105 lines
5.5 KiB
Python
105 lines
5.5 KiB
Python
import pandas as pd
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import numpy as np
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import datetime as dt
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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day_segment = snakemake.params["day_segment"]
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all_steps = snakemake.params["metrics_all_steps"]
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sedentary_bout = snakemake.params["metrics_sedentary_bout"]
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active_bout = snakemake.params["metrics_active_bout"]
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threshold_active_bout = snakemake.params['threshold_active_bout']
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include_zero_step_rows = snakemake.params["include_zero_step_rows"]
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#Read csv into a pandas dataframe
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data = pd.read_csv(snakemake.input['steps_data'],parse_dates=['local_date_time'])
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columns = list("step_" + str(day_segment) + "_" + column for column in (all_steps + sedentary_bout + active_bout))
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if (day_segment != 'daily'):
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data = data.loc[data['local_day_segment'] == str(day_segment)]
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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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#Preprocessing:
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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.index.names = ['datetime']
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resampledData['time_diff_minutes'] = resampledData['local_date_time'].diff().fillna(pd.Timedelta(seconds=0)).dt.total_seconds().div(60).astype(int)
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#Sedentary Bout when you have less than 10 steps in a minute
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#Active Bout when you have greater or equal to 10 steps in a minute
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resampledData['active_sedentary'] = np.where(resampledData['steps']<int(threshold_active_bout),'sedentary','active')
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activeData = resampledData[resampledData['active_sedentary']=="active"]
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sedentaryData = resampledData[resampledData['active_sedentary']=="sedentary"]
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#Time Calculations of sedentary/active bouts:
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resampledData['active_sedentary_groups'] = (resampledData.active_sedentary != resampledData.active_sedentary.shift()).cumsum().values
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#Get the total minutes for each episode
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minutesGroupedBy = resampledData.groupby(['local_date','active_sedentary','active_sedentary_groups'])['time_diff_minutes'].sum()
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#Get Stats for all episodes in terms of minutes
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statsMinutes = minutesGroupedBy.groupby(['local_date','active_sedentary']).agg([max,min,np.mean,np.std])
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mux = pd.MultiIndex.from_product([statsMinutes.index.levels[0], statsMinutes.index.levels[1]],names=['local_date','active_sedentary'])
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statsMinutes = statsMinutes.reindex(mux, fill_value=None).reset_index()
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statsMinutes.set_index('local_date',inplace = True)
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#Descriptive Statistics Features:
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if("sumallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_sumallsteps"] = resampledData['steps'].resample('D').sum()
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if("maxallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_maxallsteps"] = resampledData['steps'].resample('D').max()
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if("minallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_minallsteps"] = resampledData['steps'].resample('D').min()
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if("avgallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_avgallsteps"] = resampledData['steps'].resample('D').mean()
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if("stdallsteps" in all_steps):
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finalDataset["step_" + str(day_segment) + "_stdallsteps"] = resampledData['steps'].resample('D').std()
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if("countsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_countsedentarybout"] = sedentaryData['active_sedentary'].resample('D').count()
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if("countactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_countactivebout"] = activeData['active_sedentary'].resample('D').count()
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if("maxdurationsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_maxdurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['max']
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if("mindurationsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_mindurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['min']
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if("avgdurationsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_avgdurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['mean']
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if("stddurationsedentarybout" in sedentary_bout):
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finalDataset["step_" + str(day_segment) + "_stddurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['std']
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if("maxdurationactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_maxdurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['max']
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if("mindurationactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_mindurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['min']
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if("avgdurationactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_avgdurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['mean']
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if("stddurationactivebout" in active_bout):
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finalDataset["step_" + str(day_segment) + "_stddurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['std']
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#Exclude data when the total step count is ZERO during the whole epoch
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if not include_zero_step_rows:
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finalDataset["sumallsteps_aux"] = resampledData["steps"].resample("D").sum()
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finalDataset = finalDataset.query("sumallsteps_aux != 0")
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del finalDataset["sumallsteps_aux"]
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finalDataset.index.names = ['local_date']
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finalDataset.to_csv(snakemake.output[0])
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