245 lines
11 KiB
Python
245 lines
11 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/{pid}", pid = ["example01", "example02"])
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shell:
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"echo 'a748ee1a-1d0b-4ae9-9074-279a2b6ba524\nandroid\ntest01\n2020/04/23,2020/05/04\n' >> ./data/external/example01 && echo '13dbc8a3-dae3-4834-823a-4bc96a7d459d\nios\ntest02\n2020/04/23,2020/05/04\n' >> ./data/external/example02"
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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_day_segments:
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input:
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config["DAY_SEGMENTS"]["FILE"],
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"data/external/participant_files/{pid}.yaml"
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params:
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day_segments_type = config["DAY_SEGMENTS"]["TYPE"],
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pid = "{pid}"
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output:
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segments_file = "data/interim/day_segments/{pid}_day_segments.csv",
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segments_labels_file = "data/interim/day_segments/{pid}_day_segments_labels.csv",
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script:
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"../src/data/compute_day_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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day_segments = "data/interim/day_segments/{pid}_day_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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day_segments_type = config["DAY_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["DAY_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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day_segments = "data/interim/day_segments/{pid}_day_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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day_segments_type = config["DAY_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["DAY_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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day_segments = "data/interim/day_segments/{pid}_day_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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day_segments_type = config["DAY_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["DAY_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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day_segments = "data/interim/day_segments/{pid}_day_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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day_segments_type = config["DAY_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["DAY_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_foreground_with_datetime.csv"
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params:
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catalogue_source = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["CATALOGUE_SOURCE"],
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catalogue_file = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["CATALOGUE_FILE"],
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update_catalogue_file = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["UPDATE_CATALOGUE_FILE"],
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scrape_missing_genres = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["SCRAPE_MISSING_CATEGORIES"]
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output:
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"data/raw/{pid}/phone_applications_foreground_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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day_segments = "data/interim/day_segments/{pid}_day_segments.csv"
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params:
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fixed_timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"],
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day_segments_type = config["DAY_SEGMENTS"]["TYPE"],
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include_past_periodic_segments = config["DAY_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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