assignment4.ipynb

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   "source": [
    "# Assignment4 -- Calcium imaging analysis -- Solutions\n",
    "\n",
    "In this notebook we will work on calcium imaging analysis, starting from simple visualizations, preprocessing steps and finaly some simple signal analysis of the extracted calcium traces. The final aim is to extract from  *1 photon microscopy* videos the calcium traces of multiple neurons and be able to make simple decoding analysis from the traces. For that we will use an already available open source software called **CaImAn**. Particularly we will use the algorithm called **CNMF-E** (standing for *Constrained Non-Negative Matrix factorization*) for extraction cell activity of 1-photon microscopy data. \n",
    "\n",
    "For this exercise we will need:\n",
    "\n",
    "- CaImAn. Installationthrough conda-forge (August 2019) can be found here: [CaImAn](https://github.com/flatironinstitute/CaImAn/blob/master/docs/source/Installation.rst). Please install the Package-based process (no need for development mode).\n",
    "- Numpy \n",
    "- Matplotlib\n",
    "- Scipy\n",
    "- Sklearn for simple trace analysis\n",
    "- Jupyter lab or jupyter notebook\n",
    "\n",
    "Remember to *conda activate caiman* environment before running the notebook.\n",
    "\n",
    "You will find some of the required plotting function in the *auxiliary_functions.py* and *normalization_functions.py* files. You can complete the exercises by adding functions to that file or just by writting the code in a jupyter notebook cell.\n",
    "\n",
    "**Different exercises for this assigment are distributed along the notebook, there are a total of $10$ assignments. Completing $6$ out of $10$ assignments will be enough for considering succesfull completion of the assignment or completing at least one exercise per seccion of the notebook (there are a total of 5 seccions). *Extra* exercises are not mandatory.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.decomposition.incremental_pca module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.decomposition. Anything that cannot be imported from sklearn.decomposition is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "##import python standard libraries\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "import pickle\n",
    "import psutil\n",
    "import logging\n",
    "import os\n",
    "\n",
    "##import caiman and caiman funcions that will be used\n",
    "import caiman as cm\n",
    "from caiman.motion_correction import MotionCorrect, high_pass_filter_space\n",
    "from caiman.source_extraction.cnmf import params as params\n",
    "from caiman.source_extraction import cnmf\n",
    "from caiman.source_extraction.cnmf.cnmf import load_CNMF\n",
    "\n",
    "##import auxiliary functions for plotting file\n",
    "import auxiliary_functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It it recomendable to have separate folders for the raw data and the multiple files that will be created, as especified in the **README**.\n",
    "\n",
    "Here declare your work directory paths that will be used in the rest of the notebook (please change them to your ouw ones). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '/home/melisa/Documents/neuro_course/data/'           # define your data directory\n",
    "figures_dir = '/home/melisa/Documents/neuro_course/figures/'     # define your results directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 1) Videos and images\n",
    "\n",
    "A genetically enconded calcium indicator is inyected (GCaMP) so a fluorescence signal is emited when there is change in calcium concentration. Influx and outflux of calcium molecules in cells are related to the generation of spikes, so in that sense fluorecence is related to firing rate of neurons. We can capture that fluorecence with a microscope. \n",
    "\n",
    "There are two main microscopy techniques for detecting calcium fluorecence, *1-photon*   (confocal microscopes use 1-photon microscopy technique) and *2-photon microscopy*, but nowadays there are trials for using even *3-photon* imaging. The use of one or the other imaging technique will depend on the specific scientific questions of the experimenter and the available resourses. \n",
    "\n",
    "On the one hand, miniendoscopes (small microscopes with 1-photon technology) are portable and can be use in freely moving animals, while (with the current technologies) 2-photon microscopy requires a laser setup where the animal need to be head fixed (and demands muchs higher experimental costs). On the other hand, excitation plane of 2-photon microscopy is much well define as tissue emited light intensity decays as 1/I^2 from the focal plane, while it decays slower with 1-photon microscopy. This last fact makes it harded to create an apropiate pre-processing algorithm to extract calcium fluctuaction (because background also includes activity from different cells in other focal planes).  \n",
    "\n",
    "We will work on a fragment of a decoded video from **Inscopixs** miniscope (1-photon imaging). This videos have an original sampling rate of $20$Hz, but had been downsample to $10$Hz. The videos are $500$ frames lenght so the algoritms should be runnable in a personal laptop. In the data folder there are multiple examples of videos which will be used in later exercises for multitrial analysis as well as some already source extracted files.\n",
    "\n",
    "First we will work with one complete video. The entire **FOV** (field of view) from the microscope is saved in file *calcium_video.tif*, while the file *calcium_video_trial_0.tif* has the cropped **ROI** (region of interest) we will work with.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Here we declare the name of the files we will be using verify it existance in the current directory.\n",
    "\n",
    "input_tif_file_path_FOV = data_dir + 'raw/' + 'calcium_video.tif'   # define the path of the raw FOV movie\n",
    "if not os.path.isfile(input_tif_file_path_FOV):                     # verify that the file exist\n",
    "    logging.error('Calcium video file not found.')\n",
    "\n",
    "input_tif_file_path = data_dir + 'raw/' + 'caiman_video_trial_0.tif'   # define the path of the raw ROI movie\n",
    "if not os.path.isfile(input_tif_file_path):                            # verify that the file exist\n",
    "    logging.error('Calcium video cropped file not found.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "###Here an additional window will open displaying a typical 1-photon calcium imaging video. \n",
    "###There you can see the multiple cells turning on and off as a light that is being emited. \n",
    "\n",
    "# videos visualization\n",
    "FOV = cm.load(input_tif_file_path_FOV)           # load complete FOV\n",
    "\n",
    "gain = 1.         #light intensity of video\n",
    "magnification = 1 #size of display movie\n",
    "fr = 20           #frequency of visualization (it is not sampling rate from the original movie)\n",
    "\n",
    "FOV.play(gain = gain, magnification = magnification, fr = fr) #show video of calcium fluorecence\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*If a video does not pop put from the previous cell, you can visualize it directly with VLC or any multimedia reprocucer using calcium_video.avi*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen from the original video, there are regions in the border that are not iluminated, so it make no sense to use them in the processing. Files called *caiman_video_trial_#.tif* are the ones corresponding to a cropped regions of the entire video. We will work with those, and here you can see to which region of the origina video it corresponds. \n",
    "\n",
    "FOV : = entire field of view of the microscope\n",
    "\n",
    "ROI : = region of interest we will work with. It corresponds to the most iluminated area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# image visualization\n",
    "cropped_region_fig_path = figures_dir + 'FOV_calcium_video_1.png'       #path where the image will be saved\n",
    "\n",
    "auxiliary_functions.plot_FOV(FOV_file = input_tif_file_path_FOV , ROI_file = input_tif_file_path, \n",
    "                             output_file = cropped_region_fig_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For any process that tries to separate neurons from background it is important to take into account that the statistics of regions with neurons will be remarkably different from the statistics of regions with no neurons. With  1-photon imaging this is diffuse because background also includes activity from neurons in other focal planes (as explained above). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 0 : Image characteristics\n",
    "\n",
    "Fluctuations in the activitation of the neuron can be directly observed on the video or in a temporal trace plot. Plotting the temporal trace will allow us to see the calcium transient dinamic, if a pixel belonging to a neuron is selected. \n",
    "\n",
    "* Select a set of *N* random pixels from the ROI and plot the temporal evolution of pixel value. If the random pixels are choosen from an active neuron, you will be able to see the calcium transcient dynamic. \n",
    "\n",
    "**Extra exercise 0:** temporal statistics  \n",
    " * For the selected pixels, plot the histogram of pixel value. What are the difference in different regions?, and how can that help to differ a neuron from the background?\n",
    "\n",
    "* If we can already see the calcium traces from the row data, why do we need to do a segmentation of the neurons? Why, as a starter, would not be a good idea to work directly with the temporal traces? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### write your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Answer:***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.a) Summary images : mean image, correlation image and PNR image\n",
    "\n",
    "Summary images are simplification of the videos that gives information about the entire video statistics in one single frame. They are use for computing metrics about the video (quality of motion correction) or also, in the case of CNMF-E (the algorithm we will use), as seed inicialization for source extraction algorithm. \n",
    "\n",
    "The most used summary images are:\n",
    "\n",
    "- **Mean image** : compute the temporal mean of the entire ROI and create one average image.\n",
    "- **Correlation image**: for every pixel compute the correlation over time with it four (or more) nearest neightbors and compute the mean over that correlation value (this is the pixel value of each pixel in the correlation summary image). With this definition pixels corresponding to the same cell that has the same actitity and changes in luminosity will be more highly correlated while pixels belonging to the background will be less correlated (will see an example in further seccions).\n",
    "- **Peak to Noise Ratio (PNR)**: computes te the peak to noise ratio pixel by pixel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 1: video statistics\n",
    "\n",
    "* Compute the mean and correlation summary images for *caiman_video_trial_0.tif*. You can use *mean* and *corrcoef* functions from *numpy* for that aim. \n",
    "\n",
    "Note: Correlation image can be computed taking *m* nearby pixels. At this instance use 4 nearest-neighbords for each pixel. When using CaImAn we will use 8 nearest-neightbords \n",
    "\n",
    "* Plot the resulting summary images and the difference between them (make sure they are defined in the same range of relative values), and plot all in the same range [0,1].\n",
    " \n",
    "**Extra exercise 1:** compare summary images\n",
    "\n",
    "* The range of the mean summary image and the correlation image are different. Normalize the first one to the maximum pixel value to compare both of them. Which summary image gives a better understading of how to segment neurons? Now, if a summary image is the seed for a automatic detection algorith, which image do you think will be a better inicializer and why? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "###create function for summary images\n",
    "def summary_image_mean(video = None):\n",
    "    # complete here with mean code\n",
    "    si_mean = np.zeros_like(video[:,:,0])\n",
    "    return si_mean\n",
    "    \n",
    "def summary_image_correlation(video = None):\n",
    "    # complete here with image correlation code\n",
    "    si_corr = np.zeros_like(video[:,:,0])\n",
    "    return si_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "###load movie\n",
    "original_movie = cm.load(input_tif_file_path)\n",
    "mean_frame = summary_image_mean(video = original_movie)\n",
    "correlation_image = summary_image_correlation(original_movie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/ipykernel_launcher.py:16: RuntimeWarning: invalid value encountered in true_divide\n",
      "  app.launch_new_instance()\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "data": {
