def plot_philap_report_graphs_for_subject(subject_id, graphs_dir):
# Download raw data if not present
participant_details = load_philap_participant_details()
try:
airspeck_raw = load_personal_airspeck_file(subject_id,
upload_type='manual',
is_minute_averaged=False)
airspeck = airspeck_raw.resample('1min').mean()
respeck = load_respeck_file(subject_id, upload_type='manual')
except:
print(
"Please download all Peeps data via download_all_philap_data(raw_airspeck=True) "
"before calling this function")
# Delete incorrect GPS. These coordinates are just outside the larger area of Delhi
airspeck_raw.loc[airspeck_raw['gpsAccuracy'] > 1000,
'gpsLongitude':'gpsLatitude'] = np.nan
airspeck_raw.loc[airspeck_raw['gpsLatitude'] < 10,
'gpsLongitude':'gpsLatitude'] = np.nan
airspeck_raw.loc[airspeck_raw['gpsLatitude'] > 40,
'gpsLongitude':'gpsLatitude'] = np.nan
airspeck_raw.loc[airspeck_raw['gpsLongitude'] < 10,
'gpsLongitude':'gpsLatitude'] = np.nan
airspeck_raw.loc[airspeck_raw['gpsLongitude'] > 80,
'gpsLongitude':'gpsLatitude'] = np.nan
home_gps = airspeck.loc[(1 < airspeck.index.hour)
& (airspeck.index.hour <= 3)].mean()
# If there was no personal data during the night, fall back on the GPS coordinates the researchers provided
if pd.isnull(home_gps['gpsLatitude']):
home_gps = get_home_gps_for_subject(subject_id, participant_details)
# Select locations near home
radius_home = 0.002
correction_factor = airspeck['gpsAccuracy'] * 0.00001
home_mask = (np.abs(airspeck['gpsLatitude'] - home_gps['gpsLatitude']) < radius_home + correction_factor) & \
(np.abs(airspeck['gpsLongitude'] - home_gps['gpsLongitude']) < radius_home + correction_factor)
##################################
# Draw detailed exposure plot
##################################
sns.set_style('darkgrid', {'xtick.bottom': True, 'xtick.major.size': 5})
fig, ax = plt.subplots(figsize=(15, 5))
if np.count_nonzero(home_mask) > 0:
for ts in airspeck.loc[home_mask].index:
ax.axvspan(ts,
ts + pd.DateOffset(minutes=1),
facecolor=CB_color_cycle[0],
alpha=0.3,
zorder=1)
ax.scatter(airspeck.index, airspeck['pm2_5'], s=2, color='black', zorder=2)
# Plot stationary airspeck home
home_airspeck = load_static_airspeck_file(subject_id,
suffix_filename='_home')
if home_airspeck is not None and len(home_airspeck) > 0:
ax.scatter(home_airspeck.index,
home_airspeck['pm2_5'],
s=2,
color='blue')
ax.set_ylabel("PM2.5 (μg/m³)")
start = airspeck.index[0].replace(hour=0, minute=0, second=0)
end = airspeck.index[-1].replace(hour=0, minute=0,
second=0) + pd.DateOffset(days=1)
ax.set_xlim(start, end)
formatter = mdates.DateFormatter('%d.%m %Hh',
tz=dateutil.tz.gettz(
project_mapping['philap'][1]))
ax.xaxis.set_major_formatter(formatter)
ax.set_title(
"Continuous PM2.5 personal exposure levels and ambient concentrations")
fig.autofmt_xdate()
home_patch = mpatches.Patch(color=CB_color_cycle[0],
label='Home',
alpha=0.3)
airs_home_patch = Line2D(range(1),
range(1),
marker='o',
color='#00000000',
markerfacecolor="blue",
label='Home sensor')
airp_patch = Line2D(range(1),
range(1),
marker='o',
color='#00000000',
markerfacecolor="black",
label='Personal sensor')
plt.legend(handles=[home_patch, airp_patch, airs_home_patch])
plt.tight_layout()
plt.savefig(graphs_dir + "{}_detailed_exposure.png".format(subject_id),
dpi=300)
plt.show()
##################################
# Draw summary bar graph
##################################
sns.set_style('darkgrid', {'xtick.bottom': False, 'xtick.major.size': 0.0})
home = airspeck.loc[home_mask, 'pm2_5'].mean()
other = airspeck.loc[~home_mask, 'pm2_5'].mean()
overall = airspeck['pm2_5'].mean()
if home_airspeck is not None:
