deploy: 8c38366

.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 7976849ac2fc9fccb5424f2fb57474bd
+tags: 645f666f9bcd5a90fca523b33c5a78b7

.doctrees/nbsphinx/notebooks/analysis_committor.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysis of obtained models\n",
+    "\n",
+    "\n",
+    "Once we have obtained a model, we can extract useful quantities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

.doctrees/nbsphinx/notebooks/statistical_performances/overdampedOU.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "718db51f",
+   "metadata": {},
+   "source": [
+    "# Overdamped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e03f944-d2c1-48ba-88ec-494c9e7c40ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import folie as fl\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82c19e11-896e-48de-ae8d-4c614545b4e8",
+   "metadata": {},
+   "source": [
+    "Let's first simulate some trajectories with various timestep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8bf9d6de-29f8-4bc7-8b0c-92f68b262b7d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not broadcast input array from shape (25,25) into shape (25,1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dt \u001b[38;5;129;01min\u001b[39;00m list_dts:\n\u001b[1;32m      6\u001b[0m     simulator \u001b[38;5;241m=\u001b[39m fl\u001b[38;5;241m.\u001b[39mSimulator(fl\u001b[38;5;241m.\u001b[39mExactDensity(model_simu), dt)\n\u001b[0;32m----> 7\u001b[0m     data_dts\u001b[38;5;241m.\u001b[39mappend(\u001b[43msimulator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mzeros\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m25\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m25\u001b[39;49m\u001b[43m)\u001b[49m)\n",
+      "File \u001b[0;32m~/Projets/folie/folie/simulations/__init__.py:34\u001b[0m, in \u001b[0;36mSimulator.run\u001b[0;34m(self, nsteps, x0, ntrajs, save_every, **kwargs)\u001b[0m\n\u001b[1;32m     32\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransition\u001b[38;5;241m.\u001b[39mrun_step(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdt, dW[:, n])\n\u001b[1;32m     33\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m%\u001b[39m save_every \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 34\u001b[0m         \u001b[43mx_val\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msave_every\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m x\n\u001b[1;32m     35\u001b[0m data \u001b[38;5;241m=\u001b[39m Trajectories(dt\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdt \u001b[38;5;241m*\u001b[39m save_every)\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(ntrajs):\n",
+      "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (25,25) into shape (25,1)"
+     ]
+    }
+   ],
+   "source": [
+    "model_simu = fl.models.OrnsteinUhlenbeck()\n",
+    "model_simu.coefficients = np.array([0.1, 1.2, 2.0])\n",
+    "data_dts = []\n",
+    "list_dts = [1e-3, 5e-3, 1e-2, 5e-2]\n",
+    "for dt in list_dts:\n",
+    "    simulator = fl.Simulator(fl.simulations.ExactStepper(model_simu), dt)\n",
+    "    data_dts.append(simulator.run(5000, np.zeros((25,)), 25))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc477114-844e-415c-88a4-f5a19c088057",
+   "metadata": {},
+   "source": [
+    "We can then run the estimation for various likelihood at all timesteps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7756920-1780-47f4-bef7-7296d8ea127f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots(1, len(model_simu.coefficients))\n",
+    "\n",
+    "for name, transitioncls in zip(\n",
+    "    [\"Exact\", \"Euler\", \"Ozaki\", \"ShojiOzaki\", \"Elerian\", \"Kessler\", \"Drozdov\"],\n",
+    "    [\n",
+    "        fl.ExactDensity,\n",
+    "        fl.EulerDensity,\n",
+    "        fl.OzakiDensity,\n",
+    "        fl.ShojiOzakiDensity,\n",
+    "        fl.ElerianDensity,\n",
+    "        fl.KesslerDensity,\n",
+    "        fl.DrozdovDensity,\n",
+    "    ],\n",
+    "):\n",
+    "    model = fl.models.OrnsteinUhlenbeck()\n",
+    "    estimator = fl.LikelihoodEstimator(transitioncls(model))\n",
+    "    coeffs_vals = np.empty((len(data_dts), len(model.coefficients)))\n",
+    "    for n, data in enumerate(data_dts):\n",
+    "        res = estimator.fit_fetch(data)\n",
+    "        coeffs_vals[n, :] = res.coefficients\n",
+    "    for n in range(len(axs)):\n",
+    "        axs[n].plot(list_dts, np.abs(coeffs_vals[:, n] - model_simu.coefficients[n]), \"-+\", label=name)\n",
+    "    print(coeffs_vals)\n",
+    "for n in range(len(axs)):\n",
+    "    axs[n].legend()\n",
+    "    axs[n].set_yscale(\"log\")\n",
+    "    axs[n].grid()\n",
+    "    axs[n].set_xlabel(r\"$\\Delta t$\")\n",
+    "    axs[n].set_ylabel(r\"$|c-c_{real}|$\")\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8",
+   "language": "python",
+   "name": "python3.8"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.doctrees/nbsphinx/notebooks/statistical_performances/overdampedhiddenOU.ipynb

