Source code for yt.visualization.color_maps

"""


"""

#-----------------------------------------------------------------------------
# Copyright (c) 2013, yt Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
import numpy as np

import matplotlib.colors as cc
import matplotlib.cm as mcm
from . import _colormap_data as _cm
from yt.extern.six import string_types

try:
    import cmocean
except ImportError:
    cmocean = None

[docs]def is_colormap(cmap): return isinstance(cmap,cc.Colormap)
[docs]def check_color(name): try: cc.colorConverter.to_rgb(name) return True except ValueError: return False
yt_colormaps = {}
[docs]def add_cmap(name, cdict): """ Adds a colormap to the colormaps available in yt for this session """ yt_colormaps[name] = \ cc.LinearSegmentedColormap(name,cdict,256) mcm.datad[name] = cdict mcm.__dict__[name] = cdict try: # API compatibility mcm.register_cmap(name, yt_colormaps[name]) except AttributeError: pass
# The format is as follows: # First number is the number at which we are defining a color breakpoint # Second number is the (0..1) number to interpolate to when coming *from below* # Third number is the (0..1) number to interpolate to when coming *from above* # Next up is boilerplate -- the name, the colormap dict we just made, and the # number of segments we want. This is probably fine as is. cdict = {'red': ((0.0, 80/256., 80/256.), (0.2, 0.0, 0.0), (0.4, 0.0, 0.0), (0.6, 256/256., 256/256.), (0.95, 256/256., 256/256.), (1.0, 150/256., 150/256.)), 'green': ((0.0, 0/256., 0/256.), (0.2, 0/256., 0/256.), (0.4, 130/256., 130/256.), (0.6, 256/256., 256/256.), (1.0, 0.0, 0.0)), 'blue': ((0.0, 80/256., 80/256.), (0.2, 220/256., 220/256.), (0.4, 0.0, 0.0), (0.6, 20/256., 20/256.), (1.0, 0.0, 0.0))} add_cmap('bds_highcontrast', cdict) add_cmap('algae', cdict) # This next colormap was designed by Tune Kamae and converted here by Matt _vs = np.linspace(0,1,255) _kamae_red = np.minimum(255, 113.9*np.sin(7.64*(_vs**1.705)+0.701)-916.1*(_vs+1.755)**1.862 \ + 3587.9*_vs+2563.4)/255.0 _kamae_grn = np.minimum(255, 70.0*np.sin(8.7*(_vs**1.26)-2.418)+151.7*_vs**0.5+70.0)/255.0 _kamae_blu = np.minimum(255, 194.5*_vs**2.88+99.72*np.exp(-77.24*(_vs-0.742)**2.0) + 45.40*_vs**0.089+10.0)/255.0 cdict = {'red':np.transpose([_vs,_kamae_red,_kamae_red]), 'green':np.transpose([_vs,_kamae_grn,_kamae_grn]), 'blue':np.transpose([_vs,_kamae_blu,_kamae_blu])} add_cmap('kamae', cdict) # This one is a simple black & green map cdict = {'red': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0)), 'green': ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (1.0, 0.0, 0.0))} add_cmap('black_green', cdict) cdict = {'red': ((0.0, 0.0, 0.0), (1.0, 0.2, 0.2)), 'green': ((0.0, 0.0, 0.0), (1.0, 0.2, 0.2)), 'blue': ((0.0, 0.0, 0.0), (1.0, 1.0, 1.0))} add_cmap('black_blueish', cdict) # This one is a variant of a colormap commonly # used for X-ray observations by Maxim Markevitch cdict = {'red': ((0.0, 0.0, 0.0), (0.3, 0.0, 0.0), (0.352, 0.245, 0.245), (0.42, 0.5, 0.5), (0.51, 0.706, 0.706), (0.613, 0.882, 0.882), (0.742, 1.0, 1.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.585, 0.0, 0.0), (0.613, 0.196, 0.196), (0.693, 0.48, 0.48), (0.785, 0.696, 0.696), (0.885, 0.882, 0.882), (1.0, 1.0, 1.0)), 'blue': ((0.0, 0.0, 0.0), (0.136, 0.0, 0.0), (0.136, 0.373, 0.373), (0.391, 1.0, 1.0), (1.0, 1.0, 1.0))} add_cmap("purple_mm", cdict) # This one comes from # http://permalink.gmane.org/gmane.comp.python.matplotlib.devel/10518 # and is an implementation of http://arxiv.org/abs/1108.5083 # # cubehelix parameters _gamma_cubehelix = 1.0 _s_cubehelix = 0.5 _r_cubehelix = -1.5 _h_cubehelix = 1.0 _cubehelix_data = { 'red': lambda x: x**_gamma_cubehelix + (_h_cubehelix * x**_gamma_cubehelix * (1 - x**_gamma_cubehelix) / 2) * (-0.14861 * np.cos(2 * np.pi * (_s_cubehelix / 3 + _r_cubehelix * x)) + 1.78277 * np.sin(2 * np.pi * (_s_cubehelix / 3 + _r_cubehelix * x))), 'green': lambda x: x**_gamma_cubehelix + (_h_cubehelix * x**_gamma_cubehelix * (1 - x**_gamma_cubehelix) / 2) * (-0.29227 * np.cos(2 * np.pi * (_s_cubehelix / 3 + _r_cubehelix * x)) - 0.90649 * np.sin(2 * np.pi * (_s_cubehelix / 3 + _r_cubehelix * x))), 'blue': lambda x: x**_gamma_cubehelix + (_h_cubehelix * x**_gamma_cubehelix * (1 - x**_gamma_cubehelix) / 2) * (1.97294 * np.cos(2 * np.pi * (_s_cubehelix / 3 + _r_cubehelix * x))), } add_cmap("cubehelix", _cubehelix_data) # Add colormaps from cmocean, if it's installed if cmocean is not None: for cmname in cmocean.cm.cmapnames: cm = getattr(cmocean.cm, cmname) # cmocean has a colormap named 'algae', so let's avoid overwriting # yt's