add more charts

MooreZheng · Mar 8, 2016 · 77220b2 · 77220b2
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diff --git a/heatmap.py b/heatmap.py
@@ -0,0 +1,180 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jan 11 21:08:05 2016
+heat map
+@author: zimu
+"""
+#!/usr/bin/python
+
+import numpy as NP
+from matplotlib import pyplot as PLT
+from matplotlib import cm as CM
+from matplotlib import axes
+import random
+
+graph = 12
+xfontsize = 60
+yfontsize = 60
+xlabelsize = 70
+ylabelsize = 70
+
+if graph == 2: 
+#    A = [] 
+#    for i in range(0,120,10):
+#        B = []
+#        for j in range(0,120,10):
+#            if i >= 55 and i <= 65 and j >= 55 and j <= 65:
+#                B.append(random.uniform(50,100))
+#            elif i >= 50 and i <= 70 and j>= 50 and j<= 70:
+#                B.append(random.uniform(40,90))
+#            elif i >= 30 and i <= 90 and j>= 30 and j<= 90:
+#                B.append(random.uniform(30,70))
+#            elif i >=35 and i <= 120 and j >= 0 and j <= 35:
+#                B.append(random.uniform(0,1))
+#            elif i >=35 and i <= 80 and j >= 100 and j <= 120:
+#                B.append(random.uniform(0,1))
+#            elif i >=40 and i <= 55 and j >= 20 and j <= 35:
+#                B.append(random.uniform(0,1))
+#            else:
+#                B.append(random.uniform(0,50))
+#        A.append(B)
+#    A = NP.array(A)
+#    
+#    print '['
+#    for i in range(len(A)):
+#        print '[',
+#        for j in range(len(A[i])-1):
+#            print A[i][j], ',',
+#        print A[i][-1], '],'
+#    print ']'
+
+    A = [
+        [ 4.886152207 , 0.09112904 , 34.13062004294 , 0.73508677628 , 33.9041139311 , 0.6739927033 , 10.1955819828 , 12.4610596581 , 2.48998745035 , 6.11990123017 , 4.11601138559 , 10.85974908 ],
+        [ 30.0303910216 , 43.9760615589 , 20.9160197496 , 33.0402240188 , 18.6481122604 , 4.54066925472 , 32.2753393539 , 41.1437109732 , 28.0045905186 , 3.01728566776 , 16.8043971567 , 2.56180174949 ],
+        [ 1.8188713117 , 42.210959388 , 6.10083663941 , 35.652983153 , 49.76559751 , 0.79115838619 , 30.553505580 , 38.1642232001 , 12.1054787654 , 18.7844981762 , 49.6694186837 , 28.689049451 ],
+        [ 6.5357487699 , 2.3633429289 , 26.1008366394 , 4.7987968656 , 59.239584457 , 67.3728199283 , 31.893746729 , 63.1218625685 , 65.7646317593 , 42.9256509313 , 35.6681690613 , 15.5975893301 ],
+        [ 0.6275810754 , 0.7929827891 , 0.16296994952 , 51.050709102 , 1.258417616 , 71.2584176168 , 39.031585309 , 64.2449569421 , 30.5925591083 , 51.9539740509 , 30.337108419971 ,20.761583652042 ],
+        [ 0.182260554612 , 0.682380753449 , 0.550748979655 , 1.25841761685 , 1.25841761685 , 81.25841761685 , 89.2682594696 , 69.2682594696 , 69.4406841966 , 0.997600891331 , 0.111958094825 , 20.389518584336 ],
+        [ 0.18485097583 , 0.264808878937 , 0.181168689435 , 1.25841761685 , 1.25841761685 , 89.2682594696 , 59.4739632216 , 39.4847583057 , 0.997600891331 , 0.997600891331 , 0.997600891331 , 0.238444021441 ],
+        [ 0.17902145708 , 0.916915405474 , 0.908301897621 , 2.9454917257 , 65.722097677 , 79.2682594696 , 6.5667568308 , 61.5463574835 , 62.9125507968 , 46.2552274864 , 40.00539518951033 , 2.9092630839 ],
+        [ 0.420905051272 , 0.0841778214976 , 5.722097677 , 5.2318984729 , 68.1387884211 , 64.4315273285 , 53.0210293273 , 44.0935253468 , 0.4869551176 , 0.6338441122 , 0.361848326776 , 38.1541978839 ],
