diff --git a/.gitignore b/.gitignore
index ed905ee6656ece9d8ee41bdea53ea50cd86d4cf9..24fc066ae94c736ff15116af88e24899177ff374 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,4 @@
 /workspace.xml
 /.idea
 /venv
-/CA
\ No newline at end of file
+**/__pycache__/
\ No newline at end of file
diff --git a/CA-ES/es.py b/CA-ES/es.py
new file mode 100644
index 0000000000000000000000000000000000000000..f86f76d58e8a8330e89f9c2852ccabf27d8728e2
--- /dev/null
+++ b/CA-ES/es.py
@@ -0,0 +1,306 @@
+import copy
+import math
+from tqdm import trange
+import multiprocessing as mp
+
+import numpy as np
+import torch
+import torch.multiprocessing as tmp
+import torch.nn.functional as F
+from torch import tensor as tt
+
+from torch.utils.tensorboard import SummaryWriter
+from torchvision.utils import save_image
+
+from model import CellularAutomataModel
+from utils import load_emoji, save_model, to_rgb, Pool
+
+import pygame
+import time
+from PIL import Image
+import logging
+
+class ES:
+    def __init__(self, args):
+        self.population_size = args.population_size
+        self.n_iterations = args.n_iterations
+        self.pool_size = args.pool_size
+        self.batch_size = args.batch_size
+        self.eval_freq = args.eval_freq
+        self.n_channels = args.n_channels
+        self.hidden_size = args.hidden_size
+
+        self.fire_rate = args.fire_rate
+        self.lr = args.lr
+        self.sigma = args.sigma
+
+        self.size = args.size + 2 * args.padding
+        self.target_img = load_emoji(args.img, self.size)
+        self.padding = args.padding
+
+        self.logdir = args.logdir
+
+        self.decay_state = 0
+
+        p = self.padding
+        self.pad_target = F.pad(tt(self.target_img), (0, 0, p, p, p, p))
+        h, w = self.pad_target.shape[:2]
+        self.seed = np.zeros([h, w, self.n_channels], np.float64)
+        self.seed[h // 2, w // 2, 3:] = 1.0
+
+        self.net = CellularAutomataModel(n_channels=self.n_channels, fire_rate=self.fire_rate, hidden_channels=self.hidden_size)
+        self.param_shape = [tuple(p.shape) for p in self.net.parameters()]
+
+        self.pool = Pool(self.seed, self.pool_size)
+
+        if args.load_model_path != "":
+            self.load_model(args.load_model_path)
+            self.lr = 0.00075 # test
+            self.decay_state = 2
+    
+        t_rgb = to_rgb(self.pad_target).permute(2, 0, 1)
+
+        if args.mode == "train":
+            save_image(t_rgb, "%s/target_image.png" % self.logdir)
+            self.writer = SummaryWriter(self.logdir)
+
+    def load_model(self, path):
+        """Load a PyTorch model from path."""
+        self.net.load_state_dict(torch.load(path))
+        self.net.double()
+
+    def fitness_shape(self, x):
+        """Sort x and and map x to linear values between -0.5 and 0.5
+            Return standard score of x
+        """
+        shaped = np.zeros(len(x))
+        shaped[x.argsort()] = np.arange(len(x), dtype=np.float64)
+        shaped /= (len(x) - 1)
+        shaped -= 0.5
+        shaped = (shaped - shaped.mean()) / shaped.std()
+        return shaped
+
+    def update_parameters(self, fitnesses, epsilons):
+        """Update parent network weights using evaluated mutants and fitness."""
+        fitnesses = self.fitness_shape(fitnesses)
+
+        for i, e in enumerate(epsilons):
+            for j, w in enumerate(self.net.parameters()):
+                w.data += self.lr * 1 / (self.population_size * self.sigma) * fitnesses[i] * e[j]
+
+    def get_population(self):
+        """Return an array with values sampled from N(0, sigma)"""
+        epsilons = []
+
+        for _ in range(int(self.population_size / 2)):
+            e = []
+            e2 = []
+            for w in self.param_shape:
+                j = np.random.randn(*w) * self.sigma
+                e.append(j)
+                e2.append(-j)
+            epsilons.append(e)
+            epsilons.append(e2)
+
+        return np.array(epsilons, dtype=np.object)
+
+    def step(self, model_try, x):
+        """Perform a generation of CA using trained net.
+            Return output x and loss
+        """
+        torch.seed()
+        iter_n = torch.randint(30, 40, (1,)).item()
+        for _ in range(iter_n): x = model_try(x)
+
+        loss = self.net.loss(x, self.pad_target)
+        loss = torch.mean(loss)
+
+        return x, loss.item()
+
+    def fitness(self, epsilon, x0, pid, q=None):
+        """Method that start a generation of ES.
+            Return output from generation x and its fitness
+        """
+        model_try = copy.deepcopy(self.net)
+        if epsilon is not None:
+            for i, w in enumerate(model_try.parameters()):
+                w.data += torch.tensor(epsilon[i])
+
+        x, loss = self.step(model_try, x0)
+        fitness = -loss
+
+        if not math.isfinite(fitness):
+            raise ValueError('Encountered non-number value in loss. Fitness ' + str(fitness) + '. Loss: ' + str(loss))
+        q.put((x, fitness, pid))
+        return
+
+    def decay_lr(self, fitness):
+        # Fitness treshholds for adjusting learning rate
+        # fit_t1 = -0.06 # testing for size ~20
+        # fit_t2 = -0.03
+
+        fit_t1 = -0.05 # works well for size ~15
+        fit_t2 = -0.02
+
+        # fit_t1 = -0.03 # used for size 9
+        # fit_t2 = -0.01
+
+        if not self.decay_state == 2:
+            if fitness >= fit_t1 and self.decay_state == 0:
+                reduce = 0.3
+                self.lr *= reduce
+                self.decay_state += 1
+                logging.info("Fitness higher than than %.3f, lr set to %.5f (*%.2f)" % (fit_t1, self.lr, reduce))
+            elif fitness >= fit_t2 and self.decay_state == 1:
+                reduce = 0.5
+                self.lr *= reduce
+                self.decay_state += 1
+                logging.info("Fitness higher than %.3f, lr set to %.5f (*%.2f)" % (fit_t2, self.lr, reduce))
+
+
+    def evaluate_main(self, x0):
+        """Return output and fitness from a generation using unperturbed weights/coeffs"""
+        x_main, loss_main = self.step(self.net, x0.clone())
+        fit_main = - loss_main
+        return x_main, fit_main
+
+
+    def train(self):
+        """main training loop"""
+        logging.info("Starting training")
+
+        x0 = tt(np.repeat(self.seed[None, ...], self.batch_size, 0)) #seed
+        _, _ = self.step(self.net, x0.clone())
+
+        processes = []
+        q = tmp.Manager().Queue()
+
+        t = trange(self.n_iterations, desc='Mean reward:', leave=True)
+        for iteration in t:
+            batch = self.pool.sample(self.batch_size)
+            x0 = batch["x"]
+            loss_rank = self.net.loss(tt(x0), self.pad_target).numpy().argsort()[::-1]
+            x0 = x0[loss_rank]
+            x0[:1] = self.seed
+            x0 = tt(x0)
+            
+            epsilons = self.get_population()
+            fitnesses = np.zeros(self.population_size, dtype=np.float64)
+            xs = torch.zeros(self.population_size, *x0.shape, dtype=torch.float64)
+            
+            for i in range(self.population_size):
+                p = tmp.Process(target=self.fitness, args=(epsilons[i], x0.clone(), i, q))
+                p.start()
+                processes.append(p)
+
+            for p in processes:
+                p.join()
+                x, fit, pid = q.get()
+                fitnesses[pid] = fit
+                xs[pid] = x
+            processes = []
+
+            idx = np.argmax(fitnesses)
+            batch["x"][:] = xs[idx]
+            self.pool.commit(batch)
+
+            fitnesses = np.array(fitnesses).astype(np.float64)
+            self.update_parameters(fitnesses, epsilons)
+
+            # Logging
+            mean_fit = np.mean(fitnesses)
+            self.writer.add_scalar("train/fit", mean_fit, iteration)
+
+            if iteration % 10 == 0:
+                t.set_description("Mean reward: %.4f    " % mean_fit, refresh=True)
+
+            if (iteration+1) % self.eval_freq == 0:
+                self.decay_lr(mean_fit)
+                
+                # Save picture of model
+                x_eval = x0.clone()
+                model = self.net
+                pics = []
+                
+                for eval in range(36):
+                    x_eval = model(x_eval)
+                    if eval % 5 == 0:
+                        pics.append(to_rgb(x_eval).permute(0, 3, 1, 2))
+                
+                save_image(torch.cat(pics, dim=0), '%s/pic/big%04d.png' % (self.logdir, iteration), nrow=1, padding=0)
+                save_model(self.net, self.logdir + "/models/model_" + str(iteration))
+
+
+    # Do damage on model using pygame, cannot run through ssh
+    def interactive(self):
+        model = self.net
+        x_eval = tt(np.repeat(self.seed[None, ...], self.batch_size, 0))
+
+        cellsize = 50
+        imgpath = '%s/one.png' % (self.logdir)
+        
+        pygame.init()
+        surface = pygame.display.set_mode((self.size * cellsize, self.size * cellsize))
+        pygame.display.set_caption("Interactive CA-ES")
+
+        damaged = 0
+        losses = []
+
+        while True:
+            for event in pygame.event.get():
+                if event.type == pygame.QUIT:
+                    pygame.quit()
+                    return
+                elif event.type == pygame.MOUSEBUTTONDOWN:
+                    # damage
+                    if damaged == 0:
+                        dmg_size = 20
+                        mpos_x, mpos_y = event.pos
+                        mpos_x, mpos_y = mpos_x // cellsize, mpos_y // cellsize
+                        x_eval[:, mpos_y:mpos_y + dmg_size, mpos_x:mpos_x + dmg_size, :] = 0
+                        damaged = 100 # number of steps to record loss after damage has occurred
