gif_nb.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "from scatter_letters import sl"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# simple use\n",
    "sl.text_to_gif('data_')"
   ],
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     "text": [
      "DONE\n"
     ]
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   ],
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  {
   "cell_type": "markdown",
   "source": [
    "![](output/movie.gif)"
   ],
   "metadata": {}
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   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "# all parameters\n",
    "sl.text_to_gif('a', # text to be converted to gif\n",
    "               out_path='output', # relative path to save temp files and output\n",
    "               repeat=True, # repeat first letter at the end\n",
    "               intensity=10, # more info below*\n",
    "               rand=True, # True=random points, false= evenly sparced\n",
    "               gif_name='movie', # name of the output file. -> movie.gif\n",
    "               n_frames=24, # number of frames in the transition\n",
    "               bg_color='#95A4AD', # background color\n",
    "               marker='o', # marker style\n",
    "               marker_color='#283F4E', # marker color \n",
    "               marker_size=10, # marker size\n",
    "               fps=24, # frames per second\n",
    "               alpha=1, # markers opacity\n",
    "               axis_on=True, # plot spines and grid\n",
    "               sort_coords=False, # sort points in the transition - options(False, 'x', 'y')\n",
    "               sort_coords_asc=True, # True - sort ascending / False - sort descending\n",
    "               in_path='./images/letters', # for custom input paths\n",
    "               hold_frames=5) # hold the complete letter for x frames\n",
    "\n",
    "# *intensity: \n",
    "# When plotting random points (rand=True), this is how many times it'll generate 500 points at the start (before applying the mask).\n",
    "# With randoms a higher intensity means more points.\n",
    "# When plotting evenly sparced points (rand=False), this is the distance between the points.\n",
    "# With even points a lower intensity means the points will be closer to each other, so more points are plotted."
   ],
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      "DONE\n"
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   ],
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  {
   "cell_type": "markdown",
   "source": [
    "![](output/movie.gif)"
   ],
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   "source": [
    "# special masks\n",
    "sl.text_to_gif('MAC[MAC]', \n",
    "               repeat=True, \n",
    "               intensity=40, \n",
    "               rand=True, \n",
    "               gif_name='movie2', \n",
    "               n_frames=32, \n",
    "               bg_color='#ffb400',\n",
    "               marker='o', \n",
    "               marker_color='#2b2300', \n",
    "               marker_size=3, \n",
    "               fps=32,\n",
    "               alpha=0.7, \n",
    "               axis_on=False, \n",
    "               sort_coords=False, \n",
    "               sort_coords_asc=True,\n",
    "               hold_frames=20)"
   ],
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    "sl.text_to_gif('RICK[RICK][RICK]', \n",
    "                repeat=True, \n",
    "                intensity=70,\n",
    "                gif_name = 'movie3', \n",
    "                n_frames=24, \n",
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    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "DONE\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "![](output/not_rand.gif)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# not randomly positioned and sorting points for transition\n",
    "sl.text_to_gif('abc', intensity=5, rand=False, gif_name='sorted', sort_coords='y', sort_coords_asc=True)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
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      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n"
     ]
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    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n"
     ]
    },
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     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n"
     ]
    },
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     },
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     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "DONE\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "![](output/sorted.gif)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "# The program works with three methods\n",
    "dir_name = \"/images/letters/\"\n",
    "# 1- get masked data: \n",
    "# generates the random points and apply a mask to it\n",
    "# if you don't pass a in_path the program uses a default dataframe with previously generated masks\n",
    "# those include all letters from A to Z and two special images 'RICK' and 'MAC'\n",
    "# If a in_path is passed the program will look in that directory for a.png file of 1000x1000 named as the first argument\n",
    "xy = sl.get_masked_data('a', in_path=dir_name)\n",
    "\n",
    "# creates two lists, x and y.\n",
    "print('lists: ', len(xy))\n",
    "print('size:', len(xy[0]))\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(xy[0], xy[1])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-0ed8e9dacb72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# those include all letters from A to Z and two special images 'RICK' and 'MAC'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# If a in_path is passed the program will look in that directory for a.png file of 1000x1000 named as the first argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mxy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_masked_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0min_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdir_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# creates two lists, x and y.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/scatter_letters/sl.py\u001b[0m in \u001b[0;36mget_masked_data\u001b[0;34m(letter, intensity, rand, in_path)\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m             \u001b[0mresult_x\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m             \u001b[0mresult_y\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# 2- text to data\n",
