{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# Creating a plume radiance texture map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "This notebook forms part of a series on [computational optical radiometry](https://github.com/NelisW/ComputationalRadiometry#computational-optical-radiometry-with-pyradi).  The notebooks can be downloaded from [Github](https://github.com/NelisW/ComputationalRadiometry#computational-optical-radiometry-with-pyradi). These notebooks are constantly revised and updated, please revisit from time to time.  \n",
    "\n",
    "\n",
    "[<img src=\"https://zenodo.org/badge/doi/10.5281/zenodo.9910.png\"   align=\"left\"/>](http://dx.doi.org/10.5281/zenodo.9910)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "The date of this document and module versions used in this document are given at the end of the file.  \n",
    "Feedback is appreciated: neliswillers at gmail dot com."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "This notebook describes a method to create a radiance texture map of a CO$_\\textrm{2}$ aircraft engine plume in the MWIR band, starting from a temperature profile in graph format.  In this particular case study the temperature information is shown as spatial iso-temperature lines.  Plume radiance is three dimensional physical phenomenon but for this analysis it is simplified to a two dimensional projection of the radiance.\n",
    "\n",
    "The steps in this process are as follows:\n",
    "\n",
    "- Scan the graph to determine the $(d,r)$ coordinates for each of the iso-temperature lines.  These coordinates are not in a regular grid.\n",
    "- Interpolate the scanned temperature profiles from the scattered coordinates into a regular grid. The regular grid intervals can be set according to the user need. In this case a relatively dense grid is used to obtain an image of the plume radiance.\n",
    "- Calculate the radiance for each of the pixels, using the interpolated temperature values.  The radiance is calculated using spectral CO$_\\textrm{2}$ emissivity values.  Spectral emissivity shape depends on the temperature and pressure of the exhaust gas.  Accurate modelling therefore requires pressure profiles (in addition to the temperature profile), and also requires an accurate model of the spectral emissivity as function of temperature and pressure.  The radiance in each pixel is calculated using \n",
    "\\begin{equation}\n",
    "L(d,r) = \\epsilon(T,P,d,r) L_\\textrm{bb}(T)\n",
    "\\end{equation}\n",
    "where\n",
    "$\\epsilon(T,Pd,r)$ is the emissivity at location $(d,r)$ at temperature $T$ and pressure $P$, and \n",
    "$L_\\textrm{bb}(T)$ is the Planck-law radiance at temperature $T$.  This calculation does not attempt to calculate the emissivity from first principles, instead measured information is used to model the emissivity at different temperatures (disregarding pressure effects).  To obtain the wideband spectral radiance, the spectral radiance must then be integrated over the sensor spectral bandwidth.\n",
    "\\begin{equation}\n",
    "L_\\textrm{plume}(d,r) = \\int_{\\Delta\\lambda}\\tau_\\textrm{S}\\cdot\\epsilon_\\textrm{plume}(T_\\textrm{plume},d,r)\\cdot L_\\textrm{bb}(T_\\textrm{plume},d,r) \\textrm{d} \\lambda\n",
    "\\end{equation}\n",
    "where\n",
    "$\\tau_\\textrm{S}$ is the sensor spectral response, \n",
    "$\\epsilon_\\textrm{plume}(T_\\textrm{plume},d,r)$ is the plume spectral emissivity at temperature $T$ in pixel $(d,r)$, and \n",
    "$L(T_\\textrm{plume},d,r)$ is the Planck-law radiance at temperature $T$ pixel $(d,r)$.\n",
    "\n",
    "- In our intended application the emissivity texture will be used on a single polygon with a single temperature.  The radiance map is then used to calculate a scaling texture map, normalised to the maximum temperature.\n",
    "\\begin{equation}\n",
    "s(d,r) = L_\\textrm{plume}(d,r) /  L_\\textrm{bb}(T_\\textrm{max}).\n",
    "\\end{equation}\n",
    "Of course this an approximation, but it is sufficient for the current need."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Set up Python environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
