{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "54hAYgD0CX24"
   },
   "source": [
    "# 7. Creating a pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wsxg-QSpCX2-"
   },
   "source": [
    "In the previous chapter we created a procedure to isolate nuclei from images. Very often the goal is to repeat the exercise on multiple images in order to compare different conditions. Here we are going to see how to package our pipeline into a function, how to apply it to multiple images and how to plot the result for comparison. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 1461,
     "status": "ok",
     "timestamp": 1616250519165,
     "user": {
      "displayName": "Guillaume Witz",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgT0K2JVYzEsjzsS5nhkUVjUrSIJ5jHzXnBoYrmVf8=s64",
      "userId": "16033870147214403532"
     },
     "user_tz": -60
    },
    "id": "tT_GfkVoCX2_"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import skimage\n",
    "import skimage.io\n",
    "import skimage.morphology\n",
    "from scipy import ndimage as ndi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tdxZm84aCX2_"
   },
   "source": [
    "## Creating an image processing function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A-SOfCHiCX2_"
   },
   "source": [
    "We have seen previously how to define a function. It needs the ```def``` descriptor, a name and inputs. What we are doing here is just copying all the lines that we used for our pipeline in the last chapter into this function. At the moment the only input is the path to a given file to analyze. We also try to document what the function does, what its input/output are etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 600,
     "status": "ok",
     "timestamp": 1616250524551,
     "user": {
      "displayName": "Guillaume Witz",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgT0K2JVYzEsjzsS5nhkUVjUrSIJ5jHzXnBoYrmVf8=s64",
      "userId": "16033870147214403532"
     },
     "user_tz": -60
    },
    "id": "E_M5K3OzCX3A"
   },
   "outputs": [],
   "source": [
    "def my_pipeline(image_path):\n",
    "    \"\"\"\n",
    "    This function extracts information about nuclei found in the third channel of an image\n",
    "    loaded at the given path\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    image_path: str\n",
    "        path to image\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    my_table: pandas dataframe\n",
    "        dataframe with label, area, mean_intensity and extent information for each nucleus\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    image_stack = skimage.io.imread(image_path)\n",
    "    \n",
    "    image_nuclei = image_stack[:,:,2]#blue channel in RGB\n",
    "    image_signal = image_stack[:,:,1]#green channel in RGB\n",
    "\n",
    "    # filter image\n",
    "    image_nuclei = skimage.filters.median(image_nuclei, skimage.morphology.disk(5))\n",
    "\n",
    "    # create mask and clean-up\n",
    "    mask_nuclei = image_nuclei > skimage.filters.threshold_otsu(image_nuclei)\n",
    "    mask_nuclei = skimage.morphology.binary_closing(mask_nuclei, footprint=skimage.morphology.disk(5))\n",
    "    mask_nuclei = ndi.binary_fill_holes(mask_nuclei, skimage.morphology.disk(5))\n",
    "    \n",
    "    # label image\n",
    "    my_labels = skimage.morphology.label(mask_nuclei)\n",
    "\n",
    "    # measure\n",
    "    my_regions = skimage.measure.regionprops_table(my_labels, image_signal, properties=('label','area', 'mean_intensity'))\n",
    "    \n",
    "    plt.subplots(figsize=(10,10))\n",
    "    plt.imshow(mask_nuclei)\n",
    "    plt.show()\n",
    "    \n",
    "    my_table = pd.DataFrame(my_regions)\n",
    "    \n",
    "    return my_table\n",
    "                \n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2VJPAiHTCX3A"
   },
   "source": [
    "We can now test our function with a single image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 469
    },
    "executionInfo": {
     "elapsed": 2670,
     "status": "ok",
     "timestamp": 1616250561793,
     "user": {
      "displayName": "Guillaume Witz",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgT0K2JVYzEsjzsS5nhkUVjUrSIJ5jHzXnBoYrmVf8=s64",
      "userId": "16033870147214403532"
     },
     "user_tz": -60
    },
    "id": "1Kb41nglCX3B",
    "outputId": "356d42b9-de71-4303-9912-0e47292e1dec"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = my_pipeline('https://github.com/guiwitz/PyImageCourse_beginner/raw/master/images/46658_784_B12_1.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "executionInfo": {
     "elapsed": 757,
     "status": "ok",
     "timestamp": 1616250564539,
     "user": {
      "displayName": "Guillaume Witz",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgT0K2JVYzEsjzsS5nhkUVjUrSIJ5jHzXnBoYrmVf8=s64",
      "userId": "16033870147214403532"
     },
     "user_tz": -60
    },
    "id": "vGWuz-kFCX3C",
    "outputId": "2e461221-3f7c-4e18-8013-b6db1df1669f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>area</th>\n",
       "      <th>mean_intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4221</td>\n",
       "      <td>79.265103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8386</td>\n",
       "      <td>65.997734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>18258</td>\n",
       "      <td>70.261091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4043</td>\n",
       "      <td>63.026713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>73</td>\n",
       "      <td>47.438356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>49056</td>\n",
       "      <td>53.714510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>45671</td>\n",
       "      <td>53.807099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>20537</td>\n",
       "      <td>70.453912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>46853</td>\n",
       "      <td>66.137729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>43277</td>\n",
       "      <td>41.412552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>40768</td>\n",
       "      <td>58.675039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>15090</td>\n",
       "      <td>44.198144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>35404</td>\n",
       "      <td>74.771551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>47642</td>\n",
       "      <td>41.701902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>54978</td>\n",
       "      <td>35.904107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>26774</td>\n",
       "      <td>53.077874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>812</td>\n",
       "      <td>102.986453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>54298</td>\n",
       "      <td>56.110207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>29638</td>\n",
       "      <td>66.283791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label   area  mean_intensity\n",
       "0       1   4221       79.265103\n",
       "1       2   8386       65.997734\n",
       "2       3  18258       70.261091\n",
       "3       4   4043       63.026713\n",
       "4       5     73       47.438356\n",
       "5       6  49056       53.714510\n",
       "6       7  45671       53.807099\n",
       "7       8  20537       70.453912\n",
       "8       9  46853       66.137729\n",
       "9      10  43277       41.412552\n",
       "10     11  40768       58.675039\n",
       "11     12  15090       44.198144\n",
       "12     13  35404       74.771551\n",
       "13     14  47642       41.701902\n",
       "14     15  54978       35.904107\n",
       "15     16  26774       53.077874\n",
       "16     17      4      112.000000\n",
       "17     18    812      102.986453\n",
       "18     19  54298       56.110207\n",
       "19     20  29638       66.283791"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ft-4QdfbCX3D"
   },
   "source": [
    "## Adjusting the function behavior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BwfMD8xWCX3D"
   },
   "source": [
    "We have recovered the same result as previously and can analyze again our data as we did before. The mask is also plotted by default when calling the function. This is helpful to test the function and verify that nothing went dramatically wrong but we probably don't want to see this image if we analyze hundreds of images. What we can do is leave the user the choice to see it or not. Let's adjust our function to do that: we add an additional **optional** parameter ```do_plotting``` which is simply a boolean (```True```/```False```). If True, plotting is happening, if False it's not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_pipeline(image_path, do_plotting=False):\n",
    "    \"\"\"\n",
    "    This function extracts information about nuclei found in the third channel of an image\n",
    "    loaded at the given path\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    image_path: str\n",
    "        path to image\n",
    "    do_plotting: bool\n",
    "        show segmentation or not\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    my_table: pandas dataframe\n",
    "        dataframe with label, area, mean_intensity and extent information for each nucleus\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    image_stack = skimage.io.imread(image_path)\n",
    "    \n",
    "    image_nuclei = image_stack[:,:,2]#blue channel in RGB\n",
    "    image_signal = image_stack[:,:,1]#green channel in RGB\n",
    "\n",
    "    # filter image\n",
    "    image_nuclei = skimage.filters.median(image_nuclei, skimage.morphology.disk(5))\n",
    "\n",
    "    # create mask and clean-up\n",
    "    mask_nuclei = image_nuclei > skimage.filters.threshold_otsu(image_nuclei)\n",
    "    mask_nuclei = skimage.morphology.binary_closing(mask_nuclei, selem=skimage.morphology.disk(5))\n",
    "    mask_nuclei = ndi.binary_fill_holes(mask_nuclei, skimage.morphology.disk(5))\n",
    "    \n",
    "    # label image\n",
    "    my_labels = skimage.morphology.label(mask_nuclei)\n",
    "\n",
    "    # measure\n",
    "    my_regions = skimage.measure.regionprops_table(my_labels, image_signal, properties=('label','area', 'mean_intensity'))\n",
    "    \n",
    "    if do_plotting:\n",
    "        plt.subplots(figsize=(10,10))\n",
    "        plt.imshow(mask_nuclei)\n",
    "        plt.show()\n",
    "    \n",
    "    my_table = pd.DataFrame(my_regions)\n",
    "    \n",
    "    return my_table\n",
    "                \n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You see that we added an ```ìf``` statment in our code. This is the place where we control for plotting or not. To learn more about it, visit the next notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "executionInfo": {
     "elapsed": 2299,
     "status": "ok",
     "timestamp": 1616250590838,
     "user": {
      "displayName": "Guillaume Witz",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgT0K2JVYzEsjzsS5nhkUVjUrSIJ5jHzXnBoYrmVf8=s64",
