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Fixed heatmap issue#27

replace/f3be5b26fdfb28f92b158148e7858887c43b6715
Meng Li 2020-02-07 11:14:19 -05:00
parent 1cbcfcca52
commit 3d13940bd3
2 changed files with 25 additions and 25 deletions
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3
rules/reports.snakefile
View File

@ -3,7 +3,8 @@ rule heatmap_rows:
"data/raw/{pid}/{sensor}_with_datetime.csv"
params:
table = "{sensor}",
pid = "{pid}"
pid = "{pid}",
bin_size = config["PHONE_VALID_SENSED_DAYS"]["BIN_SIZE"]
output:
"reports/figures/{pid}/{sensor}_heatmap_rows.html"
script:

47
src/visualization/heatmap_rows.py
View File

@ -12,11 +12,14 @@ def getComplianceMatrix(dates, compliance_bins):
return compliance_matrix
def getHourlyRowCountHeatmap(dates, hourly_row_count, sensor_name, pid, output_path):
plot = go.Figure(data=go.Heatmap(z=hourly_row_count,
x=[x for x in range(24)],
def getRowCountHeatmap(dates, row_count_per_bin, sensor_name, pid, output_path, bin_size):
bins_per_hour = int(60 / bin_size)
x_axis_labels = ["{0:0=2d}".format(x // bins_per_hour) + ":" + \
"{0:0=2d}".format(x % bins_per_hour * bin_size) for x in range(24 * bins_per_hour)]
plot = go.Figure(data=go.Heatmap(z=row_count_per_bin,
x=x_axis_labels,
y=[datetime.datetime.strftime(date, '%Y/%m/%d') for date in dates],
colorscale='Viridis'))
colorscale="Viridis"))
plot.update_layout(title="Row count heatmap for " + sensor_name + " of " + pid)
pio.write_html(plot, file=output_path, auto_open=False)
@ -25,6 +28,7 @@ def getHourlyRowCountHeatmap(dates, hourly_row_count, sensor_name, pid, output_p
sensor_data = pd.read_csv(snakemake.input[0], encoding="ISO-8859-1")
sensor_name = snakemake.params["table"]
pid = snakemake.params["pid"]
bin_size = snakemake.params["bin_size"]
# check if we have sensor data
if sensor_data.empty:
@ -35,29 +39,24 @@ else:
start_date = sensor_data["local_date"][0]
end_date = sensor_data.at[sensor_data.index[-1],"local_date"]
# Make local hour double digit
sensor_data["local_hour"] = sensor_data["local_hour"].map("{0:0=2d}".format)
# Group and count by local_date and local_hour
sensor_data_hourly_bins = sensor_data.groupby(["local_date","local_hour"]).agg(count=("timestamp","count")).reset_index()
sensor_data["local_date_time"] = pd.to_datetime(sensor_data["local_date_time"])
sensor_data = sensor_data[["local_date_time"]]
sensor_data["count"] = 1
# Add first and last day boundaries for resampling
sensor_data_hourly_bins = sensor_data_hourly_bins.append([pd.Series([start_date, "00", 0], sensor_data_hourly_bins.columns),
pd.Series([end_date, "23", 0], sensor_data_hourly_bins.columns)])
# Rebuild local date hour for resampling
sensor_data_hourly_bins["local_date_hour"] = pd.to_datetime(sensor_data_hourly_bins["local_date"] + \
" " + sensor_data_hourly_bins["local_hour"] + ":00:00")
resampled_hourly_bins = pd.DataFrame(sensor_data_hourly_bins.resample("1H", on="local_date_hour")["count"].sum())
sensor_data = sensor_data.append([pd.Series([datetime.datetime.strptime(start_date + " 00:00:00", "%Y-%m-%d %H:%M:%S"), 0], sensor_data.columns),
pd.Series([datetime.datetime.strptime(end_date + " 23:59:59", "%Y-%m-%d %H:%M:%S"), 0], sensor_data.columns)])
# Resample into bins with the size of bin_size
resampled_bins = pd.DataFrame(sensor_data.resample(str(bin_size) + "T", on="local_date_time")["count"].sum())
# Extract list of dates for creating the heatmap
resampled_hourly_bins.reset_index(inplace=True)
resampled_hourly_bins["local_date"] = resampled_hourly_bins["local_date_hour"].dt.date
dates = resampled_hourly_bins["local_date"].drop_duplicates().tolist()
resampled_bins.reset_index(inplace=True)
resampled_bins["local_date"] = resampled_bins["local_date_time"].dt.date
dates = resampled_bins["local_date"].drop_duplicates().tolist()
# Create heatmap
hourly_row_count = getComplianceMatrix(dates, resampled_hourly_bins)
hourly_row_count = np.asarray(hourly_row_count)
hourly_row_count = np.where(hourly_row_count == 0, np.nan, hourly_row_count)
getHourlyRowCountHeatmap(dates, hourly_row_count, sensor_name, pid, snakemake.output[0])
row_count_per_bin = getComplianceMatrix(dates, resampled_bins)
row_count_per_bin = np.asarray(row_count_per_bin)
row_count_per_bin = np.where(row_count_per_bin == 0, np.nan, row_count_per_bin)
getRowCountHeatmap(dates, row_count_per_bin, sensor_name, pid, snakemake.output[0], bin_size)