      "image/png": 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PNo/fjVIUjBtAYFIXUrryXJGTX+5kYdCXwe3wJynngQ12R57oaBzlvT09Iv5woMvgL1D+1nxs+aetrjk6eceY9zYu8+4REQcPdDfcrUtpTMlelKNcg+v6HpRuYmvZ5k9n6YvbW0a6ZrZtksz+JPCswfXfDNpz4MIMa/gbKs3Lit8DASLivpl548i0+wD3ZPneGrvIzGspp3VM4jWUAa5+OjM/PLLsvYGfzMwPjWtbY9Na2qaNx8JvjMy8iDJowUp+H7gwInZSzru4lXL4/amUa+t9kXK+zW9HxCeA/6IUBaOj9q3XPYEvRsRZlBH3vknZy/piSneAhS+QFzf3/zoi/pjS3er3aYaGn7MLgBOb80VuoXST3GuNr/FuSnehRzDbC0nDZL/f8yhHIc6JcrHX71FGOryBcjL4glMov6t/jojTKUf5DgZ+ljIgwkXtvQ1NQ2a+NyJeDby5GbHwPZSBSB5EGR3yWuADmXl+c3TwnyLiZEo3yBdRvlS/cuyLT+bVlJF3PxQRr6Vs9wdSRqX8WHN+3KiPN218Y7OX+hDKZ/ErI/N9ATgmIp5GKayuy8xxR2X+nDL657sj4m+a13s5cH5m/sduvDco+XZus338O/Bd4OHAH1EGw1ooLD9A2c5OjzJK5WGU9T9XmfnNiPgk8CcR8S3K9n8yJbvXsiPuLMp6vhOl6+gsTZLZZ1COer8vyvXM7koZ+fNrDGfeJH9DpbmY8HvgZyLiPZSj7jdSTj15ESWbzlrpibvh9cDjgfOiXLfvAsqOuYdRriv4Xsq5xn/cdH//R+DzlC6rv0A5DehFLbVNFfAcv3XKzI9RNr77UEZKei/lD9mXWdqb8meUrod/3vz/A8pQ/tPwLcpoc4+kDKn9gWb55wOPWRikpTn35OmUP7hvp1y24P+ljFg5b79FGdnt9ZS92J9l/LDpy8pyjZr3U9b7Lte1admqv9/m6N0xlC/OZ1Aui/E3lMF7vjUw3xcpI3Z9l3L+zPspX5ZuY2ngCHVcZv4e5RyuTZQ/uBdQtrkLKdvdgqc3j70GeBul296TMnPdv+vMvInyGfoCZfj8f6FkxD2By5d5zg2U7ns/RClUX0Apkkbb8Ybm9U6nHNE5YZnXuwI4itLd850sbRvLXppmDf6L0r3pZymZe17T1tOBJ2fmHU0bFi4lcQhlx9CvUEbb7YJfpnTjegslC97R/DyxzPwq5cjqv2XmlVNv4cpWzezM/C5l5NrvUUYVPIXyt+kbDGfeJH9DpS77M0rviL+m5ONLKSN7bhkZIGpqmjEXfomyLT6OkiHvp5xT+HeU7Q3KQHP/Rfn7835KzhwOHJeZr2qjbapD2KtMNWtGgvtv4PTM/ON5t2cSzflTVwOvy8w/nXd7JNUjyvUav0I5V/DN827PJKJcb/WLwAmZeca82yNJfWVXT1WpGVr9YZQ96PemXAurkyLi+ZQjrldR9m6/kNI9ar2j8UnqmeZ8wMOB36F0i5zlQFZr0nRrv46yU+5QSjf8r1GOTkiS5mRmXT0j4tqI+ExEXBYRlzTT9o+ICyLiqub//Qbmf3FEbIuIKyPiiFm1U9X4Yco5cb9MuWj29lXmn6fbKMXeP1O6e34P+JnM/O+5tmpGIuL0iLgxIsZeXzGKv26298sj4hEzbp/ZpBo8knJO5k8Cz266VHZVUs5l/gDwWkqXsyeNXBe1E8wnSV3UVjbNrKtnRFwLbG7Ov1iY9grglsx8WTPIwX6Z+QdRhsR/K+W6aD9MOXfrR2Y8epmkKYiIx1MGEHlLZj5kzONHU85XOBp4NPBXmfnoGbbvWswmqZfMJ0ld1FY2zXtwl2NYGvnoLOBpA9PPbi6MeQ1loIEtc2ifpN3UXLJgpWu4HUMJtszMjwP3ioiDZtO6FdtkNkkbnPkkqYvayqZZFn4J/EtEXBoRCyPCHZiZ1wM0/y9c+PdgysheC7Y30yRtPPPe3s0mScuZ9zZvPkkaZ13b+ywHd3lsZl4XEfcFLoiIL6wwb4yZtkuf1CYETwDYZ599HvmgBz1oOi2VNoBLL730psy8z1qec8RP75M337K2XkGXXn7bFcD3ByZtzcyta3iJibb3Fk09m2A4n/Zkz0febU2XapM2tlv5+pryaT3ZBOYTfneS1mSt351qy6aZFX4LF/vNzBsj4l2U7gc3RMRBmXl9c3jyxmb27cD9Bp5+CGWEsNHX3Eq55hmbN2/OSy65pM23IFUlItY8eMzNt+zg4vMPXdNz9jzoqu9n5ua1LmvARNt7W9rIpub1FvNp39g/Hx1PbustSNX5YL59Tfm0nmwC82lcW/3uJC1vrd+dasummXT1jIh9mqGoiYh9gKdQLvx6LnB8M9vxlAsI00w/NiL2aq7/s4kygqOkFiWwc43/puBc4NnNCFWPAb650I2pbWaTVIf1ZJP5ZD5Jbastm2Z1xO9A4F0RsbDMf8zMD0TEJ4FzIuJ5wJeAZwBk5hURcQ7wOeAO4ERHpZJmIdmRUwmkRRHxVuCJwAERsZ0yzPudATLzNOA8yqhU24DvAs+dagNWZjZJVZh+NoH5JGl31ZVNMyn8MvNqysW2R6ffDIzt/5SZpwKnttw0SQPKnqvpnr6Smcet8ngCJ051oRMym6Q6tJFNYD5J2j21ZdMsB3eRVIEpdUGQpKkymyR1UU3ZZOEnaVGS7MhZDlgnSaszmyR1UW3ZZOEnaUgbXRYkaXeZTZK6qKZssvCTtCiBHRUFmKR+MJskdVFt2WThJ2lITXuuJPWH2SSpi2rKJgs/SYsSquqrLqkfzCZJXVRbNln4SRpSz9hUkvrEbJLURTVlk4WfpEVJVtVXXVI/mE2Suqi2bLLwk7QkYUc9+SWpL8wmSV1UWTZZ+ElalNTVZUFSP5hNkrqotmyy8JM0INhBzLsRkjTCbJLURXVlk4WfpEUJ7Kyoy4KkfjCbJHVRbdlk4SdpSE17riT1h9kkqYtqyqY95t0ASZIkSVK7POInaVFS154rSf1gNknqotqyycJP0pCdWU+ASeoPs0lSF9WUTRZ+khbVtudKUj+YTZK6qLZssvCTtCgJdnjqr6SOMZskdVFt2WThJ2lITV0WJPWH2SSpi2rKJgs/SYtq67IgqR/MJkldVFs2WfhJGhDsyHq6LEjqC7NJUhfVlU0WfpIWJbCzor7qkvrBbJLURbVlk4WfpCE1dVmQ1B9mk6QuqimbLPwkLcqsq8uCpH4wmyR1UW3ZZOEnacjOivZcSeoPs0lSF9WUTRZ+khaV0anq2XMlqR/MJkldVFs2WfhJGlBXlwVJfWE2SeqiurLJwk/SotpGp5LUD2aTpC6qLZss/CQN2ZH19FWX1B9mk6QuqimbLPwkLUqiqr7qkvrBbJLURbVlUz0tlSRJkiSti0f8JA3ZWdFJypL6w2yS1EU1ZZOFn6RFtQ1LLKkfzCZJXVRbNln4SVqURFUnKUvqB7NJUhfVlk0WfpKG1DQssaT+MJskdVFN2WThJ2lRJlVdiFRSP5hNkrqotmyy8JM0INhJPV0WJPWF2SSpi+rKJgs/SYuSuvZcSeoHs0lSF9WWTRZ+kobUNDqVpP4wmyR1UU3ZZOEnaVES7KxodCpJ/WA2Seqi2rLJwk/SkJr2XEnqD7NJUhfVlE0WfpIWJbCzor7qkvrBbJLURbVlk4WfpAHBjopGp5LUF2aTpC6qK5ss/CQtqm3PlaR+MJskdVFt2WThJ2lITXuuJPWH2SSpi2rKJgs/SYsyo6o9V5L6wWyS1EW1ZdNMWxoRe0bEf0bE+5r7+0fEBRFxVfP/fgPzvjgitkXElRFxxCzbKfXZjtxjTbdJRMSRzba8LSJOHvP4PSPivRHx6Yi4IiKeO/U3tnL7zCap49aaTZPkk9kkaXe1kU3QTj7NukT9HeDzA/dPBi7MzE3Ahc19IuJw4FjgwcCRwBsiYs8Zt1XSFDTb7uuBo4DDgeOabXzQicDnMvNhwBOBV0XEXWbYTLNJ6hmzSVJXtZVPMyv8IuIQ4KnAmwYmHwOc1fx8FvC0gelnZ+ZtmXkNsA3YMqu2Sn2VwE5iTbcJbAG2ZebVmfkD4GzKNj666HtERAB3B24B7pjiW1uW2SR133qyaYJ8Mpsk7ZaWsglayqdZnuP3GuD3gXsMTDswM68HyMzrI+K+zfSDgY8PzLe9mTYkIk4ATgA49NBD22iz1DMxcReEAQdExCUD97dm5taB+wcDXx64vx149MhrvA44F7iOkhG/lJk719qQdZp6NsFwPu3N3abdZqln1pVNsHI+9T6b/O4k7a5WsglayqeZFH4R8XPAjZl5aUQ8cZKnjJmWu0woK2grwObNm3d5XNLalGGJ1zw61U2ZuXmFxyfZno8ALgOeBDwQuCAi/jUzv7XWxqxFW9kEw/m0b+xvPkm7YZ3ZBCvnU++zye9O0u5pKZugpXya1RG/xwI/HxFHA3sD+0bE3wM3RMRBzV6rg4Abm/m3A/cbeP4hlGpWUst2TL8H+CTb83OBl2VmAtsi4hrgQcDF027MCLNJqoTZZDZJXdRCNkFL+TSTc/wy88WZeUhm3p9y8vGHMvNXKIcnj29mOx54T/PzucCxEbFXRBwGbKL9kJV6Lwl25tpuE/gksCkiDmtOOj6Wso0P+hLwZICIOBD4UeDqKb61scwmqQ7ryaYJ8slskrRbWsomaCmf5n0dv5cB50TE8yiNfwZAZl4REecAn6OcpHhiZu6YXzOl/tg55f1BmXlHRJwEnA/sCZzebOPPbx4/DXgpcGZEfIbSveEPMvOmqTZkbcwmqWPMJsBskjpn2tkE7eXTzAu/zLwIuKj5+WaaSnXMfKcCp86sYZLIhB3r66u+yuvmecB5I9NOG/j5OuApU1/wGphNUneZTWaT1EVtZVN57enn07yP+EnqmHWepCxJrTKbJHVRTdlk4SdpUemrPrPLe0rSRMwmSV1UWzZZ+EkasmOyC4tK0kyZTZK6qKZssvCTtGg3rkcjSa0xmyR1UW3ZZOEnaUBdXRYk9YXZJKmL6somCz9JQ3ZW1GVBUn+YTZK6qKZssvCTtKjNYYklab3MJkldVFs2WfhJGlJTlwVJ/WE2SeqimrLJwk/SojIscT17riT1g9kkqYtqy6Z6SlRJkiRJ0rp4xE/SkJpOUpbUH2aTpC6qKZss/CQtqu16NJL6wWyS1EW1ZZOFn6QhNZ2kLKk/zCZJXVRTNln4SVqSdZ2kLKknzCZJXVRZNln4SVqU1NVXXVI/mE2Suqi2bLLwkzSkpj1XkvrDbJLURTVlk4WfpEW1naQsqR/MJkldVFs2WfhJGlJTgEnqD7NJUhfVlE0WfpIWJXWdpCypH8wmSV1UWzZZ+EkaUtNJypL6w2yS1EU1ZZOFn6QlWVeXBUk9YTZJ6qLKssnCT9Ki2k5SltQPZpOkLqotmyz8JA2pKcAk9YfZJKmLasomCz9Ji2o7SVlSP5hNkrqotmyy8JM0JCsKMEn9YTZJ6qKassnCT9KQmkanktQfZpOkLqopmyz8JC3KykanktQPZpOkLqotm/aYdwMkSZIkSe3yiJ+kITX1VZfUH2aTpC6qKZss/CQNqGt0Kkl9YTZJ6qK6ssnCT9KQmvZcSeoPs0lSF9WUTRZ+khYldZ2kLKkfzCZJXVRbNln4SVqSZYQqSeoUs0lSF1WWTRZ+kobUdD0aSf1hNknqopqyycJP0qKkrr7qkvrBbJLURbVlk4WfpAF1jU4lqS/MJkldVFc2WfhJGlJTX3VJ/WE2SeqimrLJwk/SkJq6LEjqD7NJUhfVlE0WfpIWZdYVYJL6wWyS1EW1ZZOFn6QhNfVVl9QfZpOkLqopmyz8JA2pqa+6pP4wmyR1UU3ZZOEnaUhNXRYk9YfZJKmLasomCz9Ji5KoKsAk9YPZJKmLassmCz9JQyrqsSCpR8wmSV1UUzbtMe8GSJIkSZLaNZPCLyL2joiLI+LTEXFFRLykmb5/RFwQEVc1/+838JwXR8S2iLgyIo6YRTul3muGJV7LbRIRcWSzLW+LiJOXmeeJEXFZkxEfmer7Wr5dZpNUg3Vk0yT51NVsapZrPkld11I2QTv5NKsjfrcBT8rMhwEPB46MiMcAJwMXZuYm4MLmPhFxOHAs8GDgSOANEbHnjNoq9Vuu8baKZqfPcMAAABrKSURBVNt9PXAUcDhwXLOND85zL+ANwM9n5oOBZ0znzazKbJJqsdZsWiWfOp5NYD5JdZhyNkF7+TSTwi+Lbzd379zcEjgGOKuZfhbwtObnY4CzM/O2zLwG2AZsmUVbpb5rYa/VFmBbZl6dmT8AzqZs44N+GXhnZn6ptCFvnOqbWobZJNWjhb3qnc2mZlnmk1SBlo74tZJPMzvHLyL2jIjLgBuBCzLzE8CBmXk9QPP/fZvZDwa+PPD07c00SS3LXNttApNszz8C7BcRF0XEpRHx7Om8m9WZTVId1ppNE+RTp7MJzCepBi1kE7SUTzMb1TMzdwAPbw5LvisiHrLC7ONK4V1WU0ScAJwAcOihh06lnVKfJeu6Hs0BEXHJwP2tmbl14P4k2/OdgEcCTwbuCvxHRHw8M7+41sasVRvZBMP5tDd32+12Sn22zmyClfOp09kEfneSuq6lbIKW8mnml3PIzG9ExEWU/uc3RMRBmXl9RBxE2aMFpaq938DTDgGuG/NaW4GtAJs3b65pNFWpmxJYe4DdlJmbV3h8ku15e/M63wG+ExEfBR4GzOTLFUw3m5rXW8ynfWN/80naHevLJlg5n6rIJvC7k9RZ7WQTtJRPsxrV8z7N3ioi4q7AzwBfAM4Fjm9mOx54T/PzucCxEbFXRBwGbAIunkVbpb5robvCJ4FNEXFYRNyFMvjAuSPzvAf4qYi4U0TcDXg08Plpvq9xzCapHi10p+psNoH5JNWipa6ereTTrI74HQSc1YxQswdwTma+LyL+AzgnIp4HfIlmNJrMvCIizgE+B9wBnNh0d5DUtinv/83MOyLiJOB8YE/g9GYbf37z+GmZ+fmI+ABwObATeFNmfna6LRnLbJJq0a9sAvNJqkMLx83byqeZFH6ZeTnw42Om30zplzruOacCp7bcNElDJr++zFpk5nnAeSPTThu5/0rglVNf+MrtMpukKvQrm5rlmk9S57WTTdBOPs38HD9JHecZH5K6yGyS1EUVZZOFn6Qlue7RqSSpPWaTpC6qLJss/CQNq2jPlaQeMZskdVFF2WThJ2lEPXuuJPWJ2SSpi+rJJgs/ScMq2nMlqUfMJkldVFE2WfhJGlZRgEnqEbNJUhdVlE0WfpKWJFDRScqSesJsktRFlWXTHvNugCRJkiSpXR7xkzQkK+qyIKk/zCZJXVRTNln4SRpWUYBJ6hGzSVIXVZRNFn6ShlXUV11Sj5hNkrqoomyy8JM0JCracyWpP8wmSV1UUzZZ+ElaklTVZUFST5hNkrqosmyy8JM0IKrqsiCpL8wmSV1UVzZZ+EkaVtGeK0k9YjZJ6qKKssnCT9KwigJMUo+YTZK6qKJssvCTNKyiAJPUI2aTpC6qKJss/CQtSarqqy6pJ8wmSV1UWTZZ+EkaUtOwxJL6w2yS1EU1ZZOFn6RhFQWYpB4xmyR1UUXZtMekM0bEqyPi4W02RpLWymySJEla3cSFH3Bn4PyI+GxE/EFEHNJWoyTNT+Tabh1gNkk9sNZs6kg+SdrgasqmiQu/zPwt4IeBk4GHA5+PiA9GxLMj4u5tNVDSjGWs7Tbv5ppNUj+sNZs6kE+SeqCibFrLET8yc0dmvi8zjwMeA9wHOBP4akS8KSIObqGNkmYl13HrALNJ2uDWk00dySdJG1hl2bSmwi8i9o2I50XEh4GPAp8Afgr4MeDbwPun30RJWpnZJEmStLKJR/WMiLcDR1C+VJ0GvDszbxt4/IXAN6feQkmzVdlecrNJ6onKsklST1SUTWu5nMPHgZMy86vjHszMnRFx4HSaJWleKhwQwWySeqDCbJLUAzVl08SFX2b+xQTzfHf3miNp7ioKMDCbpN6oLJsk9URF2eQF3CUNqyjAJPWI2SSpiyrKJgs/SYvmfX0ZSRrHbJLURbVlk4WfpGFe+0pSF5lNkrqoomyy8JM0rKI9V5J6xGyS1EUVZZOFn6QhNXVZkNQfZpOkLqopmyz8JA2rKMAk9YjZJKmLKsomCz9JSyo7SVlST5hNkrqosmyy8JM0rKIAk9QjZpOkLqoomyz8JA2rKMAk9YjZJKmLKsomCz9JQ2rqsiCpP8wmSV1UUzbtMe8GSJIkSZLa5RE/ScMq2nMlqUfMJkldVFE2WfhJWlLZ6FSSesJsktRFlWWTXT0lSZIkaYPziJ+kYRXtuZLUI2aTpC6qKJss/CQNqyjAJPWI2SSpiyrKJgs/SYuCuvqqS+oHs0lSF9WWTTM5xy8i7hcRH46Iz0fEFRHxO830/SPigoi4qvl/v4HnvDgitkXElRFxxCzaKYmy52ottwlExJHNtrwtIk5eYb5HRcSOiPjF3XoPEzKbpIqsNZsmyKeuZlOzTPNJqkEL2QTt5NOsBne5A/i9zPwx4DHAiRFxOHAycGFmbgIubO7TPHYs8GDgSOANEbHnjNoq9VczOtVabqtptt3XA0cBhwPHNdv4uPleDpw/3Te1IrNJqsE6smm1fOp4NoH5JHVfC9kE7eXTTAq/zLw+Mz/V/Hwr8HngYOAY4KxmtrOApzU/HwOcnZm3ZeY1wDZgyyzaKvXe9PdabQG2ZebVmfkD4GzKNj7qt4B3ADfu3huYnNkkVWT6e9U7m01gPknVaOeIXyv5NPPLOUTE/YEfBz4BHJiZ10MJOOC+zWwHA18eeNr2Zpqktk0/vFbdniPiYODpwGm71fbdYDZJHTf9L1dVZFPTjvtjPknd1E7h10o+zXRwl4i4O6UqfUFmfisilp11zLRdVlNEnACcAHDooYdOq5lSr63jJOUDIuKSgftbM3Pr4EuOec7oUl4D/EFm7lghF1oz7WxqXnMxn/bmbtNoptRr6xxAYaV86nw2gd+dpK5rIZugpXyaWeEXEXemBNc/ZOY7m8k3RMRBmXl9RBzE0mHK7cD9Bp5+CHDd6Gs2K2grwObNm9e32iUNW/uWdFNmbl7h8Um2583A2U1wHQAcHRF3ZOa719yaNWojm2A4n/aN/c0naXetbytaKZ86nU3gdyepCtPPJmgpn2Y1qmcAbwY+n5mvHnjoXOD45ufjgfcMTD82IvaKiMOATcDFs2ir1GvtdFf4JLApIg6LiLtQBh84d2ixmYdl5v0z8/7A24HfnFHRZzZJNVhPNq2eT53NJjCfpCq0k03QUj7N6ojfY4FfBT4TEZc10/4QeBlwTkQ8D/gS8AyAzLwiIs4BPkcZ1erEzNwxo7ZKvTbt69Fk5h0RcRJlxKk9gdObbfz5zePzPHfGbJIq0bNsAvNJqkIb1/FrK59mUvhl5scY31cV4MnLPOdU4NTWGiVpvHYC7DzgvJFpY0MrM58z/RaMZzZJFelRNjXLM5+kGrTUYbqNfJrp4C6Suq+NPVeStLvMJkldVFM2WfhJGlZRgEnqEbNJUhdVlE0WfpKWTH7SsSTNjtkkqYsqyyYLP0mLguVPKJGkeTGbJHVRbdlk4SdpWEV7riT1iNkkqYsqyqaZXMdPkiRJkjQ/HvGTNKSm0akk9YfZJKmLasomCz9JwyoKMEk9YjZJ6qKKssnCT9KwigJMUo+YTZK6qKJssvCTtCTr6rIgqSfMJkldVFk2WfhJGlZRgEnqEbNJUhdVlE0WfpKG1LTnSlJ/mE2SuqimbLLwkzSsogCT1CNmk6QuqiibLPwkDalpz5Wk/jCbJHVRTdlk4SdpSVLVnitJPWE2SeqiyrLJwk/SsIoCTFKPmE2SuqiibLLwk7QoqKvLgqR+MJskdVFt2WThJ2lYRQEmqUfMJkldVFE2WfhJGhJZUYJJ6g2zSVIX1ZRNFn6SllR2krKknjCbJHVRZdlk4SdpSE191SX1h9kkqYtqyiYLP0nDKgowST1iNknqooqyaY95N0CSJEmS1C6P+EkaUlOXBUn9YTZJ6qKassnCT9KwigJMUo+YTZK6qKJssvCTtCTr2nMlqSfMJkldVFk2WfhJGlZRgEnqEbNJUhdVlE0WfpIWBXXtuZLUD2aTpC6qLZss/CQNy4oSTFJ/mE2SuqiibLLwkzSkpj1XkvrDbJLURTVlk4WfpCVJVX3VJfWE2SSpiyrLJgs/SUNi57xbIEm7MpskdVFN2WThJ2lYRXuuJPWI2SSpiyrKJgs/SUNq6qsuqT/MJkldVFM2WfhJWpJUNTqVpJ4wmyR1UWXZZOEnaUhNe64k9YfZJKmLasomCz9JwyoKMEk9YjZJ6qKKssnCT9KioK49V5L6wWyS1EW1ZZOFn6QlmVX1VZfUE2aTpC6qLJv2mHcDJEmSJEnt8oifpCE1dVmQ1B9mk6QuqimbLPwkDasowCT1iNkkqYsqyiYLP0lDatpzJak/zCZJXVRTNln4SVqSwM6KEkxSP5hNkrqosmyy8JM0rJ78ktQnZpOkLqoomyz8JA2pqcuCpP4wmyR1UU3ZNJPLOUTE6RFxY0R8dmDa/hFxQURc1fy/38BjL46IbRFxZUQcMYs2SmosXJNm0tsEIuLIZnveFhEnj3n8WRFxeXP794h42NTf1/JtM5+kGqw1mybIJ7NJ0m5rIZugnXya1XX8zgSOHJl2MnBhZm4CLmzuExGHA8cCD26e84aI2HNG7ZR6L3Jtt1Vfr2y/rweOAg4Hjmu280HXAE/IzIcCLwW2TvddrehMzCep89aaTavlk9kkaRqmnU3QXj7NpPDLzI8Ct4xMPgY4q/n5LOBpA9PPzszbMvMaYBuwZRbtlHov13Fb3RZgW2ZenZk/AM6mbOdLi83898z8enP348Ahu/tWJmU+SRVYTzatnk9mk6Td0042QUv5NKsjfuMcmJnXAzT/37eZfjDw5YH5tjfTdhERJ0TEJRFxyde+9rVWGyv1QQCRuaYbcMDCdtjcThh52Ym36cbzgPdP9Y2t3VTz6XZua7Wx0ka3nmyaIJ96n01+d5J2T0vZBC3lUxcHd4kx08bWxpm5leaw5ubNmys6tVLqsJ1rfsZNmbl5hccn3qYj4qcp4fW4NbdiNtaVT/vG/uaTtLvWnk2wcj71Ppv87iRNwfSzCVrKp3kWfjdExEGZeX1EHATc2EzfDtxvYL5DgOtm3jqpp2LCk47XYKJtOiIeCrwJOCozb552I9bIfJI6xmwCzCapc1rIJmgpn+bZ1fNc4Pjm5+OB9wxMPzYi9oqIw4BNwMVzaJ/UP+30U/8ksCkiDouIu1AGIDh3cIaIOBR4J/CrmfnFabyV3WQ+SV3Sznk0ZpOk3dPeOX6t5NNMjvhFxFuBJ1L6s24H/hR4GXBORDwP+BLwDIDMvCIizgE+B9wBnJiZO2bRTkk58TDDE79i5h0RcRJwPrAncHqznT+/efw04E+Ae1NGogO4Y5UuEFNjPkk1MJswm6QOmn42QXv5NJPCLzOPW+ahJy8z/6nAqe21SNJy2rgQaWaeB5w3Mu20gZ9/Hfj16S95deaTVAezaZHZJHVIWxdwbyOfuji4i6R5aqevuiTtHrNJUhdVlE3zPMdPkiRJkjQDHvGTtCQh1jcssSS1x2yS1EWVZZOFn6RhFXVZkNQjZpOkLqoomyz8JA2rJ78k9YnZJKmLKsomCz9JQ1q6EKkk7RazSVIX1ZRNFn6ShlUUYJJ6xGyS1EUVZZOFn6QlCVR0krKknjCbJHVRZdlk4SdpUZBVdVmQ1A9mk6Quqi2bLPwkDasowCT1iNkkqYsqyiYLP0nDKgowST1iNknqooqyycJP0pLK+qpL6gmzSVIXVZZNFn6ShtTUV11Sf5hNkrqopmyy8JM0rKIAk9QjZpOkLqoomyz8JA3IqgJMUl+YTZK6qK5ssvCTtCSpKsAk9YTZJKmLKssmCz9Jwyo6SVlSj5hNkrqoomyy8JM0pKaTlCX1h9kkqYtqyqY95t0ASZIkSVK7POInaVhFe64k9YjZJKmLKsomCz9JSxLYWU+ASeoJs0lSF1WWTRZ+kgbUNSyxpL4wmyR1UV3ZZOEnaVhFASapR8wmSV1UUTZZ+EkaVlGASeoRs0lSF1WUTRZ+kpZU1lddUk+YTZK6qLJssvCTNCAhK7oSqaSeMJskdVFd2WThJ2lYRV0WJPWI2SSpiyrKJgs/SUsq67IgqSfMJkldVFk2WfhJGlbRnitJPWI2SeqiirLJwk/SsIoCTFKPmE2SuqiibLLwkzSgrguRSuoLs0lSF9WVTRZ+kpYksLOe0akk9YTZJKmLKssmCz9JwyracyWpR8wmSV1UUTZZ+EkaVlGASeoRs0lSF1WUTRZ+kgZkVcMSS+oLs0lSF9WVTRZ+kpYkZNbTV11ST5hNkrqosmzaY94NkCRJkiS1yyN+koZV1GVBUo+YTZK6qKJssvCTNKyik5Ql9YjZJKmLKsomCz9JSzKruh6NpJ4wmyR1UWXZZOEnaVhFe64k9YjZJKmLKsomCz9JQ7KiPVeS+sNsktRFNWWThZ+kAVnVnitJfWE2SeqiurLJwk/SkqSq0akk9YTZJKmLKssmCz9Jwyq6EKmkHjGbJHVRRdnU6Qu4R8SREXFlRGyLiJPn3R5po0sgd+aabpNYbVuO4q+bxy+PiEdM+71Nk9kkzdZ6smmSfDKbJO2OtrIJ2smnzhZ+EbEn8HrgKOBw4LiIOHy+rZI2uMyy52ott1VMuC0fBWxqbicAfzPdNzY9ZpM0B+vJplXyyWyStNtayCZoL586W/gBW4BtmXl1Zv4AOBs4Zs5tkja8FvZaTbItHwO8JYuPA/eKiIOm+86mxmyS5qCFvepmk6Td1tIRv1byqcuF38HAlwfub2+mSWrTlPdaMdm2XNP2XlNbpY1j+nvVzSZJu6+FI360lE9dHtwlxkwbKpEj4gTKoU2A2yLis623amUHADfZBtvQgeUD/Ohan3ArXz//g/n2A9b4tL0j4pKB+1szc+vA/VW35Qnn6YqJ2jqaTx/Mt/c9n+a9fNvQrTasKZ/WmU2wcj6ZTX53sg3dWX5X2tCFbIKW8qnLhd924H4D9w8BrhucoVlBWwEi4pLM3Dy75u3KNtiGrix/oQ1rfU5mHtlCU1bdliecpysmaqv51K3l24butWEt85tNEzGbbEO1y+9SG9Yyf0vZBC3lU5e7en4S2BQRh0XEXYBjgXPn3CZJazfJtnwu8OxmhKrHAN/MzOtn3dAJmU3SxmA2SeqqVvKps0f8MvOOiDgJOB/YEzg9M6+Yc7MkrdFy23JEPL95/DTgPOBoYBvwXeC582rvaswmaWMwmyR1VVv51NnCDyAzz6O8qUlsXX2W1tmGwjbMf/nQjTYA47flJrQWfk7gxFm3a73WmE3Qjd/FvNsw7+WDbVhgGxpmUyd+D7ahmHcb5r18sA1D2sinKM+RJEmSJG1UXT7HT5IkSZI0BRui8IuIIyPiyojYFhEnz3C510bEZyLisoVRgCJi/4i4ICKuav7fb8rLPD0ibhwcfnmlZUbEi5v1cmVEHNHS8k+JiK806+GyiDi6reU3r3m/iPhwRHw+Iq6IiN9pps9yPSzXhpmsi4jYOyIujohPN8t/STN9ZutAqzObZvt5nHc+mU2Lr2c+VWAe+WQ29fe7k9nUEZlZ9Y1ywuN/AQ8A7gJ8Gjh8Rsu+FjhgZNorgJObn08GXj7lZT4eeATw2dWWCRzerI+9gMOa9bRnC8s/BXjRmHmnvvzmdQ8CHtH8fA/gi82yZrkelmvDTNYF5dotd29+vjPwCeAxs1wH3lb9HZlNM/48zjufzKbF1zSfOn6bVz6ZTf397mQ2deO2EY74bQG2ZebVmfkD4GzgmDm25xjgrObns4CnTfPFM/OjwC0TLvMY4OzMvC0zr6GM+rOlheUvZ+rLb9pwfWZ+qvn5VuDzwMHMdj0s14blTLUNWXy7uXvn5pbMcB1oVWbTjD+P884ns2mxDeZT93Upn8ymXdu24b47mU3dsBEKv4OBLw/c387KH6RpSuBfIuLSiDihmXZgNtfQaP6/7wzasdwyZ7luToqIy5vuDAuHyFtffkTcH/hxyl6buayHkTbAjNZFROwZEZcBNwIXZObc1oHGMpu683mceT71OZuaZZtP3TavdW42DevldyezaX42QuEXY6bNaqjSx2bmI4CjgBMj4vEzWu6kZrVu/gZ4IPBw4HrgVbNYfkTcHXgH8ILM/NZKs7bVjjFtmNm6yMwdmflw4BBgS0Q8ZKWmTnv5WpXZtLxZrpuZ51PfswnMpwrMa52bTUt6+d3JbJqvjVD4bQfuN3D/EOC6WSw4M69r/r8ReBfl8O8NEXEQQPP/jTNoynLLnMm6ycwbmg1pJ/BGlg6Dt7b8iLgzJTj+ITPf2Uye6XoY14Z5rIvM/AZwEXAkc/4saIjZ1IHP46y3SbNpmPnUWXNZ52bTkj5+dzKb5m8jFH6fBDZFxGERcRfgWODcthcaEftExD0WfgaeAny2WfbxzWzHA+9puy0rLPNc4NiI2CsiDgM2ARdPe+ELG0vj6ZT10NryIyKANwOfz8xXDzw0s/WwXBtmtS4i4j4Rca/m57sCPwN8gTl/FjTEbOrA53GW+WQ2LS7LfOq+meeT2TSsb9+dzKaOyA6MMLO7N+BoyuhA/wX80YyW+QDKSD+fBq5YWC5wb+BC4Krm//2nvNy3Ug6F307ZE/G8lZYJ/FGzXq4Ejmpp+X8HfAa4nLKRHNTW8pvXfBzlUPvlwGXN7egZr4fl2jCTdQE8FPjPZjmfBf5ktc9fG78Lb6v+nsymGX4e551PZtPi65lPFdxmnU9mU7+/O5lN3bhF86YkSZIkSRvURujqKUmSJElagYWfJEmSJG1wFn6SJEmStMFZ+EmSJEnSBmfhJ0mSJEkbnIWfJEmSJG1wFn6SJEmStMFZ+EmSJEnSBmfhp6mIiAdGxC0R8Yjm/g9HxE0R8cQ5N01Sj5lNkiQVkZnzboM2iIj4DeCFwCOBdwGfycwXzbdVkvrObJIkycJPUxYR5wKHAQk8KjNvm3OTJMlskiT1nl09NW1vBB4CvNYvVpI6xGySJPWaR/w0NRFxd+DTwIeBo4D/OzNvmW+rJPWd2SRJkoWfpigi3gzcIzOfGRFbgXtl5jPn3S5J/WY2SZJkV09NSUQcAxwJPL+Z9ELgERHxrPm1SlLfmU2SJBUe8ZMkSZKkDc4jfpIkSZK0wVn4SZIkSdIGZ+EnSZIkSRuchZ8kSZIkbXAWfpIkSZK0wVn4SZIkSdIGZ+EnSZIkSRuchZ8kSZIkbXAWfpIkSZK0wf0fk5br5zGAraYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plotting code is provided for once the mean and correlation summary images are computed\n",
