293 lines
14 KiB
Python
293 lines
14 KiB
Python
rule restore_sql_file:
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input:
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sql_file = "data/external/rapids_example.sql",
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db_credentials = ".env"
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params:
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group = config["DATABASE_GROUP"]
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output:
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touch("data/interim/restore_sql_file.done")
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script:
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"../src/data/restore_sql_file.py"
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rule create_example_participant_files:
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output:
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expand("data/external/participant_files/{pid}.yaml", pid = ["example01", "example02"])
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shell:
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"echo 'PHONE:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n PLATFORMS: [android]\n LABEL: test-01\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\nFITBIT:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n LABEL: test-01\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\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\n END_DATE: 2020-05-04\nFITBIT:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n LABEL: test-02\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\n' >> ./data/external/participant_files/example02.yaml"
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rule create_participants_files:
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input:
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participants_file = [] if config["CREATE_PARTICIPANT_FILES"]["SOURCE"]["TYPE"] == "AWARE_DEVICE_TABLE" else config["CREATE_PARTICIPANT_FILES"]["SOURCE"]["CSV_FILE_PATH"]
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params:
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config = config["CREATE_PARTICIPANT_FILES"]
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script:
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"../src/data/create_participants_files.R"
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rule download_phone_data:
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input:
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"data/external/participant_files/{pid}.yaml"
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params:
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source = config["PHONE_DATA_CONFIGURATION"]["SOURCE"],
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sensor = "phone_" + "{sensor}",
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table = lambda wildcards: config["PHONE_" + str(wildcards.sensor).upper()]["TABLE"],
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timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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aware_multiplatform_tables = config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["ANDROID"] + "," + config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["IOS"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["ANDROID"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["IOS"],
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output:
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"data/raw/{pid}/phone_{sensor}_raw.csv"
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script:
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"../src/data/download_phone_data.R"
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rule download_fitbit_data:
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input:
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participant_file = "data/external/participant_files/{pid}.yaml",
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input_file = [] if config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"]
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params:
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data_configuration = config["FITBIT_DATA_CONFIGURATION"],
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sensor = "fitbit_" + "{sensor}",
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table = lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"],
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output:
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"data/raw/{pid}/fitbit_{sensor}_raw.csv"
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script:
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"../src/data/download_fitbit_data.R"
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rule compute_time_segments:
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input:
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config["TIME_SEGMENTS"]["FILE"],
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"data/external/participant_files/{pid}.yaml"
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params:
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time_segments_type = config["TIME_SEGMENTS"]["TYPE"],
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pid = "{pid}"
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output:
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segments_file = "data/interim/time_segments/{pid}_time_segments.csv",
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segments_labels_file = "data/interim/time_segments/{pid}_time_segments_labels.csv",
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script:
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"../src/data/compute_time_segments.py"
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rule phone_readable_datetime:
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input:
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sensor_input = "data/raw/{pid}/phone_{sensor}_raw.csv",
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time_segments = "data/interim/time_segments/{pid}_time_segments.csv"
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params:
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timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"],
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fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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time_segments_type = config["TIME_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
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output:
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"data/raw/{pid}/phone_{sensor}_with_datetime.csv"
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script:
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"../src/data/readable_datetime.R"
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rule phone_yielded_timestamps:
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input:
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all_sensors = expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor = map(str.lower, config["PHONE_DATA_YIELD"]["SENSORS"]))
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params:
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sensors = config["PHONE_DATA_YIELD"]["SENSORS"] # not used but needed so the rule is triggered if this array changes
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output:
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"data/interim/{pid}/phone_yielded_timestamps.csv"
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script:
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"../src/data/phone_yielded_timestamps.R"
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rule phone_yielded_timestamps_with_datetime:
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input:
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sensor_input = "data/interim/{pid}/phone_yielded_timestamps.csv",
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time_segments = "data/interim/time_segments/{pid}_time_segments.csv"
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params:
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timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"],
