2022-12-02_caustique_single.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# source code\n",
    "\n",
    "- **single frame**\n",
    "- dataclass\n",
    "- image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.002832Z",
     "iopub.status.busy": "2022-09-16T06:50:17.002540Z",
     "iopub.status.idle": "2022-09-16T06:50:17.009923Z",
     "shell.execute_reply": "2022-09-16T06:50:17.009225Z"
    }
   },
   "outputs": [],
   "source": [
    "figpath = '2022-12-02_caustique_single'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.012647Z",
     "iopub.status.busy": "2022-09-16T06:50:17.012413Z",
     "iopub.status.idle": "2022-09-16T06:50:17.665184Z",
     "shell.execute_reply": "2022-09-16T06:50:17.664652Z"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.display import display\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.668239Z",
     "iopub.status.busy": "2022-09-16T06:50:17.667940Z",
     "iopub.status.idle": "2022-09-16T06:50:17.670759Z",
     "shell.execute_reply": "2022-09-16T06:50:17.670294Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECISION = 4 # the higher, the bigger the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.673182Z",
     "iopub.status.busy": "2022-09-16T06:50:17.672981Z",
     "iopub.status.idle": "2022-09-16T06:50:17.675440Z",
     "shell.execute_reply": "2022-09-16T06:50:17.674999Z"
    }
   },
   "outputs": [],
   "source": [
    "PRECISION = 10 # the higher, the bigger the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.677775Z",
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     "iopub.status.idle": "2022-09-16T06:50:17.686232Z",
     "shell.execute_reply": "2022-09-16T06:50:17.685757Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "def init(args=[], figpath=figpath, PRECISION=PRECISION):\n",
    "\n",
    "    import argparse\n",
    "    if figpath is None:\n",
    "        import datetime\n",
    "        date = datetime.datetime.now().date().isoformat()\n",
    "        figpath = f'{date}_caustique'\n",
    "\n",
    "    parser = argparse.ArgumentParser()\n",
    "    parser.add_argument(\"--tag\", type=str, default='caustique', help=\"Tag\")\n",
    "    parser.add_argument(\"--figpath\", type=str, default=figpath, help=\"Folder to store images\")\n",
    "    parser.add_argument(\"--ext\", type=str, default='png', help=\"Folder to store images\")\n",
    "    parser.add_argument(\"--nx\", type=int, default=5*2**PRECISION, help=\"number of pixels (vertical)\")\n",
    "    parser.add_argument(\"--ny\", type=int, default=5*2**PRECISION, help=\"number of pixels (horizontal)\")\n",
    "    parser.add_argument(\"--nframe\", type=int, default=1, help=\"number of frames\")\n",
    "    parser.add_argument(\"--bin_dens\", type=int, default=2, help=\"relative bin density\")\n",
    "    parser.add_argument(\"--bin_spectrum\", type=int, default=3, help=\"bin spacing in spectrum\")\n",
    "    parser.add_argument(\"--seed\", type=int, default=8601, help=\"seed for RNG\")\n",
    "    parser.add_argument(\"--H\", type=float, default=5., help=\"depth of the pool\")\n",
    "    parser.add_argument(\"--variation\", type=float, default=.4, help=\"variation of diffraction index: http://www.philiplaven.com/p20.html 1.40 at 400 nm and 1.37 at 700nm makes a 2% variation\")\n",
    "    parser.add_argument(\"--scale\", type=float, default=150, help=\"sf\")\n",
    "    parser.add_argument(\"--B_sf\", type=float, default=0.25, help=\"bandwidth in sf\")\n",
    "    parser.add_argument(\"--V_Y\", type=float, default=0.3, help=\"horizontal speed\")\n",
    "    parser.add_argument(\"--V_X\", type=float, default=0.3, help=\"vertical speed\")\n",
    "    parser.add_argument(\"--B_V\", type=float, default=1.0, help=\"bandwidth in speed\")\n",
    "    parser.add_argument(\"--theta\", type=float, default=2*np.pi*(2-1.61803), help=\"angle with the horizontal\")\n",
    "    parser.add_argument(\"--B_theta\", type=float, default=np.pi/3, help=\"bandwidth in theta\")\n",
    "    parser.add_argument(\"--min_lum\", type=float, default=.2, help=\"diffusion level for the rendering\")\n",
