61 lines
2.7 KiB
Python
61 lines
2.7 KiB
Python
import pandas as pd
|
|
import plotly.io as pio
|
|
import plotly.graph_objects as go
|
|
import datetime
|
|
|
|
def getComplianceMatrix(dates, compliance_bins):
|
|
compliance_matrix = []
|
|
for date in dates:
|
|
date_bins = compliance_bins[compliance_bins["local_date"] == date]["count"].tolist()
|
|
compliance_matrix.append(date_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)],
|
|
y=[datetime.datetime.strftime(date, '%Y/%m/%d') for date in dates],
|
|
colorscale='Viridis'))
|
|
plot.update_layout(title="Hourly row count heatmap for " + pid + " and sensor " + sensor_name)
|
|
pio.write_html(plot, file=output_path, auto_open=False)
|
|
|
|
|
|
|
|
sensor_data = pd.read_csv(snakemake.input[0])
|
|
sensor_name = snakemake.params["table"]
|
|
pid = snakemake.params["pid"]
|
|
|
|
# check if we have sensor data
|
|
if sensor_data.empty:
|
|
empty_html = open(snakemake.output[0], "w")
|
|
empty_html.write("There is no "+ sensor_name + " data for "+pid)
|
|
empty_html.close()
|
|
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()
|
|
|
|
# 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())
|
|
|
|
# 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()
|
|
|
|
# Create heatmap
|
|
hourly_row_count = getComplianceMatrix(dates, resampled_hourly_bins)
|
|
getHourlyRowCountHeatmap(dates, hourly_row_count, sensor_name, pid, snakemake.output[0])
|