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   "outputs": [],
   "source": [
    "# to prepare the environment\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import os.path\n",
    "from scipy.optimize import curve_fit\n",
    "from scipy import interpolate\n",
    "from scipy import integrate\n",
    "from scipy import signal\n",
    "from scipy import ndimage\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.constants as const\n",
    "import pickle\n",
    "\n",
    "import collections\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# %reload_ext autoreload\n",
    "# %autoreload 2\n",
    "\n",
    "import pyradi.ryplot as ryplot\n",
    "import pyradi.ryplanck as ryplanck\n",
    "import pyradi.ryfiles as ryfiles\n",
    "import pyradi.rymodtran as rymodtran\n",
    "import pyradi.ryutils as ryutils\n",
    "\n",
    "from IPython.display import HTML\n",
    "from IPython.display import Image\n",
    "from IPython.display import display\n",
    "from IPython.display import FileLink, FileLinks\n",
    "\n",
    "import matplotlib as mpl\n",
    "mpl.rc(\"savefig\", dpi=150)\n",
    "mpl.rc('figure', figsize=(10,8))\n",
    "# %config InlineBackend.figure_format = 'svg'\n",
    "# %config InlineBackend.figure_format = 'pdf'\n",
    "\n",
    "pim = ryplot.ProcessImage()\n",
    "pd.set_option('display.max_columns', 80)\n",
    "pd.set_option('display.width', 100)\n",
    "pd.set_option('display.max_colwidth', 150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Scan the temperature profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "The temperature profile is given in the form of a few graphs of iso-temperature values, redrawn as shown below.   It is assumed that these values represent temperatures in the plume as would be measured by a thermocouple (gas temperature).  For simplicity it is assumed that the plume is rotationally symmetrical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "![plume-temp.PNG](images/plume-temp.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Digitising data from a graph is a tiresome and inherently error-prone task. Manual (eyeball) reading the values is not suited for complex graphs, such as [spectroscopy data](https://www2.chemistry.msu.edu/faculty/reusch/virttxtjml/Spectrpy/InfraRed/infrared.htm).  For this purpose an alternative method was developed to scan an image of the graph using a short Python script.  The code is quite basic and therefore the procedure requires some manual preparatory work:\n",
    "\n",
    "1. Obtain a PNG image (JPEG may have too much noise) of the graph by scanning or screen grab (Snipping tool on Windows).\n",
    "2. Correct the image by removing any warping, skewing or rotation.  The end result should present the axes of the graph in a regular rectangle.  This task can be completed by using tools such as Photoshop, PrintShopPro, Gimp, ImageMagick or similar.\n",
    "3. Remove all clutter from the image that should not appear in the scan. This clutter includes grid lines and all notation on the graph.  Retain only the graph to be digitised, because the code is looking for non-zero pixels in the image.\n",
    "4.  Crop the image such that only the data portion of the graph is present in the image, in other words, the bounds of the image should correspond with the minimum and maximum values of the graph.  The image bounds are used to scale the sampled values later.  At this point the image should be sized to hold the graph line, and the $(d,r)$ values of the image edges must be known. \n",
    "5. Use the Python digitising function `extractGraph` to extract the scaled coordinates of the sampled image pixels in a text file.\n",
    "\n",
    "Instead of processing the graph image on bitmap pixel level, some graphs can be redrawn in a tool such as CorelDraw or Inscape.  Import the image and manually draw the lines with Bezier curves.   Once the line is captured in vector form, export a bitmap with this line, but with an image size required by the graph axes. In CorelDraw I do this by drawing the graph frame as a rectangle on the lower and upper $(d,r)$ graph limits, but making the rectangle line zero width, so as to not show in the image. Of course, this approach is only feasible for simple graphs.  This approach was used below.\n",