    "    parser.add_argument(\"--gamma\", type=float, default=2.8, help=\"Gamma exponant to convert luminosity to luminance\")\n",
    "    parser.add_argument(\"--fps\", type=float, default=18, help=\"frames per second\")\n",
    "    parser.add_argument(\"--multispectral\", type=bool, default=True, help=\"Compute caustics on the full spectrogram.\")\n",
    "    parser.add_argument(\"--cache\", type=bool, default=True, help=\"Cache intermediate output.\")\n",
    "    parser.add_argument(\"--verbose\", type=bool, default=False, help=\"Displays more verbose output.\")\n",
    "    parser.add_argument(\"--do_display\", type=bool, default=False, help=\"Displays images in notebook.\")\n",
    "\n",
    "    opt = parser.parse_args(args=args)\n",
    "\n",
    "    if opt.verbose:\n",
    "        print(opt)\n",
    "    return opt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.688713Z",
     "iopub.status.busy": "2022-09-16T06:50:17.688504Z",
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     "shell.execute_reply": "2022-09-16T06:50:17.691125Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving our simulations in=2022-12-02_caustique_single\n"
     ]
    }
   ],
   "source": [
    "print(f'Saving our simulations in={figpath}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.722173Z",
     "iopub.status.busy": "2022-09-16T06:50:17.721924Z",
     "iopub.status.idle": "2022-09-16T06:50:17.725840Z",
     "shell.execute_reply": "2022-09-16T06:50:17.725292Z"
    }
   },
   "outputs": [],
   "source": [
    "opt = init()\n",
    "opt.figpath = figpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.728359Z",
     "iopub.status.busy": "2022-09-16T06:50:17.728155Z",
     "iopub.status.idle": "2022-09-16T06:50:17.734829Z",
     "shell.execute_reply": "2022-09-16T06:50:17.734354Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(tag='caustique', figpath='2022-12-02_caustique_single', ext='png', nx=5120, ny=5120, nframe=1, bin_dens=2, bin_spectrum=3, seed=8601, H=5.0, variation=0.4, scale=150, B_sf=0.25, V_Y=0.3, V_X=0.3, B_V=1.0, theta=2.399988291783386, B_theta=1.0471975511965976, min_lum=0.2, gamma=2.8, fps=18, multispectral=True, cache=True, verbose=False, do_display=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## utilities\n",
    "\n",
    "Transfoming a sequence of PNG frames into gif or mp4:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.737334Z",
     "iopub.status.busy": "2022-09-16T06:50:17.737126Z",
     "iopub.status.idle": "2022-09-16T06:50:17.741165Z",
     "shell.execute_reply": "2022-09-16T06:50:17.740716Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_gif(gifname, fnames, fps, do_delete=True):\n",
    "    import imageio\n",
    "\n",
    "    with imageio.get_writer(gifname, mode='I', fps=fps) as writer:\n",
    "        for fname in fnames:\n",
    "            writer.append_data(imageio.imread(fname))\n",
    "\n",
    "    from pygifsicle import optimize\n",
    "    optimize(str(gifname))\n",
    "    if do_delete: \n",
    "        for fname in fnames: os.remove(fname)\n",
    "    return gifname\n",
    "\n",
    "# https://moviepy.readthedocs.io/en/latest/getting_started/videoclips.html#imagesequenceclip\n",
    "def make_mp4(mp4name, fnames, fps, do_delete=True):\n",
    "    import moviepy.editor as mpy\n",
    "    clip = mpy.ImageSequenceClip(fnames, fps=fps)\n",
    "    clip.write_videofile(mp4name, fps=fps, codec='libx264', verbose=False, logger=None)\n",
    "    if do_delete: \n",
    "        for fname in fnames: os.remove(fname)\n",
    "    return mp4name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Utilities to compute the spectrum of the blue sky and convert them later to RGB values (check out https://laurentperrinet.github.io/sciblog/posts/2020-07-04-colors-of-the-sky.html for details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.743669Z",
     "iopub.status.busy": "2022-09-16T06:50:17.743461Z",
     "iopub.status.idle": "2022-09-16T06:50:17.757701Z",
     "shell.execute_reply": "2022-09-16T06:50:17.757282Z"