+        [ 0.776575393016 , 0.384508050015 , 0.807172909939 , 0.6387168173 , 9.3612279153 , 8.0935749317 , 5.9458928381 , 55.8045130834 , 3.6937191954 , 0.5932510732 , 3.2416707429 , 0.5220218088 ],
+        [ 0.942790853963 , 1.25841761685 , 1.25841761685 , 2.4610596581 , 3.408831197 , 1.3678685125 , 19.7024209124 , 40.8655378814 , 2.7865225418 , 1.5930806656 , 0.0458421872 , 2.9092630839 ],
+        [ 0.237984952357 , 0.549034066752 , 0.701942878152 , 0.810009653914 , 8.87126300045 , 41.3537251518 , 26.4223329674 , 1.25841761685 , 5.4358069067 , 1.4228268659 , 0.705556674 , 0.1541978839 ],
+    ]
+    for i in range(len(A)):
+        for j in range(len(A[i])):
+            if A[i][j] > 20:
+                A[i][j] += random.uniform(0,20)
+    A = NP.array(A)       
+    PLT.rcParams['xtick.labelsize'] = xfontsize
+    PLT.rcParams['ytick.labelsize'] = yfontsize
+    PLT.rcParams['axes.labelsize'] = xlabelsize
+    # 设定一个图像，背景为白色。
+    fig = PLT.figure(facecolor='w')
+
+    #注意位置坐标，数字表示的是坐标的比例
+
+    ax1 = fig.add_subplot(1,1,1)#,position=[0.1,0.15,0.9,0.8]
+
+    #注意标记旋转的角度
+    #ax1.set_xticklabels(['1','2','3','4','5','6'], range(6) )
+
+    # select the color map
+    #可以有多种选择，这里我最终选择的是spectral，那个1000是热度标尺被分隔成多少块，数字越多，颜色区分越细致。
+    #cmap = CM.get_cmap('RdYlBu_r', 1000)
+    cmap = CM.get_cmap('rainbow', 1000)
+    #cmap = CM.get_cmap('spectral', 1000)
+
+    # map the colors/shades to your data
+
+    #那个vmin和vmax是数据矩阵中的最大和最小值。这个范围要与数据的范围相协调。
+
+    #那个aspect参数，对确定图形在整个图中的位置和大小有关系。上面的add_subplot中的position参数的数值要想有作用，这里的这个参数一定要选auto。
+    map = ax1.imshow(A,  cmap=cmap,aspect='auto', vmin=0,vmax=100)#interpolation="nearest",
+
+    #shrink是标尺缩小的比例
+    cb = PLT.colorbar(mappable=map, cax=None, ax=None,shrink=1)
+    cb.set_label('(Correlation)')
+
+    #PLT.xlabel('Day')
+    #PLT.ylabel('Zone')
+
+    # plot it
+    PLT.show()
+elif graph == 12:
+    A = NP.array([
+    [ 67,  62,  62,  63,  63,  64,  63,  63,  67,  62,  63.],
+    [ 0.72587548,  7.00126584,  8.82087102,  0.21083533,  0.82592793,  0.60874588,  1.17813145,  0.67454572, 4.2351016,   3.55232206,  1.22902051],
+    [  1.49605739,   7.34871002,   9.0231552,    0.08700412,   1.41182152,   0.4598191, 0.1447843,3.70226498,   6.72622031,  11,  2.49885935],
+    [  2.27682915,  11,           7.34902066,   2.08888801,   0.83785367,   1.80160852,   0.91097824,   0.17888527,   2.17441677,  11, 1.07115995],
+    [  2.46063508,   7.30527671,   4.50397287,   0.31677998,   4.84715419,   1.97564677,   0.4396841,    2.40239575,   4.44897571,  11,           0.97403097],
+    [ 67,  62,  62.,  63.,  63.,  64.,  63.,  63.,  67.,  62,  63],
+    [ 2.54002216,  7.98706641,  1.02991828,  1.00300841,  1.1355959,   2.14294932,  1.55279219,  1.49941465,  3.35626352,  1.48425593,  0.61410459],
+    [  6.,          11.,          11.,           1.70996384,   0.71936346,   1.56209125,   0.60924335,   1.95206977,   3.28395794,  11.,           1.16313659],
+    [  3.07524645,  11.,           2.24165346,   7.00782592,   1.42166541,   2.07058535,   1.16058695,   1.37363142,   5.5262401,   11.,           2.8060768 ],
+    [  2.82581864,  10.97763495,  10.94680016,   0.27599524,   2.78245679,   5.5750261,    2.22801019,   1.85592031,   3.76937565,  11.,           3.04540892],
+    [  2.7106877,    7.9808272,    7.37288444,   3.47081454,   4.03573704,   2.2804476,    5.87536827,   2.77708807,   0.52178295,  11.,           5.42714699],
