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Minor fix of heatmap_days_by_sensors.py

pull/95/head
Meng Li 2020-08-12 16:29:33 -04:00
parent 5c25002507
commit b3b16bdb73
1 changed files with 29 additions and 29 deletions
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58
src/visualization/heatmap_days_by_sensors.py
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@ -41,34 +41,34 @@ if row_count_sensors.empty:
empty_html = open(snakemake.output[0], "w")
empty_html.write("There are no records of sensors in database.")
empty_html.close()
# set date_idx based on the first date
reference_date = row_count_sensors.index.min()
last_date = row_count_sensors.index.max()
row_count_sensors["date_idx"] = (row_count_sensors.index - reference_date).days
row_count_sensors["local_date"] = row_count_sensors.index
row_count_sensors.set_index(["local_date", "date_idx"], inplace=True)
expected_num_of_days = int(snakemake.params["expected_num_of_days"])
if expected_num_of_days < -1:
raise ValueError("EXPECTED_NUM_OF_DAYS of HEATMAP_DAYS_BY_SENSORS section in config.yaml must be larger or equal to -1.")
# if expected_num_of_days = -1, return all dates
expected_num_of_days = (last_date - reference_date).days if expected_num_of_days == -1 else expected_num_of_days
# add empty rows to make sure different participants have the same date_idx range
date_idx_range = [idx for idx in range(expected_num_of_days)]
date_range = [reference_date + timedelta(days=idx) for idx in date_idx_range]
all_dates = pd.DataFrame({"local_date": date_range, "date_idx": date_idx_range})
all_dates.set_index(["local_date", "date_idx"], inplace=True)
row_count_sensors = row_count_sensors.merge(all_dates, left_index=True, right_index=True, how="right")
# normalize each sensor (column)
if row_count_sensors.count().max() > 1:
row_count_sensors_normalized = row_count_sensors.fillna(np.nan).apply(lambda x: (x - np.nanmin(x)) / (np.nanmax(x) - np.nanmin(x)) if np.nanmax(x) != np.nanmin(x) else (x / np.nanmin(x)), axis=0)
else:
row_count_sensors_normalized = row_count_sensors
# set date_idx based on the first date
reference_date = row_count_sensors.index.min()
last_date = row_count_sensors.index.max()
row_count_sensors["date_idx"] = (row_count_sensors.index - reference_date).days
row_count_sensors["local_date"] = row_count_sensors.index
row_count_sensors.set_index(["local_date", "date_idx"], inplace=True)
pid = sensor_path.split("/")[2]
getRowCountHeatmap(row_count_sensors_normalized, row_count_sensors, pid, snakemake.output[0])
expected_num_of_days = int(snakemake.params["expected_num_of_days"])
if expected_num_of_days < -1:
raise ValueError("EXPECTED_NUM_OF_DAYS of HEATMAP_DAYS_BY_SENSORS section in config.yaml must be larger or equal to -1.")
# if expected_num_of_days = -1, return all dates
expected_num_of_days = (last_date - reference_date).days if expected_num_of_days == -1 else expected_num_of_days
# add empty rows to make sure different participants have the same date_idx range
date_idx_range = [idx for idx in range(expected_num_of_days)]
date_range = [reference_date + timedelta(days=idx) for idx in date_idx_range]
all_dates = pd.DataFrame({"local_date": date_range, "date_idx": date_idx_range})
all_dates.set_index(["local_date", "date_idx"], inplace=True)
row_count_sensors = row_count_sensors.merge(all_dates, left_index=True, right_index=True, how="right")
# normalize each sensor (column)
if row_count_sensors.count().max() > 1:
row_count_sensors_normalized = row_count_sensors.fillna(np.nan).apply(lambda x: (x - np.nanmin(x)) / (np.nanmax(x) - np.nanmin(x)) if np.nanmax(x) != np.nanmin(x) else (x / np.nanmin(x)), axis=0)
else:
row_count_sensors_normalized = row_count_sensors
pid = sensor_path.split("/")[2]
getRowCountHeatmap(row_count_sensors_normalized, row_count_sensors, pid, snakemake.output[0])