32 lines
2.3 KiB
Python
32 lines
2.3 KiB
Python
from barnett_library import *
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from statistics import mode
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import warnings
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def barnett_daily_features(snakemake):
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accuracy_limit = 999999999 # We filter rows based on accuracy in src/data/process_location_types.R script
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location_data = pd.read_csv(snakemake.input["sensor_data"])
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features_to_compute = ["local_date", "hometime", "disttravelled", "rog", "maxdiam", "maxhomedist", "siglocsvisited", "avgflightlen", "stdflightlen", "avgflightdur", "stdflightdur", "probpause", "siglocentropy", "minsmissing", "circdnrtn", "wkenddayrtn", "minutes_data_used"]
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location_features = pd.DataFrame(columns=features_to_compute)
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if len(location_data) == 0:
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warnings.warn("Barnett's location features cannot be computed because the input data is empty.")
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else:
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datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
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datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
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segment_regex = ".*#{},{}".format(datetime_start_regex, datetime_end_regex)
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location_data = location_data[location_data["assigned_segments"].str.match(segment_regex)]
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if len(location_data) == 0:
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warnings.warn("Barnett's location features cannot be computed for data or time segments that do not span one or more entire days (00:00:00 to 23:59:59).")
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else:
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location_minutes_used = location_data.groupby(["local_date", "local_hour"])[["local_minute"]].nunique().reset_index().groupby("local_date").sum()[["local_minute"]].rename(columns={"local_minute": "minutes_data_used"})
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timezone = mode(location_data["local_timezone"].values)
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location_df = location_data[["timestamp", "double_latitude", "double_longitude", "double_altitude", "accuracy"]]
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location_df.rename(columns={"double_latitude": "latitude", "double_longitude": "longitude", "double_altitude": "altitude"})
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output_mobility = run_barnett_features_for_rapids(location_df, accuracy_limit=accuracy_limit, timezone=timezone) #make local_date as the index for the output_mobility dataframe
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location_features = output_mobility.merge(location_minutes_used, on="local_date", how="left")
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location_features.reset_index(inplace=True)
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location_features.to_csv(snakemake.output[0], index=False)
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barnett_daily_features(snakemake) |