{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Interferometric Wavefront Data\n",
    "\n",
    "In this example, we will see how to use prysm to almost entirely supplant the software that comes with a commerical interferometer to analyze the wavefront of an optic.  We begin by importing the relevant classes and setting some aesthetics for matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prysm import Interferogram, FringeZernike, sample_files, zernikefit\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We point prysm to the file, create a new interferogram, mask it to a circular region 100 mm across, subtract piston, tip/tilt and power, and evalute the PV and RMS wavefront error. We also plot the wavefront."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = sample_files('dat')  # sample Zygo .dat file, will be downloaded on demand and saved locally\n",
    "i = Interferogram.from_zygo_dat(p)\n",
    "i.crop().mask('circle', 40).crop()\n",
    "i.remove_piston_tiptilt_power()\n",
    "print(i.pv, i.rms)\n",
    "i.plot2d(clim=100, interpolation='bilinear')  # +/- 100 nm\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interferogram is cropped twice – once to enclose the valid data, then again to apply a mask centered on that region. For relatively conventional interferometry, you may want to stop here. If you want to use a different unit, that is easy enough,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i.change_z_unit('waves')\n",
    "1/i.pv, 1/i.rms  # print reciprocal -- \"one over xxx waves\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no need to crop again since the outer bound has not changed. Perhaps you wish to evaluated the RMS within the 1 - 10 mm spatial periods,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i.change_z_unit('nm')\n",
    "i.fill()\n",
    "i.bandlimited_rms(1,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This value is derived from the PSD, so you must call fill first. Do not worry about the corners of the array containing data - it will be windowed out. If you do this on a part which has a central obscuration or otherwise departs from being a circle or rectangle, the result will be correct.\n",
    "\n",
    "If you wish to decompose the wavefront into Zernike polynomials, that is easy enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do this on data which has not been filled to avoid errors introduced by the fill value.\n",
    "coefficients = zernikefit(i.phase, terms=36, norm=True, map_='Fringe')\n",
    "fz = FringeZernike(coefficients, dia=i.diameter, z_unit=i.z_unit, norm=True)\n",
    "print(fz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This print might be a bit daunting, one may prefer to see the top few terms by magnitude,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fz.top_n(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or a barplot of all terms,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fz.barplot_magnitudes(orientation='v', sort=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sample data has a circular clear aperture, but if it had a central obscuration (such as transmitted wavefront data for a telescope) that would be easy to mask too.  Here we will build a composite mask for the data as if it were a telescope with an annual aperture disrupted by a spider:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from prysm.geometry import circle, inverted_circle, generate_spider\n",
    "\n",
    "outer = circle(i.samples_x, radius=1) # radius has units of array semidiameter\n",
    "inner = inverted_circle(i.samples_x, radius=0.35)\n",
    "\n",
    "# width has units of arydiam, or pixels if arydiam=None\n",
    "spider = generate_spider(vanes=3, width=0.5, rotation=90, arydiam=i.diameter, samples=i.samples_x)\n",
    "mask = outer * inner * spider\n",
    "\n",
    "i.mask(mask)\n",
    "i.plot2d(clim=100)  # +/- 100 nm\n",
    "plt.grid(False)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}