import pandas as pd
import numpy as np
import scipy.stats as stats

#Read csv into a pandas dataframe
data = pd.read_csv(snakemake.input[0])


#Resampling each of the required features as a pandas series
data.local_date_time = pd.to_datetime(data.local_date_time)
resampledData = data.set_index(data.local_date_time)

resampledData = resampledData[~resampledData.index.duplicated()]
resampledData.rename_axis('time',axis='columns',inplace=True)

resampledData.drop(columns=['local_date_time'],inplace=True)

#Finding count grouped by day
count = pd.DataFrame()
count = resampledData['activity_type'].resample('D').count()
count = count.rename(columns={"activity_type":"count"})

#Finding most common activity of the day
mostCommonActivity = pd.DataFrame()
mostCommonActivity = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
mostCommonActivity = mostCommonActivity.rename(columns={'activity_type':'most_common_activity'})

#finding different number of activities during a day
countChanges = pd.DataFrame()
# countChanges = resampledData.to_period('D').groupby(resampledData.index)['activity_type'].value_counts()
countChanges = resampledData['activity_type'].resample('D').nunique()

#Concatenating all the processed data only, no other sensor data is added here for simplicity
finalDataset = pd.DataFrame()
finalDataset = pd.concat([count,mostCommonActivity,countChanges],axis=1)
finalDataset.rename(columns={0:"count",1:'most_common_activity','activity_type':'activity_changes_count'},inplace = True)

#Export final dataframe with extracted features to respective PID
finalDataset.to_csv(snakemake.output[0])