├── Bak–Tang–Wiesenfeld_model.ipynb
├── DLA_numpy_diffusionCA.ipynb
├── Dielectric_Breakdown.ipynb
├── Feigenbaum-Cvitanović_function_on_ℂ.ipynb
├── LICENSE
├── Nice_orbits.ipynb
├── README.md
├── Statistical_Approach_2nd_law.ipynb
├── images
    ├── calculations_second_law.jpeg
    ├── db_patterns2.png
    ├── dla_fast_2.png
    ├── feigenbaum-cvitanovic_function.jpeg
    ├── frame17800.png
    ├── popart_546_455.png
    └── prob_vs_d.png
└── videos
    ├── 2nd_law_maxwell-boltzman.mov
    ├── 2nd_law_random-uniform.mov
    └── BTW_1.mov


/Bak–Tang–Wiesenfeld_model.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "9af6743c-9924-4c0b-94f1-cae54cde3eac",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# Introduction to the Bak, Tang, Wiesenfeld Model of Self-Organized Criticality\n",
  9 |     "\n",
 10 |     "The Bak, Tang, Wiesenfeld (BTW) model is a pioneering framework in the study of self-organized criticality (SOC).\n",
 11 |     "\n",
 12 |     "### Self-Organized Criticality (SOC)\n",
 13 |     "Self-organized criticality is a property of dynamical systems that naturally evolve to a critical state, where a small perturbation can trigger a chain reaction of events. These systems do not require fine-tuning of parameters to reach the critical state, which emerges naturally through the system's dynamics.\n",
 14 |     "\n",
 15 |     "### The Sandpile Model\n",
 16 |     "The BTW model is commonly illustrated using the sandpile model, which is a cellular automaton used to simulate the behavior of SOC. In this model, sand grains are added one by one to a grid. When the number of grains at any site exceeds a critical threshold (4), the site topples, distributing grains to neighboring sites. This can cause a cascading effect, where multiple sites topple in an avalanche.\n",
 17 |     "\n",
 18 |     "### Dynamics of the Model\n",
 19 |     "1. **Adding Grains**: Grains of sand are added to random sites on the grid.\n",
 20 |     "2. **Toppling**: When the number of grains at a site exceeds a critical value (4 in this simulation), the site topples, sending grains to adjacent sites.\n",
 21 |     "3. **Avalanches**: This redistribution can cause adjacent sites to exceed their thresholds, leading to further toppling in a cascading effect, known as an avalanche.\n",
 22 |     "4. **Reaching Criticality**: Over time, the system self-organizes into a critical state where the distribution of avalanche sizes follows a power-law.\n",
 23 |     "\n",
 24 |     "\n",
 25 |     "## References\n",
 26 |     "* [Self-organized criticality: An explanation of the 1/f noise](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.59.381)\n",
 27 |     "* [How Nature Works](https://link.springer.com/book/10.1007/978-1-4757-5426-1)"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 5,
 33 |    "id": "fdb8f67b-0516-48ea-b86e-85a4adc573aa",
 34 |    "metadata": {},
 35 |    "outputs": [],
 36 |    "source": [
 37 |     "import numpy as np\n",
 38 |     "import matplotlib.pyplot as plt\n",
 39 |     "from matplotlib.colors import ListedColormap, LinearSegmentedColormap, to_rgb\n",
 40 |     "from tqdm import tqdm\n",
 41 |     "%matplotlib inline\n",
 42 |     "%config InlineBackend.figure_format = 'retina'"
 43 |    ]
 44 |   },
 45 |   {
