{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## RSNA Breast Cancer detection from Mammograms\n\n### Preamble\n\nThis notebook was written for beginners by a beginner myself. As a relative newcomer to Kaggle, I learnt a lot whilst working through this competition, and by documenting my efforts and findings I hope that this Notebook could prove similarly useful to other beginners. \n\nThis Notebook is organised into the following sections:\n1. Summary of Approach\n2. Preliminaries\n3. Image visualisation and processing\n4. Metadata processing\n5. Neural Network construction and initial train\n6. Customised data sampler for imbalanced data\n7. Selected training results\n8. Creation of competition-eligible Submission\n\n","metadata":{}},{"cell_type":"markdown","source":"### 1. Summary of Approach\n\nTo tackle this competition I decided upon the following broad approach:-\n\n* Process dicom images into a smaller png dataset for use with training\n* Pass processed images into a pre-trained image model (I used Resnet50), but with a customised final layer in order to customise the number of outputs\n* Combine the image-model outputs with processed inputs from the train/test.csv file (I call this the \"metadata\"), and pass into a small fully-connected neural-network model to perform binary classification\n* Tune the model and apply techniques to handle the data imbalance\n* PyTorch was used for all neural networks\n\nOne of the key challenges in this competition is the fact that the occurence of Cancer label is quite rare, only about ~2% of the dataset, which causes difficulties for training the model. After working through the project, in the end the two points of focus that I felt were most important were i) image processing aspects and ii) handling of imbalanced dataset.","metadata":{}},{"cell_type":"markdown","source":"### 2. Preliminaries\n\n#### Package Importation\n\nBelow we import a variety of packages that will be needed.","metadata":{}},{"cell_type":"code","source":"import torch\nimport torchvision\nimport sklearn\nimport numpy as np\nimport pandas as pd\nimport time\nimport scipy\nimport sys\nimport glob\nimport cv2 \nimport os\nimport PIL\nimport multiprocessing as mp\nimport contextlib\nimport io\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nfrom tqdm.auto import tqdm\nfrom sklearn.model_selection import train_test_split\n\n#pytorch and torchvision imports\nfrom torch import nn\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import Sampler\nfrom torchvision import transforms\nfrom torchvision.models import resnet50\n\n#for displaying images from dataset\nfrom IPython.display import Image","metadata":{"execution":{"iopub.status.busy":"2023-02-28T17:51:14.300004Z","iopub.execute_input":"2023-02-28T17:51:14.300527Z","iopub.status.idle":"2023-02-28T17:51:16.926929Z","shell.execute_reply.started":"2023-02-28T17:51:14.300434Z","shell.execute_reply":"2023-02-28T17:51:16.925687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In particular - the medical images provided are in (two types of) Dicom format, and in order to read these we will need to install and import the following packages:","metadata":{}},{"cell_type":"code","source":"## install required packages for dcm processing\n#!pip install -qU python-gdcm pydicom pylibjpeg\n!pip install -qU pylibjpeg pylibjpeg-openjpeg pylibjpeg-libjpeg pydicom python-gdcm dicomsdl\n\n#import all the required dicom packages\nimport gdcm\nimport pydicom\nimport pylibjpeg\nimport dicomsdl","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:03:50.301897Z","iopub.execute_input":"2023-02-28T08:03:50.302315Z","iopub.status.idle":"2023-02-28T08:04:08.616142Z","shell.execute_reply.started":"2023-02-28T08:03:50.30228Z","shell.execute_reply":"2023-02-28T08:04:08.614851Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**NOTE:** the above code block will *not run* with \"Internet off\" mode in Kaggle, however this will be a required condition when we eventually come to creating a competition Submission. I will cover how to achieve the necessary importations with Internet-off in Section 8.","metadata":{}},{"cell_type":"code","source":"###SCRIPTS TO INSTALL DICOM PACKAGES FROM DATASET WHEN \"INTERNET OFF\"\n!pip install pylibjpeg --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install pylibjpeg-openjpeg --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install pylibjpeg-libjpeg --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install pydicom --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install python-gdcm --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install dicomsdl --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:04:08.618094Z","iopub.execute_input":"2023-02-28T08:04:08.618482Z","iopub.status.idle":"2023-02-28T08:05:18.173226Z","shell.execute_reply.started":"2023-02-28T08:04:08.618446Z","shell.execute_reply":"2023-02-28T08:05:18.171436Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Read in .csv files and establish directory structure\n\nFirst we set out some directory structure (the paths can be found by clicking the file/directory in kaggle and clicking the icon on the right to \"copy file path\"):","metadata":{}},{"cell_type":"code","source":"#Some Kaggle directory definitions\ndata_dir=\"/kaggle/input/rsna-breast-cancer-detection/\" #base data directory in kaggle\nworking_dir=\"/kaggle/working/\" #base output directory in kaggle\nimgs_dir=\"/kaggle/input/rsna-breast-cancer-detection/train_images/\" #train dicom directory in kaggle\ntest_imgs_dir=\"/kaggle/input/rsna-breast-cancer-detection/test_images/\" #test dicom directory in kaggle\n#png_imgs_dir=\"/kaggle/input/rsna-mammograms-256px-png/png_train_images_256/\"\npng_enh_imgs_dir=\"/kaggle/input/rsna-mammogram-pngs-enhanced/png_train_images/\"","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:18.176642Z","iopub.execute_input":"2023-02-28T08:05:18.177177Z","iopub.status.idle":"2023-02-28T08:05:18.183834Z","shell.execute_reply.started":"2023-02-28T08:05:18.177118Z","shell.execute_reply":"2023-02-28T08:05:18.182493Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note in the above I have pre-emptively set out a new directory path \"png_eng_imgs_dir\" - this is intended to store all the processed dicom images as png files to be used in model training.\n\nNext we read in the train and test .csvs into pandas dataframes:","metadata":{}},{"cell_type":"code","source":"train_df=pd.read_csv(data_dir+\"train.csv\")\ntest_df=pd.read_csv(data_dir+\"test.csv\")\n\nprint(\"number of training data entries:\",len(train_df))\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:18.187657Z","iopub.execute_input":"2023-02-28T08:05:18.188508Z","iopub.status.idle":"2023-02-28T08:05:18.372306Z","shell.execute_reply.started":"2023-02-28T08:05:18.188454Z","shell.execute_reply":"2023-02-28T08:05:18.371462Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are 54706 rows of training data, and there is a dicom image in the train_images directory corresponding to each of those, organised by subfolders with the patient_id which contains .dcm files with the unique image_id (and similarly for the test.csv data).\n\nIt will prove most useful to add columns to the train/test dfs containing the filepaths of the corresponding dicom images. For the train df, we will also add a column to contain the location of the png-versions of the images that we shall create later:","metadata":{}},{"cell_type":"code","source":"# Get image path and insert it as new column into train_df\nall_dicom_img_paths = []\n#all_png_img_paths=[]\nall_png_enh_img_paths=[]\nfor k in tqdm(range(len(train_df))):\n    row = train_df.iloc[k, :]\n    all_dicom_img_paths.append(imgs_dir + str(row.patient_id) + \"/\" + str(row.image_id) + \".dcm\")\n    #all_png_img_paths.append(png_imgs_dir + str(row.patient_id) + \"/\" + str(row.image_id) + \".png\")\n    all_png_enh_img_paths.append(png_enh_imgs_dir + str(row.patient_id) + \"/\" + str(row.image_id) + \".png\")\ntrain_df[\"path\"] = all_dicom_img_paths\n#train_df[\"pngpath\"] = all_png_img_paths\ntrain_df[\"pngpath_enh\"] = all_png_enh_img_paths","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:18.373589Z","iopub.execute_input":"2023-02-28T08:05:18.374663Z","iopub.status.idle":"2023-02-28T08:05:29.778665Z","shell.execute_reply.started":"2023-02-28T08:05:18.374626Z","shell.execute_reply":"2023-02-28T08:05:29.77683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# We'll do the same for test_df but only for dicom paths since the test data only involves\n#inference and no more training\nall_dicom_img_paths = []\n\nfor k in tqdm(range(len(test_df))):\n    row = test_df.iloc[k, :]\n    all_dicom_img_paths.append(test_imgs_dir + str(row.patient_id) + \"/\" + str(row.image_id) + \".dcm\")\n    \ntest_df[\"path\"] = all_dicom_img_paths","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:29.780157Z","iopub.execute_input":"2023-02-28T08:05:29.780539Z","iopub.status.idle":"2023-02-28T08:05:29.835652Z","shell.execute_reply.started":"2023-02-28T08:05:29.780502Z","shell.execute_reply":"2023-02-28T08:05:29.834101Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 3. Image visualisation and processing\n\n#### Dicom file preliminaries\nLet's start trying to open and view some of the dicom images. Most of the images can be opened via the \"pydicom.dcmread(*filepath*)\" command. However, note that the output of this is not directly the pixels data, but a collection of metadata regarding the file as well as the pixel data:-","metadata":{}},{"cell_type":"code","source":"filepath=train_df[\"path\"].iloc[0]\nsample_dcmread1 = pydicom.dcmread(filepath)\n\nprint(\"type:\",type(sample_dcmread1))\nprint(\"file read detailed:\",sample_dcmread1)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:29.837239Z","iopub.execute_input":"2023-02-28T08:05:29.838399Z","iopub.status.idle":"2023-02-28T08:05:29.927502Z","shell.execute_reply.started":"2023-02-28T08:05:29.838347Z","shell.execute_reply":"2023-02-28T08:05:29.925994Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note that the above content can be specifically accessed through other methods, and in particular note the field \"Photometric Interpretation\" (MONOCHROME1 for the above example) and the final element which stores the pixel data itself.\n\nHaving used pydicom.dcmread() to open the file, we can now access the Photometric Interpretation and pixel data specifically using the following:-","metadata":{}},{"cell_type":"code","source":"sample_dcm_pinterp=sample_dcmread1.PhotometricInterpretation\nsample_dcm_pixels1=sample_dcmread1.pixel_array\nprint(\"Photometric interpretation:\",sample_dcm_pinterp)\nprint(\"Image shape\",sample_dcm_pixels1.shape,\" Pixel array type:\",type(sample_dcm_pixels1))\nplt.imshow(sample_dcm_pixels1,cmap=\"gray\")\nprint(\"Maximum pixel value: %d, Minimum pixel value: %d\" %(sample_dcm_pixels1.max(),\n                                                          sample_dcm_pixels1.min()))","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:29.92997Z","iopub.execute_input":"2023-02-28T08:05:29.931499Z","iopub.status.idle":"2023-02-28T08:05:33.605559Z","shell.execute_reply.started":"2023-02-28T08:05:29.93144Z","shell.execute_reply":"2023-02-28T08:05:33.604222Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's look at another sample image, this time an example of a dicom file with Photometric interpretation of \"MONOCHROME2\" :-","metadata":{}},{"cell_type":"code","source":"filepath=train_df[\"path\"].iloc[7]\nsample_dcmread2 = pydicom.dcmread(filepath)\nsample_dcm_pinterp=sample_dcmread2.PhotometricInterpretation\nsample_dcm_pixels2=sample_dcmread2.pixel_array\nprint(\"Photometric interpretation:\",sample_dcm_pinterp)\nprint(\"Image shape\",sample_dcm_pixels2.shape,\" Pixel array type:\",type(sample_dcm_pixels2))\nplt.imshow(sample_dcm_pixels2,cmap=\"gray\")\nprint(\"Maximum pixel value: %d, Minimum pixel value: %d\" %(sample_dcm_pixels2.max(),\n                                                          sample_dcm_pixels2.min()))","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:33.607099Z","iopub.execute_input":"2023-02-28T08:05:33.607454Z","iopub.status.idle":"2023-02-28T08:05:35.110333Z","shell.execute_reply.started":"2023-02-28T08:05:33.607423Z","shell.execute_reply":"2023-02-28T08:05:35.109135Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can immediately notice a number of things regarding different images:\n* MONOCHROME1 corresponds to the whitespaces having the highest pixel value (hence displaying as white) whereas MONOCHROME2 corresponds to whitespaces having the lowest pixel value (hence showing as black)\n    * see [this link](https://dicom.innolitics.com/ciods/rt-dose/image-pixel/00280004) for more information\n* there can be a *considerable* amount of empty space in the images\n* output of pixel_array method is a numpy array\n* image shapes and min-max pixel values seem non-standard\n* breast images can present as left facing or right facing\n    * *Note:* one of the columns in the train/test.csvs is \"laterality\" which can be either L or R. Cursory examination of the corresponding images will reveal that most breasts with laterality L are right facing and vice versa, but *not all* - roughly 15% are \"mis-labelled\". Hence we will not take image orientation for-granted based on the laterality, and will instead aim to standardise this via an algorithm to run on every image\n\nFurthermore, the data corresponds to *two possible types* of dicom images, whereby the second type cannot be opened by the pydicom.dcmread() method: ","metadata":{}},{"cell_type":"code","source":"#try using pydicom pixel_array method:\nfilepath=train_df[\"path\"].iloc[20]\nread=pydicom.dcmread(filepath)\npixels=read.pixel_array","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:35.115025Z","iopub.execute_input":"2023-02-28T08:05:35.115432Z","iopub.status.idle":"2023-02-28T08:05:35.46546Z","shell.execute_reply.started":"2023-02-28T08:05:35.115393Z","shell.execute_reply":"2023-02-28T08:05:35.46436Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Instead, this second type of dicom image can have pixels read using the dicomsd1.open() followed by .pixelData() methods:","metadata":{}},{"cell_type":"code","source":"filepath=train_df[\"path\"].iloc[20]\n\nsample_dcmread3 = dicomsdl.open(filepath) \nsample_dcm_pixels3=sample_dcmread3.pixelData()\n\nsample_dcm_pinterp=sample_dcmread3.PhotometricInterpretation\nprint(\"Photometric interpretation:\",sample_dcm_pinterp)\nprint(\"Image shape\",sample_dcm_pixels3.shape,\" Pixel array type:\",type(sample_dcm_pixels3))\nplt.imshow(sample_dcm_pixels3,cmap=\"gray\")\nprint(\"Maximum pixel value: %d, Minimum pixel value: %d\" %(sample_dcm_pixels3.max(),\n                                                          sample_dcm_pixels3.min()))","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:35.467052Z","iopub.execute_input":"2023-02-28T08:05:35.468283Z","iopub.status.idle":"2023-02-28T08:05:36.918007Z","shell.execute_reply.started":"2023-02-28T08:05:35.468229Z","shell.execute_reply":"2023-02-28T08:05:36.916519Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Notice also in the above sample image how \"flat\" the tonality of the breast interior is: intuitively, better contrast could help humans to detect features more easily, and stands to reason it could help ML algorithms too.\n\n#### Image processing objectives and helper functions\n\nWe now set out our aims with regards to image processing: \n* To ***standardise*** the images presented such that whitespace values and breast facing-direction are consistent across all images.\n    * Whereas there are many image recognition tasks where it is beneficial to train with flipped images or images with different backgrounds (eg: object recognition, is the image of a ball or a cat or etc.?), the task at hand is of a specific domain ie: to use mammograms to identify breast cancer where the choice of monochromacity or choice of orienting the breast image is irrelevant to the presence of cancer but simply creates confusion from a machine-learning perspective. Hence, to assist our learning we shall standardise these features away.\n* To enhance images by increasing contrast\n* To crop away empty space\n    ** The presence of cancer is obviously determined by the interior of the breast itself and the whitespace is irrelevant - having a lot of whitespace effectively reduces the amount of useful image resolution being passed to the model.\n\nBearing the above in mind, we start by writing a simple function to read the dicom image and obtain its photometric interpretation, trying both file-handling styles for the one that works:","metadata":{}},{"cell_type":"code","source":"#Helper function to read a dicom image into pixels - note there are two types of\n#dicom files presented which may need different handling, hence the below snippet:\ndef dicom_to_pixels(filepath):\n    try:\n        img = pydicom.dcmread(filepath)\n        img_pixels=img.pixel_array\n    except:\n        img = dicomsdl.open(filepath) \n        img_pixels = img.pixelData()\n        \n    return img_pixels,img.PhotometricInterpretation","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:36.919843Z","iopub.execute_input":"2023-02-28T08:05:36.920237Z","iopub.status.idle":"2023-02-28T08:05:36.92677Z","shell.execute_reply.started":"2023-02-28T08:05:36.920202Z","shell.execute_reply":"2023-02-28T08:05:36.925474Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"For the rest of the image processing, we will perform tasks in the following order:-\n1. \"normalise\" the image pixel values, which we have seen can be quite non-standard, to remap them linearly onto the range [0,1]\n2. invert the pixel values from x to 1-x depending on the Photometric Interpretation - we shall opt for all whitespaces to display as black or lowest pixel values\n3. detect if the breast is right or left facing, and if the latter flip it to be right-facing\n4. crop the image in two passes: horizontal crop first and then vertical crop\n    * we shall write our own custom routines for these two steps which will be slightly different as we will know, from the above step, that all breasts are right-facing\n5. increase the image contrast on the cropped image\n6. resize the image to our preferred resolution\n7. cast pixel values to lie in the integer range [0,255], and finally;\n8. write the result to a 3-channel pixel array that can be saved in png format.\n\nWe first write helper code to perform steps 1,3-5 of the above: ","metadata":{}},{"cell_type":"code","source":"#def normalise fn\ndef normalise_fn(img_pixels):\n    ## linearly convert pixel values from arbitrary into the continuous range [0,1]\n    pixels_out=(img_pixels - img_pixels.min()) / (img_pixels.max() - img_pixels.min())\n    return pixels_out\n\n#detect orientation - performed after converting all whitespace pixels to near-zero pixel value\ndef check_right_facing(img,band_threshold=5):\n    ## intuition: a right-facing breast will have some breast imagery on the left edge\n    # having higher pixel values and will largely have blank area on the right edge having\n    # near-zero pixel values. Hence, we form vertical bands at the edges to determine which\n    # has the higher average pixel value - if the left edge has higher values then the\n    # breast is right-facing\n    \n    #argument: band_threshold denotes how many strips of 1%-width bands to use for determination\n    #of orientation\n    \n    total_img_width=img.shape[1]\n    band_width=int(total_img_width/100) #pixel-width of a vertical band covering 1% of edge\n\n    L_img_band=img[:,:band_threshold*band_width]\n    R_img_band=img[:,-band_threshold*band_width:]\n    L_pixel_avg=L_img_band.mean()\n    R_pixel_avg=R_img_band.mean()\n    \n    if L_pixel_avg>R_pixel_avg:\n        right_facing=True\n    else:\n        right_facing=False\n    \n    return right_facing\n\n#horizontally crop away whitespace\ndef h_crop_img(img,band_threshold=5,range_threshold=0.125,avg_threshold=0.125):\n    ##intuition: input images have previously already standardised to face right\n    #hence, we can work from left to right to detect when both average pixel value and the\n    #min-max range of pixel values in a sliding vertical column fall below specified threshold\n    #values. Once this has occurred for a band_threshold number of times, we determine we have\n    #safely reached the end of useful graphical information and crop the remaining right-side\n    \n    total_img_width=img.shape[1]\n    band_width=int(total_img_width/100)\n    num_bands=int(total_img_width/band_width)\n\n    band_count=0\n    for i in range(num_bands):\n        x1,x2=i*band_width,(i+1)*band_width\n        img_band=img[:,x1:x2]\n        img_row_avgs=img_band.mean(axis=1)\n\n        pixel_range=img_row_avgs.max()-img_row_avgs.min()\n        pixel_avg=img_band.mean()\n\n        if pixel_range < range_threshold and pixel_avg<avg_threshold:\n            band_count+=1\n\n        if band_count>=5 or i==num_bands-1:\n            rightmost_col=x2\n            break\n\n    cropped_img=img[:,:rightmost_col]\n    return cropped_img\n\n#vertically crop away whitespace\ndef v_crop_img(img,band_threshold=5,range_threshold=0.125,avg_threshold=0.125):\n    ##intuition: unlike the h_crop_img function where inputs are expected to be laterally\n    #asymmetrical, we cannot make similar assumptions on the vertical symmetry as both the\n    #upper and lower edges may (or may not) contain useful breast imagery.