Question

我正在尝试根据我使用的每日时间跟踪文件制作时间跟踪图表。我编写的代码可以抓取我的文件并生成一些列表。

endTimes是特定活动以分钟为单位的时间列表，从月份的第一天的午夜0点到一个月的多少分钟。

标签是endTimes中列出的时间的标签列表。由于跟踪器在0分钟之前没有任何数据，因此它比结束时间短。大多数标签都是重复的。

类别包含标签的每个唯一值，以及我对时间的看法。

我想创建一个颜色条或一堆颜色条（每天1个），这些颜色条将描述我如何花一个月的时间并将每种标签的颜色相关联。类别中的每个值都将具有关联的颜色。更蓝更好。更多的红色更糟糕。已经是为了使jet colormap正确，但是我需要为类别中的每个值均匀地分隔出亵渎的颜色值。然后我想下一步是将其转换为列出的色彩映射，以根据与类别关联的标签的方式用于色条。

我认为这是正确的做法，但我不确定。我不确定如何将标签与颜色值相关联。

到目前为止，这是我的代码的最后一部分。我找到了一个函数来制作一个离散的色彩图。它确实如此，但它不是我想要的，我不确定发生了什么。

感谢您的帮助！

# 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()

Answer 1

听起来你想要一个堆积条形图，颜色值映射到给定范围？在这种情况下，这是一个粗略的例子：

import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np

# Generate data....
intervals, weights = [], []
max_weight = 5
for _ in range(30):
    numtimes = np.random.randint(3, 15)
    times = np.random.randint(1, 24*60 - 1, numtimes)
        times = np.r_[0, times, 24*60]
    times.sort()
    intervals.append(np.diff(times) / 60.0)
    weights.append(max_weight * np.random.random(numtimes + 1))

# Plot the data as a stacked bar chart.
for i, (interval, weight) in enumerate(zip(intervals, weights)):
    # We need to calculate where the bottoms of the bars will be.
    bottoms = np.r_[0, np.cumsum(interval[:-1])]

    # We want the left edges to all be the same, but increase with each day.
    left = len(interval) * [i]
    patches = plt.bar(left, interval, bottom=bottoms, align='center')

    # And set the colors of each bar based on the weights
    for val, patch in zip(weight, patches):
        # We need to normalize the "weight" value between 0-1 to feed it into
        # a given colorbar to generate an actual color...
        color = cm.jet(float(val) / max_weight)
        patch.set_facecolor(color)

# Setting the ticks and labels manually...
plt.xticks(range(0, 30, 2), range(1, 31, 2))
plt.yticks(range(0, 24 + 4, 4), 
           ['12am', '4am', '8am', '12pm', '4pm', '8pm', '12am'])
plt.xlabel('Day')
plt.ylabel('Hour')
plt.axis('tight')
plt.show()

enter image description here

使用python中的colorbar和colormaps创建颜色编码时间图表

1 个答案: