{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# ETHZ: 227-0966-00L\n", "# Quantitative Big Imaging\n", "# April 26, 2018\n", "\n", "## Dynamic Experiments" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "plt.rcParams[\"figure.figsize\"] = (8, 8)\n", "plt.rcParams[\"figure.dpi\"] = 150\n", "plt.rcParams[\"font.size\"] = 14\n", "plt.rcParams['font.family'] = ['sans-serif']\n", "plt.rcParams['font.sans-serif'] = ['DejaVu Sans']\n", "plt.style.use('ggplot')\n", "sns.set_style(\"whitegrid\", {'axes.grid': False})" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Literature / Useful References\n", "\n", "### Books\n", "- Jean Claude, Morphometry with R\n", " - [Online](http://link.springer.com/book/10.1007%2F978-0-387-77789-4) through ETHZ\n", " - [Buy it](http://www.amazon.com/Morphometrics-R-Use-Julien-Claude/dp/038777789X)\n", "- John C. Russ, “The Image Processing Handbook”,(Boca Raton, CRC Press)\n", " - Available [online](http://dx.doi.org/10.1201/9780203881095) within domain ethz.ch (or proxy.ethz.ch / public VPN) \n", "\n", " \n", "\n", "### Papers / Sites\n", "\n", "- Comparsion of Tracking Methods in Biology\n", " - Chenouard, N., Smal, I., de Chaumont, F., Maška, M., Sbalzarini, I. F., Gong, Y., … Meijering, E. (2014). Objective comparison of particle tracking methods. Nature Methods, 11(3), 281–289. doi:10.1038/nmeth.2808\n", " - Maska, M., Ulman, V., Svoboda, D., Matula, P., Matula, P., Ederra, C., … Ortiz-de-Solorzano, C. (2014). A benchmark for comparison of cell tracking algorithms. Bioinformatics (Oxford, England), btu080–. doi:10.1093/bioinformatics/btu080\n", "- Registration\n", " - https://itk.org/ITKSoftwareGuide/html/Book2/ITKSoftwareGuide-Book2ch3.html\n", "- Multiple Hypothesis Testing\n", " - Coraluppi, S. & Carthel, C. Multi-stage multiple-hypothesis tracking.\n", "J. Adv. Inf. Fusion 6, 57–67 (2011).\n", " - Chenouard, N., Bloch, I. & Olivo-Marin, J.-C. Multiple hypothesis\n", "tracking in microscopy images. in Proc. IEEE Int. Symp. Biomed. Imaging\n", "1346–1349 (IEEE, 2009)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ " \n", "# Previously on QBI ...\n", "\n", "- Image Enhancment \n", " - Highlighting the contrast of interest in images\n", " - Minimizing Noise\n", "- Understanding image histograms\n", "- Automatic Methods\n", "- Component Labeling\n", "- Single Shape Analysis\n", "- Complicated Shapes\n", "- Distribution Analysis" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Quantitative \"Big\" Imaging\n", "\n", "\n", "The course has covered imaging enough and there have been a few quantitative metrics, but \"big\" has not really entered.\n", "\n", "What does __big__ mean?\n", "\n", "- Not just / even large\n", "- it means being ready for _big data_\n", "- volume, velocity, variety (3 V's)\n", "- scalable, fast, easy to customize\n", "\n", "\n", "So what is \"big\" imaging\n", "\n", "- doing analyses in a disciplined manner\n", " - fixed steps\n", " - easy to regenerate results\n", " - no _magic_\n", "- having everything automated\n", " - 100 samples is as easy as 1 sample\n", "- being able to adapt and reuse analyses\n", " - one really well working script and modify parameters\n", " - different types of cells\n", " - different regions" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Objectives\n", "\n", "\n", "1. What sort of dynamic experiments do we have?\n", "1. How can we design good dynamic experiments?\n", "1. How can we track objects between points?\n", "\n", "How can we track shape?\n", "How can we track distribution?\n", "\n", "1. How can we track topology?\n", "1. How can we track voxels?\n", "1. How can we assess deformation and strain?\n", "1. How can assess more general cases?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Outline\n", "\n", "- Motivation (Why and How?)\n", "- Scientific Goals\n", "\n", "### Experiments\n", "\n", "- Simulations\n", "- Experiment Design\n", " \n", "### Object Tracking\n", "### Distribution\n", "### Topology\n", "\n", "\n", "### Voxel-based Methods\n", " - Cross Correlation\n", " - DIC\n", " - DIC + Physics\n", " \n", "### General Problems\n", " - Thickness - Lung Tissue\n", " - Curvature - Metal Systems\n", " - Two Point Correlation - Volcanic Rock" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Motivation\n", "\n", "\n", "- 3D images are already difficult to interpret on their own\n", "- 3D movies (4D) are almost impossible \n", "\n", "\n", "\n", "\n", "\n", "- 2D movies (3D) can also be challenging\n", "\n", "\n", "\n", "\n", "We can say that it looks like, but many pieces of quantitative information are difficult to extract\n", "- how fast is it going?\n", "- how many particles are present?\n", "- are their sizes constant?\n", "- are some moving faster?\n", "- are they rearranging?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Scientific Goals\n", "\n", "### Rheology\n", "\n", "Understanding the flow of liquids and mixtures is important for many processes\n", "- blood movement in arteries, veins, and capillaries\n", "- oil movement through porous rock\n", "- air through dough when cooking bread\n", "- magma and gas in a volcano\n", "\n", "\n", "### Deformation\n", "\n", "Deformation is similarly important since it plays a significant role in the following scenarios\n", "\n", "- red blood cell lysis in artificial heart valves\n", "- microfractures growing into stress fractures in bone\n", "- toughening in certain wood types" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Experiments\n", "The first step of any of these analyses is proper experimental design. Since there is always\n", "\n", "- a limited field of view\n", "- a voxel size\n", "- a maximum rate of measurements\n", "- a non-zero cost for each measurement\n", "\n", "\n", "There are always trade-offs to be made between getting the best possible high-resolution nanoscale dynamics and capturing the system level behavior. \n", "\n", "#### If we measure too fast\n", " - sample damage\n", " - miss out on long term changes\n", " - have noisy data\n", " \n", "#### Too slow\n", " - miss small, rapid changes\n", " - blurring and other motion artifacts\n", " \n", "#### Too high resolution\n", " - not enough unique structures in field of view to track\n", " \n", "#### Too low resolution\n", " - not sensitive to small changes" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Simulation\n", "\n", "In many cases, experimental data is inherited and little can be done about the design, but when there is still the opportunity, simulations provide a powerful tool for tuning and balancing a large number parameters\n", "\n", "Simulations also provide the ability to pair post-processing to the experiments and determine the limits of tracking." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# What do we start with?\n", "\n", "Going back to our original cell image\n", "\n", "1. We have been able to get rid of the noise in the image and find all the cells (lecture 2-4)\n", "1. We have analyzed the shape of the cells using the shape tensor (lecture 5)\n", "1. We even separated cells joined together using Watershed (lecture 6)\n", "1. We have created even more metrics characterizing the distribution (lecture 7)\n", "\n", "We have at least a few samples (or different regions), large number of metrics and an almost as large number of parameters to _tune_\n", "\n", "\n", "### How do we do something meaningful with it?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Basic Simulation\n", "\n", "\n", "We start with a starting image with a number of circles on a plane" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from skimage.io import imread\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "xx, yy = np.meshgrid(np.linspace(-1.5, 1.5, 15),\n", " np.linspace(-1.5, 1.5, 15))\n", "\n", "N_DISK_ROW = 2\n", "N_DISK_COL = 4\n", "DISK_RAD = 0.15\n", "disk_img = np.zeros(xx.shape, dtype=int)\n", "for x_cent in 0.7*np.linspace(-1, 1, N_DISK_COL):\n", " for y_cent in 0.7*np.linspace(-1, 1, N_DISK_ROW):\n", " c_disk = np.sqrt(np.square(xx-x_cent)+np.square(yy-y_cent)) < DISK_RAD\n", " disk_img[c_disk] = 1\n", "fig, ax1 = plt.subplots(1, 1)\n", "\n", "sns.heatmap(disk_img, annot=True, fmt='d', ax=ax1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "fig, c_ax = plt.subplots(1, 1, figsize=(5, 5), dpi=150)\n", "\n", "s_img = disk_img.copy()\n", "img_list = [s_img]\n", "for i in range(4):\n", " s_img = np.roll(s_img, -1, axis=1)\n", " s_img = np.roll(s_img, -1, axis=0)\n", " img_list += [s_img]\n", "\n", "\n", "def update_frame(i):\n", " plt.cla()\n", " sns.heatmap(img_list[i],\n", " annot=True,\n", " fmt=\"d\",\n", " cmap='nipy_spectral',\n", " ax=c_ax,\n", " cbar=False,\n", " vmin=0,\n", " vmax=1)\n", " c_ax.set_title('Iteration #{}'.format(i+1))\n", "\n", "\n", "# write animation frames\n", "anim_code = FuncAnimation(fig,\n", " update_frame,\n", " frames=len(img_list),\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", "plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Analysis\n", "\n", "- Threshold\n", "- Component Label\n", "- Shape Analysis\n", "- Distribution Analysis" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " area frame_idx label x y\n", "0 1 0 1 4.0 4.0\n", "1 1 0 2 6.0 4.0\n", "2 1 0 3 8.0 4.0\n", "3 1 0 4 10.0 4.0\n", "4 1 0 5 4.0 10.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.morphology import label\n", "from skimage.measure import regionprops\n", "import pandas as pd\n", "all_objs = []\n", "for frame_idx, c_img in enumerate(img_list):\n", " lab_img = label(c_img > 0)\n", " for c_obj in regionprops(lab_img):\n", " all_objs += [dict(label=int(c_obj.label),\n", " y=c_obj.centroid[0],\n", " x=c_obj.centroid[1],\n", " area=c_obj.area,\n", " frame_idx=frame_idx)]\n", "all_obj_df = pd.DataFrame(all_objs)\n", "all_obj_df.head(5)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, c_ax = plt.subplots(1, 1, figsize=(5, 5), dpi=150)\n", "c_ax.imshow(disk_img, cmap='bone_r')\n", "for frame_idx, c_rows in all_obj_df.groupby('frame_idx'):\n", " c_ax.plot(c_rows['x'], c_rows['y'], 's', label='Frame: %d' % frame_idx)\n", "c_ax.legend()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Describing Motion\n", "We can describe the motion in the above example with a simple vector\n", "\n", "$$ \\vec{v}(\\vec{x})=\\langle -1,-1 \\rangle $$\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Scoring Tracking\n", "\n", "In the following examples we will use simple metrics for scoring fits where the objects are matched and the number of misses is counted.\n", "\n", "There are a number of more sensitive scoring metrics which can be used, by finding the best submatch for a given particle since the number of matches and particles does not always correspond. See the papers at the beginning for more information" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Tracking: Nearest Neighbor\n", "\n", "While there exist a number of different methods and complicated approaches for tracking, for experimental design it is best to start with the simplist, easiest understood method. The limits of this can be found and components added as needed until it is possible to realize the experiment\n", "- If a dataset can only be analyzed with a multiple-hypothesis testing neural network model then it might not be so reliable\n", "\n", "We then return to _nearest neighbor_ which means we track a point ($\\vec{P}_0$) from an image ($I_0$) at $t_0$ to a point ($\\vec{P}_1$) in image ($I_1$) at $t_1$ by \n", "\n", "$$ \\vec{P}_1=\\textrm{argmin}(||\\vec{P}_0-\\vec{y}|| \\;\\forall \\vec{y}\\in I_1) $$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "format": "column", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "frame_0 = all_obj_df[all_obj_df['frame_idx'].isin([0])]\n", "frame_1 = all_obj_df[all_obj_df['frame_idx'].isin([1])]\n", "fig, c_ax = plt.subplots(1, 1, figsize=(5, 5), dpi=150)\n", "c_ax.matshow(disk_img > 1)\n", "c_ax.scatter(frame_0['x'], frame_0['y'], c='black', label='Frame: 0')\n", "c_ax.scatter(frame_1['x'], frame_1['y'], c='red', label='Frame: 1')\n", "dist_df_list = []\n", "for _, row_0 in frame_0.iterrows():\n", " for _, row_1 in frame_1.iterrows():\n", " seg_dist = np.sqrt(np.square(row_0['x']-row_1['x']) +\n", " np.square(row_0['y']-row_1['y']))\n", " c_ax.plot([row_0['x'], row_1['x']],\n", " [row_0['y'], row_1['y']], '-', alpha=1/seg_dist)\n", " dist_df_list += [dict(x0=row_0['x'],\n", " y0=row_0['y'],\n", " lab0=int(row_0['label']),\n", " x1=row_1['x'],\n", " y1=row_1['y'],\n", " lab1=int(row_1['label']),\n", " dist=seg_dist)]\n", "c_ax.legend()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "format": "column", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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distlab0lab1x0x1y0y1
01.414214114.03.04.03.0
11.414214124.05.04.03.0
23.162278134.07.04.03.0
35.099020144.09.04.03.0
45.099020154.03.04.09.0
\n", "
" ], "text/plain": [ " dist lab0 lab1 x0 x1 y0 y1\n", "0 1.414214 1 1 4.0 3.0 4.0 3.0\n", "1 1.414214 1 2 4.0 5.0 4.0 3.0\n", "2 3.162278 1 3 4.0 7.0 4.0 3.0\n", "3 5.099020 1 4 4.0 9.0 4.0 3.0\n", "4 5.099020 1 5 4.0 3.0 4.0 9.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_df = pd.DataFrame(dist_df_list)\n", "dist_df.head(5)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "\n", "fig, c_ax = plt.subplots(1, 1, figsize=(5, 5), dpi=150)\n", "c_ax.matshow(disk_img > 1)\n", "c_ax.scatter(frame_0['x'], frame_0['y'], c='black', label='Frame: 0')\n", "c_ax.scatter(frame_1['x'], frame_1['y'], c='red', label='Frame: 1')\n", "\n", "\n", "def update_frame(i):\n", " # plt.cla()\n", " c_rows = dist_df.query('lab0==%d' % i)\n", " for _, c_row in c_rows.iterrows():\n", " c_ax.quiver(c_row['x0'], c_row['y0'],\n", " c_row['x1']-c_row['x0'],\n", " c_row['y1']-c_row['y0'], scale=1.0, scale_units='xy', angles='xy',\n", " alpha=1/c_row['dist'])\n", " c_ax.set_title('Point #{}'.format(i+1))\n", "\n", "\n", "# write animation frames\n", "anim_code = FuncAnimation(fig,\n", " update_frame,\n", " frames=np.unique(dist_df['lab0']),\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", "plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, c_ax = plt.subplots(1, 1, figsize=(5, 5), dpi=150)\n", "c_ax.matshow(disk_img > 1)\n", "c_ax.scatter(frame_0['x'], frame_0['y'], c='black', label='Frame: 0')\n", "c_ax.scatter(frame_1['x'], frame_1['y'], c='red', label='Frame: 1')\n", "for _, c_rows in dist_df.groupby('lab0'):\n", " _, c_row = next(c_rows.sort_values('dist').iterrows())\n", " c_ax.quiver(c_row['x0'], c_row['y0'],\n", " c_row['x1']-c_row['x0'],\n", " c_row['y1']-c_row['y0'],\n", " scale=1.0, scale_units='xy', angles='xy')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "\n", "fig, c_ax = plt.subplots(1, 1, figsize=(5, 5), dpi=150)\n", "c_ax.matshow(disk_img > 1)\n", "\n", "\n", "def draw_timestep(i):\n", " # plt.cla()\n", " frame_0 = all_obj_df[all_obj_df['frame_idx'].isin([i])]\n", " frame_1 = all_obj_df[all_obj_df['frame_idx'].isin([i+1])]\n", " c_ax.scatter(frame_0['x'], frame_0['y'], c='black', label='Frame: %d' % i)\n", " c_ax.scatter(frame_1['x'], frame_1['y'],\n", " c='red', label='Frame: %d' % (i+1))\n", " dist_df_list = []\n", " for _, row_0 in frame_0.iterrows():\n", " for _, row_1 in frame_1.iterrows():\n", " dist_df_list += [dict(x0=row_0['x'],\n", " y0=row_0['y'],\n", " lab0=int(row_0['label']),\n", " x1=row_1['x'],\n", " y1=row_1['y'],\n", " lab1=int(row_1['label']),\n", " dist=np.sqrt(\n", " np.square(row_0['x']-row_1['x']) +\n", " np.square(row_0['y']-row_1['y'])))]\n", " dist_df = pd.DataFrame(dist_df_list)\n", " for _, c_rows in dist_df.groupby('lab0'):\n", " _, best_row = next(c_rows.sort_values('dist').iterrows())\n", " c_ax.quiver(best_row['x0'], best_row['y0'],\n", " best_row['x1']-best_row['x0'],\n", " best_row['y1']-best_row['y0'],\n", " scale=1.0, scale_units='xy', angles='xy')\n", " c_ax.set_title('Frame #{}'.format(i+1))\n", "\n", "\n", "# write animation frames\n", "anim_code = FuncAnimation(fig,\n", " draw_timestep,\n", " frames=all_obj_df['frame_idx'].max(),\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", "plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Computing Average Flow\n", "From each of these time steps we can now proceed to compute the average flow. We can perform this spatially (averaging over regions and time), temporally (averaging over regions), or spatial-temporally (averaging over regions for every time step)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average Flow:\n" ] }, { "data": { "text/plain": [ "dx -1.0\n", "dy -1.0\n", "dtype: float64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "average_field = []\n", "for i in range(all_obj_df['frame_idx'].max()):\n", " frame_0 = all_obj_df[all_obj_df['frame_idx'].isin([i])]\n", " frame_1 = all_obj_df[all_obj_df['frame_idx'].isin([i+1])]\n", " dist_df_list = []\n", " for _, row_0 in frame_0.iterrows():\n", " for _, row_1 in frame_1.iterrows():\n", " dist_df_list += [dict(x0=row_0['x'],\n", " y0=row_0['y'],\n", " lab0=int(row_0['label']),\n", " x1=row_1['x'],\n", " y1=row_1['y'],\n", " lab1=int(row_1['label']),\n", " dist=np.sqrt(\n", " np.square(row_0['x']-row_1['x']) +\n", " np.square(row_0['y']-row_1['y'])))]\n", " dist_df = pd.DataFrame(dist_df_list)\n", " for _, c_rows in dist_df.groupby('lab0'):\n", " _, best_row = next(c_rows.sort_values('dist').iterrows())\n", " average_field += [dict(frame_idx=i,\n", " x=best_row['x0'],\n", " y=best_row['y0'],\n", " dx=best_row['x1']-best_row['x0'],\n", " dy=best_row['y1']-best_row['y0'])]\n", "average_field_df = pd.DataFrame(average_field)\n", "print('Average Flow:')\n", "average_field_df[['dx', 'dy']].mean()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Spatially Averaging\n", "To spatially average we first create a grid of values and then interpolate our results onto this grid " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "format": "column", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Miniconda3\\envs\\qbi2018\\lib\\site-packages\\scipy\\interpolate\\_fitpack_impl.py:974: RuntimeWarning: No more knots can be added because the number of B-spline\n", "coefficients already exceeds the number of data points m.\n", "Probable causes: either s or m too small. (fp>s)\n", "\tkx,ky=1,1 nx,ny=7,9 m=32 fp=0.000000 s=0.000000\n", " warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n" ] }, { "data": { "text/plain": [ "(array(-1.), array(-1.))" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.interpolate import interp2d\n", "\n", "\n", "def img_intp(f):\n", " def new_f(x, y):\n", " return np.stack([f(ix, iy) for ix, iy in zip(np.ravel(x), np.ravel(y))], 0).reshape(np.shape(x))\n", " return new_f\n", "\n", "\n", "dx_func = img_intp(\n", " interp2d(average_field_df['x'], average_field_df['y'], average_field_df['dx']))\n", "dy_func = img_intp(\n", " interp2d(average_field_df['x'], average_field_df['y'], average_field_df['dy']))\n", "dx_func(8, 8), dy_func(8, 8)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g_x, g_y = np.meshgrid(np.linspace(average_field_df['x'].min(),\n", " average_field_df['x'].max(), 10),\n", " np.linspace(average_field_df['y'].min(),\n", " average_field_df['y'].max(), 10))\n", "fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 5, figsize=(24, 4))\n", "sns.heatmap(g_x, ax=ax1, annot=True)\n", "ax1.set_title('X Position')\n", "sns.heatmap(g_y, ax=ax2, annot=True)\n", "ax2.set_title('Y Position')\n", "\n", "sns.heatmap(dx_func(g_x, g_y), ax=ax3, annot=True)\n", "ax3.set_title('$\\Delta x$')\n", "sns.heatmap(dy_func(g_x, g_y), ax=ax4, annot=True)\n", "ax4.set_title('$\\Delta y$')\n", "ax5.quiver(g_x, g_y, dx_func(g_x, g_y), dy_func(g_x, g_y),\n", " scale=1.0, scale_units='xy', angles='xy')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Temporarlly Averaging\n", "Here we take the average at each time point" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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frame_idxdxdy
00-1.0-1.0
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\n", "
" ], "text/plain": [ " frame_idx dx dy\n", "0 0 -1.0 -1.0\n", "1 1 -1.0 -1.0\n", "2 2 -1.0 -1.0\n", "3 3 -1.0 -1.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "temp_avg_field = average_field_df[['frame_idx', 'dx', 'dy']].groupby(\n", " 'frame_idx').agg('mean').reset_index()\n", "temp_avg_field" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,0,'Timestep')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 4))\n", "ax1.plot(temp_avg_field['frame_idx'], temp_avg_field['dx'], 'rs-')\n", "ax1.set_title('$\\Delta x$')\n", "ax1.set_xlabel('Timestep')\n", "ax2.plot(temp_avg_field['frame_idx'], temp_avg_field['dy'], 'rs-')\n", "ax2.set_title('$\\Delta y$')\n", "ax2.set_xlabel('Timestep')\n", "ax3.quiver(temp_avg_field['dx'], temp_avg_field['dy'],\n", " scale=1, scale_units='xy', angles='xy')\n", "ax3.set_title('$\\Delta x$, $\\Delta y$')\n", "ax3.set_xlabel('Timestep')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Spatiotemporal Relationship\n", "We can also divide the images into space and time steps" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Miniconda3\\envs\\qbi2018\\lib\\site-packages\\scipy\\interpolate\\_fitpack_impl.py:974: RuntimeWarning: No more knots can be added because the number of B-spline\n", "coefficients already exceeds the number of data points m.\n", "Probable causes: either s or m too small. (fp>s)\n", "\tkx,ky=1,1 nx,ny=5,5 m=8 fp=0.000000 s=0.000000\n", " warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "\n", "g_x, g_y = np.meshgrid(np.linspace(average_field_df['x'].min(),\n", " average_field_df['x'].max(), 4),\n", " np.linspace(average_field_df['y'].min(),\n", " average_field_df['y'].max(), 4))\n", "\n", "frames = len(sorted(np.unique(average_field_df['frame_idx'])))\n", "fig, m_axs = plt.subplots(2, 3, figsize=(14, 10))\n", "for c_ax in m_axs.flatten():\n", " c_ax.axis('off')\n", "[(ax1, ax2, _), (ax3, ax4, ax5)] = m_axs\n", "\n", "\n", "def draw_frame_idx(idx):\n", " plt.cla()\n", " c_df = average_field_df[average_field_df['frame_idx'].isin([idx])]\n", " dx_func = img_intp(interp2d(c_df['x'], c_df['y'], c_df['dx']))\n", " dy_func = img_intp(interp2d(c_df['x'], c_df['y'], c_df['dy']))\n", " sns.heatmap(g_x, ax=ax1, annot=False, cbar=False)\n", " ax1.set_title('Frame %d\\nX Position' % idx)\n", " sns.heatmap(g_y, ax=ax2, annot=False, cbar=False)\n", " ax2.set_title('Y Position')\n", "\n", " sns.heatmap(dx_func(g_x, g_y), ax=ax3, annot=False, cbar=False, fmt='2.1f')\n", " ax3.set_title('$\\Delta x$')\n", " sns.heatmap(dy_func(g_x, g_y), ax=ax4, annot=False, cbar=False, fmt='2.1f')\n", " ax4.set_title('$\\Delta y$')\n", " ax5.quiver(g_x, g_y, dx_func(g_x, g_y), dy_func(g_x, g_y),\n", " scale=1.0, scale_units='xy', angles='xy')\n", "\n", "\n", "# write animation frames\n", "anim_code = FuncAnimation(fig,\n", " draw_frame_idx,\n", " frames=frames,\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", "plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Longer Series\n", "We see that this approach becomes problematic when we switch to longer series\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from skimage.morphology import label\n", "from skimage.measure import regionprops\n", "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "fig, c_ax = plt.subplots(1, 1, figsize=(5, 5), dpi=150)\n", "\n", "s_img = disk_img.copy()\n", "img_list = [s_img]\n", "for i in range(8):\n", " if i % 2 == 0:\n", " s_img = np.roll(s_img, -2, axis=0)\n", " else:\n", " s_img = np.roll(s_img, -1, axis=1)\n", " img_list += [s_img]\n", "\n", "import pandas as pd\n", "all_objs = []\n", "for frame_idx, c_img in enumerate(img_list):\n", " lab_img = label(c_img > 0)\n", " for c_obj in regionprops(lab_img):\n", " all_objs += [dict(label=int(c_obj.label),\n", " y=c_obj.centroid[0],\n", " x=c_obj.centroid[1],\n", " area=c_obj.area,\n", " frame_idx=frame_idx)]\n", "all_obj_df = pd.DataFrame(all_objs)\n", "all_obj_df.head(5)\n", "\n", "\n", "def update_frame(i):\n", " plt.cla()\n", " sns.heatmap(img_list[i],\n", " annot=True,\n", " fmt=\"d\",\n", " cmap='nipy_spectral',\n", " ax=c_ax,\n", " cbar=False,\n", " vmin=0,\n", " vmax=1)\n", " c_ax.set_title('Iteration #{}'.format(i+1))\n", "\n", "\n", "# write animation frames\n", "anim_code = FuncAnimation(fig,\n", " update_frame,\n", " frames=len(img_list),\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", "plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "\n", "fig, c_ax = plt.subplots(1, 1, figsize=(5, 5), dpi=150)\n", "c_ax.matshow(disk_img > 1)\n", "\n", "\n", "def draw_timestep(i):\n", " # plt.cla()\n", " frame_0 = all_obj_df[all_obj_df['frame_idx'].isin([i])]\n", " frame_1 = all_obj_df[all_obj_df['frame_idx'].isin([i+1])]\n", " c_ax.scatter(frame_0['x'], frame_0['y'], c='black', label='Frame: %d' % i)\n", " c_ax.scatter(frame_1['x'], frame_1['y'],\n", " c='red', label='Frame: %d' % (i+1))\n", " dist_df_list = []\n", " for _, row_0 in frame_0.iterrows():\n", " for _, row_1 in frame_1.iterrows():\n", " dist_df_list += [dict(x0=row_0['x'],\n", " y0=row_0['y'],\n", " lab0=int(row_0['label']),\n", " x1=row_1['x'],\n", " y1=row_1['y'],\n", " lab1=int(row_1['label']),\n", " dist=np.sqrt(\n", " np.square(row_0['x']-row_1['x']) +\n", " np.square(row_0['y']-row_1['y'])))]\n", " dist_df = pd.DataFrame(dist_df_list)\n", " for _, c_rows in dist_df.groupby('lab0'):\n", " _, best_row = next(c_rows.sort_values('dist').iterrows())\n", " c_ax.quiver(best_row['x0'], best_row['y0'],\n", " best_row['x1']-best_row['x0'],\n", " best_row['y1']-best_row['y0'],\n", " scale=1.0, scale_units='xy', angles='xy', alpha=0.25)\n", " c_ax.set_title('Frame #{}'.format(i+1))\n", "\n", "\n", "# write animation frames\n", "anim_code = FuncAnimation(fig,\n", " draw_timestep,\n", " frames=all_obj_df['frame_idx'].max(),\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", "plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average Flow:\n", "dx -0.500\n", "dy -0.625\n", "dtype: float64\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Miniconda3\\envs\\qbi2018\\lib\\site-packages\\scipy\\interpolate\\_fitpack_impl.py:974: RuntimeWarning: No more knots can be added because the number of B-spline\n", "coefficients already exceeds the number of data points m.\n", "Probable causes: either s or m too small. (fp>s)\n", "\tkx,ky=1,1 nx,ny=10,11 m=64 fp=5.301937 s=0.000000\n", " warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n", "C:\\Miniconda3\\envs\\qbi2018\\lib\\site-packages\\scipy\\interpolate\\_fitpack_impl.py:974: RuntimeWarning: No more knots can be added because the number of B-spline\n", "coefficients already exceeds the number of data points m.