    "figure, axes = plt.subplots(1,3)\n",
    "\n",
    "axes[0].set_xlabel('x', fontsize = 12)\n",
    "axes[0].set_ylabel('y', fontsize = 12)\n",
    "axes[0].set_title('Mean Summary Image', fontsize = 15)\n",
    "\n",
    "axes[1].set_xlabel('x', fontsize = 12)\n",
    "axes[1].set_ylabel('y', fontsize = 12)\n",
    "axes[1].set_title('Correlation Summary Image', fontsize = 15)\n",
    "\n",
    "axes[1].set_xlabel('x', fontsize = 12)\n",
    "axes[1].set_ylabel('y', fontsize = 12)\n",
    "axes[2].set_title('MSI - CSI', fontsize = 15)\n",
    "\n",
    "mesh0 = axes[0].pcolormesh(mean_frame/np.max(mean_frame), cmap = 'viridis')\n",
    "mesh1 = axes[1].pcolormesh(correlation_image, cmap = 'viridis')\n",
    "mesh2 = axes[2].pcolormesh(mean_frame/np.max(mean_frame)-correlation_image, cmap = 'viridis')\n",
    "\n",
    "vmin_corr = 0\n",
    "vmax_corr = 1\n",
    "mesh0.set_clim(vmin_corr,vmax_corr)\n",
    "mesh1.set_clim(vmin_corr,vmax_corr)\n",
    "mesh2.set_clim(vmin_corr,vmax_corr)\n",
    "\n",
    "\n",
    "figure.colorbar(mesh0,ax=axes[0])\n",
    "figure.colorbar(mesh1,ax=axes[1])\n",
    "figure.colorbar(mesh2,ax=axes[2])\n",
    "\n",
    "figure.set_size_inches(15,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Answer:***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Preprocessing calcium videos\n",
    "\n",
    "Before running into extracting neural activity it is important to take into account that videos from alive animals has intrinsic motion (you can see that from the video dispayed before) that can correspond to respiration, microscopes movements or the movements from the animal itself. This means that it is necesary to correct the motion in multiple frames and align them to a common frame. The typical calcium imaging pre-processing pipeline consists of motion correction of the videos followed by source extraction.\n",
    "\n",
    "### 2.a) Motion correction (MC)\n",
    "\n",
    "In this notebook we will use **NoRMCorre**, which is the motion correction algorithm that is implemented in the CaImAn pipeline as well. \n",
    "\n",
    "We will first perform a *rigid motion correction* and then a *non-rigid (piece-wise) motion correction* of multiple patches of the FOV (proposed in NoRMCorre). For rigid motion correction all frames are aligned to a template using a displacement vector. The displacement vector is computed by computing the maximum cross-correlation value between each frame and the template. PW-rigid (piece-wise-rigid) motion correction consists on spliting the FOV into overlapping patches and perform registration in each patch. Each patch is then splited again and for each sub-patch a displacement vector is computed. Final patch's motion correction is computed by using a smooth motion field created by all sub-patches displacement vectors. A good approach for a good motion correction would be to first run rigid motion correction to solve for all general 'big' displacements of the field of view and then run non-rigid motion correction for correcteness of local shifts (non-uniform displacements).\n",
    "\n",
    "Motion correction requires a general set of parameters definition that are an input to the algorithm. \n",
    "\n",
    "The first step into correction motion is to high pass filter the images (smoothing borders): *gSig_filt* . The size of the filter will affect how smooth or sharp the borders of different parts of the image are define. Small filter size will result in multiple images that has lower pixel by pixel correlation values, while big filter size can oversmooth the image to the point where everything will be highly correlated. This is one paremeter to optimize when runnin motion correction and depends on the size in pixels of a typical neuron.\n",
    "\n",
    "For rigid motion correlation the complete frame will be shift in a *xy* direction. This shift should be limited, as for example, there could be high cross-correlation between in one frame and the mean towards some direction because of highly correlated co-activated neurons that are 'highly' spatialy separated. The limitation for this is imposed by the  *max_shifts* parameter. Similar criteria applyes for non-rigid motion correction, where in this case the limitation is in number of pixels the patch can be shifted from the rigid version (*max_deviation_rigid*). \n",
    "\n",
    "For non-rigid motion correction size of patches and overlapping between different patches should be define, ie *strides* and *overlaps*.\n",
    "\n",
    "For more information about motion correction algorithm see [Pnevmatikakis et al,2017](https://www.sciencedirect.com/science/article/pii/S0165027017302753) *NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data*.\n",
    "\n",
    "\n",
    "#### Example of motion correction \n",
    "\n",
    "First we will define all the parameters that are relevant to perform motion correction in a rigid and non-rigid manner, and will explore the results after a first step of rigid and pw-rigid motion correction.\n",
    "\n",
    "After the example you will have to explore the effect on some of these parameters in the resulting motion corrected movie.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Changing key pw_rigid in group motion from False to True\n",
      "WARNING:root:Changing key gSig_filt in group motion from None to (5, 5)\n",
      "WARNING:root:Changing key max_shifts in group motion from (6, 6) to (25, 25)\n",
      "WARNING:root:Changing key strides in group motion from (96, 96) to (48, 48)\n",
      "WARNING:root:Changing key overlaps in group motion from (32, 32) to (96, 96)\n",
      "WARNING:root:Changing key upsample_factor_grid in group motion from 4 to 1\n",
      "WARNING:root:Changing key max_deviation_rigid in group motion from 3 to 15\n",
      "WARNING:root:Changing key min_mov in group motion from None to 326.0\n",
      "WARNING:root:No parameter save_movie_rig found!\n"
     ]
    }
   ],
   "source": [
    "## parameters_motion_correction. We create them in a dictionary to prepare for caiman paramenters requirements\n",
    "\n",
    "parameters_motion_correction = { 'pw_rigid': True,  # flag for performing piecewise-rigid motion correction (otherwise just rigid)\n",
    "                                'save_movie_rig': True, # flag to say whether the rigig motion corrected movie is saved\n",
    "                                'gSig_filt': (5, 5),     # size of high pass spatial filtering, used in 1p data\n",
    "                                'max_shifts': (25, 25),  # maximum allowed rigid shift in pixels (view the movie to get a sense of motion)\n",
    "                                'niter_rig': 1 ,\n",
    "                                'strides': (48, 48),     # create a new patch every x pixels for pw-rigid correction\n",
    "                                'overlaps': (96, 96),    # overlap between pathes (size of patch strides+overlaps)\n",
    "                                'upsample_factor_grid': 1,\n",
    "                                'num_frames_split': 80,  # length in frames of each chunk of the movie (to be processed in parallel)\n",
    "                                'max_deviation_rigid': 15, # maximum deviation allowed for patch with respect to rigid shifts\n",
    "                                'shifts_opencv': True, 'use_cuda': False, 'nonneg_movie': True,\n",
    "                                'border_nan': 'copy'}\n",
    "\n",
    "\n",
    "# load tif movie as a caiman movie object\n",
    "original_movie = cm.load(input_tif_file_path)\n",
    "# Calculate movie minimum to subtract from movie (this is necesary for running motion correction)\n",
    "min_mov = np.min(original_movie)\n",
    "# Apply the parameters to the CaImAn algorithm. \n",
    "parameters_motion_correction['min_mov'] = min_mov\n",
    "opts = params.CNMFParams(params_dict = parameters_motion_correction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is necesary to start a cluster to run the subsequent steps from the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/numpy/core/memmap.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  res = super(memmap, self).__getitem__(index)\n",
      "WARNING:root:Changing key n_processes in group patch from 8 to 1\n",
      "WARNING:root:Changing key rf in group patch from 60 to None\n",
      "WARNING:root:Changing key stride in group patch from 30 to None\n",
      "WARNING:root:Changing key p in group preprocess from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 1 to 0\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/dia.py:300: RuntimeWarning: divide by zero encountered in remainder\n",
      "  c = np.arange(num_rows, dtype=np.intc) - (offsets % max_dim)[:, None]\n"
     ]
    }
   ],
   "source": [
    "n_processes = psutil.cpu_count()\n",
    "cm.cluster.stop_server()\n",
    "# Start a new cluster\n",
    "c, dview, n_processes = cm.cluster.setup_cluster(backend='local',\n",
    "                                                 n_processes=n_processes,\n",
    "                                                single_thread=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run motion correction algorithm in two steps: first perform *rigid motion correction* and them *pw-rigid motion correction*. \n",
    "\n",
    "The object *mc* will containg the motion correction movie and all the relevant information, for example *mc.x_shifts* contains information about the rigid shifts that were perform in the x axis of the image frame by frame (*mc.y_shifts for y-axis*). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Movie average is negative. Removing 1st percentile.\n",
      "WARNING:root:Movie average is negative. Removing 1st percentile.\n",
      "WARNING:root:Movie average is negative. Removing 1st percentile.\n",
      "WARNING:root:Movie average is negative. Removing 1st percentile.\n",
      "WARNING:root:Movie average is negative. Removing 1st percentile.\n",
      "WARNING:root:Movie average is negative. Removing 1st percentile.\n"
     ]
    }
   ],
   "source": [
    "#create an object for motion correction\n",
    "mc = MotionCorrect(input_tif_file_path, dview = dview, **opts.get_group('motion'))\n",
    "# Perform rigid motion correction\n",
    "mc.motion_correct_rigid(save_movie = parameters_motion_correction['save_movie_rig'], template = None)\n",
    "m_rig = cm.load(mc.fname_tot_rig[0])\n",
    "fname_tot_rig  = m_rig.save(data_dir + 'motion_corrected/' + 'calcium_video_1' +'_rig' + '.mmap',  order='C')         \n",
    "os.remove(mc.fname_tot_rig[0]) #remove old version\n",
    "\n",
    "#perform non-rigid motion correction\n",
    "total_template_rig = mc.total_template_rig\n",
    "mc.motion_correct_pwrigid(save_movie=True, template = total_template_rig)\n",
    "fname_tot_els = mc.fname_tot_els[0]\n",
    "m_els = cm.load(fname_tot_els)\n",
    "fname_tot_els  = m_els.save(data_dir + 'motion_corrected/' + 'calcium_video_1' + '_els' + '.mmap',  order='C')\n",
    "os.remove(mc.fname_tot_els[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 2: Rigid vs Non-Rigid motion correction\n",
    "\n",
    "The objects *mc* contains information regarding the shifts produced by rigid and non-rigid motion corrections. For rigid motion correction you will find two vectors of size *500* frames corresponding to the shifts produced in each frame by rigid displacements. For non-rigid motion correction each atribute of the object contains multiple size *500* vectors corresponding to the shifts of all patches in *x* and *y* direction. \n",
    "\n",
    " * Plot the shifts produced by rigid and pw-rigid motion correction frame by frame. In the case of pw-rigid plot the mean and std from multiple patches in each direction.\n",
    " * Compare rigid and no-rigid shifts statistics. Plot histogram for both and compute relevants parametes. \n",
    " * Which mc algorithm would you expect to produce better outputs respect to the quality of the motion corrected image and why?\n",
    "\n",
    "Note: *use mc.shifts_rig, mc.x_shifts_els and mc.y_shifts_els*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rigid motion correction x-mean:  std :\n",
      "Rigid motion correction y-mean:  std :\n",
      "PW-rigid motion correction x-mean:  std :\n",
      "PW-rigid motion correction y-mean:  std :\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Complete the following script visualize rigid and elastic shifts\n",
    "\n",
    "figure = plt.figure(constrained_layout=True, figsize= (10,5))\n",
    "gs = figure.add_gridspec(3, 12)\n",
    "\n",
    "figure_ax0 = figure.add_subplot(gs[0, 0:7])\n",
    "figure_ax0.set_title('shifts_rig', fontsize = 15)\n",
    "\n",
    "figure_ax00 = figure.add_subplot(gs[0, 8:11])\n",
    "figure_ax00.set_title('Histogram', fontsize = 15)\n",
    "\n",
    "# add plot temporal evolution\n",
    "figure_ax0.legend(['x shifts','y shifts'])\n",
    "figure_ax0.set_xlabel('frames')\n",
    "figure_ax0.set_ylabel('pixels')\n",
    "\n",
    "# add histogram plot for rigid \n",
    "figure_ax00.set_xlabel('pixels')\n",
    "figure_ax00.set_ylabel('#')\n",
    "\n",
    "figure_ax1 = figure.add_subplot(gs[1, 0:7])\n",
    "figure_ax1.set_title('x_shifts_non_rig', fontsize = 15)\n",
    "figure_ax2 = figure.add_subplot(gs[1, 8:11])\n",
    "figure_ax2.set_title('Histogram', fontsize = 15)\n",
    "\n",
    "# add pixel shifts evolution for x non rigid shift\n",
    "figure_ax1.set_xlabel('frames')\n",
    "figure_ax1.set_ylabel('x shift pixels')\n",
    "\n",
    "# add histogram pixel shifts evolution for x non rigid shift\n",
    "figure_ax2.set_xlabel('pixels')\n",
    "figure_ax2.set_ylabel('#')\n",
    "\n",
    "figure_ax3 = figure.add_subplot(gs[2, 0:7])\n",
    "figure_ax3.set_title('y_shifts_non_rig', fontsize = 15)\n",
    "figure_ax4 = figure.add_subplot(gs[2, 8:11])\n",
    "figure_ax4.set_title('Histogram', fontsize = 15)\n",
    "\n",
    "# add pixel shifts evolution for y non rigid shift\n",
    "figure_ax3.set_xlabel('frames')\n",
    "figure_ax3.set_ylabel('y shift pixels')\n",
    "\n",