    }
   },
   "outputs": [],
   "source": [
    "from lambda2color import Lambda2color, xyz_from_xy\n",
    "\n",
    "# borrowed from https://github.com/gummiks/gummiks.github.io/blob/master/scripts/astro/planck.py\n",
    "def planck(wav, T):\n",
    "    import scipy.constants as const\n",
    "    c = const.c # c = 3.0e+8\n",
    "    h = const.h # h = 6.626e-34\n",
    "    k = const.k # k = 1.38e-23\n",
    "    a = 2.0*h*c**2\n",
    "    b = h*c/(wav*k*T)\n",
    "    intensity = a / ( (wav**5) * (np.exp(b) - 1.0) )\n",
    "    return intensity\n",
    "\n",
    "def scattering(wav, a=0.005, p=1.3, b=0.45):\n",
    "    \"\"\"\n",
    "    b is  proportionate  to  the  column  density  of  aerosols\n",
    "    along  the  path  of  sunlight,  from  outside  the  atmosphere\n",
    "    to  the  point  of  observation\n",
    "    \n",
    "    see https://laurentperrinet.github.io/sciblog/posts/2020-07-04-colors-of-the-sky.html for more details\n",
    "\n",
    "    \"\"\"\n",
    "    # converting wav in µm:\n",
    "    intensity = np.exp(-a/((wav/1e-6)**4)) # Rayleigh extinction by nitrogen\n",
    "    intensity *= (wav/1e-6)**-4\n",
    "    intensity *= np.exp(-b/((wav/1e-6)**p)) # Aerosols\n",
    "    return intensity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## computing the caustics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib\n",
    "mystr = \"toto\"\n",
    "md5 = hashlib.sha224(mystr.encode()).hexdigest()[:8] # an unique identifier for future tagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(b'toto', '21c043ee')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mystr.encode(), md5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.760440Z",
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   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "import hashlib\n",
    "from tqdm.notebook import tqdm, trange\n",
    "import MotionClouds as mc\n",
    "class Caustique:\n",
    "    def __init__(self, opt):\n",
    "        \"\"\"\n",
    "        Image coordinates follow 'ij' indexing, that is,\n",
    "        * their origin at the top left,\n",
    "        * the X axis is vertical and goes \"down\",\n",
    "        * the Y axis is horizontal and goes \"right\".\n",
    "\n",
    "        \"\"\"\n",
    "        self.mc = mc\n",
    "        self.ratio = opt.ny/opt.nx # ratio between height and width (>1 for portrait, <1 for landscape)\n",
    "        X = np.linspace(0, 1, opt.nx, endpoint=False) # vertical\n",
    "        Y = np.linspace(0, self.ratio, opt.ny, endpoint=False) # horizontal\n",
    "        self.xv, self.yv = np.meshgrid(X, Y, indexing='ij')\n",
    "        self.opt = opt\n",
    "        # https://stackoverflow.com/questions/16878315/what-is-the-right-way-to-treat-python-argparse-namespace-as-a-dictionary\n",
    "        self.d = vars(opt)\n",
    "        os.makedirs(self.opt.figpath, exist_ok=True)\n",
    "        self.cachepath = os.path.join('/tmp', self.opt.figpath)\n",
    "        if opt.verbose: print(f'{self.cachepath=}')\n",
    "        os.makedirs(self.cachepath, exist_ok=True)\n",
    "\n",
    "        # a standard white:\n",
    "        illuminant_D65 = xyz_from_xy(0.3127, 0.3291), \n",
    "        illuminant_sun = xyz_from_xy(0.325998, 0.335354)\n",
    "        # color conversion class\n",
    "        self.cs_srgb = Lambda2color(red=xyz_from_xy(0.64, 0.33),\n",
    "                               green=xyz_from_xy(0.30, 0.60),\n",
    "                               blue=xyz_from_xy(0.15, 0.06),\n",
    "                               white=illuminant_sun)\n",
    "        self.wavelengths = self.cs_srgb.cmf[:, 0]*1e-9\n",
    "        self.N_wavelengths = len(self.wavelengths)\n",
    "        # multiply by the spectrum of the sky\n",
    "        intensity5800 = planck(self.wavelengths, 5800.)\n",
    "        scatter = scattering(self.wavelengths)\n",
    "        self.spectrum_sky = intensity5800 * scatter\n",
    "        self.spectrum_sky /= self.spectrum_sky.max()\n",
    "\n",
