{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aac63f47",
   "metadata": {},
   "source": [
    "# Tutorial for HN module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa2f98e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary packages along with HNpy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import HavNegpy as dd\n",
    "%matplotlib qt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7af9ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract the data\n",
    "\n",
    "filename = 'hn_example_data.txt'\n",
    "col_names = ['log f', 'log eps2']\n",
    "df = pd.read_csv(filename, sep=',',index_col=False,usecols = [0,1],names=col_names,header=None,skiprows=2,encoding='unicode_escape',engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c9e0008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Example for HN fitting')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot the data \n",
    "\n",
    "x,y = df['log f'], df['log eps2']\n",
    "plt.scatter(x,y,label='example data')\n",
    "plt.xlabel('log f [Hz]')\n",
    "plt.ylabel('log $\\epsilon$\"')\n",
    "plt.legend()\n",
    "plt.title('Example for HN fitting')"
   ]
  },
  {
   "attachments": {
    "diel_loss_plot.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "a8f196d9",
   "metadata": {},
   "source": [
    "![diel_loss_plot.png](attachment:diel_loss_plot.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60a0ea31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate the HN module\n",
    "hn = dd.HN()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ade59a0",
   "metadata": {},
   "source": [
    "Select the ROI to fit the data using the select range method. \n",
    "The select range functions pops in a separate window and allows you two clicks to select the region of interest (ROI).\n",
    "The data in ROI is shown as image in the following cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae767322",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_lower_limit 2.5234707551868087 x_upper_limit 4.4693345833813005\n"
     ]
    }
   ],
   "source": [
    "#select range\n",
    "hn = dd.HN()\n",
    "x1,y1 = hn.select_range(x,y)"
   ]
  },
  {
   "attachments": {
    "ROI.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "a163d6c4",
   "metadata": {},
   "source": [
    "Plot of the ROI to fit the HN function\n",
    "![ROI.png](attachment:ROI.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec21620b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter the beta value:0.5\n",
      "enter the gamma value:1\n",
      "enter the fm:3.75\n",
      "enter the deps:0.5\n",
      "enter the cond:0\n",
      "enter the s:1\n",
      "dumped_parameters {'beta': 0.5, 'gamma': 1.0, 'freq': 5623.413251903491, 'deps': 0.5, 'cond': 0.0, 'n': 1.0}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dump the initial guess parameters using dump parameters method (varies for each fn), which dumps the parameters in a json file'\n",
    "#this is required before performing the first fitting as it takes the initial guess from the json file created\n",
    "\n",
    "hn.dump_parameters_hn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ba6f0d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded parameters \n",
      " {'beta': 0.5, 'gamma': 1.0, 'freq': 5623.413251903491, 'deps': 0.5, 'cond': 0.0, 'n': 1.0}\n"
     ]
    }
   ],
   "source": [
    "# view the initial fit based on the dumped parameters\n",
    "# the plot is shown as a image in the next cell\n",
    "\n",
    "hn.initial_view_hn(x1,y1)"
   ]
  },
  {
   "attachments": {
    "initial_guess.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "3c26a8e1",
   "metadata": {},
   "source": [
    "Plot of the initial fit based on the supplied parameters\n",
    "\n",
    "![initial_guess.png](attachment:initial_guess.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "640dca70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Choose the fit function\n",
      " 1 -- HN, 2 -- HN with cond, 3 -- Hn-flank, 4 -- double HN, 5 -- double HN with cond:1\n",
      "0.7326563571608663 0.34641197051059136 1945.8637251236246 3.2548369440810547\n",
      "log fmax: 3.8521833734663224\n",
      "fit parameters dumped for next iteration {'beta': 0.7326563571608663, 'gamma': 0.34641197051059136, 'freq': 1945.8637251236246, 'deps': 3.2548369440810547, 'cond': 0, 'n': 0}\n"
     ]
    }
   ],
   "source": [
    "# perform least squares fitting\n",
    "# the plot is shown as a image in the next cell\n",
    "\n",
    "hn.fit(x1,y1)"
   ]
  },
  {
   "attachments": {
    "fit_example.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "df8ea510",
   "metadata": {},
   "source": [
    "Plot of the final fit of the HN function to the data\n",
    "\n",
    "![fit_example.png](attachment:fit_example.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "851699f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to use an existing file to save fit results? \n",
      " eg: existing file to save HN parameters, y or n:n\n",
      "Choose the fit function\n",
      " 1 -- HN, 2 -- HN with cond, 3 -- HN-flank, 4 -- double HN, 5 -- double HN with cond:1\n",
      "Enter the analysis_file_name:hn_fit.TXT\n",
      "file did not exist, created hn_fit.TXT\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create a file to save fit results using create_analysis file method\n",
    "#before saving fit results an analysis file has to be created\n",
    "\n",
    "hn.create_analysis_file()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bece0584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#save the fit results using save_fit method of the corresponding fit function\n",
    "#takes one argument, read more on the documentation\n",
    "\n",
    "hn.save_fit_hn(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39d60211",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