 46 |    "cell_type": "code",
 47 |    "execution_count": 6,
 48 |    "id": "29982e60-a0be-45b2-ac43-118b207e2c56",
 49 |    "metadata": {},
 50 |    "outputs": [],
 51 |    "source": [
 52 |     "def create_colormap(hex_color1, hex_color2, reverse=False):\n",
 53 |     "    \"\"\"\n",
 54 |     "    Create a linearly interpolated colormap from two hex colors.\n",
 55 |     "\n",
 56 |     "    :param hex_color1: String, the first hex color (e.g., \"#FF0000\" for red)\n",
 57 |     "    :param hex_color2: String, the second hex color (e.g., \"#0000FF\" for blue)\n",
 58 |     "    :return: LinearSegmentedColormap\n",
 59 |     "    \"\"\"\n",
 60 |     "    color1 = to_rgb(hex_color1)\n",
 61 |     "    color2 = to_rgb(hex_color2)\n",
 62 |     "    if reverse:\n",
 63 |     "        colors = [color2, color1]\n",
 64 |     "    else:\n",
 65 |     "        colors = [color1, color2]\n",
 66 |     "    cmap = LinearSegmentedColormap.from_list(\"custom_colormap\", colors)\n",
 67 |     "\n",
 68 |     "    return cmap"
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "code",
 73 |    "execution_count": 7,
 74 |    "id": "d41ca6ef-f98a-430e-a68f-eb9a1c51072c",
 75 |    "metadata": {},
 76 |    "outputs": [],
 77 |    "source": [
 78 |     "sheet_map = create_colormap(\"#f4f0e8\", \"#383b3e\")"
 79 |    ]
 80 |   },
 81 |   {
 82 |    "cell_type": "code",
 83 |    "execution_count": 8,
 84 |    "id": "eb597429-eb5c-469b-9671-004234027a43",
 85 |    "metadata": {},
 86 |    "outputs": [],
 87 |    "source": [
 88 |     "class AbelianSandpile:\n",
 89 |     "    \"\"\"\n",
 90 |     "    Implements the Bak–Tang–Wiesenfeld model of the Abelian sandpile.\n",
 91 |     "\n",
 92 |     "    Attributes:\n",
 93 |     "    n (int): The size of the grid (excluding the boundary).\n",
 94 |     "    grid (numpy.ndarray): The grid representing the sandpile.\n",
 95 |     "    history (list of numpy.ndarray): A history of grid states after each step.\n",
 96 |     "    history_toppling (list of numpy.ndarray): A history of toppling events.\n",
 97 |     "\n",
 98 |     "    Methods:\n",
 99 |     "    step(): Performs a single step in the sandpile model.\n",
100 |     "    step_and_record(): Performs a single step and records the toppling events.\n",
101 |     "    check_difference(grid1, grid2): Checks the difference between two grids.\n",
102 |     "    simulate(n_step): Simulates the sandpile for a given number of steps.\n",
103 |     "    simulate_and_record(n_step): Simulates the sandpile for a given number of steps and records the toppling events.\n",
104 |     "    \"\"\"\n",
105 |     "    def __init__(self, n=100, random_state=None):\n",
106 |     "        \"\"\"\n",
107 |     "        Initializes the AbelianSandpile class.\n",
108 |     "\n",
109 |     "        Parameters:\n",
110 |     "        n (int): The size of the grid (excluding the boundary). Default is 100.\n",
111 |     "        random_state (int): The seed for the random number generator. Default is None.\n",
112 |     "        \"\"\"\n",
113 |     "        self.n = n\n",
114 |     "        np.random.seed(random_state) # Set the random seed\n",
115 |     "        self.grid = np.random.choice([0, 1, 2, 3], size=(n + 2, n + 2)).astype(np.int8)\n",
116 |     "        self.grid[0, :] = 0\n",
