88 lines
4.8 KiB
Python
88 lines
4.8 KiB
Python
import pandas as pd
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import plotly.express as px
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import yaml
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def getPidAndLabel(participant_file_paths, pids):
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pid2label, y_axis_labels = {}, []
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for participant_file_path, pid in zip(participant_file_paths, pids):
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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 = str(participant_file["PHONE"]["LABEL"])
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pid2label[pid] = label
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y_axis_labels.append(pid + "." + label)
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return pid2label, y_axis_labels
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def getPhoneDataYieldHeatmap(phone_data_yield, time, time_segment, html_file):
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if time == "RELATIVE_TIME":
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# Number of minutes after the first start date time of local segments
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phone_data_yield["local_segment_end_datetime"] = (phone_data_yield["local_segment_end_datetime"] - phone_data_yield["local_segment_start_datetime"].min()) + pd.Timestamp(2000,1,1)
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phone_data_yield["local_segment_start_datetime"] = (phone_data_yield["local_segment_start_datetime"] - phone_data_yield["local_segment_start_datetime"].min()) + pd.Timestamp(2000,1,1)
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for type in ["minutes", "hours"]:
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column_name = "phone_data_yield_rapids_ratiovalidyielded" + type
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fig = px.timeline(phone_data_yield,
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x_start="local_segment_start_datetime",
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x_end="local_segment_end_datetime",
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y="y_axis_label",
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color=column_name,
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color_continuous_scale="Viridis",
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range_color=[0, 1],
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opacity=0.7,
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hover_data={'local_segment_start_datetime':False, 'local_segment_end_datetime':False, 'local_segment':True})
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fig.update_layout(title="Heatmap of valid yielded " + type + " ratio for " + time_segment + " segments and " + time.lower().replace("_", " ") + ".<br>y-axis shows participant information (format: pid.label).<br>x-axis shows the time" + (" since their first segment" if time == "RELATIVE_TIME" else "") + ".<br>z-axis (color) shows valid yielded " + type + " ratio during a segment instance.",
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xaxis=dict(side="bottom", title="Time Since First Segment" if time == "RELATIVE_TIME" else "Time"),
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yaxis=dict(side="left", title="Participant information"),
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margin=dict(t=160))
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if time == "RELATIVE_TIME":
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fig.update_layout(xaxis_tickformat="%y years %j days<br>%X")
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html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn"))
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return html_file
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pid2label, y_axis_labels = getPidAndLabel(snakemake.input["participant_files"], snakemake.params["pids"])
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time_segments_type = snakemake.params["time_segments_type"] # FREQUENCY or PERIODIC or EVENT
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time = snakemake.params["time"] # ABSOLUTE_TIME or RELATIVE_TIME
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time_segments = pd.read_csv(snakemake.input["time_segments_file"])["label"].unique()
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phone_data_yield = pd.read_csv(snakemake.input["phone_data_yield"], parse_dates=["local_segment_start_datetime", "local_segment_end_datetime"]).sort_values(by=["pid", "local_segment_start_datetime"])
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if time_segments_type == "FREQUENCY":
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phone_data_yield["local_segment_label"] = phone_data_yield["local_segment_label"].str[:-4]
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html_file = open(snakemake.output[0], "w", encoding="utf-8")
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if phone_data_yield.empty:
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html_file.write("There is no sensor data for the sensors in [PHONE_DATA_YIELD][SENSORS].")
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else:
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# Make sure the phone_data_yield file contains both "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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phone_data_yield.loc[:, ["phone_data_yield_rapids_ratiovalidyieldedminutes", "phone_data_yield_rapids_ratiovalidyieldedhours"]] = phone_data_yield.loc[:, ["phone_data_yield_rapids_ratiovalidyieldedminutes", "phone_data_yield_rapids_ratiovalidyieldedhours"]].round(3).clip(upper=1)
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phone_data_yield["y_axis_label"] = phone_data_yield["pid"].apply(lambda pid: pid + "." + str(pid2label[pid]))
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if time_segments_type == "EVENT":
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html_file = getPhoneDataYieldHeatmap(phone_data_yield, time, "event", html_file)
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else: # FREQUENCY or PERIODIC
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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].copy()
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if not phone_data_yield_per_segment.empty:
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html_file = getPhoneDataYieldHeatmap(phone_data_yield_per_segment, time, time_segment, html_file)
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html_file.close()
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