    "# add histogram pixel shifts evolution for y non rigid shift\n",
    "figure_ax4.set_xlabel('pixels')\n",
    "figure_ax4.set_ylabel('#')\n",
    "\n",
    "\n",
    "## add corresponding values\n",
    "print('Rigid motion correction x-mean:',' std :')\n",
    "print('Rigid motion correction y-mean:', ' std :')\n",
    "\n",
    "print('PW-rigid motion correction x-mean:', ' std :')\n",
    "print('PW-rigid motion correction y-mean:', ' std :')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# play original video, rigid motion corrected and pw motion corrected\n",
    "# concatenate the three movies along axes 2\n",
    "gain = 1\n",
    "magnification = 1\n",
    "fr = 10\n",
    "movies_chained = cm.concatenate([original_movie,m_rig,m_els],axis = 2).resize(1,1,.5).play(gain = gain, magnification = magnification, fr = fr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluation metrics\n",
    "\n",
    "As proposed in [Pnevmatikakis et al. 2017](https://www.sciencedirect.com/science/article/pii/S0165027017302753). there are different measurements that can be used to evaluate the performance of the motion correction algorithm, for example the rigid vs pw results. \n",
    "\n",
    "A simple measure that is computed directly in the summary images (mean image or correlation image) is the crispness. This is done by computing the norm of the gradient field of the image at all pixels. \n",
    "If SI is the summary image then its crispness can be defined as \n",
    "\n",
    "$$C(SI) = ∥ | ∇SI | ∥_F $$\n",
    "\n",
    "\n",
    "where $ ∇I $ denotes the gradient vector field of $I$ in all directions, $|·|$ denotes the entry-wise magnitude, and $ ∥·∥_F $ denotes the Frobenius norm. Lower values of crispness means higher intrinsic motion (the image is blurrer). Higher values of crispness means there are sharp transition borders, which translates into having a better define non-moved image.\n",
    "\n",
    "There are other measures that can be use to quantify the quality of the motion that take into account the complete motion field of the video. For a matter of simplicity and computational capacity here we will implement crispness as a quality assesment measure for motion correction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 3: Quality assesment\n",
    "\n",
    "\n",
    "Quality assesment by crispness is computed in a summary image that could be any of the ones previously mentioned.  \n",
    "* Compute the mean and correlation image for the original movie, the rigid motion corrected and the pw-motion corrected. You can use your previously done function or use the method local correlations from caiman object. You can see documantion and caiman demostration for this. \n",
    "* Plot the different summary images for original movie, rigid-motion corrected movie and pw-rigid motion corrected movie.\n",
    "* Create a function that computes the crispness as define above and use it to compute quality measure in the mean and correlation summary images. For that, just remove the border in the computations. Compare the crispness result after motion correction with the original value for the correlation image.\n",
    "* Defined as above, can cripsness be used directly to compare the motion quality of differnt videos with different image resolutions? what should be taken into account in order to compare them?\n",
    "\n",
    "**Extra exercise 3**: Crispness with CaImAn \n",
    "\n",
    "Now use the method *motion_correction.compute_metrics_motion_correction* from the object *cm* to compute the same values. \n",
    "\n",
    "*The intend of this exercise is to familiarize with the CaImAn tool and not particularly relavant for understanding the methodology.*\n",
    "\n",
    "\n",
    "Tip: use the atribute *local_correlations* from original movie, m_rig and m_els."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "### compute summary images\n",
    "# mean summary image\n",
    "original_movie_mean = summary_image_mean(video = original_movie)\n",
    "m_rig_mean = summary_image_mean(video = m_rig)\n",
    "m_els_mean = summary_image_mean(video = m_els)\n",
    "\n",
    "#### complete code correlation summary image using caiman with 8 neighbours\n",
    "#original_movie_corr = \n",
    "#m_rig_corr = \n",
    "#m_els_corr = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_crispness(image = None):\n",
    "    ## complete here with crispness algorithm that uses the \n",
    "    ##four-nearest neighbords to compute the gradient\n",
    "    crispness = 0\n",
    "    return crispness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crispness Mean Original Movie:  0\n",
      "Crispness Mean Rigid MC:  0\n",
      "Crispness Mean PW-rigid MC:  0\n",
      "Crispness Corr Original Movie:  0\n",
      "Crispness Corr Rigid MC:  0\n",
      "Crispness Corr PW-rigid MC:  0\n"
     ]
    }
   ],
   "source": [
    "### manually compute crispness for summary images using function compute crispness and print values\n",
    "crispness_original_mean = compute_crispness(original_movie_mean)\n",
    "print('Crispness Mean Original Movie: ', crispness_original_mean)\n",
    "crispness_m_rig_mean = compute_crispness(m_rig_mean)\n",
    "print('Crispness Mean Rigid MC: ', crispness_m_rig_mean)\n",
    "crispness_m_els_mean = compute_crispness(m_els_mean)\n",
    "print('Crispness Mean PW-rigid MC: ', crispness_m_els_mean)\n",
    "\n",
    "crispness_original_corr = compute_crispness(original_movie_corr)\n",
    "print('Crispness Corr Original Movie: ', crispness_original_corr)\n",
    "crispness_m_rig_corr = compute_crispness(m_rig_corr)\n",
    "print('Crispness Corr Rigid MC: ', crispness_m_rig_corr)\n",
    "crispness_m_els_corr = compute_crispness(m_els_corr)\n",
    "print('Crispness Corr PW-rigid MC: ', crispness_m_els_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/ipykernel_launcher.py:8: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/ipykernel_launcher.py:10: RuntimeWarning: invalid value encountered in true_divide\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fcdbe12c050>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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4ME92IPCD/H4xsEDSxpJ2It0p56KaX8LMJimAVazu+BqCxcAB+Y5EuwN3T5yqMx2cTWYzQ69scj4BziezKVdyNtU5grYNcKqkiem/FxE/lXQxsEjSwcBNwH6QnsAuaRFwJbAKOMx3ITIbvSBYPcAdgyWdAOwBbC1pOfBhYEOAiDgaOJ10F6JlwJ+AgwZs8qCcTWYzwKDZBM4nMxu+krOpZ4EWEdcBu7QZfgewZ4fPHAUcVacBZjY8awa45CIi9u8xPoDDJr2AIXM2mc0cg2QTOJ/MbDRKzaa6Nwkxs8IF8CAjP5fazKwvziYzK1HJ2eQCzawhAgY+VG9mNmzOJjMrUcnZ5ALNrCGC4MFpv6u0mdm6nE1mVqKSs8kFmllTBKwuM2fMbJw5m8ysRAVnkws0s4YIxINtn4doZjZ9nE1mVqKSs8kFmllDBLCm0J4gMxtfziYzK1HJ2eQCzawhAnig97PnzcymlLPJzEpUcja5QDNrkDVR5qF6MxtvziYzK1Gp2eQCzawh1iAeYNZ0N8PMbB3OJjMrUcnZ5ALNrEFK7Qkys/HmbDKzEpWaTS7QzBoiEA9EmT1BZja+nE1mVqKSs8kFmllDBLCm0ItdzWx8OZvMrEQlZ5MLNLOGiCi3J8jMxpezycxKVHI2uUAza5A1hT5w0czGm7PJzEpUaja5QDNriHQutTdpMyuLs8nMSlRyNpXZKjPrW8nnUpvZ+HI2mVmJSs4mF2hmDVHy3YjMbHw5m8ysRCVnkws0swZZE2X2BJnZeHM2mVmJSs0mF2hmDbGm4J4gMxtfziYzK1HJ2eQCzaxBSj2X2szGm7PJzEpUaja5QDNriAjxYKE9QWY2vpxNZlaikrPJBZpZQwSwutBzqc1sfDmbzKxEJWeTCzSzhgjK7Qkys/HlbDKzEpWcTS7QzBpkdaHnUpvZeHM2mVmJSs0mF2hmDRGUe7tYMxtfziYzK1HJ2eQCzawhSj5Ub2bjy9lkZiUqOZtql42SZkn6laQf5d+3knSmpGvyzy0r0x4haZmkqyXtNYqGm9n6VqOOrzokzcvb7TJJh7cZ/yhJP5R0maSlkg4a+pfok7PJrHzdsqlOPjmbzGwUBs0mGE0+9XNc753AVZXfDwfOiog5wFn5dyTtDCwAng7MA74iqczy1KxBIsSDax7W8dVL3k6/DOwN7Azsn7fnqsOAKyNiF2AP4LOSNhruN+mbs8msYL2yqVc+OZvMbBQGzSYYXT7VKtAkbQ+8AvhGZfB84Lj8/jhg38rwEyPi/oi4HlgG7FZnOWY2eQGsQR1fNewGLIuI6yLiAeBE0vbcupjNJQnYDLgTWDXEr9EXZ5NZ+XplU418cjaZ2dANIZtgRPlU9xq0zwPvAzavDNsmIlYCRMRKSY/Nw7cDLqhMtzwPW4ekQ4BDAB7/+MfXbIaZdRKIB9d07XTdWtKSyu8LI2Jh5fftgJsrvy8Hntsyjy8Bi4EVpDx4fUSsmXyrBzb0bALnk9kw1cgm6J5PzqbM2WQ2PEPIJhhRPvUs0CS9Erg1Ii6RtEev6aFtuRnrDUhfbiHA3Llz1xtvZv3rcbvY2yNibpfxdbbdvYBLgZcATwTOlHRuRNzTV0OHYFTZBM4ns2GrcSvrbvnkbJoY6GwyG6oBswlGlE91jqA9H3i1pH2ATYBHSjoeuEXS7NwLNBu4NU+/HNih8vntSRWjmY1QIFYNdjeiOtvuQcAnIiKAZZKuB54KXDTIgifJ2WQ2AzibnE1mJRpCNsGI8qln2RgRR0TE9hGxI+ki1v+KiDeRDtUdmCc7EPhBfr8YWCBpY0k7AXO6NcDMhiMCVoc6vmq4GJgjaad88eoC0vZcdROwJ4CkbYCnANcN8WvU5mwymxl6ZVONfHI2mdnQDSGbYET5NMhz0D4BLJJ0cF7wfgARsVTSIuBK0gVwh0XE6gGWY2Y1BGJV73OpO38+YpWkdwBnALOAY/L2fGgefzTwUeBYSb8mHdZ/f0TcPnjrh8rZZFYQZ9NDnE1mBRk0m2B0+dRXgRYRvwB+kd/fQa4G20x3FHBUP/M2s8HVfWZHJxFxOnB6y7CjK+9XAC8faCEj4GwyK5uzydlkVqJBswlGk0+DHEEzs4IMoyfIzGzYnE1mVqKSs8kFmlmD1Hxmh5nZlHI2mVmJSs0mF2hmDRFBned5mJlNKWeTmZWo5GxygWbWEIFYU++OQ2ZmU8bZZGYlKjmbXKCZNUQAq6LnkzPMzKaUs8nMSlRyNrlAM2uQNYUGjZmNN2eTmZWo1GxygWbWEBEqtifIzMaXs8nMSlRyNrlAM2uQUs+lNrPx5mwysxKVmk0u0MwaIoBVa8rsCTKz8eVsMrMSlZxNLtDMGqLkuxGZ2fhyNplZiUrOJhdoZk0R5d6NyMzGmLPJzEpUcDa5QDNriKDcc6nNbHw5m8ysRCVnkws0s4YIVOy51GY2vpxNZlaikrPJBZpZg0ShPUFmNt6cTWZWolKzyQWaWYOsocygMbPx5mwysxKVmk0u0MwaIgJWF3qo3szGl7PJzEpUcja5QDNrjHJvF2tm48zZZGYlKjebXKCZNURQbk+QmY0vZ5OZlajkbHKBZtYUkQ7Xm5kVxdlkZiUqOJtcoJk1RACrC33gopmNL2eTmZWo5GxygWbWGOWeS21m48zZZGYlKjebXKCZNciaNWUGjZmNN2eTmZWo1GxygWbWEBHlPnDRzMaXs8nMSlRyNrlAM2uQ1YX2BJnZeHM2mVmJSs0mF2hmDVJqT5CZjTdnk5mVqNRs6nnrEkmbSLpI0mWSlko6Mg/fStKZkq7JP7esfOYIScskXS1pr1F+ATNLIl/s2ulVh6R5ebtdJunwDtPsIenSnAdnD/VL9MHZZDYz9MqmOvk0k7Ipt8X5ZFa4YWQTjCaf6txb8n7gJRGxC/AsYJ6k3YHDgbMiYg5wVv4dSTsDC4CnA/OAr0iaVecLmtkA8rnUnV695O30y8DewM7A/nl7rk6zBfAV4NUR8XRgv+F/kdqcTWYzQY9s6pVPMzCbwPlkVr4BswlGl089C7RI7su/bphfAcwHjsvDjwP2ze/nAydGxP0RcT2wDNit13LMbHCxRh1fNewGLIuI6yLiAeBE0vZc9QbglIi4CSAibh3qF+iDs8ls5uiWTTXyaUZlU16+88lsBhgwm2BE+VTr6WySZkm6FLgVODMiLgS2iYiVeUErgcfmybcDbq58fHke1jrPQyQtkbTktttuq9MMM+sh3ZGo/QvYemKby69DWj5eZ9t9MrClpF9IukTSASP7MjWMIpvyfJ1PZkPULZtq5NOMyybwvpPZTDBgNsGI8qnWTUIiYjXwrHyI7lRJz+gyebtyM9rMcyGwEGDu3LnrjTez/kRArOna53J7RMztMr7Otvsw4DnAnsDDgfMlXRARv+2rsUMyimzK83U+mQ1JjWyC7vk047IJvO9kVrohZBOMKJ/6uotjRNwl6Rek86NvkTQ7IlZKmk3qIYJUOe5Q+dj2wIp+lmNmkxOD/XNdZ9tdTgqrPwJ/lHQOsAswbTtB4GwyK924ZhM4n8xKNmA2wYjyqc5dHB+Te3+Q9HDgpcBvgMXAgXmyA4Ef5PeLgQWSNpa0EzAHuKjXcsxsUAOfR30xMEfSTpI2Il2wvrhlmh8AL5T0MEmPAJ4LXDXUr1GTs8lspuieTTXyaUZlEzifzGaGgbMJRpRPdY6gzQaOy3cp2QBYFBE/knQ+sEjSwcBN5DuSRMRSSYuAK4FVwGH5ML+ZjdoAPUERsUrSO4AzgFnAMXl7PjSPPzoirpL0U+ByYA3wjYi4YvCGT4qzyWymGK9sAueT2cww4BG0UeVTzwItIi4Hnt1m+B2kcynbfeYo4Khe8zazIQrq9vZ0nkXE6cDpLcOObvn908CnB1rQEDibzGaIMcsmcD6ZzQhDyCYYTT71dQ2amZVu8KAxMxs+Z5OZlajMbHKBZtYka6a7AWZmbTibzKxEhWaTCzSzpgigxlPvzcymlLPJzEpUcDa5QDNrkCi0J8jMxpuzycxKVGo2uUAza5JCe4LMbMw5m8ysRIVmkws0swbR4A9cNDMbOmeTmZWo1GxygWbWFCEYwu1izcyGytlkZiUqOJtcoJk1SaE9QWY25pxNZlaiQrPJBZpZkxR6sauZjTlnk5mVqNBscoFm1hQF3y7WzMaYs8nMSlRwNrlAM2sQFdoTZGbjzdlkZiUqNZs2mO4GmJmZmZmZWeIjaGYNokLvRmRm483ZZGYlKjWbXKCZNUVQ7N2IzGyMOZvMrEQFZ5MLNLMGKfVcajMbb84mMytRqdnkAs2sSQrtCTKzMedsMrMSFZpNLtDMGkJRbk+QmY0vZ5OZlajkbHKBZtYkhT7Pw8zGnLPJzEpUaDa5QDNrkFJ7gsxsvDmbzKxEpWaTCzSzJin0XGozG3POJjMrUaHZ5ALNrCkKPpfazMaYs8nMSlRwNrlAM2uSQnuCzGzMOZvMrESFZpMLNLMGUaFBY2bjzdlkZiUqNZtcoJk1SaFBY2ZjztlkZiUqNJtcoJk1RcHnUpvZGHM2mVmJCs4mF2hmTVJoT5CZjTlnk5mVqNBs2qDXBJJ2kPTfkq6StFTSO/PwrSSdKema/HPLymeOkLRM0tWS9hrlFzCzRKSeoE6vWvOQ5uXtdpmkw7tM9zeSVkt67ZCa3zdnk9nM0Cub6uTTTMqm3A7nk1nhhpFNMJp86lmgAauAd0fE04DdgcMk7QwcDpwVEXOAs/Lv5HELgKcD84CvSJpVYzlmNohIF7t2evWSt9MvA3sDOwP75+253XSfBM4Y7hfom7PJbCbokU298mkGZhM4n8zKN2A2wejyqWeBFhErI+J/8vt7gauA7YD5wHF5suOAffP7+cCJEXF/RFwPLAN2q9MYMxvQmi6v3nYDlkXEdRHxAHAiaXtu9Y/AfwK3DqHFk+ZsMptBumVT73yaUdkEziezGWOwbIIR5VOdI2gPkbQj8GzgQmCbiFgJKYiAx+bJtgNurnxseR7WOq9DJC2RtOS2227rpxlm1kGPXqCtJ7a5/Dqk5eM9t11J2wGvAY4e4dfo2zCzKc/P+WQ2RDV6qbvl04zNJvC+k1nJBswmGFE+1b5JiKTNSJXfP0fEPZI6Ttpm2HoHCSNiIbAQYO7cuYVeomc2w3Tfkm6PiLldxtfZdj8PvD8iVnfJgCk17GwC55PZ0PXeirrl04zMJvC+k1nxBssmGFE+1SrQJG1ICpjvRsQpefAtkmZHxEpJs1l7yG45sEPl49sDK2q1xswmb/DbxdbZducCJ+aA2RrYR9KqiDhtoCVPkrPJbAYYw2wC55NZ8YZzm/2R5FOduzgK+CZwVUR8rjJqMXBgfn8g8IPK8AWSNpa0EzAHuKjXcsxsCKLLq7eLgTmSdpK0EemC9cXrzD5ip4jYMSJ2BE4G3j6NxZmzyWym6JZNvfNpRmUTOJ/MZozBsglGlE91jqA9H3gz8GtJl+ZhHwA+ASySdDBwE7BfbsRSSYuAK0l3MTosIlbX+opmNpBBeoIiYpWkd5DuMDQLOCZvz4fm8aVd2+FsMpshxiybwPlkNiMMegRtVPnUs0CLiPNof34lwJ4dPnMUcNRkGmRmk1S/t6fzLCJOB05vGdY2XCLiLYMtbTDOJrMZYsyyKbfB+WRWuiFkE4wmn2rfJMTMyibqPbPDzGwqOZvMrEQlZ5MLNLMGKTVozGy8OZvMrESlZpMLNLMmGfxuRGZmw+dsMrMSFZpNLtDMmiLK7QkyszHmbDKzEhWcTS7QzBpkCM/zMDMbOmeTmZWo1GxygWbWJIX2BJnZmHM2mVmJCs0mF2hmTRHl9gSZ2RhzNplZiQrOJhdoZk1SaE+QmY05Z5OZlajQbHKBZtYQotyeIDMbX84mMytRydnkAs2sQRSFdgWZ2VhzNplZiUrNJhdoZk1R8LnUZjbGnE1mVqKCs8kFmlmTlNkRZGbjztlkZiUqNJtcoJk1SKk9QWY23pxNZlaiUrPJBZpZUwSo0J4gMxtjziYzK1HB2eQCzawhSr4bkZmNL2eTmZWo5GxygWbWJIXejcjMxpyzycxKVGg2uUAza4qC70ZkZmPM2WRmJSo4m1ygmTVIqUFjZuPN2WRmJSo1m1ygmTVIqUFjZuPN2WAldB0AACAASURBVGRmJSo1m1ygmTVFUOy51GY2xpxNZlaigrPJBZpZg5R6u1gzG2/OJjMrUanZ5ALNrCFKvl2smY0vZ5OZlajkbHKBZtYUEcUeqjezMeZsMrMSFZxNLtDMGqTUniAzG2/OJjMrUanZ5ALNrEFKPZfazMabs8nMSlRqNrlAM2uKAFYXmjRmNr6cTWZWooKzaYNeE0g6RtKtkq6oDNtK0pmSrsk/t6yMO0LSMklXS9prVA03s/UpOr9qfV6al7fdZZIObzP+jZIuz69fStpl2N+hH84ns5mhWzbVySdnk5mNwqDZBKPJp54FGnAsMK9l2OHAWRExBzgr/46knYEFwNPzZ74iaVaNZZjZEGhNdHz1/GzaVr8M7A3sDOyft+mq64EXR8QzgY8CC4f8Ffp1LM4ns+J1y6Ze+eRsMrNRGSSbYHT51LNAi4hzgDtbBs8HjsvvjwP2rQw/MSLuj4jrgWXAbr2WYWZDED1eve0GLIuI6yLiAeBE0ja9dhERv4yIP+RfLwC2H0rbJ8n5ZDYD9Mqm3vnkbDKz4Rs8m2BE+VTnCFo720TEyrzQlcBj8/DtgJsr0y3Pw9Yj6RBJSyQtue222ybZDDObIECro+ML2Hpim8uvQ1pmUXv7zQ4GfjLULzEcziezgvTKphr55GzKnE1mwzOEbIIR5dOwbxKiNsPa1p8RsZB8iG/u3LllXqFnNsOo+/M8bo+Iud0+3mZY2xlK+jtSyLygfuumnfPJbJr0yCbonk/OpomBziazoRowm2BE+TTZI2i3SJqdFzYbuDUPXw7sUJlue2DFJJdhZv2IgDVdXr3V2n4lPRP4BjA/Iu4YStuHy/lkVpJe2dQ7n5xNZjZ8g2cTjCifJlugLQYOzO8PBH5QGb5A0saSdgLmABdNchlm1qcB70R0MTBH0k6SNiJdtL54nflLjwdOAd4cEb8ddvuHxPlkVpgB75TmbDKzkRjCXRxHkk89T3GUdAKwB+kczOXAh4FPAIskHQzcBOwHEBFLJS0CrgRWAYdFxOpaX8/MBhNMnC89uY9HrJL0DuAMYBZwTN6mD83jjwY+BDyadJcxgFU9Dv2PlPPJbAZwNjmbzEo0YDbB6PKpZ4EWEft3GLVnh+mPAo7qNV8zG4He51L3+HicDpzeMuzoyvu3Am8daCFD5HwymyGcTROcTWYlGTCb0iyGn0/DvkmImU2jOs/sMDObas4mMytRqdnkAs2sSYbQE2RmNnTOJjMrUaHZ5ALNrCEUMfC51GZmw+ZsMrMSlZxNLtDMmqTQniAzG3POJjMrUaHZ5ALNrCkCKLQnyMzGmLPJzEpUcDa5QDNrEBXaE2Rm483ZZGYlKjWbXKCZNUbAmjXT3QgzsxbOJjMrUbnZ5ALNrCmCYs+lNrMx5mwysxIVnE0u0MwapNS7EZnZeHM2mVmJSs0mF2hmTVJoT5CZjTlnk5mVqNBscoFm1hQRsLrMc6nNbIw5m8ysRAVnkws0syYptCfIzMacs8nMSlRoNrlAM2uSQoPGzMacs8nMSlRoNrlAM2uKCFi9erpbYWa2LmeTmZWo4GxygWbWJIX2BJnZmHM2mVmJCs0mF2hmTREUe7GrmY0xZ5OZlajgbHKBZtYkhfYEmdmYczaZWYkKzSYXaGZNUfC51GY2xpxNZlaigrPJBZpZkxTaE2RmY87ZZGYlKjSbXKCZNUa5D1w0s3HmbDKzEpWbTS7QzJoiIKLMoDGzMeZsMrMSFZxNLtDMmqTQniAzG3POJjMrUaHZ5ALNrCkiYE2ZQWNmY8zZZGYlKjibXKCZNUgUejciMxtvziYzK1Gp2eQCzawxoti7EZnZOHM2mVmJys2mDUY1Y0nzJF0taZmkw0e1HDPLgvQ8j06vGnptt0q+mMdfLmnXYX+NUXM2mU2xXtlUI5+cTWY2dEPIJhhNPo2kQJM0C/gysDewM7C/pJ1HsSwzSwKINdHx1UvN7XZvYE5+HQJ8dahfYsScTWZTr1c29conZ5OZjcKg2QSjy6dRHUHbDVgWEddFxAPAicD8ES3LzAAiiNWrO75qqLPdzge+HckFwBaSZg/3i4yUs8lsqvXIphr55Gwys+EbPJtgRPk0qmvQtgNurvy+HHhudQJJh5CqSID7JV0xorbUtTVwu9vgNhSwfICn9PuBe/nDGT9fs2jrLpNsImlJ5feFEbGw8nvP7bbDNNsBK/tt7zSp8x2dT+Ut320oqw195VONbILu+eRsypxNbkOhyy+lDVOdTTCifBpVgaY2w9Y5Tpi/3EIASUsiYu6I2lKL2+A2lLL8iTb0+5mImDfoYtvNdhLTlKxW+51PZS3fbSivDf1M72yqxdnkNszY5ZfUhn6mH0I2wYjyaVSnOC4Hdqj8vj2wYkTLMrPhqLPdzvRte6a332wcOZvMrFQjyadRFWgXA3Mk7SRpI2ABsHhEyzKz4aiz3S4GDsh3JNoduDsiZsopROBsMpuJnE1mVqqR5NNITnGMiFWS3gGcAcwCjomIpV0+srDLuKniNiRuw/QvH6ahDZ22W0mH5vFHA6cD+wDLgD8BB011OwcxiWyCMf17KGz54DZMGLs2OJs6Gru/hQ7chulfPoxpG0aVT4pCH9BmZmZmZmY2bkb2oGozMzMzMzPrjws0MzMzMzOzQkx7gSZpnqSrJS2TdPgULvcGSb+WdOnEbTklbSXpTEnX5J9bDnmZx0i6tfrckm7LlHREXi9XS9prRMv/iKTf5fVwqaR9RrX8PM8dJP23pKskLZX0zjx8KtdDpzZMybqQtImkiyRdlpd/ZB4+ZevAenM2Te3f43Tnk7Ppofk5n2aA6cgnZ9P47js5m6ZBREzbi3Qx3bXAXwEbAZcBO0/Rsm8Atm4Z9ing8Pz+cOCTQ17mi4BdgSt6LRPYOa+PjYGd8nqaNYLlfwR4T5tph778PN/ZwK75/ebAb/OypnI9dGrDlKwL0vMwNsvvNwQuBHafynXgV8//R86mKf57nO58cjY9NE/nU+Gv6conZ9P47js5m6b+Nd1H0HYDlkXEdRHxAHAiMH8a2zMfOC6/Pw7Yd5gzj4hzgDtrLnM+cGJE3B8R15Pu/LLbCJbfydCXn9uwMiL+J7+/F7iK9DT1qVwPndrQyVDbEMl9+dcN8yuYwnVgPTmbpvjvcbrzydn0UBucT+UrKZ+cTeu3rXH7Ts6mqTfdBdp2wM2V35fT/X/4MAXwM0mXSDokD9sm8nMJ8s/HTkE7Oi1zKtfNOyRdng/jTxwaHvnyJe0IPJvUCzIt66GlDTBF60LSLEmXArcCZ0bEtK0Da8vZVM7f45Tn0zhnU16286ls07XOnU3rGst9J2fT1JjuAk1thk3Vff+fHxG7AnsDh0l60RQtt66pWjdfBZ4IPAtYCXx2KpYvaTPgP4F/joh7uk06qna0acOUrYuIWB0RzyI9TX43Sc/o1tRhL996cjZ1NpXrZsrzadyzCZxPM8B0rXNn01pjue/kbJo6012gLQd2qPy+PbBiKhYcESvyz1uBU0mHPW+RNBsg/7x1CprSaZlTsm4i4pb8B78G+DprD/+ObPmSNiRt4N+NiFPy4CldD+3aMB3rIiLuAn4BzGOa/xZsHc6mAv4ep3qbdDaty/lUrGlZ586mtcZx38nZNLWmu0C7GJgjaSdJGwELgMWjXqikTSVtPvEeeDlwRV72gXmyA4EfjLotXZa5GFggaWNJOwFzgIuGvfCJP+rsNaT1MLLlSxLwTeCqiPhcZdSUrYdObZiqdSHpMZK2yO8fDrwU+A3T/Ldg63A2FfD3OJX55Gx6aFnOp/JNeT45m9Y1bvtOzqZpENN8lxJgH9LdYK4FPjhFy/wr0p1dLgOWTiwXeDRwFnBN/rnVkJd7AukQ8IOkyv7gbssEPpjXy9XA3iNa/neAXwOXk/6YZ49q+XmeLyAdYr4cuDS/9pni9dCpDVOyLoBnAr/Ky7kC+FCvv79R/L/wq+f/J2fTFP49Tnc+OZsemp/zaQa8pjqfnE3jve/kbJr6l/IXMDMzMzMzs2k23ac4mpmZmZmZWeYCzczMzMzMrBAu0MzMzMzMzArhAq2FpA9I+sawp60xr5D0pGHMa4A2HC3p36azDZMh6QZJL53kZ18o6epht8lsuvWzPfeatoR8mkqSdszf+WGT/PzQ/m0wG3f9/Dvda1pJx0r62PBaVz5Jv5D01kl+9vGS7pM0a9jtsu4aXaBJeoukX0v6k6TfS/rqxC06O4mIj0dErT/kfqadCSLi0Ij46HS3Y5RadzQj4tyIeMp0tslsMnLHxJ/zP56/zzsem02M72d7HmTbz//4h6RdWoaflofvURn2ZEknSbpd0t2SLpf0rpn8j7+kPSQtrw5r2r8NZi15c4ukb0naTNL+kq5smfbMDsMO7zDvj0h6MM/7Lkm/lPS3E+P7+Xd6kH/T8z5jSPpcy/B98/BjK8M2yu2+RtIf8/o5RtKOk1l2KVo7vCPipojYLCJWT2e7xlFjCzRJ7wY+CbwXeBSwO/AE4Mz83JB2n5lUb6kNR7v17/8nZl29KiI2A54FPBs4Ypra8VvggIlfJD2alLm3VYY9EbgQuBn464h4FLAfMBfYfFQNc66YDc1E3uwK/A3wr8DZwNMkPQYe2rZ2AR7RMuxvgXO6zPv7ed5bA/8NnDSyb9HdtcDrWzLiAFLGVZ0MvBp4A2kfcxfgEmDPUTXMWTZeGlmgSXokcCTwjxHx04h4MCJuAF5HKtLelKf7iKSTJR0v6R7gLXnY8ZV5HSDpRkl3SPq3au9CddrKKTEHSrop9xB/sDKf3SSdn3uHVkr6UqdCsc33+YWkj+Vepfsk/VDSoyV9V9I9ki6u9tpIel4ednf++bw8fIGkJS3z/hdJi/P7dQ79S3qlpEsrPVrP7NLGp+cesjtz79oH8vCNJX1e0or8+rykjfO4PSQtl/R+Sb8HvtVuWD9t6baeJU3843BZXo+vV0vvt6Sn5fV9l6Slkl5dGXespC9L+rGkeyVdmHc6zaZVRPweOINUqAFtt+f35W1ihaS3qnI0uc20761M+79rNOG7pJ2aiSNh+wOnAg9UpjkS+GVEvCsiVuZ2Xx0Rb4iIu9rNVNL8vN3fI+laSfPy8G0lLc55s0zSP1Q+0ynXW4dtIOnwPN87JC2StFWHdhwk6aq83V8n6W15+KbAT4Btc6bcl9vW+u/Iq3Oe3JXz5WmVcTdIeo/S0cS7JX1f0iY11rnZtIiI35H+7p8RESuA64AX5dG7kp6TdnbLsA2AJfQQEatIebKd1hZ4rf9O7yrpV3l7PClvMx/rMO2zJf1Pnvb7QK9t6/ek53rtlT+/FfA8Kg8CV9oHfBkwPyIujohVEXF3RHw5Ir7ZbqaSdpB0iqTbct58KQ/fQNK/Ku1n3irp25IelcdN7FceLOkm4L/aDcvT7p73je6SdJkqZy60tOOJkv4rt+F2pf3IiYc/fwd4PPDDnGXvU8vp3jWyd1H+DvfmzJvbY31bB40s0Egb0ybAKdWBEXEfKVReVhk8n9QTsgUpFB4iaWfgK8AbgdmkXpLteiz7BcBTSL0oH6r8Q7wa+BdS79Df5vFv7+M7LQDenJf/ROB8UvGyFXAV8OHc5q2AHwNfJD2873PAj5V6tBcDT5E0pzLfNwDfa12YpF2BY4C35fl8DVisXFy1TLs58HPgp8C2wJNIDwuE9JDA3Uk7jrsAu5F63SY8Ln+HJwCHtBvWT1vosp4jYuIfi13yIfvvt3yPDYEfAj8DHgv8I/BdSdXTJfYn7WhuCSwDjmrTBrMpJWl7YG/S32S78fOAdwEvJW2fL+4yr3nAe0g5OSd/ppcVwJXAy/PvBwDfbpnmpaSsrUXSbnke7yXl84uAG/LoE0gPjN0WeC3wcUnVnut2ud467J+AfUnrYlvgD8CXOzTnVuCVwCOBg4D/T9KuEfFH0npfkTNls7zDWv0eT87t/WfgMcDppB2gagfd64B5wE6kh7G+pecKMpsmknYgPaT4V3nQOawtxl4EnAuc1zLsgoiodth0mvdGpPy4g7RNtht/KnAsaT/hBOA1XeZ1GulhyluRjsr9r15tIOXOxBkBC4AfAPdXxr8UuCgibq4xL5Q6rn4E3AjsSNqPOzGPfkt+/R3pYeCbAV9qmcWLgaeRi8bWYZK2I+33fYz0Pd8D/OdEgdvaHODfSZn3NGAH4CMAEfFm4CbykdKI+FSbz/fK3lfn77YFaZ+z9btYTU0t0LYGbs89Ma1W5vETzo+I0yJiTUT8uWXa1wI/jIjzcrB8iPQk9W6OjIg/R8RlwGWkooSIuCQiLsg9LTeQioyOO0ltfCsiro2Iu0lF5rUR8fP8HU8ind4E8Argmoj4Tl7WCcBvSBvcn0hBsz9ALtSeSqVnqOIfgK9FxIURsToijiMF1O5tpn0l8PuI+GxE/CUi7o2IC/O4NwL/NyJujYjbSMXNmyufXQN8OCLur6z/1mG12zLget6dFI6fiIgHIuK/SKG6f2WaUyLiokov37PazMdsqpwm6V7SaYO3kjtq2ngdKUOW5hw4sss8J6a9IhcgH6nZlm8DB+QOjS0i4vyW8Y8m5W9dBwPHRMSZOZ9/FxG/yTuHLwDen/PmUuAbrJsr7XK9ddjbgA9GxPKIuD9/z9eqzSlDEfHjnL8REWeTOnFeWPN7vB74cf4eDwKfAR5O6kic8MWIWBERd5I6iZwrVqLTJN1FKr7OBj6eh1ePlr2QVKCd2zLs7B7zfl2e98S/+a/tsA+3O/Aw0jbzYEScAlzUYZ67AxsCn8/Tngxc3KMdkArAPfKRrHadTf1m2W6kgua9EfHHnFvn5XFvBD4XEdflgwhHAAtacugj+XN/7jDsTcDpEXF6zrczSUcr92ltSEQsy1l0f94n+xw195FqZu95uR2rSYXxLm1mZTU0tUC7Hdi63T+0pCNht1d+79YDsm11fN6xuaPHsn9fef8n0g7/xMXxP1K6mP8eUrBt3W4GHdxSef/nNr9P3BxgW1IvTdWNrD3y9z3WFhxvAE7L36vVE4B358Pld+Xg3CHPv9UOpPO222ltz40t87gtIv7S8pnWYbXbMuB63ha4OSLWtLS3etS07f9fs2myb0RsDuxB6mzp9Le+TpbRR+6xfp50cgrwEtKR5++0GX8HKX/r6pQr2wJ3RsS9LW2sbqftvl/rsCcAp1Yy5SrSEfhtWj8oaW9JF+TTeu4i7fj0kysPrcOcLzfjXLGZZ9+I2CIinhARb68UDOcAz5S0JakoOj8ifgPMzsNekKdB0hu19nTgn1TmvSgitiBtf1cAz+nQhm2B30VEtbO8U561m7ZnnuXv9WPS2T5bR8T/a5lkMll2Y4eCs90+0sNYN4d65dkTgP1a9pFe0K6Nkh4r6URJv8v7SMfTX5b1yt7WLNukw7649dDUAu180hGWv68OVLpeYG/Wnn4H3Y+IrQS2r3z+4aSek8n4KulI1pyIeCTwAdKh5mFbQdpYqx4P/C6//xmpeH0WqVBb7/TG7GbgqBzGE69H5CNy7abtdC1Wa3sen4dNaLf+W4f105ZB1vMKYAdJ1e2iuu7MipSP6hxLOjrTzjpZRtph6GRly/jH12zDn0hH9/8P7Qu0n1Pv9KIJnXJlBbBVPrW62sbqdlo3V/ZuyZVNIl1f85B8KvV/ktbtNnkn8nTW5kqvsyrWyUBJIq1f54o1QkRcR/o7PwS4KR8JgrQvdgipw+GCPO13Y+3pwHu3mdftpKPbH5HUrghaSbo+rfrveqc8azdtrTwjHTV7N52zbLd8ankdNwOP71CotNtHWsW6nfC98uxm4DstWbZpRHyizef+PX/2mXkf6U2su4/ULc/qZK8NSSMLtEinAR4J/IekeZI2VLqJxkmkc2fbbXDtnAy8SummGxvleU62qNocuAe4T9JTSTsxo3A68GRJb5D0MEmvB3Ymnao3cQHuycCnSecqn9lhPl8HDpX0XCWbSnpFy4Y54UfA4yT9s9JNQTaX9Nw87gTgXyU9RtLWpNNEj28zj276aUuv9XwL6Tzvdi4E/gi8L//N7AG8irXnipuV7PPAy3LnS6tFwEFKN8F5BGk77GQR6SYaO+dpO5022c4HgBfn04tbfRh4nqRPS3ocgKQnKd24o93jT76Z27yn0oX020l6aqTrPn4J/LukTZRuGHQwLdcQ13A0cJSkJ+S2PEbS/DbTbQRsTLoj5SpJe7P2WjtImfLofDpUO4uAV+TvsSFpp+/+/B3MmuJc0nWu51aGnZeHLYn1LyHpKB99OwN4X5vR55OOdL8j7+PMJ51C2M75pGLnn/K0f99l2lZnk67D/Y827fs5ad/pVEnPyfPeXNKhan9TpYtIxeIn8v7LJpKen8edAPyLpJ2UHpPycdIdLdsdbevkeNK+6l6SZuX579GhgNwcuA+4S+natfe2jO+4jzTE7LUaGlmgAUS6uPEDpF7Pe1h7e+c98/UGdeaxlHS6zomkjete0nUetT7f4j2kUwrvJRUc3+8++eRExB2ka8LeTToM/z7glblXasL3SBe5ntQpBCJiCek88C+RLtRdRocL1/Ph7peRipnfA9eQLniFdNHqEuBy0p2R/icP6+c71W4LvdfzR4Dj8mkAr2tZzgOkC1z3Jp0G+xXggPyPhVnR8vUE3wbWe+B0RPyEdOOg/yZtPxPXh62XZXnaz5PuDrYs/6zbhhWVaytax11LunHPjsBSSXeTjkwtIW2vrdNfRL4hB3A3aYdpoqd5/zyfFaTrRT6cr7voxxdI19/+TOk6vguA57ZOlPPtn0iF1h9I+bK4Mv43pJ2s63KubNvy+atJvdT/QcqVV5GuCe55wwSzGeRs0s21qtv/uXlYt9vrd/Jp0k3CHlsdmLebvycVBneRtq0f0T7LJqZ9C2nbfT0tN4/rJF9vela+LrSd15I6xL9PyqcrSI8M+Xmbea0mbfdPIt2EY3luC6QboH2HtI6uB/5C2u+sLRdO80n7vLeR9nXfS/t9/CNJd9W8m3QaZ+v6+HdSp/pdkt7T5vPDyF6rQeuemmvd5N6Nu0inz10/3e0xM5sMpbvLXgFs3GdPrZlZUSRdCBwdEd+a7raYDUtjj6ANi6RXSXpEvn7tM6SjQDdMb6vMzPoj6TWSNlK6aP+TpDvUujgzsxlF0oslPS6fWngg6dEUP53udpkNU60CTelBmr9WemDokjxsK6UHE1+Tf25Zmf4IpQfYXS1pr85znhHmkw7lriA9E2hB+LCjNZCkY5QelHlFh/GS9MW8bV+u9Hy6aTXm2dSvt5FOf7mWdA3HqK6DNRs655NVPIX0GKO7SZdzvDYi+rntvdnQjCqbap3iKOkGYG71OiZJnyLdbvMTkg4HtoyI9ys93PkE1j734efAk/M5uGZWKEkvIl08/O2IeEab8fuQzo3fh3StzhciYr1rdqaSs8lsPDifzKxEo8qmQU5xnA8cl98fB+xbGX5ipIfgXU+6yLzuXXPMbJpExDlApwuiIW3b384XT18AbKH2t0Gebs4ms4ZxPplZiUaVTXUfHhekO10F8LWIWEh6HszK3LiVlTvtbEd+3kW2nHUfYgeApENIz8dgFrOe8wgeWbMpZs13L3+4PSIe089n9vq7TeOOOzt3tl5y+f1LSXeImrAwb8t1bce6D8ec2Lan89SSoWcTOJ/Muuk3n3plEzif8L6T2cCalE11C7TnR8SKHCRnSup22/F2zwlb7zzK/OUWAjxSW8VztWfNppg138/j5Bv7/cztd67mwjM6Pzdzw9nX/iUi5g7QrFrb9hQbejaB88msm37zqVc2gfMJ7zuZDaxJ2VSrQIuIFfnnrZJOJR12v0XS7NwDNJv0fDBIlWH1qe7bk26wYWYjFAQPjvZyheK2bWeTWfmmIJugwO3b+WRWtpKzqec1aEpPPd984j3wctLzcxYDB+bJDgR+kN8vBhZI2ljSTqQ7H15U80uY2QDWdPlvCBYDB+Q7Eu0O3D2dd85yNpnNHN2yyfkEOJ/MpkWp2VTnCNo2wKmSJqb/XkT8VNLFwCJJB5OejL4fQEQslbQIuBJYBRzmuxCZjV7qCZp8mEg6AdgD2FrScuDDwIYAEXE0cDrpLkTLgD8BBw3Y5EE5m8xmgEGzCZxPZjZ8JWdTzwItIq4Ddmkz/A6g7cnPEXEUcFSdBpjZcASweoBLLiJi/x7jAzhs0gsYMmeT2cwwaDaB88nMhq/kbKp7kxAzK1zAwD1BZmbD5mwysxKVnE0u0MwapMyYMbNx52wysxKVmk0u0MwaIiJ4IKb7rtJmZutyNplZiUrOJhdoZg0RlNsTZGbjy9lkZiUqOZtcoJk1RCAejHbPQzQzmz7OJjMrUcnZ5ALNrEFWt31gvZnZ9HI2mVmJSs0mF2hmDZHuRtTz2fNmZlPK2WRmJSo5m1ygmTVEep5HmT1BZja+nE1mVqKSs8kFmllDBGI1ZfYEmdn4cjaZWYlKziYXaGYNUfKhejMbX84mMytRydnkAs2sMcTqQoPGzMaZs8nMSlRuNrlAM2uIAB5k1nQ3w8xsHc4mMytRydnkAs2sISLK7Qkys/HlbDKzEpWcTS7QzBqi5J4gMxtfziYzK1HJ2eQCzawxyu0JMrNx5mwysxKVm00u0MwaIt2NqMyeIDMbX84mMytRydnkAs2sIUp+noeZjS9nk5mVqORscoFm1hCpJ8ibtJmVxdlkZiUqOZvKbJWZ9S0Qq0PT3Qwzs3U4m8ysRCVnkws0s4aIKLcnyMzGl7PJzEpUcjaV2SozmwSxhjJ7gsxsnDmbzKxE5WaTCzSzhgjggUJ7gsxsfDmbzKxEJWdTma0ys74FYk2h51Kb2fhyNplZiUrOJhdoZg1R8t2IzGx8OZvMrEQlZ1OZrTKzSRCrCz2X2szGmbPJzEpUbjbVfjqbpFmSfiXpR/n3rSSdKema/HPLyrRHSFom6WpJe42i4Wa2rtQTNKvjqw5J8/J2u0zS4W3GP0rS2dty8QAAGqxJREFUDyVdJmmppIOG/T365WwyK1uvbKqTT84mMxu2YWQTjCaf+nl89juBqyq/Hw6cFRFzgLPy70jaGVgAPB2YB3xFUr1vaGaTFiHWxAYdX73k7fTLwN7AzsD+eXuuOgy4MiJ2AfYAPitpo+F+k745m8wK1iubeuWTs8nMRmHQbILR5VOtAk3S9sArgG9UBs8HjsvvjwP2rQw/MSLuj4jrgWXAbnWWY2aTN4QjaLsByyLiuoh4ADiRtD23LmZzSQI2A+4EVg3xa/TF2WRWviH0UjubzGzohnQEbST5VPcI2ueB9wFrKsO2iYiVAPnnY/Pw7YCbK9Mtz8PWIekQSUskLXmQ+2s2w8w6E6tjg44vYOuJbS6/DmmZQZ1t90vA04AVwK+Bd0bEGqbP0LMJnE9mw9U9m2rkk7MpczaZDdPA2QQjyqeeNwmR9Erg1oi4RNIevaaHtlfbxXoDIhYCCwEeqa3WG29m/ZnoCeri9oiY22V8nW13L+BS4CXAE4EzJZ0bEff009ZhGFU2gfPJbJhqZBN0zydn08RAZ5PZ0Awhm2BE+VTnCNrzgVdLuoF02O4lko4HbpE0GyD/vDVPvxzYofL57UkVo5mN0MTzPDq9aqiz7R4EnBLJMuB64KlD+QL9czaZzQC9sqlGPjmbzGzohpBNMKJ86lmgRcQREbF9ROxIuoj1vyLiTcBi4MA82YHAD/L7xcACSRtL2gmYA1zUazlmNpiIga9BuxiYI2mnfPHqAtL2XHUTsCeApG2ApwDXDfFr1OZsMpsZemVTjXxyNpnZ0A0hm2BE+TTIc9A+ASySdHBe8H4AEbFU0iLgStIFcIdFxOoBlmNmNdXs7WkrIlZJegdwBjALOCZvz4fm8UcDHwWOlfRr0mH990fE7YO3fKicTWaFcTYBziaz4gySTTC6fOqrQIuIXwC/yO/vIFeDbaY7Cjiqn3mb2WDSofp+npzRZh4RpwOntww7uvJ+BfDygRYyAs4ms3I5m5xNZiUaRjbBaPJpkCNoZlaQdLHr4EFjZjZMziYzK1HJ2eQCzawxhtMTZGY2XM4mMytRudnkAs2sIdLFrmUGjZmNL2eTmZWo5GxygWbWIKX2BJnZeHM2mVmJSs0mF2hmDRGIVYUGjZmNL2eTmZWo5GxygWbWEMHgt4s1Mxs2Z5OZlajkbHKBZtYUIVatqfVQRTOzqeNsMrMSFZxNLtDMGiKANZTZE2Rm48vZZGYlKjmbXKCZNUQAq9aUeS61mY0vZ5OZ/f/t3X2oZPdZwPHv022SVmtp0rRlSSJdZRW3pW/GbaH1tWI3+cNtwcJG0SgpSzERBQW3CrVFAq2iiNAa1ja4irgETc0i0ZiG1iJ9SdKapknTNLeJNGuWLPW1Vdh07338Y84mN5s7M+femTPzzDnfDwx779yZ+f1m9p4v93fm3HMrqtwmF2hSj1Q9llrSsNkmSRVVbZMLNKknKp+NSNJw2SZJFVVukws0qS+y7p4gSQNmmyRVVLhNLtCknqh8LLWk4bJNkiqq3CYXaFJPJFF2T5Ck4bJNkiqq3CYXaFKPrBc9llrSsNkmSRVVbZMLNKknsvCx1JKGyzZJqqhym1ygSb0RrBc9llrSkNkmSRXVbZMLNKlHsuieIEnDZpskVVS1TS7QpJ7IhPWNmqGRNFy2SVJFldvkAk3qkQ1qhkbSsNkmSRVVbZMLNKknsvCx1JKGyzZJqqhym1ygST2SuewZSNJz2SZJFVVtkws0qScyYaPoniBJw2WbJFVUuU0u0KQeqfr3PCQNm22SVFHVNrlAk3qk6lv1kobNNkmqqGqbXKBJPZFE2bfqJQ2XbZJUUeU2TZ1VRLwgIu6OiC9GxIMR8f7m+ksi4s6IeKT59+JN93lPRKxFxMMR8bYun4CkZ+SESxsRcaDZbtci4siY2/xYRNzX9OCf5jLxHbBN0uqY1KY2fVqlNjVzsU/SCpi1TdBNn9osG88AP5GZrwVeBxyIiDcBR4C7MnMvcFfzORGxDzgEvAo4AHw4Ina1eYKSZpCQGzH2Mk2znX4IuArYB1zTbM+bb/MS4MPAT2fmq4B3zv+JtGabpFUwpU3T+rSCbQL7JNU3Y5uguz5NXaDlyLeaTy9oLgkcBI411x8D3t58fBA4nplnMvMxYA3YP20cSbPLjLGXFvYDa5n5aGY+BRxntD1v9rPArZn59dF4eXquT2AbbJO0Oia1qUWfVqpNzfj2SVoBM7YJOupTqwMvI2JXRNwHnAbuzMzPAa/IzFPNQKeAlzc3vwx4fNPdTzbXnf+YhyPi3oi499ucaTMNSRMksLERYy/Apee2ueZy+LyHaLPtfh9wcUR8MiI+HxG/0NkTaqGLNjWPa5+kOZnWphZ9Wrk2gT87SdXNoU3QUZ9anSQkM9eB1zVv0X0sIl494eZbLTefcxhnZh4FjgK8OC4peg4VaYUkMHlvzzcy88oJX2+z7T4f+EHgrcALgc9ExGcz86vbmeq8dNGm5nHtkzQv09sEk/u0cm0Cf3aSypu9TdBRn7Z1FsfM/K+I+CSj46OfjIjdmXkqInYz2kMEo5XjFZvudjnwxHbGkbQzuTHT3dtsuycZxep/gf+NiE8BrwWW9kMQ2CapuqG2CeyTVNmMbYKO+tTmLI4va/b+EBEvBH4S+ApwAri2udm1wG3NxyeAQxFxUUTsAfYCd08bR9KsZj6O+h5gb0TsiYgLGf3C+onzbnMb8MMR8fyI+A7gjcBDc30aLdkmaVVMblOLPq1Um8A+Sath5jZBR31q8w7abuBYc5aS5wG3ZObfRcRngFsi4jrg6zRnJMnMByPiFuDLwFng+uZtfkldas5GtOO7Z56NiBuAO4BdwM3N9vzu5us3ZeZDEfEPwP3ABvCRzHxgDrPfCdskrYLhtQnsk1TfjG2C7vo0dYGWmfcDr9/i+n9ndCzlVve5Ebhx2mNLmrMZfyMhM28Hbj/vupvO+/z3gd+fbaTZ2SZphQyoTWCfpJUxh9/k7KJP2/odNEnFtXs7XpIWyzZJqqhom1ygSX3iOb0kVWSbJFVUtE0u0KS+mMOx1JI0d7ZJUkWF2+QCTeqTonuCJA2cbZJUUdE2uUCTeiSK7gmSNGy2SVJFVdvkAk3qi6TsniBJA2abJFVUuE0u0KTeCCi6J0jSkNkmSRXVbZMLNKlPiu4JkjRwtklSRUXb5AJN6ouk7J4gSQNmmyRVVLhNLtCkHomie4IkDZttklRR1Ta5QJP6pGhoJA2cbZJUUdE2uUCTeqTqniBJw2abJFVUtU0u0KS+KHwstaQBs02SKircJhdoUp8U3RMkaeBsk6SKirbJBZrUI1Xfqpc0bLZJUkVV2+QCTeqTjWVPQJK2YJskVVS0TS7QpJ6IrLsnSNJw2SZJFVVukws0qU+K/rKrpIGzTZIqKtomF2hSj1TdEyRp2GyTpIqqtskFmtQXCVH0WGpJA2abJFVUuE0u0KQ+KbonSNLA2SZJFRVtkws0qUeq7gmSNGy2SVJFVdv0vGVPQJIkSZI04jtoUl8UPpZa0oDZJkkVFW6TCzSpT4oeSy1p4GyTpIqKtskFmtQTQd09QZKGyzZJqqhym6b+DlpEXBERn4iIhyLiwYj41eb6SyLizoh4pPn34k33eU9ErEXEwxHxti6fgKRNcsKlhYg40Gy3axFxZMLtfigi1iPiZ2af9M7YJmmFTGpTiz6tUpuaedgnaRXM2Cbopk9tThJyFvj1zPwB4E3A9RGxDzgC3JWZe4G7ms9pvnYIeBVwAPhwROxqMY6kWTTHUo+7TNNspx8CrgL2Adc02/NWt/sgcMd8n8C22SZpFUxp07Q+rWCbwD5J9c3YJuiuT1MXaJl5KjO/0Hz8TeAh4DLgIHCsudkx4O3NxweB45l5JjMfA9aA/W0mI2lGs+0F2g+sZeajmfkUcJzR9ny+XwH+Bjg9hxnvmG2SVshse6lXqk1gn6SVMfs7aJ30aVun2Y+IVwKvBz4HvCIzT8EoRMDLm5tdBjy+6W4nm+vOf6zDEXFvRNz7bc5sZxqSxpiyF+jSc9tcczl83t2nbrsRcRnwDuCmDp/Gts2zTc3j2SdpjlrspZ7Up5VtE/izk1TZjG2CjvrU+iQhEfEiRiu/X8vM/4mIsTfd4rrnrEEz8yhwFODFcUnRc6hIK2T63p5vZOaVE77eZtv9I+A3M3N9QgMWat5tAvskzVW7PdGT+rSSbQJ/dpJKm71N0FGfWi3QIuICRoH5y8y8tbn6yYjYnZmnImI3z7xldxK4YtPdLweeaDUbSTOZ8WxEbbbdK4HjTWAuBa6OiLOZ+bczjbxDtklaDUNrE9gnaRXM4SyOnfSpzVkcA/go8FBm/uGmL50Arm0+vha4bdP1hyLioojYA+wF7p42jqTZRY6/tHAPsDci9kTEhYx+Yf3E5htk5p7MfGVmvhL4a+CXl7g4s03SipjUphZ9Wqk2gX2SVsWMbYKO+tTmHbQ3Az8PfCki7muu+y3gA8AtEXEd8HXgnc0kHoyIW4AvMzqL0fWZud7qKUrauQRm2BOUmWcj4gZGZxjaBdzcbM/vbr5e7Xc7bJO0CobXJrBPUn0ztgm669PUBVpm/jNbH18J8NYx97kRuHEnE5K0M8H4DbWtzLwduP2867aMS2b+4ozDzcQ2SathaG1q5mCfpOLm0Sbopk+tTxIiqb45HEstSXNnmyRVVLVNLtCkPvGcXpIqsk2SKiraJhdoUl9k3T1BkgbMNkmqqHCbXKBJfVJ0T5CkgbNNkioq2iYXaFKPtDwlrCQtlG2SVFHVNrlAk3qk6lv1kobNNkmqqGqbXKBJfZGUfate0oDZJkkVFW6TCzSpJ4K6e4IkDZdtklRR5Ta5QJP6pOieIEkDZ5skVVS0TS7QpL5IiI2ipZE0XLZJUkWF2+QCTeqRqmcjkjRstklSRVXb5AJN6pGqx1JLGjbbJKmiqm1ygSb1SdE9QZIGzjZJqqhom1ygSX2RdfcESRow2ySposJtcoEm9URQ91hqScNlmyRVVLlNLtCkHql6NiJJw2abJFVUtU0u0KS+SMoeSy1pwGyTpIoKt8kFmtQjsb7sGUjSc9kmSRVVbZMLNKlHqh5LLWnYbJOkiqq2yQWa1BdZ91hqSQNmmyRVVLhNLtCkPqnZGUlDZ5skVVS0TS7QpJ6IzLJ7giQNl22SVFHlNrlAk3qk6rHUkobNNkmqqGqbXKBJPRIby56BJD2XbZJUUdU2uUCT+iKBom/VSxow2ySposJtet60G0TEzRFxOiIe2HTdJRFxZ0Q80vx78aavvSci1iLi4Yh4W1cTl/RcsTH+0ur+EQeabXctIo5s8fWfi4j7m8unI+K1834O22GfpNUwqU1t+mSbJHVh1jZBN32aukAD/gw4cN51R4C7MnMvcFfzORGxDzgEvKq5z4cjYleLMSTNQ+b4yxTNtvoh4CpgH3BNs01v9hjwo5n5GuB3gaNzfgbb9WfYJ6m+SW2a0ifbJKkzM7QJuuvT1AVaZn4K+I/zrj4IHGs+Pga8fdP1xzPzTGY+BqwB+6eNIWkOcua9QPuBtcx8NDOfAo4z2qafGSLz05n5n82nnwUun+dT2C77JK2AKW1q0SfbJGn+Zm8TdNSnNu+gbeUVmXmqGfQU8PLm+suAxzfd7mRz3XNExOGIuDci7v02Z3Y4DUnnBM0pY8dcgEvPbXPN5fB5D9F6+21cB/z9XJ/EfNgnqZBpbWrRJ9vUsE3S/MyhTdBRn+Z9kpDY4rot3x/MzKM0b/G9OC6p+Rt60qqZvLfnG5l55YSvt95+I+LHGUXmLa3ntnz2SVqW6XuiJ/XJNp270jZJ8zVbm6CjPu10gfZkROzOzFMRsRs43Vx/Erhi0+0uB57Y4RiStiOZ9Q8uttp+I+I1wEeAqzLz32cZsCP2SarENp1jm6RKZm8TdNSnnR7ieAK4tvn4WuC2TdcfioiLImIPsBe4e4djSNqW2X7RFbgH2BsReyLiQka/tH5i8w0i4ruBW4Gfz8yvzv0pzId9kkqZ0qbpfbJNkjowc5ugoz5NfQctIv4K+DFGx2CeBH4H+ABwS0RcB3wdeCdAZj4YEbcAXwbOAtdn5nqbiUia3Sx7gjLzbETcANwB7AJubrbpdzdfvwl4L/BSRmcZAzg75a3/TtknaTXYJtskVTTrO2hd9WnqAi0zrxnzpbeOuf2NwI3THlfSnGX7v9kx9iEybwduP++6mzZ9/C7gXbONMj/2SVoBtmkz2yRVMYc2QTd9mvdJQiQt0+zHUkvS/NkmSRUVbZMLNKlHot3x0pK0ULZJUkVV2+QCTeqLBNZrhkbSgNkmSRUVbpMLNKkngiy7J0jScNkmSRVVbpMLNKlPNubw266SNG+2SVJFRdvkAk3qiwRqdkbSkNkmSRUVbpMLNKlHouieIEnDZpskVVS1TS7QpN5o/VfvJWmBbJOkiuq2yQWa1BeFz0YkacBsk6SKCrfJBZrUI1XPRiRp2GyTpIqqtskFmtQXCazXPJZa0oDZJkkVFW6TCzSpN+oeSy1pyGyTpIrqtskFmtQnRc9GJGngbJOkioq2yQWa1BcJbNTcEyRpwGyTpIoKt8kFmtQbCRvry56EJJ3HNkmqqG6bXKBJfVF4T5CkAbNNkioq3CYXaFKfFD2WWtLA2SZJFRVtkws0qTfqno1I0pDZJkkV1W2TCzSpLxJYr3kstaQBs02SKircJhdoUp8U3RMkaeBsk6SKirbJBZrUG1n2l10lDZltklRR3Ta5QJP6IiGLvlUvacBsk6SKCrfJBZrUJ0Xfqpc0cLZJUkVF2+QCTeqLzLK/7CppwGyTpIoKt8kFmtQjWfTveUgaNtskqaKqbXKBJvVFJqzXDI2kAbNNkioq3KbndfXAEXEgIh6OiLWIONLVOJI2yY3xlxambbcx8sfN1++PiDfM/Tl0zDZJSzCpTS36ZJskdWLGNkE3fepkgRYRu4APAVcB+4BrImJfF2NJGslMcn197GWaltvtVcDe5nIY+JP5Potu2SZp8aa1aVqfbJOkLszaJuiuT129g7YfWMvMRzPzKeA4cLCjsSQ1ciPHXlpos90eBP48Rz4LvCQids/3WXTKNklLMKlNLfpkmyR1YsY2QUd96up30C4DHt/0+UngjZtvEBGHGa0iAc58PP/6gY7m0talwDecg3MoMD7A92/3Dt/kP+/4+MYtl064yQsi4t5Nnx/NzKObPp+63Y65zWXAqe3Od0naPEf7VG9851BrDtvqU4s2weQ+2aaGbXIORcevModFtwk66lNXC7TY4rpnLUObJ3cUICLuzcwrO5pLK87BOVQZ/9wctnufzDww67BbPewOblNZq/nbp1rjO4d6c9jO7W1TK7bJOazs+JXmsJ3bz6FN0FGfujrE8SRwxabPLwee6GgsSfPRZrtd9W171ecvDZFtklRVJ33qaoF2D7A3IvZExIXAIeBER2NJmo822+0J4BeaMxK9CfjvzFyVQ4jANkmryDZJqqqTPnVyiGNmno2IG4A7gF3AzZn54IS7HJ3wtUVxDiPOYfnjwxLmMG67jYh3N1+/CbgduBpYA/4P+KVFz3MWO2gTDPT7odj44BzOGdwcbNNYg/teGMM5LH98GOgcuupTZK7SIdqSJEmS1F+d/aFqSZIkSdL2uECTJEmSpCKWvkCLiAMR8XBErEXEkQWO+68R8aWIuO/caTkj4pKIuDMiHmn+vXjOY94cEacj4oFN140dMyLe07wuD0fE2zoa/30R8W/N63BfRFzd1fjNY14REZ+IiIci4sGI+NXm+kW+DuPmsJDXIiJeEBF3R8QXm/Hf31y/sNdA09mmxX4/LrtPtunpx7NPK2AZfbJNw/3ZyTYtQWYu7cLol+m+BnwPcCHwRWDfgsb+V+DS8677PeBI8/ER4INzHvNHgDcAD0wbE9jXvB4XAXua12lXB+O/D/iNLW479/Gbx90NvKH5+LuArzZjLfJ1GDeHhbwWjP4exouajy8APge8aZGvgZep/0e2acHfj8vuk216+jHtU/HLsvpkm4b7s5NtWvxl2e+g7QfWMvPRzHwKOA4cXOJ8DgLHmo+PAW+f54Nn5qeA/2g55kHgeGaeyczHGJ35ZX8H448z9/GbOZzKzC80H38TeIjRX1Nf5Oswbg7jzHUOOfKt5tMLmkuywNdAU9mmBX8/LrtPtunpOdin+ir1yTY9d269+9nJNi3eshdolwGPb/r8JJP/w+cpgX+MiM9HxOHmuldk83cJmn9fvoB5jBtzka/NDRFxf/M2/rm3hjsfPyJeCbye0V6QpbwO580BFvRaRMSuiLgPOA3cmZlLew20JdtU5/tx4X0acpuase1Tbct6zW3Tsw3yZyfbtBjLXqDFFtct6rz/b87MNwBXAddHxI8saNy2FvXa/AnwvcDrgFPAHyxi/Ih4EfA3wK9l5v9MumlX89hiDgt7LTJzPTNfx+ivye+PiFdPmuq8x9dUtmm8Rb42C+/T0NsE9mkFLOs1t03PGOTPTrZpcZa9QDsJXLHp88uBJxYxcGY+0fx7GvgYo7c9n4yI3QDNv6cXMJVxYy7ktcnMJ5tv+A3gT3nm7d/Oxo+ICxht4H+Zmbc2Vy/0ddhqDst4LTLzv4BPAgdY8veCnsU2Ffh+XPQ2aZuezT6VtZTX3DY9Y4g/O9mmxVr2Au0eYG9E7ImIC4FDwImuB42I74yI7zr3MfBTwAPN2Nc2N7sWuK3ruUwY8wRwKCIuiog9wF7g7nkPfu6buvEORq9DZ+NHRAAfBR7KzD/c9KWFvQ7j5rCo1yIiXhYRL2k+fiHwk8BXWPL3gp7FNhX4flxkn2zT02PZp/oW3ifb9GxD+9nJNi1BLvksJcDVjM4G8zXgtxc05vcwOrPLF4EHz40LvBS4C3ik+feSOY/7V4zeAv42o5X9dZPGBH67eV0eBq7qaPy/AL4E3M/om3l3V+M3j/kWRm8x3w/c11yuXvDrMG4OC3ktgNcA/9KM8wDw3mnff138X3iZ+v9kmxb4/bjsPtmmpx/PPq3AZdF9sk3D/tnJNi3+Es0TkCRJkiQt2bIPcZQkSZIkNVygSZIkSVIRLtAkSZIkqQgXaJIkSZJUhAs0SZIkSSrCBZokSZIkFeECTZIkSZKK+H+PhcKrHoETyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ploting code is provided once you have the correlation images for all the summary images\n",
    "\n",
    "figure, axes = plt.subplots(2,3)\n",
    "figure.set_size_inches(15,7)\n",
    "\n",
    "mesh0 = axes[0,0].pcolormesh(original_movie_mean/np.max(original_movie_mean))\n",
    "axes[0,0].set_title('Original movie mean')\n",
    "mesh1 = axes[0,1].pcolormesh(m_rig_mean/np.max(m_rig_mean))\n",
    "axes[0,1].set_title('Rigig MC mean')\n",
    "mesh2 = axes[0,2].pcolormesh(m_els_mean/np.max(m_els_mean))\n",
    "axes[0,2].set_title('PW-rigid MC mean')\n",
    "\n",
    "mesh3 = axes[1,0].pcolormesh(original_movie_corr)\n",
    "axes[1,0].set_title('Original movie correlation')\n",
    "mesh4 = axes[1,1].pcolormesh(m_rig_corr)\n",
    "axes[1,1].set_title('Rigid MC correlation')\n",
    "mesh5= axes[1,2].pcolormesh(m_els_corr)\n",
    "axes[1,2].set_title('PW-Rigid MC correlation')\n",
    "\n",
    "vmin_corr = 0\n",
    "vmax_corr = 1\n",
    "mesh0.set_clim(vmin_corr,vmax_corr)\n",
    "mesh1.set_clim(vmin_corr,vmax_corr)\n",
    "mesh2.set_clim(vmin_corr,vmax_corr)\n",
    "mesh3.set_clim(vmin_corr,vmax_corr)\n",
    "mesh4.set_clim(vmin_corr,vmax_corr)\n",
    "mesh5.set_clim(vmin_corr,vmax_corr)\n",
    "\n",
    "figure.colorbar(mesh0,ax=axes[0,0])\n",
    "figure.colorbar(mesh1,ax=axes[0,1])\n",
    "figure.colorbar(mesh2,ax=axes[0,2])\n",
    "figure.colorbar(mesh3,ax=axes[1,0])\n",
    "figure.colorbar(mesh4,ax=axes[1,1])\n",
    "figure.colorbar(mesh5,ax=axes[1,2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "crispness_orig = 0\n",
    "crispness_rig = 0\n",
    "crispness_els = 0\n",
    "\n",
    "## EXTRA: use caiman to compute crispness \n",
    "# compute borders of motion corrected movie to exclud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Crispness original: 0\n",
      "Crispness rigid: 0\n",
      "Crispness elastic: 0\n"
     ]
    }
   ],
   "source": [
    "# print crispness values from caiman rutine\n",
    "print('Crispness original: '+ str(int(crispness_orig)))\n",
    "print('Crispness rigid: '+ str(int(crispness_rig)))\n",
    "print('Crispness elastic: '+ str(int(crispness_els)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameters selection for motion correction\n",
    "\n",
    "Output and quality assesment values of motion correction will be dependent on the selected paramenters. If crispness values after motion correction are not bigger than the original that could mean that the paramenters used for motion correction were not optimal and they deteriorated the image/ In the above example some parameters could not optimal for motion correction in this particular video. For the example we used one selected value of the filter, but the performance of the motion correction algorithm will be dependent on the quality of the image that comes out from that filter and how blurry that image is.\n",
    "\n",
    "#### Exercise 4 : Motion correction parameters impact\n",
    "\n",
    "One parameter that impacts motion correction performance is the size of the spatial filter that is applyed to the video.\n",
    "\n",
    "* Use the following function from CaImAn and plot the result of different space filtering applyed to a single frame of the video:from caiman.motion_correction import high_pass_filter_space\n",
    "\n",
    "* How will this filtering affect the measure of crispness?\n",
    "\n",
    "* Acording to Pnevmatikakis et al, which other factors are relevant for pw-rigid motion correction? \n",
    "\n",
    "**Extra exercise  4:** parameters selection \n",
    "\n",
    "- Explore the impact of different parameters in the output of motion correction algorithm by measuring crispness in the correlation image of the final outcome. Particularly measure the effect of different spatial filter size by applying motion correction algorithm multiple times and computing the crispness of the output video. \n",
    "\n",
    "- Display all crispness values for rigid and not rigid motion correction and plot them agains gSig_filters values.\n",
    "\n",
    "- If possible, show the impact of the crispness value that comes from changing other relevant paraments.\n",
    "\n",
    "*Note1: The is no need to save all the motion corrected movies, just save the cripsness values.*\n",
    "\n",
    "*Note2: gSig_filters is an optional range of posible values, but if your computer gets to slow in the computation reduce the range.*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## complete here with the code for visualization of different spatial filters \n",
    "gSig_filters = 2*np.arange(0,6)+3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "crispness_rig = np.zeros_like(gSig_filters)\n",
    "crispness_els = np.zeros_like(gSig_filters)\n",
    "\n",
    "# compute crispness for different gSig_filters size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcdbe23e290>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "## plot crispness value for different gfilters size\n",
    "plt.plot(gSig_filters,crispness_rig)\n",
    "plt.plot(gSig_filters,crispness_els)\n",
    "plt.xlabel('Spatial filter size', fontsize = 12)\n",
    "plt.ylabel('Crispness', fontsize = 12)\n",
    "plt.legend(['rigid', 'pw-ridig'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Source extraction\n",
    "\n",
    "Original algorithms to perform source extraction from calcium videos were based on **PCA** and **ICA**. The disadvantage of appying this techniques directly is that they do not confine the sources to only one spatial region nor does provide good solution to deal with overlapping sources. As we are using CaImAn, in this notebook we will use a different technique called *Constrained Non-Negative Matrix Factorization* (**CNMF**). This technique (as non linear) solves the sparcity problem by adding multiple constrains to an optimation problem and also can deal better with overlapping sources. \n",
    "\n",
    "Matrix factorization part is based on the fact that a calcium imaging video can be  can be approximated as a product of two matrices: a spatial matrix (**A**) that encodes the location of each neuron in the optical field, and a temporal matrix (**C**) that characterizes the calcium concentration evolution of each neuron. This deconvolution can be constrained to the fact that spiking activity and the shape of each inferred neuron are as sparce as posible. \n",
    "\n",
    "The recorded video data can be represented by a matrix $Y∈R^{d×T}$, where $d$ is the number of pixels in the field of view and $T$ is the number of frames observed. Each neuron $i$ is characterized by its spatial *‘footprint’* vector $a_i ∈R^{d}$ characterizing the cell’s shape and location, and *‘calcium activity’* timeseries $c_i∈R^T$, modeling (up to a multiplicative and additive constant) cell i’s mean fluorescence signal at each frame. Here, both $a_i$ and $c_i$ are constrained to be nonnegative because of their physical interpretations. The background fluctuation is represented by a matrix $B∈R^{d×T}$. If the field of view contains a total number of K neurons, then the observed movie data is modeled as a superposition of all neurons’ spatiotemporal activity, plus time-varying background and additive noise:\n",
    "\n",
    "\n",
    "$$ Y = \\sum_{i=1..K} a_i . c_i^T + B + E = A C + B + E $$\n",
    "\n",
    "where $A=[a_1,…,a_K]$ and $C=[c_1,…,c_K]^T$. The noise term $E∈R^{d×T}$ is modeled as Gaussian, $E(t)∼N(0,Σ)$ is a diagonal matrix, indicating that the noise is spatially and temporally uncorrelated.\n",
    "\n",
    "Estimating the model parameters A,C in the model gives us all neurons’ spatial footprints and their denoised temporal activity. This can be achieved by minimizing the residual sum of squares (RSS), aka the Frobenius norm of the matrix Y−(AC+B),\n",
    "\n",
    "$$ ∥Y−(AC+B)∥_F $$\n",
    "\n",
    "while requiring the model variables A,C and B to follow the desired constraints, discussed above.\n",
    "\n",
    "\n",
    "\n",
    "For more information about deconvolution methods see [Pnevmatikakis, et al. 2016](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4881387/) *Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data*.\n",
    "\n",
    "\n",
    "More information about the background modeling for microendoscopic data can be found in [Pengcheng Zhou, et al. 2018](https://elifesciences.org/articles/28728) *Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data*.\n",
    "\n",
    "\n",
    "More information about **CaImAn** complete pipeline can be found in [Andrea Giovannucci, et al. 2019](https://elifesciences.org/articles/38173) *CaImAn an open source tool for scalable calcium imaging data analysis*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary images that are inicialitation for CNMF-E\n",
    "\n",
    "CNMF-E algorithm takes as inicialization threshold values for the correlation and PNR images, so all regions that surpases those thresholds will be included in the model as neural footprint templates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load motion corrected video\n",
    "input_mmap_file_path = fname_tot_els \n",
    "Yr, dims, T = cm.load_memmap(input_mmap_file_path)         # load mmap file\n",
    "images = Yr.T.reshape((T,) + dims, order='F' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute correlation and pnr images \n",
    "gSig = 9\n",
    "corr_image, pnr_image = cm.summary_images.correlation_pnr(images[::1], gSig=gSig,swap_dim=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_summary_images_fig_path = figures_dir + 'summary_images_calcium_video_1.png'\n",
    "auxiliary_functions.summary_images(corr_image = corr_image, pnr_image = pnr_image, \n",
    "                                  output_path = output_summary_images_fig_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example of source extraction\n",
    "\n",
    "Here we will run an example on the video we have been working on for source extraction. First we will define some fixed parameters for this example and later we will explore the impact of those in the source extraction output (particularly the seeds for inicialization in CNMF-E). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Changing key fr in group data from 30 to 10\n",
      "WARNING:root:Changing key decay_time in group data from 0.4 to 0.1\n",
