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[code]import matplotlib as mpl  import matplotlib.pyplot as plt

px,py = x,y#x,y

nx = ny = 100 #number of bins in x,y directions x_bins = np.linspace(px.min(), px.max(), nx+1) y_bins = np.linspace(py.min(), py.max(), ny+1)

plt.xlabel('Nonce'); plt.ylabel('Hash ($\log_{10}$)'); plt.title(r'2-D Histogram of First '+str(len(x))+' Valid Nonces and Hashes');

plt.hist2d(px, py, [x_bins,y_bins], norm=mpl.colors.LogNorm());

plt.savefig('Density Plot (log colorscale).png') plt.close();[/code]

[code]import
import matplotlib as mpl  import import matplotlib.pyplot as pltplt px,py = x,y#x,yx,y nx = ny = 100 #number of bins in x,y directions x_bins = np.linspace(px.min(), px.max(), nx+1) y_bins = np.linspace(py.min(), py.max(), ny+1)ny+1) plt.xlabel('Nonce'); plt.ylabel('Hash ($\log_{10}$)'); plt.title(r'2-D Histogram of First '+str(len(x))+' Valid Nonces and Hashes');Hashes'); plt.hist2d(px, py, [x_bins,y_bins], norm=mpl.colors.LogNorm());norm=mpl.colors.LogNorm());
plt.savefig('Density Plot (log colorscale).png') plt.close();[/code]plt.close();

This produces a nice plot:

They key is to use hist2d:

import matplotlib as mpl
import matplotlib.pyplot as plt

px,py = x,y

nx = ny = 100 #number of bins in x,y directions
x_bins = np.linspace(px.min(), px.max(), nx+1)
y_bins = np.linspace(py.min(), py.max(), ny+1)

plt.xlabel('Nonce');
plt.ylabel('Hash ($\log_{10}$)');
plt.title(r'2-D Histogram of First '+str(len(x))+' Valid Nonces and Hashes');

plt.hist2d(px, py, [x_bins,y_bins], norm=mpl.colors.LogNorm());

plt.savefig('Density Plot (log colorscale).png')
plt.close();

This produces a nice plot:

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