在Python中使用顏色條和顏色映射創建顏色編碼的時間圖

Question

我試圖根據我使用的每日時間跟蹤文件進行時間跟蹤圖。我編寫了通過我的文件搜索並生成一些列表的代碼。

endTimes是一個特別的活動分鐘從0午夜月的第一天要然而，許多分鐘都在一個月內結束的時間列表。

標籤是在endTimes中列出的時間的標籤列表。由於追蹤者在0分鐘之前沒有任何數據，因此比尾聲短。大多數標籤都是重複的。

類別包含標籤的每個唯一值，以便我認爲時間的順序。

我想創建一個彩條或colorbars的堆棧（1 eachday），將描繪我怎麼把我的時間了一個月，把與每個標籤相關的顏色。類別中的每個值都會有一個關聯的顏色。更多藍色更好。更紅更糟糕。這已經是爲了讓jet colormap正確，但是我需要爲類別中的每個值均勻地分配褻瀆顏色值。然後，我想下一步將是將其轉換爲列出的色彩圖，以便根據與類別關聯的標籤如何用於色條。

我認爲這是正確的做法，但我不確定。我不確定如何將標籤與顏色值相關聯。

到目前爲止，這是我的代碼的最後一部分。我發現了一個功能來製作一個離散的色彩地圖。它的確如此，但這不是我正在尋找的，我不確定發生了什麼。

感謝您的幫助！

# 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

這聽起來像你想要的東西像映射到給定範圍的顏色值的堆積條形圖？在這種情況下，這裏有一個粗略的例子：

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()