Delete PES_of_Hamiltonian.ipynb

23e9612b · Amir Mirzanejad · cbd7c88b · cbd7c88b
Commit 23e9612b authored 1 year ago by Amir Mirzanejad
--- a/PES_of_Hamiltonian.ipynb
+++ b/PES_of_Hamiltonian.ipynb
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "c1bacd2c-a5c0-4cd4-b78d-9728220c136c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe\n",
-       "    scrolling=\"no\"\n",
-       "    width=\"1220px\"\n",
-       "    height=\"1820\"\n",
-       "    src=\"iframe_figures/figure_33.html\"\n",
-       "    frameborder=\"0\"\n",
-       "    allowfullscreen\n",
-       "></iframe>\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# improt dependecies\n",
-    "import plotly\n",
-    "import scipy.ndimage\n",
-    "import numpy as np\n",
-    "import scipy\n",
-    "import plotly.io as pio\n",
-    "import plotly.graph_objects as go\n",
-    "\n",
-    "# Assuming Energy values from 'HubbardModelTriqs.ipynb' are stored in 'Energies_U_0.npy' file \n",
-    "Enegies_U_0 = np.load(\"Energies_U_0.npy\")\n",
-    "\n",
-    "# Apply the Gaussian filter\n",
-    "sigma = [0.1, 0.1]\n",
-    "data_smooth_U   = scipy.ndimage.gaussian_filter(Enegies_U_0[:,:,0], sigma)\n",
-    "data_smooth_1_U = scipy.ndimage.gaussian_filter(Enegies_U_0[:,:,1], sigma)\n",
-    "data_smooth_2_U = scipy.ndimage.gaussian_filter(Enegies_U_0[:,:,2], sigma)\n",
-    "\n",
-    "data_smooth   = Enegies_U_0[:,:,0]\n",
-    "data_smooth_1 = Enegies_U_0[:,:,1]\n",
-    "data_smooth_2 = Enegies_U_0[:,:,2]\n",
-    "\n",
-    "colorscale1 = [[0, 'yellow'],     [1, 'darkorange']]\n",
-    "colorscale2 = [[0, 'darkorange'], [1, '#FF9912']]\n",
-    "colorscale3 = [[0, '#FF9912'],    [1, '#FF6103']]\n",
-    "\n",
-    "fig = go.Figure()\n",
-    "\n",
-    "# Add surface trace without contours, using the smoothed data\n",
-    "fig.add_trace(go.Surface(z=data_smooth_U, colorscale=colorscale1))\n",
-    "fig.add_trace(go.Surface(z=data_smooth_1_U, colorscale=colorscale2))\n",
-    "fig.add_trace(go.Surface(z=data_smooth_2_U, colorscale=colorscale3))\n",
-    "\n",
-    "# Generate x and y indices\n",
-    "x = np.arange(0, data_smooth.shape[1])\n",
-    "y = np.arange(0, data_smooth.shape[0])\n",
-    "\n",
-    "# Add lines that correspond to constant x values\n",
-    "for i in range(data_smooth.shape[0]):\n",
-    "    fig.add_trace(go.Scatter3d(x=np.full_like(y, i), y=y, z=data_smooth[:, i], mode='lines', \n",
-    "                               line=dict(color='black', width=5), showlegend=False))    \n",
-    "for i in range(data_smooth.shape[0]):\n",
-    "    fig.add_trace(go.Scatter3d(x=np.full_like(y, i), y=y, z=data_smooth_1[:, i], mode='lines', \n",
-    "                               line=dict(color='black', width=3), showlegend=False))\n",
-    "for i in range(data_smooth.shape[0]):\n",
-    "    fig.add_trace(go.Scatter3d(x=np.full_like(y, i), y=y, z=data_smooth_2[:, i], mode='lines', \n",
-    "                               line=dict(color='black', width=3), showlegend=False))\n",
-    "\n",
-    "# Adjust position of camera    \n",
-    "camera_params = dict(up=dict(x=1.25,y=0,z=1),center=dict(x=0,y=0,z=0),eye=dict(x=2.9,y=-2.1,z=0.5)) \n",
-    "\n",
-    "fig.update_layout(scene_camera=camera_params)\n",
-    "\n",
-    "fig.update_layout(title='PES of Hamiltonian for U = 0', autosize=True,\n",
-    "    scene = dict(xaxis = dict(title='', title_font_family=\"Arial\", title_font_color='black', title_font_size=25), \n",
