Merge branch 'main' into feat/intermediate-plotting/epidemic-demo

.github/workflows/python-testing.yml

@@ -13,8 +13,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ['3.10', '3.11']
-        os: [ubuntu-latest, macOS-latest] #,windows-latest]
+        python-version: ['3.10'] #3.11
+        os: [ubuntu-latest] #, macOS-latest] #,windows-latest]
     steps:
     - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}
@@ -24,12 +24,12 @@ jobs:
     - name: Setup PDM
       uses: pdm-project/[email protected]
       with: 
-        version: 2.10.4
+        version: 2.15.4
         cache: true
 
     - name: Install dependencies
       run: |
-        pdm install -d -G tensorflow -G rebound
+        pdm install -G all
     - name: Analysing the code with pylint
       run: |
         pdm run pylint $(git ls-files '*.py')

CONTRIBUTING.md

@@ -39,7 +39,7 @@
        pyenv shell 3.10.14   #use the newly installed version
        ```
 
-   Check your installation by running `which python` which should output something along the lines of:
+   Check your installation by running `which python`. Output should be along the lines of:
    - for mise: `/Users/[user]/.local/share/mise/installs/python/3.10.14/bin/python`
 
    - for pyenv: `/Users/[user]/.pyenv/shims/python`

demos/systems/ca_demo.ipynb

@@ -0,0 +1,80 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dynadojo.systems.ca import CASystem\n",
+    "from dynadojo.wrappers import SystemChecker\n",
+    "from dynadojo.utils.ca import plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAF4CAYAAABO5xPOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAisklEQVR4nO3df2yV9fn/8deh0ANoe7BAW7q2riqTKYqKgI0/ptKAuBirzk3UiGg0mwcCMqNjAZnRpQ4zgzgmJougy1CHAZws4pAfZW6AWmWCGZ0ghhpoUTbOoXUU1t6fP/xyvlRLe049d9/3dc7zkZzEnnNz39d93vc55+X947pDnud5AgAAMKiP6wIAAAB6iiADAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALMIMgAAwCyCDAAAMIsgAwAAzCLIADDl9ttvV//+/fWvf/3ra689/vjjCoVCWr16tYPKALgQ4l5LACw5cOCARowYoQsuuEDr169PPL9nzx6de+65uvbaa/