86 lines
4.8 KiB
Python
86 lines
4.8 KiB
Python
import pandas as pd
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import numpy as np
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import plotly.graph_objects as go
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import yaml
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def getPhoneDataYieldHeatmap(data_for_plot, y_axis_labels, time_segment, type, html_file):
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fig = go.Figure(data=go.Heatmap(z=data_for_plot.values.tolist(),
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x=data_for_plot.columns.tolist(),
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y=y_axis_labels,
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hovertext=data_for_plot.values.tolist(),
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hovertemplate="Time since first segment: %{x}<br>Participant: %{y}<br>Ratiovalidyielded" + type + ": %{z}<extra></extra>",
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zmin=0, zmax=1,
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colorscale="Viridis"))
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fig.update_layout(title="Heatmap of valid yielded " + type + " ratio for " + time_segment + " segments.<br>y-axis shows participant information (format: pid.label).<br>x-axis shows the time since their first segment.<br>z-axis (color) shows valid yielded " + type + " ratio during a segment instance.")
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fig["layout"]["xaxis"].update(side="bottom")
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fig["layout"].update(xaxis_title="Time Since First Segment")
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fig["layout"].update(margin=dict(t=160))
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html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn"))
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y_axis_labels, phone_data_yield_minutes, phone_data_yield_hours = [], {}, {}
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for phone_data_yield_path, participant_file_path, time_segments_path in zip(snakemake.input["phone_data_yield"], snakemake.input["participant_file"], snakemake.input["time_segments_labels"]):
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# set pid.label as y_axis_label
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pid = phone_data_yield_path.split("/")[3]
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time_segments = pd.read_csv(time_segments_path, header=0)["label"]
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with open(participant_file_path, "r", encoding="utf-8") as f:
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participant_file = yaml.safe_load(f)
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label = participant_file["PHONE"]["LABEL"]
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y_axis_label = pid + "." + label
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y_axis_labels.append(y_axis_label)
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phone_data_yield = pd.read_csv(phone_data_yield_path, index_col=["local_segment_start_datetime"], parse_dates=["local_segment_start_datetime"])
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# make sure the phone_data_yield file contains "phone_data_yield_rapids_ratiovalidyieldedminutes" and "phone_data_yield_rapids_ratiovalidyieldedhours" columns
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if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns):
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raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].")
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if not phone_data_yield.empty:
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for time_segment in time_segments:
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phone_data_yield_per_segment = phone_data_yield[phone_data_yield["local_segment_label"] == time_segment]
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if not phone_data_yield_per_segment.empty:
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# set number of minutes after the first start date time of local segments as x_axis_label
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phone_data_yield_per_segment.index = phone_data_yield_per_segment.index - phone_data_yield_per_segment.index.min()
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phone_data_yield_minutes_per_segment = phone_data_yield_per_segment[["phone_data_yield_rapids_ratiovalidyieldedminutes"]].rename(columns={"phone_data_yield_rapids_ratiovalidyieldedminutes": y_axis_label})
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phone_data_yield_hours_per_segment = phone_data_yield_per_segment[["phone_data_yield_rapids_ratiovalidyieldedhours"]].rename(columns={"phone_data_yield_rapids_ratiovalidyieldedhours": y_axis_label})
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if time_segment not in phone_data_yield_minutes.keys():
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phone_data_yield_minutes[time_segment] = phone_data_yield_minutes_per_segment
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phone_data_yield_hours[time_segment] = phone_data_yield_hours_per_segment
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else:
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phone_data_yield_minutes[time_segment] = pd.concat([phone_data_yield_minutes[time_segment], phone_data_yield_minutes_per_segment], axis=1, sort=True)
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phone_data_yield_hours[time_segment] = pd.concat([phone_data_yield_hours[time_segment], phone_data_yield_hours_per_segment], axis=1, sort=True)
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html_file = open(snakemake.output[0], "a", encoding="utf-8")
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if len(phone_data_yield_minutes.keys()) == 0:
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html_file.write("There is no sensor data for the sensors in [PHONE_DATA_YIELD][SENSORS].")
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for time_segment in phone_data_yield_minutes.keys():
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minutes_data_for_plot = phone_data_yield_minutes[time_segment].transpose().reindex(pd.Index(y_axis_labels)).round(3)
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hours_data_for_plot = phone_data_yield_hours[time_segment].transpose().reindex(pd.Index(y_axis_labels)).round(3)
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getPhoneDataYieldHeatmap(minutes_data_for_plot, y_axis_labels, time_segment, "minutes", html_file)
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getPhoneDataYieldHeatmap(hours_data_for_plot, y_axis_labels, time_segment, "hours", html_file)
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html_file.close()
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