Source code for dataval.plots

#!/usr/bin/env python
# -*- coding: utf-8 -*-
Plotting utilities.

.. codeauthor:: Rasmus Handberg <>

import logging
import copy
import numpy as np
from bottleneck import allnan, anynan
import matplotlib
from matplotlib.ticker import MaxNLocator
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import astropy.visualization as viz

# Change to a non-GUI backend since this
# should be able to run on a cluster:

[docs] def plots_interactive(backend=('Qt5Agg', 'MacOSX', 'Qt4Agg', 'GTK3Agg', 'Qt5Cairo', 'GTK3Cairo', 'TkAgg')): """ Change plotting to using an interactive backend. Parameters: backend (str or list): Backend to change to. If not provided, will try different interactive backends and use the first one that works. .. codeauthor:: Rasmus Handberg <> """ logger = logging.getLogger(__name__) logger.debug("Valid interactive backends: %s", matplotlib.rcsetup.interactive_bk) if isinstance(backend, str): backend = [backend] for bckend in backend: if bckend not in matplotlib.rcsetup.interactive_bk: logger.debug("Interactive backend '%s' is not found", bckend) continue # Try to change the backend, and catch errors # it it didn't work: try: plt.switch_backend(bckend) except (ModuleNotFoundError, ImportError): pass else: break
[docs] def plots_noninteractive(): """ Change plotting to using a non-interactive backend, which can e.g. be used on a cluster. Will set backend to 'Agg'. .. codeauthor:: Rasmus Handberg <> """ plt.switch_backend('Agg')
[docs] def plot_image(image, ax=None, scale='log', cmap=None, origin='lower', xlabel=None, ylabel=None, cbar=None, clabel='Flux ($e^{-}s^{-1}$)', cbar_ticks=None, cbar_ticklabels=None, cbar_pad=None, cbar_size='5%', title=None, percentile=95.0, vmin=None, vmax=None, offset_axes=None, color_bad='k', **kwargs): """ Utility function to plot a 2D image. Parameters: image (2d array): Image data. ax (matplotlib.pyplot.axes, optional): Axes in which to plot. Default (None) is to use current active axes. scale (str or :py:class:`astropy.visualization.ImageNormalize` object, optional): Normalization used to stretch the colormap. Options: ``'linear'``, ``'sqrt'``, ``'log'``, ``'asinh'``, ``'histeq'``, ``'sinh'`` and ``'squared'``. Can also be a :py:class:`astropy.visualization.ImageNormalize` object. Default is ``'log'``. origin (str, optional): The origin of the coordinate system. xlabel (str, optional): Label for the x-axis. ylabel (str, optional): Label for the y-axis. cbar (string, optional): Location of color bar. Choises are ``'right'``, ``'left'``, ``'top'``, ``'bottom'``. Default is not to create colorbar. clabel (str, optional): Label for the color bar. cbar_size (float, optional): Fractional size of colorbar compared to axes. Default=0.03. cbar_pad (float, optional): Padding between axes and colorbar. title (str or None, optional): Title for the plot. percentile (float, optional): The fraction of pixels to keep in color-trim. If single float given, the same fraction of pixels is eliminated from both ends. If tuple of two floats is given, the two are used as the percentiles. Default=95. cmap (matplotlib colormap, optional): Colormap to use. Default is the ``Blues`` colormap. vmin (float, optional): Lower limit to use for colormap. vmax (float, optional): Upper limit to use for colormap. color_bad (str, optional): Color to apply to bad pixels (NaN). Default is black. kwargs (dict, optional): Keyword arguments to be passed to :py:func:`matplotlib.pyplot.imshow`. Returns: :py:class:`matplotlib.image.AxesImage`: Image from returned by :py:func:`matplotlib.pyplot.imshow`. .. codeauthor:: Rasmus Handberg <> """ logger = logging.getLogger(__name__) # Backward compatible settings: make_cbar = kwargs.pop('make_cbar', None) if make_cbar: raise FutureWarning("'make_cbar' is deprecated. Use 'cbar' instead.") if not cbar: cbar = make_cbar # Special treatment for boolean arrays: if isinstance(image, np.ndarray) and image.dtype == 'bool': if vmin is None: vmin = 0 if vmax is None: vmax = 1 if cbar_ticks is None: cbar_ticks = [0, 1] if cbar_ticklabels is None: cbar_ticklabels = ['False', 'True'] # Calculate limits of color scaling: interval = None if vmin is None or vmax is None: if allnan(image): logger.warning("Image is all NaN") vmin = 0 vmax = 1 if cbar_ticks is None: cbar_ticks = [] if cbar_ticklabels is None: cbar_ticklabels = [] elif isinstance(percentile, (list, tuple, np.ndarray)): interval = viz.AsymmetricPercentileInterval(percentile[0], percentile[1]) else: interval = viz.PercentileInterval(percentile) # Create ImageNormalize object with extracted limits: if scale in ('log', 'linear', 'sqrt', 'asinh', 'histeq', 'sinh', 'squared'): if scale == 'log': stretch = viz.LogStretch() elif scale == 'linear': stretch = viz.LinearStretch() elif scale == 'sqrt': stretch = viz.SqrtStretch() elif scale == 'asinh': stretch = viz.AsinhStretch() elif scale == 'histeq': stretch = viz.HistEqStretch(image[np.isfinite(image)]) elif scale == 'sinh': stretch = viz.SinhStretch() elif scale == 'squared': stretch = viz.SquaredStretch() # Create ImageNormalize object. Very important to use clip=False if the image contains # NaNs, otherwise NaN points will not be plotted correctly. norm = viz.ImageNormalize( data=image[np.isfinite(image)], interval=interval, vmin=vmin, vmax=vmax, stretch=stretch, clip=not anynan(image)) elif isinstance(scale, (viz.ImageNormalize, matplotlib.colors.Normalize)): norm = scale else: raise ValueError("scale {} is not available.".format(scale)) if offset_axes: extent = (offset_axes[0]-0.5, offset_axes[0] + image.shape[1]-0.5, offset_axes[1]-0.5, offset_axes[1] + image.shape[0]-0.5) else: extent = (-0.5, image.shape[1]-0.5, -0.5, image.shape[0]-0.5) if ax is None: ax = plt.gca() # Set up the colormap to use. If a bad color is defined, # add it to the colormap: if cmap is None: cmap = copy.copy(plt.get_cmap('Blues')) elif isinstance(cmap, str): cmap = copy.copy(plt.get_cmap(cmap)) if color_bad: cmap.set_bad(color_bad, 1.0) # Plotting the image using all the settings set above: im = ax.imshow( image, cmap=cmap, norm=norm, origin=origin, extent=extent, interpolation='nearest', **kwargs) if xlabel is not None: ax.set_xlabel(xlabel) if ylabel is not None: ax.set_ylabel(ylabel) if title is not None: ax.set_title(title) ax.set_xlim([extent[0], extent[1]]) ax.set_ylim([extent[2], extent[3]]) if cbar: colorbar(im, ax=ax, loc=cbar, size=cbar_size, pad=cbar_pad, label=clabel, ticks=cbar_ticks, ticklabels=cbar_ticklabels) # Settings for ticks: integer_locator = MaxNLocator(nbins=10, integer=True) ax.xaxis.set_major_locator(integer_locator) ax.xaxis.set_minor_locator(integer_locator) ax.yaxis.set_major_locator(integer_locator) ax.yaxis.set_minor_locator(integer_locator) ax.tick_params(which='both', direction='out', pad=5) ax.xaxis.tick_bottom() ax.yaxis.tick_left() return im
