OS-SIM algorithm implementaion in python

OS-SIM.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "from PIL import Image\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_tiff(path):\n",
    "    \"\"\"\n",
    "    path - Path to the multipage-tiff file\n",
    "    \"\"\"\n",
    "    img = Image.open(path)\n",
    "    images = []\n",
    "    for i in range(img.n_frames):\n",
    "        img.seek(i)\n",
    "        images.append(np.array(img))\n",
    "    return np.array(images, dtype='float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc = '20210514-1642-35_120ms.tif'\n",
    "raw = read_tiff(loc)\n",
    "print(raw.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxmin(raw):\n",
    "    mms = []\n",
    "    for i in range(raw.shape[0]//3):\n",
    "        mm = np.max(raw[0+i*3:3+i*3:1], axis=0) - np.min(raw[0+i*3:3+i*3:1], axis=0)\n",
    "        mms.append(mm)\n",
    "    mm = np.mean(np.asarray(mms), axis=0)\n",
    "    return mm\n",
    "\n",
    "def homodyne0(raw, angles, phases):\n",
    "    s = raw[0]\n",
    "    x = np.arange(1,4)\n",
    "    def g(o):\n",
    "        return np.exp(2*1j*np.pi/3*o)\n",
    "    mms = []\n",
    "    for i in range(angles):\n",
    "        mmp = []\n",
    "        for j in range(phases):\n",
    "            mmp.append(raw[j+i*phases]*g(np.ones_like(s)*(j+1)))\n",
    "        mm = np.abs(np.sum(np.asarray(mmp), axis=0))\n",
    "        mms.append(mm)\n",
    "    mm = np.mean(np.asarray(mms), axis=0)\n",
    "    return mm\n",
    "\n",
    "def homodyne1(raw):\n",
    "    s = raw[0]\n",
    "    x = np.arange(1,4)\n",
    "    def g(o):\n",
    "        return np.exp(2*1j*np.pi/3*o)\n",
    "    mms = []\n",
    "    for i in range(raw.shape[0]//3):\n",
    "        mm = np.abs(raw[0+i*3]*g(np.ones_like(s)) + raw[1+i*3]*g(np.ones_like(s)*2) +raw[2+i*3]*g(np.ones_like(s)*3))\n",
    "        mms.append(mm)\n",
    "    mm = np.mean(np.asarray(mms), axis=0)\n",
    "    return mm\n",
    "\n",
    "def homodyne2(raw):\n",
    "    mms = [] #- np.mean(raw, axis=0)\n",
    "    for i in range(raw.shape[0]//3):\n",
    "        mm = np.sqrt((np.square((raw[0+3*i]-raw[1+3*i])) + np.square((raw[0+3*i]-raw[2+3*i])) + np.square((raw[1+3*i]-raw[2+3*i]))))\n",
    "        mms.append(mm)\n",
    "    mm = np.mean(np.asarray(mms), axis=0)\n",
    "#     mm = mms[0]\n",
    "    return mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamp = datetime.datetime.now()\n",
    "t = timestamp.strftime(\"%Y%m%d-%H%M-%S\")\n",
    "fn = homodyne0\n",
    "Image.fromarray(fn(raw, 3, 6)).save(f'{fn.__name__}_{t}_'+loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loc = '20210531-2231-27_YBCO_10.5K_0.5mW_2r_-17dBm_SIM_ODMR_data'\n",
    "directory = 'C:/Users/yy3/Work/Gpufit_binary/Gpufit_1.1.0_win64_cublas_build201804131033/Gpufit_1.1.0_win64_cublas_build201804131033/python/examples/Data/06_2021/'\n",
    "all_data = np.load(directory+loc+'_ch0_sweep.npz')['sweep_images'].squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(all_data.shape)\n",
    "final_data = np.empty((2,36, 471,572))\n",
    "s = all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for f in range(s[2]):\n",
    "    for o in range(s[1]):\n",
    "        final_data[o,f,:,:] = homodyne0(all_data[:,o,f,:,:], 3, 3)\n",
    "np.savez_compressed(directory+loc+'_ch0_sweep_simmed.npz', sweep_images=(final_data))"
   ]
  }
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