2D plotting commands

Proplot adds several new features to matplotlib’s plotting commands using the intermediate PlotAxes class. For the most part, these additions represent a superset of matplotlib – if you are not interested, you can use the plotting commands just like you would in matplotlib. This section documents the features added for 2D plotting commands like contour, pcolor, and imshow.

Important

By default, proplot automatically adjusts the width of contourf and pcolor edges to eliminate the appearance of “white lines” in saved vector graphic files. However, this can significantly slow down the drawing time for large datasets. To disable this feature, pass edgefix=False to the relevant PlotAxes command, or set rc.edgefix to False to disable globally.

Data arguments

The treatment of data arguments passed to the 2D PlotAxes commands is standardized. For each command, you can optionally omit the x and y coordinates, in which case they are inferred from the data (see xarray and pandas integration). If coordinates are string labels, they are converted to indices and tick labels using IndexLocator and IndexFormatter. If coordinates are descending and the axis limits are unset, the axis direction is automatically reversed. If coordinate centers are passed to commands like pcolor and pcolormesh, they are automatically converted to edges using edges or edges2d, and if coordinate edges are passed to commands like contour and contourf, they are automatically converted to centers (notice the locations of the rectangle edges in the pcolor plots below). All positional arguments can also be specified as keyword arguments (see the documentation for each plotting command).

Note

By default, when choosing the colormap normalization range, proplot ignores data outside the x or y axis limits if they were previously fixed by set_xlim or 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 rc['cmap.inbounds'] to False (see also the rc['axes.inbounds'] setting and the user guide).

[1]:
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,
        suptitle='Standardized input demonstration',
        toplabels=('Coordinate centers', 'Coordinate edges'),
    )

    # Plot using both centers and edges as coordinates
    axs[0].pcolormesh(x, y, data)
    axs[1].pcolormesh(xedges, yedges, data)
    axs[2].contourf(x, y, data)
    axs[3].contourf(xedges, yedges, data)
_images/2dplots_2_0.svg
[2]:
import proplot as pplt
import numpy as np

# Sample data
cmap = 'turku_r'
state = np.random.RandomState(51423)
N = 80
x = y = np.arange(N + 1)
data = 10 + (state.normal(0, 3, size=(N, N))).cumsum(axis=0).cumsum(axis=1)
xlim = ylim = (0, 25)

# Plot the data
fig, axs = pplt.subplots(
    [[0, 1, 1, 0], [2, 2, 3, 3]], wratios=(1.3, 1, 1, 1.3), span=False, refwidth=2.2,
)
axs[0].fill_between(
    xlim, *ylim, zorder=3, edgecolor='red', facecolor=pplt.set_alpha('red', 0.2),
)
for i, ax in enumerate(axs):
    inbounds = i == 1
    title = f'Restricted lims inbounds={inbounds}'
    title += ' (default)' if inbounds else ''
    ax.format(
        xlim=(None if i == 0 else xlim),
        ylim=(None if i == 0 else ylim),
        title=('Default axis limits' if i == 0 else title),
    )
    ax.pcolor(x, y, data, cmap=cmap, inbounds=inbounds)
fig.format(
    xlabel='xlabel',
    ylabel='ylabel',
    suptitle='Default vmin/vmax restricted to in-bounds data'
)
_images/2dplots_3_0.svg

Pandas and xarray integration

The 2D PlotAxes commands recognize pandas and xarray data structures. If you omit x and y coordinates, the commands try to infer them from the pandas.DataFrame or xarray.DataArray. If you did not explicitly set the x or y axis label or legend or colorbar label(s), the commands try to retrieve them from the pandas.DataFrame or xarray.DataArray. The commands also recognize pint.Quantity structures and apply unit string labels with formatting specified by rc.unitformat = 'L'.

These features restore some of the convenience you get with the builtin pandas and xarray plotting functions. They are also optional – installation of pandas and xarray are not required to use proplot. The automatic labels can be disabled by setting rc.autoformat to False or by passing autoformat=False to any plotting command.