    "    def wave(self):\n",
    "        filename = f'{self.cachepath}/{self.opt.tag}_wave.npy'\n",
    "        if os.path.isfile(filename):\n",
    "            z = np.load(filename)\n",
    "        else:\n",
    "            # A simplistic model of a wave using https://github.com/NeuralEnsemble/MotionClouds\n",
    "            fx, fy, ft = mc.get_grids(self.opt.nx, self.opt.ny, self.opt.nframe)\n",
    "            env = mc.envelope_gabor(fx, fy, ft, V_X=self.opt.V_Y, V_Y=self.opt.V_X, B_V=self.opt.B_V,\n",
    "                                    sf_0=1./self.opt.scale, B_sf=self.opt.B_sf/self.opt.scale,\n",
    "                                    theta=self.opt.theta, B_theta=self.opt.B_theta)\n",
    "            z = mc.rectif(mc.random_cloud(env, seed=self.opt.seed))\n",
    "            if self.opt.cache: np.save(filename, z)\n",
    "        return z\n",
    "\n",
    "    def transform(self, z_, modulation=1.):\n",
    "        xv, yv = self.xv.copy(), self.yv.copy()\n",
    "\n",
    "        dzdx = z_ - np.roll(z_, 1, axis=0)\n",
    "        dzdy = z_ - np.roll(z_, 1, axis=1)\n",
    "        xv = xv + modulation * self.opt.H * dzdx\n",
    "        yv = yv + modulation * self.opt.H * dzdy\n",
    "\n",
    "        xv = np.mod(xv, 1)\n",
    "        yv = np.mod(yv, self.ratio)\n",
    "\n",
    "        return xv, yv\n",
    "\n",
    "    def plot(self, z, do_color=True, dpi=50):\n",
    "        \n",
    "        md5 = hashlib.sha224((self.opt.figpath + self.opt.tag).encode()).hexdigest()[:8] # an unique identifier for future tagging\n",
    "        output_filename=f'{self.opt.figpath}/{self.opt.tag}_{md5}.{self.opt.ext}'\n",
    "\n",
    "        if not os.path.isfile(output_filename):\n",
    "            #hist = self.do_raytracing(z)\n",
    "            binsx, binsy = self.opt.nx//self.opt.bin_dens, self.opt.ny//self.opt.bin_dens\n",
    "\n",
    "            subplotpars = matplotlib.figure.SubplotParams(left=0., right=1., bottom=0., top=1., wspace=0., hspace=0.,)\n",
    "\n",
    "            if self.opt.multispectral:\n",
    "\n",
    "                #image_rgb = self.cs_srgb.spec_to_rgb(hist)\n",
    "                image_rgb = np.zeros((self.opt.nx//self.opt.bin_dens,  self.opt.ny//self.opt.bin_dens, 3, self.opt.nframe))\n",
    "                for i_frame in tqdm(range(self.opt.nframe)):\n",
    "                    for ii_wavelength, i_wavelength in enumerate(range(self.opt.bin_spectrum//2, self.N_wavelengths, self.opt.bin_spectrum)):\n",
    "                        modulation = 1. + self.opt.variation/2 - self.opt.variation*i_wavelength/self.N_wavelengths\n",
    "                        # print(i_wavelength, N_wavelengths, modulation)\n",
    "                        xv, yv = self.transform(z[:, :, i_frame], modulation=modulation)\n",
    "                        hist_, edge_x, edge_y = np.histogram2d(xv.ravel(), yv.ravel(),\n",
    "                                                            bins=[binsx, binsy],\n",
    "                                                            range=[[0, 1], [0, self.ratio]],\n",
    "                                                            density=True)\n",
    "\n",
    "                        spec = np.zeros((self.N_wavelengths))\n",
    "                        spec[i_wavelength] = 1\n",
    "                        rgb = self.cs_srgb.spec_to_rgb(spec)\n",
    "                        rgb *= self.spectrum_sky[i_wavelength]\n",
    "                        image_rgb[:, :, :, i_frame] += hist_[:, :, None] * rgb[None, None, :]\n",
    "\n",
    "                # image_rgb -= image_rgb.min()\n",
    "                image_rgb /= image_rgb.max()\n",
    "            else:\n",
    "                hist = np.zeros((binsx, binsy, self.opt.nframe))\n",
    "                for i_frame in trange(self.opt.nframe):\n",
    "                    xv, yv = self.transform(z[:, :, i_frame])\n",
    "                    hist_, edge_x, edge_y = np.histogram2d(xv.ravel(), yv.ravel(),\n",
    "                                                        bins=[binsx, binsy],\n",