\n", "Probable causes: either s or m too small. (fp>s)\n", "\tkx,ky=1,1 nx,ny=10,11 m=64 fp=22.666667 s=0.000000\n", " warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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48eK0adOG2NhYtm7dSmhoKKGhoaxYsYIVK1Zgs9moWbMmwcHBBAcHU6xYMU+HfFcP0+ftfkmRVYg0wILV0yEIIR4yZucVrfUJoJrj6SN32OatRE/fMDUAIYRHSaszIYSZJKeI1KhXrx5btmyhQYMGfPrppxw5coSvvvoKb29vT4eWKjabjRo1alCjRg2GDh3KkSNHWLp0KaGhoWzatIkNGzbw0UcfUapUKYKDgwkJCaFKlSrYbDLHS3ogVR8h3MRmtTr9EEIIZ0hOEUKYycdqceohhBDOcDanSF4Rd1KmTBm2bdtG1apVmTZtGi+88EJCV/uHRYkSJfjoo4/YsGED//33H99++y1NmjThr7/+YujQoVSvXp38+fPzzjvvsGTJEiIiIjwdsrgL+eYlhJtY7uM/IYRwhuQUIYSZbBbnHkII4Qxnc4rkFXE3efLkYd26dTRt2pS1a9dSvXp1jh075umwXCJHjhy89dZbLFy4kHPnzrFy5Urat2+Pt7c306dP5+WXXyZnzpw0bNiQKVOmcOaMTIeQ1kiRVQghhBBCCCHFECGEqaTIKsySOXNm5s2bR8+ePTl8+DDVqlV7qCb6Somvry8vvPACX375JadOnWLnzp18/vnnlCxZkmXLlvHee++RP39+qlWrxqBBg9i/fz92u93TYWd4UmQVwk2sFqvTDyGEcIbkFCGEmWwWi1MPIYRwhrM5RfKKcIbVauWLL77gm2++4fLly9StW5e5c+d6Oiy3sFgsPP744/Tr14/du3dz8uRJJkyYQL169di1axefffYZZcuW5dFHH6VLly6sXbuWmJgYT4edIaWLia/27j/A6PFf8s3kiW4/dmxcHGMWLuBUeDhWq5WPX21G/hw5E9av272LRZs3YbNaKJo3P50aNcbqhvHvqndpys3r0QBEXrjC/gXrEtaVCqlJtiJ5ib1hnFS7ZqxM2NZVKtWrROV6lQHw8vEi/6P5GfDqAK5HXAfgqVeeokKdCtjtdtZ+t5YDWw64NJ6kylYoRZfubXnntS4ULFyAASO7gx3+0Mf5ovdot9zxSc8zIT4s4uLiGDh0BProUXy8fejXqweFCnp2VkSJSWJKDckr7jdlzgLWbdlGzM2bvBZSnyb16yXbps+I8WQNCKBr27ddEkNcXBxfDBrMkSNH8PHxoc/nvSlUqFDC+m9nzmTlqlVYLVbeeac1z9Stm7Du+PHjtHirJWt/XkOmTJlcEl+8vfv3M2bcRKZ//dVty5cuX8GMWXPIksWfkAYNaPxysEuOb7xPg4z3ydubPn363PY+zZo1i1WrVwNQq2ZN2rVrh91u57l69Sjs2K5c+fJ80LmzS+JLyepVK5k3dw5Wq5VixUvwaY+ebrmujCetyTzL6uXF29+OJEeRIOJiY5n9bg/O6rTTJfb69Si6f9CBrj16U6hIUbcf/8b16/T9qAMdPulNUOEiKW5zYM8uRg/ozdSFy90bHFBs8HhiI42xEqP/O8vfk0e7PYY7ef3xAkRExxK6/1+3HldyinCFt99+myJFitC4cWOaN2/OH3/8Qe/evTPUdXGhQoXo0KEDHTp04MqVK6xevZrQ0FCWL1/O2LFjGTt2LFmzZuWll14iODiYF198kaxZs3o67AwhzRdZp8+czdIVq/DLnNkjx9960CgGjunQiT3H/mDy0lD6vd0agBsxMcxYvYrJXT/G18eHQXNmse3QQZ58rIxLY7J6GbPK/T5pSYrrAwvkYseUZcREXndpHInt/GknO3/aCcDLnV5mx6odCQVWX39fajSqwbCWw/Dx9aHLpC5uLbK2avs6DRrXIyoyCoBuvTswYcQ0dmwNo9cXXalTryZrV29yeRzWDJT006q16zdy40Y0c6ZPYc++/QwfM47xI4dJTBJTuo1J8op7/b57L7v3H2L2hOFEXb/BjPmLkm2zIHQlR/88QeXyZV0Wx9p164iOjmbWzG/Zu3cvI0eNZuwY48v8latXmfvdPJaFLiEqKopXm72WUGS9du0aI0eNdsusvNO/ncWyFSvJnNn3tuUXL11iwleTWTBnJgEBAbz7fkeqVqlMgfz5TY9h7bp1RN+4wayZMx3v0yjGjhkDwOnTp1mxciWzZ83CYrHQqlUr6tati6+vL6VKlWL8uHGmx3Mv169fZ9KXE/lu/vf4Zs5Mrx7d2bxpI089XdttMUhrMs8q+1IdbF42htdoQqlnaxLyxcd8/Up7T4cFwJFDBxk7fBDn/vvPI8f/4/BBJo0awvnws3fc5tx//7Jk/hxiY2+6MTKDxZFXjw/o7vZj30uNoo+QP9CXo+fcP1mO5BThKrVr12br1q3Ur1+fPn36cPToUaZOneryG8hpUWBgIE2bNqVp06bExMSwZcsWQkNDWbJkCd999x3fffcdXl5e1K5dm+DgYBo2bEiRIkU8HfZDy+lb40opj/Q3LBhUgDHDBnvi0ADUKFOWLk2aAnD24kWyZQlIWOdtszGmQyd8fXwAo9WrO764BOTLgc3bi8rvNuSJtsFkLZTn1koL+OXMymOv1KZqh0YUeKKky+NJLKhEEHkK52Hbim0Jy6KvR3Pp7CV8fH3w8fXBHufecUJO/fU3H7btlfC8VNkS7NgaBsDm9duoVrOSW+KQia9u54mcsmvPHmpWrwpA+bJlOHjosLtDSEZico7ElDLJKbe4I6ds2b6LEv8rQufeX9ChZ3+efrLKbevDDhxiz0FN04YvujSO3bvDqF69OgDlypXjwMGDCesy+/qSL19eoqKiiIqKwuJoBWm32+k/YCCdOnYks69vivs1U8GgAowePiTZ8tOn/0aVKE7WrFmxWq2UKV2avfv2uySG3bt3U71GDcDxPh24dYM3T548fDlxIjabDavVSszNm/hkysTBQ4f477//eKdNGzp06MCJEydcEltKfHx8mPrNt/g6GhbcjL2Jj+Ma020xyCzgCTxxnXL2yJ9YvbywWCz4BmYhNsb9xcI7iYmJps/gERS8QwtS1x8/hu4Dh1GgUMrHj75xg0kjh9D2w0/dG5iDb6H/YfXJRJEeAynaazCZiymPxJFU0Uf8KPqIH5uPX/DI8Z3NKRklrwhzlShRgt9++41atWoxe/Zsnn32Wc6dO+fpsDzK29ub2rVrM2rUKP744w8OHDjA4MGDeeKJJ/jll1/o3LkzRYsWpXz58vTu3Zvt27cTFxfn6bAfKndtyaqU+h8wCqgM3HRcbOwDPtRaH3FDfDxXtw5//+PZGdNsNhvD5n/Hr/v30evNlgnLrVYr2QOMouuPWzYRFR1NpeIlXB5PbMxNjm8I4/Tvh/DLmZXKbRqwadhc7HF2bD7enNyyjxMb92CxWqjSNoTLp8O5dua8y+MCqPN6HX6e9XOy5ZfCL/HR1I+wWq2sm7cuhVe6zs8rN5I/KG/C88TdCCIjIskS4O+WOGRcRM/nlIiISLL4Z0l4brXauHnzJl5enmvULzFJTKmR0fOKu3PKxctXOHM2nImDP+fvM2fp+NkAls2chMViIfz8BSbOmMu4AZ+xat1msw99m4iICAKy3Prs2Wy3f/by5slLoyavEBsbyzutWwEwafJkatWqiVKuv04BeO6Zuvz9zz/JlhcuVJBjfx7n/Pnz+Pn7s2379oSu+Wa72/vk7e1N9uzZsdvtjBo9mpIlS1KkcGHOnzvHO61bG2Oc7d5Nz5493Tbem9VqJUeOHADMn/cdUZFRVK32pFuOHS+jd+319HXKjWuR5CgSRN/Dv5Al5yNMbNDa1Yd02mPlKnj0+KXKlr/r+iljhxPSrAU5cuV2U0S3i4u+QfjyH7i4djU+eQtQpHt/jnR9FzxYvAj09eKl0nn4+rcTPB6UzSMxZPSc4moxMTHJGnmltOxhljNnTtasWUObNm2YPXs21apVY/ny5SiVNm50eJLFYqF06dKULl2a7t27c/bsWZYvX86SJUtYs2YNAwcOZODAgeTPn5+GDRsSHByc0KtHPLh7fROcCvTQWic0S1RKVQO+AWq4MrC05pNmr3Phpfp0Hj+OKR93I7OP0Qw9Li6OqSuWcTo8nM/fbOmWcUAiwi8Ree4yAJHnLhMdcZ1MAf5cv3yN2OibnNy8lzjHne/zx/4mMF8OtxRZff19yV0wN8f23D52VMkqJQl8JJAhbxotWtoMacOJAyc4pU+5PKaU2BNd7Pj5+3H1yjW3HNfMz4ZSKhPGefg/4ArQQWt9NNH6d4G2wE1goNZ6mWkHTx2P5hR/fz8iIiMTnsfZ4zxapAOJyVkSU8oy0thTd+DWnJItMJD/FQrCx9ubooWC8PHx5sKly+TIno3V6zdz6fIV2n/al3MXLhF14wZFCwXR6MVnzQ4Df39/IiJvdfuMi7v12duy5VfCz51jxbKlALR/vwMVKlRg+YoV5Mmdhx9/XMK58+dp1/59vpk+zfTY7iUwMJBuXbvQ9ZPu5Mmdm1IlFdmzuWaMMH9/fyIiUn6fAG7cuEGfPn3w8/fns549AShdunTCNo9XrMh/4eHY7XaXnmtfTZzAnrDdAEyc9DUTxo3lr5MnGTp8hNvPcena69nrlGc+fIeDqzfyY89hZA/Kx4dr59K/7AvcvHHD1YdO0TeTv+TAXqP319BxX2Gz2dx6/DlTv+LQPuP4/UZ9ecfjXzgXzsG9uznz9ynmfzuFa1euMLJfTz7qM8htsUafOU30v8aNpeh//yb26hW8sz1CzAX3t6prUDoPj+b0J39WXy5ExvB+jaIE+HrhY7Ny9uoNtp286LZYJKe41qxZs9i+fTsRERFYLBbatWtHlSpVaN067dygcYdMmTIxc+ZMSpQoweeff061atVYtGgRderUAXD53/H0Ik+ePLRu3ZrWrVsTGRnJL7/8QmhoKEuXLmXy5MlMnjwZf39/6tWrR3BwMPXr1ydXrlyeDjvdude3Qd/EFxkAWuutGemuwM87dxB++TKv132GTN4+WCwWbIlaDo1dtBBvLy/6tmzltokJgqqUIiBvDg4u3kimQD+8fH24cdX4EuGfKxvlmz/Hr2O+x2KxkL1IPv7Z4Z4urP8r9z+O7jqabHnk1UhiomO46Sj8Rl2LwjeL5+6OHD7wB5WrVWDH1jBq1q7K9t92u+W4Jo+d+C5wTWtdTRkn5ATgeQClVF6gM0YrDF9gs1JqjdbaM1fot/NoTqlYvhzrN27hheeeYc++/RR/9FG3HFdikphcRcZkdW9OebxsaWb/EErLVxsRfv4CUVE3yBZo9Ghp0SSYFk2MCZwWr/yZ43+ddkmBFaBihQps2LiR5+vVY+/evRQvVixhXWBgAL6ZMuHjY1yzBAQEcPXqVZaFhiZs8+JL9Zn01Zcuie1ebt68yd59+/lmymRiY2N59/2OdO7wvkuOVbFCBTZs2MDzzz9vvE/Fiyess9vtfNCli/FltFWrhOWTJk8mW9astGrVCq01efPmdfkXs/YdOib8/MWAfvh4+zB81Gi3TngVT3KKZ69TIi9eThgiIOLCJWze3lhtnuux0Kqta85NZzVv49x4tI/kzMXE2T8kPG/V6Hm3FlgBsteuh2+hIvwz/Uu8sj+CNbMfMZc800V/2cHk49ZWLZydPAGZ3FpgBckpSqmqwFCtdW2lVAVgPBAL3ADe0lrfeZBhJzz33HO88847Cc8nT55Mr1697vKKh5fFYqF3794UK1aMVq1aUa9ePb7++mtatWpF7969GTBggBRaE/Hz86Nhw4Y0bNiQuLg4tm/fzpIlSwgNDWXx4sUsXrwYi8VC9erVCQkJITg4WFoHO+leRdY9SqnpwCrgMhAAvATsdXVgaUWNsmUZuWA+Xb+aSGxsLO2DQ9i8fx9RN25QIqggq7b/TpkiRfnk60kAvFyzFjXLuG6yC4DTvx+ibLO6VH2/EXa7nf0L1lG4Zjkizl0m/OAJzuw+wpOdmhAXG8c/OzXXzrrnj2muoFxcOHPrYqJWk1qc/+c8B387yCl9io7jOmK32zmx/wRHdyYvxrrLiIET6TOkG94+3vz5x0nWrNjgluOaPC5iaWAlgNZaK6VKJVpXBdjiKKreUEr9AZQDtpsZwAPyaE55pvbT/LZtOy1av4cdOwM+/8wdh5WYJCaXySjjrd6FW3NK7epV2LF3P83adcVuj6NXl3asXLeJyKjrvNqNImpMAAAgAElEQVTwBVccMkV169bht61beavl28ZYq/36MnPWbAoVLEjt2k+zdds2WrzVEqvFQsWKFXiyWjW3xXYny1etJioyklcaN8Lb25vXWrTEJ5MPbzV/g+zZXNONtW7dusb79NZb2IH+/foxc9YsChUsSGxcHDt37iQmOpotm43hHTp37sw7rVvTs2dPNm3ejM1mY0D//i6JLSWHDx0i9McfqVDxcd5v+y4AzV5vTh3HxGXuYJG+vR69Tvll9DTemj6MjzYuwMvHhx97DiPaMXGrSO7qlctMHDaQ7gOHezoULq77iaD2Xflfn+GAnb8nj/HoUAFpRUbOKUqpT4A3gfguFWOBTlrrMKVUW+BToGtqjlGwYEEqVKhAWJjR4rtixYoEBQWlZpfp3uuvv07hwoUJCQmhdevW7N27l3HjxlG8eHFatmx57x1kQFarlapVq1K1alUGDRrEsWPHWLp0KaGhoWzcuJEtW7bwySefUKJECYKDgwkODubJJ5/0eK/CtMpit995EiKllAV4GagJBGJ0S94CLNZa33P2ougr5907w5ETzqz7zdMhJHNo81+eDiGZtWEnPB3CbVYd2XbvjTxg78kNTl85BFdo4fT5EBo2+677VUq9B1QF2jj+3QL4aK1jlVItgLJa608d284EZmqtkw+W62YPY04Rwkw+gTnu69uIs3nlXjklvUptTrl55miayyk3s+b3dAjJWGJjPB1CMnavtDd78I00WE/J6p/5vs79VY9WdOqceOHYbskpKWhnKZLmckqPc66ZWC41ImLS3skS++Frng4hmcmvJJ880NMmNCnnkpwC984rSqkeQDDgA3yptXb/mDf3QSnVBOMGzSxH78N8WuszjnUdgAJa65732s/OnTvtlSrdeaLmzz//nAEDBiT83K9fP1PiT+/+/PNP6tevz+HDRq/e7Nmzc+jQIfLkyXOPV4rELl68yMqVKwkNDWXlypVcuXIFgBw5clC/fn2Cg4OpV68eAQEB99jTw2Xnzp1UqlQpxZx119Kz42JiseMhhEgFk7vLTAdKAeswLv53aq1jHeuuYLS8iBcAXDLz4A9KcooQ5sro3fAkpwhhLrNbnSmlcgM7gecwxomfAdiB/RjjyaepapvkFCHMZVZOUUrVBqpjjI3sB3xsyo5dSGv9g1KqSKLn8QXW6kBH4CkzjtOgQYOEImuDBg3M2GW6Z7fb2bFjB/7+tya4vnjxIp07d2b+/PkejCz9yZ49O2+88QZvvPEG0dHRbNiwgdDQUEJDQ5k5cyYzZ87Ex8eHZ555huDgYBo2bEiBAgU8HbZHZexpiYVwI8t9/OeEJ4DNWuvaGF8E/ky07negllLKVymVFaMYm/aaMQghUs3EnCKEEFhtFqcezlBKeQOTgfj+7qOAXlrrWoAFCHHJLyGESDOczSlO5JXngX0Y33uWAmllUt/7opRqBkwC6mutw83YZ+XKlcmbNy958+blbi1eMxKLxULVqlWpXbs2gYGBCcsXLFhAaKIx6sX98fHx4bnnnmP8+PGcOHGCsLAw+vfvT7ly5Vi5ciXt27cnKCiIypUr079/f8LCwrhbz/mHlRRZhXATq8Xq9MMJR4H2SqnfgAFAV6VUV6VUsNb6X2AcsAlYC3ymtb7usl9MCOExJuYUIYTAYrU69XDSCIxiwj+O55WA+IHwVwKumRlOCJFmOJtTnMgrOTEm9W0KtAPmOIb3SDccQ7p1BGprrf+81/bOslqt1K9fnwYNGnhkwsS0qnDhwowYMYLTp08zbtw4HnVMUNu+fXsuX77s4ejSP4vFQvny5enduzfbt2/n9OnTfPXVV7z44ovs27ePPn36ULFiRYoUKUKnTp1Ys2YN0dHRng7bLWSkWiHSIa31OZJ/ORmVaP0UYIpbgxJCCCFEuuZsK9V7UUq9DYRrrVc7xlEEsCQa1/QqkNWUgwkh0iyzcgpwHjistY4GtFLqOpAL+M+sA7iSUsqG0QjmL2CRY5b2DVrrPmbsv2HDhlgy+BBSdxIQEECnTp14//33WbZsGWPGjOGTTz5h8uTJng7toVKgQAHatWtHu3btuHbtGj/99BOhoaEsW7aMCRMmMGHCBAICAnjxxRcJDg7mxRdf5JFHHvF02C4hRVYh3ET+8AkhzCZ5RQhhJhPHZG0N2JVSzwIVgJlA7kTr08x48UII1zExp2wGPlBKjQLyAf4Yhdc0TWt9AqjmeOqyitKzz0rHgHux2WyEhIQQEhLCnj17iIyMxM/Pz9NhPZSyZMlC48aNady4MbGxsfz222+EhoayZMkSFixYwIIFC7DZbNSqVYvg4GCCg4MTWho/DKTIKoSb2KTLrhDCZJJXhBBmsvnYTNmP1jphQhel1HqM7r3DlVK1tdbrgRcxJu8UQjzETMwpy5RST2HMPWHFmDgv9h4vyzAST/Ak7q18+fKeDiHDsNls1KxZk5o1azJs2DC01gkTZ23cuJH169fTtWtXHnvssYSCa5UqVdL10BdSZBXCTTL6LOBCCPNJXhFCmMlidWlO+QiYopTyAQ4BC115MCGE55mZU7TWn5i2MyGERyil6NatG926dSM8PJwVK1YQGhrK6tWrGTx4MIMHDyZPnjw0aNCA4OBgnn322XTX4liKrEIIIYQQQgisNvNbjmitayd6+rTpBxBCpFmuyClCiIdDrly5aNmyJS1btuT69eusXbs2oZXrtGnTmDZtGpkzZ+a5554jODiYBg0akCdPHk+HfU9SZBXCTWTsRCGE2SSvCCHMZOL4iUIIITlFCOEUX19fXnrpJV566SW+/PJLdu3alVBwjX9YLBaqVq2aMKxA6dKl0+R3ISmyCuEm0q1XCGE2yStCCDNJQUQIYSbJKUKI+2W1WqlcuTKVK1emf//+nDx5kqVLl7JkyRLWr1/P1q1b6dmzJ48++mhCwbVmzZp4eaWN8qa03xfCTSz38Z8QQjhDcooQwkxWm9WphxBCOMPZnCJ5RQhxJ4ULF6Zjx46sWbOGc+fOMW/ePN544w3Onz/P6NGjqVOnDrlz56ZFixYsWLCAK1eueDTetFHqFSIDkBZnQgizSV4RQpjJ5i2FDiGEeSSnCCHMlDVrVpo1a0azZs2IiYlh8+bNhIaGsmTJEubMmcOcOXPw9vamdu3aBAcH07BhQwoXLuzWGCXrCeEmFovF6YcQQjhDcooQwkwWm9WphxBCOMPZnCJ5RQhxv7y9valTpw6jR4/m2LFj7Nu3jy+++ILHH3+cNWvW0KlTJ4oUKULFihXp06cPO3fuxG63uzwuackqhJtIizMhhNkkrwghzGSV8ROFECaSnCKEcAeLxUKZMmUoU6YMPXv25MyZMyxfvpzQ0FDWrFlDWFgY/fv3p0CBAjRs2JDg4GDq1KmDr6+v6bHILSMh3ETGZBVCmE1yihDCTBabxamHEEI4w9mcInlFCGGmfPny0aZNG0JDQzl//jw//vgjrVu3Jjo6mkmTJvHSSy+RM2dOmjRpwsyZMzl37lzCa3fu3MmcOXMeuNWrtGQVwk2kxZkQwmySV4QQZpIuu0IIM0lOEUJ4mp+fHyEhIYSEhBAbG8vvv/9OaGgooaGhLFq0iEWLFmG1WqlRowbBwcE88cQTtGjRgu+//55JkyaRN2/e+zqeFFmFcBMZF1EIYTbJK0IIM8kkNUIIM0lOEUKkJTabjSeffJInn3ySwYMH88cff7B06VJCQ0PZtGkTmzZtSth2yZIlbNy4kfHjx/PGG284/b1Lsp4QbmK1WJx+CCGEMySnCCHMZLVZnXoIIYQznM0pkleEEJ5QrFgxPvzwQ9atW8fZs2eZNWsWxYoVS1h/8eJFWrRoQaNGjThz5oxT+5SWrEK4ibQ4S70z637zdAhCuFThkAb3tb3kldSJyV7Q0yGkC3arzdMhpAs+D8HbJOMips6YyEOeDiGZGNdPpHzfsvmmvZPFNu0HT4eQzBBPB2ACySlCiPQiR44cZMqUiT///DPZuqStWu9GiqxCuIlMPiOEMJvkFSGEmWT8RCGEmSSnCCHSk+eff57w8HDsdvttj7i4uNt+vhspsgohhBBCCCGwWKUgIoQwj+QUIUR6EhgYmOp9SJFVCDexSoMzIYTJJK8IIcwk4yIKIcwkOUUIkdFIkVUIN7HJnVwhhMnMzitKqarAUK11baVUMWAGYAf2Ax201nGJts0MzAZyA1eBllrrcFMDEkK4ldVHvhoIIcwjOUUIkdFI1UcIN7FYLE4/hBDCGWbmFKXUJ8BUwNexaBTQS2tdC7AAIUle0h7Y51g/E+hlyi8lhPAYi9Xq1EMIIZzhbE6RvCKEeFhINhNCCCEEwDGgcaLnlYANjp9XAs8m2b4msOou64UQ6YzFZnPqIYQQznA2p0heEUI8LKT9vhBuYpVZwIUQJjMzr2itf1BKFUm0yKK1tjt+vgpkTfKSQODyXdYLIdIZmQlcCGEmySlCiIxGiqxCuIkMAyCEMJuL80pcop8DgEtJ1l9xLL/TeiFEOmOVLrtCCBNJThFCZDRSZBXCTaxSZBVCmMzFeWW3Uqq21no98CKwLsn6LcBLwO+O9ZtcGYwQwvWk1ZkQwkySU+5vUlEhRPonRVYh3ERqrEIIs7k4r3wETFFK+QCHgIUASqmfgAbAV8C3SqnNQDTwhkujEUK4nNVbvhoIIcyT0XOKY1LRN4EIx6L4SUXXK6UmYUwquthT8QkhzJexs54QbiQtWYUQZjM7r2itTwDVHD8fAZ5OYZt6jh+jgaamBiCE8ChpdSaEMJPklIRJRWc5niedVLQeUmQV4qEiRVYh3MQiE18JIUwmeUUIYSYpiAghzJTRc8oDTCoqhEjnpMgqhJvIxFdCCLNJXhFCmMmawQsiQghzSU5J5l6Tigoh0jkpsgrhJmZ261VKvQ287XjqC1QA8mqtLznWjwNqYNwhBQjRWl82LQAhRJogw5AIIcxkkZnAhRAmkpySzL0mFX0gkZGR+Pn5mbGrNOPmzZvYbLaHqkHB33//Tf78+R+q3+lhc+nSJbJly5aqfUiRVQg3MTOXaq1nYMxMiVJqIjA9vsDq8DjwvNb6nHlHFUKkNXKNJoQwk9VHvhoIIcwjOSWZFCcVfRBXr15l8eLFzJ49m71793Lq1Cm8vb1NC9TTxo4dy9y5c+nVqxchISFY03nBPioqikKFChEQEEC5cuUoX7485cuXp1y5cpQpUybdFcmXLFmC3W6nfv36D83n7uLFi+TKlYsyZcrw7LPP8uyzz1KrVi38/f3vaz+S9YRwE1e0OFNKVQYe01p3SLTMChQHvlZK5QGmaa2nm35wIYTHSUtWIYSZpNWZEMJMklOcm1TUWTExMaxevZrZs2cTGhpKVFQUALVq1eLff/+lYMGCZoScJvz999+EhYXRuHFjypYtS69evWjSpAk2m83ToT2Qq1ev8vrrr7Nnzx5+++03Nm3alLDOarVSvHjxhKJrfAE2KCgoTbZ6tdvt9OjRg+PHj/PPP/+QPXt2T4dkivPnz/Pcc8+xceNG9uzZw8iRI/H29qZ69eoJRdfKlSvj5XX3MqoUWYVwExdNUNMT6JdkmT8wHhgF2IB1SqkdWuu9rghACOE5MvGVEMJM1nT65VUIkTZJTjHHr7/+ypw5c5g/fz7nz58HoHTp0rRo0YI33niDwoULezhC840aNYr27dszePBgZs2aRbNmzShZsiSfffYZr7322j0LXWlN7ty5mT17NgA3btzg0KFD7Nmzh71797Jnzx727NnDggULWLBgQcJrsmfPnqzV62OPPUbmzJk99WsAxufx0KFDtGjR4qEpsAIUK1aMlStXcuPGDbZu3crPP//Mzz//zKZNm9iwYQO9e/cmMDCQOnXq0Lt37zvuJ118MvfuP8Do8V/yzeSJbj92bFwcYxYu4FR4OFarlY9fbUb+HDkT1q/bvYtFmzdhs1oomjc/nRo1dktT9updmnLzejQAkReusH/BreFcSoXUJFuRvMTeiAFg14yVCdu6SqV6lahcrzIAXj5e5H80PwNeHcD1iOsAPPXKU1SoUwG73c7a79ZyYMsBl8aTVNkKpejSvS3vvNaFgoULMGBkd7DDH/o4X/Qejd1uv/dOUsnsFmdKqWxASa110rF8IoGxWutIx3ZrgfJAhiuypsXzV2KSmMwkLVndZ8P69Uz++mu8bDZCXn6ZJk2apLjd8OHDKVK4ME1ffTVh2YULF2jZsiULFy4kU6ZMbovp8OHDDB0yBKvNho+3NwO/+ILw8HCGDx+esM2+vXsZPWYMNWrU8FhMOXLk4Ntvv2XVypVYrFbavPMOdZ95xpR4HjQmV7xP94rjr7/+4vPevbFYLBQrVowePXsm5JC//vqLD7t04YdFiwC4fPkyIcHBPFqsGAB169alefPmDxxbvIw+E7hZYmJi6NO3L//88w/R0dG89+671K5d+7ZtoqKiaNeuHX379qVo0aL3tf9f1q5lzU8/MWTIEADeeeedhHXHT5wgODiYLh98cN9xb9ywgalfT8Zm8yL45RAaNb79M6r1YYYPHYrVasXHx4d+AwaSI0cO5syexU+rVwNQo2ZN3mvb7r6PfSebNmxg6hRHTCEhvJwkpiOOmGw2K97ePvR1xDRi2FD2hoXh5290wR0xagxZAgJMiWnjhg1MSfQ+NW6c8t+DkSOGU7hwEV5p2hSABfPnsTQ0FIvFQpv32vLUU0+ZEk9ajQkkp5gl/m9P/vz5+fjjj2nevDnly5dPk60czVS8eHGmT59O7969GTJkCN988w1vvvkm/fr1o2fPnrRo0SJddlXPlCkTFSpUoEKFCgnL7HY7Z86cua3oumfPHjZv3syGDRsStrNarSilkrV6dedYr1OnTgXg3Xffdcvx3C1Tpkw8/fTTPP300wwYMIBLly6xYcOGhKLrkiVL0neRdfrM2SxdsQo/D1Xrtx40ioFjOnRiz7E/mLw0lH5vtwbgRkwMM1avYnLXj/H18WHQnFlsO3SQJx8r49KYrF7GHcHfJy1JcX1ggVzsmLKMmMjrLo0jsZ0/7WTnTzsBeLnTy+xYtSOhwOrr70uNRjUY1nIYPr4+dJnUxa1F1lZtX6dB43pERRrdKbr17sCEEdPYsTWMXl90pU69mqxdvekee0k9F+S8p4CfU1heApinlHocsAI1gW9NP3o6kBbPX4lJYjLTQ35tnWbExMQwYsQI5sydS+bMmWnZsiVPP/00OXPeKrJfuHCB3r16cfLkSYq0bJmw/NctWxg7diwXHC1P3BnT8GHD+LR7d0qWLMnC77/nm+nT+bhbN6ZNmwbATz/9RK5cuUwrsD5oTO+1bct3c+eydNkyoqKiaPbqq6YVWdPK++RMHCNHjKBDx4488cQTDBwwgPXr1lH3mWdYtnQpc+fO5dKlW0OvHzp0iBdeeIHuPXo8cEwpkYKIOZYvX062rFkZ9MUXXLp0iWavvXZbkfXAgQMM/OILzp49e9/7Hjp0KL/+9htKqYRl8Z/V06dP0+2TT3jvAb743oyJYdTIEcycPYfMmTPzztstqfVUks/osOF0+/RTlCrJDwsX8u033/Dqa6+xasVKZsyaZRTqWreiTp26FC9R4r5jSCmm0SNHMMMRU5tWLamZNKbhRkwlVEkWLVzIzBnf8OFHH6MPH2LcxC/JZnIrq5iYGEaOHMEsR0yt327JU0liunjhAp/37s3Jv07y1ltFjGUXL/L9ggXMnTef6OhomjZpTK1atUwpiqTFmOJJTjFH69atad68OU8//XS67S6fGkWLFmXy5Mn06tWLYcOGMWXKFFq3bk3//v3p3r07b7/9tqk3sT3BYrGQP39+8ufPzwsvvJCw/Pr16xw8eDCh6BpfhJ03bx7z5s1L2C5HjhzJWr2WLl0aX19fU+O8fPky8+fPp0SJEtSqVcvUfadV2bJlIyQkhJCQEMD4W3u3v99pPusVDCrAmGGDPXb8GmXK0qWJcafv7MWLZMty6w6ot83GmA6d8PXxAYxWTu64kxKQLwc2by8qv9uQJ9oGk7VQnlsrLeCXMyuPvVKbqh0aUeCJki6PJ7GgEkHkKZyHbSu2JSyLvh7NpbOX8PH1wcfXB3uc61uNJnbqr7/5sG2vhOelypZgx9YwADav30a1mpXcGo+JFPBnwhOluiqlgrXWh4A5wFZgAzBTa+3epsNpRFo8fyUmiUmkP8ePH6dgwYIEBgbi7e1NxYoV2bVr123bREVG0q5dO+o3aHDbcovVyuSvvyYwa1a3xzRk6FBKljSuA27GxuKT6AtIVGQkk776ik8//dTjMWXOnJl8+fIRFRVFVFSUqV/w08r75EwcBw8epHJlo1dQjZo12brNuJYKDAxMKKLFO3TwIIcOH+ad1q35+OOPCQ8PT1V88SxWq1MPcXf16tWjQ4eE4fKTFUWio6MZPWoURYsUSVgW3/q1VevWtHz7bbZv357ivstXqMBnn32W4rphw4fT5YMPHmgClaSf0fIVKxK2+/bP6KAhQ1DKOFdiY2+SKZMPefPkYfzEidhsNqxWKzdv3rztHEqN48ePE5Q4pgrJY/pi8BBKJI7Jx4e4uDhO/fUXgwYOoE2rloT++KMp8QCcSPI+VahYkd1JYoqMiuK9du2oX79+wrLs2bPz3fwFeHt7c/7cObIEBJiW69JiTPGczSmSV+5u2rRp1K1bN0MWWBMrWLAg48eP5/jx43Tt2pWzZ8/Srl07ihUrxvjx4xPGqH2Y+Pr68vjjj9OqVSvGjBnD2rVrOXfuHKdOnWL58uUMGjSIZs2akStXLjZs2MCYMWNo1aoVlSpVIkuWLJQpU4bmzZszdOhQVq1axZkzZ1LVi/e7774jKiqKNm3aPPQtqe8kKCjoruvTfEvW5+rW4e9/zng0BpvNxrD53/Hr/n30evNW6xSr1Up2R7eTH7dsIio6mkrFU3/X9l5iY25yfEMYp38/hF/OrFRu04BNw+Zij7Nj8/Hm5JZ9nNi4B4vVQpW2IVw+Hc61M+a2oLmTOq/X4edZyRtXXgq/xEdTP8JqtbJuXtLe7a7188qN5A/Km/A8cTKIjIgkS8D9zRb3oGwmXzxorYcneT4q0c/DgGGmHjCdSmvnr8QkMZkaj3wpcYuIa9fIkiVLwnN/Pz+uXbt22zYFgoIoEBTE5i1bblv+5JNPeiymXLlyARAWFsb8efOYNv3WHIiLFy/m2eeeM3UsrdTElCdvXho3akRsbCytE3V99mRMYN775EwccOsaxd/Pj2tXrwLw1NPJ50gpUrQo7UuXplq1aixfvpyhQ4YwYuTIVMUIMhO4WeKLnBEREXz08cd0TFRwBahYsWKy1yxevJhs2bLRr29fLl26RKvWrVnsGB4isReefz7FAuyRI0eIuHaNqlWrPlDMERERST6j/ly7evtnNKfjXNkTFsaC+fOZMnUaXt7eZMueHbvdztjRo1ElS5o2PmSymPz9k5038THt3RPG9/PnM3naNKKiomj62us0b96C2Lg42r/3LqVKlzalde21JDH5pfA+FShQgAIFCvDrls23Lffy8mL+vHlMnvQVr73+eqpjScsxxZOcIlwhX758jBw5kk8//ZRRo0YxceJEOnfuzKBBg+jWrRtt27a97xnh0xOLxUJQUBBBQUG89NJLCcujoqI4cOBAslavBw4cYO7cuQnb5cqVK8VWrz6OxiJgDF/QuXNn3nvvPcqWLZuwfMqUKXh5efHWW2+555dNhyTrOemTZq9z4aX6dB4/jikfdyOzj3GHNi4ujqkrlnE6PJzP32zplmp+RPglIs9dBiDy3GWiI66TKcCf65evERt9k5Ob9xIXcxOA88f+JjBfDrcUWX39fcldMDfH9hy7bXnJKiUJfCSQIW8a40a1GdKGEwdOcEqfcnlMKbHHxSX87Ofvx9Uryb/kuEIGvdGTJqSl81dikpjMJHnFtSZMmMDu3bs5euTIbReYEZGRBJg0tp+rY1q9ahVTp05l/IQJPPLIIwnLV6xYYUpRzoyY1q9fz7nwcJavWAHA++3bU6FChdv25e6Y4qX2fbqfOBLnjHt9xqpUqZLQBbBu3bp89eWXDxzjbTGYdONGKeUNTAeKAJmAgcBBYAZgB/YDHbTWcXfYRbr377//8mHXrrz66qu3fQm+k6NHj7Jr927279sHQGxsLHv37WPsmDEA1G/QgMaNGt3x9cuXL6dx48b3HeeXEycQtns3fxw9SpkyiT+jESmOYfrT6tVMnzaVMePGk91xrty4cYP+ffvg5+9P9x497zuGpL6aOIE9YUZMjyWOKSIixfNizerVfDNtKqPHjSd79keIjY3ltdffwNcx3FzlJ57g6JEjqSqyxr9PR5O8T5GRKcd0J81ee43GTZrQqWMHtm/fzhNPPPFQxZSUiTnFBkzB6MkXC7TSWh+7+6vEwy537twMGTKEbt26MXbsWMaNG8dHH33EkCFD6Nq1Kx06dCAgIIDdu3fj7e1NmTKuHcbL0zJnzkzlypUTesWAUSw9depUsrFe165dyy+//JKwnZeXF6VKlbptrNfTp09Tvnx5Xn31Vfr27UtkZCS7du2iSZMm5MmTJ6UQBPcosiql1mFcGCVmAexa6+ouiyoN+XnnDsIvX+b1us+QydsHi8WCzXLrj8XYRQvx9vKib8tWbpnwCiCoSikC8ubg4OKNZAr0w8vXhxtXIwDwz5WN8s2f49cx32OxWMheJB//7Djslrj+V+5/HN11NNnyyKuRxETHcNNR+I26FoVvFnPHBrkfhw/8QeVqFdixNYyatauy/bfdbjmuTFDj/pySFs9fiUliMlNGzyuuzikdO3YEjG68jRs35vLly/j5+bFr506P3cG/n5iWL1vGwoULmTptGlkTDVdw9epVomNiyJs3L2ZIbUyBgYFk8vXFx8c4rwICArjqaMXpqZjAnPfpfuIoWbJkQoFjy+bNdy109Ovbl2eefZbnn3+e37dto1Tp0g8cY2IWq2ldUVsA54EgFt8AACAASURBVLXWbyqlcgC7gTCgl9Z6vVJqEhACLDbrgGYxI6+cP3+edu3b06N7d6dblhYpWpQ8efLQpk0brl+/ztSpUynz2GPJhoq4k22//06rVq2c2jax9zsYn9GbMTE0bXLrM7p71y7eTPIZXbF8OYt+WMjkKVMTzhW73c5HH3ah8hNVePsBjp+S9oliavbKrZjCdu2iRZKYVjpi+ipRTH+dPMlnPboza+532OPi2BO2m/oNG6Yqpvj3KSbJ+7QrhfcpJSdOnGDC+HEMHzESLy8vfLy9U/03PC3GlJSJOaUhgNa6hlKqNjAKI4cIQY4cOejfvz9du3Zl/PjxjBkzhh49ejB8+HC6dOlCqVKleO+991ixYgXVqlXzdLhuZbFYKFSoEIUKFaJBoiGtIiMj2b9/f7JWr/scN/oSmz9/Pt9//z3FHJNtPqwTXpnlXi1Zu2PcMWoE3HR9OGlPjbJlGblgPl2/mkhsbCztg0PYvH8fUTduUCKoIKu2/06ZIkX55OtJALxcsxY1yzx4ywtnnP79EGWb1aXq+42w2+3sX7COwjXLEXHuMuEHT3Bm9xGe7NSEuNg4/tmpuXb2okvjiZcrKBcXzlxIeF6rSS3O/5+9+46v6f7jOP669yaRIbF31IwjJfYWFKWlNLZuq2rVqE2NX6lSq/ZIbaopKTFq1R41gxrhGEVatWLLkHHv74/EbSLBxbn3Znyej8d5POScc+9538j55OZzv+d7/r1NyP4Q/lb/5stpX2Iymbh86jLng5M3Y21l4rczGTluAI5Ojvx14Qq/b9j14gcJrdi0pqTG81cySSahKZvUFEdHR/r360e3bt0wGY34NWtGnjx5uHjxIgEBAc+cH9GaXpRp8ODBfP/99+TNl4++ffsCULFiRbp3786VK1fInz9/qsp04MABPv3kE/R6PeXKl9dsmoXU8n2y5GeoX79+jBo1iunTplGkSBHebtDgmc/Xu3dvRo4cyYoVK3BxcWHkyJGa5ES7hshKIDDR17FAReLnigfYCDQkFTZZ0aCuzJs3jwcPHuDv74+/vz8ALVq0IDIyklatWqX4mNatWvHNqFF07NSJR48e0bZNm5f6wC4sLIysWbO+SlwAHBwd+apff3p274bRZOJ9Pz9y587DXxcvsuKXAAYMGszE8d+TN29eBvT771wpoZTkaHAw0dHR5kvRv+zZizJly75ylsSZ+vTtT68e3TAZTTR9kumvi6z8JYD+AwczacL35Mmbl0H94zNVqFCRL7p1591GjejY7lMcHBxo3KQpxYoVf+08EH8u9+3Xny8Tvk9+ib5Pv/wSwJChKf8+KFy4MCVKlKB9u8/QoaNGzZpUTDTaLL1lMtOopqiqGqQoyvqELwsBL3/XOJHuZc2aleHDh9OnTx9mzZrFpEmTGDFiBHq9HqPRSP369Vm9ejUNGza0d1S7c3V1pUqVKlSpUsW8zmQyceXKFU6cOMHx48cZM2YM0dHRQPwVe+fOnQMgICAARVEonGhecfEf3YsmvVUUZQBwQVXVl34TFP3gtm3vcGSBazv22ztCMmf2hto7QjLbj1+2d4QkNp07+OKd7ODElV0Wf9w7vtk3Fp8PA4NGptvhaa9TU66sWZ/qaooQWirk1+Slzn1L64rUlJRFRkVJTRHpmouz80ud+xG/TrTonHBt2d+i51UUxR1YS3zTcqKqqvkT1tcDOqqq+snL5LOVV60rUZGRqa6mxKS6RGDje+BaxJBuf0tqK7Ori1VqClhWVxRFWUz8ByCtVFXd8jJZ0qrg4GBTxYpp9kbNdhUeHs7YsWMZM2aMeZ2joyPLli2jTZs2dkyW+n3//fcMHTqUfPny4enpSVxcHEeOHOGdd96hU6dOFC1alAoVKmTYm18FBwdTsWLFFF/8C+dkffrmOkKIV5NRC9DTpKYIoR2pK1JThNCSTsM7VyuKUpD4kaqzVFVdrihK4htyugP3NDuYxqSuCKENLWsKgKqq7RRFGQQcVBTlTVVVwzU9gEhXrl69yrJly5Ksi4mJ4YMPPuDu3bt06dLFTslSv08//ZS+ffvi6OgIQK1atdDr9fz4448ULFjQzulSN7nxlRA2YtBLM0QIoS2pK0IITTk4vXgfCyiKkgfYAnypquqTO2scUxTlLVVVdwKNgB2aHEwIkXppV1M+BTxVVR0LRABG4m+AJcQzlShRgjNnzqCqKqdPnyYkJMS89OjRg9u3bzNkyBAZtJCCxNMlnTlzhr1799K4cWNpsFpAmqxCCCGEEEIIze4EDgwFsgHDFUUZnrCuNzBNURQn4AxJ52wVQqRDGtaUVcBCRVF2A45AH1VVo7R6cpF+ubi4UK5cOcqVK5dkfVRUFOfPn+fRo0e4u7vbKV3a8OQGjJ9//rmdk6QN0mQVwkbkEzIhhNakrgghNKXdTWp6E99UfVodTQ4ghEgbtKsp4YBMoik04+zsjI+P3KD2RR4/fszixYvJkycPTZo0sXecNEGarELYiFzVK4TQmtQVIYSmNGqICCEEIDVFiDRu7dq1hIWFMWjQIPP8rOL5pMkqhI3IiDMhhNakrgghtKThpb1CCCE1RYg07scffwSgU6dOdk6SdkiTVQgbkV6IEEJrUleEEJrS6CY1QggBSE0RIg27dOkSv//+O2+99RZeXl72jpNmSJNVCBvRSzdECKExqStCCC3pDHJprxBCO1JThEi7FixYAEDnzp3tnCRtkSarEDaiQ5ohQghtSV0RQmhKLu0VQmhJaooQac6VK1coUKAACxcuJFu2bLRo0cLekdIUabIKYSMy4EwIoTWpK0IITclNaoQQWpKaIkSa07p1a/z8/Lh69Sq9evXC2dnZ3pHSFPloSQgb0et0Fi9CCGEJqSlCCC3p9AaLFiGEsISlNUXqihCpx4ULFxg2bBgAERERTJo0CZPJZOdUaYeMZBVCCCGEEELIpb1CCG1JTREiTYmJieHu3bvmrxctWsSOHTvQyaANi0mTVQgb0eulMAkhtCV1RQihJZ3cCVwIoSGpKUKkLWFhYUm+njRpEr6+vnZKkzZJk1UIG5FPf4QQWpO6IoTQlIw6E0JoSWqKEGnKrVu3zP/+6KOP6Nmzpx3TpE3SZBXCRmTAmRBCa1JXhBBa0hlkXkQhhHakpiSlKIojsBgoDMQBnVVVPWvXUEIkcvPmTQB8fHzw9/eXAR2vQD5aEkIIIYQQQsTfCdySRQghLGFpTck4daUx4KCqag1gFDDGznmESOLWrVtkzZqV1atX4+bmZu84aZI0WYWwEZ1OZ/EihBCWkJoihNCUNEOEEFqSJuvTzgEOiqLoAQ8gxs55hEgiLCyMZcuWAbBy5Uo7p0mbZLoAIWzEINf1CiE0JnVFCKElnYOjvSMIIdIRqSnJPCJ+qoCzQE6giV3TCKuJjY3l2rVrREVF4eXlZe84FomNjcXd3Z1p06axZ88egoOD7R0pTZImqxA2IqPJXt+ZvaH2jiCEVRXye7n9pa68HkPEHXtHSCbaOZu9IyRjsneAFDikwg8Y7kXF2TtCMi7OL/kAnVzk9jrGp8L3Kf19C9k7QjKG1Hf6cvjfR/aOkIyv4R97R0iucLmX219qytO+AjarqjpEUZSCwHZFUXxUVY2ydzDx8mJjY9m+fTuXLl0iNDSU0NBQrly5QmhoKP/8E3/+7t69O9U3Wa9du8a8efPw9/c35548eTLe3t52TpY2SZNVCCGEEEIIIQ0RIYS2pKY87S7/TRFwB3AEMsxcCemNg4MDBQoUYMGCBaxYsQKTKenH0rlz52blypWcPXsWHx8f3nzzzVQ1z+mdO3fo0aMHgYGBxMbGmtfXrl2b3r172zFZ2iZNViFsRAacCSG0plVdURSlPdA+4UtnoByQV1XVewnbpwE1gYcJ+/ipqnpfm6MLIVILkzREhBAakpqSzA/AAkVR9gBOwFBVVcPtnEm8hlKlShEQEMCwYcMYPXo0K1euNDdbb968yZQpU8z76nQ6ihYtio+PDz4+PpQuXRofHx+8vLxwcLB9ay579uyMHDmSrVu3EhYWBoCbmxuLFi1Cr5dz91VJk1UIG5HLeoUQWtOqrqiqughYBKAoykxgwZMGa4IKwDuqqoZpckAhROokDREhhJakpiShquojoI29cwjtlS5dml9++YXhw4czevRoVqxYwddff02zZs04efKkeTl16hRBQUEEBQWZH5spUya8vb3NTdcnDVhPT0+r9hCuX79Ox44dCQsLw2AwEBcXx+TJkylSpIjVjpkRSJNVCBuRHqsQQmta1xVFUSoBpVRV7ZFonR7wAvwVRckDzFdVdYG2RxZCpAryZkUIoSWpKSKDSdxs3bx5M5UqVaJSpUpJ9gkLCzM3XBM3X48fP55kv6xZs1K6dOlkzdds2V5//v7Dhw/TvHlzrl69Srt27ShXrhybN2+mc+fOr/3cGZ00WYWwEb28yRBCaMwKdWUo8M1T69yA6cBk4ucN26EoyhFVVU9ofXAhhH2ZDPKngRBCO1JTREb1pDmakpw5c1K3bl3q1q1rXmc0GgkNDU3SdD158iQHDhxg7969SR5foECBZFMOeHt74+xs2d0ulyxZwhdffEFsbCxTpkyhV69enD9/ntatW8vVtxqQqieEjVhhxNkQ4H3i5/OZparq/ETbmgIjgFjiL/v9UdujCyFSAy3riqIoWYGSqqrueGpTBDBVVdWIhP22A2UBabIKkd7Ipb1CCC1JTRHCInq9nsKFC1O4cGGaNm1qXh8dHc25c+eSNV83bdrEpk2bkjzey8srWfO1aNGiGAzx91aLjY1l0KBBTJ48mezZs7NixQrq168PQIkSJWz7gtMxabIKYSNafiqkKMpbQA3ib0TjCvRPtM2R+EnVKwPhwD5FUdapqnpdswBCiFRB40+bawNbU1hfAghQFKUCoAd8gcVaHlgIkUpIQ0QIoSWpKUK8FicnJ/Oo2A8//NC8/sGDB5w+fTrJlAMnT54kMDCQwMBA834uLi6UKlUKLy8vgoODOXfuHIqi8Ntvv1GsWDF7vKR0T5qsQtiIxiNZ3wFOAqsBD2BAom3ewAVVVe8CKIqyF6gFrNQ0gRDC7jSuKwrwl/kLRelLfC1ZqyjKT8ABIAZYoqrqaU2PLIRIHaQhIoTQktQUIazCw8OD6tWrU716dfM6k8nEjRs3ko16PXXqFEeOHDHvp6oqVatWTTbqtXTp0ri7u9vj5aQr0mQVwkY0HnGWEygENAGKAGsVRSmpqqqJ+Kbr/UT7PgSyaHlwIUTqoGVdUVV1wlNfT0707/HAeM0OJoRIlUzSEBFCaEhqihC2o9PpyJs3L3nz5qVBgwYABAUF8cknnwDwwQcfULJkSU6fPs3JkyfZvXs3O3fuTPIchQoVStZ8VRQFJycnW7+cNEuarEKkTbeBs6qqRgOqoihRQC7gJvAASPwRlDtwz/YRhRBCCJGmSENECKElqSlC2IXRaGT06NH873//I3PmzAQFBeHn55dkn6ioKM6cOZNkyoFTp06xfv161q9fb97PwcGBkiVLmpuuTxqwhQoVQq+Xc/xp0mQVwkYMek1Hsu4FeiuKMhnIR/zdv28nbDsDeCmKkh14RPw8ixO1PLgQInXQuK4IITI6vcHeCYQQ6YnUFCFs7uHDh7Rr147Vq1dTrFgx1qxZQ6lSpZLt5+zsTPny5SlfvnyS9Xfv3k0y1UDiBmxAQIB5v8yZM5vni33SfPXx8SFnzpxWf42pmTRZhbARLWcLUFV1vaIotYFDxN+IpgfQVlGUzKqq+ifMpbg5YdsCVVWvand0IURqofGcrEKIDE4u7RVCaElqihC2dfHiRfz8/Dh9+jQNGzbk559/Jnv27C/1HNmyZaNWrVrUqlXLvM5kMnH16tVk870ePXqUAwcOJHl8njx5kk05UKpUKVxdXTV5jamdNFmFsBGN52RFVdWBz9m2Dlin6QGFEKmO1nVFCJHByWV/QggtSU0Rwma2bt1KmzZtuHv3Lv3792fs2LE4OGjT8tPpdHh6euLp6UmjRo3M62NjYzl//nyyEa/btm1j69atSR5frFixZFMOeHl5aZYxtUhfr0YIIYQQQgjxamTUmRBCS1JThLA6k8nElClT6N+/P46OjixdutR8sytrc3BwwNvbG29vb1q3bm1eHx4eTkhISJLm68mTJwkKCiIoKMi8X6ZMmfD29k7WfPX09Eyzg0mkySqEjaTRGiGESMWkrgghNCUNESGElqSmCGFVUVFRdOnShSVLllCgQAGCgoKoVKmSvWPh5uZG5cqVqVy5cpL1t27dSjbf66lTpzh+/HiS/bJmzZpsvtfSpUuTLVs2W76MVyJNViFsRC83qBFCaEzqihBCSya9/GkghNCO1BQhrOfq1as0b96cw4cPU6NGDX799Vfy5s1r71jPlStXLurWrUvdunXN64xGI1euXEk25cCBAwfYu3dvkscXKFAg2Xyv3t7eODs72/qlPFOaqHonTp3mh+mzWDh3ps2PHWc0MiVwBX/fuoVer6d/m7bkz/Hf3dJ2HDvKqr17MOh1FMmbn57NW6C3wdwzNfq0JjYqGoCIOw84tWKHeZu3ny9ZC+cl7nEMAEcXbTTvay0VG1akUsP4T0wcnBzIXyw/o9uMJio8CoDarWpTrm45TCYT23/ezul9p62a52k+5bzpM7gLnT7oQ8FCBRg9aTCY4IJ6iTHDf8BkMlk9Q1od7p7WFa1bgdylCqMz6An94zRXD58xbytcuywFKnsTEx4JwOlfdxF+655kkkxpJpPUldTh9p27tO3QBf+pEyla+A27ZIiMjKRHt64MH/k/ihQpkuI+y39aRljYbXr17m2TTFGRkXTv1pURI/9H4acyhYWFMfzrocTExJAzZ07+980onF1cNDv2rp07mevvj4PBgF+zZrRs2TLJ9tDQUEYMH45Op6N48eIMGTrU/P4tNDSUr/r04ddVq5I8Jjg4mKFDhrB5yxbNcj5txfJlbFgbRJaEkRr9Bn/NG4UKW+14ycioM7sIPfYHp38PjP95rPkOxWs0TLL9cfhD1o3qSpZ8hQAoWLYaJeu+r3mOXbt24j/XH4ODgWZ+zWjx1HnzxMQJEyhUuBCtW7cBYNnSpWzevAkAX99adOna1ao5QkNDGTki/vwtVrw4Q4bEn78TJ07g+LFj6PV6+vbtR7lEd8z+adkywm6H0bt3n9fK9iKHdv7O1qAV6A16PAsX46Me/W3yt2Fif549z+T5y1k8YWSS9Vv2HGTeiiB06GjduD6tGtW3fhipKUJYxf79+2nRogXXr1+nc+fOTJ8+nUyZMtk71ivR6/UUKVKEIkWK0LRpU/P66OhoVFVN1nzdtGkTmzZtMu9nMBjw8vJKNuq1aNGiGAwGm7+eVN9kXbBkGes2bMJVwzfeL+NASHwzcEqPnvx58QJz163lm/YdAXgcE8OizZuY27c/zk5OfPfTUg6eCaF6qdJWzaR3iP9BOTRnTYrbPQrk4siP64mJiLJqjsSCtwQTvCUYgGY9m3Fk0xFzg9XZzZmazWsyvt14nJyd6DOnj02brB26fEiTFg2JjIhvfAwY3oMZE+dz5MBxho3pS92GvmzfvMdmeYTtZC+an6yF83Jg5ioMjg4UqVM+yXaPArk4GbCNB1dvSSbJlCYzCfuLiY1l1PeTcbbjG9uQ06f5bswYbt68keL2qKgovh09ilMnT1Kv/ts2yzT2OZkWL1zAe02a0qRpU+bOmc2vvwby8SefanLsmJgYJk6cyE/Ll+Pi4kK7du2oU6cOOXP+9yH5pIkT6fHll1SuXJlvR49m544d1Ktfn/Xr1rF8+XLu3Uv6Acn169dZumQJsbGxmmR8lnPqGYaMHIXi/aZVj/NMGn1woyiKHpgFlAUeA5+rqnpBkydPZ4zGOI6vXcy7AyfjkMmZ9d9+iWeZajhn9jDvc+fvixSqWJvKbbpYLUdMTAyTJk5k2U/x5037du2o/dR5c+fOHYYPH0bolSt8VrgdAP/88w8bNm5g6dJl6HQ6OnboQN169ShRooTVckyaNJEePb6kUuXKfPvtaHbu3EGBAp6c+PNPli77idDQUIYMHsTynwOIiopi9KhvOHnyFPXftm5TMfrxY4KW+DNy1lIyOTvz4/cjOXFoH+Wq1XrxgzUyf8Ua1m7bg4tz0t9JcXFGJi9YzsoZY3F1dqbpF32pX6My2bJ4POOZNCI1RQjNLViwgG7dumE0Gpk1axZdu3ZNlwMvnJyczE3TDz/80Lz+wYMHnD59Otl8r4GBgQQGBpr3c3FxoVSpUkmarz4+PuTJk8eq36+X/mhJURSb/hVR0LMAU8aPteUhk6hZ2oc+LeMn8L1x9y5ZM7ubtzkaDEzp0RNnJycgftSro6Oj1TO558uBwdGBSp2bUrnL+2R5I89/G3XgmjMLpVq9RdUezSlQuaTV8yTmWcKTPIXycHDDQfO66Kho7t24h5OzE07OTpiM1h81mtjfoVf5qssw89fePiU4ciB+zo+9Ow9SzbeiTXLodJYvGY216kpO5Q0eXrtN+XaNqNDhPW6euZxku0eBXBStV4Gq3ZtTtG4Fa0SQTJLJqqSmpMyW71UmTZ9Nm+ZNyZUzh60OmUx0TDQTJ0+mcOHCKW+Pjua9Jk3o+PnnNs00YfJkCj0jU9/+A2j83nsYjUZuXL9Bjhzaff8uXbpEwYIF8fDwwNHRkfLly3P06NEk+4SEhJjnLKvp68uBg/HvWzw8PJg/f36SfR8/fsy3337LkKFDNcv4LOfOnuGnxQv5snNHflq0wOrHS0ant2x5sWaAs6qq1YHBwCSr5rYya9YUvd5Ak2GzcHJxIzr8IZhMOGZKetnjnb8vcuefi/w+ZQh75o8j8v4dzXOkdN4ce+q8iYyMoGvXrrz3XhPzujx58jBz5iwMBgN6vZ7Y2BgyZXKyao4zISFUfHL+1vTl4IGD5M6dG2dnZ6KjowkPDzffrTo6OpomTZryuQ3qn4OjI4MnzSVTwmWrcXFxODq9+vfiVRTMl5epI/olW28w6Fk/bzLubq7ce/gQkwlcXWxwea2lNeXFdSVd1ZT0ymQyceNGyh+upnb37t1j0aJFPHjwwN5RnikmJoaePXvSqVMnPDw82Lp1K926dUuXDdbn8fDwoHr16nTu3Jlp06axY8cObt26xbVr19iyZQuTJk2iQ4cOlCpVitOnT7No0SL69etHw4YNyZcvn3nKgl69euHv78/+/ft5+PChZvmeOZJVUZSmwAwgBvhaVdVfEjZtBOppluAFGtSry9V/r9nqcCkyGAyM/+Vn/jh1kmGftjOv1+v1ZHOPb7oG7dtDZHQ0Fb1e7VPblxEXE8ulXcf559AZXHNmodLnTdgzfjkmowmDkyNX9p3k8u4/0el1VOnix/1/bvHo2m2r5wKo+2Fdti7dmmz9vVv36DevH3q9nh0BO1J4pPVs3bib/J7/zU2SuAhFhEeQ2d3NJjkyWvFLia3riqObMy7Z3Ale8Buu2T2o0L4Reyb8bN5+7c/zhO47RezjaMq3a0Su64W4deaK1jEkk2SymoxeV+z9XiXot01ky5qVmtWqMG/Jcmsf7pnKlSv/3O3xb0ZrsHZtylfAWMOLMul0OuJiY/mwbVuiox/T+YsvNDt2+KNHZM6c2fy1m6srjx49SjGDeXvCm+vadeok22/c2LG0++wz8uTJk2yb1uo1eIdmrdrg5paZ4QP78cfe3dTwrW314z5h0u7SXl9gE4CqqgcURbH/XTgsYK+aojcYCD3+B4dXzKVAqUronrq80SNPAcoU/Ih8Jctx6fBODq/0p/bngzXNEB6e9LxxdXPl4VPnTYECnhQo4Mm+vfvM6xwdHcmWLRsmk4kffphMyZIlKfQaU1xYksNEovPXzZVHjx7iYDCg0+tp3qwZjx49ZPiI+EvlPTw8qF6jBmvXWL/+6fV6PLJlB2D72pU8jozkzfJVrH7cxBrWqsrV6zdT3OZgMPD73oOMnrmAOlXK42Cw/kWtGb2m2NKMGTNo3rw5BQoUsPmxb968yU8//cTChQvp0aMHXbqkPOo+OjqaO3fucPfuXe7cufPcpUOHDnzwwQdWzR0eHs66desICAhg48aN1KlTh/bt2wPxDeOYmBgiIiLMS3h4eJKvX2YxGAxJLnF/WWFhYbRu3ZqdO3dSrlw5goKCKFSokEbfibRPp9ORN29e8ubNS4MGDczr4+Li+Ouvv5JNObB792527tyZ5DmKFy/O2bNnX3uKgedV1q+B8oAOWKkoirOqqosTvs5wBrb9kDuN36PX9Gn82H8ALk7xH2gbjUbmbVjPP7duMeLTdjb5gzf81j0iwu4DEBF2n+jwKDK5uxF1/xFx0bFc2XsCY0z85Wy3L17FI18OmzRZnd2cyV0wNxf/vJhkfckqJfHI7sG4T8cB8Pm4z7l8+jJ/q39bPVNKTEaj+d+ubq48fJD8Dy9ryOC9kCdsWldiwqMIv3kXU5yR8Fv3iIuNw8nNheiEOTOv7Dlhnq/41pnLeBTIafWmmGSSTFqSumLf9yqr129EBxw4HIx6/gJfjxrL9AljyJkju9WPPWvmDI4fOwbA7Ln+dplz6mkvm8nB0ZGVq1Zx8MABRg4fjv9TI0hf1owZMzh27Bjnz53Dx8fHvD48IgJ3d/ck+yZ+v5bS9idu3rzJ0aNHCf37b+bMncv9+/cZNHAg348f/1pZE5s3ZyYn/zwOJhNjJv5A5oSrpqrV9OWCqtq0yarh/IkewP1EX8cpiuKgqqp151t4fTarKcfXLePWxRAA6vcazRvlalCwTDX2L5vKpYM7KFb9v6k98pYogyHhb4+CZatz4jftPtSZ+eS8OX+O0onOm4jwZ58XT3v8+DH/GzkSNzdXhgz92uo59InP34Tt69avI2eOHMyaNZuI8HA6dGhP2TJlyG2DD0eCFvtzIeQEAH2/m8qvC2dz42ooXb8ek+o+DG3gW5X6NSozdOIs1mzdXYXCqAAAIABJREFURYt36r74Qa9DaopNHDx4kJ49ezJo0CCGDBlCv379cLHydIsxMTFs2LCBhQsX8ttvv5mn0zl58iRffPFFio3T8PBwi5+/Vi3rTLMRFRXFxo0bCQgIYN26dURGRpq3HTp0iLx585obo3FxcZodN1vCXOuv4s8//8TPz48rV67Qtm1bFixYgKurq2bZXsWsWbMYMWIEAQEBvP22baaiehVP5mv18vKiefPm5vVRUVGcOXMmSfM1NjZWk/fTz2uyRquqegdAURQ/YLuiKKHEf3iYYWwNPsKt+/f5sF59Mjk6odPpMCT6ZTF1VSCODg78r10Hm01q7lnFG/e8OQhZvZtMHq44ODvx+GF8wXLLlZWyHzfgjykr0el0ZCucj3+PnLVJrqJlinL+6Plk6yMeRhATHUNsQuM38lEkzpntd/e3s6cvUKlaOY4cOI7vW1U5vP+YTY5rkLuAg43ryt3L1yjkW4bLu/8kk4crBidHohPmKnZwdqJmvw/YO2E5cdGx5CjuyT+JbmJkLZJJMmlJ6op936ssnj3V/O8O3fswfGBfmzRYAbr3+NImx3kZL5Np3HdjeLtBQypVroybmxs6DX6Wv/wy/vgxMTG0aNGC+/fv4+rqytHgYD777LMk+5YsWZLDhw9TuXJl9u3dS+XKlVN8zty5c7Nm7Vrz1/Xr1dO0wQrwedceADx69JAOH7Zh8S+/4uLiwtEjh2nc1E/TY72IUbte4gMgcWdMn0aaITarKeWafgJATGQE26YNo16PURgcHXFwypSsMXdg+QzeKFeDQhV8ua7+SfaCxTTL0SPRedMy8XlzNPl5kxKTycRXfXpTuUoVOnToaJMcJUuW5Mjhw1SqXJl9++LP35iYGFxcXTEYDLi6ueHk5EREouaJNTVr999I/CVTx+Hg6Ej34eNsfsOr53kUHkH3keOZ993XODk54uLsbJN8UlNso3LlyixatIghQ4YwfPhw5s2bx4QJE2jVqpXmjf5z584xZ84cli1bxq1bye9DMHPmfzcr1+l0ZMuWjezZs1OqVCmyZ8+ebHmy/el11piGMSoqimnTphEUFMShQ4dSbKLmzJkTNzc3XF1dNVnc3Nxwfo3zbeXKlbRv357IyEjGjh3LoEGDUsWHN+Hh4dy+fVvTRrQtOTs7U758ecqXf/6VV6/ieU3Wy4qiTAaGq6r6UFGUFsBmIKvmKVKxmj4+TFrxC31nzyQuLo5u7/ux99RJIh8/poRnQTYdPkTpwkUY6D8HgGa+tfAt7fOCZ309/xw6g0/belTt3hyTycSpFTso5FuG8LD73Aq5zLVj56jesyXGOCP/Bqs8unHXqnmeyOWZizvX/psjqlbLWtz+9zYh+0P4W/2bL6d9iclk4vKpy5wPTt6MtZWJ385k5LgBODo58teFK/y+YZfdsmRANq0rt85cIVvR/FTv1Qp0cGb1bvKVLY4hkyP/HAzh/MYDVOnaDGNsHLcv/EPY2VBrxJBMkklYj7xXeYb79+8zetQ3TJw02d5RzO7fv8+3o75hwqTJfPDhR3w35lt+9J+LXq9n8BDt5jt1dHSkf79+dOvWDZPRiF+zZuTJk4eLFy8SEBDA119/Tb9+/Rg1ahTTp02jSJEivJ3o0jJ7yZzZnc7devBV9y9wdHSiYuXKVKvpa9MMRpNmvcR9QFNghaIo1YCTWj2xldm8pji6uFK4Uh1+nzIEvcFA1gKFKVzlLR6HP+Tg8unU7jyU8u9/xoGfpnNu9wYcMjlT7SPtP2RxdHSkX/9+dO/WDZPJiJ9fM3InnDe/BAQw9OuUR6ju2LGd4OBgoqNjzNMI9OzVi7Jly1otR9+E8zdm+jSKFinC22/Hn7/Hjx+n3WefYTTG0ahx42fOU20tVy6o7NuynuKlyjJ5SC8A6vu1pnyN5NOQ2Mr67XuJiIqiTeO3aVLPl0/7/w9HBwMlirxB03rWvyGX1BTb0Ov1tGvXjpYtWzJ27FgmTZpEmzZtqFWrFlOnTtW0kVS0aFE+/fRTihcvzh9//MEff/zBpUuXzNu///57WrZsSfbs2cmSJUuq+rDB2dmZgQMHMnDgQB4+fMiePXvYvn0727Zt4/jx4+TLl4/jx4+b53S2J6PRyIgRIxgzZgweHh6sW7eO9957z96xzJ40V1PDlVSpjc70jMKnKIoD8AmwQlXViIR1eYAhqqr2seTJox/cTnWjXq/t2G/vCMmc2Zv6/hDffvyyvSMksencwRfvZAcnruyy+GOkrYPnWHw+vD2uq/0/nrKC160rmwbMSnU1RQgtvTuh+0ud+5bWFakpKYu+82+qqynRzq9+OZm1pLpvEuCQCkdx34tKfaM58mV1e6lv1MOISIv+u91dXZ77vInuBF6G+EvtO6iqaptLq17D69aUUb+rqe506e8rc/ZZ4vC/tpk+7GX4Gv6xd4RkDIXLWaWmwPPrSlqtKVoIDg42Vaz4cjdqvnTpEgMHDiQwMBCdTkfHjh0ZM2aM1eYWv379Ovv37+ePP/7g3r17zJ07N1U1Vy1x+/Ztdu3aRdmyZSlWTLurBV7F/fv3+eSTT1i/fj2KorBmzRoURbFrpqd99913fP3112zfvp26da087UgqFBwcTMWKFVOsWc9s0ScMv1/01LobgEUNViFEUqlgVL/dSV0RQlsZva5ITRFCW0aNWoSqqhqBrto8m+1ITRFCWxm9pthLkSJFWLlyJbt27aJPnz7Mnz+fFStWMGzYMHr37k2mTJk0PV7evHlp3rx5kjkv05ocOXLQokULe8fg3Llz+Pn5cfbsWRo3bszy5cvJkiWLvWMl82Qka1prptuCfEeEsBGdXmfxIoQQlpCaIoTQkslksmgRQghLWFpTMlJdURRliKIo+xVFCVYUpZM1j1WnTh2OHDmCv78/zs7ODBo0iFKlShEUFJShvudpxcaNG6lSpQpnz55lyJAhrF27NlU2WEGmC3geabIKYSM6neWLEEJYQmqKEEJLRpNlixBCWMLSmpJR6oqiKG8BNYCaQB2goLWPaTAY6Ny5M+fPn6d///6EhobSvHlzGjRowMmTMrVtamAymRg/fjzvvfce0dHRBAQE8N1336XqBqY0WZ9NmqxC2IhOp7N4EUIIS0hNEUJoKc5k2SKEEJawtKZkoLryDvE37VoNrAPW2+rAWbJkYcKECZw+fZr333+fbdu2Ua5cObp3705YWJitYoinRERE8PHHHzNo0CAKFizIvn37aNu2rb1jvZDRaASkyZoSabIKYSMyklUIoTWpKUIILcllvUIILcl0AcnkBCoBrYmfY/YnRVFs+k7Ny8uLNWvWsGXLFry9vZk9ezZeXl5MmTKFmJgYW0bJ8EJDQ/H19eXnn3+mdu3aHDlyhPLly9s7lkVkTtZnk++IEDYiI1mFEFqTmiKE0JLRwkUIISxhaU3JQHXlNrBZVdVoVVVVIArIZY8gDRo04Pjx48ycORO9Xs9XX32Fj48PGzZssEecDGfPnj1UqlSJY8eO0aNHD7Zu3UquXHb5UXglMl3As0mTVQgbkZGsQgitSU0RQmjJZLJsEUIIS1haUzJQXdkLvKsoik5RlPyAG/GNV7twcHCge/funD9/nl69enHhwgXee+89GjVqxJkzZ+wVK92bM2cO9erV4969e/j7+zNjxgwcHR3tHeulSJP12aTJKoQQQgghhJAb1AghNCU3vkpKVdX1wDHgEPFzsvZQVTXOvqkge/bsTJ06lZMnT/Luu++yadMmypQpQ58+fbh7966946Ub0dHRdO3alW7dupEjRw527NhB586d7R3rlcicrM/mYO8AQmQUOpmvRAihMakrQggtxWWg4WRCCOuTmpKcqqoD7Z3hWby9vdm4cSMbNmygb9++TJ06lWXLljFq1Ci++OILHBykffSqbty4QatWrdi7dy+VKlVi9erVeHp62jvWK5M5WZ9NviNC2IhMFyCE0JrUFCGEluSyXiGElmS6gLSpcePGnDhxgh9++IHY2Fh69OhB+fLl2bp1q72jpUnBwcFUqlSJvXv38sknn7B79+403WAFmS7geaTJKoSN6PQ6ixchhLCE1BQhhJaMJpNFixBCWMLSmiJ1JfVxcnKiT58+nD9/nq5duxISEkKDBg1o1qwZFy5csHe8NGP58uX4+vry77//MmnSJJYsWYKLi4u9Y702abI+mzRZhRBCCCGEEJgsXIQQwhKW1hSpK6lXrly5mD17NseOHaNevXqsWbOGN998k4EDB/LgwQN7x0u14uLiGDhwIB9//DHOzs5s3LiRvn37oksnl5jJnKzPJk1WIWxEpgsQQmhNaooQQktygxohhJbkxlfpR5kyZdi6dSurV6+mYMGCTJgwAS8vL+bNm2ce1Sji3b17lyZNmjBhwgTefPNNDh8+TMOGDe0dS1MyJ+uzyXdECBvR6XQWL0IIYQmpKUIILcnciUIILcmcrOmLTqejWbNmhISE8P333xMREUHnzp2pXLkyu3fvtne8VOHMmTNUqVKFTZs24efnx4EDByhevLi9Y2lOpgt4NmmyCmEjer3O4kUIISwhNUUIoaU4k8miRQghLGFpTZG6krZkypSJgQMHcv78eTp27Mjx48epU6cOrVu35vLly/aOZzfr1q2jatWqXLhwgREjRrBq1Src3d3tHcsqpMn6bNJkFUIIIYQQQshlvUIITcl0Aelb3rx5mT9/PkeOHMHX15fAwEBKlizJsGHDePTokb3j2YzJZOLbb7/Fz88Po9HIr7/+yjfffJOuL6WXOVmfLf3+rwuRysicrEIIrUlNEUJoSS7rFUJoSaYLyBgqVKjA7t27+eWXX8iTJw9jxoxBURSWLFlibsalV48ePaJNmzYMHz6cwoULs3//flq0aGHvWFYnc7I+m3xHhLARmZNVCKE1qSlCCC0ZMVm0CCGEJSytKVJX0j6dTkebNm04e/Yso0eP5t69e7Rr147q1auzf/9+e8ezikuXLlGzZk0CAwOpX78+hw8fxsfHx96xbEKmC3g2B3sHECLD0PgjDUVRcgPBQANVVc8mWt8X6ATcSljVRVVVVduj28f245ftHUEIq3r3ZR8gH5W+lsfO2ewdIRknU6y9IyQXF2PvBMnEObraO0IyOWLv2jtCCtxeam8ZTfZ62lcsYO8IydyJTH01xcGQ+j78y+SQ+n6hHuYNe0dIptpL7i81JeNxcXFh2LBhdOjQgcGDB7Ns2TJq1KjBxx9/zLhx4/D09LR3RE1s376dNm3acPv2bfr06cOECRNwcEj/7bXz58/j5ORkbrKaTCYOHz5M5cqVzfsYjcYMPcI1475yIWxMy5GsiqI4AnOByBQ2VwA+U1X1rYQlXTRYhRDJaTmSVVGUY4qi7ExYFj61rbOiKEcURTmgKEoTq7wYIYTdGU0mixYhhLCEpTVF6kr6U6BAAZYuXcr+/fupWrUqP/30E4qiMGrUKCIiIuwd75WZTCamT59Ow4YNefjwIQsXLuSHH37IEA1WiG+ilyhRgt27dwNQtmxZtm3bZt4eERFBYGCgveKlCtJkFcJGNJ6TdSIwB/g3hW0VgSGKouxVFGWIhi9BCJHKaFVTFEVxBkj04UyHRNvyAr2AmsA7wFhFUTJZ5xUJIewpJs5k0SKEEJawtKZIXUm/qlWrxh9//MHSpUvJmjUrI0eOpGTJkgQEBGBKY831x48f8/nnn9OrVy9y587N7t27ad++vb1j2ZSnpyclS5bkzp07APz999/4+fmZtwcGBrJlyxZ7xUsVpMkqhI1oNZJVUZT2wC1VVTc/Y5cAoCtQD/CVUWdCpF8ajmQtC7gqirJFUZTtiqIkviKwCrBPVdXHqqreBy4AZazwcoQQdhZnMlm0CCGEJSytKVJX0je9Xs8nn3zCuXPnGD58OLdu3eLDDz+kVq1aHDlyxN7xLHLt2jXeeustFixYQLVq1Thy5AhVq1a1dyy7aNSokfnfJUqUoGTJkuavFyxYwIEDB+wRK9WQJqsQNqLhSNaOQANFUXYC5YAlCSPNUBRFB0xRVTVMVdVo4DegvPVelRDCnjQcHR9B/Aj5d4j/kOYnRVGeXPfkAdxPtO9DIIuGL0MIkUrIZb1CCC3JdAEiMTc3N0aNGsXZs2dp27Yt+/bto0qVKnTs2JHr16/bO94zHTx4kEqVKnHgwAE6dOjAzp07yZ8/v71j2U3jxo3N/27WrJl5QMeFCxfYtWsXISEh3L9//1kPT/ekySqErWjUZVVVtbaqqnVUVX0LOE78/KtPfit5AKcURcmc0HCtR/zNsYQQ6ZF2XdZzwDJVVU2qqp4DbgP5ErY9ANwT7esO3NP0dQghUoU4o2WLEEJYwtKaInUlYylUqBABAQHs3r2b8uXLs3DhQry8vBg3bhxRUVH2jpfE4sWLqV27Njdu3GDatGnMnz+fTJky9qxZ1atXJ0uW+PEWzZo1M69fsGAB8N/NsDIqabIKkQ4oivKRoihfJFzKOxTYAewBTququsG+6YQQaUBHYBKAoij5if/A5lrCtkNALUVRnBVFyQJ4A6fsklIIYVUy4kwIoSUZySqep1atWhw6dIj58+fj5ubGkCFDePPNN1m1apXd52uNjY3lq6++on379ri7u7NlyxZ69uxp8Q1l0zNHR0caNmxInjx5zFMmxMbGsnjxYvM+GXnKgIxxCzQhUgG9QfuCnDCaFeBsonVLgaWaH0wIkepoWFfmA4sURdkLmIhvuvZSFOWCqqprFUWZRvwHN3rga1VVU9cwAyGEJmKM0ugQQmhHaop4EYPBQMeOHWnVqhXfffcdP/zwAy1btuStt95iypQplC1b1uaZbt++Tdu2bdm2bRtlypQhKCiIIkWK2DxHataoUSOyZcuGXh8/bnPz5s38++9/9+Q+ePCgvaLZnTRZhbAR+dRLCKE1repKwhzOHz21+o9E238EftTkYEKIVCtOGiJCCA1JTRGW8vDwYNy4cXTu3JkBAwawevVqKlSoQOfOnRk9ejS5cuWySY6TJ0/i5+fHpUuXaNmyJYsWLSJz5sw2OXZa8u6775IvXz7z1wsXLqRs2bL8+eefZM2alQMHDmAymTJkD0SmCxDCRjS88ZUQQgBSU4QQ2pLLeoUQWpLpAsTLKlasGKtWrWLbtm2UKlWKuXPn4uXlxeTJk4mOjrbqsVetWkX16tW5dOkSo0ePZuXKldJgfYZ8+fLRsGFD89dTpkxhwoQJAAwaNIj58+dz9+5de8WzK2myCiGEEEIIIYgzWbYIIYQlLK0pUlfE0+rVq8fRo0eZPXs2jo6O9OvXDx8fH9avX6/5fK1Go5GRI0fSsmVL9Ho9a9asYdiwYRlyFObLeDJVAICnpydhYWEA5MyZk/fff5/s2bPbK5pdSZNVCFuRoaxCCK1JTRFCaEhGnAkhtCQjWcXrcHBwoGvXrpw/f56vvvqKv/76i6ZNm/Luu+8SEhKiyTEePnxIixYtGDVqFMWLF+fgwYO8//77mjx3RpO4yZqRSZNVCBvR6XUWL0IIYQmpKUIILcUZTRYtQghhCUtritQV8TxZs2Zl8uTJnDp1isaNG7NlyxbKlClDr169uHPnzis/74ULF6hWrRpr1qzhnXfe4dChQ3h7e2uYPGO5ffs2ADly5LBzEvuSJqsQNiJNViGE1qSmCCG0FGM0WbQIIYQlLK0pGa2uKIqSW1GUvxVFKWnvLGmJoij89ttvbNy4ES8vL6ZPn46XlxczZswgNjb2pZ5ry5YtVK5cmZCQEAYMGMBvv/1GtmzZrJQ8Y5CRrPGkySqEEEIIIYSQy3qFEJqS6QKSUxTFEZgLRNo7S1r17rvvcuLECaZOnYrRaKRnz56ULVuWLVu2vPCxJpOJSZMm0ahRI6Kioli2bBnjx4/HYDDYIHn6Jk3WeNJkFcJGZEpWIYTWpKYIIbRkNJosWoQQwhKW1pQMVlcmAnOAf+0dJC1zdHSkV69eXLhwgR49eqCqKu+88w5Nmzbl3LlzKT4mMjKSzz77jP79+5M/f3727t3Lxx9/bOPk6deT6QIy+ohgB3sHECKjkEt2hRBak7oihNCSNe/wrShKFmAZ4AE4AX1VVd2vKEo1YCoQC2xRVfUb66UQQtiSlWuKG7AcyA6EA5+qqnrLekd8fYqitAduqaq6WVGUIfbOkx7kyJGDGTNm0K1bN7766ivWr1/P5s2b6dWrF8OHDydLliwA/PPPPzRv3pwjR47g6+tLYGAgefLksXP69CUsLIxs2bLh4JCx24wyklUIG9HpdBYvQghhCakpQggtWfmy3r7ANlVV6wDtgZkJ6+cAHwG+QFVFUSq87usQQqQOVp4uoDMQrKpqLSAAGKZpeOvoCDRQFGUnUA5YoihKXvtGSh9KlSrF5s2bWbNmDYUKFWLSpEl4eXnh7+/P7t27qVSpEkeOHKFLly5s27ZNGqxWEBYWluGnCoA0MpL1xKnT/DB9FgvnznzxzhqLMxqZEriCv2/dQq/X079NW/Ln+O8HZ8exo6zauweDXkeRvPnp2bwFer31e9c1+rQmNioagIg7Dzi1Yod5m7efL1kL5yXucQwARxdtNO9rLRUbVqRSw0oAODg5kL9Yfka3GU1UeBQAtVvVplzdcphMJrb/vJ3T+05bNc/TfMp502dwFzp90IeChQowetJgMMEF9RJjhv+AyRbzAEmfw+YMjgba9G9D9nzZeRzxmKDpQYRdDTNvt8fPpWSSTJqSumIXUZGRdO/WlREj/0fhIkWSbIuMjGTsmDH8++9VYmJiGDBoEKVL+1g904mTp5gybRoLfvRPti0yMoou3bvzzYgRFClS2OpZYmJjGTlqDFevXSMmOprOHTtQt04t8/YNm7ew7OdfMOj1lPAqzteDBlj1vdOuXTvxn+uPwcFAM79mtGjZMsX9Nm7YwM8BP7NkyVLUs2eZMGGCedvJkyeY/MMUatasqXm+23fv0rZjN/ynjKdooTfM6xcHrGT1uo1ky5YVgBEDvqJIoYKaHz+xmDijNZ/+B+Bxwr8dgChFUTyATKqqXgRQFGUzUB84as0gqcH+PbtZtvBHDAYD7zZ5n8Z+LZJsv3BOZer47zAYDHgWLETfocPR6/UELF3Ejt834+bmRpuPP6Oab23NMv2RkElvMNCoyfu8l0KmKYky9UvIFBS4gi0b1gE6Pu34OdU1zLRvzy4Wz4v/PjV+34+mzZJmunvnDhO+G83DBw8wGo0M/d8oCngWZPXKX9i4fh06nY52nTpTo5Z2mRJ7HBXF+MG96NR3KPnfKJxk2+2b15n7/TeYTJDZ3YOuQ74hk7OzVXKk1kzWrCmqqk5RFOXJRJpvADesdjCNqKpq/kFMaLR2VVX1uv0SpS86nY7333+fd955h+nTpzN69Gi6dOkCgF6vZ/bs2XTt2tXOKdOv27dvU7ZsWXvHsLtU32RdsGQZ6zZswtXFxS7HPxAS/0fylB49+fPiBeauW8s37TsC8DgmhkWbNzG3b3+cnZz47qelHDwTQvVSpa2aSe8Q/7vk0Jw1KW73KJCLIz+uJyYiyqo5EgveEkzwlmAAmvVsxpFNR8wNVmc3Z2o2r8n4duNxcnaiz5w+Nm2ydujyIU1aNCQyIn5u8QHDezBj4nyOHDjOsDF9qdvQl+2b91g9h4wms72qjasSHRnNzF4zyeWZC78v/Zg/ZD5gv59LySSZtCR1xfZCTp9m7Jgx3LyZ8t9ySxYvoljxYoz69lvOnzvHuXPnrN5kXbBoMes3bMDFOfl7pdMhIYweM5YbN29aNUNiv23YRJYsHnw3aiT37t2nzSftzE3WqKgoZsz259eAZbg4OzPw6xHs2rMvSRNWSzExMUyaOJFlPy3HxcWF9u3aUbtOnWQjLdSzZwkKWg0JH7oqJUsyb378+f37li3kypXLKg3WmNhYRo3/AedMTsm2nVHPM2b4YEqVLKH5cZ9Fq0t7FUXpBHz11OoOqqoeThi1tQzoQ/zUAQ8S7fMQKKpNitQrNjaGOVMnMWPBUpxdXOjTpSPVfGuTPdFAjmXz/fmkY2eq1vBl7MivObhvL3nz5WPHlk1Mn7cYgN5fdKBcpco4p3Duv0qm2VMnMSshU+8uHan+VKYl8/35NCHTdyO/5sC+vZTy8WHtqpX4L1lO9ONoOn7Ummo1a2ny+yk2NoaZP0xi7qJlOLu40OPzDtTwrU2OROfvnOlTefudRtRr0JCjRw4TevkybpkzExS4kvk//Uz042g+a9uK6r7aZErs0rkzLJo6nrthKdfXTb8GULXO29R/vyWBC+ewe9M6GjRrrWmG1J7JRjVlO+ADNNDmaCKty5QpE7179+b06dMsWrQIAKPRyO+//07Dhg0pWjTd/5qxuYiICCIjI2UkKy85XYCiKC6KoiR/F2hFBT0LMGX8WFseMomapX3o0zL+F8+Nu3fJmtndvM3RYGBKj544O8V/S+KMRhwdHa2eyT1fDgyODlTq3JTKXd4nyxuJhrrrwDVnFkq1eouqPZpToHJJq+dJzLOEJ3kK5eHghoPmddFR0dy7cQ8nZyecnJ0w2Xhi879Dr/JVl/+uHvH2KcGRA8cB2LvzINV8K9okh0wXkJy1a0ruN3Jz9vBZAG79c4vcb+Q2b7PXz6VkkkxakpqSlC3ep0THRDNh8mQKFS6c4vYDf+zH0dGRL7t3Y96P/lSvUcOacQAoWNCTHyZOSHFbdHQ0UyZNoEjhQlbP8UTDt+vxZdcvzF8nvmOvk5MTS+f745IwciouLpZMKTQYtXLp0iUKFiyIh4cHjo6OlC9fnmNHkw6SvHfvHtOmTaX/gIHJHh8ZGcHsObMZOGiQVfJNmjGHNs2akiuFP0pC1PPMX7qcz7r1Zt6S5VY5/tO0uqxXVdX5qqqWfmo5rCiKD7ANGKqq6i7iG6zuiR7qDtyzyot7BdaqKaGXL5PfsyDuCT+XpcuU4+TxY0n2KVZC4eGDB5hMJiIiInBwcCD08iXKVKiIU6ZMOGXKRIHyVd5uAAAgAElEQVSCb/DXhQuaZLpy+TIFXpCpeAmFB09lypI1Gz8u+RkHB0fu3LlN5syZNfu9c+XSpSSZypQtx4mnMp08cZxbN2/St0dXtm7aSLmKlciaNRvzfwqIz3T7Npnd3a3yuzAmJppeI8eRr2DK9bVQMS/CHz0EIDIiHIOD9e9entoyaTVdwLNqSsK2ekAt4FervhiNqar6lqqqZ+2dIz26desWb7/9NosWLaJChQps2LCBOnXqsGrVKry9vRk6dCgPHz60d8x05clNr6TJ+oImq6IoRRRFCVIUZY6iKG8DZ4AziqI0sU08aFCvrt0nzjUYDIz/5WdmrVlNrTJlzOv1ej3Z3OPfFwbt20NkdDQVvaw/2iAuJpZLu45z5Md1nP51F2U/ett88xODkyNX9p3kxM9bOTJvPW9UL03mfDmsnumJuh/WZevSrcnW37t1j37z+tF7Vm/2Be2zWR6ArRt3ExsbZ/468ZusiPAIMru72SaI/iWWdMrWNeXaxWt4V/UG4A3vN8iSI0uSGwXZ4+dSMkkmTUlNsfn7lHLlypM377OnT7t37y4PHzxgxqzZ1KpdhymTJ1srilmD+vWf+V6pfLlyz81rDa6urri5uREeHk6/wUP5stt/DVe9Xk+OHNkBWP7LSiIiIqletYrVsoSHPyJz5sz/ZXNz5eGjR+av4+Li+OZ//6Nf/wG4ubome/zq1atp8HYDq9wpN+i3TWTLmpWaVSunuL3R23UZPuAr5k+byNETp9i1b7/mGZ4WZzJZtLwKRVHeBFYCH6mquhFAVdUHQLSiKMUURdEB7wDWv7zo2RltUlMiwh/hlujn0sXVlfDwR0n2KVDwDWZNnkCnD1py785tylaoSOHiXpw8doyI8HAe3L9HyMkTREVGWi3To6cyeRZ8g5mTJ9Dhg5bcvXObchXiByoYHBwIWvkLX37entr13tYkD0B4eHjSTG5uhD9Kmun6v9dw93Bn8sw55M6bl+VLFgHg4ODAqhUBdOvYjrfq1dcsU2IlSpUlR+5nz+uYLVdutq4NZEjnjzhxeD9ValsnR2rOZGlNeZW6oijKEEVRPk34MhyIe97+ImM4fvw4lSpVYvfu3Xz44Yfs2bOHRo0asWPHDgIDA8mfPz9jx46lRIkSLFq0CKPRqtPkZBhhYfFTqOXIYbveU2r1oj+9FhI/f9J+IBCoApQHMtyd8Aa2/ZAFAwczJXAlkdGPzeuNRiP+69dy9Nw5RnzaziYjhsJv3ePfo+cAiAi7T3R4FJkSGoVx0bFc2XsCY0wscY9juH3xKh42arI6uzmTu2BuLv55Mcn6klVK4pHdg3GfjuO7j7+jVM1SFFSsO6fY85gSFVJXN1cePnj0nL21IyNZARvXlMObDvM44jFdJnbBu5o3/5z/xzzC0F4/l5JJMmlJaoptasqsmTP44vNOfPF5J+Linv83XJasWald5y0Aatepw5mQEC2jpBnXr9+gU7cvadL4Xd57950k24xGIxOnTGP/wUNMHj/WKj+jM2fM4PNOnejTuzePwsPN6yPCI3B3/2/g5JmQEEJDr/DdmDEMHjyIv/76iwnjx5u3b9ywgeYtks4BqZXVv21i/+FgOnzZF/X8Bb4ePY6w23cAMJlMfNKmBdmyZsHR0ZHaNapy5pw2IxafJ85osmh5RWMBZ2Cqoig7FUV5Mu9VV+An4BBwTFXVg896Ahuwak1ZOHcW/bp/wYgBfYlI9HMZGRFB5szuSfadPWUik+fMY8Evq3i7URPmTPuBQoWL4NeqDUP/3959h0dRtm0c/m0aPVQBkZIXPxxRQQUEqYIVFQ0KFpAqIAhEqlIE6VKkiSJNQRREulFULO+LCoIiAaX6iGgAAUsCJJAE0vb7IyEGCGRDZkuS6zyOPczOTGauxd17N/c+88zAMObOmsH1N95EyVKlcpVp4bw3GNj7GUY+P5C4bDLNnjmVmXPf5O3la7jn/lbMmTUjY13rx55g5brP2LljOzsifshVpjfnzKZfrx4MHzzg/ExxcRQvcX6mkiVL0rjpHQA0atoMs+/fmvvo40+y5tPP+WnHdrZvy12mc1YtmsfEwb2ZOLg3qdm8Hyxf8Do9Bo9g4oL3eOrZAcyfMtaWDHkh0zmu1pQrrCsLgafS5zZ9D+hqZ3bJe5YvX06jRo04fPgwkydPZunSpRRN/wLV4XDQpk0b9u3bx4QJEzh16hRdu3alQYMGfPutZweB5UfnmqwayZr9nKwB6afyfG1ZVgtjzN8AlmUluz+ab/gyYhv/xMTQ7s67KBQYhMPhwN/xb2/61TWrCAwIYHTnrh654BVA5fo1KVGxLHvXfkOh4KIEFA7i7Km0DyDFrirFzU/dw+aZK3E4HJQOuZqj2zxzFkL12tXZv33/RcvjT8WTlJhEclLa0ybhdAKFi7t/0vdL+XnPr9S7/Ra2ffcjTZo34IctO7L/JbGLR2tKZasyv+/+nY/mfkTl6ypTNtMXDt56XiqTMomtPFJTevfp6/K2t9xyK99u2kTNG25ge0QE1a8tePN+RUcfp2dYP4Y9P4jb6188SnPsy5MJCgrk1amT3fbZqU/ftP9nSUlJtHn0UWJiYihatCjbt0fQqVOnjO1uqlWL1WvWAnD0yBGGDh3C8y+kTRtw6tQpEhOT3DYSePEbMzN+7tp3ICOf70+59FG+p+PieKRjdz5cuogiRQqzNWIHrR+83y05MstFAzVbxpjQSyz/DrjdbQfOGbfWlK49ewNpc412a/cYsTExFClalF0/buex9h3P27ZEiWCKFksbRFG2XDn27PyRkydOEBNzkpnzFhJ3+hRD+/UhpPq1ucr0dKZMT2fKtDObTOXSMx0+GMmbc15n9MRXCAgIIDAoCD9H7l7X3Z/tk5Gp0xNtMzL99ON2nujQ6bxta91yC99t3sR9D7Ri547t/Kd6dQ4djGT+7NcYN3nqv5lsqjVtu/Z0edtixUtQpFjaSNzSZcsRdzo2m9/IP5nOcXNN+Qto6bYDSJ6RkpLCyJEjmThxIiVLlmT16tXcf3/W75mFCxdm+PDhdOnSheHDh7N48WKaNGnCk08+yeTJk6latWqWvyeXd266AI1kzb7JaizLehN4xhjTBcCyrKFAgbkCXuNatZi2YjkD58wmJSWFZx8OZdPuXSScPct1lauw/oet3BTyH16YPxeA1k2a0sTNF7j4Y+s+aj1xJw16P4LT6WT3ig1Ua1KbuKgY/tkbybEdv9AwrA2pKakcjTCc/uuEW/Occ1Xlqzh+7HjG/aZtmhJ9NJq9W/Zy2Bym76y+OJ1OIndHsj/i4masp0wdP5tRk54nMCiQ3349yBeffO2R42Y+rbgA82hNiToSxX1d7uOOx+4g4XQCq6av8vrzUpmUyU6qK77xOSUmJobxY8fwyrTpdO3WjfFjx9C1UycCAgIYM36cJ6MA8PGn60mIj6dtG/eMwMzOgkWLiY09xfy3FjH/rUUAtGkdSkJCAjfcUJO1H35EnVtupvuzaY3Qp558nLtaNHdLlsDAQAYNHkTvZ5/F6UwlNLQ15StU4MCBAyx//32Gv/jiJX/30MGDVKpUyS25LuXjz/9LfEICj4W2ol/PbjwdNoigoEAa1L2VZo0auP34ickF/rRJj9SUgIBAej03kGED+uJMTeW+VqGUK1+eg7//Rviq5Tz3/DAGDh/JhJHD8ff3JzAwkAHDRlCyVCn+PHKEPk93JCAgkB59+58357EdmYYO6EtqaiotW4VyVfnyRKZn6vf8MAYNH8n49EwBgYEMGjaCildX4toa1xHWows4HNS/vRE317HnegcBAYH06T+Qwc/1welM5YGH0jP99htrVi5n4JBh9O43gCkTxhG+ehXFihfnpXEvUyI4mP+rcR29u3UGHDRo1DhjagN3Ox0bw8IZE3lu1CQ69BnEu69PIzU1BacTOvUd7JEMvpRJNUXcLSYmhvbt2/PJJ59w/fXXEx4eznXXZT+FY6VKlXj77bfp06cP/fr14/333yc8PJwXXniBF154IWMErLhGI1n/5XBeZv4Ty7L8gIeMMeGZlnUA1hhj4rPbeWJstGevcOSCYxvcP59VTu3bdMjbES7yvx8jvR3hPOt/8eaZY5e28+DXLnc4flvxgcuvh+qPt86XnZPc1pQX7nnB52qKiJ2mfDElR699V+uKakrWTsUn+FxNCXL64MlCKUneTnCRlEDf++MnIC7K2xEuElSuco5e+xP++4tLr4kX77pONSULh46f9rma4ovTcgf4+97TJ/Lk2ew3Em6vVsYtNQXyb13JrYiICGfdup75ksBXHD9+nDJlymS7nTGG0NBQjDE8+OCDLF26lJIlS+b4eE6nk2XLljFkyBD++OMPKleuzOTJk2nXrh0Oh4PPPvuM++67L/sdXcKRI0e45pprrvj3fdHRo0fP+yJ69OjRjBkzhm+++YamTZt6MZlnREREULdu3Sxr1mXfd40xqZk/ZKQvW+LKhwwRuYDD4fotn1JNEbGZaopqioiN3Dwnq89TTRGxl5vnZJV0hw753qCtKzV//nxmzZpFUtLlv+D95JNPqF+/PsYYhg8fTnh4+BU1WCFtvtb27dtjjGHUqFFER0fz1FNP0bhxY3744QdeeuklJk2adEX73rt3L61bt+bs2fzxRU5SUhJjx44lLCzsvMek6QL+5YtfboqIiIiIiIepGSIidlKT1b0OHz7MY489RoMG7p9OxhPmzZtHz549mTJlCq+88kqW2zidTiZNmkSrVq1ITk5mxYoVTJgwwZapU4oWLcro0aMxxtCuXTu2bNlC/fr12bFjB8OGDWP27Nk52l9kZCT33HMPO3bswLIsTp06leuM3rRnzx4aNmzIqFGjWLNmDfPnz89Ylx+mC1i7di2//pr7i4yqySriIRrIKiJ2U00RETupGSIidlKT1b2qVKnCypUr2bp1q7ej5NrcuXPp1asXAAkJCRw7duyibeLj42nXrh3Dhg2jatWqbN68mccee8z2LFWqVOG9995j48aNBAcHZ4yq7du3L4sXL3ZpH3/++Sf33HMPR48eJSUlhRMnTmQ0IvOalJQUJk+eTJ06dYiIiMhYnnlKh3MjWV2Z5sFXRUREEBwcnOv9ZHfhKxGxiUOdDhGxmeqKiNhJjQ4RsZNqimdUqVLF2xFyZc6cOfTu3RuAYsWKUadOHQIDA8+b9/PgwYO0bt2aH3/8kebNm7NixQquuuoqt2WKi4tj0qRJxMbGnrf86aefpnjx4rRp0+aSv3vy5Elatmx53qjISpUqsXTpUoYPH46fX94a6/jtt98SEBDA5MmT8ff3z7gVKlQIp9OJw+EgKiqKUqVKERCQd1uMAwcOtKVJnHf/BUTyGId/3iqmIuL7VFdExE5ndSVwEbGRaopkZ+vWrfz8888sXryYevXqYVnWRaf+f/3117Rt25aoqCj69u3L9OnTCQwMdGuuYsWKER4ezp49e9i0aRObNm1i48aN/PHHH7Rr144PP/yQli1bXvR78fHxPPzwwwCEhYXRrFkzmjZtSoUKFdya152aNWtGs2bNLrtNVFRUnp4qAOwbhasmq4iIiIiIaNSZiNhKNUWyU79+ferXr5/lOqfTyZw5c+jXrx8Oh4M333yTbt26eSybv78/tWvXpnbt2hkjbQ8dOsSmTZv44osvuOmmm6hcufJ5vxMbG0t4eDilS5f2WE5fEB0dTa1atbwdwyeoySriKTqrV0TsproiIjZSQ0RE7KSaIlcqMTGRvn37smDBAipWrMiaNWto2LCht2NRtWpV2rdvT/v27bNcX7FiRQ8n8r74+Hji4+Pz/EhWu6jJKuIhmjtRROymuiIidkpxqiEiIvZRTZEr8eeff9KmTRs2b97Mbbfdxtq1a7nmmmu8HUsu4dxFr9RkTaMmq4iHOPzUDBERe6muiIidNOpMROykmiI5tW3bNlq3bs2RI0fo1KkT8+bNo3Dhwt6OJZdxrslatmxZLyfxDWqyiniII49dRVBEfJ/qiojYKVEXqRERG6mmSE4sWbKE7t27k5SUxIwZMzLmYhXfFhUVBWgk6zlqsorkQZZl+QMLAAtIAboaYw5kWv8Q8BKQDCw0xizwSlARERHJM1JS1RAREfuopogrkpOTGTp0KNOmTaN06dIsX76ce+65x9uxxEVqsp5PTVYRT7H3S7iHAIwxjS3Lag5MB0IBLMsKBGYAtwFxwLeWZX1kjPnT1gQi4n36cl9EbKRTe0XETqopkp0TJ07w5JNP8vnnn3PjjTcSHh7Otdde6+1YkgOaLuB8arKKeIidcycaYz6wLGtd+t1qwF+ZVtcEfjXGnACwLGsT0BRYaVsAEfEJmpNVROykhoiI2Ek1RS5nz549hIaGcuDAAVq3bs0777xDiRIlvB1LckgjWc+nJquIp9g8n4wxJtmyrMXAI0DbTKuCgZhM908BJW09uIj4Bs1TJSI2SlZDRERspJoilxIeHk6HDh04ffo0o0ePZuTIkfjpWgN5kkaynk/PYhEPcTgcLt9cZYzpDFwHLLAsq1j64lgg81eAJYCTtj0QEfEZdtcUESnYUlKdLt1ERFzhak1RXSk4UlNTGTt2LK1btwZg7dq1jBo1Sg3WPEwjWc+nkawinmLjab2WZXUEKhtjJgLxQCppF8AC2AfUsCyrDHAaaAZMte3gIuI7NF2AiNhIVwIXETuppkhmp0+fpnPnzqxZs4bq1asTHh7OTTfd5O1YkkvnmqxlypTxchLfoCariIfYPJpsDbDIsqxvgECgP/CoZVnFjTHzLcsaCHxG2mj1hcaYI3YeXER8g111Jf2CeQuBEKAQMN4Y82Gm9QOBbsA/6Yt6GmOMLQcXEZ+h0WQiYifVFDnnt99+IzQ0lN27d3P33XezfPlyNeXyiejoaEqWLElgYKC3o/gENVlFPMXGHqsxJg54/DLrPwI+su+IIuKT7KsrHYBoY0xHy7LKAjuADzOtrwN0MsZE2HZEEfE5aoiIiJ1UUwTgv//9L48//jjHjx9nwIABTJkyhYAAtaLyi6ioKE0VkIme2SIeonkRc2/9L997O4KIW03J4fY21pWVwKpM95MvWF8XGGZZVkXg4/SpSvK8QF8syz7496jf2dPejnARv4SY7DfysGItx3k7wkUSdyzM0fZONURyJT5Jp0a74ve/E7wd4SItqgV7O0K+pJpyvuzOHMpvnE4nr776KoMHDyYgIIDFixfTqVMnb8cSm0VFRVGrVi1vx/AZarKKiIgUcMaY0wCWZZUgrdk64oJN3gdmk3ZhvbWWZbUyxqzzbEoRcbdUNURExEaqKRfJ7syhfOPMmTP06tWLxYsXU6lSJT744ANuu+02b8cSmyUkJBAfH0/ZsmW9HcVnqMkq4im6QI2I2M3eC+pVAdYCbxhj3su03AHMNMbEpN//GLgVUJNVJJ9xOtUQERH7qKZcJLszh/KFo0eP8sgjj7B161YaNmzI6tWrufrqq70dS9wgOjoaQNMFZKImq4iHOPz8vB1BRPIZu+qKZVkVgM+BvsaY/16wOhjYbVlWTSAOuJO0U91EJJ9J0ZXARcRGqinnc+HMoTzvu+++49FHH+XYsWN069aN2bNnU6hQIW/HEjdRk/ViarKKeIjmZBURu9lYV4YDpYGRlmWNTF+2AChmjJlvWdZwYANwFvivMeYTuw4sIr7DqX6IiNhINeVilzpzKD9YtGgRvXr1IiUlhddee40+ffrob+B8LioqCkDTBWSiJquIiEgBZ4zpB/S7zPp3gXc9l0hEvEGn9oqInVRTzpfNmUN5VlJSEoMGDeK1116jXLlyrFy5kubNm3s7lnjAuSarRrL+S01WEU/RnKwiYjfVFRGxkS5SIyJ2Uk25SFZnDt1vjEnwYqZciYqK4vHHH2fDhg3cfPPNfPDBB4SEhHg7lnjIuekCNJL1X2qyiniITpUQEbuproiInZxqiIiIjVRTzpfdmUN5zc6dOwkNDSUyMpLHHnuMRYsWUaxYMW/HEg/SSNaLqckq4iEOf134SkTspboiInZKSdEEiiJiH9WU/GvVqlV07tyZhIQEXn75ZYYOHaov/wsgNVkvpiariIiIiIho1JmI2Eo1Jf9JTU1l1KhRjB8/nhIlSvDhhx/SqlUrb8cSL9F0ARdTk1XEU/TNnojYTXVFRGykhoiI2Ek1JX+JjY2lQ4cOfPTRR9SoUYPw8HBq1qzp7VjiRedGsqrJ+i81WUU8RKdPiIjdVFdExE66SI2I2Ek1Jf/Yv38/oaGh7Nu3j5YtW7Js2TJKlSrl7VjiZVFRUZQsWZLAwEBvR/EZarKKeIqaISJiN9UVEbGR0+n+hohlWdcD3wMVjDFnLMu6HXgVSAY+N8aMcXsIEfEIT9QUcb/169fTrl07Tp48yZAhQ5gwYQL+/v7ejiU+IDo6WqNYL6Amq4iHOPzUDBERe6muiIidnG6+Ro1lWcHANOBspsVzgTbAb8DHlmXVMcZsd28SEfEEd9YUy7L8gelAPaAQMNoYs859Ryx4nE4nU6dOZejQoRQqVIj33nuPdu3aeTuW+JCoqChuvPFGb8fwKWqyiniKRpyJiN1UV0TERinJ7uuIWJblAOYDw4Hw9GXBQCFjzIH0+58BdwFqsorkA+6sKUBHINAY09iyrGuAx9x5sIImISGB7t27895771GlShU++OAD6tSp4+1Y4kPOnDlDXFycRrJeQE1WEU9RM0RE7Ka6IiI2susiNZZldQMGXLD4IPC+MeYny7LOLQsGYjNtcwqobksIEfE6N1/46j5gl2VZHwMOIMydBytIDh06xCOPPML27dtp2rQpq1atonz58t6OJT4mOjoagHLlynk5iW9Rk1XEQ3SBGhGxm+qKiNgp1ab5E40xbwFvZV5mWdavQLf0BmxF4HOgFVAi02YlgJO2hBARr7Orplzii5t/gDOk1ZFmwKL0/0oubNq0iTZt2vD333/z7LPPMnPmTIKCgrwdS3xQVFQUoCbrhfJEk3Xn7j3MeO0NFs2b7fFjp6SmMnPVCg7/8w9+fn4MfvwJKpX990m0Ycd21mzaiL+fg/9UrETYI4/i5+fn9lyN+j9G8plEAOKPx7J7xYaMdTVDm1AqpCIpZ5MA2P72pxnbukvde+tS7956AAQEBVDp2kqMe3wcZ+LOANCsbTNuaXELTqeT/y37H3u+3ePWPBeqdUtN+g/tSbcn+1Ol2jWMmzYUnPCr+Z0JI2d4ZlJ2zZ3oVZmfA9VrVOOliYNxOBz8su8AE196ldRUN09Ep0zK5A6qK7b59NNPWbJ0Kf5+ftS47jpeHD78vPfzY8eOMWr0aFKSk3ECL40cSUhIiMv7P378OJ07d2bVqlUUKlSIlJQUpk6dyt69e0lMSqJXr17c0cy+vw137trNzFmzWLhgvm37zKnoEyd4vFsfFsyYRPVqVTOWf7j+CxYtW0mJYsUIfeBe2rS63yN52nbrQ/FiRQGofHVFxg8blLFu43c/MOftpQDUvO7/GDGgj0e/xOjX4V66tm7KPydOAdBn/Dv8cvBPAKyQimx6ZwSV7+7P2cRkt+Zw56gzY8z/nfvZsqxI4N70C18lWpZ1LWlzst4HFMgLX509c4aXBvYhbMhIKlcLOW/diegopo8bSXJyMqXLlqPfsFEUKly4QOWJ+OZLvv5wJX5+flQKuZa2vQaeV6NjT0Tz7vRxpCQnE1y6LO37DSOo0JVnSkhIoFevXowePZr//Oc/WW6zZMkSoqKj6d+vX472ndP3g6SkJEaOHMnRo0fx8/dn1EsvnZfpnXffZe3atZQpXRqAkTl8f7rwcezevZup06bhdDopV64cL0+YQKFChXL0GM+xq6Zc4oub94F1xhgn8LVlWdfZcrACbN68eYSFhWX8/Mwzz3g5kfiycyNZNV3A+Xy+ybrwnSV89Ml6ihYp4pXjf7c3rRk4s08YPx34lXkffciYLk8DcDYpibc/W8+8gYMpHBTEy0vf5ft9e2l4401uzeQXkHYlv61zw7NcH3zNVWxbsI6k+DNuzZFZxOcRRHweAUDrsNZsW78to8FauFhhGj/SmCmdpxBUOIj+c/t7tMnatWc7Wj16LwnxCQA8P7IPr099i23f/ciICQNpcW8T/vfZRo/lEc+78Dnw3PM9eG3KAiK27mTc1KE0v6exx58DyqRM4jvOnDnD67Nns2rlSooUKcKQoUP55ptvaN68ecY2s2fP5sknnuDOO+/k282beXXWLGZMn+7S/r/dvJlXX32V6OPHM5atW7eO5ORkFi9ezF9//cUXX3xh2+NZ+PZi1n3yCUUKe+ezE0BScjJjprxK4QtGv5w4GcNrC95m1aI5lChenO79h3B73Vu55uqKbs1z9mzal81vz3rlonVx8fFMm/Mmi16dQulSJVn43kpOxMRQplQpt2bK7Nbrq9F15Jvs2HfwvOUlihVm8sAnOJvk3ubqOW4+tfdSegFLAX/gc2PM994I4U37f97LnGmTiP7nryzXr166mBYtW3FnywdZtnA+6z9cQ+jj7QtMnsSzZ/lk6ZsMmfU2QYUKs3jqGPb+sJmbGjTJ2ObL1Uu5rUVL6t/Zkk+XLWTz+g9pHvr4FR1vz549jJ8wgb/+yvrxnzlzhjFjx7J71y7uuvvuHO37St4PNm3aREpKCu+88w5btmzhtddfZ/q0aRnr9+3bx4Tx47nhhhtylCWrx+F0Ohk7dixTp06latWqrFmzhmPHjuWoaZuZm2vKJuABYLVlWTcDh9x5sPwsMTGRfv36MXfuXMqXL8/q1atp0qRJ9r8oBZpGsmbN5SGXlmV5ZRKOKpWvYeaUid44NACNb6pF/zZpc2j/deIEpYr/e0ZToL8/M/uEZfwBkZKaSmBgoNszlbi6LP6BAdTr8RC39XyYklUr/LvSAUXLleTGts1p0OcRrrnterfnyazydZWpUK0C33/y7+fjxDOJnPzrJEGFgwgqHOTxD/CHDx1hQM8RGfdr1pU3E3kAABqOSURBVLqObd/9CMCmr77n9iZ1PZLD4fBz+VYQeLKmXPgcGNjrJSK27iQgMIByV5UhOur4ZX5bmZTJdzOppvwrNzUlKCiIdxYvpkj6F7opyckXnRo3aNAgmjZtmrG+UPr6zz//nI6dOtG5Sxdmvvpqlvv3cziYP28eJYODM5Zt3rKFChUq0LdvX8aOHcsdd9xxpfEvUqVKZWZMvbiZ6ElTX5/HE60f5Kpy549u+OPoMa6vcS0lg4Px8/Pjpuuv46c9+9yexxz4jTNnztBj4HCe7jfkvGP+uHsvNaqH8MrsBXTqO4iypUt5tMEKUKdmNYY8/SAbFg7jhacfyFg+Z2RnXnp9DfFuPiPpnNRUp0u33DLGhBhjzqT//J0x5nZjzG3GmBdzvXMbeeqzSnJSEsPGT6Fy1ZAs13cLG0jze+8nNTWVqL//olTpMgUqT0BgIP0nv5ExMjU1JYWAC2r0I93CqNf8XlJTUzkZ9TclSpW+4uMlJiYyY/p0/nOJxmJiYiIPtWpF9+7dz1vurveDatWqkZySQmpqKnFxcQQEnD9Oat++fby1cCGdu3ThrbfSBnueOnWKQYMH0617d7p1787+/ftdehwHDx6kZKlSLFm6lKe7dSMmJuaKG6zgek25wrqyAHBYlvUdaRfV63XFQQuwv//+m7vvvpu5c+dSp04dtm3bpgaruERN1qxdciRrFsPt37EsqxOAMeYXt6bK5J47W3Dk6DFPHS5L/v7+TFm+jM27dzGiY+eM5X5+fpQukdZ0/eDbjSQkJlK3hvvPUkhJSub3r3/kj637KFquJPW6t2LjlPdwpjrxDwrk4Le7iPzmJxx+Dur3DCXmj384fSza7bkAWrRrwZfvfnnR8pP/nGTQm4Pw8/Njw/sbsvhN9/ny02+oVPnfETKZT/+Lj4uneIliHsnh8MA0Er7MmzXlwudAamoqV19TgflLp3P61GkiDxx25+GVSZncpiDXFTtrip+fX8apTu8tW0Z8QgINGzY8b5vS6adhRkZGMn3GDGbOmEFMTAxz5s7lvaVLKVKkCMNffJEtW7Zc9LsX3gc4eeIEBw8d4rXXXiMiIoKXRo1i0cKFOYl9SffcdRdHjh61ZV9X4oNPPqNMqVI0bnAbC959/7x1VStfw6+/HyTq+AmKFS3CdxE7qFa1stszFS5UiC5PtqVNq5Yc/OMIvZ4fybolbxIQ4M+Jk7Fs3bGT1W/NpmiRInQKG8zNN9UkpIr7c52z4rOtzFnxP2JPJ7Byel8eaHozdW8I4dONO9n5i+dqSmqK56df8SXe+qxSs9bNl13vcDhISUmhX9f2JCWe5Yku3S+7fX7L4+fnR4lSaY3cb9at5uyZBKxbbrsoU2pKClP6dSU5KZH7nuhyxce79dZbL7s+ODiYRo0aER7+71mF7nw/KFq0KEePHiW0dWtOnjzJa7Nmnff7Le+7jyeeeILixYszYMAAvv7mG7Zv306D+vV5/PHHOXjwIC+NGsXit9/O9nGcOHGCn376iaFDhlC1alXCnnuOG264gQYNGmT775YVd9YUY8xZ4Gm3HaAA2L59O61bt+bw4cO0b9+eN998M+MLZ5HsaLqArF1uuoAvgXjgKGlX67OAeYATuNP90XzLC0+04/gDD/Lca7NYMPh5igSlzUuTmprKm5+s449//uGljp09Mn9X3D8niY+KASA+KobEuDMUKlGMMzGnSUlM5uCmnaSmn1YWfeAIwVeX9UiTtXCxwpSvUp4DPx04b/n19a8nuEwwkzpOAqD7pO5E7onksPF8cwTAmWmuxKLFinIq9rRnDqy5E32qphw78hcPNX+KR598kOdH9mHEIO+NmFcmZbpiBbuu5LqmvP766+zYsQOA+fPnM/PVVzl48CDTpk7N8v186w8/8PLLLzNh/HhCQkLYtWsXJ06coG/fvkDaaed/HDnC6DFjOHzoEKVLl2bq1KlZHrtkqVLc0awZDoeDevXqcfDgwSy3y4vWfvwZDgds2bYd8+sBho+fwuuTxlKubBlKBpdgSFgvBrw4hgrlr+KG62pQumRJt2cKqXINVStXwuFwEFKlMqWCS/BP9HGurnAVpUoGc9P111GubFoTp27tm/h5/29ub7KO6f0IjW6tgcPh4NH+s4g9nTYFyacbd3LL9VVp98DtHPn7BF1aN6Vi2ZJ8MmcQd3Wb7NZMdoxSzeM89lllyYI57NuVdmbV2Blv4O/vf9ntAwICmP3uCn7c9j0zJ4zi5dfsnW/Z1/IAfLxkAb/t2wVAn7Ez+Oidufx95DBPDx2XZY32Dwhg2Ox3MT9uY+nMCYS9/JrLx7rw/SC7x3+hQ4cOue394N0lS2jUqBH9nnuOP//8kx49emTM5+p0OnnqqacokT7wp2nTpvz888/8un8/P2zdymeffQZAbGws23fsYPbrrwPQuXNnmmUxD3ipUqWoUqUK1157LQCNGzVi7969V95kVU3xWcuWLePpp58mMTGRV155hUGDBumCqpIjGsmatcs1WesBc4E5xpgvLMvaYIxp4aFcPuPLiG38ExNDuzvvolBgEA6HA/9Mp16+umYVgQEBjO7c1SMXvAKoXL8mJSqWZe/abygUXJSAwkGcPRUHQLGrSnHzU/eweeZKHA4HpUOu5ui2nz2Sq3rt6uzffvGpKPGn4klKTCI5vfGbcDqBwsXdOzn+5fy851fq3X4L2777kSbNG/DDlh1ey1LA+ExNmfXmy0wdP5tDkUeIOx3vEx8AlUmZJMdyXVPO/TEMMGbMGAKDgpg5Y0aW7+dbf/iBKVOm8Mbs2VSqVAmAa665hgoVKjB37lwCAwMJDw/Hsiwea9s222PfeuutbNy0ibvvvhtjDBUrundOUk9aPPvfuWq79B3ES8/3y2hgJien8NOefSyePZ2UlBS69x9Cv57uH4i05pPP2f9bJCMH9uXvqGji4uO5Kj3TDdb/8etvkZw4GUOJ4sXZufdn2j7k/otxjXpjLQDBxYuwY+U4aj/6InEJZ2lRvyZvh2/k5QUfZWz7y8dTeODZaZfalW2cqSluP4aP89hnlQ49nnV527nTJ9Go+d3UrlOPIkWLueUsBl/LA/Bghx4ZP7//+hQCAgPpNvzlLGv0yrnTuaVRc2rUrkOhIkVzPFVO5veDK+HO94Pg4OCMKQKCS5YkOTmZlPRBI6dPn6ZN27Z8sHYtRYoUYesPP9A6NJTY2FgefPBBHnjgAaKPH2ftmjXUufXWjOkELqVy5crEx8dz6NAhqlatyvYdO3ikdesr/FdRTfFFKSkpDB8+nClTplCyZEnWrl1Ly5YtvR1L8qBzI1nVZD3fJZusxpi/Lct6HJhqWdZtl9ouv2tcqxbTVixn4JzZpKSk8OzDoWzavYuEs2e5rnIV1v+wlZtC/sML8+cC0LpJU5rcVMutmf7Yuo9aT9xJg96P4HQ62b1iA9Wa1CYuKoZ/9kZybMcvNAxrQ2pKKkcjDKf/OuHWPOdcVfkqjh/7dy7Cpm2aEn00mr1b9nLYHKbvrL44nU4id0eyP+LiZqynTB0/m1GTnicwKJDffj3IF5987ZHjFvRvBn2pprw1Zynjpg0jKSmZMwlnGD1kijfjKJMyXbGCXFfsrCn79u1j7QcfUKdOHXr0SPujvv1TT1Gvbl1GjxnDjOnTeeWVVzKu8AxQLSSEl0aOpGPHjnTr1o2U1FQqVarEvffe69Ix2zz6KOMnTKBDx444nU5GjhiR/S/lYR9//j/iExJ4LPRBAgMDebxbbwoFBdH5ybaULuX+kaxtHryPFydOo2OfgTgcDsYOGcjS1R9Q9ZpKtGjSkH49u9JzcNp0oPe1aEaN6iFuz3RO7OkEXnp9NV8seIGzicls2LqX9Zt2eez4mRX0hogvfVY5FRvD65PHM2zCK7Rq8yRzpk1k+eI38XM46DVgSIHKc/iA4fsvP6b6DbWZPbI/AHe0asu1N93C8tcn8/SwCTRr1YYVc6bx2fLFOPwctO01wNYMMTExGe8HWSlTpozb3g86dujAqFGj6NK1K0lJSYSFhfHVhg3Ex8fTtm1bwsLC6N69O4FBQTSoX5+mTZtSq1YtRo8ezerVqzkdF8ezvVybrjQwMJDRo0czdNgwcDq5+eabsxzx6qqCXlN8zcmTJ2nXrh3r16+nZs2ahIeHU6NGDW/Hkjzq3EjWMmXcOy93XuNwOrMfcWNZVhegqzEmR1dlSIyN9rnhPMc2bPF2hIvs2+R7F0L834+R3o5wnvW/+OaFZnce/NrlDkfsr3tcfj0E/9+N+bpzcqU1pXa1O3yupojYKSc1BVyvK6opWTuTkOBzNcWR6pmryOeEX7xnvqzNER/8w71Yy3HejnCRxB0Lc/Tar9rlXZdeE4fe7pivawpcWV35+a9Yn6spvuj3EwnejnCRFtWCs99IKFykiFtqChSMunIlIiIinHXr5v5Czfv27SM0NJT9+/fz0EMPsWTJEoKD9byXK1evXj32799PTEyMt6N4XEREBHXr1s2yZl1uuoAMxpi3gbdtzCRS4DhyOLdTfqaaImIP1ZU0qiki9khNSvR2BJ+huiKSe6opvmHdunW0b9+eU6dOMXLkSEaPHu2xqQ4l/4qOjtZFr7LgUpNVRERERETyN53aKyJ2Uk3xLqfTycSJExkxYgRFihRh5cqVtHVhnmARV0RFRVGzZk1vx/A5arKKeEoBnjtRRNxEdUVEbKSGiIjYSTXFe+Li4ujatSsrV64kJCSE8PBwateu7e1Ykk+cPXuW06dP66JXWVCTVcRD7L5AjWVZDYDJxpjmFywfCHQD/klf1NMYY2w9uIj4hIJ84SsRsZ8aIiJiJ9UU74iMjCQ0NJSdO3fSokULVqxYoWaY2Co6OhpA0wVkQU1WEU9x2DfvjWVZLwAdgbgsVtcBOhljImw7oIj4JhvrioiIGiIiYifVFM/76quvaNu2LdHR0Tz33HNMnTqVwMBAb8eSfCYqKgpAzfssqMkq4iEOP1tHnB0AHgXezWJdXWCYZVkVgY+NMRPtPLCI+A6b64qIFHCpaoiIiI1UU85nWZYf8AZwM3AW6G6M+dWOfTudTmbPnk3//v3x9/dn4cKFdO3a1Y5di1xETdZL0xAYEU9xOFy/ZcMYsxpIusTq94FewJ1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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.interpolate