home_ambient = home_airspeck['pm2_5'].mean()
else:
home_ambient = np.nan
mean_values = [home, other, overall, home_ambient]
fig, ax = plt.subplots(figsize=(8, 5))
ax.set_title(
"Mean PM2.5 personal exposure levels and ambient concentrations")
ax.bar(np.arange(len(mean_values)),
mean_values,
width=0.5,
color=CB_color_cycle,
edgecolor="none")
ax.set_ylabel("PM2.5 (μg/m³)")
ax.set_xlim(-0.5, len(mean_values) - 0.5)
plt.xticks(np.arange(len(mean_values)), [
"Home\npersonal", "Other\npersonal", "Overall\npersonal",
"Home\nambient"
])
plt.savefig(graphs_dir +
"{}_mean_exposure.png".format(subject_id, subject_id),
dpi=300)
plt.show()
##################################
# Draw map
##################################
get_maps_image(airspeck_raw,
graphs_dir + "{}_airspeck_map.png".format(subject_id),
zoom=13)
##################################
# Other statistics
##################################
# Create new empty file
open(graphs_dir + "{}_stats.txt".format(subject_id), 'w').close()
# Append stats to this file
with open(graphs_dir + "{}_stats.txt".format(subject_id), 'a') as f:
f.write("Step count: {}\n".format(respeck['step_count'].sum()))
f.write(
"Mean breathing rate during night: {:.2f} breaths per minute\n".
format(respeck.loc[(0 < respeck.index.hour) &
(respeck.index.hour < 6),
'breathing_rate'].mean()))
f.write("Mean breathing rate during day: {:.2f} breaths per minute\n".
format(respeck.loc[(6 <= respeck.index.hour) &
(respeck.index.hour <= 23),
'breathing_rate'].mean()))
f.write("\nStart of recording: {}\n".format(
airspeck.index[0].replace(tzinfo=None)))
f.write("End of recording: {}\n".format(
airspeck.index[-1].replace(tzinfo=None)))
f.write("Total duration: {}\n".format(airspeck.index[-1] -
airspeck.index[0]))
f.write("Total recording time at home: {:.1f} h\n".format(
np.count_nonzero(home_mask) / 60.))
def create_dublin_pixelgram_for_subject(
subject_id, overwrite_pixelgram_if_already_exists=False):
download_respeck_and_personal_airspeck_data(subject_id,
upload_type='manual')
respeck_data = load_respeck_file(subject_id, upload_type='manual')
airspeck_data = load_personal_airspeck_file(subject_id,
upload_type='manual')
# Load correction factors for timezone
corrections = pd.read_excel(dublin_timezones_correction_filepath).replace(
np.nan, 0).set_index('subject_id')
participant_details = load_dublin_participant_details()
row = participant_details.loc[subject_id]
# Load exposure period
from_time = row['start_of_exposure_time_to_shs']
to_time = row['end_of_exposure_time_to_shs']
start_exposure = row['date_of_exposure_to_shs'].replace(
hour=from_time.hour, minute=from_time.minute,
second=from_time.second).to_pydatetime() + timedelta(
hours=int(corrections.loc[subject_id, 'shs_times_difference']))
if not pd.isnull(row['end_date_of_exposure_to_shs']):
end_exposure = row['end_date_of_exposure_to_shs'].replace(
hour=to_time.hour, minute=to_time.minute,
second=to_time.second).to_pydatetime() + timedelta(
hours=int(corrections.loc[subject_id, 'shs_times_difference']))
else:
end_exposure = row['date_of_exposure_to_shs'].replace(
hour=to_time.hour, minute=to_time.minute,
second=to_time.second).to_pydatetime() + timedelta(
hours=int(corrections.loc[subject_id, 'shs_times_difference']))
# Load recording period
from_time = row['start_time_of_monitoring']
start_recording = row['start_date_of_monitoring'].replace(
hour=from_time.hour, minute=from_time.minute,
second=from_time.second).to_pydatetime() + timedelta(
hours=int(corrections.loc[subject_id,
'recording_times_difference']))
to_time = row['end_time_of_monitoring']
end_recording = row['end_date_of_monitoring'].replace(