\n    #hence, although we adopt a similar \"sliding band\" approach, we will slide a horizontal\n    #band from top to bottom of the entire image and keep a record of where the band averages\n    #or min/max range where above threshold values - this indicates potentially useful imagery,\n    #and we record the position of such bands with a value 1 (store this array in row_indicator)\n    \n    #we assess row_indicator from both directions: if values initially start at zero and then\n    #become 1, we crop away the previous bands of zero according to a certain amount of \"buffer\n    #rows\" as given by the input band_threshold\n    \n    total_img_height=img.shape[0]\n    band_height=int(total_img_height/100)\n    num_bands=int(total_img_height/band_height)\n\n    row_indicator=np.zeros(num_bands)\n    \n    for i in range(num_bands):\n        y1,y2=i*band_height,(i+1)*band_height\n        img_band=img[y1:y2,:]\n        img_col_avgs=img_band.mean(axis=0)\n        \n        pixel_range=img_col_avgs.max()-img_col_avgs.min()\n        \n        pixel_avg=img_band.mean()\n\n        if pixel_range > range_threshold or pixel_avg>avg_threshold:\n            row_indicator[i]=1\n\n    band_count=0\n    y_min,y_max=0,total_img_height\n    for i in range(num_bands):\n        if row_indicator[i]==1:\n            y_min=np.maximum(i-band_threshold,0)*band_height\n            break\n    for i in range(num_bands-1,-1,-1):\n        if row_indicator[i]==1:\n            y_max=np.minimum((i+1+band_threshold)*band_height,total_img_height)\n            break\n    \n    cropped_img=img[y_min:y_max,:]\n    return cropped_img\n\n#sigmoid contrast fn\ndef sigmoid_contrast_fn(img_pixels,intensity):\n    ##assumes input pixels are already normalised to be continuous in the range [0,1],\n    #and the input image has been vertically and horizontally cropped\n    \n    #pre-crop the min/max pixel values would have been cast to 0 and 1 respectively, however\n    #post cropping its possible some extreme values may have been trimmed away, so we re-cast\n    #the remaining pixels to a [0,1] range\n    pixels_out=(img_pixels - img_pixels.min()) / (img_pixels.max() - img_pixels.min())\n    #in retrospect, the above line probably lends little value and could be omitted\n    \n    #Next: \"grey\" pixels having a value of 0.5 will be preserved whilst pixel values on either\n    #side will be made either darker/lighter according to an S-shaped curve, hence increasing\n    #contrast\n    \n    #The Sigmoid function is a convenient s-shaped function for the task, where the\n    #\"intensity\" argument serves to make pixels further away from 0.5 and subjected to more\n    #darkening/lightening, ie: more contrast\n    pixels_out=(2*pixels_out-1)*intensity\n    pixels_out=1/(1+np.exp(-pixels_out))\n    return pixels_out\n","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:36.928446Z","iopub.execute_input":"2023-02-28T08:05:36.92903Z","iopub.status.idle":"2023-02-28T08:05:36.951922Z","shell.execute_reply.started":"2023-02-28T08:05:36.92899Z","shell.execute_reply":"2023-02-28T08:05:36.95055Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We combine the above helper functions into a general image pre-processor \"dicom_processor\":","metadata":{}},{"cell_type":"code","source":"def dicom_processor(dicom_pixels,p_interp,\n                    contrast_enhance=False, contrast_intensity=5,\n                    flip_left_to_right_facing=False,close_crop=False,\n                    resize_res=None,png_rtn=False):\n    \n    #1. normalise pixel values to range [0,1]\n    img_pixels=normalise_fn(dicom_pixels)\n    \n    #2. MONOCHROME1 to be chromatically inverted\n    if p_interp == \"MONOCHROME1\":\n        img_pixels = 1 - img_pixels\n    \n    #3. if opted: check if breast is right-facing and if not laterally flip the image\n    if flip_left_to_right_facing:\n        right_facing=check_right_facing(img_pixels)\n        if not right_facing:\n            img_pixels=img_pixels[:,::-1]\n    \n    #4. if opted: crop horizontally and then vertically\n    if close_crop:\n        img_pixels=v_crop_img(h_crop_img(img_pixels))\n    \n    #5. if opted: apply contrast on cropped image\n    if contrast_enhance:\n        img_pixels = sigmoid_contrast_fn(img_pixels,contrast_intensity)\n    \n    #6. if opted: resize image\n    if resize_res:\n        ##NOTE: cv2.resize takes res as (W,H) but mathematically we normally use\n        #the opposite, ie: rows x col, so here we reverse it:\n        img_pixels = cv2.resize(img_pixels,resize_res[::-1])\n    \n    #7. rebase pixel values to 8-bit integer for output\n    img_pixels = (img_pixels * 255).astype(np.uint8)\n    \n    #8. if opted: write output into 3-channel format consistent with png images\n    #( these have dimension (channels, h,w) )\n    if png_rtn:\n        img_pixels=np.transpose(np.array([img_pixels,img_pixels,img_pixels]),(1,2,0))\n    \n    return img_pixels","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:05:36.95358Z","iopub.execute_input":"2023-02-28T08:05:36.954014Z","iopub.status.idle":"2023-02-28T08:05:36.971503Z","shell.execute_reply.started":"2023-02-28T08:05:36.953978Z","shell.execute_reply":"2023-02-28T08:05:36.970296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note that running dicom_processor above with all default values only performs monochromatic inversion and re-cast of pixel values to 8-bit integer, keeping all other aspects unchanged.\n\nWe'll write a helper function for comparison of images pre- and post-processing:-","metadata":{}},{"cell_type":"code","source":"def get_rnd_indices(num_images,df_wPaths,\n                    num_cancer=None,random_state=None):\n    cancer_count=0\n    cancer_indices,no_cancer_indices=[],[]\n    \n    if random_state is None:\n        np.random.seed()\n    else:\n        np.random.seed(random_state)\n    \n    random_indices=np.random.permutation(range(len(df_wPaths)))\n    \n    if num_cancer is None:\n        random_indices=random_indices[:num_images]\n    else:\n        for i,index in enumerate(random_indices):\n            row=df_wPaths.iloc[index,:]\n            if row.cancer==1 and len(cancer_indices)<num_cancer:\n                cancer_indices+=[index]\n            elif row.cancer==0 and len(no_cancer_indices)<num_images-num_cancer:\n                no_cancer_indices+=[index]\n            if len(cancer_indices)+len(no_cancer_indices)==num_images:\n                break\n        random_indices=np.random.permutation(cancer_indices+no_cancer_indices)\n    \n    return random_indices\n\ndef compareRandomImages(num_images,df_wPaths,\n                        num_cancer=None,random_state=None,\n                        contrast_enhance=False,flip_LtoR=False,\n                        crop_images=False,resize_res=False):\n    counter=0\n    num_rows=num_images\n    \n    if random_state is None:\n        np.random.seed()\n    else:\n        np.random.seed(random_state)\n    \n    random_indices=get_rnd_indices(num_images,df_wPaths,\n                    num_cancer=num_cancer,random_state=random_state)\n    \n    #plt.figure(figsize = (24,15))\n    plt.figure()\n    fig, axs = plt.subplots(num_rows, 2, figsize=(9,8*num_rows))\n    axs = axs.flatten()\n    for i,index in enumerate(random_indices):\n        row=df_wPaths.iloc[index,:]\n        laterality=str(row.laterality)\n        cancer=str(row.cancer)\n        view=str(row[\"view\"])\n        img_path=row.path\n        \n        img_pixels,interp=dicom_to_pixels(img_path)\n        img_pixels2=dicom_processor(img_pixels,interp,\n                                    flip_left_to_right_facing=flip_LtoR,\n                                    contrast_enhance=contrast_enhance,\n                                    close_crop=crop_images,\n                                   resize_res=resize_res)\n        \n        title='Index:'+str(index)+\"\\nP Interp:\"+interp+\"\\nLat:\"+laterality+\",  View:\"+view+\",  Cancer:\"+cancer\n        title2='Index:'+str(index)+\"  P Interp:\"+interp+\"\\nLat:\"+laterality+\",  View:\"+view+\",  Cancer:\"+cancer\n        \n        axs[2*i].set_title(title+'\\nImg Shape:'+str(img_pixels.shape)+\"\\nUnprocessed\")\n        axs[2*i+1].set_title(title2+'  Img Shape:'+str(img_pixels2.shape)+\"\\nProcessed\")\n        axs[2*i].imshow(img_pixels, cmap=\"gray\")\n        axs[2*i+1].imshow(img_pixels2, cmap=\"gray\")","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:10:19.157933Z","iopub.execute_input":"2023-02-28T08:10:19.158385Z","iopub.status.idle":"2023-02-28T08:10:19.177073Z","shell.execute_reply.started":"2023-02-28T08:10:19.15835Z","shell.execute_reply":"2023-02-28T08:10:19.175671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's take a look at 8 randomly selected images pre- and post-processing, 4 of which are positive for cancer:-","metadata":{}},{"cell_type":"code","source":"compareRandomImages(8,train_df,num_cancer=4,random_state=1,\n                        contrast_enhance=True,flip_LtoR=True,\n                        crop_images=True,resize_res=(512,256))","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:10:21.980508Z","iopub.execute_input":"2023-02-28T08:10:21.981489Z","iopub.status.idle":"2023-02-28T08:10:46.868636Z","shell.execute_reply.started":"2023-02-28T08:10:21.981439Z","shell.execute_reply":"2023-02-28T08:10:46.867076Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note that often a considerable amount of horizontal space can end up cropped - as such, we could qualitatively say that a rectangular final image that is taller than it is wide can often capture the breast-image in a standard shape whilst being fairly \"filled out\" and without too much distortion. Hence we have chosen an image-resize of (512,256) for our processed images.\n\nOur Neural Network will be trained on processed png inputs. Hence, we will run the dicom_processor on all training dicom images and write them to file mimicking the file structure of the provided train images. The code block below will help us accomplish this - note this code block was originally written to write the processed pngs to local drive, which I later uploaded as a dataset and added to the Kaggle notebook. The running time required, on my local CPU, was very lengthy - about 18hours.","metadata":{}},{"cell_type":"code","source":"\"\"\"\nRead dicom files and write png-conversion into new directories.\nThis took ~18 hours to run on local CPU\n\nThis code block was originally written to run and write to local machine, with the pngs\nzipped up into a dataset and added to the Kaggle notebook, however the folder destinations\ncould be rearranged to work directly in kaggle.\n\"\"\"\nlocal_working_folder=sys.path[0]\nlocal_data_dir=local_working_folder+\"\\\\data\\\\\"\nlocal_imgs_dir=local_data_dir+\"train_images\\\\\"\nlocal_test_imgs_dir=local_data_dir+\"test_images\\\\\"\nlocal_png_imgs_dir=local_data_dir+\"png_train_images\\\\\"\n\npngResolution=(512,256)\ntimeSta=time.time()\nfor k in tqdm(range(len(train_df))):\n    row = train_df.iloc[k, :]\n    dicom_img_path=row.path\n    png_img_dir=local_png_imgs_dir + str(row.patient_id)\n    png_img_filepath=png_img_dir + \"\\\\\" + str(row.image_id) + \".png\"\n    \n    fileExists=os.path.exists(png_img_filepath)\n    if not fileExists:#if file does not already exist, proceed\n        dirExists=os.path.exists(png_img_dir)\n        if not dirExists:#if dir does not already exist, create it\n            make_dir_cmd=\"\\\"\"+png_img_dir[2:]+\"\\\"\" #to write into command prompt after !mkdir\n            #create new directory\n            !mkdir {make_dir_cmd}\n        \n        #convert dicom into png and place it into the relevant folder\n        #png_pixels=dicom_to_pixels(row.path)\n        laterality=str(row.laterality)\n        png_pixels=dicom_processor(*dicom_to_pixels(row.path),\n                                   contrast_enhance=True, contrast_intensity=5,\n                                   flip_left_to_right_facing=True,\n                                   close_crop=True,\n                                   resize_res=pngResolution,png_rtn=True)\n        \n        cv2.imwrite(png_img_filepath,png_pixels)\n        \nprint(\"Time taken: %.2f secs\" %(time.time()-timeSta))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 4. Metadata processing\n\nWe will now examine the train and test dfs in more detail, and bear in mind that we will ultimately need to mirror any processing steps performed on the train data onto the data in test.csv accordingly. Also recall earlier we added new columns path and pngpath_enh to the train df and path to the test df, let us omit these for exploratory data analysis purposes:-","metadata":{}},{"cell_type":"code","source":"df=train_df.copy()\ntrain_features=list(df.drop(columns=[\"path\",\"pngpath_enh\"]))\n\ndf_test=test_df.copy()\ntest_features=list(df_test.drop(columns=[\"path\"]))\n\nprint(\"Train features:\",train_features)\nprint(\"\\nTest features:\",test_features)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:35.137684Z","iopub.execute_input":"2023-02-28T08:11:35.138161Z","iopub.status.idle":"2023-02-28T08:11:35.171931Z","shell.execute_reply.started":"2023-02-28T08:11:35.138112Z","shell.execute_reply":"2023-02-28T08:11:35.17053Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Both datasets have some patient id and image id columns that can be neglected for feature engineering purposes, however note that Site ID pertains to the site that mammograms were performed at and potentially has learnable information. Ignoring the patient/image ID columns, let's take a look at which features are common to both train/test sets and which are unique to one set or the other:","metadata":{}},{"cell_type":"code","source":"path_cols=[\"path\",\"pngpath\"]\nprint(\"Img path columns:\",path_cols)\n\nid_cols=[\"patient_id\",\"image_id\"]\nprint(\"ID columns:\",id_cols)\ncommon_features=list(set(test_features) & set(train_features))\nfor i in id_cols:\n    common_features.remove(i)\nprint(\"Common features:\",common_features)\n\ntrain_only_features=[]\nfor i in train_features:\n    if not i in test_features:\n        train_only_features+=[i]\nprint(\"Train only features:\",train_only_features)\n\ntest_only_features=[]\nfor i in test_features:\n    if not i in train_features:\n        test_only_features+=[i]\nprint(\"Test only features:\",test_only_features)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:37.946014Z","iopub.execute_input":"2023-02-28T08:11:37.946477Z","iopub.status.idle":"2023-02-28T08:11:37.956423Z","shell.execute_reply.started":"2023-02-28T08:11:37.946438Z","shell.execute_reply":"2023-02-28T08:11:37.95519Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"At this stage, the most straightforward way to proceed would be to focus on processing the common features in the train and test set consistently and training on these, since these will also be present in the test data, and this is in fact how we shall proceed. However, given luxury of more time and computing power to devote to this challenge, it is entirely conceiveable that more productive use could be made of these additional features.\n\nThe label to be predicted as 1 or 0 is \"cancer\" and we immediately notice it occurs rarely in the dataset with a rate of ~2.3%:","metadata":{}},{"cell_type":"code","source":"total_train_length,num_cancer,cancer_rate=len(df),df[\"cancer\"].sum(),df[\"cancer\"].mean()\nprint(\"Total training data rows: %d\\nNumber of cancer cases: %d\\nCancer rate: %.4f\"\n     %(total_train_length,num_cancer,cancer_rate))","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:38.763898Z","iopub.execute_input":"2023-02-28T08:11:38.764768Z","iopub.status.idle":"2023-02-28T08:11:38.772471Z","shell.execute_reply.started":"2023-02-28T08:11:38.764708Z","shell.execute_reply":"2023-02-28T08:11:38.771041Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We also quickly take a look at occurrences of null values within the common features, and it looks like we only need to handle null values in \"age\", which we shall get to later:","metadata":{}},{"cell_type":"code","source":"null_count=(1*df[common_features].isnull()).sum()\nprint(\"Common features having >=1 null value:\")\nnull_count[null_count>0]","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:39.09155Z","iopub.execute_input":"2023-02-28T08:11:39.092025Z","iopub.status.idle":"2023-02-28T08:11:39.114205Z","shell.execute_reply.started":"2023-02-28T08:11:39.091987Z","shell.execute_reply":"2023-02-28T08:11:39.112847Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Light visualisation of common features\n\nLet's make some quick examination of the common features - all of these are basically categorical except for \"age\", let's take a quick look at the population age with and without cancer:","metadata":{}},{"cell_type":"code","source":"\"\"\"\nplot histogram of population age, and draw quartiles for the whole population (green) and\nalso quartiles for the portion of population that is positive for cancer (red)\n\"\"\"\nhisto=sns.histplot(data=df[df[\"cancer\"]==1],x=\"age\",kde=True)\nquantiles_all = df[\"age\"].quantile([0.25,0.5,0.75]).to_numpy()\nquantiles_cancer = df[df[\"cancer\"]==1][\"age\"].quantile([0.25,0.5,0.75]).to_numpy()\nfor i in quantiles_all:\n    histo.axvline(i,color=\"green\")\nfor i in quantiles_cancer:\n    histo.axvline(i,color=\"red\")\nprint(\"Age quartiles for whole training population:\",quantiles_all)\nprint(\"Age quartiles for cancer-positive          :\",quantiles_cancer)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:39.37027Z","iopub.execute_input":"2023-02-28T08:11:39.37107Z","iopub.status.idle":"2023-02-28T08:11:39.725016Z","shell.execute_reply.started":"2023-02-28T08:11:39.371024Z","shell.execute_reply":"2023-02-28T08:11:39.723707Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We note that the age distribution for patients with breast cancer has all quartiles shifted by roughly +5 years compared to the population statistics.\n\nFor the other largely categorical common features, let us see if, at a glance, there is any noticeable statistical difference in cancer rate for different presentations of these features - first we make a small function to display this as a dataframe:","metadata":{}},{"cell_type":"code","source":"def make_meancount(x,y,df):\n    count_col=df.groupby(by=x,dropna=False)[y].count()\n    percentage_col=count_col/len(df)\n    mean_col=df.groupby(by=x,dropna=False)[y].mean()\n    out_df=pd.DataFrame({\"count\":count_col,\"%age of pop.\":percentage_col,\"mean/rate\":mean_col}).reset_index()\n    print(\"count and mean/rate of\",y,\"value wrt\",x,\"value\")\n    return out_df","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:39.727015Z","iopub.execute_input":"2023-02-28T08:11:39.727395Z","iopub.status.idle":"2023-02-28T08:11:39.734557Z","shell.execute_reply.started":"2023-02-28T08:11:39.727358Z","shell.execute_reply":"2023-02-28T08:11:39.733639Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"make_meancount(\"laterality\",\"cancer\",df)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:39.811764Z","iopub.execute_input":"2023-02-28T08:11:39.812412Z","iopub.status.idle":"2023-02-28T08:11:39.839584Z","shell.execute_reply.started":"2023-02-28T08:11:39.812373Z","shell.execute_reply":"2023-02-28T08:11:39.838263Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Taking the example of laterality, there doesn't appear to be a meaningful difference in cancer rate in images taken with a left vs right laterality. Let's run the same table for the other common features:","metadata":{}},{"cell_type":"code","source":"make_meancount(\"implant\",\"cancer\",df)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:40.090475Z","iopub.execute_input":"2023-02-28T08:11:40.091935Z","iopub.status.idle":"2023-02-28T08:11:40.112352Z","shell.execute_reply.started":"2023-02-28T08:11:40.091882Z","shell.execute_reply":"2023-02-28T08:11:40.11109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"make_meancount(\"site_id\",\"cancer\",df)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:40.259657Z","iopub.execute_input":"2023-02-28T08:11:40.260109Z","iopub.status.idle":"2023-02-28T08:11:40.280193Z","shell.execute_reply.started":"2023-02-28T08:11:40.260074Z","shell.execute_reply":"2023-02-28T08:11:40.278708Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"make_meancount(\"view\",\"cancer\",df)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:40.42585Z","iopub.execute_input":"2023-02-28T08:11:40.426255Z","iopub.status.idle":"2023-02-28T08:11:40.456766Z","shell.execute_reply.started":"2023-02-28T08:11:40.426221Z","shell.execute_reply":"2023-02-28T08:11:40.455122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In some of the cases above, the presence of a certain feature is too sparse (eg: view == LM, LMO, ML, AT) to make meaningful statistical statements, whilst amongst features representing a sizeable portion of the population there once again seems to be little meaningful difference in cancer rates. Intuitively, it suggests that the determination of cancer is mainly derived from analysis of the mammograms. Nevertheless, this is only a very superficial analysis and perhaps there are deeper correlations not visible at this level - hence, for our Neural Network training purposes we will still combine this metadata with the processed-images for training.","metadata":{}},{"cell_type":"markdown","source":"#### Metadata processing:\n\nWe now perform the following, with a view to eventually arrive at a purely numerical and normalised (where relevant) train df for use with training:\n* handle null values in \"age\"\n* binarise \"laterality\"\n* one-hot \"view\" and \"machine_id\"\n\nWe will choose to replace null values of age with the column mean:","metadata":{}},{"cell_type":"code","source":"mean_age=df[\"age\"].mean()\ndf[\"age\"]=df[\"age\"].fillna(mean_age)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:40.850276Z","iopub.execute_input":"2023-02-28T08:11:40.85072Z","iopub.status.idle":"2023-02-28T08:11:40.859049Z","shell.execute_reply.started":"2023-02-28T08:11:40.850681Z","shell.execute_reply":"2023-02-28T08:11:40.857608Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The laterality column is either L or R, let us remap these to integers 0 and 1 respectively:","metadata":{}},{"cell_type":"code","source":"L_R_map={\"L\":0,\"R\":1}\ndf[\"laterality\"]=df[\"laterality\"].map(L_R_map)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:41.180284Z","iopub.execute_input":"2023-02-28T08:11:41.180698Z","iopub.status.idle":"2023-02-28T08:11:41.194009Z","shell.execute_reply.started":"2023-02-28T08:11:41.180665Z","shell.execute_reply":"2023-02-28T08:11:41.192974Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Columns \"machine_id\" and \"view\" are categorical with multiple possible values - we shall apply one-hot encoding to each of these:","metadata":{}},{"cell_type":"code","source":"df[\"machine_id\"]=df[\"machine_id\"].astype(\"str\")\nCategorical_cols=[\"view\",\"machine_id\"]\nCategorical_OH_cols=pd.get_dummies(df[Categorical_cols])\nCategorical_OH_cols.