117 |     "        self.grid[-1, :] = 0\n",
118 |     "        self.grid[:, 0] = 0\n",
119 |     "        self.grid[:, -1] = 0\n",
120 |     "        self.history =[self.grid.copy()]\n",
121 |     "        self.history_toppling =[np.zeros_like(self.grid, dtype=np.int8)]\n",
122 |     "\n",
123 |     "\n",
124 |     "    def step(self):\n",
125 |     "        \"\"\"\n",
126 |     "        Performs a single step in the sandpile model. This involves adding a grain\n",
127 |     "        of sand to a random position and then performing the toppling relaxation\n",
128 |     "        process if any cell has 4 or more grains.\n",
129 |     "        \"\"\"\n",
130 |     "        self.grid[np.random.randint(1, self.n+1), np.random.randint(1, self.n+1)] += 1 #dropping a grain\n",
131 |     "        #topplig relaxation\n",
132 |     "        while(np.max(self.grid) >= 4):\n",
133 |     "            toppling = self.grid >= 4\n",
134 |     "            self.grid[toppling] -= 4\n",
135 |     "            self.grid[:-1, :][toppling[1:, :]] +=1\n",
136 |     "            self.grid[1:, :][toppling[:-1, :]] +=1\n",
137 |     "            self.grid[:, :-1][toppling[:, 1:]] +=1\n",
138 |     "            self.grid[:, 1:][toppling[:, :-1]] +=1\n",
139 |     "            self.grid[0, :] = 0\n",
140 |     "            self.grid[-1, :] = 0\n",
141 |     "            self.grid[:, 0] = 0\n",
142 |     "            self.grid[:, -1] = 0\n",
143 |     "\n",
144 |     "    def step_and_record(self):\n",
145 |     "        \"\"\"\n",
146 |     "        Performs a single step in the sandpile model and records the toppling events.\n",
147 |     "        \"\"\"\n",
148 |     "        self.grid[np.random.randint(1, self.n+1), np.random.randint(1, self.n+1)] += 1 #dropping a grain\n",
149 |     "        while(np.max(self.grid) >= 4):\n",
150 |     "            toppling = self.grid >= 4\n",
151 |     "            self.grid[toppling] -= 4\n",
152 |     "            self.grid[:-1, :][toppling[1:, :]] +=1\n",
153 |     "            self.grid[1:, :][toppling[:-1, :]] +=1\n",
154 |     "            self.grid[:, :-1][toppling[:, 1:]] +=1\n",
155 |     "            self.grid[:, 1:][toppling[:, :-1]] +=1\n",
156 |     "            self.grid[0, :] = 0\n",
157 |     "            self.grid[-1, :] = 0\n",
158 |     "            self.grid[:, 0] = 0\n",
159 |     "            self.grid[:, -1] = 0\n",
160 |     "            self.history.append(self.grid.copy())\n",
161 |     "            self.history_toppling.append(toppling.astype(np.int8))\n",
162 |     "\n",
163 |     "    @staticmethod\n",
164 |     "    def check_difference(grid1, grid2):\n",
165 |     "        \"\"\"\n",
166 |     "        Checks the difference between two grids.\n",
167 |     "\n",
168 |     "        Parameters:\n",
169 |     "        grid1 (numpy.ndarray): The first grid to compare.\n",
170 |     "        grid2 (numpy.ndarray): The second grid to compare.\n",
171 |     "\n",
172 |     "        Returns:\n",
173 |     "        int: The number of differing elements between the two grids.\n",
174 |     "        \"\"\"\n",
175 |     "        return np.sum(grid1 != grid2)\n",
176 |     "\n",
177 |     "    \n",
178 |     "    def simulate(self, n_step):\n",
179 |     "        \"\"\"\n",