hour=to_time.hour, minute=to_time.minute,
second=to_time.second).to_pydatetime() + timedelta(
hours=int(corrections.loc[subject_id,
'recording_times_difference']))
# Look up timezone
tz = timezone(project_mapping[subject_id[:3]][1])
print("Creating pixelgram for subject {}".format(subject_id))
plot_combined_pixelgram_dublin(
subject_id,
respeck_data,
airspeck_data,
exposure_period=[
tz.localize(start_exposure),
tz.localize(end_exposure)
],
recording_period=[
tz.localize(start_recording),
tz.localize(end_recording)
],
overwrite_if_already_exists=overwrite_pixelgram_if_already_exists)
def download_data_and_plot_combined_pixelgram(
subject_id,
timeframe=None,
filter_out_not_worn_respeck=True,
overwrite_pixelgram_if_already_exists=False,
subject_visit_number=None,
overwrite_data_if_already_exists=False,
upload_type='automatic'):
project_name = get_project_for_subject(subject_id)
plot_dir = project_mapping[project_name][3]
if subject_visit_number is None:
label_files = "{}".format(subject_id)
else:
label_files = "{}({})".format(subject_id, subject_visit_number)
pixelgram_filepath = plot_dir + "{}_combined_pixelgram.png".format(
label_files)
# Check if pixelgram already exists
if not overwrite_pixelgram_if_already_exists and os.path.isfile(
pixelgram_filepath):
print("Pixelgram for subject {} already exists. Skipping subject.".
format(label_files))
return
# Download data if not present
download_respeck_and_personal_airspeck_data(
subject_id,
upload_type=upload_type,
timeframe=timeframe,
overwrite_if_already_exists=overwrite_data_if_already_exists,
subject_visit_number=subject_visit_number)
# Load data and create plot
respeck_data = load_respeck_file(
subject_id,
project_name=project_name,
upload_type=upload_type,
subject_visit_number=subject_visit_number,
filter_out_not_worn=filter_out_not_worn_respeck)
airspeck_data = load_personal_airspeck_file(
subject_id,
project_name=project_name,
upload_type=upload_type,
subject_visit_number=subject_visit_number)
if len(respeck_data) == 0:
print("RESpeck data for subject {} empty. Skipping subject.".format(
label_files))
return
if len(airspeck_data) == 0:
print("Airspeck data for subject {} empty. Skipping subject.".format(
label_files))
return
if timeframe is not None:
tz = timezone(project_mapping[project_name][1])
if timeframe[0].tzinfo is None:
start_time = tz.localize(timeframe[0])
end_time = tz.localize(timeframe[1])
else:
start_time = timeframe[0]
end_time = timeframe[1]
plot_combined_pixelgram(
subject_id,
respeck_data[start_time:end_time],
airspeck_data[start_time:end_time],
pixelgram_filepath,
overwrite_if_already_exists=overwrite_pixelgram_if_already_exists,
subject_visit_number=subject_visit_number)
else:
plot_combined_pixelgram(
subject_id,
respeck_data,
airspeck_data,
pixelgram_filepath,
overwrite_if_already_exists=overwrite_pixelgram_if_already_exists,
subject_visit_number=subject_visit_number)
def download_respeck_data_and_plot_pixelgram(
subject_id,
project_name=None,
upload_type='automatic',
timeframe=None,
overwrite_pixelgram_if_already_exists=False,
filter_out_not_worn=True,
overwrite_data_if_already_exists=False,
subject_visit_number=None):
if project_name is None:
project_name = get_project_for_subject(subject_id)
plot_dir = project_mapping[project_name][3]
label_files = "{}({})".format(subject_id, subject_visit_number)
pixelgram_filepath = plot_dir + "{}_respeck_pixelgram.png".format(
label_files)
# Check if pixelgram already exists
if not overwrite_pixelgram_if_already_exists and os.path.isfile(
pixelgram_filepath):
print("Pixelgram for subject {} already exists. Skipping subject.".