@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2199d582",
+   "metadata": {},
+   "source": [
+    "# Overdamped with hidden variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad5f8a73-e432-41db-bf0b-625ff80db5b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import folie as fl\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0191d59f-9793-4efc-9fe7-fc53cd46f4d1",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1a4b8c2-948a-4bcd-b4b2-6208c1684260",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_simu = model_simu = fl.models.OrnsteinUhlenbeck(dim=3)\n",
+    "data_dts = []\n",
+    "list_dts = [1e-3, 5e-3, 1e-2, 5e-2]\n",
+    "for dt in list_dts:\n",
+    "    simulator = fl.Simulator(fl.simulations.ExactStepper(model_simu), dt, keep_dim=1)\n",
+    "    data_dts.append(simulator.run(5000, np.random.normal(loc=0.0, scale=1.0, size=(25, 3)), 25))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1d5f7af-d2e2-4511-aea2-e61c32f0929b",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "473bb54a-7291-4019-9004-a6b260e9c599",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots(1, len(model_simu.coefficients))\n",
+    "\n",
+    "for name, transitioncls in zip(\n",
+    "    [\"Euler\"],\n",
+    "    [\n",
+    "        fl.EulerDensity,\n",
+    "    ],\n",
+    "):\n",
+    "    fun_lin = fl.functions.Linear().fit(data_dts[0])\n",
+    "    fun_frct = fl.functions.Constant().fit(data_dts[0])\n",
+    "    fun_cst = fl.functions.Constant().fit(data_dts[0])\n",
+    "    model = fl.models.OverdampedHidden(fun_lin, fun_frct, fun_cst, dim=1, dim_h=2)\n",
+    "    estimator = fl.EMEstimator(transitioncls(model), max_iter=15, verbose=2, verbose_interval=1)\n",
+    "    coeffs_vals = np.empty((len(data_dts), len(model.coefficients)))\n",
+    "    for n, data in enumerate(data_dts):\n",
+    "        res = estimator.fit_fetch(\n",
+    "            data[\n",
+    "                :,\n",
+    "            ]\n",
+    "        )\n",
+    "        coeffs_vals[n, :] = res.coefficients\n",
+    "    for n in range(len(axs)):\n",
+    "        axs[n].plot(list_dts, np.abs(coeffs_vals[:, n] - model_simu.coefficients[n]), \"-+\", label=name)\n",
+    "for n in range(len(axs)):\n",
+    "    axs[n].legend()\n",
+    "    axs[n].set_yscale(\"log\")\n",
+    "    axs[n].grid()\n",
+    "    axs[n].set_xlabel(\"$\\\\Delta t\")\n",
+    "    axs[n].set_ylabel(\"$|c-c_{real}|$\")\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8",
+   "language": "python",
+   "name": "python3.8"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}