180 |     "        Simulates the sandpile for a given number of steps.\n",
181 |     "\n",
182 |     "        Parameters:\n",
183 |     "        n_step (int): The number of steps to simulate.\n",
184 |     "        \"\"\"\n",
185 |     "        for _ in tqdm(range(n_step)):\n",
186 |     "            self.step()\n",
187 |     "\n",
188 |     "    def simulate_and_record(self, n_step):\n",
189 |     "        \"\"\"\n",
190 |     "        Simulates the sandpile for a given number of steps and records the toppling events.\n",
191 |     "\n",
192 |     "        Parameters:\n",
193 |     "        n_step (int): The number of steps to simulate.\n",
194 |     "        \"\"\"\n",
195 |     "        for _ in tqdm(range(n_step)):\n",
196 |     "            self.step_and_record()\n"
197 |    ]
198 |   },
199 |   {
200 |    "cell_type": "code",
201 |    "execution_count": 9,
202 |    "id": "a3858dae-4e68-4d67-9631-9e1bc5cc6e4d",
203 |    "metadata": {},
204 |    "outputs": [
205 |     {
206 |      "name": "stderr",
207 |      "output_type": "stream",
208 |      "text": [
209 |       "100%|███████████████████████████████████| 30000/30000 [00:24<00:00, 1206.96it/s]\n"
210 |      ]
211 |     }
212 |    ],
213 |    "source": [
214 |     "model = AbelianSandpile(n=200, random_state=12345)\n",
215 |     "model.simulate(30000)"
216 |    ]
217 |   },
218 |   {
219 |    "cell_type": "code",
220 |    "execution_count": 10,
221 |    "id": "c9a707c3-e9ab-4a3e-8df4-89dfc8284b03",
222 |    "metadata": {},
223 |    "outputs": [
224 |     {
225 |      "name": "stderr",
226 |      "output_type": "stream",
227 |      "text": [
228 |       "100%|██████████████████████████████████████████| 50/50 [00:00<00:00, 216.15it/s]\n"
229 |      ]
230 |     }
231 |    ],
232 |    "source": [
233 |     "model.simulate_and_record(50)"
234 |    ]
235 |   },
236 |   {
237 |    "cell_type": "code",
238 |    "execution_count": 11,
239 |    "id": "2a5ee6f1-081a-4a5f-b1a4-a93ec6cc6ccd",
240 |    "metadata": {},
241 |    "outputs": [
242 |     {
243 |      "name": "stdout",
244 |      "output_type": "stream",
245 |      "text": [
246 |       "N° of frames: 2755\n"
247 |      ]
248 |     }
249 |    ],
250 |    "source": [
251 |     "hist_toppling = np.array(model.history_toppling)\n",
252 |     "hist_toppling_cum = np.cumsum(hist_toppling, axis=0)\n",
253 |     "toppled_sites = np.count_nonzero(hist_toppling, axis=(1,2))\n",
254 |     "print(f\"N° of frames: {len(model.history_toppling)}\")"
255 |    ]
256 |   },
257 |   {
258 |    "cell_type": "code",
259 |    "execution_count": 12,
260 |    "id": "d09c2dff-0d21-44cb-8959-037966f518b1",
261 |    "metadata": {},
262 |    "outputs": [
263 |     {
264 |      "data": {
265 |       "image/png": 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",
266 |       "text/plain": [
267 |        "<Figure size 500x600 with 2 Axes>"
268 |       ]
269 |      },
270 |      "metadata": {
271 |       "image/png": {
272 |        "height": 611,
273 |        "width": 511
274 |       }
275 |      },
276 |      "output_type": "display_data"
277 |     }
278 |    ],
279 |    "source": [
280 |     "%matplotlib inline\n",
281 |     "index = 2000\n",
282 |     "fig, axs = plt.subplot_mosaic(\"\"\"\n",
283 |     "    AAA\n",
284 |     "    AAA\n",
285 |     "    AAA\n",
286 |     "    BBB\n",
