title: How to plot two or more overlapping 3-D Gaussian surfaces in the same graph in Python?
body: <p>How can I plot two or more overlapping Gaussian surfaces in the same graph, as below?
  This is the code I have written, But the first surface is being covered by the second one. They are overlapping , But i want them to be displayed transparently</p>
  
  <p>result obtained:  <a href="https://i.stack.imgur.com/5LSsW.png" rel="nofollow noreferrer">https://i.stack.imgur.com/5LSsW.png</a></p>
  
  <p>code :<a href="https://pastebin.com/embed_iframe/ms8cngXm" rel="nofollow noreferrer">https://pastebin.com/embed_iframe/ms8cngXm</a></p>
  
  <pre><code>import matplotlib.pyplot as plt
  from mpl_toolkits.mplot3d import Axes3D
  
  def loaddata(filename,label):
      file = open(filename, 'r')
      text = file.read()
      text=text.split('\n')
      file.close()
      dataset = list()
      for line in text:
          if len(line)&gt;0:
              value = line.split()
              dataset.append([float(value[0]), float(value[1]), label])
      return dataset
  def multivariate_gaussian(pos, mu, Sigma):
      n = mu.shape[0]
      Sigma_det = np.linalg.det(Sigma)
      Sigma_inv = np.linalg.inv(Sigma)
      N = np.sqrt((2*np.pi)**n * Sigma_det)
      fac = np.einsum('...k,kl,...l-&gt;...', pos-mu, Sigma_inv, pos-mu)
      return np.exp(-fac / 2) / N
  