import interp2d\n", "average_field = []\n", "for i in range(all_obj_df['frame_idx'].max()):\n", " frame_0 = all_obj_df[all_obj_df['frame_idx'].isin([i])]\n", " frame_1 = all_obj_df[all_obj_df['frame_idx'].isin([i+1])]\n", " dist_df_list = []\n", " for _, row_0 in frame_0.iterrows():\n", " for _, row_1 in frame_1.iterrows():\n", " dist_df_list += [dict(x0=row_0['x'],\n", " y0=row_0['y'],\n", " lab0=int(row_0['label']),\n", " x1=row_1['x'],\n", " y1=row_1['y'],\n", " lab1=int(row_1['label']),\n", " dist=np.sqrt(\n", " np.square(row_0['x']-row_1['x']) +\n", " np.square(row_0['y']-row_1['y'])))]\n", " dist_df = pd.DataFrame(dist_df_list)\n", " for _, c_rows in dist_df.groupby('lab0'):\n", " _, best_row = next(c_rows.sort_values('dist').iterrows())\n", " average_field += [dict(frame_idx=i,\n", " x=best_row['x0'],\n", " y=best_row['y0'],\n", " dx=best_row['x1']-best_row['x0'],\n", " dy=best_row['y1']-best_row['y0'])]\n", "average_field_df = pd.DataFrame(average_field)\n", "print('Average Flow:')\n", "print(average_field_df[['dx', 'dy']].mean())\n", "\n", "\n", "def img_intp(f):\n", " def new_f(x, y):\n", " return np.stack([f(ix, iy) for ix, iy in zip(np.ravel(x), np.ravel(y))], 0).reshape(np.shape(x))\n", " return new_f\n", "\n", "\n", "dx_func = img_intp(\n", " interp2d(average_field_df['x'], average_field_df['y'], average_field_df['dx']))\n", "dy_func = img_intp(\n", " interp2d(average_field_df['x'], average_field_df['y'], average_field_df['dy']))\n", "\n", "g_x, g_y = np.meshgrid(np.linspace(average_field_df['x'].min(),\n", " average_field_df['x'].max(), 5),\n", " np.linspace(average_field_df['y'].min(),\n", " average_field_df['y'].max(), 5))\n", "fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(1, 5, figsize=(24, 4))\n", "sns.heatmap(g_x, ax=ax1, annot=True)\n", "ax1.set_title('X Position')\n", "sns.heatmap(g_y, ax=ax2, annot=True)\n", "ax2.set_title('Y Position')\n", "\n", "sns.heatmap(dx_func(g_x, g_y), ax=ax3, annot=True)\n", "ax3.set_title('$\\Delta x$')\n", "sns.heatmap(dy_func(g_x, g_y), ax=ax4, annot=True)\n", "ax4.set_title('$\\Delta y$')\n", "ax5.quiver(g_x, g_y, dx_func(g_x, g_y), dy_func(g_x, g_y),\n", " scale=1.0, scale_units='xy', angles='xy')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " frame_idx dx dy\n", "0 0 0.0 -2.0\n", "1 1 -1.0 0.0\n", "2 2 0.0 -2.0\n", "3 3 -1.0 0.0\n", "4 4 0.0 1.0\n", "5 5 -1.0 0.0\n", "6 6 0.0 -2.0\n", "7 7 -1.0 0.0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "temp_avg_field = average_field_df[['frame_idx', 'dx', 'dy']].groupby(\n", " 'frame_idx').agg('mean').reset_index()\n", "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 4))\n", "ax1.plot(temp_avg_field['frame_idx'], temp_avg_field['dx'], 'rs-')\n", "ax1.set_title('$\\Delta x$')\n", "ax1.set_xlabel('Timestep')\n", "ax2.plot(temp_avg_field['frame_idx'], temp_avg_field['dy'], 'rs-')\n", "ax2.set_title('$\\Delta y$')\n", "ax2.set_xlabel('Timestep')\n", "ax3.quiver(temp_avg_field['dx'], temp_avg_field['dy'],\n", " scale=1, scale_units='xy', angles='xy')\n", "ax3.set_title('$\\Delta x$, $\\Delta y$')\n", "ax3.set_xlabel('Timestep')\n", "temp_avg_field" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Miniconda3\\envs\\qbi2018\\lib\\site-packages\\scipy\\interpolate\\_fitpack_impl.py:974: RuntimeWarning: No more knots can be added because the number of B-spline\n", "coefficients already exceeds the number of data points m.\n", "Probable causes: either s or m too small. (fp>s)\n", "\tkx,ky=1,1 nx,ny=5,5 m=8 fp=0.000000 s=0.000000\n", " warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "\n", "g_x, g_y = np.meshgrid(np.linspace(average_field_df['x'].min(),\n", " average_field_df['x'].max(), 4),\n", " np.linspace(average_field_df['y'].min(),\n", " average_field_df['y'].max(), 4))\n", "\n", "frames = len(sorted(np.unique(average_field_df['frame_idx'])))\n", "fig, m_axs = plt.subplots(2, 3, figsize=(14, 10))\n", "for c_ax in m_axs.flatten():\n", " c_ax.axis('off')\n", "[(ax1, ax2, _), (ax3, ax4, ax5)] = m_axs\n", "\n", "\n", "def draw_frame_idx(idx):\n", " plt.cla()\n", " c_df = average_field_df[average_field_df['frame_idx'].isin([idx])]\n", " dx_func = img_intp(interp2d(c_df['x'], c_df['y'], c_df['dx']))\n", " dy_func = img_intp(interp2d(c_df['x'], c_df['y'], c_df['dy']))\n", " sns.heatmap(g_x, ax=ax1, annot=False, cbar=False)\n", " ax1.set_title('Frame %d\\nX Position' % idx)\n", " sns.heatmap(g_y, ax=ax2, annot=False, cbar=False)\n", " ax2.set_title('Y Position')\n", "\n", " sns.heatmap(dx_func(g_x, g_y), ax=ax3, annot=False, cbar=False, fmt='2.1f')\n", " ax3.set_title('$\\Delta x$')\n", " sns.heatmap(dy_func(g_x, g_y), ax=ax4, annot=False, cbar=False, fmt='2.1f')\n", " ax4.set_title('$\\Delta y$')\n", " ax5.quiver(g_x, g_y, dx_func(g_x, g_y), dy_func(g_x, g_y),\n", " scale=1.0, scale_units='xy', angles='xy')\n", "\n", "\n", "# write animation frames\n", "anim_code = FuncAnimation(fig,\n", " draw_frame_idx,\n", " frames=frames,\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", "plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Random Appearance / Disappearance\n", "\n", "\n", "Under perfect imaging and experimental conditions objects should not appear and reappear but due to \n", "- noise\n", "- limited fields of view / depth of field\n", "- discrete segmentation approachs\n", "- motion artifacts\n", " - blurred objects often have lower intensity values than still objects\n", "\n", "It is common for objects to appear and vanish regularly in an experiment.\n", "\n", "- See https://rawgit.com/kmader/Quantitative-Big-Imaging-2017/master/Lectures/09-Slides.html#(19)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Jitter / Motion Noise\n", "\n", "Even perfect spherical objects do not move in a straight line. The jitter can be seen as a stochastic variable with a random magnitude ($a$) and angle ($b$). This is then sampled at every point in the field\n", "\n", "$$ \\vec{v}(\\vec{x})=\\vec{v}_L(\\vec{x})+||a||\\measuredangle b $$\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Limits of Tracking\n", "\n", "We see that visually tracking samples can be difficult and there are a number of parameters which affect the ability for us to clearly see the tracking.\n", "- flow rate\n", "- flow type\n", "- density\n", "- appearance and disappearance rate \n", "- jitter\n", "- particle uniqueness\n", "\n", "\n", "\n", "\n", "We thus try to quantify the limits of these parameters for different tracking methods in order to design experiments better. \n", "\n", "### Acquisition-based Parameters\n", "\n", "- acquisition rate $\\rightarrow$ flow rate, jitter (per frame)\n", "- resolution $\\rightarrow$ density, appearance rate\n", "\n", "\n", "### Experimental Parameters\n", "\n", "- experimental setup (pressure, etc) $\\rightarrow$ flow rate/type \n", "- polydispersity $\\rightarrow$ particle uniqueness\n", "- vibration/temperature $\\rightarrow$ jitter\n", "- mixture $\\rightarrow$ density" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Basic Simulations\n", "\n", "\n", "Input flow from simulation\n", "\n", "$$ \\vec{v}(\\vec{x})=\\langle 0,0,0.05 \\rangle+||0.01||\\measuredangle b $$\n", "\n", "\n", "https://rawgit.com/kmader/Quantitative-Big-Imaging-2017/master/Lectures/09-Slides.html#(23)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Designing Experiments\n", "\n", "\n", "### How does this help us to design experiments?\n", "- density can be changed by adjusting the concentration of the substances being examined or the field of view\n", "- flow per frame (image velocity) can usually be adjusted by changing pressure or acquisition time\n", "- jitter can be estimated from images\n", "\n", "### How much is enough?\n", "\n", "- difficult to create one number for every experiment\n", "- 5% error in bubble position \n", " - $\\rightarrow$ <5% in flow field\n", " - $\\rightarrow$ >20% error in topology\n", "- 5% error in shape or volume\n", " - $\\rightarrow$ 5% in distribution or changes\n", " - $\\rightarrow$ >5% in individual bubble changes\n", " - $\\rightarrow$ >15% for single bubble strain tensor calculations" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Extending Nearest Neighbor\n", "\n", "\n", "### Bijective Requirement\n", "\n", "- We define $\\vec{P}_f$ as the result of performing the nearest neigbhor tracking on $\\vec{P}_0$\n", "- $$ \\vec{P}_f=\\textrm{argmin}(||\\vec{P}_0-\\vec{y} || \\forall \\vec{y}\\in I_1) $$\n", "- We define $\\vec{P}_i$ as the result of performing the nearest neigbhor tracking on $\\vec{P}_f$\n", "- $$ \\vec{P}_i=\\textrm{argmin}(||\\vec{P}_f-\\vec{y} || \\forall \\vec{y}\\in I_0) $$\n", "- We say the tracking is bijective if these two points are the same\n", "- $$ \\vec{P}_i \\stackrel{?}{=} \\vec{P}_0 $$\n", "\n", "\n", "\n", "### Maximum Displacement\n", "\n", "$$ \\vec{P}_1=\\begin{cases} \n", "||\\vec{P}_0-\\vec{y} ||<\\textrm{MAXD}, & \\textrm{argmin}(||\\vec{P}_0-\\vec{y} || \\forall \\vec{y}\\in I_1) \\\\\n", "\\textrm{Otherwise}, & \\emptyset \\end{cases}$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Extending Nearest Neighbor (Continued)\n", "\n", "\n", "### Prior / Expected Movement\n", "\n", "$$ \\vec{P}_1=\\textrm{argmin}(||\\vec{P}_0+\\vec{v}_{offset}-\\vec{y} || \\forall \\vec{y}\\in I_1) $$\n", "\n", "### Adaptive Movement\n", "Can then be calculated in an iterative fashion where the offset is the average from all of the $\\vec{P}_1-\\vec{P}_0$ vectors. It can also be performed \n", "$$ \\vec{P}_1=\\textrm{argmin}(||\\vec{P}_0+\\vec{v}_{offset}-\\vec{y} || \\forall \\vec{y}\\in I_1) $$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Beyond Nearest Neighbor\n", "\n", "\n", "While nearest neighbor provides a useful starting tool it is not sufficient for truly complicated flows and datasets.\n", "\n", "### Better Approaches\n", "\n", "1. Multiple Hypothesis Testing\n", "Nearest neighbor just compares the points between two frames and there is much more information available in most time-resolved datasets. This approach allows for multiple possible paths to be explored at the same time and the best chosen only after all frames have been examined\n", "\n", "\n", "\n", "### Shortcomings\n", "\n", "1. Merging and Splitting Particles\n", "The simplicity of the nearest neighbor model does really allow for particles to merge and split (relaxing the bijective requirement allows such behavior, but the method is still not suited for such tracking). For such systems a more specific, physically-based is required to encapsulate this behavior." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Voxel-based Approaches\n", "\n", "For voxel-based approachs the most common analyses are digital image correlation (or for 3D images digital volume correlation), where the correlation is calculated between two images or volumes.\n", "\n", "### Standard Image Correlation\n", "\n", "Given images $I_0(\\vec{x})$ and $I_1(\\vec{x})$ at time $t_0$ and $t_1$ respectively. The correlation between these two images can be calculated\n", "\n", "$$ C_{I_0,I_1}(\\vec{r})=\\langle I_0(\\vec{x}) I_1(\\vec{x}+\\vec{r}) \\rangle $$" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Miniconda3\\envs\\qbi2018\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n" ] }, { "data": { "text/plain": [ "Text(0.5,1,'$T_1$')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3frbWjkly/yR37b3/1+a1pyU5bKozAAAAlpm9CQDma8qnvd8vycVJntZaOy3J7ZN8JMn/nfAMAACAZWZvAoA5mvJp7wcluVeSX09ynyT3TvLLSd4+4RkAAADLzN4EAHM0Zfz8yeaPz+y9X9F7/06SM5I8tLV2wITnAAAALCt7EwDM0ZTx8+LNx7v1lmu33PxxbcJzAAAAlpW9CQDmaMrX/Pxokq8leUtr7dQkt01yZpIP9N6vmPAcAACAZWVvAoA5muzOz977T5P8dpJrknwlyZeTfDPJk6c6AwAAYJnZmwBgvqa88zO9928leeyUjwkAAFCJvQkA5mfK1/wEAAAAAFgY4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEniJwAAAABQ0vroAYA9Pfa444aef/kVVww9P0kOOuA9Q88/9NDDh55/2WXfGHo+AADADTnyyPsPPf/kZ5w89PxFsLY2/p7GjY1do0e4QeP/KQEAAAAA7AfiJwAAAABQkvgJAAAAAJQkfgIAAAAAJYmfAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEniJwAAAABQkvgJAAAAAJQkfgIAAAAAJYmfAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEniJwAAAABQkvgJAAAAAJQkfgIAAAAAJYmfAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWtjx4AWCwHH3jg6BHyqEedPvT8737360PPX1tbG3o+AABw/Q65y6+OHiHnX/Spoeff81fG/zMY7YQTnjj0/I2NXUPPXxbu/AQAAAAAShI/AQAAAICSxE8AAAAAoCTxEwAAAAAoSfwEAAAAAEoSPwEAAACAksRPAAAAAKAk8RMAAAAAKEn8BAAAAABKEj8BAAAAgJLETwAAAACgpPUpH6y1dt8kr0tyryRXJXlPkuf23n8y5TkAAADLyt4EAPMz2Z2frbVbJPlgkvclOSjJbyQ5MclzpzoDAABgmdmbAGC+pnza+52THLb5mGub13YluXLCMwAAAJaZvQkA5miy+Nl7vzzJa5P8eZKfJPn3JF/evAYAALDy7E0AMF9TP+39qiRPT3L7JPdMco8kL5nqDAAAgGVmbwKA+ZryDY8emeSU3vuRmx9/qbX2kiRnJXnRhOcAAAAsK3sTAMzRlK/5uT3JL+117adJrp7wDAAAgGVmbwKAOZryzs9zk7yytfaCJK9KcniSFyZ554RnAAAALDN7EwDM0ZRveHRxkt9P8ogklyf5eJK/S3LGVGcAAAAsM3sTAMzXlHd+pvf+90n+fsrHBAAAqMTeBADzM+VrfgIAAAAALAzxEwAAAAAoSfwEAAAAAEoSPwEAAACAksRPAAAAAKAk8RMAAAAAKEn8BAAAAABKEj8BAAAAgJLETwAAAACgJPETAAAAAChpffQAwGI58sj7jx4hJz/j5NEjDLW2Nv77Uhsbu0aPAADAArvjHQ8Zev4n/+VzQ88/evv2oecvghN/78lDzz/vo28dej7LY/yGDQAAAACwH4ifAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEniJwAAAABQkvgJAAAAAJQkfgIAAAAAJYmfAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEniJwAAAABQkvgJAAAAAJQkfgIAAAAAJYmfAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlLQ+egBYNIfc5VeHnn/+RZ8aev49f2Xsn38RnHDCE4eev7Gxa+j5AAAstue94vWjR8ifPv8Ph55/4Ve/OvT857z07KHnJ8mrX/z00SPAUnDnJwAAAABQkvgJAAAAAJQkfgIAAAAAJYmfAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAlrd/U39haOyTJhUme2nv/xOa130xyVpKjklyW5OW99zdPMCcAAMBSsTMBwHg36c7P1toDMvsifsSWa3dO8qEkb09ypyRPSfLa1tr9JpgTAABgadiZAGAx7HP8bK09Mcm7k5yx10+dkuTy3vvZvfdreu/nJ3lXkj+++WMCAAAsBzsTACyOm3Ln57lJjui9n7PX9aOSfGGvaxcnOfqmDAYAALCk7EwAsCD2+TU/e+/fvo6fOjDJj/a6dmWSA/b1DAAAgGVlZwKAxTHlu73/KMnt9rp2uyRXTHgGAADAsrIzAcCcTRk/v5jZ0zi2usfmdQAAgFVnZwKAOdvnp71fj79J8mettWcmOTvJbyV5fJKTJzwDAABgWdmZAGDOJrvzs/d+eZITkjwmyeVJ3pTkGb33j091BgAAwLKyMwHA/N2sOz9772t7ffy5JA+4WRMBAAAUYWcCgLGmfM1PAAAAAICFIX4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEniJwAAAABQkvgJAAAAAJQkfgIAAAAAJa2PHoDFcsc7HjL0/E/+y+eGnp8kR2/fPnqEoU78vSePHiHnffSto0cAAICF9aoXnDZ6hIWYAeDGcOcnAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEniJwAAAABQkvgJAAAAAJQkfgIAAAAAJYmfAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEniJwAAAABQkvgJAAAAAJQkfgIAAAAAJYmfAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEniJwAAAABQkvgJAAAAAJQkfgIAAAAAJa2PHoA9Pe8Vrx96/p8+/w+Hnn/hV7869Pwkec5Lzx56/qtf/PSh5wMAAABU4c5PAAAAAKAk8RMAAAAAKEn8BAAAAABKEj8BAAAAgJLETwAAAACgJPETAAAAAChJ/AQAAAAAShI/AQAAAICSxE8AAAAAoCTxEwAAAAAoSfwEAAAAAEpav6m/sbV2SJILkzy19/6JzWunJHlRkl9L8v0kb03yst77rps/KgAAwPKwMwHAeDfpzs/W2gMy+yJ+xJZrxyR5R5IXJrlTkpOSnJrkWTd7SgAAgCViZwKAxbDP8bO19sQk705yxl4/tSPJG3rvH+y97+q9/2uSv03ywJs9JQAAwJKwMwHA4rgpd36em+SI3vs5Wy/23t/fez9998ettdsmeViSnTdvRAAAgKViZwKABbHPr/nZe//2Df2a1tqBSd6X5Kokr70JcwEAACwlOxMALI7J3+29tdYye22b9SS/03u/YuozAAAAlpWdCQDmZ9L42Vp7aJLPJvlIkhN77/8x5eMDAAAsMzsTAMzXPj/t/bq01u6f2Yt1n9Z7f8tUjwsAAFCBnQkA5m+y+JnkBUluleSs1tpZW65f0Hs/acJzAAAAlpGdCQDm7GbFz9772pa/f8TNHwcAAKAOOxMAjDX5Gx4BAAAAACwC8RMAAAAAKEn8BAAAAABKEj8BAAAAgJLETwAAAACgJPETAAAAAChJ/AQAAAAAShI/AQAAAICSxE8AAAAAoCTxEwAAAAAoSfwEAAAAAEpaHz0Ae3rVC05b6fMBAAAAYCru/AQAAAAAShI/AQAAAICSxE8AAAAAoCTxEwAAAAAoSfwEAAAAAEoSPwEAAACAksRPAAAAAKAk8RMAAAAAKEn8BAAAAABKEj8BAAAAgJLETwAAAACgJPETAAAAAChJ/AQAAAAAShI/AQAAAICSxE8AAAAAoCTxEwAAAAAoSfwEAAAAAEoSPwEAAACAksRPAAAAAKAk8RMAAAAAKEn8BAAAAABKEj8BAAAAgJLETwAAAACgJPETAAAAAChJ/AQAAAAAShI/AQAAAICSxE8AAAAAoCTxEwAAAAAoSfwEAAAAAEoSPwEAAACAktZHD7DFbZJkx44dg8cAgLG2fC28zcAxAFhM9iYAyI3fmxYpfu5IkjPPPHPwGACwMHYk+fToIQBYKDsSexMAbLEj17M3LVL8PDfJ45NckuTHY0cBgKFuk9kX8HMHzwHA4rE3AcDMjdqb1jY2NuYyDQAAAADAPHnDIwAAAACgJPETAAAAAChJ/AQAAAAAShI/AQAAAICSxE8AAAAAoCTxEwAAAAAoSfwEAAAAAEpaHz3AFFprhyb56yQPSnJNkncmeXbv/ZqRc81Ta+3oJK9OckySq5Ocl+T03vv3hg42Z621Wyb5WJJLeu+nDh5nrlprByV5XZKHZfaNjU8mOa33funQweaotXbfzP4Z3CvJVUnek+S5vfefDB1sDlprhyS5MMlTe++f2Lz2m0nOSnJUksuSvLz3/uZhQ+5H1/HnPyXJi5L8WpLvJ3lrkpf13neNmhMARlr1vcnO9P/Zm1Z3b7Izre7OlKzu3lTlzs9zkvwwybYk90vykCTPGjrRHLXWbpvkw0k+neRumf1He3Bm/8KumhcnOX70EIO8P8kBSY5Isj3Jz5K8cehEc9Rau0WSDyZ5X5KDkvxGkhOTPHfkXPPQWntAZl/Ajthy7c5JPpTk7UnulOQpSV7bWrvfkCH3o+v48x+T5B1JXpjZn/+kJKdmhb42AMAvsLJ7k53pWuxNK7g32ZlWd2dKVntvWvr42Vq7e2bfuXxu7/3K3vvXkrwsydOHDjZf25N8PslLe+9X994vT/JXSR44dqz5aq39bpJTMvtitlI2P2HdP8mpvfcf9N6vSPK0JM8bO9lc3TnJYZl9XlvbvLYryZXDJpqD1toTk7w7yRl7/dQpSS7vvZ/de7+m935+kncl+eN5z7g/Xc+ff0eSN/TeP9h739V7/9ckf5sV+7wIALvZm+xMu9mbVnpvsjPtaSV2psTetPTxM7Pv2H2/9/6tLdcuTrK9tXanQTPNVZ85qff+sy2XH51k56iZ5m3zKTxvTvK4FP/EfR3ul9m/909rrX21tXZpkj9PshJP3UiSzf+BfW1mf+6fJPn3JF/evFbZuUmO6L2fs9f1o5J8Ya9rFyc5ei5Tzc8v/PP33t/fez9998ebd3s8LCv0eREA9rLSe5OdacbetNp7k51pZXemZMX3pgrx88AkP9rr2u5P4gfMeZbhWmtrrbWXJ3l4kj8ZPc88bN66/84kr+m9f370PIMclNlrtvx6kvskuXeSX87s9v2VsPnvwVWZ3b1w+yT3THKPJC8ZOdf+1nv/9nW8Ttd1fW4s9Xnxev78P9daOzDJBzL796P6/9gBwHWxN21axZ0psTdtWum9yc50LSuxMyX2pgrx80dJbrfXtd0fXzHnWYZqrd0hs9fu+F9JHth73/s7GFU9P8mPe+9/OXqQgXa/OPUze+9X9N6/k9nt7A9trZX7xH0dHpnklN7763vvP+m9fymzL+J/NHiuUa7rc+OqfV5smb2uzXqS39l8ahMArCJ7U1Z6Z0rsTYm9yc60JzvTpup7U4X4+cUkB7fW7rrl2j2SfLP3/p+DZpq71toRSf4pyR2SHLtiX8SfkORBrbUftNZ+kNlTOB63+fer4uLM/nu+9ZZrt9z8ce3av7yk7Ul+aa9rP83snTxX0RczexrHVvfYvL4SWmsPTfLZJB9JcmLv/T8GjwQAI6383rTiO1Nib0rsTXamPa38zpSsxt60PnqAm6v3/pXW2j8keV1r7X8nuUuSF2X2OiYrYfMdys7f/Ospvfddg0eaq977kVs/bq29bfP6qSPmGeSjSb6W5C2ttVOT3DbJmUk+UO07Ntfj3CSvbK29IMmrkhye2TvWvXPoVOP8TZI/a63X5CL+AAABOElEQVQ9M8nZSX4ryeOTnDx0qjlprd0/sxfqPq33/pbR8wDAaKu+N636zpTYmzat+t5kZ9rTSu9MyersTRXu/ExmL1S9nuTfkvxjZrX6ZUMnmq8nZfYdnD9I8l+ttR/u/mvwXMxJ7/2nSX47yTVJvpLZi1Z/M8mTR841T733i5P8fpJHJLk8yceT/F2u/W52K2HzxcxPSPKYzP55vCnJM3rvHx862Py8IMmtkpy19XNia+3DowcDgIFWeW+yM7Hye5OdaU92piQrsjetbWxsjJ4BAAAAAGByVe78BAAAAADYg/gJAAAAAJQkfgIAAAAAJYmfAAAAAEBJ4icAAAAAUJL4CQAAAACUJH4CAAAAACWJnwAAAABASeInAAAAAFCS+AkAAAAAlCR+AgAAAAAliZ8AAAAAQEn/D/4gILdvsK0MAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from skimage.io import imread\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from keras.datasets import mnist\n", "(img_data, dig_label), _ = mnist.load_data()\n", "bw_img = img_data[0, ::2, ::2]\n", "shift_img = np.roll(np.roll(bw_img, -2, axis=0), 2, axis=1)\n", "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6), dpi=100)\n", "ax1.imshow(bw_img, cmap='bone')\n", "ax1.set_title('$T_0$')\n", "ax2.imshow(shift_img, cmap='bone')\n", "ax2.set_title('$T_1$')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6), dpi=100)\n", "sns.heatmap(bw_img, ax=ax1, cbar=False, annot=True, fmt='d', cmap='bone')\n", "sns.heatmap(shift_img, ax=ax2, cbar=False, annot=True, fmt='d', cmap='bone')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 6), dpi=100)\n", "\n", "mx, my = np.meshgrid(np.arange(-4, 6, 2),\n", " np.arange(-4, 6, 2))\n", "\n", "nx = mx.ravel()\n", "ny = my.ravel()\n", "out_score = np.zeros(nx.shape, dtype=np.float32)\n", "\n", "\n", "def update_frame(i):\n", " a_img = bw_img\n", " b_img = np.roll(np.roll(shift_img, nx[i], axis=1), ny[i], axis=0)\n", " ax1.cla()\n", " sns.heatmap(a_img, ax=ax1, cbar=False, annot=True, fmt='d', cmap='bone')\n", " ax2.cla()\n", " sns.heatmap(b_img, ax=ax2, cbar=False, annot=True, fmt='d', cmap='bone')\n", "\n", " out_score[i] = np.mean(a_img*b_img)\n", " ax3.cla()\n", " sns.heatmap(out_score.reshape(mx.shape), ax=ax3,\n", " cbar=False, annot=True, fmt='2.1f', cmap='RdBu')\n", " ax3.set_xticklabels(mx[0, :])\n", " ax3.set_yticklabels(my[:, 0])\n", " ax1.set_title('Iteration #{}'.format(i+1))\n", " ax2.set_title('X-Offset: %d\\nY-Offset: %d' % (nx[i], ny[i]))\n", " ax3.set_title(r'$\\langle I_0(\\vec{x}) I_1(\\vec{x}+\\vec{r}) \\rangle$')\n", "\n", "\n", "# write animation frames\n", "anim_code = FuncAnimation(fig,\n", " update_frame,\n", " frames=len(nx),\n", " interval=300,\n", " repeat_delay=4000).to_html5_video()\n", "plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Mean Squared Error\n", "We can also use MSE or RMSE " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'$\\\\langle (I_0(\\\\vec{x})-I_1(\\\\vec{x}+\\\\vec{r}))^2 \\\\rangle$')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 6), dpi=100)\n", "out_score = np.zeros(nx.shape, dtype=np.float32)\n", "for i in range(len(nx)):\n", " a_img = bw_img\n", " b_img = np.roll(np.roll(shift_img, nx[i], axis=1), ny[i], axis=0)\n", " out_score[i] = np.mean(np.square(a_img-b_img))\n", "\n", "# get the minimum\n", "i_min = np.argmin(out_score)\n", "b_img = np.roll(np.roll(shift_img, nx[i_min], axis=1), ny[i_min], axis=0)\n", "\n", "sns.heatmap(a_img, ax=ax1, cbar=False, annot=True, fmt='d', cmap='bone')\n", "sns.heatmap(b_img, ax=ax2, cbar=False, annot=True, fmt='d', cmap='bone')\n", "sns.heatmap(out_score.reshape(mx.shape), ax=ax3, cbar=False,\n", " annot=True, fmt='2.1f', cmap='viridis')\n", "ax3.set_xticklabels(mx[0, :])\n", "ax3.set_yticklabels(my[:, 0])\n", "ax1.set_title('Iteration #{}'.format(i+1))\n", "ax2.set_title('X-Offset: %d\\nY-Offset: %d' % (nx[i_min], ny[i_min]))\n", "ax3.set_title(r'$\\langle (I_0(\\vec{x})-I_1(\\vec{x}+\\vec{r}))^2 \\rangle$')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Real Example \n", "## Bone Slice Registration" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'$T_1$')" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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V3S/xHncxjfM6E1gTdG5Ge3kpz+cPydaK4rwRctkMyoUzGs1Y/2xeEuewn17DGUO2l7y5eRzLrh+Fsqvrh3j/dMpr+CHZmkxSZiprJlJl2qMPBlF0fHp6F+8/vYjtV0UUnJ39HUJjWmKBfRYXCOrr4XmMP1XiF5Htu2iPtISyTEB/+3f3Kps3/gsFY4wxxhhjjDHGGGOMMcYcxR8UjDHGGGOMMcYYY4wxxhhzFH9QMMYYY4wxxhhjjDHGGGPMUfxBwRhjjDHGGGOMMcYYY4wxR3nhpMzX14/15Mnb7/3n4ZBFhffO7oeyk4sovEiFFSDnW82j3IaEVRKLeEjYMThjqSzJRYbdWJZJxNooEC0XzjwvJFD9pV/630PZ2X8d+0mShneiyOxf+YHvC2WfevAa3v/2p6KwhuREN49ZdFoqJ8qEVTSuqKy+4G92JNuugwQoExORyIUkSJtEbkYiIxqTmViJ5g9JQTM5HkmNeyA8yqTW9Lsk1aY5LbEcDYVlidS5t4117fSi3CdrP7q2Bn2Sye1IutYAORoJ2yQWEdG7fvzT34L3P/rqd4WyCUigttsoJpKy+MWs17FfNiA1zuj1oiCRyjJQNkzzX9xXJKama+sgCpb4XRcgBW5kcix4/nIZx0UmFaa4NpvFvqaYIEm9bmxrkjyRqFli6RsJmBeLaXJ/bL9tPb5/lis0WzFWkUhsMIgSMUm6e+9joezsfpSTZXIwev/mmkSkPNdpDaO8ZgvCLkmqQaykuJa1H1Hp2kKRMsU/6dm1olFBPG7My8Juu3tm/k4uORbW6mVS5k0iCiyVImbzO8vHDhnejXs5iaWqtO8qFYJK0raCALjegH0f5LIkZEyfD3nzzZMJXju/YdntIZRfShxL1wsQhSaxlLh8+zKUTS5jfpJBUt6rZC0sJRNN0rig9ZHGmSStQXQ7vY5zbUPybEm7bdm6R6JuifcImYCZoD0yjekq8tMOtF8mGqXnT69i+2VrfqlUNxP10h6T9tj75DkkRad9ZxXRdal8WOK4SlJsSZpBu1KOl9WV8tFsXhGlOV4j2WMTW5gXm0L5rlRNFt9M9kOHZGsNrUt334z7hnPYCzzlU6GEzo0efpmlzF/54q+Fsqvrd5NnRVqt+P7ZHpGgfefFnTdC2f1v4TPCk/O4b6xXGCu0BlcRkNNcWc7L5yrth3sgeu4kUvNSsrWGoFiRzenbcaGmsnjmv1AwxhhjjDHGGGOMMcYYY8xR/EHBGGOMMcYYY4wxxhhjjDFH8QcFY4wxxhhjjDHGGGOMMcYcxR8UjDHGGGOMMcYYY4wxxhhzFH9QMMYYY4wxxhhjjDHGGGPMUcqV7d8kdruNdtv3Tdybzar43nYvWtUzyABOVvpmk5uIDO6tTrSa77Zsuq/VozX7fDAIZYPTaAWXpLt3oy39N37jH+G13yzW62hA/7t/93N4bavVCWW1/zBe9zt/22fw/u956xOhbA9W9S/96pfx/vXqYSibXk1D2S4xsNfq8VvcbhufP7+Z4/3L6SKUdXqxTdYrttLT+L1tZX+vTtAmktTpx2edXAxDWe+kh/cT80l811Y7zglJanVjOb1/M7mfbPWC91+vOH5Mr2NfT55MYp2gnSRJuzivqf2z+YvA/f0ht3+9Ed+f2qQO4/Tpo6iuMf6s7nH73f+W+6HsjUefDGU314/4+fXyum5hDi6Xs1DWaHCspri0XMaxmj1/vY5zlcvic57WK47hZjOWbTZxTkv8rttdbJP6nt+f7t8rjt/NhutP6yK9K72nJLXacQ61Wl14DscqygEWC5j/CVtoV3r/9ZrHOtV1OLwTyk5P7+H9PZjDMP20mvPzF7BW0PqRQTG00YztRzmNJLXacVzBkEhZr2L7U/3nE34nyqGaUKdOUv/b1zZo3TDmJadWqz2zpm/XnDcSLZg3Wd5RusfabjiWlzK8E3PRKs/fQy4uSSvIm6vUtdUpez7FbEnabeK6Pb2O6/OTt5/g/ZT3E6d3T7Gc2q/RirG0St5ab8TFLHt/gt5/B/l1FboDzttp30ZsYM2ShAtfE9ov2zel+4lCtoXjZ5PsGwla97N9b5YjHNKA/Ul2/3IWc8n1svzcB6EES0kuC3Mqm2e0b6IyOgvKoFysCnRuIeVnB4d0BzG/laTBedwPEgvY92fQvrV0TEnSbld2bieVz4E6nMVlUF83muX/u+yTi5NQlsWE7OzkkDc+Fc8CJem1X38Qyh5+Oe7Hs7GeteshN1eXRddJfMYzOONxlpUfksUKqn8SFpDSeZnt2wg695LKYwWRrShtiEE0fjNu50WUIxD+CwVjjDHGGGOMMcYYY4wxxhzFHxSMMcYYY4wxxhhjjDHGGHMUf1AwxhhjjDHGGGOMMcYYY8xR/EHBGGOMMcYYY4wxxhhjjDFHeeGkzP3+qU6G70t62m2WG9Ua8VsISTgyORkJdEmCQvI/iQWeJIHJpLznD85CWQcE0OdnLCc7P38dy180SAgqSb/wC38jlHV/IkpY+j/O/f8vvvVWKFt8e5TL3IBoV5KuH16FMpKwZHKqUivlas6iUxp/JJ/cJOOXBNR1qH8mxyLpGonwqgiHCJbQlAtjMjEQydWo/1Y1FubQ/CU5UiasovhT5bo9SOdI7lZFWEWsFtn7x7aug5wpE35RXCUp7y4R7a7nMS42QFQssayXxv9mz3Kp2ew61gukxp0OiwhJbL7bxffPpMqlciuqU3Z/sxnHRQvKpHIBcSZwp/ZfraJ0keokSa1WmZQ5k2pvt7FdG/V4Lf2mJDVJFg2xpl5PRJrQ17tteepEOcDlO3H9yeRspdLCTJxF5bT+ZLlOu/t8IkmKYZRrZfOEpM41EuklwrHba2CjMG4b8zKx2+2eid9Z3kF5VxcEskOQR0rlUuLpFefdSAVRIAkQKW5QzJSkyWVZvbI4QXkTxaeMCexHLkHAfPnocfFvUt5C8k+J34ty4VKhqyRt1jFvmU/KpczUJlQniQXQRCbqpH1DuscDAUGzZAAAIABJREFUlpBP905i3kFlVZhe8b6Z8vkZSJlJ3lyF9iOe5yRrbxbKYyVpC21Ne8Rs30h5C+0FMurPuf6XysazXDob14dQO0l8xpSdEZRKZTOBdKnAtXB7I4nHZXZGR5AsPcsbaY9NdJK5Svk4Sr0rxMomnPHtNskeWWXrSrvHc/X1T0ZZ89mD81CWrZVbiOs0/htfLpeKk5Q52/eUztVOPzmjqcdYWUX13myVvdd+z/OnuYt9zaLocoF8FalydnZ1SBarbseaUsm8d1fGGGOMMcYYY4wxxhhjjDmKPygYY4wxxhhjjDHGGGOMMeYo/qBgjDHGGGOMMcYYY4wxxpij+IOCMcYYY4wxxhhjjDHGGGOO8sJJme+/8abUinLeQ0guNgc50QpEo1IiugR5YVcstth2orCE5CqZsItEVsRJIkTs9Vi69bLw+PFXQ9nf+Tv/Qyi788YdvP/NP3YRyroghyIJjCR1QFgyOI/jbrPi+2n8kPBsMWXhTSZSOoTGpMTSqiZJmZP7Sc5zDmWZ2IgE0vT+mbCpXih73oOES2I5DZVlwqLTe6exTiDMykSlpcKc9YLn/xriUgMkQJlwikQ61FftRAq+nMVxTb/56MuP8P6HX3oYyx5+KZRdXr6D95PodzCIonpJ6nbjvKzXY1tttzynSLpHUuGMWq1srDYTqTTJqqs8p9+PsnQSMWbCNxIw12pRtr1eZwL52K401kleLEnz+U3RtdlYp75aQ9k2kVpT+7daMS5UkWOtoK2WsKZl0G9max3JySiu1BusHKNnkRyPhH8SC7lIikzCUonXIIp1vSFL0Un6SH2Vyd1uS6Fbzym5N+ZFpF6vPxP/s7yH9k0duDabSwQs5Slb2CNhfEryY5LiUo5I8lpJun4c1yJicMp70CyfLmV2Fdfd2c08lC0W8bqM3iDuBUl6L7FUm6ScqwXnAiQgpbWoVF4rsWi1imi2VBgpsWizSp+SqJQgeXHGDvL2ZpvH73wSx0o21olSKfA0kZdTOa3P/TNeyzuw/rahrEr8qTLWKMWj8Uf7WykXmB5C8vgMev422fcSlEtJUncQ25DypkxqvYCxRvE7E6CXvkMmj6azD4pfmcC63iiTMtcb/PxSqXOWNxPbBkipK0jhsz0S0YD3ojKaf5K0b8f+o/nTqxDrsK+zfR/ENbq/m0iZaQ2k9sv6b5Wc3ZT8plQui2/CmM5o0LlZsm8t3bdlrG5JrVudsjXSf6FgjDHGGGOMMcYYY4wxxpij+IOCMcYYY4wxxhhjjDHGGGOO4g8KxhhjjDHGGGOMMcYYY4w5ij8oGGOMMcYYY4wxxhhjjDHmKP6gYIwxxhhjjDHGGGOMMcaYo5Spm7+J3P+W+2qdD977z5lBm2zls5tZKFtMF3g/GcQ3YJXPnr9eRQP4GqzgrW40bUtsdt/Bs+4NT/H+s4s7WP4y8+TJ26Hsf/3pv4zXfuc/952h7Id+6PtC2fmDc37Wbz4JZbtNtLJn/Y8GdbCtzydzvJ+oYmAns32zDeM3Mc1v13H8Xb17VfSbktTpd4quncOclHj+7baxrbP213oTiqj9mzCnM+h+KsvKdzCnl8sYEyRpDeX7aXzXrP2orQjqJ0lqd9uhjMbf9HqK919dPQplTx5/NZTN5jd4f7c7CGVZWzcaca7V6/FbOMXU7Hfp/lqNv6836nGtaXd6oazV4ramMbxaxbiw