236 lines
11 KiB
Python
236 lines
11 KiB
Python
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 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"
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rule prepare_participants_csv:
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input:
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username_list = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"]
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params:
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data_configuration = config["PHONE_DATA_STREAMS"][config["PHONE_DATA_STREAMS"]["USE"]],
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participants_table = "participants",
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device_id_table = "light_sensor",
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start_end_date_table = "esm"
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output:
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participants_file = config["CREATE_PARTICIPANT_FILES"]["CSV_FILE_PATH"]
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script:
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"../src/data/streams/translate_usernames_into_participants_data.R"
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rule create_participants_files:
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input:
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participants_file = config["CREATE_PARTICIPANT_FILES"]["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 pull_phone_data:
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input: unpack(pull_phone_data_input_with_mutation_scripts)
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params:
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data_configuration = config["PHONE_DATA_STREAMS"][config["PHONE_DATA_STREAMS"]["USE"]],
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sensor = "phone_" + "{sensor}",
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tables = lambda wildcards: config["PHONE_" + str(wildcards.sensor).upper()]["CONTAINER"],
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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/streams/pull_phone_data.R"
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rule process_time_segments:
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input:
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segments_file = config["TIME_SEGMENTS"]["FILE"],
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participant_file = "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/datetime/process_time_segments.R"
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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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pid_file = "data/external/participant_files/{pid}.yaml",
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tzcodes_file = input_tzcodes_file,
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params:
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device_type = "phone",
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timezone_parameters = config["TIMEZONE"],
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pid = "{pid}",
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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/datetime/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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pid_file = "data/external/participant_files/{pid}.yaml",
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tzcodes_file = input_tzcodes_file,
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params:
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device_type = "phone",
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timezone_parameters = config["TIMEZONE"],
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pid = "{pid}",
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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/datetime/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 = optional_phone_yield_input_for_locations,
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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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accuracy_limit = config["PHONE_LOCATIONS"]["ACCURACY_LIMIT"]
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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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pid_file = "data/external/participant_files/{pid}.yaml",
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tzcodes_file = input_tzcodes_file,
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params:
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device_type = "phone",
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timezone_parameters = config["TIMEZONE"],
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pid = "{pid}",
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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/datetime/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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pid_file = "data/external/participant_files/{pid}.yaml",
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tzcodes_file = input_tzcodes_file,
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params:
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device_type = lambda wildcards: wildcards.sensor.split("_")[0],
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timezone_parameters = config["TIMEZONE"],
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pid = "{pid}",
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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/datetime/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 pull_wearable_data:
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input: unpack(pull_wearable_data_input_with_mutation_scripts)
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params:
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data_configuration = lambda wildcards: config[wildcards.device_type.upper() +"_DATA_STREAMS"][config[wildcards.device_type.upper() +"_DATA_STREAMS"]["USE"]],
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device_type = "{device_type}",
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sensor = "{device_type}" + "_" + "{sensor}",
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pid = "{pid}",
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tables = lambda wildcards: config[wildcards.device_type.upper() + "_" + str(wildcards.sensor).upper()]["CONTAINER"],
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wildcard_constraints:
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device_type="(empatica|fitbit)"
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output:
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"data/raw/{pid}/{device_type}_{sensor}_raw.csv"
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script:
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"../src/data/streams/pull_wearable_data.R"
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rule fitbit_readable_datetime:
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input:
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sensor_input = "data/raw/{pid}/fitbit_{sensor}_raw.csv",
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time_segments = "data/interim/time_segments/{pid}_time_segments.csv",
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pid_file = "data/external/participant_files/{pid}.yaml",
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tzcodes_file = input_tzcodes_file,
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params:
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device_type = "fitbit",
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timezone_parameters = config["TIMEZONE"],
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pid = "{pid}",
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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}_with_datetime.csv"
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script:
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"../src/data/datetime/readable_datetime.R"
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rule fitbit_steps_intraday_exclude_sleep:
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input:
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sensor_data = "data/raw/{pid}/fitbit_steps_intraday_with_datetime.csv",
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sleep_data = optional_steps_sleep_input
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params:
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exclude_sleep = config["FITBIT_STEPS_INTRADAY"]["EXCLUDE_SLEEP"]
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output:
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"data/interim/{pid}/fitbit_steps_intraday_with_datetime_exclude_sleep.csv"
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script:
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"../src/data/fitbit_steps_intraday_exclude_sleep.py"
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rule empatica_readable_datetime:
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input:
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sensor_input = "data/raw/{pid}/empatica_{sensor}_raw.csv",
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time_segments = "data/interim/time_segments/{pid}_time_segments.csv",
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pid_file = "data/external/participant_files/{pid}.yaml",
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tzcodes_file = input_tzcodes_file,
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params:
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device_type = "empatica",
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timezone_parameters = config["TIMEZONE"],
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pid = "{pid}",
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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/datetime/readable_datetime.R"
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