Note

For every plotting command, you can pass a Dataset, DataFrame, or dict to the data keyword with strings as data arguments instead of arrays – just like matplotlib. For example, ax.plot('y', data=dataset) and ax.plot(y='y', data=dataset) are translated to ax.plot(dataset['y']). This is the preferred input style for most seaborn plotting commands. Also, if you pass a pint.Quantity or DataArray containing a pint.Quantity, proplot will automatically call setup_matplotlib so that the axes become unit-aware.

[3]:
import xarray as xr
import numpy as np
import pandas as pd

# DataArray
state = np.random.RandomState(51423)
linspace = np.linspace(0, np.pi, 20)
data = 50 * state.normal(1, 0.2, size=(20, 20)) * (
    np.sin(linspace * 2) ** 2
    * np.cos(linspace + np.pi / 2)[:, None] ** 2
)
lat = xr.DataArray(
    np.linspace(-90, 90, 20),
    dims=('lat',),
    attrs={'units': '\N{DEGREE SIGN}N'}
)
plev = xr.DataArray(
    np.linspace(1000, 0, 20),
    dims=('plev',),
    attrs={'long_name': 'pressure', 'units': 'hPa'}
)
da = xr.DataArray(
    data,
    name='u',
    dims=('plev', 'lat'),
    coords={'plev': plev, 'lat': lat},
    attrs={'long_name': 'zonal wind', 'units': 'm/s'}
)

# DataFrame
data = state.rand(12, 20)
df = pd.DataFrame(
    (data - 0.4).cumsum(axis=0).cumsum(axis=1)[::1, ::-1],
    index=pd.date_range('2000-01', '2000-12', freq='MS')
)
df.name = 'temperature (\N{DEGREE SIGN}C)'
df.index.name = 'date'
df.columns.name = 'variable (units)'
[4]:
import proplot as pplt
fig = pplt.figure(refwidth=2.5, share=False, suptitle='Automatic subplot formatting')

# Plot DataArray
cmap = pplt.Colormap('PuBu', left=0.05)
ax = fig.subplot(121, yreverse=True)
ax.contourf(da, cmap=cmap, colorbar='t', lw=0.7, ec='k')

# Plot DataFrame
ax = fig.subplot(122, yreverse=True)
ax.contourf(df, cmap='YlOrRd', colorbar='t', lw=0.7, ec='k')
ax.format(xtickminor=False, yformatter='%b', ytickminor=False)
_images/2dplots_6_0.svg

Changing the colormap

It is often useful to create custom colormaps on-the-fly, without explicitly calling the Colormap constructor function. You can do so using the cmap and cmap_kw keywords, available with most PlotAxes 2D plot commands. For example, to create and apply a monochromatic colormap, you can use cmap='color_name' (see the colormaps section for more info). You can also create on-the-fly “qualitative” DiscreteColormaps by passing lists of colors to the keyword c, color, or colors.

Proplot defines global defaults for four different colormap types: sequential (setting rc['cmap.sequential']), diverging (setting rc['cmap.diverging']), cyclic (setting rc['cmap.cyclic']), and qualitative (setting rc['cmap.qualitative']). To use the default colormap for a given type, pass sequential=True, diverging=True, cyclic=True, or qualitative=True to any plotting command. If the colormap type is not explicitly specified, sequential is used with the default linear normalizer when data is strictly positive or negative, and diverging is used with the diverging normalizer when the data limits or colormap levels cross zero (see below).