    "# receives string and split the letters to apply the previous method\n",
    "# to use special masks here you need to put the name of the mask or file between brackets - example '[RICK]'\n",
    "coords = sl.text_to_data('abc', repeat=True, intensity = 10, rand=True, in_path=None)\n",
    "\n",
    "# creates two lists, x and y.\n",
    "print(len(coords), 'lists, one for each letter and one for the repeated letter')\n",
    "print(len(coords[0]), 'lists inside each of the above for x and y')\n",
    "print('size:', len(coords[0][0]))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f29ecefc28c647b18580663461ddc9c6",
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       "version_minor": 0
      },
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       "HBox(children=(HTML(value='Coordinates'), FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "4 lists, one for each letter and one for the repeated letter\n",
      "2 lists inside each of the above for x and y\n",
      "size: 1082\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "# 3- build gif\n",
    "# Uses the coordinates lists generate in the previous method to plot the images and transitions, and create the gif\n",
    "sl.build_gif(coords, \n",
    "          out_path='output', gif_name = 'movie', n_frames=10, \n",
    "          bg_color='#95A4AD', marker='.', marker_color='#283F4E',\n",
    "          marker_size = 25, fps=4, alpha=1, axis_on=True,\n",
    "          sort_coords = False, sort_coords_asc=False, hold_frames=5)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac1aec27fb204e78b2b698bc9822b7c2",
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       "HBox(children=(HTML(value='Plotting'), FloatProgress(value=0.0, max=3.0), HTML(value='')))"
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    },
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     "output_type": "stream",
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      "\n"
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    },
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     },
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    },
    {
     "output_type": "stream",
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     "text": [
      "\n"
     ]
    },
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       "HBox(children=(HTML(value='Removing Imgs'), FloatProgress(value=0.0, max=33.0), HTML(value='')))"
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     "name": "stdout",
     "text": [
      "\n",
      "DONE\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "letter = 'a'\n",
    "dir_name = \"/images/letters/\"\n",
    "title = letter.upper() + '.png'\n",
    "file_img = dir_name +  title\n",
    "#mask = cv2.imread(os.path.join(in_path, f'{letter.upper()}.png',0))\n",
    "mask = cv2.imread(file_img)\n",
    "mask = cv2.flip(mask, 0)\n",
    "plt.imshow(mask)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8c3c808be0>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import imageio\n",
    "import random\n",
    "import cv2\n",
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "import math\n",
    "from collections import OrderedDict\n",
    "import gc\n",
    "\n",
    "def get_masked_data(letter, intensity, rand, in_path):\n",
    "    \"\"\"\n",
    "    Receives a 'letter', which is the name of a file at /images/letters\n",
    "    Convert the letter into a list of x coordinates \n",
    "    and a list of y coordinates to create the shape of that letter.\n",
    "    Don't require the mask to be a letter, it can also be used \n",
    "    with other 1000x1000 png images. \n",
    "    \n",
    "    letter:     A string with the name of the png file that contains a mask\n",
    "    intensity:  This needs some work. When randonly positioning the points\n",
    "                the intensity is how many times 500 points will be generated.\n",
    "                With evenly sparced points, it controls the distance between the\n",
    "                points.\n",
    "    rand: If true generates random points, if false generate evenly sparced points\n",
    "    \"\"\"\n",
    "    # get mask from image\n",
    "    if in_path:\n",
    "        file_img = letter.upper() + '.png'\n",
    "        mask = cv2.imread(os.path.join(in_path, file_img),0)\n",
    "        #mask = cv2.imread(file_img)\n",
    "        mask = cv2.flip(mask, 0)\n",
    "        #plt.imshow(mask)\n",
    "    else:\n",
    "        dir_path = os.path.dirname(os.path.realpath(__file__))\n",
    "        mask = pd.read_pickle(os.path.join(dir_path,'masks.pkl'))\n",
    "        mask = mask[mask['letter'] == letter.upper()]['mask'].values[0]\n",
    "    # fill a plot with random points\n",
    "    if rand:\n",
    "        random.seed(420)\n",
    "        x = []\n",
    "        y = []\n",
    "        \n",
    "        for i in range(intensity):\n",
    "            x = x + random.sample(range(0, 1000), 500)\n",
    "            y = y + random.sample(range(0, 1000), 500)\n",
    "\n",
    "    # fill a plot with evenly sparced points\n",
    "    else:\n",
    "        base = np.arange(0, 1000, intensity, dtype=int).tolist()\n",
    "        y = []\n",
    "        x = []\n",
    "\n",
    "        for i in base:\n",
    "            x_temp = [i]*len(base)\n",
    "            y_temp = base\n",
    "            x = x + x_temp\n",
    "            y = y + y_temp\n",
    "\n",
    "    # get only the coordinates inside the mask\n",
    "    result_x = []\n",
    "    result_y = []\n",
    "\n",
    "    for i in range(len(x)):\n",
    "        if mask[y[i]][x[i]] == 0:\n",
    "            result_x.append(x[i])\n",
    "            result_y.append(y[i])\n",
    "            \n",
    "    # return a list of x and y positions\n",
    "    return result_x, result_y\n",
    "\n",
    "# The program works with three methods\n",
    "img_str = 'logo'\n",
    "#pathin = 'images'\n",
    "#img_path = str(pathin)\n",
    "# 1- get masked data: \n",
    "# generates the random points and apply a mask to it\n",
    "# if you don't pass a in_path the program uses a default dataframe with previously generated masks\n",
    "# those include all letters from A to Z and two special images 'RICK' and 'MAC'\n",
