\n", - " return function (event) {\n", - " return fig.mouse_event(event, name);\n", - " };\n", - " }\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mousedown',\n", - " on_mouse_event_closure('button_press')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseup',\n", - " on_mouse_event_closure('button_release')\n", - " );\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband_canvas.addEventListener(\n", - " 'mousemove',\n", - " on_mouse_event_closure('motion_notify')\n", - " );\n", - "\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseenter',\n", - " on_mouse_event_closure('figure_enter')\n", - " );\n", - " rubberband_canvas.addEventListener(\n", - " 'mouseleave',\n", - " on_mouse_event_closure('figure_leave')\n", - " );\n", - "\n", - " canvas_div.addEventListener('wheel', function (event) {\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " on_mouse_event_closure('scroll')(event);\n", - " });\n", - "\n", - " canvas_div.appendChild(canvas);\n", - " canvas_div.appendChild(rubberband_canvas);\n", - "\n", - " this.rubberband_context = rubberband_canvas.getContext('2d');\n", - " this.rubberband_context.strokeStyle = '#000000';\n", - "\n", - " this._resize_canvas = function (width, height, forward) {\n", - " if (forward) {\n", - " canvas_div.style.width = width + 'px';\n", - " canvas_div.style.height = height + 'px';\n", - " }\n", - " };\n", - "\n", - " // Disable right mouse context menu.\n", - " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", - " event.preventDefault();\n", - " return false;\n", - " });\n", - "\n", - " function set_focus() {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'mpl-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'mpl-button-group';\n", - " continue;\n", - " }\n", - "\n", - " var button = (fig.buttons[name] = document.createElement('button'));\n", - " button.classList = 'mpl-widget';\n", - " button.setAttribute('role', 'button');\n", - " button.setAttribute('aria-disabled', 'false');\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - "\n", - " var icon_img = document.createElement('img');\n", - " icon_img.src = '_images/' + image + '.png';\n", - " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", - " icon_img.alt = tooltip;\n", - " button.appendChild(icon_img);\n", - "\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " var fmt_picker = document.createElement('select');\n", - " fmt_picker.classList = 'mpl-widget';\n", - " toolbar.appendChild(fmt_picker);\n", - " this.format_dropdown = fmt_picker;\n", - "\n", - " for (var ind in mpl.extensions) {\n", - " var fmt = mpl.extensions[ind];\n", - " var option = document.createElement('option');\n", - " option.selected = fmt === mpl.default_extension;\n", - " option.innerHTML = fmt;\n", - " fmt_picker.appendChild(option);\n", - " }\n", - "\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "};\n", - "\n", - "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", - " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", - " // which will in turn request a refresh of the image.\n", - " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", - "};\n", - "\n", - "mpl.figure.prototype.send_message = function (type, properties) {\n", - " properties['type'] = type;\n", - " properties['figure_id'] = this.id;\n", - " this.ws.send(JSON.stringify(properties));\n", - "};\n", - "\n", - "mpl.figure.prototype.send_draw_message = function () {\n", - " if (!this.waiting) {\n", - " this.waiting = true;\n", - " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " var format_dropdown = fig.format_dropdown;\n", - " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", - " fig.ondownload(fig, format);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", - " var size = msg['size'];\n", - " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", - " fig._resize_canvas(size[0], size[1], msg['forward']);\n", - " fig.send_message('refresh', {});\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", - " var x0 = msg['x0'] / fig.ratio;\n", - " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", - " var x1 = msg['x1'] / fig.ratio;\n", - " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", - " x0 = Math.floor(x0) + 0.5;\n", - " y0 = Math.floor(y0) + 0.5;\n", - " x1 = Math.floor(x1) + 0.5;\n", - " y1 = Math.floor(y1) + 0.5;\n", - " var min_x = Math.min(x0, x1);\n", - " var min_y = Math.min(y0, y1);\n", - " var width = Math.abs(x1 - x0);\n", - " var height = Math.abs(y1 - y0);\n", - "\n", - " fig.rubberband_context.clearRect(\n", - " 0,\n", - " 0,\n", - " fig.canvas.width / fig.ratio,\n", - " fig.canvas.height / fig.ratio\n", - " );\n", - "\n", - " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", - " // Updates the figure title.\n", - " fig.header.textContent = msg['label'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", - " var cursor = msg['cursor'];\n", - " switch (cursor) {\n", - " case 0:\n", - " cursor = 'pointer';\n", - " break;\n", - " case 1:\n", - " cursor = 'default';\n", - " break;\n", - " case 2:\n", - " cursor = 'crosshair';\n", - " break;\n", - " case 3:\n", - " cursor = 'move';\n", - " break;\n", - " }\n", - " fig.rubberband_canvas.style.cursor = cursor;\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_message = function (fig, msg) {\n", - " fig.message.textContent = msg['message'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", - " // Request the server to send over a new figure.\n", - " fig.send_draw_message();\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", - " fig.image_mode = msg['mode'];\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", - " for (var key in msg) {\n", - " if (!(key in fig.buttons)) {\n", - " continue;\n", - " }\n", - " fig.buttons[key].disabled = !msg[key];\n", - " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", - " if (msg['mode'] === 'PAN') {\n", - " fig.buttons['Pan'].classList.add('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " } else if (msg['mode'] === 'ZOOM') {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.add('active');\n", - " } else {\n", - " fig.buttons['Pan'].classList.remove('active');\n", - " fig.buttons['Zoom'].classList.remove('active');\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Called whenever the canvas gets updated.\n", - " this.send_message('ack', {});\n", - "};\n", - "\n", - "// A function to construct a web socket function for onmessage handling.\n", - "// Called in the figure constructor.\n", - "mpl.figure.prototype._make_on_message_function = function (fig) {\n", - " return function socket_on_message(evt) {\n", - " if (evt.data instanceof Blob) {\n", - " /* FIXME: We get \"Resource interpreted as Image but\n", - " * transferred with MIME type text/plain:\" errors on\n", - " * Chrome. But how to set the MIME type? It doesn't seem\n", - " * to be part of the websocket stream */\n", - " evt.data.type = 'image/png';\n", - "\n", - " /* Free the memory for the previous frames */\n", - " if (fig.imageObj.src) {\n", - " (window.URL || window.webkitURL).revokeObjectURL(\n", - " fig.imageObj.src\n", - " );\n", - " }\n", - "\n", - " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", - " evt.data\n", - " );\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " } else if (\n", - " typeof evt.data === 'string' &&\n", - " evt.data.slice(0, 21) === 'data:image/png;base64'\n", - " ) {\n", - " fig.imageObj.src = evt.data;\n", - " fig.updated_canvas_event();\n", - " fig.waiting = false;\n", - " return;\n", - " }\n", - "\n", - " var msg = JSON.parse(evt.data);\n", - " var msg_type = msg['type'];\n", - "\n", - " // Call the \"handle_{type}\" callback, which takes\n", - " // the figure and JSON message as its only arguments.\n", - " try {\n", - " var callback = fig['handle_' + msg_type];\n", - " } catch (e) {\n", - " console.log(\n", - " \"No handler for the '\" + msg_type + \"' message type: \",\n", - " msg\n", - " );\n", - " return;\n", - " }\n", - "\n", - " if (callback) {\n", - " try {\n", - " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", - " callback(fig, msg);\n", - " } catch (e) {\n", - " console.log(\n", - " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", - " e,\n", - " e.stack,\n", - " msg\n", - " );\n", - " }\n", - " }\n", - " };\n", - "};\n", - "\n", - "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", - "mpl.findpos = function (e) {\n", - " //this section is from http://www.quirksmode.org/js/events_properties.html\n", - " var targ;\n", - " if (!e) {\n", - " e = window.event;\n", - " }\n", - " if (e.target) {\n", - " targ = e.target;\n", - " } else if (e.srcElement) {\n", - " targ = e.srcElement;\n", - " }\n", - " if (targ.nodeType === 3) {\n", - " // defeat Safari bug\n", - " targ = targ.parentNode;\n", - " }\n", - "\n", - " // pageX,Y are the mouse positions relative to the document\n", - " var boundingRect = targ.getBoundingClientRect();\n", - " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", - " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", - "\n", - " return { x: x, y: y };\n", - "};\n", - "\n", - "/*\n", - " * return a copy of an object with only non-object keys\n", - " * we need this to avoid circular references\n", - " * http://stackoverflow.com/a/24161582/3208463\n", - " */\n", - "function simpleKeys(original) {\n", - " return Object.keys(original).reduce(function (obj, key) {\n", - " if (typeof original[key] !== 'object') {\n", - " obj[key] = original[key];\n", - " }\n", - " return obj;\n", - " }, {});\n", - "}\n", - "\n", - "mpl.figure.prototype.mouse_event = function (event, name) {\n", - " var canvas_pos = mpl.findpos(event);\n", - "\n", - " if (name === 'button_press') {\n", - " this.canvas.focus();\n", - " this.canvas_div.focus();\n", - " }\n", - "\n", - " var x = canvas_pos.x * this.ratio;\n", - " var y = canvas_pos.y * this.ratio;\n", - "\n", - " this.send_message(name, {\n", - " x: x,\n", - " y: y,\n", - " button: event.button,\n", - " step: event.step,\n", - " guiEvent: simpleKeys(event),\n", - " });\n", - "\n", - " /* This prevents the web browser from automatically changing to\n", - " * the text insertion cursor when the button is pressed. We want\n", - " * to control all of the cursor setting manually through the\n", - " * 'cursor' event from matplotlib */\n", - " event.preventDefault();\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", - " // Handle any extra behaviour associated with a key event\n", - "};\n", - "\n", - "mpl.figure.prototype.key_event = function (event, name) {\n", - " // Prevent repeat events\n", - " if (name === 'key_press') {\n", - " if (event.which === this._key) {\n", - " return;\n", - " } else {\n", - " this._key = event.which;\n", - " }\n", - " }\n", - " if (name === 'key_release') {\n", - " this._key = null;\n", - " }\n", - "\n", - " var value = '';\n", - " if (event.ctrlKey && event.which !== 17) {\n", - " value += 'ctrl+';\n", - " }\n", - " if (event.altKey && event.which !== 18) {\n", - " value += 'alt+';\n", - " }\n", - " if (event.shiftKey && event.which !== 16) {\n", - " value += 'shift+';\n", - " }\n", - "\n", - " value += 'k';\n", - " value += event.which.toString();\n", - "\n", - " this._key_event_extra(event, name);\n", - "\n", - " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", - " return false;\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", - " if (name === 'download') {\n", - " this.handle_save(this, null);\n", - " } else {\n", - " this.send_message('toolbar_button', { name: name });\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", - " this.message.textContent = tooltip;\n", - "};\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", - "\n", - "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", - "\n", - "mpl.default_extension = \"png\";/* global mpl */\n", - "\n", - "var comm_websocket_adapter = function (comm) {\n", - " // Create a \"websocket\"-like object which calls the given IPython comm\n", - " // object with the appropriate methods. Currently this is a non binary\n", - " // socket, so there is still some room for performance tuning.\n", - " var ws = {};\n", - "\n", - " ws.close = function () {\n", - " comm.close();\n", - " };\n", - " ws.send = function (m) {\n", - " //console.log('sending', m);\n", - " comm.send(m);\n", - " };\n", - " // Register the callback with on_msg.\n", - " comm.on_msg(function (msg) {\n", - " //console.log('receiving', msg['content']['data'], msg);\n", - " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", - " ws.onmessage(msg['content']['data']);\n", - " });\n", - " return ws;\n", - "};\n", - "\n", - "mpl.mpl_figure_comm = function (comm, msg) {\n", - " // This is the function which gets called when the mpl process\n", - " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", - "\n", - " var id = msg.content.data.id;\n", - " // Get hold of the div created by the display call when the Comm\n", - " // socket was opened in Python.\n", - " var element = document.getElementById(id);\n", - " var ws_proxy = comm_websocket_adapter(comm);\n", - "\n", - " function ondownload(figure, _format) {\n", - " window.open(figure.canvas.toDataURL());\n", - " }\n", - "\n", - " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", - "\n", - " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", - " // web socket which is closed, not our websocket->open comm proxy.\n", - " ws_proxy.onopen();\n", - "\n", - " fig.parent_element = element;\n", - " fig.cell_info = mpl.find_output_cell(\"
\");\n", - " if (!fig.cell_info) {\n", - " console.error('Failed to find cell for figure', id, fig);\n", - " return;\n", - " }\n", - " fig.cell_info[0].output_area.element.one(\n", - " 'cleared',\n", - " { fig: fig },\n", - " fig._remove_fig_handler\n", - " );\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_close = function (fig, msg) {\n", - " var width = fig.canvas.width / fig.ratio;\n", - " fig.cell_info[0].output_area.element.off(\n", - " 'cleared',\n", - " fig._remove_fig_handler\n", - " );\n", - "\n", - " // Update the output cell to use the data from the current canvas.\n", - " fig.push_to_output();\n", - " var dataURL = fig.canvas.toDataURL();\n", - " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", - " // the notebook keyboard shortcuts fail.\n", - " IPython.keyboard_manager.enable();\n", - " fig.parent_element.innerHTML =\n", - " '';\n", - " fig.close_ws(fig, msg);\n", - "};\n", - "\n", - "mpl.figure.prototype.close_ws = function (fig, msg) {\n", - " fig.send_message('closing', msg);\n", - " // fig.ws.close()\n", - "};\n", - "\n", - "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", - " // Turn the data on the canvas into data in the output cell.\n", - " var width = this.canvas.width / this.ratio;\n", - " var dataURL = this.canvas.toDataURL();\n", - " this.cell_info[1]['text/html'] =\n", - " '';\n", - "};\n", - "\n", - "mpl.figure.prototype.updated_canvas_event = function () {\n", - " // Tell IPython that the notebook contents must change.\n", - " IPython.notebook.set_dirty(true);\n", - " this.send_message('ack', {});\n", - " var fig = this;\n", - " // Wait a second, then push the new image to the DOM so\n", - " // that it is saved nicely (might be nice to debounce this).\n", - " setTimeout(function () {\n", - " fig.push_to_output();\n", - " }, 1000);\n", - "};\n", - "\n", - "mpl.figure.prototype._init_toolbar = function () {\n", - " var fig = this;\n", - "\n", - " var toolbar = document.createElement('div');\n", - " toolbar.classList = 'btn-toolbar';\n", - " this.root.appendChild(toolbar);\n", - "\n", - " function on_click_closure(name) {\n", - " return function (_event) {\n", - " return fig.toolbar_button_onclick(name);\n", - " };\n", - " }\n", - "\n", - " function on_mouseover_closure(tooltip) {\n", - " return function (event) {\n", - " if (!event.currentTarget.disabled) {\n", - " return fig.toolbar_button_onmouseover(tooltip);\n", - " }\n", - " };\n", - " }\n", - "\n", - " fig.buttons = {};\n", - " var buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " var button;\n", - " for (var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " /* Instead of a spacer, we start a new button group. */\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - " buttonGroup = document.createElement('div');\n", - " buttonGroup.classList = 'btn-group';\n", - " continue;\n", - " }\n", - "\n", - " button = fig.buttons[name] = document.createElement('button');\n", - " button.classList = 'btn btn-default';\n", - " button.href = '#';\n", - " button.title = name;\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', on_click_closure(method_name));\n", - " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", - " buttonGroup.appendChild(button);\n", - " }\n", - "\n", - " if (buttonGroup.hasChildNodes()) {\n", - " toolbar.appendChild(buttonGroup);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = document.createElement('span');\n", - " status_bar.classList = 'mpl-message pull-right';\n", - " toolbar.appendChild(status_bar);\n", - " this.message = status_bar;\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = document.createElement('div');\n", - " buttongrp.classList = 'btn-group inline pull-right';\n", - " button = document.createElement('button');\n", - " button.classList = 'btn btn-mini btn-primary';\n", - " button.href = '#';\n", - " button.title = 'Stop Interaction';\n", - " button.innerHTML = '';\n", - " button.addEventListener('click', function (_evt) {\n", - " fig.handle_close(fig, {});\n", - " });\n", - " button.addEventListener(\n", - " 'mouseover',\n", - " on_mouseover_closure('Stop Interaction')\n", - " );\n", - " buttongrp.appendChild(button);\n", - " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", - " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", - "};\n", - "\n", - "mpl.figure.prototype._remove_fig_handler = function (event) {\n", - " var fig = event.data.fig;\n", - " fig.close_ws(fig, {});\n", - "};\n", - "\n", - "mpl.figure.prototype._root_extra_style = function (el) {\n", - " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", - "};\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function (el) {\n", - " // this is important to make the div 'focusable\n", - " el.setAttribute('tabindex', 0);\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " } else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager) {\n", - " manager = IPython.keyboard_manager;\n", - " }\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which === 13) {\n", - " this.canvas_div.blur();\n", - " // select the cell after this one\n", - " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", - " IPython.notebook.select(index + 1);\n", - " }\n", - "};\n", - "\n", - "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", - " fig.ondownload(fig, null);\n", - "};\n", - "\n", - "mpl.find_output_cell = function (html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i = 0; i < ncells; i++) {\n", - " var cell = cells[i];\n", - " if (cell.cell_type === 'code') {\n", - " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", - " var data = cell.output_area.outputs[j];\n", - " if (data.data) {\n", - " // IPython >= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] === html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "};\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel !== null) {\n", - " IPython.notebook.kernel.comm_manager.register_target(\n", - " 'matplotlib',\n", - " mpl.mpl_figure_comm\n", - " );\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure()\n", - "plt.plot(filtered_EDA, color='red')\n", - "plt.scatter(peaks, filtered_EDA[peaks], color=\"green\")\n", - "plt.scatter(peak_starts, filtered_EDA[peak_starts], color=\"green\", marker=\">\", alpha=0.5)\n", - "plt.scatter(peak_ends, filtered_EDA[peak_ends], color=\"green\", marker=\"s\", alpha=0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Features, related to the peaks, are then calculated.\n", - "These include the number and rate (peaks per second) of the peaks, as well as the average and maximum of their characteristics (duration, increase and decrease time etc.)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "avgPeakAmplitudeChangeAfter 0.564829\n", - "avgPeakAmplitudeChangeBefore 0.000000\n", - "avgPeakChangeRatio 0.096774\n", - "avgPeakDecreaseTime 2.818182\n", - "avgPeakDuration 49.454545\n", - "avgPeakIncreaseTime 0.272727\n", - "maxPeakAmplitudeChangeAfter 1.660329\n", - "maxPeakAmplitudeChangeBefore 1.400329\n", - "maxPeakChangeRatio 0.259259\n", - "maxPeakDecreaseTime 6.750000\n", - "maxPeakDuration 8.500000\n", - "maxPeakIncreaseTime 1.750000\n", - "maxPeakResponseSlopeAfter -0.065041\n", - "maxPeakResponseSlopeBefore 0.233815\n", - "numPeaks 11.000000\n", - "powerPeaks 3.643448\n", - "ratePeaks 0.091667\n", - "Name: 131, dtype: float64\n" - ] - } - ], - "source": [ - "print(df_EDA.loc[no_interest, df_EDA.columns.str.contains('Peak')])" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 239/239 [01:04<00:00, 3.69it/s]\n" - ] - } - ], - "source": [ - "calculatedFeatures_EDA = calculateFeatures(eda_data_2D, fs=int(sampleRate_EDA), featureNames=gsrFeatureNames)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cf/main.py index 54cb417d..1e9accdd 100644 --- a/src/features/empatica_electrodermal_activity/cf/main.py +++ b/src/features/empatica_electrodermal_activity/cf/main.py @@ -1,8 +1,6 @@ import pandas as pd from scipy.stats import entropy -import sys -sys.path.insert(1, '/workspaces/rapids/calculatingfeatures') from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures From 1902d02a86da93e0c4653f799783a13a137a958a Mon Sep 17 00:00:00 2001 From: Primoz Date: Fri, 25 Mar 2022 16:27:28 +0000 Subject: [PATCH 13/57] Updating conda env. --- environment.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/environment.yml b/environment.yml index 1bdd2760..937162e3 100644 --- a/environment.yml +++ b/environment.yml @@ -145,6 +145,7 @@ dependencies: - biosppy==0.8.0 - build==0.7.0 - cached-property==1.5.2 + - calculatingfeatures==0.1.1 - configargparse==0.15.1 - decorator==4.4.2 - h5py==3.6.0 @@ -157,6 +158,7 @@ dependencies: - pulp==2.4 - pyparsing==2.4.7 - pyrsistent==0.15.5 + - pywavelets==1.3.0 - ratelimiter==1.2.0.post0 - shortuuid==1.0.8 - snakemake==5.30.2 From 393dab72f5a4e7d528957f148b8bbcb99cf22ae9 Mon Sep 17 00:00:00 2001 From: = <=> Date: Mon, 28 Mar 2022 12:37:02 +0000 Subject: [PATCH 14/57] Added components for the temperature features extraction. --- config.yaml | 23 ++++---- environment.yml | 52 ++++--------------- .../cf/main.py | 2 +- src/features/empatica_temperature/cf/main.py | 44 ++++++++++++++++ 4 files changed, 70 insertions(+), 51 deletions(-) create mode 100644 src/features/empatica_temperature/cf/main.py diff --git a/config.yaml b/config.yaml index 290b8b89..faaf1286 100644 --- a/config.yaml +++ b/config.yaml @@ -495,9 +495,17 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py + CF: + COMPUTE: True + FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", + "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", + "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", + "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", + "sumOfSquareComponents"] + SRC_SCRIPT: src/features/empatica_temperature/cf/main.py # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/ EMPATICA_ELECTRODERMAL_ACTIVITY: @@ -509,14 +517,11 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CF: COMPUTE: True - FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', - 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats', - 'maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', - 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', - 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', - 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', - 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', - 'significantIncrease', 'significantDecrease'] + FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', + 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', + 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', + 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange', + 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ diff --git a/environment.yml b/environment.yml index 937162e3..7476307b 100644 --- a/environment.yml +++ b/environment.yml @@ -13,34 +13,22 @@ dependencies: - attrs=20.3.0 - binaryornot=0.4.4 - blas=1.0 - - brotli=1.0.9 - brotlipy=0.7.0 - bzip2=1.0.8 - - ca-certificates=2022.2.1 - - certifi=2021.10.8 + - ca-certificates=2021.7.5 + - certifi=2021.5.30 - cffi=1.14.4 - chardet=3.0.4 - click=7.1.2 - colorama=0.4.4 - cookiecutter=1.6.0 - cryptography=3.3.1 - - cycler=0.11.0 - datrie=0.8.2 - - dbus=1.13.18 - docutils=0.16 - - expat=2.4.4 - - fontconfig=2.13.1 - - fonttools=4.25.0 - - freetype=2.11.0 - future=0.18.2 - - giflib=5.2.1 - gitdb=4.0.5 - gitdb2=4.0.2 - gitpython=3.1.11 - - glib=2.69.1 - - gst-plugins-base=1.14.0 - - gstreamer=1.14.0 - - icu=58.2 - idna=2.10 - imbalanced-learn=0.6.2 - importlib-metadata=2.0.0 @@ -49,10 +37,7 @@ dependencies: - jinja2=2.11.2 - jinja2-time=0.2.0 - joblib=1.0.0 - - jpeg=9d - jsonschema=3.2.0 - - kiwisolver=1.3.2 - - lcms2=2.12 - ld_impl_linux-64=2.36.1 - libblas=3.8.0 - libcblas=3.8.0 @@ -66,38 +51,24 @@ dependencies: - libgfortran - liblapack=3.8.0 - libopenblas=0.3.10 - - libpng=1.6.37 - libstdcxx-ng=11.2.0 - - libtiff=4.2.0 - - libuuid=1.0.3 - - libwebp=1.2.2 - - libwebp-base=1.2.2 - - libxcb=1.14 - libxgboost=0.90 - - libxml2=2.9.12 - libzlib=1.2.11 - lightgbm=3.1.1 - llvm-openmp=10.0.0 - - lz4-c=1.9.3 - markupsafe=1.1.1 - - matplotlib=3.5.1 - - matplotlib-base=3.5.1 - mkl - mkl-service=2.3.0 - mkl_fft=1.2.0 - mkl_random=1.1.1 - more-itertools=8.6.0 - - munkres=1.1.4 - ncurses=6.2 - numpy=1.19.2 - numpy-base=1.19.2 - openblas=0.3.4 - - openssl=1.1.1m - - packaging=21.3 + - openssl=1.1.1k - pandas=1.1.5 - pbr=5.5.1 - - pcre=8.45 - - pillow=9.0.1 - pip=20.3.3 - plotly=4.14.1 - poyo=0.5.0 @@ -106,28 +77,24 @@ dependencies: - pycparser=2.20 - pyerfa=1.7.1.1 - pyopenssl=20.0.1 - - pyqt=5.9.2 - pysocks=1.7.1 - python=3.7.9 - python-dateutil=2.8.1 - python_abi=3.7 - pytz=2020.4 - pyyaml=5.3.1 - - qt=5.9.7 - readline=8.0 - requests=2.25.0 - retrying=1.3.3 - scikit-learn=0.23.2 - scipy=1.5.2 - setuptools=51.0.0 - - sip=4.19.8 - six=1.15.0 - smmap=3.0.4 - smmap2=3.0.1 - sqlite=3.33.0 - threadpoolctl=2.1.0 - tk=8.6.10 - - tornado=6.1 - tqdm=4.62.0 - urllib3=1.25.11 - wheel=0.36.2 @@ -138,23 +105,26 @@ dependencies: - yaml=0.2.5 - zipp=3.4.0 - zlib=1.2.11 - - zstd=1.4.9 - pip: - amply==0.1.4 - - bidict==0.21.4 + - bidict==0.22.0 - biosppy==0.8.0 - - build==0.7.0 - cached-property==1.5.2 - calculatingfeatures==0.1.1 - configargparse==0.15.1 + - cycler==0.11.0 - decorator==4.4.2 + - fonttools==4.31.2 - h5py==3.6.0 - ipython-genutils==0.2.0 - jupyter-core==4.6.3 + - kiwisolver==1.4.1 + - matplotlib==3.5.1 - nbformat==5.0.7 - opencv-python==4.5.5.64 + - packaging==21.3 - peakutils==1.3.3 - - pep517==0.12.0 + - pillow==9.0.1 - pulp==2.4 - pyparsing==2.4.7 - pyrsistent==0.15.5 @@ -162,7 +132,7 @@ dependencies: - ratelimiter==1.2.0.post0 - shortuuid==1.0.8 - snakemake==5.30.2 - - tomli==2.0.1 - toposort==1.5 - traitlets==4.3.3 + - typing-extensions==4.1.1 prefix: /opt/conda/envs/rapids diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cf/main.py index 1e9accdd..c4dbfda9 100644 --- a/src/features/empatica_electrodermal_activity/cf/main.py +++ b/src/features/empatica_electrodermal_activity/cf/main.py @@ -18,7 +18,7 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment eda_intraday_features = pd.DataFrame() - # apply a method from calculate features module + # apply methods from calculate features module eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=4, featureNames=features)) diff --git a/src/features/empatica_temperature/cf/main.py b/src/features/empatica_temperature/cf/main.py new file mode 100644 index 00000000..8c273a87 --- /dev/null +++ b/src/features/empatica_temperature/cf/main.py @@ -0,0 +1,44 @@ +import pandas as pd +from scipy.stats import entropy + +from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames +from CalculatingFeatures.calculate_features import calculateFeatures + +import sys + + +def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment): + temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features) + + if not temperature_intraday_data.empty: + temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment) + + if not temperature_intraday_data.empty: + + temperature_intraday_features = pd.DataFrame() + + # apply methods from calculate features module + temperature_intraday_features = \ + temperature_intraday_data.groupby('local_segment').apply(\ + lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=4, featureNames=features)) + + temperature_intraday_features.reset_index(inplace=True) + + return temperature_intraday_features + + +def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_intraday_features = provider["FEATURES"] + # name of the features this function can compute + base_intraday_features_names = genericFeatureNames + # the subset of requested features this function can compute + intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) + + # extract features from intraday data + temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data, + intraday_features_to_compute, time_segment, + filter_data_by_segment) + + return temperature_intraday_features \ No newline at end of file From ca59a54d8fb09549740588165a3ece0213507f28 Mon Sep 17 00:00:00 2001 From: = <=> Date: Mon, 28 Mar 2022 13:50:08 +0000 Subject: [PATCH 15/57] Get a sample rate from two sequential timestamps. --- .../empatica_electrodermal_activity/cf/main.py | 13 +++++++++++-- src/features/empatica_temperature/cf/main.py | 14 +++++++++++--- 2 files changed, 22 insertions(+), 5 deletions(-) diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cf/main.py index c4dbfda9..997ff8a0 100644 --- a/src/features/empatica_electrodermal_activity/cf/main.py +++ b/src/features/empatica_electrodermal_activity/cf/main.py @@ -7,11 +7,20 @@ from CalculatingFeatures.calculate_features import calculateFeatures pd.set_option('display.max_columns', None) +def getSampleRate(data): + try: + timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0] + except: + raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.") + + return 1000/timestamps_diff def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment): eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features) - if not eda_intraday_data.empty: + if not eda_intraday_data.empty: + sample_rate = getSampleRate(eda_intraday_data) + eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment) if not eda_intraday_data.empty: @@ -21,7 +30,7 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment # apply methods from calculate features module eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=4, featureNames=features)) + lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ... eda_intraday_features.reset_index(inplace=True) diff --git a/src/features/empatica_temperature/cf/main.py b/src/features/empatica_temperature/cf/main.py index 8c273a87..ec9a8f2a 100644 --- a/src/features/empatica_temperature/cf/main.py +++ b/src/features/empatica_temperature/cf/main.py @@ -4,13 +4,21 @@ from scipy.stats import entropy from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures -import sys +def getSampleRate(data): + try: + timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0] + except: + raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.") + + return 1000/timestamps_diff def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment): temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features) - if not temperature_intraday_data.empty: + if not temperature_intraday_data.empty: + sample_rate = getSampleRate(temperature_intraday_data) + temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment) if not temperature_intraday_data.empty: @@ -20,7 +28,7 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim # apply methods from calculate features module temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=4, featureNames=features)) + lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ... temperature_intraday_features.reset_index(inplace=True) From 8b76c96e472ac63af6186ec57537c55797385b0f Mon Sep 17 00:00:00 2001 From: = <=> Date: Mon, 28 Mar 2022 14:18:29 +0000 Subject: [PATCH 16/57] Cleaning existing CF mains' and preparing src script for ACC. --- config.yaml | 37 +++++--- .../empatica_accelerometer/cf/main.py | 86 +++++++++++++++++++ .../cf/main.py | 4 +- src/features/empatica_temperature/cf/main.py | 6 +- 4 files changed, 114 insertions(+), 19 deletions(-) create mode 100644 src/features/empatica_accelerometer/cf/main.py diff --git a/config.yaml b/config.yaml index faaf1286..0ec7eb4b 100644 --- a/config.yaml +++ b/config.yaml @@ -477,9 +477,20 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py + CF: + COMPUTE: