def test_scale_transformed_breaks():
df = pd.DataFrame({'x': [1, 10, 100, 1000], 'y': range(4)})
p = (ggplot(df, aes('x', 'y')) +
geom_bin2d(breaks=([5, 50, 500], [0.5, 1.5, 2.5])))
out1 = layer_data(p)
out2 = layer_data(p + scale_x_log10())
np.testing.assert_allclose(out1.xmax, [50, 500])
np.testing.assert_allclose(out2.xmax, np.log10([50, 500]))
def test_scale_transformed_breaks():
df = pd.DataFrame({
'x': [1, 10, 100, 1000],
'y': range(4)
})
p = (ggplot(df, aes('x', 'y'))
+ geom_bin2d(breaks=([5, 50, 500], [0.5, 1.5, 2.5]))
)
out1 = layer_data(p)
out2 = layer_data(p + scale_x_log10())
np.testing.assert_allclose(out1.xmax, [50, 500])
np.testing.assert_allclose(out2.xmax, np.log10([50, 500]))
p9.scale_y_timedelta(labels=timedelta_format("d")) + p9.annotate(
"text",
x=10,
y=timedelta(days=1450),
label=f"Y={results_2.slope:.2f}*X+{results_2.intercept:.2f}",
) + p9.labs(
x="Euclidean Distance of Preprints First and Final Versions",
y="Time Elapsed Until Preprint is Published",
) + p9.theme_seaborn(
context="paper",
style="ticks", font="Arial", font_scale=1.3))
print(g)
g = (p9.ggplot(published_date_distances,
p9.aes(x="doc_distances", y="time_to_published")) +
p9.geom_bin2d(bins=100) + p9.scale_fill_distiller(trans="log",
direction=-1,
type="seq",
palette="YlGnBu",
name="log(count)") +
p9.geom_line(stat="smooth",
method="lm",
linetype="dashed",
se=False,
alpha=1,
size=0.7) +
p9.scale_y_timedelta(labels=timedelta_format("d")) + p9.annotate(
"text",
x=7.5,
y=timedelta(days=1490),
label=f"Y={results_2.slope:.2f}*X+{results_2.intercept:.2f}",
def test_drop_false():
p = ggplot(df, aes('x', 'y')) + geom_bin2d(binwidth=2, drop=False)
assert p + _theme == 'drop_false'
def test_drop_true():
p = ggplot(df, aes('x', 'y')) + geom_bin2d(binwidth=2, drop=True)
assert p + _theme == 'drop_true'
def test_drop_false():
p = ggplot(df, aes('x', 'y')) + geom_bin2d(binwidth=2, drop=False)
assert p + _theme == 'drop_false'
def test_drop_true():
p = ggplot(df, aes('x', 'y')) + geom_bin2d(binwidth=2, drop=True)
assert p + _theme == 'drop_true'
def histogram2d(
data: pd.DataFrame,
column1: str,
column2: str,
fig: plt.Figure = None,
ax: plt.Axes = None,
fig_width: int = 6,
fig_height: int = 6,
trend_line: str = "auto",
lower_quantile1: float = 0,
upper_quantile1: float = 1,
lower_quantile2: float = 0,
upper_quantile2: float = 1,
transform1: str = "identity",
transform2: str = "identity",
equalize_axes: bool = False,
reference_line: bool = False,
plot_density: bool = False,
) -> Tuple[plt.Figure, plt.Axes, p9.ggplot]:
"""
Creates an EDA plot for two continuous variables.
