/
stats.py
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/
stats.py
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# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.11.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %% [raw] raw_mimetype="text/restructuredtext"
# .. _pandas: https://pandas.pydata.org
#
# .. _xarray: http://xarray.pydata.org/en/stable/
#
# .. _seaborn: https://seaborn.pydata.org
#
# .. _ug_stats:
#
# Statistical plotting
# ====================
#
# This section documents a few very basic additions to matplotlib's plotting commands
# that can be useful for statistical analysis. The :ref:`1D plotting <ug_1dplots>`
# section should be read before this section. Some of these tools will be
# expanded in the future, but for a more comprehensive suite of statistical
# plotting utilities, you may be interested in `seaborn`_ (we try to ensure
# that seaborn plotting commands are compatible with ProPlot figures and axes).
# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_errorbars:
#
# Error bars and shading
# ----------------------
#
# Error bars and error shading can be quickly added on-the-fly to
# `~proplot.axes.PlotAxes.line`, `~proplot.axes.PlotAxes.linex`
# (equivalently, `~proplot.axes.PlotAxes.plot`,
# `~proplot.axes.PlotAxes.plotx`), `~proplot.axes.PlotAxes.scatter`,
# `~proplot.axes.PlotAxes.scatterx`, `~proplot.axes.PlotAxes.bar`, and
# `~proplot.axes.PlotAxes.barh` plots using any of several keyword arguments.
#
# If you pass 2D arrays to these commands with ``mean=True``, ``means=True``,
# ``median=True``, or ``medians=True``, the means or medians of each column are
# drawn as lines, points, or bars, while *error bars* or *error shading*
# indicates the spread of the distribution in each column. Invalid data is
# ignored. You can also specify the error bounds *manually* with the `bardata`,
# `boxdata`, `shadedata`, and `fadedata` keywords. These commands can draw and
# style thin error bars (the ``bar`` keywords), thick "boxes" overlaid on top of
# these bars (the ``box`` keywords; think of them as miniature boxplots), a
# transparent primary shading region (the ``shade`` keywords), and a more
# transparent secondary shading region (the ``fade`` keywords). See the documentation
# on the plotting commands for details.
# %%
import numpy as np
import pandas as pd
# Sample data
# Each column represents a distribution
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'
# Calculate error data
# Passed to 'errdata' in the 3rd subplot example
means = data.mean(axis=0)
means.name = data.name # copy name for formatting
fadedata = np.percentile(data, (5, 95), axis=0) # light shading
shadedata = np.percentile(data, (25, 75), axis=0) # dark shading
# %%
import proplot as pplt
import numpy as np
# Loop through "vertical" and "horizontal" versions
varray = [[1], [2], [3]]
harray = [[1, 1], [2, 3], [2, 3]]
for orientation, array in zip(('horizontal', 'vertical'), (harray, varray)):
# Figure
fig = pplt.figure(refwidth=4, refaspect=1.5, share=False)
axs = fig.subplots(array, hratios=(2, 1, 1))
axs.format(abc='A.', suptitle=f'Indicating {orientation} error bounds')
# Medians and percentile ranges
ax = axs[0]
kw = dict(
color='light red', edgecolor='k', legend=True,
median=True, barpctile=90, boxpctile=True,
# median=True, barpctile=(5, 95), boxpctile=(25, 75) # equivalent
)
if orientation == '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 orientation == '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]
kw = dict(
shadedata=shadedata, fadedata=fadedata,
label='mean', shadelabel='50% CI', fadelabel='90% CI',
color='ocean blue', barzorder=0, boxmarker=False,
)
if orientation == 'horizontal':
ax.linex(means, legend='b', legend_kw={'ncol': 1}, **kw)
else:
ax.line(means, legend='ll', **kw)
ax.format(title='Line plot')
# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_boxplots:
#
# Box plots and violin plots
# --------------------------
#
# Vertical and horizontal box and violin plots can be drawn using
# `~proplot.axes.PlotAxes.boxplot`, `~proplot.axes.PlotAxes.violinplot`,
# `~proplot.axes.PlotAxes.boxploth`, and `~proplot.axes.PlotAxes.violinploth` (or
# their new shorthands, `~proplot.axes.PlotAxes.box`, `~proplot.axes.PlotAxes.violin`,
# `~proplot.axes.PlotAxes.boxh`, and `~proplot.axes.PlotAxes.violinh`). The
# ProPlot versions employ aesthetically pleasing defaults and permit flexible
# configuration using keywords like `color`, `barcolor`, and `fillcolor`.
# They also automatically apply axis labels based on the `~pandas.DataFrame`
# or `~xarray.DataArray` column labels. Violin plot error bars are controlled
# with the same keywords used for :ref:`on-the-fly error bars <ug_errorbars>`.
# %%
import proplot as pplt
import numpy as np
import pandas as pd
# Sample data
N = 500
state = np.random.RandomState(51423)
data1 = state.normal(size=(N, 5)) + 2 * (state.rand(N, 5) - 0.5) * np.arange(5)
data1 = pd.DataFrame(data1, columns=pd.Index(list('abcde'), name='label'))
data2 = state.rand(100, 7)
data2 = pd.DataFrame(data2, columns=pd.Index(list('abcdefg'), name='label'))
# Figure
fig, axs = pplt.subplots([[1, 1, 2, 2], [0, 3, 3, 0]], span=False)
axs.format(
abc='A.', titleloc='l', grid=False,
suptitle='Boxes and violins demo'
)
# Box plots
ax = axs[0]
obj1 = ax.box(data1, means=True, marker='x', meancolor='r', fillcolor='gray4')
ax.format(title='Box plots')
# Violin plots
ax = axs[1]
obj2 = ax.violin(data1, fillcolor='gray6', means=True, points=100)
ax.format(title='Violin plots')
# Boxes with different colors
ax = axs[2]
ax.boxh(data2, cycle='pastel2')
ax.format(title='Multiple colors', ymargin=0.15)
# %% [raw] raw_mimetype="text/restructuredtext" tags=[]
# .. _ug_hist:
#
# Histograms and kernel density
# -----------------------------
#
# Vertical and horizontal histograms can be drawn with
# `~proplot.axes.PlotAxes.hist` and `~proplot.axes.PlotAxes.histh`.
# As with the other plotting commands, multiple histograms can be
# drawn by passing 2D arrays instead of 1D arrays, and the color
# cycle used to color histograms can be changed on-the-fly using
# the `cycle` and `cycle_kw` keywords. Likewise, 2D histograms can
# be drawn with the `~proplot.axes.PlotAxes.hist2d`
# `~proplot.axes.PlotAxes.hexbin` commands, and their colormaps can
# be changed on-the-fly with the `cmap` and `cmap_kw` keywords (see
# the :ref:`2D plotting section <ug_apply_cmap>`). Marginal distributions
# for the 2D histograms can be added using :ref:`panel axes <ug_panels>`.
#
# In the future, ProPlot will include options for adding "smooth" kernel density
# estimations to histograms plots using a `kde` keyword. It will also include
# separate `proplot.axes.PlotAxes.kde` and `proplot.axes.PlotAxes.kde2d` commands.
# The `~proplot.axes.PlotAxes.violin` and `~proplot.axes.PlotAxes.violinh` commands
# will use the same algorithm for kernel density estimation as the `kde` commands.
# %%
import proplot as pplt
import numpy as np
# Sample data
M, N = 300, 3
state = np.random.RandomState(51423)
x = state.normal(size=(M, N)) + state.rand(M)[:, None] * np.arange(N) + 2 * np.arange(N)
# Sample overlayed histograms
fig, ax = pplt.subplots(refwidth=4, refaspect=(3, 2))
ax.format(suptitle='Overlaid histograms', xlabel='distribution', ylabel='count')
res = ax.hist(
x, pplt.arange(-3, 8, 0.2), filled=True, alpha=0.7, edgecolor='k',
cycle=('indigo9', 'gray3', 'red9'), labels=list('abc'), legend='ul',
)
# %%
import proplot as pplt
import numpy as np
# Sample data
N = 500
state = np.random.RandomState(51423)
x = state.normal(size=(N,))
y = state.normal(size=(N,))
bins = pplt.arange(-3, 3, 0.25)
# Histogram with marginal distributions
fig, axs = pplt.subplots(ncols=2, refwidth=2.3)
axs.format(
abc='A.', abcloc='l', titleabove=True,
ylabel='y axis', suptitle='Histograms with marginal distributions'
)
colors = ('indigo9', 'red9')
titles = ('Group 1', 'Group 2')
for ax, which, color, title in zip(axs, 'lr', colors, titles):
ax.hist2d(
x, y, bins, vmin=0, vmax=10, levels=50,
cmap=color, colorbar='b', colorbar_kw={'label': 'count'}
)
color = pplt.scale_luminance(color, 1.5) # histogram colors
px = ax.panel(which, space=0)
px.histh(y, bins, color=color, fill=True, ec='k')
px.format(grid=False, xlocator=[], xreverse=(which == 'l'))
px = ax.panel('t', space=0)
px.hist(x, bins, color=color, fill=True, ec='k')
px.format(grid=False, ylocator=[], title=title, titleloc='l')