True + FEATURES: ["meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", + "postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand", + "coefficientOfVariationBand", "amplitudeBand", "totalEnergyBand", "dominantFrequencyEnergyBand", "meanCrossingRateBand", + "correlationBand", "quartilesMagnitudesBand", "interQuartileRangeMagnitudesBand", "areaUnderAccelerationMagnitude", + "peaksDataLow", "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", "totalKineticEnergyBand", "squareSumOfComponent", + "sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow", + "pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] + SRC_SCRIPT: src/features/empatica_accelerometer/cf/main.py + # See https://www.rapids.science/latest/features/empatica-heartrate/ EMPATICA_HEARTRATE: @@ -495,16 +506,16 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CF: - COMPUTE: True + COMPUTE: False FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", - "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", - "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", - "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", - "sumOfSquareComponents"] + "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", + "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", + "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", + "sumOfSquareComponents"] SRC_SCRIPT: src/features/empatica_temperature/cf/main.py # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/ @@ -512,16 +523,16 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CF: - COMPUTE: True + COMPUTE: False FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', - 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', - 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', - 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange', - 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] + 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', + 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', + 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', + 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ diff --git a/src/features/empatica_accelerometer/cf/main.py b/src/features/empatica_accelerometer/cf/main.py new file mode 100644 index 00000000..9bf9308b --- /dev/null +++ b/src/features/empatica_accelerometer/cf/main.py @@ -0,0 +1,86 @@ +import pandas as pd +from scipy.stats import entropy + +from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray, convertInputInto2d, gsrFeatureNames +from CalculatingFeatures.calculate_features import calculateFeatures + + +def getSampleRate(data): + try: + timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0] + except: + raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.") + + return 1000/timestamps_diff + +def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment, filter_data_by_segment): + acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features) + + if not acc_intraday_data.empty: + sample_rate = getSampleRate(acc_intraday_data) + + acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment) + + if not acc_intraday_data.empty: + + acc_intraday_features = pd.DataFrame() + + # apply methods from calculate features module + acc_intraday_features = \ + acc_intraday_data.groupby('local_segment').apply(\ + lambda x: calculateFeatures(convertInputInto2d(x['accelerometer'], x.shape[0]), fs=sample_rate, featureNames=features)) + + acc_intraday_features.reset_index(inplace=True) + + return acc_intraday_features + + +def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_intraday_features = provider["FEATURES"] + # name of the features this function can compute + base_intraday_features_names = gsrFeatureNames + # the subset of requested features this function can compute + intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) + + # extract features from intraday data + eda_intraday_features = extractAccFeaturesFromIntradayData(eda_intraday_data, + intraday_features_to_compute, time_segment, + filter_data_by_segment) + + return eda_intraday_features + + def dbdp_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + # name of the features this function can compute + base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) + + acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) + if not acc_data.empty: + acc_data = filter_data_by_segment(acc_data, time_segment) + + if not acc_data.empty: + acc_features = pd.DataFrame() + # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) + magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) + acc_data = acc_data.assign(magnitude = magnitude.values) + + if "maxmagnitude" in features_to_compute: + acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + if "minmagnitude" in features_to_compute: + acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() + if "avgmagnitude" in features_to_compute: + acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() + if "medianmagnitude" in features_to_compute: + acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() + if "stdmagnitude" in features_to_compute: + acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() + + acc_features = acc_features.reset_index() + + return acc_features \ No newline at end of file diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cf/main.py index 997ff8a0..97b5f8ea 100644 --- a/src/features/empatica_electrodermal_activity/cf/main.py +++ b/src/features/empatica_electrodermal_activity/cf/main.py @@ -4,8 +4,6 @@ from scipy.stats import entropy from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures -pd.set_option('display.max_columns', None) - def getSampleRate(data): try: @@ -30,7 +28,7 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment # apply methods from calculate features module eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ... + lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, featureNames=features)) eda_intraday_features.reset_index(inplace=True) diff --git a/src/features/empatica_temperature/cf/main.py b/src/features/empatica_temperature/cf/main.py index ec9a8f2a..200a295e 100644 --- a/src/features/empatica_temperature/cf/main.py +++ b/src/features/empatica_temperature/cf/main.py @@ -1,7 +1,7 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames +from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures @@ -13,7 +13,7 @@ def getSampleRate(data): return 1000/timestamps_diff -def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment): +def extractAccFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment): temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not temperature_intraday_data.empty: @@ -28,7 +28,7 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim # apply methods from calculate features module temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) # To-Do: Hardcoded f/s ... + lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) temperature_intraday_features.reset_index(inplace=True) From a9244a60fc3f8f07e6ce19ef8c638e27280f42ab Mon Sep 17 00:00:00 2001 From: = <=> Date: Mon, 28 Mar 2022 14:26:37 +0000 Subject: [PATCH 17/57] Corrections for TEMP cf src script. --- src/features/empatica_temperature/cf/main.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/features/empatica_temperature/cf/main.py b/src/features/empatica_temperature/cf/main.py index 200a295e..c7cc962a 100644 --- a/src/features/empatica_temperature/cf/main.py +++ b/src/features/empatica_temperature/cf/main.py @@ -1,7 +1,7 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames +from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures @@ -13,7 +13,7 @@ def getSampleRate(data): return 1000/timestamps_diff -def extractAccFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment): +def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment): temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not temperature_intraday_data.empty: From ab0b9227d7febc682f73cc169f79f5392bf3e330 Mon Sep 17 00:00:00 2001 From: = <=> Date: Tue, 29 Mar 2022 09:41:51 +0000 Subject: [PATCH 18/57] Added ACC calculated features and shorter version of ACC data. --- config.yaml | 14 ++--- data/external/empatica/empatica1/E4 Data.zip | Bin 1678743 -> 1320626 bytes .../empatica_accelerometer/cf/main.py | 51 ++++-------------- .../cf/main.py | 4 +- src/features/empatica_temperature/cf/main.py | 2 +- 5 files changed, 22 insertions(+), 49 deletions(-) diff --git a/config.yaml b/config.yaml index 0ec7eb4b..01575201 100644 --- a/config.yaml +++ b/config.yaml @@ -482,13 +482,14 @@ EMPATICA_ACCELEROMETER: SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CF: COMPUTE: True - FEATURES: ["meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", + FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features + "meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", "postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand", "coefficientOfVariationBand", "amplitudeBand", "totalEnergyBand", "dominantFrequencyEnergyBand", "meanCrossingRateBand", "correlationBand", "quartilesMagnitudesBand", "interQuartileRangeMagnitudesBand", "areaUnderAccelerationMagnitude", "peaksDataLow", "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", "totalKineticEnergyBand", "squareSumOfComponent", "sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow", - "pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] + "pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] # Acc features SRC_SCRIPT: src/features/empatica_accelerometer/cf/main.py @@ -506,11 +507,11 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CF: - COMPUTE: False + COMPUTE: True FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", @@ -523,11 +524,11 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CF: - COMPUTE: False + COMPUTE: True FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', @@ -559,6 +560,7 @@ EMPATICA_TAGS: PROVIDERS: # None implemented yet + ######################################################################################################################## # PLOTS # ######################################################################################################################## diff --git a/data/external/empatica/empatica1/E4 Data.zip b/data/external/empatica/empatica1/E4 Data.zip index bb1e36fe7f3420a0072bb7889cec4e6c8d0cc1a6..ad07905e024983096c31c93e794f6b31da32ba5e 100644 GIT binary patch delta 27028 zcmV(rK<>YnyJoV@P>0z&61Ukq6EAuXn^}ESObe+ZuWbMTdl>?^7q$}u1%GrO^`tKG z40z#1!jH*+8c(WpJHCG3b{%c*=X$DRAGx`nVJzZlh8J%d*T-kwn=dnH&MO;!j&I{- z7bjPbj_8d>e?F-A6k6XL{*y?a73!ItdBNAk)?U+;YgK%h>rImKLXGEs&qs5?$AvQ+w+?6?$K&xP1O9`=-F3hC)5VSysRjEDt9r z2KvsrTqCTgW)@NE7Kxpq!KPp_j`BH?2s{6sI{^wyt`b&=1K&FfZhsrmt)=4$O3UbC zSyBrmCOc=A+%2*RwfkI@cZ>P_8c7un4IFwBF`QI7DVF20o^4GT;9S(>*P<0p3jOLy zo#)yVuvCnI?ixKV3KHiUgRgwp`1$s=ZsJ&2W1RoB(g_qG>La0ZB|}nf@l?ZDgQ3OiE~}rp9dl4 zk!i6;mBufE>qElC6^5InDA&y%dTWaq}($(GM5lq$}P zl~t|S1XQ({DrlB!l!J9VOEA#uUZM)y1OZ?TC_>`QJwpv9yF~%47;91ows|zT<3w+6 z+^7&VW+M5ks0vB(=)`_OMB#F5+>%pm>F*(o6uK~Z8h@Y?r3Q`Q0P809Z1T*V*|Z8} zHdm*zTZ`$;;4wu(hsID96J|tg7qqBPJJ?v+Mo)Cfdlj{*}q=&0+~WU){L z$b~g+ra5&A}va z5Rv-Os()0uY}Fk{*aRr2K`SX0I*pqa+j!@qh0X^DnyD9A*DzU*I#1|8ifiZrHP3J~ zWHUp9c`tx{W|_-e5m#26cvPnRQ5Fu*QYHQ_9o03>;B*)e=@g0_Ixc`@_Gx0bS*Dq? zoQG|#omA$*W;!D3o_Vy$GtTXlBJB$DDG~mz`G1JS<3ztsU4V20bs^8C6}eO1@&uSU z$mp}&SHxI4RxpA9)@FUM}N+lu^K4j zEDAxWJPx)MP*nVMQkh?e+QKr6sA~f0KELLDs;QqJ2Z!FME>;q)6#c?-MhaElZRn#0 zAb(isrzYStYW;e0Cyq5qER>^0kP7?Fi6|ae^WX`XT)K|YHX|FrXT&60ii{Uo^46ye zGLz!qt!byABE@Q+sZ8$g=yJAk7qnzea?6^%}2$V11o z6^bl3S(yh>|LB@zNOg)FJd?u}d69+sfnilfZ*IQI>JL~+qm`g54F;nmVrKn~=fs;{ z3AuJk2>UGzJT=JGY|U-KPBd{M-4NS>n&!w*b!D7k5pGw}y~jK$(>_M{g80FmHh+4I zx~N#BdrT6*j|sZe<%%$ivRmO!>+)fSVxSqoTH6JxrBm8s+`I5sbDGhDe@yyC>j?i`B70Q!*0Ydrck zA=!a@E=%_q>Qj=;22cBdYS9|Nq2u2WI&@{)eraKeE}Q&dEf%=S!|fak^?wl)RnO%j zH|%LcR8n(fCH6Eu)uer5dlIaT1`B3e!o&xQ^X))3*_l9PrEWU-(ZLWg_O`1!m}ysD zYrEEwG%1=7%sNdxvXI+ODSIVrRPEeT2}R52k;OR160WO;H1 zHBWJ%{Hk><*=h=~c6ha(K7XdHtg_1e_nV*8k(T6nS>QpFR6fYsbJA}xqbO9)Bu|UQ zbXh}z5zZLPA;JH`V947PJ6hOl7#v)^DJ}bEk;P~5BvX^Y>}rxWx?fM3yTG5ZpZ*oO zLUSV@t8QST**pNLj;ikBgL~+r>LlI#b|k;F!XfE&=3yr2vQ#piUVkELs<=zXv5S?C z1a(}CMxLkSyQQ^gTZssHU9DjyAe}eg1r!~b23C2@NK
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ym`MM(c^+XzNRrah|C7|XW#B(GeadG1Pot?Tp=11qA+Vn3016UPLn_(x?tcM%eTk3& diff --git a/src/features/empatica_accelerometer/cf/main.py b/src/features/empatica_accelerometer/cf/main.py index 9bf9308b..7b2fdf71 100644 --- a/src/features/empatica_accelerometer/cf/main.py +++ b/src/features/empatica_accelerometer/cf/main.py @@ -1,9 +1,10 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray, convertInputInto2d, gsrFeatureNames +from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames, frequencyFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures +import sys def getSampleRate(data): try: @@ -17,7 +18,7 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not acc_intraday_data.empty: - sample_rate = getSampleRate(acc_intraday_data) + sample_rate = getSampleRate(acc_intraday_data) acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment) @@ -27,20 +28,24 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment # apply methods from calculate features module acc_intraday_features = \ - acc_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['accelerometer'], x.shape[0]), fs=sample_rate, featureNames=features)) + acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ + convertInputInto2d(x['double_values_0'], x.shape[0]), \ + convertInputInto2d(x['double_values_1'], x.shape[0]), \ + convertInputInto2d(x['double_values_2'], x.shape[0]), \ + fs=int(sample_rate), featureNames=features)) acc_intraday_features.reset_index(inplace=True) return acc_intraday_features + def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_intraday_features = provider["FEATURES"] # name of the features this function can compute - base_intraday_features_names = gsrFeatureNames + base_intraday_features_names = accelerometerFeatureNames + frequencyFeatureNames # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) @@ -49,38 +54,4 @@ def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segmen intraday_features_to_compute, time_segment, filter_data_by_segment) - return eda_intraday_features - - def dbdp_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): - - acc_data = pd.read_csv(sensor_data_files["sensor_data"]) - requested_features = provider["FEATURES"] - # name of the features this function can compute - base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) - - acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) - if not acc_data.empty: - acc_data = filter_data_by_segment(acc_data, time_segment) - - if not acc_data.empty: - acc_features = pd.DataFrame() - # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) - magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) - acc_data = acc_data.assign(magnitude = magnitude.values) - - if "maxmagnitude" in features_to_compute: - acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() - if "minmagnitude" in features_to_compute: - acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() - if "avgmagnitude" in features_to_compute: - acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() - if "medianmagnitude" in features_to_compute: - acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() - if "stdmagnitude" in features_to_compute: - acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() - - acc_features = acc_features.reset_index() - - return acc_features \ No newline at end of file + return eda_intraday_features \ No newline at end of file diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cf/main.py index 97b5f8ea..eb4ce9d7 100644 --- a/src/features/empatica_electrodermal_activity/cf/main.py +++ b/src/features/empatica_electrodermal_activity/cf/main.py @@ -1,7 +1,7 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, gsrFeatureNames +from CalculatingFeatures.helper_functions import convertInputInto2d, gsrFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures @@ -28,7 +28,7 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment # apply methods from calculate features module eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, featureNames=features)) + lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features)) eda_intraday_features.reset_index(inplace=True) diff --git a/src/features/empatica_temperature/cf/main.py b/src/features/empatica_temperature/cf/main.py index c7cc962a..53e3aa01 100644 --- a/src/features/empatica_temperature/cf/main.py +++ b/src/features/empatica_temperature/cf/main.py @@ -28,7 +28,7 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim # apply methods from calculate features module temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) + lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features)) temperature_intraday_features.reset_index(inplace=True) From 470993eeb03102c66762e80ec394eed70b79f10b Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 30 Mar 2022 15:00:11 +0000 Subject: [PATCH 19/57] Modification of getSampleRate method for all CF scripts. --- src/features/empatica_accelerometer/cf/main.py | 11 ++++++----- .../empatica_electrodermal_activity/cf/main.py | 6 +++--- src/features/empatica_temperature/cf/main.py | 6 +++--- 3 files changed, 12 insertions(+), 11 deletions(-) diff --git a/src/features/empatica_accelerometer/cf/main.py b/src/features/empatica_accelerometer/cf/main.py index 7b2fdf71..8b400970 100644 --- a/src/features/empatica_accelerometer/cf/main.py +++ b/src/features/empatica_accelerometer/cf/main.py @@ -8,10 +8,11 @@ import sys def getSampleRate(data): try: - timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0] + timestamps_diff = data['timestamp'].diff().dropna().mean() except: - raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.") + raise Exception("Error occured while trying to get the mean sample rate from the data.") + print(1000/timestamps_diff) return 1000/timestamps_diff def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment, filter_data_by_segment): @@ -41,7 +42,7 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): - eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) + acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_intraday_features = provider["FEATURES"] # name of the features this function can compute @@ -50,8 +51,8 @@ def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segmen intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - eda_intraday_features = extractAccFeaturesFromIntradayData(eda_intraday_data, + acc_intraday_features = extractAccFeaturesFromIntradayData(acc_intraday_data, intraday_features_to_compute, time_segment, filter_data_by_segment) - return eda_intraday_features \ No newline at end of file + return acc_intraday_features \ No newline at end of file diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cf/main.py index eb4ce9d7..1c7d1735 100644 --- a/src/features/empatica_electrodermal_activity/cf/main.py +++ b/src/features/empatica_electrodermal_activity/cf/main.py @@ -7,10 +7,10 @@ from CalculatingFeatures.calculate_features import calculateFeatures def getSampleRate(data): try: - timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0] + timestamps_diff = data['timestamp'].diff().dropna().mean() except: - raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.") - + raise Exception("Error occured while trying to get the mean sample rate from the data.") + return 1000/timestamps_diff def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment): diff --git a/src/features/empatica_temperature/cf/main.py b/src/features/empatica_temperature/cf/main.py index 53e3aa01..04cbeb0e 100644 --- a/src/features/empatica_temperature/cf/main.py +++ b/src/features/empatica_temperature/cf/main.py @@ -7,10 +7,10 @@ from CalculatingFeatures.calculate_features import calculateFeatures def getSampleRate(data): try: - timestamps_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0] + timestamps_diff = data['timestamp'].diff().dropna().mean() except: - raise Exception("Error occured while trying to get the sample rate from the first two sequential timestamps.") - + raise Exception("Error occured while trying to get the mean sample rate from the data.") + return 1000/timestamps_diff def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment): From a357138f6e0f84628d10d7d23ff657a10b35a2f6 Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 30 Mar 2022 15:01:24 +0000 Subject: [PATCH 20/57] Added CF for HRV and shortened test data --- config.yaml | 9 ++- data/external/empatica/empatica1/E4 Data.zip | Bin 1320626 -> 201671 bytes .../empatica_blood_volume_pulse/cf/main.py | 54 ++++++++++++++++++ 3 files changed, 61 insertions(+), 2 deletions(-) create mode 100644 src/features/empatica_blood_volume_pulse/cf/main.py diff --git a/config.yaml b/config.yaml index 01575201..eb93ba28 100644 --- a/config.yaml +++ b/config.yaml @@ -541,9 +541,14 @@ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: False - FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] + COMPUTE: True + FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features + "maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] # HRV features SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py + CF: + COMPUTE: True + FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR'] + SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cf/main.py # See 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zwVGgSflYxerZ0<0wP+#{-KGP+G8Q)6gc4`v(I4CObdnqOCz}bND)ARK*u&60f7{hAVJU|643wJ2>w?{u|a{r z72qDZK$j8z3-;eAFAWBc4?_RE1O_B6N2s(9Ta0za0s=+1fk06I4~5_7`N&UV3B?o! zGzcuj|Dyj_Ou0mXz{IvO0D%;-!Tv|$|DZD7Kq)^^A#h3mzmUIR|9fIt!;Ou^6g^Z3 z3 Date: Wed, 30 Mar 2022 16:17:07 +0000 Subject: [PATCH 21/57] Modified README for RAPIDS-CalculatingFeatures integration. --- README.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/README.md b/README.md index 8c33f4c6..d03ec29d 100644 --- a/README.md +++ b/README.md @@ -11,3 +11,23 @@ For more information refer to our [documentation](http://www.rapids.science) By [MoSHI](https://www.moshi.pitt.edu/), [University of Pittsburgh](https://www.pitt.edu/) + +## Installation + +For RAPIDS installation refer to to the [documentation](https://www.rapids.science/1.8/setup/installation/) + +## CalculatingFeatures + +This RAPIDS extension uses CalculatingFeatures library accessible [here](https://repo.ijs.si/matjazbostic/calculatingfeatures). + +To use CalculatingFeatures library: +- Follow the installation instructions in the [README.md](https://repo.ijs.si/matjazbostic/calculatingfeatures/-/blob/master/README.md). + +- Copy built calculatingfeatures folder into the RAPIDS workspace. + +- Install the CalculatingFeatures package by: + ``` + pip install "path/to/the/calculatingfeatures/folder" + You can then delete the folder after the package is installed. + CalculatingFeatures package has to be built and installed everytime to get the newest version. + ``` \ No newline at end of file From c050174ca359ff32f8331aa3efad13092efefe34 Mon Sep 17 00:00:00 2001 From: Primoz Date: Thu, 31 Mar 2022 09:16:00 +0000 Subject: [PATCH 22/57] Various minimal changes. --- config.yaml | 4 ++-- data/external/timesegments_frequency.csv | 2 +- environment.yml | 2 +- src/features/empatica_accelerometer/cf/main.py | 1 - 4 files changed, 4 insertions(+), 5 deletions(-) diff --git a/config.yaml b/config.yaml index eb93ba28..933601f2 100644 --- a/config.yaml +++ b/config.yaml @@ -28,11 +28,11 @@ TIME_SEGMENTS: &time_segments TIMEZONE: TYPE: SINGLE SINGLE: - TZCODE: America/New_York + TZCODE: Europe/Ljubljana MULTIPLE: TZCODES_FILE: data/external/multiple_timezones_example.csv IF_MISSING_TZCODE: STOP - DEFAULT_TZCODE: America/New_York + DEFAULT_TZCODE: Europe/Ljubljana FITBIT: ALLOW_MULTIPLE_TZ_PER_DEVICE: False INFER_FROM_SMARTPHONE_TZ: False diff --git a/data/external/timesegments_frequency.csv b/data/external/timesegments_frequency.csv index e56782d1..22684a47 100644 --- a/data/external/timesegments_frequency.csv +++ b/data/external/timesegments_frequency.csv @@ -1,2 +1,2 @@ label,length -thirtyminutes,30 \ No newline at end of file +fiveminutes,5 \ No newline at end of file diff --git a/environment.yml b/environment.yml index 7476307b..7276ba1f 100644 --- a/environment.yml +++ b/environment.yml @@ -118,7 +118,7 @@ dependencies: - h5py==3.6.0 - ipython-genutils==0.2.0 - jupyter-core==4.6.3 - - kiwisolver==1.4.1 + - kiwisolver==1.4.2 - matplotlib==3.5.1 - nbformat==5.0.7 - opencv-python==4.5.5.64 diff --git a/src/features/empatica_accelerometer/cf/main.py b/src/features/empatica_accelerometer/cf/main.py index 8b400970..6ebf2068 100644 --- a/src/features/empatica_accelerometer/cf/main.py +++ b/src/features/empatica_accelerometer/cf/main.py @@ -12,7 +12,6 @@ def getSampleRate(data): except: raise Exception("Error occured while trying to get the mean sample rate from the data.") - print(1000/timestamps_diff) return 1000/timestamps_diff def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment, filter_data_by_segment): From 1c42347b9b5dd0c408b6fbd377ae74e27e7f6ab0 Mon Sep 17 00:00:00 2001 From: = <=> Date: Mon, 4 Apr 2022 12:19:33 +0000 Subject: [PATCH 23/57] Small changes. --- README.md | 3 +-- config.yaml | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index d03ec29d..9524b94f 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,6 @@ To use CalculatingFeatures library: - Install the CalculatingFeatures package by: ``` - pip install "path/to/the/calculatingfeatures/folder" - You can then delete the folder after the package is installed. + pip install "path/to/the/calculatingfeatures/folder" CalculatingFeatures package has to be built and installed everytime to get the newest version. ``` \ No newline at end of file diff --git a/config.yaml b/config.yaml index 933601f2..bc43d0d7 100644 --- a/config.yaml +++ b/config.yaml @@ -31,7 +31,7 @@ TIMEZONE: TZCODE: Europe/Ljubljana MULTIPLE: TZCODES_FILE: data/external/multiple_timezones_example.csv - IF_MISSING_TZCODE: STOP + IF_MISSING_TZCODE: USE_DEFAULT DEFAULT_TZCODE: Europe/Ljubljana FITBIT: ALLOW_MULTIPLE_TZ_PER_DEVICE: False From 74cf4ada1c20c093648fd9cf5061640c456e5a18 Mon Sep 17 00:00:00 2001 From: = <=> Date: Tue, 12 Apr 2022 14:00:44 +0000 Subject: [PATCH 24/57] Cr-feat window length for all empaticas sensors. --- config.yaml | 36 ++++++++++++------- .../empatica_accelerometer/{cf => cr}/main.py | 35 ++++++++++++------ .../{cf => cr}/main.py | 28 ++++++++++----- .../{cf => cr}/main.py | 27 +++++++++----- .../empatica_temperature/{cf => cr}/main.py | 29 +++++++++------ 5 files changed, 105 insertions(+), 50 deletions(-) rename src/features/empatica_accelerometer/{cf => cr}/main.py (55%) rename src/features/empatica_blood_volume_pulse/{cf => cr}/main.py (60%) rename src/features/empatica_electrodermal_activity/{cf => cr}/main.py (61%) rename src/features/empatica_temperature/{cf => cr}/main.py (59%) diff --git a/config.yaml b/config.yaml index bc43d0d7..77d36d68 100644 --- a/config.yaml +++ b/config.yaml @@ -477,10 +477,10 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py - CF: + CR: COMPUTE: True FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features "meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", @@ -490,7 +490,10 @@ EMPATICA_ACCELEROMETER: "peaksDataLow", "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", "totalKineticEnergyBand", "squareSumOfComponent", "sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow", "pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] # Acc features - SRC_SCRIPT: src/features/empatica_accelerometer/cf/main.py + WINDOWS: + COMPUTE: False + WINDOW_LENGTH: 10 # specify window length in seconds + SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py # See https://www.rapids.science/latest/features/empatica-heartrate/ @@ -507,48 +510,57 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py - CF: + CR: COMPUTE: True FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] - SRC_SCRIPT: src/features/empatica_temperature/cf/main.py + WINDOWS: + COMPUTE: False + WINDOW_LENGTH: 90 # specify window length in seconds + SRC_SCRIPT: src/features/empatica_temperature/cr/main.py # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py - CF: + CR: COMPUTE: True FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] - SRC_SCRIPT: src/features/empatica_electrodermal_activity/cf/main.py + WINDOWS: + COMPUTE: False + WINDOW_LENGTH: 80 # specify window length in seconds + SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features "maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] # HRV features SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py - CF: + CR: COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR'] - SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cf/main.py + WINDOWS: + COMPUTE: False + WINDOW_LENGTH: 4 # specify window length in seconds + SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/ EMPATICA_INTER_BEAT_INTERVAL: diff --git a/src/features/empatica_accelerometer/cf/main.py b/src/features/empatica_accelerometer/cr/main.py similarity index 55% rename from src/features/empatica_accelerometer/cf/main.py rename to src/features/empatica_accelerometer/cr/main.py index 6ebf2068..43812eb8 100644 --- a/src/features/empatica_accelerometer/cf/main.py +++ b/src/features/empatica_accelerometer/cr/main.py @@ -14,7 +14,7 @@ def getSampleRate(data): return 1000/timestamps_diff -def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment, filter_data_by_segment): +def extractAccFeaturesFromIntradayData(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment): acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not acc_intraday_data.empty: @@ -27,12 +27,20 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment acc_intraday_features = pd.DataFrame() # apply methods from calculate features module - acc_intraday_features = \ - acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ - convertInputInto2d(x['double_values_0'], x.shape[0]), \ - convertInputInto2d(x['double_values_1'], x.shape[0]), \ - convertInputInto2d(x['double_values_2'], x.shape[0]), \ - fs=int(sample_rate), featureNames=features)) + if window_length is None: + acc_intraday_features = \ + acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ + convertInputInto2d(x['double_values_0'], x.shape[0]), \ + convertInputInto2d(x['double_values_1'], x.shape[0]), \ + convertInputInto2d(x['double_values_2'], x.shape[0]), \ + fs=int(sample_rate), featureNames=features)) + else: + acc_intraday_features = \ + acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ + convertInputInto2d(x['double_values_0'], window_length*int(sample_rate)), \ + convertInputInto2d(x['double_values_1'], window_length*int(sample_rate)), \ + convertInputInto2d(x['double_values_2'], window_length*int(sample_rate)), \ + fs=int(sample_rate), featureNames=features)) acc_intraday_features.reset_index(inplace=True) @@ -40,18 +48,23 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, time_segment -def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): +def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_intraday_features = provider["FEATURES"] + + if provider["WINDOWS"]["COMPUTE"]: + requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] + else: + requested_window_length = None + # name of the features this function can compute base_intraday_features_names = accelerometerFeatureNames + frequencyFeatureNames # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - acc_intraday_features = extractAccFeaturesFromIntradayData(acc_intraday_data, - intraday_features_to_compute, time_segment, - filter_data_by_segment) + acc_intraday_features = extractAccFeaturesFromIntradayData(acc_intraday_data, intraday_features_to_compute, + requested_window_length, time_segment, filter_data_by_segment) return acc_intraday_features \ No newline at end of file diff --git a/src/features/empatica_blood_volume_pulse/cf/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py similarity index 60% rename from src/features/empatica_blood_volume_pulse/cf/main.py rename to src/features/empatica_blood_volume_pulse/cr/main.py index 3423a770..8b7f0c81 100644 --- a/src/features/empatica_blood_volume_pulse/cf/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -15,7 +15,7 @@ def getSampleRate(data): return 1000/timestamps_diff -def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, time_segment, filter_data_by_segment): +def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment): bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not bvp_intraday_data.empty: @@ -27,28 +27,38 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, time_segment bvp_intraday_features = pd.DataFrame() - # apply methods from calculate features module - bvp_intraday_features = \ - bvp_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + # apply methods from calculate features module + if window_length is None: + bvp_intraday_features = \ + bvp_intraday_data.groupby('local_segment').apply(\ + lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + else: + bvp_intraday_features = \ + bvp_intraday_data.groupby('local_segment').apply(\ + lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) bvp_intraday_features.reset_index(inplace=True) return bvp_intraday_features -def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): +def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): bvp_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_intraday_features = provider["FEATURES"] + + if provider["WINDOWS"]["COMPUTE"]: + requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] + else: + requested_window_length = None + # name of the features this function can compute base_intraday_features_names = hrvFeatureNames + frequencyFeatureNames # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - bvp_intraday_features = extractBVPFeaturesFromIntradayData(bvp_intraday_data, - intraday_features_to_compute, time_segment, - filter_data_by_segment) + bvp_intraday_features = extractBVPFeaturesFromIntradayData(bvp_intraday_data, intraday_features_to_compute, + requested_window_length, time_segment, filter_data_by_segment) return bvp_intraday_features \ No newline at end of file diff --git a/src/features/empatica_electrodermal_activity/cf/main.py b/src/features/empatica_electrodermal_activity/cr/main.py similarity index 61% rename from src/features/empatica_electrodermal_activity/cf/main.py rename to src/features/empatica_electrodermal_activity/cr/main.py index 1c7d1735..c4f11349 100644 --- a/src/features/empatica_electrodermal_activity/cf/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -13,7 +13,7 @@ def getSampleRate(data): return 1000/timestamps_diff -def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment, filter_data_by_segment): +def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment): eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not eda_intraday_data.empty: @@ -26,27 +26,38 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, time_segment eda_intraday_features = pd.DataFrame() # apply methods from calculate features module - eda_intraday_features = \ - eda_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + if window_length is None: + eda_intraday_features = \ + eda_intraday_data.groupby('local_segment').apply(\ + lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + else: + eda_intraday_features = \ + eda_intraday_data.groupby('local_segment').apply(\ + lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) + eda_intraday_features.reset_index(inplace=True) return eda_intraday_features -def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): +def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_intraday_features = provider["FEATURES"] + + if provider["WINDOWS"]["COMPUTE"]: + requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] + else: + requested_window_length = None + # name of the features this function can compute base_intraday_features_names = gsrFeatureNames # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - eda_intraday_features = extractEDAFeaturesFromIntradayData(eda_intraday_data, - intraday_features_to_compute, time_segment, - filter_data_by_segment) + eda_intraday_features = extractEDAFeaturesFromIntradayData(eda_intraday_data, intraday_features_to_compute, + requested_window_length, time_segment, filter_data_by_segment) return eda_intraday_features \ No newline at end of file diff --git a/src/features/empatica_temperature/cf/main.py b/src/features/empatica_temperature/cr/main.py similarity index 59% rename from src/features/empatica_temperature/cf/main.py rename to src/features/empatica_temperature/cr/main.py index 04cbeb0e..b804298c 100644 --- a/src/features/empatica_temperature/cf/main.py +++ b/src/features/empatica_temperature/cr/main.py @@ -4,6 +4,7 @@ from scipy.stats import entropy from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames from CalculatingFeatures.calculate_features import calculateFeatures +import sys def getSampleRate(data): try: @@ -13,7 +14,7 @@ def getSampleRate(data): return 1000/timestamps_diff -def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment): +def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment): temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not temperature_intraday_data.empty: @@ -25,28 +26,36 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, tim temperature_intraday_features = pd.DataFrame() - # apply methods from calculate features module - temperature_intraday_features = \ - temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + # apply methods from calculate features module + if window_length is None: + temperature_intraday_features = \ + temperature_intraday_data.groupby('local_segment').apply(\ + lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + else: + temperature_intraday_features = \ + temperature_intraday_data.groupby('local_segment').apply(\ + lambda x: calculateFeatures(convertInputInto2d(x['temperature'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) temperature_intraday_features.reset_index(inplace=True) return temperature_intraday_features -def cf_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): +def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_intraday_features = provider["FEATURES"] + if provider["WINDOWS"]["COMPUTE"]: + requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] + else: + requested_window_length = None + # name of the features this function can compute base_intraday_features_names = genericFeatureNames # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data, - intraday_features_to_compute, time_segment, - filter_data_by_segment) - + temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data, intraday_features_to_compute, + requested_window_length, time_segment, filter_data_by_segment) return temperature_intraday_features \ No newline at end of file From 3c058e446365c5767f074e97e07546d60409f34f Mon Sep 17 00:00:00 2001 From: = <=> Date: Wed, 13 Apr 2022 13:18:23 +0000 Subject: [PATCH 25/57] Add option to calculate features within windows and store it in CSV (all sensors). --- config.yaml | 14 +++++++------- rules/features.smk | 18 ++++++++++++------ .../empatica_accelerometer/cr/main.py | 4 +++- .../empatica_blood_volume_pulse/cr/main.py | 4 +++- .../cr/main.py | 4 +++- src/features/empatica_temperature/cr/main.py | 5 ++++- src/features/entry.py | 19 +++++++++++++++++-- src/features/utils/utils.py | 4 ++-- 8 files changed, 51 insertions(+), 21 deletions(-) diff --git a/config.yaml b/config.yaml index 77d36d68..12e70b42 100644 --- a/config.yaml +++ b/config.yaml @@ -481,7 +481,7 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features "meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", "postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand", @@ -510,7 +510,7 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: @@ -521,7 +521,7 @@ EMPATICA_TEMPERATURE: "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: - COMPUTE: False + COMPUTE: True WINDOW_LENGTH: 90 # specify window length in seconds SRC_SCRIPT: src/features/empatica_temperature/cr/main.py @@ -530,7 +530,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: @@ -541,7 +541,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] WINDOWS: - COMPUTE: False + COMPUTE: True WINDOW_LENGTH: 80 # specify window length in seconds SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py @@ -550,7 +550,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features "maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] # HRV features SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py @@ -558,7 +558,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR'] WINDOWS: - COMPUTE: False + COMPUTE: True WINDOW_LENGTH: 4 # specify window length in seconds SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py diff --git a/rules/features.smk b/rules/features.smk index f0fea945..1b6e0ad8 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -770,7 +770,8 @@ rule empatica_accelerometer_python_features: provider_key = "{provider_key}", sensor_key = "empatica_accelerometer" output: - "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}.csv" + "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}.csv", + "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -796,7 +797,8 @@ rule empatica_heartrate_python_features: provider_key = "{provider_key}", sensor_key = "empatica_heartrate" output: - "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}.csv" + "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}.csv", + "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -822,7 +824,8 @@ rule empatica_temperature_python_features: provider_key = "{provider_key}", sensor_key = "empatica_temperature" output: - "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}.csv" + "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}.csv", + "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -848,7 +851,8 @@ rule empatica_electrodermal_activity_python_features: provider_key = "{provider_key}", sensor_key = "empatica_electrodermal_activity" output: - "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}.csv" + "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}.csv", + "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -874,7 +878,8 @@ rule empatica_blood_volume_pulse_python_features: provider_key = "{provider_key}", sensor_key = "empatica_blood_volume_pulse" output: - "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}.csv" + "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}.csv", + "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -900,7 +905,8 @@ rule empatica_inter_beat_interval_python_features: provider_key = "{provider_key}", sensor_key = "empatica_inter_beat_interval" output: - "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}.csv" + "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}.csv", + "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv" script: "../src/features/entry.py" diff --git a/src/features/empatica_accelerometer/cr/main.py b/src/features/empatica_accelerometer/cr/main.py index 43812eb8..9618a88c 100644 --- a/src/features/empatica_accelerometer/cr/main.py +++ b/src/features/empatica_accelerometer/cr/main.py @@ -53,7 +53,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_intraday_features = provider["FEATURES"] - if provider["WINDOWS"]["COMPUTE"]: + calc_windows = kwargs.get('calc_windows', False) + + if provider["WINDOWS"]["COMPUTE"] and calc_windows: requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] else: requested_window_length = None diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index 8b7f0c81..6d8afcd4 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -47,7 +47,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_intraday_features = provider["FEATURES"] - if provider["WINDOWS"]["COMPUTE"]: + calc_windows = kwargs.get('calc_windows', False) + + if provider["WINDOWS"]["COMPUTE"] and calc_windows: requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] else: requested_window_length = None diff --git a/src/features/empatica_electrodermal_activity/cr/main.py b/src/features/empatica_electrodermal_activity/cr/main.py index c4f11349..b621f040 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -46,7 +46,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_intraday_features = provider["FEATURES"] - if provider["WINDOWS"]["COMPUTE"]: + calc_windows = kwargs.get('calc_windows', False) + + if provider["WINDOWS"]["COMPUTE"] and calc_windows: requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] else: requested_window_length = None diff --git a/src/features/empatica_temperature/cr/main.py b/src/features/empatica_temperature/cr/main.py index b804298c..20a8af85 100644 --- a/src/features/empatica_temperature/cr/main.py +++ b/src/features/empatica_temperature/cr/main.py @@ -45,7 +45,10 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) requested_intraday_features = provider["FEATURES"] - if provider["WINDOWS"]["COMPUTE"]: + + calc_windows = kwargs.get('calc_windows', False) + + if provider["WINDOWS"]["COMPUTE"] and calc_windows: requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] else: requested_window_length = None diff --git a/src/features/entry.py b/src/features/entry.py index 2f65f8ad..761235f1 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -7,6 +7,13 @@ provider = snakemake.params["provider"] provider_key = snakemake.params["provider_key"] sensor_key = snakemake.params["sensor_key"] +calc_windows = False +try: + calc_windows = provider["WINDOWS"]["COMPUTE"] +except KeyError: + print("Compute window key not found in config.yaml!") + + if sensor_key == "all_cleaning_individual" or sensor_key == "all_cleaning_overall": # Data cleaning sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files) @@ -14,6 +21,14 @@ else: # Extract sensor features del sensor_data_files["time_segments_labels"] time_segments_file = snakemake.input["time_segments_labels"] - sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file) -sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file + sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows) + # Calculation over multiple windows in case of Empatica's CR-features + if calc_windows: + sensor_features.to_csv(snakemake.output[1], index=False) + sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False) + elif "empatica" in sensor_key and provider_key == "dbdp": + pd.DataFrame().to_csv(snakemake.output[1], index=False) + + +sensor_features.to_csv(snakemake.output[0], index=False) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index d2986699..063afc9d 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -88,7 +88,7 @@ def chunk_episodes(sensor_episodes): return merged_sensor_episodes -def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file): +def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False): import pandas as pd from importlib import import_module, util @@ -106,7 +106,7 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file time_segments_labels["label"] = [""] for time_segment in time_segments_labels["label"]: print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, time_segment)) - features = feature_function(sensor_data_files, time_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) + features = feature_function(sensor_data_files, time_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes, calc_windows=calc_windows) if not "local_segment" in features.columns: raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)") features.columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features.columns] From 075c64d1e50bd78d130875e918c6aa5e46c4a128 Mon Sep 17 00:00:00 2001 From: = <=> Date: Thu, 14 Apr 2022 11:51:53 +0000 Subject: [PATCH 26/57] HRV: changed wrapper calcFeat method with specialized one. --- config.yaml | 28 +++++--- .../empatica_blood_volume_pulse/cr/main.py | 28 +++++--- .../empatica_inter_beat_interval/cr/main.py | 66 +++++++++++++++++++ src/features/entry.py | 2 +- 4 files changed, 104 insertions(+), 20 deletions(-) create mode 100644 src/features/empatica_inter_beat_interval/cr/main.py diff --git a/config.yaml b/config.yaml index 12e70b42..8a1516de 100644 --- a/config.yaml +++ b/config.yaml @@ -510,18 +510,18 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: - COMPUTE: True + COMPUTE: False WINDOW_LENGTH: 90 # specify window length in seconds SRC_SCRIPT: src/features/empatica_temperature/cr/main.py @@ -530,18 +530,18 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] WINDOWS: - COMPUTE: True + COMPUTE: False WINDOW_LENGTH: 80 # specify window length in seconds SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py @@ -551,15 +551,15 @@ EMPATICA_BLOOD_VOLUME_PULSE: PROVIDERS: DBDP: COMPUTE: True - FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features - "maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] # HRV features + FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py CR: COMPUTE: True - FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR'] + FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features + 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: COMPUTE: True - WINDOW_LENGTH: 4 # specify window length in seconds + WINDOW_LENGTH: 10 # specify window length in seconds SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/ @@ -570,6 +570,14 @@ EMPATICA_INTER_BEAT_INTERVAL: COMPUTE: False FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py + CR: + COMPUTE: False + FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features + 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features + WINDOWS: + COMPUTE: True + WINDOW_LENGTH: 4 # specify window length in seconds + SRC_SCRIPT: src/features/inter_beat_interval/cr/main.py # See https://www.rapids.science/latest/features/empatica-tags/ EMPATICA_TAGS: diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index 6d8afcd4..bdf26f75 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -1,8 +1,8 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, frequencyFeatureNames -from CalculatingFeatures.calculate_features import calculateFeatures +from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, hrvFreqFeatureNames +from CalculatingFeatures.hrv import extractHrvFeatures, extractHrvFeatures2D, extractHrvFeatures2DWrapper import sys @@ -13,13 +13,15 @@ def getSampleRate(data): except: raise Exception("Error occured while trying to get the mean sample rate from the data.") - return 1000/timestamps_diff + return int(1000/timestamps_diff) def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment): bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features) - if not bvp_intraday_data.empty: - sample_rate = getSampleRate(bvp_intraday_data) + if not bvp_intraday_data.empty: + sample_rate = getSampleRate(bvp_intraday_data) + + print(bvp_intraday_data.shape) bvp_intraday_data = filter_data_by_segment(bvp_intraday_data, time_segment) @@ -31,12 +33,20 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_lengt if window_length is None: bvp_intraday_features = \ bvp_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + lambda x: + extractHrvFeatures2DWrapper( + convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), + sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, featureNames=features)) + else: bvp_intraday_features = \ bvp_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) - + lambda x: + extractHrvFeatures2DWrapper( + convertInputInto2d(x['blood_volume_pulse'], window_length*sample_rate), + sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, featureNames=features)) + print(sample_rate) + print(bvp_intraday_features) bvp_intraday_features.reset_index(inplace=True) return bvp_intraday_features @@ -55,7 +65,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length = None # name of the features this function can compute - base_intraday_features_names = hrvFeatureNames + frequencyFeatureNames + base_intraday_features_names = hrvFeatureNames + hrvFreqFeatureNames # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py new file mode 100644 index 00000000..f8d38b66 --- /dev/null +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -0,0 +1,66 @@ +import pandas as pd +from scipy.stats import entropy + +from CalculatingFeatures.helper_functions import convertInputInto2dTime, convert2DEmpaticaToArray hrvFeatureNames, hrvFreqFeatureNames +from CalculatingFeatures.calculate_features import calculateFeatures + +import sys + + +def getSampleRate(data): + try: + timestamps_diff = data['timestamp'].diff().dropna().mean() + except: + raise Exception("Error occured while trying to get the mean sample rate from the data.") + + return 1000/timestamps_diff + +def extractIBIFeaturesFromIntradayData(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment): + ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features) + + if not ibi_intraday_data.empty: + sample_rate = getSampleRate(ibi_intraday_data) + + ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment) + + if not ibi_intraday_data.empty: + + ibi_intraday_features = pd.DataFrame() + + # apply methods from calculate features module + # if window_length is None: + # ibi_intraday_features = \ + # ibi_intraday_data.groupby('local_segment').apply(\ + # lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + # else: + # ibi_intraday_features = \ + # ibi_intraday_data.groupby('local_segment').apply(\ + # lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) + + ibi_intraday_features.reset_index(inplace=True) + + return ibi_intraday_features + + +def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_intraday_features = provider["FEATURES"] + + calc_windows = kwargs.get('calc_windows', False) + + if provider["WINDOWS"]["COMPUTE"] and calc_windows: + requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"] + else: + requested_window_length = None + + # name of the features this function can compute + base_intraday_features_names = hrvFeatureNames + hrvFreqFeatureNames + # the subset of requested features this function can compute + intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) + + # extract features from intraday data + ibi_intraday_features = extractBVPFeaturesFromIntradayData(ibi_intraday_data, intraday_features_to_compute, + requested_window_length, time_segment, filter_data_by_segment) + + return ibi_intraday_features \ No newline at end of file diff --git a/src/features/entry.py b/src/features/entry.py index 761235f1..f7127f35 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -27,7 +27,7 @@ else: if calc_windows: sensor_features.to_csv(snakemake.output[1], index=False) sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False) - elif "empatica" in sensor_key and provider_key == "dbdp": + elif "empatica" in sensor_key: pd.DataFrame().to_csv(snakemake.output[1], index=False) From 8c8fe1fec7602a8e74cd45d8dd1b5a56ebbfbe88 Mon Sep 17 00:00:00 2001 From: = <=> Date: Tue, 19 Apr 2022 13:24:46 +0000 Subject: [PATCH 27/57] Modifications, mostly imports, after changes in cr-features module. --- config.yaml | 16 ++++---- .../empatica_accelerometer/cr/main.py | 36 ++++++++-------- .../empatica_blood_volume_pulse/cr/main.py | 31 +++++++------- .../cr/main.py | 16 ++++---- .../empatica_inter_beat_interval/cr/main.py | 41 +++++++++++-------- src/features/empatica_temperature/cr/main.py | 20 ++++----- 6 files changed, 85 insertions(+), 75 deletions(-) diff --git a/config.yaml b/config.yaml index 8a1516de..5d81159b 100644 --- a/config.yaml +++ b/config.yaml @@ -491,7 +491,7 @@ EMPATICA_ACCELEROMETER: "sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow", "pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] # Acc features WINDOWS: - COMPUTE: False + COMPUTE: True WINDOW_LENGTH: 10 # specify window length in seconds SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py @@ -521,7 +521,7 @@ EMPATICA_TEMPERATURE: "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: - COMPUTE: False + COMPUTE: True WINDOW_LENGTH: 90 # specify window length in seconds SRC_SCRIPT: src/features/empatica_temperature/cr/main.py @@ -541,7 +541,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] WINDOWS: - COMPUTE: False + COMPUTE: True WINDOW_LENGTH: 80 # specify window length in seconds SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py @@ -550,11 +550,11 @@ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: @@ -567,17 +567,17 @@ EMPATICA_INTER_BEAT_INTERVAL: CONTAINER: IBI PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: COMPUTE: True WINDOW_LENGTH: 4 # specify window length in seconds - SRC_SCRIPT: src/features/inter_beat_interval/cr/main.py + SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py # See https://www.rapids.science/latest/features/empatica-tags/ EMPATICA_TAGS: diff --git a/src/features/empatica_accelerometer/cr/main.py b/src/features/empatica_accelerometer/cr/main.py index 9618a88c..ca2bf5ee 100644 --- a/src/features/empatica_accelerometer/cr/main.py +++ b/src/features/empatica_accelerometer/cr/main.py @@ -1,24 +1,24 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convert3DEmpaticaToArray, convertInputInto2d, accelerometerFeatureNames, frequencyFeatureNames -from CalculatingFeatures.calculate_features import calculateFeatures +from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features +from cr_features.calculate_features import calculate_features import sys -def getSampleRate(data): +def get_sample_rate(data): try: timestamps_diff = data['timestamp'].diff().dropna().mean() except: raise Exception("Error occured while trying to get the mean sample rate from the data.") - return 1000/timestamps_diff + return int(1000/timestamps_diff) -def extractAccFeaturesFromIntradayData(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment): +def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment): acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not acc_intraday_data.empty: - sample_rate = getSampleRate(acc_intraday_data) + sample_rate = get_sample_rate(acc_intraday_data) acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment) @@ -29,18 +29,18 @@ def extractAccFeaturesFromIntradayData(acc_intraday_data, features, window_lengt # apply methods from calculate features module if window_length is None: acc_intraday_features = \ - acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ - convertInputInto2d(x['double_values_0'], x.shape[0]), \ - convertInputInto2d(x['double_values_1'], x.shape[0]), \ - convertInputInto2d(x['double_values_2'], x.shape[0]), \ - fs=int(sample_rate), featureNames=features)) + acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \ + convert_to2d(x['double_values_0'], x.shape[0]), \ + convert_to2d(x['double_values_1'], x.shape[0]), \ + convert_to2d(x['double_values_2'], x.shape[0]), \ + fs=sample_rate, feature_names=features)) else: acc_intraday_features = \ - acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ - convertInputInto2d(x['double_values_0'], window_length*int(sample_rate)), \ - convertInputInto2d(x['double_values_1'], window_length*int(sample_rate)), \ - convertInputInto2d(x['double_values_2'], window_length*int(sample_rate)), \ - fs=int(sample_rate), featureNames=features)) + acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \ + convert_to2d(x['double_values_0'], window_length*sample_rate), \ + convert_to2d(x['double_values_1'], window_length*sample_rate), \ + convert_to2d(x['double_values_2'], window_length*sample_rate), \ + fs=sample_rate, feature_names=features)) acc_intraday_features.reset_index(inplace=True) @@ -61,12 +61,12 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length = None # name of the features this function can compute - base_intraday_features_names = accelerometerFeatureNames + frequencyFeatureNames + base_intraday_features_names = accelerometer_features + frequency_features # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - acc_intraday_features = extractAccFeaturesFromIntradayData(acc_intraday_data, intraday_features_to_compute, + acc_intraday_features = extract_acc_features_from_intraday_data(acc_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) return acc_intraday_features \ No newline at end of file diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index bdf26f75..daf3a079 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -1,13 +1,13 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convertInputInto2d, hrvFeatureNames, hrvFreqFeatureNames -from CalculatingFeatures.hrv import extractHrvFeatures, extractHrvFeatures2D, extractHrvFeatures2DWrapper +from cr_features.helper_functions import convert_to2d, hrv_features, hrv_freq_features +from cr_features.hrv import extract_hrv_features_2d_wrapper import sys -def getSampleRate(data): +def get_sample_rate(data): try: timestamps_diff = data['timestamp'].diff().dropna().mean() except: @@ -15,11 +15,11 @@ def getSampleRate(data): return int(1000/timestamps_diff) -def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment): +def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment): bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not bvp_intraday_data.empty: - sample_rate = getSampleRate(bvp_intraday_data) + sample_rate = get_sample_rate(bvp_intraday_data) print(bvp_intraday_data.shape) @@ -29,24 +29,25 @@ def extractBVPFeaturesFromIntradayData(bvp_intraday_data, features, window_lengt bvp_intraday_features = pd.DataFrame() + print(features) + # apply methods from calculate features module if window_length is None: bvp_intraday_features = \ bvp_intraday_data.groupby('local_segment').apply(\ lambda x: - extractHrvFeatures2DWrapper( - convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), - sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, featureNames=features)) + extract_hrv_features_2d_wrapper( + convert_to2d(x['blood_volume_pulse'], x.shape[0]), + sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=False, feature_names=features)) else: bvp_intraday_features = \ bvp_intraday_data.groupby('local_segment').apply(\ lambda x: - extractHrvFeatures2DWrapper( - convertInputInto2d(x['blood_volume_pulse'], window_length*sample_rate), - sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, featureNames=features)) - print(sample_rate) - print(bvp_intraday_features) + extract_hrv_features_2d_wrapper( + convert_to2d(x['blood_volume_pulse'], window_length*sample_rate), + sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) + bvp_intraday_features.reset_index(inplace=True) return bvp_intraday_features @@ -65,12 +66,12 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length = None # name of the features this function can compute - base_intraday_features_names = hrvFeatureNames + hrvFreqFeatureNames + base_intraday_features_names = hrv_features + hrv_freq_features # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - bvp_intraday_features = extractBVPFeaturesFromIntradayData(bvp_intraday_data, intraday_features_to_compute, + bvp_intraday_features = extract_bvp_features_from_intraday_data(bvp_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) return bvp_intraday_features \ No newline at end of file diff --git a/src/features/empatica_electrodermal_activity/cr/main.py b/src/features/empatica_electrodermal_activity/cr/main.py index b621f040..7cb6e5b2 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -1,8 +1,8 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convertInputInto2d, gsrFeatureNames -from CalculatingFeatures.calculate_features import calculateFeatures +from cr_features.helper_functions import convert_to2d, gsr_features +from cr_features.calculate_features import calculate_features def getSampleRate(data): @@ -11,9 +11,9 @@ def getSampleRate(data): except: raise Exception("Error occured while trying to get the mean sample rate from the data.") - return 1000/timestamps_diff + return int(1000/timestamps_diff) -def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment): +def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment): eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not eda_intraday_data.empty: @@ -29,11 +29,11 @@ def extractEDAFeaturesFromIntradayData(eda_intraday_data, features, window_lengt if window_length is None: eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, feature_names=features)) else: eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['electrodermal_activity'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) + lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), fs=sample_rate, feature_names=features)) eda_intraday_features.reset_index(inplace=True) @@ -54,12 +54,12 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length = None # name of the features this function can compute - base_intraday_features_names = gsrFeatureNames + base_intraday_features_names = gsr_features # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - eda_intraday_features = extractEDAFeaturesFromIntradayData(eda_intraday_data, intraday_features_to_compute, + eda_intraday_features = extract_eda_features_from_intraday_data(eda_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) return eda_intraday_features \ No newline at end of file diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index f8d38b66..39e8f59c 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -1,25 +1,27 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convertInputInto2dTime, convert2DEmpaticaToArray hrvFeatureNames, hrvFreqFeatureNames -from CalculatingFeatures.calculate_features import calculateFeatures +from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features +from cr_features.calculate_features import calculate_features import sys +pd.set_option('display.max_rows', 1000) -def getSampleRate(data): + +def get_sample_rate(data): try: timestamps_diff = data['timestamp'].diff().dropna().mean() except: raise Exception("Error occured while trying to get the mean sample rate from the data.") - return 1000/timestamps_diff + return int(1000/timestamps_diff) -def extractIBIFeaturesFromIntradayData(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment): +def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment): ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not ibi_intraday_data.empty: - sample_rate = getSampleRate(ibi_intraday_data) + sample_rate = get_sample_rate(ibi_intraday_data) ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment) @@ -27,15 +29,22 @@ def extractIBIFeaturesFromIntradayData(ibi_intraday_data, features, window_lengt ibi_intraday_features = pd.DataFrame() + print(ibi_intraday_data.head(100)) + sys.exit() + # apply methods from calculate features module - # if window_length is None: - # ibi_intraday_features = \ - # ibi_intraday_data.groupby('local_segment').apply(\ - # lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], x.shape[0]), fs=int(sample_rate), featureNames=features)) - # else: - # ibi_intraday_features = \ - # ibi_intraday_data.groupby('local_segment').apply(\ - # lambda x: calculateFeatures(convertInputInto2d(x['blood_volume_pulse'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) + if window_length is None: + ibi_intraday_features = \ + ibi_intraday_data.groupby('local_segment').apply(\ + extract_hrv_features_2d_wrapper( + convert_to2d(x['inter_beat_interval'], window_length*sample_rate), + sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) + else: + ibi_intraday_features = \ + ibi_intraday_data.groupby('local_segment').apply(\ + extract_hrv_features_2d_wrapper( + convert_to2d(x['blood_volume_pulse'], window_length*sample_rate), + sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) ibi_intraday_features.reset_index(inplace=True) @@ -55,12 +64,12 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length = None # name of the features this function can compute - base_intraday_features_names = hrvFeatureNames + hrvFreqFeatureNames + base_intraday_features_names = hrv_features + hrv_freq_features # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - ibi_intraday_features = extractBVPFeaturesFromIntradayData(ibi_intraday_data, intraday_features_to_compute, + ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) return ibi_intraday_features \ No newline at end of file diff --git a/src/features/empatica_temperature/cr/main.py b/src/features/empatica_temperature/cr/main.py index 20a8af85..78eaa952 100644 --- a/src/features/empatica_temperature/cr/main.py +++ b/src/features/empatica_temperature/cr/main.py @@ -1,24 +1,24 @@ import pandas as pd from scipy.stats import entropy -from CalculatingFeatures.helper_functions import convert1DEmpaticaToArray, convertInputInto2d, genericFeatureNames -from CalculatingFeatures.calculate_features import calculateFeatures +from cr_features.helper_functions import convert_to2d, generic_features +from cr_features.calculate_features import calculate_features import sys -def getSampleRate(data): +def get_sample_rate(data): try: timestamps_diff = data['timestamp'].diff().dropna().mean() except: raise Exception("Error occured while trying to get the mean sample rate from the data.") - return 1000/timestamps_diff + return int(1000/timestamps_diff) -def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment): +def extract_temp_features_from_intraday_data(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment): temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not temperature_intraday_data.empty: - sample_rate = getSampleRate(temperature_intraday_data) + sample_rate = get_sample_rate(temperature_intraday_data) temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment) @@ -30,11 +30,11 @@ def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, win if window_length is None: temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['temperature'], x.shape[0]), fs=int(sample_rate), featureNames=features)) + lambda x: calculate_features(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, feature_names=features)) else: temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convertInputInto2d(x['temperature'], window_length*int(sample_rate)), fs=int(sample_rate), featureNames=features)) + lambda x: calculate_features(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, feature_names=features)) temperature_intraday_features.reset_index(inplace=True) @@ -54,11 +54,11 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length = None # name of the features this function can compute - base_intraday_features_names = genericFeatureNames + base_intraday_features_names = generic_features # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) # extract features from intraday data - temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data, intraday_features_to_compute, + temperature_intraday_features = extract_temp_features_from_intraday_data(temperature_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) return temperature_intraday_features \ No newline at end of file From 66451160e96afcffb6877a69a5278236937c7706 Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 20 Apr 2022 10:44:51 +0000 Subject: [PATCH 28/57] Calculating HRV features with IBI.csv. --- config.yaml | 2 +- src/data/streams/empatica_zip/container.py | 6 +++-- src/data/streams/empatica_zip/format.yaml | 1 + src/data/streams/rapids_columns.yaml | 1 + .../empatica_inter_beat_interval/cr/main.py | 27 +++++++++++++------ 5 files changed, 26 insertions(+), 11 deletions(-) diff --git a/config.yaml b/config.yaml index 5d81159b..77d7a8e5 100644 --- a/config.yaml +++ b/config.yaml @@ -576,7 +576,7 @@ EMPATICA_INTER_BEAT_INTERVAL: 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: COMPUTE: True - WINDOW_LENGTH: 4 # specify window length in seconds + WINDOW_LENGTH: 120 # specify window length in seconds SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py # See https://www.rapids.science/latest/features/empatica-tags/ diff --git a/src/data/streams/empatica_zip/container.py b/src/data/streams/empatica_zip/container.py index 542acdcd..46e7019d 100644 --- a/src/data/streams/empatica_zip/container.py +++ b/src/data/streams/empatica_zip/container.py @@ -62,13 +62,15 @@ def extract_empatica_data(data, sensor): df.index.name = 'timestamp' elif sensor == 'EMPATICA_INTER_BEAT_INTERVAL': + df = pd.read_csv(sensor_data_file, names=['timestamp', column], header=None) + df['timings'] = df['timestamp'] timestampstart = float(df['timestamp'][0]) - df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart + df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart df = df.drop([0]) df[column] = df[column].astype(float) df = df.set_index('timestamp') - + else: raise ValueError( "sensor has an invalid name: {}".format(sensor)) diff --git a/src/data/streams/empatica_zip/format.yaml b/src/data/streams/empatica_zip/format.yaml index d8f0aac9..90180c13 100644 --- a/src/data/streams/empatica_zip/format.yaml +++ b/src/data/streams/empatica_zip/format.yaml @@ -50,6 +50,7 @@ EMPATICA_INTER_BEAT_INTERVAL: TIMESTAMP: timestamp DEVICE_ID: device_id INTER_BEAT_INTERVAL: inter_beat_interval + TIMINGS: timings MUTATION: COLUMN_MAPPINGS: SCRIPTS: # List any python or r scripts that mutate your raw data diff --git a/src/data/streams/rapids_columns.yaml b/src/data/streams/rapids_columns.yaml index 06dc8f99..43e6b649 100644 --- a/src/data/streams/rapids_columns.yaml +++ b/src/data/streams/rapids_columns.yaml @@ -217,6 +217,7 @@ EMPATICA_INTER_BEAT_INTERVAL: - TIMESTAMP - DEVICE_ID - INTER_BEAT_INTERVAL + - TIMINGS EMPATICA_TAGS: - TIMESTAMP diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index 39e8f59c..8988f729 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -1,12 +1,14 @@ import pandas as pd -from scipy.stats import entropy +import numpy as np from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features -from cr_features.calculate_features import calculate_features +from cr_features.hrv import extract_hrv_features_2d_wrapper +import math import sys pd.set_option('display.max_rows', 1000) +pd.set_option('display.max_columns', None) def get_sample_rate(data): @@ -28,23 +30,32 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_ if not ibi_intraday_data.empty: ibi_intraday_features = pd.DataFrame() + # np.set_printoptions(threshold=sys.maxsize) + # print(ibi_intraday_data.groupby('local_segment').apply(lambda x: math.ceil(x['timings'].iloc[-1]))) + # nekaj = ibi_intraday_data.groupby('local_segment').apply(lambda x: \ + # convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1]) - print(ibi_intraday_data.head(100)) - sys.exit() + # sys.exit() # apply methods from calculate features module if window_length is None: ibi_intraday_features = \ ibi_intraday_data.groupby('local_segment').apply(\ + lambda x: extract_hrv_features_2d_wrapper( - convert_to2d(x['inter_beat_interval'], window_length*sample_rate), - sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) + signal_2D = \ + convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[0], + ibi_timings = \ + convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1], + sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) else: ibi_intraday_features = \ ibi_intraday_data.groupby('local_segment').apply(\ + lambda x: extract_hrv_features_2d_wrapper( - convert_to2d(x['blood_volume_pulse'], window_length*sample_rate), - sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) + signal_2D = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[0], + ibi_timings = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1], + sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) ibi_intraday_features.reset_index(inplace=True) From 56383679994a448f6b32e028a889600d490e8315 Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 25 Apr 2022 13:07:03 +0000 Subject: [PATCH 29/57] Implementation of the second order features. --- config.yaml | 19 +++--- environment.yml | 9 +-- rules/features.smk | 18 ++++-- src/features/__init__.py | 0 src/features/cr_features_helper_methods.py | 30 ++++++++++ .../empatica_accelerometer/cr/main.py | 6 ++ .../empatica_blood_volume_pulse/cr/main.py | 6 ++ .../cr/main.py | 6 ++ .../empatica_inter_beat_interval/cr/main.py | 22 +++---- src/features/empatica_temperature/cr/main.py | 7 +++ src/features/entry.py | 12 ++-- src/features/utils/utils.py | 60 +++++++++++++++---- 12 files changed, 147 insertions(+), 48 deletions(-) create mode 100644 src/features/__init__.py create mode 100644 src/features/cr_features_helper_methods.py diff --git a/config.yaml b/config.yaml index 77d7a8e5..503e5872 100644 --- a/config.yaml +++ b/config.yaml @@ -493,6 +493,7 @@ EMPATICA_ACCELEROMETER: WINDOWS: COMPUTE: True WINDOW_LENGTH: 10 # specify window length in seconds + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py @@ -510,11 +511,11 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", @@ -523,6 +524,7 @@ EMPATICA_TEMPERATURE: WINDOWS: COMPUTE: True WINDOW_LENGTH: 90 # specify window length in seconds + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_temperature/cr/main.py # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/ @@ -530,11 +532,11 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', @@ -543,6 +545,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: WINDOWS: COMPUTE: True WINDOW_LENGTH: 80 # specify window length in seconds + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ @@ -550,16 +553,17 @@ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: COMPUTE: True WINDOW_LENGTH: 10 # specify window length in seconds + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/ @@ -576,7 +580,8 @@ EMPATICA_INTER_BEAT_INTERVAL: 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: COMPUTE: True - WINDOW_LENGTH: 120 # specify window length in seconds + WINDOW_LENGTH: 2000 # specify window length in seconds + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py # See https://www.rapids.science/latest/features/empatica-tags/ diff --git a/environment.yml b/environment.yml index 7276ba1f..169138a1 100644 --- a/environment.yml +++ b/environment.yml @@ -110,12 +110,13 @@ dependencies: - bidict==0.22.0 - biosppy==0.8.0 - cached-property==1.5.2 - - calculatingfeatures==0.1.1 - configargparse==0.15.1 + - cr-features==0.1.8 - cycler==0.11.0 - decorator==4.4.2 - - fonttools==4.31.2 + - fonttools==4.33.2 - h5py==3.6.0 + - hmmlearn==0.2.7 - ipython-genutils==0.2.0 - jupyter-core==4.6.3 - kiwisolver==1.4.2 @@ -124,7 +125,7 @@ dependencies: - opencv-python==4.5.5.64 - packaging==21.3 - peakutils==1.3.3 - - pillow==9.0.1 + - pillow==9.1.0 - pulp==2.4 - pyparsing==2.4.7 - pyrsistent==0.15.5 @@ -134,5 +135,5 @@ dependencies: - snakemake==5.30.2 - toposort==1.5 - traitlets==4.3.3 - - typing-extensions==4.1.1 + - typing-extensions==4.2.0 prefix: /opt/conda/envs/rapids diff --git a/rules/features.smk b/rules/features.smk index 1b6e0ad8..b72e3cbb 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -771,7 +771,8 @@ rule empatica_accelerometer_python_features: sensor_key = "empatica_accelerometer" output: "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}.csv", - "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv" + "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv", + "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows_SO_features.csv" script: "../src/features/entry.py" @@ -798,7 +799,8 @@ rule empatica_heartrate_python_features: sensor_key = "empatica_heartrate" output: "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}.csv", - "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv" + "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv", + "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows_SO_features.csv" script: "../src/features/entry.py" @@ -825,7 +827,8 @@ rule empatica_temperature_python_features: sensor_key = "empatica_temperature" output: "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}.csv", - "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv" + "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv", + "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows_SO_features.csv" script: "../src/features/entry.py" @@ -852,7 +855,8 @@ rule empatica_electrodermal_activity_python_features: sensor_key = "empatica_electrodermal_activity" output: "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}.csv", - "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv" + "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv", + "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows_SO_features.csv" script: "../src/features/entry.py" @@ -879,7 +883,8 @@ rule empatica_blood_volume_pulse_python_features: sensor_key = "empatica_blood_volume_pulse" output: "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}.csv", - "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv" + "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv", + "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows_SO_features.csv" script: "../src/features/entry.py" @@ -906,7 +911,8 @@ rule empatica_inter_beat_interval_python_features: sensor_key = "empatica_inter_beat_interval" output: "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}.csv", - "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv" + "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv", + "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows_SO_features.csv" script: "../src/features/entry.py" diff --git a/src/features/__init__.py b/src/features/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/features/cr_features_helper_methods.py b/src/features/cr_features_helper_methods.py new file mode 100644 index 00000000..c9ebceba --- /dev/null +++ b/src/features/cr_features_helper_methods.py @@ -0,0 +1,30 @@ +import pandas as pd + +def extract_second_order_features(intraday_features, so_features_names): + if not intraday_features.empty: + so_features = pd.DataFrame() + if "mean" in so_features_names: + so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_mean")], axis=1) + if "median" in so_features_names: + so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_median")], axis=1) + if "sd" in so_features_names: + so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).std().add_suffix("_SO_sd")], axis=1) + if "max" in so_features_names: + so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).max().add_suffix("_SO_max")], axis=1) + if "min" in so_features_names: + so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).min().add_suffix("_SO_min")], axis=1) + + so_features.reset_index(inplace=True) + + else: + so_features = pd.DataFrame(columns=["local_segment"]) + + return so_features + +def get_sample_rate(data): + try: + timestamps_diff = data['timestamp'].diff().dropna().mean() + except: + raise Exception("Error occured while trying to get the mean sample rate from the data.") + + return int(1000/timestamps_diff) \ No newline at end of file diff --git a/src/features/empatica_accelerometer/cr/main.py b/src/features/empatica_accelerometer/cr/main.py index ca2bf5ee..71c13827 100644 --- a/src/features/empatica_accelerometer/cr/main.py +++ b/src/features/empatica_accelerometer/cr/main.py @@ -3,6 +3,7 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features from cr_features.calculate_features import calculate_features +from cr_features_helper_methods import get_sample_rate, extract_second_order_features import sys @@ -69,4 +70,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen acc_intraday_features = extract_acc_features_from_intraday_data(acc_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) + if calc_windows: + so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] + acc_second_order_features = extract_second_order_features(acc_intraday_features, so_features_names) + return acc_intraday_features, acc_second_order_features + return acc_intraday_features \ No newline at end of file diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index daf3a079..0a78b41d 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -3,6 +3,7 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, hrv_features, hrv_freq_features from cr_features.hrv import extract_hrv_features_2d_wrapper +from cr_features_helper_methods import get_sample_rate, extract_second_order_features import sys @@ -74,4 +75,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen bvp_intraday_features = extract_bvp_features_from_intraday_data(bvp_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) + if calc_windows: + so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] + bvp_second_order_features = extract_second_order_features(bvp_intraday_features, so_features_names) + return bvp_intraday_features, bvp_second_order_features + return bvp_intraday_features \ No newline at end of file diff --git a/src/features/empatica_electrodermal_activity/cr/main.py b/src/features/empatica_electrodermal_activity/cr/main.py index 7cb6e5b2..3e09b84d 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -3,6 +3,7 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, gsr_features from cr_features.calculate_features import calculate_features +from cr_features_helper_methods import get_sample_rate, extract_second_order_features def getSampleRate(data): @@ -62,4 +63,9 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen eda_intraday_features = extract_eda_features_from_intraday_data(eda_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) + if calc_windows: + so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] + eda_second_order_features = extract_second_order_features(eda_intraday_features, so_features_names) + return eda_intraday_features, eda_second_order_features + return eda_intraday_features \ No newline at end of file diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index 8988f729..02c0cd94 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -3,6 +3,7 @@ import numpy as np from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features from cr_features.hrv import extract_hrv_features_2d_wrapper +from cr_features_helper_methods import get_sample_rate, extract_second_order_features import math import sys @@ -11,14 +12,6 @@ pd.set_option('display.max_rows', 1000) pd.set_option('display.max_columns', None) -def get_sample_rate(data): - try: - timestamps_diff = data['timestamp'].diff().dropna().mean() - except: - raise Exception("Error occured while trying to get the mean sample rate from the data.") - - return int(1000/timestamps_diff) - def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment): ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features) @@ -30,13 +23,7 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_ if not ibi_intraday_data.empty: ibi_intraday_features = pd.DataFrame() - # np.set_printoptions(threshold=sys.maxsize) - # print(ibi_intraday_data.groupby('local_segment').apply(lambda x: math.ceil(x['timings'].iloc[-1]))) - # nekaj = ibi_intraday_data.groupby('local_segment').apply(lambda x: \ - # convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1]) - - # sys.exit() - + # apply methods from calculate features module if window_length is None: ibi_intraday_features = \ @@ -82,5 +69,10 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen # extract features from intraday data ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) + if calc_windows: + so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] + ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names) + return ibi_intraday_features, ibi_second_order_features + return ibi_intraday_features \ No newline at end of file diff --git a/src/features/empatica_temperature/cr/main.py b/src/features/empatica_temperature/cr/main.py index 78eaa952..7f556b32 100644 --- a/src/features/empatica_temperature/cr/main.py +++ b/src/features/empatica_temperature/cr/main.py @@ -3,6 +3,7 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, generic_features from cr_features.calculate_features import calculate_features +from cr_features_helper_methods import get_sample_rate, extract_second_order_features import sys @@ -61,4 +62,10 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen # extract features from intraday data temperature_intraday_features = extract_temp_features_from_intraday_data(temperature_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) + + if calc_windows: + so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] + temperature_second_order_features = extract_second_order_features(temperature_intraday_features, so_features_names) + return temperature_intraday_features, temperature_second_order_features + return temperature_intraday_features \ No newline at end of file diff --git a/src/features/entry.py b/src/features/entry.py index f7127f35..26aa1adc 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -22,13 +22,17 @@ else: del sensor_data_files["time_segments_labels"] time_segments_file = snakemake.input["time_segments_labels"] - sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows) - # Calculation over multiple windows in case of Empatica's CR-features if calc_windows: - sensor_features.to_csv(snakemake.output[1], index=False) - sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False) + first_order_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows) + first_order_features.to_csv(snakemake.output[1], index=False) + second_order_features.to_csv(snakemake.output[2], index=False) + calc_windows = False elif "empatica" in sensor_key: pd.DataFrame().to_csv(snakemake.output[1], index=False) + pd.DataFrame().to_csv(snakemake.output[2], index=False) + + + sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows) sensor_features.to_csv(snakemake.output[0], index=False) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 063afc9d..832620d6 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -93,6 +93,8 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file from importlib import import_module, util sensor_features = pd.DataFrame(columns=["local_segment"]) + sensor_fo_features = pd.DataFrame(columns=["local_segment"]) + sensor_so_features = pd.DataFrame(columns=["local_segment"]) time_segments_labels = pd.read_csv(time_segments_file, header=0) if "FEATURES" not in provider: raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key.upper())) @@ -106,23 +108,57 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file time_segments_labels["label"] = [""] for time_segment in time_segments_labels["label"]: print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, time_segment)) + features = feature_function(sensor_data_files, time_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes, calc_windows=calc_windows) - if not "local_segment" in features.columns: - raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)") - features.columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features.columns] - sensor_features = pd.concat([sensor_features, features], axis=0, sort=False) + + # In case of calc_window = True + if isinstance(features, tuple): + if not "local_segment" in features[0].columns or not "local_segment" in features[1].columns: + raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)") + features[0].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[0].columns] + features[1].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[1].columns] + if not features[0].empty: + sensor_fo_features = pd.concat([sensor_features, features[0]], axis=0, sort=False) + if not features[1].empty: + sensor_so_features = pd.concat([sensor_features, features[1]], axis=0, sort=False) + else: + if not "local_segment" in features.columns: + raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)") + features.columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features.columns] + sensor_features = pd.concat([sensor_features, features], axis=0, sort=False) else: for feature in provider["FEATURES"]: sensor_features[feature] = None - segment_colums = pd.DataFrame() - sensor_features['local_segment'] = sensor_features['local_segment'].str.replace(r'_RR\d+SS', '') - split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True) - new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"]) - segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns - for i in range(segment_colums.shape[1]): - sensor_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) + + if calc_windows: + segment_colums = pd.DataFrame() + sensor_fo_features['local_segment'] = sensor_fo_features['local_segment'].str.replace(r'_RR\d+SS', '') + split_segemnt_columns = sensor_fo_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True) + new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"]) + segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns + for i in range(segment_colums.shape[1]): + sensor_fo_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) + + segment_colums = pd.DataFrame() + sensor_so_features['local_segment'] = sensor_so_features['local_segment'].str.replace(r'_RR\d+SS', '') + split_segemnt_columns = sensor_so_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True) + new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"]) + segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns + for i in range(segment_colums.shape[1]): + sensor_so_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) - return sensor_features + return sensor_fo_features, sensor_so_features + + else: + segment_colums = pd.DataFrame() + sensor_features['local_segment'] = sensor_features['local_segment'].str.replace(r'_RR\d+SS', '') + split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True) + new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"]) + segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns + for i in range(segment_colums.shape[1]): + sensor_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) + + return sensor_features def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files): from importlib import import_module, util From f62a1302dd483fdaf12e0a9dcb22e145f7cb493e Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 9 May 2022 11:01:52 +0000 Subject: [PATCH 30/57] Cr-features corrections for ACC and TEMP sensors --- config.yaml | 12 ++++++------ environment.yml | 2 +- src/features/cr_features_helper_methods.py | 2 +- src/features/empatica_accelerometer/cr/main.py | 18 +++++------------- .../empatica_blood_volume_pulse/cr/main.py | 4 ---- .../empatica_electrodermal_activity/cr/main.py | 10 +--------- src/features/empatica_temperature/cr/main.py | 6 +++--- 7 files changed, 17 insertions(+), 37 deletions(-) diff --git a/config.yaml b/config.yaml index 503e5872..ea6aa2be 100644 --- a/config.yaml +++ b/config.yaml @@ -477,11 +477,11 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features "meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", "postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand", @@ -523,7 +523,7 @@ EMPATICA_TEMPERATURE: "sumOfSquareComponents"] WINDOWS: COMPUTE: True - WINDOW_LENGTH: 90 # specify window length in seconds + WINDOW_LENGTH: 600 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_temperature/cr/main.py @@ -544,7 +544,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] WINDOWS: COMPUTE: True - WINDOW_LENGTH: 80 # specify window length in seconds + WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py @@ -562,7 +562,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: COMPUTE: True - WINDOW_LENGTH: 10 # specify window length in seconds + WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py @@ -580,7 +580,7 @@ EMPATICA_INTER_BEAT_INTERVAL: 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: COMPUTE: True - WINDOW_LENGTH: 2000 # specify window length in seconds + WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py diff --git a/environment.yml b/environment.yml index 169138a1..4244da98 100644 --- a/environment.yml +++ b/environment.yml @@ -111,7 +111,7 @@ dependencies: - biosppy==0.8.0 - cached-property==1.5.2 - configargparse==0.15.1 - - cr-features==0.1.8 + - cr-features==0.1.9 - cycler==0.11.0 - decorator==4.4.2 - fonttools==4.33.2 diff --git a/src/features/cr_features_helper_methods.py b/src/features/cr_features_helper_methods.py index c9ebceba..b963b46e 100644 --- a/src/features/cr_features_helper_methods.py +++ b/src/features/cr_features_helper_methods.py @@ -6,7 +6,7 @@ def extract_second_order_features(intraday_features, so_features_names): if "mean" in so_features_names: so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_mean")], axis=1) if "median" in so_features_names: - so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_median")], axis=1) + so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).median().add_suffix("_SO_median")], axis=1) if "sd" in so_features_names: so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).std().add_suffix("_SO_sd")], axis=1) if "max" in so_features_names: diff --git a/src/features/empatica_accelerometer/cr/main.py b/src/features/empatica_accelerometer/cr/main.py index 71c13827..ce2fc2f9 100644 --- a/src/features/empatica_accelerometer/cr/main.py +++ b/src/features/empatica_accelerometer/cr/main.py @@ -2,19 +2,11 @@ import pandas as pd from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features -from cr_features.calculate_features import calculate_features +from cr_features.calculate_features_old import calculateFeatures from cr_features_helper_methods import get_sample_rate, extract_second_order_features import sys -def get_sample_rate(data): - try: - timestamps_diff = data['timestamp'].diff().dropna().mean() - except: - raise Exception("Error occured while trying to get the mean sample rate from the data.") - - return int(1000/timestamps_diff) - def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment): acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features) @@ -30,18 +22,18 @@ def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_ # apply methods from calculate features module if window_length is None: acc_intraday_features = \ - acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \ + acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ convert_to2d(x['double_values_0'], x.shape[0]), \ convert_to2d(x['double_values_1'], x.shape[0]), \ convert_to2d(x['double_values_2'], x.shape[0]), \ - fs=sample_rate, feature_names=features)) + fs=sample_rate, featureNames=features)) else: acc_intraday_features = \ - acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \ + acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ convert_to2d(x['double_values_0'], window_length*sample_rate), \ convert_to2d(x['double_values_1'], window_length*sample_rate), \ convert_to2d(x['double_values_2'], window_length*sample_rate), \ - fs=sample_rate, feature_names=features)) + fs=sample_rate, featureNames=features)) acc_intraday_features.reset_index(inplace=True) diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index 0a78b41d..44357e7f 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -21,8 +21,6 @@ def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_ if not bvp_intraday_data.empty: sample_rate = get_sample_rate(bvp_intraday_data) - - print(bvp_intraday_data.shape) bvp_intraday_data = filter_data_by_segment(bvp_intraday_data, time_segment) @@ -30,8 +28,6 @@ def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_ bvp_intraday_features = pd.DataFrame() - print(features) - # apply methods from calculate features module if window_length is None: bvp_intraday_features = \ diff --git a/src/features/empatica_electrodermal_activity/cr/main.py b/src/features/empatica_electrodermal_activity/cr/main.py index 3e09b84d..6630027a 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -6,19 +6,11 @@ from cr_features.calculate_features import calculate_features from cr_features_helper_methods import get_sample_rate, extract_second_order_features -def getSampleRate(data): - try: - timestamps_diff = data['timestamp'].diff().dropna().mean() - except: - raise Exception("Error occured while trying to get the mean sample rate from the data.") - - return int(1000/timestamps_diff) - def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment): eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not eda_intraday_data.empty: - sample_rate = getSampleRate(eda_intraday_data) + sample_rate = get_sample_rate(eda_intraday_data) eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment) diff --git a/src/features/empatica_temperature/cr/main.py b/src/features/empatica_temperature/cr/main.py index 7f556b32..a71b0a69 100644 --- a/src/features/empatica_temperature/cr/main.py +++ b/src/features/empatica_temperature/cr/main.py @@ -2,7 +2,7 @@ import pandas as pd from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, generic_features -from cr_features.calculate_features import calculate_features +from cr_features.calculate_features_old import calculateFeatures from cr_features_helper_methods import get_sample_rate, extract_second_order_features import sys @@ -31,11 +31,11 @@ def extract_temp_features_from_intraday_data(temperature_intraday_data, features if window_length is None: temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculate_features(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, feature_names=features)) + lambda x: calculateFeatures(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) else: temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculate_features(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, feature_names=features)) + lambda x: calculateFeatures(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, featureNames=features)) temperature_intraday_features.reset_index(inplace=True) From 9cf9e1fe14e8926fdd19dbbeefc87443865c33b4 Mon Sep 17 00:00:00 2001 From: Primoz Date: Tue, 10 May 2022 11:36:49 +0000 Subject: [PATCH 31/57] Testing and modifying the code with different E4 data. --- config.yaml | 6 +++--- data/external/participant_files/p01.yaml | 10 +++++----- src/features/cr_features_helper_methods.py | 8 ++++++-- src/features/empatica_accelerometer/cr/main.py | 4 ++-- src/features/empatica_blood_volume_pulse/cr/main.py | 12 ++---------- .../empatica_electrodermal_activity/cr/main.py | 4 ++-- src/features/empatica_inter_beat_interval/cr/main.py | 5 ++--- src/features/empatica_temperature/cr/main.py | 12 ++---------- src/features/utils/utils.py | 4 ++-- 9 files changed, 26 insertions(+), 39 deletions(-) diff --git a/config.yaml b/config.yaml index ea6aa2be..97785709 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p01] +PIDS: [p02, p01] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -477,11 +477,11 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features "meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", "postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand", diff --git a/data/external/participant_files/p01.yaml b/data/external/participant_files/p01.yaml index 90f48512..fe394a76 100644 --- a/data/external/participant_files/p01.yaml +++ b/data/external/participant_files/p01.yaml @@ -1,9 +1,9 @@ PHONE: - DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id - PLATFORMS: [android] # or ios - LABEL: MyTestP01 # any string - START_DATE: 2020-01-01 # this can also be empty - END_DATE: 2021-01-01 # this can also be empty + DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id + PLATFORMS: [android] # or ios + LABEL: MyTestP01 # any string + START_DATE: 2020-01-01 # this can also be empty + END_DATE: 2021-01-01 # this can also be empty EMPATICA: DEVICE_IDS: [empatica1] LABEL: test01 diff --git a/src/features/cr_features_helper_methods.py b/src/features/cr_features_helper_methods.py index b963b46e..f8dd3456 100644 --- a/src/features/cr_features_helper_methods.py +++ b/src/features/cr_features_helper_methods.py @@ -1,4 +1,7 @@ import pandas as pd +import math as m + +import sys def extract_second_order_features(intraday_features, so_features_names): if not intraday_features.empty: @@ -21,10 +24,11 @@ def extract_second_order_features(intraday_features, so_features_names): return so_features -def get_sample_rate(data): +def get_sample_rate(data): # To-Do get the sample rate information from the file's metadata try: timestamps_diff = data['timestamp'].diff().dropna().mean() + print("Timestamp diff:", timestamps_diff) except: raise Exception("Error occured while trying to get the mean sample rate from the data.") - return int(1000/timestamps_diff) \ No newline at end of file + return m.ceil(1000/timestamps_diff) \ No newline at end of file diff --git a/src/features/empatica_accelerometer/cr/main.py b/src/features/empatica_accelerometer/cr/main.py index ce2fc2f9..9a11a752 100644 --- a/src/features/empatica_accelerometer/cr/main.py +++ b/src/features/empatica_accelerometer/cr/main.py @@ -3,7 +3,7 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features from cr_features.calculate_features_old import calculateFeatures -from cr_features_helper_methods import get_sample_rate, extract_second_order_features +from cr_features_helper_methods import extract_second_order_features import sys @@ -11,7 +11,7 @@ def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_ acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not acc_intraday_data.empty: - sample_rate = get_sample_rate(acc_intraday_data) + sample_rate = 32 acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment) diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index 44357e7f..7e991159 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -3,24 +3,16 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, hrv_features, hrv_freq_features from cr_features.hrv import extract_hrv_features_2d_wrapper -from cr_features_helper_methods import get_sample_rate, extract_second_order_features +from cr_features_helper_methods import extract_second_order_features import sys -def get_sample_rate(data): - try: - timestamps_diff = data['timestamp'].diff().dropna().mean() - except: - raise Exception("Error occured while trying to get the mean sample rate from the data.") - - return int(1000/timestamps_diff) - def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment): bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not bvp_intraday_data.empty: - sample_rate = get_sample_rate(bvp_intraday_data) + sample_rate = 64 bvp_intraday_data = filter_data_by_segment(bvp_intraday_data, time_segment) diff --git a/src/features/empatica_electrodermal_activity/cr/main.py b/src/features/empatica_electrodermal_activity/cr/main.py index 6630027a..dee4869f 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -3,14 +3,14 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, gsr_features from cr_features.calculate_features import calculate_features -from cr_features_helper_methods import get_sample_rate, extract_second_order_features +from cr_features_helper_methods import extract_second_order_features def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment): eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not eda_intraday_data.empty: - sample_rate = get_sample_rate(eda_intraday_data) + sample_rate = 4 eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment) diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index 02c0cd94..4d8c4fd1 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -3,7 +3,7 @@ import numpy as np from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features from cr_features.hrv import extract_hrv_features_2d_wrapper -from cr_features_helper_methods import get_sample_rate, extract_second_order_features +from cr_features_helper_methods import extract_second_order_features import math import sys @@ -16,8 +16,7 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_ ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not ibi_intraday_data.empty: - sample_rate = get_sample_rate(ibi_intraday_data) - + ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment) if not ibi_intraday_data.empty: diff --git a/src/features/empatica_temperature/cr/main.py b/src/features/empatica_temperature/cr/main.py index a71b0a69..1eea539a 100644 --- a/src/features/empatica_temperature/cr/main.py +++ b/src/features/empatica_temperature/cr/main.py @@ -3,23 +3,15 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, generic_features from cr_features.calculate_features_old import calculateFeatures -from cr_features_helper_methods import get_sample_rate, extract_second_order_features +from cr_features_helper_methods import extract_second_order_features import sys -def get_sample_rate(data): - try: - timestamps_diff = data['timestamp'].diff().dropna().mean() - except: - raise Exception("Error occured while trying to get the mean sample rate from the data.") - - return int(1000/timestamps_diff) - def extract_temp_features_from_intraday_data(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment): temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features) if not temperature_intraday_data.empty: - sample_rate = get_sample_rate(temperature_intraday_data) + sample_rate = 4 temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 832620d6..90608ab1 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -118,9 +118,9 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file features[0].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[0].columns] features[1].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[1].columns] if not features[0].empty: - sensor_fo_features = pd.concat([sensor_features, features[0]], axis=0, sort=False) + sensor_fo_features = pd.concat([sensor_fo_features, features[0]], axis=0, sort=False) if not features[1].empty: - sensor_so_features = pd.concat([sensor_features, features[1]], axis=0, sort=False) + sensor_so_features = pd.concat([sensor_so_features, features[1]], axis=0, sort=False) else: if not "local_segment" in features.columns: raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + provider["SRC_SCRIPT"] + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)") From 31e36e7400d2dff152f8420d56369d2b3a8b6141 Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 11 May 2022 08:50:15 +0000 Subject: [PATCH 32/57] Alternating Second order and full segment features corresponding to config settings. --- config.yaml | 8 ++++---- rules/features.smk | 18 ++++++------------ src/features/entry.py | 16 +++++++--------- 3 files changed, 17 insertions(+), 25 deletions(-) diff --git a/config.yaml b/config.yaml index 97785709..324bba7e 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p02, p01] +PIDS: [p01] #,p02] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -477,11 +477,11 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features "meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", "postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand", @@ -502,7 +502,7 @@ EMPATICA_HEARTRATE: CONTAINER: HR PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py diff --git a/rules/features.smk b/rules/features.smk index b72e3cbb..1b6e0ad8 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -771,8 +771,7 @@ rule empatica_accelerometer_python_features: sensor_key = "empatica_accelerometer" output: "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}.csv", - "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv", - "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows_SO_features.csv" + "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -799,8 +798,7 @@ rule empatica_heartrate_python_features: sensor_key = "empatica_heartrate" output: "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}.csv", - "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv", - "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows_SO_features.csv" + "data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -827,8 +825,7 @@ rule empatica_temperature_python_features: sensor_key = "empatica_temperature" output: "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}.csv", - "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv", - "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows_SO_features.csv" + "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -855,8 +852,7 @@ rule empatica_electrodermal_activity_python_features: sensor_key = "empatica_electrodermal_activity" output: "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}.csv", - "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv", - "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows_SO_features.csv" + "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -883,8 +879,7 @@ rule empatica_blood_volume_pulse_python_features: sensor_key = "empatica_blood_volume_pulse" output: "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}.csv", - "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv", - "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows_SO_features.csv" + "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv" script: "../src/features/entry.py" @@ -911,8 +906,7 @@ rule empatica_inter_beat_interval_python_features: sensor_key = "empatica_inter_beat_interval" output: "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}.csv", - "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv", - "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows_SO_features.csv" + "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv" script: "../src/features/entry.py" diff --git a/src/features/entry.py b/src/features/entry.py index 26aa1adc..5517cdcc 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -11,7 +11,7 @@ calc_windows = False try: calc_windows = provider["WINDOWS"]["COMPUTE"] except KeyError: - print("Compute window key not found in config.yaml!") + print("WINDOWS COMPUTE key not found in config.yaml!") if sensor_key == "all_cleaning_individual" or sensor_key == "all_cleaning_overall": @@ -23,16 +23,14 @@ else: time_segments_file = snakemake.input["time_segments_labels"] if calc_windows: - first_order_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows) - first_order_features.to_csv(snakemake.output[1], index=False) - second_order_features.to_csv(snakemake.output[2], index=False) - calc_windows = False + window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True) + second_order_features.to_csv(snakemake.output[0], index=False) + window_features.to_csv(snakemake.output[1], index=False) elif "empatica" in sensor_key: pd.DataFrame().to_csv(snakemake.output[1], index=False) - pd.DataFrame().to_csv(snakemake.output[2], index=False) - sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=calc_windows) + sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False) - -sensor_features.to_csv(snakemake.output[0], index=False) +if not calc_windows: + sensor_features.to_csv(snakemake.output[0], index=False) From c1564f0caeba18fa5b6365e8508f958e7b4f63ed Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 11 May 2022 14:21:21 +0000 Subject: [PATCH 33/57] Changed wrapper method calculate_feature to its newest version (for TEMP and ACC). --- config.yaml | 22 +++++++-------- .../empatica_accelerometer/cr/main.py | 27 +++++++++++++++---- src/features/empatica_temperature/cr/main.py | 16 +++++++++-- 3 files changed, 47 insertions(+), 18 deletions(-) diff --git a/config.yaml b/config.yaml index 324bba7e..9b49664b 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p01] #,p02] +PIDS: [p01] #, p02] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -477,7 +477,7 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: @@ -502,7 +502,7 @@ EMPATICA_HEARTRATE: CONTAINER: HR PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py @@ -511,7 +511,7 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: @@ -532,18 +532,18 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] WINDOWS: - COMPUTE: True + COMPUTE: False WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py @@ -553,11 +553,11 @@ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: @@ -571,11 +571,11 @@ EMPATICA_INTER_BEAT_INTERVAL: CONTAINER: IBI PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: diff --git a/src/features/empatica_accelerometer/cr/main.py b/src/features/empatica_accelerometer/cr/main.py index 9a11a752..3821de20 100644 --- a/src/features/empatica_accelerometer/cr/main.py +++ b/src/features/empatica_accelerometer/cr/main.py @@ -3,6 +3,7 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features from cr_features.calculate_features_old import calculateFeatures +from cr_features.calculate_features import calculate_features from cr_features_helper_methods import extract_second_order_features import sys @@ -19,21 +20,37 @@ def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_ acc_intraday_features = pd.DataFrame() - # apply methods from calculate features module + # # apply methods from calculate features module + # if window_length is None: + # acc_intraday_features = \ + # acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ + # convert_to2d(x['double_values_0'], x.shape[0]), \ + # convert_to2d(x['double_values_1'], x.shape[0]), \ + # convert_to2d(x['double_values_2'], x.shape[0]), \ + # fs=sample_rate, featureNames=features)) + # else: + # acc_intraday_features = \ + # acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ + # convert_to2d(x['double_values_0'], window_length*sample_rate), \ + # convert_to2d(x['double_values_1'], window_length*sample_rate), \ + # convert_to2d(x['double_values_2'], window_length*sample_rate), \ + # fs=sample_rate, featureNames=features)) + + # apply methods from calculate features module if window_length is None: acc_intraday_features = \ - acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ + acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \ convert_to2d(x['double_values_0'], x.shape[0]), \ convert_to2d(x['double_values_1'], x.shape[0]), \ convert_to2d(x['double_values_2'], x.shape[0]), \ - fs=sample_rate, featureNames=features)) + fs=sample_rate, feature_names=features)) else: acc_intraday_features = \ - acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ + acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \ convert_to2d(x['double_values_0'], window_length*sample_rate), \ convert_to2d(x['double_values_1'], window_length*sample_rate), \ convert_to2d(x['double_values_2'], window_length*sample_rate), \ - fs=sample_rate, featureNames=features)) + fs=sample_rate, feature_names=features)) acc_intraday_features.reset_index(inplace=True) diff --git a/src/features/empatica_temperature/cr/main.py b/src/features/empatica_temperature/cr/main.py index 1eea539a..70a2567e 100644 --- a/src/features/empatica_temperature/cr/main.py +++ b/src/features/empatica_temperature/cr/main.py @@ -3,6 +3,7 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, generic_features from cr_features.calculate_features_old import calculateFeatures +from cr_features.calculate_features import calculate_features from cr_features_helper_methods import extract_second_order_features import sys @@ -19,15 +20,26 @@ def extract_temp_features_from_intraday_data(temperature_intraday_data, features temperature_intraday_features = pd.DataFrame() + # apply methods from calculate features module + # if window_length is None: + # temperature_intraday_features = \ + # temperature_intraday_data.groupby('local_segment').apply(\ + # lambda x: calculateFeatures(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) + # else: + # temperature_intraday_features = \ + # temperature_intraday_data.groupby('local_segment').apply(\ + # lambda x: calculateFeatures(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, featureNames=features)) + # apply methods from calculate features module if window_length is None: temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) + lambda x: calculate_features(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, feature_names=features)) else: temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculateFeatures(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, featureNames=features)) + lambda x: calculate_features(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, feature_names=features)) + temperature_intraday_features.reset_index(inplace=True) From 6864cfe77524d851660bac2f66600d0617bb0399 Mon Sep 17 00:00:00 2001 From: Primoz Date: Fri, 13 May 2022 13:35:34 +0000 Subject: [PATCH 34/57] Changes after thorough testing with available data. --- config.yaml | 22 +++++++++---------- .../empatica_accelerometer/cr/main.py | 20 ++--------------- .../empatica_blood_volume_pulse/cr/main.py | 2 +- src/features/empatica_temperature/cr/main.py | 14 ++---------- 4 files changed, 16 insertions(+), 42 deletions(-) diff --git a/config.yaml b/config.yaml index 9b49664b..0bca22c5 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p01] #, p02] +PIDS: [p01, p02] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -477,7 +477,7 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: @@ -502,7 +502,7 @@ EMPATICA_HEARTRATE: CONTAINER: HR PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py @@ -511,7 +511,7 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: @@ -532,18 +532,18 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] WINDOWS: - COMPUTE: False + COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py @@ -553,11 +553,11 @@ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: @@ -571,11 +571,11 @@ EMPATICA_INTER_BEAT_INTERVAL: CONTAINER: IBI PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: diff --git a/src/features/empatica_accelerometer/cr/main.py b/src/features/empatica_accelerometer/cr/main.py index 3821de20..77d18bfe 100644 --- a/src/features/empatica_accelerometer/cr/main.py +++ b/src/features/empatica_accelerometer/cr/main.py @@ -20,22 +20,6 @@ def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_ acc_intraday_features = pd.DataFrame() - # # apply methods from calculate features module - # if window_length is None: - # acc_intraday_features = \ - # acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ - # convert_to2d(x['double_values_0'], x.shape[0]), \ - # convert_to2d(x['double_values_1'], x.shape[0]), \ - # convert_to2d(x['double_values_2'], x.shape[0]), \ - # fs=sample_rate, featureNames=features)) - # else: - # acc_intraday_features = \ - # acc_intraday_data.groupby('local_segment').apply(lambda x: calculateFeatures( \ - # convert_to2d(x['double_values_0'], window_length*sample_rate), \ - # convert_to2d(x['double_values_1'], window_length*sample_rate), \ - # convert_to2d(x['double_values_2'], window_length*sample_rate), \ - # fs=sample_rate, featureNames=features)) - # apply methods from calculate features module if window_length is None: acc_intraday_features = \ @@ -43,14 +27,14 @@ def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_ convert_to2d(x['double_values_0'], x.shape[0]), \ convert_to2d(x['double_values_1'], x.shape[0]), \ convert_to2d(x['double_values_2'], x.shape[0]), \ - fs=sample_rate, feature_names=features)) + fs=sample_rate, feature_names=features, show_progress=False)) else: acc_intraday_features = \ acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \ convert_to2d(x['double_values_0'], window_length*sample_rate), \ convert_to2d(x['double_values_1'], window_length*sample_rate), \ convert_to2d(x['double_values_2'], window_length*sample_rate), \ - fs=sample_rate, feature_names=features)) + fs=sample_rate, feature_names=features, show_progress=False)) acc_intraday_features.reset_index(inplace=True) diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index 7e991159..562c5a46 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -27,7 +27,7 @@ def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_ lambda x: extract_hrv_features_2d_wrapper( convert_to2d(x['blood_volume_pulse'], x.shape[0]), - sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=False, feature_names=features)) + sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) else: bvp_intraday_features = \ diff --git a/src/features/empatica_temperature/cr/main.py b/src/features/empatica_temperature/cr/main.py index 70a2567e..36e720bd 100644 --- a/src/features/empatica_temperature/cr/main.py +++ b/src/features/empatica_temperature/cr/main.py @@ -20,25 +20,15 @@ def extract_temp_features_from_intraday_data(temperature_intraday_data, features temperature_intraday_features = pd.DataFrame() - # apply methods from calculate features module - # if window_length is None: - # temperature_intraday_features = \ - # temperature_intraday_data.groupby('local_segment').apply(\ - # lambda x: calculateFeatures(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, featureNames=features)) - # else: - # temperature_intraday_features = \ - # temperature_intraday_data.groupby('local_segment').apply(\ - # lambda x: calculateFeatures(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, featureNames=features)) - # apply methods from calculate features module if window_length is None: temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculate_features(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, feature_names=features)) + lambda x: calculate_features(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, feature_names=features, show_progress=False)) else: temperature_intraday_features = \ temperature_intraday_data.groupby('local_segment').apply(\ - lambda x: calculate_features(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, feature_names=features)) + lambda x: calculate_features(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, feature_names=features, show_progress=False)) temperature_intraday_features.reset_index(inplace=True) From 1471c86c620b4f7333932b6ae6ebf869e7cffb8f Mon Sep 17 00:00:00 2001 From: Primoz Date: Fri, 13 May 2022 13:37:12 +0000 Subject: [PATCH 35/57] Cr-features version update in rapids venv. --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 4244da98..19e4a252 100644 --- a/environment.yml +++ b/environment.yml @@ -111,7 +111,7 @@ dependencies: - biosppy==0.8.0 - cached-property==1.5.2 - configargparse==0.15.1 - - cr-features==0.1.9 + - cr-features==0.1.11 - cycler==0.11.0 - decorator==4.4.2 - fonttools==4.33.2 From 2a8f58f5c86aadbb08a9869127e8e9a140fcf7a1 Mon Sep 17 00:00:00 2001 From: Primoz Date: Fri, 20 May 2022 13:18:45 +0000 Subject: [PATCH 36/57] Patching IBI with BVP. WIP --- Snakefile | 2 ++ config.yaml | 25 ++++++++++--------- data/external/timesegments_periodic.csv | 2 +- rules/features.smk | 1 + .../cr/main.py | 11 ++++++-- .../empatica_inter_beat_interval/cr/main.py | 16 ++++++++++-- src/features/entry.py | 3 ++- src/features/utils/utils.py | 4 +++ 8 files changed, 46 insertions(+), 18 deletions(-) diff --git a/Snakefile b/Snakefile index 7dc5ad3a..d060e9f7 100644 --- a/Snakefile +++ b/Snakefile @@ -359,6 +359,8 @@ for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys(): if config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_raw.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_{language}_{provider_key}.csv", pid=config["PIDS"], language=get_script_language(config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/empatica_inter_beat_interval.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) diff --git a/config.yaml b/config.yaml index 0bca22c5..69e14921 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p01, p02] +PIDS: [p01] #p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -477,11 +477,11 @@ EMPATICA_ACCELEROMETER: CONTAINER: ACC PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features "meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", "postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand", @@ -502,7 +502,7 @@ EMPATICA_HEARTRATE: CONTAINER: HR PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py @@ -511,11 +511,11 @@ EMPATICA_TEMPERATURE: CONTAINER: TEMP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", @@ -532,11 +532,11 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: CONTAINER: EDA PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', @@ -553,11 +553,11 @@ EMPATICA_BLOOD_VOLUME_PULSE: CONTAINER: BVP PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: @@ -571,13 +571,14 @@ EMPATICA_INTER_BEAT_INTERVAL: CONTAINER: IBI PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py CR: COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features - 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features + 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features + PATCH_WITH_BVP: True WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds diff --git a/data/external/timesegments_periodic.csv b/data/external/timesegments_periodic.csv index 0a26eb6b..a2c4d9ac 100644 --- a/data/external/timesegments_periodic.csv +++ b/data/external/timesegments_periodic.csv @@ -1,3 +1,3 @@ label,start_time,length,repeats_on,repeats_value daily,00:00:00,23H 59M 59S,every_day,0 -night,00:00:00,5H 59M 59S,every_day,0 +E4baseline,01:00:00,3H,every_day,0 diff --git a/rules/features.smk b/rules/features.smk index 1b6e0ad8..76b2e94c 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -899,6 +899,7 @@ rule empatica_blood_volume_pulse_r_features: rule empatica_inter_beat_interval_python_features: input: sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", + bvp_sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv", time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][wildcards.provider_key.upper()], diff --git a/src/features/empatica_electrodermal_activity/cr/main.py b/src/features/empatica_electrodermal_activity/cr/main.py index dee4869f..9499deec 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -1,10 +1,17 @@ import pandas as pd +import numpy as np from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, gsr_features from cr_features.calculate_features import calculate_features from cr_features_helper_methods import extract_second_order_features +import sys + +#pd.set_option('display.max_columns', None) +#pd.set_option('display.max_rows', None) +#np.seterr(invalid='ignore') + def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment): eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features) @@ -14,7 +21,7 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_ eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment) - if not eda_intraday_data.empty: + if not eda_intraday_data.empty: eda_intraday_features = pd.DataFrame() @@ -28,7 +35,6 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_ eda_intraday_data.groupby('local_segment').apply(\ lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), fs=sample_rate, feature_names=features)) - eda_intraday_features.reset_index(inplace=True) return eda_intraday_features @@ -58,6 +64,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen if calc_windows: so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] eda_second_order_features = extract_second_order_features(eda_intraday_features, so_features_names) + return eda_intraday_features, eda_second_order_features return eda_intraday_features \ No newline at end of file diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index 4d8c4fd1..607e5638 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -2,14 +2,14 @@ import pandas as pd import numpy as np from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features -from cr_features.hrv import extract_hrv_features_2d_wrapper +from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features from cr_features_helper_methods import extract_second_order_features import math import sys pd.set_option('display.max_rows', 1000) -pd.set_option('display.max_columns', None) +#pd.set_option('display.max_columns', None) def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment): @@ -48,9 +48,21 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_ return ibi_intraday_features +def patch_IBI_with_BVP(bvp_intraday_data): + # get features method is used because + hrv_time_and_freq_features, sample, rr, timings, peak_indx = \ + get_HRV_features(bvp_intraday_data['blood_volume_pulse'].to_numpy(), hampel_fiter=False, median_filter=False, mod_z_score_filter=True, sampling=64, feature_names=['meanHr']) + + def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + print(sensor_data_files) ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) + if provider["PATCH_WITH_BVP"]: + bvp_intraday_data = pd.read_csv(sensor_data_files["bvp_sensor_data"]) + patch_IBI_with_BVP(bvp_intraday_data) + # sys.exit() + requested_intraday_features = provider["FEATURES"] calc_windows = kwargs.get('calc_windows', False) diff --git a/src/features/entry.py b/src/features/entry.py index 5517cdcc..d8c7e843 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -1,6 +1,8 @@ import pandas as pd from utils.utils import fetch_provider_features, run_provider_cleaning_script +import sys + sensor_data_files = dict(snakemake.input) provider = snakemake.params["provider"] @@ -29,7 +31,6 @@ else: elif "empatica" in sensor_key: pd.DataFrame().to_csv(snakemake.output[1], index=False) - sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False) if not calc_windows: diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index 90608ab1..a7bf9fe3 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -169,3 +169,7 @@ def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data sensor_features = cleaning_function(sensor_data_files, provider) return sensor_features + + +def empatica_patch_IBI_with_BVP(bvp_data): + pass From bb62497ba6d09991b7bfe6767d852be77b6b619c Mon Sep 17 00:00:00 2001 From: Primoz Date: Tue, 24 May 2022 11:07:18 +0000 Subject: [PATCH 37/57] Patching IBI with BVP - selecting appropriate pipeline entry point. WIP --- Snakefile | 2 -- config.yaml | 9 ++++++--- src/data/streams/empatica_zip/container.py | 10 ++++++++++ 3 files changed, 16 insertions(+), 5 deletions(-) diff --git a/Snakefile b/Snakefile index d060e9f7..7dc5ad3a 100644 --- a/Snakefile +++ b/Snakefile @@ -359,8 +359,6 @@ for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys(): if config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_raw.