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:41.445282Z","iopub.execute_input":"2023-02-28T08:11:41.445996Z","iopub.status.idle":"2023-02-28T08:11:41.53413Z","shell.execute_reply.started":"2023-02-28T08:11:41.445932Z","shell.execute_reply":"2023-02-28T08:11:41.53274Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we append the one-hot encoded columns to the dataframe:","metadata":{}},{"cell_type":"code","source":"Categorical_OH_list=list(Categorical_OH_cols)\ndf=pd.concat([df,Categorical_OH_cols],join=\"inner\",axis=1)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:41.745118Z","iopub.execute_input":"2023-02-28T08:11:41.745564Z","iopub.status.idle":"2023-02-28T08:11:41.764494Z","shell.execute_reply.started":"2023-02-28T08:11:41.74553Z","shell.execute_reply":"2023-02-28T08:11:41.763006Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The resulting train df, including all the processed columns but still including train-only columns, looks as below:","metadata":{}},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:42.079786Z","iopub.execute_input":"2023-02-28T08:11:42.080196Z","iopub.status.idle":"2023-02-28T08:11:42.10528Z","shell.execute_reply.started":"2023-02-28T08:11:42.080161Z","shell.execute_reply":"2023-02-28T08:11:42.104261Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Normalise dataframe and trim input columns\n\nWe will now trim down to a dataframe X of only common columns, and will form the labels dataframe Y:","metadata":{}},{"cell_type":"code","source":"NonOH_train_cols=[\"age\",\"laterality\",\"site_id\",\"implant\"]\nTrain_cols_list=NonOH_train_cols+Categorical_OH_list\nYlabel=\"cancer\"","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:42.408821Z","iopub.execute_input":"2023-02-28T08:11:42.409543Z","iopub.status.idle":"2023-02-28T08:11:42.416712Z","shell.execute_reply.started":"2023-02-28T08:11:42.4095Z","shell.execute_reply":"2023-02-28T08:11:42.414893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_df=df[Train_cols_list].copy()\nY_df=df[Ylabel].copy()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:42.588183Z","iopub.execute_input":"2023-02-28T08:11:42.588647Z","iopub.status.idle":"2023-02-28T08:11:42.611278Z","shell.execute_reply.started":"2023-02-28T08:11:42.588596Z","shell.execute_reply":"2023-02-28T08:11:42.609774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Number of features:\",len(list(X_df)))\nX_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:42.713652Z","iopub.execute_input":"2023-02-28T08:11:42.714133Z","iopub.status.idle":"2023-02-28T08:11:42.735052Z","shell.execute_reply.started":"2023-02-28T08:11:42.714093Z","shell.execute_reply":"2023-02-28T08:11:42.733787Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Y_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:42.877679Z","iopub.execute_input":"2023-02-28T08:11:42.878389Z","iopub.status.idle":"2023-02-28T08:11:42.896262Z","shell.execute_reply.started":"2023-02-28T08:11:42.87834Z","shell.execute_reply":"2023-02-28T08:11:42.894375Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"From dataframe X_df we will form another dataframe Xn_df where we choose to normalise the non -view and -machine-id columns, however in retrospect the only column that really requires normalisation is \"age\" due to its range of possible numerically-ordered values:","metadata":{}},{"cell_type":"code","source":"#normalise X metadata\nXn_df=X_df.copy()\nX_mean=X_df[NonOH_train_cols].mean()\nX_stdev=X_df[NonOH_train_cols].std()\nXn_df[NonOH_train_cols]=(X_df[NonOH_train_cols]-X_mean)/X_stdev\nXn_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:43.197503Z","iopub.execute_input":"2023-02-28T08:11:43.197946Z","iopub.status.idle":"2023-02-28T08:11:43.233775Z","shell.execute_reply.started":"2023-02-28T08:11:43.197912Z","shell.execute_reply":"2023-02-28T08:11:43.23256Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We shall add back the path columns to the X dataframes as later we shall write code that expects to extract the paths from the features dataframes:","metadata":{}},{"cell_type":"code","source":"#append the pngpaths as the last column to X_df and Xn_df\nX_df=pd.concat([X_df,df[\"pngpath_enh\"]],axis=1,join=\"inner\")\nXn_df=pd.concat([Xn_df,df[\"pngpath_enh\"]],axis=1,join=\"inner\")","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:43.511237Z","iopub.execute_input":"2023-02-28T08:11:43.511685Z","iopub.status.idle":"2023-02-28T08:11:43.527879Z","shell.execute_reply.started":"2023-02-28T08:11:43.511645Z","shell.execute_reply":"2023-02-28T08:11:43.526304Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Thus far we have run through all the metadata preprocessing steps for the training set, but we will also need to process the test data in the same way for inference. We collect the salient steps above into a function definition that can be applied to the test.csv in order to similarly preprocess it","metadata":{}},{"cell_type":"code","source":"def prep_normalised_X(inp_df,age_mean,\n                        Categorical_OH_col_names,#Non_OH_col_names,\n                        norm_mean,norm_stdev):\n    \n    df=inp_df.copy()\n    \n    df[\"age\"]=df[\"age\"].fillna(age_mean)\n    \n    L_R_map={\"L\":0,\"R\":1}\n    df[\"laterality\"]=df[\"laterality\"].map(L_R_map)\n    \n    df[\"machine_id\"]=df[\"machine_id\"].astype(\"str\")\n    \n    Categorical_col_prefixes=[\"view_\",\"machine_id_\"]\n    \n    df[Categorical_OH_col_names]=0 \n    \n    for i in range(len(df)):\n        row=df.iloc[i,:]\n        view_col_val=row[\"view\"]\n        machine_col_val=row[\"machine_id\"]\n        df[\"view_\"+str(view_col_val)][i]=1\n        df[\"machine_id_\"+str(machine_col_val)][i]=1\n    \n    #normalise\n    df[list(norm_mean.index)]=(df[list(norm_mean.index)]-norm_mean)/norm_stdev\n    \n    return df","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:43.820863Z","iopub.execute_input":"2023-02-28T08:11:43.821315Z","iopub.status.idle":"2023-02-28T08:11:43.831583Z","shell.execute_reply.started":"2023-02-28T08:11:43.821275Z","shell.execute_reply":"2023-02-28T08:11:43.830358Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We'll run the preprocessing routine on the test_df now:","metadata":{}},{"cell_type":"code","source":"test_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:44.117992Z","iopub.execute_input":"2023-02-28T08:11:44.118919Z","iopub.status.idle":"2023-02-28T08:11:44.13511Z","shell.execute_reply.started":"2023-02-28T08:11:44.118874Z","shell.execute_reply":"2023-02-28T08:11:44.133651Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_mean=X_df[NonOH_train_cols].mean()\nX_stdev=X_df[NonOH_train_cols].std()\n\nXn_test_df=prep_normalised_X(test_df.copy(),\n                             df[\"age\"].mean(),\n                             Categorical_OH_list,X_mean,X_stdev)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:44.287943Z","iopub.execute_input":"2023-02-28T08:11:44.288373Z","iopub.status.idle":"2023-02-28T08:11:44.323745Z","shell.execute_reply.started":"2023-02-28T08:11:44.288336Z","shell.execute_reply":"2023-02-28T08:11:44.322437Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Xn_test_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:44.496042Z","iopub.execute_input":"2023-02-28T08:11:44.496501Z","iopub.status.idle":"2023-02-28T08:11:44.524759Z","shell.execute_reply.started":"2023-02-28T08:11:44.496461Z","shell.execute_reply":"2023-02-28T08:11:44.523294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We wish to ensure the processed test data will have the same column structure as the Xn_df that will be used in training, hence the last few lines below, and note we also start to form a column of the submission df out of the \"prediction id\" column in the test.csv:","metadata":{}},{"cell_type":"code","source":"submission_df_detailed=Xn_test_df.pop(\"prediction_id\")\nXn_test_df=Xn_test_df[list(Xn_df.drop(columns=\"pngpath_enh\"))+[\"path\"]]\nXn_test_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:44.749076Z","iopub.execute_input":"2023-02-28T08:11:44.749555Z","iopub.status.idle":"2023-02-28T08:11:44.780234Z","shell.execute_reply.started":"2023-02-28T08:11:44.749514Z","shell.execute_reply":"2023-02-28T08:11:44.778794Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 5. Neural Network construction and initial train\n\nWe shall implement a neural network architecture in Pytorch. A simple Pytorch NN workflow may consist of the following steps:-\n* define a Dataset, and most importantly the get_item() method of the Dataset which determines the form of data that is fed into the model\n    * for vision tasks it is typical to include some final img-processing steps as required by the vision model into the Dataset definition, including whether or not to perform random image augmentation during training mode\n* define a Dataloader which determines how elements of the dataset shall be fed to the model\n    * Dataloaders make use of samplers to determine the (random) manner in which items of the Dataset are fed - these samplers can be customised, which we shall do in order to improve training on our dataset with sparse labels\n* define the model architecture\n* write a training loop using the model and DataLoader, including the optimiser and loss/evaluation functions used, as well as elements such as learning rate decay and/or regularisers\n* divide the data into a training and dev set, train the model and assess the outcome, then tweak hyperparameters and repeat\n\nFor the model training/validation cycle a typical approach to help robustly select the best model is to use k-fold validation - however, in this Notebook we shall only present results based on a single fold, as this Notebook is primarily just for learning purposes and we found running training cycles on Kaggle-supplied GPU to be extremely time consuming.\n\nWe shall now address the first step which is to define the Pytorch Dataset:\n\n#### Dataset definition\n\nOur processed train images have been saved as png files, however Pytorch expects tensor inputs for everything. Hence, let's first write a function that we will put into the get_item() method of our Dataset to grab the png files from their filepath and convert them into tensors - we'll also put a toggle in this function that will perform some slight image augmentation if the user specifies training mode:","metadata":{}},{"cell_type":"code","source":"def img_to_tensor_preprocess(img_pixels,training=False,\n                             re_size_shape=None,max_rotation=7.5):\n    img_out=transforms.ToTensor()(img_pixels)\n    img_out=(img_out.type(torch.float32))#.to(device)\n    \n    if training:\n        orig_img_HW=img_out.shape[1],img_out.shape[2]\n        rnd_angle=(2*np.random.rand()-1)*max_rotation\n        #rotate the image by a small angle about its centre - this function will return\n        #the section of the image with the same dimensions about the centre, ie: there will\n        #be some spaces at the corner(s) consisting of blank space that has been rotated in\n        img_out=transforms.functional.rotate(img_out,angle=rnd_angle) \n        crop_tuple=recrop_and_resize_post_rotation(orig_img_HW,rnd_angle)\n        #create a crop from the image centre such that there is no \"corner whitespace\"\n        #included as a result of the rotation\n        img_out=transforms.functional.center_crop(img_out,crop_tuple)\n        img_out=transforms.Resize(orig_img_HW)(img_out)\n        \n    if re_size_shape:\n        img_out=transforms.Resize(re_size_shape)(img_out)\n        \n    #normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\n    #parameters above are apparently the standard for torchvision model inputs (from: from:https://github.com/pytorch/examples/blob/97304e232807082c2e7b54c597615dc0ad8f6173/imagenet/main.py#L197-L198)\n    \n    #img_out=normalize(img_out)\n    \n    return img_out\n\ndef recrop_and_resize_post_rotation(input_HW,rotate_angle):\n    ##intuition: pursuant to the image being rotated about its centre by a small angle,\n    #this function works out how to crop from the centre so as to not include any whitespace\n    #introduced to the image as a result of the rotation\n    orig_angle_radians,new_angle_radians,scale_factor,crop_shape=[],[],[],[]\n    for i in range(2):\n        orig_angle_radians+=[np.arctan(input_HW[(i+1)%2]/input_HW[i])]\n        new_angle_radians+=[orig_angle_radians[i]+np.abs(rotate_angle*np.pi/180)]\n        scale_factor+=[np.abs(np.cos(new_angle_radians[i])/np.cos(orig_angle_radians[i]))]\n        crop_shape+=[int(input_HW[i]*scale_factor[i])]\n    return tuple(crop_shape)","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:11:45.059263Z","iopub.execute_input":"2023-02-28T08:11:45.059676Z","iopub.status.idle":"2023-02-28T08:11:45.07257Z","shell.execute_reply.started":"2023-02-28T08:11:45.059642Z","shell.execute_reply":"2023-02-28T08:11:45.071279Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's visualise a few examples of the rotation and cropping:","metadata":{}},{"cell_type":"code","source":"#check a few png images and their rotations\nnum_images=4\n\nnp.random.seed(3)\nrandom_indices=np.random.permutation(list(range(len(Xn_df))))[:num_images]\n    \nplt.figure()\nfig, axs = plt.subplots(num_images, 2, figsize=(9,6.5*num_images))\naxs = axs.flatten()\nfor i,index in enumerate(random_indices):\n    row=Xn_df.loc[index,:]\n    img_path=row.pngpath_enh\n    img_pixels=cv2.imread(img_path)\n    rotated_img_tensor=img_to_tensor_preprocess(img_pixels,training=True)\n    axs[2*i].set_title('Index:'+str(index)+\", Orig png\")\n    axs[2*i].imshow(img_pixels, cmap=\"gray\")\n    axs[2*i+1].set_title('Index:'+str(index)+\", Rotated and cropped tensor (as image)\")\n    axs[2*i+1].imshow(np.transpose(np.asarray(rotated_img_tensor),(1,2,0)), cmap=\"gray\")\nplt.tight_layout()\n","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:17:00.403305Z","iopub.execute_input":"2023-02-28T08:17:00.403853Z","iopub.status.idle":"2023-02-28T08:17:02.344166Z","shell.execute_reply.started":"2023-02-28T08:17:00.403807Z","shell.execute_reply":"2023-02-28T08:17:02.342914Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Notice that our rotation algorithm only uses a small angle of rotation (otherwise one may end up with images where parts of the breast which are the most relevant information get cropped away),\n\nAlso, note that in the code we had commented out the line to apply ***\"transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\"***. This line is commonly used with Resnet models as this replicates the parameters with which 223x223 images were normalised for training. However, once again the domains of use are quite different - Resnet was originally built to detect 1000 different kinds of objects in colour images, whereas our application is a specific domain for which we simply use Resnet as a trained feature extractor, hence on the one hand it may not be strictly necessary to include this line, but on the other hand we noted that its inclusion would cause our input images to be potentially \"overly contrasty\", ie: the imagery of the breast becomes all very white or very dark without much differentiation for the algorithm to learn from - the below block of code illustrates this:","metadata":{}},{"cell_type":"code","source":"#compare a few processed images and how they would appear if the standard Resnet normalisation\n#were also applied\nnormalize=transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\n\nnum_images=4\n\nnp.random.seed(2)\nrandom_indices=np.random.permutation(list(range(len(Xn_df))))[:num_images]\n    \nplt.figure()\nfig, axs = plt.subplots(num_images, 2, figsize=(9,6.5*num_images))\naxs = axs.flatten()\nfor i,index in enumerate(random_indices):\n    row=Xn_df.loc[index,:]\n    img_path=row.pngpath_enh\n    img_pixels=cv2.imread(img_path)\n    img_as_tensor=img_to_tensor_preprocess(img_pixels,training=False)\n    normalized_tensor=normalize(img_as_tensor)\n    axs[2*i].set_title('Index:'+str(index)+\", Orig png\")\n    axs[2*i].imshow(img_pixels, cmap=\"gray\")\n    axs[2*i+1].set_title('Index:'+str(index)+\", Normalized tensor (as image)\")\n    axs[2*i+1].imshow(np.transpose(np.asarray(normalized_tensor),(1,2,0)), cmap=\"gray\")\nplt.tight_layout()","metadata":{"execution":{"iopub.status.busy":"2023-02-28T08:25:36.715236Z","iopub.execute_input":"2023-02-28T08:25:36.715969Z","iopub.status.idle":"2023-02-28T08:25:38.506805Z","shell.execute_reply.started":"2023-02-28T08:25:36.715928Z","shell.execute_reply":"2023-02-28T08:25:38.504905Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"For the above reason, we chose to omit the use of the Resnet normalize line.\n\nHaving defined our img-to-tensor conversion function, with a toggle for training-mode augmentation, we can define our pytorch Dataset: this expects to take in the normalised Dataframe of metadata, a df including including just the image paths, and optionally a df of Labels:","metadata":{}},{"cell_type":"code","source":"##custom Dataset - takes input X and Y (optional) dataframes and df of image paths\n##if inp_type is \"png\" or \"dicom\" respectively, the path df should correspondingly point to\n#this type of image\nclass pd_df_toDataset(Dataset):\n    #must define __init__ to set the arguments of this class.\n    def __init__(self,X_df,Y_df,path_df,\n                 training=False,inp_type=\"png\",dicom_conv_res=(512,256)):\n        #X_df and Y_df are assumed to be fully preprocessed to be numerical\n    \n        #now define class methods that initialise upon instantiation\n        self.X_ds=torch.tensor(X_df.to_numpy(),dtype=torch.float32)#.to(device)\n        if Y_df is None:\n            self.Y_ds=None\n        else:\n            self.Y_ds=torch.reshape(torch.tensor(Y_df.to_numpy(),dtype=torch.float32),(-1,1))#.to(device)\n            \n        self.path_df=path_df\n        self.train=training\n        self.input_type=inp_type\n        self.conv_res=dicom_conv_res\n        \n    #Dataset class requires that we must define __len__ and __getitem__ methods\n    def __len__(self):\n        return len(self.X_ds)\n   \n    def __getitem__(self,idx):#outputs a row of X metadata, the Y label, and processed image as tensor\n        img_path=self.path_df.iloc[idx]\n        if self.input_type==\"png\":\n            img_pixels=cv2.imread(img_path)\n        elif self.input_type==\"dicom\":\n            img_pixels=dicom_processor(*dicom_to_pixels(img_path),\n                                       contrast_enhance=True, contrast_intensity=5,\n                                       flip_left_to_right_facing=True,close_crop=True,\n                                       resize_res=self.conv_res,png_rtn=True)\n        \n        #apply tensor conversion and apply final image transforms, incl randomisation\n        #for training set\n        img=img_to_tensor_preprocess(img_pixels,training=self.train)\n        \n        if self.Y_ds is None:\n            return self.X_ds[idx],img\n        else:\n            return self.X_ds[idx],self.Y_ds[idx],img","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Neural network architecture\n\nWe shall choose an NN architecture as follows: \n\n1. input image, passed to --> \n2. ResNet model (44 outputs), output concatenated with Xmetadata (20 inputs), passed to -->\n3. fully-connected Neural Network (64 inputs, hidden layer architecture [32,16,4])\n4. --> one real-valued output per sample \n5. --> apply sigmoid to get prediction of cancer probability.\n\nIn this order, we shall define the building blocks of the architecture:\n\n##### Pre-trained ResNet layer\n\nWe will use a pretrained model from torchvision. However, the original models were designed to produce 1000 outputs being the probabilities of the input image corresponding to 1000 broad classes of objects - for our purposes, we wish to use the feature-extraction properties of the pretrained outputs but do not need the original 1000 outputs, instead we will replace the final fully-connected layer of the ResNet with a custom fully-connected layer to output 44 outputs. Furthermore, whilst we will freeze the rest of the weights of the pre-trained model, this new layer will have trainable weights.","metadata":{}},{"cell_type":"code","source":"#helper function to instantiate the vision model,\n#replacing the final fc layer with one that has a customised #outputs:\ndef make_Vizmodel(num_outputs):\n    #weights = ResNet50_Weights.DEFAULT #kaggle needs update for this\n    #vizmodel = resnet50(weights=weights) #kaggle needs update for this\n    vizmodel=resnet50(pretrained=True) #deprecated, but works for Kaggle\n    for param in vizmodel.parameters():\n        param.requires_grad = False #lock down existing layer weights\n\n    num_ftrs = vizmodel.fc.in_features \n    #fc.in_features is an internal variable in the torchvision model that contains the number\n    #of incoming features to the final fc layer (which by default outputs 1000 nodes, but we\n    #will replace this)\n    vizmodel.fc = nn.Linear(num_ftrs, num_outputs)\n    #Note: Parameters of newly constructed modules have requires_grad=True by default\n    \n    return vizmodel","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Nest we will build the FC NN model that the vision model's output will feed into. Note that, whilst in the below we have allowed for the FC model to include dropout and leak-ReLU layers, in this Notebook we shall only make use of the model instantiated without dropout and with regular ReLU.","metadata":{}},{"cell_type":"code","source":"#FC NN definition\n\"\"\"Custom fully-connected nn creator\nfwd pass repeatedly applies the fwdblock according to the layer architecture supplied\nConstructor takes the following inputs:\n - input_dims\n - the hidden-layer architecture, input as a list\n - flag to indicate whether batchnorm layers should be enabled (no if we are performing\nbatch training)\n - leakyReLU parameter (the slope when x<0)\n - dropout parameter (the proportion of input nodes to \"switch off\" or set to zero)\n\"\"\"\n#component/helper function for custom nn.Module class\ndef fwdblock(input_dim,output_dim,final_layer=False,\n             batchnorm=True,leaky_param=0.0,dropout_p=0.0):\n    \n    layer_list=[]\n    layer_list.append(nn.Dropout(p=dropout_p,inplace=True))\n    layer_list.append(nn.Linear(input_dim, output_dim))\n    if final_layer is False:\n        if batchnorm is True:\n            layer_list.append(nn.BatchNorm1d(output_dim))\n        layer_list.append(nn.LeakyReLU(leaky_param,inplace=True))\n    \n    return nn.Sequential(*layer_list)\n\nclass FCnn_wLayers(nn.Module):\n    \n    def __init__(self, input_dim=64,layers=[32,16,4],\n                 batchnorm=True,leaky_param=0.0,dropout_p=0.0):\n        super(FCnn_wLayers, self).__init__()\n        #functions that inherit from __init__ args such as input_dim should be set here\n        #these functions will also be what shows up when the model is Printed\n        self.L1=fwdblock(input_dim, layers[0],\n                         batchnorm=batchnorm,leaky_param=leaky_param)\n        self.numLayers=len(layers)\n        self.Lmid=[]\n        for n in range(self.numLayers-1):\n            self.Lmid.append(\n                fwdblock(layers[n],layers[n+1],\n                         batchnorm=batchnorm,\n                         leaky_param=leaky_param)\n            )\n        self.Lfinal=fwdblock(layers[-1],1,final_layer=True,\n                             batchnorm=batchnorm,leaky_param=leaky_param)\n        \n        self.Lmid_layers=nn.Sequential(*self.Lmid) # recall * is the unpacking operator\n        #note that if n=1 and hence self.Lmid is [], self.Lmid_layers will be the identity operator\n        \n    #mandatory to define the forward pass\n    def forward(self,inpFeatures):\n        #inpFeatures is the n-Samples of feature Vectors with M features (ie: n x m input tensor)\n        yOut=self.L1(inpFeatures)\n        yOut=self.Lmid_layers(yOut)\n        yOut=self.Lfinal(yOut)\n        return yOut","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Nest we write a function to combine the two model forwards in the intended order given the necessary inputs, and a helper function that instantiates the Combined Model together with the combined forward:","metadata":{}},{"cell_type":"code","source":"#helper function to take a batch of meta-features X and images as tensors and\n#output batch of outputs / probabilities as logits\ndef combined_model_fwd(batchX,batch_imgs,FCmodel,Vizmodel):\n    assert len(batchX)==len(batch_imgs)\n    \n    #apply vision model to the img_minibatch\n    img_batch_encoding=Vizmodel(batch_imgs)\n    \n    #concatenate the img encoding with the batch of X metadata\n    currX=torch.cat((batchX,img_batch_encoding),1)\n    #run the concatenated X through the FC model\n    currPred=FCmodel(currX)\n    \n    return currPred\n\n#Combined model instantiator - uses the combined model fwd\nclass Viz_n_FC_model(nn.Module):\n    def __init__(self, vizmodel_output_dim=44,Xmeta_input_dim=20,\n                 layers=[32,16,4],\n                 batchnorm=True,leaky_param=0.0,dropout_p=0.0):\n        super(Viz_n_FC_model, self).