63 lines
2.8 KiB
Python
63 lines
2.8 KiB
Python
import pandas as pd
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import numpy as np
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import plotly.io as pio
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import plotly.graph_objects as go
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import datetime
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def getComplianceMatrix(dates, compliance_bins):
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compliance_matrix = []
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for date in dates:
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date_bins = compliance_bins[compliance_bins["local_date"] == date]["count"].tolist()
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compliance_matrix.append(date_bins)
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return compliance_matrix
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def getRowCountHeatmap(dates, row_count_per_bin, sensor_name, pid, output_path, bin_size):
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bins_per_hour = int(60 / bin_size)
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x_axis_labels = ["{0:0=2d}".format(x // bins_per_hour) + ":" + \
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"{0:0=2d}".format(x % bins_per_hour * bin_size) for x in range(24 * bins_per_hour)]
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plot = go.Figure(data=go.Heatmap(z=row_count_per_bin,
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x=x_axis_labels,
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y=[datetime.datetime.strftime(date, '%Y/%m/%d') for date in dates],
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colorscale="Viridis"))
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plot.update_layout(title="Row count heatmap for " + sensor_name + " of " + pid)
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pio.write_html(plot, file=output_path, auto_open=False)
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sensor_data = pd.read_csv(snakemake.input[0], encoding="ISO-8859-1")
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sensor_name = snakemake.params["table"]
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pid = snakemake.params["pid"]
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bin_size = snakemake.params["bin_size"]
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# check if we have sensor data
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if sensor_data.empty:
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empty_html = open(snakemake.output[0], "w")
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empty_html.write("There is no "+ sensor_name + " data for "+pid)
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empty_html.close()
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else:
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start_date = sensor_data["local_date"][0]
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end_date = sensor_data.at[sensor_data.index[-1],"local_date"]
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sensor_data["local_date_time"] = pd.to_datetime(sensor_data["local_date_time"])
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sensor_data = sensor_data[["local_date_time"]]
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sensor_data["count"] = 1
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# Add first and last day boundaries for resampling
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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),
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pd.Series([datetime.datetime.strptime(end_date + " 23:59:59", "%Y-%m-%d %H:%M:%S"), 0], sensor_data.columns)])
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# Resample into bins with the size of bin_size
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resampled_bins = pd.DataFrame(sensor_data.resample(str(bin_size) + "T", on="local_date_time")["count"].sum())
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# Extract list of dates for creating the heatmap
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resampled_bins.reset_index(inplace=True)
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resampled_bins["local_date"] = resampled_bins["local_date_time"].dt.date
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dates = resampled_bins["local_date"].drop_duplicates().tolist()
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# Create heatmap
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row_count_per_bin = getComplianceMatrix(dates, resampled_bins)
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row_count_per_bin = np.asarray(row_count_per_bin)
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row_count_per_bin = np.where(row_count_per_bin == 0, np.nan, row_count_per_bin)
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getRowCountHeatmap(dates, row_count_per_bin, sensor_name, pid, snakemake.output[0], bin_size)
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