✨ Port sim_analysis_script_6d_disp to notebook

pyglotaran_examples/test/simultaneous_analysis_6d_disp/sim_analysis_script_6d_disp.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "### Load data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from glotaran.io import load_dataset\n",
+    "\n",
+    "experiment_data = {\n",
+    "    \"dataset1\": load_dataset(\"equareaIRFdispscalsim6a.ascii\"),\n",
+    "    \"dataset2\": load_dataset(\"equareaIRFdispscalsim6b.ascii\"),\n",
+    "    \"dataset3\": load_dataset(\"equareaIRFdispscalsim6c.ascii\"),\n",
+    "    \"dataset4\": load_dataset(\"equareaIRFdispscalsim6d.ascii\"),\n",
+    "    \"dataset5\": load_dataset(\"equareaIRFdispscalsim6e.ascii\"),\n",
+    "    \"dataset6\": load_dataset(\"equareaIRFdispscalsim6f.ascii\"),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "### Load model and parameters, define scheme\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from glotaran.io import load_model, load_parameters\n",
+    "from glotaran.project.scheme import Scheme\n",
+    "\n",
+    "model = load_model(\"model.yml\")\n",
+    "parameters = load_parameters(\"parameters.yml\")\n",
+    "\n",
+    "scheme = Scheme(\n",
+    "    model,\n",
+    "    parameters,\n",
+    "    experiment_data,\n",
+    "    maximum_number_function_evaluations=9,  # TRF needs 8m LM needs 127\n",
+    "    # optimization_method=\"Levenberg-Marquardt\", #LM needs nfev=127\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4",
+   "metadata": {},
+   "source": [
+    "### Optimization\n",
+    "\n",
+    "Fitting model to the data according to the scheme, optimizing the parameters.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from glotaran.optimization.optimize import optimize\n",
+    "\n",
+    "result = optimize(scheme)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from pyglotaran_extras.io import setup_case_study\n",
+    "\n",
+    "from glotaran.io import save_result\n",
+    "\n",
+    "results_folder, _ = setup_case_study(output_folder_name=\"pyglotaran_examples_results\")\n",
+    "save_result(result, results_folder / \"result.yml\", allow_overwrite=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from pyglotaran_extras import plot_overview\n",
+    "\n",
+    "plot_overview(result.data[\"dataset1\"]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "plot_overview(result.data[\"dataset2\"]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "plot_overview(result.data[\"dataset3\"]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_overview(result.data[\"dataset4\"]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_overview(result.data[\"dataset5\"]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_overview(result.data[\"dataset6\"]);"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pygta-staging",
+   "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"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}