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refactor(summarize_cadence): use new "true cadence" calculation for the cadence metrics #142

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refactor(summarize_cadence): use new "true cadence" calculation for the cadence metrics #142

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76 changes: 53 additions & 23 deletions src/stepcount/stepcount.py
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Original file line number Diff line number Diff line change
Expand Up @@ -263,7 +263,7 @@ def main():
info.update({f'WalkingAdjusted(mins)_Hour{h:02}_Weekday': steps_summary_adj['weekday_hour_walks'].loc[h] for h in range(24)})

# Cadence summary
cadence_summary = summarize_cadence(Y, model.steptol)
cadence_summary = summarize_cadence(T_steps)
# overall stats
info['CadencePeak1(steps/min)'] = cadence_summary['cadence_peak1']
info['CadencePeak30(steps/min)'] = cadence_summary['cadence_peak30']
Expand All @@ -278,7 +278,7 @@ def main():
info['Cadence95th(steps/min)_Weekday'] = cadence_summary['weekday_cadence_p95']

# Cadence summary, adjusted
cadence_summary_adj = summarize_cadence(Y, model.steptol, adjust_estimates=True)
cadence_summary_adj = summarize_cadence(T_steps, 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']
Expand Down Expand Up @@ -808,52 +808,82 @@ def _tdelta_to_str(tdelta):


def summarize_cadence(
Y: pd.Series,
steptol: int = 3,
T_steps: pd.Series,
peak1_min_walk_per_day: int = 10,
peak30_min_walk_per_day: int = 30,
p95_min_walk_per_day: int = 10,
min_cadence: int = 50,
max_cadence: int = 150,
rolling_min_periods: int = 10,
adjust_estimates: bool = False
):
"""
Summarize cadence information from a series of step counts.
Summarize cadence information from a series of step timestamps.

Parameters:
- Y (pd.Series): A pandas Series of step counts.
- steptol (int, optional): The minimum number of steps per window for the window to be considered valid for calculation. Defaults to 3 steps per window.
- T_steps (pd.Series): A pandas Series of timestamps of the steps.
- peak1_min_walk_per_day (int, optional): The minimum number of walking minutes per day for the peak1 cadence. Defaults to 10 minutes.
- peak30_min_walk_per_day (int, optional): The minimum number of walking minutes per day for the peak30 cadence. Defaults to 30 minutes.
- p95_min_walk_per_day (int, optional): The minimum number of walking minutes per day for the 95th percentile cadence. Defaults to 30 minutes.
- min_cadence (int, optional): The minimum cadence to consider. Defaults to 50 steps per minute.
- max_cadence (int, optional): The maximum cadence to consider. Defaults to 150 steps per minute.
- rolling_min_periods (int, optional): The minimum number of observations needed for the rolling mean cadence. Defaults to 10 observations.
- adjust_estimates (bool, optional): Whether to adjust estimates to account for missing data. Defaults to False.

Returns:
- dict: A dictionary containing various summary cadence statistics.

Example:
summary = summarize_cadence(Y, steptol=3, adjust_estimates=True)
summary = summarize_cadence(T_steps, adjust_estimates=True)
"""

# TODO: split walking and running cadence?

def _cadence_max(x, steptol, walktol=30, n=1):
y = x[x >= steptol]
if len(T_steps) == 0:
return {
'daily': pd.DataFrame(columns=['CadencePeak1(steps/min)', 'CadencePeak30(steps/min)', 'Cadence95th(steps/min)']),
'cadence_peak1': np.nan,
'cadence_peak30': np.nan,
'cadence_p95': np.nan,
# weekend stats
'weekend_cadence_peak1': np.nan,
'weekend_cadence_peak30': np.nan,
'weekend_cadence_p95': np.nan,
# weekday stats
'weekday_cadence_peak1': np.nan,
'weekday_cadence_peak30': np.nan,
'weekday_cadence_p95': np.nan,
}

def _max(x, min_walk=30, n=1):
# if not enough walking time, return NA.
# note: walktol in minutes, x must be minutely
if len(y) < walktol:
if x.count() < min_walk:
return np.nan
return y.nlargest(n, keep='all').mean()
return x.nlargest(n, keep='all').mean()

def _cadence_p95(x, steptol, walktol=30):
y = x[x >= steptol]
def _p95(x, min_walk=30):
# if not enough walking time, return NA.
# note: walktol in minutes, x must be minutely
if len(y) < walktol:
if x.count() < min_walk:
return np.nan
return y.quantile(.95)

dt = utils.infer_freq(Y.index).total_seconds()
steptol_in_minutes = steptol * 60 / dt # rescale steptol to steps/min
minutely = Y.resample('T').sum().rename('Steps') # steps/min
return x.quantile(.95)

T_steps = T_steps.copy()
T_steps.index = T_steps
cad = 60 / T_steps.diff().dt.total_seconds() # steps/min
# set very low and very high cadences to NA
cad = cad.where(cad.between(min_cadence, max_cadence))
# 1min moving average
cad = cad.rolling('T', min_periods=rolling_min_periods).mean()
# resample to minutely
minutely = cad.resample('T').mean().rename('Cadence(steps/min)')

# cadence https://jamanetwork.com/journals/jama/fullarticle/2763292

daily_cadence_peak1 = minutely.resample('D').agg(_cadence_max, steptol=steptol_in_minutes, walktol=10, n=1).rename('CadencePeak1(steps/min)')
daily_cadence_peak30 = minutely.resample('D').agg(_cadence_max, steptol=steptol_in_minutes, walktol=30, n=30).rename('CadencePeak30(steps/min)')
daily_cadence_p95 = minutely.resample('D').agg(_cadence_p95, walktol=10, steptol=steptol_in_minutes).rename('Cadence95th(steps/min)')
daily_cadence_peak1 = minutely.resample('D').agg(_max, min_walk=peak1_min_walk_per_day, n=1).rename('CadencePeak1(steps/min)')
daily_cadence_peak30 = minutely.resample('D').agg(_max, min_walk=peak30_min_walk_per_day, n=30).rename('CadencePeak30(steps/min)')
daily_cadence_p95 = minutely.resample('D').agg(_p95, min_walk=p95_min_walk_per_day).rename('Cadence95th(steps/min)')

with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='Mean of empty slice')
Expand Down
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