287 |     "    \"\"\", figsize=(5,6),layout=\"constrained\", height_ratios=[1, 1, 1, 0.5])\n",
288 |     "fig.set_facecolor(\"#f4f0e8\")\n",
289 |     "axs[\"A\"].contourf(hist_toppling_cum[index], vmin=2, vmax=14, origin=\"lower\", cmap=\"coolwarm_r\", alpha=0.5) #RdYlBu\n",
290 |     "axs[\"A\"].imshow(hist_toppling[index],cmap=sheet_map, origin=\"lower\")#, interpolation=\"bicubic\")\n",
291 |     "axs[\"A\"].axis(\"off\")\n",
292 |     "axs[\"B\"].set_facecolor(\"#f4f0e8\")\n",
293 |     "axs[\"B\"].plot(toppled_sites[:index], color=\"#383b3e\", linewidth=0.5)\n",
294 |     "axs[\"B\"].set_xlabel(\"Time step\", color=\"#383b3e\", fontsize=8)\n",
295 |     "axs[\"B\"].set_ylabel(\"N° of topplings\", color=\"#383b3e\", fontsize=8)\n",
296 |     "axs[\"B\"].tick_params(axis='both', labelsize=6, color=\"#383b3e\")\n",
297 |     "fig.suptitle(\"Bak–Tang–Wiesenfeld model\", fontsize=10, color = \"#383b3e\")\n",
298 |     "plt.show()"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "code",
303 |    "execution_count": null,
304 |    "id": "60893539-086c-4f41-97bf-dd5c3f1450f4",
305 |    "metadata": {},
306 |    "outputs": [],
307 |    "source": []
308 |   }
309 |  ],
310 |  "metadata": {
311 |   "kernelspec": {
312 |    "display_name": "Python 3 (ipykernel)",
313 |    "language": "python",
314 |    "name": "python3"
315 |   },
316 |   "language_info": {
317 |    "codemirror_mode": {
318 |     "name": "ipython",
319 |     "version": 3
320 |    },
321 |    "file_extension": ".py",
322 |    "mimetype": "text/x-python",
323 |    "name": "python",
324 |    "nbconvert_exporter": "python",
325 |    "pygments_lexer": "ipython3",
326 |    "version": "3.11.7"
327 |   }
328 |  },
329 |  "nbformat": 4,
330 |  "nbformat_minor": 5
331 | }
332 | 


--------------------------------------------------------------------------------
/DLA_numpy_diffusionCA.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": null,
  6 |    "id": "4597fa9a",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import matplotlib.pyplot as plt\n",
 12 |     "import matplotlib.colors as mcolors\n",
 13 |     "%config InlineBackend.figure_format = 'retina'"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "code",
 18 |    "execution_count": null,
 19 |    "id": "b2e945ac",
 20 |    "metadata": {},
 21 |    "outputs": [],
 22 |    "source": [
 23 |     "def sheet_cmap(reverse=False):\n",
 24 |     "    # a nice colormap\n",
 25 |     "    one = \"#f4f0e8\".lstrip('#')\n",
 26 |     "    two = \"#383b3e\".lstrip('#')\n",
 27 |     "    one = tuple(int(one[i:i+2], 16)/255 for i in (0, 2, 4))\n",
 28 |     "    two = tuple(int(two[i:i+2], 16)/255 for i in (0, 2, 4))\n",
 29 |     "    if reverse:\n",
 30 |     "        one, two = two, one\n",
 31 |     "    colors = [one, two]\n",
 32 |     "    cmap = mcolors.LinearSegmentedColormap.from_list('sheet_cmap', colors, N=256)\n",
 33 |     "    return cmap"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "code",
 38 |    "execution_count": null,
 39 |    "id": "6b3fc332",
 40 |    "metadata": {},
 41 |    "outputs": [],
 42 |    "source": [
 43 |     "# Configuration params\n",
 44 |     "size = (2000,2000)\n",
 45 |     "n_particles = 800000\n",
 46 |     "rng = np.random.default_rng()\n",
 47 |     "\n",
