Tags: plot*

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  1. 2023-10-10 Tags: , , , by klotz
  2. 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

  3. 2021-07-20 Tags: , , , by klotz
  4. 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()

    2017-07-30 Tags: , , , , by klotz
  5. 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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