2fBYqdXiGKZ3arViTMjK6V3X6yXev1zFNbTViuOn24l9Kkm7fYxBs1kcF5tNjAlP6wXlWVwoZL+PYyVrfyqn/ltBOz19Vnz/ZjO2X6MVx5kk9Yf9WCfKP1YxJkoc1ykudAc8froncazTtbQmSlID1lWindw/OI3jqt6I47+RxPrd9v3nV1kPjHlZaDTrat6KH7sNr4U7GP9bmJ/LGa8FBF07uynPe4lsLS/N0bNY2IG8h8hi2QZ+l9KWbHmqN2OM7/RiLD6r3f/GFbzFxWsXoax3wrF8cjUp/l1ivy2L5dtk/F0+jHkjkY2/wflJKOsPYX2CNUviftnCXuJ52e14ANQbMe+itYz2ZxLvm6qQnUeE5yfzbz6F8QNpT7aXpfnXgn0j5ecZWd5ANNtl126SMbFL9tOHpPvWQirdn441zicPycZU6RrQgJgmcR/WYVxQ/2fQvMr2jaX/c2mK6Vk5jYvsjIXoQN5MZ4kSnxFUiVVZPvw80PNL1wRJ2sOYyM5oaa7SvEi27QjFiqz/szXskF3y/um4LITO06qcUVH9s7yGuD0vS/dN/gsFY4wxxhhjjDHGGGOMMcYcxR8UjDHGGGOMMcYYY4wxxhhzlMp/EzMajX6PpP9E0ndJmkn6HyX9+Hg8no9Go98u6b+Q9N2S3pX0H4/H45/8EOtrjDHGGGOMMS803jMZY4wxxphXlUp/oTAaje5L+puS/itJ55L+WUk/LOlPj0ajC0l/S9Jf/tp/9+9I+s9Ho9E//2FW2BhjjDHGGGNeVLxnMsYYY4wxrzKV/kJhPB6/OxqNHozH45vRaFSTdFdSV0//lzWflfRoPB7/xNcu/z9Ho9FflfTvSfr7pc9o99rq3pKcZBISEomROKKVSLi2INIgicwmk6qCgJnkKplch6RLJK98/YylzG986g0sf5kheeWXvvyreO3f+5//Xij72FsfC2UPzs/w/tknHlSs3bP0TqP0i+QoJNaRWJiCot5k/JBUl8b/as7CpclllKJOLqNw6wQkaJJ0di+Oyxo8P5OTrWD+kNQ4E86QsKfRjPM3ExNR+5OEiur0tDy+VyYyI+i9UC645N+kutJQyYRbJPwhOdYikYpvtyAnBHkxyWclqd2Ocqp6nfuK4jKJhtttHiutpA6HrBIpMYuQSOqcyJGgrUj0S9c9vTb24WQSRbuDAa8V/X6MgSgVzwTu21jXRqOKSC2OYfrNXEoNUm14fjOrE/TVYhHj32rF/U91rZOoG8a0JHU6UaqMuUYmB4MciIR77Q6PvyVJwXGtwdvROtqA5++TWE8xlOJnJpDvQcVICp09fzl/v1+7iSzSmI+Cb8aeSXqaO35QYSstW+tl+W+RALmKqFEV5mQdcjwSNfcSKS9BuVAV0SuR5e27bcxF2r1YRu+UcXo3rvu0P8iYXs+Kry2Wgj6nlJb291K56DITpeK4rmDVbrTKRLckX864vT59ozJJmlM+nqx7BMnaa5CLkChcynOU8Jxk/tO5ywbOQqhOGVWuLZWSZv2cybJjnSrEtAbs5ZM+nd3EubrKYm1hHTL5cmkM6p/G/FYql8pSLpdx8ySeUWRzhagitaUYXkWKTGL4KqJeehattRnZ2c/zsIQcg+Z0RrsX5xXtBSRuf8or5hOWOhMo1U5i/bown8rOmEtjTfZ8WsPoymytJD6oKDo7xzukskNhPB5/fSf+G5I+L+mrkv5bPf2T3c8fXP7Lkr636jOMMcYYY4wx5mXFeyZjjDHGGPOq8jxS5rckfUzSVtJfkzSUND24ZiaJ/yfOxhhjjDHGGPNq4z2TMcYYY4x5pfjAHxTG4/F8PB5/RdKfkvSv6mlifPi3T31J8d8WMMYYY4wxxphXHO+ZjDHGGGPMq0ZVKfPvGI1GvzIajW7/o1EdSSs9/VPd7z645TOSvvB8VTTGGGOMMcaYlwPvmYwxxhhjzKtMJSmzpH+op/8Lmj8/Go3+tKQ3JP1nkn5ST/+E98+PRqM/IeknJP1OSf+WpH+9ygOWs+Uz4qFMLEJS1UYzCj+qCGNIOLQB0bIkFCGRyKs/5OcTj25AitthYc3r3/56KLt79834m4++Uvz8l4mvfvUfh7LrR9eh7Ac++e14/0U/9ssvg4iKflMSi7yATO5GIhaSyyymLJyhsbqEa0liJEk3j+P/CI7mTya3IxES1f/ynUu+H0RQJDLLxDYkWyZxzDppP3r/OQivSH4qsXSLpKDUphJLv0h0mkl0Gq0Y/2hMkChe4vhJotheEr8Gg/NQNjy5KPpNiWXNqYC7EcdAsxn7v17jtm62QHoIwqPmvlyqTWRS4cXi8F+1kFarOC6z99+R1BnK5nOe6yQQJlFyRgPbH0RUFYRPNcVr2y0WAbZI4A2i5UaTY8UaZNvX17FsNuNYT7LsJjzr9PQe3n9+/iCUXZy/FsoGSa7SAWldi6T0iUiQRGYkZSZhoyQ1E7H9IalIEqSfG3hWFusphxqcD0JZJpK8LTfLZJHGfER85Hsm6WnukUnJb0MCRYoF6f2FUtJKVPjJTGB4SBYLKMesQy5H+WUGrdv0m1UolSBKHDczUeN2Gxub5J+ZKJNYzGIuQ6JdSWq1ygSsnT73X+8k3k99lT1/dhVzMSLr//4w7odIvtoB0XbGDNZHys8l4blDlbFC7dLqxDmRrfln986KnpOlgnSeQmtydu7SP4vr/vOKbonndIqnUFVRKp24v0ngnj+rrF06FQTuNYifVQTwRBXRMMXv0vxU4j16RhUBM0FnP9l+mKA1jMqy9ZvWapx/SZvQWlsqZc+gPUqrWyYvlqQFnBtNCmO6VC1WFOdFyVqfnf0UP79wr0Ly5vQ3P2Besk72lodU+guF8Xg80dM/1f1tkt6W9HOS/jdJ//54PH4k6V+W9G9IeiTpv5H0x8bj8d+u8gxjjDHGGGOMeVnxnskYY4wxxrzKVP0LBY3H41+W9HuT/+4XJf3Q81bKGGOMMcYYY15WvGcyxhhjjDGvKh9YymyMMcYYY4wxxhhjjDHGmN86+IOCMcYYY4wxxhhjjDHGGGOO4g8KxhhjjDHGGGOMMcYYY4w5SmWHwkfN7HqqmyeT9/7zOjFtky2dbPNkFZekVju++m4TrejNdrmBvAVW95OLE7y2Abb6J7NZKDuDMkk6u3cWyl578IlQ9ujRV/D+l52aoq2c+vq018P7h91uKKP2z0zvk1tj9OtsVvHa/R5vVxPGXx0M7FsYk5K0nEfb/Wa1CWWzax4/VP8aPJ/qJEnXj65D2fwmPms5i/V8+rD4u4OzQSjr9Dt4O83/6WV8p8V0gfdTvTbQ1q0YUiRJbfgv6p04/hoNjj+NZqw/xZqs/6mvV4tVKNutdng/xcp6g+YUx7+zu+eh7ObmQShrJg2428X3qtX4+/ZecRItFnGs7ff8ro1GnGv0rHo96SvqQ5jY601sf0lar+NY28P9NZgTT/8LGCvwThnrdazXdkuxisdapx1jZacT42q3G+fv09/th7INtNVux/1Hcx37FNYESVospqGM3n+1muP9VK9mM86LweAU77937+Oh7M4bd0JZ74TXKoLGSrvHc43m+nYd+3qzjjFF4nWBnp/F+u02th/GX4hpkrSH+2n+ZLlis/n+/F01eY4b8zLT7LTUgnl+COVzFF8zdrU4Fyk+7mDOpr8JOU6WNxOUt9QrxNLtJtZ1m6zlBMXHjEZh/NlteS1+XqivqrQ1/ib0dR32t9KzsfgbQfsjidcyYpGsRdl+4JDeacxZMuhdG63y/Gx4Z/hczyp9J4nXbWrrTtLONXpXyI+3yfilvGMN++bVMtm3JDnCIVn8obwB50Ryf3YeUEp/GMfV4Lw8VnWSHK+URrN8XNIZA0FtKknbJJ88hM5NJKkOsYLGWqeXnBHAHrsNZ3QZlKNOk/MUgmJQe1cebOmMoAZrdTeJqVR/WivXyZyidZlyhWzfRlD8Sfe9AL3TfML7NiI7TyKycX1Ilj/ReUwVStuK+jSD5jSNqcNnbSBuE/4LBWOMMcYYY4wxxhhjjDHGHMUfFIwxxhhjjDHGGGOMMcYYcxR/UDDGGGOMMcYYY4wxxhhjzFH8QcEYY4wxxhhjjDHGGGOMMUd54aTMm9XmGbnePpGY7GuxfLOJchEqk6QdiLhIWNYfRiGlxNIlkhuRqPlpvaLk4vLyJpT9ZiJFJZHRyfACr32ZyUSpLRCFnpxHAfYChKSS9HgSRZ3vXF6FMhINS9L0KgqAJ5fxNzPRJMlhSCQ1vYq/KfG8IIlPNv76p1EERXKe2Q0Lb0g4QwLhTEJHHpgqIr/1Mj6f2j8TQzWhXbqDOKZIQiaxCIreNZPCU/whN1E9kdOVSkmz/idZPM0famdJ6oO07vU3vzWULWZR1CyxAHezYTkXCXSn0ygFXyxi/JRYSkxS306HRXwkG261Yv9vt4nUFjq2BbLqTE5FsmESVWfPX6+jtK+KFLlNUuZ2jB/tFq+VxG4X67pccqwtrSuJviVptaL3h7kCfSrxWDk7i+P6zp038f6LB1Fgfu/Nu6Gsm4hEl7NY/yoiRoLiQv+Mxz+NSxKhZfF7cB7nD127T8YfxWASxq3mHKt2t2Joth4b8zLTbDWe2X+QvFIqFz1mUNxdz2DdToSGmQAy3J7s+0iA3OrGdadUfixxLkvxReK8q1RYKHH7VYlJJICkuN1K8kasU4W8G+sE8bl3wrlAVn5IJk9ttsukqptk31QqS6a9gMT7tibk+JlodreN44fGVCYPPb8fc4nVMIo2M3lwJhs+pLSdMzJR6b4Z37W5Lx+rpXnPOhGibinvoDmdzMlSAWz2/lRO+dHJWdyLfRhk+1GCzrMoF83iH8nus/0k0erEPVLzJNa/nUiZaTtVRZbePYl9ReMnGxObQik1nTs+LScBd3mspjWQ1s+MPbQV9ckCxkQGSuGfc0xWkTrTeVAm9W6DmL50/mbMQOq9KpSfS/yuVcY00WxzrLo9fhrJOdgh/gsFY4wxxhhjjDHGGGOMMcYcxR8UjDHGGGOMMcYYY4wxxhhzFH9QMMYYY4wxxhhjjDHGGGPMUfxBwRhjjDHGGGOMMcYYY4wxR3nhpMzb7e4ZuWsmZyJRHwkzMrkW0QERUy8RJaIUEiQ0i0zYAtIfEo7UQaImSUuQEzUazydSehEhIabEUleSi33x4SO8/wv/6NdC2Vf+8VdC2TaRei9nUaRy/ThKYek6SSitI7lRJkodglT39O5pKMuENyQAHYDIb7vl+dOYwu+SiK+CMIfkWNcPo3xXYjlXFSl0C4Q7JCKjOSlJ7V68vwX3ZxIsEojukrYmSJJDouTBGQuDLl6LAvf+MI6J+YSFSzRWSWSXCf9IjkSieonb6vHbcV6/+86v4/2TSZStkwCY5MuSNBzeKbo2i1VNiMt1uHaXSJVJwFyvw1oBZRJLrfE5iUiTYsBkehnKprPYztnvzmcxVtJvSpmUOdaJ2jmD2v/8nAXig0EUId69+7FQ9vHv+Djef+/j90LZ8E6M1dlaf/1ubL/VIvZpNtdIOkrS1lY3EWFCDGuCCCwXPsa4QNdmcj8SjNKYopgiHQioK6xHxrwstNqtZySCmaiwVFZMudQ3evYh2VpSKjDOYiHJ5GmPVqX+VQTOlI834FWz95+CLDjdIwD0/tRWzVa51JXqmu2bsxztkCqiSNq3U34tsQCayNqf1jh6frbvp/fCfVuW90L70bVZ/amvSZSaidbXIGvGtbTCXmRbQUqOY7WCQJyeRfLbpPnwXUkAn0mdp0mOeki3w3sJylGoT6qIbjOpL46LCvkPCXzrkDdmvbddk9S4PNbS83fQV9lcoeIt7CWz+2k/TnOVcukMit/Z+Kccu0r/tcA1vE5k8cQa9hhEJjXGa0E2T+M0Y3AKa1VyRlYK5S8SrzV0Le1PMijWVFn/CTrLyqAzIjpLPIT6jfBfKBhjjDHGGGOMMcYYY4wx5ij+oGCMMcYYY4wxxhhjjDHGmKP4g4IxxhhjjDHGGGOMMcYYY47iDwrGGGOMMcYYY4wxxhhjjDmKPygYY4wxxhhjjDHGGGOMMeYo5XrqbxLr5VqrWyb27qCL15FZfbWIBvftJpraJal30gtl/WE0YJNpPnvWehmt6JlVnepPtvfTu6d4P5m9hycXeG0ptVr8vtTrsQG804ltRcymV1i+XM2L7t9uN1h+ff0olF2+cxmffz3F+7/w858PZVcPr0NZOzGok1n+9rj9RtdJ0moRze7z2U28sMbjr9mKY6V/OojXJQZ6up+s9v3TOE8kaXhnGMq26zjX1svYJpK0j7J7nCvzax4nNP/28KP0ThLHhSr3E3Q/tUnGdhPHymbN47/T74Syk4vYJ+0ej99mK8af7TY+P+s/ojeMY6WTzJ96Mz4/Y3YV53Ad4vJ+z229Wse59s7bca5NJk/w/h3EoHknvmu3y7Hy5OQ8lDVoXidznSZLDa6lNUWSdrtY//Ua1i9oJ0laLmehbAXxe7Va4P1U180mzvXs/i1cu9nGsm43xj9J6vfjGkrXnp89wPvPL14LZfc/Hq+98+Zdvv+1uC63O61QNrvhWLeEWEfxO4PWmh3Ev8WEx98O5vAGYvV6xbGKoLxuk8TKLcTAFeRa9TrH6satazcV6mjMy8JqudZyzvH7NhR3CMpFJM5nsj0W0Sp8fpZ3UT5Oz6f8MGNwFteCRoX8hJ41n3As31GOBWtxBu2HGs3YVrTmSrzHzfY4BMVPyntnNzFnyNgq9h/lklXI1gKCcmHay2XlNO+onyVey5Dk3KHb5/OQkjpJ0nJGuQDXFavVKMtbs/avw1itAuX9lAtl85f3aJBfJPGj2SybK/1hcm4C+7YW7NGrxNQNbabFORLtESh+ZFC7UptKUg3GAMaa5P50P3QAjemsXnRGt99l9YexBn1FfSpJDYgrFJezM85SKJeXpB28F82VrP1uHsc9cnaeRQzO47pKcaHKWG9A/eksV8rb5ZDs/Qncy67L92K0b7q5nBTf32zGZ2XTh6AxmbXfbUr7yH+hYIwxxhhjjDHGGGOMMcaYo/iDgjHGGGOMMcYYY4wxxhhjjuIPCsYYY4wxxhhjjDHGGGOMOYo/KBhjjDHGGGOMMcYYY4wx5igvnJR5OV1ofkvoRMIaieUcJMLK5IUnF1GaQxKWTIpK0iUqy+REfZBODaFOJ+cs97m4exbKPvPbvzeUfeH//Q68//LynVD2xhufCmXf9V3/At5/fi8KKEnc8fDtr+L9v/ALfyOUzedRTrLZsByp3Y4im6uHUQCdyZlICksC7Kz/SeRCIrlMeELPmjyJ1y5mLHcjXxEJXzK5HcmtqK3qieiV5uWuDVLjRC5GAs8ViMSy9qfnN9tR+JcJp6hdtluSSrNUvZRMykyy49223K5DIqfeSbncaT6JAlxq6+z9Ka61oP0zKXQD5UKJXAxEeH0QKd7dsVR3s4nvNZtFAfujR1/B+69vHsfnb6MAu9ViOReJFEnuVBPPlXodpG0gJaYySVosotSa3n+dSJF3+7iu0f15rI5xjdpqOLyD99NYq9ViWSbM64BAuwXXNposDK3XywSd60TkR3NoMY1tffXOJd4/vY6CS5T7JcJTios0p6hOEq91VJZJmWkNRBFdLRm/UC+qf5YrPhtXKhjMjHlJqNdrz8TJTP66mJYJCKuJCmNZu8drIcWtKgJkYg+iyEyKjHWCHKWT1J+gfUcmfyepZacTn5XlQlvYY85u4rtWEZU++NaYN1URNS9nMT5n44/qiiTvT3kzkQk5qf1ofc6kzAS9U9p/hXLL550T6wWvpSRgprpm9ScnK+UdrS7nIh0YV61O+Vgj1rDvTccPzMs5jNVMPjsYxn07ie5PLuL+QJJ6zytFh/1kNtczMfch2RkZ9SvVNRPAt5MxUMoKxnAVgS+tC5RLZgJ1on8a++/8fjyLk8Sycjh3IFF2FbI9OrUVxcVsnGT7gUOycwd6L4rfOH/F86r0ORKPS8oLaH+VQaLr7Iyt+P7kjBgF8nBGlcVqOvuidW16Fc8HpGcF5Ktkb3WI/0LBGGOMMcYYY4wxxhhjjDFH8QcFY4wxxhhjjDHGGGOMMcYcxR8UjDHGGGOMMcYYY4wxxhhzFH9QMMYYY4wxxhhjjDHGGGPMUV44KfNsdqObm/flhCRRklj0R24YkoB9o/JDMmENCUNIApI9Z3AWZcsXr52HsrsnLGU+68f3/57f/T2hbDn/d/H+PbTrd/zAW6HszU+9ifd3W/H95yBcefybUWgqSZ/+/ljX/+tn/5dQ9u67X8T7X3/9k6GM5DQkipWk+99yP5SRhGZ2VS5sIQHv8A7LmaivlyD1vH43Ct8kaVMoJ8qEQ/VGHNckh8nkagSNf5LQSCwMqjJ/SMDZhZhQRW5GbZVJoLBdEzkO3x+vJXlwE+TFklRPZNOHZKJUkgaS8CcTnWJbV5BLrWCs5yLCWK8+yM2yutKzLp/E+T+ZsBR3Po8C4hUIjGezG7yfxjW9a6PBsardjnNguYxxab1mkeBuB7JxqP8mkTo3oV6tVhRxNROpcbcb17DT03uh7PycpdqDQRThkRwvk2PNbqJ0ajJ5EsponEnc1pMnsU+yWEkC9BrI6klKL3EMbbYqzDUQcZGILVsrSORG608zibUU16its1hN/UrzJ1sTb/dLFamfMS8L+/3+mTmRiTJJoEvrYwYJ3knAnOW9WfkhWSy9eTIpKsven/LJ/ukglDULJYSSVHsc130SmmblWd5CUF+VyjMlzvEp7lYRTVKOmcXiNgiwiXqyFtCzSEqbyZ9L26o/jPltVi96p32y7yGq7LFU+LtZO9P4z8YqQXvcKs+nvKUBe5xs/JWe8WR7ieuHMZdfQn5UZS9De6HugEW1lPdlezSCxvoNxB+pXEw/vOAzJooLuyRHJkggjdclUt4tCMQJks+m18L4y8bK81J69rBacN5fWq1sraP3orw/k3pnsuVDMgE5jXU+D+IXXcF5Hp37ZtC70hlhJdE3iZIrxO/uIMaqux+7i9fS2SM9v8r4pbi6Sep/e1jRHhB/v7gmxhhjjDHGGGOMMcYYY4z5LYs/KBhjjDHGGGOMMcYYY4wx5ij+oGCMMcYYY4wxxhhjjDHGmKP4g4IxxhhjjDHGGGOMMcYYY47iDwrGGGOMMcYYY4wxxhhjjDlK8592BQ6p1xtqNI5Xi2zVp/dOQ9lr3/Ya3j847Ycysorvd2yab7ZbsQys7nSdJPXP4vNb3XYoW25inSTpeh4N6t1+tLJ/5w98J947+QCkAAAgAElEQVTf6Ufb+Me/NbbVa6dneH+zEd91uYm28Ncv+P77H7sXyi5evwhlv/YPfg3vb/diW82upqGMrO6StF6sQhn1fwY9X51oW2+2eSw3WrH8DAz27Q6Pn+l1NMCv5vGdtlu2s9fWcfw0YPzWavE6SdqvYl/vYK5k7V+vx989uRjGOrVinSQevzT+KU5IbLZfTOaxbLrE+6ldN2tua6IG77/fxvZbbWKfSlKjGd+L7t/t4ph8Wh6vbcCcbjR5/NKz6O3XS24Tmmv1OvfVbhOfRfOKxkT2rOn1J0LZcrXA+6+u3glli0WMNdfXD/H+xfwmlO0V+6XdjvP/aXkc1/s992spnU5cf/rNZK3qx3nZbMb4l63b9Xosp988v3sH7z+7F9cQihXrVbJWPrwKZbsvxzE1m8XrMmj+31xO+PmPrkNZuxfHagfKJKk7iOV1mKvZ/CF2MH/TtRLKaVloJWvdBtaK6WWcP1msp7GerUvE9ta70nsb87Kz3+5xTT5ks4lxqw55XxZLBueDUDa8OAllF69zLG8l+ewhs6uY30qcz2Vxh7h4Le4x2rDvyphDjkgM78T1SeJ8nnLBjDXE0pvHMb/IoLxpDzki7SUkXguu3o3rJsX3jOHd2FZV4vtiGvO22Q2PH4LW3SyX7A5iLlZl/FDatl7GtqZ+fnp//IEdzOk9lElS/yzOXzq3yFgX9ku2zq6wPI4p3F+L9yhV5s9yHvdzlJ9kUPzrwb6d9rcS9xXNlQ2cpUhc1xWcZUh5PhfuT65r92Jdky0S/y6dsUDZevF85y40JyXugw7MVRpTGdl5ArGFMwY6I8jWlNJxWWX8T57AXjQ5IyDojLU3jHvJDHqnTbJvw2BZAco1KFY3KvQp7dtwM5RA4y/b99EauJzF+PW8ZwHpuct2j///N/yt56qJMcYYY4wxxhhjjDHGGGN+S+APCsYYY4wxxhhjjDHGGGOMOYo/KBhjjDHGGGOMMcYYY4wx5ij+oGCMMcYYY4wxxhhjjDHGmKO8cFLme288kG5JKDOpMQk3Tu9GYcgJCMOkXHZ7SCZlJTnGCiQajVYiVQUBIUlxM9EkiUpJuJIJe0j48eQy3r9J5EpbkLrOF7FNMrkWlb/2iSiFboK8WGLRJcnJSNglSQuSm4Ccpn/KotT+KciterGumTCOhEkkp6llcjyQipNca3bDwh8SgdFYqeCbQZFYJoyhudobxrbuJHKuVieW10FU3EykwjT/6P0ziWBjV3Z/JlaicYlyoqQDuvso8qGm3iZyNpJLUZuQxEySplexriTHymQ+JMXOhFckTWLhEvfV+f3zUEbzL5MKTyZPQtnN9aNQdpVImafTKC1cr2O7brflIr5WK/Z/M5Eqk0B5MIii4+GQRZqnF1Fk2QVpYSYHo7hA8T8TIfYg1tG1mdSX+np4HkWQmZyq3S0TKC9mvNYslzEG01yl9f/ptTDXYf3d7bj9SeBeRWRKfUVS92yuL+dRkEl9ksktqa7Y/0n73RaRkpTUmJed+XSh6fX78yxb90tFj5X2TRAfMtEmiSoJii+S1IQYf3Y/rmWNLO+DGEFSzEUiyqQcL9tjEBS3KZfNoDWK8n6Sb0vc/1UExtQu9P5VRJGl+V0G7RGriJJL1xdJ6kCOS3lvtu8gJlcTKOUxtYV9P0qZk+YnATDlTZkoNNuPx+dzBaaX8V13sO+eXfOYzOLSIXQ+kz2fYgr1sySdnMfnVxlrFJf3+1hG5zuStIG8KWvr0hwvO6Oh85AqAmuKC1uQEleBRMvpGUXh4UWVnLD03DCDYu0yWT8yWfYhVWJtNi+Ik4u4R6KyKuOf1vXJE4p/fB4yu47xa3DGMYFk23TulLUflkNZtu+hvILmT3buQfO3yr4tO7s+hOLfIY1G2VzyXygYY4wxxhhjjDHGGGOMMeYo/qBgjDHGGGOMMcYYY4wxxpij+IOCMcYYY4wxxhhjjDHGGGOO8oH+QdnRaNSQ9H9I+ifj8fjHvlb2+yT9BUmflPRFSX9yPB7/zIdUT2OMMcYYY4x5qfC+yRhjjDHGvGp8UEPdfyTpd0n6J5I0Go3ekvTXJf0BST8j6UclfW40Gr01Ho+/XOWHzx+cSwWSjzqIqNogcM3kYCS9WYFUeAKiYkmaXZNoMco5SNgksZyLhB30ThKLmJ68fRnKLt+NZRKLvEjUOwUJkcTClA0IdzJhC8mdSA40vBMlMNnzpyBsSeVkJIUF4VkmXKG6thKBOLEC2S2N6Wz8tGBcUJ1IiCqxXG56FduPxqnE7UJ9ko3f/jCKVlEAnAhz6Pkk/CEhaQYJizJhD8mZaE7OoE2zZ5EcKpPDkchvu43PzyRaLRAYk7Awk2hR+6OUG8ReUiIny0SMbZ4D8X4upzF49827oYzkzRkkXHr4JZYyf/VL/18ou7p+N5SlY60dZeXtwjaRpGYj9vX5xWuh7P7HH+D9JMKjWEfjT+I1GAXkidSZ5hXNH4rpEseFHsSfTI6JIjgQaWVS6m4/9hXJtRqJ8JDccrTWZskcrQttkC52T+I4k6TdNrYfCveStZbaP5vrBLU15QWZMPJ2vaoIUI35kPnI9k31eu0ZuW22lpTmQ5mok+KOSPRYQRRZiULR5uB8UPyTJIDN9g3UriTPJPltBsWy03tRHplB+4YaxOwM2gtkUP/jXiiRl7Yh7yTR5+C0vP+qiGKJOsyJbN+0hX6lXIRE4RlU/xrsRSWp2S6bv9eProufT3vMxpBFnaUC2Ez+Snu8KqLYUln31cMrLC8VqGe5GO1nqkh9S+Mv5sfiud5KpK6CtqIcKWvTeqGINZMHU45Hv9lMzk2oXlUEwB8F1P5V3n9+E/eNWd5M0H58nYyVBexRCdoLSVJ3EPcI1P5Zn2Sy4UNmVyxgL5UKZzk9zbVMNl8KCeSzfSffH8cElUnSkAT0hfmPxCkY7eUwpzu8JpHEH1L5nzwajUa/R9Jn9TQR/jp/WNLPj8fjnx6Px5vxePw5ST8n6Y9U/X1jjDHGGGOMednxvskYY4wxxryKVPqgMBqNHkj6SUl/UNLtz0rfLenzB5f/sqTvfa7aGWOMMcYYY8xLhvdNxhhjjDHmVaX4g8JoNKpL+iuS/uJ4PP4HB//1UNLh3wLOJPG/l2OMMcYYY4wxryDeNxljjDHGmFeZKn+h8GckLcbj8X8J/91U0uE/xNWXxAICY4wxxhhjjHk18b7JGGOMMca8slSRMv8hSW+ORqOvW377kjQajX5E0k9I+v6D6z8j6RerVmiz2jwjOSFRqCS1CuUsixlLeEj0OAU51+XbT/B+kjKTnCeTa5HIpNEEUWMi8SERFglvMmEVyYn6Z1GERRKab/S7h2T9R7CoOhHegHGkCe3XS0STJGeh38zkdCQgnd3E8bNOhD0EjQkapxILNEkYk7U/PYvkWCTqrkLWftTW1FZZ+9Vqsa2ryOFIikr3Z+1HbU1ypkzuRpAc7SSRC9YbsV4k1c7mT6NVNi+z60h0jMKf5P03GxDQFwrXpFyASzSgrUh4lAmb+F1j/7/xqTfw/td+PcqOH375UShDKblYFk/zZ3LNIrrNJs7hTq98rJ2cx7bC+ZvIHfdtiKvQf5nAm9ag0vghsciM+rTK+COy+0mATHFll0ittyDEovUnk2LT+Kf3z8b/FqRj1FfZ80kEuN/H8ZfJ6ShW07pO10nS8ta4LJU6GvMh8U3ZN53dP9fu1tjO5hJJIQkS1WbQuj+f8L6rVGCY5Y00fzt9yOUrSN8p7mWiSRIoUnxezSuIjiFHopid0Qep5qb7fKLibPzQupvlLQS1K/VpJqos/c0q8lZ6p2wtmzyZFP1mFdF1NtYJei86d6hXyI9Jvlrl/kWFfSPNlUzKS5RKjbP5N5lwjnxIJnqlsV5FNExQrMrc15RLZXs0PCOCvUSpfFnifQ/l1xK3AdWJ4rfEcbnCVEllyYdUWSuqCODxPCjZj5dCfUXrryTVCqXItBeQpMFZ3PfRGUWV+LVaxPmTidJLY9A8kU/TfoDGbzb+CIprmdQYz5NgXc9iGp3xELR+Sbwu0BkflR2yKlxPiz8ojMfjT9/+z6PR6C99rfzHRqPRpyX9B6PR6PdL+ilJPyrphyX98dLfN8YYY4wxxpiXHe+bjDHGGGPMq0wlKXPGeDz+FUk/IunPSnoi6c9J+ux4PP7VD+P3jTHGGGOMMeZlx/smY4wxxhjzslPlnzx6hvF4/GMH//lnJf3s81bIGGOMMcYYY14VvG8yxhhjjDGvEh/KXygYY4wxxhhjjDHGGGOMMebVxh8UjDHGGGOMMcYYY4wxxhhzlA/8Tx59VFw9vNbjh1fv/efeSQ+vI1v9fgcG+g1b2W+eTELZ5dtPYtmjx3j/eh3N2CcnF6FseBFN6RKbvetgZV8nVnl6f7KNZwZ0Mqv3h/1Q1uq08P4tPIsM9Js1138D70XPoudI0moRbevUpuf3z/B+MruT7Xx6PSt+/gyupesktr0T7R7b1fvDOC+abe4rYruN7Urtn/X/brcLZevlOtapye9J47dWi+MnM9jT+CWonlk5jbXthscfjd8NxJpG8v5UTm1dT8YJ1Z/i3w7KJH7XNYzVpPm1WcW+pmtpnEk8/ju9OCezcho/a6iTJO22sa3a3TivtnCdJK1gXAvatQO/KUmvf/sboezs/nkoW85j/JG4r2j8N77EY2U+jXGJ1lWKiZJUr8d5KcWyxqCL99cacVyt5rFsl4wViiE1qFMWq2hdpbJ0sENcojGFvylua4pr+yRWUQ5A/U9tKkmNQWw/mn8UvyVewxst+E1YfyVpC8+idTF7fwLjX9Z/xrzinN4bqtbn9ec2k8tpKMO5lKyFG8iHKBeiXFgqz9sGZwMsz2L8Idm+idb9Tqd8G0xxvzS+Z5ycx3dtdY735TeqU/b8LJ89hNd8XjeyHJ+guD+9imNym+wbCcpxs7WolGwtXzY5RzskG+fNVhxrtO+pJ/WnvIfowv4wvTY5YyGy/fgh2V60x8chgQa0kyRtN2U5Au1PJKnZLIsf/VNuk2Y71otiTRZ/qP9Ln5OR7TEJGGqqwV7q6bUwLuFR2R6PaMF7tbu87yBo/ZlB/MioV2griqGUY+dnFGWxIjvjKa1TlTMGimvZvpXygmxeEUtoq+tbZ7tf5/Ldy+LfpPGTnZHQfqYBZ3zZGVEjWQMOyfq/1Y2xhuZUBu6R4Fm77IyKznigTUpysmVyNnCI/0LBGGOMMcYYY4wxxhhjjDFH8QcFY4wxxhhjjDHGGGOMMcYcxR8UjDHGGGOMMcYYY4wxxhhzFH9QMMYYY4wxxhhjjDHGGGPMUV44KfP8ZqbpZRQmH0JSQBJ1ZsKN2VWUhs1uosB4uWS5GP0uSTwyORFJt0j4QsIqSWrB75JUOBNuTEBKTXKtTKpcKpXNJEQkUO3ty+VmJBwh4U4mGiUREMm9WolUmWTXJFHJhCnULiRXy+YCldNYG5xF0bbEcjqSA2WiXJKDoQA8kQLT/ST8ySRcOK/hWesFS4RoruHzEzkhCZBJGNQ7YVEtDWuSks5vOP6UCnfWSx6/JOejmNpJhFEUv0qFf1Iiys2EWdBY9KxMKkuCP4orVeRi1P8ZFCupLJVjteOzSO6UieRorlCszt5pCXGN5IwnF2zcwzUQ2n85KxeBUqzMpMooK1dZm0i8rlOdaP6n5RXkXLV6bH+KtZnIksppXa8iRaY+zeZ/A16f4iLFH4kFsQt4/2z+3x4r60IpojEvE/uttNu+H+dICChJZ/fPQhnlSFneMAXZMsUSyoUlXvdJQJrl7ZQ3U3wgUXRGu1D0LEmrefzdeiPGchItS+UC3Ex+S3u81SKWZVJYosr7oxSU9jLZWgL3T57chLLVnIWmtUJRZhVO754WX0vjF3ORCtAePet/mqu0l+r2ed9BVOn/NeQtFCsyKXa7x/PikCryUtoLr1ZJ/KnHsXp6Efv//rc+KH4+nSVl4z87TzmkVH4tVZMy0743I4vBh9C5Wwb1axorADpPqRTrKowrinW0787OqEr3k1n8aGI+HOtf2k9PfxPWz6T+dEZYZV2ltYr2LVXiJ/V1u8vjj/ZtnUGMi9kZV6kYPdt3UFtlbc2/G+cqtSmVSdIqqdchJbGmNB75LxSMMcYYY4wxxhhjjDHGGHMUf1AwxhhjjDHGGGOMMcYYY8xR/EHBGGOMMcYYY4wxxhhjjDFH8QcFY4wxxhhjjDHGGGOMMcYc5YUz1LW77WckI5kokeQWJALL5D51kAaRnGO3O8f7G2AaPH/tIpRlUlYStpBoMBNukFxkBwLbTAp8c/MoPgsE1MtEjjU4jwLO/jAKxzIJGUqJSb5ZQX6K8s3kfhKpoXwzkZGQ3Ijqn4lmSfhC9c/EPvNpFObs52XyVomlzCQPJQmZxCImkntlDppsXh9Sq7EECEVO0KckLJN4rhCZqJbiD5XVEmFVHfqF5lo2/+lZNKcyKXwd2oVE73WQ70o8L6itqwjDMuETCYFI6p7JkUhuRXXNpLLUB1TXbKyVzvUMmpc0Vm5LOZ+5H+dlvHaRjDWUU4EoOhM3kaCPpN6ZXI1EhBQXMykyxYravoIUGWJgG8Z1FmtprpA0tdnk+pMsmaTYGTRWSCrdTOYqSd+oTzMR4xZiLcUfymkkntdZXkPcFsuXrjvGvEzMJjNNbt7P3+/07hTfSzL0JeSSEq8blAtWEd1S3Gh3WZRIIZbW3Wx9pbpmOT5BsTTLkYhS2WwmbyVRI8oXK+QXVaD6Z+tuKUvYty+mnIuUkjVzE+J/FQEu5diCJSVbCwkSUG8TUeliFgXOlJ/R+ipJwzvDUHZyEcsySIA8uy6+HcdKI8l7iNlNPKMgUXwVzh7EM57BaZk8WuK9AMlnJY6f8+t4bam8Wcr7mqC5lkH5GO3x24n/m8YwnZts1uVznc4jTs7L+4r2SBvYS0oc69q9uG/JoLpW2fe1E1nwIVWkzES276J2mSRtReC6BOO/k7QpnZ1Q/M1iPe3bqpxHZPv5Q6qcUdNeJhM1UzHlKtkZLfUrnlEV5CSl4mz/hYIxxhhjjDHGGGOMMcYYY47iDwrGGGOMMcYYY4wxxhhjjDmKPygYY4wxxhhjjDHGGGOMMeYo/qBgjDHGGGOMMcYYY4wxxpij+IOCMcYYY4wxxhhjjDHGGGOOEpXZ/5Q5uTPU8pZ0mqzyGft2vLaemLpb63htpxtt4+vVEO8ng/vp3dNQ1jvt4/1bsHWvFtEAvt2wXXs1X8G18Tczg3ir1cXy0ueXmMElaZ/YwTf7+K6rbaxrvcHPaUD7N1vR4E7XSWxAJ1t6ZlBfTOahbLWM17bVwfu36/j8BhjsOz2+fwP1X68XoWy34/bfQr+QQb5W5/artWK/0FCjeSJJLZjXdXjWFvpU4roSjSbfv9/BWOvE+d9s8/yZ3cxC2WIa+z+bJ02IVTSm5pPYp5LUhlg1OB3AczjE1yAudgc81oj5JLb/cgp1TeIv9XUWq6gNafxT/JOk1nlsl3ojjot0rsDvbjbx+Vmsw/uh/llIXcNYmV1PQ9kcxo8ktXtxrNC82CX1p7myg1idPZ/YUvvBcyRpvYS1DuragD6VpO4grnU01rLxh/0K43pdGJMknn/Z+++gnOZ1O1krlrMYlxbTSaxTMgA7i9h+A8gLOn1+PsV6jP/JWkH9ul7G/CF7fm/Ye+//HyRtZMzLzHK21Pzm/fh73brG67J84JD1Ks4viWMEzTvKxb/2A0XP3yX7DgzRtD4lazmVL2Yxb8nWcoql2bpBUNyi9a0KzWb5Np7WHdp3VIHeqQpNeP5sHvNrSdpty9bYbN9C75qNFX5+2bWdfsw5MyoMHwbzAz43acEeJ8ubCcq7sryFoH1bp192FiFx3kq5dKdX/pt0xrRalM9J2su0kjhL719vxvvbzdhPEr8/9UkG1TWjBWMoO88iSs+osramuLqF+V/ljJDmOuXXGf2T8vyRxsDspnyPRLGG8maKn+lv0voH5xYSr3VE4ZKe35/k/YOzGEOf91k0pmj9l3hcUvtn0Fyjc5ts/aG4Snv5bE3sli9BR+mf9o5fJP+FgjHGGGOMMcYYY4wxxhhjCvAHBWOMMcYYY4wxxhhjjDHGHMUfFIwxxhhjjDHGGGOMMcYYcxR/UDDGGGOMMcYYY4wxxhhjzFFeOClzq918RrJSRRhFwrBMQtMCtwoJgDP5K8llSNhRSTRZQapMIqwFSFGz+jcbJEWMwp9UdHgSpUckEN4kwqfZVZSKkiiZhJqS1B9GOVAXnt+D6ySWglJbZcKqUhFbJvwpFSB3QMIisYhns4rvShIuiYU/1CeZ6HMAolsSRpEETmIp+a4GZRWESSS3qSIca4AcK5O7ddaxXVckp8ukxBBrSNiTSdH7Z7GvSZyTzX8av3RtNs5JoEoiuCr3Z8K7DQgiSRpJEjGJ5wBJIzeJW5DiGpVlckWc6zAsMrnaDuRQFCuryCEb8P5VhFO01kyuoug3g0SSJFyTeF6RtLOZCNhb3fheONaS9ostzffvK8gdVxBXqE8kjpUUa2/Lh29DY22zjm+1nLOEjWIQSUtJVC5JXZAu0vzbJu1H9ULhXzJ+bs+LVoX1xJiXhdn1VJMnN+/959v//21oP0Wi5mwt7cJ+oMoejQSqtJZkcxnrBHuEdrIWkGhyBzlithaXCogriWqTuEnQukm5ZAbt8arkDc8L7RsJyvkkaXpV1v6NZEiylJSlnKXQXqye5P1EH0S3mSiUoHMPWt8l6fFvPi770WRM0H7seaXk8wqi2ullzDFXSd5C0H52chn3vdm+ifaIRNYklLednJ+Eskw+2+6WS4FpP1JlrFNcoX1PFUggm+XN80lZXVeVYm2F8wDYo9K+NYOu7fbLRNMSx0AqywTsBM3V+YTnX+nZSRUpNu1b02v78V2ff/zFsUbxU5K2u/j+WVwlnnddoPupbHDK9uVsP3lIltPcHitDEGQT/gsFY4wxxhhjjDHGGGOMMcYcxR8UjDHGGGOMMcYYY4wxxhhzFH9QMMYYY4wxxhhjjDHGGGPMUfxBwRhjjDHGGGOMMcYYY4wxR3nhpMzbze4ZCeB2w6JLEmmQcCmTMpOwgn4zE5NQOQl3SEIjSasFiCYrCENIrlLl/dudMrkJCdsklgORnC0T6yymIEeD+rcTKTFRAwlOJgVugNxseGdY/CxqVxIeZf1PbdWCtiZRr8QiHpL7NEDCJkkrEL2SsC5rv/Ui1ovm1GrG85dEeNR/JCSVWDi128Y5kQnrSNbd70epaSa1pr4ikR+JnSTu/8FZlHNVkbMRmdwOBeQgHCKJn8SxjgTumcQsa1eCxuX0GkRqifAJ52UFgS71YQvmZT0TtkH9SdhFommJBcQUf7K1gqBrSfgkcVwhUe7sesbPghhSSa4Fax3Na4ofEq+rFP9SgXhhW2drJT1/dhPbimLK1x4G18b43xqwHI36leZfLmWO/U91JWFoBsnZqE8kaQ7janJVPv9vy8YX986kH/x0aTWNeSmYXk51/fD6vf9cRZRKotJM8E5SwV4FKTOtcSSFrFJ/ikXzRJ5IP0vxOcsPSEZPeR/9psTrfpW1hKgiai0lTSUg76si2uyAQJs4S/atecUKn095W/as54DkxRm87nM7kUCT8vYsF6NcuopolASsJxdx31wqBJU4JmSiXtyjVhHlQvyi+JPNf9zjwB4py8VpP0S5WCsR3TYrtCv1QSabRuAdqkidJyDQXsO5VyYlLuWjksqj7DzZo5VC+5Z6YpDfQlyi+Z9JdUtDZfZOW9h38nP4QaVS4Qya60SV+E11zeYakcUlgvZNNNKzM0JieOc0lHUH5ev/7DrGupJco7Tf/BcKxhhjjDHGGGOMMcYYY4w5ij8oGGOMMcYYY4wxxhhjjDHmKP6gYIwxxhhjjDHGGGOMMcaYo/iDgjHGGGOMMcYYY4wxxhhjjuIPCsYYY4wxxhhjjDHGGGOMOUq5cvubxH6/0+6WSXsHpuyn18WyzXpT/hywda/AQF+rs8GczOI7+M1uv4v3owEcbN9VDOZ0be+En0/l61Vsv3ZiQCczOBnUN5vyPiHbetZ+nX40mzebsU5VDPZ7GFSdHhvUzx+ch7LVYhXK1lAmSbtdfBaNiWa7vP33S5gUCY0GjJVhr/j+xWxRdN0a5pTEc5XmWnb/fDqLv7mJbd1u8zvVYK6cnA/i/d023k9ttV3H/qMxJXH/4XWtBpYvprH9af7SOJekDVxLZTQnn9Yr1r/di21FMUGSGvBejQa/ax3GBcXK+X6O92MMgkvrjSTWd2MM2G0hfkCZJDWa8b2ydiH2zTiGqF+yvqJ1iZ6fvX+tFutP47cOMUXiGLyFNqGYLkl1GFe01g1g/kr8rjRXKX5L0nIZ5xrFhUaT35/iehYXCBo/dD+9k1S+Vre6PH6orel+mv/Z8ymvy9p/C/NqOVvGsjmvSa3F+++1SOK5MS819drT//saNZXvWyi+ZLHk5skklG2SawnKW7J5T/ROYt5VT/IGgnKcKmsx5e2U41cI77juZetDth99HihvKM1PJc47M0r3s9n7dwe8HzyE8tOnz4e8B8b/87KHcZJeC5fuIL+VpC3smzZ0lpCcm/BvVpi/sBafv3YRymgvVYVs/uy2kPdAnbJ9P46rCrlUC/bjFD6qxBTKu7NcisjmCpVXmdd09kPnZrQmSNLN45viZxXXCdqF1gSpvA9W8yzvhBw1uRaBWHNydlJ8O+f4ZfM/g+JC1k60byRoTZW4/jSmWklOTvXa0XlGPQkW0P4deFbpmiJJs+tp8bU0V/C6CrkCQXuhjPWS4yJxe65Rjkj4LxSMMcYYY4wxxhhjjDHGGHMUf1AwxhhjjDHGGGOMMcYYY8xR/EHBGGOMMcYYY4wxxhhjjDFH8QcFY4wxxhhjjDHGGGOMMcYc5YWTMtfrjVTOeRsSYJbKCyXp5jLKNSbXV6Gs1WJhB4msSMpIEh+JpU0sj331AD4AACAASURBVGThSakIrZaIMkmORXKUdiIlboHch+RISuRU5IEhOVkm3CGpK0l0dktuJ2o/ktBk4wf7GsYEScAkaQVyFJTwJHKu3YJEVHGspFJgmCtU/+x+klaRaLmKfJREt+slS5Bms+t47TpemwmHWMoa+6TRLJNPSyylzUS5xdKu5DoSyVEw32ai2DX8LozVKsImiml7cf+jlDvtq3gtjZVM+FcqoKWYJkkNkL3T/M+kxDQCSBq3SoZaqUipilQXpfbJWkXtNyC3XSKlpnWBpPDZ86mt6drBKYsASfpJcSmTW80n0eBdKgfMGJz2Q9nJxbD4fpabcv0b2zh+aa5mIsg95GOZ9BLvL8x10vkHY4Wk2Nn9t6/tQ7sb87Jzcn7yjDA1m5+Z+P4Qii8Sr9uzm1koy/Y9tEdrQi6fQfVvw7qXrSUEiTazWMrXlos6S+uV5zKxLMuRS6nD+lrblEtlq+X4ZfvWTIpLZwOUH2TQvktQluWiKDWH8VtFfkl7vC3JR8VScNzLD8rHf+8knnEsph+N6HNwHqW0lAtl0H6K2i8TAu8KBbZZ/5du23CcJeBZTvr88nlJ5wGlclVJalTI8QgUS1eQldMeF8/4kt9sdcvqv91wm9C4WlKsrxD/6DyrSp9Sm+6TfcvsJu5biExK34XyNZyRZHSgrqf3zorvp/GbnYfi82GPQPlDtibhHi+Jy0R/yGeXh1SJFetENo+/WygQzwTwt8dl6Rj1XygYY4wxxhhjjDHGGGOMMeYo/qBgjDHGGGOMMcYYY4wxxpij+IOCMcYYY4wxxhhjjDHGGGOOUvkfSRuNRv+mpL8q6fY/svk/jcfjPzQajX6fpL8g6ZOSvijpT47H45/5UGpqjDHGGGOMMS8B3jMZY4wxxphXlQ9iXflBSf/9eDz+t28XjkajtyT9dUl/QNLPSPpRSZ8bjUZvjcfjL5f+eKNRe0aylEnESA5GcqwFCBWlcgHvBkSvkrTdFgpvEplFfxhFRCSVzCQcpQLeOQjTJGk5jyIXkhrWC8UiGZkUmspJJEf9JElTkGqTVDO7n+Qua2jTdiLV7YJcp4rok9qf5Ey5HAwEuliW/BESjFWSe2VSaHo+9V8mgSKREvVJJrxrNqNIhsr6Jyw6pbhC45/aVOL4Q3KyTJhXR7lcBWFWoYA+638SsFOfVhLuwVzL+o+EQVUE0CRlPTlnKS/Nqyb0dXcQ5XQSiwBRAA/xQ+J2JdHzfsexggRzNP4yKfTJWWwXnOuJsI7GGokIaUxJLPciEVYWK0lW3RvGNs3mGrUVSbEnT1jkt1yUidmrzJUmtAmtKVJ5XMjmOsrSk3UR74f3orJ1MteX9PzC35Q4VxjQmE7knJ3e+7F+CPcZ8xHzke6ZJGkw7Gl5a05n+6ZWh2P0IZmDb3rF+4lDKsXCCgJllLEXvpPE+yncSyaiRJRCVpBqUi5D6y6tr5K0X5e16/OKoncV5JOZQJlYqqxem025fLMBeX/6u5Cj7bbl70o5VnMLxyjJ+Ke2plwsE5WXCpBpLyPxvgPvT+bUYhZzIdrLZlJkynFoTmec3j0NZdQmlLNLyb67gui0VMqanjuB6BWve878LIP2WHQWJZVLXbP8tNuP+6kqeWe2hsXf5PenvqJYn81VaiuSKmfQHCQpOInWM+hds/VzeOf5pNo0BuncLQXar1VhrS8948wg2TQK0BMW0xjrqgjoSSBP0Ht+GNeW5gB0bidJa73/rpt12bt8kH/y6Acl/SKU/2FJPz8ej396PB5vxuPx5yT9nKQ/8gGeYYwxxhhjjDEvK94zGWOMMcaYV5JKn7BGo1Fd0vdLmo5Gox+X1JD0tyT9KUnfLenzB7f8sqTv/RDqaYwxxhhjjDEvPN4zGWOMMcaYV5mqf6FwX9IvSfprkr5L0u+Q9JakvyJpKOnw72Fmkk6es47GGGOMMcYY87LgPZMxxhhjjHllqfQXCuPx+G1Jv/tW0Re/9r+6+b8l/W1Jh/8YW18S/2N6xhhjjDHGGPOK4T2TMcYYY4x5lan6Tx79M5L+oKQ/Mx6Pv27H6EjaSfr7kr7v4JbPiP/tUGOMMcYYY4x55fCeyRhjjDHGvMpU1YA/lvRHJT0ejUZ/UdKbkv5TSX9J0n8n6U+MRqPfL+mnJP2opB+W9McrVajTUrvbfu8/D++c4nWrxTKUXT28DmXrxIpNBvNuN9ruM6n4ZhNt37ObaEBfzmI9sx8enA9C2dnZGd++jfUnK/mWrPaSVotYfzKgZ1Z1MtOvwSpOVvun5bH+88kcryVW89ius5t4f1p/qBeNlU6/U1wnssqvEis8tXWtVgtl26z96vFassq3E9N8u9cOZa1OLGu2OUSQbX6ziu+atT+19RzGKo1zSer14r8KQH11csH/ekBv2Atl7V68v97gfxWO+mUN77+EcSrxe1H7t7pJ/3XjtfV6rGsDxqSUj4tDVjCnJX5/Gn9Z/KF22SV9TXOV3vUE4qckdU9iG1D7NxrcVjQv6dr1juf6CtqA4l+2VlBcp3nVH8b1S+K4RHN1veS+bnfjvGi04m/SmJR4rV3N4vOn1zO+H/qf3j+bq1yneH8tu5/i8rZ8rWzA7y5hXk0vY/4gcaymuZK9P41Vqj/NKSnv10PWyVpHMWRTIdZvoP+bMKaz8d8ddN///09i3DfmI+Qj3zNJUqvXUefWPNlCfilJm3VZjk25iCSdPyiLBVneML3iGFcKrVsTKKP4LnHcWc1jXbNYRrTaMe+h9VHiXILiNuXXGfROleoPeVuzVX40UIO9SB3KJOG+l/KebN+EJH1NYC4HawlcVuk3s/FHOQLd34QxJUmdftm71pK1PMtRSq/bFPbL1btXWN6DNbpDe5lk34K/edINZdleojmJ43pyOQllsyQXpVx8t41ttdmUz9+Ts7hHrTJ/Myifoz1yFqsoR6xSL4prdYgr2RkDQeNyveK1hvYNVeIK5c31BsSPCrFyVmH9Kz2Pyfb41K9V4jqNdYq1WawjqpzxZWcnh9D+SpJaheNqn9SfcpgsrhHp2e8BtH5n9E95j0NkMfCQrE9ur0vdWtneupJDYTwef0nSvybpR/Q0Uf5FSf+PpD86Ho9/5Wvlf1bSE0l/TtJnx+Pxr1Z5hjHGGGOMMca8rHjPZIwxxhhjXmWq/oWCxuPxz+mpWIz+u5+V9LPPWyljjDHGGGOMeVnxnskYY4wxxryqVPoLBWOMMcYYY4wxxhhjjDHG/NbEHxSMMcYYY4wxxhhjjDHGGHOUyv/k0UdN96Sn/i0ZBImKJal+E+UoN49vQhkJp7JykmtlkBR4ARIOErVKLE0iCUwmTyW5DIluSf4rsTAHRa+JqJTefwLCGRIlS4lo9JpEz9x+JKehskw0SXKfJrQfCbOy+0k+mt1PsuDb8sj3fjORMlP/07uS0FNiqSXJhVLhD5Rj/RMxzPWjKFAnCc42uZ/mL8m5uv1YJnG/0FzN+o/GL8Wf+Q3Lveh3Ty6GoSwTZmXj4pBUrgb3o2g7EQZl7RKfz+U0/taJHImkf1UgORPFxSoCaoqfaT03ICKE+JlJqBpNitXlAnKaK6VSdSkRGbai3DZba2muUFzPxjSNYSrLBOAExap7b97Fa0kWXUVAT1CszkR+jXW8lkRo2VylZ9H47YKwT2IxPAmUqwj7UGqe9D/mABzWk2fV8f835lVhu94+E9MpF5HKRX3ZWnJ6J+YoJPrMuIZ6US6YQTJ7YnbFeVcVASJBe0QSdWbyy1LBPe0lJM5RKBZTfpU+i+SlSSyuQfxsNmHfkOQCRLMd649C0IQltVUFqzKN3yy/pf6rIjrNcqxDMikx7XFpTmdSdqwT5B00pqQ8Rz6E5MsS5wiPvvo4lDWTfU9prMlEv7Qf7q5iMkGidonfn/L+7CyJcuT+WblolfJ22p9IPC46Ko/VRJbjEzQvKP+qIuAmqXEmlV3SGVMFgXD/LJ49Nttx/GS/SWLv7DyQGN45DWVV2p+k2lXWP+qrIaz/mUCeoL7K9g2lsu70LKJwDVjOea0hAfPVk0dFv/kU3k8ektWfYiCfW1eRmseybEzenj89OIMgvLsyxhhjjDHGGGOMMcYYY8xR/EHBGGOMMcYYY4wxxhhjjDFH8QcFY4wxxhhjjDHGGGOMMcYcxR8UjDHGGGOMMcYYY4wxxhhzlBdOytxqt9S+JZPJJEBLECATg1OWOpOUsQ7iiUxORAJkEqFlok2SLZPUcZnIgUj4cvUwSkSml1Fik8FSZv7mRFJXEmnNEimtQGTTgT7JJEzdQSwnUSkJ0yRpuyGpZGxrkh9LLFIhEVbWfp1erCsJV3aJFDupVCgi+aWUvBfMtaz9SdZN12ZyK5oXNKcyCdEJyNq7J1EUm4l9diAsormSiV6prhSrsvcnuR2Nv0zit4H7ScKVxU+qVxWpKsnNWP6ayO3o/kTORX1AouIMmuu0fmRyL2pDetdsrJJIbfKkwloBce30XhR29YZx/EscqyguZVLhUtlwJtJrg9SX+j+TWqNsHuZFqShckjrwfIrJTysQfxfzj6z9CkVw9J6SMK5T/CrNiSTOP9JYA+OXKJWoSYkwLpFSY15GUupMLnZr/q4qCDSNeVnYbjbPjP8s7yCqzFtaCygXzOhBjva8zG/iup1JjUvXCNofSlKjUCCa5QK07hFZLoZrIaxbVfqU4jvlohkN2GNXeT6tW6XyX0mqQZtk/Ud7FOrTbN/WB9lwlrcQpe+1ryBPpb1c6ZotSSfnLGAntoVS9yyXwj1Shb6meZX1NUHzn8Zqld+sAgmYqzxrvo+xropolwSsrRXHKmoXOk/IYhr1VZUcnaBx3YD4L/G+7/9n715jdU3v+r7/1nM+rOM+zdgztmMwXTDjGGJzCII2pEmQQitCQEVFRErVNpGqVg2KlLyANlKqRi0vokRIeVG1SZMQV8gVhDQuiBQBbgIGghobnEkemzHYnj2Hvffa6/ScT6sv9tiz9vp//zzXPdvjzN7+fV7hi+d+7vu+7ut4P7P2j2RjFYXdXsC8ku2PSsdQem8hSbvX4x6PDE+HWE7vI0mVUF9qq/0KoeI0VtJ7w0yVdxS0LqD3pkcvc9DyvXsvFZ1nb+8GllOANcn6BD0Xmn+qBHXTvlHcfB6elwv7rf9CwczMzMzMzMzMzMzMNvIPCmZmZmZmZmZmZmZmtpF/UDAzMzMzMzMzMzMzs438g4KZmZmZmZmZmZmZmW3kHxTMzMzMzMzMzMzMzGyj8njvr5D5ZKbp8I107smQE8Dn01kom45iWZbATmnjlJZdq/FvLrV6LG8063CdnI69gLT6yXlMIJ+O+PyUVj6DtPTVIp5HkmqNeK11KGt1Wnj8GtLuG83YnNrJ8atlPL7ZivXf7rXxeEqbb/egrFt+/tFpEndeePxKMUF9tYRUdUmj01Eom47j82t3+f4bUFeUFk/PSdJDfexLZdB/Wkn9bR9sY/lVZ0dnWD46iXVN7a+7HZ+pJPX34vnpWrP+u4Bx4eICnt+K628J/fdiHb+TnpMk1eowLsDzy8YvOhdd0xTGBElaLeK9UltJxz8opzGV2qTE40/2WSpvtuOzrsP4I0nL+SKU8VibzDWzeLzguWTnpzF4NqG5Cg/Hdr29H9t/NlZSG6JxPWtrE5hr6KP9vT4eX2/Eetm9vlt8/gvog9TWaEyWpDE8a5p/afyRpAZcf/tarL819EmJ2xXdK60fJF5rVPkczUF0/7NxbJMS9x9q67R+kqRaI17XYjaPZVPoZyofa5YwpkkPz3Xz5BmbPc7qjcZDa439W3v4uWyMit/H/YT6GK1bM2sYi2h8z9B6jJYN2VhEaH2RzUU0lmbj9qOg9Z3EYylptsq39vRMsnVr+XdWeabx/NlailD9b+/zWoTWjVXQXNjZ7hYf34E1Gq0vaX0ucV1RXWfr9jrUFa0vs7U4rTFHJ3EvS9cp5e8jrsr63xzWzbSXzJ4z7Sepr+zdiOvTrHwE6+MMjjWFY7LEY2X2rAl9NnsmazhXqxPHhfWSx78VnIv2vVlbI7TGzfY9WRsMxyfvWLJ3R1eNz+K7FEk8MYH9m/tYvpu0wXB+2N9I+RhyVdbX6nXYo8MzzfYNeE0w12TPfwntko7P3nHRuHRy9ziUnZ7cweNrW9R+41ixf+M6Hr9zbSeUtSqsS8gWtP/s/mmTPoc9Vg32UpLUvTSv0bxF/BcKZmZmZmZmZmZmZma2kX9QMDMzMzMzMzMzMzOzjfyDgpmZmZmZmZmZmZmZbeQfFMzMzMzMzMzMzMzMbKO3XSjzarl6KPiLgjkkDoWkoL8hBHM8OD5+AQVFUpnEoWUUNJqFg5WGCq5W8Z4efBbqhcJLk1BkCtmgoN0s3IoCMCkUNrOYxvuioOUs1Pjs/nkoa0M4zO4NDqej+qegSgqhkaRGq+y3uAmEH0scqk0hLvUdDjyqQzgbtZ8sMIfKR2exr1D4ucTtmsKdTu+e4vHnJ/H5NZuxrTaScDkMNYXPZoFVpTlQWZumcLxWPdYJBZVLUhPCrTAUPglaXUPgFNV/FnhIQUjU/7Lxi4Ks6JlQsJckbVEQWfJQqK2XhhNK3NbH57FfZnMN1RX3NZ5rJsOy0MruDof70XOhIL3sWVEbpCAnGpMkDhWmcMzs/PXCILUsSI/mdQwX3Sp/ftQmqE9K0k5hAPYc5jQpC1CP4VhZqDCVlwZtS7xWoXk1vX44F/X/rK+Xyuof57AV1Gky1l++r16FsE+zx8WFLh7qp30YszLUb7OgRiqn+SFFawQYH7OxiCxgLM3WXXT9VUJRCc1bWVBoFoB4Fe1PJF6P0