Change the parallelization to be handled by joblib (#19)

* convert the inference scripts to use joblib

* remove unnecessary imports

* change to joblib

* pass by reference

* pass by reference

* update to parallelize

* fixes

* update data files

* fix setup file

* add configuration model

* rename r

* update

* added a script to collate the cm data

* updated collation scripts

* added data

* fix num samples/num posterior vals mismatch

* add stochastic root finding back in

* updated script for fig. 1b

* added script to generate fig 1c

* updates

* updates

* renaming and reorganizing

* updates

* rename p to alpha

Extra/plot_frac_vs_beta.ipynb

@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import json\n",
+    "import numpy as np\n",
+    "from lcs import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"Data/frac_vs_beta.json\") as file:\n",
+    "    data = json.load(file)\n",
+    "beta = np.array(data[\"beta\"], dtype=float)\n",
+    "frac = np.array(data[\"fraction\"], dtype=float)\n",
+    "ps = np.array(data[\"ps\"], dtype=float)\n",
+    "sps = np.array(data[\"sps\"], dtype=float)\n",
+    "fce = np.array(data[\"fce\"], dtype=float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cmasher as cmr\n",
+    "\n",
+    "cmap = cmr.sunburst\n",
+    "cmap = cmr.ember\n",
+    "\n",
+    "ps_summary = ps.mean(axis=2).T\n",
+    "\n",
+    "c = plt.imshow(\n",
+    "    to_imshow_orientation(ps_summary),\n",
+    "    extent=(min(frac), max(frac), min(beta), max(beta)),\n",
+    "    aspect=\"auto\",\n",
+    "    cmap=cmap,\n",
+    ")\n",
+    "plt.xlabel(r\"$f$\")\n",
+    "plt.ylabel(r\"$\\beta$\")\n",
+    "plt.colorbar(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cmasher as cmr\n",
+    "\n",
+    "cmap = cmr.sunburst\n",
+    "cmap = cmr.ember\n",
+    "\n",
+    "sps_summary = sps.mean(axis=2).T\n",
+    "\n",
+    "c = plt.imshow(\n",
+    "    to_imshow_orientation(sps_summary),\n",
+    "    extent=(min(frac), max(frac), min(beta), max(beta)),\n",
+    "    aspect=\"auto\",\n",
+    "    cmap=cmap,\n",
+    ")\n",
+    "plt.xlabel(r\"$f$\")\n",
+    "plt.ylabel(r\"$\\beta$\")\n",
+    "plt.colorbar(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cmasher as cmr\n",
+    "\n",
+    "cmap = cmr.sunburst\n",
+    "cmap = cmr.ember\n",
+    "\n",
+    "fce_summary = fce.mean(axis=2).T\n",
+    "\n",
+    "c = plt.imshow(\n",
+    "    to_imshow_orientation(fce_summary),\n",
+    "    extent=(min(frac), max(frac), min(beta), max(beta)),\n",
+    "    aspect=\"auto\",\n",
+    "    cmap=cmap,\n",
+    ")\n",
+    "plt.xlabel(r\"$f$\")\n",
+    "plt.ylabel(r\"$\\beta$\")\n",
+    "plt.colorbar(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hyper",
+   "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": "3.10.0"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}