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