Matplotlib Techniques

arrows.py

import matplotlib.pyplot as plt

plt.xlim([-1,20])
plt.ylim([-1,20])
plt.arrow(0, 0, 10, 10,head_width=0.5, head_length=0.5)
plt.arrow(10,10, 8,4,head_width=0.5, head_length=0.5)

barPlot.py

import matplotlib.pyplot as plt

if __name__ == "__main__":
    errors = [0.338, 0.325, 0.305, 0.272, 0.267]
    plt.bar(range(len(errors)), errors)
    plt.xticks(range(len(errors)), ["5", "10", "30", "50",'100'])
    plt.xlabel('Iterations (T)')
    plt.ylabel('Test Error')
    plt.ylim([0,0.6])
    plt.xticks(rotation=90)
    
    for i, error in enumerate(errors):
        plt.text(i-0.25, error+0.01 , str(error), color='blue', fontweight='bold')

contours.py

import os
import cv2
import copy
import pprint
import numpy as np
from shapely.geometry import Polygon
from descartes import PolygonPatch
import matplotlib.pyplot as plt

%matplotlib inline

def find_contours(img_url_local, class_id_study=0, only_classes=0,verbose=0, show_image=0):
    if os.path.exists(img_url_local):
        img = cv2.imread(img_url_local, cv2.IMREAD_UNCHANGED)
        h, w = len(img), len(img[0])
        img_class_ids, counts = np.unique(img, return_counts=True)
        if verbose or only_classes:
            pprint.pprint(dict(zip(img_class_ids, counts)))
            if only_classes:
                return [], [], [], h, w

        for class_id in img_class_ids:
            if class_id not in [class_id_study]:
                continue
            if verbose:
                print (' - class_id : ', class_id)
            img_tmp = copy.deepcopy(img)
            img_tmp[img_tmp != class_id] = 0
            img_tmp[img_tmp == class_id] = 255
            if verbose:
                print (np.unique(img_tmp, return_counts=True))
            if show_image:
                plt.imshow(img_tmp, cmap='gray')
                
            im2, contours, hierarchy = cv2.findContours(img_tmp, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

            return im2, contours, hierarchy, h, w
    else:
        print (' Check image URL : ', img_url_local)
        return []

def get_contour_pts(contours, hierarchy, verbose=0):
    polygons_pts = []
    for contour_id, contour_topology in enumerate(hierarchy[0]):
        if verbose:
            print (' - hierarchy : ', contour_topology)
        next_contour    =  contour_topology[0]
        prev_contour    =  contour_topology[1]
        child_contour   =  contour_topology[2]
        parent_countour =  contour_topology[3]
        
        if child_contour == -1 and parent_countour == -1:
            polygon_pts = np.array([pts[0] for pts in contours[contour_id]])
            polygons_pts.append(polygon_pts)
        else:
            print (' - Special case noticed! Please handle.')
    
    return polygons_pts
    
def draw_scatter(polygons_pts, h, w):
    f, axarr= plt.subplots(1, figsize=(10,10))
    axarr.set_xlim(0, w)
    axarr.set_ylim(h, 0)
    
    for polygon_pts in polygons_pts:
        plt.scatter(polygon_pts[:,0], polygon_pts[:,1]) 
    
def draw_polygon(polygons_pts, h, w, title='', save=False):
    fig, ax = plt.subplots(1, figsize=(10,10))
    ax.set_xlim(0, w)
    ax.set_ylim(h, 0)
    
    for polygon_pts in polygons_pts:
        print (polygon_pts.shape)
        if len(polygon_pts) > 2:
            poly    = Polygon(polygon_pts)
            patch   = PolygonPatch(poly, facecolor=[0,0,0.5], edgecolor=[1,1,1], alpha=0.8)
            ax.add_patch(patch)
        
    plt.title(title)
    plt.tight_layout()    


    
if __name__ == "__main__":
    ## PARAMS
    img_url_local                  = '../data/cityscapes/stuttgart_000189_000019_gtFine_labelIds.png'
    