vObT7j+aD1ZJRSVQn1JtpbC74Twz36y7yRtmPe2D+Jeqoos6JRke1RC+3FaS82SPkXtlx4fvYuQOICY9ghZj6C+OisMSs50d3qhrDSQVuI9Ju3lJd5LUKh2lVBzqmsKOpf4vRUZnnL9TUflAbidPqyxK9zXBewRaKykvUj+nbFsNs3aOpWVr3tLx7XSMU3ioOPpmK+f1t178D7q+jMc6lsqu098R1MYVJ0dz3vJ8gB12gtn9VdlXCX0Pmj3Wqz/OgQtZ/p7cV7av8Wh2qUBzNmYgGHLFfZYi1lcF1Wp08vttwZzNPFfKJiZmZmZmZmZmZmZ2Ub+QcHMzMzMzMzMzMzMzDbyDwpmZmZmZmZmZmZmZraRf1AwMzMzMzMzMzMzM7ON3nYJdc1OS+1LwRNZ4AsFUVHgxCwJp5pQKCcE+VCwisSBJT3FcKE0mAOCVChcZWueBMu0IRwpCeAlLQhVpHtKw40gpIPCcbLAHDoXlWXhVBSqOoIA7lkS7tZoxKZPQZlbSSguhVJWCQWmcDYKX+1CYJTEz5qC2CiYReIgMNJocfulcKcq4XrTaQydms/LQ80pCIwC57JQZ3ouGEKVJOZQOBGdKzue+k8DwqGywCMMRa8QuEPjKrXpLFyQ7pUCqyhQV+K6zu6V0Fg9POYgMwobpwDiVhIKS22QQtfomUjSchn7YKtF9c9jJQVB0ViRjTXUBhfTeE1ZuOYcxhBqFzR+StKEgiQrPGv67ALGlaz907yKodbZWJGsAeBEWIx9BcLRsvqnvkLzKrUJSarDWEXzT5VQ6Sw0lI8vG1dpTpakZSO2H3qmWeDpYv5GXZfOe2aPk4vl6qE5KQvfKw6qLB3zxGNxlYB3UhpeLPG4kY0lJFujlKK5cP9WDH+UpK1kjr5q/oW7XA5r/Cr7vt5e3KNWgaHA7fissvZH8xa1tSrPhPYoWTMvrf9s30roXUQV1Cez9REFkJNmFvRJ70ig/SxhLS9JEwirHsFaOr0uWEsfPB3XLZ0er2XI2dZZKKP9gSSd3IkBzrtQ191tPn+VAPZS5/fPQ1n23qMKCrvOwupL0b3297aLjz96+SiUnd+Pzy9TJUCe9i2E9uKSVKuXnau3w+9oaFy6+a6boWznoEr93Q9lqyW3dbouWv9m+7Y17FGqzDX0jmYyjO/t6HMZfO+Y7JtxPwT5ydv7cX+d6cFevFFhrUSyNdFqwc+1FM3BVb7zcl8rneP8FwpmZmZmZmZmZmZmZraRf1AwMzMzMzMzMzMzM7ON/IOCmZmZmZmZmZmZmZlt5B8UzMzMzMzMzMzMzMxsI/+gYGZmZmZmZmZmZmZmGz1a5PtboNVpqX0p9bpe5988FvOYVt3djqnma0i6lqS9m3uhbBsSvJsdThAnzXZMdU8TvOG6KFW93uQE8fU6pm43mvFxXsDnJGkOyeqjk1G8zgXXH93rfDovuk5JarbitbbgO7Pz1xvx+ufTmFY/G03x+FWr7FlV6SB1SHvvbkPSvKR2r138WZLV61UNqOcH54p9pQVtvdXl9l+De12M4zNZQj+VpNUqljebsU56O/E6JanTi3W1mMfnT+1cklrdeK6LdXz+2fizBeX1eqyTrdoWHk/nIrXk/NTWVjB+1Gp8PLU/ev7Z+DODZ312dBbKaEzKvjcbKwV1Rc81O3wKY8BiFttKN2lrdbiHerN8rO72e6GM+hXVv8R9fQltvQFjmiSNT+O4fnrvNJSd3DnB42lcp+e/WvGYtIZympezMY3qhZ511qca8FxqjfidWV8pvX+avyVpCXMYtT9qpw++t3Ss5+dPaP2U3X+zG783a6tkOY/XT+fP5nq6LprXsuu/PK7QusPscTcZTTU6G3/pf1/+vzehvkzr+yrmML5VkfXTGqyxGi1Yi8H6KEPz9ixZN5a6SIbsra2ydd/WFq8bcd9YuBeQpCbMEXSq7PyE1m3LBa/7s/VsOH8ylk+Hk1BGc3Gm3vjy/zeU9ESztQjNe7RuyKqf9shV0FpkBOvD4ek5Hn9+fr/oPDs717D8YG8/lF17R/xstm8gtMeq0n+HJ8NQRv1MknauxfazD++S0nNBXdMeeb0qGyekant8ku67QAe+c3xePtec3o37DtqLZKqMS6XjQqfP7112b+yGMqpr2vdL0u71eHwb3juMz+KYJknDk9gH790+ws+S0jlwMuR9B6HxK5ur6VHR+E91kqHjs303PStqP1X2MlU+u1rGuqJ5IWvTzXY8V5VxjfZDFxXWRZfXFRfZgubqOYu/3czMzMzMzMzMzMzMvmr5BwUzMzMzMzMzMzMzM9vIPyiYmZmZmZmZmZmZmdlG/kHBzMzMzMzMzMzMzMw2etsl1G1tbaXhepdRkE0Nwnn6+9t4fG8XgjI7MfAoCyyhwJfS8EZJqkE4VJXAJQrSoXCSWp0DTygcZHgcQ2DoniQObVtBENcsOf5iFQNHKBynvx+DsiUO16IguCwwh4KcJhT4lYSgUFArhcBkQZn0rCiEJQtconIK+qzX+P5Lw2WyUGFCfXIx5/prQCjq/vUYznXrPbf4eKhXCnyaJaHQFBpHfSoLF6TAo3oXgoaTcDZqVzR+UXjy6x8ORRTuk7UfygGicL+tJT9/CpKj9pcFftG9Zp+l4tLnJ+UBa1dl4bc41sLzz54VlVPoXJaNNjyOoXHU17IA8fF5HNdorLtIwh2pXVE4FwWVSzyv0VifhXNR/VGoc9ZXqa7omWbth46neT2b6xczngPDeZJQYppXqf+kc00j1jXN31mIG50L+1/SflbLWE4BzFk/pSA2fP7LZK1xaQymazF73K1W64f6D4XXZmJMKK9FH5SX7VEePSiY1x2lG9ZsLKIxvkpAPBlBqGt2/9l+5KrTuyd8Lgh1JQdPH2B5th4sRQGcFKD8qPuGcYVQ8dK9XJXzZ6j+slBSQnVFQePdnfh+4sHx8V47PQ6VJaPT2FapTU2n8XOS1GzCvr0d9+jXnrqOx19/5kYou/EMf5bQWnYb3vFka2Hq6zQmZO2HnvWycH/x4Lris6a9eBY0S7K+RnvMrF5IC9bj6X6Uzj+K785oLTyFz2VK5x9JasL7PDp+99oOHk99kNai2VxF9T+7Xb5vOIE54OzeGX6WlIZyr2Avn9nei309WysQ+my27i8dl7P2X/Ie+cHn+Hj63ioB5oTGCtrLZErfZUh8/zT+lKzVSsPQ/RcKZmZmZmZmZmZmZma2kX9QMDMzMzMzMzMzMzOzjfyDgpmZmZmZmZmZmZmZbeQfFMzMzMzMzMzMzMzMbKO3XSizdPFQ8EUWVEiBJysI1cxCXCiAkAJ7KHxQ4nCZBQQQZyEahRkXKQrJoMCYehrKGo+nOh2fczhWbVQWTpMFvlBgCD3rZpt/86JQ5A480ywwi8JyKRxnmgTDUJBQcxHP1epyYA0FSW0fxLIsWIaCOsen8VllgSsdCFeiEKas/U6gXVC412zG7afdjoFHe7f2Q9nu9V08ntoK1XXW/i5grKDArywwiUKzqIzOk30vBY1TeK3EQbcUbpjd/3wC49c8tuksjIfa5T48v2yco3DU5ZxDdQk96yrXWgUGqUH9t5bc1ynciS41CxWew7xCgVVZXdPxVP9ZUHsb2iCFSmf1T2HNtSQAmFBYNo3/WTQYBnFSgHgSoE7ltC6g+VPidYEoCCw5P32W6i8LFyMYKp88E+qX1Kay66cgsmXS1skKPjqhUGYY06SHgzRnyXxs9jhrNBsPzf+rdhZ0GDsTzQXZuoeOrxLUSWPpHMbNbC571FBEmsup7KKZzAUwF9E1pevGwnlveMLhy6VhrQdPcSgzob1Mtm+YwbhPocBVgoLXnXiu4fE5fpbaKsnmF1qL4DUl56HnWquwbqd1Ux+CThvJc27AGp/2DVVcf2e81yprCQpavZ4ELd96961Q1uqUjx8j2ONS/dG7EInbetZXCe07KUA8C8SlUO/+frz+7L3T+f3YL+hdgCQdv3o/lFEoeDZ+71AdZmtUsF6VfbY0PFiSejvxs81k30L7GTy+QqjwEELNh8ccYD6G9yG4x0vmNPpslQB5bBe4F+W+3ob3QbTvytrqo3rUca0kbPjB55Lzw7qgVr5t1SzZj1xFY3pmXaH/0VqrSgD35f5D8xbxXyiYmZmZmZmZmZmZmdlG/kHB5FI+EgAAIABJREFUzMzMzMzMzMzMzMw28g8KZmZmZmZmZmZmZma2UeUMhcPDw2uS/rak/0gPfpD4mKT/ajAYvHJ4ePhtkn5C0vOS7kr6HweDwd/9Ml6vmZmZmZnZ25r3TGZmZmb2pHozf6Hw05K2JX2tpHdLWkn6Xw8PDw8k/ZykfyhpX9J/IelvHR4efuuX6VrNzMzMzMweB94zmZmZmdkTqdJfKBweHn5I0h+V9NRgMDh7vewvSHqHpB+QdDQYDP7O6x//pcPDww9L+q8l/WbpOWaTuaaj6Zf+93IOqeiSZuNZKMs+Sygtnb5zC1LRM7VGjADvJOnYlKxOCeoN+E5JuoBo8vk0fi5LBa83429JdP4sqZzqqg7XWkvq7wKS7Wu1eE10HklazOKzbjTj+RvbXT4/1Atd03pZnopOx2cJ7s12LKc2sUja9GoZnz+dn+pJkpZwXx14VrUWt98ZPGvqU5l2pxPKWlAnWftbwfVT+2km9b9cxGutN+LxrW4rlElSqxPL6fktV/E5ZZ/dguvP2k93txfKqE3Pp1x/1K5n01iWPdN6Heq6sE4kaQ3j13LBfa0G52p326GM2o8kTZMxuBRda30rtv92L16TJNVhXKJ+uRrCAK5kXqL5A9qvJK1XMK7RPTW4nvq7/VBG/SIba6ivUlu9PO9fRm2Yxk/q/xLXFbWf9YrbH42rdP+rpP12oF1QX1kkfZXG6i7Ma1vQTyTuVzR/ZH11uYxjAN1rdjy13xWMv802j7UNmINoXZOd//K40HjEscCsiq/EnkmSdg62tbo0/g9PuJ2f3z+PhVvla1yai7M1CqGxlGRjAaHxKVO6n8vWjbifwbmY922laC/z+jfHEhjTOv24vs7QWDxP5vLi76zwTGgu6mT7NtrjTOO1ZnO5Cttf1k5p3yCYtufJvqXVoX1f/Fx/bxuP7+6U9bXs/nu7sV6prRw8tY/Hnx8Pi87fh/2JxOu2yXASyrK15Oh0FMrmk7i+yNaCdP68r0WtTlzL0bWuk30f7QXIzgE/fxqXzu+f4WdP7pwWnSur6+29uO7P3ieRGTwX6lbZWEX7qb0bu8XH07hIaH8iSWf3Yv1RnWb1Pz6P7bqKTi/eV6Mf74nm5EyVeam0r5bO6VK+RyelbS17x5jth0rhHhH2zZlsDLiq3qsw/gj2nUn7pedC40/2TC7vUbP5+KqqNf6tkl6Q9BcODw9/9/Dw8BVJf1PSK3rwJ7u/c+XzL0j6xornMDMzMzMze1x5z2RmZmZmT6yqPyhck/QBSV8n6Y9I+iZJz+jBn+zuSLr68/FYD/7U18zMzMzM7KuB90xmZmZm9sSq+oPCF/+G6UcGg8H5YDB4TdKPSfoeSVuSrv6dW08S/I2tmZmZmZnZE8l7JjMzMzN7YlX9QeGF14+5/A85ffEfZfqEHvwJ72XPSfrUm7s0MzMzMzOzx473TGZmZmb2xKqaUPf/SPqspL93eHj4n0nqSvobkn5W0v8h6X84PDz8EUl/R9J3SvphSX+mygkmZ2MNz8Zf+t+rJFyIgpjos2nQIIQKUuBIFlhBgS8YSpwEg1BgBwVVVgmFxuPrm4MK/6CyZRLYQ4E7ohyuCqHSFHg1HXNQJ4XFbm3FEJvs+dcasbwHQVJp4At9L3w2C0w5fvU+ll+VBd5Q0CqGsCTtj/rPpB7rOjv/6ATCsSBUtN3mcC4K8hvCd2ZBvRSkRZdKfUKS6vV4fHcnXmsWYkShzNRWsJ/owb9rEI+Pzyprf3T+KoGjFMpMIUhZKDMFxQqefx5uB+dPxvoq4yqhUFcK8srmGqoDCjKjEKusnPoKzT8Sh9o24Z62tjjIsgZzwGIex98sAL3djfdKdZq1VQp2p2dNoeQSt6FphXAwGmvomdCYKvFYT0HVVCZxEBb19WysoNDMGoxfVQLYqa8vZnw8hWZSKHUWOEhj1Qr6bwtC0CSel+lZZ/d/eVzs7/tfk7GvqLd8zyRJ3e2eFpfGlGwsG0OoaWl4oMR9mcILMxgKXXgeicdiWstlaN4i2VxM2sm1kmyNcNXB09ewvEooZCkaS2l9IeX7qavmsL+Q8v3QVVWeaXsf1uIVwkdpfs3a6Xod5y3cyydrEVpjrSDoNcs5paBaUm+Ur4+3kwBgQqHEtEdfJWPKKQTdkqz+qP2UvouRpO42vCOAtUi2byS0F6gyftBeNgvlprB6uidJWsC+hdaCmdJQ4wyFZZeOv5LUKWzr01Hyjgj2mLRGHJ/TblwaQgA57cVo/JB4rKQ9UjZW9fb43clVWVtvVBgDH8XRK2XvsiRp7+ZeKMuusw771jn0taxFl74jyNYa9I5ytSgfF0rXVRcV1l/0jjTThj1adq/k8hBcOu9X+guFwWCwkPTH9ODV8WckfVrSS5L+88FgcCTpT0n6TyQdSfrfJP23g8Hgl6ucw8zMzMzM7HHlPZOZmZmZPckq/wQ5GAxelvSfJv+/35L0HY96UWZmZmZmZo8r75nMzMzM7ElVNUPBzMzMzMzMzMzMzMy+CvkHBTMzMzMzMzMzMzMz2+jRUlfeAuPhRMPTN8JQKLxQ4iAUCqGhwByJAzMo3IQCezIUeJGFI1FoFZ2fAiUlqQWha3MIfMqun0JJ6w0I+kzqn8KiKbwxu38MtYVrzZ5/aUhIFoxC19+kuk7yqqhdUZscQQieVB7uk4VTUTgWhWt1trt4PAXOUCgnhXdKfP2k3eHAKAqKxaDVJISOggAp3ChrJ9Qu271YV1mIHgVp0bmy/keBUfSss1B2an8zCKc6h2Cp7HgKJc5C9ErHynUSqk3SsQaqgPpKFiCNQW5w/VngHYV+VQldo3OtoF6y68/CZoOkrVNYN5VlgY9ZWHA4fdLWqV9QOFwb5iSJQ7umNNfPea6ndkVtoiTU94soKDgLx7uAaqUguGyuaxbO1Vk4GwV5cSg491XqaxTKnIWzdmEOWsD6J2t/tC6gc2X99/K6qEqYu9njotFqqLl6Y61Cc7kknd47K/q+WrJu34E1ZjbuEbquKvMrrdGzMHhCQZu07ioND5Z4Ls/Wjdl+9KpsLUDo/pfJXMjHx7mkdH2fqRIeTc+0v9/Hz9IcSWvkKuGv9PxoLytx+yHZvpPK56s4Fw+PORSaQl1pfZOtBait0PHZWoTq9fK480VZ/y0NFW0l/YfWHQ1YC2VrAao/koVK036e+mqV/kfr46z9Xn8Hh7WTnWs7oYzWfWt4l/TlQP2SnkvWVyiA997LR6FsfMqhyqNzGuvLx/Vms3xeI7RGxfabzF9ZHyhF56qCXj1R/+3DXijT34vjemnQvMTPukpfI70dvn56R0XoXa4kXVyUjXXF+3vl8xLqlYVa44OWNDx5Y6yrFdaxd1dmZmZmZmZmZmZmZraRf1AwMzMzMzMzMzMzM7ON/IOCmZmZmZmZmZmZmZlt5B8UzMzMzMzMzMzMzMxsI/+gYGZmZmZmZmZmZmZmG3GU/NvI1hYnba/XMZl6tYgJ7lmCdq0Wf0upN2La+kWSgE3XVYe0dromSVqvYnmnH1PlOz1Omq/X4/UvKqSdb8H9470m908J9qrFOmk0uImVJqi3OpxUT8+v0SpvzutVTEun+6fPSdIK0u6X8KzpOWfo+MkoptpLUm8Vr3X/qf1Q1t/rF5+/Bs9vldw/1csK7nU556R7alZLqNN6k59pf68Jn439L3t+hO6/1eX2V6/Hc9H1Z/2Hxo8mtN9mm8+/Xsa6Pjs6i2X3z/H4R9WAsZLGhNo2/2ZNfW0+4bGa2tVsPAtl1H8krtfudhfKeKzFayrs/5I0Pot9mMaFZKpD63Vs11lfozZI7Yrq5MHhMNdCv6pVaOvUr6qcv/Q8r39B0XdeJGPFRb3s/GsYkyVeg1BZdv11mEPrjdivmu04JmafbcC4mp2f2lWnH/t6b6eHx9dgrTKtTUNZtlZbwVhHY0I21l6+/mw9ZvY4W69WWi3faOe0PpCk1Sr2ZVyjclfE9VgnGbfJCObCxax83/KoaI2B636YXzN0/bgWFO8xCa1vMrRHGtcnxcfTWJrNhW8FqqtsLdPfj/sZ3IsmqK3TuRqwl8jOj2uJbM0C7y2WNL8l7ef07il/79XTwHky49PYJ/du7uFnaY3Bexlei9BcXgW1y8UK1lLJfypL7z1Itk6YnMd+NZuU91Vqq7RvP3jqAI/fvbEbyt517Tp+dq9XNi7/65duY/ntF18OZdiuk7aG88Iorvuyse7sXmzr916+F8omE97jLpdl88r2Ntd1lXGF0DsuekfVTt5xldqC9fWD88MevQd79MI+IfEaPXtHR+h9FL13ySw6cVxZLnmszN4nXDUZ8lyZ7aeuysZ6mkNWS9i3V9iTlO6FpfK9Tvad00t9dVb4jP0XCmZmZmZmZmZmZmZmtpF/UDAzMzMzMzMzMzMzs438g4KZmZmZmZmZmZmZmW3kHxTMzMzMzMzMzMzMzGyjt10oc7ffUf9S8E6VoL75NIa7UAiGxOEkJAss2YIgEQp3WiZByXRdFBiF4YNKgjwwXIwDNyi0jcJdssAQCvJoQ9AnBcNI0nQcw3kmw1iWBS3Tc7mAILWs/RAMf0xCpKheKXyyCSEyktSF66dQ0s6Ig2IpNK5KuB2F4/T3OFSTUGANtdXzIw5MorZOITZZUCj1P6r/LDAL+yq0dTqPxEGpdP+LJFyOnl+tEfsPBU1XkQU+lQbtpkGv0K/pOyk8XuJ6zYMkaayP/Tqrawq4o8CwedJ/6L5aENh1ccFtbXweQ++o/WXhVttQ11X6On0vhe5lAeB0/Tyucl+hAGYKPMvCoaiuaFzOxgq6L/podjxdF83r2VwxhSA6Cszq7XKIHs0hvNbg9k/hcLT+yfo6jaEUyp5ZzGJboSA+amdSeZBpGip9KbQtW0+ZPc6Gx0OdXgo8zuayZjP28dPxWfF5cCyAoOYMrXuqBBBTH6e9QLZuo7G0yliW1etVpWOWVG3fgdcEczEGbSfWq/Kgx1oj1jXdazYWYwAnzK80Z0jSdBTP1YC1eIba7+h0FD+YXD/t0WiPmq3lStvF6GSI5WdHsa/SvjmbSynUmM51fp/3bdsH26Hs5rtuhrJOn9+v0PsAaqtZmzyiUF64/8zOtZ3iz5bqbsc9ehbouw9hy0+956lQdnMvhi9L0lN7cd3+vlu3Nl3il8wgwPaTv/95/Oydz98p+s7sXlsw1jZbsf1kaN2+sx+fH5VJUrsfn0urMGhX4nc8VUJxaQzo7ZS/Yyldq9JeXOJ9F41V2fF4Lhj/s/mbwuazAGSymMa5lgKYs/Dh0nVFum+CuaoB7afT43d0hOqviioBzjSHZu9jyeX2UxpQ7b9QMDMzMzMzMzMzMzOzjfyDgpmZmZmZmZmZmZmZbeQfFMzMzMzMzMzMzMzMbCP/oGBmZmZmZmZmZmZmZhu97UKZV8uVlpdCNBdZKDMFqEKQUhYOtYZwIgrCSkNRIcdjCeGfWTAIXRcFJc4nfP8UxEWBHVmIDIaLUVAr3FOG7ikLBysN2szCySiAd7WKzTm7/0aLAoPi8dn1l4aiNuE8Egf2UPvJwlAo6HMM4WIUdCxxkBSFIGXtf/c6BBBDnWSBPxSORiE8SffFdnEBIUrTcw4BoiAv6n8UIphZQ9A6htBJEpRj/0naP/X1GgS+UTCVxOMf1XUWuEWoq1HQsyStprGtZ30VgxihXe1c41Bb6mvUr8enHGRH6FqzAPnSILisr1AA8BD6ehYuRQGVNP5l10+o/1E4psTPih41Bz3zWEfzKs0JEo+hFCSahXqXBrhn6PrpWmn8kXgMoDYxn/JaA4O4CsNNH3w0fpb69XLM4Yi0hqHQyEkyVtMY1NuN4XbZ8798/dl8YvY4Wy6WWlwaE6oENZ5/4aj4PJ/5ndiX69DvtpK+SKoEGBOai2tbfH5az2ZrdEJrrCpBnTSW0vyUhYfSXIJjcYX5qYpGDfZYsO5erfn8FApMsnX7dARzXIX6L11LZHp7/VCGzzRpU1lYcTjPDq9l927thzJaN528dozH3381ltO+8f5rPCZQOdUpBTVLUn+vLJSW1jcS79uGxxwgTWgvQHvkehI034d1x41nb4Sya++4jsff2o+hyu+AsuvbvGfY7cb93IW4/Z+OYx/62Y99PJT9yk/9Ch7/ud97Acuveu4bvwXLn/+O50JZB/ajezfi/UvS/s3Y1quMtbTGJxR0LpW3q61k3Uz7Pgp1z5S+Y6oS9LuCdxTZvoveEVD9U/iyJA2PY9h79j6XZPuxUrRHpXvK6pnGBQplpjXVW6VC88fP4nvX5B3nm9k3+S8UzMzMzMzMzMzMzMxsI/+gYGZmZmZmZmZmZmZmG/kHBTMzMzMzMzMzMzMz28g/KJiZmZmZmZmZmZmZ2Ub+QcHMzMzMzMzMzMzMzDYqi0H/ChqdjXV+/4109SyBm9LGG62YwN3ux6RuSao3YjklgFMqtvRwAvYbn43XOhvP+HhIhm924vWvFnx+Smuna8oS4C/W8Xi61vT66/F7qewC6lTi+6LnR/eUoc+uVxyLTu1nqxaPp2uSpHY3Hk9p6TX4TklaQ/1TLDtdp8TtcjKclHylJKm70wtlrU5Mq8/un66ru90JZdSnJKkxjEPPcD0MZZPzMR4/Po/3WoP2t5jO8Xi6/navHcqy/n+xjsev4fmn7acVz9VoxjFpkZyf+iWdv97kIb4B5Y1WLKNxUpKW0H8Xk1jXdE2SNE/GdVKvx2vo7cb2S9cv8bOajKD+qE9KanfhWVWoK3qu9UY8nsYfKevX8Z6WC65r6hfT0bToPFLS1mD+URw+JElNqKtOP44Vk2G8JkmaQ7s6OzrjkwG6fporsrmO5lAaP7Pnt4LnQs8vq39aV9Czps9J2bxYPlbQGE5tnfpZptWO42IzGSu7O91Qtr2/HcpKlgrtbmx3Zo+7Wr3+UJ/M1s2713dj2e6NUDb49G8Wn3s6OQ9lh3/4g8XHZ/M2aeK4UX481ctqyfMmoTUCzU+0v8r0dvuhLBuLqbzWKB93ybrC/dNagmT7FhUeT3Uq8Xp4PonzdlZ/+J0V1qLUfhowF87gmiRpAXN0uxsXTp3tOOdJ0g3ov2Tn2g6W793YC2W0ljq7H/u0pIfezXzR6b3TUJY9fxp/aC+Sof10VlekC5/t78f+R5+TpIOn9kPZs888Fcqe2ov1LEmdZhy/+u24v5gved/36ml8Vp+9cwc/+ys/9/FQ9rN//++Hsipjfb8X72t39zp+9kPfHeeAD33TNxSfa76IfeXeML4jyCxWMFZAX1/MeKw5fu246Dz03kTi9Sy9T6AxIUN9JR1rwehsFMqGx+V1SvNvhvoqjcvZO84qcyih91F8ngr7FnjWWZ1k7wNK0bqEzpXVE737pLaevfe4rPQ9rP9CwczMzMzMzMzMzMzMNvIPCmZmZmZmZmZmZmZmtpF/UDAzMzMzMzMzMzMzs438g4KZmZmZmZmZmZmZmW30tgtlng6nGp+9EcSahdpS2Cl9Ngv8oXANCpyi8FGJQ6MoBKOWBHNQ0CgFXWZBkUsI7ODwUA6M4VDreP4phJdK0hxCPSmAOQuqJHUK7EqObxcGCOehxhDOM41lFJT94FwQyrqm9sOBZyt4rhRYk4UCz6dlQaN1CISVOIjs6JX7oSzrP9TWtuDxZYF5awgiW85jiM48CVWeQTkGpiVBn/Rce3sxaLUG4enZda3nFJTLz4/qj2Ttl55LDe6Jwn8flMf7ovaXnZ/GvymEylL4r8Thellbo+dC/S8LVxqdUhBVDJejoGBJ6kCQHQU+pX0d2sAKQtdqMCdIPFf0IZQ2CwcjFACchRNSH6ZxvQZtKvvsGp5VFmRIoX/3770ayhqN7P6vxWuCsWI65lDk7b1Y1zR+pKHCEPBHbTULVT6F9kvzfzZWYWgYzatJEOYWzMul6yeJ+ypdU7vHYyL1Swo6p/WXJPUvPevSoDazx8l6vX4oPD1dN8AYtXcthjK3WhxKeu/eS6Hs7OwolPW3Y3ipJP173/T+UEbjZjaWlIYDTs7HWE4BxNkejdAeh76T1jeZZrs8lLM0QLgF62upfI2QtR8qpz1K9vzWUH8rWDdl6368JgiVrMN5pGqhmmQF3zs6i20tu35aY9O6p0rQKs37tGaUpGvviAG6VDY65aDW49dOQtnJnVi2XvFa+PRu/OxWsm4hVQK0+XjaN8b1XbYXoLDr7U787GjGa9k7ZzFUmQKYs+OPbt8LZb/7r34XP/uxn/toKHvh3/wafrbUaBzX4rdvfwY/S++u3nvzZijbhfrL/N7du6HsC/fjewtJWq5iXzuFd2wrKJM4gJxsH8T28+D4+KzJNHnHR+i9QZXxa3QS9xJZKDKeP1mjk95eDDun8T97x9VI9oPhmpJ3KVmw+lXZvpPQO+ZsTUL3Wrp+kaqF1RPqf/jeN3lHeFlpwLT/QsHMzMzMzMzMzMzMzDbyDwpmZmZmZmZmZmZmZraRf1AwMzMzMzMzMzMzM7ON/IOCmZmZmZmZmZmZmZlt9LYLZa7Vtx4KdMrCcUpDIrLAEgqtoiCjLOiTgibpOymQUOJwDgocykKRKbOJAqfSwA04P4V4UPivlISOwXdmz4/CXaj+KTxWkro7MaiV6p+ClqUkVBvCvShYJStfQBAXhQdLHKTVhrrK2u8KguAoXCerf7p+DOxJwrXoezu9CoGXGKAc+0p2/YTqJBsn+rux/ZSGp0ocxLeqx/qnED8pCQWfU9Arjx9UV9T+W1lgE9Q/tUnqE1ISxHce65pC7CQObaslQYIYCgzXT89f4n5JqP9J0u713XhNUK8nd47xeAz3gvkjC/UlHRg/+xBeLXFdYShxnQO0STaukskwfu+92zHI8+jlWCZJR0e3Q9l8HgOUb9x4Fo+nID26/2ys6e3EcaEH8082VlO/ov4/4RxEbUFoGI3fWSgzjYENCGvPxnqqKwrC5J6ehGZCW88CQ6n/UwB6SRBtFlJv9jir1WoP7ZuydSv1ERrLrl17Go//7Gc/UXQ92Vhy89kYyllljcd7pDi/VAn1pXGndH8pcahzo0J4LK2xsrVMHfZzNO5na/FsPxquKXl+tEesJwHMhD5L4ZO1eoXwSphLsvDLTj+um6oEZa6T/cBVFL6cobX06V0OdM3Kr6I1qyTt39oLZdR+9zsHeHwH9kgHT8UA9iHsJSVpdBrLz4/iPZ3e4/s8uhfXgscnr+FnSacTg2L3926FspvvfCcev3cz1l+V9kPjF/WpyTCubyXp5CiGEr90+9P42d/7vd8uvq5SNK5//vMv4Gc/9n/+Sij72uffG8r+9Ac+UHz+Z65dC2WvnsagaEkaTuO8QPU6PObw5dFJsiC/or8f25QkncPx2fsAQuMazXXVxspHW//SHifbdxBaf8y2+B0jnYvm5R68y8nge7Nkrsve/VyVzZX82bLvlHiuwu9MrnMBe8wq57/8jjvZWgX+CwUzMzMzMzMzMzMzM9vIPyiYmZmZmZmZmZmZmdlG/kHBzMzMzMzMzMzMzMw28g8KZmZmZmZmZmZmZma2kX9QMDMzMzMzMzMzMzOzjR4t8vst0Oy01O5tTrdeQoJ1s916pHNvQVg6pZI/+Gz8cL0ef59pJqnq9WYsXy2WoawGqeaSVINzkdWSU+XrjXj8GhLQ6TofnD9eF93r9n4fj+/uxGT25Tze/2I2x+Pps61ufP6NJtdfp98pOhedR+Jk+NUq1nWtxs+p2YkJ9m24/nYvXqfE9zo6GRVdpyRNzsehjNLiazXoFMm5hsfDeJ0d7pPd7W78bDvWSfPGHh6/B+Wjs3hNs/EMj6d+Rc9queLnT3VVeh5J2oL+S/1/DW1K4rpaQ1tfJu0P2yV09Yt1Mn7A9dO4TWOKJC2hvL8bxwSJxwqqq8UszgkSPyvq//s39/H4/Vuxra1X8TtP753i8TQurGZQL0mbojZE7YeuSZIWs9gHaFygOU3iuqbjL5K2OjyN/fLk7nEoOzu9i8fP55NQ1uvFZ7J/4zoev3ttJ5RVWSvQGoDmzwytVebQVmt1rv8OjJV0/dlYjW0l6deE+vAcyrK1ErX/djeWZXNFHdp/di6zr0ZbWw+P39m6lfoyrSVv3XwPHv/udz9XdD3vfBcff/DUQSgr3ctI0tHLR6GM5qcmrI8ydP89WHNkaC0xHU2Lj6exLJuLSSPZY5aei9ZNWxfJujFZz4bjK1w/zUXtBu97aN9A6rA/fXBdsazVjevWCpevBfS11YLXvQtYC2RrfELXRWuBbN98Dnu0BuzxszZF9drf2w5lbajTB+eKx88ncX26XPJa/uj+K6HsM5/+l6FsBmvGTKMR6+/atXcUHz+bxb306em94uM7nfiOpNXi9o/nn8b1tSTN5uVjULk4foxGvO/5Z//s74Wy4egklJ382H+Jx//JP/KBois66PM7plfuxz3G3S/EPQbtTySpt8ffe1W27ymVjanUB6uMq/SOjsbPMbw3ydC9NtO+XjYv0fo+U2mug2utMi9vwX6Kxr983x3H4Cr7FlqXVEHvbmiPmV3S5aZW2uz8FwpmZmZmZmZmZmZmZraRf1AwMzMzMzMzMzMzM7ON/IOCmZmZmZmZmZmZmZltVClD4fDw8Icl/S9XiluSLgaDQfvw8PB7JP24pK+R9HlJf2UwGHz0y3KlZmZmZmZmb3PeM5mZmZnZk6zSDwqDweDDkj78xf99eHj4jKR/KemvHh4efp2kn5b0Q5I+Kun7JX3k8PDw6waDwe3Sc2xtbT0UPJKFgm5BqCgFcWUBzxTusYYA4ywEhYKUKPBjNuVwJLquJQQ5ZYFljcIgkyUElknSxQWE0sJ3ZiFYFMpKISLbBzEQU+IAxjnU1SoJdaVwq8U0lmUhLtRWKGh0mYS40LOicBkKX5akZgtCdSHchepE4tC9GYRbjU5iCFd2rj4EaG/f5FDkCwiQpVDarP1RqCmFj7agniQOianBd2btl0KJKTBnljz/yTCGftGdsK0oAAAgAElEQVRY0elzsA6FKpcG5kncflqdOKZkwT4UVj05j/eUBcZRYBH16Wz8nE9iu07Hagpyg36RBS7RGLB/KwYwX3+GQ30pFJpCybN7pbGmSrgWHU+BVxT+K0nTETxraL9Z/dFYQ+FOFAImSU2o/91rcVyp13msXkEwOh1Pz1Ti0MI6BZgnQaZUr+t1Wai7xH2N2i9dk8RBYPT8aUzOzkXjctYmaVyidVl2fgplps9SP5d4DKQ+TWOqJK31xly9qhDqZvaovhJ7JulBf74cApgFvHd3y8KGa7Vnsby386eLjn/3cxzKTGtMlCQF0rgzhfE1UxoAmc2lhNZIWcB8lQBqPB7WXVUShHGPBXMB7W8enKrsXNlYThbz8qBKmguzdWOpKgHehEKZx+cxqFfidRfJ2inNexQKnpnC+efJHqPKdV2V7RtoLqeu3u7yPW1vx1D3rVq8pk4nBkVLUqMRn/XuLq/7ycsvvwil5e2XTKdxj05ljxsKxv7lX/5wKJuMz/D4z/3wD4Syb/n3v7H4/LQepzXidhK+TPsWaquL5B3NZFgWAJy9I6Kxjvbz2bpbhUNwlb3oOjsXyNYgV2Xv+Ai9d8zQ+zB675Chul7D+iN7R0PtgvZCGXou2bqC0L6TZKHMl/d90+Q93FVvenVzeHi4JeknJf3fg8HgH0n685L++WAw+NnBYLAcDAYfkfQxSX/xzZ7DzMzMzMzsceU9k5mZmZk9aR7lP9f6c5Kel/RnXv/fz0v6nSufeUFS+U+KZmZmZmZmTw7vmczMzMzsifKm/kLh8PCwJum/l/Q3BoPB+evFO5Ku/jsQY0n8t2dmZmZmZmZPKO+ZzMzMzOxJ9Gb/yaM/Lukdkv7upbKRpKv/QGdP0rnMzMzMzMy+unjPZGZmZmZPnDf7Tx79gKR/PBgMLv/XNZ+S9MErn3tO0m9V+eJ6o/5Q4OAcgjUkqdmOl07hTPs3OaiRwsEmEKR09wsc4kHhVsPjGKSTBT5RYMyaQoGnHFhyAeEcTQjsWiXhWhSuROFOWQgThipCcEcW+ELlpUHPknQB4SYUmJIFbdJn6ZqyEBoKMG1BuE7jIgmKhABwClHJQo2prY7OYlDsasWBMZ1ebP/0/Heucag2hducHcVwJbpOidvlLgRFd7c5nKsJ7b/djf1/e4//Yz8K9T6/H/fxWVAtPSuqv+4Oh0LvHMTr4hCn8lD34sBD8bOi+88Cn0oDl7LANwq7pvBXSVpBX8mCkMgeBIvffNfNUJa1FWqrFLpH4YaS1INQZwqAzwL7KOCO+l9WfxjADefKQpxoXuKgY15O9Ldjve5ci+1ne7/8P8ztQbgoBRVn1hAEltUfldOzXidzHT3rKkFkuFaAPkFrEkkan8W2SvNiHULtMzh/J/VPn6U6zdofjYs1uNbuNo+1jUtrmM4jhqKavUlv2Z5Jej2UOVnrXkZrdLIN65OsnMan7WQtQqGwJAtPpHVLtkckHRgjaCzM9g2lQYfZvqVRGGpbJWiagnozeK/JuoXQs6bxvQaBoplWuzwUtA5hz1VClelaq4Q6n9w5CWU0v45P415MkpaF8z7tbyVed1CkbaNR1s8k3jdm7Z9Q+2sWjjMSr4UoPFeSbt16dyj70Ie+u/hc168/E8re+/73Fh//yV+LQ/Nv/dbPh7Lh8Lj4O7/a/fpv/FMsv3vvpVD2yV/+rlD2nuffg8dfe/paKKP56z3v/0N/8AVeQv3/td9/DT87gnHhUS3hvUXWU1uwnaH3Kc3ru494VWw+jdeKe9lk30JKQ+EfnCtWwBa8I82UvmOgd0mSJDhXHQLkM/TukdYFVKcS75to35Xtey+vwdqFwd1v9geF75T0E1fKflLSXz48PPxBST8j6fslfZekv/Qmz2FmZmZmZva48p7JzMzMzJ44b/Y/1/oaSbcvFwwGg38r6fsk/aikY0l/TdIPDAaDTz/SFZqZmZmZmT1+vGcyMzMzsyfOm/oLhcFggH8POxgMfkHSLzzSFZmZmZmZmT3mvGcyMzMzsyeR/0FZMzMzMzMzMzMzMzPbyD8omJmZmZmZmZmZmZnZRm82lPkt0+m2HkoizxKoa4Vp3dnx63VMxsbPJunWq+UqlC0WMa283uRU71o9/pbT7DRj2ZITvPGyIBV8ueQM+PoCzt9uhbKsnsejmCBeJa19uYjXtYY6zWxB/dHxqwv+Tkpmp7T0reT5U9o81V/2/GeQ1j46GcWys3M8/nx4jOVX7WwfYPn2XvwL/OvvuBbKrj0dyyTp5M5JKKO6Wq+4/02h/dQb8ZmuVvz8duD579/ci9/Z5CHu9N5pKFvOoU1Cn5L4XhuteK7udpePr8Xrp7bSaPXw+Mtj5JeOhzY5POb2c3o33j/VyUVy/3QuGj+pnUv8XFfpWFdW1/u39vH43eu7oazdbYcyapOSdH4/1uHRy/dC2SwZ/3o7/AyvovYnSfNJ/F4av1rtOH9I0haM4TT/tGD+eXB8rCs6vgFtQuJn1YB+mfVVev6tDsxVcE2StIDnsoC6pmuSeFyndn1xwe23Xoe5IqlrQv2K5jpqJxLPazTW1uA6JV7rUFktef7tXmw/2CYaXP80VtD9T87GeLwutf9WMp+bPc4azYaa0Keuon5bBY77cN5sLLr3Upw3l4t4TdNxMhcfDzddoiSpBusrSRKMpSvYi2TrlmyOv6qRrPtXheNPtu6iuSCra5Ktca5qdeOcJ/FcXKvHMlofZtbreDw9E0manMcx/gL28vVkLqG2ejHiuia0x8W9ZPL8qF1SnTZhfSNJdVrjwLlGZ3EvKXFboXVntu8iOO7gP/DGa4EarEXWq/g5ieu1v3sYyp5+79N4/Ps++L5Q9vzhe/Gz5Fu/59tC2Tf89AdC2c//4w/j8a+++nuhbLWK7Wex4PHnq8mLL/6rUPbKKy+Gsmu/yc/6qafjc/22P/4nQtm3f++34/HUV/dvxj1m9t6LxhpCe9HMukK/bPfiO4KDp/h9EJ4LxtWT1+J7J9pfZOWl84/E+7HSOs3QXJW9o5pPYx+sMtcK9t20F8zeMWb1elU2V5LFDMYamL8eXMAb9VL6btZ/oWBmZmZmZmZmZmZmZhv5BwUzMzMzMzMzMzMzM9vIPyiYmZmZmZmZmZmZmdlG/kHBzMzMzMzMzMzMzMw2etuFMs/ny4eCr7JgCgqnGp3EwK7s+NkkBm5QENcphM9K0ug0hh5RiMvOAacTUagqBUVSYJHEAZwU9JoFflDgCQWmjE45BK30XJPhBI+nuq4S6kzhLBQO1Whx+CUFeVHgXG83CcXdiWG7FMCb1f8Q2iqFMk9nHK5FQU57ezdC2cFT1/H468/E8uvPxOM7EKIlcSjqNrT1LJwNg8wglHeatB961hTulz1/CsehILosVJuCbulZ0zgj5QG8JdckcZBTE8aELJSewnmo/2fHt+H505iQhRP292K/2rm2g5/tQqgxhkInQXg41t8uG/8lDiA/OzqL50/qqjSUeTHj8Y/CcquMdRTkR20lC+wsDe3LQnWzsOSrsrGSrp/qugZ9MkPhZlmPxM/C+LWV3Cc9q/pFfKZZECLdKwVYd/pxTSFxECLVadZ+lstYM4tpvP/sOWPYPLSVbKyjdklrhWyuuDwuzJJ7NHucNdvNh9YEy0cMX85kY/RVtJeReCyjtXgjWTc2C/tvNhc02zzGXJWF6mZhuaXnKc2Eryf7vmyNUSqbI64qnbMlzLmuhNYX2VpsDKHMhNY3ktTf7Ycymh8zFMpJc2GV50TPOrt+QvVH63uJ9y3ZvEtoPd+GNpUFzZa2q6yf07qHZGtZ2mOfTeO64blnnsXjn70e983P/0j87Hd8/3fi8b/x0d8IZZ/8eCy7/dKn8fhXXv1sKBuP417kSUX3Oh6f42dfu/O5UHZ8/Gooe+Z978Tjf/D7/mTRNVF4sSS9NHip6HjqkxkaazK47k724+TkTnxHNYT+k6E+mO3RCa0LlvPysYrQHicLWp7DGJq9zyH0PuRR55oqSvf4Jc+kdNz1XyiYmZmZmZmZmZmZmdlG/kHBzMzMzMzMzMzMzMw28g8KZmZmZmZmZmZmZma2kX9QMDMzMzMzMzMzMzOzjd52ocz1ev3hwL4WB0ZQaNYKApizEA0KB6IQjCGEL0vSZBKDYHZ29kPZ/lMHeDwF+B6/dhzKZhCUKkmC+6dwpCyEi85PAdDDYw68WS7KwmGy8NlavSyIZgVBvZI0PovhXK1ODCGhOpE4FLK/Ez9L4dUSh0JTnWbBKhTaRUHJWT1NhrFd7F7fLfpOSbr17luhrL8XA8uyoGy6fjo+C+GiAGAKYqNgHokDmCcQ2JYFHtHzo+tfLcrCk7PPHr9yHz9LAa4U2LNb56Biqn8K3FmvykOQqgT99iCUnIIIs8A3Oj4bq2gMoLD482MOkB+fxnZBAchZkB6FM9G4tl5zENwanguGWyZttd2NbYVCktLrLwy7z4IkqQ9SEFk2Vq1hXuYyrr8tuC4aVbIgULp/6hfNNgeW0RhGx2f1T8U4riTPvwXhWtQvaUyT+PopsCvrf9TWqwSGUdh0lXA2en7Up7P6v3yvpeFiZo+TWr2u2qUg4y0YX6UslC9+tkrQLgW8Z+HF9NkszJ7QdVUJSiwNy81CnQntRatcEz6TCg+A5ofS8Gmp2vhcel80v1c5/2LO+8saBF3id8L+QOJQZzp/ZjqMAb6ztyA8s8rzp7rKQp0p7JiWoln4Oq3nO7DvzayT/fxV9S6vxeiyKCh2lLy3uffS3VD2ItTVaMbt/OZO3GM/cxDf8fzZb/lmPJ7Kf+el7w1lv/rrv43H/+rP/Goo++Qnfgk/++JnPwmlj5ig/rbE97RYxGf4+c//m1D2f/3vP4XHf8d/8MFQ9jW34nuTP/bcN+DxH4Oyo9tHoSwbU0vfkVQZv86OygO86X3KCNbdGRor6vXYr0vfBUp8/9lYRaHUhOYESZqcxbGeJKdXF94nVpGFfcfz8wXQvm97P77janU3B0Vf298uuhb/hYKZmZmZmZmZmZmZmW3kHxTMzMzMzMzMzMzMzGwj/6BgZmZmZmZmZmZmZmYb+QcFMzMzMzMzMzMzMzPbyD8omJmZmZmZmZmZmZnZRo1/1xewSa3BSd0tSPCmtOvs+E4/JnBfXMS0+EaTq6jd7sXv3O7Gsh4nfdchgbvZiudaJOdXrSwZfbVYYjklyC/n8bOdnXhPkqR1rKv5bBE/tuQE+gs4Hj8Hz+TB8TEBfTKM9zSbzPH4VrsZyragTvt7MRVdkrpQL1naOqH219uJbergqQM8/vz+WdH5+7vxOyWpCfc/m8xC2fB4iMePTkehbA51Xavxb5ZY/3D99az/d1rx/ND+VvfP8Xj6Xnr+2wc7eHyjFa//HM51cucEj1/Atfb3Y1vb3tvG49fQf6bDSSijZypxv2p32/H8yf3v3dwLZZ1+PL7e4PGrBuPfch7rRJKGx1Cvd09DGdW/JI3PY1tdLmNbbTRim5KkTi/29fZu/OxqGcckSao34r1m8xKhvrqG8e/ktWM8fr2CZ92Lzyq7/ukY5grqa9B+JKkG90/zRzbW07hAZasVzzULmNdoXKLnJEm1etmzWiVzneC+Wt3Yflodrj96VutVfFa1ZE1A41obxk8aUyVelzSgLEN1Te13MeX+PxvHMYym2lbS/i6PazTGmT3uarWth/pZ1j9pjqX+ReuLKpbJWEhjyRaMD7QWlqSda3E9sr7H8xbBPR5cUx32l5I0OoG1BNTpcsH3n+3HrtqC9ZHEcwENhlXm0jrNb8mUR987n8a1FI3vVeB9VrBO1jLkAubSDO0n6f4z2Rx71SppP6QJexFcc4nnfZxMM/D8Z/AuoQrq/xlao9JeIkN7xNN7cS+RPdPbMC7cfuZGKFt9Hbep5599NpR929e+L5R94F3vxuO/7VveH8o+/vE/ip/9xC99IpR98v/7F6Hs3r2X8PjVktdjV52d3cPy2TzuR/9do33fb/zGR/GzH//VHw5lH/xz31d8rn43vuN5Gfbjdz5/B48fncR3L9QuaX+Tob1kht5RkOy9F8718D4zO57GStrLZOgdIY0f2Tum3Ru78fxw+uwdSansvUX27uyq7PrpWmle7e3yO87Lz6WXvQe+wn+hYGZmZmZmZmZmZmZmG/kHBTMzMzMzMzMzMzMz28g/KJiZmZmZmZmZmZmZ2Ub+QcHMzMzMzMzMzMzMzDZ624UytzrNh4IjKJhEkuYQ+kVBhxkKUKTAkoOnORSXwv262xzAjOeHICjK0aLwRYlDbamMQogkDjAmWTDIVh0CbKH+s0BLClKiEJjzoxg+LEkruC0KJ6FgFkmaQhDdahUDdLPwaAo3oXCZ5ZIDcxoQVkuhvL09DqejwJoJhPJmQasUREXXtEiCcqn/1KBNNFrcJ3sQFk3PKg3sonA4aD9UJxL3K+o//X0ORd4+iOXU/7P6o3A/CsfLwgGpnILcKCj7wblivTbgmVB4eIbONZ/F+5Q4XGpyNsbPnkMwOIVNz6ccQF2vUxAU3H8SZNmHPkjjWha4WBowmI3VdDwFCVI9SeVBlBlqKwsYFrO2XluW/XcL2fxNAaFLqOt1EsqMzwXCwbKxfrmKx9N3ZkGYtFagAOEsVJnWClkAdSkKclutuP/RWE9zXdbOaV3VgPpfdrn/UMAnhosmQZSX55BsPDV7kmShtLQepGErC0okc5iLF0mo6QTGOFp3ZUGDpUrDjyUe36p8bxbATKqEShKa95cwlmf7RvzOCgHcNMfRupX2AlVUCRpdw/OrEgp9cVF+rTTHValr2vdSnWZrKUJzKYYvS2q2aY9efv9TCGDO1p2kNGiU2nQGQ9WT8YP2SEO4/sk5r4VoP0r7nlky/p1O4n70/RDUXE/GiW96Twxrfu6ZZ/CzL/+Jb49lJz8Uyl741It4/CuffTmUHb8W35Hcf/kIjz87O47f+Uo81507n8PjKUB5NIrvg6ZT3mNKZeMa7QUl6bO//dlQ9rl7d4u+U5Lm8O5nDO3q+LVYT5I0g3dUNFlXmn8qzOul80K7n6z7CwPoaX8n8bpEleaqWEb339/jUGIa62kv1+yUB12PT+Pzn3d5rMj2g1dl9UfwHWUy1l7eK5Wew3+hYGZmZmZmZmZmZmZmG/kHBTMzMzMzMzMzMzMz28g/KJiZmZmZmZmZmZmZ2Ub+QcHMzMzMzMzMzMzMzDZ624UyD0+GOr1//qX/nYVrUWDIsjBoWeIgCvzOCuFAghCNZRZUCaFBFCREgUsSh1atV3T9fH6qq04vBrBS+KzEQVwULpXVP90XBa1mgWkUElIlXI2eNYUrDY/PQ5mUBRDHe82uqY0B1hBYlwTbUBAVhZpOk1Di+Um8Vwq1pBCv7LowHCwJ6qTrp3A1CiSVpPFpDGKivpr1X3ou2P+S8Nq9G3uh7ODWfijbubaLx1OA8Hwa+2rW/yncbCUKT8XDsa1Q0PBixoFBRy/fC2UjCBwan3Ng23wRw90aDR4rWq1uKKOxJgvl7fTi8XT/jSTIjdo6BT5R4N6Dc8U+gP03aas4Lyl+trfDAe4UxLQNAfDd5Hiq10YrPutsrqE+TEHP2fXTHELrgvmMg6NorqXQRpp/pCTI6hGDOKmtZGP1+UnsQxiKDO1M4nGN1w881tIYStef9T8a16n/UeChlMyBcajVRRLEeXldlPVxs8fZYrbQ/FIQaDYWk2yN/lagcaMOa5xKQcnw2Srjc5UAX1rjl4ZPStIS5i0Kiq2iyrMuDVfMwutp30ey8E+ai6scP5/Fe92CNlVLbpMCxElnJ64ZJa6X0qBhiefiCjmpqAehorSXf3D+smul8HaJ1w3ZuoXQurlKX6d1K+0xqzwTCr/NwtPp+Z1enIYy2p9I0qdgjfX5o7iXurXL+8Z37h8Uf/br3/nOorL/8Lnn8Pi75/Hdx52zGIr86kkMapak41Hco9NeIHvHcvszt0PZC7/2Qih78Xc/ice/+NlPYPlVN2++i8ufvRnKTsflbb0H7062oa9u7/M7tixY/VHQuruzzWMduYDnlzR1NKkwVlAwfbbHLkXr/0Yy19EctIKg7dqi/Jpo/s/m2q2tsvcO9J0Sj6s0/2fveJaX7muevIe6yn+hYGZmZmZmZmZmZmZmG/kHBTMzMzMzMzMzMzMz28g/KJiZmZmZmZmZmZmZ2Ub+QcHMzMzMzMzMzMzMzDbyDwpmZmZmZmZmZmZmZrZRWXTzV9B8utBsPPvS/15DqrgkXaxj+WIe065XSQJ2o9UMZXSu2WQWyrLP1icxrXtc51RzSiunVPOLJEJ9DQne9NnseEowX0KC+RLqVJLae+1Y1otlmfUS0uIhgTxLYO/v9+PxdK/wndlnl4tYp1n7Ob17huVX0TOVpDqkzY9Ox6Fs7+YeHt/qxAR4eqbUzl+/snh8LbZJaqcSt//FOqbF0zVJSTJ94TORpPF57FeXx41NutvdUHbw1H4o278VyyRp9/puKHv2+rVQtveBXvE1vXD7dih7+cWX8bPU/2u1WNcduE9J2oLPUv2d3j3F4++9ci+UTafDULZccvun/tdqdvCz7W4cV+j49ZrbOrXhZitOfdSnMnT+ejJW4fnb8Vr7u9xWqA/Mp7GvNZO+Tu2itxvHT+oTEs+BzXZ8rtlYSeWrZbymyZDnShwrSDLXNZrxWdN3ZmNNDcbFNaw/MvS9dZhXl9CnJe6XNH9mqF1TndTr5e2X1gXnx7H/S9LwZBTKqP/lc1VE/S9rf7VLc+2qwnMze1wsF0stZm/MCclQmO4HrqL5SeJ1axV1GEuq9HtC41O2bqU9BlnMeN2ygHVzbyfOm7SWlqRWPY7Fs9U0liX7TpKdi2T7uasaMD5Lj/78aS3SgvVddv5O4fkXU35+2feG8/R5LUrWPdrLPto8U+WZ0roxW/cTnsuP8bNDmOOzdVMpalPZ+LN9sBPKaH+QwXcsj7omgDE1Gz9O78X3BmdHsezVZEy8/cz1UPY1Tz+Fn332WtyP3tqN+9Zaskc/6MV2RWXvvXkTj5/M4xg2gzXaOpmTJh/8w6HsC3/qW0PZp377M3j8Z37r01h+1bOHz2L5c9/89aGs3y5va7TWfOf7ngll2fx3/Frsg7NxnCvovWeG1t3ZWEf7HpLtheaTuEetsm+iPW6VPTrZSsYVsoSxYhkvSZNhfCYSrzVoXqfzSPxcs3GFNErnyoL2007GiKv8FwpmZmZmZmZmZmZmZraRf1AwMzMzMzMzMzMzM7ON/IOCmZmZmZmZmZmZmZlt5B8UzMzMzMzMzMzMzMxso7ddKHO331H/UkhFFiI2h3CKCwpKhvBBicOhlot4PIWfZihEJDueQlFXFYIW6w0KIoshHHQeKQlcgRCRLASEwmEo6DEL5Z3C8eOzGN6YZchRgCgFbrWTEJdmO5avVvFZjSBQUuIgJQoVzcLVZpN4/6OTGHh1fv8cj98+2A5lN98Vw5G29+PnJA7iqkGbykLcTu6chLJzqJNGEoKzcy2Ga1Fbyfo/Bd50t2O4UBYUvv/UQSi79e5boezGXgyxkqSn92JY9tc+FcOxdjoceHTvPD7X3/79z4Wyu5+/g8dTUCsFRbeTwKUswDd8Luk/23vx+e3sx7Is8In6X5b7s4JxfYWh1EkQYjeeq7cTw8VoTJWkOQT8UZBbFq6FobgUSpvMVY0WBEBD4FJ2/5fDOr+IxioakyUO16IA3DRUGI6n+svGGrovqr+srTUgLJu+MwvypLUCzRXZWEVzKIaKQzuXeA6jPpE9fxpXKUA8C2XudWJfoWdFc4LEbY3it7O1AsGgangm0sMBlY2kjs0eZ61OqyhIlsZikq0bCY1b1Oezz65hLZfNxaXfmQ0lNELTuLeq8VhCoZLj83ivWagsoXmT9nJSMm/A8bQ/lqTVIwbo0rhP+94M1Qs9/+aS648CiGmNSvtDifcNq2W8p9LwbqlaUOajhlrTWpbecWT7XjI+G4ey2YjXgrTuqzJv9/fi86O2Tvcp8bqvNGg+QwHY2f6I1k20vs/6P4VC00uOrE9Ru361e4qfnS3juW5D2HYrGWtIs1H+ypCey9EwvuM4Gcf2l54frvVrv/49+Nls73/V9l4fy+uw7r8L+/Ys1JocbMdzXcB7G4n7wAkENZ/c4edPbY3mf9rfSrwfov5Xpf+3Csf/DM3102SsInSttL+VuP3ivjWZU2mspKEKxwTx++xsj/0oSt5xzwrnE/+FgpmZmZmZmZmZmZmZbeQfFMzMzMzMzMzMzMzMbCP/oGBmZmZmZmZmZmZmZhtVzlA4PDz8oKS/LekDevBP4X5E0l8dDAazw8PD75H045K+RtLnJf2VwWDw0S/j9ZqZmZmZmb2tec9kZmZmZk+qSj8oHB4e1iR9VNL/LOm7JL1T0i9Kund4ePhTkn5a0g+9/pnvl/SRw8PDrxsMBrdLz1FvNh4Kt0xDhSHciMIltpKgRQrn6EK4EYWISNJ0FEMBKVyDYybLw0GaHT5/sx7LKURkMeHADwo9W6/i8SMIbJKk6SgGwFGQVVb/EwgyGx7HwBsKb5U43IrqNAtKpcAeClyi8FZJ2r8VQ3kp8CYLqrz/yv1QRuFY91+9x8e/dhTKKJyGgpolqb8bw4GoTqdJuB6F7lFQdavLwUgYVEtBoUlQ7fZ+vP4bz8Z7PXg6hi9L0s39+Pzesb8fyq5vczjhbjeGY/Va8Z6+cBSfkyT9k1/5eCj72Ec+Fspe/PQn8PhmK9brN3zgm0PZ89/xPB6//2y8/71RDHXeuxnrROLQMAoiy4KxKByQ2o/E48ICwgGpTUnSLgSA9yF0koKeJb5XDLqtECS5WsbApSzAna6Lzr+EECiJx/BFEsRFstCokmuS+FlTuCU9U0lqUoA1lKVBfjRX1CBcMFlrkDo8vypKg64lSXBdvV4cf7Kxsvj8SftvwxhO/ZoCMyVel82gT2WBodSuKQiTgrKlh9tVr0KooNmj+hYD55wAABn+SURBVErsmSSpv9fXCtZPV22djEIZBQ0uZhxUSAGm/DkeS2ksWMyorOg0kvJQRdIuCK6WpNqcr38xLwtFzMbSUtm+g9C6IQtlLpWtJQgFZWZoj0r7luz8NO+0KoSCXkAsd2lQefqdFUJFKdS6yrqDUj2pT2VraWor58cxKDcL/+xBgO2yQlujump04rhVS8YZ6uu0FsxClel9Dr2joDWPxO2vAeuubC1coakiWuMMz+OYLknjSVlYbZVQWlIllHd8Fq+V2l+myrj42udeK/pcFpS+ez3uh6vobse5hr4ze0e1dyPu0Uk2fi3uFY5ryfOj57+usO+htpoFGJc6vx/fBZze41Bqkr0jIKXtPxuraD9apa/QGiJ7R0AasNfp7cZ9o2D8lPRQu2gme+urqv6TRweS3vH6cV8821rSWNKfl/TPB4PBzw4Gg+VgMPiIpI9J+osVz2FmZmZmZva48p7JzMzMzJ5YlX5QGAwGR5L+lqS/KWkm6QuSPv162fOSfufKIS9I+sZHv0wzMzMzM7O3P++ZzMzMzOxJVukHhdf/fHci6b+R1Jf0fknPSfrrknYkXf17prEk/jdDzMzMzMzMnjDeM5mZmZnZk6zqPyj7ZyX9wGAw+PrX//e/Pjw8/OuSfkLSv5B09R8460mK/+iVmZmZmZnZk8l7JjMzMzN7YlXNUHi3pKupNAtJc0mf0oM/4b3sudfLzczMzMzMvhp4z2RmZmZmT6yqf6HwC5L+p8PDwx+V9OOS3iPpv5P0jyT9pKS/fHh4+IOSfkbS90v6Lkl/qcoJ1uvVQ+ngjRpf4sUqpo3PZ4v4fUkqOYVt9/b6oaze5PPXm8tQRqnmzTYnuDeS770qSxCnZPr1Ot5rmlQOFVCDsPvFnFPZT+/GZPU5pM03O3z/a3h+lFafpcLT86M6zVLRl/PYViiVvbMNqeiSrl27HsrqzViBu9d28Pi9G7uh7OzoLJRRqr0knVHa/Z2TUJY9/93r8fwtaKvrNR9P7bLVvbpvlrrbHTy+C/Xa34/9jz4nSfs390PZM8/eCmVP7+3h8Z1WvNd+O15/5rXT2P5fvHMnlP2/P//rePw/+Qf/IJQNPv2boWy1iuOMJO3uxPa3uxvLPvTdH8Tjn3vfHwpl7UbsP2fTCR5/dD4MZYsljH8wTknSeBS/dzGL/V+Sjl87DmU01rQ6sf9KUgOe9QqudTrke13BWEXnqkP9SdJyHp/h+Ozqv3TBfVqStmCw6/Rjv7qA8V/ivkpj1SoZa+l716s4LtTqMCiLx4CterwmqicpG2ti/Wdzbb0e75XaxFaNr3+5iNfVgHZdS9o63Re19dWKz0/Pn8Z1mlMlfn6T4TSUzcY8V9Jc2929+h9W52N1uxfHVXr+1Kclblf0nVn7v7yGoOPM3kJv+Z6J0PgsSfNp7GPTURwL1jA/ZmguarZ5LqaxmNboF8m6E8e95LOE7p/QnC/xHoPm0swM6prPw9+5BXMhzXt1GF8laQFzUTbvkhrMkW2Yi+k6M3T+bC1Cc1SVtkrvCAjNuZLUbMXnn+1xyWIazz9J1p2E1q30TDN0LrqmDM7xvdj/s303obVA1v5pMUL71myep+dKa0FaX2ZoTKLn9OVA710mZ+NH+s7xObe/0rGyyviXjeuExlp6H0LzlyTd+dxrj3T+lz9zu+hzVeafm++6GcpuvTu+t5CkTr9srdqE9ivxuysaKyu8IqyE6pr6Sjb/UB+i8Ws0iu+9JGkygXcUi7L5V5IajTgGtNtx39Pvx3dREo81NC7RnCrxHF5lru7txuff243v2Bowp121De/GSdVQ5hck/ceSvlfSkaRflvRPJf3YYDD4t5K+T9KPSjqW9Nf04E99P13lHGZmZmZmZo8r75nMzMzM7ElW9S8UNBgMflHSLyb/v1/Qg/8ix8zMzMzM7KuS90xmZmZm9qSqmqFgZmZmZmZmZmZmZmZfhfyDgpmZmZmZmZmZmZmZbVT5nzx6q11c/AFBwpdQEAoFMGeBLRQk04bwxSxwZQHhThSY0Yag2qycwjHSuoDrWv3/7d19jKVXQcfx38zcufM+uzvbbikldWFpTlNMKyK1iYj1LaRF8KWKETRdeUvwDWy0hoIaKcZg1BIIwbcWIkikoUK0sRYCFcUSJUYR3XraSkltu4Xu68zO7My9d+74x3PXnc75nb3Pddu5z8N8P0kDe+Y+9577nOc5zznPM3N+nbQsF7jhgi7dd8oF87hwl9Zq+p4uqFry39+Fu+UCt1xo2rIJOm2t+nAoF7Y9bsPh7OaW26e5oE4XxLNwcRqqu3w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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from