Untitled2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tifffile as tf\n",
    "from BackgroundFinder import BackgroundFinder\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/MarcusF/anaconda3/lib/python3.6/site-packages/tifffile/tifffile.py:2236: UserWarning: ome-xml: not an ome-tiff master file\n",
      "  warnings.warn('ome-xml: not an ome-tiff master file')\n"
     ]
    }
   ],
   "source": [
    "with tf.TiffFile('./260522_CecB-  PCPG vesicles- after flushing_1_MMStack_Pos6.ome.tif') as tif:\n",
    "    images = tif.asarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bgf = BackgroundFinder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bgf.get_background(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bgf.get_data_gradient()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This has taken  0.0437\n",
      "This has taken  0.0010\n",
      "This has taken  0.0007\n",
      "This has taken  0.0007\n",
      "This has taken  0.0006\n",
      "-4.4394360404069175\n",
      "-6.051674013329674\n",
      "-7.05770377867178\n",
      "-7.427319962427788\n",
      "-7.222057243298551\n",
      "-6.571181721895854\n",
      "-5.6367652101102355\n",
      "-4.579768331053671\n",
      "-3.53479995384261\n",
      "-2.595844185178218\n",
      "-1.8145519992278043\n",
      "-1.206036822271403\n",
      "-0.7599963613301162\n",
      "-0.4516992357726073\n",
      "-0.2514122372709996\n",
      "-0.13067415155819595\n",
      "-0.06498465203387505\n",
      "-0.036974803933195716\n",
      "-0.03011460823547188\n",
      "-0.033433428147607774\n",
      "-0.041094511582299015\n",
      "-0.050289378418315045\n",
      "-0.059085400318425435\n",
      "-0.06288049577473642\n",
      "-0.06131469097227019\n",
      "-0.05493996248679997\n",
      "-0.04432265848351813\n",
      "-0.030667871417149883\n",
      "-0.015300110915243859\n",
      "0.000798219599441069\n"
     ]
    }
   ],
   "source": [
    "bgf.find_correct_gaussian_scale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1945"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bgf.peak_max_arg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bgf.plot_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}