import pandas as pd import numpy as np 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 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")) plot.update_layout(title="Row count heatmap for " + sensor_name + " of " + pid) pio.write_html(plot, file=output_path, auto_open=False) 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: 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"] 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 = 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_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 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)