examples/test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  ++++++++++++++++++++++++++++++++++++++++++++\n",
      "  +     MAD-X 5.08.01  (64 bit, Darwin)      +\n",
      "  + Support: mad@cern.ch, http://cern.ch/mad +\n",
      "  + Release   date: 2022.02.25               +\n",
      "  + Execution date: 2022.11.12 14:34:48      +\n",
      "  ++++++++++++++++++++++++++++++++++++++++++++\n"
     ]
    }
   ],
   "source": [
    "# numpy: our main numerical package\n",
    "import numpy as np\n",
    "# matplotlib and seaborn: our plotting packages\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# widget for producing animations:\n",
    "from ipywidgets import interactive\n",
    "# Note: to produce animations you might need to configure your jupyter lab properly:\n",
    "# > pip install ipywidgets\n",
    "# > jupyter nbextension enable --py widgetsnbextension\n",
    "# > jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
    "\n",
    "# linear algebra and optimisation algorithms\n",
    "from numpy.linalg import norm\n",
    "from scipy.optimize import minimize\n",
    "# some other useful package\n",
    "from copy import deepcopy\n",
    "\n",
    "# our optics package\n",
    "from cpymad.madx import Madx\n",
    "madx = Madx()\n",
    "\n",
    "import PyNAFF as pnf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Matrix definition\n",
    "Omega=np.array([[0, 1],[-1,0]])\n",
    "M=np.array([[1, 0],[1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum and multiplication of matrices\n",
    "Omega - M.T @ Omega @ M\n",
    "# M.T means the \"traspose of M\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function definition\n",
    "def Q(f=1):\n",
    "    return np.array([[1, 0],[-1/f,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 1.]),\n",
       " array([[ 0.00000000e+00,  2.22044605e-16],\n",
       "        [ 1.00000000e+00, -1.00000000e+00]]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eigenvalues and eigenvector\n",
    "np.linalg.eig(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entire array: [0.43916602 0.62954114 0.03002016 0.7456181  0.59105602 0.8016237\n",
      " 0.19775254 0.7935036  0.88258795 0.05175047]\n",
      "********************************************************************************\n",
      "Specific element (5th): 0.8016236996624654\n",
      "Last element: 0.051750471327519865\n",
      "Specific elements (from 3rd to 7th): [0.7456181  0.59105602 0.8016237  0.19775254]\n",
      "First 5 elements array: [0.43916602 0.62954114 0.03002016 0.7456181  0.59105602]\n",
      "Last 5 elements array: [0.8016237  0.19775254 0.7935036  0.88258795 0.05175047]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create an array\n",
    "array1d = np.random.uniform(size=10)\n",
    "# Print selected elements\n",
    "print(f'Entire array: {array1d}\\n{80*\"*\"}')\n",
    "print(f'Specific element (5th): {array1d[5]}')\n",
    "print(f'Last element: {array1d[-1]}')\n",
    "print(f'Specific elements (from 3rd to 7th): {array1d[3:7]}')\n",
    "print(f'First 5 elements array: {array1d[:5]}')\n",
    "print(f'Last 5 elements array: {array1d[5:]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entire array: [0.49567513 0.82782758 0.0736709  0.20687557 0.35800835 0.02396125\n",
      " 0.49164229 0.05844169 0.23879471 0.41256486]\n",
      "\n",
      "Each element muliplied by 5: [2.47837563 4.13913788 0.3683545  1.03437785 1.79004175 0.11980625\n",
      " 2.45821145 0.29220844 1.19397357 2.0628243 ]\n",
      "\n",
      "Each element squared: [2.45693830e-01 6.85298495e-01 5.42740151e-03 4.27975016e-02\n",
      " 1.28169979e-01 5.74141500e-04 2.41712142e-01 3.41543079e-03\n",
      " 5.70229154e-02 1.70209763e-01]\n",
      "\n",
      "Square root of each element: [0.70404199 0.9098503  0.27142384 0.45483576 0.59833799 0.15479422\n",
      " 0.70117208 0.24174716 0.48866626 0.64231212]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create an array\n",
    "array1d = np.random.uniform(size=10)\n",
    "\n",
    "print(\"Entire array: \" + str(array1d) + \"\\n\")\n",
    "print(\"Each element muliplied by 5: \" + str(5 * array1d) + \"\\n\")\n",
    "print(\"Each element squared: \" + str(array1d**2) + \"\\n\")\n",
    "print(\"Square root of each element: \" + str(np.sqrt(array1d)) + \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 1.]),\n",
       " array([[ 0.00000000e+00,  2.22044605e-16],\n",
       "        [ 1.00000000e+00, -1.00000000e+00]]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Matrix definition\n",
    "Omega=np.array([[0, 1],[-1,0]])\n",
    "M=np.array([[1, 0],[1,1]])\n",
    "\n",
    "# Sum and multiplication of matrices\n",
    "Omega - M.T @ Omega @ M\n",
    "# M.T means the \"traspose of M\".\n",
    "\n",
    "# Function definition\n",
    "def Q(f=1):\n",
    "    return np.array([[1, 0],[-1/f,1]])\n",
    "\n",
    "#Eigenvalues and eigenvectors\n",