    "                                                        range=[[0, 1], [0, self.ratio]],\n",
    "                                                        density=True)\n",
    "                #hist /= hist.max()\n",
    "\n",
    "            fnames = []\n",
    "            for i_frame in range(self.opt.nframe):\n",
    "                fig, ax = plt.subplots(figsize=(self.opt.ny/self.opt.bin_dens/dpi, self.opt.nx/self.opt.bin_dens/dpi), subplotpars=subplotpars)\n",
    "                if self.opt.multispectral:\n",
    "                    ax.imshow(image_rgb[:, :, :, i_frame] ** (1/self.opt.gamma), vmin=0, vmax=1)\n",
    "                else:\n",
    "                    if do_color:\n",
    "                        bluesky = np.array([0.268375, 0.283377]) # xyz\n",
    "                        sun = np.array([0.325998, 0.335354]) # xyz\n",
    "                        # ax.pcolormesh(edge_y, edge_x, hist[:, :, i_frame], vmin=0, vmax=1, cmap=plt.cm.Blues_r)\n",
    "                        # https://en.wikipedia.org/wiki/CIE_1931_color_space#Mixing_colors_specified_with_the_CIE_xy_chromaticity_diagram\n",
    "                        L1 = 1 - hist[:, :, i_frame]\n",
    "                        L2 = hist[:, :, i_frame]\n",
    "                        image_denom = L1 / bluesky[1] + L2 / sun[1]\n",
    "                        image_x = (L1 * bluesky[0] / bluesky[1] + L2 * sun[0] / sun[1]) / image_denom\n",
    "                        image_y = (L1 + L2) / image_denom \n",
    "                        image_xyz = np.dstack((image_x, image_y, 1 - image_x - image_y))\n",
    "                        image_rgb = self.cs_srgb.xyz_to_rgb(image_xyz)\n",
    "                        image_L = self.opt.min_lum + (1-self.opt.min_lum)* L2 ** .61803\n",
    "                        ax.imshow(image_L[:, :, None]*image_rgb, vmin=0, vmax=1)\n",
    "\n",
    "                    else:\n",
    "                        ax.imshow(1-image_L, vmin=0, vmax=1)\n",
    "\n",
    "                fname = f'{self.cachepath}/{self.opt.tag}_frame_{i_frame:04d}.png'\n",
    "                fig.savefig(fname, dpi=dpi)\n",
    "                fnames.append(fname)\n",
    "                plt.close('all')\n",
    "\n",
    "            if self.opt.nframe==1:\n",
    "                shutil.copyfile(fname, output_filename)\n",
    "                return output_filename\n",
    "            else:\n",
    "                if self.opt.ext == 'gif':\n",
    "                    return make_gif(output_filename, fnames, fps=self.opt.fps)\n",
    "                else:\n",
    "                    return make_mp4(output_filename, fnames, fps=self.opt.fps)\n",
    "\n",
    "    def show(self, output_filename, width=1024):\n",
    "        from IPython.display import HTML, Image, display\n",
    "        if self.opt.nframe==1:\n",
    "            display(Image(url=output_filename.replace(self.opt.ext, 'png'), width=width))\n",
    "        else:\n",
    "            if self.opt.ext == 'gif':\n",
    "                return display(Image(url=output_filename, width=width))\n",
    "            else:\n",
    "                #import moviepy.editor as mpy\n",
    "                #return mpy.ipython_display(output_filename, width=width)\n",
    "                # https://github.com/NeuralEnsemble/MotionClouds/blob/master/MotionClouds/MotionClouds.py#L858\n",
    "                opts = ' loop=\"1\" autoplay=\"1\" controls '\n",
    "                html = HTML(f'<video {opts} src=\"{output_filename}\" type=\"video/{self.opt.ext}\" width={width}\\>')\n",
    "                html.reload()\n",
    "                return display(html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# a simple caustics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## generating the caustics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:17.864888Z",
     "iopub.status.busy": "2022-09-16T06:50:17.864663Z",
     "iopub.status.idle": "2022-09-16T06:50:19.896847Z",
