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chore(outputs): concat enmo and steps output files
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@chanshing
chanshing committed Apr 26, 2024
1 parent 4baa3f4 commit 55b5f16
Showing 1 changed file with 84 additions and 52 deletions.
136 changes: 84 additions & 52 deletions src/stepcount/stepcount.py
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Original file line number Diff line number Diff line change
Expand Up @@ -88,82 +88,114 @@ def main():
# ENMO summary
enmo_summary = summarize_enmo(data)
info['ENMO(mg)'] = enmo_summary['avg']
enmo_summary['minutely'].to_csv(f"{outdir}/{basename}-MinutelyENMO.csv.gz")
enmo_summary['hourly'].to_csv(f"{outdir}/{basename}-HourlyENMO.csv.gz")
enmo_summary['daily'].to_csv(f"{outdir}/{basename}-DailyENMO.csv.gz")

# ENMO summary, adjusted
enmo_summary_adj = summarize_enmo(data, adjust_estimates=True)
info['ENMOAdjusted(mg)'] = enmo_summary_adj['avg']
enmo_summary_adj['minutely'].to_csv(f"{outdir}/{basename}-MinutelyENMOAdjusted.csv.gz")
enmo_summary_adj['hourly'].to_csv(f"{outdir}/{basename}-HourlyENMOAdjusted.csv.gz")
enmo_summary_adj['daily'].to_csv(f"{outdir}/{basename}-DailyENMOAdjusted.csv.gz")

# Steps summary
summary = summarize_steps(Y, model.steptol)
summary['minutely'].to_csv(f"{outdir}/{basename}-MinutelySteps.csv.gz")
summary['hourly'].to_csv(f"{outdir}/{basename}-HourlySteps.csv.gz")
info['TotalSteps'] = summary['total']
info['StepsDayAvg'] = summary['daily_avg']
info['StepsDayMed'] = summary['daily_med']
info['StepsDayMin'] = summary['daily_min']
info['StepsDayMax'] = summary['daily_max']
info['TotalWalking(mins)'] = summary['total_walk']
info['WalkingDayAvg(mins)'] = summary['daily_walk_avg']
info['WalkingDayMed(mins)'] = summary['daily_walk_med']
info['WalkingDayMin(mins)'] = summary['daily_walk_min']
info['WalkingDayMax(mins)'] = summary['daily_walk_max']
info['Steps5thAt'] = summary['daily_ptile_at_avg']['p05_at']
info['Steps25thAt'] = summary['daily_ptile_at_avg']['p25_at']
info['Steps50thAt'] = summary['daily_ptile_at_avg']['p50_at']
info['Steps75thAt'] = summary['daily_ptile_at_avg']['p75_at']
info['Steps95thAt'] = summary['daily_ptile_at_avg']['p95_at']
steps_summary = summarize_steps(Y, model.steptol)
info['TotalSteps'] = steps_summary['total']
info['StepsDayAvg'] = steps_summary['daily_avg']
info['StepsDayMed'] = steps_summary['daily_med']
info['StepsDayMin'] = steps_summary['daily_min']
info['StepsDayMax'] = steps_summary['daily_max']
info['TotalWalking(mins)'] = steps_summary['total_walk']
info['WalkingDayAvg(mins)'] = steps_summary['daily_walk_avg']
info['WalkingDayMed(mins)'] = steps_summary['daily_walk_med']
info['WalkingDayMin(mins)'] = steps_summary['daily_walk_min']
info['WalkingDayMax(mins)'] = steps_summary['daily_walk_max']
info['Steps5thAt'] = steps_summary['daily_ptile_at_avg']['p05_at']
info['Steps25thAt'] = steps_summary['daily_ptile_at_avg']['p25_at']
info['Steps50thAt'] = steps_summary['daily_ptile_at_avg']['p50_at']
info['Steps75thAt'] = steps_summary['daily_ptile_at_avg']['p75_at']
info['Steps95thAt'] = steps_summary['daily_ptile_at_avg']['p95_at']

# Steps summary, adjusted
summary_adj = summarize_steps(Y, model.steptol, adjust_estimates=True)
summary_adj['minutely'].to_csv(f"{outdir}/{basename}-MinutelyStepsAdjusted.csv.gz")
summary_adj['hourly'].to_csv(f"{outdir}/{basename}-HourlyStepsAdjusted.csv.gz")
info['TotalStepsAdjusted'] = summary_adj['total']
info['StepsDayAvgAdjusted'] = summary_adj['daily_avg']
info['StepsDayMedAdjusted'] = summary_adj['daily_med']
info['StepsDayMinAdjusted'] = summary_adj['daily_min']
info['StepsDayMaxAdjusted'] = summary_adj['daily_max']
info['TotalWalkingAdjusted(mins)'] = summary_adj['total_walk']
info['WalkingDayAvgAdjusted(mins)'] = summary_adj['daily_walk_avg']
info['WalkingDayMedAdjusted(mins)'] = summary_adj['daily_walk_med']
info['WalkingDayMinAdjusted(mins)'] = summary_adj['daily_walk_min']
info['WalkingDayMaxAdjusted(mins)'] = summary_adj['daily_walk_max']
info['Steps5thAtAdjusted'] = summary_adj['daily_ptile_at_avg']['p05_at']
info['Steps25thAtAdjusted'] = summary_adj['daily_ptile_at_avg']['p25_at']
info['Steps50thAtAdjusted'] = summary_adj['daily_ptile_at_avg']['p50_at']
info['Steps75thAtAdjusted'] = summary_adj['daily_ptile_at_avg']['p75_at']
info['Steps95thAtAdjusted'] = summary_adj['daily_ptile_at_avg']['p95_at']