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_raw.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_{language}_{provider_key}.csv", pid=config["PIDS"], language=get_script_language(config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower())) files_to_compute.extend(expand("data/processed/features/{pid}/empatica_inter_beat_interval.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) diff --git a/config.yaml b/config.yaml index 69e14921..bef26ca6 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p01] #p02, p03] +PIDS: [p01] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -43,12 +43,15 @@ TIMEZONE: # See https://www.rapids.science/latest/setup/configuration/#data-stream-configuration PHONE_DATA_STREAMS: - USE: aware_csv + USE: aware_postgresql # AVAILABLE: aware_mysql: DATABASE_GROUP: MY_GROUP + aware_postgresql: + DATABASE_GROUP: PSQL_STRAW + aware_csv: FOLDER: data/external/aware_csv @@ -177,7 +180,7 @@ PHONE_BLUETOOTH: # See https://www.rapids.science/latest/features/phone-calls/ PHONE_CALLS: - CONTAINER: calls.csv + CONTAINER: call PROVIDERS: RAPIDS: COMPUTE: False diff --git a/src/data/streams/empatica_zip/container.py b/src/data/streams/empatica_zip/container.py index 46e7019d..4edb8369 100644 --- a/src/data/streams/empatica_zip/container.py +++ b/src/data/streams/empatica_zip/container.py @@ -8,6 +8,8 @@ import csv from collections import OrderedDict from io import BytesIO, StringIO +from cr_features.hrv import get_HRV_features + def processAcceleration(x, y, z): x = float(x) y = float(y) @@ -94,6 +96,14 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download print("Extracting {} data from {} for {}".format(sensor, zipfile, device)) with ZipFile(zipfile, 'r') as zipFile: listOfFileNames = zipFile.namelist() + if sensor == "EMPATICA_INTER_BEAT_INTERVAL": + extracted_bvp_data = extract_empatica_data(zipFile.read('BVP.csv'), "EMPATICA_BLOOD_VOLUME_PULSE") + hrv_time_and_freq_features, sample, bvp_rr, bvp_timings, peak_indx = \ + get_HRV_features(extracted_bvp_data['blood_volume_pulse'].to_numpy(), ma=False, detrend=False, m_deternd=False, + low_pass=False, winsorize=True, winsorize_value=25, + hampel_fiter=False, median_filter=False, mod_z_score_filter=True, + sampling=64, feature_names=['meanHr']) + print(bvp_rr, bvp_timings) for fileName in listOfFileNames: if fileName == sensor_csv: participant_data = pd.concat([participant_data, extract_empatica_data(zipFile.read(fileName), sensor)], axis=0) From 5532043b1f4e4f71ddab9e140c503d44712f07b6 Mon Sep 17 00:00:00 2001 From: Primoz Date: Wed, 25 May 2022 19:39:47 +0000 Subject: [PATCH 38/57] Patching IBI with BVP - completed. --- rules/features.smk | 1 - src/data/streams/empatica_zip/container.py | 63 ++++++++++++++++--- .../empatica_inter_beat_interval/cr/main.py | 12 ---- src/features/utils/utils.py | 6 +- 4 files changed, 54 insertions(+), 28 deletions(-) diff --git a/rules/features.smk b/rules/features.smk index 76b2e94c..1b6e0ad8 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -899,7 +899,6 @@ rule empatica_blood_volume_pulse_r_features: rule empatica_inter_beat_interval_python_features: input: sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", - bvp_sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv", time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: provider = lambda wildcards: config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][wildcards.provider_key.upper()], diff --git a/src/data/streams/empatica_zip/container.py b/src/data/streams/empatica_zip/container.py index 4edb8369..80224893 100644 --- a/src/data/streams/empatica_zip/container.py +++ b/src/data/streams/empatica_zip/container.py @@ -2,13 +2,16 @@ from zipfile import ZipFile import warnings from pathlib import Path import pandas as pd +import numpy as np from pandas.core import indexing import yaml import csv from collections import OrderedDict from io import BytesIO, StringIO +import sys, os -from cr_features.hrv import get_HRV_features +from cr_features.hrv import get_HRV_features, get_patched_ibi_with_bvp +from cr_features.helper_functions import empatica1d_to_array, empatica2d_to_array def processAcceleration(x, y, z): x = float(x) @@ -88,6 +91,10 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download participant_data = pd.DataFrame(columns=columns_to_download.values()) participant_data.set_index('timestamp', inplace=True) + with open('config.yaml', 'r') as stream: + config = yaml.load(stream, Loader=yaml.FullLoader) + cr_ibi_provider = config['EMPATICA_INTER_BEAT_INTERVAL']['PROVIDERS']['CR'] + available_zipfiles = list((Path(data_configuration["FOLDER"]) / Path(device)).rglob("*.zip")) if len(available_zipfiles) == 0: warnings.warn("There were no zip files in: {}. If you were expecting data for this participant the [EMPATICA][DEVICE_IDS] key in their participant file is missing the pid".format((Path(data_configuration["FOLDER"]) / Path(device)))) @@ -96,17 +103,15 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download print("Extracting {} data from {} for {}".format(sensor, zipfile, device)) with ZipFile(zipfile, 'r') as zipFile: listOfFileNames = zipFile.namelist() - if sensor == "EMPATICA_INTER_BEAT_INTERVAL": - extracted_bvp_data = extract_empatica_data(zipFile.read('BVP.csv'), "EMPATICA_BLOOD_VOLUME_PULSE") - hrv_time_and_freq_features, sample, bvp_rr, bvp_timings, peak_indx = \ - get_HRV_features(extracted_bvp_data['blood_volume_pulse'].to_numpy(), ma=False, detrend=False, m_deternd=False, - low_pass=False, winsorize=True, winsorize_value=25, - hampel_fiter=False, median_filter=False, mod_z_score_filter=True, - sampling=64, feature_names=['meanHr']) - print(bvp_rr, bvp_timings) for fileName in listOfFileNames: if fileName == sensor_csv: - participant_data = pd.concat([participant_data, extract_empatica_data(zipFile.read(fileName), sensor)], axis=0) + if sensor == "EMPATICA_INTER_BEAT_INTERVAL" and cr_ibi_provider.get('PATCH_WITH_BVP', False): + participant_data = \ + pd.concat([participant_data, patch_ibi_with_bvp(zipFile.read('IBI.csv'), zipFile.read('BVP.csv'))], axis=0) + #print("patch with ibi") + else: + participant_data = pd.concat([participant_data, extract_empatica_data(zipFile.read(fileName), sensor)], axis=0) + #print("no patching") warning = False if warning: warnings.warn("We could not find a zipped file for {} in {} (we tried to find {})".format(sensor, zipFile, sensor_csv)) @@ -117,4 +122,42 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download participant_data["device_id"] = device return(participant_data) +def patch_ibi_with_bvp(ibi_data, bvp_data): + ibi_data_file = BytesIO(ibi_data).getvalue().decode('utf-8') + ibi_data_file = StringIO(ibi_data_file) + + ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file) + + bvp_data_file = BytesIO(bvp_data).getvalue().decode('utf-8') + bvp_data_file = StringIO(bvp_data_file) + + bvp_data, bvp_start_timestamp, sample_rate = empatica1d_to_array(bvp_data_file) + + hrv_time_and_freq_features, sample, bvp_rr, bvp_timings, peak_indx = \ + get_HRV_features(bvp_data, ma=False, + detrend=False, m_deternd=False, low_pass=False, winsorize=True, + winsorize_value=25, hampel_fiter=False, median_filter=False, + mod_z_score_filter=True, sampling=64, feature_names=['meanHr']) + + ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr, min_length=10) + + df = \ + pd.DataFrame(np.array([ibi_timings, ibi_rr]).transpose(), columns=['timestamp', 'inter_beat_interval']) + df.loc[-1] = [ibi_start_timestamp, 'IBI'] # adding a row + df.index = df.index + 1 # shifting index + df = df.sort_index() # sorting by index + + # Repeated as in extract_empatica_data for IBI + df['timings'] = df['timestamp'] + timestampstart = float(df['timestamp'][0]) + df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart + df = df.drop([0]) + df['inter_beat_interval'] = df['inter_beat_interval'].astype(float) + df = df.set_index('timestamp') + + # format timestamps + df.index *= 1000 + df.index = df.index.astype(int) + return(df) + # print(pull_data({'FOLDER': 'data/external/empatica'}, "e01", "EMPATICA_accelerometer", {'TIMESTAMP': 'timestamp', 'DEVICE_ID': 'device_id', 'DOUBLE_VALUES_0': 'x', 'DOUBLE_VALUES_1': 'y', 'DOUBLE_VALUES_2': 'z'})) \ No newline at end of file diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index 607e5638..8b4cab5d 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -48,21 +48,9 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_ return ibi_intraday_features -def patch_IBI_with_BVP(bvp_intraday_data): - # get features method is used because - hrv_time_and_freq_features, sample, rr, timings, peak_indx = \ - get_HRV_features(bvp_intraday_data['blood_volume_pulse'].to_numpy(), hampel_fiter=False, median_filter=False, mod_z_score_filter=True, sampling=64, feature_names=['meanHr']) - - def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): - print(sensor_data_files) ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) - if provider["PATCH_WITH_BVP"]: - bvp_intraday_data = pd.read_csv(sensor_data_files["bvp_sensor_data"]) - patch_IBI_with_BVP(bvp_intraday_data) - # sys.exit() - requested_intraday_features = provider["FEATURES"] calc_windows = kwargs.get('calc_windows', False) diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py index a7bf9fe3..7303ac86 100644 --- a/src/features/utils/utils.py +++ b/src/features/utils/utils.py @@ -168,8 +168,4 @@ def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data cleaning_function = getattr(cleaning_module, provider_key.lower() + "_cleaning") sensor_features = cleaning_function(sensor_data_files, provider) - return sensor_features - - -def empatica_patch_IBI_with_BVP(bvp_data): - pass + return sensor_features \ No newline at end of file From fbf6a77dfc681c22c841f66bffa3b0528c3641ec Mon Sep 17 00:00:00 2001 From: Primoz Date: Thu, 2 Jun 2022 06:41:53 +0000 Subject: [PATCH 39/57] Small misc changes --- config.yaml | 36 +-- renv.lock | 396 ++++++++++++------------ renv/activate.R | 3 - tests/scripts/missing_values_heatmap.py | 18 ++ 4 files changed, 229 insertions(+), 224 deletions(-) create mode 100644 tests/scripts/missing_values_heatmap.py diff --git a/config.yaml b/config.yaml index bef26ca6..5aba8dde 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p01] #p01, p02, p03] +PIDS: [p02, p03] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -484,18 +484,11 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: False - FEATURES: ["fqHighestPeakFreqs", "fqHighestPeaks", "fqEnergyFeat", "fqEntropyFeat", "fqHistogramBins","fqAbsMean", "fqSkewness", "fqKurtosis", "fqInterquart", # Freq features - "meanLow", "areaLow", "totalAbsoluteAreaBand", "totalMagnitudeBand", "entropyBand", "skewnessBand", "kurtosisBand", - "postureDistanceLow", "absoluteMeanBand", "absoluteAreaBand", "quartilesBand", "interQuartileRangeBand", "varianceBand", - "coefficientOfVariationBand", "amplitudeBand", "totalEnergyBand", "dominantFrequencyEnergyBand", "meanCrossingRateBand", - "correlationBand", "quartilesMagnitudesBand", "interQuartileRangeMagnitudesBand", "areaUnderAccelerationMagnitude", - "peaksDataLow", "sumPerComponentBand", "velocityBand", "meanKineticEnergyBand", "totalKineticEnergyBand", "squareSumOfComponent", - "sumOfSquareComponents", "averageVectorLength", "averageVectorLengthPower", "rollAvgLow", "pitchAvgLow", "rollStdDevLow", - "pitchStdDevLow", "rollMotionAmountLow", "rollMotionRegularityLow", "manipulationLow", "rollPeaks", "pitchPeaks", "rollPitchCorrelation"] # Acc features + COMPUTE: True + FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features WINDOWS: COMPUTE: True - WINDOW_LENGTH: 10 # specify window length in seconds + WINDOW_LENGTH: 15 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py @@ -518,15 +511,12 @@ EMPATICA_TEMPERATURE: FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: False - FEATURES: ["autocorrelations", "countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", - "longestStrikeBelowMean", "stdDev", "median", "meanChange", "numberOfZeroCrossings", "absEnergy", "linearTrendSlope", - "ratioBeyondRSigma", "binnedEntropy", "numOfPeaksAutocorr", "numberOfZeroCrossingsAutocorr", "areaAutocorr", - "calcMeanCrossingRateAutocorr", "countAboveMeanAutocorr", "sumPer", "sumSquared", "squareSumOfComponent", - "sumOfSquareComponents"] + COMPUTE: True + FEATURES: ["countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", + "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: COMPUTE: True - WINDOW_LENGTH: 600 # specify window length in seconds + WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_temperature/cr/main.py @@ -539,15 +529,15 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', - 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'maxPeakResponseSlopeBefore', 'maxPeakResponseSlopeAfter', - 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] + 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', + 'significantDecrease'] WINDOWS: COMPUTE: True - WINDOW_LENGTH: 300 # specify window length in seconds + WINDOW_LENGTH: 60 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py @@ -560,7 +550,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: diff --git a/renv.lock b/renv.lock index 0920a32c..8db3499c 100644 --- a/renv.lock +++ b/renv.lock @@ -13,168 +13,168 @@ "Package": "BDgraph", "Version": "2.63", "Source": "Repository", - 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"Repository": "CRAN", + "Repository": "RSPM", "Hash": "7d597f982ee6263716b6a2f28efd29fa" }, "rlang": { "Package": "rlang", "Version": "0.4.10", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "599df23c40a4fce9c7b4764f28c37857" }, "rmarkdown": { "Package": "rmarkdown", "Version": "2.6", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "bc4bac38960b446c183957bfd563e763" }, "robustbase": { "Package": "robustbase", "Version": "0.93-6", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "241454e22c9d26b11669b78d06706b41" }, "rpart": { "Package": "rpart", "Version": "4.1-15", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "9787c1fcb680e655d062e7611cadf78e" }, "rprojroot": { "Package": "rprojroot", "Version": "2.0.2", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "249d8cd1e74a8f6a26194a91b47f21d1" }, "rstudioapi": { "Package": "rstudioapi", "Version": "0.13", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "06c85365a03fdaf699966cc1d3cf53ea" }, "rvest": { "Package": "rvest", "Version": "0.3.6", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "a9795ccb2d608330e841998b67156764" }, "scales": { "Package": "scales", "Version": "1.1.1", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "6f76f71042411426ec8df6c54f34e6dd" }, "selectr": { "Package": "selectr", "Version": "0.4-2", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "3838071b66e0c566d55cc26bd6e27bf4" }, "seriation": { "Package": "seriation", "Version": "1.3.1", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "76ad2df9a7784e78dfaebe1ac9e295c3" }, "shape": { "Package": "shape", "Version": "1.4.5", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "58510f25472de6fd363d76698d29709e" }, "shiny": { "Package": "shiny", "Version": "1.5.0", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "ee4ed72d7a5047d9e73cf922ad66e9c9" }, "sourcetools": { "Package": "sourcetools", "Version": "0.1.7", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "947e4e02a79effa5d512473e10f41797" }, "spatial": { "Package": "spatial", "Version": "7.3-12", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "58a02ce0150652b96c044bc67a0df2e5" }, "stringi": { "Package": "stringi", "Version": "1.5.3", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "a063ebea753c92910a4cca7b18bc1f05" }, "stringr": { @@ -1252,175 +1252,175 @@ "Package": "survival", "Version": "3.2-7", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "39c4ac6d22dad33db0ee37b40810ea12" }, "sys": { "Package": "sys", "Version": "3.4", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "b227d13e29222b4574486cfcbde077fa" }, "testthat": { "Package": "testthat", "Version": "3.0.1", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "17826764cb92d8b5aae6619896e5a161" }, "tibble": { "Package": "tibble", "Version": "3.0.4", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "71dffd8544691c520dd8e41ed2d7e070" }, "tidygraph": { "Package": "tidygraph", "Version": "1.2.0", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "5375980d0787633ca62c265b28bedb41" }, "tidyr": { "Package": "tidyr", "Version": "1.1.2", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "c40b2d5824d829190f4b825f4496dfae" }, "tidyselect": { "Package": "tidyselect", "Version": "1.1.0", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "6ea435c354e8448819627cf686f66e0a" }, "tidyverse": { "Package": "tidyverse", "Version": "1.3.0", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "bd51be662f359fa99021f3d51e911490" }, "timeDate": { "Package": "timeDate", "Version": "3043.102", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "fde4fc571f5f61978652c229d4713845" }, "tinytex": { "Package": "tinytex", "Version": "0.28", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "ff73961a77fba606fbf944dc3468e5b9" }, "tmvnsim": { "Package": "tmvnsim", "Version": "1.0-2", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "50470c7ed0cc8099cbc17b38f0ef621f" }, "tweenr": { "Package": "tweenr", "Version": "1.0.1", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "fc77eb5297507cccfa3349a606061030" }, "utf8": { "Package": "utf8", "Version": "1.1.4", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "4a5081acfb7b81a572e4384a7aaf2af1" }, "vctrs": { "Package": "vctrs", "Version": "0.3.8", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "ecf749a1b39ea72bd9b51b76292261f1" }, "viridis": { "Package": "viridis", "Version": "0.5.1", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "6f6b49e5b3b5ee5a6d0c28bf1b4b9eb3" }, "viridisLite": { "Package": "viridisLite", "Version": "0.3.0", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "ce4f6271baa94776db692f1cb2055bee" }, "waldo": { "Package": "waldo", "Version": "0.2.3", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "181d1a31b1ba2009ef20926f2ee0570c" }, "whisker": { "Package": "whisker", "Version": "0.4", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "ca970b96d894e90397ed20637a0c1bbe" }, "withr": { "Package": "withr", "Version": "2.3.0", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "7307d79f58d1885b38c4f4f1a8cb19dd" }, "xfun": { "Package": "xfun", "Version": "0.19", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "a42372606cb76f34da9d090326e9f955" }, "xml2": { "Package": "xml2", "Version": "1.3.2", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "d4d71a75dd3ea9eb5fa28cc21f9585e2" }, "xtable": { "Package": "xtable", "Version": "1.8-4", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "b8acdf8af494d9ec19ccb2481a9b11c2" }, "xts": { "Package": "xts", "Version": "0.12.1", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "ca2fd4ad8ef78cca3aa2b30f992798a8" }, "yaml": { "Package": "yaml", "Version": "2.2.1", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "2826c5d9efb0a88f657c7a679c7106db" }, "zoo": { "Package": "zoo", "Version": "1.8-9", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Hash": "035d1c7c12593038c26fb1c2fd40c4d2" } } diff --git a/renv/activate.R b/renv/activate.R index d3b271d8..6841a967 100644 --- a/renv/activate.R +++ b/renv/activate.R @@ -14,9 +14,6 @@ local({ # signal that we're loading renv during R startup Sys.setenv("RENV_R_INITIALIZING" = "true") on.exit(Sys.unsetenv("RENV_R_INITIALIZING"), add = TRUE) - - if(grepl("Darwin", Sys.info()["sysname"], fixed = TRUE) & grepl("ARM64", Sys.info()["version"], fixed = TRUE)) # M1 Macs - Sys.setenv("TZDIR" = file.path(R.home(), "share", "zoneinfo")) # signal that we've consented to use renv options(renv.consent = TRUE) diff --git a/tests/scripts/missing_values_heatmap.py b/tests/scripts/missing_values_heatmap.py new file mode 100644 index 00000000..827343dd --- /dev/null +++ b/tests/scripts/missing_values_heatmap.py @@ -0,0 +1,18 @@ +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt + + +# path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants +# path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" +path = "/rapids/data/interim/p02/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" +# path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" +# path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv" +# path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" + +df = pd.read_csv(path) +print(df) +sns.heatmap(df.isna(), cbar=False) +plt.savefig('eda_windows_p02_window_60_more_peaks.png', bbox_inches='tight') + + From d300f0f8f027a06902ac2e7ab70a27f7554a68b3 Mon Sep 17 00:00:00 2001 From: primoz Date: Thu, 2 Jun 2022 11:43:49 +0000 Subject: [PATCH 40/57] Fixed RAPIDS bug: error when IBI.csv is empty. --- .gitignore | 2 +- config.yaml | 2 +- credentials.yaml | 7 +++++++ environment.yml | 2 +- src/data/streams/empatica_zip/container.py | 23 ++++++++++++++++++---- 5 files changed, 29 insertions(+), 7 deletions(-) create mode 100644 credentials.yaml diff --git a/.gitignore b/.gitignore index 6cf83e29..e783159f 100644 --- a/.gitignore +++ b/.gitignore @@ -114,7 +114,7 @@ sn_profile_*/ settings.dcf tests/fakedata_generation/ site/ -credentials.yaml +!credentials.yaml # Docker container and other files .devcontainer diff --git a/config.yaml b/config.yaml index 5aba8dde..248342e1 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p02, p03] #p01, p02, p03] +PIDS: [p03] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: diff --git a/credentials.yaml b/credentials.yaml new file mode 100644 index 00000000..7b20cd95 --- /dev/null +++ b/credentials.yaml @@ -0,0 +1,7 @@ +PSQL_STRAW: + database: staw + user: staw_db + password: kizi-x2yf-mate + host: 212.235.208.113 + port: 5432 + \ No newline at end of file diff --git a/environment.yml b/environment.yml index 19e4a252..b0faa871 100644 --- a/environment.yml +++ b/environment.yml @@ -111,7 +111,7 @@ dependencies: - biosppy==0.8.0 - cached-property==1.5.2 - configargparse==0.15.1 - - cr-features==0.1.11 + - cr-features==0.1.13 - cycler==0.11.0 - decorator==4.4.2 - fonttools==4.33.2 diff --git a/src/data/streams/empatica_zip/container.py b/src/data/streams/empatica_zip/container.py index 80224893..f1fe9bcd 100644 --- a/src/data/streams/empatica_zip/container.py +++ b/src/data/streams/empatica_zip/container.py @@ -68,8 +68,11 @@ def extract_empatica_data(data, sensor): elif sensor == 'EMPATICA_INTER_BEAT_INTERVAL': - df = pd.read_csv(sensor_data_file, names=['timestamp', column], header=None) - df['timings'] = df['timestamp'] + df = pd.read_csv(sensor_data_file, names=['timings', column], header=None) + df['timestamp'] = df['timings'] + if df.empty: + df = df.set_index('timestamp') + return df timestampstart = float(df['timestamp'][0]) df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart df = df.drop([0]) @@ -126,7 +129,19 @@ def patch_ibi_with_bvp(ibi_data, bvp_data): ibi_data_file = BytesIO(ibi_data).getvalue().decode('utf-8') ibi_data_file = StringIO(ibi_data_file) - ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file) + + + # Begin with the cr-features part + try: + ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file) + except IndexError: + # Checks whether IBI.csv is empty + df_test = pd.read_csv(ibi_data_file, names=['timings', 'inter_beat_interval'], header=None) + print(df_test) + if df_test.empty: + df_test['timestamp'] = df_test['timings'] + df_test = df_test.set_index('timestamp') + return df_test bvp_data_file = BytesIO(bvp_data).getvalue().decode('utf-8') bvp_data_file = StringIO(bvp_data_file) @@ -139,7 +154,7 @@ def patch_ibi_with_bvp(ibi_data, bvp_data): winsorize_value=25, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, sampling=64, feature_names=['meanHr']) - ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr, min_length=10) + ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr, min_length=None) df = \ pd.DataFrame(np.array([ibi_timings, ibi_rr]).transpose(), columns=['timestamp', 'inter_beat_interval']) From 2acf6ff9fb802a71fa12c9c982beeb8cbe444a64 Mon Sep 17 00:00:00 2001 From: primoz Date: Fri, 3 Jun 2022 12:34:36 +0000 Subject: [PATCH 41/57] Exception handling in case of empty ibi. Changes of the method EDA uses in main.py. Other small corrections. --- config.yaml | 8 ++++---- src/data/streams/empatica_zip/container.py | 9 ++++----- src/features/empatica_blood_volume_pulse/cr/main.py | 4 ++-- src/features/empatica_electrodermal_activity/cr/main.py | 5 +++-- src/features/empatica_inter_beat_interval/cr/main.py | 4 ++-- tests/scripts/missing_values_heatmap.py | 9 +++++++-- 6 files changed, 22 insertions(+), 17 deletions(-) diff --git a/config.yaml b/config.yaml index 248342e1..eafe27fe 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p03] #p01, p02, p03] +PIDS: [p02] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features WINDOWS: COMPUTE: True @@ -511,7 +511,7 @@ EMPATICA_TEMPERATURE: FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: @@ -529,7 +529,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', diff --git a/src/data/streams/empatica_zip/container.py b/src/data/streams/empatica_zip/container.py index f1fe9bcd..93a6e5d8 100644 --- a/src/data/streams/empatica_zip/container.py +++ b/src/data/streams/empatica_zip/container.py @@ -129,19 +129,18 @@ def patch_ibi_with_bvp(ibi_data, bvp_data): ibi_data_file = BytesIO(ibi_data).getvalue().decode('utf-8') ibi_data_file = StringIO(ibi_data_file) - - # Begin with the cr-features part try: ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file) - except IndexError: + except IndexError as e: # Checks whether IBI.csv is empty df_test = pd.read_csv(ibi_data_file, names=['timings', 'inter_beat_interval'], header=None) - print(df_test) if df_test.empty: df_test['timestamp'] = df_test['timings'] df_test = df_test.set_index('timestamp') return df_test + else: + raise IndexError("Something went wrong with indices. Error that was previously caught:\n", repr(e)) bvp_data_file = BytesIO(bvp_data).getvalue().decode('utf-8') bvp_data_file = StringIO(bvp_data_file) @@ -154,7 +153,7 @@ def patch_ibi_with_bvp(ibi_data, bvp_data): winsorize_value=25, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, sampling=64, feature_names=['meanHr']) - ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr, min_length=None) + ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr) df = \ pd.DataFrame(np.array([ibi_timings, ibi_rr]).transpose(), columns=['timestamp', 'inter_beat_interval']) diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index 562c5a46..aa7a4186 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -1,7 +1,7 @@ import pandas as pd from scipy.stats import entropy -from cr_features.helper_functions import convert_to2d, hrv_features, hrv_freq_features +from cr_features.helper_functions import convert_to2d, hrv_features from cr_features.hrv import extract_hrv_features_2d_wrapper from cr_features_helper_methods import extract_second_order_features @@ -55,7 +55,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length = None # name of the features this function can compute - base_intraday_features_names = hrv_features + hrv_freq_features + base_intraday_features_names = hrv_features # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) diff --git a/src/features/empatica_electrodermal_activity/cr/main.py b/src/features/empatica_electrodermal_activity/cr/main.py index 9499deec..25ecba59 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -4,6 +4,7 @@ from scipy.stats import entropy from cr_features.helper_functions import convert_to2d, gsr_features from cr_features.calculate_features import calculate_features +from cr_features.gsr import extractGsrFeatures2D from cr_features_helper_methods import extract_second_order_features import sys @@ -29,11 +30,11 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_ if window_length is None: eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], x.shape[0]), fs=sample_rate, feature_names=features)) + lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, threshold=0, featureNames=features)) else: eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: calculate_features(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), fs=sample_rate, feature_names=features)) + lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, threshold=0, featureNames=features)) eda_intraday_features.reset_index(inplace=True) diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index 8b4cab5d..0e27a79f 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -1,7 +1,7 @@ import pandas as pd import numpy as np -from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features, hrv_freq_features +from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features from cr_features_helper_methods import extract_second_order_features @@ -61,7 +61,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length = None # name of the features this function can compute - base_intraday_features_names = hrv_features + hrv_freq_features + base_intraday_features_names = hrv_features # the subset of requested features this function can compute intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) diff --git a/tests/scripts/missing_values_heatmap.py b/tests/scripts/missing_values_heatmap.py index 827343dd..2f2364f2 100644 --- a/tests/scripts/missing_values_heatmap.py +++ b/tests/scripts/missing_values_heatmap.py @@ -5,14 +5,19 @@ import matplotlib.pyplot as plt # path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants # path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" -path = "/rapids/data/interim/p02/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" +path = "/rapids/data/interim/p03/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" df = pd.read_csv(path) print(df) +is_NaN = df. isnull() +row_has_NaN = is_NaN. any(axis=1) +rows_with_NaN = df[row_has_NaN] +print(rows_with_NaN.size) + sns.heatmap(df.isna(), cbar=False) -plt.savefig('eda_windows_p02_window_60_more_peaks.png', bbox_inches='tight') +plt.savefig('eda_windows_p03_window_60_thresh_default.png', bbox_inches='tight') From 2c7ac21465f5721e78d85be49c08bd31c8120650 Mon Sep 17 00:00:00 2001 From: primoz Date: Mon, 6 Jun 2022 13:51:15 +0000 Subject: [PATCH 42/57] Added standardization on SO features. --- credentials.yaml | 1 - src/features/entry.py | 16 +++++++++------- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/credentials.yaml b/credentials.yaml index 7b20cd95..3a1c51e0 100644 --- a/credentials.yaml +++ b/credentials.yaml @@ -4,4 +4,3 @@ PSQL_STRAW: password: kizi-x2yf-mate host: 212.235.208.113 port: 5432 - \ No newline at end of file diff --git a/src/features/entry.py b/src/features/entry.py index d8c7e843..a13a93de 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -1,5 +1,6 @@ import pandas as pd from utils.utils import fetch_provider_features, run_provider_cleaning_script +from sklearn.preprocessing import StandardScaler import sys @@ -9,12 +10,7 @@ provider = snakemake.params["provider"] provider_key = snakemake.params["provider_key"] sensor_key = snakemake.params["sensor_key"] -calc_windows = False -try: - calc_windows = provider["WINDOWS"]["COMPUTE"] -except KeyError: - print("WINDOWS COMPUTE key not found in config.yaml!") - +calc_windows = True if (provider.get("WINDOWS", False) and provider["WINDOWS"].get("COMPUTE", False)) else False if sensor_key == "all_cleaning_individual" or sensor_key == "all_cleaning_overall": # Data cleaning @@ -26,8 +22,14 @@ else: if calc_windows: window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True) - second_order_features.to_csv(snakemake.output[0], index=False) + + # Z-score SO features by columns + if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False): + second_order_features[second_order_features.columns[4:]] = StandardScaler().fit_transform(second_order_features[second_order_features.columns[4:]]) + window_features.to_csv(snakemake.output[1], index=False) + second_order_features.to_csv(snakemake.output[0], index=False) + elif "empatica" in sensor_key: pd.DataFrame().to_csv(snakemake.output[1], index=False) From 64e41cfa35f890dfcd5dfa64148a3af565047a32 Mon Sep 17 00:00:00 2001 From: primoz Date: Tue, 7 Jun 2022 10:39:48 +0000 Subject: [PATCH 43/57] Second order features standardization in config.yaml. --- config.yaml | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/config.yaml b/config.yaml index eafe27fe..c74d544e 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p02] #p01, p02, p03] +PIDS: [p031] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -183,7 +183,7 @@ PHONE_CALLS: CONTAINER: call PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES_TYPE: EPISODES # EVENTS or EPISODES CALL_TYPES: [missed, incoming, outgoing] FEATURES: @@ -484,12 +484,13 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features WINDOWS: COMPUTE: True WINDOW_LENGTH: 15 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] + STANDARDIZE_SO_FEATURES: True SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py @@ -498,7 +499,7 @@ EMPATICA_HEARTRATE: CONTAINER: HR PROVIDERS: DBDP: - COMPUTE: False + COMPUTE: True FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py @@ -511,13 +512,14 @@ EMPATICA_TEMPERATURE: FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: False - FEATURES: ["countAboveMean", "countBelowMean", "maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", + COMPUTE: True + FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] + STANDARDIZE_SO_FEATURES: True SRC_SCRIPT: src/features/empatica_temperature/cr/main.py # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/ @@ -529,7 +531,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', @@ -539,6 +541,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: COMPUTE: True WINDOW_LENGTH: 60 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] + STANDARDIZE_SO_FEATURES: True SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ @@ -557,6 +560,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] + STANDARDIZE_SO_FEATURES: True SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/ @@ -576,6 +580,7 @@ EMPATICA_INTER_BEAT_INTERVAL: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] + STANDARDIZE_SO_FEATURES: True SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py # See https://www.rapids.science/latest/features/empatica-tags/ From f371249b99c60bd859e969301561d7c9c187b159 Mon Sep 17 00:00:00 2001 From: primoz Date: Thu, 9 Jun 2022 13:35:15 +0000 Subject: [PATCH 44/57] First order features standardization WIP --- config.yaml | 14 +++++++------- data/external/timesegments_periodic.csv | 1 - .../empatica_blood_volume_pulse/cr/main.py | 10 +++++++++- .../empatica_inter_beat_interval/cr/main.py | 11 +++++++++-- src/features/entry.py | 18 +++++++++++++++--- 5 files changed, 40 insertions(+), 14 deletions(-) diff --git a/config.yaml b/config.yaml index c74d544e..2c3b5ab7 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p031] #p01, p02, p03] +PIDS: [p03] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -183,7 +183,7 @@ PHONE_CALLS: CONTAINER: call PROVIDERS: RAPIDS: - COMPUTE: True + COMPUTE: False FEATURES_TYPE: EPISODES # EVENTS or EPISODES CALL_TYPES: [missed, incoming, outgoing] FEATURES: @@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features WINDOWS: COMPUTE: True @@ -499,7 +499,7 @@ EMPATICA_HEARTRATE: CONTAINER: HR PROVIDERS: DBDP: - COMPUTE: True + COMPUTE: False FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_SCRIPT: src/features/empatica_heartrate/dbdp/main.py @@ -512,7 +512,7 @@ EMPATICA_TEMPERATURE: FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: @@ -531,14 +531,14 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] WINDOWS: - COMPUTE: True + COMPUTE: False WINDOW_LENGTH: 60 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] STANDARDIZE_SO_FEATURES: True diff --git a/data/external/timesegments_periodic.csv b/data/external/timesegments_periodic.csv index a2c4d9ac..117313e8 100644 --- a/data/external/timesegments_periodic.csv +++ b/data/external/timesegments_periodic.csv @@ -1,3 +1,2 @@ label,start_time,length,repeats_on,repeats_value daily,00:00:00,23H 59M 59S,every_day,0 -E4baseline,01:00:00,3H,every_day,0 diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index aa7a4186..9e6db52b 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -1,5 +1,5 @@ import pandas as pd -from scipy.stats import entropy +from sklearn.preprocessing import StandardScaler from cr_features.helper_functions import convert_to2d, hrv_features from cr_features.hrv import extract_hrv_features_2d_wrapper @@ -7,6 +7,8 @@ from cr_features_helper_methods import extract_second_order_features import sys +# pd.set_option('display.max_rows', 1000) +pd.set_option('display.max_columns', None) def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment): bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features) @@ -64,8 +66,14 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length, time_segment, filter_data_by_segment) if calc_windows: + if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False): + fo_columns = bvp_intraday_features.columns.values[2:] + fo_columns_z_score = [col + "_zscore" for col in fo_columns] + bvp_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(bvp_intraday_features[fo_columns]) + so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] bvp_second_order_features = extract_second_order_features(bvp_intraday_features, so_features_names) + return bvp_intraday_features, bvp_second_order_features return bvp_intraday_features \ No newline at end of file diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index 0e27a79f..41af4cf1 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -1,4 +1,5 @@ import pandas as pd +from sklearn.preprocessing import StandardScaler import numpy as np from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features @@ -8,8 +9,8 @@ from cr_features_helper_methods import extract_second_order_features import math import sys -pd.set_option('display.max_rows', 1000) -#pd.set_option('display.max_columns', None) +# pd.set_option('display.max_rows', 1000) +pd.set_option('display.max_columns', None) def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment): @@ -69,8 +70,14 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) if calc_windows: + if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False): + fo_columns = ibi_intraday_features.columns.values[2:] + fo_columns_z_score = [col + "_zscore" for col in fo_columns] + ibi_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(ibi_intraday_features[fo_columns]) + so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names) + return ibi_intraday_features, ibi_second_order_features diff --git a/src/features/entry.py b/src/features/entry.py index a13a93de..46895c71 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -21,12 +21,24 @@ else: time_segments_file = snakemake.input["time_segments_labels"] if calc_windows: - window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True) + window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True) + # # Get basic stats from all participant's windows + # fo_means_stds = pd.DataFrame({"mean": window_features.mean(), "median": window_features.median(), "sd": window_features.std(), + # "min": window_features.min(), "max": window_features.max()}) + + # fo_columns = window_features.columns.values[5:] + # fo_columns_z_score = [col + "_zscore" for col in fo_columns] + # window_features[fo_columns_z_score] = StandardScaler().fit_transform(window_features[fo_columns]) + + # print(fo_means_stds) + # Z-score SO features by columns - if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False): - second_order_features[second_order_features.columns[4:]] = StandardScaler().fit_transform(second_order_features[second_order_features.columns[4:]]) + # if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False): + # for indx, fo_mean_std in fo_means_stds.iterrows(): + # print(indx, fo_mean_std) + # sys.exit() window_features.to_csv(snakemake.output[1], index=False) second_order_features.to_csv(snakemake.output[0], index=False) From e1d7607de4e7d78bb2fb2ec4de30af84edac5b2d Mon Sep 17 00:00:00 2001 From: primoz Date: Fri, 10 Jun 2022 12:34:48 +0000 Subject: [PATCH 45/57] Extraction of additional SO features. Min/max has been changed to nsmallest/nlargest means. --- config.yaml | 44 ++++++++++++------- src/features/cr_features_helper_methods.py | 17 +++++-- .../empatica_blood_volume_pulse/cr/main.py | 2 +- .../empatica_inter_beat_interval/cr/main.py | 4 +- src/features/entry.py | 16 ------- tests/scripts/zero_vals.py | 27 ++++++++++++ 6 files changed, 72 insertions(+), 38 deletions(-) create mode 100644 tests/scripts/zero_vals.py diff --git a/config.yaml b/config.yaml index 2c3b5ab7..ce7fafd0 100644 --- a/config.yaml +++ b/config.yaml @@ -484,13 +484,13 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features WINDOWS: COMPUTE: True WINDOW_LENGTH: 15 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] - STANDARDIZE_SO_FEATURES: True + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows'] + STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py @@ -512,14 +512,14 @@ EMPATICA_TEMPERATURE: FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] - STANDARDIZE_SO_FEATURES: True + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows'] + STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_temperature/cr/main.py # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/ @@ -531,17 +531,17 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', 'significantDecrease'] WINDOWS: - COMPUTE: False + COMPUTE: True WINDOW_LENGTH: 60 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] - STANDARDIZE_SO_FEATURES: True + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', count_windows, eda_num_peaks_non_zero] + STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ @@ -559,8 +559,8 @@ EMPATICA_BLOOD_VOLUME_PULSE: WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] - STANDARDIZE_SO_FEATURES: True + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows'] + STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/ @@ -579,8 +579,8 @@ EMPATICA_INTER_BEAT_INTERVAL: WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'max', 'min'] - STANDARDIZE_SO_FEATURES: True + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows'] + STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py # See https://www.rapids.science/latest/features/empatica-tags/ @@ -662,4 +662,18 @@ ALL_CLEANING_OVERALL: COMPUTE: True MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5 CORR_THRESHOLD: 0.95 - SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R \ No newline at end of file + SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R + + +######################################################################################################################## +# Z-score standardization # +######################################################################################################################## +STANDARDIZATION: + COMPUTE: True + EXCECUTE_FULL_PIPELINE: False # Standardization to be calculated from feature extraction step including merging all sensors and participants steps (in seperate standardization file) +EMPATICA_STANDARDIZATION: + PROVIDERS: + CR: + COMPUTE: False + TYPE: FROM_FIRST_ORDER # FROM_FIRST_ORDER or FROM_SECOND_ORDER(not implemented) + SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R diff --git a/src/features/cr_features_helper_methods.py b/src/features/cr_features_helper_methods.py index f8dd3456..6f2d3b2e 100644 --- a/src/features/cr_features_helper_methods.py +++ b/src/features/cr_features_helper_methods.py @@ -6,16 +6,25 @@ import sys def extract_second_order_features(intraday_features, so_features_names): if not intraday_features.empty: so_features = pd.DataFrame() + #print(intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).nsmallest()) if "mean" in so_features_names: so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_mean")], axis=1) if "median" in so_features_names: so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).median().add_suffix("_SO_median")], axis=1) if "sd" in so_features_names: so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).std().add_suffix("_SO_sd")], axis=1) - if "max" in so_features_names: - so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).max().add_suffix("_SO_max")], axis=1) - if "min" in so_features_names: - so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).min().add_suffix("_SO_min")], axis=1) + if "nlargest_mean" in so_features_names: # largest 5 -- maybe there is a faster groupby solution? + for column in intraday_features.columns[2:]: + so_features[column+"_SO_nlargest_mean"] = intraday_features.drop("level_1", axis=1).groupby("local_segment")[column].apply(lambda x: x.nlargest(5).mean()) + if "nsmallest_mean" in so_features_names: # smallest 5 -- maybe there is a faster groupby solution? + for column in intraday_features.columns[2:]: + so_features[column+"_SO_nsmallest_mean"] = intraday_features.drop("level_1", axis=1).groupby("local_segment")[column].apply(lambda x: x.nsmallest(5).mean()) + if "count_windows" in so_features_names: + so_features["SO_windowsCount"] = intraday_features.groupby(["local_segment"]).count()["level_1"] + + # numPeaksNonZero specialized for EDA sensor + if "eda_num_peaks_non_zero" in so_features_names and "numPeaks" in intraday_features.columns: + so_features["SO_numPeaksNonZero"] = intraday_features.groupby("local_segment")["numPeaks"].apply(lambda x: (x!=0).sum()) so_features.reset_index(inplace=True) diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index 9e6db52b..edf52076 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -66,7 +66,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length, time_segment, filter_data_by_segment) if calc_windows: - if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False): + if provider["WINDOWS"].get("STANDARDIZE_FEATURES", False): fo_columns = bvp_intraday_features.columns.values[2:] fo_columns_z_score = [col + "_zscore" for col in fo_columns] bvp_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(bvp_intraday_features[fo_columns]) diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index 41af4cf1..1137b802 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -33,7 +33,7 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_ signal_2D = \ convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[0], ibi_timings = \ - convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1], + convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1], sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features)) else: ibi_intraday_features = \ @@ -70,7 +70,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) if calc_windows: - if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False): + if provider["WINDOWS"].get("STANDARDIZE_FEATURES", False): fo_columns = ibi_intraday_features.columns.values[2:] fo_columns_z_score = [col + "_zscore" for col in fo_columns] ibi_intraday_features[fo_columns_z_score] = StandardScaler().fit_transform(ibi_intraday_features[fo_columns]) diff --git a/src/features/entry.py b/src/features/entry.py index 46895c71..4cee0155 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -22,23 +22,7 @@ else: if calc_windows: window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True) - - # # Get basic stats from all participant's windows - # fo_means_stds = pd.DataFrame({"mean": window_features.mean(), "median": window_features.median(), "sd": window_features.std(), - # "min": window_features.min(), "max": window_features.max()}) - - # fo_columns = window_features.columns.values[5:] - # fo_columns_z_score = [col + "_zscore" for col in fo_columns] - # window_features[fo_columns_z_score] = StandardScaler().fit_transform(window_features[fo_columns]) - - # print(fo_means_stds) - # Z-score SO features by columns - # if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False): - # for indx, fo_mean_std in fo_means_stds.iterrows(): - # print(indx, fo_mean_std) - - # sys.exit() window_features.to_csv(snakemake.output[1], index=False) second_order_features.to_csv(snakemake.output[0], index=False) diff --git a/tests/scripts/zero_vals.py b/tests/scripts/zero_vals.py new file mode 100644 index 00000000..9e13ecf5 --- /dev/null +++ b/tests/scripts/zero_vals.py @@ -0,0 +1,27 @@ +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt + + +# path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants +# path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" +path = "/rapids/data/interim/p031/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" +# path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" +# path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv" +# path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" + +df = pd.read_csv(path) +print(df) +is_NaN = df.isnull() +df = df[df["empatica_electrodermal_activity_cr_numPeaks"]] +print(df) + + +# row_has_NaN = is_NaN. any(axis=1) +# rows_with_NaN = df[row_has_NaN] +# print(rows_with_NaN.size) + +# sns.heatmap(df.isna(), cbar=False) +plt.savefig('eda_windows_p03_window_60_thresh_default.png', bbox_inches='tight') + + From 094743244d8117b391f17ac1d03f8d977139a689 Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 13 Jun 2022 10:51:22 +0000 Subject: [PATCH 46/57] Added SO feature for sum all rows that are non zero for BVP and IBI sensors. --- config.yaml | 8 ++++---- src/features/cr_features_helper_methods.py | 8 ++++++-- src/features/empatica_blood_volume_pulse/cr/main.py | 2 +- src/features/empatica_electrodermal_activity/cr/main.py | 4 ++-- src/features/empatica_inter_beat_interval/cr/main.py | 2 +- 5 files changed, 14 insertions(+), 10 deletions(-) diff --git a/config.yaml b/config.yaml index ce7fafd0..c150b3cc 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p03] #p01, p02, p03] +PIDS: [p01] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -559,7 +559,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows'] + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_zero'] STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py @@ -575,11 +575,11 @@ EMPATICA_INTER_BEAT_INTERVAL: COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features - PATCH_WITH_BVP: True + PATCH_WITH_BVP: False WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows'] + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_zero'] STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py diff --git a/src/features/cr_features_helper_methods.py b/src/features/cr_features_helper_methods.py index 6f2d3b2e..bf25296d 100644 --- a/src/features/cr_features_helper_methods.py +++ b/src/features/cr_features_helper_methods.py @@ -24,8 +24,12 @@ def extract_second_order_features(intraday_features, so_features_names): # numPeaksNonZero specialized for EDA sensor if "eda_num_peaks_non_zero" in so_features_names and "numPeaks" in intraday_features.columns: - so_features["SO_numPeaksNonZero"] = intraday_features.groupby("local_segment")["numPeaks"].apply(lambda x: (x!=0).sum()) - + so_features["SO_numPeaksNonZero"] = intraday_features.groupby("local_segment")["numPeaks"].apply(lambda x: (x!=0).sum()) + + # numWindowsNonZero specialized for BVP and IBI sensors + if "hrv_num_windows_non_zero" in so_features_names and "meanHr" in intraday_features.columns: + so_features["SO_numWindowsNonZero"] = intraday_features.groupby("local_segment")["meanHr"].apply(lambda x: (x!=0).sum()) + so_features.reset_index(inplace=True) else: diff --git a/src/features/empatica_blood_volume_pulse/cr/main.py b/src/features/empatica_blood_volume_pulse/cr/main.py index edf52076..243f7e72 100644 --- a/src/features/empatica_blood_volume_pulse/cr/main.py +++ b/src/features/empatica_blood_volume_pulse/cr/main.py @@ -64,7 +64,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen # extract features from intraday data bvp_intraday_features = extract_bvp_features_from_intraday_data(bvp_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) - + if calc_windows: if provider["WINDOWS"].get("STANDARDIZE_FEATURES", False): fo_columns = bvp_intraday_features.columns.values[2:] diff --git a/src/features/empatica_electrodermal_activity/cr/main.py b/src/features/empatica_electrodermal_activity/cr/main.py index 25ecba59..1d56839d 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -30,11 +30,11 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_ if window_length is None: eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, threshold=0, featureNames=features)) + lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features)) else: eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, threshold=0, featureNames=features)) + lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features)) eda_intraday_features.reset_index(inplace=True) diff --git a/src/features/empatica_inter_beat_interval/cr/main.py b/src/features/empatica_inter_beat_interval/cr/main.py index 1137b802..29d00b23 100644 --- a/src/features/empatica_inter_beat_interval/cr/main.py +++ b/src/features/empatica_inter_beat_interval/cr/main.py @@ -69,6 +69,7 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen # extract features from intraday data ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute, requested_window_length, time_segment, filter_data_by_segment) + if calc_windows: if provider["WINDOWS"].get("STANDARDIZE_FEATURES", False): fo_columns = ibi_intraday_features.columns.values[2:] @@ -80,5 +81,4 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen return ibi_intraday_features, ibi_second_order_features - return ibi_intraday_features \ No newline at end of file From 402059871ff330eec8cf3c80e02a496d590d9137 Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 13 Jun 2022 14:12:03 +0000 Subject: [PATCH 47/57] Making standardization as a rule. WIP: done only for BVP. --- Snakefile | 2 + config.yaml | 14 +++---- rules/features.smk | 14 +++++++ src/features/cr_features_helper_methods.py | 39 ++++++++++++------- src/features/entry.py | 1 - .../empatica_standardization/main.py | 33 ++++++++++++++++ 6 files changed, 81 insertions(+), 22 deletions(-) create mode 100644 src/features/standardization/empatica_standardization/main.py diff --git a/Snakefile b/Snakefile index 7dc5ad3a..25e0fc08 100644 --- a/Snakefile +++ b/Snakefile @@ -354,6 +354,8 @@ for provider in config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/processed/features/{pid}/empatica_blood_volume_pulse.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + files_to_compute.extend(expand("data/interim/{pid}/empatica_blood_volume_pulse_features/standardization_empatica_blood_volume_pulse_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["EMPATICA_STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower())) + for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys(): if config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["COMPUTE"]: diff --git a/config.yaml b/config.yaml index c150b3cc..432b1364 100644 --- a/config.yaml +++ b/config.yaml @@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features WINDOWS: COMPUTE: True @@ -512,7 +512,7 @@ EMPATICA_TEMPERATURE: FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: @@ -531,7 +531,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', @@ -572,10 +572,10 @@ EMPATICA_INTER_BEAT_INTERVAL: FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features - PATCH_WITH_BVP: False + PATCH_WITH_BVP: True WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds @@ -674,6 +674,6 @@ STANDARDIZATION: EMPATICA_STANDARDIZATION: PROVIDERS: CR: - COMPUTE: False + STANDARDIZE: True TYPE: FROM_FIRST_ORDER # FROM_FIRST_ORDER or FROM_SECOND_ORDER(not implemented) - SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R + SRC_SCRIPT: src/features/standardization/empatica_standardization/main.py diff --git a/rules/features.smk b/rules/features.smk index 1b6e0ad8..6a1e0a97 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -896,6 +896,20 @@ rule empatica_blood_volume_pulse_r_features: script: "../src/features/entry.R" +rule empatica_blood_volume_pulse_python_cr_features_standardization: + input: + windows_features_data = "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv" + params: + provider = config["EMPATICA_STANDARDIZATION"]["PROVIDERS"]["CR"], + provider_key = "{provider_key}", + sensor_key = "empatica_blood_volume_pulse", + provider_main = config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"]["CR"] + output: + "data/interim/{pid}/empatica_blood_volume_pulse_features/standardization_empatica_blood_volume_pulse_python_{provider_key}.csv", + "data/interim/{pid}/empatica_blood_volume_pulse_features/standardization_empatica_blood_volume_pulse_python_{provider_key}_windows.csv" + script: + "../src/features/standardization/empatica_standardization/main.py" + rule empatica_inter_beat_interval_python_features: input: sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", diff --git a/src/features/cr_features_helper_methods.py b/src/features/cr_features_helper_methods.py index bf25296d..c140fe99 100644 --- a/src/features/cr_features_helper_methods.py +++ b/src/features/cr_features_helper_methods.py @@ -3,37 +3,48 @@ import math as m import sys -def extract_second_order_features(intraday_features, so_features_names): +def extract_second_order_features(intraday_features, so_features_names, prefix=""): + + if prefix: + groupby_cols = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime'] + else: + groupby_cols = ['local_segment'] + if not intraday_features.empty: so_features = pd.DataFrame() #print(intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).nsmallest()) if "mean" in so_features_names: - so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).mean().add_suffix("_SO_mean")], axis=1) + so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).mean().add_suffix("_SO_mean")], axis=1) + if "median" in so_features_names: - so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).median().add_suffix("_SO_median")], axis=1) + so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).median().add_suffix("_SO_median")], axis=1) + if "sd" in so_features_names: - so_features = pd.concat([so_features, intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).std().add_suffix("_SO_sd")], axis=1) + so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).std().add_suffix("_SO_sd")], axis=1) + if "nlargest_mean" in so_features_names: # largest 5 -- maybe there is a faster groupby solution? - for column in intraday_features.columns[2:]: - so_features[column+"_SO_nlargest_mean"] = intraday_features.drop("level_1", axis=1).groupby("local_segment")[column].apply(lambda x: x.nlargest(5).mean()) + for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]: + so_features[column+"_SO_nlargest_mean"] = intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols)[column].apply(lambda x: x.nlargest(5).mean()) + if "nsmallest_mean" in so_features_names: # smallest 5 -- maybe there is a faster groupby solution? - for column in intraday_features.columns[2:]: - so_features[column+"_SO_nsmallest_mean"] = intraday_features.drop("level_1", axis=1).groupby("local_segment")[column].apply(lambda x: x.nsmallest(5).mean()) + for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]: + so_features[column+"_SO_nsmallest_mean"] = intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols)[column].apply(lambda x: x.nsmallest(5).mean()) + if "count_windows" in so_features_names: - so_features["SO_windowsCount"] = intraday_features.groupby(["local_segment"]).count()["level_1"] + so_features["SO_windowsCount"] = intraday_features.groupby(groupby_cols).count()[prefix+"level_1"] # numPeaksNonZero specialized for EDA sensor - if "eda_num_peaks_non_zero" in so_features_names and "numPeaks" in intraday_features.columns: - so_features["SO_numPeaksNonZero"] = intraday_features.groupby("local_segment")["numPeaks"].apply(lambda x: (x!=0).sum()) + if "eda_num_peaks_non_zero" in so_features_names and prefix+"numPeaks" in intraday_features.columns: + so_features[prefix+"SO_numPeaksNonZero"] = intraday_features.groupby(groupby_cols)[prefix+"numPeaks"].apply(lambda x: (x!=0).sum()) # numWindowsNonZero specialized for BVP and IBI sensors - if "hrv_num_windows_non_zero" in so_features_names and "meanHr" in intraday_features.columns: - so_features["SO_numWindowsNonZero"] = intraday_features.groupby("local_segment")["meanHr"].apply(lambda x: (x!=0).sum()) + if "hrv_num_windows_non_zero" in so_features_names and prefix+"meanHr" in intraday_features.columns: + so_features[prefix+"SO_numWindowsNonZero"] = intraday_features.groupby(groupby_cols)[prefix+"meanHr"].apply(lambda x: (x!=0).sum()) so_features.reset_index(inplace=True) else: - so_features = pd.DataFrame(columns=["local_segment"]) + so_features = pd.DataFrame(columns=groupby_cols) return so_features diff --git a/src/features/entry.py b/src/features/entry.py index 4cee0155..dea1e1df 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -1,6 +1,5 @@ import pandas as pd from utils.utils import fetch_provider_features, run_provider_cleaning_script -from sklearn.preprocessing import StandardScaler import sys diff --git a/src/features/standardization/empatica_standardization/main.py b/src/features/standardization/empatica_standardization/main.py new file mode 100644 index 00000000..2e2fb0f6 --- /dev/null +++ b/src/features/standardization/empatica_standardization/main.py @@ -0,0 +1,33 @@ +import pandas as pd +import numpy as np +from sklearn.preprocessing import StandardScaler + +import sys + +sys.path.append('/rapids/src/features/') +from cr_features_helper_methods import extract_second_order_features + +sensor_data_files = dict(snakemake.input) + +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] +sensor_key = snakemake.params["sensor_key"] + +pd.set_option('display.max_columns', None) + +if provider_key == "cr": + provider_main = snakemake.params["provider_main"] + prefix = sensor_key + "_" + provider_key + "_" + + windows_features_data = pd.read_csv(sensor_data_files["windows_features_data"]) + excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', + prefix + "level_1"] + windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)] = \ + StandardScaler().fit_transform(windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)]) + + windows_features_data.to_csv(snakemake.output[1], index=False) + + if provider_main["WINDOWS"]["COMPUTE"] and "SECOND_ORDER_FEATURES" in provider_main["WINDOWS"]: + so_features_names = provider_main["WINDOWS"]["SECOND_ORDER_FEATURES"] + windows_so_features_data = extract_second_order_features(windows_features_data, so_features_names, prefix) + windows_so_features_data.to_csv(snakemake.output[0], index=False) \ No newline at end of file From 9ea39dc5573be3bb540de649744120b91b57104d Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 13 Jun 2022 18:17:30 +0000 Subject: [PATCH 48/57] Standardization as a Snakefile's rule enabled for all E4 sensors. --- Snakefile | 15 +++- config.yaml | 20 ++--- rules/features.smk | 84 +++++++++++++++---- .../{empatica_standardization => }/main.py | 16 ++-- 4 files changed, 102 insertions(+), 33 deletions(-) rename src/features/standardization/{empatica_standardization => }/main.py (78%) diff --git a/Snakefile b/Snakefile index 25e0fc08..8d0870c3 100644 --- a/Snakefile +++ b/Snakefile @@ -317,6 +317,8 @@ for provider in config["EMPATICA_ACCELEROMETER"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/processed/features/{pid}/empatica_accelerometer.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower())) for provider in config["EMPATICA_HEARTRATE"]["PROVIDERS"].keys(): if config["EMPATICA_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]: @@ -336,6 +338,8 @@ for provider in config["EMPATICA_TEMPERATURE"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/processed/features/{pid}/empatica_temperature.