    "\n",
    "For alternative means to digitise graphs, see [this post](https://plus.google.com/communities/108773711053400791849), [this tool](http://markummitchell.github.io/engauge-digitizer/), or  [this tool](http://im2graph.co.il/).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "One of these 'clean-up' bitmaps is shown below. Note that there is no other marks in the image than the line to be scanned. The size of the image corresponds to the graph frame, which in turn corresponds to the graph scale values for $x$ and $y$.\n",
    "    \n",
    "![plume-015b.PNG](data/plume-015b.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "Note that the lines should have however small but different $(r,d)$ values, even if they appear to be falling on each other (see the figure below).  This requirement stems from the fact that all the sample points are eventually combined into a single data set.  Values with the same $(r,d)$ values, but different temperature values, lead to ambiguity in subsequent interpolation.  The value last presented in the data array will be used and this may lead to incorrect interpolation.  Essentially we must help the interpolation algorithm by ensuring that valid gradients may be calculated.  High or infinite gradients can be modelled by shifting the $(r,d)$ location a minute distance from each other, such as to present numerically feasible and stable gradients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "![plume-temp-zoom.PNG](images/plume-temp-zoom.PNG)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "The graphs are sampled into a scattered set of $(d,r,T)$ coordinates. These coordinate sets are sampled on the lines in the graphs above and are not in a regular grid (the next step creates datasets on regular grids).  The `pyradi.ryutils.extractGraph` function digitises the lines in the above graphs, one line at a time, creating the scatter data set.  Subsequent interpolation requires that the full graph domain (distance from tailpipe) and range (radial distance) be covered, hence the graphs above show values for the full $d$ domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "run_control": {
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   "source": [
    "def extractGraph(filename, xmin, xmax, ymin, ymax, outfile=None,doPlot=False,\\\n",
    "        xaxisLog=False, yaxisLog=False, step=None, value=None):\n",
    "    \"\"\"Scan an image containing graph lines and produce (x,y,value) data.\n",
    "    \n",
    "    This function processes an image, calculate the location of pixels on a \n",
    "    graph line, and then scale the (r,c) or (x,y) values of pixels with non-zero \n",
    "    values. The \n",
    "\n",
    "    Get a bitmap of the graph (scan or screen capture).\n",
    "    Take care to make the graph x and y axes horizontal/vertical.\n",
    "    The current version of the software does not work with rotated images.\n",
    "    Bitmap edit the graph. Clean the graph to the maximum extent possible,\n",
    "    by removing all the clutter, such that only the line to be scanned is visible.\n",
    "    Crop only the central block that contains the graph box, by deleting\n",
    "    the x and y axes notation and other clutter. The size of the cropped image \n",
    "    must cover the range in x and y values you want to cover in the scan. The \n",
    "    graph image/box must be cut out such that the x and y axes min and max\n",
    "    correspond exactly with the edges of the bitmap.\n",
    "    You must end up with nothing in the image except the line you want \n",
    "    to digitize.\n",
    "\n",
    "    The current version only handles single lines on the graph, but it does\n",
    "    handle vertical and horizontal lines.\n",
    "    \n",
    "    The function can also write out a value associated with the (x,y) coordinates \n",
    "    of the graph, as the third column. Normally these would have all the same \n",
    "    value if the line represents an iso value.\n",
    "\n",
    "    The x,y axes can be lin/lin, lin/log, log/lin or log/log, set the flags.\n",
    "\n",
