Allow user to modify and visualize the potential OTF.

osscar-org · Oct 12, 2023 · 97c04d0 · 97c04d0
1 parent 6e6f287
commit 97c04d0
Showing 1 changed file with 20 additions and 18 deletions.
diff --git a/notebook/quantum-mechanics/msoft.ipynb b/notebook/quantum-mechanics/msoft.ipynb
@@ -76,15 +76,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "af938e9c",
    "metadata": {},
    "outputs": [],
    "source": [
     "\"\"\"\n",
     "Notebook demonstrating the use of the MSOFT algorithm for quantum dynamics.\n",
     "\"\"\"\n",
-    "%matplotlib ipympl\n",
+    "%matplotlib widget\n",
     "import numpy as np\n",
     "from math import pi\n",
     "from numpy.fft import fft as fft\n",
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "e04d5db5",
    "metadata": {},
    "outputs": [],
@@ -243,7 +243,7 @@
     "    def _get_potential_energy(self, Nsteps):\n",
     "        epot = sum(np.conj(self.psi_x[:,0])*self.h[:,0,0]*self.psi_x[:,0] + \\\n",
     "                        np.conj(self.psi_x[:,0])*self.h[:,0,1]*self.psi_x[:,1] + \\\n",
-    "                        np.conj(self.psi_x[:,1])*self.h[:,0,1]*self.psi_x[:,0] + \\\n",
+    "                        np.conj(self.psi_x[:,1])*self.h[:,1,0]*self.psi_x[:,0] + \\\n",
     "                       np.conj(self.psi_x[:,1])*self.h[:,1,1]*self.psi_x[:,1] )*self.dx\n",
     "        self.epot[int(self.t/self.dt)] = np.real(epot)\n",
     "\n",
@@ -274,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "id": "8056c5b0",
    "metadata": {},
    "outputs": [],
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "id": "9fd44a50",
    "metadata": {},
    "outputs": [],
@@ -376,14 +376,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "id": "2b699f3d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "36ab3f1aab3f480e96b8b453ceb610be",
+       "model_id": "5d395b2b21c24046ad6d6361add28e98",
        "version_major": 2,
        "version_minor": 0
       },
@@ -397,7 +397,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9738d2f57db2421e923a92913f85630a",
+       "model_id": "cdafe23deb834e7eb633251c48452c35",
        "version_major": 2,
        "version_minor": 0
       },
@@ -584,13 +584,15 @@
     "    # pot_prev_V2_ad_x_line, = pot_ax.plot(S.x, S.E_eigvals[:,1], c='cyan')#, label=r'$V_{ad}(x)$')\n",
     "    # pot_prev_V1_di_x_line, = pot_ax.plot(S.x, S.h[:,0,0], c='green', label=r'$V_{di}(x)$')\n",
     "    # pot_prev_V2_di_x_line, = pot_ax.plot(S.x, S.h[:,1,1], c='green')#, label=r'$V_{di}(x)$')\n",
-    "    # pot_prev_V2_di_coupling_x_line, = pot_ax.plot(S.x,S.h[:1,0], c='red', label='$h_{12}(x)$')#, label=r'$V_{di}(x)$')\n",
+    "#     pot_prev_V2_di_coupling_x_line, = pot_ax.plot(S.x,S.h[:,1,0], c='red', label='$h_{12}(x)$')#, label=r'$V_{di}(x)$')\n",
     "    # pot_ax.legend()\n",
+    "    pot_prev_V2_di_coupling_x_line.set_data(S.x,S.h[:,1,0])#, label=r'$V_{di}(x)$')\n",
+    "\n",
     "    pot_prev_V1_ad_x_line.set_data(S.x, S.E_eigvals[:,0])#, c='purple', label=r'$V_{ad}(x)$')\n",
     "    pot_prev_V2_ad_x_line.set_data(S.x, S.E_eigvals[:,1])#,c='purple')\n",
     "    pot_prev_V1_di_x_line.set_data(S.x, S.h[:,0,0])#, c='green', label=r'$V_{di}(x)$')\n",
     "    pot_prev_V2_di_x_line.set_data(S.x, S.h[:,1,1])#,c ='green')\n",
-    "    #pot_prev_V2_di_coupling_x_line.set_data(S.x,0.1*np.ones(len(S.h)))#,  c='red', label='$h_{12}(x)$')\n",
+    "#     pot_prev_V2_di_coupling_x_line.set_data(S.x,0.1*np.ones(len(S.h)))#,  c='red', label='$h_{12}(x)$')\n",
     "    S.evolution(1)\n",
     "\n",
     "\n",
@@ -610,14 +612,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "b19b0ff6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "10835ccaa2b8438a9b2f8282ef552bc2",
+       "model_id": "e102426b9323454c8710d188dd555974",
        "version_major": 2,
        "version_minor": 0
       },
@@ -690,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "30286f7c",
    "metadata": {
     "tags": []
@@ -706,7 +708,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "818118610dd44191a74310c72993e0bf",
+       "model_id": "518d73fad551454882ec94287e7faead",
        "version_major": 2,
        "version_minor": 0
       },
@@ -731,7 +733,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "59280bcfc1fa4370bc09718703991588",
+       "model_id": "0ff114d986e844fab740d2f00ce5ac02",
        "version_major": 2,
        "version_minor": 0
       },
@@ -745,7 +747,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "62887066e63741ba8479efb064f17c01",
+       "model_id": "c7d8d84df2024e6e84d555705772c748",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1030,7 +1032,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,