  #this is just for maptype ignore this
  _viridis_data = [[0.267004, 0.004874, 0.329415],
                   [0.268510, 0.009605, 0.335427],
                   [0.269944, 0.014625, 0.341379],
                   [0.271305, 0.019942, 0.347269],
                   [0.272594, 0.025563, 0.353093],
                   [0.273809, 0.031497, 0.358853],
                   [0.274952, 0.037752, 0.364543],
                   [0.276022, 0.044167, 0.370164],
                   [0.277018, 0.050344, 0.375715],
                   [0.277941, 0.056324, 0.381191],
                   [0.278791, 0.062145, 0.386592],
                   [0.279566, 0.067836, 0.391917],
                   [0.280267, 0.073417, 0.397163],
                   [0.280894, 0.078907, 0.402329],
                   [0.281446, 0.084320, 0.407414],
                   [0.281924, 0.089666, 0.412415],
                   [0.282327, 0.094955, 0.417331],
                   [0.282656, 0.100196, 0.422160],
                   [0.282910, 0.105393, 0.426902],
                   [0.283091, 0.110553, 0.431554],
                   [0.283197, 0.115680, 0.436115],
                   [0.283229, 0.120777, 0.440584],
                   [0.283187, 0.125848, 0.444960],
                   [0.283072, 0.130895, 0.449241],
                   [0.282884, 0.135920, 0.453427],
                   [0.282623, 0.140926, 0.457517],
                   [0.282290, 0.145912, 0.461510],
                   [0.281887, 0.150881, 0.465405],
                   [0.281412, 0.155834, 0.469201],
                   [0.280868, 0.160771, 0.472899],
                   [0.280255, 0.165693, 0.476498],
                   [0.279574, 0.170599, 0.479997],
                   [0.278826, 0.175490, 0.483397],
                   [0.278012, 0.180367, 0.486697],
                   [0.277134, 0.185228, 0.489898],
                   [0.276194, 0.190074, 0.493001],
                   [0.275191, 0.194905, 0.496005],
                   [0.274128, 0.199721, 0.498911],
                   [0.273006, 0.204520, 0.501721],
                   [0.271828, 0.209303, 0.504434],
                   [0.270595, 0.214069, 0.507052],
                   [0.269308, 0.218818, 0.509577],
                   [0.267968, 0.223549, 0.512008],
                   [0.266580, 0.228262, 0.514349],
                   [0.265145, 0.232956, 0.516599],
                   [0.263663, 0.237631, 0.518762],
                   [0.262138, 0.242286, 0.520837],
                   [0.260571, 0.246922, 0.522828],
                   [0.258965, 0.251537, 0.524736],
                   [0.257322, 0.256130, 0.526563],
                   [0.255645, 0.260703, 0.528312],
                   [0.253935, 0.265254, 0.529983],
                   [0.252194, 0.269783, 0.531579],
                   [0.250425, 0.274290, 0.533103],
                   [0.248629, 0.278775, 0.534556],
                   [0.246811, 0.283237, 0.535941],
                   [0.244972, 0.287675, 0.537260],
                   [0.243113, 0.292092, 0.538516],
                   [0.241237, 0.296485, 0.539709],
                   [0.239346, 0.300855, 0.540844],
                   [0.237441, 0.305202, 0.541921],
                   [0.235526, 0.309527, 0.542944],
                   [0.233603, 0.313828, 0.543914],
                   [0.231674, 0.318106, 0.544834],
                   [0.229739, 0.322361, 0.545706],
                   [0.227802, 0.326594, 0.546532],
                   [0.225863, 0.330805, 0.547314],
                   [0.223925, 0.334994, 0.548053],
                   [0.221989, 0.339161, 0.548752],
                   [0.220057, 0.343307, 0.549413],
                   [0.218130, 0.347432, 0.550038],
                   [0.216210, 0.351535, 0.550627],
                   [0.214298, 0.355619, 0.551184],
                   [0.212395, 0.359683, 0.551710],
                   [0.210503, 0.363727, 0.552206],
                   [0.208623, 0.367752, 0.552675],
                   [0.206756, 0.371758, 0.553117],
                   [0.204903, 0.375746, 0.553533],
                   [0.203063, 0.379716, 0.553925],
                   [0.201239, 0.383670, 0.554294],
                   [0.199430, 0.387607, 0.554642],
                   [0.197636, 0.391528, 0.554969],
                   [0.195860, 0.395433, 0.555276],
                   [0.194100, 0.399323, 0.555565],
                   [0.192357, 0.403199, 0.555836],
                   [0.190631, 0.407061, 0.556089],
                   [0.188923, 0.410910, 0.556326],
                   [0.187231, 0.414746, 0.556547],
                   [0.185556, 0.418570, 0.556753],
                   [0.183898, 0.422383, 0.556944],
                   [0.182256, 0.426184, 0.557120],
                   [0.180629, 0.429975, 0.557282],
                   [0.179019, 0.433756, 0.557430],
                   [0.177423, 0.437527, 0.557565],
                   [0.175841, 0.441290, 0.557685],
                   [0.174274, 0.445044, 0.557792],
                   [0.172719, 0.448791, 0.557885],
                   [0.171176, 0.452530, 0.557965],
                   [0.169646, 0.456262, 0.558030],
                   [0.168126, 0.459988, 0.558082],
                   [0.166617, 0.463708, 0.558119],
                   [0.165117, 0.467423, 0.558141],
                   [0.163625, 0.471133, 0.558148],
                   [0.162142, 0.474838, 0.558140],
                   [0.160665, 0.478540, 0.558115],
                   [0.159194, 0.482237, 0.558073],
                   [0.157729, 0.485932, 0.558013],
                   [0.156270, 0.489624, 0.557936],
                   [0.154815, 0.493313, 0.557840],
                   [0.153364, 0.497000, 0.557724],
                   [0.151918, 0.500685, 0.557587],
                   [0.150476, 0.504369, 0.557430],
                   [0.149039, 0.508051, 0.557250],
                   [0.147607, 0.511733, 0.557049],
                   [0.146180, 0.515413, 0.556823],
                   [0.144759, 0.519093, 0.556572],
                   [0.143343, 0.522773, 0.556295],
                   [0.141935, 0.526453, 0.555991],
                   [0.140536, 0.530132, 0.555659],
                   [0.139147, 0.533812, 0.555298],
                   [0.137770, 0.537492, 0.554906],
                   [0.136408, 0.541173, 0.554483],
                   [0.135066, 0.544853, 0.554029],
                   [0.133743, 0.548535, 0.553541],
                   [0.132444, 0.552216, 0.553018],
                   [0.131172, 0.555899, 0.552459],
                   [0.129933, 0.559582, 0.551864],
                   [0.128729, 0.563265, 0.551229],
                   [0.127568, 0.566949, 0.550556],
                   [0.126453, 0.570633, 0.549841],
                   [0.125394, 0.574318, 0.549086],
                   [0.124395, 0.578002, 0.548287],
                   [0.123463, 0.581687, 0.547445],
                   [0.122606, 0.585371, 0.546557],
                   [0.121831, 0.589055, 0.545623],
                   [0.121148, 0.592739, 0.544641],
                   [0.120565, 0.596422, 0.543611],
                   [0.120092, 0.600104, 0.542530],
                   [0.119738, 0.603785, 0.541400],
                   [0.119512, 0.607464, 0.540218],
                   [0.119423, 0.611141, 0.538982],
                   [0.119483, 0.614817, 0.537692],
                   [0.119699, 0.618490, 0.536347],
                   [0.120081, 0.622161, 0.534946],
                   [0.120638, 0.625828, 0.533488],
                   [0.121380, 0.629492, 0.531973],
                   [0.122312, 0.633153, 0.530398],
                   [0.123444, 0.636809, 0.528763],
                   [0.124780, 0.640461, 0.527068],
                   [0.126326, 0.644107, 0.525311],
                   [0.128087, 0.647749, 0.523491],
                   [0.130067, 0.651384, 0.521608],
                   [0.132268, 0.655014, 0.519661],
                   [0.134692, 0.658636, 0.517649],
                   [0.137339, 0.662252, 0.515571],
                   [0.140210, 0.665859, 0.513427],
                   [0.143303, 0.669459, 0.511215],
                   [0.146616, 0.673050, 0.508936],
                   [0.150148, 0.676631, 0.506589],
                   [0.153894, 0.680203, 0.504172],
                   [0.157851, 0.683765, 0.501686],
                   [0.162016, 0.687316, 0.499129],
                   [0.166383, 0.690856, 0.496502],
                   [0.170948, 0.694384, 0.493803],
                   [0.175707, 0.697900, 0.491033],
                   [0.180653, 0.701402, 0.488189],
                   [0.185783, 0.704891, 0.485273],
                   [0.191090, 0.708366, 0.482284],
                   [0.196571, 0.711827, 0.479221],
                   [0.202219, 0.715272, 0.476084],
                   [0.208030, 0.718701, 0.472873],
                   [0.214000, 0.722114, 0.469588],
                   [0.220124, 0.725509, 0.466226],
                   [0.226397, 0.728888, 0.462789],
                   [0.232815, 0.732247, 0.459277],
                   [0.239374, 0.735588, 0.455688],
                   [0.246070, 0.738910, 0.452024],
                   [0.252899, 0.742211, 0.448284],
                   [0.259857, 0.745492, 0.444467],
                   [0.266941, 0.748751, 0.440573],
                   [0.274149, 0.751988, 0.436601],
                   [0.281477, 0.755203, 0.432552],
                   [0.288921, 0.758394, 0.428426],
                   [0.296479, 0.761561, 0.424223],
                   [0.304148, 0.764704, 0.419943],
                   [0.311925, 0.767822, 0.415586],
                   [0.319809, 0.770914, 0.411152],
                   [0.327796, 0.773980, 0.406640],
                   [0.335885, 0.777018, 0.402049],
                   [0.344074, 0.780029, 0.397381],
                   [0.352360, 0.783011, 0.392636],
                   [0.360741, 0.785964, 0.387814],
                   [0.369214, 0.788888, 0.382914],
                   [0.377779, 0.791781, 0.377939],
                   [0.386433, 0.794644, 0.372886],
                   [0.395174, 0.797475, 0.367757],
                   [0.404001, 0.800275, 0.362552],
                   [0.412913, 0.803041, 0.357269],
                   [0.421908, 0.805774, 0.351910],
                   [0.430983, 0.808473, 0.346476],
                   [0.440137, 0.811138, 0.340967],
                   [0.449368, 0.813768, 0.335384],
                   [0.458674, 0.816363, 0.329727],
                   [0.468053, 0.818921, 0.323998],
                   [0.477504, 0.821444, 0.318195],
                   [0.487026, 0.823929, 0.312321],
                   [0.496615, 0.826376, 0.306377],
                   [0.506271, 0.828786, 0.300362],
                   [0.515992, 0.831158, 0.294279],
                   [0.525776, 0.833491, 0.288127],
                   [0.535621, 0.835785, 0.281908],
                   [0.545524, 0.838039, 0.275626],
                   [0.555484, 0.840254, 0.269281],
                   [0.565498, 0.842430, 0.262877],
                   [0.575563, 0.844566, 0.256415],
                   [0.585678, 0.846661, 0.249897],
                   [0.595839, 0.848717, 0.243329],
                   [0.606045, 0.850733, 0.236712],
                   [0.616293, 0.852709, 0.230052],
                   [0.626579, 0.854645, 0.223353],
                   [0.636902, 0.856542, 0.216620],
                   [0.647257, 0.858400, 0.209861],
                   [0.657642, 0.860219, 0.203082],
                   [0.668054, 0.861999, 0.196293],
                   [0.678489, 0.863742, 0.189503],
                   [0.688944, 0.865448, 0.182725],
                   [0.699415, 0.867117, 0.175971],
                   [0.709898, 0.868751, 0.169257],
                   [0.720391, 0.870350, 0.162603],
                   [0.730889, 0.871916, 0.156029],
                   [0.741388, 0.873449, 0.149561],
                   [0.751884, 0.874951, 0.143228],
                   [0.762373, 0.876424, 0.137064],
                   [0.772852, 0.877868, 0.131109],
                   [0.783315, 0.879285, 0.125405],
                   [0.793760, 0.880678, 0.120005],
                   [0.804182, 0.882046, 0.114965],
                   [0.814576, 0.883393, 0.110347],
                   [0.824940, 0.884720, 0.106217],
                   [0.835270, 0.886029, 0.102646],
                   [0.845561, 0.887322, 0.099702],
                   [0.855810, 0.888601, 0.097452],
                   [0.866013, 0.889868, 0.095953],
                   [0.876168, 0.891125, 0.095250],
                   [0.886271, 0.892374, 0.095374],
                   [0.896320, 0.893616, 0.096335],
                   [0.906311, 0.894855, 0.098125],
                   [0.916242, 0.896091, 0.100717],
                   [0.926106, 0.897330, 0.104071],
                   [0.935904, 0.898570, 0.108131],
                   [0.945636, 0.899815, 0.112838],
                   [0.955300, 0.901065, 0.118128],
                   [0.964894, 0.902323, 0.123941],
                   [0.974417, 0.903590, 0.130215],
                   [0.983868, 0.904867, 0.136897],
                   [0.993248, 0.906157, 0.143936]]
  from matplotlib.colors import ListedColormap
  