[5]:
import proplot as pplt
import numpy as np

# Sample data
N = 18
state = np.random.RandomState(51423)
data = np.cumsum(state.rand(N, N), axis=0)

# Custom defaults of each type
pplt.rc['cmap.sequential'] = 'PuBuGn'
pplt.rc['cmap.diverging'] = 'PiYG'
pplt.rc['cmap.cyclic'] = 'bamO'
pplt.rc['cmap.qualitative'] = 'flatui'

# Make plots. Note the default behavior is sequential=True or diverging=True
# depending on whether data contains negative values (see below).
fig = pplt.figure(refwidth=2.2, span=False, suptitle='Colormap types')
axs = fig.subplots(ncols=2, nrows=2)
axs.format(xformatter='none', yformatter='none')
axs[0].pcolor(data, sequential=True, colorbar='l', extend='max')
axs[1].pcolor(data - 5, diverging=True, colorbar='r', extend='both')
axs[2].pcolor(data % 8, cyclic=True, colorbar='l')
axs[3].pcolor(data, levels=pplt.arange(0, 12, 2), qualitative=True, colorbar='r')
types = ('sequential', 'diverging', 'cyclic', 'qualitative')
for ax, typ in zip(axs, types):
    ax.format(title=typ.title() + ' colormap')
pplt.rc.reset()
_images/2dplots_8_0.svg
[6]:
import proplot as pplt
import numpy as np

# Sample data
N = 20
state = np.random.RandomState(51423)
data = np.cumsum(state.rand(N, N), axis=1) - 6

# Continuous "diverging" colormap
fig = pplt.figure(refwidth=2.3, spanx=False)
ax = fig.subplot(121, title="Diverging colormap with 'cmap'", xlabel='xlabel')
ax.contourf(
    data,
    norm='div',
    cmap=('cobalt', 'white', 'violet red'),
    cmap_kw={'space': 'hsl', 'cut': 0.15},
    colorbar='b',
)

# Discrete "qualitative" colormap
ax = fig.subplot(122, title="Qualitative colormap with 'colors'")
ax.contourf(
    data,
    levels=pplt.arange(-6, 9, 3),
    colors=['red5', 'blue5', 'yellow5', 'gray5', 'violet5'],
    colorbar='b',
)
fig.format(xlabel='xlabel', ylabel='ylabel', suptitle='On-the-fly colormaps')
_images/2dplots_9_0.svg

Changing the normalizer

Matplotlib colormap “normalizers” translate raw data values into normalized colormap indices. In proplot, you can select the normalizer from its “registered” name using the Norm constructor function. You can also build a normalizer on-the-fly using the norm and norm_kw keywords, available with most 2D PlotAxes commands. If you want to work with the normalizer classes directly, they are available in the top-level namespace (e.g., norm=pplt.LogNorm(...) is allowed). To explicitly set the normalization range, you can pass the usual vmin and vmax keywords to the plotting command. See below for more details on colormap normalization in proplot.

[7]:
import proplot as pplt
import numpy as np

# Sample data
N = 20
state = np.random.RandomState(51423)
data = 11 ** (0.25 * np.cumsum(state.rand(N, N), axis=0))

# Create figure
gs = pplt.GridSpec(ncols=2)
fig = pplt.figure(refwidth=2.3, span=False, suptitle='Normalizer types')

# Different normalizers
ax = fig.subplot(gs[0], title='Default linear normalizer')
ax.pcolormesh(data, cmap='magma', colorbar='b')
ax = fig.subplot(gs[1], title="Logarithmic normalizer with norm='log'")
ax.pcolormesh(data, cmap='magma', norm='log', colorbar='b')
[7]:
<matplotlib.collections.QuadMesh at 0x7f70a023a940>
_images/2dplots_11_1.svg

Special normalizers

Proplot includes two new “continuous” normalizers. The SegmentedNorm normalizer provides even color gradations with respect to index for an arbitrary monotonically increasing or decreasing list of levels. This is automatically applied if you pass unevenly spaced levels to a plotting command, or it can be manually applied using e.g. norm='segmented'. This can be useful for datasets with unusual statistical distributions or spanning many orders of magnitudes.

The DivergingNorm normalizer ensures that colormap midpoints lie on some central data value (usually 0), even if vmin, vmax, or levels are asymmetric with respect to the central value. This is automatically applied if your data contains negative and positive values (see below), or it can be manually applied using e.g. diverging=True or norm='diverging'. It can also be configured to scale colors “fairly” or “unfairly”:

  • With fair scaling (the default), gradations on either side of the midpoint have equal intensity. If vmin and vmax are not symmetric about zero, the most intense colormap colors on one side of the midpoint will be truncated.

