Hi,
I had the same problem a while ago when Matplotlib got the new "Viridis"
color scheme and I wanted to use this (and some more) in my application.
Until now the format of the colormaps for the ImageView widget are pretty
simple and, as Vincent wrote before, you can just add your own at the top
of the GradientEditorItem.py.
The Matplotlib cmaps work a bit different and there're actually several
modes how to define your colors. For example is "viridis" just a list of
256 RGB-tuples, while "cubehelix" is generated by a parameterizable
function. So you have to convert each of these into Pyqtgraph's format. I
started doing that, but after the first two I decided to just convert them
all.
So ... in the attached file is the ImageView example from PyQtgraph,
extended with a converter function that converts any Matplotlib colormap to
PyQtgraph's format and a modified ImageView that "monkey patches" the new
colormap into the context menu of the GradientEditorWidget. I hope you find
it useful. :)
I planed to make a pull request with the converter added to colormap.py
and an extended init-function for the GradientEditor that accepts
additional colormaps. If somebody wants to include it before I find the
time .... feel free. :)
Sebastian
Am Dienstag, 15. November 2016 19:16:34 UTC+1 schrieb Bobby Henley:
>
> Hi, I'm very new to the idea of colormaps, I've always just used
> matplotlib and had it done for me.
> Does pyqtgraph have any built in colormaps in the way that matplotlib does?
> I'm now writing something where I would like to display my stack of images
> in ImageView with something like a cubehelix colormap. Can someone point me
> in the direction of a straightforward idiot proof tutorial on how this is
> done? I've searched for similiar topics in the group but its all going over
> my head.
>
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# -*- coding: utf-8 -*-
# author: Sebastian Hoefer
"""
This is an example how to use an ImageView with Matplotlib Colormaps (cmap).
The function 'cmapToColormap' converts the Matplotlib format to the internal
format of PyQtGraph that is used in the GradientEditorItem. The function
itself has no dependencies on Matplotlib! Hence the weird if clauses with
'hasattr' instead of 'isinstance'.
The class 'MplCmapImageView' demonstrates, how to integrate converted
colormaps into a GradientEditorWidget. This is just monkey patched into the
class and should be implemented properly into the GradientEditorItem's
constructor. But this is one way to do it, if you don't want to touch your
PyQtGraph installation.
The 'main' block is just the modified 'ImageView' example from pyqtgraph.
"""
import numpy as np
from pyqtgraph.Qt import QtCore, QtGui
import pyqtgraph
# additional imports for this to work ...
import collections
import matplotlib.cm
def cmapToColormap(cmap, nTicks=16):
"""
Converts a Matplotlib cmap to pyqtgraphs colormaps. No dependency on matplotlib.
Parameters:
*cmap*: Cmap object. Imported from matplotlib.cm.*
*nTicks*: Number of ticks to create when dict of functions is used. Otherwise unused.
"""
# Case #1: a dictionary with 'red'/'green'/'blue' values as list of ranges (e.g. 'jet')
# The parameter 'cmap' is a 'matplotlib.colors.LinearSegmentedColormap' instance ...
if hasattr(cmap, '_segmentdata'):
colordata = getattr(cmap, '_segmentdata')
if ('red' in colordata) and isinstance(colordata['red'], collections.Sequence):
# print("[cmapToColormap] RGB dicts with ranges")
# collect the color ranges from all channels into one dict to get unique indices
posDict = {}
for idx, channel in enumerate(('red', 'green', 'blue')):
for colorRange in colordata[channel]:
posDict.setdefault(colorRange[0], [-1, -1, -1])[idx] = colorRange[2]
indexList = list(posDict.keys())
indexList.sort()
# interpolate missing values (== -1)
for channel in range(3): # R,G,B
startIdx = indexList[0]
emptyIdx = []
for curIdx in indexList:
if posDict[curIdx][channel] == -1:
emptyIdx.append(curIdx)
elif curIdx != indexList[0]:
for eIdx in emptyIdx:
rPos = (eIdx - startIdx) / (curIdx - startIdx)
vStart = posDict[startIdx][channel]
vRange = (posDict[curIdx][channel] - posDict[startIdx][channel])
posDict[eIdx][channel] = rPos * vRange + vStart
startIdx = curIdx
del emptyIdx[:]
for channel in range(3): # R,G,B
for curIdx in indexList:
posDict[curIdx][channel] *= 255
posList = [[i, posDict[i]] for i in indexList]
return posList
# Case #2: a dictionary with 'red'/'green'/'blue' values as functions (e.g. 'gnuplot')
elif ('red' in colordata) and isinstance(colordata['red'], collections.Callable):
# print("[cmapToColormap] RGB dict with functions")
indices = np.linspace(0., 1., nTicks)
luts = [np.clip(np.array(colordata[rgb](indices), dtype=np.float), 0, 1) * 255 \
for rgb in ('red', 'green', 'blue')]
return list(zip(indices, list(zip(*luts))))
# If the parameter 'cmap' is a 'matplotlib.colors.ListedColormap' instance, with the attributes 'colors' and 'N'
elif hasattr(cmap, 'colors') and hasattr(cmap, 'N'):
colordata = getattr(cmap, 'colors')
# Case #3: a list with RGB values (e.g. 'seismic')
if len(colordata[0]) == 3:
# print("[cmapToColormap] list with RGB values")
indices = np.linspace(0., 1., len(colordata))
scaledRgbTuples = [(rgbTuple[0] * 255, rgbTuple[1] * 255, rgbTuple[2] * 255) for rgbTuple in colordata]
return list(zip(indices, scaledRgbTuples))