format(label_files))
return
# Download files if they weren't there yet before
download_respeck_data(
subject_id,
upload_type=upload_type,
timeframe=timeframe,
overwrite_if_already_exists=overwrite_data_if_already_exists,
subject_visit_number=subject_visit_number)
respeck_data = load_respeck_file(subject_id,
project_name,
subject_visit_number=subject_visit_number,
upload_type=upload_type,
filter_out_not_worn=filter_out_not_worn)
if len(respeck_data) == 0:
print(
"File for subject {} empty. Skipping subject.".format(subject_id))
return
if timeframe is not None:
tz = timezone(project_mapping[project_name][1])
if timeframe[0].tzinfo is None:
start_time = tz.localize(timeframe[0])
end_time = tz.localize(timeframe[1])
else:
start_time = timeframe[0]
end_time = timeframe[1]
plot_respeck_pixelgram(
subject_id,
respeck_data[start_time:end_time],
pixelgram_filepath,
overwrite_if_already_exists=overwrite_pixelgram_if_already_exists,
subject_visit_number=subject_visit_number)
else:
plot_respeck_pixelgram(
subject_id,
respeck_data,
pixelgram_filepath,
overwrite_if_already_exists=overwrite_pixelgram_if_already_exists,
subject_visit_number=subject_visit_number)
def plot_peeps_report_graphs_for_subject(subject_id,
subject_visit_number=1,
graphs_dir,
max_value=100):
# Download raw data if not present
#download_all_peeps_data(download_raw_airspeck_data=True, phase=phase)
participant_details = load_peeps_participant_details()
calibration_date_pers, is_calibrated_pm_pers, airspeck = get_resampled_data(
subject_id, subject_visit_number)
if not is_calibrated_pm_pers:
airspeck['pm2_5'] = airspeck['pm2_5'] * 0.60456 + 37.26849
#calibration_date_pers = 'Calibrated with median'
respeck = load_respeck_file(subject_id,
'peeps',
subject_visit_number=subject_visit_number,
upload_type='manual')
#All subject details
all_visit_subj_details = participant_details.loc[
participant_details['Subject ID'] == subject_id]
#Details for specific visit
subj_details = all_visit_subj_details[
all_visit_subj_details['Visit number'] == subject_visit_number]
infer = subj_details['Infer Home Coordinates'][0]
if infer:
home_gps = airspeck.loc[(1 < airspeck.index.hour)
& (airspeck.index.hour <= 3)].mean()
print('Subject ID: ' + str(subject_id) + ' home_gps: ' + str(home_gps))
# If there was no personal data during the night, fall back on the GPS coordinates the researchers provided
#if pd.isnull(home_gps['gpsLatitude']):
else:
home_gps = get_home_gps_for_subject(subject_id, participant_details)
# Select locations near home
#radius_home = 0.01
radius_home = 0.002
home_id = get_home_id_for_subject(subject_id, participant_details)
correction_factor = airspeck['gpsAccuracy'] * 0.00001
home_mask = (np.abs(airspeck['gpsLatitude'] - home_gps['gpsLatitude']) < radius_home + correction_factor) & \
(np.abs(airspeck['gpsLongitude'] - home_gps['gpsLongitude']) < radius_home + correction_factor)
all_factors_ids = ['33B45C90B13731DE', 'E1EFA8FCA05B3FF9']
#Declare work and commute masks in case there is no work
work_mask = np.zeros(len(airspeck)).astype(bool)
commute_mask = np.zeros(len(airspeck)).astype(bool)
radius_work = 0.01
if subject_id in ['PEV018', 'PEV066', 'PEV047', 'PEV076']:
radius_work = 0.002