+
+            x_eval = model(x_eval)
+            image = to_rgb(x_eval).permute(0, 3, 1, 2)
+            
+            save_image(image, imgpath, nrow=1, padding=0)
+
+            if damaged > 0:
+                loss= self.net.loss(x_eval, self.pad_target)
+                losses.append(loss)
+                if damaged == 1:
+                    lossfile = open('%s/losses.txt' % self.logdir, "w")
+                    for l in range(len(losses)):
+                        lossfile.write('%d,%.06f\n' % (l, losses[l]))
+                    lossfile.close()
+                    losses.clear()
+                damaged -= 1
+
+            # Saving and loading each image as a quick hack to get rid of the batch dimension in tensor
+            image = np.asarray(Image.open(imgpath))
+
+            self.game_update(surface, image, cellsize)
+            time.sleep(0.05)
+            pygame.display.update()
+
+    def game_update(self, surface, cur_img, sz):
+        nxt = np.zeros((cur_img.shape[0], cur_img.shape[1]))
+
+        for r, c, _ in np.ndindex(cur_img.shape):
+            pygame.draw.rect(surface, cur_img[r,c], (c*sz, r*sz, sz, sz))
+
+        return nxt
+
+    def generate_graphic(self):
+        model = self.net
+        x_eval = tt(np.repeat(self.seed[None, ...], self.batch_size, 0))
+        pics = []
+        pics.append(to_rgb(x_eval).permute(0, 3, 1, 2))
+
+        for eval in range(40):
+            x_eval = model(x_eval)
+            if eval in [10, 20, 30, 39]: # frames to save img of
+                pics.append(to_rgb(x_eval).permute(0, 3, 1, 2))
+
+        save_image(torch.cat(pics, dim=0), '%s/graphic.png' % (self.logdir), nrow=len(pics), padding=0)
+        
\ No newline at end of file
diff --git a/CA-ES/logs/CARROT-graphic_12-04-2022_10-15-16/graphic.png b/CA-ES/logs/CARROT-graphic_12-04-2022_10-15-16/graphic.png
new file mode 100644
index 0000000000000000000000000000000000000000..360c7c22419960ee60235281d691d7fc89638581
Binary files /dev/null and b/CA-ES/logs/CARROT-graphic_12-04-2022_10-15-16/graphic.png differ
diff --git a/CA-ES/logs/CARROT-graphic_12-04-2022_10-15-16/logfile.log b/CA-ES/logs/CARROT-graphic_12-04-2022_10-15-16/logfile.log
new file mode 100644
index 0000000000000000000000000000000000000000..0b5a29602cbc271fe02289dfba4fecd1f3c75c3f
--- /dev/null
+++ b/CA-ES/logs/CARROT-graphic_12-04-2022_10-15-16/logfile.log
@@ -0,0 +1,19 @@
+INFO:root:
+Arguments:
+mode: 'graphic'
+population_size: 16
+n_iterations: 100
+pool_size: 1024
+batch_size: 1
+eval_freq: 500
+n_channels: 16
+hidden_size: 32
+fire_rate: 0.5
+lr: 0.005
+sigma: 0.01
+img: '🥕'
+size: 15
+padding: 0
+logdir: 'logs/CARROT-graphic_12-04-2022_10-15-16'
+load_model_path: 'model_999500'
+
diff --git a/CA-ES/main.py b/CA-ES/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..3468f92f7b03d94a641e7e86586e6b1075de0cd1
--- /dev/null
+++ b/CA-ES/main.py
@@ -0,0 +1,63 @@
+import torch
+import argparse
+import time
+import os
+import unicodedata
+import logging
+
+from es import ES
+
+# rabbit 🐰
+# carrot 🥕
+# watermelon 🍉
+
+if __name__ == '__main__':
+
+    emoji = '🥕'
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--mode", type=str, default="graphic", metavar="train/interactive/graphic", help="Decides mode to run")
+
+    parser.add_argument("--population_size", type=int, default=16, metavar=128, help="Population size")
+    parser.add_argument("--n_iterations", type=int, default=100, help="Number of iterations to train for.")
+    parser.add_argument("--pool_size", type=int, default=1024, help="Size of the training pool, zero if training without pool")
+    parser.add_argument("--batch_size", type=int, default=1, help="Batch size.")
+    parser.add_argument("--eval_freq", type=int, default=500, help="Frequency for various saving/evaluating/logging",)
+    parser.add_argument("--n_channels", type=int, default=16, help="Number of channels of the input tensor")
+    parser.add_argument("--hidden_size", type=int, default=32, help="Number of hidden channels")
+
+    parser.add_argument("--fire_rate", type=float, default=0.5, metavar=0.5, help="Cell fire rate")
+    parser.add_argument("--lr", type=float, default=0.005, metavar=0.005, help="Learning rate") 
+    parser.add_argument("--sigma", type=float, default=0.01, metavar=0.01, help="Sigma")
+
+    parser.add_argument("--img", type=str, default=emoji, metavar="🐰", help="The emoji to train on")
+    parser.add_argument("--size", type=int, default=15, help="Image size")
+    parser.add_argument("--padding", type=int, default=0, help="Padding. The shape after padding is (h + 2 * p, w + 2 * p).")
+    parser.add_argument("--logdir", type=str, default="logs", help="Logging folder for new model")
+    parser.add_argument("--load_model_path", type=str, default="model_999500", help="Path to pre trained model")
+
+    args = parser.parse_args()
+
+    if not os.path.isdir(args.logdir):
+        raise Exception("Logging directory '%s' not found in base folder" % args.logdir)
+
+    args.logdir = "%s/%s-%s_%s" % (args.logdir, unicodedata.name(args.img), args.mode, time.strftime("%d-%m-%Y_%H-%M-%S"))
+    os.mkdir(args.logdir)
+
+    logging.basicConfig(filename='%s/logfile.log' % args.logdir, encoding='utf-8', level=logging.INFO)
+    argprint = "\nArguments:\n"
+    for arg, value in vars(args).items():
+        argprint += ("%s: %r\n" % (arg, value))
+    logging.info(argprint)
+
+    es = ES(args)
+    torch.set_num_threads(1) # disable pytorch's built in parallelization
+
+    match args.mode:
+        case "train": 
+            os.mkdir(args.logdir + "/models")
+            os.mkdir(args.logdir + "/pic")
+            es.train()
+        case "interactive": es.interactive()
+        case "graphic": es.generate_graphic()
diff --git a/CA-ES/model.py b/CA-ES/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..868ca49d66c0b3b9fedb49838c93d46c389cff87
--- /dev/null
+++ b/CA-ES/model.py
@@ -0,0 +1,69 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class CellularAutomataModel(nn.Module):
+    def __init__(self, n_channels, hidden_channels, fire_rate):
+        super().__init__()
+        self.n_channels = n_channels
+        self.hidden_channels = hidden_channels
+        self.fire_rate = fire_rate
+
+        self.fc0 = nn.Linear(self.n_channels * 3, self.hidden_channels, bias=False)
+        self.fc1 = nn.Linear(self.hidden_channels, self.n_channels, bias=False)
+        with torch.no_grad(): self.fc1.weight.zero_()
+
+        identity = np.float64([0, 1, 0])
+        identity = torch.from_numpy(np.outer(identity, identity))
+        sobel_x = torch.from_numpy(np.outer([1, 2, 1], [-1, 0, 1]) / 8.0)  # sobel filter
+        sobel_y = sobel_x.T
+        self.kernel = torch.cat([
+            identity[None, None, ...], 
+            sobel_x[None, None, ...], 
+            sobel_y[None, None, ...]], 
+            dim=0).repeat(self.n_channels, 1, 1, 1)
+
+        for param in self.parameters(): param.requires_grad = False
+
+        self.double()
+
+    def perceive(self, x):
+        """Percieve neighboors with two sobel filters and one single-entry filter"""
+        y = F.conv2d(x.permute(0, 3, 1, 2), self.kernel, groups=16, padding=1)
+        y = y.permute(0, 2, 3, 1)
+        return y
+    
+    def loss(self, x, y):
+        """mean squared error"""
+        return torch.mean(torch.square(x[..., :4] - y), [-2, -3, -1])
+
+    def forward(self, x, fire_rate=None, step_size=1.0):
+        """Forward a cell grid through the network and return the cell grid with changes applied."""
+        y = self.perceive(x)
+        pre_life_mask = get_living_mask(x)
+        dx1 = self.fc0(y)
+        dx1 = F.relu(dx1)
+        dx2 = self.fc1(dx1)
+        dx = dx2 * step_size
+
+        if fire_rate is None:
+            fire_rate = self.fire_rate
+
+        update_mask_rand = torch.rand(*x[:, :, :, :1].shape)
+        update_mask = update_mask_rand <= fire_rate
+        x += dx * update_mask.double()
+        post_life_mask = get_living_mask(x)
+        life_mask = pre_life_mask.bool() & post_life_mask.bool()
+        res = x * life_mask.double()
+
+        return res
+
+
+def get_living_mask(x):
+    """returns boolean vector of the same shape as x, except for the last dimension.
+    The last dimension is a single value, true/false, that determines if alpha > 0.1"""
+    alpha = x[:, :, :, 3:4]
+    m = F.max_pool3d(alpha, kernel_size=3, stride=1, padding=1) > 0.1
+    return m
+
diff --git a/CA-ES/saved_models/model_999500 b/CA-ES/saved_models/model_999500
new file mode 100644
index 0000000000000000000000000000000000000000..af0a267609c32d1e859c5b49744606d0d694d1b5
Binary files /dev/null and b/CA-ES/saved_models/model_999500 differ
diff --git a/CA-ES/utils.py b/CA-ES/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed3546088589f6161efa254354387fff2998e864
--- /dev/null
+++ b/CA-ES/utils.py
@@ -0,0 +1,67 @@
+import requests
+import torch
+from torchvision.utils import save_image
+import numpy as np
+import PIL.Image, PIL.ImageDraw
+import io
+
+
+def load_emoji(emoji_code, img_size):
+    """Loads image of emoji with code 'emoji' from google's emojirepository"""
+    emoji_code = hex(ord(emoji_code))[2:].lower()
+    url = 'https://raw.githubusercontent.com/googlefonts/noto-emoji/main/png/128/emoji_u%s.png' % emoji_code
+    req = requests.get(url)
+    img = PIL.Image.open(io.BytesIO(req.content))
+    img.thumbnail((img_size, img_size), PIL.Image.ANTIALIAS)
+    img = np.float64(img) / 255.0
+    img[..., :3] *= img[..., 3:]
+
+    return img
+
+def to_alpha(x):
+    """Return the alpha channel of an image."""
+    return torch.clamp(x[..., 3:4], 0.0, 1.0)
+
+def to_rgb(x):
+    """Return the three first channels (RGB) with alpha deducted."""
