63 lines
3.1 KiB
Python
63 lines
3.1 KiB
Python
import pandas as pd
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import numpy as np
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import scipy.stats as stats
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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day_segment = snakemake.params["segment"]
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features = snakemake.params["features"]
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#Read csv into a pandas dataframe
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data = pd.read_csv(snakemake.input['gar_events'],parse_dates=['local_date_time'])
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ar_deltas = pd.read_csv(snakemake.input['gar_deltas'],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
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columns = list("ar_" + str(day_segment) + "_" + column for column in features)
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if data.empty:
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finalDataset = pd.DataFrame(columns = columns)
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else:
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finalDataset = pd.DataFrame()
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ar_deltas = splitOvernightEpisodes(ar_deltas, [],['activity'])
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if day_segment != "daily":
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ar_deltas = splitMultiSegmentEpisodes(ar_deltas, day_segment, [])
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data.local_date_time = pd.to_datetime(data.local_date_time)
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resampledData = data.set_index(data.local_date_time)
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resampledData.drop(columns=['local_date_time'],inplace=True)
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if(day_segment!='daily'):
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resampledData = resampledData.loc[resampledData['local_day_segment'] == str(day_segment)]
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if resampledData.empty:
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finalDataset = pd.DataFrame(columns = columns)
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else:
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#Finding the count of samples of the day
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if("count" in features):
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finalDataset["ar_" + str(day_segment) + "_count"] = resampledData['activity_type'].resample('D').count()
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#Finding most common activity of the day
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if("mostcommonactivity" in features):
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finalDataset["ar_" + str(day_segment) + "_mostcommonactivity"] = resampledData['activity_type'].resample('D').apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None)
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#finding different number of activities during a day
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if("countuniqueactivities" in features):
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finalDataset["ar_" + str(day_segment) + "_countuniqueactivities"] = resampledData['activity_type'].resample('D').nunique()
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#finding Number of times activity changed
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if("activitychangecount" in features):
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resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1))
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resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
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finalDataset["ar_" + str(day_segment) + "_activitychangecount"] = resampledData['different_activity'].resample('D').sum()
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deltas_features = {'sumstationary':['still','tilting'],
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'summobile':['on_foot','running','on_bicycle'],
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'sumvehicle':['in_vehicle']}
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for column, activity_labels in deltas_features.items():
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if column in features:
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finalDataset["ar_" + str(day_segment) + "_"+str(column)] = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))]
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.groupby(['local_start_date'])['time_diff']
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.agg({"ar_" + str(day_segment) + "_" + str(column) :'sum'}))
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finalDataset.index.names = ['local_date']
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finalDataset.to_csv(snakemake.output[0]) |