71 lines
3.5 KiB
Python
71 lines
3.5 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]
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compliance_matrix.append(date_bins["has_row"].tolist())
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return compliance_matrix
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def getComplianceHeatmap(dates, compliance_matrix, 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=compliance_matrix,
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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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colorbar={'tick0': 0,'dtick': 1}))
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plot.update_layout(title="Compliance heatmap. Five-minute bins showing how many sensors logged at least one row of data in that period for " + pid)
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pio.write_html(plot, file=output_path, auto_open=False)
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# get current patient id
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pid = snakemake.params["pid"]
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sensors_dates = []
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sensors_five_minutes_row_is = pd.DataFrame()
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for sensor_path in snakemake.input:
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sensor_data = pd.read_csv(sensor_path)
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# check if the sensor is off
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if sensor_data.empty:
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continue
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# create a dataframe contains 2 columns: local_date_time, has_row
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sensor_data["has_row"] = [1]*sensor_data.shape[0]
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sensor_data["local_date_time"] = pd.to_datetime(sensor_data["local_date_time"])
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sensed_bins = sensor_data[["local_date_time", "has_row"]]
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# get the first date and the last date of current sensor
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start_date = datetime.datetime.combine(sensed_bins["local_date_time"][0].date(), datetime.time(0,0,0))
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end_date = datetime.datetime.combine(sensed_bins["local_date_time"][sensed_bins.shape[0]-1].date(), datetime.time(23,59,59))
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# add the above datetime with has_row=0 to our dataframe
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sensed_bins.loc[sensed_bins.shape[0], :] = [start_date, 0]
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sensed_bins.loc[sensed_bins.shape[0], :] = [end_date, 0]
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# get bins with 5 min
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sensor_five_minutes_row_is = pd.DataFrame(sensed_bins.resample("5T", on="local_date_time")["has_row"].sum())
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sensor_five_minutes_row_is["has_row"] = (sensor_five_minutes_row_is["has_row"]>0).astype(int)
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# merge current sensor with previous sensors
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if sensors_five_minutes_row_is.empty:
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sensors_five_minutes_row_is = sensor_five_minutes_row_is
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else:
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sensors_five_minutes_row_is = pd.concat([sensors_five_minutes_row_is, sensor_five_minutes_row_is]).groupby("local_date_time").sum()
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sensors_five_minutes_row_is.reset_index(inplace=True)
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# resample again to impute missing dates
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sensors_five_minutes_row_is_successive = pd.DataFrame(sensors_five_minutes_row_is.resample("5T", on="local_date_time")["has_row"].sum())
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# get sorted date list
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sensors_five_minutes_row_is_successive.reset_index(inplace=True)
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sensors_five_minutes_row_is_successive["local_date"] = sensors_five_minutes_row_is_successive["local_date_time"].apply(lambda x: x.date())
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dates = list(set(sensors_five_minutes_row_is_successive["local_date"]))
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dates.sort()
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compliance_matrix = getComplianceMatrix(dates, sensors_five_minutes_row_is_successive)
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# get heatmap
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getComplianceHeatmap(dates, compliance_matrix, pid, snakemake.output[0], 5) |