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GitHub - brad-darksbian/fed_dashboard: A simple Dash and Plotly dashboard to review and compare… A simple dashboard to view federal economic data. This system uses the included CSV file of federal economic data to… github.com
import numpy as np import matplotlib.pyplot as plt from scipy.interpolate import griddata
dat = np.genfromtxt('dat.xyz', delimiter=' ',skip_header=0) X_dat = dat :,0 » Y_dat = dat :,1 » Z_dat = dat :,2 »
X, Y, Z, = np.array( » ), np.array( » ), np.array( » ) for i in range(len(X_dat)): X = np.append(X,X_dat i » ) Y = np.append(Y,Y_dat i » ) Z = np.append(Z,Z_dat i » )
xi = np.linspace(X.min(),X.max(),1000) yi = np.linspace(Y.min(),Y.max(),1000)
zi = griddata((X, Y), Z, (xi None,: » , yi :,None » ), method='cubic')
zmin = 3 zmax = 12 zi (zi<zmin) | (zi>zmax) » = None
CS = plt.contourf(xi, yi, zi, 15, cmap=plt.cm.rainbow,
vmax=zmax, vmin=zmin)
plt.colorbar()
plt.show()
t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. The technique can be implemented via Barnes-Hut approximations, allowing it to be applied on large real-world datasets. We applied it on data sets with up to 30 million examples. The technique and its variants are introduced in the following papers:
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