      "WARNING:root:Changing key rf in group patch from None to 60\n",
      "WARNING:root:Changing key stride in group patch from None to 30\n",
      "WARNING:root:Changing key p_tsub in group patch from 2 to 1\n",
      "WARNING:root:Changing key low_rank_background in group patch from True to None\n",
      "WARNING:root:Changing key nb_patch in group patch from 1 to 0\n",
      "WARNING:root:Changing key del_duplicates in group patch from False to True\n",
      "WARNING:root:Changing key p in group preprocess from 2 to 1\n",
      "WARNING:root:Changing key min_corr in group init from 0.85 to 0.7\n",
      "WARNING:root:Changing key min_pnr in group init from 20 to 7\n",
      "WARNING:root:Changing key K in group init from 30 to None\n",
      "WARNING:root:Changing key gSig in group init from [5, 5] to (4, 4)\n",
      "WARNING:root:Changing key gSiz in group init from None to (17, 17)\n",
      "WARNING:root:Changing key ring_size_factor in group init from 1.5 to 1.4\n",
      "WARNING:root:Changing key tsub in group init from 2 to 1\n",
      "WARNING:root:Changing key nb in group init from 1 to 0\n",
      "WARNING:root:Changing key method_init in group init from greedy_roi to corr_pnr\n",
      "WARNING:root:Changing key center_psf in group init from False to True\n",
      "WARNING:root:Changing key normalize_init in group init from True to False\n",
      "WARNING:root:Changing key nb in group spatial from 1 to 0\n",
      "WARNING:root:Changing key p in group temporal from 2 to 1\n",
      "WARNING:root:Changing key nb in group temporal from 1 to 0\n",
      "WARNING:root:Changing key merge_thr in group merging from 0.8 to 0.7\n"
     ]
    }
   ],
   "source": [
    "# create dictionary with source extraction parameters\n",
    "\n",
    "parameters_source_extraction ={ 'fr': 10,               # movie frame rate\n",
    "                               'decay_time': 0.1,       # length of a typical transient in seconds\n",
    "                               'min_corr': 0.7,   # min peak value from correlation image\n",
    "                               'min_pnr': 7,     # min peak to noise ration from PNR image\n",
    "                                'p': 1,                # order of the autoregressive system \n",
    "                               'K': None,              # upper bound on number of components per patch, in general None\n",
    "                               'gSig': (4, 4),         # gaussian width of a 2D gaussian kernel, which approximates a neuron\n",
    "                               'gSiz': (17, 17),       # average diameter of a neuron, in general 4*gSig+1\n",
    "                               'ring_size_factor': 1.4, # radius of ring is gSiz*ring_size_factor\n",
    "                               'merge_thr': 0.7, 'rf': 60,\n",
    "                               'stride': 30, 'tsub': 1, 'ssub': 2, 'p_tsub': 1, 'p_ssub': 2, 'low_rank_background': None,\n",
    "                               'nb': 0, 'nb_patch': 0, 'ssub_B': 2, 'init_iter': 2,\n",
    "                               'method_init': 'corr_pnr', 'method_deconvolution': 'oasis',\n",
    "                               'update_background_components': True,\n",
    "                               'center_psf': True, 'border_pix': 0, 'normalize_init': False,\n",
    "                               'del_duplicates': True, 'only_init': True}\n",
    "# create caiman parameters objects\n",
    "opts = params.CNMFParams(params_dict=parameters_source_extraction)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Changing key n_processes in group patch from 1 to 8\n",
      "WARNING:root:Changing key init_batch in group online from 200 to 500\n",
      "WARNING:root:Changing key medw in group spatial from None to (3, 3)\n",
      "WARNING:root:Changing key n_pixels_per_process in group preprocess from None to 5142\n",
      "WARNING:root:Changing key n_pixels_per_process in group spatial from None to 5142\n",
      "WARNING:root:Changing key n_pixels_per_process in group preprocess from 5142 to 3600\n",
      "WARNING:root:Changing key n_pixels_per_process in group spatial from 5142 to 3600\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<caiman.source_extraction.cnmf.cnmf.CNMF at 0x7fcdbfba80d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run source extraction (fit the model to the data)\n",
    "cnmf_object = cnmf.CNMF(n_processes=n_processes, dview=dview, params=opts)\n",
    "cnmf_object.fit(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the cnmf object as a hdf5 file\n",
    "output_cnmf_file_path = data_dir + 'source_extracted/' + 'calcium_video_0_cnmf.hdf5'\n",
    "cnmf_object.save(output_cnmf_file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main outputs from CNMF are the matrix **A** containing the footprints and the matrix **C** containing the temporal calcium traces. Here we show the result of the A matrix by ploting the footprints on top on the correlation image and also ploting the temporal traces. \n",
    "\n",
    "*Note: Footprints are save in cnmf_object.estimates.A and temporal traces in cnmf_object.estimates.C *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of componentes = 24\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display footprints in the ROI\n",
    "figure, axes = plt.subplots(1)\n",
    "pos = axes.imshow(corr_image)\n",
    "coordinates = cm.utils.visualization.get_contours(cnmf_object.estimates.A, np.shape(corr_image), 0.2, 'max')\n",
    "for c in coordinates:\n",
    "    v = c['coordinates']\n",
    "    c['bbox'] = [np.floor(np.nanmin(v[:, 1])), np.ceil(np.nanmax(v[:, 1])),\n",
    "                    np.floor(np.nanmin(v[:, 0])), np.ceil(np.nanmax(v[:, 0]))]\n",
    "    axes.plot(*v.T, c='r')\n",
    "figure.colorbar(pos, ax=axes)\n",
    "\n",
    "print('Total number of componentes = ' + f'{cnmf_object.estimates.A.shape[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot temporal traces\n",
    "figure, axes = plt.subplots(1)\n",
    "C_0 = cnmf_object.estimates.C.copy()\n",
    "C_0[1] += C_0[0].min()\n",
    "for i in range(1, len(C_0)):\n",
    "    C_0[i] += C_0[i].min() + C_0[:i].max()\n",
    "    axes.plot(C_0[i])\n",
    "axes.set_xlabel('t [frames]')\n",
    "axes.set_yticks([])\n",
    "axes.set_ylabel('activity')\n",
    "figure.set_size_inches([50., .5 * len(C_0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parameters selection\n",
    "\n",
    "\n",
    "There are many paramenters involved in the source extraction algorithm but we will concentrate on the impact of two of them, *min_corr* and *min_pnr*, that are the seed for the 1p source extraction algorithm. \n",
    "\n",
    "Computation of source extraction is slow, and here we will run it multiple times, so ***be patient***.\n",
    "\n",
    "You can change the *corr_values* and *pnr_values* range, but take into account that the lower those values are, the most permisive the conditions for select a region as a cell is. This will increase the number of cells that are detected but will also slow down the extraction algoritm. If you are having speed problems you can also try increasing the range of values of corr and pnr, which will result in a lower number of components detected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise 5: Source extraction parameters impact\n",
    "\n",
    "* For a fixed minimum correlation value (ie 0.6) select a range of possible PNR minimun values and study the impact of those in the final source extraction output.\n",
    "\n",
    "* For a fixed value of PNR (ie 6) select a range of minimum correlation valur and study the impact of those in the final source extraction output.\n",
    "\n",
    "* How do these paramenters affect the spatial sparcity of the output? Which set of parameter selection procedes the higher number of neurons? Is maximal number of neurons always better? Why?  \n",
    "\n",
    "* Generate a plot of number of neurons vs PNR (or min corr value) to show how these parameters affect the source extracted signals.\n",
    "\n",
    "* Are the temporal traces affected by these parameters?\n",
    "\n",
    "**Extra exercise 5:** cell selection visualization\n",
    "\n",
    "* For different PNR and minimum correlation value plot the correlation image and the selected cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## complete with code for exploration of corr_value and pnr_values in the result of source extraction\n",
    "\n",
    "#suggested corr and pnr values\n",
    "corr_values = [0.5,0.6, 0.7, 0.8,0.9]\n",
    "pnr_values = [5,6,7,8,9]\n",
    "\n",
    "num_of_components_diff_corr = np.zeros_like(corr_values)\n",
    "num_of_components_diff_pnr = np.zeros_like(pnr_values)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "## complete with code for visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Multisession registration\n",
    "\n",
    "Experiments take lond periods of time and multiple trails or sessions that are not necesarily continuos in time. For calcium imaging there is one main restriction related to long recording periods that the photobleacing effect  (alteration of fluorophore molecule such that it permanently is unable to fluoresce). Because of this, periods of registration are normally alternated with resting periods. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.a) Photobleaching effect\n",
    "\n",
    "Because of prolonged exposure to light emision fluorecense saturates, producing a darker image, a phenomena known as phobleaching. You will be provided with multiple videos that were registered continously with small resting periods in the middle. \n",
    "\n",
    "##### Exercise 6: photobleaching effect\n",
    "\n",
    "* Study the effect of photobleaching in the raw signal. For that aim use the videos called *'caiman_video_trial_'+ f'{trial}' +'.tif'* to create a new multisession video.\n",
    "* Repeat exercise 0 in this new signal. How is bleaching reflected in the temporal evolution of a set of random pixels for these signals? \n",
    "\n",
    "\n",
    "**Extra exercise 6:** temporal statistics\n",
    "\n",
    "* Study the differences in the statistics of pixel value in a trial by trial (histogram, mean and std).\n",
    "\n",
    "Note: you may use *cm.concatenate()* method to create a new movie from a list of caiman objects.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a movie concatenating all segments on multiple trials\n",
    "output_tif_file_concatenated = data_dir + 'raw/' + 'movie_concatenated.tif'\n",
    "movie_list = []\n",
    "## complete with concatenation\n",
    "\n",
    "concatenated_movie = cm.concatenate(movie_list,axis = 0)\n",
    "concatenated_movie.save(output_tif_file_concatenated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# temporal evolution visualization and statistics of pixels with multiple trials for concatenated_movie\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.b) Registration\n",
    "\n",
    "The next problem is that ideally one would like to analyze the evolution of the activity of one single cell over the entire experiment. One solution could be to use the concatenated movies as produced in the previos step and run an entire source extraction algorithm over that new video. That approch would be an easy solution to deal with tracking the same neurons, nevertheless it has some disadvantages. For example, because of bleaching the signals are non stationary, and the quality of them deteriorates over time ( a fact that is not included in the model we are currently using for source extraction). The other disadvantage is the resources that are required to perform source extraction in a longer video (principaly RAM memory). \n",
    "\n",
    "One solution to this problem is to perform a trial by trial source extraction, and then match the different components in multiple sessions that belong to the same cell. This procedure is called registration and uses the footprints of the extracted cells to perform a matching. There are different ways to perform registration, for example in [Sheintuch, et al. 2017](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5670033/): *Tracking the Same Neurons across Multiple Days in Ca2+ Imaging Data*. In this case, we will use the algorithm proposed in the CaImAn software that is based in the *Hungarian Matching Algorithm*. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example of registration\n",
    "\n",
    "In the *'data/source_extracted/'* directory you will find multiple *.hdf5* files corresponding to a group of different sessions that were recording in the same mouse. This files were produced following the steps above, the only difference might be in the selection of motion correction or source extraction paramenters. \n",
    "\n",
    "One step that is important before registration is that all videos should be aligned. In this particulared case we have used the motion correction rutine in the concatenated file (produced as in the previous step) and use as a templete the first session. After that the video was redivided into the original time-session to produce different source extracted files that are given in this directory.\n",
    "\n",
    "We will first try and example with two sessions and you will run the rest for more sessions and create the apropiate new temporal traces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "### import registration and normalization functions\n",
    "from caiman.base.rois import register_multisession\n",
    "import normalization_functions as normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list with the files path that are going to be registered (this are the extracted files)\n",
    "source_extracted_files_list = [data_dir + 'source_extracted/' + 'calcium_video_1_cnmf.hdf5', \n",
    "                          data_dir + 'source_extracted/' + 'calcium_video_2_cnmf.hdf5']\n",
    "# create a list with the files path containing the different correlation image (summary image)\n",
    "correlation_files_list = [data_dir + 'source_extracted/' + 'calcium_video_1_corr.npy', \n",
