38 lines
1.7 KiB
Python
38 lines
1.7 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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day_segment = snakemake.params["segment"]
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#Read csv into a pandas dataframe
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data = pd.read_csv(snakemake.input[0])
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columns = ['count','most_common_activity','count_unique_activities','activity_change_count']
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columns = list("ar_" + str(day_segment) + "_" + column for column in columns)
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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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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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count = resampledData['activity_type'].resample('D').count()
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#Finding most common activity of the day
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mostCommonActivity = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
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#finding different number of activities during a day
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uniqueActivities = resampledData['activity_type'].resample('D').nunique()
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#finding Number of times activity changed
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resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1),inplace=True)
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resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
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countChanges = resampledData['different_activity'].resample('D').sum()
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finalDataset = pd.concat([count, mostCommonActivity, uniqueActivities, countChanges],axis=1)
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finalDataset.index.names = ['local_date']
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finalDataset.columns=columns
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finalDataset.to_csv(snakemake.output[0]) |