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import altair as alt
import gradio as gr
import numpy as np
import pandas as pd
from vega_datasets import data
def make_plot(plot_type):
if plot_type == "scatter_plot":
cars = data.cars()
return alt.Chart(cars).mark_point().encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin',
)
elif plot_type == "heatmap":
# Compute x^2 + y^2 across a 2D grid
x, y = np.meshgrid(range(-5, 5), range(-5, 5))
z = x ** 2 + y ** 2
# Convert this grid to columnar data expected by Altair
source = pd.DataFrame({'x': x.ravel(),
'y': y.ravel(),
'z': z.ravel()})
return alt.Chart(source).mark_rect().encode(
x='x:O',
y='y:O',
color='z:Q'
)
elif plot_type == "us_map":
states = alt.topo_feature(data.us_10m.url, 'states')
source = data.income.url
return alt.Chart(source).mark_geoshape().encode(
shape='geo:G',
color='pct:Q',
tooltip=['name:N', 'pct:Q'],
facet=alt.Facet('group:N', columns=2),
).transform_lookup(
lookup='id',
from_=alt.LookupData(data=states, key='id'),
as_='geo'
).properties(
width=300,
height=175,
).project(
type='albersUsa'
)
elif plot_type == "interactive_barplot":
source = data.movies.url
pts = alt.selection(type="single", encodings=['x'])
rect = alt.Chart(data.movies.url).mark_rect().encode(
alt.X('IMDB_Rating:Q', bin=True),
alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),
alt.Color('count()',
scale=alt.Scale(scheme='greenblue'),
legend=alt.Legend(title='Total Records')
)
)
circ = rect.mark_point().encode(
alt.ColorValue('grey'),
alt.Size('count()',
legend=alt.Legend(title='Records in Selection')
)
).transform_filter(
pts
)
bar = alt.Chart(source).mark_bar().encode(
x='Major_Genre:N',
y='count()',
color=alt.condition(pts, alt.ColorValue("steelblue"), alt.ColorValue("grey"))
).properties(
width=550,
height=200
).add_selection(pts)
plot = alt.vconcat(
rect + circ,
bar
).resolve_legend(
color="independent",
size="independent"
)
return plot
elif plot_type == "radial":
source = pd.DataFrame({"values": [12, 23, 47, 6, 52, 19]})
base = alt.Chart(source).encode(
theta=alt.Theta("values:Q", stack=True),
radius=alt.Radius("values", scale=alt.Scale(type="sqrt", zero=True, rangeMin=20)),
color="values:N",
)
c1 = base.mark_arc(innerRadius=20, stroke="#fff")
c2 = base.mark_text(radiusOffset=10).encode(text="values:Q")
return c1 + c2
elif plot_type == "multiline":
source = data.stocks()
highlight = alt.selection(type='single', on='mouseover',
fields=['symbol'], nearest=True)
base = alt.Chart(source).encode(
x='date:T',
y='price:Q',
color='symbol:N'
)
points = base.mark_circle().encode(
opacity=alt.value(0)
).add_selection(
highlight
).properties(
width=600
)
lines = base.mark_line().encode(
size=alt.condition(~highlight, alt.value(1), alt.value(3))
)
return points + lines
with gr.Blocks() as demo:
button = gr.Radio(label="Plot type",
choices=['scatter_plot', 'heatmap', 'us_map',
'interactive_barplot', "radial", "multiline"], value='scatter_plot')
plot = gr.Plot(label="Plot")
button.change(make_plot, inputs=button, outputs=[plot])
demo.load(make_plot, inputs=[button], outputs=[plot])
if __name__ == "__main__":
demo.launch()
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