plot_character.py
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'''
Take a file and plot its data onto a 2d or 3d axis depending on the data.
Automatic detection of the number of dimension.
'''
import os
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import argparse
import json
import pandas as pd
# Defining useful functions
'''
Read the file whose content is metas and vectors.
Returns two numpy array : (metas, vectors)
'''
def read_vector_file(filename, toy_version=False):
vectors = np.empty((0, 1), np.float32)
metas = np.empty((0, 4), np.float32)
with open(filename, "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)
return (metas, vectors)
# Defining argparse
parser = argparse.ArgumentParser(description='Plot a file of 2d ou 3d dimension')
parser.add_argument('vectorfile', type=str,
help='the path of the vectors file')
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
VECTORFILE_PATH=args.vectorfile
OUTFILE_PATH = args.output
TOY_VERSION = args.toy
# Get Vectors
metas, vectors = read_vector_file(VECTORFILE_PATH, toy_version = TOY_VERSION)
vectors_T = np.transpose(vectors)
print("Number of characters: " + str(len(np.unique(np.transpose(metas)[1]))))
df = pd.DataFrame(dict(
x=vectors_T[0],
y=vectors_T[1],
character=np.transpose(metas)[1]
))
groups = df.groupby('character')
# Plot
fig, ax = plt.subplots()
for character, group in groups:
ax.plot(group.x, group.y, marker='o', linestyle='', ms=2, label=character)
plt.savefig(OUTFILE_PATH)
print("Your plot is saved well (no check of this affirmation)")