MAINT: tweak the frustrated Ising example notebook, show multiprocessing

ev-br · Jul 8, 2021 · d569a28 · d569a28
1 parent 0cbd1fc
commit d569a28
Show file tree

Hide file tree

Showing 2 changed files with 61 additions and 40 deletions.
diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
@@ -40,5 +40,5 @@ jobs:
 
         # also check that the 2D triangular Ising example runs
         pip install jupyter
-        ipython -c"%run examples/frustrated_2d_ising.ipynb"
+        ipython -c"%run frustrated_2d_ising.ipynb"
 
diff --git a/examples/frustrated_2d_ising.ipynb b/examples/frustrated_2d_ising.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "488bd36c",
+   "id": "b92b380b",
    "metadata": {},
    "source": [
     "# Simulate a frustrated 2D Ising model on the triangular lattice. \n",
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "601e580b",
+   "id": "0bee3da3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "23f5fd9b",
+   "id": "da0196e3",
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "a6cf11a6",
+   "id": "5628ca52",
    "metadata": {},
    "outputs": [
     {
@@ -197,53 +197,74 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "788c0558",
+   "cell_type": "markdown",
+   "id": "2a2d9104",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.   1.   0.   1.   1.  -0.1  0.   0.   0.   0.   0.   0.   1.   0.   0.  -0.1]\n",
-      " [ 1.   0.   1.   0.   0.   1.  -0.1  0.   0.   0.   0.   0.  -0.1  1.   0.   0. ]\n",
-      " [ 0.   1.   0.   1.   0.   0.   1.  -0.1  0.   0.   0.   0.   0.  -0.1  1.   0. ]\n",
-      " [ 1.   0.   1.   0.  -0.1  0.   0.   1.   0.   0.   0.   0.   0.   0.  -0.1  1. ]\n",
-      " [ 1.   0.   0.  -0.1  0.   1.   0.   1.   1.  -0.1  0.   0.   0.   0.   0.   0. ]\n",
-      " [-0.1  1.   0.   0.   1.   0.   1.   0.   0.   1.  -0.1  0.   0.   0.   0.   0. ]\n",
-      " [ 0.  -0.1  1.   0.   0.   1.   0.   1.   0.   0.   1.  -0.1  0.   0.   0.   0. ]\n",
-      " [ 0.   0.  -0.1  1.   1.   0.   1.   0.  -0.1  0.   0.   1.   0.   0.   0.   0. ]\n",
-      " [ 0.   0.   0.   0.   1.   0.   0.  -0.1  0.   1.   0.   1.   1.  -0.1  0.   0. ]\n",
-      " [ 0.   0.   0.   0.  -0.1  1.   0.   0.   1.   0.   1.   0.   0.   1.  -0.1  0. ]\n",
-      " [ 0.   0.   0.   0.   0.  -0.1  1.   0.   0.   1.   0.   1.   0.   0.   1.  -0.1]\n",
-      " [ 0.   0.   0.   0.   0.   0.  -0.1  1.   1.   0.   1.   0.  -0.1  0.   0.   1. ]\n",
-      " [ 1.  -0.1  0.   0.   0.   0.   0.   0.   1.   0.   0.  -0.1  0.   1.   0.   1. ]\n",
-      " [ 0.   1.  -0.1  0.   0.   0.   0.   0.  -0.1  1.   0.   0.   1.   0.   1.   0. ]\n",
-      " [ 0.   0.   1.  -0.1  0.   0.   0.   0.   0.  -0.1  1.   0.   0.   1.   0.   1. ]\n",
-      " [-0.1  0.   0.   1.   0.   0.   0.   0.   0.   0.  -0.1  1.   1.   0.   1.   0. ]]\n"
-     ]
-    }
-   ],
    "source": [
-    "L=4\n",
-    "Js = np.zeros((L**2, L**2), dtype=float)\n",
+    "## Use multiprocessing for running several simulations in parallel\n",
     "\n",
-    "get_J(Js, 1.0, -0.1, L, L)\n",
+    "(this can be streamlined, of course)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "181e48c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from multiprocessing import Pool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "94b126b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param = [\n",
+    "    # L, T, J, Jd, num_sweeps, seed\n",
+    "    (4, 1.0, 1, -0.1, 100, 0),\n",
+    "    (4, 1.5, 1, -0.1, 100, 0),\n",
+    "    (4, 2.0, 1, -0.1, 100, 0),\n",
+    "]\n",
     "\n",
-    "with np.printoptions(linewidth=120, precision=2):\n",
-    "    print(Js)"
+    "with Pool(processes=2) as pool:\n",
+    "    res = pool.starmap(simulate, param)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "57db2306",
+   "execution_count": 42,
+   "id": "0d5a40b7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "raise ValueError"
+    "res_sequential = [simulate(*par) for par in param ]"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "990cbb4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy.testing import assert_allclose\n",
+    "\n",
+    "assert_allclose(res,\n",
+    "                res_sequential,\n",
+    "                atol=1e-14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fed870b8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {