diff --git a/rules/reports.smk b/rules/reports.smk index caed8993..a2c24468 100644 --- a/rules/reports.smk +++ b/rules/reports.smk @@ -35,7 +35,9 @@ rule heatmap_sensor_row_count_per_time_segment: participant_file = "data/external/participant_files/{pid}.yaml", time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - pid = "{pid}" + pid = "{pid}", + sensor_names = config["HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT"]["SENSORS"], + time_segments_type = config["TIME_SEGMENTS"]["TYPE"] output: "reports/interim/{pid}/heatmap_sensor_row_count_per_time_segment.html" script: diff --git a/src/visualization/heatmap_sensor_row_count_per_time_segment.py b/src/visualization/heatmap_sensor_row_count_per_time_segment.py index 6b62e6e1..df89a5dc 100644 --- a/src/visualization/heatmap_sensor_row_count_per_time_segment.py +++ b/src/visualization/heatmap_sensor_row_count_per_time_segment.py @@ -1,89 +1,91 @@ import pandas as pd import numpy as np -import plotly.graph_objects as go +import plotly.express as px from importlib import util from pathlib import Path import yaml - -def getRowCountHeatmap(data_for_plot, scaled_data_for_plot, pid, time_segment, html_file): - - fig = go.Figure(data=go.Heatmap(z=scaled_data_for_plot.values.tolist(), - x=data_for_plot.columns, - y=data_for_plot.index, - hovertext=data_for_plot.values.tolist(), - hovertemplate="Segment start: %{x}
Sensor: %{y}
Row count: %{hovertext}", - zmin=0, zmax=1, - colorscale='Viridis')) - - fig.update_layout(title="Heatmap of sensor row count for " + time_segment + " segments. Pid: " + pid +". Label: " + label + "
y-axis shows the included sensors.
x-axis shows the start (date and time) of a time segment.
z-axis (color) shows row count per sensor per segment instance.") - fig["layout"].update(margin=dict(t=160)) - - html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) - - - - # import filter_data_by_segment from src/features/utils/utils.py spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "features" / "utils" / "utils.py")) mod = util.module_from_spec(spec) spec.loader.exec_module(mod) filter_data_by_segment = getattr(mod, "filter_data_by_segment") +def getRowCountHeatmap(data_for_plot, pid, time_segment, html_file): + + fig = px.timeline(data_for_plot, + x_start="local_segment_start_datetime", + x_end="local_segment_end_datetime", + y="sensor", + color="scaled_value", + color_continuous_scale="Peach", #"Viridis", + opacity=0.7, + hover_data={"local_segment_start_datetime":False, "local_segment_end_datetime":False, "local_segment":True, "value":True, "scaled_value":True}) + + fig.update_layout(title="Heatmap of sensor row count for " + time_segment + " segments. Pid: " + pid +". Label: " + label + "
y-axis shows the included sensors.
x-axis shows time segments.
z-axis (color) shows row count per sensor per segment instance.", + xaxis=dict(side="bottom", title="Time Segments"), + yaxis=dict(side="left", title="Sensors"), + margin=dict(t=160)) + + html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) + + return html_file - -phone_data_yield = pd.read_csv(snakemake.input["phone_data_yield"], index_col=["local_segment_start_datetime"], parse_dates=["local_segment_start_datetime"]) -# make sure the phone_data_yield file contains "phone_data_yield_rapids_ratiovalidyieldedminutes" and "phone_data_yield_rapids_ratiovalidyieldedhours" columns -if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns): - raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].") -phone_data_yield = phone_data_yield[["local_segment_label", "phone_data_yield_rapids_ratiovalidyieldedminutes", "phone_data_yield_rapids_ratiovalidyieldedhours"]] - -time_segments = pd.read_csv(snakemake.input["time_segments_labels"], header=0)["label"] -pid = snakemake.params["pid"] - with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: participant_file = yaml.safe_load(f) label = participant_file["PHONE"]["LABEL"] -sensor_names = [] -sensors_row_count = dict(zip(time_segments, [pd.DataFrame()] * len(time_segments))) +pid = snakemake.params["pid"] +sensor_names = [sensor_name.lower() for sensor_name in snakemake.params["sensor_names"]] +time_segments_type = snakemake.params["time_segments_type"] +time_segments_labels = pd.read_csv(snakemake.input["time_segments_labels"], header=0)["label"] -for sensor_path in snakemake.input["all_sensors"]: +phone_data_yield = pd.read_csv(snakemake.input["phone_data_yield"], index_col=["local_segment"], parse_dates=["local_segment_start_datetime", "local_segment_end_datetime"]) #index_col=["local_segment_start_datetime"], + +# make sure the phone_data_yield file contains "phone_data_yield_rapids_ratiovalidyieldedminutes" and "phone_data_yield_rapids_ratiovalidyieldedhours" columns +if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns): + raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].") + +# extract row count +sensors_row_count = pd.DataFrame() +for sensor_path, sensor_name in zip(snakemake.input["all_sensors"], sensor_names): sensor_data = pd.read_csv(sensor_path, usecols=["assigned_segments"]) - sensor_name = sensor_path.split("/")[-1].replace("_with_datetime.csv", "") - sensor_names.append(sensor_name) - + + sensor_row_count = pd.DataFrame() if not sensor_data.empty: - for time_segment in time_segments: + for time_segment in time_segments_labels: sensor_data_per_segment = filter_data_by_segment(sensor_data, time_segment) if not sensor_data_per_segment.empty: - # extract local start datetime of the segment from "local_segment" column - sensor_data_per_segment["local_segment_start_datetime"] = pd.to_datetime(sensor_data_per_segment["local_segment"].apply(lambda x: x.split("#")[1].split(",")[0])) - sensor_row_count = sensor_data_per_segment.groupby("local_segment_start_datetime")[["local_segment"]].count().rename(columns={"local_segment": sensor_name}) - sensors_row_count[time_segment] = pd.concat([sensors_row_count[time_segment], sensor_row_count], axis=1, sort=False) + sensor_row_count = pd.concat([sensor_row_count, sensor_data_per_segment.groupby(["local_segment"])[["local_segment"]].count().rename(columns={"local_segment": sensor_name})], axis=0, sort=False) + sensors_row_count = pd.concat([sensors_row_count, sensor_row_count], axis=1, sort=False) + +sensors_row_count.index.name = "local_segment" +sensors_row_count.index = sensors_row_count.index.str.replace(r"_RR\d+SS", "") +data_for_plot = phone_data_yield.rename(columns={"phone_data_yield_rapids_ratiovalidyieldedminutes": "ratiovalidyieldedminutes","phone_data_yield_rapids_ratiovalidyieldedhours": "ratiovalidyieldedhours"}).merge(sensors_row_count, how="left", left_index=True, right_index=True).reset_index() + + +if time_segments_type == "FREQUENCY": + data_for_plot["local_segment_label"] = data_for_plot["local_segment_label"].str[:-4] +elif time_segments_type == "EVENT": + data_for_plot["local_segment_label"] = "event" -# add phone data yield features and plot heatmap -html_file = open(snakemake.output[0], "a", encoding="utf-8") sensor_names.extend(["ratiovalidyieldedminutes", "ratiovalidyieldedhours"]) -for time_segment in time_segments: - if not phone_data_yield.empty: - phone_data_yield_per_segment = phone_data_yield[phone_data_yield["local_segment_label"] == time_segment].rename(columns={"phone_data_yield_rapids_ratiovalidyieldedminutes": "ratiovalidyieldedminutes","phone_data_yield_rapids_ratiovalidyieldedhours": "ratiovalidyieldedhours"}).round(3) - if not phone_data_yield_per_segment.empty: - sensors_row_count[time_segment] = pd.concat([sensors_row_count[time_segment], phone_data_yield_per_segment], axis=1, sort=True) - - # consider all the sensors - data_for_plot = sensors_row_count[time_segment].transpose().reindex(pd.Index(sensor_names)) - - if data_for_plot.empty: +html_file = open(snakemake.output[0], "a", encoding="utf-8") +for time_segment in set(data_for_plot["local_segment_label"]): + if not data_for_plot.empty: + data_for_plot_per_segment = data_for_plot[data_for_plot["local_segment_label"] == time_segment] + if data_for_plot_per_segment.empty: html_file.write("There are no records of selected sensors in database for " + time_segment + " segments. Pid: " + pid + ". Label: " + label + ".
") else: + data_for_plot_per_segment = data_for_plot_per_segment.reindex(columns=["local_segment", "local_segment_start_datetime", "local_segment_end_datetime"] + sensor_names).set_index(["local_segment", "local_segment_start_datetime", "local_segment_end_datetime"]) # except for phone data yield sensor, scale each sensor (row) to the range of [0, 1] - scaled_data_for_plot = data_for_plot.copy() - scaled_data_for_plot.loc[sensor_names[:-2]] = scaled_data_for_plot.fillna(np.nan).loc[sensor_names[:-2]].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=1) - - getRowCountHeatmap(data_for_plot, scaled_data_for_plot, pid, time_segment, html_file) + scaled_data_for_plot_per_segment = data_for_plot_per_segment.copy() + scaled_data_for_plot_per_segment[sensor_names[:-2]] = scaled_data_for_plot_per_segment.fillna(np.nan)[sensor_names[:-2]].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) + data_for_plot_processed = pd.concat([data_for_plot_per_segment.stack(dropna=False).to_frame("value"), scaled_data_for_plot_per_segment.stack(dropna=False).to_frame("scaled_value")], axis=1).reset_index().rename(columns={"level_3": "sensor"}) + data_for_plot_processed[["value", "scaled_value"]] = data_for_plot_processed[["value", "scaled_value"]].round(3).clip(upper=1) + getRowCountHeatmap(data_for_plot_processed, pid, time_segment, html_file) html_file.close()