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bin/plot.py
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''' Take a file and plot its data onto a 2d or 3d axis depending on the data. ''' import os import numpy as np from sklearn.cluster import KMeans import matplotlib.pyplot as plt import argparse import json # Defining argparse parser = argparse.ArgumentParser(prog='Plotter', description='Plot a file of 2d ou 3d dimension') parser.add_argument('filepath', type=str, help='the path of the file you want to plot') parser.add_argument('-o-', '--output', type=str, default='plot.pdf', help='the path of the ploted file') parser.add_argument('-t', '--toy', action='store_true', help='test the script on a toy example. Do not test all the file content') args = parser.parse_args() # Editing global variable FILE_PATH=args.filepath OUTFILE_PATH = args.output TOY_VERSION = args.toy # Defining vectors with default number of column vectors = np.empty((0, 64), np.float32) metas = np.empty((0, 4), np.float32) # READ DATA with open(os.path.join(FILE_PATH), "r") as f: for i, line in enumerate(f): if TOY_VERSION == True and i > 100: break spl_line = line.split(" ") if(len(vectors) == 0): vectors = np.empty((0, len(spl_line[1:])), np.float32) metas = np.append( metas, np.asarray([spl_line[0].split(",")]), axis=0) vectors = np.append( vectors, np.asarray([spl_line[1:]], dtype=np.float32), axis=0) vectors_T = np.transpose(vectors) # Plot the file plt.plot(vectors, 'ro') fig, ax = plt.subplots() if(vectors_T.shape[0] == 2): ax.scatter(vectors_T[0], vectors_T[1]) #c=close, s=volume, alpha=0.5) else: ax.scatter(vectors_T[0], vectors_T[1], vectors_T[2]) ax.set_xlabel('Axe 1', fontsize=15) ax.set_ylabel('Axe 2', fontsize=15) if(vectors_T.shape[0] == 3): ax.set_zlabel('Axe 3', fontsize15=15) ax.set_title('Volume and percent change') plt.savefig(OUTFILE_PATH) |