Source code for rsatoolbox.vis.colors

#!/usr/bin/python
# -*- coding: UTF-8 -*-
"""
Definition of rsatoolbox's colors

@author: iancharest
"""

import numpy as np
from skimage.color import rgb2hsv, hsv2rgb
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from scipy.interpolate import interp1d


[docs]def color_scale(n_cols, anchor_cols=None, monitor=False): """ linearly interpolates between a set of given anchor colours to give n_cols and displays them if monitor is set Args: n_cols (int): number of colors for the colormap anchor_cols (numpy.ndarray, optional): what color space to interpolate. Defaults to None. monitor (boolean, optional): quick visualisation of the resulting colormap. Defaults to False. Returns: numpy.ndarray: n_cols x 3 RGB array. """ if anchor_cols is None: # if no anchor_cols provided, use red to blue anchor_cols = np.array([[1, 0, 0], [0, 0, 1]]) # define color scale n_anchors = anchor_cols.shape[0] # simple 1D interpolation fn = interp1d( range(n_anchors), anchor_cols.T, ) cols = fn(np.linspace(0, n_anchors - 1, n_cols)).T # optional visuals if monitor: reshaped_cols = cols.reshape((n_cols, 1, 3)) width = int(n_cols / 2) mapping = np.tile(reshaped_cols, (width, 1)) plt.imshow(mapping) plt.show() return cols
[docs]def rdm_colormap(n_cols=256, monitor=None): """this function provides a convenient colormap for visualizing dissimilarity matrices. it goes from blue to yellow and has grey for intermediate values. Args: n_cols (int, optional): precision of the colormap. Defaults to 256. Returns: [matplotlib ListedColormap]: this matplotlib color object can be used as a cmap in any plot. Example: .. code-block:: python import numpy as np import matplotlib.pyplot as plt from rsatoolbox.vis.colors import rdm_colormap plt.imshow(np.random.rand(10,10),cmap=rdm_colormap()) plt.colorbar() plt.show() (ported from Niko Kriegeskorte's RDMcolormap.m) """ # blue-cyan-gray-red-yellow with increasing V (BCGRYincV) anchor_cols = np.array([ [0, 0, 1], [0, 1, 1], [.5, .5, .5], [1, 0, 0], [1, 1, 0], ]) # skimage rgb2hsv is intended for 3d images (RGB) # here we add a new axis to our 2d anchorCols to satisfy # skimage, and then squeeze anchor_cols_hsv = rgb2hsv(anchor_cols[np.newaxis, :]).squeeze() inc_v_weight = 1 anchor_cols_hsv[:, 2] = (1 - inc_v_weight) * anchor_cols_hsv[:, 2] + \ inc_v_weight * np.linspace(0.5, 1, anchor_cols.shape[0]).T # anchorCols = brightness(anchorCols) anchor_cols = hsv2rgb(anchor_cols_hsv[np.newaxis, :]).squeeze() cols = color_scale(n_cols, anchor_cols, monitor) cmap = ListedColormap(cols) cmap.set_bad('white') return cmap