50 lines
2.5 KiB
Python
50 lines
2.5 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 getDatesComplianceMatrix(phone_sensed_bins):
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dates = phone_sensed_bins.index
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compliance_matrix = []
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for date in dates:
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compliance_matrix.append(phone_sensed_bins.loc[date, :].tolist())
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return dates, 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="Heatmap sensed bins.<br>Five-minute bins showing how many sensors logged at least one row of data in that period for " + pid + "<br>Label: " + label + ", device_id: " + device_id)
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pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn")
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# get current patient id
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pid = snakemake.params["pid"]
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bin_size = snakemake.params["bin_size"]
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with open(snakemake.input["pid_file"], encoding="ISO-8859-1") as external_file:
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external_file_content = external_file.readlines()
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device_id = external_file_content[0].split(",")[-1]
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label = external_file_content[2]
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phone_sensed_bins = pd.read_csv(snakemake.input["sensor"], parse_dates=["local_date"], index_col="local_date")
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if phone_sensed_bins.empty:
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empty_html = open(snakemake.output[0], "w", encoding="ISO-8859-1")
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empty_html.write("There is no sensor data for " + pid + "<br>Label: " + label + ", device_id: " + device_id)
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empty_html.close()
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else:
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# resample to impute missing dates
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phone_sensed_bins = phone_sensed_bins.resample("1D").asfreq().fillna(0)
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# get dates and compliance_matrix
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dates, compliance_matrix = getDatesComplianceMatrix(phone_sensed_bins)
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# convert compliance_matrix from list to np.array and replace 0 with np.nan
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compliance_matrix = np.asarray(compliance_matrix)
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compliance_matrix = np.where(compliance_matrix == 0, np.nan, compliance_matrix)
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# get heatmap
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getComplianceHeatmap(dates, compliance_matrix, pid, snakemake.output[0], bin_size) |