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower())) for provider in config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"].keys(): if config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"][provider]["COMPUTE"]: @@ -345,6 +349,8 @@ for provider in config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/processed/features/{pid}/empatica_electrodermal_activity.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower())) for provider in config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"].keys(): if config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"][provider]["COMPUTE"]: @@ -354,7 +360,8 @@ for provider in config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/processed/features/{pid}/empatica_blood_volume_pulse.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") - files_to_compute.extend(expand("data/interim/{pid}/empatica_blood_volume_pulse_features/standardization_empatica_blood_volume_pulse_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["EMPATICA_STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower())) + if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower())) for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys(): @@ -365,6 +372,8 @@ for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys(): files_to_compute.extend(expand("data/processed/features/{pid}/empatica_inter_beat_interval.csv", pid=config["PIDS"])) files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower())) if isinstance(config["EMPATICA_TAGS"]["PROVIDERS"], dict): for provider in config["EMPATICA_TAGS"]["PROVIDERS"].keys(): @@ -404,6 +413,10 @@ for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys(): if config["ALL_CLEANING_OVERALL"]["PROVIDERS"][provider]["COMPUTE"]: files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +".csv")) +# Put the for loop over STANDARDIZATION providers if all are COMPUTE == True +# then merge all that are set to True in z_all_sensors for all and each participant +# See the logic behind: in each sensor the "data/processed/features/all_participants/all_sensor_features.csv" is listed + rule all: input: files_to_compute diff --git a/config.yaml b/config.yaml index 432b1364..7f235b12 100644 --- a/config.yaml +++ b/config.yaml @@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features WINDOWS: COMPUTE: True @@ -512,7 +512,7 @@ EMPATICA_TEMPERATURE: FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: @@ -531,7 +531,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic', 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', @@ -559,7 +559,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_zero'] + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_nan'] STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py @@ -572,14 +572,14 @@ EMPATICA_INTER_BEAT_INTERVAL: FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features PATCH_WITH_BVP: True WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_zero'] + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_nan'] STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py @@ -669,11 +669,7 @@ ALL_CLEANING_OVERALL: # Z-score standardization # ######################################################################################################################## STANDARDIZATION: - COMPUTE: True - EXCECUTE_FULL_PIPELINE: False # Standardization to be calculated from feature extraction step including merging all sensors and participants steps (in seperate standardization file) -EMPATICA_STANDARDIZATION: PROVIDERS: CR: - STANDARDIZE: True - TYPE: FROM_FIRST_ORDER # FROM_FIRST_ORDER or FROM_SECOND_ORDER(not implemented) - SRC_SCRIPT: src/features/standardization/empatica_standardization/main.py + COMPUTE: True + SRC_SCRIPT: src/features/standardization/main.py diff --git a/rules/features.smk b/rules/features.smk index 6a1e0a97..0f8f4d73 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -775,6 +775,20 @@ rule empatica_accelerometer_python_features: script: "../src/features/entry.py" +rule empatica_accelerometer_python_features_standardization: + input: + windows_features_data = "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv" + params: + provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"], + provider_key = "{provider_key}", + sensor_key = "empatica_accelerometer", + provider_main = config["EMPATICA_ACCELEROMETER"]["PROVIDERS"]["CR"] + output: + "data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_python_{provider_key}.csv", + "data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_python_{provider_key}_windows.csv" + script: + "../src/features/standardization/main.py" + rule empatica_accelerometer_r_features: input: sensor_data = "data/raw/{pid}/empatica_accelerometer_with_datetime.csv", @@ -829,6 +843,20 @@ rule empatica_temperature_python_features: script: "../src/features/entry.py" +rule empatica_temperature_python_features_standardization: + input: + windows_features_data = "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv" + params: + provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"], + provider_key = "{provider_key}", + sensor_key = "empatica_temperature", + provider_main = config["EMPATICA_TEMPERATURE"]["PROVIDERS"]["CR"] + output: + "data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_python_{provider_key}.csv", + "data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_python_{provider_key}_windows.csv" + script: + "../src/features/standardization/main.py" + rule empatica_temperature_r_features: input: sensor_data = "data/raw/{pid}/empatica_temperature_with_datetime.csv", @@ -856,6 +884,20 @@ rule empatica_electrodermal_activity_python_features: script: "../src/features/entry.py" +rule empatica_electrodermal_activity_python_features_standardization: + input: + windows_features_data = "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv" + params: + provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"], + provider_key = "{provider_key}", + sensor_key = "empatica_electrodermal_activity", + provider_main = config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"]["CR"] + output: + "data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_python_{provider_key}.csv", + "data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_python_{provider_key}_windows.csv" + script: + "../src/features/standardization/main.py" + rule empatica_electrodermal_activity_r_features: input: sensor_data = "data/raw/{pid}/empatica_electrodermal_activity_with_datetime.csv", @@ -883,6 +925,20 @@ rule empatica_blood_volume_pulse_python_features: script: "../src/features/entry.py" +rule empatica_blood_volume_pulse_python_cr_features_standardization: + input: + windows_features_data = "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv" + params: + provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"], + provider_key = "{provider_key}", + sensor_key = "empatica_blood_volume_pulse", + provider_main = config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"]["CR"] + output: + "data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_python_{provider_key}.csv", + "data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_python_{provider_key}_windows.csv" + script: + "../src/features/standardization/main.py" + rule empatica_blood_volume_pulse_r_features: input: sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv", @@ -896,20 +952,6 @@ rule empatica_blood_volume_pulse_r_features: script: "../src/features/entry.R" -rule empatica_blood_volume_pulse_python_cr_features_standardization: - input: - windows_features_data = "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv" - params: - provider = config["EMPATICA_STANDARDIZATION"]["PROVIDERS"]["CR"], - provider_key = "{provider_key}", - sensor_key = "empatica_blood_volume_pulse", - provider_main = config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"]["CR"] - output: - "data/interim/{pid}/empatica_blood_volume_pulse_features/standardization_empatica_blood_volume_pulse_python_{provider_key}.csv", - "data/interim/{pid}/empatica_blood_volume_pulse_features/standardization_empatica_blood_volume_pulse_python_{provider_key}_windows.csv" - script: - "../src/features/standardization/empatica_standardization/main.py" - rule empatica_inter_beat_interval_python_features: input: sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", @@ -924,6 +966,20 @@ rule empatica_inter_beat_interval_python_features: script: "../src/features/entry.py" +rule empatica_inter_beat_interval_python_features_standardization: + input: + windows_features_data = "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv" + params: + provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"], + provider_key = "{provider_key}", + sensor_key = "empatica_inter_beat_interval", + provider_main = config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"]["CR"] + output: + "data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_python_{provider_key}.csv", + "data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_python_{provider_key}_windows.csv" + script: + "../src/features/standardization/main.py" + rule empatica_inter_beat_interval_r_features: input: sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv", diff --git a/src/features/standardization/empatica_standardization/main.py b/src/features/standardization/main.py similarity index 78% rename from src/features/standardization/empatica_standardization/main.py rename to src/features/standardization/main.py index 2e2fb0f6..b6026d0b 100644 --- a/src/features/standardization/empatica_standardization/main.py +++ b/src/features/standardization/main.py @@ -4,9 +4,6 @@ from sklearn.preprocessing import StandardScaler import sys -sys.path.append('/rapids/src/features/') -from cr_features_helper_methods import extract_second_order_features - sensor_data_files = dict(snakemake.input) provider = snakemake.params["provider"] @@ -16,12 +13,14 @@ sensor_key = snakemake.params["sensor_key"] pd.set_option('display.max_columns', None) if provider_key == "cr": + sys.path.append('/rapids/src/features/') + from cr_features_helper_methods import extract_second_order_features + provider_main = snakemake.params["provider_main"] prefix = sensor_key + "_" + provider_key + "_" windows_features_data = pd.read_csv(sensor_data_files["windows_features_data"]) - excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', - prefix + "level_1"] + excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', prefix + "level_1"] windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)] = \ StandardScaler().fit_transform(windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)]) @@ -30,4 +29,9 @@ if provider_key == "cr": if provider_main["WINDOWS"]["COMPUTE"] and "SECOND_ORDER_FEATURES" in provider_main["WINDOWS"]: so_features_names = provider_main["WINDOWS"]["SECOND_ORDER_FEATURES"] windows_so_features_data = extract_second_order_features(windows_features_data, so_features_names, prefix) - windows_so_features_data.to_csv(snakemake.output[0], index=False) \ No newline at end of file + windows_so_features_data.to_csv(snakemake.output[0], index=False) + else: + pd.DataFrame().to_csv(snakemake.output[0], index=False) + +else: + pass #To-Do for the rest of the sensors. \ No newline at end of file From 212cf300f8fa562b9a6138de548b8621f5222bf7 Mon Sep 17 00:00:00 2001 From: Primoz Date: Tue, 14 Jun 2022 15:09:14 +0000 Subject: [PATCH 49/57] Debugging EDA signal - preliminary step for imputation. --- config.yaml | 8 ++--- eda_windows_p01_window_non_zero.png | Bin 0 -> 100008 bytes maxPeakAmplitudeChangeBefore.png | Bin 0 -> 100014 bytes src/features/cr_features_helper_methods.py | 4 +-- tests/scripts/zero_vals.py | 39 ++++++++++++++++----- 5 files changed, 37 insertions(+), 14 deletions(-) create mode 100644 eda_windows_p01_window_non_zero.png create mode 100644 maxPeakAmplitudeChangeBefore.png diff --git a/config.yaml b/config.yaml index 7f235b12..3d164496 100644 --- a/config.yaml +++ b/config.yaml @@ -484,7 +484,7 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features WINDOWS: COMPUTE: True @@ -512,7 +512,7 @@ EMPATICA_TEMPERATURE: FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: @@ -553,7 +553,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: @@ -572,7 +572,7 @@ EMPATICA_INTER_BEAT_INTERVAL: FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py CR: - COMPUTE: True + COMPUTE: False FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features PATCH_WITH_BVP: True diff --git a/eda_windows_p01_window_non_zero.png b/eda_windows_p01_window_non_zero.png new file mode 100644 index 0000000000000000000000000000000000000000..18afe5e80f73da72480128eb7b3276c45e5b2eff GIT binary patch literal 100008 zcmagG2|Sl;*FF9v$`n$fWK07hWN0!AiHd|o=8_Z{B10roC8@|z5mJVXQDlllhKeGT zOobAeDr5Su>#K8~^SJt!D7Y7S|oduKOGhySiIC+fsBbU7Z{pT^){E37oQZb~);JVv~fd zgwzHBJ6BgHm#rH&9{>9V5{}MCHnv)hmEc1dowN?PP$+DcKZI+UjE2VaX&Tw0W zihMd_^hqvATPB4?SO27TMkoK7p_6y_HFeC~teu%UJy?C@omYJRTSd3;Co(2Xc2#g& z3k0yMGU%;NeaxfGAc()yFp}S!!e}{E@!u)D^up8^Y-7?QKTbTgEF7;FR3BO|KwcN} zPEVPCE zAL3(UFT^Q$CA###)Y8$Z%scu<@^C$OWmT2)#9&7W4-E}Xw7er<=An`chhM~&S646E 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bytes src/features/cr_features_helper_methods.py | 8 +++---- tests/scripts/zero_vals.py | 22 +++++-------------- 7 files changed, 18 insertions(+), 27 deletions(-) delete mode 100644 eda_windows_p01_window_non_zero.png delete mode 100644 maxPeakAmplitudeChangeBefore.png diff --git a/config.yaml b/config.yaml index 3d164496..85477dd9 100644 --- a/config.yaml +++ b/config.yaml @@ -489,7 +489,7 @@ EMPATICA_ACCELEROMETER: WINDOWS: COMPUTE: True WINDOW_LENGTH: 15 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows'] + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'] STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py @@ -518,7 +518,7 @@ EMPATICA_TEMPERATURE: WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows'] + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'] STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_temperature/cr/main.py @@ -536,11 +536,11 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', - 'significantDecrease'] + 'significantDecrease', maxPeakResponseSlopeBefore, maxPeakResponseSlopeAfter] WINDOWS: COMPUTE: True WINDOW_LENGTH: 60 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', count_windows, eda_num_peaks_non_zero] + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', count_windows, eda_num_peaks_non_zero] STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py @@ -559,7 +559,7 @@ EMPATICA_BLOOD_VOLUME_PULSE: WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_nan'] + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan'] STANDARDIZE_FEATURES: False SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py @@ -579,7 +579,7 @@ EMPATICA_INTER_BEAT_INTERVAL: WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds - SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest_mean', 'nsmallest_mean', 'count_windows', 'hrv_num_windows_non_nan'] + SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan'] 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b/tests/scripts/missing_values_heatmap.py index 2f2364f2..3826e371 100644 --- a/tests/scripts/missing_values_heatmap.py +++ b/tests/scripts/missing_values_heatmap.py @@ -5,7 +5,7 @@ import matplotlib.pyplot as plt # path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants # path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" -path = "/rapids/data/interim/p03/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" +path = "/rapids/data/interim/p02/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" @@ -18,6 +18,6 @@ rows_with_NaN = df[row_has_NaN] print(rows_with_NaN.size) sns.heatmap(df.isna(), cbar=False) -plt.savefig('eda_windows_p03_window_60_thresh_default.png', bbox_inches='tight') +plt.savefig('eda_windows_p02_window_60_thresh_default.png', bbox_inches='tight') From c851ab0763592f66d9efda4c6e12c7fe324b9d17 Mon Sep 17 00:00:00 2001 From: Primoz Date: Tue, 21 Jun 2022 14:09:49 +0000 Subject: [PATCH 52/57] Fill EDA NaN values where numPeak is zero. Other small changes. --- calculatingfeatures | 1 + config.yaml | 3 ++- eda_windows_p01_window_60_thresh_default.png | Bin 124792 -> 0 bytes eda_windows_p02_window_60_thresh_default.png | Bin 128612 -> 0 bytes .../empatica_electrodermal_activity/cr/main.py | 5 +++++ src/features/entry.py | 3 ++- tests/scripts/missing_values_heatmap.py | 2 +- 7 files changed, 11 insertions(+), 3 deletions(-) create mode 160000 calculatingfeatures delete mode 100644 eda_windows_p01_window_60_thresh_default.png delete mode 100644 eda_windows_p02_window_60_thresh_default.png diff --git a/calculatingfeatures b/calculatingfeatures new file mode 160000 index 00000000..98e6207e --- /dev/null +++ b/calculatingfeatures @@ -0,0 +1 @@ +Subproject commit 98e6207ecc23a23b1f621a11f28eb87cbfb7f9da diff --git a/config.yaml b/config.yaml index 9ff1e19b..16c1ed9e 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p02] #p01, p02, p03] +PIDS: [p01] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -542,6 +542,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: WINDOW_LENGTH: 60 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', count_windows, eda_num_peaks_non_zero] STANDARDIZE_FEATURES: False + IMPUTE_NANS: False SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ diff --git a/eda_windows_p01_window_60_thresh_default.png b/eda_windows_p01_window_60_thresh_default.png deleted file mode 100644 index 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b/src/features/empatica_electrodermal_activity/cr/main.py index 1d56839d..6b9e6fac 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -63,6 +63,11 @@ def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segmen requested_window_length, time_segment, filter_data_by_segment) if calc_windows: + if provider["WINDOWS"]["IMPUTE_NANS"]: + eda_intraday_features[eda_intraday_features["numPeaks"] == 0] = \ + eda_intraday_features[eda_intraday_features["numPeaks"] == 0].fillna(0) + pd.set_option('display.max_columns', None) + so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"] eda_second_order_features = extract_second_order_features(eda_intraday_features, so_features_names) diff --git a/src/features/entry.py b/src/features/entry.py index dea1e1df..288ba168 100644 --- a/src/features/entry.py +++ b/src/features/entry.py @@ -28,7 +28,8 @@ else: elif "empatica" in sensor_key: pd.DataFrame().to_csv(snakemake.output[1], index=False) - sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False) + if not calc_windows: + sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False) if not calc_windows: sensor_features.to_csv(snakemake.output[0], index=False) diff --git a/tests/scripts/missing_values_heatmap.py b/tests/scripts/missing_values_heatmap.py index 3826e371..20e7d33f 100644 --- a/tests/scripts/missing_values_heatmap.py +++ b/tests/scripts/missing_values_heatmap.py @@ -5,7 +5,7 @@ import matplotlib.pyplot as plt # path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants # path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" -path = "/rapids/data/interim/p02/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" +path = "/rapids/data/interim/p01/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" From 505c3a86b9d7ee9826559e069a684b07a98ca093 Mon Sep 17 00:00:00 2001 From: Primoz Date: Thu, 30 Jun 2022 15:15:37 +0000 Subject: [PATCH 53/57] Testing different EDA findPeaks parameters. --- .gitignore | 3 +++ config.yaml | 4 ++-- .../empatica_electrodermal_activity/cr/main.py | 6 ++++-- ...{missing_values_heatmap.py => missing_vals.py} | 15 ++++++++++----- tests/scripts/zero_vals.py | 9 ++++----- 5 files changed, 23 insertions(+), 14 deletions(-) rename tests/scripts/{missing_values_heatmap.py => missing_vals.py} (65%) diff --git a/.gitignore b/.gitignore index e783159f..eb1b7009 100644 --- a/.gitignore +++ b/.gitignore @@ -118,3 +118,6 @@ site/ # Docker container and other files .devcontainer + +# Calculating features module +calculatingfeatures/ diff --git a/config.yaml b/config.yaml index 16c1ed9e..5833db60 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p01] #p01, p02, p03] +PIDS: [p02] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -536,7 +536,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: 'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore', 'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio', 'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease', - 'significantDecrease', maxPeakResponseSlopeBefore, maxPeakResponseSlopeAfter] + 'significantDecrease'] WINDOWS: COMPUTE: True WINDOW_LENGTH: 60 # specify window length in seconds diff --git a/src/features/empatica_electrodermal_activity/cr/main.py b/src/features/empatica_electrodermal_activity/cr/main.py index 6b9e6fac..0b09f02b 100644 --- a/src/features/empatica_electrodermal_activity/cr/main.py +++ b/src/features/empatica_electrodermal_activity/cr/main.py @@ -30,11 +30,13 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_ if window_length is None: eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features)) + lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features, + threshold=.01, offset=1, riseTime=5, decayTime=15)) else: eda_intraday_features = \ eda_intraday_data.groupby('local_segment').apply(\ - lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features)) + lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features, + threshold=.01, offset=1, riseTime=5, decayTime=15)) eda_intraday_features.reset_index(inplace=True) diff --git a/tests/scripts/missing_values_heatmap.py b/tests/scripts/missing_vals.py similarity index 65% rename from tests/scripts/missing_values_heatmap.py rename to tests/scripts/missing_vals.py index 20e7d33f..db8eb193 100644 --- a/tests/scripts/missing_values_heatmap.py +++ b/tests/scripts/missing_vals.py @@ -2,22 +2,27 @@ import pandas as pd import seaborn as sns import matplotlib.pyplot as plt +participant = "p02" # path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants # path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" -path = "/rapids/data/interim/p01/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" +path = f"/rapids/data/interim/{participant}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" df = pd.read_csv(path) print(df) -is_NaN = df. isnull() -row_has_NaN = is_NaN. any(axis=1) +is_NaN = df.isnull() +row_has_NaN = is_NaN.any(axis=1) rows_with_NaN = df[row_has_NaN] -print(rows_with_NaN.size) + +print("All rows:", len(df.index)) +print("\nCount NaN vals:", rows_with_NaN.size) +print("\nDf mean:") +print(df.mean()) sns.heatmap(df.isna(), cbar=False) -plt.savefig('eda_windows_p02_window_60_thresh_default.png', bbox_inches='tight') +plt.savefig(f'eda_{participant}_windows_NaN.png', bbox_inches='tight') diff --git a/tests/scripts/zero_vals.py b/tests/scripts/zero_vals.py index d53d826e..d657673c 100644 --- a/tests/scripts/zero_vals.py +++ b/tests/scripts/zero_vals.py @@ -3,9 +3,11 @@ import seaborn as sns import matplotlib.pyplot as plt from itertools import compress +participant = "p02" + # path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants # path = "/rapids/data/interim/p03/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv" -path = "/rapids/data/interim/p01/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" +path = f"/rapids/data/interim/{participant}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv" # path = "/rapids/data/interim/p02/empatica_temperature_features/empatica_temperature_python_cr_windows.csv" @@ -17,9 +19,6 @@ columns_num_peaks_zero = df_num_peaks_zero.columns[df_num_peaks_zero.isna().any( df_num_peaks_non_zero = df[df["empatica_electrodermal_activity_cr_numPeaks"] != 0] df_num_peaks_non_zero = df_num_peaks_non_zero[columns_num_peaks_zero] -print(df_num_peaks_non_zero[df_num_peaks_non_zero["empatica_electrodermal_activity_cr_maxPeakAmplitudeChangeBefore"] != 0]) - - pd.set_option('display.max_columns', None) df_q = pd.DataFrame() @@ -27,7 +26,7 @@ for col in df_num_peaks_non_zero: df_q[col] = pd.to_numeric(pd.cut(df_num_peaks_non_zero[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False)) sns.heatmap(df_q) -plt.savefig('eda_windows_p01_window_values_non_zero_peak_distribution_0thresh.png', bbox_inches='tight') +plt.savefig(f'eda_{participant}_window_non_zero_peak_other_vals.png', bbox_inches='tight') plt.close() # Filter columns that do not contain 0 From a5480f13693bf043030fd5b907310cbb68ad9cbc Mon Sep 17 00:00:00 2001 From: Primoz Date: Mon, 4 Jul 2022 13:00:47 +0000 Subject: [PATCH 54/57] Few changes during addition to file structure. --- calculatingfeatures | 1 - config.yaml | 88 ++++++++++++------- data/external/empatica/empatica1/E4 Data.zip | Bin 1320626 -> 2190678 bytes 3 files changed, 55 insertions(+), 34 deletions(-) delete mode 160000 calculatingfeatures diff --git a/calculatingfeatures b/calculatingfeatures deleted file mode 160000 index 98e6207e..00000000 --- a/calculatingfeatures +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 98e6207ecc23a23b1f621a11f28eb87cbfb7f9da diff --git a/config.yaml b/config.yaml index 5833db60..0ab0bcba 100644 --- a/config.yaml +++ b/config.yaml @@ -3,7 +3,7 @@ ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files -PIDS: [p02] #p01, p02, p03] +PIDS: [p031] #p01, p02, p03] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: @@ -51,7 +51,7 @@ PHONE_DATA_STREAMS: aware_postgresql: DATABASE_GROUP: PSQL_STRAW - + aware_csv: FOLDER: data/external/aware_csv @@ -80,12 +80,12 @@ PHONE_ACCELEROMETER: # See https://www.rapids.science/latest/features/phone-activity-recognition/ PHONE_ACTIVITY_RECOGNITION: CONTAINER: - ANDROID: plugin_google_activity_recognition + ANDROID: google_ar IOS: plugin_ios_activity_recognition EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same AR episode. PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] ACTIVITY_CLASSES: STATIONARY: ["still", "tilting"] @@ -105,16 +105,17 @@ PHONE_APPLICATIONS_CRASHES: # See https://www.rapids.science/latest/features/phone-applications-foreground/ PHONE_APPLICATIONS_FOREGROUND: - CONTAINER: applications_foreground + CONTAINER: applications APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + PACKAGE_NAMES_HASHED: True UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: - COMPUTE: False - INCLUDE_EPISODE_FEATURES: False + COMPUTE: True + INCLUDE_EPISODE_FEATURES: True SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] @@ -124,7 +125,7 @@ PHONE_APPLICATIONS_FOREGROUND: dating: ["com.tinder", "com.relance.happycouple", "com.kiwi.joyride"] SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps EXCLUDED_CATEGORIES: [] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] # TODO list system apps? FEATURES: APP_EVENTS: ["countevent", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"] @@ -134,7 +135,7 @@ PHONE_APPLICATIONS_FOREGROUND: # See https://www.rapids.science/latest/features/phone-applications-notifications/ PHONE_APPLICATIONS_NOTIFICATIONS: - CONTAINER: applications_notifications + CONTAINER: notifications APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" @@ -148,7 +149,7 @@ PHONE_BATTERY: EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] SRC_SCRIPT: src/features/phone_battery/rapids/main.py @@ -157,12 +158,12 @@ PHONE_BLUETOOTH: CONTAINER: bluetooth PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R DORYAB: - COMPUTE: False + COMPUTE: True FEATURES: ALL: DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"] @@ -183,7 +184,7 @@ PHONE_CALLS: CONTAINER: call PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES_TYPE: EPISODES # EVENTS or EPISODES CALL_TYPES: [missed, incoming, outgoing] FEATURES: @@ -193,7 +194,7 @@ PHONE_CALLS: SRC_SCRIPT: src/features/phone_calls/rapids/main.R # See https://www.rapids.science/latest/features/phone-conversation/ -PHONE_CONVERSATION: +PHONE_CONVERSATION: # TODO Adapt for speech CONTAINER: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio @@ -212,14 +213,34 @@ PHONE_CONVERSATION: # See https://www.rapids.science/latest/features/phone-data-yield/ PHONE_DATA_YIELD: - SENSORS: [] + SENSORS: [#PHONE_ACCELEROMETER, + PHONE_ACTIVITY_RECOGNITION, + PHONE_APPLICATIONS_FOREGROUND, + PHONE_APPLICATIONS_NOTIFICATIONS, + PHONE_BATTERY, + PHONE_BLUETOOTH, + PHONE_CALLS, + PHONE_LIGHT, + PHONE_LOCATIONS, + PHONE_MESSAGES, + PHONE_SCREEN, + PHONE_WIFI_VISIBLE] PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1, minimum percentage of valid minutes in an hour to be considered valid. SRC_SCRIPT: src/features/phone_data_yield/rapids/main.R +PHONE_ESM: + CONTAINER: esm + PROVIDERS: + STRAW: + COMPUTE: False + SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"] + FEATURES: [mean] + SRC_SCRIPT: src/features/phone_esm/straw/main.py + # See https://www.rapids.science/latest/features/phone-keyboard/ PHONE_KEYBOARD: CONTAINER: keyboard @@ -231,10 +252,10 @@ PHONE_KEYBOARD: # See https://www.rapids.science/latest/features/phone-light/ PHONE_LIGHT: - CONTAINER: light + CONTAINER: light_sensor PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_SCRIPT: src/features/phone_light/rapids/main.py @@ -248,7 +269,7 @@ PHONE_LOCATIONS: PROVIDERS: DORYAB: - COMPUTE: False + COMPUTE: True FEATURES: ["locationvariance","loglocationvariance","totaldistance","avgspeed","varspeed", "numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","avglengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","timeathome", "homelabel"] DBSCAN_EPS: 100 # meters DBSCAN_MINSAMPLES: 5 @@ -263,7 +284,7 @@ PHONE_LOCATIONS: SRC_SCRIPT: src/features/phone_locations/doryab/main.py BARNETT: - COMPUTE: False + COMPUTE: True FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features @@ -278,10 +299,10 @@ PHONE_LOG: # See https://www.rapids.science/latest/features/phone-messages/ PHONE_MESSAGES: - CONTAINER: messages + CONTAINER: sms PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] @@ -293,7 +314,7 @@ PHONE_SCREEN: CONTAINER: screen PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True REFERENCE_HOUR_FIRST_USE: 0 IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable IGNORE_EPISODES_LONGER_THAN: 360 # in minutes, set to 0 to disable @@ -315,12 +336,13 @@ PHONE_WIFI_VISIBLE: CONTAINER: wifi PROVIDERS: RAPIDS: - COMPUTE: False + COMPUTE: True FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R + ######################################################################################################################## # FITBIT # ######################################################################################################################## @@ -484,13 +506,13 @@ EMPATICA_ACCELEROMETER: FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features WINDOWS: COMPUTE: True WINDOW_LENGTH: 15 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'] - STANDARDIZE_FEATURES: False + STANDARDIZE_FEATURES: True SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py @@ -512,14 +534,14 @@ EMPATICA_TEMPERATURE: FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean", "stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"] WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'] - STANDARDIZE_FEATURES: False + STANDARDIZE_FEATURES: True SRC_SCRIPT: src/features/empatica_temperature/cr/main.py # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/ @@ -541,7 +563,7 @@ EMPATICA_ELECTRODERMAL_ACTIVITY: COMPUTE: True WINDOW_LENGTH: 60 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', count_windows, eda_num_peaks_non_zero] - STANDARDIZE_FEATURES: False + STANDARDIZE_FEATURES: True IMPUTE_NANS: False SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py @@ -554,14 +576,14 @@ EMPATICA_BLOOD_VOLUME_PULSE: FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features WINDOWS: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan'] - STANDARDIZE_FEATURES: False + STANDARDIZE_FEATURES: True SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/ @@ -573,7 +595,7 @@ EMPATICA_INTER_BEAT_INTERVAL: FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py CR: - COMPUTE: False + COMPUTE: True FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features 'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features PATCH_WITH_BVP: True @@ -581,7 +603,7 @@ EMPATICA_INTER_BEAT_INTERVAL: COMPUTE: True WINDOW_LENGTH: 300 # specify window length in seconds SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan'] - STANDARDIZE_FEATURES: False + STANDARDIZE_FEATURES: True SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py # See https://www.rapids.science/latest/features/empatica-tags/ diff --git a/data/external/empatica/empatica1/E4 Data.zip b/data/external/empatica/empatica1/E4 Data.zip index ad07905e024983096c31c93e794f6b31da32ba5e..aa26b26f736d77e41af3a1baf5aa268a6b6de605 100644 GIT binary patch delta 889635 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