# Case #3: a list of tuples with positions and RGB-values (e.g. 'terrain')
# -> this section is probably not needed anymore!?
elif len(colordata[0]) == 2:
# print("[cmapToColormap] list with positions and RGB-values. Just scale the values.")
scaledCmap = [(idx, (vals[0] * 255, vals[1] * 255, vals[2] * 255)) for idx, vals in colordata]
return scaledCmap
# Case #X: unknown format or datatype was the wrong object type
else:
raise ValueError("[cmapToColormap] Unknown cmap format or not a cmap!")
class MplCmapImageView(pyqtgraph.ImageView):
def __init__(self, additionalCmaps=[], setColormap=None, **kargs):
super(MplCmapImageView, self).__init__(**kargs)
self.gradientEditorItem = self.ui.histogram.item.gradient
self.activeCm = "grey"
self.mplCmaps = {}
if len(additionalCmaps) > 0:
self.registerCmap(additionalCmaps)
if setColormap is not None:
self.gradientEditorItem.restoreState(setColormap)
def registerCmap(self, cmapNames):
""" Add matplotlib cmaps to the GradientEditors context menu"""
self.gradientEditorItem.menu.addSeparator()
savedLength = self.gradientEditorItem.length
self.gradientEditorItem.length = 100
# iterate over the list of cmap names and check if they're avaible in MPL
for cmapName in cmapNames:
if not hasattr(matplotlib.cm, cmapName):
print('[extendedimageview] Unknown cmap name: \'{}\'. Your Matplotlib installation might be outdated.'.format(cmapName))
else:
# create a Dictionary just as the one at the top of GradientEditorItem.py
cmap = getattr(matplotlib.cm, cmapName)
self.mplCmaps[cmapName] = {'ticks': cmapToColormap(cmap), 'mode': 'rgb'}
# Create the menu entries
# The following code is copied from pyqtgraph.ImageView.__init__() ...
px = QtGui.QPixmap(100, 15)
p = QtGui.QPainter(px)
self.gradientEditorItem.restoreState(self.mplCmaps[cmapName])
grad = self.gradientEditorItem.getGradient()
brush = QtGui.QBrush(grad)
p.fillRect(QtCore.QRect(0, 0, 100, 15), brush)
p.end()
label = QtGui.QLabel()
label.setPixmap(px)
label.setContentsMargins(1, 1, 1, 1)
act = QtGui.QWidgetAction(self.gradientEditorItem)
act.setDefaultWidget(label)
act.triggered.connect(self.cmapClicked)
act.name = cmapName
self.gradientEditorItem.menu.addAction(act)
self.gradientEditorItem.length = savedLength
def cmapClicked(self, b=None):
"""onclick handler for our custom entries in the GradientEditorItem's context menu"""
act = self.sender()
self.gradientEditorItem.restoreState(self.mplCmaps[act.name])
self.activeCm = act.name
## Start Qt event loop unless running in interactive mode.
if __name__ == '__main__':
import sys
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
app = QtGui.QApplication([])
## Create window with ImageView widget
win = QtGui.QMainWindow()
win.resize(800,800)
# Instantiate the modified ImageView class ...
# imv = pyqtgraph.ImageView()
imv = MplCmapImageView(additionalCmaps=['jet', 'viridis', 'seismic', 'cubehelix'])
win.setCentralWidget(imv)
win.show()
win.setWindowTitle('pyqtgraph example: ImageView')
## Create random 3D data set with noisy signals
img = pyqtgraph.gaussianFilter(np.random.normal(size=(200, 200)), (5, 5)) * 20 + 100
img = img[np.newaxis,:,:]
decay = np.exp(-np.linspace(0,0.3,100))[:,np.newaxis,np.newaxis]
data = np.random.normal(size=(100, 200, 200))
data += img * decay
data += 2
## Add time-varying signal
sig = np.zeros(data.shape[0])
sig[30:] += np.exp(-np.linspace(1,10, 70))
sig[40:] += np.exp(-np.linspace(1,10, 60))
sig[70:] += np.exp(-np.linspace(1,10, 30))
sig = sig[:,np.newaxis,np.newaxis] * 3
data[:,50:60,50:60] += sig
## Display the data and assign each frame a time value from 1.0 to 3.0
imv.setImage(data, xvals=np.linspace(1., 3., data.shape[0]))
QtGui.QApplication.instance().exec_()
