kitty-plotnine (k-nine) is a command-line tool that allows users to create plots directly within their terminal using the plotnine library and the terminal graphics protocol supported by terminals like Kitty, Konsole, and WezTerm. It aims for simplicity and ease of use, enabling one-liner plotting for quick data visualization. The tool supports various data formats like CSV, space-separated numbers, and JSONL. It offers features like customization through the Grammar of Graphics, faceting, and even documentation lookup for plotnine elements. It's designed for those who prefer a terminal-centric workflow and quick visual analysis.
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()
