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91 lines
2.7 KiB
Python
91 lines
2.7 KiB
Python
# type: ignore
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import gradio as gr
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import xyzservices.providers as xyz
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from bokeh.models import ColumnDataSource, Whisker
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from bokeh.plotting import figure
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from bokeh.sampledata.autompg2 import autompg2 as df
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from bokeh.sampledata.penguins import data
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from bokeh.transform import factor_cmap, jitter, factor_mark
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def get_plot(plot_type):
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if plot_type == "map":
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plot = figure(
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x_range=(-2000000, 6000000),
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y_range=(-1000000, 7000000),
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x_axis_type="mercator",
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y_axis_type="mercator",
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)
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plot.add_tile(xyz.OpenStreetMap.Mapnik) # type: ignore
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return plot
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elif plot_type == "whisker":
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classes = sorted(df["class"].unique())
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p = figure(
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height=400,
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x_range=classes,
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background_fill_color="#efefef",
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title="Car class vs HWY mpg with quintile ranges",
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)
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p.xgrid.grid_line_color = None
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g = df.groupby("class")
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upper = g.hwy.quantile(0.80)
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lower = g.hwy.quantile(0.20)
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source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))
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error = Whisker(
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base="base",
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upper="upper",
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lower="lower",
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source=source,
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level="annotation",
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line_width=2,
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)
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error.upper_head.size = 20
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error.lower_head.size = 20
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p.add_layout(error)
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p.circle(
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jitter("class", 0.3, range=p.x_range),
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"hwy",
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source=df,
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alpha=0.5,
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size=13,
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line_color="white",
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color=factor_cmap("class", "Light6", classes),
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)
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return p
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elif plot_type == "scatter":
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SPECIES = sorted(data.species.unique())
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MARKERS = ["hex", "circle_x", "triangle"]
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p = figure(title="Penguin size", background_fill_color="#fafafa")
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p.xaxis.axis_label = "Flipper Length (mm)"
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p.yaxis.axis_label = "Body Mass (g)"
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p.scatter(
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"flipper_length_mm",
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"body_mass_g",
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source=data,
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legend_group="species",
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fill_alpha=0.4,
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size=12,
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marker=factor_mark("species", MARKERS, SPECIES),
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color=factor_cmap("species", "Category10_3", SPECIES),
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)
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p.legend.location = "top_left"
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p.legend.title = "Species"
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return p
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with gr.Blocks() as demo:
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with gr.Row():
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plot_type = gr.Radio(value="scatter", choices=["scatter", "whisker", "map"])
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plot = gr.Plot()
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plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])
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demo.load(get_plot, inputs=[plot_type], outputs=[plot])
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if __name__ == "__main__":
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demo.launch()
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