70 lines
4.1 KiB
Python
70 lines
4.1 KiB
Python
import numpy as np
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import pandas as pd
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import plotly.io as pio
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import plotly.graph_objects as go
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from datetime import datetime, timedelta
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def getRowCountHeatmap(row_count_sensors_normalized, row_count_sensors, pid, output_path):
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plot = go.Figure(data=go.Heatmap(z=row_count_sensors_normalized.T.values.tolist(),
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x=[datetime.strftime(idx[0], "%Y/%m/%d")+"("+str(idx[1])+")" for idx in row_count_sensors.index],
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y=row_count_sensors.columns.tolist(),
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hovertext=row_count_sensors.T.values.tolist(),
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hovertemplate="Date: %{x}<br>Sensor: %{y}<br>Row count: %{hovertext}<extra></extra>",
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colorscale="Viridis"))
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plot.update_layout(title="Row count heatmap for " + pid)
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pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn")
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phone_valid_sensed_days = pd.read_csv(snakemake.input["phone_valid_sensed_days"], parse_dates=["local_date"], index_col=["local_date"])
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phone_valid_sensed_days = phone_valid_sensed_days[phone_valid_sensed_days["is_valid_sensed_day"] == True]
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row_count_sensors = pd.DataFrame()
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for sensor_path in snakemake.input["sensors"]:
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sensor_name = sensor_path.split("/")[-1].replace("_with_datetime.csv", "")
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# plugin_studentlife_audio_android or plugin_studentlife_audio => conversion; plugin_google_activity_recognition or plugin_ios_activity_recognition => AR; applications_foreground => apps
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sensor_name = sensor_name.replace("plugin_studentlife_audio_android", "conversion").replace("plugin_studentlife_audio", "conversion") \
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.replace("plugin_google_activity_recognition", "AR").replace("plugin_ios_activity_recognition", "AR") \
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.replace("applications_foreground", "apps")
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sensor_data = pd.read_csv(sensor_path, encoding="ISO-8859-1", parse_dates=["local_date"], dtype={"label": str})
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if sensor_data.empty:
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row_count_sensor = pd.DataFrame(columns=[sensor_name])
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else:
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row_count_sensor = sensor_data[["timestamp", "local_date"]].groupby(["local_date"]).count().rename(columns={"timestamp": sensor_name})
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row_count_sensors = row_count_sensors.join(row_count_sensor, how="outer")
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row_count_sensors.index = pd.to_datetime(row_count_sensors.index)
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row_count_sensors = row_count_sensors.join(phone_valid_sensed_days[["valid_sensed_hours"]], how="outer")
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# set date_idx based on the first date
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reference_date = row_count_sensors.index.min()
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last_date = row_count_sensors.index.max()
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row_count_sensors["date_idx"] = (row_count_sensors.index - reference_date).days
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row_count_sensors["local_date"] = row_count_sensors.index
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row_count_sensors.set_index(["local_date", "date_idx"], inplace=True)
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expected_num_of_days = int(snakemake.params["expected_num_of_days"])
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if expected_num_of_days < -1:
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raise ValueError("EXPECTED_NUM_OF_DAYS of HEATMAP_DAYS_BY_SENSORS section in config.yaml must be larger or equal to -1.")
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# if expected_num_of_days = -1, return all dates
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expected_num_of_days = (last_date - reference_date).days if expected_num_of_days == -1 else expected_num_of_days
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# add empty rows to make sure different participants have the same date_idx range
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date_idx_range = [idx for idx in range(expected_num_of_days)]
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date_range = [reference_date + timedelta(days=idx) for idx in date_idx_range]
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all_dates = pd.DataFrame({"local_date": date_range, "date_idx": date_idx_range})
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all_dates.set_index(["local_date", "date_idx"], inplace=True)
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row_count_sensors = row_count_sensors.merge(all_dates, left_index=True, right_index=True, how="right")
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# normalize each sensor (column)
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if row_count_sensors.count().max() > 1:
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row_count_sensors_normalized = row_count_sensors.fillna(np.nan).apply(lambda x: (x - np.nanmin(x)) / (np.nanmax(x) - np.nanmin(x)) if np.nanmax(x) != np.nanmin(x) else (x / np.nanmin(x)), axis=0)
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else:
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row_count_sensors_normalized = row_count_sensors
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pid = sensor_path.split("/")[2]
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getRowCountHeatmap(row_count_sensors_normalized, row_count_sensors, pid, snakemake.output[0])
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