 48 |     "# field: 1 for moving particles, 0 otherwise\n",
 49 |     "field = np.zeros(size[0]*size[1], dtype=np.int8).reshape(size)\n",
 50 |     "particles_pos = np.unravel_index(np.random.choice(np.arange(size[0]*size[1]), n_particles, replace=False), size)\n",
 51 |     "field[particles_pos] = 1\n",
 52 |     "\n",
 53 |     "# sticked_field: 1 for sticked particles, 0 otherwise\n",
 54 |     "sticked_field = np.zeros(size[0]*size[1], dtype=np.int8).reshape(size)\n",
 55 |     "sticked_field[size[0]//2, size[1]//2] = 1\n",
 56 |     "\n",
 57 |     "#array view for margolous blocking of field\n",
 58 |     "field_b_even = field.reshape((-1, 2, field.shape[1]//2, 2))  \n",
 59 |     "field_blocks_even = field_b_even.transpose((0,2,1,3))\n",
 60 |     "\n",
 61 |     "field_b_odd = field[1:-1,1:-1].reshape((-1, 2, field.shape[1]//2-1, 2))  \n",
 62 |     "field_blocks_odd = field_b_odd.transpose((0,2,1,3))"
 63 |    ]
 64 |   },
 65 |   {
 66 |    "cell_type": "code",
 67 |    "execution_count": null,
 68 |    "id": "9c671650",
 69 |    "metadata": {},
 70 |    "outputs": [],
 71 |    "source": [
 72 |     "#DLA simulation\n",
 73 |     "n_iter = 10000\n",
 74 |     "for i in range(n_iter):\n",
 75 |     "    #randomly move particles\n",
 76 |     "    if i%2==0:\n",
 77 |     "        rng.shuffle(field_blocks_even, axis=3)\n",
 78 |     "        rng.shuffle(field_blocks_even, axis=2)\n",
 79 |     "    else:\n",
 80 |     "        rng.shuffle(field_blocks_odd, axis=3)\n",
 81 |     "        rng.shuffle(field_blocks_odd, axis=2)\n",
 82 |     "    \n",
 83 |     "    #check for sticking\n",
 84 |     "    stick_1_0 = (field[2:,1:-1]+sticked_field[1:-1,1:-1]==2) & (sticked_field[2:,1:-1]==0)\n",
 85 |     "    field[2:,1:-1][stick_1_0]=0\n",
 86 |     "    sticked_field[2:,1:-1][stick_1_0]=1\n",
 87 |     "    \n",
 88 |     "    stick_m1_0 = (field[0:-2,1:-1]+sticked_field[1:-1,1:-1]==2) & (sticked_field[0:-2,1:-1]==0)\n",
 89 |     "    field[0:-2,1:-1][stick_m1_0]=0\n",
 90 |     "    sticked_field[0:-2,1:-1][stick_m1_0]=1\n",
 91 |     "    \n",
 92 |     "    stick_0_1 = (field[1:-1, 2:]+sticked_field[1:-1,1:-1]==2) & (sticked_field[1:-1, 2:]==0)\n",
 93 |     "    field[1:-1, 2:][stick_0_1]=0\n",
 94 |     "    sticked_field[1:-1, 2:][stick_0_1]=1\n",
 95 |     "    \n",
 96 |     "    stick_0_m1 = (field[1:-1, 0:-2]+sticked_field[1:-1,1:-1]==2) & (sticked_field[1:-1, 0:-2]==0)\n",
 97 |     "    field[1:-1, 0:-2][stick_0_m1]=0\n",
 98 |     "    sticked_field[1:-1, 0:-2][stick_0_m1]=1\n"
 99 |    ]
100 |   },
101 |   {
102 |    "cell_type": "code",
103 |    "execution_count": null,
104 |    "id": "bbdcba57",
105 |    "metadata": {},
106 |    "outputs": [],
107 |    "source": [
108 |     "print(f\"Sticked particles: {np.count_nonzero(sticked_field)} - total particles: {np.count_nonzero(sticked_field) + np.count_nonzero(field)}\")"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": null,
114 |    "id": "320314b6",
115 |    "metadata": {},
116 |    "outputs": [],
117 |    "source": [
118 |     "fig, ax = plt.subplots(figsize=(10,10))\n",
119 |     "fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None)\n",
120 |     "ax.spines[['top', 'right', 'bottom', 'left']].set_visible(False)\n",
121 |     "ax.set_aspect('equal', 'box')\n",
122 |     "ax.set_xlim([0,size[0]])\n",