__init__()\n        #functions that inherit from __init__ args such as input_dim should be set here\n        #these functions will also be what shows up when the model is Printed\n        self.vizmodel=make_Vizmodel(vizmodel_output_dim)\n        self.FCmodel=FCnn_wLayers(input_dim=Xmeta_input_dim+vizmodel_output_dim,\n                                  layers=layers,\n                                  batchnorm=batchnorm,leaky_param=leaky_param,\n                                  dropout_p=dropout_p)\n        \n    #mandatory to define the forward pass\n    def forward(self,inpXmeta,inp_imgBatch):\n        #inpFeatures is the n-Samples of feature Vectors with M features (ie: n x m input tensor)\n        fwd=combined_model_fwd(inpXmeta,inp_imgBatch,self.FCmodel,self.vizmodel)\n        return fwd","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The models and their forward passes have all been defined - given a batch of $m$ inputs, the output will be a tensor of $m$ real-valued outputs which can be mapped to a probability using the sigmoid function to make a prediction. Bearing this in mind, before we define the training loop, let us create a few helper functions and definitions that we will need, including a function to generate evaluation metrics according to the Probabilistic F-score that will be used in the competition for scoring:","metadata":{}},{"cell_type":"code","source":"\"\"\"\nvariety of defns and helper fns\n\"\"\"\n#specify the device name - in Pytorch we must always take care to consistently case models\n#and their tensor inputs to the same type of device\ndevice=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n#helper function for prediction from logits input\ndef yPredict(yOut):\n    return nn.Sigmoid()(yOut)\n\n\ndef evalmetrics(predictions,labels,style=\"prob\",beta=1.0):\n    #outputs fscore/precision/recall in either probabilistic or traditional version\n    #beta is a scaler to favour either precision or recall.\n    #beta=1 is balanced, <1 favours precision, >1 favours recall\n    #style=\"prob\" denotes probabilistic output, \"trad\" denotes traditional\n    #can take tensor input\n    \n    ##with edits on: https://www.kaggle.com/code/sohier/probabilistic-f-score/notebook\n    assert style==\"prob\" or style==\"trad\"\n    \n    y_true_count = 0\n    tp,fp=0,0\n    \n    tensor_flag=(type(predictions)==torch.Tensor)\n    \n    for idx in range(len(labels)):\n        if tensor_flag:\n            pred=predictions[idx].item()\n        else:\n            pred=predictions[idx]\n        \n        if style==\"trad\":\n            pred=int(np.round(pred,0))\n        \n        if (labels[idx]):\n            y_true_count += 1\n            tp += pred\n        else:\n            fp += pred\n            \n    beta_squared = beta * beta\n    if tp+fp>0:\n        precision=tp/(tp+fp)\n    else:\n        precision=0\n    \n    if y_true_count>0:\n        recall=tp/y_true_count\n    else:\n        recall=1.0\n    \n    if (precision > 0 and recall > 0):\n        fscore = (1 + beta_squared) * (precision * recall) / (beta_squared * precision + recall)\n    else:\n        fscore=0\n    \n    return fscore,precision,recall\n\n#run through a Dataset via its Dataloader to generate a batch of model logits-outputs together\n#with the true y_labels - these can then be passed into the evalmetrics function to generate\n#metrics. Intended for use with a DataLoader loaded with the dev or test set.\ndef make_yhatlogits_y_batch(model,DL,torch_device):\n    yhatlogits_batch,y_batch=None,None\n    for Z in DL:\n        Xmeta_minibatch,Y_minibatch,pngs_minibatch=Z[0].to(torch_device),Z[1].to(torch_device),Z[2].to(torch_device)\n        minibatch_output=model(Xmeta_minibatch,pngs_minibatch)\n        if yhatlogits_batch is None:\n            yhatlogits_batch=minibatch_output\n            y_batch=Y_minibatch\n        else:\n            yhatlogits_batch=torch.cat((yhatlogits_batch,minibatch_output))\n            y_batch=torch.cat((y_batch,Y_minibatch))\n    \n    return yhatlogits_batch,y_batch","metadata":{"execution":{"iopub.status.busy":"2023-02-28T14:04:05.831759Z","iopub.execute_input":"2023-02-28T14:04:05.832165Z","iopub.status.idle":"2023-02-28T14:04:05.848007Z","shell.execute_reply.started":"2023-02-28T14:04:05.83213Z","shell.execute_reply":"2023-02-28T14:04:05.846496Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Neural Network training loop\n\nWe will now write the training loop for our NN. Note a few of the choices made in the implementation:-\n* use of Adam optimizer\n* use of exponential learning rate decay\n* the Loss function used is [Binary Cross Entropy with logits loss](https://pytorch.org/docs/stable/generated/torch.nn.BCEWithLogitsLoss.html) - this can take an argument pos_weight which if >1 makes the loss function prioritise recall (weights the term when true label = 1 more heavily, hence penalising false negative predictions more heavily) and if <1 prioritises precision (weights the term when true label = 0 more heavily, hence penalising false positive predictions more heavily)\n\nThis training function can also be fed a dev set, in which case it will also evaluate metrics over the dev set at every interval of minibatch_display_step number of minibatches, and can also plot charts of the training / dev performance for loss, fscore, precision and recall,","metadata":{}},{"cell_type":"code","source":"\"\"\"\nCustom function to train an NN premised on the following:-\n    - for minibatch tracking, tracks: minibatch mean loss\n    - for epochal / batch metrics tracking, tracks: loss, acc, prec, recall, fscore\n    - Adam optimiser and Exponential learning rate decay are used\nArguments are:\n    - model: The model instance name\n        note: only model instantiation is needed\n    - dataIterable: The data iterable (typically dataloader or tqdm version thereof)\n        - the underlying dataset is expected to be an output of pd_df_toDataset function\n    - n_epochs: the number of epochs to train\n    - lr: the Learning Rate for the optimiser (currently hardcoded as Adam)\n    - Dev_DL: a dataloader containing the dev dataset\n        - the underlying dataset is expected to be the output of pd_df_toDataset function\n    - lr_decay_gamma: the gamma in the exp lr decay rate\n    - l2penalty: the lambda in L2 regularisation\n    - epoch_calc_steps: # of epoch steps at which to calculate batch metrics\n    - epoch_calc_display: flag to display epoch calcs\n    - minibatch_display: flag as to whether minibatch metrics (currently just loss) are also posted\n    - minibatch_display_step: # of minibatch steps to display minibatch metrics\n    - display_results: flag to indicate whether to display training results, ie:\n        print the graphs and time-taken\nReturns:\n    - tuple of sampled-Epochs, hist of training loss and metrics, and dev loss and metrics (if applicable)\n    - if no batch_Train_tuple is supplied, returns 0\n\"\"\"\ndef Train_NN(model,\n             dataIterable,\n             n_epochs=50,\n             lr=0.001,\n             Dev_DL=None,\n             lr_decay_gamma=1.0, #exponential decay in scheduler\n             l2penalty=0.0, #lambda term in L2 regularisation\n             BCEweight=1.0, #weight in BCE loss function - <1 prioritises precision\n             epoch_calc_steps=10,\n             epoch_calc_display=True,\n             minibatch_display=False,\n             minibatch_display_step=16,\n             calc_minibatch_train_metrics=True,\n             display_results=True\n            ):\n    \n    ##define Optimiser here, which may include regularization weight\n    optimiser=torch.optim.Adam(model.parameters(), lr=lr, weight_decay=l2penalty)\n    ##Scheduler Code goes here (example scheduler used below):\n    scheduler = torch.optim.lr_scheduler.ExponentialLR(optimiser, gamma=lr_decay_gamma) \n    #set the criterion function\n    pos_weight=torch.ones([1])*BCEweight\n    criterion = torch.nn.BCEWithLogitsLoss(pos_weight=pos_weight).to(device)\n    \n    #batch_Dev_tuple represents a \"manageable\" set of dev data to run metrics on\n    if Dev_DL is not None:\n        batch_dev_metrics_flag=True\n    else:\n        batch_dev_metrics_flag=False\n                \n    if model.training is False:\n        model.train()\n    \n    cur_step = 0\n    mean_loss = 0\n    mean_metrics=np.array((0,0,0))\n    plot_metric_names=[\"Loss\",\"Fscore\",\"Precision\",\"Recall\"]\n\n    if calc_minibatch_train_metrics or batch_dev_metrics_flag or minibatch_display:\n        minibatch_calc_flag=True\n        epoch_progress=0\n        d_epoch=minibatch_display_step/len(dataIterable)\n    else:\n        minibatch_calc_flag=False\n    \n    #list of variables for metrics tracking (classifier task version)\n    x_axis=[] \n    train_metrics=[[],[],[],[]] #intended to contain 4 arrays, each representing\n    #time-series of the training/dev loss/fscore/precision/recall respectively\n    dev_metrics=[[],[],[],[]]\n    \n    timeSta=time.time()\n    \n    for epoch in range(n_epochs):\n        \n        #in each epoch we should run through all batches in the Dataloader:\n        for XY in tqdm(dataIterable):#cycling through the dataloader (minibatches)\n            currXmeta,currY,batchpngs = XY[0].to(device),XY[1].to(device),XY[2].to(device)\n            cur_batch_size = len(currY)\n            \n            currOut=model(currXmeta,batchpngs)\n            \n            # Zero out the gradients in optimiser before backpropagation\n            optimiser.zero_grad()\n\n            # Calculate current loss from running inputs into FCmodel\n            cur_loss=criterion(currOut,currY)\n            \n            # Update gradients - note both the FCmodel and final FC layer of Vizmodel\n            #(which requires grad) will be taken into account\n            cur_loss.backward(retain_graph=True)\n            \n            # Update optimizer (this updates all weights according to the gradients)\n            optimiser.step()\n            \n            # create history of loss and eval metrics on the dev-set at every\n            #minibatch calc step\n            if minibatch_calc_flag:\n                # Keep track of the average loss\n                mean_loss +=cur_loss.item() / minibatch_display_step\n                model.eval()\n                with torch.no_grad():\n                    currPred=yPredict(currOut)\n                    minibatch_evalmetrics=evalmetrics(currPred,currY)\n                    mean_metrics=mean_metrics+np.array(minibatch_evalmetrics)/minibatch_display_step\n                model.train()\n                \n                if (cur_step+1) % minibatch_display_step == 0:\n                    epoch_progress+=d_epoch\n                    x_axis+=[epoch_progress]\n                    \n                    if minibatch_display:\n                        print(f\"Epoch prog: {epoch_progress}, Step {cur_step+1}: Mean Loss: {mean_loss}\")\n                    \n                    if calc_minibatch_train_metrics:\n                        train_metrics[0]+=[mean_loss]\n                        for i in range(3):\n                            train_metrics[i+1]+=[mean_metrics[i]]\n                    if batch_dev_metrics_flag:\n                        model.eval()\n                        with torch.no_grad():\n                            devOut,Y_dev_Tensor=make_yhatlogits_y_batch(model,Dev_DL,\n                                                                  torch_device=device)\n                            \n                            dev_metrics[0]+=[criterion(devOut,Y_dev_Tensor).item()]\n                            devPred=yPredict(devOut)\n                            metrics=evalmetrics(devPred,Y_dev_Tensor)\n                            \n                            for i in range(3):\n                                dev_metrics[i+1]+=[metrics[i]]\n                        model.train()\n                    \n                    mean_loss = 0\n                    mean_metrics = np.array((0,0,0))\n                cur_step += 1\n                #end if-block \"minibatch_calc_flag\"\n            \n        #insert code below for per-Epoch tasks\n        \n        ##If Scheduler is used, scheduler step would appear here (typically is an epoch-like frequency of update)\n        scheduler.step()\n    \n    #Training Loop over all Epochs completed, now insert end-of-training code\n    timeEnd=time.time()\n    \n    if display_results is True:\n        #code to print charts of per-epoch training progress\n        \n        plt.figure()\n        fig, axs = plt.subplots(2, 2, figsize=(18,18))\n        axs = axs.flatten()\n        for i in range(4):\n            if calc_minibatch_train_metrics is True:\n                axs[i].plot(x_axis,train_metrics[i],label=\"Train (averaged)\")\n                axs[i].plot([],[],label=\"Dev (batch)\")\n            if batch_dev_metrics_flag is True:\n                ax2=axs[i].twinx()\n                ax2.plot(x_axis,dev_metrics[i],label=\"Dev (batch)\",color=\"orange\")\n            axs[i].set_title(plot_metric_names[i]+\" training progression\")\n            axs[i].set_ylabel(plot_metric_names[i])\n            axs[i].set_xlabel(\"Epoch progress\")\n            axs[i].legend()\n    \n    #code to output metrics history\n    print(\"Time Taken: %.2f secs\" %(timeEnd-timeSta))\n\n    if calc_minibatch_train_metrics or batch_dev_metrics_flag:\n        output=[x_axis]\n        if calc_minibatch_train_metrics:\n            output+=[train_metrics]\n        if batch_dev_metrics_flag:\n            output+=[dev_metrics]\n        return tuple(output)\n    else:\n        return 0","metadata":{"execution":{"iopub.status.busy":"2023-02-28T14:04:07.996228Z","iopub.execute_input":"2023-02-28T14:04:07.996645Z","iopub.status.idle":"2023-02-28T14:04:08.02972Z","shell.execute_reply.started":"2023-02-28T14:04:07.99661Z","shell.execute_reply":"2023-02-28T14:04:08.028354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We also write a function Make_NN which, when fed tuples of the training and dev dfs and the required hyperparameters, \"puts everything together\" by instantiating the NN models and training them: ","metadata":{}},{"cell_type":"code","source":"\"\"\"\ncustom fn to: \ni) define the Vision and FC NN models\nii) train them\niii) generate evaluation metrics\n\nmain inputs:\ntrain_tuple_df: assumed to be a tuple of (X_train_df,Y_train_df) where the last column\nof X_train_df is the filepaths of each processed mammogram in png format\ndev_tuple_df: optional, but otherwise assumed to be same format as train_tuple_df\n\nnoteworthy inputs:\nadj_final_bias : if True, applies an adjustment to the bias in the final output unit of the\n    neural network so as to `statistically minimise' the initial loss when the product of BCEweight and\n    ratio of positive labels over negative labels is not 1\nuse_cust_sampler : if True, applies the \"target ratio sampler\" to the training Dataloader with\n    target ratio equal to the supplied tgt_pos_ratio\ntrain_ds_randrotate : if True, applies random rotation as image augmentation to each image in the\n    training set as described further above\n\"\"\"\n\ndef Make_NN(train_tuple_df,dev_tuple_df,#feature & labels inputs\n            layer_archt,leaky_param,dropout_p,#FC NN-model parameters\n            num_epochs,lr,halflife,l2penalty,BCEweight,#training parameters\n            batch_size=1024,vizmodel_FClayer_output=44,\n            minibatch_display_step=16,\n            random_state=0,display_result=True,\n            adj_final_bias=False,#whether to adjust final bias to address train-data imbalance\n            use_cust_sampler=False,tgt_pos_ratio=0.5,sampler_rnd_state=0,#cust sampler parameters\n            train_ds_randrotate=False,\n            df_path_columnname=\"pngpath\"):\n    \n    X_train_df=train_tuple_df[0].copy()\n    Y_train_df=train_tuple_df[1].copy()\n    train_num_pos=Y_train_df.sum().item()\n    pngpath_train_df=X_train_df.pop(df_path_columnname)\n    \n    Train_DS=pd_df_toDataset(X_train_df,Y_train_df,\n                             pngpath_train_df,training=train_ds_randrotate)\n    \n    Dev_DL=None\n    \n    if dev_tuple_df is not None:\n        X_dev_df=dev_tuple_df[0].copy()\n        Y_dev_df=dev_tuple_df[1].copy()\n        pngpath_dev_df=X_dev_df.pop(df_path_columnname)\n        \n        Dev_DS=pd_df_toDataset(X_dev_df,Y_dev_df,pngpath_dev_df)\n        Dev_DL=DataLoader(Dev_DS,batch_size=batch_size)\n    \n    if use_cust_sampler:\n        cust_sampler=tgt_ratio_sampler(Train_DS,train_num_pos,tgt_pos_ratio,\n                                  random_state=sampler_rnd_state)\n    else:\n        cust_sampler=None\n    \n    dataIter = DataLoader(#use custom_sampler\n        Train_DS,\n        batch_size=batch_size,num_workers=mp.cpu_count(),\n        sampler=cust_sampler\n    )\n\n    if random_state is None:\n        torch.random.seed()\n        np.random.seed()\n    else:\n        torch.manual_seed(random_state)\n        np.random.seed(random_state)\n    \n    num_meta_features=len(list(X_train_df))\n    \n    #create the combined viz and FC model\n    model=Viz_n_FC_model(vizmodel_output_dim=vizmodel_FClayer_output,\n                         Xmeta_input_dim=num_meta_features,\n                 layers=layer_archt,\n                 batchnorm=True,leaky_param=leaky_param,dropout_p=dropout_p)\n    \n    if adj_final_bias is True:\n        init_bias=model.FCmodel.Lfinal[1].bias.data\n        if use_cust_sampler:\n            init_bias_value=np.log(BCEweight*tgt_pos_ratio/(1-tgt_pos_ratio))\n        else:\n            num_train_pos=Y_train_df.sum().item()\n            num_train_neg=len(Y_train_df)-num_train_pos\n            init_bias_value=np.log(BCEweight*num_train_pos/num_train_neg)\n        \n        model.FCmodel.Lfinal[1].bias.data=torch.tensor(torch.ones_like(init_bias)*init_bias_value,\n                                                       dtype=torch.float32)\n    \n    model.to(device)\n    \n    if halflife>0:\n        gamma=np.exp(-np.log(2)/halflife) #this is the per-epoch learning rate multiplier\n    else:\n        gamma=1.0\n    \n    print(\"training...\")\n    #train the model from Training Function (outputs a modelHist similar to TF version)\n    modelHist=Train_NN(model,\n                       dataIter,n_epochs=num_epochs,lr=lr,\n                       lr_decay_gamma=gamma,l2penalty=l2penalty,\n                       Dev_DL=Dev_DL,\n                       epoch_calc_display=False,\n                       minibatch_display=False,\n                       minibatch_display_step=minibatch_display_step,\n                       calc_minibatch_train_metrics=True,\n                       display_results=display_result,\n                       BCEweight=BCEweight\n                      )\n\n    #output final Training and Dev set metrics\n    model.eval(); #use semi-colon to avoid throwing architecture summary to console\n    \n    with torch.no_grad():\n        Y_dev_logits_Tensor,Y_dev_Tensor=make_yhatlogits_y_batch(model,Dev_DL,\n                                                                 torch_device=device)\n        Y_dev_pred_Tensor=yPredict(Y_dev_logits_Tensor)\n        dev_metrics=evalmetrics(Y_dev_pred_Tensor,Y_dev_Tensor)\n        print(\"Dev metrics: fscore %.6f, precision %.6f, recall %.6f\" %dev_metrics)\n    \n    #return the final batch dev metrics, the trained models, and modelHist\n    return dev_metrics,model,modelHist","metadata":{"execution":{"iopub.status.busy":"2023-02-28T14:04:09.207519Z","iopub.execute_input":"2023-02-28T14:04:09.207981Z","iopub.status.idle":"2023-02-28T14:04:09.228547Z","shell.execute_reply.started":"2023-02-28T14:04:09.207941Z","shell.execute_reply":"2023-02-28T14:04:09.227493Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note for now that the code of Make_NN above includes options for a number of features that we shall discuss in more detail later that are mainly to help handle imbalanced datasets, such as:\n* adj_final_bias : toggles whether to adjust the bias in the final output neuron in order to \"statistically minimise\" the initial loss\n* use_cust_sampler : whether or not to use a customised sampler with the dataloader\n\n##### Neural Network initial train\n\nWe are now in a position to try an initial training run of our neural network - let us first create a train/dev data-split, choosing a dev set size of 2048 samples. Once again, ideally we would make a k- such splits and train / evaluate on each of them in order to judge model performance, but in this notebook we shall simply demonstrate the use of a single fold:-","metadata":{}},{"cell_type":"code","source":"#basic train/dev split setup\nX_train,X_dev,Y_train,Y_dev=train_test_split(Xn_df,Y_df,\n                                             test_size=2048,random_state=0)\n\ntrain_length, train_cancer_count, train_cancer_rate = len(Y_train),Y_train.sum(), Y_train.mean()\ndev_length,dev_cancer_count, dev_cancer_rate = len(Y_dev),Y_dev.sum(), Y_dev.mean()\n\nprint(\"Train set: total size %d, cancer count %d, cancer rate %.4f\" \n      %(train_length, train_cancer_count, train_cancer_rate))\nprint(\"  Dev set: total size %d, cancer count %d, cancer rate %.4f\" \n      %(dev_length, dev_cancer_count, dev_cancer_rate))","metadata":{"execution":{"iopub.status.busy":"2023-02-28T13:08:35.956223Z","iopub.execute_input":"2023-02-28T13:08:35.956648Z","iopub.status.idle":"2023-02-28T13:08:35.985524Z","shell.execute_reply.started":"2023-02-28T13:08:35.956615Z","shell.execute_reply":"2023-02-28T13:08:35.984473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The code block below will instantiate and train our prescribed model for N epochs with initial learning rate of 0.001 and minibatch size of 128 - the pos_weight in the loss function is neutral and default dataloader sampler is used, nor are training images augmented. Feel free to run this code-block to try - on Kaggle using P100 GPU it runs in about 1hour:","metadata":{}},{"cell_type":"code","source":"r\"\"\"\nExample of NN training\n======================\n-dataloader is as per the actual distribution of pos/neg samples (~2% cancer rate)\n-BCE wgt of 1.0 (neutral)\n\"\"\"\n#inputs\ntrain_tuple_df=(X_train,Y_train)\ndev_tuple_df=(X_dev,Y_dev)\n\n#training and architecture params\nnum_epochs=5\nbatch_size=128\nlayer_archt=[32,16,4]\nlr=0.001 #learning rate\nhalflife=100 #exponential decay of learning-rate such that by 100 epochs the lr will be halved\nminibatch_display_step=2048/batch_size\n\n#imbalanced data handling params\nBCEweight=1.0\nuse_cust_sampler=False\nadj_final_bias=False\nrand_rotate_flag=False\n\n#unused and/or static params\nleaky_param, dropout_p, l2penalty=0.0,0.0,0.0\npath_columnname=\"pngpath_enh\"\n\nwith contextlib.redirect_stderr(io.StringIO()):\n    metrics,model,model_hist=Make_NN(\n        train_tuple_df,dev_tuple_df,#feature & labels inputs\n        layer_archt,leaky_param,dropout_p,#FC NN-model parameters\n        num_epochs,lr,halflife,l2penalty,BCEweight,#training parameters\n        batch_size=batch_size,minibatch_display_step=minibatch_display_step,\n        display_result=True,\n        adj_final_bias=adj_final_bias,\n        use_cust_sampler=use_cust_sampler,train_ds_randrotate=rand_rotate_flag,\n        df_path_columnname=path_columnname\n    )","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We attach images of the output results from the above:\n![train_Img1a.png](attachment:90328186-918a-44a7-a695-08e3e97631a6.png)\n![train_Img1b.png](attachment:e4ac4a35-4fdc-4334-aaf4-486e16efd028.png)\n\nThe training metric values are on the left axis of the charts whilst dev metrics are on the right axis. Notice that, although both train and dev losses seem to pleasantly decline over the 5 epochs, the training fscore pretty much just moves around like noise and in absolute terms bare improves - with a training set performance like this, there isn't really much point to even discuss the dev set performance.\n\n##### Loss declines but evaluation metrics (eg: Fscore) don't improve?