    ## STEPS
    _, contours, hierarchy, h, w   = find_contours(img_url_local, class_id_study=4, only_classes=0
                                                   , verbose=0, show_image=0)
    if len(contours):
        polygons_pts                = get_contour_pts(contours, hierarchy, verbose=0)
        # draw_scatter(polygons_pts, h, w) 
        draw_polygon(polygons_pts, h, w)

errorBar.py

import matplotlib.pyplot as plt

def getStats(X):
    x = np.arange(len(X))
    y = np.mean(X, axis=1)
    yerr = np.std(X, axis=1)
    return x,y,yerr

def plot1(X):
    x, y, yerr = getStats(X)
    plt.errorbar(x, y, yerr, label='', marker='s')
  
    plt.legend(loc = 'upper right') # plt.legend(fancybox=True, shadow=True, fontsize=8)
    plt.xticks([])
    plt.ylim([-0.5, 0.5])
    plt.xaxis.set_tick_params(labelsize=20)
    plt.grid(True)
    plt.title('Title', fontsize=10);    
    # plt.xscale('log', basex=2)
    
    
if __name__ == "__main__":
  X = [[], [], ... , [], 
  plot1(X)
  
  

forLatex.py

"""
To save images to embed in latex
"""

import os
import matplotlib as mpl
import matplotlib.pyplot as plt

mpl.rcParams['figure.dpi']= 100  # keep this low, else it takes times to render on matplotlib
mpl.rc("savefig", dpi=300)       # keep this high.
inline_rc = dict(mpl.rcParams)   # for resetting purposes
%matplotlib inline

def plot(data): # data is a numpy array
	f, axarr = plt.subplots(1, figsize=(10,10))
	plt.plot(data[:,0], data[:,1], color='blue', label='MyData', linestyle=':', alpha=1.0, marker='o') # linestyle = ['-', '--', ':'] 

	# Styling
	# mpl.rcParams.update(mpl.rcParamsDefault) # set this since every run needs this
	mpl.rcParams.update(inline_rc)             # set this since every run needs this
	plt.rc('legend', fontsize=20)
	plt.rc('xtick', labelsize=15)
	plt.rc('ytick', labelsize=15)
	# axarr.xaxis.set_major_locator(plt.MaxNLocator(5)) # to limit the number of ticks
	axarr.xaxis.get_label().set_fontsize(20)
	axarr.yaxis.get_label().set_fontsize(20)
	
	plt.xlabel('X Axis')
	plt.ylabel('Y axis')
	plt.title('My Plot', fontsize=30)
	plt.legend(loc='upper left')

	if not os.path.isdir('./images/'):
		os.mkdir('images')
	filename = 'images/image_' + str(datetime.datetime.now().replace(microsecond=0)) + '.png' 
	f.savefig(filename, bbox_inches = 'tight',pad_inches = 0, format='png')

heatmap.py

import seaborn as sns

def heatmap(data):
  sns.heatmap(tmp, annot=True, fmt='g')mp = 

if __name__ == "__main__":
  data =  [[214051, 9114],[135, 210]]
  heatmap(data)

plotBar.py

import matplotlib.pyplot as plt

def plot(data):
	f, axarr = plt.subplots(2,1, sharex=True)
    axarr[0].bar(range(len(data)), data)
    axarr[0].set_title('Data1')
    axarr[0].set_xticks(range(len(data)))   
	axarr[0].set_xticklabels(['Exact','0.1','0.33','0.5','1','3','5','10','100'])
	
    axarr[0].bar(range(len(data)), data)
    axarr[0].set_title('Data2')
    

sample_lineplot.py

import matplotlib.pyplot as plt
%matplotlib inline

def plot():
  # Keep a length of 12
  x_0 = [1.0, 1.2, 1.3, 2.5, 3.0, 3.1] # len=6
  x_1 = [3.1, 2.9, 2.0, 1.0, 1.1, 1.1, 1.0, 1.2, 1.3, 3.0, 3.5, 3.5] #len=18
  x_2 = [3.4, 3.0, 2.0, 1.1, 1.1, 1.2, 1.1, 1.4, 1.2, 4.0, 4.5, 5.0] #len=30
  x_3 = [5.1, 4.5, 4.0, 1.9, 1.1, 1.2, 1.3, 1.0, 1.5, 5.5, 6.7, 7.0] #len=42
  x_4 = [7.1, 6.2, 5.9, 2.9, 2.0, 1.4, 1.3, 1.2, 1.3, 4.0, 4.5, 4.6] #len=54
  x_5 = [4.5, 4.5, 4.4, 3.0, 2.1, 1.1, 1.2, 1.3, 1.3, 4.4, 5.0, 5.1] #len=66
  x_6 = [5.1, 4.9, 4.8, 2.1, 1.3, 1.4, 1.2, 1.1, 1.2, 5.0, 5.2, 5.3] #len=78
  x_plot = x_0 + x_1 + x_2 + x_3 + x_4 + x_5 + x_6