    "    Args:\n",
    "        | filename: name of the image file\n",
    "        | xmin: the value corresponding to the left side (column=0)\n",
    "        | xmax: the value corresponding to the right side (column=max)\n",
    "        | ymin: the value corresponding to the bottom side (row=bottom)\n",
    "        | ymax: the value corresponding to the top side (row=top)\n",
    "        | outfile: write the sampled points to this output file\n",
    "        | doPlot: plot the digitised graph for visual validation\n",
    "        | xaxisLog: x-axis is in log10 scale (min max are log values)\n",
    "        | yaxisLog: y-axis is in log10 scale (min max are log values)\n",
    "        | step: if not None only ouput every step values\n",
    "        | value: if not None, write this value as the value column\n",
    "\n",
    "    Returns:\n",
    "        | outA: a numpy array with columns (xval, yval, value)\n",
    "        | side effect: a file may be written\n",
    "        | side effect: a graph may be displayed\n",
    "        \n",
    "    Raises:\n",
    "        | No exception is raised.\n",
    "\n",
    "    Author: neliswillers@gmail.com\n",
    "    \"\"\"\n",
    "\n",
    "    from scipy import ndimage\n",
    "    from skimage.morphology import medial_axis\n",
    "    if doPlot:\n",
    "        import pylab\n",
    "        import matplotlib.pyplot as pyplot\n",
    "     \n",
    "    #read image file, as grey scale\n",
    "    img = ndimage.imread(filename, True)\n",
    "\n",
    "    # find threshold 50% up the way\n",
    "    halflevel = img.min() + (img.max()-img.min()) /2\n",
    "    # form binary image by thresholding\n",
    "    img = img < halflevel\n",
    "    #find the skeleton one pixel wide\n",
    "    imgskel = medial_axis(img)\n",
    "    \n",
    "    #if doPlot:\n",
    "        # pylab.imshow(imgskel)\n",
    "        # pylab.gray()\n",
    "        # pylab.show()\n",
    "\n",
    "    # set up indices arrays to get x and y indices\n",
    "    ind = np.indices(img.shape)\n",
    "\n",
    "    #skeletonise the graph to one pixel only\n",
    "    #then get the y pixel value, using indices\n",
    "    yval = ind[0,...] * imgskel.astype(float)\n",
    "    \n",
    "    #if doPlot:\n",
    "        # pylab.imshow(yval>0)\n",
    "        # pylab.gray()\n",
    "        # pylab.show()\n",
    "        \n",
    "    # invert y-axis origin from left top to left bottom\n",
    "    yval = yval.shape[0] - np.max(yval, axis=0)\n",
    "    #get indices for only the pixels where we have data\n",
    "    wantedIdx = np.where(np.sum(imgskel, axis = 0) > 0)\n",
    "\n",
    "    # convert to original graph coordinates\n",
    "    cvec = np.arange(0.0,img.shape[1])\n",
    "\n",
    "    xval = xmin + (cvec[wantedIdx] / img.shape[1]) * (xmax - xmin)\n",
    "    xval = xval.reshape(-1,1)\n",
    "    yval = ymin + (yval[wantedIdx] / img.shape[0]) * (ymax - ymin)\n",
    "    yval = yval.reshape(-1,1)\n",
    "\n",
    "    if xaxisLog:\n",
    "        xval =  10** xval\n",
    "\n",
    "    if yaxisLog:\n",
    "        yval =  10 ** yval\n",
    "\n",
    "    #build the result array\n",
    "    outA = np.hstack((xval,yval))\n",
    "    if value is not None:\n",
    "        outA = np.hstack((outA,value*np.ones(yval.shape)))\n",
    "\n",
    "    # process step intervals\n",
    "    if step is not None:\n",
    "        # collect the first value, every step'th value, and last value\n",
    "        outA = np.vstack((outA[0,:],outA[1:-2:step,:],outA[-1,:]))\n",
    "\n",
    "    #write output file\n",
    "    if outfile is not None > 0 :\n",
    "        np.savetxt(outfile,outA)\n",
    "\n",
    "    if doPlot:\n",
    "        fig = pyplot.figure()\n",
    "        ax=fig.add_subplot(1,1,1)\n",
    "        ax.plot(xval,yval)\n",
    "        if xaxisLog:\n",
    "            ax.set_xscale('log')\n",
    "        if yaxisLog:\n",
    "            ax.set_yscale('log')\n",
    "        pylab.show()\n",
    "\n",
    "    return outA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# to digitise one graph line\n",
    "def prepareDataPoints(dicLines, gscale):\n",