    "np.linalg.eig(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'My title')"
      ]
     },
     "execution_count": 9,
     "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": [
    "# check a simple plot\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot([0,10],[0,10],'ob-')\n",
    "plt.xlabel('My x-label [arb. units]')\n",
    "plt.ylabel('My y-label [arb. units]')\n",
    "plt.title('My title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f8e9eebaa60>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "rs = np.random.RandomState(11)\n",
    "x = rs.normal(size=1000)\n",
    "y = rs.normal(size=1000)\n",
    "sns.jointplot(x=x, y=y, kind=\"hex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LOCATION</th>\n",
       "      <th>INDICATOR</th>\n",
       "      <th>SUBJECT</th>\n",
       "      <th>MEASURE</th>\n",
       "      <th>FREQUENCY</th>\n",
       "      <th>TIME</th>\n",
       "      <th>Value</th>\n",
       "      <th>Flag Codes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AUS</td>\n",
       "      <td>GDP</td>\n",
       "      <td>TOT</td>\n",
       "      <td>MLN_USD</td>\n",
       "      <td>A</td>\n",
       "      <td>1960</td>\n",
       "      <td>25073.260</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUS</td>\n",
       "      <td>GDP</td>\n",
       "      <td>TOT</td>\n",
       "      <td>MLN_USD</td>\n",
       "      <td>A</td>\n",
       "      <td>1961</td>\n",
       "      <td>25364.881</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUS</td>\n",
       "      <td>GDP</td>\n",
       "      <td>TOT</td>\n",
       "      <td>MLN_USD</td>\n",
       "      <td>A</td>\n",
       "      <td>1962</td>\n",
       "      <td>27955.357</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AUS</td>\n",
       "      <td>GDP</td>\n",
       "      <td>TOT</td>\n",
       "      <td>MLN_USD</td>\n",
       "      <td>A</td>\n",
       "      <td>1963</td>\n",
       "      <td>30432.979</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AUS</td>\n",
       "      <td>GDP</td>\n",
       "      <td>TOT</td>\n",
       "      <td>MLN_USD</td>\n",
       "      <td>A</td>\n",
       "      <td>1964</td>\n",
       "      <td>32745.289</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LOCATION INDICATOR SUBJECT  MEASURE FREQUENCY  TIME      Value Flag Codes\n",
       "0      AUS       GDP     TOT  MLN_USD         A  1960  25073.260        NaN\n",
       "1      AUS       GDP     TOT  MLN_USD         A  1961  25364.881        NaN\n",
       "2      AUS       GDP     TOT  MLN_USD         A  1962  27955.357        NaN\n",
       "3      AUS       GDP     TOT  MLN_USD         A  1963  30432.979        NaN\n",
       "4      AUS       GDP     TOT  MLN_USD         A  1964  32745.289        NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a simple pandas dataframe (GDP world statistics)\n",
    "myDF=pd.read_csv('https://stats.oecd.org/sdmx-json/data/DP_LIVE/.GDP.../OECD?contentType=csv&detail=code&separator=comma&csv-lang=en')\n",
    "myDF[(myDF['TIME']==2018) & (myDF['MEASURE']=='MLN_USD')]\n",
    "myDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22ad969afc924182b87d3d3be3e27294",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='f', max=1.0), Output(layout=Layout(height='300px')))…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to have the animation you need to configure properly your jupyter lab\n",
    "# From https://towardsdatascience.com/interactive-controls-for-jupyter-notebooks-f5c94829aee6\n",
    "\n",
    "# > pip install ipywidgets\n",
    "# > jupyter nbextension enable --py widgetsnbextension\n",
    "# > jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
    "\n",
    "# Possibly you need also nodejs \n",
    "# https://anaconda.org/conda-forge/nodejs\n",
    "# > conda install -c conda-forge nodejs \n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interactive\n",
    "\n",
    "t = np.linspace(0,10,1000)\n",
    "def plotIt(f):\n",
    "    plt.plot(t, np.sin(2*np.pi*f*t))\n",
    "    plt.grid(True)\n",
    "    \n",
    "interactive_plot = interactive(plotIt,f=(0,1,.1),continuous_update=True)\n",
    "output = interactive_plot.children[-1]\n",
    "output.layout.height = '300px'\n",
    "interactive_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12344998270771194"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = np.linspace(1, 3000, num=3000, endpoint=True)\n",
    "Q = 0.12345\n",
    "signal = np.sin(2.0*np.pi*Q*t)\n",
    "pnf.naff(signal, 200, 1, 0 , False, window=1)\n",
    "# outputs an array of arrays for each frequency. Each sub-array includes:\n",
    "# [order of harmonic, frequency, Amplitude, Re{Amplitude}, Im{Amplitude]\n",
    "\n",
    "# My frequency is simply \n",
    "pnf.naff(signal, 200, 1, 0 , False)[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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