     "shell.execute_reply": "2022-09-16T06:50:19.896285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5120, 5120, 1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Caustique(opt)\n",
    "z = c.wave()\n",
    "z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:19.899518Z",
     "iopub.status.busy": "2022-09-16T06:50:19.899287Z",
     "iopub.status.idle": "2022-09-16T06:50:19.902454Z",
     "shell.execute_reply": "2022-09-16T06:50:19.902008Z"
    }
   },
   "outputs": [],
   "source": [
    "output_filename = f'{opt.figpath}/{opt.tag}.{opt.ext}'\n",
    "if not os.path.isfile(output_filename):\n",
    "    c = Caustique(opt)\n",
    "    z = c.wave()\n",
    "    output_filename = c.plot(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:19.904998Z",
     "iopub.status.busy": "2022-09-16T06:50:19.904782Z",
     "iopub.status.idle": "2022-09-16T06:50:19.908171Z",
     "shell.execute_reply": "2022-09-16T06:50:19.907731Z"
    }
   },
   "outputs": [],
   "source": [
    "if opt.do_display: c.show(output_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# exploring parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:19.910794Z",
     "iopub.status.busy": "2022-09-16T06:50:19.910587Z",
     "iopub.status.idle": "2022-09-16T06:50:19.913072Z",
     "shell.execute_reply": "2022-09-16T06:50:19.912580Z"
    }
   },
   "outputs": [],
   "source": [
    "N_scan = 9\n",
    "base = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## water depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:19.915420Z",
     "iopub.status.busy": "2022-09-16T06:50:19.915215Z",
     "iopub.status.idle": "2022-09-16T06:50:21.641723Z",
     "shell.execute_reply": "2022-09-16T06:50:21.641154Z"
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[39], line 11\u001b[0m\n\u001b[1;32m      9\u001b[0m opt \u001b[39m=\u001b[39m init()\n\u001b[1;32m     10\u001b[0m opt\u001b[39m.\u001b[39mfigpath \u001b[39m=\u001b[39m figpath\n\u001b[0;32m---> 11\u001b[0m c \u001b[39m=\u001b[39m Caustique(opt)\n\u001b[1;32m     13\u001b[0m c\u001b[39m.\u001b[39mopt\u001b[39m.\u001b[39mH \u001b[39m=\u001b[39m H_\n\u001b[1;32m     14\u001b[0m c\u001b[39m.\u001b[39mopt\u001b[39m.\u001b[39mtag \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mopt\u001b[39m.\u001b[39mtag\u001b[39m}\u001b[39;00m\u001b[39m_H_\u001b[39m\u001b[39m{\u001b[39;00mH_\u001b[39m:\u001b[39;00m\u001b[39m.3f\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\n",
      "Cell \u001b[0;32mIn[34], line 18\u001b[0m, in \u001b[0;36mCaustique.__init__\u001b[0;34m(self, opt)\u001b[0m\n\u001b[1;32m     16\u001b[0m X \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlinspace(\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, opt\u001b[39m.\u001b[39mnx, endpoint\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m) \u001b[39m# vertical\u001b[39;00m\n\u001b[1;32m     17\u001b[0m Y \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mlinspace(\u001b[39m0\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mratio, opt\u001b[39m.\u001b[39mny, endpoint\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m) \u001b[39m# horizontal\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mxv, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39myv \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mmeshgrid(X, Y, indexing\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mij\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m     19\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mopt \u001b[39m=\u001b[39m opt\n\u001b[1;32m     20\u001b[0m \u001b[39m# https://stackoverflow.com/questions/16878315/what-is-the-right-way-to-treat-python-argparse-namespace-as-a-dictionary\u001b[39;00m\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mmeshgrid\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/stim/lib/python3.10/site-packages/numpy/lib/function_base.py:4990\u001b[0m, in \u001b[0;36mmeshgrid\u001b[0;34m(copy, sparse, indexing, *xi)\u001b[0m\n\u001b[1;32m   4987\u001b[0m     output \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mbroadcast_arrays(\u001b[39m*\u001b[39moutput, subok\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m   4989\u001b[0m \u001b[39mif\u001b[39;00m copy:\n\u001b[0;32m-> 4990\u001b[0m     output \u001b[39m=\u001b[39m [x\u001b[39m.