+    rgb, a = x[..., :3], to_alpha(x)
+    return 1.0 - a + rgb
+
+def to_rgba(x):
+    """Return the four first channels (RGBA) of an image."""
+    return x[..., :4]
+
+def save_model(ca, base_fn):
+    """Save a PyTorch model to a specific path."""
+    torch.save(ca.state_dict(), base_fn)
+
+def visualize(xs, step_i, nrow=1):
+    """Save a batch of multiple x's to file"""
+    for i in range(len(xs)):
+        xs[i] = to_rgb(xs[i]).permute(0, 3, 1, 2)
+    save_image(torch.cat(xs, dim=0), './logg/pic/p%04d.png' % step_i, nrow=nrow, padding=0)
+
+class Pool:
+    """Class for storing and providing samples of different stages of growth."""
+    def __init__(self, seed, size):
+        self.size = size
+        self.slots = np.repeat([seed], size, 0)
+        self.seed = seed
+
+    def commit(self, batch):
+        """Replace existing slots with a batch."""
+        indices = batch["indices"]
+        for i, x in enumerate(batch["x"]):
+            if (x[:, :, 3] > 0.1).any():  # Avoid committing dead image
+                self.slots[indices[i]] = x.copy()
+
+    def sample(self, c):
+        """Retrieve a batch from the pool."""
+        indices = np.random.choice(self.size, c, False)
+        batch = {
+            "indices": indices,
+            "x": self.slots[indices]
+        }
+        return batch
+
+    
\ No newline at end of file
diff --git a/automata_ex/__pycache__/model.cpython-310.pyc b/automata_ex/__pycache__/model.cpython-310.pyc
deleted file mode 100644
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diff --git a/automata_ex/readme.md b/automata_ex/readme.md
index a93cd282c8e05a99c22c98e674f59b7539cf3b2d..7a70de093f8f1b5198199b64f0d9d834f2ec9083 100644
--- a/automata_ex/readme.md
+++ b/automata_ex/readme.md
@@ -13,7 +13,7 @@ Get parameters:
 ``python train.py --help``
 
 Run args suggestion:
-``python train.py -d cuda -n 10000 -b 4 rabbit.png``
+``python train.py -p 0 -n 10000 -s 9 -hch 32 rabbit.png``
 Remove '-d cuda' to train using cpu
 
 In pytorch edit configuration and add parameters:
diff --git a/automata_ex/train.py b/automata_ex/train.py
index 43775f095d2024a1c040a9b03eaba22255c3e4d1..2b14c1ec56174256434ea2528052c15a6401b6a8 100644
--- a/automata_ex/train.py
+++ b/automata_ex/train.py
@@ -5,6 +5,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 from PIL import Image
+from torchvision.utils import save_image
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 from model import CAModel
@@ -12,6 +13,7 @@ import io
 import os
 import requests
 import unicodedata
+import json
 
 def load_image(path, size=40):
     """Load an image.
@@ -63,6 +65,8 @@ def to_rgb(img_rgba):
     rgb, a = img_rgba[:, :3, ...], torch.clamp(img_rgba[:, 3:, ...], 0, 1)
     return torch.clamp(1.0 - a + rgb, 0, 1)
 
+def save_model(PATH, model):
+    torch.save(model.state_dict(), PATH)
 
 def make_seed(size, n_channels):
     """Create a starting tensor for training.
@@ -123,7 +127,7 @@ def main(argv=None):
             "-n",
             "--n-batches",
             type=int,
-            default=5000,
+            default=10000,
             help="Number of batches to train for.",
     )
     parser.add_argument(
@@ -177,10 +181,15 @@ def main(argv=None):
     if not os.path.isdir(args.logdir):
         raise Exception("Logging directory '%s' not found in base folder" % args.logdir)
 
-    args.logdir = "%s/%s-%s_%s" % (args.logdir, unicodedata.name(args.img), args.mode, time.strftime("%d-%m-%Y_%H:%M:%S"))
+    # make log dir
+    args.logdir = "%s/%s-%s_%s" % (args.logdir, unicodedata.name(args.img), args.mode, time.strftime("%d-%m-%Y_%H-%M-%S"))
     os.mkdir(args.logdir)
     os.mkdir(args.logdir + "/models")
     os.mkdir(args.logdir + "/pic")
+    print(f'logs saved to dir: {args.logdir}')
+
+    with open(f'{args.logdir}/args.json', 'w') as f:
+        f.write(json.dumps(vars(args),indent=4))
 
     # Misc
     device = torch.device(args.device)
@@ -234,6 +243,7 @@ def main(argv=None):
         pool[remaining_pool] = x[remaining_batch].detach()
 
         if it % args.eval_frequency == 0:
+            save_model(f'{args.logdir}/models/model_{it}.pt', model)
             x_eval = seed.clone()  # (1, n_channels, size, size)
 
             eval_video = torch.empty(1, args.eval_iterations, 3, *x_eval.shape[2:])
@@ -243,6 +253,7 @@ def main(argv=None):
                 x_eval_out = to_rgb(x_eval[:, :4].detach().cpu())
                 eval_video[0, it_eval] = x_eval_out
 
+            save_image(x_eval_out, f'{args.logdir}/pic/im_{it}.png')
             writer.add_video("eval", eval_video, it, fps=60)
 
 
diff --git a/damage_ca/damage.py b/damage_ca/damage.py
new file mode 100644
index 0000000000000000000000000000000000000000..08904bf88a92202f952d47d5f96101b9841bdb1f
--- /dev/null
+++ b/damage_ca/damage.py
@@ -0,0 +1,92 @@
+import torch
+from torch.utils.tensorboard import SummaryWriter
+from torchvision.utils import save_image
+from model import CellularAutomataModel
+from utils import load_emoji, to_rgb, get_gaussian_kernel, adv_attack
+import torch.nn.functional as F
+from torch import tensor as tt
+import numpy as np
+
+class Damage():
+    def __init__(self, args):
+        self.device = torch.device('cpu')
+        self.n_iterations = args.n_iterations
+        self.batch_size = args.batch_size
+        self.dmg_freq = args.dmg_freq
+        self.alpha = args.alpha
+        self.padding = args.padding
+        self.sigma = args.sigma
+        self.size = args.size+2 +self.padding
+        self.logdir = args.logdir
+        self.max_dmg_freq = args.max_dmg_freq
+        self.load_model_path = args.load_model_path
+        self.n_channels = args.n_channels
+        self.target_img = load_emoji(args.img, self.size) #rgba img
+        self.mode = args.mode # 0 for blur, 1 for pixel removal, 2 for adversarial attck
+        self.eps = args.eps
+
+        p = self.padding
+        self.pad_target = F.pad(tt(self.target_img), (0, 0, p, p, p, p))
+        h, w = self.pad_target.shape[:2]
+
+        whidden = torch.concat((self.pad_target, torch.zeros((self.size,self.size,12))), axis=2)
+        self.batch_target = tt(np.repeat(whidden[None, ...], self.batch_size, 0))
+
+        self.seed = np.zeros([h, w, 16], np.float64)
+        self.seed[h // 2, w // 2, 3:] = 1.0
+        self.x0 = tt(np.repeat(self.seed[None, ...], self.batch_size, 0)) #seed
+
+        t_rgb = to_rgb(self.pad_target).permute(2, 0, 1)
+        self.net = CellularAutomataModel(n_channels=16, fire_rate=0.5, hidden_channels=32).to(self.device)
+        save_image(t_rgb, "%s/target_image.png" % self.logdir)
+        self.writer = SummaryWriter(self.logdir)
+
+        
+    def load_model(self, path):
+        """Load a PyTorch model from path."""