  x_0_ = [1.1, 1.1, 1.4, 2.4, 3.1, 3.2]
  x_1_ = [3.2, 2.7, 1.9, 1.1, 1.2, 1.1, 1.0, 1.1, 1.4, 3.1, 3.5, 3.2]
  x_2_ = [3.1, 3.2, 2.5, 1.3, 1.1, 1.2, 1.1, 1.5, 1.2, 4.1, 4.6, 5.2]
  x_3_ = [5.0, 4.6, 4.0, 2.2, 1.5, 1.2, 1.4, 1.0, 1.9, 6.0, 6.7, 6.9]
  x_4_ = [6.5, 6.1, 6.0]
  x_5_ = []
  x_6_ = []
  x_plot_ = x_0_ + x_1_ + x_2_ + x_3_ + x_4_ + x_5_ + x_6_


  f, axarr = plt.subplots(1, figsize=(10,10))
  axarr.plot(x_plot, linestyle='--', label='Prediction')
  axarr.plot(x_plot_, label='Actual')
  axarr.set_ylabel('Average Bird Density (birds/km2)', fontsize=15)
  axarr.set_xlabel('Time', fontsize=15)
  axarr.set_xticks([])
  axarr.set_xticks([5,17,29,41,53,65,77])
  axarr.set_xticklabels(["13/05", "14/05", "15/05", "16/05",'17/05', '18/05', '19/05'])
  axarr.set_yticks([1,2,3,4,5,6,7])
  axarr.set_yticklabels(['0','30','60','90', '120', '150', '180', '210'])
  axarr.legend(fontsize=11)

  vspan_c = 'b'
  axarr.axvspan(3, 8, facecolor=vspan_c, alpha=0.1)
  axarr.axvspan(15, 20, facecolor=vspan_c, alpha=0.1)
  axarr.axvspan(27, 32, facecolor=vspan_c, alpha=0.1)
  axarr.axvspan(39, 44, facecolor=vspan_c, alpha=0.1)
  axarr.axvspan(51, 56, facecolor=vspan_c, alpha=0.1)
  axarr.axvspan(63, 68, facecolor=vspan_c, alpha=0.1)
  axarr.axvspan(75, 80, facecolor=vspan_c, alpha=0.1)

  plt.text(65.8, 6.5, 'Note : The stripes \nrepresent night', color='blue', fontweight='bold', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
 
if __name__ == "__main__":
  plot()

seaborn_boxplot.py

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

x     = [[], [], ..., []]
y     = [[], [], ..., []]
label = [[], [], ..., []]
data  = pd.DataFrame({'x': x, 'y':y, 'CE': sns_label_ce, 'DICE': sns_label_dice, 'HD95': sns_label_hd95})
palette = {'label1': ['r','g','b'], ...}

sns.set(style="darkgrid")
plt.figure(figsize=(10,5), dpi=200)

bplt = sns.boxplot(x='x', y='y', hue='label', palette=palette, data=data)
bplt.set_xlabel('',fontsize=15)
bplt.set_ylabel('',fontsize=15)
bplt.set_xticklabels(bplt.get_xticklabels(),size = 15)
bplt.set_ylim([0.0, 0.9])
# plt.setp(bplt.get_legend().get_texts(), fontsize='15')
handles, labels = bplt.get_legend_handles_labels()
bplt.legend(handles=handles[1:], labels=labels[1:],fontsize=15)
plt.savefig('plot.png', bbox_inches='tight')

subplot_single.py

from shapely import geometry
from descartes import PolygonPatch
import matplotlib.pyplot as plt

def random():
	f, axarr = plt.subplots(1)
	axarr.imshow(img_array)