print('Smaller work radius set: {}'.format(radius_work))
work_id = get_work_id_for_subject(subject_id, participant_details)
hasWorkLocation = True
if (work_id == 'Not deployed'):
hasWorkLocation = False
work_airspeck = []
if hasWorkLocation: #We need a work location to establish commuting
if subject_visit_number == 1:
work_gps = peeps_work_id_to_gps_phase1[get_work_id_for_subject(
subject_id, participant_details)]
if subject_visit_number == 2:
work_gps = peeps_work_id_to_gps[get_work_id_for_subject(
subject_id, participant_details)]
use_all_features = False
calibration_date_work, is_calibrated_spmwork, is_calibrated_sgaswork, work_airspeck = load_static_airspeck_file(
work_id,
sensor_label="{}".format(subject_id),
suffix_filename='_work',
project_name='peeps',
upload_type='automatic',
calibrate_pm_and_gas=False,
return_calibration_flag=False,
use_all_features_for_pm_calibration=use_all_features)
if pd.isnull(work_gps['gpsLatitude']):
work_airspeck_lat = work_airspeck.gpsLatitude.loc[
work_airspeck.gpsLatitude > 0].dropna().mean()
work_airspeck_lng = work_airspeck.gpsLongitude.loc[
work_airspeck.gpsLongitude > 0].dropna().mean()
work_gps = {
'gpsLatitude': work_airspeck_lat,
'gpsLongitude': work_airspeck_lng
}
if pd.isnull(work_gps['gpsLatitude']):
print('Assuming work is wherever subjcet is between 10 and 11am')
work_gps = airspeck.loc[(10 < airspeck.index.hour)
& (airspeck.index.hour <= 11)].mean()
work_mask = (np.abs(airspeck['gpsLatitude'] - work_gps['gpsLatitude']) < radius_work + correction_factor) & \
(np.abs(airspeck['gpsLongitude'] - work_gps['gpsLongitude']) < radius_work + correction_factor)
# Go through whole array and search for commuting
begin_commute = 0
last_location = ""
for idx in range(1, len(airspeck)):
if (home_mask[idx] == True and home_mask[idx - 1] == False and last_location == "work") or \
(work_mask[idx] == True and work_mask[idx - 1] == False and last_location == "home"):
#print('Finished commute')
#commute_length = min(idx - begin_commute, 180) #if the commute is more than 3 hours it's not really a commute
#print(commute_length)
if (idx - begin_commute < 180):
commute_mask[begin_commute:idx] = True
elif (home_mask[idx] == False and home_mask[idx - 1] == True):
print('Entering leaving home commute')
begin_commute = idx
last_location = "home"
elif (work_mask[idx] == False and work_mask[idx - 1] == True):
print('Entering leaving work commute')
begin_commute = idx
last_location = "work"
#print('hello2')
##################################
# Draw detailed exposure plot
##################################
sns.set_style('whitegrid', {'xtick.bottom': True, 'xtick.major.size': 5})
fig, ax = plt.subplots(figsize=(15, 5))
#print(airspeck.loc[home_mask])
if np.count_nonzero(home_mask) > 0:
for ts in airspeck.loc[home_mask].index:
ax.axvspan(ts,
ts + pd.DateOffset(minutes=1),
facecolor=CB_color_cycle[0],
alpha=0.3,
zorder=1,
lw=0)
if np.count_nonzero(work_mask) > 0:
for ts in airspeck.loc[work_mask].index:
ax.axvspan(ts,
ts + pd.DateOffset(minutes=1),
facecolor=CB_color_cycle[1],
alpha=0.3,
zorder=1,
lw=0)
#print(np.count_nonzero(commute_mask))
if np.count_nonzero(commute_mask) > 0:
for ts in airspeck.loc[commute_mask].index:
ax.axvspan(ts,
ts + pd.DateOffset(minutes=1),
facecolor=CB_color_cycle[2],
alpha=0.3,
zorder=1,
lw=0)
#airspeck_plot = airspeck.resample('10min').mean()
ax.scatter(airspeck.resample('10min').mean().index,
airspeck.resample('10min').mean()['pm2_5'],
s=2,
color='black',
zorder=2)
# Plot stationary airspeck home
use_all_features = False
#if home_id in all_factors_ids:
# use_all_features = True
calibration_date_home, is_calibrated_spmhome, is_calibrated_sgashome, home_airspeck = load_static_airspeck_file(
home_id,
sensor_label="{}".format(subject_id),
suffix_filename='_home',
project_name='peeps',
upload_type='automatic',
calibrate_pm_and_gas=False,
use_all_features_for_pm_calibration=use_all_features,
return_calibration_flag=False)
home_airspeck = load_static_airspeck_file(subject_id,
suffix_filename='_home')
#home_aispeck_plot = home_airspeck.resample('10min').mean()
start_personal = airspeck.index[0].replace(hour=0, minute=0, second=0)
end_personal = airspeck.index[-1].replace(hour=0, minute=0,
second=0) + pd.DateOffset(days=1)
start_home = start_personal
end_home = end_personal
if len(home_airspeck) > 0:
ax.scatter(home_airspeck.resample('10min').mean().index,
home_airspeck.resample('10min').mean()['pm2_5'],
s=2,
color=CB_color_cycle[0],
zorder=2)
start_home = home_airspeck.index[0].replace(hour=0, minute=0, second=0)
end_home = home_airspeck.index[-1].replace(
hour=0, minute=0, second=0) + pd.DateOffset(days=1)
# Plot stationary airspeck work
#work_airpseck is already loaded above
#work_airspeck = load_static_airspeck_file(subject_id, suffix_filename='_work')
#work_aispeck_plot = work_airspeck.resample('10min').mean()
start_work = start_personal
end_work = end_personal
if len(work_airspeck) > 0:
ax.scatter(work_airspeck.resample('10min').mean().index,
work_airspeck.resample('10min').mean()['pm2_5'],
s=2,
color=CB_color_cycle[1],
zorder=2)
start_work = work_airspeck.index[0].replace(hour=0, minute=0, second=0)
end_work = work_airspeck.index[-1].replace(
hour=0, minute=0, second=0) + pd.DateOffset(days=1)
ax.set_ylabel("PM2.5 (μg/m³)")
ax.set_xlim(min(start_personal, start_home, start_work),
max(end_personal, end_home, end_work))
formatter = mdates.DateFormatter('%d.%m %Hh',
tz=dateutil.tz.gettz(
project_mapping['peeps'][1]))
ax.xaxis.set_major_formatter(formatter)
ax.set_title(
"Continuous PM2.5 personal exposure levels and ambient concentrations")
fig.autofmt_xdate()
home_patch = mpatches.Patch(color=CB_color_cycle[0],
label='Home',
alpha=0.3)
work_patch = mpatches.Patch(color=CB_color_cycle[1],
label='Work',
alpha=0.3)
commute_patch = mpatches.Patch(color=CB_color_cycle[2],
label='Commute',
alpha=0.5)
airs_home_patch = Line2D(range(1),
range(1),
marker='o',
color='#00000000',
markerfacecolor=CB_color_cycle[0],
label='Home sensor')
airp_patch = Line2D(range(1),
range(1),
marker='o',
color='#00000000',
markerfacecolor="black",
label='Personal sensor')
airs_work_patch = Line2D(range(1),
range(1),
marker='o',
color='#00000000',
markerfacecolor=CB_color_cycle[1],
label='Work sensor')
plt.legend(handles=[
home_patch, work_patch, commute_patch, airp_patch, airs_home_patch,
airs_work_patch
])
plt.tight_layout()