+        self.net.load_state_dict(torch.load(path))
+        self.net.double()
+
+
+    def run(self):
+        imgpath = '%s/damaged.png' % (self.logdir)
+        self.load_model(self.load_model_path) # model loaded
+        x_eval = self.x0.clone()
+        blur = get_gaussian_kernel(sigma=self.sigma,channels=16).to(self.device)
+        l_func = torch.nn.MSELoss()
+        dmg_count = 0
+        for i in range(self.n_iterations):
+            loss=self.net.loss(x_eval, self.pad_target).mean()
+            self.writer.add_scalar("dmg/loss", loss, i)
+
+            x_eval = self.net(x_eval) #update step
+            
+            if i % self.dmg_freq == 0 and i != 0 and dmg_count != self.max_dmg_freq: # do damage
+                if self.mode == 0:
+                    gblur = blur(x_eval.permute(0, 3, 1, 2).type(torch.float32)) # returns (ch, s, s)
+                    x_eval = gblur.permute(0,2,3,1).type(torch.float64)
+                    #self.writer.add_image(f'after_{i}', to_rgb(x_eval)[0].permute(2, 0, 1))
+                elif self.mode == 1:
+                    #lower half
+                    y_pos = (self.size // 2) + 1
+                    x_pos = 0
+                    dmg_size = self.size
+                    x_eval[:, y_pos:y_pos + dmg_size, x_pos:x_pos + dmg_size, :] = 0
+                elif self.mode==2:
+                    e = x_eval.detach().cpu()
+                    e.requires_grad = True
+                    l = l_func(e, self.batch_target)
+                    self.net.zero_grad()
+                    l.backward()
+                    x_eval = adv_attack(x_eval, self.eps, e.grad.data)
+                dmg_count += 1
+                image = to_rgb(x_eval).permute(0, 3, 1, 2)
+                save_image(image, imgpath, nrow=1, padding=0)
+
+        imgpath = '%s/done.png' % (self.logdir)
+        image = to_rgb(x_eval).permute(0, 3, 1, 2)
+        save_image(image, imgpath, nrow=1, padding=0)
+        
+        
+                    
+    
\ No newline at end of file
diff --git a/damage_ca/logs/CARROT_29-04-2022_14-41-56_mode2/damaged.png b/damage_ca/logs/CARROT_29-04-2022_14-41-56_mode2/damaged.png
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index 0000000000000000000000000000000000000000..f55dc5f4ec91bbb0306348b02279fa09f8497024
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diff --git a/damage_ca/logs/CARROT_29-04-2022_14-41-56_mode2/logfile.log b/damage_ca/logs/CARROT_29-04-2022_14-41-56_mode2/logfile.log
new file mode 100644
index 0000000000000000000000000000000000000000..e9603e500b54c801e2684182c0e9220f7ff23c71
--- /dev/null
+++ b/damage_ca/logs/CARROT_29-04-2022_14-41-56_mode2/logfile.log
@@ -0,0 +1,17 @@
+INFO:root:
+Arguments:
+mode: 2
+n_iterations: 1000
+batch_size: 1
+n_channels: 16
+dmg_freq: 51
+max_dmg_freq: 5
+sigma: 0.2
+alpha: 0.005
+padding: 0
+eps: 0.007
+img: '🥕'
+size: 15
+logdir: 'logs/CARROT_29-04-2022_14-41-56_mode2'
+load_model_path: 'models/model_999500'
+
diff --git a/damage_ca/logs/CARROT_29-04-2022_14-41-56_mode2/target_image.png b/damage_ca/logs/CARROT_29-04-2022_14-41-56_mode2/target_image.png
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diff --git a/damage_ca/logs/CARROT_29-04-2022_14-42-09_mode2/damaged.png b/damage_ca/logs/CARROT_29-04-2022_14-42-09_mode2/damaged.png
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diff --git a/damage_ca/logs/CARROT_29-04-2022_14-42-09_mode2/done.png b/damage_ca/logs/CARROT_29-04-2022_14-42-09_mode2/done.png
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diff --git a/damage_ca/logs/CARROT_29-04-2022_14-42-09_mode2/logfile.log b/damage_ca/logs/CARROT_29-04-2022_14-42-09_mode2/logfile.log
new file mode 100644
index 0000000000000000000000000000000000000000..60c1ceb6c4896648eca7af1eaf3998432d6c654f
--- /dev/null
+++ b/damage_ca/logs/CARROT_29-04-2022_14-42-09_mode2/logfile.log
@@ -0,0 +1,17 @@
+INFO:root:
+Arguments:
+mode: 2
+n_iterations: 1000
+batch_size: 1
+n_channels: 16
+dmg_freq: 51
+max_dmg_freq: 5
+sigma: 0.2
+alpha: 0.005
+padding: 0
+eps: 0.07
+img: '🥕'
+size: 15
+logdir: 'logs/CARROT_29-04-2022_14-42-09_mode2'
+load_model_path: 'models/model_999500'
+
diff --git a/damage_ca/logs/CARROT_29-04-2022_14-42-09_mode2/target_image.png b/damage_ca/logs/CARROT_29-04-2022_14-42-09_mode2/target_image.png
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diff --git a/damage_ca/logs/CARROT_29-04-2022_14-42-33_mode2/damaged.png b/damage_ca/logs/CARROT_29-04-2022_14-42-33_mode2/damaged.png
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diff --git a/damage_ca/logs/CARROT_29-04-2022_14-42-33_mode2/logfile.log b/damage_ca/logs/CARROT_29-04-2022_14-42-33_mode2/logfile.log
new file mode 100644
index 0000000000000000000000000000000000000000..50b8466348b92c8db3a6d99e136aaf4d45668118
--- /dev/null
+++ b/damage_ca/logs/CARROT_29-04-2022_14-42-33_mode2/logfile.log
@@ -0,0 +1,17 @@
+INFO:root:
+Arguments:
+mode: 2
+n_iterations: 1000
+batch_size: 1
+n_channels: 16
+dmg_freq: 51
+max_dmg_freq: 1
+sigma: 0.2
+alpha: 0.005
+padding: 0
+eps: 0.007
+img: '🥕'
+size: 15
+logdir: 'logs/CARROT_29-04-2022_14-42-33_mode2'
+load_model_path: 'models/model_999500'
+
diff --git a/damage_ca/logs/CARROT_29-04-2022_14-42-33_mode2/target_image.png b/damage_ca/logs/CARROT_29-04-2022_14-42-33_mode2/target_image.png
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diff --git a/damage_ca/main.py b/damage_ca/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..992b3117577a026fbb35f24a32e3b6f1d25184af
--- /dev/null
+++ b/damage_ca/main.py
@@ -0,0 +1,47 @@
+import argparse
+import os
+import unicodedata
+import logging
+from damage import Damage
+import time
+if __name__ == '__main__':
+
+    emoji = '🥕'
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--mode", type=int, default=0, help="0 for gaussian blur, 1 for pixel removal, 2 for adversarial attacks")
+    parser.add_argument("--n_iterations", type=int, default=1000, help="Number of iterations to test for.")
+    parser.add_argument("--batch_size", type=int, default=8, help="Batch size.")
+    parser.add_argument("--n_channels", type=int, default=16, help="Number of channels of the input tensor")
+    parser.add_argument("--dmg_freq", type=int, default=51, help="Frequency for damaging",)
+    parser.add_argument("--max_dmg_freq", type=int, default=-1, help="Limit the number of times of damaging, -1 if not specified")
+    
+    parser.add_argument("--sigma", type=float, default=0.2, help="Sigma for gaussian blur, greater value means more blurry")
+    parser.add_argument("--alpha", type=float, default=0.005, metavar=0.005, help="Alpha for how much noise to add")
+    parser.add_argument("--padding", type=int, default=0, help="Padding for image")
+    parser.add_argument("--eps", type=float, default=0.007, help="Epsion scales the amount of damage done from adversarial attacks")
+    parser.add_argument("--img", type=str, default=emoji, metavar="🐰", help="The emoji to train on")
+    parser.add_argument("--size",type=int, default=15, help="size of image")
+    parser.add_argument("--logdir", type=str, default="logs", help="Logging folder for new model")
+    parser.add_argument("--load_model_path", type=str, default="models/model_999500", help="Path to pre trained model")
+
+    args = parser.parse_args()
+
+    if not os.path.isdir(args.logdir):
+        raise Exception("Logging directory '%s' not found in base folder" % args.logdir)
+
+    args.logdir = "%s/%s_%s_mode%s" % (args.logdir, unicodedata.name(args.img), time.strftime("%d-%m-%Y_%H-%M-%S"), args.mode)
+    os.mkdir(args.logdir)
+
+    print(args.logdir)
+    logging.basicConfig(handlers=[logging.FileHandler(filename=f'{args.logdir}/logfile.log', encoding='utf-8', mode='a+')], level=logging.INFO)
+    argprint = "\nArguments:\n"
+    for arg, value in vars(args).items():
+        argprint += ("%s: %r\n" % (arg, value))
+    logging.info(argprint)
+
+    #dmg here
+    dmg = Damage(args)
+    dmg.run()
+    
\ No newline at end of file
diff --git a/damage_ca/model.py b/damage_ca/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..868ca49d66c0b3b9fedb49838c93d46c389cff87
--- /dev/null
+++ b/damage_ca/model.py
@@ -0,0 +1,69 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class CellularAutomataModel(nn.Module):
+    def __init__(self, n_channels, hidden_channels, fire_rate):
+        super().__init__()
+        self.n_channels = n_channels
+        self.hidden_channels = hidden_channels
+        self.fire_rate = fire_rate
+
+        self.fc0 = nn.Linear(self.n_channels * 3, self.hidden_channels, bias=False)
+        self.fc1 = nn.Linear(self.hidden_channels, self.n_channels, bias=False)
+        with torch.no_grad(): self.fc1.weight.zero_()
+
+        identity = np.float64([0, 1, 0])
+        identity = torch.from_numpy(np.outer(identity, identity))
+        sobel_x = torch.from_numpy(np.outer([1, 2, 1], [-1, 0, 1]) / 8.0)  # sobel filter
+        sobel_y = sobel_x.T
+        self.kernel = torch.cat([
+            identity[None, None, ...], 
+            sobel_x[None, None, ...], 
+            sobel_y[None, None, ...]], 
+            dim=0).repeat(self.n_channels, 1, 1, 1)
+
+        for param in self.parameters(): param.requires_grad = False
+
+        self.double()
+
+    def perceive(self, x):
+        """Percieve neighboors with two sobel filters and one single-entry filter"""
+        y = F.conv2d(x.permute(0, 3, 1, 2), self.kernel, groups=16, padding=1)
+        y = y.permute(0, 2, 3, 1)
+        return y
+    
+    def loss(self, x, y):
+        """mean squared error"""
+        return torch.mean(torch.square(x[..., :4] - y), [-2, -3, -1])
+
+    def forward(self, x, fire_rate=None, step_size=1.0):
+        """Forward a cell grid through the network and return the cell grid with changes applied."""
+        y = self.perceive(x)
+        pre_life_mask = get_living_mask(x)
+        dx1 = self.fc0(y)
+        dx1 = F.relu(dx1)
+        dx2 = self.fc1(dx1)
+        dx = dx2 * step_size
+
+        if fire_rate is None:
+            fire_rate = self.fire_rate
+
+        update_mask_rand = torch.rand(*x[:, :, :, :1].shape)
+        update_mask = update_mask_rand <= fire_rate
+        x += dx * update_mask.double()
+        post_life_mask = get_living_mask(x)
+        life_mask = pre_life_mask.bool() & post_life_mask.bool()
+        res = x * life_mask.double()
+
+        return res
+
+
+def get_living_mask(x):
+    """returns boolean vector of the same shape as x, except for the last dimension.
+    The last dimension is a single value, true/false, that determines if alpha > 0.1"""
+    alpha = x[:, :, :, 3:4]
+    m = F.max_pool3d(alpha, kernel_size=3, stride=1, padding=1) > 0.1
+    return m
+
diff --git a/damage_ca/models/model_999500 b/damage_ca/models/model_999500
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index 0000000000000000000000000000000000000000..af0a267609c32d1e859c5b49744606d0d694d1b5
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diff --git a/damage_ca/readme.md b/damage_ca/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..a187b2cf9ba408e1cbf46e0477bd766e743e3089
--- /dev/null
+++ b/damage_ca/readme.md
@@ -0,0 +1,40 @@
+# Damage script
+
+``python main.py --batch_size=1 --mode=0 --max_dmg_freq=-1 --load_model_path="models/model_999500"``
+
+## Types of damage
+
+* Gaussian blur
+Requires sigma value. Greater sigma equals more blurryness
+default argument: --sigma = 0.2
+mode 0
+
+* Removal of pixels
+mode 1
+
+* Adversarial attacks
+Requires epsilon value. Greater epsiolon equals more noise.
+default argument: --eps=0.007
+mode 2
+
+## Parameters:  
+optional arguments:  
+  -h, --help            show this help message and exit  
+  --mode MODE           0 for gaussian blur, 1 for pixel removal, 2 for adversarial attacks  
+  --n_iterations N_ITERATIONS Number of iterations to test for.  