XKK684rBBAb2KPDABTCgsL9atf/UobNmzQ888/n3j+vvvuU79+/fTUU085rA5Ab2OPDABzPM/T5Zdfrvr6eu3cuVNr167V5MmTtXDhQk2fPt11eQB6EUEGgEkffvihLrzwQlVXV+uvf/2rSktLtXXrVvXpw45mIJsQZACY9fOf/1w1NTXKycnR22+/rYsuush1SQB6Gf/rAsCsIUOGSJJKSko0cuRIx9UAcIEgA8CkhoYGzZs3TyNHjlRDQ4Pmz5/vuiQADhBkAJg0bdo0SdLrr7+um2++Wb/85S/18ccfO64KQG8jyAAwZ+XKlfrTn/6kRx99VKWlpVqwYIFyc3MVjUZdlwagl3GyLwBTDh8+rHPOOUdDhw7VO++8o5ycHEnSwoULNWPGDP3xj3/UzTff7LhKAL2FIAPAlBkzZug3v/mNtmzZojFjxiSeb2tr09ixY9XY2KidO3cqLy/PYZUAeguHlgCYUVdXp0WLFum+++7rEGIkKScnR4sXL1ZjY6PmzJnjqEIAvY09MgAAwCz2yAAAALMIMgAAwCyCDAAAMIsgAwAAzCLIAAAAswgyAADArL6uC/Bbe3u79u3bp7y8PIVCIdflAACAJHiep8OHD6ukpER9+px8v0vGB5l9+/aprKzMdRkAAKAHGhoaVFpaetLXTQSZRYsW6YknnlBjY6NGjRqlp59+WmPHjk3q36bSpjwWiyU9bSQSSfs8LSw7WcnWKKW/TpfLtsLC9pssK2OYrZ9bC+vth2z9DkznsuPxuMrKyrr9HQ98Z9+XX35Zd9xxhxYvXqxx48ZpwYIFWr58uerr61VYWNjtv4/H40m/sam8FckepvLj7XW57GSlchgv3XW6XLYVFrbfZFkZw2z93FpYbz9k63dgOpd9/Pc7FospPz//pNMF/mTfJ598Uvfcc4+mTp2qc845R4sXL9bAgQP13HPPuS4NAAA4Fuggc/ToUdXV1amqqirxXJ8+fVRVVaXNmzd3+m9aW1sVj8c7PAAAQGYKdJD5/PPP1dbWpqKiog7PFxUVqbGxsdN/U1NTo0gkknhwoi8AAJkr0EGmJ2bPnq1YLJZ4NDQ0uC4JAAD4JNBXLQ0ZMkQ5OTlqamrq8HxTU5OKi4s7/TfhcFjhcLg3ygMAAI4Feo9Mbm6uRo8erXXr1iWea29v17p161RZWemwMgAAEASB3iMjSbNmzdKUKVN08cUXa+zYsVqwYIFaWlo0depU16UBAADHAh9kfvSjH+mzzz7Tww8/rMbGRl1wwQVas2bN104Ahl3Z2lfEpSD3jkh1nlZuPWJhu3TZS8uPcXT5nvvxXrpcdpC338A3xPumaIjnhssflwzfpAPJZZCxItO2S4JM+mRSkKEhHgAAQAoIMgAAwCyCDAAAMIsgAwAAzCLIAAAAswgyAADALIIMAAAwiyADAADMIsgAAACzAn+LAvQcnVG7Z6EDsR+dnC10h87WDsB+8GO8071tuGzpb6WrsMvPhMvvoe6wRwYAAJhFkAEAAGYRZAAAgFkEGQAAYBZBBgAAmEWQAQAAZhFkAACAWQQZAABgFkEGAACYRWffHkp318ZUOhy67AibLD/Wx49lp1umdSdNluttKFkWOqOmItn1cbnefizbQldsP1joYp3OcYzH44pEIt3Ohz0yAADALIIMAAAwiyADAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALMIMgAAwKys6ewbi8WUn5/f5TQuu9H60RnVD5nWKdNCx1M/JLs+Fsbbj2Vb6VScrctO9zi67phr5fs/qNgjAwAAzCLIAAAAswgyAADALIIMAAAwiyADAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALOy5hYFkUgkrfPLpDbrftyawWVL/0xrx54sP9qcZ1rrdAu3HrByywULLNxiw695Jivd36sutrVA75H5xS9+oVAo1OExYsQI12UBAICACPwemXPPPVdvvvlm4u++fQNfMgAA6CWBTwV9+/ZVcXGx6zIAAEAABfrQkiR99NFHKikp0RlnnKHbbrtNe/fu7XL61tZWxePxDg8AAJCZAh1kxo0bp6VLl2rNmjV65plntGfPHl1++eU6fPjwSf9NTU2NIpFI4lFWVtaLFQMAgN4U8gydzn7o0CGdfvrpevLJJ3X33Xd3Ok1ra6taW1sTf8fjcRNhhquW3CzbwpVQybJytYvLq3z8wFVLweXyqiXXV0IlK8hXLcXjcUUiEcViMeXn5590usCfI3OiQYMG6Tvf+Y527dp10mnC4bDC4XAvVgUAAFwJ9KGlr2pubtbu3bs1bNgw16UAAIAACHSQeeCBB1RbW6tPPvlEf//733XDDTcoJydHkydPdl0aAAAIgEAfWvr00081efJkHTx4UEOHDtVll12mLVu2aOjQoa5LM3FuRaZ1oPSDhfWxcH6Dy/OSXI+hy/FJ97L9GEc/lp2sTDuvzo9lZ8JvlKmTfXvi+MlC6ebyBCnXX9yuWAiPfnB5wm2m/RD4IZO+QgkybuZpYdnJ8uO3rLuTfQN9aAkAAKArBBkAAGAWQQYAAJhFkAEAAGYRZAAAgFkEGQAAYBZBBgAAmEWQAQAAZgW6s2+QWWgoZoWVxmfp5rKpYrq3oUzqbpvNLDRfTGWeyXK9DVlosueH7padbENb9sgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALMIMgAAwCyCDAAAMIsgAwAAzCLIAAAAswgyAADALG5RcAKXLbL94LI1eJDbXp8o01qdJ8vlOKb7PXd9ew8L76WFFviuxzFZLr/7XY5jkH/z2CMDAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALMIMgAAwCyCDAAAMIsgAwAAzKKzr88sdNd13bEx3V06Xa6PH91J/ehiaqHzssvPTiosdM11NT/JbZdZK2OTSR3TXXz/skcGAACYRZABAABmEWQAAIBZBBkAAGAWQQYAAJhFkAEAAGYRZAAAgFkEGQAAYBZBBgAAmJU1nX1jsZjy8/O7nMaPrqyuO0a6mJ9k571MN9fvZbr5MTbp7mhsYbuQ3K6PlfcoWRa6Q1v43LrWXZ3xeFyRSKTb+TjdI7Np0yZdd911KikpUSgU0qpVqzq87nmeHn74YQ0bNkwDBgxQVVWVPvroIzfFAgCAwHEaZFpaWjRq1CgtWrSo09fnz5+vhQsXavHixdq6datOOeUUTZw4UUeOHOnlSgEAQBA5PbQ0adIkTZo0qdPXPM/TggULNGfOHF1//fWSpBdeeEFFRUVatWqVbrnllt4sFQAABFBgT/bds2ePGhsbVVVVlXguEolo3Lhx2rx5s8PKAABAUAT2ZN/GxkZJUlFRUYfni4qKEq91prW1Va2trYm/4/G4PwUCAADnArtHpqdqamoUiUQSj7KyMtclAQAAnwQ2yBQXF0uSmpqaOjzf1NSUeK0zs2fPViwWSzwaGhp8rRMAALgT2CBTUVGh4uJirVu3LvFcPB7X1q1bVVlZedJ/Fw6HlZ+f3+EBAAAyk9NzZJqbm7Vr167E33v27NG2bdtUUFCg8vJyzZw5U4899piGDx+uiooKzZ07VyUlJaqurnZXNAAACAynQebdd9/VVVddlfh71qxZkqQpU6Zo6dKlevDBB9XS0qJ7771Xhw4d0mWXXaY1a9aof//+rkoGAAABEvKs9DLuoeMtjl3doiBZLm9l4Pp2Ai43wXS/7360Os80FsbbQo2Sje+hZPnx2bHyfWXhe8jFZyLZ3+/AniMDAADQHYIMAAAwiyADAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALMIMgAAwCyntygIGpcdG/3oxOhHl06X83TZbdVKh1BXUllvl11MM+k9T4WFjr0uPzuuu/X68R65WraL7ubskQEAAGYRZAAAgFkEGQAAYBZBBgAAmEWQAQAAZhFkAACAWQQZAABgFkEGAACYRZABAABm0dm3h1x2o7XQndRlV0uXnV5ddLXsybLTzY/1trCdS5m1Pi6331S4fM8zabxd6+49isfjikQi3c6HPTIAAMAsggwAADCLIAMAAMwiyAAAALMIMgAAwCyCDAAAMIsgAwAAzOpRkNm9e7fmzJmjyZMn68CBA5Kk119/XR9++GFaiwMAAOhKykGmtrZW5513nrZu3aoVK1aoublZkvSPf/xD8+bNS3uBAAAAJ5NykPnZz36mxx57TGvXrlVubm7i+auvvlpbtmxJa3EAAABdSfkWBdu3b9eyZcu+9nxhYaE+//zztBTlh2TaHPvBQjtrP1qNu2xZ77J1ukuZtt4ut0uXt9hIhYXPjoVx9OO2JhbW249lpyJddaa8R2bQoEHav3//155///339a1vfSstRQEAACQj5SBzyy236KGHHlJjY6NCoZDa29v1t7/9TQ888IDuuOMOP2oEAADoVMhLcX/R0aNHFY1GtXTpUrW1talv375qa2vTrbfeqqVLlyonJ8evWnsk2btnusahpfTNM9MOsSB9Mu3QUrIs1JgKl4eWksWhpe4lW2csFlN+fv7J55NqkDlu79692rFjh5qbm3XhhRdq+PDhPZmN7wgy3XO90RNk0FsIMl2z8tkhyAR32alIV5BJ+WTf48rLy1VeXt7Tfw4AAPCNpRxkPM/TK6+8og0bNujAgQNqb2/v8PqKFSvSVhwAAEBXUg4yM2fO1LPPPqurrrpKRUVFZnZFAgCAzJNykPn973+vFStW6Nprr/WjHgAAgKSlfPl1JBLRGWec4UctAAAAKUn5qqXnn39ea9as0XPPPacBAwb4VVfaWLlqKRUWzthPRboPT2baFWCZxsL4+HHVnR8svJfJcnlVjOvxdv0dnAwXvyfHf7+7u2op5T0yP/zhD/Wf//xHhYWFOu+883TRRRd1eKRi06ZNuu6661RSUqJQKKRVq1Z1eP3OO+9UKBTq8LjmmmtSLRkAAGSolM+RmTJliurq6nT77bd/45N9W1paNGrUKN1111268cYbO53mmmuu0ZIlSxJ/h8PhHi8PAABklpSDzJ///Ge98cYbuuyyy77xwidNmqRJkyZ1OU04HFZxcfE3XhYAAMg8KR9aKisr6/JYVbpt3LhRhYWFOvvss/WTn/xEBw8e7HL61tZWxePxDg8AAJCZUg4yv/71r/Xggw/qk08+8aGcjq655hq98MILWrdunX71q1+ptrZWkyZNUltb20n/TU1NjSKRSOJRVlbme50AAMCNlK9aOu200/TFF1/of//7nwYOHKh+/fp1eP3f//53zwoJhbRy5UpVV1efdJqPP/5YZ555pt58802NHz++02laW1vV2tqa+Dsej2dcmOGqpa5l0pUcmcjC+Li+iiVZFt7LZHHVUrAF+aqllM+RWbBgwTep6xs544wzNGTIEO3ateukQSYcDnNCMAAAWaJHVy258umnn+rgwYMaNmyYsxoAAEBwJBVk4vF4YrdOdyfPpnIicHNzs3bt2pX4e8+ePdq2bZsKCgpUUFCgRx55RDfddJOKi4u1e/duPfjggzrrrLM0ceLEpJcBAAAyV1LnyOTk5Gj//v0qLCxUnz59Oj1W5nmeQqFQlyfiftXGjRt11VVXfe35KVOm6JlnnlF1dbXef/99HTp0SCUlJZowYYIeffRRFRUVJb2MZI+xSRzrTka6z89xzeX6uDzXKd3zzLTxToWVc9GSYWE7T2WeyXL9HWjhe8iFtJ4js379ehUUFEiSlixZorKyMuXk5HSYpr29XXv37k2pyCuvvLLLN/GNN95IaX4AACC7pHzV0ol7Z0508OBBFRYWprRHpjewR6Z7Vv7PwQ8W/k+IPTLpwx6ZrlnYzlOZZ7Jcfwda+B5ywbd7LR0/hPRVzc3N6t+/f6qzAwAA6LGkr1qaNWuWpC8T4dy5czVw4MDEa21tbdq6dasuuOCCtBcIAABwMkkHmffff1/Sl3tktm/frtzc3MRrubm5GjVqlB544IH0VwgAAHASSQeZDRs2SJKmTp2qp556qlfvtwQAANCZlBviLVmyxI86AAAAUpbyyb4AAABBQZABAABmEWQAAIBZKZ8jk8lc3so905rxuXwvXa5PKuuSSdtQKmiy1zUrTc9cNnTMNNnaoDJd2CMDAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALMIMgAAwCyCDAAAMIsgAwAAzMqazr6RSKTbabK1s2QqXSAtdJbMtPXxg4X1dlljtnbXTYWFz4TL9fZjvDNt2ekaH/bIAAAAswgyAADALIIMAAAwiyADAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALOyprNvLBZTfn5+2uaXSV2AXXcxTffyM2lsUuHHe+5H59h0L9tlx1zJThfrZGTaevux/brspmxh2S6wRwYAAJhFkAEAAGYRZAAAgFkEGQAAYBZBBgAAmEWQAQAAZhFkAACAWQQZAABgFkEGAACYRZABAABmZc0tClxJdyttP/ixbCvtuS2878muj8uW8S5b27tc70zjslW/H+NoZZ7p5sfn0Y/fsu6mjcfjikQi3c7H6R6ZmpoajRkzRnl5eSosLFR1dbXq6+s7THPkyBFFo1ENHjxYp556qm666SY1NTU5qhgAAASJ0yBTW1uraDSqLVu2aO3atTp27JgmTJiglpaWxDT333+/XnvtNS1fvly1tbXat2+fbrzxRodVAwCAoAh5Abql5WeffabCwkLV1tbqiiuuUCwW09ChQ7Vs2TL94Ac/kCTt3LlT3/3ud7V582Zdcskl3c7z+K6pdN/9OlkWDi35IdMOLVlZn3RzedjRwvuTCtYH35SF0wDSWWOyv9+BOtk3FotJkgoKCiRJdXV1OnbsmKqqqhLTjBgxQuXl5dq8eXOn82htbVU8Hu/wAAAAmSkwQaa9vV0zZ87UpZdeqpEjR0qSGhsblZubq0GDBnWYtqioSI2NjZ3Op6amRpFIJPEoKyvzu3QAAOBIYIJMNBrVjh079NJLL32j+cyePVuxWCzxaGhoSFOFAAAgaAJx+fW0adO0evVqbdq0SaWlpYnni4uLdfToUR06dKjDXpmmpiYVFxd3Oq9wOKxwOOx3yQAAIACc7pHxPE/Tpk3TypUrtX79elVUVHR4ffTo0erXr5/WrVuXeK6+vl579+5VZWVlb5cLAAACxukemWg0qmXLlunVV19VXl5e4ryXSCSiAQMGKBKJ6O6779asWbNUUFCg/Px8TZ8+XZWVlUldsQQAADKb08uvT3bZ15IlS3TnnXdK+rIh3k9/+lO9+OKLam1t1cSJE/Xb3/72pIeWvsqvy68z7ZLYdLPSXTdZflxG7/LSfJadPi633yBfOntcpo23H9K97pny+5Ts73eg+sj4gSDjBkHGzTxZdnqWnQqCTNcybbz9QJDpnMk+MgAAAKkgyAAAALMIMgAAwCyCDAAAMIsgAwAAzCLIAAAAswgyAADALIIMAAAwKxA3jewNkUgkrfNLtumPhUZqrptGuWzq5bJZV7r50YTQQgM51+vt+vOTDD/W28I4upqf5M92we9J59gjAwAAzCLIAAAAswgyAADALIIMAAAwiyADAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALOy5hYFQeeyzbkf7az94EcbcQu3HvADt4VI37ItfCYy6TYKrudpgYXxTif2yAAAALMIMgAAwCyCDAAAMIsgAwAAzCLIAAAAswgyAADALIIMAAAwiyADAADMIsgAAACz6Ox7gkzrJOpHd0c/Oola6L5poRttKtI9ji7HO9Pe81Ske7tMZRxdfiZcrreV7S1ZLj+P6fqNYo8MAAAwiyADAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALMIMgAAwCyCDAAAMCtrOvvGYjHl5+d3OY3L7o5WuvD6IVs7hCbL5TytbEMuufwucDk+6V52Ku+jy+66Fj6Pfiw7yJzukampqdGYMWOUl5enwsJCVVdXq76+vsM0V155pUKhUIfHj3/8Y0cVAwCAIHEaZGpraxWNRrVlyxatXbtWx44d04QJE9TS0tJhunvuuUf79+9PPObPn++oYgAAECRODy2tWbOmw99Lly5VYWGh6urqdMUVVySeHzhwoIqLi3u7PAAAEHCBOtk3FotJkgoKCjo8/4c//EFDhgzRyJEjNXv2bH3xxRcuygMAAAETmJN929vbNXPmTF166aUaOXJk4vlbb71Vp59+ukpKSvTBBx/ooYceUn19vVasWNHpfFpbW9Xa2pr4Ox6P+147AABwIzBBJhqNaseOHXrrrbc6PH/vvfcm/vu8887TsGHDNH78eO3evVtnnnnm1+ZTU1OjRx55xPd6AQCAe4E4tDRt2jStXr1aGzZsUGlpaZfTjhs3TpK0a9euTl+fPXu2YrFY4tHQ0JD2egEAQDA43SPjeZ6mT5+ulStXauPGjaqoqOj232zbtk2SNGzYsE5fD4fDCofD6SwTAAAElNMgE41GtWzZMr366qvKy8tTY2OjJCkSiWjAgAHavXu3li1bpmuvvVaDBw/WBx98oPvvv19XXHGFzj//fJelAwCAAAh5Dlsknqyj4JIlS3TnnXeqoaFBt99+u3bs2KGWlhaVlZXphhtu0Jw5c7rt0ntcPB5XJBJx1tnXZddEP2q00Ek0FXS47ZrLbShbZdrn0WXX2mRZ+dxma2ff7n6/nQaZ3nA8yCQjlbci3YPvctmpsBCO/HgvM+0LJJPa6rsebwvbUKZtvxZk2jaULD/Wu7sgE4iTfQEAAHqCIAMAAMwiyAAAALMIMgAAwCyCDAAAMIsgAwAAzCLIAAAAswgyAADALIIMAAAwy+m9lvD/WemS6UedmdTh1o9lW2jxTnv59LHSJdnC7RGyVba95+yRAQAAZhFkAACAWQQZAABgFkEGAACYRZABAABmEWQAAIBZBBkAAGAWQQYAAJhFkAEAAGZlTWffWCym/Pz8Lqfxo6OmHzKtk6nLDqEupXt9rGy/6ebHutDBunvp7hZs5fPtskuySy66BcfjcUUikW7nwx4ZAABgFkEGAACYRZABAABmEWQAAIBZBBkAAGAWQQYAAJhFkAEAAGYRZAAAgFkEGQAAYBZBBgAAmJU1tyhINyvttF3JtFs4+NFmPd3zTOU9d9lePtl5WmjbLrltwW/htgd+bL8uWbklRroF+VYT7JEBAABmEWQAAIBZBBkAAGAWQQYAAJhFkAEAAGYRZAAAgFkEGQAAYBZBBgAAmJXxDfGON/GJx+OOK8kumfZ++7E+6Z6nhRr9mqdLmbY+ybKw/SJ9XHwXHH+9u2Z8Ic9KO8Ue+vTTT1VWVua6DAAA0AMNDQ0qLS096esZH2Ta29u1b98+5eXlJVonx+NxlZWVqaGhQfn5+Y4rxIkYm2BjfIKLsQkuxqZnPM/T4cOHVVJSoj59Tn4mTMYfWurTp89Jk1x+fj4bVUAxNsHG+AQXYxNcjE3qIpFIt9Nwsi8AADCLIAMAAMzKyiATDoc1b948hcNh16XgKxibYGN8gouxCS7Gxl8Zf7IvAADIXFm5RwYAAGQGggwAADCLIAMAAMwiyAAAALOyMsgsWrRI3/72t9W/f3+NGzdOb7/9tuuSss6mTZt03XXXqaSkRKFQSKtWrerwuud5evjhhzVs2DANGDBAVVVV+uijj9wUm2Vqamo0ZswY5eXlqbCwUNXV1aqvr+8wzZEjRxSNRjV48GCdeuqpuummm9TU1OSo4uzxzDPP6Pzzz080VqusrNTrr7+eeJ1xCY7HH39coVBIM2fOTDzH+Pgj64LMyy+/rFmzZmnevHl67733NGrUKE2cOFEHDhxwXVpWaWlp0ahRo7Ro0aJOX58/f74WLlyoxYsXa+vWrTrllFM0ceJEHTlypJcrzT61