    "    for i,item in enumerate(dicLines.keys()):\n",
    "\n",
    "        out = extractGraph(item, gscale[0], gscale[1], gscale[2],gscale[3],\n",
    "            None, False, False, False, step=dicLines[item][0], value=dicLines[item][1] )\n",
    "        #convert to Kelvin\n",
    "        out[:,2] += 273\n",
    "        if i==0:\n",
    "            outA = out\n",
    "        else:\n",
    "            outA = np.vstack((outA, out))  \n",
    "    return outA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "A dictionary stores the digitised data. The dictionary keys are the descriptions of the different data set. Each dictionary entry has three entries: (1) a list of the bitmaps for each of the lines, and the sample interval and iso-temperature (in degrees C) for the line, (2) the graph domain and range limits in metres, and (3) the scatter data set as digitised.\n",
    "\n",
    "The quality of the subsequent grid sampling depends on the fineness of the scattered data. On the other hand, too dense sampling creates too large a data set.  By experiment the optimal number of samples were found, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(531, 3)\n"
     ]
    }
   ],
   "source": [
    "#to digitise all the lines in all the graphs, and create one data structure for all data\n",
    "datasets = {\n",
    "    'Plume' :[{\n",
    "    'data/plume-015a.PNG':[25,15],\n",
    "    'data/plume-015b.PNG':[15,15],\n",
    "    'data/plume-032.PNG':[15,32],\n",
    "    'data/plume-060.PNG':[15,60],\n",
    "    'data/plume-116.PNG':[10,116],\n",
    "    'data/plume-171.PNG':[5,171],\n",
    "    'data/plume-282.PNG':[3,282],\n",
    "    'data/plume-394a.PNG':[3,394],\n",
    "    'data/plume-394b.PNG':[3,394],\n",
    "    },[0, 85.3, 0, 3.048] ],\n",
    "}\n",
    "\n",
    "for key in datasets.keys():\n",
    "    datasets[key].append(prepareDataPoints(datasets[key][0],datasets[key][1]))\n",
    "    print(datasets[key][2].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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26FZJq4HVwDeb20TrRPleJhheV93XV2huoYpGUkfdyhkcbWIrvY5WrapdquCk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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc561d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to plot the sampled temperature iso lines\n",
    "def plotScatterDataSet(dataset,key, ikey):\n",
    "    z = dataset[2]\n",
    "    p = ryplot.Plotter(ikey,1,1,figsize=(12,3));\n",
    "    p.plot(1,z[:,0], z[:,1],  xlabel='Distance from tail pipe m', ylabel='Radial distance m',\n",
    "                 ptitle='Samples: {}, temperature K'.format(key),markers=['.'],linestyle='' );\n",
    "\n",
    "for ikey,key in enumerate(datasets.keys()):\n",
    "    plotScatterDataSet(datasets[key],key, ikey)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Interpolate the scanned temperature profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "The scattered point digitised data sets must now be interpolated and resampled on a regular grid. This is done using the `scipy.interpolate.griddata` function.  To create symmetry in the boundary conditions, the scatter data set is mirrored along both axes. Linear interpolation works the best, cubic interpolation results in too many artefacts (possibly because of the sparse input data). After interpolation only the rear half, behinf the tailpipe, is retained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mtgp4SNIRwBpgMMM+HKdtivK6u2DvLvXs2y1xX3st9JrH3nEcp1eZlhgbqIT0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\nBvwJ8OnYbLdde7j9mhB2SnMUQfo8zGzczJ7Gbdc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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe35d400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to interpolate the temperature data to a regular grid\n",
    "def interpolateDataSet(dataset,key, ikey, numDsamples, numRsamples):\n",
    "    # get the scatter data set\n",
    "    z = dataset[2]\n",
    "    #mirror around the centre line\n",
    "    zn = z.copy()\n",
    "    zn[:,1] = -zn[:,1]\n",
    "    z = np.append(z,zn,axis=0)\n",
    "    #mirror around the tail pipe\n",
    "    zn = z.copy()\n",
    "    zn[:,0] = -zn[:,0]\n",
