diff --git a/vizualize_atari.py b/vizualize_atari.py
deleted file mode 100644
index c67c3b8..0000000
--- a/vizualize_atari.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# Copied from https://github.com/emansim/baselines-mansimov/blob/master/baselines/a2c/visualize_atari.py
-# and https://github.com/emansim/baselines-mansimov/blob/master/baselines/a2c/load.py
-# Thanks to the author and OpenAI team!
-
-import glob
-import json
-import os
-
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.signal import medfilt
-matplotlib.rcParams.update({'font.size': 8})
-
-
-def smooth_reward_curve(x, y):
-    # Halfwidth of our smoothing convolution
-    halfwidth = min(31, int(np.ceil(len(x) / 30)))
-    k = halfwidth
-    xsmoo = x[k:-k]
-    ysmoo = np.convolve(y, np.ones(2 * k + 1), mode='valid') / \
-        np.convolve(np.ones_like(y), np.ones(2 * k + 1), mode='valid')
-    downsample = max(int(np.floor(len(xsmoo) / 1e3)), 1)
-    return xsmoo[::downsample], ysmoo[::downsample]
-
-
-def fix_point(x, y, interval):
-    np.insert(x, 0, 0)
-    np.insert(y, 0, 0)
-
-    fx, fy = [], []
-    pointer = 0
-
-    ninterval = int(max(x) / interval + 1)
-
-    for i in range(ninterval):
-        tmpx = interval * i
-
-        while pointer + 1 < len(x) and tmpx > x[pointer + 1]:
-            pointer += 1
-
-        if pointer + 1 < len(x):
-            alpha = (y[pointer + 1] - y[pointer]) / \
-                (x[pointer + 1] - x[pointer])
-            tmpy = y[pointer] + alpha * (tmpx - x[pointer])
-            fx.append(tmpx)
-            fy.append(tmpy)
-
-    return fx, fy
-
-
-def load_data(indir, smooth, bin_size):
-    datas = []
-    infiles = glob.glob(os.path.join(indir, '*monitor.json'))
-
-    for inf in infiles:
-        with open(inf, 'r') as f:
-            t_start = float(json.loads(f.readline())['t_start'])
-            for line in f:
-                tmp = json.loads(line)
-                t_time = float(tmp['t']) + t_start
-                tmp = [t_time, int(tmp['l']), float(tmp['r'])]
-                datas.append(tmp)
-
-    datas = sorted(datas, key=lambda d_entry: d_entry[0])
-    result = []
-    timesteps = 0
-    for i in range(len(datas)):
-        result.append([timesteps, datas[i][-1]])
-        timesteps += datas[i][1]
-
-    if len(result) < bin_size:
-        return [None, None]
-
-    x, y = np.array(result)[:, 0], np.array(result)[:, 1]
-
-    if smooth == 1:
-        x, y = smooth_reward_curve(x, y)
-
-    if smooth == 2:
-        y = medfilt(y, kernel_size=9)
-
-    x, y = fix_point(x, y, bin_size)
-    return [x, y]
-
-
-color_defaults = [
-    '#1f77b4',  # muted blue
-    '#ff7f0e',  # safety orange
-    '#2ca02c',  # cooked asparagus green
-    '#d62728',  # brick red
-    '#9467bd',  # muted purple
-    '#8c564b',  # chestnut brown
-    '#e377c2',  # raspberry yogurt pink
-    '#7f7f7f',  # middle gray
-    '#bcbd22',  # curry yellow-green
-    '#17becf'  # blue-teal
-]
-
-
-def visdom_plot(viz, win, folder, game, name, bin_size=100, smooth=1):
-    tx, ty = load_data(folder, smooth, bin_size)
-    if tx is None or ty is None:
-        return win
-
-    fig = plt.figure()
-    plt.plot(tx, ty, label="{}".format(name))
-    plt.xticks([4*1e6, 4*2e6, 4*4e6, 4*6e6, 4*8e6, 4*10e6],
-               ["1M", "2M", "4M", "6M", "8M", "10M"])
-    plt.xlabel('Number of Timesteps')
-    plt.ylabel('Rewards')
-
-    plt.xlim(0, 40e6)
-
-    plt.title(game)
-    plt.legend(loc=4)
-    plt.show()
-    plt.draw()
-
-    image = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-    image = image.reshape(fig.canvas.get_width_height()[::-1] + (3, ))
-    plt.close(fig)
-
-    # Show it in visdom
-    image = np.transpose(image, (2, 0, 1))
-    return viz.image(image, win=win)
-
-
-if __name__ == "__main__":
-    from visdom import Visdom
-    viz = Visdom()
-    visdom_plot(
-        viz, None, '/tmp/gym/', 'BreakOut', 'a2c', bin_size=100, smooth=1)