57 lines
2.2 KiB
Python
57 lines
2.2 KiB
Python
import numpy as np
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import pandas as pd
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import plotly.graph_objects as go
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def getCorrMatrixHeatmap(corr_matrix, time_segment, html_file):
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feature_names = corr_matrix.columns
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fig = go.Figure(data=go.Heatmap(z=corr_matrix.values.tolist(),
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x=feature_names,
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y=feature_names,
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colorscale="Viridis",
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zmin=-1, zmax=1))
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fig.update_layout(title="Correlation matrix of features of " + time_segment + " segments.")
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html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn"))
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time_segments_type = snakemake.params["time_segments_type"]
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min_rows_ratio = snakemake.params["min_rows_ratio"]
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corr_threshold = snakemake.params["corr_threshold"]
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corr_method = snakemake.params["corr_method"]
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features = pd.read_csv(snakemake.input["all_sensor_features"])
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if time_segments_type == "FREQUENCY":
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features["local_segment_label"] = features["local_segment_label"].str[:-4]
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if time_segments_type == "EVENT":
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features["local_segment_label"] = "event"
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time_segments = set(features["local_segment_label"])
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html_file = open(snakemake.output[0], "a", encoding="utf-8")
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if features.empty:
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html_file.write("There are no features for any participant.")
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else:
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for time_segment in time_segments:
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features_per_segment = features[features["local_segment_label"] == time_segment]
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if features_per_segment.empty:
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html_file.write("There are no features for " + time_segment + " segments.<br>")
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else:
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# drop useless columns
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features_per_segment = features_per_segment.drop(["pid", "local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"], axis=1).astype(float)
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# get correlation matrix
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corr_matrix = features_per_segment.corr(method=corr_method, min_periods=min_rows_ratio * features_per_segment.shape[0])
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# replace correlation coefficients less than corr_threshold with NA
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corr_matrix[(corr_matrix > -corr_threshold) & (corr_matrix < corr_threshold)] = np.nan
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# plot heatmap
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getCorrMatrixHeatmap(corr_matrix, time_segment, html_file)
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html_file.close()
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