import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_pweave 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)
"""Test smoother."""
import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_pweave import show_test
import numpy as np
import pandas as pd
#' <h2>Smoother</h2>
@viz_reg_test
def test_smoother():
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)
# return ar.autoplot(data.head({nrows}), columns={vars})
#
# show_test(test_autoplot_{n})
# """.format(
# nrows=nrows, vars=vars, n=n
# )
# )
#' <h3> Test autoplot #1</h3>
@viz_reg_test
def test_autoplot_1():
return ar.autoplot(data.head(10), columns=["x"])
show_test(test_autoplot_1)
#' <h3> Test autoplot #2</h3>
@viz_reg_test
def test_autoplot_2():
return ar.autoplot(data.head(50), columns=["x"])
show_test(test_autoplot_2)
#' <h3> Test autoplot #3</h3>
@viz_reg_test
import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_pweave import show_test
from vega_datasets import data
#' <h2>Lineplot</h2>
@viz_reg_test
def test_lineplot():
return ar.lineplot(data.iowa_electricity(),
x="year",
y="net_generation",
color="source")
show_test(test_lineplot)
#' <h2>Scatterplot</h2>
@viz_reg_test
def test_scatterplot():
return ar.scatterplot(
data.iris(),
x="petalWidth",
y="petalLength",
color="sepalWidth",
tooltip="species",
)
show_test(test_scatterplot)
#'<h2>Scatterplot alternate data syntax</h2>
@viz_reg_test
def test_scatterplot_alternate_data():
d = data.iris()
return ar.scatterplot(
x=d["petalWidth"],
y=d["petalLength"],
color=d["sepalWidth"],
tooltip=d["species"],
)
import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_pweave import show_test
from vega_datasets import data
#' <h2>Barchart</h2>
@viz_reg_test
def test_barchart_color():
source = data.barley()
return ar.barchart(source, x="year", y="mean(yield)", color=True)
show_test(test_barchart_color)
import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_pweave import show_test
import numpy as np
import pandas as pd
#' <h2>Stripplot</h2>
@viz_reg_test
def test_stripplot():
x = np.array(range(100)) // 10
data = pd.DataFrame(dict(x=x, y=np.random.normal(size=len(x))))
return ar.stripplot(data)
show_test(test_stripplot)
#' <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.heatmap(source,
x="IMDB_Rating",
y="Rotten_Tomatoes_Rating",
color="",
aggregate="count")
show_test(test_count_heatmap)
import altair as alt
import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_pweave import show_test
from vega_datasets import data
#' <h2>Areaplot</h2>
@viz_reg_test
def test_areaplot():
return alt.vconcat(*map(
lambda stack: ar.areaplot(
data.iowa_electricity(),
x="year",
y="net_generation",
color="source",
stack=stack,
),
ar.StackType,
))
show_test(test_areaplot)
import altair_recipes as ar
from altair_recipes.common import viz_reg_test, gather
from altair_recipes.display_pweave 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():
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"])
show_test(test_layered_histogram_wide)
import altair_recipes as ar
from altair_recipes.common import viz_reg_test
from altair_recipes.display_pweave import show_test
import numpy as np
import pandas as pd
#' <h2>Autocorrelation</h2>
@viz_reg_test
def test_autocorrelation():
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_pweave 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)
#' <h2>Boxplot with color</h2>
@viz_reg_test
def test_boxplot_color():