import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_altair import show_test
from vega_datasets import data
#' <h2>Boxplot from melted data</h2>
@viz_reg_test
def test_boxplot_melted():
return ar.boxplot(data.iris(), columns="petalLength", group_by="species")
show_test(test_boxplot_melted)
#' <h2>Boxplot from cast data</h2>
@viz_reg_test
def test_boxplot_cast():
iris = data.iris()
return ar.boxplot(iris, columns=list(iris.columns[:-1]))
show_test(test_boxplot_cast)
import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_altair import show_test
import numpy as np
import pandas as pd
#' <h2>Autocorrelation</h2>
@viz_reg_test
def test_autocorrelation():
np.random.seed(0)
data = pd.DataFrame(dict(x=np.random.uniform(size=100)))
return ar.autocorrelation(data, column="x", max_lag=15)
show_test(test_autocorrelation)
import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_altair import show_test
import numpy as np
import pandas as pd
#' <h2>Qqplot</h2>
@viz_reg_test
def test_qqplot():
df = pd.DataFrame({
"Trial A": np.random.normal(0, 0.8, 1000),
"Trial B": np.random.normal(-2, 1, 1000),
"Trial C": np.random.uniform(3, 2, 1000),
})
return ar.qqplot(df, x="Trial A", y="Trial C")
show_test(test_qqplot)
#' <h2>Heatmap</h2>
@viz_reg_test
def test_heatmap():
# Compute x^2 + y^2 across a 2D grid
x, y = np.meshgrid(range(-5, 6), range(-5, 6))
z = x**2 + y**2
# Convert this grid to columnar data expected by Altair
data = pd.DataFrame({"x": x.ravel(), "y": y.ravel(), "z": z.ravel()})
return ar.heatmap(data, x="x", y="y", color="z")
show_test(test_heatmap)
#' <h2>Count Heatmap</h2>
@viz_reg_test
def test_count_heatmap():
source = data.movies.url
return ar.count_heatmap(source,
x="IMDB_Rating",
y="Rotten_Tomatoes_Rating")
show_test(test_count_heatmap)
#' <h2>Scatter</h2>
@viz_reg_test
def test_scatter():
return ar.scatter(
data.iris(),
x="petalWidth",
y="petalLength",
color="sepalWidth",
tooltip="species",
)
show_test(test_scatter)
#' <h2>Multiscatter at defaults</h2>
@viz_reg_test
def test_multiscatter_defaults():
return ar.multiscatter(data.iris())
show_test(test_multiscatter_defaults)
#' <h2>Multiscatter with explicit parameters</h2>
"""Test smoother."""
import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_altair import show_test
import numpy as np
import pandas as pd
#' <h2>Smoother</h2>
@viz_reg_test
def test_smoother():
np.random.seed(0)
x = np.random.uniform(size=100)
data = pd.DataFrame(dict(x=x, y=np.random.uniform(size=100) + 10 * x))
return ar.smoother(data, *list(data.columns))
show_test(test_smoother)
from altair_recipes.common import viz_reg_test, gather
from altair_recipes.display_altair import show_test
import numpy as np
import pandas as pd
from vega_datasets import data
#' <h2>Histogram</h2>
@viz_reg_test
def test_histogram():
return ar.histogram(data.movies(), column="IMDB_Rating")
show_test(test_histogram)
#' <h2>Layered Histogram from wide data</h2>
@viz_reg_test
def test_layered_histogram_wide():
np.random.seed(0)
df = pd.DataFrame(
{
"Trial A": np.random.normal(0, 0.8, 1000),
"Trial B": np.random.normal(-2, 1, 1000),
"Trial C": np.random.normal(3, 2, 1000),
}
)
return ar.layered_histogram(df, columns=["Trial A", "Trial B", "Trial C"])