\n\nWhy does the loss decline so pleasantly despite the model obviously not really learning anything? At initialisation, with all weights randomly set, the model will essentially make random guesses centred around 0.5 - suppose the model were to make a prediction vector that is simply always 0.5 (call this $constPred=0.5$), then the probabilistic fscore would be:\n\n$pTP = constPred \\times numPos, pFP = constPred \\times numNeg$\n\n$pPrecision = \\frac{pTP}{pTP+pFP}=\\frac{constPred \\times numPos}{constPred \\times (numPos+numNeg)}=\\frac{numPos}{n}$\n\n$pRecall = \\frac{pTP}{numPos} = constPred$\n\n$pFscore = \\frac{2 \\times pPrecision \\times pRecall}{pPrecision+pRecall}=2 \\times constPred \\times \\frac{posRatio}{posRatio+constPred}$\n\nsetting $posRatio = \\frac{numPos}{n}$ and where $numPos$ is the number of positive samples in the dataset, $numNeg$ is the number of negative samples and $n = numPos+numNeg$ is the total number of samples in the dataset.\n\nHence, given that the fraction of true labels in the training dataset (ie: $\\frac{numPos}{n}$ is ~2%, this gives an initial probabilistic fscore of around 3.846% - indeed, we can roughly see the training fscore hovering about this value, meaning that we are just making random guesses.\n\nThe first thing that this struggling model learns is that the simplest way to reduce the loss is simply to *reduce the mean* of its random guesses, eg: from random guesses averaging 0.5, make the random guesses average lower at 0.4, etc etc. This trivially lowers the BCE loss since the penalty for cases where the true label is zero (the vast majority) are penalised far less. Imagining a model that shifts to random guesses with a mean of around 0.1 (roughly corresponds to the final training recall score in the charts above), by the same calculation above its probabilistic fscore would be around 3.333% - not actually that much different from our initial pFscore and well within the realm of the fluctuations we observe. Hence, this portion of model learning satisfies the mathematical objective function but in substance is not really learning anything useful other than the fact that \"positive true labels are sparse\".\n\n##### Strategies for improving performance on imbalanced datasets\n\nIn our training code above we have built-in two possible mechanisms to improve the training performance on imbalanced datasets, both of which we will proceed to discuss:\n\n1. **Adjustment to initial bias in final neuron** - as discussed above, a randomly initialised model will tend to produce random guess of probability ~0.5, a reasonable starting point if the data were *balanced* with 50% of samples being positive. However, as seen above, in the presence of an imbalanced dataset the path of least resistance is for the model to simply \"learn sparsity\" and reduce the average of its random guesses to something closer to the actual distribution of positives within the data, but essentially still making random guesses - this stage of \"lowest hanging fruit\" will likely occur before the model can learn anything more discerning. By adjusting the bias term in the final neuron, we can essentially help the model to learn sparsity instantly by tuning the initial outputs to have a random average that is equal to the true ratio of positives in the population - indeed, this would \"statistically minimise\" the initial loss, hence the model can immediately begin to learn other things.\n\n2. **Over-sampling of positive samples** - another method is, in any given epoch of training, to ensure the model sees as many positive examples as negative examples by over-sampling the positive samples. This allows training to proceed as though the underlying dataset were balanced, a far easier situation for the model to commence learning. The downside is that the model will overfit more rapidly to the few positive samples since these are being over-sampled - however, the hope is that with variance reduction techniques we can still get the model to reasonably generalise.\n\nIn my own efforts, both these methods were attempted, however I found that over-sampling was much more effective to at least arrive at a situation where the model can learn the training set, failing which there is little basis to hope that a good generalised performance can be achieved.\n\nTo achieve this, the default Sampler in the Pytorch Dataloader should be replaced with one that can achieve the desired oversampling. In the next section, we describe the customised sampler class that was built for this purpose.\n\n### 6. Customised data sampler for imbalanced data\n\nThe code for our customised sampler, to be used with the Pytorch Dataloader, is as below. The assumed inputs are an ordered dataset such that the first num_pos samples are all the positive-classified members of the dataset, and the value of num_pos is given to the function. A target ratio is also supplied, this being the desired ratio of positive samples to be seen in a single training epoch, achieved by over-sampling. \n\nThis particular implementation produces a \"fixed epoch length\" by presuming that the epoch ends when all the *numNeg* negative samples have been seen once, where this represents a proportion *1 - tgtRatio* of the whole epoch size - since *numNeg* is known, a deterministic value for the whole epoch length can be fixed (for a balanced target ratio of 0.5 and a true ratio for positives of 0.02, the modified epoch length will be almost double the original size of the dataset).\n\nAs for the remaining *modifiedEpochLength - numNeg* samples seen during the epoch: these are oversampled from the limited number of positive samples. The oversampling is performed in such a manner that all available positive samples are fully cycled through once-each before the entire batch is made available for further such once-each oversampling. This avoids the possiblity that, by chance, positive example A is repeated in the epoch twice as often as positive example B, 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"},"e4ac4a35-4fdc-4334-aaf4-486e16efd028.png":{"image/png":"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"}}},{"cell_type":"code","source":"class tgt_ratio_sampler(Sampler):\n    r\"\"\"custom Sampler to generate batches from a dataset such that the ratio of \n    positive samples in each batch corresponds, on average, to a specified target\n    ratio. Has \"memory\" such that, for the class that is being undersampled vs the true\n    ratio, repetition will sample without-replacement until all samples of that class are\n    exhausted before random-recycling for further sampling.\n    \n    takes inputs:\n    dataset: an ordered binary-classified dataset where it is presumed that the first\n    num_pos elements consist of the positive (class=1) samples and the remaining elements\n    consist of the negative (class=0) samples.\n    \n    num_pos: an integer indicating the number of positive samples in the dataset, which as\n    above are presumed to be ordered first in the dataset\n    \n    tgt_ratio: the target ratio for appearance of positive samples in the sampled batch\n    \n    random_state: seed for numpy randomiser\n    \"\"\"\n    def __init__(self,dataset,num_pos,tgt_ratio,random_state=None):\n        \n        assert tgt_ratio>0.0 and tgt_ratio<1.0\n        \n        self.num_samples = len(dataset)\n        self.indices = np.array(range(self.num_samples))\n        self.tgt_ratio=tgt_ratio\n        #self.tgt_pos=int(np.round(self.num_samples*self.tgt_ratio))\n        self.tgt_neg=self.num_samples-num_pos\n        self.epochlength=int(np.round(self.tgt_neg/(1-self.tgt_ratio)))\n        self.tgt_pos=self.epochlength-self.tgt_neg\n        \n        self.base_binary_list=np.append(np.ones(self.tgt_pos),np.zeros(self.tgt_neg))\n        index_list=np.array(range(self.num_samples))\n        self.base_pos_list=index_list[:num_pos]\n        self.base_neg_list=index_list[num_pos:]\n        \n        self.pos_idx=0\n        self.neg_idx=0\n        self.rnd_pos_list=np.random.permutation(self.base_pos_list)\n        self.rnd_neg_list=np.random.permutation(self.base_neg_list)\n        \n        np.random.seed(random_state)\n        \n    def __iter__(self):\n        idx = 0\n        rnd_binary_list=np.random.permutation(self.base_binary_list)\n        \n        while idx < self.epochlength:\n            rnd_binary=rnd_binary_list[idx]\n            if rnd_binary==1:\n                yield self.rnd_pos_list[self.pos_idx]\n                self.pos_idx+=1\n                if self.pos_idx==len(self.rnd_pos_list):\n                    self.rnd_pos_list=np.random.permutation(self.base_pos_list)\n                    self.pos_idx=0\n            else:\n                yield self.rnd_neg_list[self.neg_idx]\n                self.neg_idx+=1\n                if self.neg_idx==len(self.rnd_neg_list):\n                    self.rnd_neg_list=np.random.permutation(self.base_neg_list)\n                    self.neg_idx=0\n                    stop_flag=1\n            \n            idx += 1\n                \n    def __len__(self):\n        return self.epochlength","metadata":{"execution":{"iopub.status.busy":"2023-02-28T15:08:44.022699Z","iopub.execute_input":"2023-02-28T15:08:44.023478Z","iopub.status.idle":"2023-02-28T15:08:44.037633Z","shell.execute_reply.started":"2023-02-28T15:08:44.023439Z","shell.execute_reply":"2023-02-28T15:08:44.036396Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's illustrate the custom sampler in action by creating a toy dataset of integers from 0 to 9, and instruct the sampler that the first two indices (ie: 0 and 1) correspond to the only positive samples, whilst requesting it to produce an observed ratio of 0.5 (balanced). We will use a dataloader to cycle through for 3 epochs in minibatches of 4 each:","metadata":{}},{"cell_type":"code","source":"toy_DS=torch.Tensor(list(range(10)))\ntoy_sampler=tgt_ratio_sampler(toy_DS,\n                              num_pos=2,tgt_ratio=0.5,random_state=1)\ntoy_DL=DataLoader(toy_DS,batch_size=4,\n        sampler=toy_sampler)\n\nfor epoch in range(3):\n    for i,minibatch in enumerate(toy_DL):\n        print(\"Epoch:\",epoch,\" minibatch:\",i,\" sampled indices:\",list(np.array(minibatch).astype(int)))        ","metadata":{"execution":{"iopub.status.busy":"2023-02-28T15:13:36.00727Z","iopub.execute_input":"2023-02-28T15:13:36.008032Z","iopub.status.idle":"2023-02-28T15:13:36.019963Z","shell.execute_reply.started":"2023-02-28T15:13:36.00798Z","shell.execute_reply":"2023-02-28T15:13:36.018595Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note that:\n* there are 4 minibatches of 4 samples per epoch, ie: 16 samples seen per epoch - this is because all 8 negative samples (indices 2-9) need to be seen once each, and this should represent half of all samples in the epoch, which makes the epoch-length 16 samples long\n* indices 2-9 are indeed only seen once each per epoch\n* indices 0-1 occur multiple times per epoch to make up the difference, however each must be seen exactly once before one can be repeated\n\nIn preparation to use the DataLoader with customised sampler, we will need to prep the training dataset so as to arrange all positive samples first, as required by the above sampler (note that we don't re-arrange the dev set similarly, as this by definition is not used in training but only inference).","metadata":{}},{"cell_type":"code","source":"#test setup for use with weighted-sampler\n\nX_temp,X_dev,Y_temp,Y_dev=train_test_split(Xn_df,Y_df,\n                                             test_size=2048,random_state=0)\n#note: this first train_test split is fully randomised, so X_temp and Y_temp will have\n#positive and negative samples randomly scattered\n\nnum_train_pos=Y_temp.sum()\npos_index_filter=(Y_temp==1)\nX_pos,X_neg=X_temp[pos_index_filter],X_temp[~pos_index_filter]\n\n#create the label columns:\nfor i,item in enumerate([X_neg,X_pos]):\n    item[\"cancer\"]=i%2\n\n#combine X_pos_train and X_neg_train to form a train set\n#Note: no shuffling as the custom dataloader sampler expects the dataset\n# to have all positive samples arranged at the top\nX_train=pd.concat([X_pos,X_neg])\nY_train=X_train.pop(\"cancer\")\n\nprint(\"Y_dev cancer count: %d, Y_dev cancer rate: %.5f\" %(Y_dev.sum().item(),Y_dev.sum().item()/len(Y_dev)))\nprint(\"Y_train cancer count: %d, Y_train cancer rate: %.5f\" %(Y_train.sum().item(),Y_train.sum().item()/len(Y_train)))","metadata":{"execution":{"iopub.status.busy":"2023-02-28T15:34:11.598581Z","iopub.execute_input":"2023-02-28T15:34:11.599063Z","iopub.status.idle":"2023-02-28T15:34:11.6474Z","shell.execute_reply.started":"2023-02-28T15:34:11.599024Z","shell.execute_reply":"2023-02-28T15:34:11.645915Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are 1102 positive samples in Y_train - let's check the row numbers 1100:1105 to observe that the boundary between positive and negative samples is correctly applied:","metadata":{}},{"cell_type":"code","source":"Y_train.iloc[1100:1105]","metadata":{"execution":{"iopub.status.busy":"2023-02-28T15:35:57.181392Z","iopub.execute_input":"2023-02-28T15:35:57.182593Z","iopub.status.idle":"2023-02-28T15:35:57.191358Z","shell.execute_reply.started":"2023-02-28T15:35:57.182542Z","shell.execute_reply":"2023-02-28T15:35:57.189821Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can now re-run the code to instantiate and train our model but with a few tweaks to the parameters to ensure we are using the customised sampler with the above re-ordered datasets. Feel free to run the below code-block for yourselves if you'd like to try, but please note that on Kaggle P100 GPU it needed 110 minutes to run owing to the almost twice-as-long epoch length:","metadata":{}},{"cell_type":"code","source":"r\"\"\"\nsame code as before, only changed parameters are listed:-\n\"\"\"\n#inputs\ntrain_tuple_df=(X_train,Y_train)\ndev_tuple_df=(X_dev,Y_dev)\n\n#imbalanced data handling params\nuse_cust_sampler=True\n\nwith contextlib.redirect_stderr(io.StringIO()):\n    metrics,model,model_hist=Make_NN(\n        train_tuple_df,dev_tuple_df,#feature & labels inputs\n        layer_archt,leaky_param,dropout_p,#FC NN-model parameters\n        num_epochs,lr,halflife,l2penalty,BCEweight,#,#training parameters\n        batch_size=batch_size,minibatch_display_step=minibatch_display_step,\n        display_result=True,\n        adj_final_bias=adj_final_bias,\n        use_cust_sampler=use_cust_sampler,train_ds_randrotate=rand_rotate_flag,\n        df_path_columnname=path_columnname\n    )","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Below we show some images from the training result after running the above code:\n![train_Img2a.png](attachment:5b381cd1-eb5e-416e-bc8c-b139f075412a.png)\n![train_Img2b.png](attachment:5e6c969d-13c5-4f5e-9bbd-1d7bc2959fde.png)\n\nFirstly, recall that the training set and dev sets now have very distribution distributions - the former has been made into a \"balanced\" dataset whilst the latter is reflective of the true cancer-rate in the population. Hence, training metrics and dev metrics are shown on different axes (left and right respectively) and we can see they occupy quite different ranges.\n\nHowever, most importantly we see that the training performance is now excellent after only 5 epochs - fscore, precision and recall are all close to 97%, and seeing as half the training set consists of positive samples we can be sure that the model has learned a good many of the cancer-positive samples provided.\n\nOn the other hand, the dev performance in absolute terms can certainly not be considered good - the final fscore, precision and recall end up around 10%, 8% and 14% respectively. However, these are high enough that compared to a random guess model on a dataset with only ~2-3% positive-rate we can almost certainly say that in general the model has definitely learned. Furthermore, whilst very noisy, we can identify some upward trend in fscore and precision, even if recall is generally trending downward. From here, we could optimistically say we are in a position of \"very rapid overfit to the training set\", and given the large gap between training and dev performance we can attempt to employ techniques to trade off the superb (but overtly so) training performance in favour of raising the generalised performance (ie: variance reduction techniques).","metadata":{},"attachments":{"5b381cd1-eb5e-416e-bc8c-b139f075412a.png":{"image/png":"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"},"5e6c969d-13c5-4f5e-9bbd-1d7bc2959fde.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### 7. Selected training results\n\nFirst we show the results if, from the previous code block, we were to a) reduce the minibatch-size from 128 to 32, and b) reduce the learning-rate from 0.001 to 0.0001. Both of these are efforts to trade-off training performance for dev-performance or better generalisation - using a smaller minibatch creates more \"randomness\" in training which tends to reduce overfitting.\n\n##### Training result with lr=0.0001 and minibatch-size=32 with custom-sampler\n![train_Img3a.png](attachment:a49e1997-d0fb-4cb8-a96b-137caf8900e9.png)\n![train_Img3b.png](attachment:41c8e932-fed5-43eb-af68-c400802a3662.png)\n\nNotice in the above that although the final dev performance is slightly worse than the case with higher learning rate and batch-size, the trend seems more stable - this, coupled with the fact that the training performance looks to not have fully plateaud, suggests that longer training under these parameters could potentially produce a better generalised model.\n\nNext, if we were to turn on the \"training-mode\" in our training dataset which performs random image-augmentation (in this case, recall this is a light-rotation of input images) as a further generalisation technique, our training performance looks as below:-\n\n##### Training result with lr=0.0001 and minibatch-size=32 with custom-sampler\n![train_Img4a.png](attachment:913f4de9-19a7-4c6e-8787-770ebcf8e928.png)\n![train_Img4b.png](attachment:a32d3298-2bca-45e5-b452-fe2b0ba2fa2d.png)\n\nHere, although once again the final dev metrics after 5 epochs are slightly worse-off than previously, the dev fscore and precision appear to have a fairly stable increasing trend, whilst the training metrics only achieve about 70% after 5 epochs but still has a healthy positive slope, once again suggestive that with longer training times the training-performance can continue to productively \"drag up\" the dev performance to new heights.\n\nUnfortunately, owing to the very lengthy training times even with just 5-10 epochs on Kaggle GPU, we did not substantively extend our findings beyond what is shown here, but were pleased to see positive trends in our attempts at variance reduction, so as to be confident that with more time and computing power we could be headed in the right direction.\n\n\n### 8. Creation of competition-eligible Submission\n\nThis next section is very specific to Kaggle competition submission requirements, and will hopefully be of assistance to any other beginners wishing to make a competition-eligible submission.\n\nSome of the idiosyncrasies of Kaggle submission requirements that we will need to work around are:\n* \"Internet-off\" mode must be applied to the submission workbook - in particular, if using any publicly available pre-trained models, even if we have saved our trained weights and loaded them as a local dataset, this means we cannot download the base model from the internet on the fly as we would otherwise do. Hence, the simpler solution is to *save the entire trained model* and load this as a Kaggle dataset with the submission notebook.\n* \"Internet-off\" also means that pip install commands won't work as internet is required for these - but then, how do we install all the necessary dicom processing packages? The solution is to download the packages offline and add them to the notebook as a Kaggle dataset, and point the installation commands to these \"local\" packages\n* The notebook must successfully produce a submission.csv file in the required format by running on the supplied test.csv file and test images, even though these are a very small subsample - in actual scoring, behind the scenes Kaggle will replace these tiny samples with the full test datasets which your code will run against to produce a full-sized submission.csv.\n\nBelow, I will go through how I tackled each of these requirements, before reproducing the final code-block that I used to make a successful submission.