[docs] def plot_image_fit_residuals(fig, image, fit, residuals=None, percentile=95.0): """ Make a figure with three subplots showing the image, the fit and the residuals. The image and the fit are shown with logarithmic scaling and a common colorbar. The residuals are shown with linear scaling and a separate colorbar. Parameters: fig (fig object): Figure object in which to make the subplots. image (2D array): Image numpy array. fit (2D array): Fitted image numpy array. residuals (2D array, optional): Fitted image subtracted from image numpy array. Returns: list: List with Matplotlib subplot axes objects for each subplot. """ if residuals is None: residuals = image - fit # Calculate common normalization for the first two subplots: vmin_image, vmax_image = viz.PercentileInterval(percentile).get_limits(image) vmin_fit, vmax_fit = viz.PercentileInterval(percentile).get_limits(fit) vmin = np.nanmin([vmin_image, vmin_fit]) vmax = np.nanmax([vmax_image, vmax_fit]) norm = viz.ImageNormalize(vmin=vmin, vmax=vmax, stretch=viz.LogStretch()) # Add subplot with the image: ax1 = fig.add_subplot(131) im1 = plot_image(image, ax=ax1, scale=norm, cbar=None, title='Image') # Add subplot with the fit: ax2 = fig.add_subplot(132) plot_image(fit, ax=ax2, scale=norm, cbar=None, title='PSF fit') # Calculate the normalization for the third subplot: vmin, vmax = viz.PercentileInterval(percentile).get_limits(residuals) v = np.max(np.abs([vmin, vmax])) # Add subplot with the residuals: ax3 = fig.add_subplot(133) im3 = plot_image(residuals, ax=ax3, scale='linear', cmap='seismic', vmin=-v, vmax=v, cbar=None, title='Residuals') # Make the common colorbar for image and fit subplots: cbar_ax12 = fig.add_axes([0.125, 0.2, 0.494, 0.03]) fig.colorbar(im1, cax=cbar_ax12, orientation='horizontal') # Make the colorbar for the residuals subplot: cbar_ax3 = fig.add_axes([0.7, 0.2, 0.205, 0.03]) fig.colorbar(im3, cax=cbar_ax3, orientation='horizontal') # Add more space between subplots: plt.subplots_adjust(wspace=0.4, hspace=0.4) return [ax1, ax2, ax3]
[docs] def colorbar(im, ax=None, loc='right', pad=None, size='5%', label=None, ticks=None, ticklabels=None): """ Draw colorbar next to the given axes. Returns: :class:`matplotlib.colorbar.Colorbar`: Colorbar handle. .. codeauthor:: Rasmus Handberg <> """ if ax is None: ax = plt.gca() fig = ax.figure # Create new colorbar axes: divider = make_axes_locatable(ax) if loc == 'top': pad = 0.05 if pad is None else pad cax = divider.append_axes('top', size=size, pad=pad) orientation = 'horizontal' elif loc == 'bottom': pad = 0.35 if pad is None else pad cax = divider.append_axes('bottom', size=size, pad=pad) orientation = 'horizontal' elif loc == 'left': pad = 0.35 if pad is None else pad cax = divider.append_axes('left', size=size, pad=pad) orientation = 'vertical' else: pad = 0.05 if pad is None else pad cax = divider.append_axes('right', size=size, pad=pad) orientation = 'vertical' cb = fig.colorbar(im, cax=cax, orientation=orientation) if loc == 'top': cax.xaxis.set_ticks_position('top') cax.xaxis.set_label_position('top') elif loc == 'left': cax.yaxis.set_ticks_position('left') cax.yaxis.set_label_position('left') if label is not None: cb.set_label(label) if ticks is not None: cb.set_ticks(ticks) if ticklabels is not None: cb.set_ticklabels(ticklabels) #cax.yaxis.set_major_locator(matplotlib.ticker.AutoLocator()) #cax.yaxis.set_minor_locator(matplotlib.ticker.AutoLocator()) cax.tick_params(which='both', direction='out', pad=5) cb.set_alpha(1) cb.draw_all() return cb