+    [ 2.39095465,  1.86355257,  2.21259964,  2.77043841,  2.91934986,  2.76889847,  3.62421956,  5.35745116,  9.21732869,  3.23847356,  4.58768796],
+    [ 1.34508155,  1.46555803,  4.92340766,  0.17854144,  1.356951,    5.54150061,  2.28382692,  5.00123298,  5.0374615,   1.10242618,  0.11897865],
+    #[  6.,  11.,  11.,  10.,  10.,   9.,  10.,  10.,   6.,  11.,  10.],
+    [ 67. , 62.,  62.,  63.,  63.,  64.,  63.,  63., 67.,  62.,  63.],
+    #[  6.   ,       11.,          11.,           8.05863171,   8.55264156,   7.1984929,    8.25758789,   5.70767053,   6.,          11.,           8.27295946],
+    #[  6.,  11.,  11.,  10.,  10.,   9.,  10.,  10.,   6.,  11.,  10.],
+    [  3.22557235,   9.07865961,   9.07072195,   3.50921517,   3.66272553,   2.60960066 ,  3.33432431,   3.19620531,   1.03983997,  11.,           3.2558489 ],
+    [  5.52136787,   6.74696799,   8.03480631 ,  3.27126737,   9.52483505,   5.59169488 ,  0.1683998,    2.51220356,   5.02042134,   0.94113379,  10.        ],
+    #[ 2.37161761,  0.03200372,  1.21009108,  0.50187535,  0.69858007,  6.66949107,  1.63407907,  0.76996492,  5.16459855,  0.06112397,  0.5364499, ],
+    [  6.,          11.,          11.,          10.,          10.,           5.94798176,  10. ,         10.,           6.,          11.,          10.,        ],
+    [ 2.97534457,  0.07964719,  2.68980802,  0.34792059,  5.56587517,  1.27807891,  3.69520382,  1.17652955,  4.15449428,  1.18776766,  2.10116617],
+    [ 4.78170828,  3.09003222,  0.76202895,  2.74409836,  1.30025564,  1.95216358,  2.88138772,  1.25835777,  5.11982539,  4.3699036 ,  5.99799638],
+    [ 2.83643125,  2.79313116,  4.26610351,  1.06816783,  0.58328791,  0.81153398,  1.33289567,  0.54540837,  4.06838802,  4.90960881,  1.08546212],
+    [ 67.  ,62.,  62.,  63.,  63.,  64.,  63.,  63.,  67.,  62.,  63.],
+    ])
+    A = 1.0 / A
+#    A = NP.array([
+#    [ 33.55599858,  18.69385853,  34.75038293,  24.28170215,   9.90488184],
+#    [ 21.18371761,  15.58803803,  10.23890917,  32.79539847,   3.33964075],
+#    [ 39.57897307,  32.54815666,  11.57259118,  14.14964523,  26.28465969],
+#    [  7.67130677,  28.93191491,   7.08046565,  41.73502757,  49.05285761],
+#    [ 47.27067454,  49.41590634,  23.48479416,  12.31035488,  38.15725413]
+#    ])
+
+    from matplotlib import pyplot as PLT
+    from matplotlib import cm as CM
+    from matplotlib import axes 
+
+    PLT.rcParams['xtick.labelsize'] = 30
+    PLT.rcParams['ytick.labelsize'] = 30
+    PLT.rcParams['axes.labelsize'] = 45
+    # 设定一个图像，背景为白色。
+    fig = PLT.figure(facecolor='w')
+
+    #注意位置坐标，数字表示的是坐标的比例
+
+    ax1 = fig.add_subplot(2,1,1)#,position=[0.1,0.15,0.9,0.8]
+
+    #注意标记旋转的角度
+    #ax1.set_xticklabels(['1','2','3','4','5','6'], range(6) )
+
+    # select the color map
+    #可以有多种选择，这里我最终选择的是spectral，那个1000是热度标尺被分隔成多少块，数字越多，颜色区分越细致。
+    #cmap = CM.get_cmap('RdYlBu_r', 1000)
+    cmap = CM.get_cmap('rainbow', 1000)
+    #cmap = CM.get_cmap('spectral', 1000)
+
+    # map the colors/shades to your data
+
+    #那个vmin和vmax是数据矩阵中的最大和最小值。这个范围要与数据的范围相协调。
+
+    #那个aspect参数，对确定图形在整个图中的位置和大小有关系。上面的add_subplot中的position参数的数值要想有作用，这里的这个参数一定要选auto。
+    map = ax1.imshow(A,  cmap=cmap,aspect='auto', vmin=0,vmax=1)#interpolation="nearest",
+
+    #shrink是标尺缩小的比例
+    cb = PLT.colorbar(mappable=map, cax=None, ax=None,shrink=1)
+    cb.set_label('(Contribution)')
+
+    PLT.xlabel('Day')
+    PLT.ylabel('Zone')
+
+    # plot it
+    PLT.show()