algae or any other colormap we've already added if cmname in yt_colormaps: cmname = cmname + '_cmocean' yt_colormaps[cmname] = cm try: mcm.register_cmap(cmname, yt_colormaps[cmname]) except AttributeError: # for old versions of matplotlib this won't work, so we avoid # erroring out but don't worry about registering with matplotlib pass # Add colormaps in _colormap_data.py that weren't defined here _vs = np.linspace(0,1,256) for k,v in list(_cm.color_map_luts.items()): if k not in yt_colormaps and k not in mcm.cmap_d: cdict = { 'red': np.transpose([_vs,v[0],v[0]]), 'green': np.transpose([_vs,v[1],v[1]]), 'blue': np.transpose([_vs,v[2],v[2]]) } add_cmap(k, cdict) def _extract_lookup_table(cmap_name): cmap = mcm.get_cmap(cmap_name) if not cmap._isinit: cmap._init() r = cmap._lut[:-3, 0] g = cmap._lut[:-3, 1] b = cmap._lut[:-3, 2] a = np.ones(b.shape) return [r, g, b, a]
[docs]def show_colormaps(subset="all", filename=None): """ Displays the colormaps available to yt. Note, most functions can use both the matplotlib and the native yt colormaps; however, there are some special functions existing within image_writer.py (e.g. write_image() write_bitmap(), etc.), which cannot access the matplotlib colormaps. In addition to the colormaps listed, one can access the reverse of each colormap by appending a "_r" to any map. If you wish to only see certain colormaps, include them in the cmap_list attribute. Parameters ---------- subset : string, or list of strings, optional valid values : "all", "yt_native", or list of cmap names default : "all" As mentioned above, a few functions can only access yt_native colormaps. To display only the yt_native colormaps, set this to "yt_native". If you wish to only see a few colormaps side by side, you can include them as a list of colormap names. Example: ['algae', 'gist_stern', 'kamae', 'spectral'] filename : string, opt default: None If filename is set, then it will save the colormaps to an output file. If it is not set, it will "show" the result interactively. """ import matplotlib.pyplot as plt import matplotlib.cm as cm a=np.outer(np.arange(0,1,0.01), np.ones(10)) if subset == "all": maps = [ m for m in cm.cmap_d if (not m.startswith("idl")) & (not m.endswith("_r"))] elif subset == "yt_native": maps = [ m for m in _cm.color_map_luts if (not m.startswith("idl")) & (not m.endswith("_r"))] else: try: maps = [ m for m in cm.cmap_d if m in subset] if len(maps) == 0: raise AttributeError except AttributeError: raise AttributeError("show_colormaps requires subset attribute " "to be 'all', 'yt_native', or a list of " "valid colormap names.") maps = list(set(maps)) maps.sort() # scale the image size by the number of cmaps plt.figure(figsize=(2.*len(maps)/10.,6)) plt.subplots_adjust(top=0.7,bottom=0.05,left=0.01,right=0.99) l = len(maps)+1 for i,m in enumerate(maps): plt.subplot(1,l,i+1) plt.axis("off") plt.imshow(a, aspect='auto',cmap=plt.get_cmap(m),origin="lower") plt.title(m,rotation=90, fontsize=10, verticalalignment='bottom') if filename is not None: plt.savefig(filename, dpi=100, facecolor='gray') else: plt.show()
[docs]def make_colormap(ctuple_list, name=None, interpolate=True): """ This generates a custom colormap based on the colors and spacings you provide. Enter a ctuple_list, which consists of tuples of (color, spacing) to return a colormap appropriate for use in yt. If you specify a name, it will automatically be added to the current session as a valid colormap. Output colormap is in the format yt expects for adding a colormap to the current session: a dictionary with the appropriate RGB channels each consisting of a 256x3 array : First number is the number at which we are defining a color breakpoint Second number is the (0..1) number to interpolate to when coming *from below* Third number is the (0..1) number to interpolate to when coming *from above* Parameters ---------- ctuple_list: list of (color, float) tuples The ctuple_list consists of pairs of (color, interval) tuples identifying the colors to use in the colormap and the intervals they take to change to the next color in the list. A color can either be a string of the name of a color, or it can be an array of 3 floats, each representing the intensity of R, G, and B on a scale of 0 to 1. Valid color names and their equivalent arrays are listed below. Any interval can be given for the different color tuples, and the total of all the intervals will be scaled to the 256 output elements. If a ctuple_list ends with a color and a non-zero interval, a white 0-interval would be added to the end to finish the interpolation. To avoid finishing with white, specify your own zero-interval color at the end. name: string, optional If you wish this colormap to be added as a valid colormap to the current session, specify a name here. Default: None interpolation: boolean, optional Designates whether or not the colormap will interpolate between the colors provided or just give solid colors across the intervals. Default: True Preset Color Options -------------------- 'white' : np.array([255, 255, 255 ])/255. 