    "                          data_dir + 'source_extracted/' + 'calcium_video_2_corr.npy']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "## multiple list created to append the relevant information for the registration and creation of a unique time trace\n",
    "A_components_list = []  ## list for contour matrix on multiple trials\n",
    "FOV_size = []  ## list for the cn filter dim (to verify it is always the same dims)\n",
    "#C_traces_list = []  ## list with traces for each trial\n",
    "for i in range(len(source_extracted_files_list)):\n",
    "    cnm = load_CNMF(source_extracted_files_list[i])\n",
    "    cn_filter = np.load(correlation_files_list[i])\n",
    "    FOV_size.append(cn_filter.shape)\n",
    "    A_components_list.append(cnm.estimates.A[:, cnm.estimates.idx_components])\n",
    "    #raw_normed, cnm_normed, res_normed, s_normed, noise_levels =  normalization.normalize_traces(cnm.estimates.C,\n",
    "    #                                                                                             cnm.estimates.YrA,cnm.estimates.S,1,offset_method=\"denoised_floor\")\n",
    "    #C_traces_list.append(cnm_normed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/_index.py:126: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.\n",
      "  self._set_arrayXarray(i, j, x)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells video 1 = 61\n",
      "Number of cells video 2 = 36\n",
      "Number of cells in spatial union = 81\n"
     ]
    }
   ],
   "source": [
    "# define parameters for the hungarian matching algorithm and run multisession registrations\n",
    "thresh_cost = 0.9\n",
    "max_dist = 15\n",
    "spatial_union, assignments, match = register_multisession(A=A_components_list, dims=FOV_size[0], thresh_cost= thresh_cost, max_dist=max_dist)\n",
    "\n",
    "print('Number of cells video 1 = ' + f'{A_components_list[0].shape[1]}')\n",
    "print('Number of cells video 2 = ' + f'{A_components_list[1].shape[1]}')\n",
    "print('Number of cells in spatial union = ' + f'{spatial_union.shape[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 7: Registration footprints\n",
    "\n",
    " For visualization of the original cells in each video and the overlap we can plot the correlation image for each video with their footprints and the spatial union. Here you can see that some neurons are active in both trials, while some are only actitive in one single trial. Spatial union represents all cells that were active in one or in both of them.\n",
    "\n",
    "* Create a figure as describe above. For spatial union you can use one of the two correlation images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display footprints in the ROI for video 1, 2 and overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 8: Registration temporal traces\n",
    "\n",
    "Use the multiple files provided in *'/raw/source_extracted/'* directory and create the new calcium trace for all time for each detected neuron after running registration. If posible use the five files provided.\n",
    "\n",
    "Originaly we proposed that registration method would be better than just doing an alignment and run the entire algorithm in one single video. One reason, as explained before, is the underling model. But, this does not solved the fact that each session have different baseline level, and different signal to noise ratio. Because of that, before doing a concatenation of temporal calcium traces it is required to do a normalization of them, preferably taking into account signal to noise ratio variability. \n",
    "\n",
    "Use the rutines provided in normalization_functions file (from [zbarry](https://gist.github.com/zbarry)) before applying the concatenation for temporal traces. \n",
    "\n",
    "*Note: If plotting multiple traces of concatenated files is not posible, just select a few of them.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/_index.py:126: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.\n",
      "  self._set_arrayXarray(i, j, x)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/_index.py:126: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.\n",
      "  self._set_arrayXarray(i, j, x)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/_index.py:126: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.\n",
      "  self._set_arrayXarray(i, j, x)\n",
      "/home/melisa/anaconda3/envs/caiman/lib/python3.7/site-packages/scipy/sparse/_index.py:126: SparseEfficiencyWarning: Changing the sparsity structure of a csc_matrix is expensive. lil_matrix is more efficient.\n",
      "  self._set_arrayXarray(i, j, x)\n"
     ]
    }
   ],
   "source": [
    "# create a list with the files path that are going to be registered (these are the extracted files)\n",
    "source_extracted_files_list = [data_dir + 'source_extracted/' + 'calcium_video_1_cnmf.hdf5', \n",
    "                               data_dir + 'source_extracted/' + 'calcium_video_2_cnmf.hdf5',\n",
    "                               data_dir + 'source_extracted/' + 'calcium_video_3_cnmf.hdf5',\n",
    "                               data_dir + 'source_extracted/' + 'calcium_video_4_cnmf.hdf5',\n",
    "                               data_dir + 'source_extracted/' + 'calcium_video_5_cnmf.hdf5']\n",
    "# create a list with the files path containing the different correlation image (summary image)\n",
    "correlation_files_list = [data_dir + 'source_extracted/' + 'calcium_video_1_corr.npy', \n",
    "                          data_dir + 'source_extracted/' + 'calcium_video_2_corr.npy',\n",
    "                          data_dir + 'source_extracted/' + 'calcium_video_3_corr.npy',\n",
    "                          data_dir + 'source_extracted/' + 'calcium_video_4_corr.npy',\n",
    "                          data_dir + 'source_extracted/' + 'calcium_video_5_corr.npy']\n",
    "\n",
    "## multiple list created to append the relevant information for the registration and creation\n",
    "## of a unique time trace\n",
    "\n",
    "\n",
    "\n",
    "# define parameters for the hungarian matching algorithm and run multisession registrations\n",
    "#thresh_cost = 0.9\n",
    "#max_dist = 15\n",
    "#spatial_union, assignments, match ...\n",
    "# define parameters for the hungarian matching algorithm and run multisession registrations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "## create new temporal traces on lenght the new registrated signals\n",
    "traces = np.zeros((spatial_union.shape[0], total_C_size))\n",
    "##complete with value assignation in the trace\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'activity')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot temporal traces\n",
    "figure, axes = plt.subplots(1)\n",
    "C_0 = traces.copy()\n",
    "C_0[1] += C_0[0].min()\n",
    "for i in range(1, 5):\n",
    "    C_0[i] += C_0[i].min() + C_0[:i].max()\n",
    "    axes.plot(C_0[i])\n",
    "axes.set_xlabel('t [frames]')\n",
    "axes.set_yticks([])\n",
    "axes.set_ylabel('activity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Analysis of calcium traces\n",
    "\n",
    "For multiple sessions or trials, source extraction can be run separatelly and a registration step is followed as was explained and done before. In directory */data/traces/* you will find a file called *traces_example.npy* where there are calcium traces corresponding to $42$ sessions that were registered all together following the previously described steps. The $42$ sessions are alternating between two behavioural conditions **A** and **B**. From some individual neurons it is already posible to see condition specificity responses. To do a simple analysis on the *population level* we will train a *support vector machine* (**SVM**) classifier to distinguish between these two conditions. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load traces file and temporal alignment information (timeline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of neurons = 262\n",
      "Number of temporal frames = 130631\n"
     ]
    }
   ],
   "source": [
    "### load traces corresponding to registration of 42 trials, from \n",
    "traces_example_file_path = data_dir + 'traces/' + 'traces_example.npy'\n",
    "traces = np.load(traces_example_file_path)\n",
    "print('Number of neurons = ' + f'{traces.shape[0]}')\n",
    "print('Number of temporal frames = ' + f'{traces.shape[1]}')\n",
    "\n",
    "### load timeline concatenation information (this refers to the aligning concatenation time points)\n",
    "timeline_file_path = data_dir + 'traces/' + 'traces_timeline.pkl'\n",
    "timeline_file = open(timeline_file_path, 'rb')\n",
    "timeline_info = pickle.load(timeline_file)\n",
    "\n",
    "timeline_1 = np.zeros(len(timeline_info) + 1)\n",
    "for i in range(len(timeline_info)):\n",
    "    timeline_1[i] = timeline_info[i][1]\n",
    "timeline_1[len(timeline_info)] = traces.shape[1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we show the behaviour of on neurons in the two different conditions (one condition is marked in light blue)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### plot one example of temporal traces after concatenation\n",
    "cell_number = 1\n",
    "figure = plt.figure(constrained_layout=True)\n",
    "gs = figure.add_gridspec(1, 1)\n",
    "figure_ax1 = figure.add_subplot(gs[0, 0])\n",
    "figure_ax1.plot(traces[cell_number], color = 'k')\n",
    "for i in range(0,len(timeline_1)-1,2):\n",
    "    [x_, _x, y_, _y] = [0,18,timeline_1[i], timeline_1[i+1]]\n",
    "    rect = Rectangle((y_, x_), _y - y_, _x - x_, fill=True, color='lightblue')\n",
    "    figure_ax1.add_patch(rect)\n",
    "figure_ax1.set_xlabel('t [frames]')\n",
    "figure_ax1.set_ylabel('activity')\n",
    "figure.set_size_inches([50., 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'cell #')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# represent neural activity matrix as a binary matrix\n",
    "threshold = 1\n",
    "binary_traces = np.zeros(traces.shape)\n",
    "for i in range(traces.shape[0]):\n",
    "    position = np.where(traces[i,:]>threshold)\n",
    "    binary_traces[i,position] = 1\n",
    "plt.imshow(binary_traces, cmap = 'viridis', aspect='auto')\n",
    "plt.xlabel('time (frames)')\n",
    "plt.ylabel('cell #')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Excersice 9: SVM on temporal calcium traces  \n",
    "\n",
    "Train a binary classifier to separate the two conditions using svm from sklearn library. For that aim, you may follow the next steps:\n",
    "\n",
    "- Resample temporal data by avaraging timepoints every re_sf frames. We recommend to use arround 500 frames, but you can try how this impacts the accuracy of the classifier. And, use a binarized version of this by selecting an adecuate threshold for the mean (as shown before).\n",
    "\n",
    "- Create the accurate labels for this two condicions in the resample timeline.\n",
    "\n",
    "- Separate training and testing data set by selecting randomly N temporal frames and evaluate the result in the rest of the temporal frames. Take into account that as the labels are half and half if you select point uniformly, the dataset will be balanced enough. Also N should not be smaller that the number of neurons that you use.\n",
    "\n",
    "- Use a subset of neurons from the original traces file. \n",
    "\n",
    "- Evaluate the accuracy and plot the original vector condition (labels) and the tested one (resulted labels from svm).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(262, 261)\n"
     ]
    },
    {
     "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": [
    "# import svm from sklearn library (see documentation)\n",
    "from sklearn import svm\n",
    "import scipy\n",
    "\n",
    "neural_activity = traces\n",
    "re_sf = 500 #resampling value\n",
    "reshape_neural_activity = np.reshape(neural_activity[:, :int(int(neural_activity.shape[1] / re_sf) * re_sf)],\n",
    "                                         (neural_activity.shape[0], int(neural_activity.shape[1] / re_sf), re_sf))\n",
    "resample_neural_activity = np.mean(reshape_neural_activity, axis=2)\n",
    "\n",
    "print(resample_neural_activity.shape)\n",
    "\n",
    "### create condition and resample condition labels\n",
    "labels = np.ones((traces.shape[1],1))\n",
    "# add conditions where labels = 0\n",
    "# reshape and resample labels\n",
    "\n",
    "# represent neural activity matrix as a binary matrix. \n",
    "# select a threshold \n",
    "threshold = 1\n",
    "binary_traces = np.zeros_like(resample_neural_activity)\n",
    "# create spikes ...\n",
    "    \n",
    "plt.imshow(binary_traces, cmap = 'viridis', aspect='auto')\n",
    "plt.xlabel('time (frames)')\n",
    "plt.ylabel('cell #')\n",
    "\n",
    "\n",
    "resample_neural_activity = binary_traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training points = 0\n",
      "Training points with condition A = 0\n",
      "Number of testing points = 0\n"
     ]
    }
   ],
   "source": [
    "# separate training and testing data\n",
    "# all_data_id = ..\n",
    "\n",
    "#select temporal points for traning\n",
    "training_temporal_points = []\n",
    "\n",
    "#create temporal points for testing\n",
    "testing_temporal_points = []\n",
    "\n",
    "#resample labels as well\n",
    "training_labels = resample_labels[training_temporal_points]\n",
    "testing_labels = resample_labels[testing_temporal_points]\n",
    "\n",
    "print('Number of training points = '+f'{len(training_temporal_points)}')\n",
    "print('Training points with condition A = ' + f'{sum(training_labels)}')\n",
    "print('Number of testing points = ' + f'{len(testing_temporal_points)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize svm object\n",
    "clf = svm.SVC()\n",
    "# fit dataset\n",
    "clf.fit(training_data_set.T, training_labels.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate svm\n",
    "svm_prediction = clf.predict(testing_data_set.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# measure performance\n",
    "overlap = 0\n",
    "\n",
    "print(overlap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(2,1)\n",
    "# plot testing labes and svm prediction"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}