      "userId": "16033870147214403532"
     },
     "user_tz": -60
    },
    "id": "OV520FszCX3D"
   },
   "outputs": [],
   "source": [
    "table = my_pipeline('https://github.com/guiwitz/PyImageCourse_beginner/raw/master/images/46658_784_B12_1.tif')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YZVwVJx1CX3E"
   },
   "source": [
    "Now indeed our function doesn't produce any output if we don't explicitly ask for it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Os88I7H6CX3E"
   },
   "source": [
    "## Analyzing multiple images "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e8-WS0jYCX3E"
   },
   "source": [
    "The point of creating an image processing pipeline was to be able to easily analyze multiple images. We can do that now by using a simple for loop. First we create a list of files we want to analyze and that we can see [here](http://flagella.crbs.ucsd.edu/images/13601) and [here](http://flagella.crbs.ucsd.edu/images/13585)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 520,
     "status": "ok",
     "timestamp": 1616250985105,
     "user": {
      "displayName": "Guillaume Witz",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgT0K2JVYzEsjzsS5nhkUVjUrSIJ5jHzXnBoYrmVf8=s64",
      "userId": "16033870147214403532"
     },
     "user_tz": -60
    },
    "id": "pmnqlw5JCX3E"
   },
   "outputs": [],
   "source": [
    "files_to_analyze = [\n",
    "    'https://github.com/guiwitz/PyImageCourse_beginner/raw/master/images/46658_784_B12_1.tif',\n",
    "    'https://github.com/guiwitz/PyImageCourse_beginner/raw/master/images/27897_273_C8_2.tif'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t8fdtYunCX3E"
   },
   "source": [
    "Now we create a ```for``` loop that will go through this list of files and analyze each of them with our function. To learn more about ```for``` loops visit the next chapter on Logical Flow. Before the loop, we create an empty list that is going to be filled with the output of the function. We also add to the output DataFrame a column with the filename that we can recover by splitting the address ```files_to_analyze[0].split('/')[-1]```. Note that the ```split``` function is a method of string objects that allows to split string where we find a certain character (here /):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "executionInfo": {
     "elapsed": 2305,
     "status": "ok",
     "timestamp": 1616250988822,
     "user": {
      "displayName": "Guillaume Witz",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgT0K2JVYzEsjzsS5nhkUVjUrSIJ5jHzXnBoYrmVf8=s64",
      "userId": "16033870147214403532"
     },
     "user_tz": -60
    },
    "id": "WlUASgblCX3F"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_tables = []\n",
    "for file in files_to_analyze:\n",
    "    \n",
    "    #use the function\n",
    "    new_table = my_pipeline(file)\n",
    "    \n",
    "    #add image index\n",
    "    new_table['filename'] = file.split('/')[-1]\n",
    "    \n",
    "    #append the result to the list\n",
    "    all_tables.append(new_table)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can concatenate our results into a single table. For this we use a Pandas function called ```concat``` which just glues two DataFrames together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "executionInfo": {
     "elapsed": 700,
     "status": "ok",
     "timestamp": 1616250990994,
     "user": {
      "displayName": "Guillaume Witz",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgT0K2JVYzEsjzsS5nhkUVjUrSIJ5jHzXnBoYrmVf8=s64",
      "userId": "16033870147214403532"
     },
     "user_tz": -60
    },
    "id": "pB5JtOFICX3F",
    "outputId": "831249cb-686c-4c41-cde4-820dabd6bf25"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>area</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4221</td>\n",
       "      <td>79.265103</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8386</td>\n",
       "      <td>65.997734</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>18258</td>\n",
       "      <td>70.261091</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4043</td>\n",
       "      <td>63.026713</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>73</td>\n",
       "      <td>47.438356</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>49056</td>\n",
       "      <td>53.714510</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>45671</td>\n",
       "      <td>53.807099</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>20537</td>\n",
       "      <td>70.453912</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>46853</td>\n",
       "      <td>66.137729</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>43277</td>\n",
       "      <td>41.412552</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>40768</td>\n",
       "      <td>58.675039</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>15090</td>\n",
       "      <td>44.198144</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>35404</td>\n",
       "      <td>74.771551</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>47642</td>\n",
       "      <td>41.701902</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>54978</td>\n",
       "      <td>35.904107</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>26774</td>\n",
       "      <td>53.077874</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>812</td>\n",
       "      <td>102.986453</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>54298</td>\n",