Args:
data: pandas DataFrame containing data to be plotted
column1: name of column to plot on the x axis
column2: name of column to plot on the y axis
fig: matplotlib Figure generated from blank ggplot to plot onto. If specified, must also specify ax
ax: matplotlib axes generated from blank ggplot to plot onto. If specified, must also specify fig
fig_width: figure width in inches
fig_height: figure height in inches
trend_line: Trend line to plot over data. Default is to plot no trend line. Other options are passed
to `geom_smooth <https://plotnine.readthedocs.io/en/stable/generated/plotnine.geoms.geom_smooth.html>`_.
lower_quantile1: Lower quantile of column1 data to remove before plotting for ignoring outliers
upper_quantile1: Upper quantile of column1 data to remove before plotting for ignoring outliers
lower_quantile2: Lower quantile of column2 data to remove before plotting for ignoring outliers
upper_quantile2: Upper quantile of column2 data to remove before plotting for ignoring outliers
transform1: Transformation to apply to the column1 data for plotting:
- **'identity'**: no transformation
- **'log'**: apply a logarithmic transformation with small constant added in case of zero values
- **'log_exclude0'**: apply a logarithmic transformation with zero values removed
- **'sqrt'**: apply a square root transformation
transform2: Transformation to apply to the column2 data for plotting. Same options as for column1.
equalize_axes: Square the aspect ratio and match the axis limits
reference_line: Add a y = x reference line
plot_density: Overlay a 2d density on the given plot
Returns:
Tuple containing matplotlib figure and axes along with the plotnine ggplot object
Examples:
.. plot::
import pandas as pd
import intedact
data = pd.read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2018/2018-09-11/cats_vs_dogs.csv")
intedact.histogram2d(data, 'n_dog_households', 'n_cat_households', equalize_axes=True, reference_line=True);
"""
data = trim_quantiles(data,
column1,
lower_quantile=lower_quantile1,
upper_quantile=upper_quantile1)
data = trim_quantiles(data,
column2,
lower_quantile=lower_quantile2,
upper_quantile=upper_quantile2)
data = preprocess_transformations(data, column1, transform=transform1)
data = preprocess_transformations(data, column2, transform=transform2)
# draw the scatterplot
gg = p9.ggplot(data, p9.aes(x=column1, y=column2)) + p9.geom_bin2d()
# overlay density
if plot_density:
gg += p9.geom_density_2d()
# add reference line
if reference_line:
gg += p9.geom_abline(color="black")
# add trend line
if trend_line != "none":
gg += p9.geom_smooth(method=trend_line, color="red")
gg += p9.labs(fill="")
# handle axes transforms
gg, xlabel = transform_axis(gg, column1, transform1, xaxis=True)
gg, ylabel = transform_axis(gg, column2, transform2, xaxis=False)
if fig is None and ax is None:
gg.draw()
fig = plt.gcf()
ax = fig.axes[0]
else:
_ = gg._draw_using_figure(fig, [ax])
if equalize_axes:
fig, ax, gg = match_axes(fig, ax, gg)
fig.set_size_inches(fig_width, fig_width)
else:
fig.set_size_inches(fig_width, fig_height)
ax.set_ylabel(ylabel)
ax.set_xlabel(xlabel)
return fig, ax, gg
p9.scale_y_timedelta(labels=timedelta_format("d")) + p9.annotate(
"text",
x=10,
y=timedelta(days=1450),
label=f"Y={results_2.slope:.2f}*X+{results_2.intercept:.2f}",
) + p9.labs(
x="Eucledian Distance of Preprints First and Final Versions",
y="Time Elapsed Until Preprint is Published",
) + p9.theme_seaborn(
context="paper",
style="ticks", font="Arial", font_scale=1.3))
print(g)
g = (p9.ggplot(published_date_distances,
p9.aes(x="doc_distances", y="time_to_published")) +
p9.geom_bin2d(bins=100) + p9.scale_fill_distiller(
trans="log",
direction=-1,
type="seq",
palette="YlGnBu",
name="log(count)",
labels=log_format(base=10),
) + p9.geom_line(stat="smooth",
method="lm",
linetype="dashed",
se=False,
alpha=1,
size=0.7) +
p9.scale_y_timedelta(labels=timedelta_format("d")) + p9.annotate(
"text",
x=10,