+  --batch_size BATCH_SIZE  
+  Batch size.  
+  --n_channels N_CHANNELS  
+                        Number of channels of the input tensor  
+  --dmg_freq DMG_FREQ   Frequency for damaging  
+  --max_dmg_freq MAX_DMG_FREQ  
+                        Limit the number of times of damaging, -1 if not specified       
+  --sigma SIGMA         Sigma for gaussian blur, greater value means more blurry  
+  --alpha 0.005         Alpha for how much noise to add  
+  --padding PADDING     Padding for image  
+  --eps EPS             Epsion scales the amount of damage done from adversarial attacks  
+  --img 🐰               The emoji to train on  
+  --size SIZE           size of image  
+  --logdir LOGDIR       Logging folder for new model  
+  --load_model_path LOAD_MODEL_PATH  
+                        Path to pre trained model
diff --git a/damage_ca/utils.py b/damage_ca/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0667de132a35cd37a8f57fa7134272e32a5a0cd1
--- /dev/null
+++ b/damage_ca/utils.py
@@ -0,0 +1,114 @@
+import requests
+import torch
+from torchvision.utils import save_image
+import numpy as np
+import PIL.Image, PIL.ImageDraw
+import io
+import math
+
+
+def load_emoji(emoji_code, img_size):
+    """Loads image of emoji with code 'emoji' from google's emojirepository"""
+    emoji_code = hex(ord(emoji_code))[2:].lower()
+    url = 'https://raw.githubusercontent.com/googlefonts/noto-emoji/main/png/128/emoji_u%s.png' % emoji_code
+    req = requests.get(url)
+    img = PIL.Image.open(io.BytesIO(req.content))
+    img.thumbnail((img_size, img_size), PIL.Image.ANTIALIAS)
+    img = np.float64(img) / 255.0
+    img[..., :3] *= img[..., 3:]
+
+    return img
+
+# Adversarial attack
+def adv_attack(image, epsilon, data_grad):
+    # Collect the element-wise sign of the data gradient
+    sign_data_grad = data_grad.sign()
+    # Create the perturbed image by adjusting each pixel of the input image
+    perturbed_image = image + epsilon*sign_data_grad
+    # Adding clipping to maintain [0,1] range
+    perturbed_image = torch.clamp(perturbed_image, 0, 1)
+    # Return the perturbed image
+    return perturbed_image
+
+def get_gaussian_kernel(kernel_size=3, sigma=1.0, channels=3, padding=1):
+    """ Create a model for applying 2d convolutional gaussian blur filter"""
+    # Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
+    x_coord = torch.arange(kernel_size)
+    x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
+    y_grid = x_grid.t()
+    xy_grid = torch.stack([x_grid, y_grid], dim=-1)
+
+    mean = (kernel_size - 1)/2.
+    variance = sigma**2.
+
+    # Calculate the 2-dimensional gaussian kernel which is
+    # the product of two gaussian distributions for two different
+    # variables (in this case called x and y)
+    gaussian_kernel = (1./(2.*math.pi*variance)) *\
+                      torch.exp(
+                          -torch.sum((xy_grid - mean)**2., dim=-1) /\
+                          (2*variance)
+                      )
+
+    # Make sure sum of values in gaussian kernel equals 1.
+    gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
+
+    # Reshape to 2d depthwise convolutional weight
+    gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size)
+    gaussian_kernel = gaussian_kernel.repeat(channels, 1, 1, 1)
+
+    gaussian_filter = torch.nn.Conv2d(in_channels=channels, out_channels=channels,
+                                kernel_size=kernel_size, groups=channels, padding=padding, bias=False)
+
+    gaussian_filter.weight.data = gaussian_kernel
+    gaussian_filter.weight.requires_grad = False
+    
+    return gaussian_filter
+
+def to_alpha(x):
+    """Return the alpha channel of an image."""
+    return torch.clamp(x[..., 3:4], 0.0, 1.0)
+
+def to_rgb(x):
+    """Return the three first channels (RGB) with alpha deducted."""
+    rgb, a = x[..., :3], to_alpha(x)
+    return 1.0 - a + rgb
+
+def to_rgba(x):
+    """Return the four first channels (RGBA) of an image."""
+    return x[..., :4]
+
+def save_model(ca, base_fn):
+    """Save a PyTorch model to a specific path."""
+    torch.save(ca.state_dict(), base_fn)
+
+def visualize(xs, step_i, nrow=1):
+    """Save a batch of multiple x's to file"""
+    for i in range(len(xs)):
+        xs[i] = to_rgb(xs[i]).permute(0, 3, 1, 2)
+    save_image(torch.cat(xs, dim=0), './logg/pic/p%04d.png' % step_i, nrow=nrow, padding=0)
+
+class Pool:
+    """Class for storing and providing samples of different stages of growth."""
+    def __init__(self, seed, size):
+        self.size = size
+        self.slots = np.repeat([seed], size, 0)
+        self.seed = seed
+
+    def commit(self, batch):
+        """Replace existing slots with a batch."""
+        indices = batch["indices"]
+        for i, x in enumerate(batch["x"]):
+            if (x[:, :, 3] > 0.1).any():  # Avoid committing dead image
+                self.slots[indices[i]] = x.copy()
+
+    def sample(self, c):
+        """Retrieve a batch from the pool."""
+        indices = np.random.choice(self.size, c, False)
+        batch = {
+            "indices": indices,
+            "x": self.slots[indices]
+        }
+        return batch
+
+    
\ No newline at end of file
diff --git a/ES_ex/ES.py b/etc/ES_ex/ES.py
similarity index 100%
rename from ES_ex/ES.py
rename to etc/ES_ex/ES.py
diff --git a/ES_ex/ES_test.py b/etc/ES_ex/ES_test.py
similarity index 100%
rename from ES_ex/ES_test.py
rename to etc/ES_ex/ES_test.py
diff --git a/es_automata/ES.py b/etc/es_automata/ES.py
similarity index 100%
rename from es_automata/ES.py
rename to etc/es_automata/ES.py
diff --git a/es_automata/__init__.py b/etc/es_automata/__init__.py
similarity index 100%
rename from es_automata/__init__.py
rename to etc/es_automata/__init__.py
diff --git a/es_automata/animation.mp4 b/etc/es_automata/animation.mp4
similarity index 100%
rename from es_automata/animation.mp4
rename to etc/es_automata/animation.mp4
diff --git a/es_automata/model.py b/etc/es_automata/model.py
similarity index 100%
rename from es_automata/model.py
rename to etc/es_automata/model.py
diff --git a/es_automata/rabbit.png b/etc/es_automata/rabbit.png
similarity index 100%
rename from es_automata/rabbit.png
rename to etc/es_automata/rabbit.png
diff --git a/es_automata/readme.md b/etc/es_automata/readme.md
similarity index 100%
rename from es_automata/readme.md
rename to etc/es_automata/readme.md
diff --git a/es_automata/requirements.txt b/etc/es_automata/requirements.txt
similarity index 96%
rename from es_automata/requirements.txt
rename to etc/es_automata/requirements.txt