'gray' : np.array([130, 130, 130])/255. 'dgray' : np.array([80, 80, 80])/255. 'black' : np.array([0, 0, 0])/255. 'blue' : np.array([0, 0, 255])/255. 'dblue' : np.array([0, 0, 160])/255. 'purple' : np.array([100, 0, 200])/255. 'dpurple' : np.array([66, 0, 133])/255. 'dred' : np.array([160, 0, 0])/255. 'red' : np.array([255, 0, 0])/255. 'orange' : np.array([255, 128, 0])/255. 'dorange' : np.array([200,100, 0])/255. 'yellow' : np.array([255, 255, 0])/255. 'dyellow' : np.array([200, 200, 0])/255. 'green' : np.array([0, 255, 0])/255. 'dgreen' : np.array([0, 160, 0])/255. Examples -------- To obtain a colormap that starts at black with equal intervals in green, blue, red, yellow in that order and interpolation between those colors. (In reality, it starts at black, takes an interval of 10 to interpolate to green, then an interval of 10 to interpolate to blue, then an interval of 10 to interpolate to red.) >>> cm = make_colormap([('black', 10), ('green', 10), ('blue', 10), ... ('red', 0)]) To add a colormap that has five equal blocks of solid major colors to the current session as "steps": >>> make_colormap([('red', 10), ('orange', 10), ('yellow', 10), ... ('green', 10), ('blue', 10)], name="steps", ... interpolate=False) To add a colormap that looks like the French flag (i.e. equal bands of blue, white, and red) using your own RGB keys, then to display it: >>> make_colormap([([0,0,1], 10), ([1,1,1], 10), ([1,0,0], 10)], ... name='french_flag', interpolate=False) >>> show_colormaps(['french_flag']) """ # aliases for different colors color_dict = { 'white' : np.array([255, 255, 255 ])/255., 'gray' : np.array([130, 130, 130])/255., 'dgray' : np.array([80, 80, 80])/255., 'black' : np.array([0, 0, 0])/255., 'blue' : np.array([0, 0, 255])/255., 'dblue' : np.array([0, 0, 160])/255., 'purple' : np.array([100, 0, 200])/255., 'dpurple' : np.array([66, 0, 133])/255., 'dred' : np.array([160, 0, 0])/255., 'red' : np.array([255, 0, 0])/255., 'orange' : np.array([255, 128, 0])/255., 'dorange' : np.array([200,100, 0])/255., 'yellow' : np.array([255, 255, 0])/255., 'dyellow' : np.array([200, 200, 0])/255., 'green' : np.array([0, 255, 0])/255., 'dgreen' : np.array([0, 160, 0])/255.} cmap = np.zeros((256,3)) # If the user provides a list with a non-zero final interval, it # doesn't make sense because you have an interval but no final # color to which it interpolates. So provide a 0-length white final # interval to end the previous interval in white. if ctuple_list[-1][1] != 0: ctuple_list.append(('white', 0)) # Figure out how many intervals there are total. rolling_index = 0 for i, (color, interval) in enumerate(ctuple_list): if isinstance(color, string_types): ctuple_list[i] = (color_dict[color], interval) rolling_index += interval scale = 256./rolling_index n = len(ctuple_list) # Step through each ctuple and interpolate from one color to the # next over the interval provided rolling_index = 0 for i in range(n-1): color, interval = ctuple_list[i] interval *= scale next_index = rolling_index + interval next_color, next_interval = ctuple_list[i+1] if not interpolate: next_color = color # Interpolate the R, G, and B channels from one color to the next # Use np.round to make sure you're on a discrete index interval = np.round(next_index)-np.round(rolling_index) for j in np.arange(3): cmap[int(np.rint(rolling_index)):int(np.rint(next_index)), j] = \ np.linspace(color[j], next_color[j], interval) rolling_index = next_index # Return a dictionary with the appropriate RGB channels each consisting of # a 256x3 array in the format that is expected by add_cmap() to add a # colormap to the session. # The format is as follows: # First number is the number at which we are defining a color breakpoint # Second number is the (0..1) number to interpolate to when coming *from below* # Third number is the (0..1) number to interpolate to when coming *from above* _vs = np.linspace(0,1,256) cdict = {'red': np.transpose([_vs, cmap[:,0], cmap[:,0]]), 'green': np.transpose([_vs, cmap[:,1], cmap[:,1]]), 'blue': np.transpose([_vs, cmap[:,2], cmap[:,2]])} if name is not None: add_cmap(name, cdict) return cdict