Create test_altair.py

test_altair.py

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+import altair as alt
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+from streamlit_vega_lite import vega_lite_component, altair_component, _component_func
+
+hist_data = pd.DataFrame(np.random.normal(42, 10, (200, 1)), columns=["abc"])
+print(hist_data)
+
+@st.cache_resource
+def altair_histogram():
+    brushed = alt.selection_interval(encodings=["x"], name="brushed")
+
+    return (
+        alt.Chart(hist_data)
+        .mark_bar()
+        .encode(alt.X("abc:Q", bin=True), y="count()")
+        .add_selection(brushed)
+    )
+
+chart = altair_histogram()
+res = st.altair_chart(chart, use_container_width=True)
+# print(res)
+event_dict = altair_component(altair_chart=altair_histogram())
+chart_dict = chart.to_dict()
+print(chart_dict)
+altair_chart = chart.copy()
+datasets = {}
+
+def id_transform(data):
+    """Altair data transformer that returns a fake named dataset with the
+    object id."""
+    name = f"d{id(data)}"
+    datasets[name] = data
+    return {"name": name}
+
+alt.data_transformers.register("id", id_transform)
+
+with alt.data_transformers.enable("id"):
+    chart_dict = altair_chart.to_dict()
+# st.write(event_dict)
+
+event_dict = _component_func(spec=chart_dict, **datasets, key=None, default={})
+# print(chart_dict)
+
+r = event_dict.get("abc")
+if r:
+    filtered = hist_data[(hist_data.abc >= r[0]) & (hist_data.abc < r[1])]
+    st.write(filtered)