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Improvements to plotting tool #240

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merged 3 commits into from
Nov 14, 2023
Merged

Improvements to plotting tool #240

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e227378
fix(accPlot): support missing acceleration trace
chanshing Nov 14, 2023
0bde87b
fix(accPlot): more consistent DST handling: mask DST hour
chanshing Nov 14, 2023
fd33adf
fix(accPlot): check dataframe index is datetime
chanshing Nov 14, 2023
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42 changes: 28 additions & 14 deletions src/accelerometer/accPlot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,8 +75,10 @@ def main(): # noqa: C901

# read time series file to pandas DataFrame
data = pd.read_csv(
args.timeSeriesFile, index_col='time',
parse_dates=['time'], date_parser=utils.date_parser
args.timeSeriesFile,
index_col='time',
parse_dates=['time'],
date_parser=utils.date_parser
)

# set backend if run from main
Expand All @@ -92,16 +94,16 @@ def main(): # noqa: C901


def plotTimeSeries( # noqa: C901
data,
title=None,
showFirstNDays=None
):
data,
title=None,
showFirstNDays=None
):
"""Plot overall activity and classified activity types

:param pd.DataFrame data: Input DataFrame with time series data
Index: DatetimeIndex
Columns (4 class example):
Name: acc, dtype=float
Name: acc, dtype=float (optional)
Name: light, dtype=Any numeric, value=0 or 1
Name: moderate-vigorous, dtype=Any numeric, value=0 or 1
Name: sedentary, dtype=Any numeric, value=0 or 1
Expand All @@ -119,9 +121,20 @@ def plotTimeSeries( # noqa: C901
>>> fig.show()
"""

# double check time index
if 'time' in data.columns:
data = data.set_index('time')
# check index is datetime
if not isinstance(data.index, pd.DatetimeIndex):
raise ValueError("Index must be a DatetimeIndex")

# use tz-naive local time
if data.index.tz is not None:
data.index = (
# convoluted way to mask the ambiguous DST pushback hour, if any
data.index
.tz_localize(None)
.tz_localize(data.index.tz, ambiguous='NaT', nonexistent='NaT')
.tz_localize(None)
)
data = data[data.index.notnull()]

# fix gaps or irregular sampling
if pd.infer_freq(data) is None:
Expand All @@ -143,12 +156,12 @@ def plotTimeSeries( # noqa: C901

# setup plotting range
MAXRANGE = 2 * 1000 # 2g (above this is very rare)
data['acc'] = data['acc'].rolling('1T').mean() # minute average
data['acc'] = data['acc'].clip(0, MAXRANGE)
if 'acc' in data:
data['acc'] = data['acc'].rolling('1T').mean() # minute average
data['acc'] = data['acc'].clip(0, MAXRANGE)
data[labels] = data[labels].astype('f4') * MAXRANGE

# number of rows to display in figure (all days + legend)
data.index = data.index.tz_localize(None, ambiguous='NaT', nonexistent='NaT') # tz-unaware local time
groupedDays = data.groupby(data.index.date)
nrows = len(groupedDays) + 1

Expand All @@ -164,7 +177,8 @@ def plotTimeSeries( # noqa: C901

ax = fig.add_subplot(nrows, 1, i + 1)

ax.plot(group.index, group['acc'].to_numpy(), c='k')
if 'acc' in group:
ax.plot(group.index, group['acc'].to_numpy(), c='k')

if len(labels) > 0:
ax.stackplot(group.index,
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