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import gradio as gr
import numpy as np
from data import temp_sensor_data, food_rating_data
with gr.Blocks() as scatter_plots:
with gr.Row():
start = gr.DateTime("2021-01-01 00:00:00", label="Start")
end = gr.DateTime("2021-01-05 00:00:00", label="End")
apply_btn = gr.Button("Apply", scale=0)
with gr.Row():
group_by = gr.Radio(["None", "30m", "1h", "4h", "1d"], value="None", label="Group by")
aggregate = gr.Radio(["sum", "mean", "median", "min", "max"], value="sum", label="Aggregation")
temp_by_time = gr.ScatterPlot(
temp_sensor_data,
x="time",
y="temperature",
)
temp_by_time_location = gr.ScatterPlot(
temp_sensor_data,
x="time",
y="temperature",
color="location",
)
time_graphs = [temp_by_time, temp_by_time_location]
group_by.change(
lambda group: [gr.ScatterPlot(x_bin=None if group == "None" else group)] * len(time_graphs),
group_by,
time_graphs
)
aggregate.change(
lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs),
aggregate,
time_graphs
)
# def rescale(select: gr.SelectData):
# return select.index
# rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])
# for trigger in [apply_btn.click, rescale_evt.then]:
# trigger(
# lambda start, end: [gr.ScatterPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs
# )
price_by_cuisine = gr.ScatterPlot(
food_rating_data,
x="cuisine",
y="price",
)
with gr.Row():
price_by_rating = gr.ScatterPlot(
food_rating_data,
x="rating",
y="price",
color="wait",
show_actions_button=True,
)
price_by_rating_color = gr.ScatterPlot(
food_rating_data,
x="rating",
y="price",
color="cuisine",
# color_map={"Italian": "red", "Mexican": "green", "Chinese": "blue"},
)
if __name__ == "__main__":
scatter_plots.launch()
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