  • With unfair scaling, gradations on either side of the midpoint are warped so that the full range of colormap colors is always traversed. This configuration should be used with care, as it may lead you to misinterpret your data.

The below examples demonstrate how these normalizers affect the interpretation of different datasets.

[8]:
import proplot as pplt
import numpy as np

# Sample data
state = np.random.RandomState(51423)
data = 11 ** (2 * state.rand(20, 20).cumsum(axis=0) / 7)

# Linear segmented norm
fig, axs = pplt.subplots(ncols=2, refwidth=2.4)
fig.format(suptitle='Segmented normalizer demo')
ticks = [5, 10, 20, 50, 100, 200, 500, 1000]
for ax, norm in zip(axs, ('linear', 'segmented')):
    m = ax.contourf(
        data, levels=ticks, extend='both',
        cmap='Mako', norm=norm,
        colorbar='b', colorbar_kw={'ticks': ticks},
    )
    ax.format(title=norm.title() + ' normalizer')
_images/2dplots_13_0.svg
[9]:
import proplot as pplt
import numpy as np

# Sample data
state = np.random.RandomState(51423)
data1 = (state.rand(20, 20) - 0.485).cumsum(axis=1).cumsum(axis=0)
data2 = (state.rand(20, 20) - 0.515).cumsum(axis=0).cumsum(axis=1)

# Figure
fig, axs = pplt.subplots(nrows=2, ncols=2, refwidth=2.2, order='F')
axs.format(suptitle='Diverging normalizer demo')
cmap = pplt.Colormap('DryWet', cut=0.1)

# Diverging norms
i = 0
for data, mode, fair in zip(
    (data1, data2), ('positive', 'negative'), ('fair', 'unfair'),
):
    for fair in ('fair', 'unfair'):
        norm = pplt.Norm('diverging', fair=(fair == 'fair'))
        ax = axs[i]
        m = ax.contourf(data, cmap=cmap, norm=norm)
        ax.colorbar(m, loc='b')
        ax.format(title=f'{mode.title()}-skewed + {fair} scaling')
        i += 1
_images/2dplots_14_0.svg

Discrete levels

By default, proplot uses DiscreteNorm to “discretize” the possible colormap colors for contour and pseudocolor PlotAxes commands (e.g., contourf, pcolor). This is analogous to matplotlib.colors.BoundaryNorm, except DiscreteNorm can be paired with arbitrary continuous normalizers specified by norm (see above). Discrete color levels can help readers discern exact numeric values and tend to reveal qualitative structure in the data. DiscreteNorm also repairs the colormap end-colors by ensuring the following conditions are met:

  1. All colormaps always span the entire color range regardless of the extend parameter.

  2. Cyclic colormaps always have distinct color levels on either end of the colorbar.

To explicitly toggle discrete levels on or off, change rc['cmap.discrete'] or pass discrete=False or discrete=True to any plotting command that accepts a cmap argument. The level edges or centers used with DiscreteNorm can be explicitly specified using the levels or values keywords, respectively (arange and edges are useful for generating levels and values lists). You can also pass an integer to these keywords (or to the N keyword) to automatically generate approximately this many level edges or centers at “nice” intervals. The algorithm used to generate levels is similar to matplotlib’s algorithm for generarting contour levels. The default number of levels is controlled by rc['cmap.levels'], and the level selection is constrainted by the keywords vmin, vmax, locator, and locator_kw – for example, vmin=100 ensures the minimum level is greater than or equal to 100, and locator=5 ensures a level step size of 5 (see this section for more on locators). You can also use the keywords negative, positive, or symmetric to ensure that your levels are strictly negative, positive, or symmetric about zero, or use the nozero keyword to remove the zero level (useful for single-color contour plots).

