Spaces:
Runtime error
Runtime error
File size: 4,816 Bytes
ed4ba7d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 |
from pathlib import Path
from typing import List, Dict, Tuple
import matplotlib.colors as mpl_colors
import pandas as pd
import seaborn as sns
import shinyswatch
import shiny.experimental as x
from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
sns.set_theme()
www_dir = Path(__file__).parent.resolve() / "www"
df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
species: List[str] = df["Species"].unique().tolist()
species.sort()
app_ui = x.ui.page_fillable(
shinyswatch.theme.minty(),
x.ui.layout_sidebar(
x.ui.sidebar(
# Artwork by @allison_horst
ui.input_selectize(
"xvar",
"X variable",
numeric_cols,
selected="Bill Length (mm)",
),
ui.input_selectize(
"yvar",
"Y variable",
numeric_cols,
selected="Bill Depth (mm)",
),
ui.input_checkbox_group(
"species", "Filter by species", species, selected=species
),
ui.hr(),
ui.input_switch("by_species", "Show species", value=True),
ui.input_switch("show_margins", "Show marginal plots", value=True),
),
ui.output_ui("value_boxes"),
x.ui.output_plot("scatter", fill=True),
ui.help_text(
"Artwork by ",
ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
class_="text-end",
),
fill=True,
fillable=True,
),
)
def server(input: Inputs, output: Outputs, session: Session):
@reactive.Calc
def filtered_df() -> pd.DataFrame:
"""Returns a Pandas data frame that includes only the desired rows"""
# This calculation "req"uires that at least one species is selected
req(len(input.species()) > 0)
# Filter the rows so we only include the desired species
return df[df["Species"].isin(input.species())]
@output
@render.plot
def scatter():
"""Generates a plot for Shiny to display to the user"""
# The plotting function to use depends on whether margins are desired
plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
plotfunc(
data=filtered_df(),
x=input.xvar(),
y=input.yvar(),
palette=palette,
hue="Species" if input.by_species() else None,
hue_order=species,
legend=False,
)
@output
@render.ui
def value_boxes():
df = filtered_df()
def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
return x.ui.value_box(
title,
count,
{"class_": "pt-1 pb-0"},
showcase=x.ui.bind_fill_role(
ui.tags.img(
{"style": "object-fit:contain;"},
src=showcase_img,
),
item=True,
),
theme_color=None,
style=f"background-color: {bgcol};",
height="90px",
full_screen=True,
)
if not input.by_species():
return penguin_value_box(
"Penguins",
len(df.index),
bg_palette["default"],
# Artwork by @allison_horst
showcase_img="penguins.png",
)
value_boxes = [
penguin_value_box(
name,
len(df[df["Species"] == name]),
bg_palette[name],
# Artwork by @allison_horst
showcase_img=f"{name}.png",
)
for name in species
# Only include boxes for _selected_ species
if name in input.species()
]
return x.ui.layout_column_wrap(1 / len(value_boxes), *value_boxes)
# "darkorange", "purple", "cyan4"
colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
palette: Dict[str, Tuple[float, float, float]] = {
"Adelie": colors[0],
"Chinstrap": colors[1],
"Gentoo": colors[2],
"default": sns.color_palette()[0], # type: ignore
}
bg_palette = {}
# Use `sns.set_style("whitegrid")` to help find approx alpha value
for name, col in palette.items():
# Adjusted n_colors until `axe` accessibility did not complain about color contrast
bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1]) # type: ignore
app = App(
app_ui,
server,
static_assets=str(www_dir),
)
|