example nb of current interface

8e5b7d74 · Kostas Vilkelis · 19c3b598 · 8e5b7d74
Commit 8e5b7d74 authored Feb 23, 2024 by Kostas Vilkelis
--- a/examples/testInterface.ipynb
+++ b/examples/testInterface.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from codes import utils, model, solvers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def random_guess(vectors, int_model):\n",
+    "    \"\"\"\n",
+    "    Generate random guess.\n",
+    "\n",
+    "    Parameters:\n",
+    "    -----------\n",
+    "    vectors : list of tuples\n",
+    "        Hopping vectors for the mean-field corrections.\n",
+    "    \"\"\"\n",
+    "    scale = 0.1*(1+np.max(np.abs([*int_model.values()])))\n",
+    "    return utils.generate_guess(\n",
+    "        vectors=vectors,\n",
+    "        ndof=len([*int_model.values()][0]),\n",
+    "        scale=scale\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 4.81536544e-01,  6.41071801e-03, -1.76048506e-07, ...,\n",
+       "        0.00000000e+00,  1.33679796e-06,  0.00000000e+00])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "U0 = 1\n",
+    "nk = 100\n",
+    "filling = 2\n",
+    "\n",
+    "hopp = np.kron(np.array([[0, 1], [0, 0]]), np.eye(2))\n",
+    "tb_model = {(0,): hopp + hopp.T.conj(), (1,): hopp, (-1,): hopp.T.conj()}\n",
+    "int_model = {\n",
+    "    (0,): U0 * np.kron(np.eye(2), np.ones((2, 2))),\n",
+    "}\n",
+    "\n",
+    "testModel = model.MfModel(tb_model, int_model, filling)\n",
+    "guess = utils.tb2grid(random_guess(tb_model.keys(), int_model), nk)\n",
+    "solvers.kspace_solver(testModel, guess)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
+%% Cell type:code id: tags:
+
+``` python
+import numpy as np
+from codes import utils, model, solvers
+```
+
+%% Cell type:code id: tags:
+
+``` python
+def random_guess(vectors, int_model):
+    """
+    Generate random guess.
+
+    Parameters:
+    -----------
+    vectors : list of tuples
+        Hopping vectors for the mean-field corrections.
+    """
+    scale = 0.1*(1+np.max(np.abs([*int_model.values()])))
+    return utils.generate_guess(
+        vectors=vectors,
+        ndof=len([*int_model.values()][0]),
+        scale=scale
+    )
+```
+
+%% Cell type:code id: tags:
+
+``` python
+U0 = 1
+nk = 100
+filling = 2
+
+hopp = np.kron(np.array([[0, 1], [0, 0]]), np.eye(2))
+tb_model = {(0,): hopp + hopp.T.conj(), (1,): hopp, (-1,): hopp.T.conj()}
+int_model = {
+    (0,): U0 * np.kron(np.eye(2), np.ones((2, 2))),
+}
+
+testModel = model.MfModel(tb_model, int_model, filling)
+guess = utils.tb2grid(random_guess(tb_model.keys(), int_model), nk)
+solvers.kspace_solver(testModel, guess)
+```
+
+%% Output
+
+    array([ 4.81536544e-01,  6.41071801e-03, -1.76048506e-07, ...,
+            0.00000000e+00,  1.33679796e-06,  0.00000000e+00])
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```