Eu preciso fazer um gráfico que funcione como um gráfico de densidade para regiões de alta densidade no gráfico, mas abaixo de alguns limites usa pontos individuais. Não encontrei nenhum código existente semelhante ao que preciso na galeria de miniaturas do matplotlib ou nas pesquisas do Google. Eu tenho um código de trabalho eu escrevi a mim mesmo, mas é um pouco complicado e (mais importante) leva um tempo inaceitavelmente longo quando o número de pontos / caixas é grande. Aqui está o código:

<code>import numpy as np
import math
import matplotlib as mpl
import matplotlib.pyplot as plt
import pylab
import numpy.random

#Create the colormap:
halfpurples = {'blue': [(0.0,1.0,1.0),(0.000001, 0.78431373834609985, 0.78431373834609985),
(0.25, 0.729411780834198, 0.729411780834198), (0.5,
0.63921570777893066, 0.63921570777893066), (0.75,
0.56078433990478516, 0.56078433990478516), (1.0, 0.49019607901573181,
0.49019607901573181)],

    'green': [(0.0,1.0,1.0),(0.000001,
    0.60392159223556519, 0.60392159223556519), (0.25,
    0.49019607901573181, 0.49019607901573181), (0.5,
    0.31764706969261169, 0.31764706969261169), (0.75,
    0.15294118225574493, 0.15294118225574493), (1.0, 0.0, 0.0)],

    'red': [(0.0,1.0,1.0),(0.000001,
    0.61960786581039429, 0.61960786581039429), (0.25,
    0.50196081399917603, 0.50196081399917603), (0.5,
    0.41568627953529358, 0.41568627953529358), (0.75,
    0.32941177487373352, 0.32941177487373352), (1.0,
    0.24705882370471954, 0.24705882370471954)]} 

halfpurplecmap = mpl.colors.LinearSegmentedColormap('halfpurples',halfpurples,256)

#Create x,y arrays of normally distributed points
npts = 1000
x = numpy.random.standard_normal(npts)
y = numpy.random.standard_normal(npts)

#Set bin numbers in both axes
nxbins = 25
nybins = 25

#Set the cutoff for resolving the individual points
minperbin = 1

#Make the density histrogram
H, yedges, xedges = np.histogram2d(y,x,bins=(nybins,nxbins))
#Reorient the axes
H =  H[::-1]

extent = [xedges[0],xedges[-1],yedges[0],yedges[-1]]

#Compute all bins where the density plot value is below (or equal to) the threshold
lowxleftedges = [[xedges[i] for j in range(len(H[:,i])) if H[j,i] <= minperbin] for i in range(len(H[0,:]))] 
lowxrightedges = [[xedges[i+1] for j in range(len(H[:,i])) if H[j,i] <= minperbin] for i in range(len(H[0,:]))] 
lowyleftedges = [[yedges[-(j+2)] for j in range(len(H[:,i])) if H[j,i] <= minperbin] for i in range(len(H[0,:]))]
lowyrightedges = [[yedges[-(j+1)] for j in range(len(H[:,i])) if H[j,i] <= minperbin] for i in range(len(H[0,:]))]

#Flatten and convert to numpy array
lowxleftedges = np.asarray([item for sublist in lowxleftedges for item in sublist])
lowxrightedges = np.asarray([item for sublist in lowxrightedges for item in sublist])
lowyleftedges = np.asarray([item for sublist in lowyleftedges for item in sublist])
lowyrightedges = np.asarray([item for sublist in lowyrightedges for item in sublist])

#Find all points that lie in these regions
lowdatax = [[x[i] for j in range(len(lowxleftedges)) if lowxleftedges[j] <= x[i] and x[i] <= lowxrightedges[j] and lowyleftedges[j] <= y[i] and y[i] <= lowyrightedges[j]] for i in range(len(x))]
lowdatay = [[y[i] for j in range(len(lowyleftedges)) if lowxleftedges[j] <= x[i] and x[i] <= lowxrightedges[j] and lowyleftedges[j] <= y[i] and y[i] <= lowyrightedges[j]] for i in range(len(y))]

#Flatten and convert into numpy array
lowdatax = np.asarray([item for sublist in lowdatax for item in sublist])
lowdatay = np.asarray([item for sublist in lowdatay for item in sublist])

#Plot
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot(lowdatax,lowdatay,linestyle='.',marker='o',mfc='k',mec='k')
cp1 = ax1.imshow(H,interpolation='nearest',extent=extent,cmap=halfpurplecmap,vmin=minperbin)
fig1.colorbar(cp1)

fig1.savefig('contourtest.eps')
</code>

Este código produz uma imagem semelhante a esta:

No entanto, quando usado em conjuntos de dados maiores, o programa leva vários segundos a minutos. Alguma idéia de como acelerar isso? Obrigado!