#!/usr/bin/env python3

import numpy as np
import pandas as pd

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE

import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.cm as cm
plt.style.use('bmh')

import os
import sys

import argparse

parser = argparse.ArgumentParser()
parser.add_argument('workDir', type=str)
parser.add_argument('--names', type=str, nargs='+', required=True)
args = parser.parse_args()

y = pd.read_csv("{}/labels.csv".format(args.workDir)).as_matrix()[:, 0]
X = pd.read_csv("{}/reps.csv".format(args.workDir)).as_matrix()

target_names = np.array(args.names)
colors = cm.gnuplot2(np.linspace(0, 0.7, len(target_names)))

X_pca = PCA(n_components=50).fit_transform(X, X)
tsne = TSNE(n_components=2, init='random', random_state=0)
X_r = tsne.fit_transform(X_pca)

for c, i, target_name in zip(colors,
                             list(range(1, len(target_names) + 1)),
                             target_names):
    plt.scatter(X_r[y == i, 0], X_r[y == i, 1],
                c=c, label=target_name)
plt.legend()
plt.savefig("{}/tsne.pdf".format(args.workDir))