\n\n##### Saving of entire model\nAs stated above, under Internet-off conditions, it is easier to save the entire model to local drive, then load it as a private Kaggle Dataset with the submission notebook. To save a Pytorch model, one can use the below lines of code:","metadata":{},"attachments":{"a49e1997-d0fb-4cb8-a96b-137caf8900e9.png":{"image/png":"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FuHLwIzrziwUbWdGauGpGtiRhxWuiH+8O1+dxuT6PZ/sWVuxlWxdaoQwGQ9vBW2MVe6QxJZ6tlx5//HG1jmOFHi9YlJHWL+I6hqxevVoVoSW1ZY85n6dfu7bHu45jjG+jGGb/Ok+y1AwGQzM3LWbdH/ZeYu80xkaVyV9BuS8OpOcjM78Y6Xk1/6Xlym1+CcICfOHTuWpgY4YQawlxQGD5fMYy0G1I6xdnUvTtp6WlqSraXMeKuqyMS2gdYwVZVrpdunSpcjWyGjYHEqau8pb94ViygaZ9tlrhgMZ0WFbnZXVdVptlXSTWDuEyK/Ey44eDHl+bgyOFFKuE0wJHE7me2XGZafzGtWdoL9BywdYZo0ePbtPHNccpjiPs08jJGc/rQ5kFOJxViAwPxqqcolKEBvqis+M7YHVq1hfiWMXxgGMgxytO9BgryfGK4wHbhrDKNu9rdxob8p566qlqDGWtI1qzOS7Ros9lTtDYd47tlDi+sLo2xxcKKY5VrKZNCyEbczPhh9s43nEiyv3ge1NEsVsCW6dQRDGriVW/uZ33TTynob3x888/q56NjaVFhZQvyvDp+hQ8+f02zNuahB82H67x7/tNhzF3azJmDumCyKCq8v2ErUVY1p9C6cILL1QDCjt7c5BiWwBagihyWOyRPY3YroBQ4LAUAwcJtgbhoMIZIzt3cz852Nx+++2qfQFFFQeiYcOGoU+fPqqBJV+Xr0lrE1ut8P054LF1CWeffCwHHwovCjrdJ44XGWKElKG90R6FVHZ2FoLCwvHsTzvwxqLd+GlL7WPVdxsTsfFgFo4a2AUhMvFzQkGjG2FzrGERWjah5XtRHHGixUkZxw66FHlLKIw4tlF0ccjmhJBjKMcVjle85Xo2s+VEkes4BlJscT1fkxZ4TjYpoNjXju/NApKcaHLMZINw3mdbF4o9Tip53wgpQ3vFW0KqRYMY8ovLcP30fvjprmPxzW0za/379vaZ+OrW6egbfeTJzJOeMyrOnjhDoxWIVWo5CHA9Z2W0OnGA4YClYfAoU5zZz4jdwSmqOFikp6eri8GECROU25CWKza5JRyI+Hj2ujvzzDPVIMPX17VmGEvBppR8PgPpadlijzfuI+/rfnQGg6FtIKc3/Dt3wiPnjMScO+oeq7674xi8ctUkdAuz+kg6oaWHYxAb4NKNR4s3xxP2hKSI4nZO/ljQlGOXhhZxjjV8HEUVxdBvf/tbNS6x5xut34z34KTv5JNPVuMYi0NycvfMM88oKxXHHz1eUeQyHIL975566in1eG7nH/eB45UpRmoweEaLWqQoPnS37MbAgYGWHTagpXhihVoOIOziPXDgQCWi6ILjHweIcePGqedxMOJgdvTRRyvXHgebUaNGqQGEz6WQ4kyMLj0ONHQjUqzxddhYk69LSxQHRj6fwuymm25SwbacCdIUTwsUByea9DkjZHNMba43FilDe6M9u/a8Bc95Wpq0u5+WKH5fXEcBxQkYhREfx/GGpKRmICY6Sm1jYWM+nuMo941tljiB41hDyzyFGMdATvr4G3As5CSSk0JanjgxZBuUsWPHYt68eUrAcXziuMXPyddlsD3HKr6fsUgZ2iveski1WNYexQUtNS2Ztce4Agaja1dbQ/nqq6+wa9cuZaHSpnh3MH6BIoyuPmKy9gzeYHNCNkrKyzCud1V6elk5sDslG/1iQuHv6+HxVZIBHPoZ6HuhvaL+tMesPYoPuvNbjOQFOJxRitKwUejVwxJWDYGhDkyuodi9+uqraxVG/A0Ze0poGTNZe4bWTE5hCRbuTMO+1FzcMHOAx2Nem8/aaw3QMtRYEUXOPfdc3H333bWKKMLZrRZRBkN9WLAzBVsTs+2l6szekIB/ztluL1msjc/AKU8vFDGVa6+pne+2pKMwdQuw7o/2GkOrYdtziC1d0igRRWhBv/LKK3HLLbfUaV3SIspgaO1kFpTg9x+swfebDuGEYd08nzh6kQ4tpAyGtsJDX2zE0j3p9lJ1corKsDMpx16yeH7eDvSJDsKS3Wn2GovNh7Lw2LebVQaa5nBWAc5/cQm+23gI8DFxMa0P+a1K8+37BkPH45CMUUz2KC49suzQmwt3Y0SPMDx/+QQMiwu31zYvDRJSzBxh/A9N3po//OEPqjYKY5Dop7/gggtUIKMT+uK9HTdBlxoz58444wyVvecJTP+lmZtZK+48m3Q5MkbClU8++cS+BxUDxTIHBkNzsGhnGhIyjryYlpaVIzW3EMk5VZ3mP10Vj6KSCjxxwWh8u17EkYNlIqyW78nAJa8uRXpusVr36q+7ccvxg7AnORMFBVXJGJpPV8crsWVoHP/+979VDak///nPHtdo2rxhBQ5sW4PkgzvsNdVhYgyz65wwXpMhFJoXXnjBvmcwtE3+8uUm/LQ1CZ/I2OYkLbcI7y3ej5uPHWSvaRnqLaQYHEmhxLggfYLOnj0bZ599NiZPnqxSdllW4Nlnn1XlATQMWGQ8UvUBRERVmQzmBZlAYbYHf1lARXVFStFD1xrTfunr5MDCWCyKOGbEcF8ZKMk6UhRNDHBnjRYGY3KwYSoygze5noFnFFmsAfWXv/xFpSpTNPLzEQZmUnwxmJZBmQzu9HRANBgayq7kHPSIDFSTkPziUuQVleLbDYfU7KygpAzdwgIRGuCL1DxLBL25eB8+vHEKZg7uimx5LGsbaX7ZkYr/u2Qsnr9sPO75dL0aiFbuzcCzFw3DCcO6IDe/AHd+shXXvrMSN72/Cme9sBDztiWjrLzZQilbL8V51hjkdmxy/slj+FgXWKeJMUpnnXUW7rzzTrWOZQgWLFkjM7MXkJe8BVk5uWrM0rz43FPo0zsCyQe24esfFqsyLhx/OFaxVAFLr3Ac5sSP4y0TYxiLybFq7ty5arxmkgzvGwytmYLiMhzKrJoQah7+arPK1v/k5mkyFiVh0a4UvDR/F2797xpc9toyvHrNRESFVC+J1NzUW0hRuFCQMNaH2SGcDY0cOVINCCw5wABqPoYZdPTD63IDzI6juHGWH4BfILD+I+CjK4FPrq/j7zqZGt8IZB20n2zBi4u2KjHzhFYjzvzefvttNXBx9scAWGbZ8bHcNwor7j/FHT8DxRH3i5/hoosuUiKLWTC6NAKzXwifz7iqL774Qg1OtLAxCNNgaEoW70rDWWPiUCyiKbugVLnr7vhoHZKzC1QJkbAgPwyPC0diVgFyCksR7OdTGeg9vnck1hyocgluSshETLAfJvSNQkyoH654YzmmD7YSPsb1CEN4YCdcc1QvPHbuaNx3+jB8cP1UvHzFRPSManx2bdtFJnzlIkYXPQvMusbN2OT4+/S3cnsDMOdeYPUDQFGK/RqoLCfAsgMcg1566SVVKuXOux9AxfLbcWDjj1i6bHWlZT0jOw+dSmV8CfJFuHz9Pj6+auLH7a+//roqg8AxlqUUOBnkemYCcvzi789kGr7+ZZddpsq7GAytmQ+WH8CVbyzDnR+vxW6ZPJJV+9OxOyUHt584EMH+vjIO9sQrC/aqYre3nTAY/7vxKBw1oIt6bEtSbyHlbNxJ6w9PZJYc+N3vfqdOaKbL8jHMymO2B2uZEKb1XnXVVdWDHEtlcBpyCnDqY8BJD9fx91fgxIeA0CMzR/R7cKZGocRUXwqo66+/HkOHDlVF5yiyCEWXFl4sYsfaURRVnLHxczAgk+KQQooZhdxfDk6EoouPp8DytovS0HH4eOUBvLtkn71UN+sOZODiCb1QXF6Bw9mFIqxSERXsL7O3IiWkfDt3EsEUhcSMQhFThegTUyV6zhvXE79uT1X3M5RlqhO6hVsJDzcfMwi5RaWY1C9aLXdGKQJ8KuS1wtA7KhD9u4QisoVneq0DGS86y/cw7nIZhx51GZfssemUx4Ez/g2c/ARw+v/JoLQX2PgP4PA8+zWqWruwDAzFDi1Kp5x2Ft586nZ0KpH1B9bhiy+/xLSjj1aPKy+XX6tYRHChvyrmy7GHYy5FEWtIcazjZI6VyDmGcfzl43ThYE4AdcsZjlkGQ3Ox5VC2TPbWIj79SMusO/JLSvHLtiTcfeoQDOwailcX7lHrF+5MwfFDuiEy2LrGnzM2Di9cPg5XT+uHYXFh6OqmVltLUG8hRaHENFjWJGENJVpxODAwjZAzLp7MbIXA7BDed0JxxRiqSipkYAmNFYUyHogb49mfb/WsNw4atD4x3ZqxUnfddZcapFhkjibyJ598UlmjWB+KUBhx0KHI4+DCNGAKJA46NI3TksYaUnQP0jzOcgX8LBz0aIXic3jLAY2CLCwsTL2uweAJdJF9uuog/rt8v72mCrrZCkuqu4pL5fHbk3Jx0shYDOkeiveX7pfHFeKW4wZinwxSdO9R088Y3AU7k3Ox43A2hsVWHZNTBkRj62Gr/+SS3akY07OqHtLAbqGYf/dxOHGYPTmpKJWrt1xwy46MkzII0TKh6jHWzbg0WgbGrsBWWqHOB5bJ5DB2AnDMW8D+j+wnQ03qON6wdN/999+vuiP8ungFdv0kYmzIFGzfuAxBAf4IkPGLxETKWNW5EHkVMUhIOIhbb/29CqFg/SdayjnpY9Yxxzha1Gkp/+tf/6rucxtFFscvWtwpvAyGhvLxinjleiMHM/IxX0RPbSzbk4rEzALc/P4aJGcfOZ7MWl0VX03W7M9EeJAfzhjdA7cdPxjFxeXYn5Ynr5OOCyb2sh8ll3+fzmoS2VTQo8VQIbZdIrTyUuc8/PDD6tr/z3/+E5deeqnqHFANEQPNxoYNGypefvlldV+ETEVqaqq639ysXbu20e8dHx9fsXLlSnupYaSkpFTILNVeMnQEth3OqrjxnZUVR/19XkVuYYm9tqJiya6UiivfWFrxly83Vfzn550Vv+5IVusz8oorLnllsbq/9kBGxfCH51S8NH9nxaKdKRXPzd1RsS4+o+JZuT2clV9x7yfrK95etLfik1Xx6vGaWz5YVfHztqSKv8/eUvHekr32WjfEf1lR8VFQRUVJrr2i/hw8eLDigw8+aDXHdUJCgjrfZQKnlouLiytWrFhRsWfPHrUsE6SKpUuXHrG/ixcvrvjuu+/U/f3796vbGsnaXlHxy9kVFVueqqjIO1BRkb3TWl8oY8ycidb92lhylhwYL1ZUbP6zLFj7qYlf9mJF+ZLrKip+PdNeU39komnfaxj5+fkVSUlJ9pKhIxL2h88rPlxhnQfn/mdhxcWvLKk2frly76x1FZsOZlYslnHq5H/Pr0jOKbS3VFQs2J5cEXHb5xUlpVXn3NM/bq94e7F1TpLP18Sr93nw8w32mqbhscces+9ZY8E777yj7suERN3u3Lmz4vLLL6/Iy8tTy48++mjFr7/+qu47qbdFylvIe9v3mh9WNm9sIVC6KlkBuLG05PdgaH4Y2H38sK6IDvGTmZ2VCcdaT498vQU3zhyAK6f2Qd/oYLzx6x7c/MFqbD+crVxsZHTPCMj1HscN7Ybe8ph9qXkqk48ztOjQACRk5qu/XpFB6vGaY4d0w0crDmBDfCZOGl5LLaJy2yLFv3YCk0aYiMIkEsJEGM4ymWhCF/7zzz+vMn/feOMNtV1Da46mmhXdHcFxwNRXgOF3y/3eQJhtiQ+QMSZQ/nJ3WcvuKMsHUjYDg64FkrcCOZZLQ9OrRzQ6hfSkb85eU3/YR7SxmHGq4/L5moM4dVQsvl2fiKd/2o4An84YHhuGVDvrV7MrOVeOE+v+ThnTOEYdPagLLpjQC3//dou1QXhr0V6MiAvHxgTLUk42HszEzEFd7SWGJPRS1vhrZ/S31zQNL774ojo/6FljuI4+72nRpSs+NjYW1113nRof6CZnv0uOG6+8Iue7gxYTUnQDMtib1YOb+4+lD9ytr88fX6Oxr8PP7xywDW2ff/+4Hf+YsxXZBVUXPg4uFD2MUaKpelzfKJVRt+OwFVD58JebcMvxA3GMCJ4hMkCdNbYH3v3tVIztFYlr31khQsqKK/Tp3AmL7ztBBZbHhPjjcHYR1h3IQq+oQPh17iyiqgBbEkV4dbWEl+bkEd2QklOsinTGuYisatC1R3c7A6vbAcxeo6ueTcqZ5Ubokuckiu7/1157Td1nFh1d9zoRhpluzNjV5yYfw/CFGs930UJZxRFy32V7Xhnyo05D0a5ZyJT71bbxL6cAuYc3oMS/DzJLgkUr5aMgaS0y5QLF7Zl55Sg6vBH5vkx8KZR1mcjKzqn+Gh78MSHG3XpP/vQ4Z0IYOi5P/bgDj58zEueN74HXft2Lj28+Gl3DA9R4ouF4N+2fc/H+sn2Il8lddEiActWRm48biPBgP1XC4O+zt6BvTAjuOmUovlxnlWbJLypBbnGZCjXQMFzh29tmYpDLWOZt6F5nzCETQBgmxCQQljbiLf+oU1iNQGsVxk7TRc9xxEmLCSkGpDMwkoHizf3HQdTd+vr+NfZ1GGtlaFtwZs4SBO5m6Kv3Z2DOpkT4yijwu/dWYfleqxjmMz/twINfbMIVbyxTNZ+GdAvDlH5R2J+ej0I77oBB4a4wDuqTm47GGWPi7DVAl1AruDIkwBfhgT74aVsSYiOsuEE/ufCzSkGUDFpOOKgdPTBGBr86quqXy77wc7UTixRjGniOcqzRv9cxxxyjRBGFEmegHCj550we0eVTuJ5QRLDcSY3nu7+f/PnIfZft8jP49j4dPinzEBjE7Y5t/AsMgn/BbnTuPhOB8tN0jhwB38yV8ljrdQKDOsOncB/8okdCNDQCfMplfWD11/DgrzHjFJ9LIemNnqiGtsehrEKk5xVhqEzeaFmad9cxav2UftF4+ZcqS+sbC/fg1uMGY2dSLv740TqcNNx5beuEB04fgemDYlTpluum91PZxCvsAsOvLdqLowe0TKs4p7WZ5zgTN3744QdVl5IlRViF4KOPPlItoph0Rkv2Tz/9pPpbOmkxIUU442Pgd0f9M9aotkduUZmqa3Iw/cgClQwg/93MAbj3tGG4fGofPD93F258dyX2pOTiHxeOlsFkOO6TP1qWYsICUCYn8ZfrDqqA8JoY1TMCvWsoPdCvSyiSswsRY4srugt7RsjF2U2LhBNHdMMNdZrJ6dqTG7r42gEcFFkTjmZ4ZrFxUOQ5p832DBpl3bs333xTCQUKB8LsOFqxnKVauM3dOVzrn4gr/6jB8K0oQGBF0ZHbRWj5lxyGjwglSlyfmInwK9gO0cfWdvkZfYuT4Bc5UPa7s7xeJwSKOjvidZr4TwtKQ8eD4QDHD7PCATrLZEOXQZnYJ1qJJhYEJkv3pOKRc0bKmBSCz1bE45ThsWq9JsCvM04bFYcnLhijHtOnSzBSZFJJPl+dIO/ROvo4MrD8tttuUw27mf1PizZ7A7MsEs8DFiK/6aabjgjraVEhZTC0Fi57dVllzFJtZOQX4dPVB/H52ur1zDYfykZFeYWatZFzx/XEe7+dgiuO6ot/XzoOfaKDMWNIV4yTmRiJCPJDjszOnhGxdeH4qqyU+jC5XxSC5OTuagupyyb3xpljqg9gGpZHuOboOoSUcu3xtv1k7VFE3Xjjjcp9R1HArF1msjGLjdm6zDZmJhw7MzhhhrFX6CSqqJMIkbwjszQVxZmipuzZeHeZ7ecnW/c1eQdkexd5DZFapk2MoZlZsTcd10zray9V4ePTCXeeNBinP7cIF72yGON7WZPByyb1xn+umqgmirXBUISYUH98tf6QGlPH2uNiW8UIKUOHh33nlu1Nw8r9lqm5pLQcH608YNddqk5qTrFyy327IdFeY8F03in9Y1R6roaWJwaGB7ixENFFtzFBLqKiXHqJyGoIFFLXz+ynZorkfBFxevbYIGiJopBqJxYpEh0drczynE2yblxQUJBqHk4zPqHbjiZ7XcC0SegsQqrQTbp4mQh31okKsAsKBssFiz9lqUPIMl6N+8ayL2VeEncGgwvbDmdjw0GOR1X8b/l+NTb2j3Hf4Pr2E4fg/jOGYfrALrjyaEtshQT64tYTPGvXMrlvNGatOICzRh8Z1tDWMELK0OHZygDtLiFYsccqXPnztmT847utuPS1Zfhle1VlarJAln8zta8SR6zbpGGdFbrPPCWQ1cflv6uOOnK25ynhQf74/XFe7DHF9kvtTEi1CnxD3QspWqOK0qqEFAWx+g1sIcUMPmb+kc4ipMqNkDJ4n0teXYI/frwOF768BJvsTLo7ZHnu1iT8+bRh6G7HYLrjhGHd8MeThyqLe32Z3D8an689hHPGVcWAtlWMkDJ0eFYfyMQ10/siLbcYJWXl+GDZAfxw5zF46qIx+PNn65W20Hy6Nh4T+kTh9ycMxKyVVgPN13/dg9NHxqmed57CauRPXjwGvztmoL2mFcBgc4Y8VLSf8getArruChLsBQe0RpXIhcvP0bGeliftwsvcIIrbtjD6cL1pHG2omwPpeXj2px3IKax+HidlF+KwSy+7V37dhQExoZhzxzFYev+JuEsE1JnPL0RsWADeunYK+nd1b43yBkO7h6oOCn1qsHi1JYyQMnRoGPCdmJmPYwd3RVcRQu8t3Y8Av06IjQjCmN6ROGNULN5etFc9NlcGJoqtPtFBsr4HFuxIxluL96qYqX9dNFo9pj4wiNxdYHjLYWUQqkbiBu/hH+neIlWYIqJVvnM/R4q3j1xUtGDKPwCwhhTxDZP1nrXbMHRs7vtsI95dug+pOdVjHa95awUe/noTSuxMtRTZ/sPGJJUIQxhu8NJVE/D3C0bj/jOs/rJNSVRIAG49fhDCmXXRxjFCytDmKSwpV7MtDcWOziapiW2Hc7A1MQclZRXw7dwZEcH+GNQtFM/N24GJfaPsRwF3nzIU326w6p2s2p+BkT0iLBeM8Ph5o7F8dxoGdw8RQdQOMjB1KrCxSHmXwK6WG8+VnF3WNgaka3yC5XewhVThYSDAtkgFyDFZXFW3x2Bwx5drExDi74sbjxmgmp1rmAxDAcU+m+8s2ofMgmI8+u1mTBsYU630x6CuYap+XXPQPTwAtxw3CGFMU23jGCFlaPPM3ZKI+z/fKOLJcsL9fc5WfLa6eladK28t2qM6jReJCAsJ8EFkkB+m9o9GYXF5tW7ioTJbYlPfk59egD/OWo/bHYGUU+Txr149Cf/5zQR7TRuHAoqTQ2ews6HxBPeyYqFcydoo2/rZCzaMp9KWJ2bshfSx7jOOqsiK4TMY3ME+niyI+fo1k3D6qFg8PXeHvQX49w/b8NqVk/C3c0biG5kYXvDSEnQLDcBtHgaGNw2dbIt8lZBrqxghZWjzsB3BzqQc5BWXqHqSKTmFeHvJPnvrkdCVdyizUKXc3vfFBkSIWGLmGyuCs7M4u4o7eeCM4fjXhWNw3fS+OHaou4Dytj8QKFj+wJ9BzSYWx6sE9QCKs+T7dUbbCfkJQPgQe8HGT469EjuJoSAZCJTnEsZZGSFlqIUfthxGD7tzAdtKhQX6YvX+dHy97hCKy8pV5fDOnTvhqz/MwM93H4eHzx6JIH9TI8wbGCFlaFNsPpSFtNzqFpN9qSKMsguRV1iKgpIy5XPfcshqv1Ih/z3y9WZ1X7P2YKYaZN68erKyXEWGWDVPgvx8cOqoOHXryoS+Ubj9BJeLXnuDFinfSJnael9IzZo1S9VqKiiwXpttWO68804899xzavmdd97B9ddfrwpktjv8IqzvttzF0kdhpPvyaQKiRUjZLryyPMv1R5Rrz4170GCwmbslSSXCaO44aTDOemERvl5/CA+eOcJe226mfa0KI6QMbYYymVX9Z94u/Li5euAuW7ZM7RuNfen5yCooxqBuYarG0toDmVi6Ow0/yQBz7dsrlKgiS3alq5YpneToX/7AiTh1hPsilh2OMrnYB8qFvJHZYWzJ4qzLxPYsn376KU4++WR1S+bPn4/LLrtMNf/+7LPPsH37dtUQlFXG2x2+IVbWnWsdKNaIiqi6wCkYE1VoW55KZDJAtyDxE4FbVL2/l6E9U4Env9+O2S716moiMatQdVs4wWExv2B8L7z0mwl445pJqj+noekwQsrQZqB5+mBWAfakVs9e6iT/HSXC6KAIKcZJFZeW4XfH9MdX6xLwxdpDePKi0crk/chXW1Rw+cH0PJw0wgripQm8R5TnZQvaNSqDTGa0ZQ2voM0WLJs3b1aCaN26dWrdhg0bMHHiRMyYMQO7d+9W6/Ly8lSvSbZxYbFM/i1YsEC1ZWh3MIC8c4B8rw6LlBJFMvz6V1kQFCoWyq5dxlgp7drzDTIu1w7EtsQczNuWhFcWVPWzq4118ZkqJKGv3eBcwyK9hqbHCClDm6GgpFx1GWcVXk1ecamqIE6hlF1UhvziMpRXVODUkXGYtfIAEjMLMLlfNB4+awTKZZZ39ZvL5PGdERdhxRIYHLBZMVP1G+naY9+6uLg4VUmcsF/Vrl27kJCQoBqVEwouNgzNzMxUwovtWu655x6sXLlSbW9XUAT5yJ/zey0RIeUqoghdezo7j8JJjm0FhZhpEdNh+GVHCi6b3Acj4iIwb6tL2yAHc7cexl2z1uG1BbtV8ouhZTBCytBmyBXRFB7oiwRHT7yVe9LRv0uw+kvPLcK6AxmIY8ClXH+GxIbj/PE9VWkCVhJ/7NxReOE3E/D0JWPtZxuqwTgeXsgbccFmM+B+/fqp7umDBlnxPzExMUoo/f3vf8dxxx2HRYsWYebMmfjnP/+J2bNn409/+pMSWbfffrtqDtruYCaef7gVcK7JO1SVkefEP8Iq0km3H5+n8Q2WL7eqkr6h/cAiwD9sOmwvWcxen4hzx8Xh3tOG4pGvNyG3yOo2sGRXKq56azmyC0uQLxPHb+RxF03sjVevnojTR7X9CuFtFSOkDG2G3EIRUkH+CPL3YQiBYr7M3IbFhaNvTChSRUit3J+hCl2S5y8fh3PH264RGxadY80ogxtokVKuvcZZpEpLS5W1ycmjjz6Kjz76CKNGjVIuvm7duin33xNPPKHceo8//rjaToHV7qAIUgU1qyypSF0CRLkpm9FZJgEUsvwNGBelUetNQc72yOp9Gbjlv6tFFFn16v7+3VZM7B+F6JAANV5NHRCDdxZbRYGX7knDxvgsFbawaHeKav7LeE92VaBl3tAyGCFlaDOwiXCXED9VFPP7zVYQ5ioZhAZ1DRWB5SuztjKsOZCJnlFy0RH6RIeoYpsGD2H5A168TXNcLyMXOAaclzosSgnfAnEn2AsOVHNiEVGl8htUax0TIELXrjxvaFdsPJiJiyf2xn/m78LJT/+irE0Pnl5VWfz66f2w6ZAlwhfvSsV7v52KORsP4++zt+KccdUnioaWwVxlDK0OFpbT8D7joEhKdhEiZJZ20vCu+HDlAbXuUGY+hsaGw9+nM4pLy0VsFaG3LaQM9YTNigMbF2xuqAHGOOmmw/FfyLIfENLfWnbSWQQXyx5QTDljqJj1R4MDxa6hXbFRRNL0wTF47cqJOH98Lzxx3mj4OVpHjZCJIyeFd89ap7LvxvSKwJmj4zCgWyiOGWKXxzC0KEZIGVodrPvEgHGyKSETL8zbqe6nikgK8ffB8cO6Y+7mZNz32QZM7R9TadLuHhGgakAFmyJzDYOuPZ8wuTWVzb0OhZO2KO39AOh/lXXfFVqkGB/FSugBMfZKgUKMOOOs6kPyIvuOoTXAMITS8nI1SWR7q/7RoSrj7vfHD3LropspQuv1hXtxy7FWk/PLp/TBK1e0k44K7YAGCamkpCR8/vnnKCyscgH8+uuvWLx4sbq/detWfPHFF+rWibOnj8HgjoLiMnywfL8aaMiS3WnYmWy5RHKKSlUhTT+fzogLD1SxTs9eNl5tI93DAjG4uyNA11A/WP6Alg/jQvI+FFK0JlEgVYhQijvV3uACs/sqZFguOChCysXawPGzoXFSy2+Ecdm2DnIKS/C791Zj66Ec5BWUItCvM7rKJLA2pvSLxpvXTEKvaCv+kxXK20V/z3ZCvYVUSUkJfvzxR5XS/I9//EOt27hxo0pb3rJli6pM/PPPPyMqKko9xgkzeoyYMtTG/O3JKhtl+2GrMvn3m5NUbBQpKC5HTIgVKL7wvhNw/+nD1CCkuWFGf7xw+UR7yVBvaJFS9YqM+8jrqGy8fCBhNhAlx2hwT3uDCz5yQeUYmbMXCHRtR+QrJ0ECsH+WvewhLLDKqui0dBlanJfn70FCZj5mrYpHUk4h+saEIFYmhrUR4OeDiyf1tpcMrY16C6msrCyVlTNlyhSEhYWpwnqsDXPgwAGkp6erqsaRkZFqec+ePfazgNTUVPzyyy/VrFgGgyvfbTyESf2jsMu2Qq2Lz1AWKLZ+KSguRZQtpFTmngvBAb4q6NzQACrsLLvO8v3SMmXwLgEiilKXAjv+Awy83l5ZAyy5n79fhFRV82wFy1Os/RPw06X2Cg8pZqCyiGMKZUOLcjirED9sOYSPfncUft6WrDL2+thWJkPbpd5CygktTAEBAdixYwfOPvtsDBgwQNWDYeuHE044AV999ZX9SCAiIgLDhg1TQsvQ0ahQpQt03FNtbDmUjTNGx2F/mp2CL0+JiwhU5vCi0jJ0sfviGbyM+m06yYjA0hLVSxcYvAArlm9/Axh+j/v6UU74WxSmyG/hkjQRPgLoeY6cEKOB7KrO/nXCsgsUx8bS2OI889N23HXyMNVRYfrAGLzwyy4RUiY5pq1TbyEVHR2tKhe/99578Pf3R1pamhJJq1atwsGDB5V1im6+JUuWKPeehgKqd+/eqqKxoWOxLTEXZzy/EPHptWeDUUQxYe8sEVIJGfkq1XdA11B0Cw9EWm6xcu11DzNCqmmQL54upU6c6BiLlNcJigMmPAL0vcReUQusO1WYZLn5nBz1JjDiXiC0L5C10V7pAbrAJ2OzDC0GG67vScnHmWOswpl/PWckUrKLMbBbmFo2tF3qLaTYjPTcc8/FtGnTcNtttyEoKAjjxo3DlVdeqSxR/fv3R9++fTFhwgTccsst9rMsaMFiA1NDx+LXHcnYnZIrYqj2bLCNCZmYOiAaXcMD1DV9wfYUnDKim4qL4iDEHnpdjJBqIsqVQUqmPPJnhJTXYfPh0X+1F+qAFc2LkkVI1VA4NmyICKkt9oIHlGRbbj1jkWpBKvDs3J04e1xV9fGQAF+88Juxqr2VoXWwc+dOvPvuuyqEiRQUFKjCwd99952KD6fH7X//+58KXXLSINdeYGAgBg8erCoSh4dbReMYWM7+WoS3bA/B7QbD5kPZqjpvfKbtrquBnUm5OGZQNwT7+SJUBpkFO1Jw8ohYxIT5Y8PBLJUWzKBLQxOg5jcyHPjIOWuy9loWZk4WipDSJQ9cCRsGZG+zFzygNMcSUSZGqskpLa/Akl1pqieoExYKZtP181wKaJ47rpcq2WJoGWgI0lA0MY6bxiJ2WSAUThRRbHtFr9rXX3+NnJwcdeukUTFSBkNdMK6J8U3njI3D4Uwr0WCtDCovyOzMSWJWAQ5k5GNonFW+oHt4IH7ckoRJ/aLRIyIY6+OzRJibw/UISrzVf00HmzNN3wipFoWZk0WpIqiqd/KvJGokkBdvL3hASab8pvL76oKghiYjObsAd81aiyvfWm6vsXh5/m5cObWPanFlaD289NJLuO6667Bw4UKVCEdhddVVVyE7O1u1uQoJCVEN11mpgMsUWzfddJO6dWKuTIYm5UUZQI4Z2lWl+GYWWDEatDR9vj4Bj3y1CV+sTcCTP2zDPbPWY5RdwZf0jg5Cvxgrm6VPVBD2peWp6uUGB7n7gO+9Ve7BdrlTSBlaFvY75O9AQeWOcBFSqh6VvVwXJVYpkSPqSCXNtwSbwWtsS8zBeeN74ewxcbjx3VVYtT8d7y/dB9/OnZR13dC6+P3vf4+3335b9fik607DygS0TNG7tn79eiWwdu/erWK8+TjXMk7mymRoMrIKS/DW4r24aEJvVSyTAePkcFYBnrt0rKrkuzM5B5P7ReE/v5mA208cDH/b6tQ9Iki1QiBxkUFgsd/QAHORr0ZRilUfyBswdpFp96xVZAVLGVoK/0gr6J8uPneomlQijpy9+2qDMVIUXWUuMYqb/g4c/MZeMHiDQ5mF6BYegBtnDsTlU3vjmZ924s+fblQN1A2tD6dlKSYmRlmiTj/9dPTo0QPz58/Hhg0bcMEFF6hkutjYWFXa6ayzzlK3ToyQMjQZNGdfM60vQgN9RQwFVlYr53U6JjQA103vjz+dOgzHD+1eWR9KM7lvFB45R2beAmOjIoP9ER1ihFQ1VJXrcrlA1h575hnatSdCioKKTXPbAcuWLcM333yD/HwrYzQjI0OZ6WnKZ/ILYx3mzJmDxESrCbamRQsHszUMA81dyx84CYiW38jDdjHF6VZNKteq6OypmPKLvWDwBntS8zBAJojkhGHd8ZJMEFc/fBL8TBXyVg+tTTfffDNmzZqFa665BjNmzMCoUaPwzjvvKBcg62Zy/WeffYYbbrjBfpaFEVKGJuFAeh72puTidzIzIwweZ/uXzIIS+HXujIjA2mMFwoP8ML5PVfmM0b3CESavYXBQIhdGBhF7RUjZaCFV7sXXbEGee+45HDp0SAWMEray4ix006ZNWLduHf773/9i0qRJajbqpEW7MPhTSImIqilrj7CcQpGH1siiDCCkt3wo1/YyFZZ72OA1thzKUk3UNRHBfoiLqEUQG1oVdOdRMBEGl3OZCXXO+pehoUe2ITNCyuAxn605WFlxvC4+WhGPcX0i1UBCWJ3cV2ZliZkF6qBj6m99+N3MAabTuSvl+XItLPGOkGIwMgVUJ/4uctsO+rLRLM8yLOeddx62b9+u1rHOHQsIU1x1794dQ4YMUSJLb2dAKc34tGKxTl6LQNedcuvVco4ExVqWJk8oESEV1ENuHXXc2PePWXxsguzp6xiq8dW6BMxaWT3on4k1XUJb6LgxtBhGSBk8oqy8At+sP4SFO1PsNRasDfXp6oMoLrVdQzZvLtyLs8dWT/Ud0CUY7yzeh+4RgTLbt1d6yKiekRjYzTQkrgbjZFiqwBuiR9d3UxYp+XHYn62NQyvT4cOHVZN1Zt8Q3mcMxLHHHquycR577DGV2vzhhx+q7ZyB0kJ1xhlnoLi4hQpY+sq+hvTlztgr3BA6ACg8bC/UQakIKPbtq3DESJXKfbr2IkYA+YfslQZNfkkZSsuqj2lOXl2wW00WX5y/C3lFVpDyNxsOYXD3MDVpNHQszC9u8Aj2ukvIKMDWRPbtquKhLzfh0a+34GBG1WyXPaSGxoWhV1T1HlKDu4bi/eX7MTyuyvRtaASMeaFFihfKRsOLhggoVdmcFqm2L6R69uypsm1ocTr++ONVFwbWu2MWDuvDUGgxVorWqOnTp9vPsmDrqxYrHhw5Cpj2viWoaiKot9VGxhMYZO4fLT+xoyBnuaxjmYsuRwM51UuRGICnf9yOl37ZZS9V54Ol+zBfxrj3fjsV950xDA98vgk5RaV4Yd5OnDaqquCmoeNghJTBI3IKSmTmVSqCqeoCy+7lGflF+N0x/fHxqoP2WuDr9Qn482lD7aUq+sSEqHywvnZZA0MjoZBSFilvuPb4j/w6lTFS7SPY/IEHHsD111+P0aNHq0LCbLbOPqDsDcpAUvYHpevvzDPPtJ9hQRdfi8FCnHTd1YafnEOexrGxPYx/lNxWpXcrK6aPvAYtW/lV566BVCAxsxCvLaxquq/5aUsSPlh+AO/+dgr8fDrhdBFOGw9l4fRnfsVpI2Nxysju9iMNHQkjpAwekZFfjG4RgcoqRVil938yoHz0u6Nx2ZTe+HiFVTK/RNbHpxVg+qAj45mGdAvFsNgw9OtiXHRega49KlO6aBoNY6TkhhapduLaI+wL2q1bN+Wy0+491oZhz1AGk9Nqxe1tDoot13IGNUFRTCHFuCgNjxnfMBFkkfJbe5j910FIzi6W46UT0nNLUFBc9Z2l5xWrce6ZS8ciwJGF978bpuKNaybjrlOGonNLJSgYWhQjpAwesXxvOib1iVKxAyQtpwhdQ60WFl3DAnGyzMSemL1V9ZMaGuteKMVGBuHL389QNaEMXoBZWMF9RNXaBRcbBU1S8sOouBy5SLQD1167xi9cBJAj8UMFjbs5DpTFUgRXQBf5iZ0WKfl9WfCTlq/8RFluHxZIb3AoswDB/j44Z1wPFf+p2XgwC0EBPhgeZ9W308RGBGFYnJXpZeiYGCFl8IiftyZj2oAYmYl1UkGYCTLYxDiyU/506lDsSMpBnszgrjyqr732SMKC6petZ6gFWhVCB8kF1QtCill7FFAcEujeM+1EWjesfs5irNoaefBzIKF6/y8FrVBltmuvzCGk+PuyBU1wHFAgQorFXQ2KtQcyEBHkhz8cPwjztiXba4FvNx7C6J7VRZTBQIyQMnjEyv3pmD64i6o8fjirCAdFSHUJq2qqSqvUu9dPwSNnj8SIHmawaRZYRypyuGWNaCyq/AFNhTIksARCO3HttVsCY4DCNEtMEQaMZ2+x7jthXBSz9QK7Vrc68ff1Y5mFIDmOMjwPXO8A/LIjBaNEMPEvOacIuUWWe2/lvgycOtIEkxuOxAgpQ53sT8tHeKAfAv180D8mRFmjkrMLEWXXiDK0EHTthQ2TC6GXhBRdexRT7PPWTgpytlsoghjbVCwiiBQmAfluyiEwO6+TvxVYrjL17ExENrtmvSrC7aYEQiX70/Iwpb9VDHh4bBiW7E7DzuRcxIUHmkQZg1uMkDK4xZn5vTY+A+P7WgPLoO5h2Juai5ScYnSXgcXQgjDOJbinddtYmApfaZFijJSHgcyGloGlEfibsXkxyU8QYWWLKieMi2IsFIPTKaSYwUcowhloTgJjgdzd1v0ODgsGBwf4IDbcqkZ+1bR+ePHnXfjDf9fg0sm91TqDwRUjpAxHwFiniY/9ZC9BFeG87uh+6v6onuHYcigbh7MK0Te6ljo3hqaFQpfuGRVE7I1Ufb4gRZRtkTKuvdYNxS7bxGRvF1Ek4ih3v3sXL3sm6nYzvK+FlNMiRTGet9e67ympK+w77Yv3l+7DRRN62UvAuN6ReOy8kSrI/OQRprSBwT1GSBmOYEN8Fvan52PVPmuGuzc1H0cNsHqRje0ZhU2HspBTVIKu4VUxUobmhtlYcmGkkIKdos0LKUsiNARlkXIIKePaa/0Ey+Qme6tlXSqT391ZJ0pD4US3no+2SNmPYcYfGx+TsEFAgYdV0gmD09fcaS+0H5btScPSPem4fsYAe43FmF6R+PL30+vd1srQcTBCynAEO5KycfW03li8O0VZnrqEVsVC9YkJVvECvN6GBZoYqRZDVakulwtkoIgg2w+77Rlg/yfW/XrD13C69hyByYbWSdhAKzaqmN0G+Nu5sUxSOPEY6ewv9x1Ciq5bCiwS2r8q1soTSuT9eHy0VOX3JqDArkz+m6P62GsMBs8xQspQDVYvzy0uwx0nDsXGhGws2pWCQV2r10hJsWtIhfgzXd7QIjCtvRMtgvIb6AtaykIgf791v76wkjctUkpIyczb1JFq/bBPXpEIoJwdVlPicoewydxqHRcVtkWKhVZpneKygkLctrCEiiCjyPIUWj7V8dF+hNTyfWno3zUUF080cVCG+mOElKEaqbkcUCvQr0sIAn0644s1CRgaW11IdQ8LRK8oEx/VolBI+YiI8pELJN1yhIUVnUUX6wWFWSf5k7uqaraxSLV6IoZbt8kLgPCh8ts7xNCyK4HcPbJOBLIKNqeVUX5jxlMRCmddnTukj/U4T+PimC2o6lfZx10bY+2BTOxLybOXLHYk5WFUD9MD1NAwGiSkVq5cicsuuwwHD1ZVfX3ooYfw6KOPoqCgAJs3b8Y111yjmoM6YZsGtmUwtF4y83ghtn6j7hGBWLkv/YgidH1ighAbYeKjWhRWrIaIKLpsKKTosilOsS6WDUFZtexz01d+2wYLMkOz4Rtq/f4ZMs6GjxCB4xAHtFRRDFM4qebHtD7JsVH5u9IN6Ij5YQwVXXaewED1EhFSbdC1t+1wNh78YiOueXsFyh37vzEhE8NMM3VDA6m3kNJC6fHHH8drr72m1m3cuBFhYWGqb1VycjLmz5+PW2+9FXPmzFHbNXl5eXLutR9zcHtk9sZEDOlutXiZ0DcSUwfEINalzMFpI+Nk9lZdXBmaGbppGBSugojlAsmLYGGqbGigAFKZf/Zw0Fl+71IjpNoEAV1l9rMOiBorx4HDIkVRVXDIWkfBRY1Msa0tUso17HDN+0fLoeNhnBSPtXIRUi3Z2LkBFJeW4/7PN+CBM4bhwbOG4fwXF6t6eGRXci4GdzM9QA0No95CKicnR3VGHzRoEKKiotQy79P6tG7dOtUYNCsrS3VZ9/X1reyivmPHDrz00kvIzXX0hzK0KnIKS/HZmoM4ZrDVcJiC6ZUrJyLYJVvl1hMGYYQxg7csvBCylYuySMl9VrjmNdLZBqQ2GKTszPCrrCMl6OKNXmTTpk346aefUFxsXciLiorUhGv16tXq/oIFC/D1119j1apVarvBQ0J6WzFLkWPk92NAuUxUKYppecqLt8STr32uUnDr8ge8z2NHwxgrT4tysgBsKV17bUtIXf3WclwwvjdmyPh2yog4nD0uDv/6YTu+WpeAQV1DTVaeocHUW0gFBARUuucyMzOVJWrx4sX4wx/+gAsvvBBJSUkICgpSAyb/6M4jQ4YMwcMPP4zwcHMBbg3sScnF1zKAaMpkAL73k3W46+QhGCCDCvHp3MkMLq0VXggZFE6rFIN+aX0I9Km6UNbFtueBlCX2gtBJXkMFmwu0clUGJdefwMDAyvNe88QTT2DRokX45Zdf1DIFE8UVl7dv3w4fHx/Mnj0bn3/+udpu8JCwwfJ7BVq1oCisS0RQU+hQQBUctASVcu0RCiwtpOSWv7OGPRtzttsLdUABTs+yjs1rAzBEgYk0V02r6gN69bR+CPbzwX2fb8SZY0RIGgwNpN5CKiIiAqGhoTj22GMxcuRINcscPXo0/vWvf+HNN99EbGwsBg8ejJNPPlndOqFrT1uoDC3LZa8uwyPfbsb2w1ZcxP2fbUDXsABcUUvDYUMrQrlsWH3ZtiKlrgIix8oFzsMgcRZg5EVXo2KutJCSC7NOk68nnGh99dVXOP300/Hf//5XrduyZYsaK+juX7ZsmVqXkJCAs846CyeeeCISExMxY8YMNXbccccdarvBQ0L6WdYkQpdscboVH0VRxUbEPE7YnFhvZ3wTUeUPrOrdCgqyrK32Qh3Qbag0VOsO02AUycu/7EJyTiG+WX8Id5401N5i4S/indXKcwpLcNqoWHutwVB/6i2kyMUXX6xM8ZdccokSTN27d1eD5wcffICYmBicffbZajsfZ2h9PDN3J04d1R1vXzMFf/hwLe78eB38fX3w13NG2Y8wtHroWumsL4QiptKXA1FjbCHlwQWOF9hqWVqO5/hHWRfcBkx66KY799xzVXzkFVdcodZx8pWRkYHs7GxlwSa0QBHGXPr5+ann0dLNscRQDyiARv/Nus8kASWk0kTRdpP7Mkkqld/Z38669ZPjhW5AWqOUkBJhpQkWIZEfby/UAUsf8HChS7kVs+lQJj5eGY/f/3cNCkvKMG2gXYDUARsT73z8DHvJYGgYDRJShrZLQkYBVuxNxWPnjcbY3pG45ZiBmNIvCo+fPwq+nW3rhqH1Q8GkL4R0tdOawAKNvMipYp11wAupSmG3UW4aezjwC7e2NTBzr7CwsJrlmUkoaWlpuO+++zBhwgSsWbNGWbGZsPLxxx/j6KOPVi6+MWNECBrqR2AXoPc51n1anmhlLBYhRStVmYhhVjDXLjxaoFgFnccHXXy0UGn8RGQUJtsLdcBjj0OFV1oTNR3syDCuTxQ+vfloPHDGcAT7uw9TCDL18Aw2CxcuxFVXXYWUlBR7jeVJe++991BSUqIS7K6//nr88MMP9lYLI6Q6GMv3pmFUXFXG3QUTe+E3U407r81BwaSFFH0YOdusOBe2dvEkdoXPd6bL08SgdTQvuMzoakSclCuvvPIKXn31VRUSMGzYMBUzyXCAv/3tbyqmiutpyTI0ArYLKrQtUhRSPC4YO6eDyjsHi9Bixp0IZB4nzhipoG6WQPIkyYACzV8uHZ7G47UQu5NzERXMGEIgMtgRWG8w2DAhTsPEuZ07d+KBBx7AW2+9Za8FvvzySxUHzooDrEpw55134vjjj7e3Whgh1cHYmJCFfl1NMc02Dy1GKkZK4IWSF8dgEcS8eHokpOSiSWuFhq4araQo0FgJW6fK7/1ApmUNrJhuQ7FEtz8JDrZak3Tp0qUy+YTbtbvP0EAC6c6TmTRjo4LZeFd+1PwDcnxYYgK+tpCipVElK9jrCY8h3zDg8Fx7RQ3w2CoRAR4gv6Unls8W5GBGPnpFafe3wXAkTHhZsmSJSpJjmIG/vz+GDx+uBFZZWRl2796tYjwZwsTkOY5ZtE4x49iJEVIdiNyiEuxNzcMIh0XK0Eaha067ZmhB4n0ddOyRkKJrr8BeECjAtJDiRVU1LrYvlPv+B9Wo1tC68e9qWaOYtRfUXX5OmW3TIqUFEwUyrZAU3XKRUAHpTrpOEyG1wF6oASYl8LjwkzGkgQkJzYGyHuQUoU+0JdoNBncwDIGxmxRJFFG6ziXXc2LHjGKGJnz//feqhNPNN9+sLOk6aUZjhFQH4lBGoQq67BdjBpc2D0UQW7kQumh44fQPl4smXS4eWAr4fMbLVMIBxBpELIuUXGTp2mNAsV+YrDPHTKsnUIQUBS/Fkn8X+ROxw1pS2iLF35FJCrRIqTpkDosU6XEGkLm+doGkyibI81hSwRPB3kLkF5ehqLQMcRHGImWomcmTJ+O0005D7969ERkZidLSUpVBPGrUKFWOhS48dnGZNm0aBg4ciHvuuUdlGzPW04kRUh2I9QczMKR7GKJCTLxAm4cXtMoLoQ8Qzr5rtCjJKe1JkHh5ISpT4RUionRBTlq3lPWCMTPyWuzTpuOxDK0XuvYKD1vuOwah0/3GMhcUxURZkWiJpEB2Hj824UOs5+RXtf46Ah4PtHCxWjqtmq2E1fvTsSOp6nhmSYPQAF8z1hlqhQHkTq677jpV747xmmeeeaaqiRkdHY3f/va3Kvv4qaeeUhnJrEzgxAipDsSe1HwM7manQhvaNrqyOelyFDDoZusiSdHjiUWKJmxVnVpDa5QjRkpdKPOs9+GF01kF29A6oQhiyYPiDKiWL3T1FctvqI8TWhVZ/kBn27m69gK7A0G9rLiqmqAAozCjtcvpGm5h3ly0B498vcleAjLzSxAe6Kf+DIamxgipDkR8Wh4m9Im0lwxtGraCoWgiPU6xUuAZv0LXnkcuF7mYVit/IELK9uzBR0STn1x0KbRovaCIcmZ4GVon7LtXSiElfyxhoQLOBW156n0BUJQIpCyyrFPuhv+gWCC3FiFFke4jQorPrybEHagaZM3n9isvr0B+UTlSc4qwaKeVtp6UU4huYQEIC3QRiwZDE2CEVAchK78Yh3OKMbKnadHTPqBJ2uUioSwM9bBIubr29GjA0gc+tkWK2X2Mxaos/tn2cO2mwDgI5zpm57QL6NpjVXPl4ZXfjH34iBZSAdHAsD8Dq261rE/uYPPjpHn2ghv08eAnE7JyZ4ydg4Ui2JKrZzU1JXO3HkZEsB8ePXck3ltmZZdm5pciJtSIf0PzYIRUB+HuT9erdgjWKGto8+jYJSd0udANV1eMFEWU6qvnFBA0RzmODWYC0uKg3IXymm3UIsVG6c8//zwOHrTifpiF88knn2DWrFmqbsxHH32kasZs3LhRbde49gpsE1Ao0SJFlFVRxI6fCGKnC6/n6YB/LaUL6B6srTAnjwe6fmnxqlYZ3wGLwzobYjcxi3eloW90CKYN7AJ/+d0OpOehtLwMwabQpqGZMEKqA7BwZwq2HsrGxRNtU7+h7aMuhK4WKVvw1BUEzIuhCkB2nP5O1x7xlYulCkxmdiBde20v2Dw1NRWbN29WGTbffPONWseaMOzpxyDTL774QhXYmzRpkrrvhNt1c/Y2BWOgKIiZMMA6Y8EyeapwERSnLAKG19DTkL91WS0iiFZKiuyaXHs8LinmeOw0A8VlZdiUmI1pA0QACieP6o5/zdmG4tIKU4TT0GwYIdWO+XjFATzw+Ua8NH83nr+8erqmoY3DrD1flwsFLQ8UUszIqw1upzVLl0/QOIUDL6i0eqksLXlsGxQV8fHxKmWZBfYoqkiPHj1UTZg9e/ao2jFxcXGqTgxrxtDdxzoybP/AVhAsEtrmUMkBtpCi+A2OU7qqGmx0HFa9gW8lKiDd6fJ1geKJbl7/SPdCinWsGKPVTIHo8ekF6BISgCFxoWr53LE9RVxV4OWfd6FfF1Oyw9A8GCHVjvlibQIum9Ibfzl7BMabIPP2hSrI6SKkaGVSFoU6rAEMVKdrj1anyguei2uPr8OLMjO82qJlRmA/v5UrV6rKxX369FHrWKn4pptuUv396L6jhYpuP/bT4jKtUKeeeiruvvtuVem4zaF+RhG+rPXE5tN9L7VcfJ6iLJqO2KeUpUDmFntBYIyUdu2VuxFSLAYqGr+pSiMwsPz/ftimBC9hGxgKphgRU5rnLxsvc4pOiAxyOT8MhibCCKl2SlpeEfx9O2NMr0iMiAtH5zZ6MTTUgI5VcULXHq1MdblV1EXOtkhVxrI4/XqCEmQiuJgByAtzG4RtHlgLZtu2bTjuuOOwdetWzJw5U/XOSkhIUM1Hx40bh59++gm33nqr/SyL/Hw3IqEtQKskfzv+tnTBDbrRKtbqKRRSpQ7XXuIPVpafRgkpeX1fxki5+Y4YX8VDyZkR6kVW7svAMz/twP9WxKvlz9YcxNT+lltPwybE3/xhhmkPY2g2jJBqo5SWVc9EKpOZmpO1BzLRx5i22yfMOFMXNDcXCmVRqMu1J0KK1iu6cVi8UeEipPjafBxjXlyD2tsQV155Jf785z+jb9++ysXHCsZ33XUXrr32WrWdhfe4ne69dkFAlGWJqpw41XMCRZegM8uRQpt1qTQqyUGOHT95nLtg85ydQGQP2VZDRl8jeXvJXrxxzWQs3pWKZ+fuUJapE4Z1s7dWERbop6yLBkNzYIRUm6QC93yyAXtSrFiGjPxi3DVrnQq81Ow4nIPYMBeLhaHtkr0NyE+w7qumsyJyXGOciCcWKSWk6P7xlwueHQ9ju0oqYQsQWhzoqulsBHmbwU9EFFvBNBSdsKCPB1qndCYgUTXKaPWSscVdHFTOLiB6onXseJlth7ORnleMM0bHYWyvSDw+ewv+du4oe6vB0HIYIdUG+XV7Kj5eGY/vN1mNZH/Znoy3ZKa2ZFeaWuYguCM5B0NjTRXzVkMRs6kawdZ/A0l2bR7GLtE959YiRZdcHUJKPVcumCyVUM2155jBq75sIrIKk6x4GEPbgLWkGtMXUQkpEUnF6dYyRZTz2KWIpwjn49wdZ4UyJoUNlm3et0i99useTB0Qre5fNrU3lt53ImJNLz1DK8AIqTYGTdlfbziEOXfOwOLdlnD6ftNhvHXNZPy81ar/kpRThEOZhRhmhFTrIG0V8E0/e6GBMOCXoobwYsY+eO5KElAc0f1SG9otSJedFlLKhecYDhgDw228MNJdZGgbBMZZ1sTGwOOqyBZSdP1qUUW0q1cdZ4wqd4FZeyF9vW6R+t/yAzicVYhrjrLOo4hAPwzubsY3Q+vACKk2As3aDLL8an0iisrKMK53FLqE+itrFMXV2WN7ILe4BHlFpUgWIRUe6IuuYWa21irwxkUlb3/VxU0FgdutW1xRgeHV4+eOQPVLk+fyT7v22ApG1Zay8bNdewUpcmE2F6w2Q2BXSyQ3BpWEkGndp5gudVikKJ4ootgmhseRK9zONjNeLH/w0YoD+GR1PP514Rh0CXPjzjYYWhgjpNoIczYlYuGuFPwot6eMkIFKGBEbruIEhnQLQ6CvDyKC/PHekn14ft4uDOkejkA/8/M2O0m/AFmOdHFC9wgDc3Wz2NooyQNydstjXWKW8hJESNkVp+nao1jiBc0Vulzq6nNWSteeXJCcMVK6XpSG7iFmXhWlGiHVlgjoYv22jUG59uwAc8bIFdmiirCtkC9dhyKkXC1SFPs8hoJ6WMeOF8jML8a/f9yuAsx7R5tYPUPrxFxp2wBFJWXYnJCNh88cgZeunIizx8hAJUwdGIOdSbkY399yvfxmSh8cyCjAZZN74o8nD1brDM3MzpeBRJdeZXSPMJ6kMkOuFjI3AmvulouUS+YdF4vlgsaMKrpXdPaUK1zH7bWhCnKKiFKWB3ufKrjO8XoqaF3WMZ3d38RItRlYNyr2JHuhgTBgnS46wuPWGWzOY5CuQx4rtGI6yd5uiTjGaVGsezJxqIMfNh3G+eN7ISbEjfXVYGglNEhIJSUl4bPPPkNhoTXYZ2VlqdosbLPA/lUsevf5559j/fr1arvGpKM2jJTcIuQWlSI2vLrJfnC3MDx01nCM62UJKcYM/OOC0ThxeCz8fIxGbhFUQUJHTAlhKjiNRNr6Uxu5u4CcrUdepKhxWERTZVHJH+OclGXABU9ce7QkqJpT8qfT1JX4clqk5MJFF2JJhryPVTXa0AaIGG4FezeGwBhLMBFaJp0uXx57PO64zrWnY+4eS4RRZPFYVZbThsMSLxsPZeH00ZYF3mBordT7astO6fPmzVOVgp988km1Ljg4GOPHj1dtF9LS0rB06VLExMRgwIABaruGQsqIqfqzKzkX3cIC0TW8usk+JMAXN84caGZrrQkGhBcethdsGGtEbVNTbZ3DP8k2O6bkwKeWK8UZY0JXXaCIHF6c6DLJ2mw1nmW9IFcojuq6gNHKQIuTElLatSfrnBdMXY9KufaMkOpQ8NhijBTdy+zXx2NBWzk5UaC4YsC5q9inSzqgq3WccntdLuY62Cnj3v60fJkgmuPP0Lqpt5DKyMhAUVERJk+erAQUrVF+fn6qEWhkZCSio6PV8o4dO5RlSpOSkqJ6XLXJtgstQF5xKeZts7K0tiZmY3hsOHw6GxHa6mFsScEhe8GGTWD50+kAXldW3Slq+VXrftJcIHywCByHO4XCzD9aXoNWIjl/0lYC0ePtja6IGPLItScXR58Q2SdHjJSra48p7BWy4ww8N3Qc6JorFgFdZFsjeSzomCn20eM6d1l7PE6ZsceDXYnyxgkpBplP7BuF0AB5L4OhFVNvIeW0KLHJJ0UTOXToENLT0xEeHq6qBZ9xxhn46quv1DZCkTVq1Cj4+xvriSek5xbjjg/XYcXedGQVFGNIrJmVtQkYI67jSzTMfArhxcmR/eSEViZapfIOyhkZCAT3k+c4rFe0CjFOKbCLVZSTcUsseugO1odydbm4QksCrVF+wXK/qGqdM9icF0pljZBbU5CzYxEQI8ecCCa68VSZDBmzaYkitGDSQkXR7Vpmg1ZNJbplm3LtNU5IrY/PwpT+Vt0og6E1U28hRUHETumvvPKKElKHD1tujF9//bWyzcKqVaswf/78SpFFeJ/tGfhcQ90Ul5UjNjwQf/5sA3Yn52F0L9N0uNVDwUSXBi8+OumOt1wfOaJqVu+Kvwgksu0poMdpQFCcCCYRVRpm69FlEioCK3WJdYGLmWZvdEFVnLbFUU1U0LUnFzufMLn42RZiXWhRwwkTPwdjpYw7vmNB1x5LHuj4OB4rPIYJBZWKzaNQcrFIUVgp4cXLCl17nsVIlZRVYKVMGPOLqx7PBJt8+ZvY1wgpQ+un3kKKQug3v/kNzj77bNx3333o3t1qiHnRRRep9WTEiBGqSSh7WDlhfJXu2m2onQPp+ThrbBwumtATmQUl6Gbqp7R+aImiSyO4hwihvfZKmZUzZilsCFCYaq9zIudDSG+g5znAhufk5Lnfsj5lb7W3C/mJ8phBIqT6A4d/lvcQcRNQg7D2pZCqI/WcQosXPF0riugAdCdKSMnrGToWrEXFY4SuOmbo8ZiiqCKsZabElRwrrjFQymVsB6JTlLvL2ktdBuToc8Nizf50nPviYpWBrGE4Q7+YYAT41vsSZTA0Ow06StlVXVufQkKs+Al9Sxgn1atXL2N9agSbE7IQGuCLW08YjPtOG2avNbQoGevsOzXAGCg/ucj4icihm46oWbmcZsFyvhS7uPwIL0a0BnWdLieWLEeOlOdHALmOi01RilXkMKiPFR9Fy1RNKIuUi6XAFWU5EGHOi6RucMwMPadFivC1mIVl6FjwN6ebjhMDJablONEWKU4MuI6uu2oNkWVCwOPKV44rLaRcLVJ73wfmnSSTgR/sFRYfr4rH4G6hWH2gymK7Lj4T0wfZllqDoZVj5H4rZdOhbMRFyoAlTOxnzNutgo2PAgnf2AtuYHCuPytLy6xcB5yrDDpanfpaFyZXKKIopiJHAactstYFimjKj7fuE2YCsshhcC9LrEXVFGgu0IJUV1VpFVguFzwfEX36sapSOpWcA8ZHBVoWZ0MHIkDGG2WRSrVEE49nxkbResm+ixRJSizZjyesb6YsnXL8cbtr5XPWmNr+PDD8Llmo8krkFpZiU0I2Xr5qEt5cVDV5WLUvA9MHx9hLBkPrxgipVsQbC/eoAEtCi1S/mCorn6GFoZuCxSs3/tVe4QZanOgWoWsuf7+1Ts3K5YrDAHJallyh0NIxSLRKEYouVonW8IIWIiKKF7hQ2RYx2t7gBo+ElFzgeHFkrIsOGFa3LkIqcozs09H2gqHDwKw9VYyVrj0RUhRPjI1inShmjxLGAiorlLY60c0ny6rQK13Ecuu0jKYsA/pfCXSRY5zJEzazVsfj3LE9MCIuDL1k4jh3i5WpnJpbhL5RHiTYHPoO2PW6vWAw1I+AgKqQmeLiYrz88su4/fbb8b///c9eCyxatAiXXnopcnNz8cILL+D3v/893n77bXurhRFSrYjXFuzBkt3WIJMtM7W4cLkoGloHyh0mv0ehiCVnILgTFuKkW46uvQJHg2G6QRjT5E7g0Bql3CQOGGzu7M9HawCb0ZLJrwHhg6z77lBCynbX1YQSUvI4XvD0e6sYKZf9GPhbufhdYy8YOgw8Jkrk+GNyBOOhKLh5PObtE32kXb0UUjIB0IkNzNBTlir5UwkXtEjZIp1krQeiJskde0Ji85MIp4smySRBYAjDPZ+uxwOfb0B5RQX8fJ0mrxrghGPf++7PLYOhDlhE/J///Cc2bNiAvLw8hIWF4fnnn0d8fLxKpiMsPs6Y8M4yPlJsUUyxQoETl5HT0JLEZ+RjR1IOUrILEejvg+hQmdUZWgcUJ8xg63sZkLzAXukCLxDMeGI8kw4sp0ChG4TxSG5bZnBGz4uSAz6fF6RSWxAxg4quPRJ3siWAakLVf6pLSMkFjgHEfB3lzquwBZ+LRUq5dapmbIYOhLLAMuZPJga+8kehQotUcG9ruz5WGEulbrWQkvU8pnirrZ2EAeZhA+R4kuPOLqWwcGeqMsZ2tyeM4/tG4e5ThiDI3w/XHF1LHGA15NhNWCj75rDgGgwewnqY11xzDQYOHIjS0tLKZDjGd1M40TLF2yVLlqi6mLzPP9ekOSOkWgnL9qTh5BHdVSHOn7enYIBx67Uu6Oool5Nn4O+A1BX2Shc4gw8WERTcR4SUbZFSM3a5wNDd57ywaCiWXIUR3SO84BTbr8F2HczY8wQ+V1/cakJZn4KrLnp0z7A3Wm0CzdCx4KShMMVyJ/OPxykr6ofZ1lBtLKo81iikeDxxUiCXFR6HTkFPt3dAd3ldEeZ2Ydq3F+/F6XYDds1VR/XDX84ajjPtfqJ1wnpnfMvDc61lg6EesOpAXFycSpajNYpWqNmzZ6uEuszMTFxwwQXK1Tds2DD1uMDAQHz44Ycqoc6JEVKthE9WxeO0UbHoERGEHzYnYnwfUzeqVUH3Gl0cQV3kApEPlQbuCjObArqJaJLfjhcYPo7iiReXzrRI6XgSByzS6RtmLzigK48XMsILkqe1nCiMtOWLVjHXFHWiXHtyQePFjhc/7peK1TLDgcGGx4aySMmxSasUj2W69rRFSiETCy2knK49oo4re+JA95sSWXJ8daLFNB/xeXLol5XjhOGNTGagpaz3WUD6KntFE1FXJqyhTaLdd4QiaebMmUhOTsZVV12lamSy/mXfvn1x3nnnKfF01llnKWsUXX1OzMjZSvh1RwpOkkFlULdQfLshEZNMRd/WBYWTvqhQVGmLkxO6LJjlRsFF6w7jSpR4EhHEmk0UKjqmRMNWHP5uhBSDylnBnCbk+liK1IVMLmp83pq7qmf/aWiB0jFSunCicu3ZF8FGos3fThjEeckll6iWUoSzvSuvvBIPP/ywWt6+fTumTJmCd999Vy0bWhgeG8xC5fHOiQHj/1TSQx/7ATZaYGghpTM/OXnQgegsG8KAdcLyCBX52HCwBON7R6JnlJWZ3GAYi9jvyqqYxKZi9e3WpMfQrmF/4Ouuu071CqYVSpdwYm9hjmksKn7FFVega9euar3GCKlWwIG0fBSUlqNbeCAm9YtSineK3BpaEcy440WF0FqUNN+6r+GFhBcVJbbkosGsOMZMcb0WFXSXuGbu8eKkX9dJ+DCZyYsIUmUJ5LU8ha4OWqRYDX3P+/L6Ln3/6Nqnm5IxW7zo8YJJCxUtZ14QUhxsUlNTsXbtWjWzI4WFhVi4cCFuu+02fPTRR2odOx88+OCDmDZtGn744Qc888wzmDNnjopXMLQCaJFSVlg5nhlgTlFPMRVSvRG9OpbUrS2kQHFOeMzbs/205SgP7oNkHmYQIVWUhdd+3ass8I2Gx3n0BOscyq7q7ep1MjfJuePGNW8wCEZItQK2JWVjUh9LOPXvEooRPcJNo86WJvFH+44NZ6Osu0S6TAMOfm7d1/BCovrSiTihRYpxJTrgnOKFUOToCtEaXpzcufZYCb3goLyuDN6cxXsKLQG0RvHCJ7uEdJciovQQKosU91X+VCV0uRhyHd0vjYS9OGl12rVrV2VmC61N48aNUx0PEhMT1bqSkhJlNucMcN26dSq1+LvvvjsirdjQQijLkhxHFP/qGCmQY5V10pzHqhxMTosUJwxajNP6qt0m+Xuws7AXzn1hLf746VZs3ZeAu08dgpE93Ewg6gvdjzyOo8cBGavtlU0AJzT6PDYYXDBCqhWw+WA2Thttzc7YEuGd66aq+4YWIl5E0sIL5cJhBcUqyvLkgmK7IbrNkNnvNuu+pjLGiBcSucAwyy7/gNpUCYWUa2yVKplguz2cRI4GsuQ9GIPC7DlPoRhiPBVbyfQ6wWrJ4YTWKr2f3B9m+fE91P7bF8FGwDZQgwcPxsUXX6xM44RiacuWLUpQxcZaxzlFVEFBAQ4cOKAyZrp164YzzzwTW7duRVGRi/vT0Pyo40KOCRVHJ38U2yqQ3AEFTKWrmqpdtlc+xnFpKU3C7P1RuHF6L/xm2hDEBpfgGBcPYYMplfOS+8pkDNdz0puwHIRRUoYacCukGGR1ww034PLLL8dpp51mrzU0BZn5xdienIMZjnYI/buYjL0WgxagHf8REXIecPAre6VQnFMlaFjniQU2U5day4TuMRa05IWHMJZEVTfXwkXg812rm+vYK1cYI8XmxJlycWA3fk9RViURUvs+AIbdLrP0tdZ6jQpEl+3cJ/7RGkbBSLeFjm9pJEwjdgZxMhuGacbvv/8+Tj31VCxduhQnnHAC3nrrLaxevVr16Tz55JPxxz/+Ud13FskztBB0/fLYZZsgVZtMhARLezjhsa6bXstvroQV/4iyTsnzacn1j8Dy5BBcMqkrpo7og6h6eKrrpESEFK1m7G+Z24SuPU6kaOk1GNzgVkjR1E4RxTS/77//3l5raApmb0hEt9AA9PLq6GJoMCmLRCj1Bkb/BUhfY68UeCHhBUXT52J57GJ7QVAxRiJEdN2lEBFa+QlyIcmusmRRSDmtXER1069BODMW68AnIqSqBzbWirYqMT6r57mWcHIUQLSEnQgp5eMT2DCZVdiVwGq8Raombr31VhVw3r9/fxUXFRERoeKi2PicnH766SrQnAHnhlYABTb/tDhi4oSvSyYxLZqV2WwU4rKsj78K+xgrTUNydhBCwrohlDq9nJcce7LhDXhe0mJGkecuAcRb0LVJYWgwuMGtkOIMktk0nB0ef/zx9lpDU/Cv77fj0inesnMb6qTgMLDqbnvBDXTr9ThFREysXBvSrccTBt6yIbGm7yVA0i/2gqBdH9oiFTbMmiHTdedjW5woqI4QUiJy2BTWHVHjZH8+led50CpDo6xfMuBHs4q0QOtZ9k7rPlGCiY+xhVToICBnjzxFZttecO0Z2gkM3lZCSo5nCiS6+dj+yImySOlgcwopWa6Ms5NjjAacklR8t7McPbvZdaEoeuiKo0vOUxZfCWRsshdcoKWI+8fWNYwLbCpYR8tYpAw14FZITZ8+XZVN/93vfof33nvPXmvwFqv2peOM5xbi7BcW4a5TBmNkDzcxMoamgf3Dtj9dJZCc5Gy3rFCxIqRU5p0In+RfrW0q0NZRkoIChduX/daqk6MLEmrrU1B3y43HistsD0OYycfChE5UjJQttFzpcarsU4IM4Iw/8RDlrhPhNfR2a7m7TISc7j2Vpi6P0aIpZjKQusQaCVxjYFo5jLmia3DNmjVYtWoVDh48snUPU5WZCcjKxISP/b//+z9lFUtKSsLRRx+tWj7QCu8kOLiDW4gDouR4l3NAWaRE+PCYCnMpCkvhpItuMs5OiS5bSPGW7u6SNOzODMCxw+3Jojo+5fV43HsChX/qQvdlPAi3830DulhirtR2NXodeR/X0iUGg41bITVv3jxV52XMmDHKJG/wLot3peKMMXH4+g/Tce3RHlasNngHxmxQlzCGyJU8ES2qbo6dTcQGq7QIEbrg2EPPydEyyYgaA6y8zbrQ8OLBiw6hi49WrUPfVAkwCiYW7XTCZWb5uYPNi7tPqRI9njLmcdl3O2Eh9sQj46R0jBSh4KN1IE9ECK0FbQgKpx07dqgMQYqqlJTqpSXY0mHQoEF45JFHsHix5YadMGEC7r33XhX0zqrGFEy8P3ToULWdsV0//vijcjsGBdmiuCPCY5LngnLXyXHtJ8vVinEKzPzUrj3t2tYWKbrBywtQnpeHPt2iMSTWYVXlceZpTSbGECpx5GYyoc4dEXs8lpWAq7AmSt5GGaLkHy0aDQYX3AqpqKgobNu2TXU9di2Fbmg8WxNzcOKwbmCquKGZYdxS79OAxHkymLu42bK2WjFDnOGS6PEiQtZb9xnU6ppdR4vU0Dvkjgzy+z+RQT2k6kJCIkfK+8ytEmAsbOhsRkxKa3Ht8fXjzpTn1bOmWNRY+44QPhzYK6Lx+0nA+gftfeSFxz71ub/hIiIKEuX9HPveBmADUWb4sZSCa4A7YVYgxRArFvMxGsZ+6srEb775pqp99eyzz6pl3qfYOvHEE6s9p8PB+CeW8CAUU3TzsdisE54nFba4KKNrj6LLFv20OolAz83PwaRBPdHLeerweZ4KKVpwWeJAV1B3UijHrNPdzvMl36Vumjdg6QMKKddz12CwOUJIsZ4L670wNorVO//617/aWwzegF3Nk3MKMTzO5aJs8D7uYhpYkqD3BSJyRgCH5tgrbXJ3yfpR9oIQOca6TV8tryUXaVYnd8ek/wALfm89ximOux8jP7jcatcdRQ2D2Xe/ZS0TzuQ526+JMQ+LWLvNXmgAtK6duxeYKPuYq8sxcB8doqmXfB/qguh2XtVqOeqoo1SQOt1048ePR69evewtFqNGjcL69etVbapJkyap7u7kyy+/xPXXX68KhVKM8U9XMCZdunRRpRsozjostKiyNx7hMR3UUw4Zh2ghtCzpxtqVMVL2McTJQXk+Vm9dhzGDRlQ/sjghcXVx1wQFPgVtiRuXXZ4cz7RIaYLk98+XY93bqPgoOZF1hqLB4MIRI2dCQoIylW/cuFFVKF6woIZO94YG8fqCPTh6YD3S2Q0NZ9drMti6DKzFbMTa1aqGzG72ThjTxCBxJyyMSSHFiwlTwd3BGjb9j5fB3pkdJ0RPlgFYbnUmX7eZcvV/U4SU/G0XYUMo9upTubwh0LIQHAeU6f2T015f8Ei3Y2UVLVVtK0aKlvOMjAw8/fTTapzScVBO7r//fiWoKLpYDJRWJq4jtD5xnPP398cdd9CyWEWHFlEKORZ0vB+hNVVbajV07VX22iu2hJXGNxBJKWlITd4LnwiXBsS0sNqNi+tEWUrlWBVRdgSMc/R3iDs2DM+3Cr56FWWREiFlYqQMNXCEkGKsAAcYNudj/AEDMg0NZ83+dCRkVs1kXl24B2d52tnc0DhYOsCZsUbYP4wXCLrbdMaRhgGwESPtBZtYFrVcIYNpmQzoNQgpMmMWMMQlnpCuhm5T5L3smCvSdTow6j7L3aaQAdp5wWoqKALpniQUhU4hRQPVqcusAPo2Rnx8vLI6sVwLi4G6wjjPc845B+Hh4SpeioVAWWWdUECxu/uFF15oale5wkrh3U+0FwQmL/DYdaIaEGshJcKTwkoTFIxv1u7ClLgSOb4dxz9hzGCRh0KKvSiZWaqPXSd07flX1d9DcF95XZcWTN6AGYt0G7MEgsHghiOEFKFF6qefflLm7Vmz5AJhaBAHMwpw/+ebcMXry7A1MRtPzN6C8b0jMMy49ZoHBp66FsAslIGW5QhYedyZLs0WLxwoWdjPSZ9LrMKcjC2qTfAEyoDOWCNXTlsur1nd5YTw0fLeCXJxyLEtUo4LUFPBfWeBT5W1R7eeSzxU+BBZ37YsUmT48OF4/fXX8be//U0Fjxu8BEtvxJ1sLwhhImZcC8OydYwqRCuoYPOqy8nB5FJ0CSpF34h8Ob5dYvwCu3nu2qNLjwKfx64rzIily1HD/pSqCK6XoVika88IKUMNuBVSM2fOVEGcEydOxN13u6+54xrY6brMxrsdnXlbDuOYIV3w9CXjcLmIKbp5Xrpyor3V0ORwdurqQmCvO8YksSaOU2SxvIG77LlgGagLZFbsGkjeGEL7yX7kWjEezYXKopKLnoploZuSZqi2D/v5sSP7P/7xD0RGumRVGpoWHlO0RBEVH2gJ8bnbs/D4D/twbM9CEUAi3J0B4US59uxg86QF8pgaBArdhqwTpYSUG4sUXXvOYrV8HM8rWpC8Cc8bFuM0rj1DDRwhpDZt2oR9+/apoPNvv/22ssmoEzYXfeyxx/D111+rZcZSvfjii7jxxhuVNYvZfn/5y19UdowTmtU7Uqba3K3JOHdcT0zoG4VPb5qGe04bhgDftjfrb7OojvUuQorLFEV0tzljmtJXyqx7sL3gQkh39yKrMdAKxcD35rICMb6Fdaw4Y2dGVhu0PrmDPfwYfsDxasOGDfZaQ7NA17W2SNHS6e+PVNFEj83ejCumDUNUsfwedL25Wlxp2dJ1pDY8UnXfFZ6r/GO7JXdCqkiuTc4ioQyGV3Fb9j55C8ZIsWBtk9WoMrR1jhBSNJX369dPFbJjZgtjpZwwuJO9+JjNx9gEDmDMcrnttttw7LHHqnIJK1asUNsZv6DjFvLz81VgZ0cJ4ly0MxWhQb4Y1dOKDxjUPUw1JDY0IwyBcm0SzFkls+g4m3YOzgdlUsDgcHdEjLIGaW+imhJvlX1pxvIinL1nbLRElbesay3McccdpyZ1rA3F7D1DM8JzSNeR6lyB1IxCXPraSrx+5QTMHN8fyBPRzlpqrvA4pOWI5O+RY9G6q8h0VDCnwKLlKphCyiWRgxTJtiDH6zOrlrGA3i5TUGpbokzWnqEGjriyMw2YFiXC+isPPviguq9xBnSylYwWRrRcpaWlKSHFdbQ8MYBTP54meM4YmWrcEfjHnK24Ykpfe8nQYrC1SzXkeGSWHGfJbNSrYdFKxgm5o/9VRwbaNhbGc7CFjL9dGqE5YCmEHHlPZ72fNg6z8FiuhR0YTKZdM6MsUvY5JIfTCz/vwWkj4zCkm4iZYqojubyEDLC2O6Frj+cen8telM4okAXnVL0mrVG0LjH+0F0dKSaLOOO2aHHlPrmzXjUGlbUnmIKchhpwayLp27evio1id3ZnfRUSEhKi4qEYQ0U3no5LoHVKx0mxWvD+/fuVeGJmDGGNl6uvvlo9v72TmFmALYnZmDHYkVFiaF5Y8C8kwJr56ng9zlTpgqBrjzBzT7v+GJjO7CB39L8S6FndMttoaOWiqKmpGGdTQEsAxVs7ElI7d+6sbKxON5+hGaH1x56MlBaWoldMGC6faidWKPex3Ia7OaeYtUdLFoPFeZ4yvopoAaQnONnb5ZiVx+r2L06YHML6Tv4O1x5jsSim3AWmNwbuKyunuxNzBoPgVkgxZfiMM85QIoh9qJywUvCMGTNU24U//elPqscVrU60PtEdSM4991x88MEHuPTSS9WyhgXwXIPS2yNzNiXilmMH2ksdFFXArgUtBBQMEaNlUJWBWs8kOcByANY95ZhNR0tUDkskyKivW8O4QtHhbeERNR7I2mS5GZsL1rvK0q699iGk+vTpo0ILON5ERNTw+xmaBsYN2gHYqdl5OHNsD/TSh7OKi5LLi2s5EcK4JlqacraJkOIkx74mqEBxeT09blBIBcZZQso10LtMJkUMRHcWyaWI4iTJtZ5bY6HlSwk049ozuMetkFq7dq2qIcV04vvuu89eWwXLIjBLhoMYKwbTasVK6N26dVPbmUXDGCma3DsazFbcnZKH00a1vZo8XmXfh0CiZSloEfIPilBibzAZqCuFFK1TDiHP+A0O1jl7LFdbc0IBxUxBuiKaC5Z8yNnVboRUdna26rHHJsSMx8zJ8bIlwlA7LDTbyRI9P2w6hB6RDlHDGDyKolA3CRy0SNHCxGbfNDRpaxOFEe/rTEAKK8ZhMT5Rx2JpaLVy1kLTsPWRq/WqsVD0sdSDdvEZDC64FVKMdYqLi1MZfKYgZ/1IzS1GSWk54iLkxOvIsPZSZjNlUSUvAtaxWrUj2CIv3ipdQBeDLrxJIcUBUcOmwAy+ZvXz6GYuS0GrGOvS1NR2pimgkGLlZ16A2oGQevXVV5V4+te//qXGKxNs3sz4BSEtKxez1mVi/rbDcnxZYRwKxv6NkHOSwsYVfezzHOVhSOsSKaHrXUSQtkjR1UcLE61Beh0nQstvsiYE7gLZVVa4FZfrNWgNoxvTlD8w1IBbIUXXHaubs5s6gzgNnnMgLQ8Vci53DevglZKztwJFqfZCE8AaMox3Ilki2JLmWQOvJn+fCKX+lmjQ65nlw9mwhgU0UxYAuSKkYk+yVzYTPjLLZgkGZ2XmpiZUhCMn1YwVU93y2zaMwTzllFNUJjGbDHeEsIHWRF6JP9btT8LKval46sLhclw5xHlnGf9GP2wvuIHWHU5gorpb5yWh651iRVuUWLCWvfQY+6Tjk2hdZuunFbfINnfWXFrCvCykOBFTbkzj2mvv0IjEpDg2PCdMjmO1AcZ8E97SwMSST07cCqk5c+aoF7jyyiuxdatcEA0es3R3OvrFNKOVobXCGWNhWjUjkVeZd5y8xw7rfkGSHPGZ1iCrYY8uFuijRUoLKVqkGGCuCektg/kBa7trZ/umhkVBmb3UnDFSnMFTSOnU8zYO++e98847quzKxx9/jG3bttlbDM3Bf1ck4sTRcfi/8wehS6hcSirqYeXkuMBzlL0sy2yXLF17ZaVV1idaqmixpfvbmTnH7Nqwge6PY2XtcowD3kAFm4sw9LbL0NCqYAz3Z599prq6fPTRR2odBRWF1BtvvKEmbrSCr169WgkuJ26FFOOdWPqAWTCMlTJ4zk/bkjCyhwl6Ra4MZqwq3lRxBcnbgXy7HQStUxyUnaZ3urBYY4ZCRVc354yXnec1rE9TKOtooXGtvtzUMPaDjWD515zQaKOzoto4TGq56667cNNNN+Gee+5RgsrQPPAw+nEr2y3Z4qlYZvAUG57CTFpOYILkHNSCiK49iIiqFE2yXdWG8pV1triidYjB6uOeBIbdZa1zwixYdi/wJrSGUcx5W6AZWhxnj82srCxVcYAVC2hxYmkVJrAcPHhQGZTYJD0hIUGJLF/f6hb9I4TUmjVr8Nxzzyn1xYGJRTkNnlEug8O+lDxM7ufSW6ojwqQdFVBdQ9XixsDWLmpGawspuhAZQO18r0LZxiamFE5sVEyYas10ag0zizg20mTf3BYpdsqnm7E5XXskWP46UHcBQ9OwcFe2KndQmQGrMtvqE84gUowB6aG9RPjYWXYsvkmBpV1onPjohsd6ksSSB4zvY5ulwb+z1jmhlZeTKG+ihZSOtTS0G7788ks89dRTyl1HUaVb2zFMgJ1YCKsTnH/++cqo9O6776qOLq4hT0cIKdZkOeGEEzB16lR8/vnn9lqDJ6w/mIXe0UEICWj78SeNgjNNf/kOGCTaFG4kWqKoBdiAWCEHf/QkyzKl4cCrLFKhIqBsIaVmuC4il15YWoUoxJoTzrL7/QaIcNPouCkJ4ed3a4g2GDzmu42HcOlUuuW0kJLzrT7nEN1kPvL4gG4yXthjBM9PdWuLJrr5dM03lc0n57kWNTXhy8mbl2MzuT/KImXvV2si4VuoenSGBnHeeecpa/aoUaNUjUtaoVhxgCVVGCrArGAttFhsfNasWXjyyScxYED1QrNHjKhDhgxRvfR+/PFH9UIPPfSQvcVQEzrIdf2BDBw/zFEgrqPCZsG0RtHM3hSzOKZNh0VXDbwUUqxXU5RsLdIEzzgLBohSzFXOcPOrW6QIC2M6+3U1JwOvl5m1m8rPTQkDzo1FytAIVu/PwLI9KZg8UCYq5SKkKHLYRkXVjvIQxi76x8j5KOdeZQyj3HIo1YKFVyctpFSpgzI51YtrF1KcOFVOsBrBnveBlGXWfZY/UO1wWqGQSpoPZG23Fwz1hYXFNbRA0fJ09tlnq5qYFFM9evRQnrlbbrkFXbt2xbhx43DmmWfikksusZ9lcYSQuuiii1SNKKqyZ555Bo8//ri9xVATM5+crxoUr9iXjquO6mev7cDkJ0AVuwyMEtFju9+8CWs/xR5viSWKJmXq7yNnhR0AqFx8dkwFBV2lkMq2Aq6d9DhVRFkNFc3bI2G0gBmLlKF+FJSUYV9qLh6fvRX3f74Br181Gb5BdO2JwKC7jX3o6hMjxeK3FPXBPapce3S9B/J8ZayUwPRnWpgIrV2csPK9amsgTld+oRdK9mSsBnJsgaKy9ujaa4WxhRSYujuDodEwDoq1MRkjTiHFNniTJ09WNTMJDU0UUyxM7sSMqF4gp6gUry/YjTX7M9Et3FGnqKOi2q0MkdlmdyBzvb3SixTEAzFHy4ArM8XCZDmKObuNlkHWHoA5IKsCNUJnmuTt4FVud5Y/ICP+BMSdbi90AFjd3FikDPUgt7AEj327WUTUNoQF+uCTm4+2+ulViLihpaZczitltamnkKI1ilZrLZwYE8UxQy/zONVJIKrJdpm8Dy1ftYyxtGBpV35j4D4wZouoz8YLpxU/o8jcaN9pYVhXq9TLwfWGemOEVCM5lFmA6QNj8OfTh+KYIY4Gmu2ZzM0y0MgsrSYobmjlCaKQaoIBh0Hm3WfIICIzxIwNMovtJgOuw9XHrB0GnRJ/1qCxLVKM13I2OSV8bku59loCFiG1AyoNBk+4+9MNKCmpwEtXTsAdJw5BRJDtwqOIUrFDMhaoEgH1iJGiiGIdNR8RSBQqhHWjeN7ytVgjjoepfk3V645CirFVzSCkimVfdJIKLd8MEdBCiufPipvktgktVB7XRJPHsfGzoUUxQqqRzNmYiEHdwjGhbzSevGicvbads+waYOtT9oIb8g5atV5YEDN9rb3SQzhQZtbRfJbB5jFTrIHs8E9QzYZDeomAs4NMGSPBGlKETU31gKgaFtfiFugIsBWOq5g0GGrg+82Jol/K8X+XjIW/j8vlQgupEsY2iZiqT4wUXeyqt6UPKnvY0QIUIEKKpQ5c+9opQSXnL9ezZUxN0BXY2ELAHCcYsM5QAKJaxNDFaAspCrW0lTLOVK8l5DU2PAwsv85eqAOKvKYsfGzwCCOkGsni3WkY0r2WE7s9QsGS+KMMNDrY2wXGKNHywZYkLEPgKcw+WXw5sPQqe4UbGPDpdE2lLrXjsbrJgGKXP2DMABudEg7MOiuIh3s7adbbYChAB95gLxgMtTN7QyL+eLJMimrCR8RThZyTnAAp95uH9DpbjsWp1rmsLVKq3EG4JaRo5XJWLudr043FeMfaar4pa5Wn1pwa4BhDsaTHtzL5bJyAaUsuLdvFso/s59kUcPzyNM6L+6Tr5BlaDCOkGsmmhKyOJ6QYIMpZYaqd1eJKsczUWMOJLjPOJDljrQsOWpsescoYsKZTTc/hoKuDWukeSJGZIZsT002gYxo4sNCtSFjaQM8sVY+5egz27RFeEDqSK9PQKA5nFaF3TG1WXDmnyuhyE2FRn0lKt2OsFk3UJox7IkokhckyX0/uM35Kw3FECal8a+ypCVrItLuxoXA/CmVSpmMuKey0kFIxSTKeyC4id4+13dtwQph/wPqsdULXnhFSLY0RUo0gp7AUGXklGBrrqJbd3uHAR+tP/yusxsTuKEiWQdLOhKPAyd1l3ddsew5YIs93krbKGjhH3ieDFuMcHCnMjLn6cQaw93/WIKdcAgIFFPcnpI8lrvTAw9kkq5YTDrraIsUGqu2gx5zB0Bys2Z8O386dEBZQi8tOnU8iNFQQeD1cexrWktJlBWjVYp033qpuA45xla9NlxtFTG3dALg/nUX0OGvK1Re+d5lMymghI8q1x32R96d1imVWaPhqKosUY0A55nEcrQuKPE5cDS2KEVKNgIHmkcENGDzaMuzYTtHS40wgy11vM1E2HAg17KVV4BhwOKtLXwkkLbBX2Gz8GxB3qnWfg6aeZXFAXPE7yyWV+IMsJ1S57RgHxUkwi/qpYFTbRcCgVboVia4jxQBW1q7p6BYpg8FDlu5JR59oh3vNHTxXWZKAF/SGnFs8J7VFiuOGPwWLvJZy7TksYcraJUKGbWRqs0gRX1E5rGXXUOiq5KVRNz9WAk/vi9xnbFTkaBFS+y3R5W0YGhE1Hsj2oHckLWTGItXiGCHVCA5m5GN0T9s60lHIXAcVk8RZoTvzucrecYhLBp3rnniEtZ4446Ig0uTsAVIXAr3Pt5aZRq0zbzjro1Ca+LQMWjJgpK2pElJqP+T71yFTqmaUDHQUe7SaEcZMRIwFNv/DfqwRUgaDJ6yLz8DEvi7lQlyhqOG5SmtRg4QULVL2BEgJKY4rImRUg3GHYKLliplsLLVQp5CScaAxAdhKGKo7alHtFydkFC0quFvGsPARMjbJGOaNmlWu8PMzKSRfxrG6UIHx9lhpaDGMkKoPcnKtkFnaQ19uxMKdKSKkCnDs0A4Wb5K6BAgdaN1XqctqxKmCViEOeproiSJs9toLQvYWef5gETo95bH2THTz34DpH1v3CWMcdOB43gEgRB5LwkcB25+zhBWJkMHsYsdsLGQAsEUEEwdgWrA0ox8GVj8irykDTuXM0tCc5OTkqOafTrjM5qDk0KFD2L17N3JzdWKAd8jOzlaNRjVc5vvwvcvKylTH93379tlbDZrMgmLkFZWJkLLLiNQE67KxYTjFhnLz1RNfCiR7HGDVcp7btCDTXeXrcOF1kgkRxxYGevvV4tojTHRxTtTqC8URM1tL5TMRfjZdGJT3i2S7iu+Ssc8Tq1F9KZPPGDnOsxgsZZEyQqqlMULKQ0rKynHnx2vx4oJdGNwtFI99swUfr4zH+D51nNTtDdaFCrartzPYO2eHdV/DoHFnnZdIET/OWIKdr1oZO5x5skwCydpZZY0iFFKlduA4gy71wNl1GpAm719TCQO68zaJKOt/VfXHMLi6q8zwMjfIEV+Hq8LQaNgZvTPj0Rw88MADePbZZ7Fxo1VXbPPmzaqL+htvvIGMjAzce++9WLt2LVJSvNDew8GDDz6Ip59+Ghs2yG8vJCYmYsmSJXj44YcRHx+Pl19+WTUg/eKLL9R2DSsbd2SSsorQMzIIsRF1fA+08hYdFoEhF/SGWKSU9VqeS2jtUQKm0LL66FpwhFZqipi6gs0Jk2Ea49rj5I19OinsFBRSHE9kP2kB4r4xPpO18tLXWA/xFioMQf6iJ4uQEoGvigvXBoWUHctlaDGMkPKA/Wn5+M3rSzGgawheuHw8rjm6Px44czj2peUi0L+DpdPniOhhx3ZCIZUry07UTNIx0LHGkzNoMuFLIPZkK0CcAoszKlq2nLByMeOcCAc1BqwTxg2EMS6qBjHEuIX+1wDdj7dXOBjzmPwjM0jdrd7QJFBEbdmyBW+++SbWr7eq2h8+bAX+Xn/99Zg7d666zz6e7Gk1c+ZMrF69Wgmofv36oX///mq7N6CVix3dr732WsyfP1+tGzp0KK666ipl+WKD0vz8fPz+979XAo8WKnZ/X7lypRJ5/v4Oy2oHYtHOFDz45Ub06xJSdwN27UZjTbeGWKQovrRRm69B8cSaVIxDYukSTaWQosVb7tdGoIwRjem3x3GJoQF8P0KBp0oxsFSDrKMFiIHwUWOPnEg2FiWk5PNHjrAEal2CkHFc9m4aWg4jpDzg563JOHpQV9x+4hCEB1rxP8cN7YZ1D5+KwV3rmB21J5Q/PrPKtceZHyuLOymQE985k+QAxOeR1OWyTQYoDoTRIoqYzZefKAOmXapAw9mfNlfTLVgZE9UTmPic5S50R58LgKPetBdcYCD72L/LWOiI3zJ4HYoRCqJzzjlH9aUiaWlpiI2NVb2r6OIjfBxFDntZlZaW4vvvv1cut/vvv19t9wZ0G3bv3l01G83LqyqnwQ7ubDzKfQgPD1d9s7gvxcXF6NSpk+qldfzxx6v96mjMWh2P5+buwP9dNBa3Hm9n3tYGLcuMeaQFsiE12ljWgLWkaMkuk++bpU9Y+kC51+zznighVWSNJXUJNlrM8xuRtccgcpZv0VnAquQK91P2gVYqihuOUb3OkcfWo06eJ/B74ESSn5FJPZy41gQnoXQvasOZocVokJBKTk7GV199peILNJz9ff3112qGt2fPHrVdm/HbOolZBTiq/5FBl/6+ndXA22EokMGNMzLdkZ1Zc0dYpNJl0HPUfyH6O8pYL4LmROt+1DgZhBKsGR3N5E5o0dKNTBl/4Ux37nMxEDHcXqgHDESPPcEaoAxNBi06FCUUL7qx58iRI7Fp0ya89dZbGDFiBLKyshAXF4cffvgBv/zyixJYy5cvrxRc3mLMmDHKKsb3pajT8ViffvoprrnmGmX94rj1wQcfICQkpHJ/2QWezUvLPW7T0T5Iyy3Cj5sO458XjkX/Li7ncE3wXKXVmBf9Bgkpe2LD2CdapOjGp2uPlcVZCkFDccGxxxMhFSLHUGUR3gZAaxY7JejXoGDhBIzFR5VFSiaTFFL87PzMuqOCN2D9PB3KwIljriO+1BX1XYjAcwlTNTQ/9RZSHFw4+MXExKjYA81jjz2mBsng4GAVgxAREVHZMVnDuAPX2InWDo/R4rJyRAd3TDN/NVil3N/RXoQm9GwX07bqAu9i9VExTzIoZW8VIXWatU4FocfL3z6ZebqIVLrydFoxZ7t1BZcaWhUUU64i5PHHH8dZZ52FM844Q00+pk6diuOOO05ZhsaOHauEF61AdP95k0ceeURZx04//XTlquO+PfHEE2obBd+tt96K6dOnK3efk44mosgLP+9EVLAfBnarh5Wd2WwshKtKizRASGkLMQOsWctEWbBLLXHidO0pwWALKdQhpAJESKnXayC0OAU5hBRderxuMYlG1ZgSoaeDzyPHWAk43oIWKT0eUkiyZlVNaFHZgK/d4F3qrWrS09NVfMGMGTPUzI1WqRUrViA6OlpZomgO5yBEa5QzS4dxEjSp02LVligoLlXW04COFgvlDgY/Ro60FwTGOTkzS2iGZjA5qxY7oRDKFBHFDJee51rr2IeP2S85u6pqPmkCRazpKuXK9Wd6w7V1Bg4ciFGjRqmJFt1pFDWMV+J6jiODBg3C8OHDlavPm/B1+b60OPF9KeIGDx5sbwV69eqltnPi15FZtCsVGw5m4ZFzRtlrPIRWGSaXKNdXAybJylgt/7DxLrNtKQwYB8WsXVqRNcq1J+tds4LdwXGJQdocuBsCn6uae1O0EX4uGf9pdeM2WqV03Gavs4DdNYQTNAQWAQ3UoQwyLupx0B3cPwrRlrZN7HxFhMEqe6FjUu+fgAORdmdxdsdBkMJJxxgsWLAAV199NS666CIVsKnhjPOoo45Sj2tLZBeUoFQ+X3igcQkpN1u4w63GxqO6KjFZfRfQ92Kgh11YU8PMHsZDqcByewbKY4gm8jwRYjrmSqPM+zIb5OMZHK5nfwaDwess25OGv327RSXS1Blc7gqLXzITtiHWKE0nETwUECqInNcWEQhljBVyCCYtpJR4qOO9+Fg+nxYsVyrFUU3IvvD1GbbAW45vqrWU/FFIqXGJQsoek6InWJZ2XcKhsfD1tdWfpSB00o07aLnjeMqvw7XJc3PBMTrhG5kob7ZXdEzqLaQ4c6NwYiozs1727t2r4g8YRLpmzRq1ffHixZg9e7YSWRofHx8VF9HWXHu5xWUokc8b7F/1WToshSKknBYpmqCZYUNTN4MueX/IbfZGB8xwoTXKGetElLtvpxWP4EQX1qRbj7Pd2pqUGgyGBsNaUXTp3XrsQPSMYop/fRHhoyxSjRBSfA0KBl2apEzEjgq6dky6GejNMcETIUVoqWG2nZP9HwNb/20v1ABjMymaKKSoUOgiVGUd5D5FCwWfihO1hRQtchyvkhdZy42FAe46yJ6Zg7VVTmfGHveNl1QKx5aA3zG/j8Y2im7j1FvVML2Z6cQM1mSNlt69eyu3HmOk7rzzTkyaNEllvVx44YVquxOdXtyWyMovVqI70K9tCUCPoZXJk8wTnrQschfhavrnbFIGmwOfAt1Pste5wDIJSXOBULv+lIbxFZx9crsTBnLSxM9AS1W/xWAwNAWbEzIxoGsozh3v4l6vD4yBbGxZEfbQ0+c6W7TQ1acsVDYq0FsmaspKXYdrjzAhhrFOTktNvIxRWVvshRpgbJZ2U/IzFWbJe8st77MoKIUWxYNzcse4z5SF9kIj4eempZ8w2Nxd94hKZOxkPCq/eo7BLQF/K4pgXZ2+g9IgdUDrErNaiHbVMZCcMQiE8Qjczse1dTYczEZcpD1Tao/8egGw7317oRZY04SzPPrtnTAegSdS8nyo4pvu4OwubaUMPo7gUcKid6EDrBmdE2bocNbJYHRWTjYYDE3CntQ89I+hKLDCNRqEasfUmLFexhUWldT14TjZLqOFyiGYaHlhZp9ys3nwXnTJrb8fWHadvULgxKyuoqEsu8ISDISPpcDjZ+P7Utgp156LG4+P5755A75+pUVKxsfaXIbaIkWRWdxCQoqiUlnpjJAy1ML6+Az0jmqnQorB4cnL5I4Hgyhnairq3sV6xHortFTRdceWLe6gkCopsQdcBxRJQXb7Fyd0FXCwZFVzCi2DwdAkLN+bhuFxLhOZ+qKqgNv3G4IWKLojAq0sFBDO7F+OB7xgq0uWB0Lq6PeA0Y/LGMLGwhQjsoOc8NUVI8Vgch3szfeny4wiiu9LYadillwum4xp0jWnGgs/oxZSTLKhRaomL45uFE0XKHsQtgRKSDEezQgpgwv5xaX45/fbMH9bEjYfysLQxg40rZV9HwF9zhaRlGKvqAXGFnSdKSeui+hi8HnyQhlg5GQKraEqNcWXvwySbKngZOwTwKi/2AsOODNlECuFXkNqRhkMBo9YeyATo3pZnoQGw8lOXZl0tcFzvSRTXsOuXaUmUvxzCCZlkRKRwfHHkzhbdjkIk/0K7CXiKA2qrQvfw10AuhNmyfk7hBQTYvje3BfuJ5d1xp6GwosuOW9AV2SgwwrPEAeWlHEH3ZwUhhRe3nr/+kIB5W3XHmNxCxpRULUFMELKDVkFJVi4IwXP/7wTmfml6KdM3+0MHvycrQ26yYolqIuDXwBxbmKgwgYDqcuODBh3wngFNuEMdC1zIAKLf65whsXBisU+XTP6DAaDV/h8bQKGdg9HWIDD8tMQep0HDHWTZOIpqqyAjEeqDYtAC5UzPoowRkkV6fXAGuUktK+MI7vk9TNEDMnlzjUA3ZUSuvZsIaXFm4qPkucy5IAWI9csYo5VKn6rMWY5jbyGsoDZdBKBWpNIoZBSMaYxsl8t5NrjvvG9vena2/qMFc/WQjgLjTOmu6CgQMV3a4qKjnS3GiHlhoLiMsRFBOLfl4zDPy8c3T5LH7CqeIX8/GyOyWrktbHjBavbebAbsRQmQid1qfU6NREcCxz1logtlwrmNcHZLQPRWbcqxAgpg8HbFJeW4yWZKN5wjBd6GzIouss0e6EB0GXGWCSVfGIvH2HhEjHDC7arRbwuGLeZutKySHEyV1eZAFqcdPgCJ4DKlSfvyffVhYV1ZweNDk6vzW2YtsqzzD6KsQqHWOT3UKPb0LZIqXIx3M8WgLFozNpzlsFpLLQg1pat6EWcVQQomr799lu8+OKLqug4yczMVJ0PXnvtNVUD85tvvsEzzzyDefPmqe0aI6TckJZXjCB/X/SNDsGJw136wLUXChjIHQoEsVZJDamzHFQWnAvs/xA4rYaCa4xxSk1xb63SMEidLjrXWWZt0LyfI/sYOcxeYTAYvAGv1Q9/vQkXTeyFyf1aQTKHskixEbB27YlgcZY+IHRxqdIE9bRIMfQgfYVMHGUsYVZxXRXPVVVzO26T9e9UQUxOpOV9uV+MoWLJAycUO7Re1eY2ZDJO4o/2ghsqRZj8OM4MSAacs4kySz+s/qO90obPoVWKwk+31GpuKIC5D96qo0UYOK9KKjQ9LDBOsUQrFFtUcZnVB5YuXaqsUEyau/TSS1URcm7fsWOH6oawdu1a+xUsjJByQ4YIqcggP/h0rufspy2RtlpmkraFiAXxNPs/AQrttgQbH7OsUCfX0gKBA5xoHoR60OC0PnB2ykmJu2B0g8HQYN5cuAfpuUX47cxWYu3lpIlCQJc/oIhyFVK0+GjrUH2gFZy9ACmk2M6F1h3lEquBwjQZc2yLFOvfUUhxjFMtYmiRomvPRUhxokiBV5vbkFl9NdV6orVs09/tBRfXHgP5ue8Mc1j/rL1SQ+uViCnl2qvhtZsaTsJ95bdyXEIaTTMGr2/ZskXVvExISFDLtFCx2gCrEWh33rp165TAYmUCFiNndwbXephGSLkhI78Y4UGchbRjMtY5XHUyOOnB5ddL5KQWAcVp6+F5wPh/WetrRA6hKBFR3q73ZOpHGQxNwudrD+KW4wbBz6eVTBQZ1F1KwWJbpBgr5VqElxYfutUoauoD209RPDFxhaJEWU5qEVIUa/52mRa6zBigrrIHbSHF7a77pvapDiHFz1eT5Z8T1z3v2PsmOLMV2c80d78lpgi9BBo1Zssf95cxZi0BLVLMvnZa0RoLBWddsWxegv0+r7jiCtWqijUyGf/Epuq0PrGNVV5eHo455hjVSio7O1uJq927dys3oBMjpNzAauYRQa4++nYGeyOF6NICchho0zLPh4Ik4LMuwLjHj5x9ucLZZNyp1qDjTTgbdAlFMBgMjWPxzhT07xqK8X1carq1JBROFAgMNVDLYdbF2YnKoKPYasC4zOemLbfcZGVygWb9pZqgJURXWFcCJc16T5W1RyEl+0CXnxNWXVdCopaLf76IpZpincoK5KKzxxJLvEA7rXEUfwyWZ0096t4cR29TPpafhcHxZS3k2qOljeUfvKnJKVa9GXNVC2xrp4mMjMSECRPw/PPP44YbbsD69etVj2AWG2dXFootNl3//PPPccEFF9jPsjBCyg0MNg8L8KLCbo2workO/lamZDkpKbJpJp74NDDqIaDnWWpzrfA1xj5mCR9vQgGnTewGg8ErvLdsP26c6YUAc29C4cKLZ2fbCs1JGQPYnVAMUcTUN0aKMOyAVh+WFaDgqYxHcoMSUrZFiGNakQg8FrzkGMkYT+6Dr8tYp2Kk5DG1VSEvTrEEkzuUK0tu6QHg+ziFFEsbUGQxZihcRFXeXnuDwM9BIUXRqWpl2fC1mgta0fh7UaB6C29nAdaD8ePH4y9/+Qv69++PsWPHKvHEZS2cRo4cifvuu09td2KElBuKREiFBtknU3uEWXq0JOkCmRwoOAgw4LNCphZ0+Q1zCWysCQ5snLnVN5umLlhTRvW7MhgM3mB9fCbiM/IxrncrskYRZelhrA2DLYVe5xxZX45WIbrZGjLORI+X1w6zxioVFF7LRZ+CgCUHCAWTek97mQKHgs+1/AFFFN17DAivCRY0ppByF59F9xg7Rhz6wdo/Z1IOA8mzNlviovsxLkKKiklej0KG5RcIawPW9vm8DYUULXTefE9m7FHQNqcgbCRGSLkhs6AYXUMcB3N7I3OrFTugYX0UBnsy6LE+mXVNicoorKU2lcFgqJOycutqlF1Ygoe+3IjbTvByUog3YDkBCgXGIBHWlnMt3ksxwxpPDbFIdZ0OhPS17lOo6PibzX+XL0bGQie0hPjawolN2TnppFAiTIAplu/T1bXHMZNWLC1mlGByUQFFSbJeBII7Swvdml2OAlKXWMKNk1wNJ7u5B0VA7bd6+uXssjcQEVG0StG1V2q/9/oHZHXzuMUUdMHRi+HNmCYKS4pSZxJUK8cIKTdkFZYiJtQ+mdojOdvlxJ1qLwg6G6VYZk2ubVxaiuB+QJ+L7AWDwVBfckQ8/e2bLfjnnK349487cMrIWJw+yqUobmuAAoXX4doCyZVQEdHASV99iRoPTLQz3ug609aTbc8AB7+17hOKAVV8055M0vJEt5vO0FKuPbl1DWOgpUpZumT/aE1ZcYvljnNCYaDKBLixWlFIRY60tufutl5Pwz6k1BP+chsjYzbjVyuhkJI/xihRxJXLfVYEr7XRsZfh+7KHqjctUoy7ohh0Z71rpRgh5YbcohJEBbcyIRX/GbDuQXuhkfBk7TLFXhBU+rEMGLRIBfexV7YwISKkBlxtLxgMhvqyKzkX6+IzMKBriCrAed3RrSw2SsOJnGgQFVZQE0pkyeVKW63qAy1e4XY9OhX/xDfjjYiArE2WUCEMbWDNKL0bFEwqQNxeoVx7cntEk3WKL9k3Cj0Ki4Sv5bVcihxTZKnHuLFIKbemvGbUOHmc7IxrBhyv0hyXI0fIGO1onaKEhggY/0i5L7cFB+X1C6zA9OaCsVm03HnTIsWyA/w8rla9VkyHF1Ll5RWqbpQ2gZOConIE+9cyO2pueJDu+x+w82Vg/+f2ygagMyGyd1jF6TS6jgt77rE/VWuAY1dDzPgGg0Hx5bpDOHZIV1wyqQ/+ccFohLbWDg0sdWJrmxpRcUhyudJFOxsKY6101h7FGbsnFNpWnqJUGQsd3xGDuNnDTo9DFDi8YroTc8rSJcKGFin+6Vp8lcj1RbkV3bjdWBqB1qXeF8otr0Mul+V+lwIx9sSXY7h24/H9+FkYR8VWLYyf4kfLlfG9qVAFSh3QkxHYXfbFjUBsKPwMyqpmhFSb4aOV8bjolaW446O1KhiTlIgi9vNtQFBjU5GxVg5YmWmcuhJYf4/VkqUhzD3OElE8GdhoVKN6SOVaA4mOJTAYDG2a9QcycPKIWHupFaNbw1CM1IS2SOnSBA2Fr6OFFJNZGCtKSw5RFinH5I0WqVKZxOoAd+4fDVpuJ3jcdxE2HFtpdWJWtIbuPFqN+Dx3af3a6hV7MhA98cjXP/q/1nrCbGZdRkFnH3L/KuR18+RzBEaIkHJxK3oLVmb/xSWTm9cNhoNo1x73KXW5db8xUCTqz9cG6NBCKq+oFN+sT8CdJw1WdVVmrbROKBqn/Hxa0VeTtEBOpAlA+EBgxIPAgc/sDS4wyJH+ZXesl+exDsu6P8tjLMFYCWdpPCG4vrW49gwGQ4NJyS5CZ59OGN3LJTC6NUKLOIdbV5eWEwogCgbd2Lih0CqkzV8UcGxqrAO4KaScdarowlMP1ZNq2Uml59zspxJbcuFgvBNdUkUOixQrq6tGyPI8dzFStDDxfekNGCXjtKugdAorp5CiYKvcJs/Jl/dhqQcW8GwKdr0uB9Zi+WwOtyVjyFiiQX+ugkPAgjOt+w2FXyW/DyOk2gZztyahd1Qwzh7bA2ePiUOZ/HDbk7LRJbQBAY1NScoioM8l1v2uR1uCx12q7e43gZ9PtBccpK8BdrwMnL3FCkx0nghEF8RjjFSoaRJsMLR1ftmZjOMGt5E6bCx1wounzo5zh4ptopCxrVcNhaKNF30VXyRvyhCHve9a2zj++Tlen2Vd1DDruExSSCmzlAuVQkomq8FxcpttrSeMSaXVhi3H3Lr2RBjpaul1FUCmG48hGITPq2zuLO/NzL7ocS6ZfV4iYY68VwAw6UVrUq6pdO3ZFinGe+XI99gY+B3TferNuKsmpkMLqfnbU3CGCCgSEeSn4qKW705H1zDHrKSloYmYB2vUGGvZT2aYnFWVuIgh+sgzNwMHV9grHLD9wIR/W/dZPHPCU9Z9jQqilBOfVXxD+9krDQZDW+Xb9YkytrXCDD13sI4dDStuXWY23EZLjXYDNhSGMdCSwxgciikm3WhXFMsrOF+f2khdy3mHyPtT8LizSKkxVEQELTRhg+XWEUuUt09EGd1f8jocp11hgLhPmL1QB2wyz1IKhJ9Bi0+KDlqDmKHI7GtvE/8p0PcyYPDNco35Wj6f7fmgNY3lIPTEni5G/XU1BFrz+DurWDY3xoJWSocWUruTczFtQIy6H+Dng4hgf8zdliRCypF+2tIsvhLo78heY3VebUJ2UignEWdtXeQkTl5grxSYDsuZVq+zrWXGBfQ6z7qvofpnoTkOBN6uUG4wGJqVL9clIKewFIO61WHdaC0olx1v6xJS8qeLdjYUCh6KGYopuo443tHKxZAI/jlrOBEZaitdbapxsTzW3X7SkkQhRosT409LbPcboWtPV2rXtaY0FHMURMws9ARayVjckzDeSsWOCRUiOgplrI+QCbcWit6EYrDrNPns8ltFjLDEFKElSgXf08InuIsBqw/cd17HlGvPWKRaPWv2Z6B/l2AloDQT+0Th+02J6BPdyFmPt0iab/VfGnCNvULgjIonM7NNnPDAZl+mif8GDnxirxQOzbHEE/3YNUErlwq0bDs+aYPBcCTpeUX4cPl+/Pm0ofaaNgAt7MqlVouQ4qVKXWAbOTZTiFHsKGuOffljOAN78dHy71q1nA+p7PQvt4yb0uLFCZ9Hl1uRjKORY+X1ZVKqoauNSTyqDpW8hxOKObro/D2cwPJ9dOYcY6W0RYqWIVqFIobJrXw+TqC9CUWNruE14l7g8E/WfQo4ih7l2hRU/Se51Rar+sLn83vSLtg2gj5C6gU7H7Mrsiu5ublqGykoKFC3rZX525IxpHv1g3dUzwgE+/shJqQVuPY4U9n/ETDJLiTnJHIUkPClvWCz9Smgx+mW5Sl9nb1S2PgI0Odie6EGaJrlydmGZgAGg+FIfth0GBP7RmOqbWlvG8hlyIcus1ouR8rdI6LB1WJUX2iR4sWfQkqXUuh2rFVVnAKEFh8n1CkqQJ23sg9KaLmzSMlrMfaUPfWiREhR2Og6SLTmsO0W45no+nNClxxLB3haH0t7DwhFh4od431bGDKondYxWqe8CUWN/h7Ch1R9Nu6/sh7ZQorfqxJSDbz+K4Err6des50LqXnz5uGdd97Bd999Z68BnnvuOXz22WdKYC1ZsgQvv/yy6pLsxMfHR4RrYxyojWNTQpU77NedKRgeW332ERnsh6uP6ts6TOL0d/PgjXZUINfwRKWlScMCbMzYo3+ccDDgjCBthXXiO6uYu0OZpUVIOWppGQztgU2bNuHrr79GOas+2yxYsACffPIJSkpKMGfOHLz77rvYsaN67Z2WHKcaw/9WxOOsthIbpVEuOwac12aRkt+DlngtHBoKhZhy7dHyYQuprjOsfnYcA2sTUtwHii93Fimup1WfYRS0PrG9iXbjcV1QT0scuFpqlFiQ1+M2T6ArkvGsxOnao0tMiUzZx8BYEVLOCuhegNci/V4MeOfvpYLeRUIoy5597dCuPVrZGgJ/G34XFJ3tOWsvMzMTBw4cwA033IA1a9aowWjjxo1q4LnooosQGhqKpUuX4tprr8XOnTsrBzDeJicnVxvQmpPsghJc8PJSFJRYVpedSbkY3fvI1OC/nTcKPaNagWuPMVAcNFz7OpGoCdZJTzMyYZ2p6EnWfUIf9vcTgZ+OAY79yl5ZCwEyeOQnVJ0oBkM74a9//Svi4+MrJ33Lly9HQkICTj31VPj5+eHVV1/F5Zdfrrq8OykuLm5zYuqZH7djcPdQjOjRxuIcaYniZE672twiv4V6TG1iywOYoaxjiPh6JPYkEVIipDl5ZZ8/J35aKAi8pZCpFFYOlMtNBBPHbRbyVLFYtqigm49lZbiO7kMnSqDIZ+I2T2CGHOv9EX4GLSzpdtP9U2n94mfxJvzs+runiKJoy2YWuD7W7O9IWeLk1unarA8MVuf3wTY9ut5XG8D+9J5D4UQCAgIQEhKC0tJSZGVlKdH07LPPYvPmzWoAioiIUI/Rjz948CC+/fbbFnP5fbU+QbVJ+HlbsqofVVxWhm5hjTQTNyW6OJw7ccODOnyU5dcn2ZuA7o6yB/2vBIbdC5y7BwjzoJyBMgUzWN0IKUP7gdaoyZMn4/zzz8eGDRvUur179yItLQ2LFy9GmYwBgwYNUlYpjluEoQlbtmzBokWL4Ovbds6HxMxCfL3hMP5x/mh7TVtCxjladNwJFA0tFEySYYPcxqAu0FpI2RNmCmauo/hwtUgpsWULCI67yipmLzvha1FElTFgXe5TaNCNR6sK3ysgUtbJe7vGSCmXmbyuxxYpEWx8D6Lcefbz+H5aSDHzWpdI8AZ002nrnSZCrj8sqUPrlJMK7pPcurowPUWJQ/ks/E50ALsTXhe9bW3zAvUWUrQ4cbDRlqmgoCD07dsXsbGxOProo5GRkYGwsDAlrCiwKKZInz59cPPNN6vnNzdFJeWYtzUZN87sjzkbEpGeW4x+Mc2/H/UiY71Vj6QmepwBpK+17nM2FXucdZ9EymA66IaqE6sueOAyY48nusHQTggMDFRxm5zM0fpEOMkbPXq02vbrr7/iqaeeQrdu3VQYAsc1bqd1aty4cUpotRXmbUvChRN6VkueaTvIPqtaUrVYAGkFGfWAZdlpDLTg0J1G156zJlUAE25ECGkrlYb75WN/p7S0KBekm8smxRNdbhQQFH0UfKqBsLwPx1ZCsaSLaWqUkOLn91C0c6ymsGEMrcpws59HS5kq+imwT6m2WnkDijLX+l3sC3jwG4fw5Jcj+8Tee7zbGNee+g3kRVStLxcOz7OaTbcy6i2kKJyOOuooFQN10003Kbde7969MWLECBVnMHHiRJx88skqLuGcc86xn2VBa1RLuPZW7E1DWKAP/nDCIGw6lIPFe1Ixc0gtWWzNzaHvLOHkJGWhqNZB9oIb+l0mz1lt3Wd6rdO1V194gnNGyC7eBkM7gdam9PR0vPTSS5g2bRoOHTqESZMmqUnetm3blNWc1ihapwYPHqxEFOHkLyYmpsXCEOoL+4TuTMrBtIFtKcDcgeqhx4tnHWKC41RtYssTKDzoMlKWD4dookAromCQcdCJ05UX3NsqSBnoZoJL117uLnlNezLKOKnsbfI+uSI2Iq11FAja3adRQkpen5YyT6Dlic+hEFQV0e19o2VIZ2aH9hdR6FJnsDGwbpWrwGRmIjMgKSAJfxeeL+UiGgPl93R1YXoKrWz+8l5KRFGRuVAqYpf1ElsZ9RZSZOTIkbj//vsxbNgwNbsjNJ9TWFFo9evXD3/6058wfrwd/NzCLBMhNX1QV0QE+SPY3wdfrzuEmbLcKuBJves1YMG59gqbdBFJLOxWEzxZWE+Es5PGxg1wlsYSCPwzGNoRzz//PB566CHMmDED0dHRytp06aWX4qqrrsKECRMwdepUFe95xRVX2M+waCsiiuxKzsXetHwM6upysWsrKIuMCBhW/m5q6JZTJQcocBzjXfgw2Q8ZB1XAtgO60vRlkrtHwefOIkWLDcdibaFhcHnebqh6Uiw/QyhGKICc6My7ygrldUAxyf2nmFLFLy1LKwKiLSsYoahjVh0f4yl0OTIo3h0FIqR05XUNxW+MTN4rvy8KqVL5DuTzhg2yrHP1QWcBUuAyg7Mmi1SZrPOmtc1LNEhItTUOpOdjXG/rAJ/aPxqLd6Wib4zLzKOloL+XbVsYo5S1xV4p8KBmH6jaoPBhwB/9742hUkhx0DAYGgctO9q6o9m6dSu++uqrSncZb7lMa5CG2XSMX/ImjHNiqAGhO4+Eh4dXhhhQXEVGNvL8aQHScovx9uK96v6iXSnoFhqAiGD7otrW8JPvf+AN1jjY5FBIycWYLjjnxDGwh/zFVgkTjXLl1WEpIxQUahy1x1Dl2ku0xIy/bSmi2HJt7cWq5mzE7Ml7EJ3NpuK85LW0AOt7eZVXguu4L3yMp7Bm4eYn7AUXaKnTlicn4/8P6H2+dV+5LelylH2jZaymnq/uoADd+i/rPve50rXnxiLFEj3swNHK6BBCKjGzAL2jrDodp42KQ6mcRwF+zTD78YTcHdbJevxPVs0nQlcdDybXQD5XGFS470PL5NwYeBJwJsUBzWBoJBRJRUVF1aw69913HxITE/H999+r5V9++UUlq6xbtw67d+9Gamoq/v73vyu3m6Fu1sZn4LFvt+LuT9bh563JuGBCL3tLG4SZdLHHN89EjtYvXuxpgdEuNxLczRpHKUCc0LrjSRKOsszI5ZRZdYS3jF09NBuIsIuj8rVcW8SoQG75/J7Gp3L/KCZ0dXaO3YTfHyuqE74e99nVjVgb7NPH5vju4GTf3bUhcoxV05DwN1RWKBE/vG45W+TUBWN9E6xxQe2zcq9SSFkZ9tXgOm8XG/UC7V5IFZWUwdfHByEB1skQFxGAk4Z3Q4BvI91hnsITtjYT6/YXrZlE92OtoD4Wb2OTy3APKhPTfJzwjRy49snbUHjSMY2VvmmDoRGwVhyTUGbPnl1Zm4nWKMZO0v2/erUV10fLE0MAjj32WKxfvx7z589X5VP4fEPdfLshAe//dgrCA/1UNvLRg9pofFRzQ7dcpUVKp+4LIQOBHqfIRdzF8kJx586V5woFDkUaSw8QWrcSf7bqU42zrS10j7m2iGHwOWONXC1hNaFce7L/dAmykKe7sgmchKug9HrEKTHQPm+PveACY6QYjF8btKpRPNGIRItUfbL2stbL57etTyoJwN5/1wxHwmtpUT0+VzPR7oXU95sPY2DXKjdeXEQQHj1nJKKaywy++e9WhfKaSJKTrbudcRd3GrDrdTmwtltF4uqCJy1nPews3hg4AATIia9N0AZDA6EVihm8jElipi6hS42FelmbyZk9Ryic5s6dq+o5vfjiiyp7ri3FJ7UExTI5XLM/C5P6ReP+04fhqUvG2lsMdWNbpFyFVMRwYMjtckW0LTwaXtQ9dbtRJFFAEdajmiZj+finLAFFGPvj6m5TMUEUUh56SCi4OF7zeWWl1rIrSojI56iPmKG7LEfElDtU1l4dQoqfQQkfWqSi5X49svYYPM74KqKElFyvVakIN/vDx7l8ha2Bdi+kZq2Mx1GOdgl+vp3Rr0sIfH2a4aOzaGbiDzIlf9pe4cIh2RY+QmZDdkovSxakyYx9m5x8vc601tUGTcYM+guzTbqNYdILVf5ug6GBsIQAE066d++O4GBrdt+/f39VCuU///kPjjnmGFUQk1m+33zzjYqTeuSRR5SYuuWWW3DaaafJId3uh6VG8fh3W3HO2B4IkLHM39en9cR7tgV4bHWiRUou+s6aURQndE+54mnrFjL8XiDG7iJBKxbHc2dAOwUVRZwTBmfTmuMptHxRaNCNRlHmLkidIoTvT7HlCbRwUbTU9HC2m3Gtr+UKvydatQhde65lHmqDBaW114blE5SQEmGmaim6QKHlxXmWaywn68299tprKsOX0HL++uuv44cf5FotsKvLm2++qUISnLT7EWvNgQxMHSAKuSVgrBPdbwzUy9xkr3Sw501gwr/tBYEBioNvAlJ2A9GT7ZW1ENTLqsrL28bi6YzIYKgDiilXqxKtTcyemz59uiotwMzf66+/HnfccYeq40SYPUehZaiZ9LxiVVT4jycPsdcY6ocIJmZKK4uUBzFZ3WZaGdKeMOyPliWqJmgpcm0Mz5ig+tTv4zithRTrYdWU7cfHeZq1p1yAvnL9sRaPgFl7zngyd6gYKW2R6iLvXQ8hVcigfNvMxIKelULKTZwVH+fFS1VhoaUeOV5lZ2djxYoVOOGEE1QLPL3+rLPOUuEJhw8fVttZY44TQSftUkjtT8vH/5YfQIYMOvS8tlgFc2bhscHj2MeBlSKQnLA/XkEK0GWKvcKm51nA1BqyJ1zhSXuUiDFt0TIYWil04blmz7GOE61XmpbuxdkW+GVHMk4c1g0+zVEqoD3C8geqjpRckF3deO7oc5FMaifaC42EQsrVIkUx5Ml+OFGNi3Plc9C1V8NzK11tHkBRRkFGIaXbjjkpkXVON6g7VExTpnweueLyusQaV55AsUfLmc6gZCYihRS/E7euPdsi5am1rQ7Y/YCWcXZeYegBxybWn2MtOS537doVcXFxKiSB67ht5cqVKqbTSbsUUr/uSMGDX2zEvZ+tx7T+LWSNIjk7ReQMsDIqCtOAQ9/aG4Q9b4vCm2YvOKDplhV8PYHmW8ZJuWaaGAyGdgf7hP6yLQUXTvSCBbqjwgs+6y+p9ioexj55C1rAXPvHMQut3kIqWIQKXXu1iDDGarE0jiewphYLgrKMDt14rlB0emKRYpwV3YRB3eU1PbRIsbQCrXK0+vGWAslXRJvTVVgNEcJULU6RSCvVxkeBXKscSH1grcuzzz67Mp6TGcf5+fmqG4Ke1DEkgSVSoqKicOGFF+KSSy7BqlWr1DZNuxRSy/em4vNbpmNPch6OGdLIjLbGkH/QCmIkjEHa/7l1nwXFWOq+l8x2DAaDoQ4Kikvx5qK9KCwpw+hedVzUDDVDNxqFAWseNfcEVAkpO6haw+X67ge7UFD8UEDUKKTkuucunMQdtG5xUs5aWu762FGwUdzUBoUPrVkUUv7y3p42LWYpA/aD5eegiKLA5ffEbERXIUVrl8pwlPvVMhJlfcIcYJOHnhwHznhMCqUuXbpg1qxZKvQgJSUFe/bswY8//qgsU+zMsmTJEtUNgdudtEshlZpTgvF9I/Hu9VNk9tbTXtsC5O6rqrPR5Wg5UESlU0RtfEwE1gggZoK1zWAwGGrgtV/34JRnfsXSXWm47cTB3gwR6XhQtFCA0EXkSX0ob6JET5lc9x1WKVqkfOoppFQ9Ktn/2tyCdK/l7LIX3MD4ozV3W+In/5AlzliR3FVIqVIL8j6ulc1dofBhchU/G9+bQtUTCpOBEHlfVfdKxBdFLq1bFLxaLPE6qr4zEUx0ZfJncwop/pbhg4Gtb8rzG+7yY2jBKaecgjPPPFNZqVist2fPnnjvvfdUFwS69saMGYNTTz31iPZ37U5IbUvMRkSwdYL0jg5GeFALur3YZ0kX1eSBGnsy8POpchDLwTvpeWu9wWAw1MDsDYlYvjcdH9wwBf+98SiM7llHGrqhdig8KKRK5ILbIiERIoOd7j2KlJrinGpCWbZEPNRmkWJbGvb6qwnGRe182coqz5HHscF9xDCoauxOKIiURUquX7WhXHG2RYpCiLeErc721VL+h4HmkSMtIUV3Hd2ujCXj69E9yP1ceKGIvQPyYMoVxmDJvjgD0RmPxWKqY+4A1t1nr2wYjIWi9YkwXoriiW49WqpovWJXBH3fSbsTUqsPZCA2XH6ElubwL0DoQDlJHPPHfr8B4k4Bjn6vhU5ig8HQVkjOLsST32/DQ2cOR9+YOiwCBs/QQooupJpESFNCwUBLkkZl7VnJFx7DwGxWRKdYqek6wgk8449qgk2N+TqHRUhlb7eEFIuSUtg4UUJKvq+60Fl2urCmjj9jaQP2kq2JfAqp0ZaYpLtSlT+gkKJFSpaZMchm0CoLWF6bnzkoVt7L4fajFYuCi4VPuQ9eCkSvD+1OSG1NzEb/LnWo5+aAtaBYmsAJlfq4f9gLBoPBUDO3f7QWfzxlSOsYz9oLdEGx9AFdaqwd1dwwFskZJ0VrCq0v9YFCQxXblEl6TUKKdZ9YANRtwLbA+F3WDSyR10lfYSUt8c/18dw/TwQnhRTdbbaOUp+TUNQUWDWZFHQ3OgPFKejY2oYCkxYoijYG06vfSURcQbw8RkQSXXvKvUfXYXdLOGn4W1JgsdL75Jes5zYz7UpI5ReXYVdSHsb1bgbzNwPfMjfKjy2q2RX6mQ99D/S/yl5hMBgMnrNwZyoCfHxw3rgWjPFsj6ggZrl488LbEkJKVVZ3CKlyuX7U5TZzhZakMhEj9HbUJnL8w0S41ND2hZag0MFWBw120qBrLKSv9d04KRehxUrldaEsUhQ3DosUr5F09zF2SrPxcUu4aSgqGX+lBS5dln4ipJRrTz5jruw/vy5lZaKYkt+NQfHMWtTQ9UgqRM7QWNECJVTalZA6mJEPf99O6B1jVVRuUqiIl4pQolnUlYNfAwOvtxcMBoPBc0rLKvD52njcfNxAe43Ba9BlVJQmd7TppJlRAsMRI0UrjGt/v7pgTSdaipRrrxYhFT5chJTV7/IIKMQoYGiVihhtBYgH97GEjxOKG38PhZRTMFGk0oVZJMKM4oiZgSRHrpe0NBFlmZPfgY/l5+B3oVydzNqzLVzM6gsSocnXZmwZv7uQOJf95Pcpfy3YEaFdCamlu9MwLDYMMSHNYNpjsbDk9VZhTVcyZP3Aa+0Fg8Fg8Jw5mxKRmlOMCX3raMthqD90hXESTGtJS+Dq2mMmt099LVIUUhQjFA+1CKmocTU3IqZLj9abkN7AjA+tdbRgKauSA4qbumpIESV8RCzp71UJKdk/VaZBPi9rTBF2+9AxVxRYjKmiNYq/C+PWVBajLPOPrjoGv0dNsH8zuu/kNdkXVgszogSW3Gp3YgvQroTUnI2JuHiSF4vVUU2vukOOV5oVXUhfIwehDHSH7Qqn2aL850wEVt9pqeXQQdZ6g8FgqAdvLNyDm44ZoHrpGbyMElK0nMjFtyWgeGFwtIaCqL4WKfbvYwuWclpzaoiRIqzInlNDkUqWHaCLjIQPtW41zuBy1UrHA4sU6zupuCUtpHzlc4pQYmNlBrLTskRyD8k62yKlqrPL78DPT+Gl1vMzyXGvWt/IOgopxlDp0gp8fDBde7JflVCYyvoWcdVatJsz9au1CegWEYTB3esoHFYftvwTiP8U2G313VEHig7GY2XyATcCKb9Yy4fnAl2ny0EnB3n0JDlIrf5hBoPB4CmzNxzCkO5hmDHYLpti8C68SPOC3FIXXb9IIPE7IGMdkJ8g1xOZrNc3OFpn7dVlkeJ1qNAR6O2EFcyD3cTf0UVHS5GGFqlAD4SUskjJrY5PohDic7mPFEK6PpUKdbINE7TGURgxHooCVwkpPp9WKlnH+3Q/MgORMVEqtkweHxgny46AdWb00RJmhFTjSMktwr2frsedJw2213iB9LVA2hrg+B/kDX6VH6sEWHQxsOr31o+WuV6E1BVWMBxh4DlNqWMeBYb/0VpnMBgMHpJbWIqXf9mDO7w5jhmqw4stxQddRy1Br3PkurJSJudvApset+KS/Os5+WdwermIDgrC2op5homAUfFgbqDXJMCN65gCyGkxo9jxJEZKC7rKW/l+aTWi+NFB7HxPKg4tpFSQuB30T8uaWi/iyRlEH9Lf2k9lgZLrLoUX2984LVKVrlJbxLUAbV5IJWTk49o3l+PZy8ZjgLfShGnaXH8/MOhaqzI500I/EoXMgpq+Mhv45XQgqLdsGyPLYSK0lsoouMuaARgMBkN9kcnZH2etxbFDu6JXVDMky3RY5JJH64eyeLQArCN41FvAxGfl7xlgqggqWpjqC5v7UkjVhSrcaWe1OWHsEssouELLkjOUhYIloIu9UAtamPL5hFYmWr0okiKGQsUUpy4TYcRgexFPRFmYRBwRH5Y/oDjkMsUUV8p2uh11JXcVCyXrguMst6CG1ipa0lqQNi+krn57Oa6bMQBnjJYv11usvVfUvMwK+11pLQ+60Trwh98DjP+XVQtj4G+tbXEirvZ9IAdbDBDaz1pnMBgM9eCVX3ejS2gA7j3VJV7F4F060/rhL+KlBcUqLS4UGLz4c38aAusuaRFSG7QMOYPbNRQsrNfkCg0F1aqGi5Dy90RI2RYkXROLt3n7rc8YNhwoEiFFgwMzCWlNIxRFWgxyX5RrzyEOKXYjx9ufwbZe8S+gm+yXQ0ix7Vp9S0h4mQYJqRUrVuD666/H7t271XJeXh5++9vf4tFHH0Vubi6++uor3HDDDfj444/Vdg3LquuOyt4gPb8YuYVluKgx3dBz5TOkLrcXhP2yzxmbgHH/tFcIFFVD/2DdZ8bEsd8AfS6xlnudD+x5R0TUEGubwWAw1IM1BzLw8cp43HmSjCGGJoYWKREh9c2Ua23U1ftOQ4GjLVJaUNF7ElRDM39m7lHopK0WTVMs94tknQfZoxSnvLRryxQFFIt+0vrHzEC6CNNXiTAaAZUxSJRlSlukKKTkMU634tH/k8eL8FKB61wvj2U8VKWEsJ/LfoHuRGEzUm8hlZ+fjy1btuCvf/0r3n33XbUuMzMT/v7+uOeeexAaGorDhw/jN7/5Dc4/X0SGg+zsbBHCHqjoWiiT5xcWW6p16a5UTB/kgVqujY1/AxZdZN1nhsDyG4AZH9WucCPkx2XvPNL7PCBHDgDdnNhgMBg8ZE9yLv786Qa8cfUkdA9vWfdEh4DB5ry4t/CFt9H4cNLugVGikwgblhEg6x+0ssppGaK4cQc9Kyzps/x6K46L78EaU3VBq5ESUvYxzOzEfHkdWpXYG5CWpKwNVjiMLqZJd52GmXvKtecQUt2PsW795DUo6JzCi9JFC8OSNOv5LUi9hRQtTuWiCvv27au6I1McRUREoKysDP/617/UcmxsLGbNmoWPPqpqVkjx9fTTTyMnxxFt3wBemr8LD3whP4jw644UXHlUX3W/QaSttbIJaD488JkcOG8Dox/17MBxEtMHiJ5gLxgMBkPdJGUX4tFvtuDuk4diYDdebAxNjg421xPhtop/qHwWuvfqgBYiLViYGKWy0N8CwkdY61yh9WnTE5Y7b/+H8txCWwjVAUUU/5wxUhRSFDgUU3TtFYrgYfFPHd/EAHRtsKCwpbuOsV+usNUN45aVG9AWUiy+qd2CfF138V7NSL2FFDsia/dcWlqa6oZMK9Rrr72GUaNGYc+ePTj33HPxyiuvYOPGjepxZMSIEXjyySeV6GoM321MxLbDuUjMKlR/Y3t5UCysJg7NBmJPtYL/llxpNVjsf4W9sR5MeUUUvogpg8Fg8JC7Zq3DpH5ROG10rL3G0OTQIsUMMR3L01aha9KTsgl0AeoMN4qRrseKgmdD/f7WOlfoBsxcDUx61oo9Yr1ETzMcqSa0RYpilQ2H+T1zHetAFYsoCx9SFd/EIp3axapjpNzFc1EksUq6MzidgrjSIpUhj2nZ37PeQorCKSwsDGPGjMGkSZMwe/ZsbNiwAUcddRR+/PFHJZj+9re/YeLEiRg7dqz9LAttzWooyTmFOJhegCun9cHjszcjKtgfPp0tUdcgWM+j36WiDkV995Vb/shsiFhfep5uHTgGg6FNQAu6prHhBvUlr6gU5764CMcM7orbTjSlDpoVZZFqB649WnI8ETiqcbEdPE5hM/R2oM+FVqked1CcDf4DEDkaiJ4i18gfPL+28XvVgobPKaKQku+Z1j+WYfCV717VhBJhx3OuIMWKySL8PMrl5wg21/AxpSICWdxTZ/zxd9SPZW3HtmaRIhdddJEST7Q8nXnmmUpULVu2DG+88YaKlXr44YexevVqXHmlnfXmJdbHZ6r04HPH9sSv21MRHuyBaZOwqqqz7sS+D4F5JwADb7CqrpJp7wBjn7DuGwyGdsH777+P119/HcnJyWqZoQdffPEFfvrpJ5SUlOD7779XsZ5O6zlhYkxTsDMpF9e/swLnjuuJm441vfRaBHXBb9kLb6OhuNDWn9rg59QxScUiXLg881O57tWQ5T5ERNSEp637A64SAUS3oIfGD1r6KmOVOlnX3QB6oOQ+A/wjRli3tHrxr0jOSZY2IKqAp0vWnsY3XASUCCm2kNHuTN7qIHpau9qikGopNh3KxhljYhES4Is/nTYM54yxRVBdrBIVvuIW6/7WZ61YqHFPmsbCBkM7htnECxYsQPfu3TFnzhy1bu3atfDx8cGMGTOQkZGBRYsWYeTIkfj666/Vdk2RXECaQky9s3Qvzh/fE9dPr8G1YmgGRBhQTLVlOotw8Mi1R2uO7Uqj8Kgrs5yCRpVWEMKGANGDLeuRJ1BIVVqv5DkUYbqYJ/ejMiGLr1cKFKbL+9muPf4eyrXnRrTRPUnXHvsA6jILqpJ6vnWfQqqFY97ajJAqlx8zM78EA7pYyvOqaX0xuX+Mul8nWduUKMbcmUCCDJi0PsWY4pkGQ3tmx44dKtRg2rRpiI+3mosfOHAAq1atwocffoiff/4ZvXv3VkKKlnRmJJPFixfj008/hZ+fdy+2h7MKsHBnKs4d78V+oIb6M+A6IPYke6GNQguMJ0JKZcPZ3hgKJE8aEGvY5mzQ70S8eOgGHXANEGS3naEw4jU3wBZSdN2pgtWyku45BpUztklXdacAc60jpaFFio8vFcGkY9tUbz8da5Up623LVgvRZoRUel4xikrLEB2sFW8tsBR9xnrr/v6PgK5Hi3h6D+h9KXDCj1bTQ4PB0K4ZNGgQtm3bpsIMmElMunbtitNPPx3Dhw9XVqekpCTs3LlT3Q8Oti4Y06dPx3XXXafWeZPbP1qHe08ZiiA/xncYWoxe57b9cjW00lBM1AUFCl1irMPEuCJPMvA0jMFiEWpPC01Peq4qiJ3PpZDSDY8DugMxU6z7tHAxcJzXaV3VnXWo1BPcSBLlwhSBVcS+hPZnZiZgmS0QGQNmXHuekZRdhPzickR6IqQSvgXmHWeZ/jb/C+hrF89kUU1ttjQYDO0aJsUw6WXXrl047rjjcOjQIUyePBkJCQnq/gUXXKASYlhg+LTTTrOfZVFY6GiT0Uhe/XU3bnx3FQJ8OuPssWYSZ/AC6jpG4VEHymVGaw7LDjWjgKe1zE9Ej47jGnyLCKmJ9i6Xy/8ipGhh0jFSjHlioLo7l6tyscsfBZMOsOfrVsY9y+u1tTpSLcWHK/ZjaPdQ+Pt6sMuJPwH9rwMWnGM1F449wd5gMBg6EjfddBNuueUWZZ1irFRMTAzOO+88JaIotJgwc+2116oM5KZg/cEs/Lw1Cc9eOg5vXjfZXmswNJJuxwKj/mIv1IKqbF5kxRHVx63XWGhhYja8jpliA2XllhMxxxIUFbJPrEiuY7bYa4/b3cauyeP5nFJ5vHbtUVBpIaVEmPeElGtlAddld1m+bUJIlZaV48fNSbhkUg3VWJ1Q6bJCOXvi0ec65FZ7g8Fg6IgwuNx56+vrq/403o6FqqICT3y7GY+fPwYhgb7w92kTw62hLRAUC0RVLy/kFlY253WQxS9rytRrCnzknGIpIdfMQgolxkuxiCYLhWohxfWMxaoMVndFzh0W9awsr2B/LkKXZQPLWThb1lEgLVmyBO+9915lFi9d///973/x2WefqSxfxle+/fbbyortpE2c2d9vSsS0gdHoGmab9WojY42Vcskf5qg3RLX/1d5gMBgMzcc7i/epDOPB3Vo2fsPQgaFoYXVyChdaiJoLZvD5RYnAcblmq2KoFFKsMWXfV+tFDDHuya1FiojgYRkHCkOiyiiIEONnY+Nn1/fxEIojTVZWFrZv367CAHSWb0hICM444wwVCkBRRaHF8ICFCxeq7Zo2IaQ+X5uAu04ZZi/VAau2hgyw7rPTdHMePAaDwSCw/QsngHeebBoRG1oQBmHTcsNinIHNaJGiq41B6q4uNxqAuC7/kCWqKpsvywZam7jOHRRLqvCmDjb3l89VbLv3LEtzQ1i+fLmqNcc4ytLSUmWd7tevn7JeFxcXq64tFFO0XPE+C/mOHj36CPdeqxdSCRkFKC0H+kZ7aLpLWw7ETLMXDAaDofl55dc9OHlEXONaWBkMjYWuMwaaF8QDQR6ExniL7scBU14XYVTlQq+E4q7goKgPxkU5LEm0ntVkkaLgyttvPZfweWW58tnyLVHVQI4//nhcddVVKoaS4oliKjExUYkozUMPPYQ+ffogMjJSxUuxrIorrV5I7U/LQ3SIB19Utnw4Nh7Olx8oaoy90mAwGJqXFXvTsGJPGi6d0owXLoPBHRQgJSI2GDccbNd4ai6qwo+qQ4tU4eEjRRYtUnT3uYO1pHJFSGlXYGCsCCu51rMfIBstNxCnYKJQ6t+/P3744QdVAoWuPPYOzszMVCKL5VDYCo9FfqdMsUs52LR6IXUwowDhgXWY7hLnAnNFAa//CxB3ChBqWi8YDG2J5u5315Q8+s1WPHXRWIQGuJmNGwzNCYUH6y1RSIU0s5CqCVqVCh3tYTQMGNcxUK7QslbIFjd2sHnESCBnl4irPSKqulrrGgndd8ceeywuu+wyHHPMMejRo4dy87366qs48cQTVdFeCqyrr75abXfS6oXUgYz82i1SWVuArU8CJ84Dzpb7Yx6rWQkbDIYmhwOSa3uVDz74AK+99lplzzvO9j766CNVQZytXObOnauqjW/evFltb6tsS8xBoF9nDO9hZyMZDC2JrwiTijLLlRbUSirqU0Ax2JyV051QYPnVEMJDtx+v67ogZ0hvK9g8ZxsQ4PI6jSQw0HoPWqE4jvGW63SGX0DAkWKv1Qup5JwidA+zVagTFtr8SVTh4suBnmeLQh1ubzAYDC0FBxvGGeTk5KhbwuKWP/74o5rhfffdd2od04jj4uLU49kAfebMmepxs2bNUtvbKmviM3DaKKuKusHQ4tDCw5JAqk+dh3HGTQ3djcXMInSxJLFUQieXcgkaugMpZHQ5Bf8YUTTy/PS1XrNINYZWLaRKy8uRnF2IPjEuB8Cu14GUX4GxTwAzPwOG3mZvMBgMLQlnb0whZi2WdevWqXXOnnf79u1T62iFYgDn0KFDkZubq0TU+vXrMW7cOLW9LcJ+oDsO52BUT7vthcHQ0jAom82KyxsXlO1VKKTopgtwyajvfSEw+EZ7wQW6KFkiwdf+DBRVESOA1KVet0g1hFYtpHIKS1FYXI7u4bYKZQGvlIUyMr8IHPUO0G0mEDbI2mYwGFocWqHYduXWW2+trBY+YMAAJa4orGiVIuxrx9osFFYsjslAz4svvhgr/7+9c4GtqsrC8CLlMbxpESgqIA9BRCGID1CRaqhEAQ0EFSFB0ChqRDQRnxhHEaWaQNAQ0QTGRyCohGAENUh9gILACERUQJyMgILypiiISjr3W5zDlEKZ9g5tzy7/lzS9Z9/b29PTc9dee+21/rVihT8fImu3FNj6X/ZZp+Zl6GcmRHnijY2JSO1LPU5fJuCkQuL47zuO3dqr3/Zw7tPxwPmKtaZiGnU2+3VLypg0iwYqj0Q7Ur8e/MtqVq9m9eukvNGDW82Wp7zVdZPNevwjEeE8IcSxIHJXtK0C+iuXXHKJNxAmaRMHCidr48aNvgVIVIotP7RcBgwYEP1UeCzesMNOb/g3a1A7ISt/IZAT8IbAhw4fJwHynTAPbM2VFlc9T/0t7hhG0HialnHVK19iJNGO1K5f/7B6dWpZ/XrVzBbmmmV2M+s5O/W9a/QKIUQI3HnnnXbHHXdY27ZtrWnTppadnW2DBg2yG2+80aNUV111lQ0ZMsRVg0Nl2b93Wk6Hyt9mEOIIaDXhuBSvkKtMaqTOhbztsjhSnmxORKrIIgU5h9qnV2wPwRJItCO1de9By27Y0OqsGWuWnXKkzhkdPSOECI24p13c545yYnKqgKqY8ut5V/6Qz7ln/5/Wu2PlbzMIcRQ4ITgvSYHqPBwp+gWWFv4GIlKx/EFMk17HbhFWAol2pHb+bpa5/59mu5eYdZsUjQohRLLYvPOAZdapYbVrJiQPRYgYErVjRfAk4I5UypOq1TgaKAUZOFDFIlLQZVwidCMT7Ugd/PMva7FjulnzvtGIEEIkjyX/2mnd26qvp0ggHpFKkK6ZR5eqm9XMigZKQY2GqZ87jjRCvdaH36uSScuRIpGUTslFoYSZxNFYobjo43SpsXe1dcvcZtZyaDQihBDJI3/tL9argwpgRAKh4q241EBl4o5dZsr7KMNWfp3TzfptiA6SR1qO1OLFi23OnDnekwboRTNhwgQvXab8mY7KM2fOtHnz5vnzMbEyaGlpvn+RNW3fP3UTSOBOCJFMDvxxyLbtO2CtG6cmCCGSRla31GTaJzpICL6tVzZ/oMQ+fAmgzI4UkSga+Q0cONDViSl1jgX1qLwhYXTp0qXWr18/L3cuHpUqizPV6vd8q9VucHQkhBDJY9GG7Vavdg2rSSm2EEkDJ6rN8OggIbAlV4Uo8yc/7pbckGq6OnXswIEDXsp89tln29ixY2379u0elaKkGacqfj29tebOneuvLy2n9cu3Q/XOiY6EECJ59D6nmU0bFq5sg6jiVEtN83wliZz5RFWig/Ap89WtW7euR5m2bt3qzhFlyxyPHDnSNWKIWDVo0MC+/vprj1TFDf7QjqGz8vEa/pVEZoOGllF1rrUQogqSkTJSdWtVfsKrEKJyKLMjRRTqggsu8K7tiOmhTrxjxw6bOnWqP9eiRQvLycmxjz/+2HJzc6OfOryll5WVdUxXeCGEEEKIUEnLq6Gx6KhRo9yhYkuPLu4oF990000ecWrfvr3de++93haiKFT7/b+VfEIIIYQQSUHhISGEEEKINJEjJYQQQgiRJnKkhBBCCCHSRI6UEKLKguTKpEmTvIIYvv32Wxs/fry98cYbfjxx4kTLy8uz1atX+7EQQpQVOVJCiCoJxS10YMjMzLT333/fxzZt2mRt2rSx66+/3o8XLlzohTIdO3b04xg08IQQojRUuCNVs2ax7s1CiCoDEigZGRnRUeVClInK4v79+9vatWt9rFGjRjZ//nybMmWKH7dq1crGjBljK1eu9ONYI++rr76yevUS1DFfCJFYqqUMR4XpESDS+fTTT3srGVrLlATO1qFDh/wrJJB+QMk9NIkHzpv/Byv4kOA+4ZxR0g+J2rVrl0nhPykgvksrqJJAI27Lli1+P9HloLLZuXOnPfHEEzZ8+HBvZ0XkKWbatGnWoUMHu/zyy239+vX2wgsvuHPFZ/ebb76xd955x77//nvr0aPHCW1V9eqHhThDuwf57HDOoX3m/9c9mFSIcHKtQ5rT0H7kvEO7Tzhv7u+DBw9GI8eHzzWi4s8++2w0kj4V6kjxD8HQngguwIcffmhnnXWWK6WHYqD4gJN3QY9B1N9DufE47xkzZlifPn1ckT6k837zzTft/PPPd92yUO4TIjZ8cB955BHbv39/NJp8cP5efPFFu/vuu90AHc9sYMCYKIjknHZaMrrNjx492h0jmqqTJ8W5P/XUU+5APfDAA653h6AwY8V17+gpykRSkonkORw0/u5u3bqd0OFKCpwrX/n5+Xbuued6K69QJnfuwcmTJ9t9990X1GeHhcUHH3zgmotEQEO43twjOCKfffaZ39tsj4cwN7CY++233+zdd9+1YcOGndDp5nNNqzui1P83OFJJ46OPPircuHFjdBQOKYekMPWPi47CYdasWYUpwxQdhcPs2bMLU5NddBQOeXl50aOwmDJlSvRIxKxatapw9erV0VE4pBarhanVeHQUDilnPnoUFlzvH3/8MToKg9TCoHDBggWFBQUF0UgYpBxAn4srkgqNSJWW3bt3++qDqENIECZkJZ6UHJHSsm3bNmvcuHFw502DbKIf3CshQVslVqah8dNPP9kZZ5wRHQmIqwFDy6fatWuXnzM7ACGRckbszDPPjI7CgTmNaDTRqVAgArVnzx7fqYi3sEOA82ZuaNasWTRS/iTSkRJCCCGECIHEyR9QMTNkyBAbNGhQNBIG5HWFuFJiZXr11VfbyJEj/dqHwnfffWcDBgywESNGRCPh8Mwzz/j1DgkiAfTYfOihh/yeEeaN2a+44oojmlSh8Oijj3olI7lhIcF1vuaaa05KcnBFQoEV89msWbOikTAg5+/WW28NTmPt+eeftyuvvNLzHiuKxDlSy5cv98nx0ksvtRUrVkSjySc3N9cTV0OrKCG8v2DBAuvateuREvEQIMH88ccft71790YjYUCFGMmNoW2jknjavXt3u+uuuywrKysaPbVhYl+0aJFvf/z888/RaPLBkRo8eHAQyfExbJzQFB89MCb2kKpeqWCluIHtvZDgWlNQcVKSsSsQ7muUASqycjhxjhT5OvzjqBT44YcfotEwYB85pL1kIEeCnB2+Lrroomg0DBBRDKlCkkqjTz/91LWNQpP2aNmypT344INefbRq1apo9NSFiZxKJsDJpFIoFOJ8rpCcearIsFXLli3zaENoeZEXXnihX+9QMmlw+rjWBAhCcriBQMxtt93mjndFOdyJc6Q6d+5sixcv9tVez549o9Hks2/fPm8/QTl0KKX4wIfk/vvvt5ycnKCcwM2bN7vOD0nyoUQDmAw43/feey+4VTXnSmI15cWhOYHlARM5nxeiuSQRZ2dnR88kH+wUzvCGDRuikTDgWrOtRypCKPcgizz0E3G2CwoKfJ4IAWwVqSpsR7799tvRaBgwpxElbtq0aYVd74y/p4geJ4ImTZp4xj1bNyFFSMghYZsJJ6pdu3YeEg0BbjiEC6mQpOKQCo0QoEJy6dKlvtIjdycEuCfQ7sFpRScNQchQwCDRToVqQ7b4QtuaLA/YDv/88889QoIeUyisWbPGI2hs0aLVFwrkRWKjcEq4D0OwsTgkX375pS/2iO40b948eibZMB8w//LFeYcynwH3CQGNa6+91rW7KgJV7QkhhBBCpEnitvaEEEIIIUJBESlxFK+//rrnwRA6L02OGmF2wtYtWrTwnxNCiPKGdIRPPvnEe5tS4V0agVtyZ2gXFEoqgAgHzXziKGLNE3rYHY+ifjePSUAmVylWeC5KaXz00lTcFX2fUCr0hBDlx8qVKz3hn/ydONG/aAJ68WR07AaLvpkzZ0YjR1PUrsSPT2S/SnpOturURBEpcRSUdL/22mvWqVMne/XVV90wUG1CF32EzjAOaHSQQEkCIk2aqeyg9QFl1TSJBV6DdgpNqvv27Wtz5szxMaqFeO0999xjL7300pEkRhJ26b6PAFwcFRs1apR37H/44Ye9Umfo0KH+PEKcobW2EEKcPCh8QGeQgg0art9yyy128cUXexNaIk/I6OA4XXfddTZ37lyXHMH+sEjs0qWL2xOeo4IR20aUCptHxIoEcewS3ykeOu+882zJkiX+fhRD0Swdm4TuHo2tKbzADiLKjJg054Wdw34Vb4QtqiaKSImjwFBgYKjmoVQf8T4qklBBxhg999xzNn36dHea0BWiCgVj9thjj7mhif1ytvpQlsWosTocPny4f0fVm0oQNMKoXMOI8R7169d3w4NhwvigAcL47bff7u9BqfnAgQO9DD800VMhxMkFW3LZZZe5TUBLrnXr1m6P2OIbP3682x4WbG+99ZbbkJtvvtkrklnc0VUglkzBWaL6EseICmaiXNgh3g+BZfQMZ8yY4b8HMVqcJnq4YZ+o0u7du7erxFOpzWKQinN+B04b2nzi1ECOlDgKtGWISNH+AsdowoQJvrpDkwPtozFjxniUiPJpNF2QfSBKhYNT1JHCKKGsQRktThdbfxgXfp7SVBw1muDy/rwWw0jkixYQOG2U27Ma5D2/+OILN3y0JqHcPDQ1cyHEyQU7M3/+fHvllVc86o3NYJG3adMmGzdunKtaY5eIYr/88su+pYeTgz1hIcZ3wDFat26d5eXleTSeBRt2B+ds4sSJHlkaMWKEzZs3zyP0RKrin0XKhN/N7+HnYkkWbBfj+fn5/jpR9dHWnjgGDAmGYerUqb6NVlIXbZyfkvSEWPWxMkT3pSTo7UdLIBJGb7jhhmj0MPF7F/0dGCwltAshYmJbRWT7ySefjEYPO1pEm2JKslVs7bE4I2pVnKL2BgeNlilE2tEniomnT36XbNWpixwpUSJss6Fum04+Eis5cgtOJORG3gHVN4TE47YVQghRVnCIiBCVFaJHRLPYyisJHCRysoiu9+rVKxoV4r/IkRJCCCGESBPFHoUQQggh0kSOlBBCCCFEmsiREkIIIYRIEzlSQgghhBBpIkdKCCGEECJN5EgJIYQQQqSJHCkhhBBCiDSRIyWEEEIIkSZypIQQQggh0sLsP8cfAHtlNv4pAAAAAElFTkSuQmCC"},"913f4de9-19a7-4c6e-8787-770ebcf8e928.png":{"image/png":"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"},"a32d3298-2bca-45e5-b452-fe2b0ba2fa2d.png":{"image/png":"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"}}},{"cell_type":"code","source":"model_path=\"INSERT FILEPATH HERE\"\ntorch.save(model,model_path) #where model is your trained model instance","metadata":{"execution":{"iopub.status.busy":"2023-02-26T21:40:02.075392Z","iopub.execute_input":"2023-02-26T21:40:02.076745Z","iopub.status.idle":"2023-02-26T21:40:02.551352Z","shell.execute_reply.started":"2023-02-26T21:40:02.07671Z","shell.execute_reply":"2023-02-26T21:40:02.550295Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"##### Loading of required dicom packages\n\nChallenges involving dicom images are not new in Kaggle - another user STPETE_ISHII has prepared this [Kaggle Dataset](https://www.kaggle.com/datasets/stpeteishii/read-dicom-set) containing all the necessary packages - as stated in his dataset page, this should be added as a dataset to the submission note and the following lines of code run to install the necessary packages under Internet-off conditions:","metadata":{}},{"cell_type":"code","source":"###SCRIPTS TO INSTALL DICOM PACKAGES FROM DATASET WHEN \"INTERNET OFF\"\n!pip install pylibjpeg --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install pylibjpeg-openjpeg --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install pylibjpeg-libjpeg --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install pydicom --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install python-gdcm --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read\n!pip install dicomsdl --no-index --find-links=file:///kaggle/input/read-dicom-set/dicom_read","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"##### Other tips for submission\nAs there is a limit of only 5 submissions a day, if you attempt to run a submission and something goes wrong, you've used one precious attempt. Hence, to try to minimise this possibility, once you think you're ready: put the notebook into Internet-off mode, restart the kernel and Run All - make sure it runs to completion without any exceptions, and double check that the final submission.csv file produced is in the competition-required format.\n\nFor this particular challenge, for training purposes it was in the long-run very time-saving to first convert all the training dicom images to png for use in training. However, for submission purposes, the hidden test images will all be dicom files, so our code needs to be able to form the relevant filepaths and convert from dicom-to-png on the fly (but at least only for inference purposes!). \n\n##### Code for Submission\n\nIn my Submission code I first performed the following steps, all of which have already been discussed and shown above:\n* first import block\n* followed by dicom installation scripts shown above in this section\n* followed by dicom package import statements\n* followed by all the steps necessary to append a column with test_image filepaths to a test_df from the supplied test.csv\n* followed by all the metadata processing steps in Section 4 so as to arrive at \"Xn_test_df\", a dataframe of all the processed and normalised test data together with a column containing all the test image dicom paths, and \"submission_df_detailed\", a dataframe with length equal to the length of the test.csv and only containing the column of \"prediction_id\" (which, as seen in the publicly visible test.csv, there can be multiple instances of the same prediction_id in this column, corresponding to more than one image taken for that patient at the same laterality)\n* set the device consistent with the device used for the trained model, and define the yPredict() function\n\nAfter all these blocks, I used the following lines of code:-","metadata":{}},{"cell_type":"code","source":"### Load the saved model:\nmodel_path=\"YOUR MODEL PATH HERE\"\ntest_model=torch.load(model_path)\ntest_model.to(device)\ntest_model.eval();\n\n#prepare the dataset and dataloader to use with loaded model for inference\ntest_X=Xn_test_df.copy()\ntest_dicom_paths=test_X.pop(\"path\")\n\ntest_DS=pd_df_toDataset(test_X,None,test_dicom_paths,inp_type=\"dicom\",training=False)\n\nbat_size=64\n##NOTE for the models I worked on, found that 64 was a safe batch size for performing\n#inference with Kaggle GPUs whilst avoiding overflow\n\ntest_DL=DataLoader(test_DS,batch_size=bat_size,num_workers=mp.cpu_count())\n#NOTE: no custom sampler is used here, as this is just for inference\n\n## The For-loop below is to generate minibatches of predictions and then concatenate them\n# into a prediction vector with length equal to the test-size\ntest_pred=None\nwith torch.no_grad():\n    for i in test_DL:\n        X_batch=i[0].to(device)\n        img_batch=i[1].to(device)\n        minibatch_pred=yPredict(test_model(X_batch,img_batch))\n        if test_pred is None:\n            test_pred = minibatch_pred\n        else:\n            test_pred=torch.cat((test_pred,minibatch_pred))\n            \n#adds the predictions column to the dataframe containing prediction_ids, with header \"cancer\"\nsubmission_df_detailed=pd.concat([submission_df_detailed,pd.DataFrame({\"cancer\":np.array(test_pred.cpu().squeeze())})],\n               join=\"inner\",axis=1)\n\n#for a given prediction_id, evaluates the mean of all predictions and saves these\n#into a new dataframe that only contains unique prediction_ids against the averaged predictions\nsubmission_df=submission_df_detailed.groupby(by=\"prediction_id\").mean().reset_index()\n\n#write to \"submission.csv\" file\nsubmission_df.to_csv(\"submission.csv\",index=False)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}