wplotlib 0.27

A library that simplifies some basic plotting.

wplotlib

Chieh's quick plot library

Pip Installation

pip install wplotlib

Example Line Plot Usage

from wplotlib import lines
from sklearn.cluster import SpectralClustering
	
textstr = ''.join(( r'$\mu=%.2f$' % (0.1, ), r'$\mathrm{median}=%.2f$' % (0, ), r'$\sigma=%.2f$' % (33, )))
x = np.linspace(0, 10, 1000)
y = np.sin(x)

lp = lines()
lp.plot_line(x, y, 'Title Here', 'X axis', 'Y axis', imgText=textstr)#, outpath)	#x can be set to None

Example Scatter Plot Usage

import wplotlib 
	
textstr = '\n'.join(( r'$\mu=%.2f$' % (0.1, ), r'$\mathrm{median}=%.2f$' % (0, ), r'$\sigma=%.2f$' % (33, )))
x = np.linspace(0, 10, 40)
y = np.sin(x)

lp = wplotlib.scatter(figsize=(10,5))		# (width, height)
lp.plot_scatter(x, y, 'Title Here', 'X axis', 'Y axis', imgText=textstr)#, outpath)

Example Multiple Line subPlot Usage

import numpy as np
from sklearn.cluster import SpectralClustering

from wplotlib import lines
from wplotlib import heatMap
from wplotlib import cluster_plot

textstr = '\n'.join(( r'$\mu=%.2f$' % (0.1, ), r'$\mathrm{median}=%.2f$' % (0, ), r'$\sigma=%.2f$' % (33, )))
x = np.linspace(0, 10, 1000)
y = np.sin(x)
y2 = np.log(x)

lp = lines(figsize=(10,5))
lp.plot_line(x, y, 'First Plot', 'X axis', 'Y axis', imgText=textstr, subplot=121)
lp.plot_line(x, y2, '2nd Plot', 'X axis', 'Y axis', imgText=textstr, subplot=122)
lp.show(save_path='twoSubplots.png') 
# when making subplots, you have to call show explicitly after all the plots, if you include path, then it will save instead of show

Example Single Histogram

from wplotlib import histograms
	
x = [21,22,23,4,5,6,77,8,9,10,31,32,33,34,35,36,37,18,49,50,100]
H = histograms()
H.histogram(x, num_bins=10, title='Basic Histogram', xlabel='value', ylabel='count', 
			facecolor='blue', α=0.5, path=None, normalize=True)

Example of Two Histograms one is log scaled

from wplotlib import histograms
	
x = [21,22,23,4,5,6,77,8,9,10,31,32,33,34,35,36,37,18,49,50,100]
H = histograms(figsize=(10,5))
H.histogram(x, num_bins=10, title='Basic Histogram', facecolor='blue', α=0.5, path=None, subplot=121)
H.histogram(x, num_bins=10, title='Y Log Scaled Histogram', facecolor='blue', α=0.5, path=None, subplot=122, ylogScale=True)
H.show()

Example Heat Map Usage

from wplotlib import heatMap

X1 = np.random.randn(100,2)
X2 = np.random.randn(100,2) + 5
X = np.vstack((X1,X2))

Y1 = np.ones(100)
Y2 = np.zeros(100)
Y = np.hstack((Y1,Y2))

clf = SpectralClustering(n_clusters=2)
allocation = clf.fit_predict(X)
kernel = clf.affinity_matrix_
axis_label = range(kernel.shape[0])
hMap = heatMap()
sorted_kernel = hMap.sort_kernel(kernel, allocation)

hMap.draw_HeatMap(kernel, title='Drawing Unsorted Heat Map', fsize=14)
hMap.draw_HeatMap(sorted_kernel, title='Drawing Sorted Heat Map', fsize=14)
hMap.draw_HeatMap(sorted_kernel, title='Drawing Sorted Heat Map', use_seaborn=True, vmin=0, vmax=1, center=None, linewidths=0, cmap=None, fsize=14)
#cmap types: "Blues", "YlGnBu", "BuPu", "Greens", None

This code results in

Example Clustering Plots

from wplotlib import cluster_plot
import numpy as np

n = 100
x1 = np.random.randn(n,2) + np.array([4,4])
x2 = np.random.randn(n,2) + np.array([-4,-4])
X = np.vstack((x1,x2))

Y = np.concatenate([np.zeros(n), np.ones(n)])

cluster_plot(X, Y, title='Clustering Results')

This code results in