index bd1c5c0ccbbe98e7f1583d020cecd7e770fcca71..95be3c2c4f9ccb346372609947e15539e5ad38c2 100644
--- a/es_automata/requirements.txt
+++ b/etc/es_automata/requirements.txt
@@ -1,359 +1,359 @@
-# This file may be used to create an environment using:
-# $ conda create --name <env> --file <this file>
-# platform: win-64
-_ipyw_jlab_nb_ext_conf=0.1.0=py38_0
-absl-py=1.0.0=pyhd8ed1ab_0
-alabaster=0.7.12=pyhd3eb1b0_0
-anaconda=2021.05=py38_0
-anaconda-client=1.7.2=py38_0
-anaconda-navigator=2.0.3=py38_0
-anaconda-project=0.9.1=pyhd3eb1b0_1
-anyio=2.2.0=py38haa95532_2
-appdirs=1.4.4=py_0
-argh=0.26.2=py38_0
-argon2-cffi=20.1.0=py38h2bbff1b_1
-asn1crypto=1.4.0=py_0
-astroid=2.5=py38haa95532_1
-astropy=4.2.1=py38h2bbff1b_1
-async_generator=1.10=pyhd3eb1b0_0
-atomicwrites=1.4.0=py_0
-attrs=20.3.0=pyhd3eb1b0_0
-autopep8=1.5.6=pyhd3eb1b0_0
-babel=2.9.0=pyhd3eb1b0_0
-backcall=0.2.0=pyhd3eb1b0_0
-backports=1.0=pyhd3eb1b0_2
-backports.functools_lru_cache=1.6.4=pyhd3eb1b0_0
-backports.shutil_get_terminal_size=1.0.0=pyhd3eb1b0_3
-backports.tempfile=1.0=pyhd3eb1b0_1
-backports.weakref=1.0.post1=py_1
-bcrypt=3.2.0=py38he774522_0
-beautifulsoup4=4.9.3=pyha847dfd_0
-bitarray=1.9.2=py38h2bbff1b_1
-bkcharts=0.2=py38_0
-black=19.10b0=py_0
-blas=1.0=mkl
-bleach=3.3.0=pyhd3eb1b0_0
-blosc=1.21.0=h19a0ad4_0
-bokeh=2.3.2=py38haa95532_0
-boto=2.49.0=py38_0
-bottleneck=1.3.2=py38h2a96729_1
-brotli=1.0.9=ha925a31_2
-brotlipy=0.7.0=py38h2bbff1b_1003
-bzip2=1.0.8=he774522_0
-ca-certificates=2021.4.13=haa95532_1
-certifi=2020.12.5=py38haa95532_0
-cffi=1.14.5=py38hcd4344a_0
-chardet=4.0.0=py38haa95532_1003
-charls=2.2.0=h6c2663c_0
-click=7.1.2=pyhd3eb1b0_0
-cloudpickle=1.6.0=py_0
-clyent=1.2.2=py38_1
-colorama=0.4.4=pyhd3eb1b0_0
-comtypes=1.1.9=py38haa95532_1002
-conda=4.11.0=py38haa244fe_0
-conda-build=3.21.4=py38haa95532_0
-conda-content-trust=0.1.1=pyhd3eb1b0_0
-conda-env=2.6.0=1
-conda-package-handling=1.7.3=py38h8cc25b3_1
-conda-repo-cli=1.0.4=pyhd3eb1b0_0
-conda-token=0.3.0=pyhd3eb1b0_0
-conda-verify=3.4.2=py_1
-console_shortcut=0.1.1=4
-contextlib2=0.6.0.post1=py_0
-coverage=6.3.1=py38h294d835_0
-cryptography=3.4.7=py38h71e12ea_0
-cudatoolkit=10.2.89=h74a9793_1
-curl=7.71.1=h2a8f88b_1
-cycler=0.10.0=py38_0
-cython=0.29.23=py38hd77b12b_0
-cytoolz=0.11.0=py38he774522_0
-dask=2021.4.0=pyhd3eb1b0_0
-dask-core=2021.4.0=pyhd3eb1b0_0
-decorator=5.0.6=pyhd3eb1b0_0
-defusedxml=0.7.1=pyhd3eb1b0_0
-diff-match-patch=20200713=py_0
-distributed=2021.4.0=py38haa95532_0
-docutils=0.17=py38haa95532_1
-entrypoints=0.3=py38_0
-et_xmlfile=1.0.1=py_1001
-fastcache=1.1.0=py38he774522_0
-ffmpeg=1.4=pypi_0
-filelock=3.0.12=pyhd3eb1b0_1
-flake8=3.9.0=pyhd3eb1b0_0
-flask=1.1.2=pyhd3eb1b0_0
-freetype=2.10.4=hd328e21_0
-fsspec=0.9.0=pyhd3eb1b0_0
-future=0.18.2=py38_1
-get_terminal_size=1.0.0=h38e98db_0
-gevent=21.1.2=py38h2bbff1b_1
-giflib=5.2.1=h62dcd97_0
-glob2=0.7=pyhd3eb1b0_0
-greenlet=1.0.0=py38hd77b12b_2
-grpcio=1.35.0=py38hc60d5dd_1
-h5py=2.10.0=py38h5e291fa_0
-hdf5=1.10.4=h7ebc959_0
-heapdict=1.0.1=py_0
-html5lib=1.1=py_0
-icc_rt=2019.0.0=h0cc432a_1
-icu=58.2=ha925a31_3
-idna=2.10=pyhd3eb1b0_0
-imagecodecs=2021.3.31=py38h5da4933_0
-imageio=2.9.0=pyhd3eb1b0_0
-imagesize=1.2.0=pyhd3eb1b0_0
-importlib-metadata=3.10.0=py38haa95532_0
-importlib_metadata=3.10.0=hd3eb1b0_0
-iniconfig=1.1.1=pyhd3eb1b0_0
-intel-openmp=2021.2.0=haa95532_616
-intervaltree=3.1.0=py_0
-ipykernel=5.3.4=py38h5ca1d4c_0
-ipython=7.22.0=py38hd4e2768_0
-ipython_genutils=0.2.0=pyhd3eb1b0_1
-ipywidgets=7.6.3=pyhd3eb1b0_1
-isort=5.8.0=pyhd3eb1b0_0
-itsdangerous=1.1.0=pyhd3eb1b0_0
-jdcal=1.4.1=py_0
-jedi=0.17.2=py38haa95532_1
-jinja2=2.11.3=pyhd3eb1b0_0
-joblib=1.0.1=pyhd3eb1b0_0
-jpeg=9b=hb83a4c4_2
-json5=0.9.5=py_0
-jsonschema=3.2.0=py_2
-jupyter=1.0.0=py38_7
-jupyter-packaging=0.7.12=pyhd3eb1b0_0
-jupyter_client=6.1.12=pyhd3eb1b0_0
-jupyter_console=6.4.0=pyhd3eb1b0_0
-jupyter_core=4.7.1=py38haa95532_0
-jupyter_server=1.4.1=py38haa95532_0
-jupyterlab=3.0.14=pyhd3eb1b0_1
-jupyterlab_pygments=0.1.2=py_0
-jupyterlab_server=2.4.0=pyhd3eb1b0_0
-jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
-keyring=22.3.0=py38haa95532_0
-kiwisolver=1.3.1=py38hd77b12b_0
-krb5=1.18.2=hc04afaa_0
-lazy-object-proxy=1.6.0=py38h2bbff1b_0
-lcms2=2.12=h83e58a3_0
-lerc=2.2.1=hd77b12b_0
-libaec=1.0.4=h33f27b4_1
-libarchive=3.4.2=h5e25573_0
-libcurl=7.71.1=h2a8f88b_1
-libdeflate=1.7=h2bbff1b_5
-libiconv=1.15=h1df5818_7
-liblief=0.10.1=ha925a31_0
-libpng=1.6.37=h2a8f88b_0
-libprotobuf=3.19.1=h23ce68f_0
-libsodium=1.0.18=h62dcd97_0
-libspatialindex=1.9.3=h6c2663c_0
-libssh2=1.9.0=h7a1dbc1_1
-libtiff=4.2.0=hd0e1b90_0
-libuv=1.40.0=he774522_0
-libxml2=2.9.10=hb89e7f3_3
-libxslt=1.1.34=he774522_0
-libzopfli=1.0.3=ha925a31_0
-llvmlite=0.36.0=py38h34b8924_4
-locket=0.2.1=py38haa95532_1
-lxml=4.6.3=py38h9b66d53_0
-lz4-c=1.9.3=h2bbff1b_0
-lzo=2.10=he774522_2
-m2w64-gcc-libgfortran=5.3.0=6
-m2w64-gcc-libs=5.3.0=7
-m2w64-gcc-libs-core=5.3.0=7
-m2w64-gmp=6.1.0=2
-m2w64-libwinpthread-git=5.0.0.4634.697f757=2
-markdown=3.3.4=py38haa95532_0
-markupsafe=1.1.1=py38he774522_0
-matplotlib=3.3.4=py38haa95532_0
-matplotlib-base=3.3.4=py38h49ac443_0
-mccabe=0.6.1=py38_1
-menuinst=1.4.16=py38he774522_1
-mistune=0.8.4=py38he774522_1000
-mkl=2021.2.0=haa95532_296
-mkl-service=2.3.0=py38h2bbff1b_1
-mkl_fft=1.3.0=py38h277e83a_2
-mkl_random=1.2.1=py38hf11a4ad_2
-mock=4.0.3=pyhd3eb1b0_0
-more-itertools=8.7.0=pyhd3eb1b0_0
-mpmath=1.2.1=py38haa95532_0
-msgpack-python=1.0.2=py38h59b6b97_1
-msys2-conda-epoch=20160418=1
-multipledispatch=0.6.0=py38_0
-mypy_extensions=0.4.3=py38_0
-navigator-updater=0.2.1=py38_0
-nbclassic=0.2.6=pyhd3eb1b0_0
-nbclient=0.5.3=pyhd3eb1b0_0
-nbconvert=6.0.7=py38_0
-nbformat=5.1.3=pyhd3eb1b0_0
-nest-asyncio=1.5.1=pyhd3eb1b0_0
-networkx=2.5=py_0
-nltk=3.6.1=pyhd3eb1b0_0
-nose=1.3.7=pyhd3eb1b0_1006
-notebook=6.3.0=py38haa95532_0
-numba=0.53.1=py38hf11a4ad_0
-numexpr=2.7.3=py38hb80d3ca_1
-numpy=1.20.1=py38h34a8a5c_0
-numpy-base=1.20.1=py38haf7ebc8_0
-numpydoc=1.1.0=pyhd3eb1b0_1
-olefile=0.46=py_0
-openjpeg=2.3.0=h5ec785f_1
-openpyxl=3.0.7=pyhd3eb1b0_0
-openssl=1.1.1k=h2bbff1b_0
-packaging=20.9=pyhd3eb1b0_0
-pandas=1.2.4=py38hd77b12b_0
-pandoc=2.12=haa95532_0
-pandocfilters=1.4.3=py38haa95532_1
-paramiko=2.7.2=py_0
-parso=0.7.0=py_0
-partd=1.2.0=pyhd3eb1b0_0
-path=15.1.2=py38haa95532_0
-path.py=12.5.0=0
-pathlib2=2.3.5=py38haa95532_2
-pathspec=0.7.0=py_0
-patsy=0.5.1=py38_0
-pep8=1.7.1=py38_0
-pexpect=4.8.0=pyhd3eb1b0_3
-pickleshare=0.7.5=pyhd3eb1b0_1003
-pillow=8.2.0=py38h4fa10fc_0
-pip=21.0.1=py38haa95532_0
-pkginfo=1.7.0=py38haa95532_0
-pluggy=0.13.1=py38haa95532_0
-ply=3.11=py38_0
-powershell_shortcut=0.0.1=3
-prometheus_client=0.10.1=pyhd3eb1b0_0