  viridis = ListedColormap(_viridis_data, name='viridis')
  
  plt.register_cmap(name='viridis', cmap=viridis)
  plt.set_cmap(viridis)
  
  filename=r"C:/Users/santhoskumar/Desktop/random/pattern/class1_rw.txt"
  label=0
  dataset1= loaddata(filename,label)
  print('Loaded data file {0} with {1} rows'.format(filename, len(dataset1)))
  filename = r"C:/Users/santhoskumar/Desktop/random/pattern/class2_rw.txt"
  label=1
  dataset2 = loaddata(filename,label)
  print('Loaded data file {0} with {1} rows'.format(filename, len(dataset2)))
  filename = r'C:/Users/santhoskumar/Desktop/random/pattern/class3_rw.txt'
  label=2
  dataset3 = loaddata(filename,label)
  print('Loaded data file {0} with {1} rows'.format(filename, len(dataset3)))
  
  
  
  N = 600
  X = np.linspace(200, 800, N)
  Y = np.linspace(300, 1200, N)
  X, Y = np.meshgrid(X, Y)
  
  dataset=np.array(dataset1)
  x,y,label=dataset.T
  dat=x,y
  
  dat=np.array(dat)
  cov=np.cov(dat)
  mu=np.mean(dat,axis=1)
  print(mu)
  
  # Pack X and Y into a single 3-dimensional array
  pos = np.empty(X.shape + (2,))
  pos[:, :, 0] = X
  pos[:, :, 1] = Y
  
  
  # The distribution on the variables X, Y packed into pos.
  Z = multivariate_gaussian(pos, mu, cov)
  minn=1e-15
  for i in range(len(Z)):
      for j in range(len(Z[i])):
          Z[i][j]*=1e4
  
  fig = plt.figure()
  ax = fig.gca(projection='3d')
  ax1=fig.gca(projection='3d')
  
  ax.plot_surface(X, Y, Z,rstride=30,cstride=30, linewidth=1,antialiased=True,cmap=viridis)
  cset = ax.contourf(X, Y, Z,zdir='z',offset=-0.4,cmap=viridis)
  
  
  ax.set_zlim(-0.4,0.40)
  ax.set_zticks(np.linspace(0,0.40,5))
  ax.view_init(27, -21)
  
  dataset=np.array(dataset2)
  x,y,label=dataset.T
  dat=x,y
  
  dat=np.array(dat)
  cov=np.cov(dat)
  mu=np.mean(dat,axis=1)
  print(mu)
  
  # Pack X and Y into a single 3-dimensional array
  pos = np.empty(X.shape + (2,))
  
  pos[:, :, 0] = X
  pos[:, :, 1] = Y
  
  
  # The distribution on the variables X, Y packed into pos.
  Z = multivariate_gaussian(pos, mu, cov)
  minn=1e-15
  for i in range(len(Z)):
      for j in range(len(Z[i])):
          Z[i][j]*=1e4
  
  
  ax.plot_surface(X, Y, Z,rstride=20,cstride=20, linewidth=1,antialiased=True,color='red',cmap=viridis)
  cset1 = ax.contourf(X, Y, Z,zdir='z',offset=-0.4,cmap=viridis)
  
  ax.set_zlim(-0.4,0.40)
  ax.set_zticks(np.linspace(0,0.40,5))
  ax.view_init(27, -21)
  plt.subplots_adjust(hspace=0.5)
  
  plt.show()
  
  
  </code></pre>