delete unnecessary files

0d4558e9 · juandaanieel · c7a171eb · c7a171eb · 0d4558e9 · 0d4558e9
Commit 0d4558e9 authored Nov 15, 2021 by juandaanieel
--- a/Untitled.ipynb
+++ b/Untitled.ipynb
--- a/docs/adiabatic_berry.md
+++ b/docs/adiabatic_berry.md
@@ -214,7 +214,7 @@ $$

 ## Summary

-The Berry phase can be expressed in terms of an arbitrary time-independent parameter $\mathbf{R}$ as,
+The Berry phase can be expressed in terms of an arbitrary time-dependent parameter $\mathbf{R}(t)$ as,
 $$
 \gamma_n(t) = i \int_{\vec{R}(0)}^{\vec{R}(t)} \langle{ \psi_n | \vec{\nabla_R}\psi_n} \rangle
 \cdot d\vec{R}.

--- a/docs/adiabatic_landau_zener.md
+++ b/docs/adiabatic_landau_zener.md
--- a/docs/adiabatic_theorem.md
+++ b/docs/adiabatic_theorem.md
--- a/docs/wkb_connection.md
+++ b/docs/wkb_connection.md
--- a/docs/wkb_smooth.md
+++ b/docs/wkb_smooth.md
--- a/docs/wkb_tunel.md
+++ b/docs/wkb_tunel.md
--- a/src/Untitled.ipynb
+++ b/src/Untitled.ipynb
--- a/src/__init__.py
+++ b/src/__init__.py
--- a/src/code/__init__.py
+++ b/src/code/__init__.py
--- a/src/figs.ipynb
+++ b/src/figs.ipynb
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cb56fad1",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f86ce09d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f235889adc0>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = np.linspace(0, np.pi, 100)\n",
-    "y = -np.cos(x)\n",
-    "plt.plot(x,y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a497f818",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def f_cos(x, resolution=0.1):\n",
-    "    np.\n",
-    "    n = x/resolution\n",
-    "    return cos()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.8.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
-%% Cell type:code id:cb56fad1 tags:
-
-``` python
-import matplotlib.pyplot as plt
-import numpy as np
-```
-
-%% Cell type:code id:f86ce09d tags:
-
-``` python
-x = np.linspace(0, np.pi, 100)
-y = -np.cos(x)
-plt.plot(x,y)
-```
-
-%% Output
-
-    [<matplotlib.lines.Line2D at 0x7f235889adc0>]
-
-
-
-%% Cell type:code id:a497f818 tags:
-
-``` python
-def f_cos(x, resolution=0.1):
-    np.
-    n = x/resolution
-    return cos()
-```