plt.savefig(graphs_dir + "{}_detailed_exposure.png".format(subject_id),
dpi=300)
plt.show()
##################################
# Draw summary bar graph
##################################
sns.set_style('darkgrid', {'xtick.bottom': False, 'xtick.major.size': 0.0})
home = airspeck.loc[home_mask, 'pm2_5'].mean()
work = airspeck.loc[work_mask, 'pm2_5'].mean()
commute = airspeck.loc[commute_mask, 'pm2_5'].mean()
other = airspeck.loc[~(work_mask | home_mask | commute_mask),
'pm2_5'].mean()
overall = airspeck['pm2_5'].mean()
home_ambient = home_airspeck['pm2_5'].mean()
work_ambient = work_airspeck['pm2_5'].mean()
mean_values = [
home, work, commute, other, overall, home_ambient, work_ambient
]
fig, ax = plt.subplots(figsize=(8, 5))
ax.set_title(
"Mean PM2.5 personal exposure levels and ambient concentrations")
ax.bar(np.arange(7),
mean_values,
width=0.5,
color=CB_color_cycle,
edgecolor="none")
plt.xticks(np.arange(7), [
"Home\npersonal", "Work\npersonal", "Commute\npersonal",
"Other\npersonal", "Overall\npersonal", "Home\nambient",
"Work\nambient"
])
ax.set_ylabel("PM2.5 (μg/m³)")
plt.savefig(graphs_dir +
"{}_mean_exposure.png".format(subject_id, subject_id),
dpi=300)
plt.show()
##################################
# Draw map
##################################
#print(airspeck)
get_maps_image(airspeck,
graphs_dir + "{}_airspeck_map.png".format(subject_id),
zoom=13,
max_value=max_value)
##################################
# Other statistics
##################################
# Append stats to this file
with open(graphs_dir + "{}_stats.txt".format(subject_id), 'a') as f:
if not is_calibrated_pm_pers:
calibration_date_pers = 'Calibrated with median'
if not is_calibrated_spmhome:
calibration_date_home = 'Calibrated with median'
if not is_calibrated_spmwork:
calibration_date_work = 'Calibrated with median'
f.write(
"The personal, home and work data in this report were calibrated using colocation data from the following dates. If colocation data was unavailable, a median calibration based on other similar sensors was used.\n"
)
f.write("Personal sensor: {}\n".format(calibration_date_pers))
f.write("Home sensor: {}\n".format(calibration_date_home))
f.write("Work sensor: {}\n".format(calibration_date_work))
f.write("Step count: {}\n".format(respeck['step_count'].sum()))
f.write(
"Mean breathing rate during night: {:.2f} breaths per minute\n".
format(respeck.loc[(0 < respeck.index.hour) &
(respeck.index.hour < 6),
'breathing_rate'].mean()))
f.write("Mean breathing rate during day: {:.2f} breaths per minute\n".
format(respeck.loc[(6 <= respeck.index.hour) &
(respeck.index.hour <= 23),
'breathing_rate'].mean()))
f.write("\nStart of recording: {}\n".format(
airspeck.index[0].replace(tzinfo=None)))
f.write("End of recording: {}\n".format(
airspeck.index[-1].replace(tzinfo=None)))
f.write("Total duration: {}\n".format(airspeck.index[-1] -
airspeck.index[0]))
f.write("Total recording time at work: {:.1f} h\n".format(
np.count_nonzero(work_mask) / 60.))
f.write("Total recording time at home: {:.1f} h\n".format(
np.count_nonzero(home_mask) / 60.))
f.write(
"Total recording time during the journey between home and work: {:.1f} h\n"
.format(np.count_nonzero(commute_mask) / 60.))