tbWKRqPasmWL1q5dq2PHjmnChAlqaWlJTHP//ffrtdde0/Lly1VbW6t9+/bpxhtvdFh1digtLdXjjz+uuro6vfvuu7r66qt1/fXX68MPP5TEuATFO++8o2effVbnn39+h+cZH594WWbs2LFeNBpN/N3W1uaVlJR4NTU1DqvKbpK8lStXJv5ub2/3iouLvSeeeCLx3KFDh7xwOOy9+OKLDirMbgcOHPAkebW1tZ7nfTkW/fr185YvX56Y5p///Kcnydu8ebOrMrPWaaed5v3ud79jXALi8OHD3vDhw721a9d63/ve97wZM2Z4nsfnxk9ZtUfm6NGjqqurU1VVVeK5Pn36qKqqSps3b3ZYGU60Z88eNTY2dhinSCSicePGMU4OxGIxSVJBQYEkqa6uTseOHeswPiNGjFB5eTnj04va2tr00ksvqaWlRZWVlYxLQESjUX3/+9/vMA4Snxs/ZfxNI0/0+eefq62tTUVFRR2eLyoq0s6dOx1Vha9qbGyUpE7H6fhr6B3t7e2aOXOmLr30Uo0cOVLSl+OTm5urQYMGdZiW8ekd27dvV2VlpY4cOaJTTz1VK1eu1DnnnKNt27YxLo699NJLeu+99/TOO+987TU+N/7JqiADIDXRaFQ7duzQW2+95boU/D9nn322tm3bplgspldeeUVTpkxRbW2t67KyXkNDg2bMmKG1a9eqf//+rsvJKll1aGnIkCHKycn52lniTU1NKi4udlQVvur4WDBObk2bNk2rV6/Whg0bVFpamni+uLhYR48e1aFDhzpMz/j0jtzcXJ111lkaPXq0ampqNGrUKD311FOMi2N1dXU6cOCALrroIvXt21d9+/ZVbW2tFi5cqL59+6qoqIjx8UlWBZnc3FyNHj1a69atSzzX3t6udevWqbKy0mFlOFFFRYWKi4s7jFM8HtfWrVsZp17geZ6mTZumlStXav369aqoqOjw+ujRo9WvX78O41NfX6+9e/cyPg60t7ertbWVcXFs/Pjx2r59u7Zt25Z4XHzxxbrtttsS/834+CPrDi3NmjVLU6ZM0cUXX6yxY8dqwYIFamlp0dSpU12XllWam5u1a9euxN979uzRtm3bVFBQoPLycs2cOVOPPfaYhg8froqKCs2dO1clJSWqrq52V3SWiEajWrZsmV599VXl5eUljt9HIhENGDBAkUhEd999t2bNmqWCggLl5+dr+vTpqqys1CWXXOK4+sw2e/ZsTZo0SeXl5Tp8+LCWLVumjRs36o033mBcHMvLy0ucR3bcKaecosGDByeeZ3x84vqyKReefvppr7y83MvNzfXGjh3rbdmyxXVJWWfDhg2epK89pkyZ4nnel5dgz5071ysqKvLC4bA3fvx4r76+3m3RWaKzcZHkLVmyJDHNf//7X+++++7zTjvtNG/gwIHeDTfc4O3fv99d0Vnirrvu8k4//XQvNzfXGzp0qDd+/HjvL3/5S+J1xiVYTrz82vMYH7+EPM/zHGUoAACAbySrzpEBAACZhSADAADMIsgAAACzCDIAAMAsggwAADCLIAMAAMwiyAAAALMIMgAAwCyCDAAAMIsgAwAAzCLIAAAAswgyAADArP8DXrVubOdPzUEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latent_dim = 4\n",
+    "embed_dim = 48\n",
+    "timesteps = 30\n",
+    "n = 1\n",
+    "system = SystemChecker(CASystem(latent_dim, embed_dim, seed=1))\n",
+    "x0 = system.make_init_conds(n=n)\n",
+    "x = system.make_data(x0, timesteps=timesteps)\n",
+    "plot([x], labels=[\"X\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}