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fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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time_segments_type = config["TIME_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
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output:
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"data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv"
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script:
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"../src/data/readable_datetime.R"
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rule unify_ios_android:
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input:
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sensor_data = "data/raw/{pid}/{sensor}_with_datetime.csv",
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participant_info = "data/external/participant_files/{pid}.yaml"
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params:
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sensor = "{sensor}",
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output:
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"data/raw/{pid}/{sensor}_with_datetime_unified.csv"
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script:
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"../src/data/unify_ios_android.R"
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rule process_phone_locations_types:
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input:
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locations = "data/raw/{pid}/phone_locations_raw.csv",
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phone_sensed_timestamps = "data/interim/{pid}/phone_yielded_timestamps.csv",
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params:
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consecutive_threshold = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD"],
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time_since_valid_location = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION"],
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locations_to_use = config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"]
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output:
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"data/interim/{pid}/phone_locations_processed.csv"
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script:
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"../src/data/process_location_types.R"
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rule phone_locations_processed_with_datetime:
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input:
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sensor_input = "data/interim/{pid}/phone_locations_processed.csv",
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time_segments = "data/interim/time_segments/{pid}_time_segments.csv"
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params:
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timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"],
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fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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time_segments_type = config["TIME_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
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output:
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"data/interim/{pid}/phone_locations_processed_with_datetime.csv"
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script:
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"../src/data/readable_datetime.R"
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rule resample_episodes:
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input:
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"data/interim/{pid}/{sensor}_episodes.csv"
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output:
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"data/interim/{pid}/{sensor}_episodes_resampled.csv"
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script:
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"../src/features/utils/resample_episodes.R"
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rule resample_episodes_with_datetime:
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input:
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sensor_input = "data/interim/{pid}/{sensor}_episodes_resampled.csv",
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time_segments = "data/interim/time_segments/{pid}_time_segments.csv"
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params:
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timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"],
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fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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time_segments_type = config["TIME_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
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output:
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"data/interim/{pid}/{sensor}_episodes_resampled_with_datetime.csv"
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script:
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"../src/data/readable_datetime.R"
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rule phone_application_categories:
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input:
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"data/raw/{pid}/phone_applications_{type}_with_datetime.csv"
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params:
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catalogue_source = lambda wildcards: config["PHONE_APPLICATIONS_" + str(wildcards.type).upper()]["APPLICATION_CATEGORIES"]["CATALOGUE_SOURCE"],
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catalogue_file = lambda wildcards: config["PHONE_APPLICATIONS_" + str(wildcards.type).upper()]["APPLICATION_CATEGORIES"]["CATALOGUE_FILE"],
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update_catalogue_file = lambda wildcards: config["PHONE_APPLICATIONS_" + str(wildcards.type).upper()]["APPLICATION_CATEGORIES"]["UPDATE_CATALOGUE_FILE"],
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scrape_missing_genres = lambda wildcards: config["PHONE_APPLICATIONS_" + str(wildcards.type).upper()]["APPLICATION_CATEGORIES"]["SCRAPE_MISSING_CATEGORIES"]
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output:
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"data/raw/{pid}/phone_applications_{type}_with_datetime_with_categories.csv"
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script:
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"../src/data/application_categories.R"
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rule fitbit_parse_heartrate:
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input:
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participant_file = "data/external/participant_files/{pid}.yaml",
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raw_data = "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv"
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params:
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timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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table = lambda wildcards: config["FITBIT_HEARTRATE_"+str(wildcards.fitbit_data_type).upper()]["TABLE"],
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column_format = config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["COLUMN_FORMAT"],
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fitbit_data_type = "{fitbit_data_type}"
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output:
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"data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv"
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script:
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"../src/data/fitbit_parse_heartrate.py"
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rule fitbit_parse_steps:
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input:
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participant_file = "data/external/participant_files/{pid}.yaml",