skimage.io import imread\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from skimage.filters import median\n", "import numpy as np\n", "full_img = imread(\"ext-figures/bonegfiltslice.png\")\n", "full_shift_img = median(\n", " np.roll(np.roll(full_img, -15, axis=0), 15, axis=1), np.ones((1, 3)))\n", "\n", "\n", "def g_roi(x): return x[5:90, 150:275]\n", "\n", "\n", "bw_img = g_roi(full_img)\n", "\n", "shift_img = g_roi(full_shift_img)\n", "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6), dpi=100)\n", "ax1.imshow(bw_img, cmap='bone')\n", "ax1.set_title('$T_0$')\n", "ax2.imshow(shift_img, cmap='bone')\n", "ax2.set_title('$T_1$');" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6), dpi=100)\n", "\n", "\n", "def g_roi(x): return x[20:30, 210:225]\n", "\n", "\n", "sns.heatmap(g_roi(full_img), ax=ax1, cbar=False,\n", " annot=True, fmt='d', cmap='bone')\n", "sns.heatmap(g_roi(full_shift_img), ax=ax2, cbar=False,\n", " annot=True, fmt='d', cmap='bone')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "format": "tab", "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 6), dpi=100)\n", "\n", "\n", "def g_roi(x): return x[20:30:2, 210:225:2]\n", "\n", "\n", "mx, my = np.meshgrid(np.arange(-10, 12, 4),\n", " np.arange(-10, 12, 4))\n", "\n", "nx = mx.ravel()\n", "ny = my.ravel()\n", "out_score = np.zeros(nx.shape, dtype=np.float32)\n", "\n", "\n", "def update_frame(i):\n", " a_img = g_roi(full_img)\n", " b_img = g_roi(\n", " np.roll(np.roll(full_shift_img, nx[i], axis=1), ny[i], axis=0))\n", " ax1.cla()\n", " sns.heatmap(a_img, ax=ax1, cbar=False, annot=True, fmt='d', cmap='bone')\n", " ax2.cla()\n", " sns.heatmap(b_img, ax=ax2, cbar=False, annot=True, fmt='d', cmap='bone')\n", " out_score[i] = np.mean(np.square(a_img-b_img))\n", " ax3.cla()\n", " sns.heatmap(out_score.reshape(mx.shape), ax=ax3,\n", " cbar=False, annot=True, fmt='2.1f', cmap='RdBu')\n", " ax1.set_title('Iteration #{}'.format(i+1))\n", " ax2.set_title('X-Offset: %d\\nY-Offset: %d' % (2*nx[i], 2*ny[i]))\n", " ax3.set_xticklabels(mx[0, :])\n", " ax3.set_yticklabels(my[:, 0])\n", "\n", "\n", "# write animation frames\n", "anim_code = FuncAnimation(fig,\n", " update_frame,\n", " frames=len(nx),\n", " interval=300,\n", " repeat_delay=2000).to_html5_video()\n", "plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'$\\\\langle (I_0(\\\\vec{x})-I_1(\\\\vec{x}+\\\\vec{r}))^2 \\\\rangle$')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 6), dpi=100)\n", "\n", "\n", "mx, my = np.meshgrid(np.arange(-20, 25, 5),\n", " np.arange(-20, 25, 5))\n", "\n", "nx = mx.ravel()\n", "ny = my.ravel()\n", "out_score = np.zeros(nx.shape, dtype=np.float32)\n", "\n", "out_score = np.zeros(nx.shape, dtype=np.float32)\n", "\n", "\n", "def g_roi(x): return x[5:90, 150:275]\n", "\n", "\n", "for i in range(len(nx)):\n", " a_img = g_roi(full_img)\n", " b_img = g_roi(\n", " np.roll(np.roll(full_shift_img, nx[i], axis=1), ny[i], axis=0))\n", " out_score[i] = np.mean(np.square(a_img-b_img))\n", "\n", "# get the minimum\n", "i_min = np.argmin(out_score)\n", "b_img = g_roi(\n", " np.roll(np.roll(full_shift_img, nx[i_min], axis=1), ny[i_min], axis=0))\n", "ax1.imshow(a_img, cmap='bone')\n", "ax1.set_title('$T_0$')\n", "ax2.imshow(b_img, cmap='bone')\n", "ax2.set_title('$T_1$ Registered')\n", "sns.heatmap(out_score.reshape(mx.shape), ax=ax3, cbar=False,\n", " annot=True, fmt='2.1f', cmap='viridis')\n", "ax3.set_xticklabels(mx[0, :])\n", "ax3.set_yticklabels(my[:, 0])\n", "ax1.set_title('Iteration #{}'.format(i+1))\n", "ax2.set_title('X-Offset: %d\\nY-Offset: %d' % (nx[i_min], ny[i_min]))\n", "ax3.set_title(r'$\\langle (I_0(\\vec{x})-I_1(\\vec{x}+\\vec{r}))^2 \\rangle$')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Registration\n", "\n", "Before any meaningful tracking tasks can be performed, the first step is to register the measurements so they are all on the same coordinate system. \n", "\n", "Often the registration can be done along with the tracking by separating the movement into actual sample movement and other (camera, setup, etc) if the motion of either the sample or the other components can be well modeled.\n", "\n", "In medicine this is frequently needed because different scanners produce different kinds of outputs with different scales, positioning and resolutions. This is also useful for 'follow-up' scans with patients to identify how a disease has progressed. With scans like chest X-rays it isn't uncommon to have multiple (some patients have hundreds) all taken under different conditions\n", "\n", "![Chest XRays](ext-figures/cxr_nih.gif)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## The Process\n", "We informally followed a process before when trying to match the two images together, but we want to make this more generic for a larger spectrum of problems. We thus follow the model set forward by tools like [ITK](https://itk.org/ITKSoftwareGuide/html/Book2/ITKSoftwareGuide-Book2ch3.html) with the components divided into the input data (*Moving Image* and *Fixed Image* sometimes called *Reference Image*). The *Transform* operation to transform the moving image. The interpolator to handle bringing all of the points onto a pixel grid. The *Metric* which is the measure of how well the transformed moving image and fixed image match and finally the *Optimizer*" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "format": "tab", "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "Fixed Image\\nReference Image\n", "\n", "Fixed Image\n", "Reference Image\n", "\n", "\n", "Metric\n", "\n", "Metric\n", "\n", "\n", "Fixed Image\\nReference Image->Metric\n", "\n", "\n", "\n", "\n", "Moving Image\n", "\n", "Moving Image\n", "\n", "\n", "Interpolator\n", "\n", "Interpolator\n", "\n", "\n", "Moving Image->Interpolator\n", "\n", "\n", "\n", "\n", "Transform\n", "\n", "Transform\n", "\n", "\n", "Transform->Interpolator\n", "\n", "\n", "Transform Parameters\n", "\n", "\n", "Interpolator->Metric\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import SVG\n", "from subprocess import check_output\n", "import pydot\n", "import os\n", "def show_graph(graph):\n", " try:\n", " return SVG(graph.create_svg())\n", " except AttributeError as e:\n", " output = check_output('dot -Tsvg', shell = True, input=g.to_string().encode())\n", " return SVG(output.decode())\n", " \n", "g = pydot.Graph(graph_type = 'digraph')\n", "fixed_img = pydot.Node('Fixed Image\\nReference Image', shape = 'folder', style=\"filled\", fillcolor=\"lightgreen\")\n", "moving_img = pydot.Node('Moving Image', shape = 'folder', style=\"filled\", fillcolor=\"lightgreen\")\n", "trans_obj = pydot.Node('Transform', shape = 'box', style = 'filled', fillcolor = 'yellow')\n", "g.add_node(fixed_img)\n", "g.add_node(moving_img)\n", "g.add_node(trans_obj)\n", "g.add_edge(pydot.Edge(fixed_img, 'Metric'))\n", "g.add_edge(pydot.Edge(moving_img, 'Interpolator'))\n", "g.add_edge(pydot.Edge(trans_obj, 'Interpolator', label = 'Transform Parameters'))\n", "g.add_edge(pydot.Edge('Interpolator', 'Metric'))\n", "show_graph(g)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "format": "tab", "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "Fixed Image\\nReference Image\n", "\n", "Fixed Image\n", "Reference Image\n", "\n", "\n", "Metric\n", "\n", "Metric\n", "\n", "\n", "Fixed Image\\nReference Image->Metric\n", "\n", "\n", "\n", "\n", "Moving Image\n", "\n", "Moving Image\n", "\n", "\n", "Interpolator\n", "\n", "Interpolator\n", "\n", "\n", "Moving Image->Interpolator\n", "\n", "\n", "\n", "\n", "Transform\n", "\n", "Transform\n", "\n", "\n", "Transform->Interpolator\n", "\n", "\n", "Transform Parameters\n", "\n", "\n", "Optimizer\n", "\n", "Optimizer\n", "\n", "\n", "Metric->Optimizer\n", "\n", "\n", "\n", "\n", "Interpolator->Metric\n", "\n", "\n", "\n", "\n", "Optimizer->Transform\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g.add_edge(pydot.Edge('Metric', 'Optimizer'))\n", "g.add_edge(pydot.Edge('Optimizer', trans_obj))\n", "show_graph(g)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Images\n", "### Fixed Image\n", "The fixed image (or reference image) is the image that will be left untouched and used for comparison\n", "### Moving Image\n", "The moving image will be transformed (translated, scaled, rotated, deformed, ...) to try and match as closely as possible the fixed image.\n", "\n", "## Transform\n", "The transform specifies the transformations which can take place on the moving image, a number of different types are possible, but the most frequent types are listed below.\n", "- Affine\n", "- Translation\n", "- Scaling\n", "- Deformable\n", "- Shearing\n", "\n", "## Interpolator\n", "The interpolator is the component applies the transform to the moving image. The common ways of interpolating are\n", "- Nearest Neighbor\n", "- Bilinear\n", "- Bicubic\n", "- Bspline\n", "- ... \n", "\n", "## Metric\n", "\n", "The metric is how the success of the matching of the two images is measured. The goal is to measure similarity between images.\n", "- Mean Squared Error - the simplist metric to use just recording the raw difference, but often this can lead to unusual matches since noise and uneven illumination can lead to high MSE for images that match well.\n", "- SSIM similarity metric\n", "- Correlation Factor\n", "\n", "## Optimizer\n", "\n", "The optimizer component is responsible for updating the parameters based on the metric. A standard approach with this is gradient descent where the gradient is calculated and a small step (determined by the learning rate) is taken in the direction of maximum descent.\n", "- Gradient Descent\n", "- Adam \n", "- Stochastic Gradient Descent\n", "- AdaGrad\n", "- AdaDelta\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "Fixed Image\\nReference Image\n", "\n", "Fixed Image\n", "Reference Image\n", "\n", "\n", "Metric\n", "\n", "Metric\n", "\n", "\n", "Fixed Image\\nReference Image->Metric\n", "\n", "\n", "\n", "\n", "Moving Image\n", "\n", "Moving Image\n", "\n", "\n", "Interpolator\n", "\n", "Interpolator\n", "\n", "\n", "Moving Image->Interpolator\n", "\n", "\n", "\n", "\n", "Transform\n", "\n", "Transform\n", "\n", "\n", "Transform->Interpolator\n", "\n", "\n", "Transform Parameters\n", "\n", "\n", "Transform->Interpolator\n", "\n", "\n", "#1\n", "\n", "\n", "Transform->Interpolator\n", "\n", "\n", "#2\n", "\n", "\n", "Transform->Interpolator\n", "\n", "\n", "#3\n", "\n", "\n", "Transform->Interpolator\n", "\n", "\n", "#4\n", "\n", "\n", "Transform->Interpolator\n", "\n", "\n", "#5\n", "\n", "\n", "Transform->Interpolator\n", "\n", "\n", "#6\n", "\n", "\n", "Optimizer\n", "\n", "Optimizer\n", "\n", "\n", "Metric->Optimizer\n", "\n", "\n", "\n", "\n", "Metric->Optimizer\n", "\n", "\n", "\n", "\n", "Metric->Optimizer\n", "\n", "\n", "\n", "\n", "Metric->Optimizer\n", "\n", "\n", "\n", "\n", "Metric->Optimizer\n", "\n", "\n", "\n", "\n", "Metric->Optimizer\n", "\n", "\n", "\n", "\n", "Metric->Optimizer\n", "\n", "\n", "\n", "\n", "Interpolator->Metric\n", "\n", "\n", "\n", "\n", "Interpolator->Metric\n", "\n", "\n", "\n", "\n", "Interpolator->Metric\n", "\n", "\n", "\n", "\n", "Interpolator->Metric\n", "\n", "\n", "\n", "\n", "Interpolator->Metric\n", "\n", "\n", "\n", "\n", "Interpolator->Metric\n", "\n", "\n", "\n", "\n", "Interpolator->Metric\n", "\n", "\n", "\n", "\n", "Optimizer->Transform\n", "\n", "\n", "\n", "\n", "Optimizer->Transform\n", "\n", "\n", "\n", "\n", "Optimizer->Transform\n", "\n", "\n", "\n", "\n", "Optimizer->Transform\n", "\n", "\n", "\n", "\n", "Optimizer->Transform\n", "\n", "\n", "\n", "\n", "Optimizer->Transform\n", "\n", "\n", "\n", "\n", "Optimizer->Transform\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for i in range(6):\n", " g.add_edge(pydot.Edge('Interpolator', 'Metric'))\n", " g.add_edge(pydot.Edge('Metric', 'Optimizer'))\n", " g.add_edge(pydot.Edge('Optimizer', trans_obj))\n", " g.add_edge(pydot.Edge(trans_obj, 'Interpolator', label = '#{}'.format(i+1)))\n", "show_graph(g)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "Fixed Image\\nReference Image\n", "\n", "Fixed Image\n", "Reference Image\n", "\n", "\n", "Metric\\nMean Squared Error\n", "\n", "Metric\n", "Mean Squared Error\n", "\n", "\n", "Fixed Image\\nReference Image->Metric\\nMean Squared Error\n", "\n", "\n", "\n", "\n", "Moving Image\n", "\n", "Moving Image\n", "\n", "\n", "Interpolator\\nNearest Neighbor\n", "\n", "Interpolator\n", "Nearest Neighbor\n", "\n", "\n", "Moving Image->Interpolator\\nNearest Neighbor\n", "\n", "\n", "\n", "\n", "Transform\n", "\n", "Transform\n", "\n", "\n", "Transform->Interpolator\\nNearest Neighbor\n", "\n", "\n", "Transform Parameters\n", "\n", "\n", "Interpolator\\nNearest Neighbor->Metric\\nMean Squared Error\n", "\n", "\n", "\n", "\n", "Optimizer\\nGrid Search\n", "\n", "Optimizer\n", "Grid Search\n", "\n", "\n", "Optimizer\\nGrid Search->Transform\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g = pydot.Graph(graph_type = 'digraph')\n", "fixed_img = pydot.Node('Fixed Image\\nReference Image', shape = 'folder', style=\"filled\", fillcolor=\"lightgreen\")\n", "moving_img = pydot.Node('Moving Image', shape = 'folder', style=\"filled\", fillcolor=\"lightgreen\")\n", "trans_obj = pydot.Node('Transform', shape = 'box', style = 'filled', fillcolor = 'yellow')\n", "g.add_node(fixed_img)\n", "g.add_node(moving_img)\n", "g.add_node(trans_obj)\n", "g.add_edge(pydot.Edge(fixed_img, 'Metric\\nMean Squared Error'))\n", "g.add_edge(pydot.Edge(moving_img, 'Interpolator\\nNearest Neighbor'))\n", "g.add_edge(pydot.Edge(trans_obj, 'Interpolator\\nNearest Neighbor', label = 'Transform Parameters'))\n", "g.add_edge(pydot.Edge('Interpolator\\nNearest Neighbor', 'Metric\\nMean Squared Error'))\n", "#g.add_edge(pydot.Edge('Metric\\nMean Squared Error', 'Optimizer\\nGrid Search', style = ''))\n", "g.add_edge(pydot.Edge('Optimizer\\nGrid Search', trans_obj))\n", "show_graph(g)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'$T_1$')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from skimage.io import imread\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from skimage.filters import median\n", "import numpy as np\n", "full_img = imread(\"ext-figures/bonegfiltslice.png\")\n", "full_shift_img = median(\n", " np.roll(np.roll(full_img, -15, axis=0), 15, axis=1), np.ones((1, 3)))\n", "\n", "def g_roi(x): return x[5:90, 150:275]\n", "\n", "bw_img = g_roi(full_img)\n", "shift_img = g_roi(full_shift_img)\n", "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 6), dpi=100)\n", "ax1.imshow(bw_img, cmap='bone')\n", "ax1.set_title('$T_0$')\n", "ax2.imshow(shift_img, cmap='bone')\n", "ax2.set_title('$T_1$')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting affine_op.py\n" ] } ], "source": [ "%%file affine_op.py\n", "\"\"\"\n", "Code taken from https://github.com/kevinzakka/spatial-transformer-network/blob/master/transformer.py\n", "\"\"\"\n", "import tensorflow as tf\n", "def affine_transform(input_fmap, theta, out_dims=None, **kwargs):\n", " \"\"\"\n", " Spatial Transformer Network layer implementation as described in [1].\n", " The layer is composed of 3 elements:\n", " - localisation_net: takes the original image as input and outputs \n", " the parameters of the affine transformation that should be applied\n", " to the input image.\n", " - affine_grid_generator: generates a grid of (x,y) coordinates that \n", " correspond to a set of points where the input should be sampled \n", " to produce the transformed output.\n", " - bilinear_sampler: takes as input the original image and the grid\n", " and produces the output image using bilinear interpolation.\n", " Input\n", " -----\n", " - input_fmap: output of the previous layer. Can be input if spatial\n", " transformer layer is at the beginning of architecture. Should be \n", " a tensor of shape (B, H, W, C). \n", " - theta: affine transform tensor of shape (B, 6). Permits cropping, \n", " translation and isotropic scaling. Initialize to identity matrix. \n", " It is the output of the localization network.\n", " Returns\n", " -------\n", " - out_fmap: transformed input feature map. Tensor of size (B, H, W, C).\n", " Notes\n", " -----\n", " [1]: 'Spatial Transformer Networks', Jaderberg et. al,\n", " (https://arxiv.org/abs/1506.02025)\n", " \"\"\"\n", " # grab input dimensions\n", " B = tf.shape(input_fmap)[0]\n", " H = tf.shape(input_fmap)[1]\n", " W = tf.shape(input_fmap)[2]\n", " C = tf.shape(input_fmap)[3]\n", "\n", " # reshape theta to (B, 2, 3)\n", " theta = tf.reshape(theta, [B, 2, 3])\n", "\n", " # generate grids of same size or upsample/downsample if specified\n", " if out_dims:\n", " out_H = out_dims[0]\n", " out_W = out_dims[1]\n", " batch_grids = affine_grid_generator(out_H, out_W, theta)\n", " else:\n", " batch_grids = affine_grid_generator(H, W, theta)\n", "\n", " x_s = batch_grids[:, 0, :, :]\n", " y_s = batch_grids[:, 1, :, :]\n", "\n", " # sample input with grid to get output\n", " out_fmap = bilinear_sampler(input_fmap, x_s, y_s)\n", "\n", " return out_fmap\n", "\n", "def get_pixel_value(img, x, y):\n", " \"\"\"\n", " Utility function to get pixel value for coordinate\n", " vectors x and y from a 4D tensor image.\n", " Input\n", " -----\n", " - img: tensor of shape (B, H, W, C)\n", " - x: flattened tensor of shape (B*H*W, )\n", " - y: flattened tensor of shape (B*H*W, )\n", " Returns\n", " -------\n", " - output: tensor of shape (B, H, W, C)\n", " \"\"\"\n", " shape = tf.shape(x)\n", " batch_size = shape[0]\n", " height = shape[1]\n", " width = shape[2]\n", "\n", " batch_idx = tf.range(0, batch_size)\n", " batch_idx = tf.reshape(batch_idx, (batch_size, 1, 1))\n", " b = tf.tile(batch_idx, (1, height, width))\n", "\n", " indices = tf.stack([b, y, x], 3)\n", "\n", " return tf.gather_nd(img, indices)\n", "\n", "def affine_grid_generator(height, width, theta):\n", " \"\"\"\n", " This function returns a sampling grid, which when\n", " used with the bilinear sampler on the input feature \n", " map, will create an output feature map that is an \n", " affine transformation [1] of the input feature map.\n", " Input\n", " -----\n", " - height: desired height of grid/output. Used\n", " to downsample or upsample. \n", " - width: desired width of grid/output. Used\n", " to downsample or upsample. \n", " - theta: affine transform matrices of shape (num_batch, 2, 3). \n", " For each image in the batch, we have 6 theta parameters of \n", " the form (2x3) that define the affine transformation T.\n", " Returns\n", " -------\n", " - normalized gird (-1, 1) of shape (num_batch, 2, H, W).\n", " The 2nd dimension has 2 components: (x, y) which are the \n", " sampling points of the original image for each point in the\n", " target image.\n", " Note\n", " ----\n", " [1]: the affine transformation allows cropping, translation, \n", " and isotropic scaling.\n", " \"\"\"\n", " # grab batch size\n", " num_batch = tf.shape(theta)[0]\n", "\n", " # create normalized 2D grid\n", " x = tf.linspace(-1.0, 1.0, width)\n", " y = tf.linspace(-1.0, 1.0, height)\n", " x_t, y_t = tf.meshgrid(x, y)\n", "\n", " # flatten\n", " x_t_flat = tf.reshape(x_t, [-1])\n", " y_t_flat = tf.reshape(y_t, [-1])\n", "\n", " # reshape to [x_t, y_t , 1] - (homogeneous form)\n", " ones = tf.ones_like(x_t_flat)\n", " sampling_grid = tf.stack([x_t_flat, y_t_flat, ones])\n", "\n", " # repeat grid num_batch times\n", " sampling_grid = tf.expand_dims(sampling_grid, axis=0)\n", " sampling_grid = tf.tile(sampling_grid, tf.stack([num_batch, 1, 1]))\n", "\n", " # cast to float32 (required for matmul)\n", " theta = tf.cast(theta, 'float32')\n", " sampling_grid = tf.cast(sampling_grid, 'float32')\n", "\n", " # transform the sampling grid - batch multiply\n", " batch_grids = tf.matmul(theta, sampling_grid)\n", " # batch grid has shape (num_batch, 2, H*W)\n", "\n", " # reshape to (num_batch, H, W, 2)\n", " batch_grids = tf.reshape(batch_grids, [num_batch, 2, height, width])\n", "\n", " return batch_grids\n", "\n", "def bilinear_sampler(img, x, y):\n", " \"\"\"\n", " Performs bilinear sampling of the input images according to the \n", " normalized coordinates provided by the sampling grid. Note that \n", " the sampling is done identically for each channel of the input.\n", " To test if the function works properly, output image should be\n", " identical to input image when theta is initialized to identity\n", " transform.\n", " Input\n", " -----\n", " - img: batch of images in (B, H, W, C) layout.\n", " - grid: x, y which is the output of affine_grid_generator.\n", " Returns\n", " -------\n", " - interpolated images according to grids. Same size as grid.\n", " \"\"\"\n", " # prepare useful params\n", " B = tf.shape(img)[0]\n", " H = tf.shape(img)[1]\n", " W = tf.shape(img)[2]\n", " C = tf.shape(img)[3]\n", "\n", " max_y = tf.cast(H - 1, 'int32')\n", " max_x = tf.cast(W - 1, 'int32')\n", " zero = tf.zeros([], dtype='int32')\n", "\n", " # cast indices as float32 (for rescaling)\n", " x = tf.cast(x, 'float32')\n", " y = tf.cast(y, 'float32')\n", "\n", " # rescale x and y to [0, W/H]\n", " x = 0.5 * ((x + 1.0) * tf.cast(W, 'float32'))\n", " y = 0.5 * ((y + 1.0) * tf.cast(H, 'float32'))\n", "\n", " # grab 4 nearest corner points for each (x_i, y_i)\n", " # i.e. we need a rectangle around the point of interest\n", " x0 = tf.cast(tf.floor(x), 'int32')\n", " x1 = x0 + 1\n", " y0 = tf.cast(tf.floor(y), 'int32')\n", " y1 = y0 + 1\n", "\n", " # clip to range [0, H/W] to not violate img boundaries\n", " x0 = tf.clip_by_value(x0, zero, max_x)\n", " x1 = tf.clip_by_value(x1, zero, max_x)\n", " y0 = tf.clip_by_value(y0, zero, max_y)\n", " y1 = tf.clip_by_value(y1, zero, max_y)\n", "\n", " # get pixel value at corner coords\n", " Ia = get_pixel_value(img, x0, y0)\n", " Ib = get_pixel_value(img, x0, y1)\n", " Ic = get_pixel_value(img, x1, y0)\n", " Id = get_pixel_value(img, x1, y1)\n", " \n", " # recast as float for delta calculation\n", " x0 = tf.cast(x0, 'float32')\n", " x1 = tf.cast(x1, 'float32')\n", " y0 = tf.cast(y0, 'float32')\n", " y1 = tf.cast(y1, 'float32')\n", "\n", " # calculate deltas\n", " wa = (x1-x) * (y1-y)\n", " wb = (x1-x) * (y-y0)\n", " wc = (x-x0) * (y1-y)\n", " wd = (x-x0) * (y-y0)\n", "\n", " # add dimension for addition\n", " wa = tf.expand_dims(wa, axis=3)\n", " wb = tf.expand_dims(wb, axis=3)\n", " wc = tf.expand_dims(wc, axis=3)\n", " wd = tf.expand_dims(wd, axis=3)\n", "\n", " # compute output\n", " out = tf.add_n([wa*Ia, wb*Ib, wc*Ic, wd*Id])\n", "\n", " return out" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "import tensorflow as tf\n", "from affine_op import affine_transform\n", "g = tf.Graph()\n", "with g.as_default():\n", " init = tf.global_variables_initializer()\n", " # tf Graph Input\n", " fixed_img = tf.placeholder(\"float\", shape = (1, None, None, 1), name = 'FixedImage')\n", " moving_img = tf.placeholder(\"float\", shape = (1, None, None, 1), name = 'MovingImage')\n", " # Initialize the variables (i.e. assign their default value)\n", " \n", " with tf.name_scope('transform_parameters'): # Set transform parameters\n", " x_offset = tf.Variable(0.0, name=\"x_offset\")\n", " y_offset = tf.Variable(0.0, name=\"y_offset\")\n", " # we keep scale and rotation fixed\n", " scale = tf.placeholder(\"float\", shape=tuple(), name = \"scale\")\n", " rotation = tf.placeholder(\"float\", shape = tuple(), name = \"rotation\")\n", "\n", " with tf.name_scope('transformer_and_interpolator'):\n", " flat_mat = tf.tile([tf.cos(rotation), -tf.sin(rotation), x_offset, \n", " tf.sin(rotation), tf.cos(rotation), y_offset], (1,))\n", " flat_mat = tf.reshape(flat_mat, (1, 6))\n", " trans_tensor = affine_transform(moving_img, flat_mat)\n", "\n", " with tf.name_scope('metric'):\n", " mse = tf.reduce_mean(tf.square(fixed_img-trans_tensor), name = 'MeanSquareError')\n", " optimizer = tf.train.GradientDescentOptimizer(1e-5).minimize(mse)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from IPython.display import clear_output, Image, display, HTML\n", "def strip_consts(graph_def, max_const_size=32):\n", " \"\"\"Strip large constant values from graph_def.\"\"\"\n", " strip_def = tf.GraphDef()\n", " for n0 in graph_def.node:\n", " n = strip_def.node.add() \n", " n.MergeFrom(n0)\n", " if n.op == 'Const':\n", " tensor = n.attr['value'].tensor\n", " size = len(tensor.tensor_content)\n", " if size > max_const_size:\n", " tensor.tensor_content = \"\"%size\n", " return strip_def\n", "\n", "def show_graph(graph_def, max_const_size=32):\n", " \"\"\"Visualize TensorFlow graph.\"\"\"\n", " if hasattr(graph_def, 'as_graph_def'):\n", " graph_def = graph_def.as_graph_def()\n", " strip_def = strip_consts(graph_def, max_const_size=max_const_size)\n", " code = \"\"\"\n", " \n", " \n", " \n", "
\n", " \n", "
