creating a color coded time chart using colorbar and colormaps in python
- by Rusty
I'm trying to make a time tracking chart based on a daily time tracking file that I used. I wrote code that crawls through my files and generates a few lists.
endTimes is a list of times that a particular activity ends in minutes going from 0 at midnight the first day of the month to however many minutes are in a month.
labels is a list of labels for the times listed in endTimes. It is one shorter than endtimes since the trackers don't have any data about before 0 minute. Most labels are repeats.
categories contains every unique value of labels in order of how well I regard that time.
I want to create a colorbar or a stack of colorbars (1 for eachday) that will depict how I spend my time for a month and put a color associated with each label. Each value in categories will have a color associated. More blue for more good. More red for more bad. It is already in order for the jet colormap to be right, but I need to get desecrate color values evenly spaced out for each value in categories. Then I figure the next step would be to convert that to a listed colormap to use for the colorbar based on how the labels associated with the categories.
I think this is the right way to do it, but I am not sure. I am not sure how to associate the labels with color values.
Here is the last part of my code so far. I found one function to make a discrete colormaps. It does, but it isn't what I am looking for and I am not sure what is happening.
Thanks for the help!
# now I need to develop the graph
import numpy as np
from matplotlib import pyplot,mpl
import matplotlib
from scipy import interpolate
from scipy import *
def contains(thelist,name):
# checks if the current list of categories contains the one just read
for val in thelist:
if val == name:
return True
return False
def getCategories(lastFile):
'''
must determine the colors to use
I would like to make a gradient so that the better the task, the closer to blue
bad labels will recieve colors closer to blue
read the last file given for the information on how I feel the order should be
then just keep them in the order of how good they are in the tracker
use a color range and develop discrete values for each category by evenly spacing them out
any time not found should assume to be sleep
sleep should be white
'''
tracker = open(lastFile+'.txt') # open the last file
# find all the categories
categories = []
for line in tracker:
pos = line.find(':') # does it have a : or a ?
if pos==-1: pos=line.find('?')
if pos != -1: # ignore if no : or ?
name = line[0:pos].strip() # split at the : or ?
if contains(categories,name)==False: # if the category is new
categories.append(name) # make a new one
return categories
# find good values in order of last day
newlabels=[]
for val in getCategories(lastDay):
if contains(labels,val):
newlabels.append(val)
categories=newlabels
# convert discrete colormap to listed colormap python
for ii,val in enumerate(labels):
if contains(categories,val)==False:
labels[ii]='sleep'
# create a figure
fig = pyplot.figure()
axes = []
for x in range(endTimes[-1]%(24*60)):
ax = fig.add_axes([0.05, 0.65, 0.9, 0.15])
axes.append(ax)
# figure out the colors to use
# stole this function to make a discrete colormap
# http://www.scipy.org/Cookbook/Matplotlib/ColormapTransformations
def cmap_discretize(cmap, N):
"""Return a discrete colormap from the continuous colormap cmap.
cmap: colormap instance, eg. cm.jet.
N: Number of colors.
Example
x = resize(arange(100), (5,100))
djet = cmap_discretize(cm.jet, 5)
imshow(x, cmap=djet)
"""
cdict = cmap._segmentdata.copy()
# N colors
colors_i = np.linspace(0,1.,N)
# N+1 indices
indices = np.linspace(0,1.,N+1)
for key in ('red','green','blue'):
# Find the N colors
D = np.array(cdict[key])
I = interpolate.interp1d(D[:,0], D[:,1])
colors = I(colors_i)
# Place these colors at the correct indices.
A = zeros((N+1,3), float)
A[:,0] = indices
A[1:,1] = colors
A[:-1,2] = colors
# Create a tuple for the dictionary.
L = []
for l in A:
L.append(tuple(l))
cdict[key] = tuple(L)
# Return colormap object.
return matplotlib.colors.LinearSegmentedColormap('colormap',cdict,1024)
# jet colormap goes from blue to red (good to bad)
cmap = cmap_discretize(mpl.cm.jet, len(categories))
cmap.set_over('0.25')
cmap.set_under('0.75')
#norm = mpl.colors.Normalize(endTimes,cmap.N)
print endTimes
print labels
# make a color list by matching labels to a picture
#norm = mpl.colors.ListedColormap(colorList)
cb1 = mpl.colorbar.ColorbarBase(axes[0],cmap=cmap
,orientation='horizontal'
,boundaries=endTimes
,ticks=endTimes
,spacing='proportional')
pyplot.show()
