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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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import sys |
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import glob |
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# import inspect |
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# import imageio |
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import hiplot as hip |
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import numpy as np |
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import pandas as pd |
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import streamlit as st |
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import plotly.express as px |
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def _score_statistics(search_data): |
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values_ = search_data["score"].values |
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mean_ = values_.mean() |
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std_ = values_.std() |
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min_ = np.amin(values_) |
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max_ = np.amax(values_) |
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df_data = pd.DataFrame( |
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[[mean_, std_, min_, max_]], |
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index=["score"], |
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columns=["mean", "std", "min", "max"], |
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) |
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col1, col2 = st.beta_columns(2) |
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col1.header("Score statistics") |
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col1.text("") |
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col2.text("") |
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col1.table(df_data) |
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def _score_statistics_plot(search_data): |
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fig = px.histogram( |
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search_data, x="score", nbins=int(len(search_data)) |
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).update_layout(width=1000, height=300) |
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col2.plotly_chart(fig) |
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_score_statistics_plot(search_data) |
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def _1d_scatter(search_data): |
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para_names = search_data.columns.drop("score") |
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st.text("") |
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col1, col2 = st.beta_columns(2) |
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col1.header("1D Scatter plot") |
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col1.text("") |
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scatter1_para1 = col1.selectbox( |
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"1D scatter plot parameter 1", |
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para_names, |
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index=0, |
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) |
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def _1d_scatter_plot(search_data): |
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fig = px.scatter( |
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search_data, x=scatter1_para1, y=search_data["score"] |
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).update_layout(width=1000, height=600) |
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col2.plotly_chart(fig) |
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_1d_scatter_plot(search_data) |
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def _2d_scatter(search_data): |
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para_names = search_data.columns.drop("score") |
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st.text("") |
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col1, col2 = st.beta_columns(2) |
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col1.header("2D Scatter plot") |
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col1.text("") |
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scatter2_para1 = col1.selectbox( |
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"2D scatter plot parameter 1", |
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para_names, |
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index=0, |
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) |
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scatter2_para2 = col1.selectbox( |
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"2D scatter plot parameter 2", |
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para_names, |
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index=1, |
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) |
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def _2d_scatter_plot(search_data): |
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fig = px.scatter( |
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search_data, x=scatter2_para1, y=scatter2_para2, color="score" |
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).update_layout(width=1000, height=600) |
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col2.plotly_chart(fig) |
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_2d_scatter_plot(search_data) |
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def _3d_scatter(search_data): |
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para_names = search_data.columns.drop("score") |
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st.text("") |
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col1, col2 = st.beta_columns(2) |
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col1.header("3D Scatter plot") |
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col1.text("") |
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scatter3_para1 = col1.selectbox( |
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"3D scatter plot parameter 1", |
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para_names, |
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index=0, |
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) |
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scatter3_para2 = col1.selectbox( |
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"3D scatter plot parameter 2", |
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para_names, |
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index=1, |
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) |
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scatter3_para3 = col1.selectbox( |
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"3D scatter plot parameter 3", |
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para_names, |
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index=2, |
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) |
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def _3d_scatter_plot(search_data): |
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fig = px.scatter_3d( |
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search_data, |
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x=scatter3_para1, |
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y=scatter3_para2, |
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z=scatter3_para3, |
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color="score", |
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).update_layout(width=1000, height=600) |
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col2.plotly_chart(fig) |
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_3d_scatter_plot(search_data) |
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def _parallel_coordinates(search_data): |
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st.text("") |
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col1, col2 = st.beta_columns(2) |
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col1.header("Parallel Corrdinates") |
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col1.text("") |
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col2.text("") |
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xp = hip.Experiment.from_dataframe(search_data) |
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ret_val = xp.display_st(key="key1") |
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plots_dict = { |
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"score_statistics": _score_statistics, |
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"1d_scatter": _1d_scatter, |
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"2d_scatter": _2d_scatter, |
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"3d_scatter": _3d_scatter, |
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"parallel_coordinates": _parallel_coordinates, |
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} |
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st.set_page_config(page_title="Hyperactive Dashboard", layout="wide") |
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path = sys.argv[1] |
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streamlit_plot_args = sys.argv[2:] |
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search_data = pd.read_csv(path) |
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# print("\n search_data \n", search_data) |
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st.title("Hyperactive Dashboard") |
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st.text("") |
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st.text("") |
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if len(search_data) > 0: |
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# --- # create plots in order of "streamlit_plot_args" |
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for streamlit_plot_arg in streamlit_plot_args: |
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plots_dict[streamlit_plot_arg](search_data) |
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else: |
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st.subheader("---> Error: Search data is empty!") |
SimonBlanke /
Hyperactive