	poly = geometry.Polygon(contour_pts)
	patch = PolygonPatch(poly, facecolor = [0,1,0], edgecolor = [0,1,0], alpha = 0.35)
	axarr.add_patch(patch)

	frame1 = plt.gca()
	frame1.axes.get_xaxis().set_visible(False)
	frame1.axes.get_yaxis().set_visible(False)
	frame1.set_xlim(0,w)
	frame1.set_ylim(0,h)
	plt.savefig(img_name, bbox_inches = 'tight', pad_inches = 0, dpi = 100)

def draw_polygon(points, title='', save=False):
    pt_max_x = np.max(points[:,0]) 
    pt_max_y = np.max(points[:,1])
    
    poly    = Polygon(pts)
    patch   = PolygonPatch(poly, facecolor=[0,0,0.5], edgecolor=[1,1,1], alpha=0.8)
    
    fig, ax = plt.subplots(1, figsize=(10,10))
    ax.set_xlim(0, pt_max_x)
    ax.set_ylim(0, pt_max_y)
    ax.add_patch(patch)
    plt.title(title)
    plt.tight_layout()
	
	if save:
		plt.savefig('img_name', bbox_inches = 'tight', pad_inches = 0, dpi = 100)

if __name__ == "__main__":
    pts = np.array([[0,0],[1,1],[0,1]]) 
    pts = np.array([[1136, 844],[1104, 913],[1550, 969], [1104, 873]]) 
    draw_polygon(pts)

subplot_single_animate.py

from IPython import display
import matplotlib.pyplot as plt

f, axarr = plt.subplots(1)
w,h = 20, 20
frame1 = plt.gca()
frame1.set_xlim(0,w)
frame1.set_ylim(0,h)
cmap = plt.cm.rainbow
color_me = np.random.rand(w)

for i in range(w):
	axarr.scatter(i,i, c = cmap(color_me[i]))
	display.display(plt.gcf())
	display.clear_output(wait=True)
	plt.pause(0.2)

subplot_single_animate_v2.py

import matplotlib
import matplotlib.pyplot as plt
from IPython import display # [for Jupyter]

def plot_data(fig,axarr,data, IS_IPYTHON):
  axarr.cla() # clear axis
  axarr.set_title('Training...')
  axarr.set_xlabel('X-axis')
  axarr.set_ylabel('Y-axis')
  axarr.plot(data)
  
  if IS_IPYTHON:
    display.display(fig) # display the figure once again
    display.clear_output(wait=True) # [for jupyter] wait to clear output of the current running cell when new output is received

  plt.pause(0.1) # just for lulz

if __name__ == "__main__":
  IS_IPYTHON = 'inline' in matplotlib.get_backend()
  print (' - IS_IPYTHON : ', IS_IPYTHON)
    
  data = []
  f,axarr = plt.subplots(1)
  for i in range(10):
      data.append(i+1)
      plot_data(f, axarr, data, IS_IPYTHON)

  if IS_IPYTHON:
    display.display(f) # [for jupyter] to keep the plot from disappearing in the end
  else:
    plt.show()         # [for scripts] to keep the plot from disappearing in the end
  print ('Complete')

tSNE.py

import random
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from sklearn.manifold import TSNE

def plot_SNE(X,Y):
	X_embedded = TSNE(n_components=2).fit_transform(X)
	
	number_of_colors = len(np.unique(Y))
	color_palette = np.array(["#"+''.join([random.choice('0123456789ABCDEF') for j in range(6)]) for i in range(number_of_colors)])
	colors = list(color_palette[Y])
	# Also try this -- https://sashat.me/2017/01/11/list-of-20-simple-distinct-colors/

	f, axarr = plt.subplots(1, figsize=(15,15))
	axarr.scatter(X_embedded[:,0], X_embedded[:,1], color=colors) 
	
	axarr.set_title('t-SNE Embedding', fontsize=20)
	legend_elements = []
	for i in range(len(color_palette)):
		legend_elements.append(
			Line2D([0], [0], marker='o', color=colors[-1], label=str(i+1), 
				   markerfacecolor=color_palette[i], markersize=20)
		)
	plt.legend(handles=legend_elements, loc='upper left')
	plt.rc('legend', fontsize=30)