-prompt-toolkit=3.0.17=pyh06a4308_0
-prompt_toolkit=3.0.17=hd3eb1b0_0
-protobuf=3.19.1=py38hd77b12b_0
-psutil=5.8.0=py38h2bbff1b_1
-ptyprocess=0.7.0=pyhd3eb1b0_2
-py=1.10.0=pyhd3eb1b0_0
-py-lief=0.10.1=py38ha925a31_0
-pycodestyle=2.6.0=pyhd3eb1b0_0
-pycosat=0.6.3=py38h2bbff1b_0
-pycparser=2.20=py_2
-pycurl=7.43.0.6=py38h7a1dbc1_0
-pydocstyle=6.0.0=pyhd3eb1b0_0
-pyerfa=1.7.3=py38h2bbff1b_0
-pyflakes=2.2.0=pyhd3eb1b0_0
-pygments=2.8.1=pyhd3eb1b0_0
-pylint=2.7.4=py38haa95532_1
-pyls-black=0.4.6=hd3eb1b0_0
-pyls-spyder=0.3.2=pyhd3eb1b0_0
-pynacl=1.4.0=py38h62dcd97_1
-pyodbc=4.0.30=py38ha925a31_0
-pyopenssl=20.0.1=pyhd3eb1b0_1
-pyparsing=2.4.7=pyhd3eb1b0_0
-pyqt=5.9.2=py38ha925a31_4
-pyreadline=2.1=py38_1
-pyrsistent=0.17.3=py38he774522_0
-pysocks=1.7.1=py38haa95532_0
-pytables=3.6.1=py38ha5be198_0
-pytest=6.2.3=py38haa95532_2
-python=3.8.8=hdbf39b2_5
-python-dateutil=2.8.1=pyhd3eb1b0_0
-python-jsonrpc-server=0.4.0=py_0
-python-language-server=0.36.2=pyhd3eb1b0_0
-python-libarchive-c=2.9=pyhd3eb1b0_1
-python_abi=3.8=2_cp38
-pytorch=1.10.2=py3.8_cuda10.2_cudnn7_0
-pytorch-model-summary=0.1.1=py_0
-pytorch-mutex=1.0=cuda
-pytz=2021.1=pyhd3eb1b0_0
-pywavelets=1.1.1=py38he774522_2
-pywin32=227=py38he774522_1
-pywin32-ctypes=0.2.0=py38_1000
-pywinpty=0.5.7=py38_0
-pyyaml=5.4.1=py38h2bbff1b_1
-pyzmq=20.0.0=py38hd77b12b_1
-qdarkstyle=2.8.1=py_0
-qt=5.9.7=vc14h73c81de_0
-qtawesome=1.0.2=pyhd3eb1b0_0
-qtconsole=5.0.3=pyhd3eb1b0_0
-qtpy=1.9.0=py_0
-regex=2021.4.4=py38h2bbff1b_0
-requests=2.25.1=pyhd3eb1b0_0
-rope=0.18.0=py_0
-rtree=0.9.7=py38h2eaa2aa_1
-ruamel_yaml=0.15.100=py38h2bbff1b_0
-scikit-image=0.18.1=py38hf11a4ad_0
-scikit-learn=0.24.1=py38hf11a4ad_0
-scipy=1.6.2=py38h66253e8_1
-seaborn=0.11.1=pyhd3eb1b0_0
-send2trash=1.5.0=pyhd3eb1b0_1
-setuptools=52.0.0=py38haa95532_0
-simplegeneric=0.8.1=py38_2
-singledispatch=3.6.1=pyhd3eb1b0_1001
-sip=4.19.13=py38ha925a31_0
-six=1.15.0=py38haa95532_0
-snappy=1.1.8=h33f27b4_0
-sniffio=1.2.0=py38haa95532_1
-snowballstemmer=2.1.0=pyhd3eb1b0_0
-sortedcollections=2.1.0=pyhd3eb1b0_0
-sortedcontainers=2.3.0=pyhd3eb1b0_0
-soupsieve=2.2.1=pyhd3eb1b0_0
-sphinx=4.0.1=pyhd3eb1b0_0
-sphinxcontrib=1.0=py38_1
-sphinxcontrib-applehelp=1.0.2=pyhd3eb1b0_0
-sphinxcontrib-devhelp=1.0.2=pyhd3eb1b0_0
-sphinxcontrib-htmlhelp=1.0.3=pyhd3eb1b0_0
-sphinxcontrib-jsmath=1.0.1=pyhd3eb1b0_0
-sphinxcontrib-qthelp=1.0.3=pyhd3eb1b0_0
-sphinxcontrib-serializinghtml=1.1.4=pyhd3eb1b0_0
-sphinxcontrib-websupport=1.2.4=py_0
-spyder=4.2.5=py38haa95532_0
-spyder-kernels=1.10.2=py38haa95532_0
-sqlalchemy=1.4.7=py38h2bbff1b_0
-sqlite=3.35.4=h2bbff1b_0
-statsmodels=0.12.2=py38h2bbff1b_0
-sympy=1.8=py38haa95532_0
-tbb=2020.3=h74a9793_0
-tblib=1.7.0=py_0
-tensorboard=1.15.0=py38_0
-terminado=0.9.4=py38haa95532_0
-testpath=0.4.4=pyhd3eb1b0_0
-textdistance=4.2.1=pyhd3eb1b0_0
-threadpoolctl=2.1.0=pyh5ca1d4c_0
-three-merge=0.1.1=pyhd3eb1b0_0
-tifffile=2021.4.8=pyhd3eb1b0_2
-tk=8.6.10=he774522_0
-toml=0.10.2=pyhd3eb1b0_0
-tomli=2.0.1=pyhd8ed1ab_0
-toolz=0.11.1=pyhd3eb1b0_0
-torchsummary=1.5.1=pypi_0
-torchvision=0.11.3=py38_cu102
-tornado=6.1=py38h2bbff1b_0
-tqdm=4.59.0=pyhd3eb1b0_1
-traitlets=5.0.5=pyhd3eb1b0_0
-typed-ast=1.4.2=py38h2bbff1b_1
-typing_extensions=3.7.4.3=pyha847dfd_0
-ujson=4.0.2=py38hd77b12b_0
-unicodecsv=0.14.1=py38_0
-urllib3=1.26.4=pyhd3eb1b0_0
-vc=14.2=h21ff451_1
-vs2015_runtime=14.27.29016=h5e58377_2
-watchdog=1.0.2=py38haa95532_1
-wcwidth=0.2.5=py_0
-webencodings=0.5.1=py38_1
-werkzeug=1.0.1=pyhd3eb1b0_0
-wheel=0.36.2=pyhd3eb1b0_0
-widgetsnbextension=3.5.1=py38_0
-win_inet_pton=1.1.0=py38haa95532_0
-win_unicode_console=0.5=py38_0
-wincertstore=0.2=py38_0
-winpty=0.4.3=4
-wrapt=1.12.1=py38he774522_1
-xlrd=2.0.1=pyhd3eb1b0_0
-xlsxwriter=1.3.8=pyhd3eb1b0_0
-xlwings=0.23.0=py38haa95532_0
-xlwt=1.3.0=py38_0
-xmltodict=0.12.0=py_0
-xz=5.2.5=h62dcd97_0
-yaml=0.2.5=he774522_0
-yapf=0.31.0=pyhd3eb1b0_0
-zeromq=4.3.3=ha925a31_3
-zfp=0.5.5=hd77b12b_6
-zict=2.0.0=pyhd3eb1b0_0
-zipp=3.4.1=pyhd3eb1b0_0
-zlib=1.2.11=h62dcd97_4
-zope=1.0=py38_1
-zope.event=4.5.0=py38_0
-zope.interface=5.3.0=py38h2bbff1b_0
-zstd=1.4.5=h04227a9_0
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: win-64
+_ipyw_jlab_nb_ext_conf=0.1.0=py38_0
+absl-py=1.0.0=pyhd8ed1ab_0
+alabaster=0.7.12=pyhd3eb1b0_0
+anaconda=2021.05=py38_0
+anaconda-client=1.7.2=py38_0
+anaconda-navigator=2.0.3=py38_0
+anaconda-project=0.9.1=pyhd3eb1b0_1
+anyio=2.2.0=py38haa95532_2
+appdirs=1.4.4=py_0
+argh=0.26.2=py38_0
+argon2-cffi=20.1.0=py38h2bbff1b_1
+asn1crypto=1.4.0=py_0
+astroid=2.5=py38haa95532_1
+astropy=4.2.1=py38h2bbff1b_1
+async_generator=1.10=pyhd3eb1b0_0
+atomicwrites=1.4.0=py_0
+attrs=20.3.0=pyhd3eb1b0_0
+autopep8=1.5.6=pyhd3eb1b0_0
+babel=2.9.0=pyhd3eb1b0_0
+backcall=0.2.0=pyhd3eb1b0_0
+backports=1.0=pyhd3eb1b0_2
+backports.functools_lru_cache=1.6.4=pyhd3eb1b0_0
+backports.shutil_get_terminal_size=1.0.0=pyhd3eb1b0_3
+backports.tempfile=1.0=pyhd3eb1b0_1
+backports.weakref=1.0.post1=py_1
+bcrypt=3.2.0=py38he774522_0
+beautifulsoup4=4.9.3=pyha847dfd_0
+bitarray=1.9.2=py38h2bbff1b_1
+bkcharts=0.2=py38_0
+black=19.10b0=py_0
+blas=1.0=mkl
+bleach=3.3.0=pyhd3eb1b0_0
+blosc=1.21.0=h19a0ad4_0
+bokeh=2.3.2=py38haa95532_0
+boto=2.49.0=py38_0
+bottleneck=1.3.2=py38h2a96729_1
+brotli=1.0.9=ha925a31_2
+brotlipy=0.7.0=py38h2bbff1b_1003
+bzip2=1.0.8=he774522_0
+ca-certificates=2021.4.13=haa95532_1
+certifi=2020.12.5=py38haa95532_0
+cffi=1.14.5=py38hcd4344a_0
+chardet=4.0.0=py38haa95532_1003
+charls=2.2.0=h6c2663c_0
+click=7.1.2=pyhd3eb1b0_0
+cloudpickle=1.6.0=py_0
+clyent=1.2.2=py38_1
+colorama=0.4.4=pyhd3eb1b0_0
+comtypes=1.1.9=py38haa95532_1002
+conda=4.11.0=py38haa244fe_0
+conda-build=3.21.4=py38haa95532_0
+conda-content-trust=0.1.1=pyhd3eb1b0_0
+conda-env=2.6.0=1
+conda-package-handling=1.7.3=py38h8cc25b3_1
+conda-repo-cli=1.0.4=pyhd3eb1b0_0
+conda-token=0.3.0=pyhd3eb1b0_0
+conda-verify=3.4.2=py_1
+console_shortcut=0.1.1=4
+contextlib2=0.6.0.post1=py_0
+coverage=6.3.1=py38h294d835_0
+cryptography=3.4.7=py38h71e12ea_0
+cudatoolkit=10.2.89=h74a9793_1
+curl=7.71.1=h2a8f88b_1
+cycler=0.10.0=py38_0
+cython=0.29.23=py38hd77b12b_0
+cytoolz=0.11.0=py38he774522_0
+dask=2021.4.0=pyhd3eb1b0_0
+dask-core=2021.4.0=pyhd3eb1b0_0
+decorator=5.0.6=pyhd3eb1b0_0
+defusedxml=0.7.1=pyhd3eb1b0_0
+diff-match-patch=20200713=py_0
+distributed=2021.4.0=py38haa95532_0
+docutils=0.17=py38haa95532_1
+entrypoints=0.3=py38_0
+et_xmlfile=1.0.1=py_1001
+fastcache=1.1.0=py38he774522_0
+ffmpeg=1.4=pypi_0
+filelock=3.0.12=pyhd3eb1b0_1
+flake8=3.9.0=pyhd3eb1b0_0
+flask=1.1.2=pyhd3eb1b0_0
+freetype=2.10.4=hd328e21_0
+fsspec=0.9.0=pyhd3eb1b0_0
+future=0.18.2=py38_1
+get_terminal_size=1.0.0=h38e98db_0
