{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting XShooter data with xtool ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from xtool.data import XShooterData, Order\n",
    "from xtool.model import OrderModel, VirtualPixelWavelength, GenericBackground, MoffatTrace, SlopedMoffatTrace\n",
    "\n",
    "from collections import OrderedDict\n",
    "from scipy import sparse\n",
    "from scipy import optimize\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading XShooter data ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xd = XShooterData('xtool_ds/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "current_order = xd[17]\n",
    "current_order.enable_flags_as_mask() # important to update the data to include the bad pixels from the pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generating a virtual pixel table for \"Wavelength\"-pixels ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "virt_pix = VirtualPixelWavelength.from_order(current_order, wavelength_sampling=0.03)\n",
    "pixel_table = virt_pix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### Initializing the two Models ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "background_mdl = GenericBackground(pixel_table, virt_pix.wavelength_pixels)\n",
    "trace_mdl = SlopedMoffatTrace(pixel_table, virt_pix.wavelength_pixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "order_model = OrderModel([background_mdl, trace_mdl])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show fittable parameters ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<OrderModel(background_level=[    0.     0.  2023. ...,  2008.  2008.  2008.], amplitude=[ nan  nan  nan ...,  nan  nan  nan], trace_pos=0.0, trace_slope=0.0, sigma=1.0, beta=1.5 [f])>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initializing the fitter ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from xtool.model.fitters import Fitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fitter = Fitter(order_model, current_order)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Differential Evolution fit ####\n",
    "\n",
    "Differential evolution is a very thorough but slow processs. It does not use starting values but moves within the bounds of the parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 1: f(x)= 4.22785e+06\n",
      "differential_evolution step 2: f(x)= 4.22785e+06\n",
      "differential_evolution step 3: f(x)= 2.95083e+06\n",
      "differential_evolution step 4: f(x)= 2.95083e+06\n",
      "differential_evolution step 5: f(x)= 2.26008e+06\n",
      "differential_evolution step 6: f(x)= 2.26008e+06\n",
      "differential_evolution step 7: f(x)= 2.26008e+06\n",
      "differential_evolution step 8: f(x)= 2.26008e+06\n",
      "differential_evolution step 9: f(x)= 2.06608e+06\n",
      "differential_evolution step 10: f(x)= 2.06231e+06\n",
      "differential_evolution step 11: f(x)= 2.00095e+06\n",
      "differential_evolution step 12: f(x)= 1.96445e+06\n",
      "differential_evolution step 13: f(x)= 1.96445e+06\n",
      "differential_evolution step 14: f(x)= 1.96445e+06\n",
      "differential_evolution step 15: f(x)= 1.89723e+06\n",
      "differential_evolution step 16: f(x)= 1.89723e+06\n",
      "differential_evolution step 17: f(x)= 1.89723e+06\n",
      "differential_evolution step 18: f(x)= 1.89723e+06\n",
      "differential_evolution step 19: f(x)= 1.89654e+06\n",
      "differential_evolution step 20: f(x)= 1.89654e+06\n",
      "differential_evolution step 21: f(x)= 1.89654e+06\n",
      "Fit finished in 1475.61700892 s\n"
     ]
    }
   ],
   "source": [
    "order_model.beta.fixed = False\n",
    "dresult = fitter.fit_differential_evolution(disp=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     fun: 1896544.4149950482\n",
       " message: 'Optimization terminated successfully.'\n",
       "    nfev: 1425\n",
       "     nit: 21\n",
       " success: True\n",
       "       x: array([-2.44188896, -0.26832235,  0.19794569,  2.0046534 ])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dresult"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nelder Mead / Simplex ####\n",
    "\n",
    "This one is much faster algorithm that will use starting values, but is unbounded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wkerzend/anaconda3/envs/xtool/lib/python2.7/site-packages/scipy/optimize/_minimize.py:394: RuntimeWarning: Method Nelder-Mead cannot handle constraints nor bounds.\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit finished in 851.559898853 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wkerzend/anaconda3/envs/xtool/lib/python2.7/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:243: SparseEfficiencyWarning: splu requires CSC matrix format\n",
      "  warn('splu requires CSC matrix format', SparseEfficiencyWarning)\n",
      "/Users/wkerzend/anaconda3/envs/xtool/lib/python2.7/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:161: SparseEfficiencyWarning: spsolve is more efficient when sparse b is in the CSC matrix format\n",
      "  'is in the CSC matrix format', SparseEfficiencyWarning)\n"
     ]
    }
   ],
   "source": [
    "order_model.trace_pos = -2\n",
    "order_model.sigma = 0.1\n",
    "order_model.trace_slope = 0.\n",
    "order_model.beta.fixed = False\n",
    "order_model.beta = 2.0\n",
    "result = fitter.fit_scipy_minimize('Nelder-Mead')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "spec = order_model.model_list[1].to_spectrum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1231a7a50>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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JWPKoY96Tw+nJ4yc/yT5NtgVlp52y1z3/fOf9d7/LHOfd01eFTz7JfSJ84UL4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\nycMYY4xv/x/lFdNIhxwu1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12294d4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "plot(spec.wavelength, spec.flux)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting in DS9 ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pyds9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = pyds9.DS9()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_model.trace_pos = result.x[0]\n",
    "order_model.trace_slope = result.x[1]\n",
    "order_model.sigma = result.x[2]\n",
    "order_model.beta = result.x[3]\n",
    "model = order_model.evaluate_to_frame(current_order, trace_pos=order_model.trace_pos, \n",
    "                                      trace_slope=order_model.trace_slope, sigma=order_model.sigma, \n",
    "                                      beta=order_model.beta.value)\n",
    "d.set('frame 1')\n",
    "d.set_np2arr(current_order.data.filled())\n",
    "\n",
    "d.set('frame 2')\n",
    "d.set_np2arr(model.filled())\n",
    "\n",
    "d.set('frame 3')\n",
    "d.set_np2arr((current_order.data.filled() - model.filled()) / current_order.uncertainty.filled())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}