123 |     "ax.set_ylim([0,size[1]])\n",
124 |     "#ax.set_axis_off()\n",
125 |     "ax.imshow(sticked_field*2+field, cmap=sheet_cmap())\n",
126 |     "plt.savefig(\"./dla_fast_2.png\", dpi=600)\n",
127 |     "plt.show()"
128 |    ]
129 |   },
130 |   {
131 |    "cell_type": "code",
132 |    "execution_count": null,
133 |    "id": "fe22cacf",
134 |    "metadata": {},
135 |    "outputs": [],
136 |    "source": []
137 |   }
138 |  ],
139 |  "metadata": {
140 |   "kernelspec": {
141 |    "display_name": "Python 3 (ipykernel)",
142 |    "language": "python",
143 |    "name": "python3"
144 |   },
145 |   "language_info": {
146 |    "codemirror_mode": {
147 |     "name": "ipython",
148 |     "version": 3
149 |    },
150 |    "file_extension": ".py",
151 |    "mimetype": "text/x-python",
152 |    "name": "python",
153 |    "nbconvert_exporter": "python",
154 |    "pygments_lexer": "ipython3",
155 |    "version": "3.11.5"
156 |   }
157 |  },
158 |  "nbformat": 4,
159 |  "nbformat_minor": 5
160 | }
161 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2025 Simone Conradi
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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 1 | # Python_Simulations
 2 | Various Python Simulations
 3 | 
 4 | ## Bak–Tang–Wiesenfeld_model.ipynb
 5 | A simulation of the Bak Tang Wiesenfeld  model described in:
 6 | _Self-organized criticality: An explanation of the 1/f noise, Per Bak, Chao Tang, and Kurt Wiesenfeld Phys. Rev. Lett. 59, 381 – Published 27 July 1987_
 7 | 
 8 | https://github.com/profConradi/Python_Simulations/assets/17752153/88ea1823-d70b-4a8d-89e6-0de61b30d2d5
 9 | 
10 | 
11 | 
12 | 
13 | 
14 | ## Dielectric_Breakdown.ipynb
15 | A simulation of the dielectric breakdown model described in:
16 | _Fractal Dimension of Dielectric Breakdown, L. Niemeyer, L. Pietronero, ' and H. J. Wiesmann Brown Boveri Research Center, CH-5405 Baden, Switzerland_
17 | 
18 | <img src="/images/db_patterns2.png" width="500"/>
19 | 
20 | 
21 | ## Statistical_Approach_2nd_law.ipynb
22 | A simulation of the Second Law of Thermodynamics as described in:
23 | _A statistical approach to the second law of thermodynamics using a computer simulation L Bellomonte and R M Sperandeo-Mineo Eur. J. Phys. 18 (1997) 321–326._
24 | 
25 | <img src="/images/frame17800.png" width="500"/>
26 | 
27 | Video:
28 | https://github.com/profConradi/Python_Simulations/assets/17752153/ed271bf7-6b1f-4b6a-a19d-0993bc7264fa
29 | 
30 | 
31 | ## DLA_numpy_diffusionCA.ipynb
32 | A simulation of Diffusion Limited Aggregation using a numpy vectorized approach and in which particles diffusion is simulated using a 2x2 block cellular automata.
33 | 
34 | <img src="/images/dla_fast_2.png" width="500"/>
35 | 
36 | ## Feigenbaum-Cvitanović_function_on_ℂ.ipynb
37 | Plotting the numerical solution to the Feigenbaum-Cvitanović functional equation in the complex plane.  
38 | 
39 | <img src="/images/feigenbaum-cvitanovic_function.jpeg" width="500"/>
40 | 
41 | 
42 | ## Nice_orbits.ipynb
43 | Plotting nice orbits of a 2d dynamical system  
44 | 
45 | <img src="/images/popart_546_455.png" width="500"/>
46 | 


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/videos/2nd_law_random-uniform.mov:
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