\u001b[39mcopy() \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m output]\n\u001b[1;32m   4992\u001b[0m \u001b[39mreturn\u001b[39;00m output\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/stim/lib/python3.10/site-packages/numpy/lib/function_base.py:4990\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   4987\u001b[0m     output \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mbroadcast_arrays(\u001b[39m*\u001b[39moutput, subok\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m   4989\u001b[0m \u001b[39mif\u001b[39;00m copy:\n\u001b[0;32m-> 4990\u001b[0m     output \u001b[39m=\u001b[39m [x\u001b[39m.\u001b[39;49mcopy() \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m output]\n\u001b[1;32m   4992\u001b[0m \u001b[39mreturn\u001b[39;00m output\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "opt = init()\n",
    "opt.figpath = figpath\n",
    "\n",
    "c = Caustique(opt)\n",
    "# compute just once\n",
    "z = c.wave()\n",
    "\n",
    "for H_ in c.opt.H*np.logspace(-1, 1, N_scan, base=base):\n",
    "    opt = init()\n",
    "    opt.figpath = figpath\n",
    "    c = Caustique(opt)\n",
    "\n",
    "    c.opt.H = H_\n",
    "    c.opt.tag = f'{opt.tag}_H_{H_:.3f}'\n",
    "    output_filename = c.plot(z)\n",
    "    print(f'H = {H_:.3f} -> {output_filename=} ')\n",
    "    if opt.do_display: c.show(output_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## refraction index variation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:21.644704Z",
     "iopub.status.busy": "2022-09-16T06:50:21.644457Z",
     "iopub.status.idle": "2022-09-16T06:50:23.391217Z",
     "shell.execute_reply": "2022-09-16T06:50:23.390608Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variation = 0.010 -> output_filename=None\n",
      "variation = 0.017 -> output_filename=None\n",
      "variation = 0.028 -> output_filename=None\n",
      "variation = 0.046 -> output_filename=None\n",
      "variation = 0.077 -> output_filename=None\n",
      "variation = 0.129 -> output_filename=None\n",
      "variation = 0.215 -> output_filename=None\n",
      "variation = 0.359 -> output_filename=None\n",
      "variation = 0.599 -> output_filename=None\n"
     ]
    }
   ],
   "source": [
    "opt = init()\n",
    "opt.figpath = figpath\n",
    "\n",
    "c = Caustique(opt)\n",
    "z = c.wave()\n",
    "\n",
    "for variation_ in np.logspace(-2, 0, N_scan, base=10, endpoint=False):\n",
    "    opt = init()\n",
    "    opt.figpath = figpath\n",
    "    c = Caustique(opt)\n",
    "    c.opt.variation = variation_\n",
    "    c.opt.tag = f'{opt.tag}_variation_{variation_:.3f}'\n",
    "    output_filename = c.plot(z)\n",
    "    print(f'variation = {variation_:.3f} -> {output_filename=}')\n",
    "    if opt.do_display: c.show(output_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## other variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-09-16T06:50:23.394466Z",
     "iopub.status.busy": "2022-09-16T06:50:23.394219Z",
     "iopub.status.idle": "2022-09-16T06:50:23.686162Z",
     "shell.execute_reply": "2022-09-16T06:50:23.685512Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======scale======\n",
      "scale=scale(default)*0.500=7.500E+01\n",
      "Doing  2022-12-02_caustique_single/caustique_scale_modul_0.500.png\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a92663c68d484334a1498f22d9d86ae2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[21], line 17\u001b[0m\n\u001b[1;32m     15\u001b[0m     z \u001b[39m=\u001b[39m c\u001b[39m.\u001b[39mwave()\n\u001b[1;32m     16\u001b[0m     mcname \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mopt\u001b[39m.\u001b[39mfigpath\u001b[39m}\u001b[39;00m\u001b[39m/\u001b[39m\u001b[39m{\u001b[39;00mc\u001b[39m.\u001b[39mopt\u001b[39m.\u001b[39mtag\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\n\u001b[0;32m---> 17\u001b[0m     output_filename \u001b[39m=\u001b[39m c\u001b[39m.\u001b[39;49mplot(z)\n\u001b[1;32m     18\u001b[0m \u001b[39mif\u001b[39;00m opt\u001b[39m.\u001b[39mdo_display: c\u001b[39m.\u001b[39mshow(output_filename)\n",