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raw_data = "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv"
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params:
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timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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table = lambda wildcards: config["FITBIT_STEPS_"+str(wildcards.fitbit_data_type).upper()]["TABLE"],
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column_format = config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["COLUMN_FORMAT"],
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fitbit_data_type = "{fitbit_data_type}"
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output:
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"data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv"
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script:
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"../src/data/fitbit_parse_steps.py"
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rule fitbit_parse_sleep:
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input:
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participant_file = "data/external/participant_files/{pid}.yaml",
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raw_data = "data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv"
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params:
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timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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table = lambda wildcards: config["FITBIT_SLEEP_"+str(wildcards.fitbit_data_type).upper()]["TABLE"],
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column_format = config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["COLUMN_FORMAT"],
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fitbit_data_type = "{fitbit_data_type}",
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sleep_episode_timestamp = config["FITBIT_SLEEP_SUMMARY"]["SLEEP_EPISODE_TIMESTAMP"]
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output:
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"data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed.csv"
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script:
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"../src/data/fitbit_parse_sleep.py"
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# rule fitbit_parse_calories:
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# input:
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# data = expand("data/raw/{{pid}}/fitbit_calories_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))
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# params:
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# timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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# table = config["FITBIT_CALORIES"]["TABLE"],
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# table_format = config["FITBIT_CALORIES"]["TABLE_FORMAT"]
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# output:
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# summary_data = "data/raw/{pid}/fitbit_calories_summary_parsed.csv",
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# intraday_data = "data/raw/{pid}/fitbit_calories_intraday_parsed.csv"
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# script:
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# "../src/data/fitbit_parse_calories.py"
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rule fitbit_readable_datetime:
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input:
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sensor_input = "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed.csv",
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time_segments = "data/interim/time_segments/{pid}_time_segments.csv"
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params:
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fixed_timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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time_segments_type = config["TIME_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
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output:
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"data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed_with_datetime.csv"
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script:
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"../src/data/readable_datetime.R"
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from pathlib import Path
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rule unzip_empatica_data:
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input:
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input_file = Path(config["EMPATICA_DATA_CONFIGURATION"]["SOURCE"]["FOLDER"]) / Path("{pid}") / Path("{suffix}.zip"),
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participant_file = "data/external/participant_files/{pid}.yaml"
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params:
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sensor = "{sensor}"
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output:
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sensor_output = "data/raw/{pid}/empatica_{sensor}_unzipped_{suffix}.csv"
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script:
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"../src/data/empatica/unzip_empatica_data.py"
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rule extract_empatica_data:
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input:
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input_file = "data/raw/{pid}/empatica_{sensor}_unzipped_{suffix}.csv",
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participant_file = "data/external/participant_files/{pid}.yaml"
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params:
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data_configuration = config["EMPATICA_DATA_CONFIGURATION"],
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sensor = "{sensor}",
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table = lambda wildcards: config["EMPATICA_" + str(wildcards.sensor).upper()]["TABLE"],
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output:
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sensor_output = "data/raw/{pid}/empatica_{sensor}_raw_{suffix}.csv"
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script:
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"../src/data/empatica/extract_empatica_data.py"
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rule join_empatica_data:
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input:
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input_files = get_all_raw_empatica_sensor_files,
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output:
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sensor_output = "data/raw/{pid}/empatica_{sensor}_joined.csv"
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script:
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"../src/data/empatica/join_empatica_data.R"
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rule empatica_readable_datetime:
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input:
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sensor_input = "data/raw/{pid}/empatica_{sensor}_joined.csv",
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time_segments = "data/interim/time_segments/{pid}_time_segments.csv"
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params:
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timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"],
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fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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time_segments_type = config["TIME_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
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output:
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"data/raw/{pid}/empatica_{sensor}_with_datetime.csv"
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script:
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"../src/data/readable_datetime.R"
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