data = (state.rand(40, 40) - 0.5).cumsum(axis=0).cumsum(axis=1)
# Generate figure
fig, axs = plot.subplots(ncols=2, nrows=2, axwidth=1.7, span=False)
axs.format(
xlabel='x axis',
ylabel='y axis',
suptitle='Modifying diverging colormaps',
)
# Cutting out central colors
levels = plot.arange(-10, 10, 2)
for i, (ax, cut) in enumerate(zip(axs, (None, None, 0.2, -0.1))):
levels = plot.arange(-10, 10, 2)
if i == 1 or i == 3:
levels = plot.edges(levels)
if i == 0:
title = 'Even number of levels'
elif i == 1:
title = 'Odd number of levels'
else:
title = 'Sharper cutoff' if cut > 0 else 'Expanded center'
title = f'{title}\ncut = ${cut}$'
ax.format(title=title)
m = ax.contourf(
data,
cmap='Div',
cmap_kw={'cut': cut},
extend='both',
levels=levels,
colorbar='b',
# limits if they were previously fixed by `~matplotlib.axes.Axes.set_xlim` or
# `~matplotlib.axes.Axes.set_ylim` (or, equivalently, by passing `xlim` or
# `ylim` to `proplot.axes.CartesianAxes.format`). This can be useful if you
# wish to restrict the view along the *x* or *y* axis within a large dataset.
# To disable this feature, pass ``inbounds=False`` to the plotting command or
# set :rcraw:`cmap.inbounds` to ``False`` (see also the :rcraw:`axes.inbounds`
# setting and the :ref:`user guide <ug_1dstd>`).
# %%
import proplot as pplt
import numpy as np
# Sample data
state = np.random.RandomState(51423)
x = y = np.array([-10, -5, 0, 5, 10])
xedges = pplt.edges(x)
yedges = pplt.edges(y)
data = state.rand(y.size, x.size) # "center" coordinates
lim = (np.min(xedges), np.max(xedges))
with pplt.rc.context({'cmap': 'Grays', 'cmap.levels': 21}):
# Figure
fig = pplt.figure(refwidth=2.3, share=False)
axs = fig.subplots(ncols=2, nrows=2)
axs.format(
xlabel='xlabel',
ylabel='ylabel',
xlim=lim,
ylim=lim,
xlocator=5,
ylocator=5,
title, cmap_kw = f'left={coord}', {'left': coord}
else:
title, cmap_kw = f'right={coord}', {'right': coord}
ax.pcolormesh(data,
cmap=cmap,
cmap_kw=cmap_kw,
colorbar='b',
colorbar_kw={'locator': 'null'})
ax.format(xlabel='x axis', ylabel='y axis', title=title)
# Cutting central colors
levels = plot.arange(-10, 10, 2)
for i, (ax, cut) in enumerate(zip(axs[3:], (None, None, 0.1, 0.2))):
if i == 0:
title = 'With central level'
levels = plot.edges(plot.arange(-10, 10, 2))
else:
title = 'Without central level'
levels = plot.arange(-10, 10, 2)
if cut is not None:
title = f'cut = {cut}'
m = ax.contourf(
data,
cmap='Div',
cmap_kw={'cut': cut},
extend='both',
levels=levels,
)
ax.format(xlabel='x axis',
ylabel='y axis',
title=title,
# positional arguments across all 2D plotting methods. Among other things,
# it guesses coordinate *edges* for `~matplotlib.axes.Axes.pcolor` and
# `~matplotlib.axes.Axes.pcolormesh` plots when you supply coordinate
# *centers*, and calculates coordinate *centers* for
# `~matplotlib.axes.Axes.contourf` and `~matplotlib.axes.Axes.contour` plots
# when you supply coordinate *edges*. Notice the locations of the rectangle
# edges in the ``pcolor`` plots shown below.
# %%
import proplot as plot
import numpy as np
# Figure and sample data
state = np.random.RandomState(51423)
x = y = np.array([-10, -5, 0, 5, 10])
xedges = plot.edges(x)
yedges = plot.edges(y)
data = state.rand(y.size, x.size) # "center" coordinates
lim = (np.min(xedges), np.max(xedges))
with plot.rc.context({'image.cmap': 'Grays', 'image.levels': 21}):
fig, axs = plot.subplots(ncols=2, nrows=2, share=False)
axs.format(
xlabel='xlabel', ylabel='ylabel',
xlim=lim, ylim=lim, xlocator=5, ylocator=5,
suptitle='Standardized input demonstration'
)
axs[0].format(title='Supplying coordinate centers')
axs[1].format(title='Supplying coordinate edges')
# Plot using both centers and edges as coordinates
axs[0].pcolormesh(x, y, data)