      "Cell \u001b[0;32mIn[13], line 90\u001b[0m, in \u001b[0;36mCaustique.plot\u001b[0;34m(self, z, do_color, dpi)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[39m# print(i_wavelength, N_wavelengths, modulation)\u001b[39;00m\n\u001b[1;32m     89\u001b[0m xv, yv \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform(z[:, :, i_frame], modulation\u001b[39m=\u001b[39mmodulation)\n\u001b[0;32m---> 90\u001b[0m hist_, edge_x, edge_y \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mhistogram2d(xv\u001b[39m.\u001b[39;49mravel(), yv\u001b[39m.\u001b[39;49mravel(),\n\u001b[1;32m     91\u001b[0m                                     bins\u001b[39m=\u001b[39;49m[binsx, binsy],\n\u001b[1;32m     92\u001b[0m                                     \u001b[39mrange\u001b[39;49m\u001b[39m=\u001b[39;49m[[\u001b[39m0\u001b[39;49m, \u001b[39m1\u001b[39;49m], [\u001b[39m0\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mratio]],\n\u001b[1;32m     93\u001b[0m                                     density\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n\u001b[1;32m     95\u001b[0m spec \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mzeros((\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mN_wavelengths))\n\u001b[1;32m     96\u001b[0m spec[i_wavelength] \u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mhistogram2d\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/stim/lib/python3.10/site-packages/numpy/lib/twodim_base.py:825\u001b[0m, in \u001b[0;36mhistogram2d\u001b[0;34m(x, y, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m    823\u001b[0m     xedges \u001b[39m=\u001b[39m yedges \u001b[39m=\u001b[39m asarray(bins)\n\u001b[1;32m    824\u001b[0m     bins \u001b[39m=\u001b[39m [xedges, yedges]\n\u001b[0;32m--> 825\u001b[0m hist, edges \u001b[39m=\u001b[39m histogramdd([x, y], bins, \u001b[39mrange\u001b[39;49m, normed, weights, density)\n\u001b[1;32m    826\u001b[0m \u001b[39mreturn\u001b[39;00m hist, edges[\u001b[39m0\u001b[39m], edges[\u001b[39m1\u001b[39m]\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mhistogramdd\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
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      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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   ],
   "source": [
    "for variable in ['scale', 'B_sf', 'B_theta', 'V_X', 'B_V', 'gamma', ]: #  'theta', 'V_Y'\n",
    "    print(f'======{variable}======')\n",
    "    for modul in np.logspace(-1, 1, N_scan, base=base):\n",
    "        opt = init()\n",
    "        opt.figpath = figpath\n",
    "\n",
    "        c = Caustique(opt)\n",
    "        c.d[variable] *= modul\n",
    "        c.opt.tag = f'{opt.tag}_{variable}_modul_{modul:.3f}'\n",
    "        output_filename = f'{opt.figpath}/{c.opt.tag}.{opt.ext}'\n",
    "\n",
    "        z = c.wave()\n",
    "        mcname = f'{opt.figpath}/{c.opt.tag}'\n",
    "        output_filename = c.plot(z)\n",
    "        print(f'{variable}={variable}(default)*{modul:.3f}={c.d[variable]:.3E} -> {output_filename=}')\n",
    "        if opt.do_display: c.show(output_filename)"
   ]
  },
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   "metadata": {},
   "source": [
    "\n",
    "## installation\n",
    "\n",
    "Install [dependencies](https://pip.pypa.io/en/stable/user_guide/#requirements-files), then this notebook:\n",
    "\n",
    "```\n",
    "python3 -m pip install -r requirements.txt\n",
    "```"
   ]
  },
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     "iopub.status.busy": "2022-09-16T06:50:23.689202Z",
     "iopub.status.idle": "2022-09-16T06:50:30.588537Z",
     "shell.execute_reply": "2022-09-16T06:50:30.587403Z"
    },
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   },
   "outputs": [],
   "source": [
    "# %pip install --upgrade -r requirements.txt"
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