[10]:
import proplot as pplt
import numpy as np

# Sample data
state = np.random.RandomState(51423)
data = 10 + state.normal(0, 1, size=(33, 33)).cumsum(axis=0).cumsum(axis=1)

# Figure
fig, axs = pplt.subplots([[1, 1, 2, 2], [0, 3, 3, 0]], ref=3, refwidth=2.3)
axs.format(yformatter='none', suptitle='Discrete vs. smooth colormap levels')

# Pcolor
axs[0].pcolor(data, cmap='viridis', colorbar='l')
axs[0].set_title('Pcolor plot\ndiscrete=True (default)')
axs[1].pcolor(data, discrete=False, cmap='viridis', colorbar='r')
axs[1].set_title('Pcolor plot\ndiscrete=False')

# Imshow
m = axs[2].imshow(data, cmap='oslo', colorbar='b')
axs[2].format(title='Imshow plot\ndiscrete=False (default)', yformatter='auto')
_images/2dplots_16_0.svg
[11]:
import proplot as pplt
import numpy as np

# Sample data
state = np.random.RandomState(51423)
data = (20 * (state.rand(20, 20) - 0.4).cumsum(axis=0).cumsum(axis=1)) % 360
levels = pplt.arange(0, 360, 45)

# Figure
gs = pplt.GridSpec(nrows=2, ncols=4, hratios=(1.5, 1))
fig = pplt.figure(refwidth=2.4, right=2)
fig.format(suptitle='DiscreteNorm end-color standardization')

# Cyclic colorbar with distinct end colors
cmap = pplt.Colormap('twilight', shift=-90)
ax = fig.subplot(gs[0, 1:3], title='distinct "cyclic" end colors')
ax.pcolormesh(
    data, cmap=cmap, levels=levels,
    colorbar='b', colorbar_kw={'locator': 90},
)

# Colorbars with different extend values
for i, extend in enumerate(('min', 'max', 'neither', 'both')):
    ax = fig.subplot(gs[1, i], title=f'extend={extend!r}')
    ax.pcolormesh(
        data[:, :10], levels=levels, cmap='oxy',
        extend=extend, colorbar='b', colorbar_kw={'locator': 180}
    )
_images/2dplots_17_0.svg

Auto normalization

By default, colormaps are normalized to span from roughly the minimum data value to the maximum data value. However in the presence of outliers, this is not desirable. Proplot adds the robust keyword to change this behavior, inspired by the xarray keyword of the same name. Passing robust=True to a PlotAxes 2D plot command will limit the default colormap normalization between the 2nd and 98th data percentiles. This range can be customized by passing an integer to robust (e.g. robust=90 limits the normalization range between the 5th and 95th percentiles) or by passing a 2-tuple to robust (e.g. robust=(0, 90) limits the normalization range between the data minimum and the 90th percentile). This can be turned on persistently by setting rc['cmap.robust'] to True.

Additionally, similar to xarray, proplot can automatically detect “diverging” datasets. By default, the 2D PlotAxes commands will apply the diverging colormap rc['cmap.diverging'] = 'BuRd' (rather than rc['cmap.sequential'] = 'Fire') and the diverging normalizer DivergingNorm (rather than Normalize – see above) if the following conditions are met:

  1. If discrete levels are enabled (see above) and the level list includes at least 2 negative and 2 positive values.

  2. If discrete levels are disabled (see above) and the normalization limits vmin and vmax are negative and positive.

  3. A colormap was not explicitly passed, or a colormap was passed but it matches the name of a known diverging colormap.

The automatic detection of “diverging” datasets can be disabled by setting rc['cmap.autodiverging'] to False.