    "    z = np.append(z,zn,axis=0)\n",
    "\n",
    "    #create the regular grid , complex j signifies the number of samples, not step size\n",
    "    xnew, ynew = np.mgrid[-dataset[1][1]:dataset[1][1]:numDsamples * 1j, -dataset[1][3]:dataset[1][3]:numRsamples * 1j]\n",
    "    #interpolate\n",
    "    znew = interpolate.griddata(z[:,0:-1], z[:,-1], (xnew, ynew), method='linear', rescale=True)\n",
    "    # select only upper-right quadrant, behind tailpipe and above centreline\n",
    "    select = xnew.__ge__(0) & xnew.__le__(80.) & ynew.__ge__(0)\n",
    "    #number of selected points along the plume centreline:\n",
    "    numd = np.sum(select[:, -1])\n",
    "    #select and reshape back into 2D\n",
    "    xnew = xnew[select].reshape(numd,-1)\n",
    "    ynew = ynew[select].reshape(numd,-1)\n",
    "    znew = znew[select].reshape(numd,-1)\n",
    "\n",
    "    p = ryplot.Plotter(ikey,1,1,figsize=(12,3));\n",
    "    p.meshContour(1, xnew, ynew, znew, xlabel='Distance from tail pipe $d$ m', ylabel='Radial distance $r$ m',\n",
    "                  ptitle='{}, temperature K'.format(key), levels=100, contourLine=False, cbarshow=True);\n",
    "    # remove the plot frame to not obscure data \n",
    "    cp = p.getSubPlot(1)\n",
    "    cp.spines['top'].set_visible(False)\n",
    "    cp.spines['right'].set_visible(False)\n",
    "    cp.spines['bottom'].set_visible(False)\n",
    "    cp.spines['left'].set_visible(False)\n",
    "\n",
    "    return xnew, ynew, znew\n",
    "\n",
    "for ikey,key in enumerate(datasets.keys()):\n",
    "    resolution = 0.1  # metre\n",
    "    numd = 2 * np.max(datasets[key][2][:,0]) / resolution\n",
    "    numr = 2 * np.max(datasets[key][2][:,1]) / resolution\n",
    "    xnew, ynew, znew = interpolateDataSet(datasets[key],key, ikey, numd, numr)\n",
    "    datasets[key].append([xnew, ynew, znew])\n",
    "\n",
    "# at this point the data set dictionary has been appended to, and contains the following:\n",
    "# datasets[key][0]: list of the input graph lines\n",
    "# datasets[key][1]: list with the graph domain and range coverage\n",
    "# datasets[key][2]: single numpy array with _all_ the sampled points (d,r,T) \n",
    "# datasets[key][3]: list of arrays with regular grid data [x-mesh-grid, y-mesh-grid, temperature]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "At this point the temperatures are available on a fine regular grid as a function of down stream distance and radial displacement from the centre line.  The next step is to convert this to radiance. All imported and calculated data are stored in the `datasets` dictionary, for reuse later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Sensor spectral response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "The sensor spectral responses are used to support spectral integration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe88d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to load and plot the sensor responses\n",
    "sensors = ['Unity3_5.txt']\n",
    "titles = ['MWIR']\n",
    "q = ryplot.Plotter(1,1,1,figsize=(12,4))\n",
    "sensorSpcs = {}\n",
    "for i, (sensor, title) in enumerate(zip(sensors, titles) ):\n",
    "    sdata = np.loadtxt('data/{}'.format(sensor),comments='%')\n",
    "    sensorSpcs[sensor] = [sdata]\n",
    "    wn = sensorSpcs[sensor][0][:,1]\n",
    "    taus = sensorSpcs[sensor][0][:,2]\n",
    "    q.plot(1,wn,taus,xlabel='Wavenumber cm$^{-1}$', ylabel='Response', ptitle='Sensor response',\n",
    "        pltaxis=[500, 4000, 0, 1.1],label=[title],plotCol=[q.plotCol[i]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Plume emissivity\n",
    "\n",
    "In the code used for emissivity calculation, a dictionary of function names is used to calculate the spectral emissivity.  The dictionary uses the spectral band (`NIR`,`SWIR`,`MWIR`,`LWIR`) as key and takes wavenumber and temperature as variables.  The code is implemented and used as shown in the following pseudocode:\n",
    "\n",
    "    def emisLWIR(wn, temperature=None):\n",
    "        .....\n",
    "        return emiss\n",
    "\n",
    "    def emisMWIR(wn, temperature=None):\n",
