Add a couple of notebooks to start from

nanotech-empa · Jan 28, 2022 · 401d054 · 401d054
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diff --git a/Untitled.ipynb b/Untitled.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2c23f7af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%aiida\n",
+    "import aiida_nanotech_empa.utils.stm_tools as stm\n",
+    "import aiida_nanotech_empa.utils.gaussian_wcs_postprocess as pp\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import ipywidgets as ipw\n",
+    "from IPython.display import clear_output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6a09415c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node = load_node(1081)\n",
+    "out = ipw.Output()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9cfd3ffc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cpa = node.outputs.gs_cube_planes\n",
+    "cpa_dict = stm.process_cube_planes_array(cpa) # changes for different multiplicity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9230c09b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replot(_=None):\n",
+    "    with out:\n",
+    "        clear_output()\n",
+    "        #pp.plot_cube_images(node.outputs.gs_cube_images)\n",
+    "        sop = dict(node.outputs.gs_out_params) # different for different multiplicity\n",
+    "        _fig, axs = plt.subplots(nrows=1, ncols=5)\n",
+    "        i_spin = 0\n",
+    "        stm.plot_mapping(sop, cpa, orbitals.value, spin.value, kind='orb', h=heights.value, ax=axs[3], extrap_h=3.0, fwhm=0.05)\n",
+    "        #plt.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8baa4d3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sop = dict(node.outputs.gs_out_params) # different for different multiplicity\n",
+    "len(sop['homos']) # finding how many spins are there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "24ee5e1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "orbitals = ipw.Dropdown(description=\"Orbitals\", options = sorted(cpa_dict['mo_planes'].keys()))\n",
+    "orbitals.observe(replot, names='value')\n",
+    "extrapolation_planes = ipw.Dropdown(description=\"Extrapolation planes\", options = cpa_dict['heights'])\n",
+    "extrapolation_planes.observe(replot, names='value')\n",
+    "heights = ipw.FloatSlider(description=\"Heights\", value=extrapolation_planes.value+3)\n",
+    "ipw.dlink((extrapolation_planes, 'value'), (heights, 'max'), transform=lambda v: v+5.0)\n",
+    "ipw.dlink((extrapolation_planes, 'value'), (heights, 'min'))\n",
+    "heights.observe(replot, names='value')\n",
+    "spin = ipw.Dropdown(description='Spin:', value = 0, options= {'Up': 0, 'Down':1})\n",
+    "spin.observe(replot, names='value')\n",
+    "kind = ipw.Dropdown(description=\"Kind\", options = [('Orbital', 'orb'), ('Orbitalˆ2', 'orb2'), ('STS','sts')])\n",
+    "kind.observe(replot, names='value')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ad3eb18e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2c93474b6eb4f7ea8ab213b0ee839d1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Dropdown(description='Orbitals', options=(0, 1, 2, 3, 4, 5, 6, 7, 8), value=0)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3790e4012a494e69b85419fc1a548966",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Dropdown(description='Extrapolation planes', options=(3.0, 4.0), value=3.0)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a74b65ad8b4945eea5a1b76cf12118ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "FloatSlider(value=6.0, description='Heights', max=8.0, min=3.0)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33b47df773304fb48b98246d5b6bcdeb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Dropdown(description='Spin:', options={'Up': 0, 'Down': 1}, value=0)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c4d39f51fde04bd1a9051b3db33be682",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Dropdown(description='Kind', options=(('Orbital', 'orb'), ('Orbitalˆ2', 'orb2'), ('STS', 'sts')), value='orb')"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b37028f76778450391db70b730cc68fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(orbitals, extrapolation_planes, heights, spin, kind, out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77239ed6",
+   "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": "3.7.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}