\n", " \"\"\".format(data=repr(str(strip_def)), id='graph'+str(np.random.rand()))\n", "\n", " iframe = \"\"\"\n", " \n", " \"\"\".format(code.replace('\"', '"'))\n", " display(HTML(iframe))\n", "show_graph(g)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "format": "tab", "scrolled": true, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From C:\\Miniconda3\\envs\\qbi2018\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py:118: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n", "Instructions for updating:\n", "Use `tf.global_variables_initializer` instead.\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Start training\n", "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "import numpy as np\n", "def make_feed_dict(f_img, m_img):\n", " return {fixed_img: np.expand_dims(np.expand_dims(f_img,0), -1),\n", " moving_img: np.expand_dims(np.expand_dims(m_img,0), -1),\n", " rotation: 0.0}\n", "loss_history = []\n", "optimize_iters = 10\n", "with tf.Session(graph = g) as sess:\n", " plt.close('all')\n", " fig, m_axs = plt.subplots(2, 2, figsize=(10, 10), dpi=100)\n", " tf.initialize_all_variables().run()\n", " # Run the initializer\n", " sess.run(init)\n", " # Fit all training data\n", " const_feed_dict = make_feed_dict(bw_img, shift_img)\n", " def update_frame(i):\n", " global loss_history\n", " (ax1, ax2), (ax4, ax3) = m_axs\n", " for c_ax in m_axs.flatten():\n", " c_ax.cla()\n", " c_ax.axis('off')\n", " f_mse, x_pos, y_pos, rs_img = sess.run([mse, x_offset, y_offset, trans_tensor], \n", " feed_dict=const_feed_dict)\n", " loss_history += [f_mse]\n", " \n", " ax1.imshow(bw_img, cmap='bone')\n", " ax1.set_title('$T_0$')\n", " ax2.imshow(shift_img, cmap='bone')\n", " ax2.set_title('$T_1$')\n", " #ax3.imshow(rs_img[0,:,:,0], cmap = 'bone')\n", " #ax3.set_title('Output')\n", " ax4.imshow(bw_img*1.0-rs_img[0,:,:,0], cmap = 'RdBu', vmin = -100, vmax = 100)\n", " ax4.set_title('Difference\\nMSE: %2.2f' % (f_mse))\n", " ax3.semilogy(loss_history)\n", " ax3.set_xlabel('Iteration')\n", " ax3.set_ylabel('MSE (Log-scale)')\n", " ax3.axis('on')\n", " \n", " for _ in range(1):\n", " sess.run(optimizer, feed_dict=const_feed_dict)\n", " # write animation frames\n", " anim_code = FuncAnimation(fig,\n", " update_frame,\n", " frames=optimize_iters,\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", " plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "g_roi = tf.Graph()\n", "with g_roi.as_default():\n", " init = tf.global_variables_initializer()\n", " # tf Graph Input\n", " fixed_img = tf.placeholder(\"float\", shape = (1, None, None, 1), name = 'FixedImage')\n", " moving_img = tf.placeholder(\"float\", shape = (1, None, None, 1), name = 'MovingImage')\n", " # Initialize the variables (i.e. assign their default value)\n", " \n", " with tf.name_scope('transform_parameters'): # Set transform parameters\n", " x_offset = tf.Variable(0.0, name=\"x_offset\")\n", " y_offset = tf.Variable(0.0, name=\"y_offset\")\n", " # we keep rotation fixed\n", " rotation = tf.placeholder(\"float\", shape = tuple(), name = \"rotation\")\n", "\n", " with tf.name_scope('transformer_and_interpolator'):\n", " flat_mat = tf.tile([tf.cos(rotation), -tf.sin(rotation), x_offset, \n", " tf.sin(rotation), tf.cos(rotation), y_offset], (1,))\n", " flat_mat = tf.reshape(flat_mat, (1, 6))\n", " trans_tensor = affine_transform(moving_img, flat_mat)\n", "\n", " with tf.name_scope('metric'):\n", " diff_tensor = (fixed_img-trans_tensor)[:, 25:75, 25:110, :]\n", " mse = tf.reduce_mean(tf.square(diff_tensor), name = 'MeanSquareError')\n", " optimizer = tf.train.GradientDescentOptimizer(2e-6).minimize(mse)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Start training\n", "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "from matplotlib import patches \n", "optimize_iters = 20\n", "loss_history = []\n", "with tf.Session(graph = g_roi) as sess:\n", " plt.close('all')\n", " fig, m_axs = plt.subplots(2, 3, figsize=(9, 4), dpi=100)\n", " tf.initialize_all_variables().run()\n", " # Run the initializer\n", " sess.run(init)\n", " # Fit all training data\n", " const_feed_dict = make_feed_dict(bw_img, shift_img)\n", " def update_frame(i):\n", " global loss_history\n", " (ax1, ax2, ax5), (ax3, ax4, ax6) = m_axs\n", " for c_ax in m_axs.flatten():\n", " c_ax.cla()\n", " c_ax.axis('off')\n", " f_mse, x_pos, y_pos, rs_img, diff_img = sess.run([mse, x_offset, y_offset, trans_tensor, diff_tensor], \n", " feed_dict=const_feed_dict)\n", " loss_history += [f_mse]\n", " \n", " ax1.imshow(bw_img, cmap='bone')\n", " ax1.set_title('$T_0$')\n", " ax2.imshow(shift_img, cmap='bone')\n", " ax2.set_title('$T_1$')\n", " ax3.imshow(rs_img[0,:,:,0], cmap = 'bone')\n", " ax3.set_title('Output')\n", " ax4.imshow(bw_img*1.0-rs_img[0,:,:,0], cmap = 'RdBu', vmin = -100, vmax = 100)\n", " ax4.set_title('MSE: %2.2f' % (f_mse))\n", " rect = patches.Rectangle((25,25), 85, 50 ,linewidth=2,edgecolor='g',facecolor='none')\n", " # Add the patch to the Axes\n", " ax4.add_patch(rect)\n", " ax5.semilogy(loss_history)\n", " ax5.set_xlabel('Iteration')\n", " ax5.set_ylabel('MSE (Log-scale)')\n", " ax5.axis('on')\n", " \n", " ax6.imshow(diff_img[0, :, :, 0], cmap = 'RdBu', vmin = -100, vmax = 100)\n", " ax6.set_title('ROI')\n", " for _ in range(5):\n", " sess.run(optimizer, feed_dict=const_feed_dict)\n", " # write animation frames\n", " anim_code = FuncAnimation(fig,\n", " update_frame,\n", " frames=optimize_iters,\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", " plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Smoother Gradient\n", "We can use a distance map of the segmentation to give us a smoother gradient" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.ndimage import distance_transform_edt\n", "from skimage.filters import threshold_otsu\n", "fig, [(ax1, ax2),(ax3, ax4)] = plt.subplots(2, 2, figsize = (8, 8))\n", "thresh_img = bw_img>threshold_otsu(bw_img)\n", "dist_start_img = distance_transform_edt(thresh_img)\n", "dist_shift_img = distance_transform_edt(shift_img>threshold_otsu(bw_img))\n", "\n", "ax1.imshow(bw_img, cmap = 'bone')\n", "ax2.imshow(thresh_img, cmap = 'bone')\n", "ax3.imshow(dist_start_img, cmap = 'jet')\n", "ax3.set_title('dmap Fixed Image')\n", "ax4.imshow(dist_shift_img, cmap = 'jet')\n", "ax4.set_title('dmap Moving Image');" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Start training\n", "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "from matplotlib import patches \n", "optimize_iters = 20\n", "loss_history = []\n", "with tf.Session(graph = g_roi) as sess:\n", " plt.close('all')\n", " fig, m_axs = plt.subplots(2, 3, figsize=(12, 4), dpi=100)\n", " # Run the initializer\n", " tf.initialize_all_variables().run()\n", " # Fit all training data\n", " const_feed_dict = make_feed_dict(dist_start_img, dist_shift_img)\n", " real_image_feed_dict = make_feed_dict(bw_img, shift_img)\n", " def update_frame(i):\n", " global loss_history\n", " (ax1, ax2, ax5), (ax3, ax4, ax6) = m_axs\n", " for c_ax in m_axs.flatten():\n", " c_ax.cla()\n", " c_ax.axis('off')\n", " f_mse, x_pos, y_pos, rs_img, diff_img = sess.run([mse, x_offset, y_offset, trans_tensor, diff_tensor], \n", " feed_dict=const_feed_dict)\n", " real_rs_img, real_diff_img = sess.run([trans_tensor, diff_tensor], \n", " feed_dict=real_image_feed_dict)\n", " \n", " loss_history += [f_mse]\n", " \n", " ax1.imshow(bw_img, cmap='bone')\n", " ax1.set_title('$T_0$')\n", " ax2.imshow(shift_img, cmap='bone')\n", " ax2.set_title('$T_1$')\n", " ax3.imshow(real_rs_img[0,:,:,0], cmap = 'bone')\n", " ax3.set_title('Output')\n", " ax4.imshow(dist_start_img*1.0-rs_img[0,:,:,0], cmap = 'RdBu', vmin = -10, vmax = 10)\n", " ax4.set_title('MSE: %2.2f' % (f_mse))\n", " rect = patches.Rectangle((25,25), 75, 50, linewidth=2,edgecolor='g',facecolor='none')\n", " # Add the patch to the Axes\n", " ax4.add_patch(rect)\n", " ax5.semilogy(loss_history)\n", " ax5.set_xlabel('Iteration')\n", " ax5.set_ylabel('MSE\\n(Log-scale)')\n", " ax5.axis('on')\n", " \n", " ax6.imshow(diff_img[0, :, :, 0], cmap = 'RdBu', vmin = -10, vmax = 10)\n", " ax6.set_title('ROI')\n", " for _ in range(200):\n", " sess.run(optimizer, feed_dict=const_feed_dict)\n", " # write animation frames\n", " anim_code = FuncAnimation(fig,\n", " update_frame,\n", " frames=optimize_iters,\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", " plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting backport_ssim.py\n" ] } ], "source": [ "%%file backport_ssim.py\n", "# backporting new tensorflow ops is soo much fun\n", "_SSIM_K1 = 0.01\n", "_SSIM_K2 = 0.03\n", "from tensorflow.python.framework import constant_op, dtypes, ops\n", "from tensorflow.python.ops import array_ops, control_flow_ops, check_ops, math_ops, nn_ops, nn\n", "def _ssim_helper(x, y, reducer, max_val, compensation =1.0):\n", " r\"\"\"Helper function for computing SSIM.\n", " SSIM estimates covariances with weighted sums. The default parameters\n", " use a biased estimate of the covariance:\n", " Suppose `reducer` is a weighted sum, then the mean estimators are\n", " \\mu_x = \\sum_i w_i x_i,\n", " \\mu_y = \\sum_i w_i y_i,\n", " where w_i's are the weighted-sum weights, and covariance estimator is\n", " cov_{xy} = \\sum_i w_i (x_i - \\mu_x) (y_i - \\mu_y)\n", " with assumption \\sum_i w_i = 1. This covariance estimator is biased, since\n", " E[cov_{xy}] = (1 - \\sum_i w_i ^ 2) Cov(X, Y).\n", " For SSIM measure with unbiased covariance estimators, pass as `compensation`\n", " argument (1 - \\sum_i w_i ^ 2).\n", " Arguments:\n", " x: First set of images.\n", " y: Second set of images.\n", " reducer: Function that computes 'local' averages from set of images.\n", " For non-covolutional version, this is usually tf.reduce_mean(x, [1, 2]),\n", " and for convolutional version, this is usually tf.nn.avg_pool or\n", " tf.nn.conv2d with weighted-sum kernel.\n", " max_val: The dynamic range (i.e., the difference between the maximum\n", " possible allowed value and the minimum allowed value).\n", " compensation: Compensation factor. See above.\n", " Returns:\n", " A pair containing the luminance measure, and the contrast-structure measure.\n", " \"\"\"\n", " c1 = (_SSIM_K1 * max_val) ** 2\n", " c2 = (_SSIM_K2 * max_val) ** 2\n", "\n", " # SSIM luminance measure is\n", " # (2 * mu_x * mu_y + c1) / (mu_x ** 2 + mu_y ** 2 + c1).\n", " mean0 = reducer(x)\n", " mean1 = reducer(y)\n", " num0 = mean0 * mean1 * 2.0\n", " den0 = math_ops.square(mean0) + math_ops.square(mean1)\n", " luminance = (num0 + c1) / (den0 + c1)\n", "\n", " # SSIM contrast-structure measure is\n", " # (2 * cov_{xy} + c2) / (cov_{xx} + cov_{yy} + c2).\n", " # Note that `reducer` is a weighted sum with weight w_k, \\sum_i w_i = 1, then\n", " # cov_{xy} = \\sum_i w_i (x_i - \\mu_x) (y_i - \\mu_y)\n", " # = \\sum_i w_i x_i y_i - (\\sum_i w_i x_i) (\\sum_j w_j y_j).\n", " num1 = reducer(x * y) * 2.0\n", " den1 = reducer(math_ops.square(x) + math_ops.square(y))\n", " c2 *= compensation\n", " cs = (num1 - num0 + c2) / (den1 - den0 + c2)\n", "\n", " # SSIM score is the product of the luminance and contrast-structure measures.\n", " return luminance, cs\n", "\n", "\n", "def _fspecial_gauss(size, sigma):\n", " \"\"\"Function to mimic the 'fspecial' gaussian MATLAB function.\"\"\"\n", " size = ops.convert_to_tensor(size, dtypes.int32)\n", " sigma = ops.convert_to_tensor(sigma)\n", "\n", " coords = math_ops.cast(math_ops.range(size), sigma.dtype)\n", " coords -= math_ops.cast(size - 1, sigma.dtype) / 2.0\n", "\n", " g = math_ops.square(coords)\n", " g *= -0.5 / math_ops.square(sigma)\n", "\n", " g = array_ops.reshape(g, shape=[1, -1]) + array_ops.reshape(g, shape=[-1, 1])\n", " g = array_ops.reshape(g, shape=[1, -1]) # For tf.nn.softmax().\n", " g = nn_ops.softmax(g)\n", " return array_ops.reshape(g, shape=[size, size, 1, 1])\n", "\n", "\n", "def _ssim_per_channel(img1, img2, max_val=1.0):\n", " \"\"\"Computes SSIM index between img1 and img2 per color channel.\n", " This function matches the standard SSIM implementation from:\n", " Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image\n", " quality assessment: from error visibility to structural similarity. IEEE\n", " transactions on image processing.\n", " Details:\n", " - 11x11 Gaussian filter of width 1.5 is used.\n", " - k1 = 0.01, k2 = 0.03 as in the original paper.\n", " Args:\n", " img1: First image batch.\n", " img2: Second image batch.\n", " max_val: The dynamic range of the images (i.e., the difference between the\n", " maximum the and minimum allowed values).\n", " Returns:\n", " A pair of tensors containing and channel-wise SSIM and contrast-structure\n", " values. The shape is [..., channels].\n", " \"\"\"\n", " filter_size = constant_op.constant(11, dtype=dtypes.int32)\n", " filter_sigma = constant_op.constant(1.5, dtype=img1.dtype)\n", "\n", " shape1, shape2 = array_ops.shape_n([img1, img2])\n", " checks = [\n", " control_flow_ops.Assert(math_ops.reduce_all(math_ops.greater_equal(\n", " shape1[-3:-1], filter_size)), [shape1, filter_size], summarize=8),\n", " control_flow_ops.Assert(math_ops.reduce_all(math_ops.greater_equal(\n", " shape2[-3:-1], filter_size)), [shape2, filter_size], summarize=8)]\n", "\n", " # Enforce the check to run before computation.\n", " with ops.control_dependencies(checks):\n", " img1 = array_ops.identity(img1)\n", "\n", " # TODO(sjhwang): Try to cache kernels and compensation factor.\n", " kernel = _fspecial_gauss(filter_size, filter_sigma)\n", " kernel = array_ops.tile(kernel, multiples=[1, 1, shape1[-1], 1])\n", "\n", " # The correct compensation factor is `1.0 - tf.reduce_sum(tf.square(kernel))`,\n", " # but to match MATLAB implementation of MS-SSIM, we use 1.0 instead.\n", " compensation = 1.0\n", "\n", " # TODO(sjhwang): Try FFT.\n", " # TODO(sjhwang): Gaussian kernel is separable in space. Consider applying\n", " # 1-by-n and n-by-1 Gaussain filters instead of an n-by-n filter.\n", " def reducer(x):\n", " shape = array_ops.shape(x)\n", " x = array_ops.reshape(x, shape=array_ops.concat([[-1], shape[-3:]], 0))\n", " y = nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='VALID')\n", " return array_ops.reshape(y, array_ops.concat([shape[:-3],\n", " array_ops.shape(y)[1:]], 0))\n", "\n", " luminance, cs = _ssim_helper(img1, img2, reducer, max_val, compensation)\n", "\n", " # Average over the second and the third from the last: height, width.\n", " axes = constant_op.constant([-3, -2], dtype=dtypes.int32)\n", " ssim_val = math_ops.reduce_mean(luminance * cs, axes)\n", " cs = math_ops.reduce_mean(cs, axes)\n", " return ssim_val, cs" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "from backport_ssim import _ssim_per_channel\n", "g_roi_ssim = tf.Graph()\n", "with g_roi_ssim.as_default():\n", " init = tf.global_variables_initializer()\n", " # tf Graph Input\n", " fixed_img = tf.placeholder(\"float\", shape = (1, None, None, 1), name = 'FixedImage')\n", " moving_img = tf.placeholder(\"float\", shape = (1, None, None, 1), name = 'MovingImage')\n", " # Initialize the variables (i.e. assign their default value)\n", " \n", " with tf.name_scope('transform_parameters'): # Set transform parameters\n", " x_offset = tf.Variable(0.0, name=\"x_offset\")\n", " y_offset = tf.Variable(0.0, name=\"y_offset\")\n", " # we keep rotation fixed\n", " rotation = tf.placeholder(\"float\", shape = tuple(), name = \"rotation\")\n", "\n", " with tf.name_scope('transformer_and_interpolator'):\n", " flat_mat = tf.tile([tf.cos(rotation), -tf.sin(rotation), x_offset, \n", " tf.sin(rotation), tf.cos(rotation), y_offset], (1,))\n", " flat_mat = tf.reshape(flat_mat, (1, 6))\n", " trans_tensor = affine_transform(moving_img, flat_mat)\n", "\n", " with tf.name_scope('metric'):\n", " ssim, _ = _ssim_per_channel(fixed_img[:, 20:75, 25:100, :]/255.0,\n", " trans_tensor[:, 20:75, 25:100, :]/255.0, \n", " max_val = 1.0)\n", " mssim = tf.reduce_mean(ssim, name = 'MeanSSIM')\n", " rev_mssim = 1-mssim # since we can only minimize\n", " optimizer = tf.train.GradientDescentOptimizer(5e-2).minimize(rev_mssim)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "format": "tab", "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Start training\n", "from matplotlib.animation import FuncAnimation\n", "from IPython.display import HTML\n", "from matplotlib import patches \n", "optimize_iters = 15\n", "loss_history = []\n", "with tf.Session(graph = g_roi_ssim) as sess:\n", " plt.close('all')\n", " fig, m_axs = plt.subplots(2, 3, figsize=(11, 5), dpi=100)\n", " tf.initialize_all_variables().run()\n", " # Run the initializer\n", " sess.run(init)\n", " # Fit all training data\n", " const_feed_dict = make_feed_dict(bw_img, shift_img)\n", " def update_frame(i):\n", " global loss_history\n", " (ax1, ax2, ax5), (ax3, ax4, ax6) = m_axs\n", " for c_ax in m_axs.flatten():\n", " c_ax.cla()\n", " c_ax.axis('off')\n", " f_ssim, x_pos, y_pos, rs_img = sess.run([mssim, x_offset, y_offset, trans_tensor], \n", " feed_dict=const_feed_dict)\n", " loss_history += [f_ssim]\n", " \n", " ax1.imshow(bw_img, cmap='bone')\n", " ax1.set_title('$T_0$')\n", " ax2.imshow(shift_img, cmap='bone')\n", " ax2.set_title('$T_1$')\n", " ax3.imshow(rs_img[0,:,:,0], cmap = 'bone')\n", " ax3.set_title('Output')\n", " ax4.imshow(bw_img*1.0-rs_img[0,:,:,0], cmap = 'RdBu', vmin = -100, vmax = 100)\n", " ax4.set_title('Difference\\nSSIM: %2.2f' % (f_ssim))\n", " rect = patches.Rectangle((25,20), 75, 55 ,linewidth=2,edgecolor='g',facecolor='none')\n", " # Add the patch to the Axes\n", " ax4.add_patch(rect)\n", " ax5.plot(loss_history)\n", " ax5.set_xlabel('Iteration')\n", " ax5.set_ylabel('SSIM')\n", " ax5.axis('on')\n", " \n", " for _ in range(1):\n", " sess.run(optimizer, feed_dict=const_feed_dict)\n", " # write animation frames\n", " anim_code = FuncAnimation(fig,\n", " update_frame,\n", " frames=optimize_iters,\n", " interval=1000,\n", " repeat_delay=2000).to_html5_video()\n", " plt.close('all')\n", "HTML(anim_code)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# ITK and Simple ITK\n", "For medical imaging the standard tools used are ITK and SimpleITK and they have been optimized over decades to deliver high-performance registration tasks. They are a bit clumsy to use from python, but they offer by far the best established tools for these problems.\n", "\n", "[https://itk.org/ITKSoftwareGuide/html/Book2/ITKSoftwareGuide-Book2ch3.html]" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "import SimpleITK as sitk\n", "def register_img(fixed_arr, \n", " moving_arr,\n", " use_affine = True,\n", " use_mse = True,\n", " brute_force = True):\n", " fixed_image = sitk.GetImageFromArray(fixed_arr)\n", " moving_image = sitk.GetImageFromArray(moving_arr)\n", " transform = sitk.AffineTransform(2) if use_affine else sitk.ScaleTransform(2)\n", " initial_transform = sitk.CenteredTransformInitializer(sitk.Cast(fixed_image,moving_image.GetPixelID()), \n", " moving_image, \n", " transform, \n", " sitk.CenteredTransformInitializerFilter.GEOMETRY)\n", " ff_img = sitk.Cast(fixed_image, sitk.sitkFloat32)\n", " mv_img = sitk.Cast(moving_image, sitk.sitkFloat32)\n", " registration_method = sitk.ImageRegistrationMethod()\n", " if use_mse:\n", " registration_method.SetMetricAsMeanSquares()\n", " else:\n", " registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)\n", " \n", " if brute_force:\n", " sample_per_axis = 12\n", " registration_method.SetOptimizerAsExhaustive([sample_per_axis//2,0,0])\n", " # Utilize the scale to set the step size for each dimension\n", " registration_method.SetOptimizerScales([2.0*3.14/sample_per_axis, 1.0,1.0])\n", " else:\n", " registration_method.SetMetricSamplingStrategy(registration_method.RANDOM)\n", " registration_method.SetMetricSamplingPercentage(0.25)\n", "\n", " registration_method.SetInterpolator(sitk.sitkLinear)\n", "\n", " registration_method.SetOptimizerAsGradientDescent(learningRate=1.0, \n", " numberOfIterations=200, \n", " convergenceMinimumValue=1e-6,\n", " convergenceWindowSize=10)\n", " # Scale the step size differently for each parameter, this is critical!!!\n", " registration_method.SetOptimizerScalesFromPhysicalShift() \n", "\n", " registration_method.SetInitialTransform(initial_transform, inPlace=False)\n", " final_transform_v1 = registration_method.Execute(ff_img, \n", " mv_img)\n", " print('Optimizer\\'s stopping condition, {0}'.format(registration_method.GetOptimizerStopConditionDescription()))\n", " print('Final metric value: {0}'.format(registration_method.GetMetricValue()))\n", " resample = sitk.ResampleImageFilter()\n", " resample.SetReferenceImage(fixed_image)\n", " \n", " # SimpleITK supports several interpolation options, we go with the simplest that gives reasonable results. \n", " resample.SetInterpolator(sitk.sitkBSpline) \n", " resample.SetTransform(final_transform_v1)\n", " return sitk.GetArrayFromImage(resample.Execute(moving_image))" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimizer's stopping condition, GradientDescentOptimizerv4Template: Convergence checker passed at iteration 48.\n", "Final metric value: 1518.9840880660756\n", "255 255\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "reg_img = register_img(bw_img, shift_img, brute_force = False, use_mse = True)\n", "print(reg_img.max(), bw_img.max())\n", "fig, (ax1, ax2, ax2d, ax3, ax4) = plt.subplots(1, 5, figsize=(20, 5), dpi=100)\n", "ax1.imshow(bw_img, cmap='bone')\n", "ax1.set_title('$T_0$')\n", "ax2.imshow(shift_img, cmap='bone')\n", "ax2.set_title('$T_1$')\n", "ax2d.imshow(1.0*bw_img-shift_img, cmap='RdBu', vmin = -100, vmax = 100)\n", "ax2d.set_title('$T_1$ Registerd Difference')\n", "ax3.imshow(reg_img, cmap='bone')\n", "ax3.set_title('$T_1$ Registered')\n", "ax4.imshow(1.0*bw_img-reg_img, cmap='RdBu', vmin = -127, vmax = 127)\n", "ax3.set_title('$T_1$ Registerd Difference');" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Subdividing the data\n", "\n", "We can approach the problem by subdividing the data into smaller blocks and then apply the digital volume correlation independently to each block.\n", "- information on changes in different regions\n", "- less statistics than a larger box" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introducing Physics\n", "\n", "\n", "DIC or DVC by themselves include no _sanity check_ for realistic offsets in the correlation itself. The method can, however be integrated with physical models to find a more optimal solutions.\n", "\n", "- information from surrounding points\n", "- smoothness criteria\n", "- maximum deformation / force\n", "- material properties\n", "\n", "\n", "\n", "$$ C_{\\textrm{cost}} = \\underbrace{C_{I_0,I_1}(\\vec{r})}_{\\textrm{Correlation Term}} + \\underbrace{\\lambda ||\\vec{r}||}_{\\textrm{deformation term}} $$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Distribution Metrics\n", "\n", "\n", "As we covered before distribution metrics like the distribution tensor can be used for tracking changes inside a sample. Of these the most relevant is the texture tensor from cellular materials and liquid foam. The texture tensor is the same as the distribution tensor except that the edges (or faces) represent physically connected / touching objects rather than touching Voronoi faces (or conversely Delaunay triangles).\n", "\n", "These metrics can also be used for tracking the behavior of a system without tracking the single points since most deformations of a system also deform the distribution tensor and can thus be extracted by comparing the distribution tensor at different time steps. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Quantifying Deformation: Strain \n", "\n", "\n", "We can take any of these approaches and quantify the deformation using a tool called the strain tensor. Strain is defined in mechanics for the simple 1D case as the change in the length against the change in the original length.\n", "$$ e = \\frac{\\Delta L}{L} $$ \n", "While this defines the 1D case well, it is difficult to apply such metrics to voxel, shape, and tensor data. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Strain Tensor\n", "\n", "There are a number of different ways to calculate strain and the strain tensor, but the most applicable for general image based applications is called the [infinitesimal strain tensor](http://en.wikipedia.org/wiki/Infinitesimal_strain_theory), because the element matches well to square pixels and cubic voxels.\n", "\n", "```{r, fig.height=6}\n", "b.sq.ele<-0.4*data.frame(dx=c(-1,1,1,-1,-1),dy=c(-1,-1,1,1,-1))\n", "s.grid<-expand.grid(x=c(0:3),y=c(0:3))\n", "sq.eles<-ddply(cbind(s.grid,id=1:nrow(s.grid)),.(x,y),function(in.pt) {\n", " data.frame(x=in.pt[1,\"x\"]+b.sq.ele$dx,\n", " y=in.pt[1,\"y\"]+b.sq.ele$dy,\n", " id=in.pt[1,\"id\"])\n", "})\n", "ggplot(sq.eles,aes(x,y))+\n", " geom_polygon(aes(group=id))+\n", " coord_equal()+\n", " theme_bw(25)\n", "```\n", "\n", "\n", "A given strain can then be applied and we can quantify the effects by examining the change in the small element. \n", "\n", "```{r, fig.height=6}\n", "s.grid<-expand.grid(x=c(0:3),y=c(0:3))\n", "rh.eles<-ddply(cbind(s.grid,id=1:nrow(s.grid)),.(x,y),function(in.pt) {\n", " out.df<-data.frame(x=in.pt[1,\"x\"]+b.sq.ele$dx/(in.pt[1,\"x\"]+1),\n", " y=in.pt[1,\"y\"]+b.sq.ele$dy/(in.pt[1,\"y\"]+1),\n", " id=in.pt[1,\"id\"],\n", " x.rel.sz=1,\n", " y.rel.sz=1,\n", " x.sz=1/(in.pt[1,\"y\"]+1),\n", " y.sz=1/(in.pt[1,\"x\"]+1))\n", " out.df$e11<-with(out.df,(x.sz-x.rel.sz)/x.rel.sz)\n", " out.df$e22<-with(out.df,(y.sz-y.rel.sz)/y.rel.sz)\n", " out.df$e12<-0.5*with(out.df,(x.sz-x.rel.sz)/y.rel.sz+(y.sz-y.rel.sz)/x.rel.sz)\n", " \n", " out.df$vol<-with(out.df,e11+e22)\n", " out.df$dva<-with(out.df,sqrt((e11-vol/2)^2+(e22-vol/2)^2))\n", " out.df\n", "})\n", "ggplot(rh.eles,aes(x,y))+\n", " geom_polygon(aes(group=id))+\n", " coord_equal()+\n", " theme_bw(25)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Types of Strain\n", "\n", "We catagorize the types of strain into two main catagories:\n", "\n", "$$ \\underbrace{\\mathbf{E}}_{\\textrm{Total Strain}} = \\underbrace{\\varepsilon_M \\mathbf{I_3}}_{\\textrm{Volumetric}} + \\underbrace{\\mathbf{E}^\\prime}_{\\textrm{Deviatoric}} $$\n", "\n", "### Volumetric / Dilational\n", "\n", "The isotropic change in size or scale of the object. \n", "\n", "### Deviatoric\n", "\n", "The change in the proportions of the object (similar to anisotropy) independent of the final scale\n", "\n", "\n", "\n", "```{r, fig.height=4}\n", "ggplot(rh.eles,aes(x,y))+\n", " geom_polygon(aes(group=id,fill=vol),color=\"black\")+\n", " coord_equal()+\n", " labs(fill=\"Volumetric\\nStrain\")+\n", " scale_fill_gradient2()+\n", " theme_bw(25)\n", "```\n", "\n", "```{r, fig.height=4}\n", "ggplot(rh.eles,aes(x,y))+\n", " geom_polygon(aes(group=id,fill=dva),color=\"black\")+\n", " coord_equal()+\n", " labs(fill=\"Deviatoric\\nStrain\")+\n", " scale_fill_gradient2()+\n", " theme_bw(25)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Two Point Correlation - Volcanic Rock\n", "\n", "\n", "__Data provided by Mattia Pistone and Julie Fife__\n", "The air phase changes from small very anisotropic bubbles to one large connected pore network. The same tools cannot be used to quantify those systems. Furthermore there are motion artifacts which are difficult to correct.\n", "\n", "\n", "\n", "\n", "\n", "We can utilize the two point correlation function of the material to characterize the shape generically for each time step and then compare.\n", "\n", "" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "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": "3.6.4" }, "livereveal": { "autolaunch": true, "scroll": true } }, "nbformat": 4, "nbformat_minor": 2 }