+gevent=21.1.2=py38h2bbff1b_1
+giflib=5.2.1=h62dcd97_0
+glob2=0.7=pyhd3eb1b0_0
+greenlet=1.0.0=py38hd77b12b_2
+grpcio=1.35.0=py38hc60d5dd_1
+h5py=2.10.0=py38h5e291fa_0
+hdf5=1.10.4=h7ebc959_0
+heapdict=1.0.1=py_0
+html5lib=1.1=py_0
+icc_rt=2019.0.0=h0cc432a_1
+icu=58.2=ha925a31_3
+idna=2.10=pyhd3eb1b0_0
+imagecodecs=2021.3.31=py38h5da4933_0
+imageio=2.9.0=pyhd3eb1b0_0
+imagesize=1.2.0=pyhd3eb1b0_0
+importlib-metadata=3.10.0=py38haa95532_0
+importlib_metadata=3.10.0=hd3eb1b0_0
+iniconfig=1.1.1=pyhd3eb1b0_0
+intel-openmp=2021.2.0=haa95532_616
+intervaltree=3.1.0=py_0
+ipykernel=5.3.4=py38h5ca1d4c_0
+ipython=7.22.0=py38hd4e2768_0
+ipython_genutils=0.2.0=pyhd3eb1b0_1
+ipywidgets=7.6.3=pyhd3eb1b0_1
+isort=5.8.0=pyhd3eb1b0_0
+itsdangerous=1.1.0=pyhd3eb1b0_0
+jdcal=1.4.1=py_0
+jedi=0.17.2=py38haa95532_1
+jinja2=2.11.3=pyhd3eb1b0_0
+joblib=1.0.1=pyhd3eb1b0_0
+jpeg=9b=hb83a4c4_2
+json5=0.9.5=py_0
+jsonschema=3.2.0=py_2
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+jupyter_client=6.1.12=pyhd3eb1b0_0
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+jupyter_core=4.7.1=py38haa95532_0
+jupyter_server=1.4.1=py38haa95532_0
+jupyterlab=3.0.14=pyhd3eb1b0_1
+jupyterlab_pygments=0.1.2=py_0
+jupyterlab_server=2.4.0=pyhd3eb1b0_0
+jupyterlab_widgets=1.0.0=pyhd3eb1b0_1
+keyring=22.3.0=py38haa95532_0
+kiwisolver=1.3.1=py38hd77b12b_0
+krb5=1.18.2=hc04afaa_0
+lazy-object-proxy=1.6.0=py38h2bbff1b_0
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+libaec=1.0.4=h33f27b4_1
+libarchive=3.4.2=h5e25573_0
+libcurl=7.71.1=h2a8f88b_1
+libdeflate=1.7=h2bbff1b_5
+libiconv=1.15=h1df5818_7
+liblief=0.10.1=ha925a31_0
+libpng=1.6.37=h2a8f88b_0
+libprotobuf=3.19.1=h23ce68f_0
+libsodium=1.0.18=h62dcd97_0
+libspatialindex=1.9.3=h6c2663c_0
+libssh2=1.9.0=h7a1dbc1_1
+libtiff=4.2.0=hd0e1b90_0
+libuv=1.40.0=he774522_0
+libxml2=2.9.10=hb89e7f3_3
+libxslt=1.1.34=he774522_0
+libzopfli=1.0.3=ha925a31_0
+llvmlite=0.36.0=py38h34b8924_4
+locket=0.2.1=py38haa95532_1
+lxml=4.6.3=py38h9b66d53_0
+lz4-c=1.9.3=h2bbff1b_0
+lzo=2.10=he774522_2
+m2w64-gcc-libgfortran=5.3.0=6
+m2w64-gcc-libs=5.3.0=7
+m2w64-gcc-libs-core=5.3.0=7
+m2w64-gmp=6.1.0=2
+m2w64-libwinpthread-git=5.0.0.4634.697f757=2
+markdown=3.3.4=py38haa95532_0
+markupsafe=1.1.1=py38he774522_0
+matplotlib=3.3.4=py38haa95532_0
+matplotlib-base=3.3.4=py38h49ac443_0
+mccabe=0.6.1=py38_1
+menuinst=1.4.16=py38he774522_1
+mistune=0.8.4=py38he774522_1000
+mkl=2021.2.0=haa95532_296
+mkl-service=2.3.0=py38h2bbff1b_1
+mkl_fft=1.3.0=py38h277e83a_2
+mkl_random=1.2.1=py38hf11a4ad_2
+mock=4.0.3=pyhd3eb1b0_0
+more-itertools=8.7.0=pyhd3eb1b0_0
+mpmath=1.2.1=py38haa95532_0
+msgpack-python=1.0.2=py38h59b6b97_1
+msys2-conda-epoch=20160418=1
+multipledispatch=0.6.0=py38_0
+mypy_extensions=0.4.3=py38_0
+navigator-updater=0.2.1=py38_0
+nbclassic=0.2.6=pyhd3eb1b0_0
+nbclient=0.5.3=pyhd3eb1b0_0
+nbconvert=6.0.7=py38_0
+nbformat=5.1.3=pyhd3eb1b0_0
+nest-asyncio=1.5.1=pyhd3eb1b0_0
+networkx=2.5=py_0
+nltk=3.6.1=pyhd3eb1b0_0
+nose=1.3.7=pyhd3eb1b0_1006
+notebook=6.3.0=py38haa95532_0
+numba=0.53.1=py38hf11a4ad_0
+numexpr=2.7.3=py38hb80d3ca_1
+numpy=1.20.1=py38h34a8a5c_0
+numpy-base=1.20.1=py38haf7ebc8_0
+numpydoc=1.1.0=pyhd3eb1b0_1
+olefile=0.46=py_0
+openjpeg=2.3.0=h5ec785f_1
+openpyxl=3.0.7=pyhd3eb1b0_0
+openssl=1.1.1k=h2bbff1b_0
+packaging=20.9=pyhd3eb1b0_0
+pandas=1.2.4=py38hd77b12b_0
+pandoc=2.12=haa95532_0
+pandocfilters=1.4.3=py38haa95532_1
+paramiko=2.7.2=py_0
+parso=0.7.0=py_0
+partd=1.2.0=pyhd3eb1b0_0
+path=15.1.2=py38haa95532_0
+path.py=12.5.0=0
+pathlib2=2.3.5=py38haa95532_2
+pathspec=0.7.0=py_0
+patsy=0.5.1=py38_0
+pep8=1.7.1=py38_0
+pexpect=4.8.0=pyhd3eb1b0_3
+pickleshare=0.7.5=pyhd3eb1b0_1003
+pillow=8.2.0=py38h4fa10fc_0
+pip=21.0.1=py38haa95532_0
+pkginfo=1.7.0=py38haa95532_0
+pluggy=0.13.1=py38haa95532_0
+ply=3.11=py38_0
+powershell_shortcut=0.0.1=3
+prometheus_client=0.10.1=pyhd3eb1b0_0
+prompt-toolkit=3.0.17=pyh06a4308_0
+prompt_toolkit=3.0.17=hd3eb1b0_0
+protobuf=3.19.1=py38hd77b12b_0
+psutil=5.8.0=py38h2bbff1b_1
+ptyprocess=0.7.0=pyhd3eb1b0_2
+py=1.10.0=pyhd3eb1b0_0
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+pycodestyle=2.6.0=pyhd3eb1b0_0
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+pycparser=2.20=py_2
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+pydocstyle=6.0.0=pyhd3eb1b0_0
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+pyls-black=0.4.6=hd3eb1b0_0
+pyls-spyder=0.3.2=pyhd3eb1b0_0
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+python-language-server=0.36.2=pyhd3eb1b0_0
+python-libarchive-c=2.9=pyhd3eb1b0_1
+python_abi=3.8=2_cp38
+pytorch=1.10.2=py3.8_cuda10.2_cudnn7_0
+pytorch-model-summary=0.1.1=py_0
+pytorch-mutex=1.0=cuda
+pytz=2021.1=pyhd3eb1b0_0
+pywavelets=1.1.1=py38he774522_2
+pywin32=227=py38he774522_1
+pywin32-ctypes=0.2.0=py38_1000
+pywinpty=0.5.7=py38_0
+pyyaml=5.4.1=py38h2bbff1b_1
+pyzmq=20.0.0=py38hd77b12b_1
+qdarkstyle=2.8.1=py_0
+qt=5.9.7=vc14h73c81de_0
+qtawesome=1.0.2=pyhd3eb1b0_0
+qtconsole=5.0.3=pyhd3eb1b0_0
+qtpy=1.9.0=py_0
+regex=2021.4.4=py38h2bbff1b_0
+requests=2.25.1=pyhd3eb1b0_0
+rope=0.18.0=py_0
+rtree=0.9.7=py38h2eaa2aa_1
+ruamel_yaml=0.15.100=py38h2bbff1b_0
+scikit-image=0.18.1=py38hf11a4ad_0
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+singledispatch=3.6.1=pyhd3eb1b0_1001
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+snowballstemmer=2.1.0=pyhd3eb1b0_0
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+sortedcontainers=2.3.0=pyhd3eb1b0_0
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+sphinxcontrib-applehelp=1.0.2=pyhd3eb1b0_0
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+sphinxcontrib-htmlhelp=1.0.3=pyhd3eb1b0_0
+sphinxcontrib-jsmath=1.0.1=pyhd3eb1b0_0
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diff --git a/es_automata/train.py b/etc/es_automata/train.py
similarity index 100%
rename from es_automata/train.py
rename to etc/es_automata/train.py
diff --git a/es_automata/utils/__init__.py b/etc/es_automata/utils/__init__.py
similarity index 100%
rename from es_automata/utils/__init__.py
rename to etc/es_automata/utils/__init__.py
diff --git a/es_automata/utils/hyperparams.py b/etc/es_automata/utils/hyperparams.py
similarity index 100%
rename from es_automata/utils/hyperparams.py
rename to etc/es_automata/utils/hyperparams.py
diff --git a/es_automata/utils/imageutils.py b/etc/es_automata/utils/imageutils.py
similarity index 100%
rename from es_automata/utils/imageutils.py
rename to etc/es_automata/utils/imageutils.py
diff --git a/es_automata/utils/video.py b/etc/es_automata/utils/video.py
similarity index 100%
rename from es_automata/utils/video.py
rename to etc/es_automata/utils/video.py