-    "                 yaxis = dict(title='', title_font_family=\"Arial\", title_font_color='black', title_font_size=25),\n",
-    "                 zaxis = dict(title='', title_font_family=\"Arial\", title_font_color='black', title_font_size=25, \n",
-    "                              tickfont=dict(size=15), range=[-3.1,-1]),),\n",
-    "                 width=1200, height=1800, margin=dict(l=60, r=60, b=60, t=90))\n",
-    "\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "beaf20ba-ea31-4a8b-87cd-ffa142c41e2c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.10.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
-%% Cell type:code id:c1bacd2c-a5c0-4cd4-b78d-9728220c136c tags:
-
-``` python
-# improt dependecies
-import plotly
-import scipy.ndimage
-import numpy as np
-import scipy
-import plotly.io as pio
-import plotly.graph_objects as go
-
-# Assuming Energy values from 'HubbardModelTriqs.ipynb' are stored in 'Energies_U_0.npy' file
-Enegies_U_0 = np.load("Energies_U_0.npy")
-
-# Apply the Gaussian filter
-sigma = [0.1, 0.1]
-data_smooth_U   = scipy.ndimage.gaussian_filter(Enegies_U_0[:,:,0], sigma)
-data_smooth_1_U = scipy.ndimage.gaussian_filter(Enegies_U_0[:,:,1], sigma)
-data_smooth_2_U = scipy.ndimage.gaussian_filter(Enegies_U_0[:,:,2], sigma)
-
-data_smooth   = Enegies_U_0[:,:,0]
-data_smooth_1 = Enegies_U_0[:,:,1]
-data_smooth_2 = Enegies_U_0[:,:,2]
-
-colorscale1 = [[0, 'yellow'],     [1, 'darkorange']]
-colorscale2 = [[0, 'darkorange'], [1, '#FF9912']]
-colorscale3 = [[0, '#FF9912'],    [1, '#FF6103']]
-
-fig = go.Figure()
-
-# Add surface trace without contours, using the smoothed data
-fig.add_trace(go.Surface(z=data_smooth_U, colorscale=colorscale1))
-fig.add_trace(go.Surface(z=data_smooth_1_U, colorscale=colorscale2))
-fig.add_trace(go.Surface(z=data_smooth_2_U, colorscale=colorscale3))
-
-# Generate x and y indices
-x = np.arange(0, data_smooth.shape[1])
-y = np.arange(0, data_smooth.shape[0])
-
-# Add lines that correspond to constant x values
-for i in range(data_smooth.shape[0]):
-    fig.add_trace(go.Scatter3d(x=np.full_like(y, i), y=y, z=data_smooth[:, i], mode='lines',
-                               line=dict(color='black', width=5), showlegend=False))
-for i in range(data_smooth.shape[0]):
-    fig.add_trace(go.Scatter3d(x=np.full_like(y, i), y=y, z=data_smooth_1[:, i], mode='lines',
-                               line=dict(color='black', width=3), showlegend=False))
-for i in range(data_smooth.shape[0]):
-    fig.add_trace(go.Scatter3d(x=np.full_like(y, i), y=y, z=data_smooth_2[:, i], mode='lines',
-                               line=dict(color='black', width=3), showlegend=False))
-
-# Adjust position of camera
-camera_params = dict(up=dict(x=1.25,y=0,z=1),center=dict(x=0,y=0,z=0),eye=dict(x=2.9,y=-2.1,z=0.5))
-
-fig.update_layout(scene_camera=camera_params)
-
-fig.update_layout(title='PES of Hamiltonian for U = 0', autosize=True,
-    scene = dict(xaxis = dict(title='', title_font_family="Arial", title_font_color='black', title_font_size=25),
-                 yaxis = dict(title='', title_font_family="Arial", title_font_color='black', title_font_size=25),
-                 zaxis = dict(title='', title_font_family="Arial", title_font_color='black', title_font_size=25,
-                              tickfont=dict(size=15), range=[-3.1,-1]),),
-                 width=1200, height=1800, margin=dict(l=60, r=60, b=60, t=90))
-
-fig.show()
-```
-
-%% Output
-
-
-%% Cell type:code id:beaf20ba-ea31-4a8b-87cd-ffa142c41e2c tags:
-
-``` python
-```