    "# If a in_path is passed the program will look in that directory for a .png file of 1000x1000 named as the first argument\n",
    "xy = get_masked_data(img_str,\n",
    "                    intensity=20,\n",
    "                    rand=True,\n",
    "                    in_path='./images/letters')\n",
    "\n",
    "# creates two lists, x and y.\n",
    "print('lists: ', len(xy))\n",
    "print('size:', len(xy[0]))\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(xy[0], xy[1])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "lists:  2\n",
      "size: 1262\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "# The program works with three methods\n",
    "img_str = 'logo'\n",
    "#pathin = 'images'\n",
    "#img_path = str(pathin)\n",
    "# 1- get masked data: \n",
    "# generates the random points and apply a mask to it\n",
    "# if you don't pass a in_path the program uses a default dataframe with previously generated masks\n",
    "# those include all letters from A to Z and two special images 'RICK' and 'MAC'\n",
    "# If a in_path is passed the program will look in that directory for a .png file of 1000x1000 named as the first argument\n",
    "xy = sl.get_masked_data(img_str, \n",
    "                        intensity=20, \n",
    "                        rand=True,\n",
    "                        in_path='./images/letters')\n",
    "\n",
    "# creates two lists, x and y.\n",
    "print('lists: ', len(xy))\n",
    "print('size:', len(xy[0]))\n",
    "plt.scatter(xy[0], xy[1])\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "lists:  2\n",
      "size: 1262\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "# all parameters\n",
    "sl.text_to_gif('_[LOGO]', # text to be converted to gif\n",
    "               out_path='output', # relative path to save temp files and output\n",
    "               repeat=True, # repeat first letter at the end\n",
    "               intensity=10, # more info below*\n",
    "               rand=True, # True=random points, false= evenly sparced\n",
    "               gif_name='movie', # name of the output file. -> movie.gif\n",
    "               n_frames=24, # number of frames in the transition\n",
    "               bg_color='#95A4AD', # background color\n",
    "               marker='o', # marker style\n",
    "               marker_color='#283F4E', # marker color \n",
    "               marker_size=10, # marker size\n",
    "               fps=24, # frames per second\n",
    "               alpha=1, # markers opacity\n",
    "               axis_on=True, # plot spines and grid\n",
    "               sort_coords=False, # sort points in the transition - options(False, 'x', 'y')\n",
    "               sort_coords_asc=True, # True - sort ascending / False - sort descending\n",
    "               in_path='./images/letters', # for custom input paths\n",
    "               hold_frames=5) # hold the complete letter for x frames\n",
    "\n",
    "# *intensity: \n",
    "# When plotting random points (rand=True), this is how many times it'll generate 500 points at the start (before applying the mask).\n",
    "# With randoms a higher intensity means more points.\n",
    "# When plotting evenly sparced points (rand=False), this is the distance between the points.\n",
    "# With even points a lower intensity means the points will be closer to each other, so more points are plotted."
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "5df1302c09264666b1991af85efb6479"
      },
      "text/plain": [
       "Coordinates:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "73b622e1b1ef42278c00a336a38b99e5"
      },
      "text/plain": [
       "Plotting:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7080ce6a44bc4fd1aae4f88258ae4914"
      },
      "text/plain": [
       "Frames:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9ae033d7395a4d24a20efbd7b166b852"
      },
      "text/plain": [
       "Frames:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "583cb9b43335445ca925f13c25912475"
      },
      "text/plain": [
       "Gif:   0%|          | 0/65 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0f960b591fdd43e785b261dcce1836ed"
      },
      "text/plain": [
       "Removing Imgs:   0%|          | 0/50 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "DONE\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "source": [
    "sl.text_to_gif('_[LOGO][MARCIO][PUC]', \n",
    "                out_path='output', # relative path to save temp files and output\n",
    "                repeat=True, \n",
    "                intensity=90,\n",
    "                rand=True, # True=random points, false= evenly sparced\n",
    "                gif_name = 'movie1', \n",
    "                n_frames=24, \n",
    "                bg_color='#53abee', \n",
    "                marker_color='#1D1D1D', \n",
    "                marker_size = 2,\n",
    "                marker='o', # marker style\n",
    "                fps=24,\n",
    "                alpha=0.7,\n",
    "                axis_on=True, # plot spines and grid\n",
    "                sort_coords=False, # sort points in the transition - options(False, 'x', 'y')\n",
    "                sort_coords_asc=True, # True - sort ascending / False - sort descending\n",
    "                in_path='./images/letters', # for custom input paths\n",
    "                hold_frames=40)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c7c5971e55ec4dc5b6a356ece8e33322"
      },
      "text/plain": [
       "Coordinates:   0%|          | 0/20 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f6a8036a926b45e1a806182f55d797c4"
      },
      "text/plain": [
       "Plotting:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "adca29bec04c4fac81923636da3c159d"
      },
      "text/plain": [
       "Frames:   0%|          | 0/25 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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    "sl.text_to_gif('RICK[RICK][RICK]', \n",
    "                out_path='output', # relative path to save temp files and output\n",
    "                repeat=True, \n",
    "                intensity=70,\n",
    "                gif_name = 'movie3', \n",
    "                n_frames=24, \n",
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    "                fps=24,\n",
    "                alpha=0.3,\n",
    "                in_path='./images/letters', # for custom input paths\n",
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