import os

import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector


def main(save_path, vectors, metadata):
    """
    :param save_path: str, dir to save results
    :param vectors: 2D ndarray, axis 0 is samples, axis 1 is data
    :param metadata: pandas dataframe with metadata
    """
    os.makedirs(save_path, exist_ok=True)
    metadata_path = os.path.join(save_path, 'metadata.tsv')
    images = tf.Variable(vectors, name='data')
    metadata.to_csv(metadata_path, sep='\t', index=False)
    with tf.Session() as sess:
        saver = tf.train.Saver([images])
        sess.run(images.initializer)
        saver.save(sess, os.path.join(save_path, 'images.ckpt'))
        config = projector.ProjectorConfig()
        embedding = config.embeddings.add()
        embedding.tensor_name = images.name
        embedding.metadata_path = os.path.basename(metadata_path)
        projector.visualize_embeddings(tf.summary.FileWriter(save_path), config)

def demo():
    import pandas as pd
    import numpy as np
    vectors = np.random.randn(5, 100)
    metadata = pd.DataFrame(np.random.randn(5, 2), columns=['label1', 'label2'])
    main('demo', vectors, metadata)

demo()