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
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
# Load data from CSV
dat = np.genfromtxt('dat.xyz', delimiter=' ',skip_header=0)
X_dat = dat :,0 »
Y_dat = dat :,1 »
Z_dat = dat :,2 »
# Convert from pandas dataframes to numpy arrays
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 » )
# create x-y points to be used in heatmap
xi = np.linspace(X.min(),X.max(),1000)
yi = np.linspace(Y.min(),Y.max(),1000)
# Z is a matrix of x-y values
zi = griddata((X, Y), Z, (xi None,: » , yi :,None » ), method='cubic')
# I control the range of my colorbar by removing data
# outside of my range of interest
zmin = 3
zmax = 12
zi (zi<zmin) | (zi>zmax) » = None
# Create the contour plot
CS = plt.contourf(xi, yi, zi, 15, cmap=plt.cm.rainbow,
vmax=zmax, vmin=zmin)
plt.colorbar()
plt.show()
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
# Load data from CSV
dat = np.genfromtxt('dat.xyz', delimiter=' ',skip_header=0)
X_dat = dat :,0 »
Y_dat = dat :,1 »
Z_dat = dat :,2 »
# Convert from pandas dataframes to numpy arrays
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 » )
# create x-y points to be used in heatmap
xi = np.linspace(X.min(),X.max(),1000)
yi = np.linspace(Y.min(),Y.max(),1000)
# Z is a matrix of x-y values
zi = griddata((X, Y), Z, (xi None,: » , yi :,None » ), method='cubic')
# I control the range of my colorbar by removing data
# outside of my range of interest
zmin = 3
zmax = 12
zi (zi<zmin) | (zi>zmax) » = None
# Create the contour plot
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:
