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from bokeh.models import ColumnDataSource, CustomJS | |
from bokeh.palettes import Category20 | |
from bokeh.plotting import figure | |
from bokeh.transform import linear_cmap | |
import numpy as np | |
import pandas as pd | |
import streamlit as st | |
from sklearn.datasets import make_blobs | |
from state import provide_state | |
st.set_page_config(layout="wide") | |
from streamlit_bokeh_events import streamlit_bokeh_events | |
from functools import partial | |
@st.cache | |
def load_data(): | |
X, y = make_blobs(n_features=2, centers=3) | |
return pd.DataFrame({"x": X[:, 0], "y": X[:, 1], "label": y}) | |
@provide_state | |
def main(state): | |
df = load_data() | |
st.title("Clustering") | |
st.subheader("Umap Embedding") | |
col1, col2 = st.beta_columns([2,1]) | |
with col1: | |
p1 = figure(sizing_mode="stretch_both", | |
tools="pan,box_select,lasso_select,wheel_zoom,reset", | |
active_drag="lasso_select", | |
active_scroll="wheel_zoom", | |
**(state.zoom or {})) | |
cmap = linear_cmap("label", palette=Category20[20], low=df["label"].min(), high=df["label"].max()) | |
cds = ColumnDataSource(df) | |
cds.selected.indices = state.selected_points or [] | |
p1.circle("x", "y", source=cds, color=cmap, line_color=cmap) | |
cds.selected.js_on_change("indices", CustomJS(args=dict(source=cds, plot=p1), | |
code=""" | |
document.dispatchEvent(new CustomEvent("select_event", | |
{detail: { | |
indices: cb_obj.indices, | |
zoom: { | |
y_range: [plot.y_range.start, plot.y_range.end], | |
x_range: [plot.x_range.start, plot.x_range.end] | |
} | |
} | |
})) | |
""" | |
) | |
) | |
event_result = streamlit_bokeh_events(p1, "select_event", key="select_event") | |
with col2: | |
if event_result is not None: | |
state.selected_points = event_result["select_event"]["indices"] | |
state.zoom = event_result["select_event"]["zoom"] | |
st.dataframe(df.iloc[event_result["select_event"]["indices"]]) | |
main() |
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