    "        .....\n",
    "        return emiss\n",
    "\n",
    "    emisFnLU = {'LWIR': emisLWIR, 'MWIR': emisMWIR}\n",
    "\n",
    "When a spectral emissivity is required it is calculated as follows:\n",
    "\n",
    "    wn = np.linspace(...)\n",
    "    emislw = emisFnLU['LWIR'](wn,300)\n",
    "    emismw = emisFnLU['MWIR'](wn,300)\n",
    "\n",
    "Plume temperature is only used in the MWIR calculation and ignored in the other spectral bands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "### MWIR plume spectral emisivity\n",
    "\n",
    "The plume spectral emissivity is determined as follows:\n",
    "\n",
    "1.  A measured emissivity near the tail pipe for a jet engine at 800 K is available (red line in the graph below).  This curve shows the pressure and temperature broadened emissivity over a distance of 100 m through the atmosphere.  From this graph two important extreme values for emissivity can be determined: the emissivity at 800 K, given by the outer shape of the curve and the emissivity at near-atmospheric temperatures given by the atmospheric attenuation (the inner shape).\n",
    "2.  Determine approximate shapes for the 800 K and 300 K gas masses (see the graph below).\n",
    "3.  Fit a curve to the two extreme emissivity shapes and determine the centre and width of these shapes.\n",
    "4.  Given the centre and width at the two extremes calculate the emissivity shape by linear interpolation of the centre and width at the required temperature.  The exact law to be used for this interpolation is not known and a simple linear interpolation method is used.\n",
    "5. Given the temperature-derived centre and width, calculate the emissivity at that tempereture and use for radiance calculation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sKvdrOkb20DGy7bJjVAJsA7fg0zgm5rInIICzmzaxARgF/Pjjj3+kbQAnd57k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\nnn+awoYAlR2eV7Vt62gsYFdKlSmlNiilrj+Cc4UQJzHPAT/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4+gzOix0OnrXZ0HHpgRBC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lAAAf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\nGGNMgbDk3BhjjDHGmALx/wFR+ODj5/B9NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe973048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to digitise emissivity graphs and fit curves to emissivity model\n",
    "\n",
    "def prepareEmis(dicLines, gscale):\n",
    "    for i,item in enumerate(dicLines.keys()):\n",
    "\n",
    "        out = ryutils.extractGraph(item, gscale[0], gscale[1], gscale[2],gscale[3],\n",
    "            None, False, False, False, step=dicLines[item][0], value=dicLines[item][1] )\n",
    "        if i==0:\n",
    "            outA = out\n",
    "        else:\n",
    "            outA = np.vstack((outA, out))  \n",
    "    return outA\n",
    "\n",
    "emissets = {\n",
    "    300: [\n",
    "        {'data/emis-MWIR-300K.png':[10,None]},\n",
    "        [1600., 3400, 0., 1.0], [2335, 80]\n",
    "    ],\n",
    "    800: [\n",
    "        {'data/emis-MWIR-800K.png':[10,None]},\n",
    "        [1600., 3400, 0., 1.0],  [2308, 191] \n",
    "    ],\n",
    "}\n",
    "\n",
    "p = ryplot.Plotter(1,1,1,figsize=(12,4));\n",
    "for ikey,key in enumerate(emissets.keys()):\n",
    "    emissets[key].append(prepareEmis(emissets[key][0],emissets[key][1]))\n",
    "    p.plot(1,emissets[key][3][:,0],emissets[key][3][:,1],label=['Emissivity {} K eyeball estimate'.format(key)]);\n",
    "    \n",
    "    emisF =ryutils.sfilter(emissets[key][3][:,0],center=emissets[key][2][0], \n",
    "                           width=emissets[key][2][1], exponent=3, taupass=1, taustop=0.)\n",
    "    p.plot(1,emissets[key][3][:,0],emisF,\n",
    "           label=[r'Emissivity {} K fitted: $\\tilde{{\\nu}}_c$ {} $\\Delta\\tilde{{\\nu}}$ {} cm$^{{-1}}$ '.format(key,emissets[key][2][0],emissets[key][2][1] )]);\n",
    "emis = np.loadtxt('data/emis-800K-MWIR.txt')\n",
    "p.plot(1, emis[:,1],emis[:,2],'Emissivity; measured and model','Wavenumber cm$^{-1}$',\n",