steps_summary_adj = summarize_steps(Y, model.steptol, adjust_estimates=True)
info['TotalStepsAdjusted'] = steps_summary_adj['total']
info['StepsDayAvgAdjusted'] = steps_summary_adj['daily_avg']
info['StepsDayMedAdjusted'] = steps_summary_adj['daily_med']
info['StepsDayMinAdjusted'] = steps_summary_adj['daily_min']
info['StepsDayMaxAdjusted'] = steps_summary_adj['daily_max']
info['TotalWalkingAdjusted(mins)'] = steps_summary_adj['total_walk']
info['WalkingDayAvgAdjusted(mins)'] = steps_summary_adj['daily_walk_avg']
info['WalkingDayMedAdjusted(mins)'] = steps_summary_adj['daily_walk_med']
info['WalkingDayMinAdjusted(mins)'] = steps_summary_adj['daily_walk_min']
info['WalkingDayMaxAdjusted(mins)'] = steps_summary_adj['daily_walk_max']
info['Steps5thAtAdjusted'] = steps_summary_adj['daily_ptile_at_avg']['p05_at']
info['Steps25thAtAdjusted'] = steps_summary_adj['daily_ptile_at_avg']['p25_at']
info['Steps50thAtAdjusted'] = steps_summary_adj['daily_ptile_at_avg']['p50_at']
info['Steps75thAtAdjusted'] = steps_summary_adj['daily_ptile_at_avg']['p75_at']
info['Steps95thAtAdjusted'] = steps_summary_adj['daily_ptile_at_avg']['p95_at']

# Cadence summary
cadence_summary = summary_cadence(Y, model.steptol)
info['CadencePeak1(steps/min)'] = cadence_summary['cadence_peak1']
info['CadencePeak30(steps/min)'] = cadence_summary['cadence_peak30']
info['Cadence95th(steps/min)'] = cadence_summary['cadence_p95']
summary['daily'] = pd.concat([summary['daily'], cadence_summary['daily_cadence']], axis=1)
summary['daily'].to_csv(f"{outdir}/{basename}-DailySteps.csv.gz")

# Cadence summary, adjusted
cadence_summary_adj = summary_cadence(Y, model.steptol, adjust_estimates=True)
info['CadencePeak1Adjusted(steps/min)'] = cadence_summary_adj['cadence_peak1']
info['CadencePeak30Adjusted(steps/min)'] = cadence_summary_adj['cadence_peak30']
info['Cadence95thAdjusted(steps/min)'] = cadence_summary_adj['cadence_p95']
summary_adj['daily'] = pd.concat([summary_adj['daily'], cadence_summary_adj['daily_cadence']], axis=1)
summary_adj['daily'].to_csv(f"{outdir}/{basename}-DailyStepsAdjusted.csv.gz")

# Save info
# Save Info.json
with open(f"{outdir}/{basename}-Info.json", 'w') as f:
json.dump(info, f, indent=4, cls=NpEncoder)

hourly = pd.concat([
steps_summary['hourly'],
enmo_summary['hourly'],
], axis=1)
hourly.to_csv(f"{outdir}/{basename}-Hourly.csv.gz")
del hourly # free memory

hourly_adj = pd.concat([
steps_summary_adj['hourly'],
enmo_summary_adj['hourly'],
], axis=1)
hourly_adj.to_csv(f"{outdir}/{basename}-HourlyAdjusted.csv.gz")
del hourly_adj # free memory

minutely = pd.concat([
steps_summary['minutely'],
enmo_summary['minutely'],
], axis=1)
minutely.to_csv(f"{outdir}/{basename}-Minutely.csv.gz")
del minutely # free memory

minutely_adj = pd.concat([
steps_summary_adj['minutely'],
enmo_summary_adj['minutely'],
], axis=1)
minutely_adj.to_csv(f"{outdir}/{basename}-MinutelyAdjusted.csv.gz")
del minutely_adj # free memory

daily = pd.concat([
steps_summary['daily'],
cadence_summary['daily'],
enmo_summary['daily'],
], axis=1)
daily.to_csv(f"{outdir}/{basename}-Daily.csv.gz")
# del daily # still needed for printing

daily_adj = pd.concat([
steps_summary_adj['daily'],
cadence_summary_adj['daily'],
enmo_summary_adj['daily'],
], axis=1)
daily_adj.to_csv(f"{outdir}/{basename}-DailyAdjusted.csv.gz")
# del daily_adj # still needed for printing

# Print
print("\nSummary\n-------")
print(json.dumps(info, indent=4, cls=NpEncoder))
print("\nEstimated Daily Stats\n---------------------")
print(summary['daily'])
print(daily)
print("\nEstimated Daily Stats (Adjusted)\n---------------------")
print(summary_adj['daily'])
print(daily_adj)

after = time.time()
print(f"Done! ({round(after - before,2)}s)")
Expand Down Expand Up @@ -429,14 +461,14 @@ def na_to_median(x):
cadence_peak30 = np.round(daily_cadence_peak30.median())
cadence_p95 = np.round(daily_cadence_p95.median())

daily_cadence = pd.concat([
daily = pd.concat([
daily_cadence_peak1.round().astype(pd.Int64Dtype()),
daily_cadence_peak30.round().astype(pd.Int64Dtype()),
daily_cadence_p95.round().astype(pd.Int64Dtype()),
], axis=1)

return {
'daily_cadence': daily_cadence,
'daily': daily,
'cadence_peak1': nanint(cadence_peak1),
'cadence_peak30': nanint(cadence_peak30),
'cadence_p95': nanint(cadence_p95),
Expand Down

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