       "      <td>56.110207</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>29638</td>\n",
       "      <td>66.283791</td>\n",
       "      <td>46658_784_B12_1.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31346</td>\n",
       "      <td>91.784279</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1022</td>\n",
       "      <td>118.964775</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>32149</td>\n",
       "      <td>108.144048</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>21866</td>\n",
       "      <td>87.044407</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>25406</td>\n",
       "      <td>82.585177</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>26270</td>\n",
       "      <td>93.764675</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>19557</td>\n",
       "      <td>101.465102</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>16367</td>\n",
       "      <td>70.146331</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>28357</td>\n",
       "      <td>77.683218</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>19552</td>\n",
       "      <td>73.210158</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>19288</td>\n",
       "      <td>75.883036</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>58364</td>\n",
       "      <td>87.722003</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>22228</td>\n",
       "      <td>105.550837</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>49020</td>\n",
       "      <td>73.625255</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>2009</td>\n",
       "      <td>68.762071</td>\n",
       "      <td>27897_273_C8_2.tif</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label   area  mean_intensity             filename\n",
       "0       1   4221       79.265103  46658_784_B12_1.tif\n",
       "1       2   8386       65.997734  46658_784_B12_1.tif\n",
       "2       3  18258       70.261091  46658_784_B12_1.tif\n",
       "3       4   4043       63.026713  46658_784_B12_1.tif\n",
       "4       5     73       47.438356  46658_784_B12_1.tif\n",
       "5       6  49056       53.714510  46658_784_B12_1.tif\n",
       "6       7  45671       53.807099  46658_784_B12_1.tif\n",
       "7       8  20537       70.453912  46658_784_B12_1.tif\n",
       "8       9  46853       66.137729  46658_784_B12_1.tif\n",
       "9      10  43277       41.412552  46658_784_B12_1.tif\n",
       "10     11  40768       58.675039  46658_784_B12_1.tif\n",
       "11     12  15090       44.198144  46658_784_B12_1.tif\n",
       "12     13  35404       74.771551  46658_784_B12_1.tif\n",
       "13     14  47642       41.701902  46658_784_B12_1.tif\n",
       "14     15  54978       35.904107  46658_784_B12_1.tif\n",
       "15     16  26774       53.077874  46658_784_B12_1.tif\n",
       "16     17      4      112.000000  46658_784_B12_1.tif\n",
       "17     18    812      102.986453  46658_784_B12_1.tif\n",
       "18     19  54298       56.110207  46658_784_B12_1.tif\n",
       "19     20  29638       66.283791  46658_784_B12_1.tif\n",
       "0       1  31346       91.784279   27897_273_C8_2.tif\n",
       "1       2   1022      118.964775   27897_273_C8_2.tif\n",
       "2       3  32149      108.144048   27897_273_C8_2.tif\n",
       "3       4  21866       87.044407   27897_273_C8_2.tif\n",
       "4       5  25406       82.585177   27897_273_C8_2.tif\n",
       "5       6  26270       93.764675   27897_273_C8_2.tif\n",
       "6       7  19557      101.465102   27897_273_C8_2.tif\n",
       "7       8      2       20.500000   27897_273_C8_2.tif\n",
       "8       9  16367       70.146331   27897_273_C8_2.tif\n",
       "9      10  28357       77.683218   27897_273_C8_2.tif\n",
       "10     11  19552       73.210158   27897_273_C8_2.tif\n",
       "11     12  19288       75.883036   27897_273_C8_2.tif\n",
       "12     13  58364       87.722003   27897_273_C8_2.tif\n",
       "13     14  22228      105.550837   27897_273_C8_2.tif\n",
       "14     15  49020       73.625255   27897_273_C8_2.tif\n",
       "15     16   2009       68.762071   27897_273_C8_2.tif"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_info = pd.concat(all_tables)\n",
    "complete_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W6Sj4FKNCX3F"
   },
   "source": [
    "We can then check if there's any difference in intensity in the nucleus between the two images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "executionInfo": {
     "elapsed": 545,
     "status": "ok",
     "timestamp": 1616250992296,
     "user": {
      "displayName": "Guillaume Witz",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgT0K2JVYzEsjzsS5nhkUVjUrSIJ5jHzXnBoYrmVf8=s64",
      "userId": "16033870147214403532"
     },
     "user_tz": -60
    },
    "id": "4A4SF_onCX3G",
    "outputId": "7c1db87e-a09a-4ed7-a9de-079ec8934491"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(all_tables[0]['mean_intensity'], bins = np.arange(0,150,10), color = 'red')\n",
    "plt.hist(all_tables[1]['mean_intensity'], bins = np.arange(0,150,10), alpha = 0.5, color = 'blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course our actual goal is not just to measure the intensity within the nucleus but to compare the intensity inside and on the edge of the nuclei. This is demonstrated in the [Demo](Demo.ipynb) notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "colab": {
   "collapsed_sections": [],
   "name": "13-Pipeline.ipynb",
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