    "       'Emissivity -',label=['Measured value at 800 K, near tailpipe'])  ;  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kpaUxZ84cHnzwwTar45pMurXWVcAC4D/ACWCL1vq4Umq+Ump+dbIQ4JhS6iSm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\nWymllFJKqUH2v3bFhn8mRIZfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe90a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to calculate spectral emissivity at different temperatures\n",
    "\n",
    "def emistemp(emissets, wn, temperature):\n",
    "    wncent = np.interp(temperature, np.asarray([300,800]), np.asarray([emissets[300][2][0],emissets[800][2][0]]))\n",
    "    wnwid  = np.interp(temperature, np.asarray([300,800]), np.asarray([emissets[300][2][1],emissets[800][2][1]]))\n",
    "    emisF =ryutils.sfilter(wn,center=wncent,width=wnwid, exponent=3, taupass=1, taustop=0.)\n",
    "    return emisF, wncent, wnwid\n",
    "\n",
    "def emisMWIR(wn, temperature):\n",
    "    emisF,_,_ = emistemp(emissets, wn, temperature)\n",
    "    return emisF\n",
    "\n",
    "emisFnLU = {'MWIR': emisMWIR}\n",
    "\n",
    "wn = emissets[300][3][:,0]\n",
    "\n",
    "p = ryplot.Plotter(1,1,1,figsize=(12,4));\n",
    "for temperature in [300, 400, 500, 600, 700, 800]:\n",
    "    emisF, wncent,wnwid = emistemp(emissets, wn, temperature)\n",
    "    p.plot(1,wn,emisF,'Emissivity model','Wavenumber cm$^{-1}$','Emissivity -',\n",
    "           label=[r'Emissivity {} K fitted: $\\tilde{{\\nu}}_c$ {} $\\Delta\\tilde{{\\nu}}$ {} cm$^{{-1}}$ '.format(temperature,wncent,wnwid )]);\n",
    "#     emisF = emisFnLU['MWIR'](wn, temperature)\n",
    "#     p.plot(1,wn,emisF,'Emissivity model','Wavenumber cm$^{-1}$',\n",
    "#        'Emissivity -',label=[r'Emissivity {} K fitted'.format(temperature)]);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Plume radiance calculation\n",
    "\n",
    "\\begin{equation}\n",
    "L_\\textrm{plume}(d,r) = \\int_{\\Delta\\lambda}\\tau_\\textrm{S}\\cdot\\epsilon_\\textrm{plume}(T_\\textrm{plume},d,r)\\cdot L_\\textrm{bb}(T_\\textrm{plume},d,r) \\textrm{d} \\lambda\n",
    "\\end{equation}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "At this point we have the sensor response, models for emissivity and the plume spatial temperature distribution.  The spectral integral can now be calculated for each pixel. Observe that the pixel location only provides temperature and that the integral can be simplified into the following steps:\n",
    "\n",
    "1. Calculate this integral and create a lookup table of temperature to radiance.\n",
    "\\begin{equation}\n",
    "f_{L_\\textrm{plume}}: T_\\textrm{plume} \\rightarrow \\int_{\\Delta\\lambda}\\tau_\\textrm{S}\\cdot\\epsilon_\\textrm{plume}(T_\\textrm{plume})\\cdot L_\\textrm{bb}(T_\\textrm{plume}) \\textrm{d} \\lambda\n",
    "\\end{equation}\n",
    "\n",
    "2. Look up the temperature at location $(d,r)$, and then look up the radiance at this temperature\n",
    "\\begin{equation}\n",
    "f_{T_\\textrm{plume}}: (d,r) \\rightarrow T_\\textrm{plume}\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "L_\\textrm{plume}(d,r) = f_{L_\\textrm{plume}}(f_{T_\\textrm{plume}}(d,r) )\n",
    "\\end{equation}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "# To be completed ...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "source": [
    "## Python and [module versions, and dates](https://github.com/rasbt/watermark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.6.1\n",
      "IPython 5.3.0\n",
      "\n",
      "numpy 1.12.1\n",
      "scipy 0.19.0\n",
      "pyradi 1.1.3\n",
      "\n",
      "compiler   : MSC v.1900 64 bit (AMD64)\n",
      "system     : Windows\n",
      "release    : 7\n",
      "machine    : AMD64\n",
      "processor  : Intel64 Family 6 Model 94 Stepping 3, GenuineIntel\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n",
      "Git hash   : c3b80d743fe636f891ac6123908a9228c749c47e\n"
     ]
    }
   ],
   "source": [
    "# to get software versions\n",
    "# https://github.com/rasbt/watermark\n",
    "# you only need to do this once\n",
    "# pip install watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%watermark -v -m -p numpy,scipy,pyradi -g "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "frozen": false,
     "read_only": false
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}