indicate_error for x direction

[kinyatoride]

Description

It would be really nice if indicate_error can also show error bars in x direction.

Steps to reproduce

import numpy as np
import proplot as plot

x = np.arange(10)
rng = np.random.default_rng()
y = rng.standard_normal((100, 10))

fig, ax = plot.subplots()
ax.plot(y, x, means=True)

Equivalent steps in matplotlib

errorbar has the xerr option to do this

[lukelbd]

Added this as part of a giant refactor. Here's the massive new website example showcasing this:

import proplot as plot
import numpy as np
import pandas as pd

# Sample data
state = np.random.RandomState(51423)
data = state.rand(20, 8).cumsum(axis=0).cumsum(axis=1)[:, ::-1]
data = data + 20 * state.normal(size=(20, 8)) + 30
data = pd.DataFrame(data, columns=np.arange(0, 16, 2))
data.columns.name = 'column number'
data.name = 'variable'

# Loop through "vertical" and "horizontal" versions
array_vertical = [[1], [2], [3]]
array_horizontal = [[1, 1], [2, 3], [2, 3]]
for name, array in zip(('horizontal', 'vertical'), (array_horizontal, array_vertical)):
 # Figure
 fig, axs = plot.subplots(
 array, refaspect=1.5, refwidth=4,
 share=0, hratios=(2, 1, 1)
 )
 axs.format(abc=True, suptitle=f'Indicating {name} error bounds')

 # Medians and percentile ranges
 ax = axs[0]
 kw = dict(
 color='light red', legend=True,
 median=True, barpctile=90, boxpctile=True,
 # median=True, barpctile=(5, 95), boxpctile=(25, 75) # equivalent
 )
 if name == 'horizontal':
 ax.barh(data, **kw)
 else:
 ax.bar(data, **kw)
 ax.format(title='Bar plot')

 # Means and standard deviation range
 ax = axs[1]
 kw = dict(
 color='denim', marker='x', markersize=8**2, linewidth=0.8,
 label='mean', shadelabel=True,
 mean=True, shadestd=1,
 # mean=True, shadestd=(-1, 1) # equivalent
 )
 if name == 'horizontal':
 ax.scatterx(data, legend='b', legend_kw={'ncol': 1}, **kw)
 else:
 ax.scatter(data, legend='ll', **kw)
 ax.format(title='Marker plot')

 # User-defined error bars
 ax = axs[2]
 means = data.mean(axis=0)
 means.name = data.name
 shadedata = np.percentile(data, (25, 75), axis=0) # dark shading
 fadedata = np.percentile(data, (5, 95), axis=0) # light shading
 method = ax.plotx if name == 'horizontal' else ax.plot
 kw = dict(
 shadedata=shadedata, fadedata=fadedata,
 label='mean', shadelabel='50% CI', fadelabel='90% CI',
 color='ocean blue', barzorder=0, boxmarker=False,
 )
 if name == 'horizontal':
 ax.plotx(means, legend='b', legend_kw={'ncol': 1}, **kw)
 else:
 ax.plot(means, legend='ll', **kw)
 ax.format(title='Line plot')

[lukelbd]

I went about this by adding plotx and scatterx commands (note the trailing x), analogous to fill_betweenx, hlines, and barh, that use the y axis as the dependent variable. They take input arguments as e.g. ax.plotx(y) or ax.plotx(y, x). Had been meaning to do this anyway! The website now showcases these commands under the "1D plotting" section.