[12]:
import proplot as pplt
import numpy as np
N = 20
state = np.random.RandomState(51423)
data = N * 2 + (state.rand(N, N) - 0.45).cumsum(axis=0).cumsum(axis=1) * 10
fig, axs = pplt.subplots(
    nrows=2, ncols=2, refwidth=2,
    suptitle='Auto normalization demo'
)

# Auto diverging
pplt.rc['cmap.sequential'] = 'lapaz_r'
pplt.rc['cmap.diverging'] = 'vik'
for i, ax in enumerate(axs[:2]):
    ax.pcolor(data - i * N * 6, colorbar='b')
    ax.format(title='Diverging ' + ('on' if i else 'off'))

# Auto range
pplt.rc['cmap.sequential'] = 'lajolla'
data = data[::-1, :]
data[-1, 0] = 2e3
for i, ax in enumerate(axs[2:]):
    ax.pcolor(data, robust=bool(i), colorbar='b')
    ax.format(title='Robust ' + ('on' if i else 'off'))
pplt.rc.reset()
_images/2dplots_19_0.svg

Quick labels

You can now quickly add labels to contour, contourf, pcolor, pcolormesh, and heatmap, plots by passing labels=True to the plotting command. The label text is colored black or white depending on the luminance of the underlying grid box or filled contour (see the section on colorspaces). Contour labels are drawn with clabel and grid box labels are drawn with text. You can pass keyword arguments to these functions by passing a dictionary to labels_kw, and you can change the label precision using the precision keyword. See the plotting command documentation for details.

[13]:
import proplot as pplt
import pandas as pd
import numpy as np

# Sample data
state = np.random.RandomState(51423)
data = state.rand(6, 6)
data = pd.DataFrame(data, index=pd.Index(['a', 'b', 'c', 'd', 'e', 'f']))

# Figure
fig, axs = pplt.subplots(
    [[1, 1, 2, 2], [0, 3, 3, 0]],
    refwidth=2.3, share='labels', span=False,
)
axs.format(xlabel='xlabel', ylabel='ylabel', suptitle='Labels demo')

# Heatmap with labeled boxes
ax = axs[0]
m = ax.heatmap(
    data, cmap='rocket',
    labels=True, precision=2, labels_kw={'weight': 'bold'}
)
ax.format(title='Heatmap with labels')

# Filled contours with labels
ax = axs[1]
m = ax.contourf(
    data.cumsum(axis=0), cmap='rocket',
    labels=True, labels_kw={'weight': 'bold'}
)
ax.format(title='Filled contours with labels')

# Line contours with labels and no zero level
data = 5 * (data - 0.45).cumsum(axis=0) - 2
ax = axs[2]
ax.contour(
    data, nozero=True, color='gray8',
    labels=True, labels_kw={'weight': 'bold'}
)
ax.format(title='Line contours with labels')
_images/2dplots_21_0.svg

Heatmap plots

The heatmap command can be used to draw “heatmaps” of 2-dimensional data. This is a convenience function equivalent to pcolormesh, except the axes are configured with settings suitable for heatmaps: fixed aspect ratios (ensuring “square” grid boxes), no gridlines, no minor ticks, and major ticks at the center of each box. Among other things, this is useful for displaying covariance and correlation matrices, as shown below. heatmap should generally only be used with CartesianAxes.

[14]:
import proplot as pplt
import numpy as np
import pandas as pd

# Covariance data
state = np.random.RandomState(51423)
data = state.normal(size=(10, 10)).cumsum(axis=0)
data = (data - data.mean(axis=0)) / data.std(axis=0)
data = (data.T @ data) / data.shape[0]
data[np.tril_indices(data.shape[0], -1)] = np.nan  # fill half with empty boxes
data = pd.DataFrame(data, columns=list('abcdefghij'), index=list('abcdefghij'))

# Covariance matrix plot
fig, ax = pplt.subplots(refwidth=4.5)
m = ax.heatmap(
    data, cmap='ColdHot', vmin=-1, vmax=1, N=100, lw=0.5, ec='k',
    labels=True, precision=2, labels_kw={'weight': 'bold'},
    clip_on=False,  # turn off clipping so box edges are not cut in half
)
ax.format(
    suptitle='Heatmap demo', title='Table of correlation coefficients',
    xloc='top', yloc='right', yreverse=True, ticklabelweight='bold',
    alpha=0, linewidth=0, tickpad=4,
)
_images/2dplots_23_0.svg