{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse images to determine asymmetry of comets ##\n", "\n", "Look at images, find good comets using three thresholds and determine their asymmetry." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Some useful routines\n", "\n", "Convert RGBA images to RGB and RGB to greyscale. Ceiling division. Analysis routines...\n", "\n", "Note that must set negative = 1 in call to rgb2grey in processImage for MC data, negative = 0 for real images." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Date and time 2021-06-03 14:06:43.249654\n", " \n", "Date and time 2021-06-03 14:06:43.641679\n", "Time since last check is 0:00:00.392025\n" ] } ], "source": [ "import datetime\n", "now = datetime.datetime.now()\n", "print(\"Date and time \",str(now))\n", "#\n", "import numpy as np\n", "#\n", "def rgba2rgb(rgba, background = (255, 255, 255)):\n", " '''\n", " Function to convert RGBA images into RGB format. Input RGBA image (and background); output RGB image.\n", " '''\n", " rows, cols, chans = rgba.shape\n", " #\n", " debug = False\n", " #\n", " if debug:\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running rgba2rgb\")\n", " if chans == 4:\n", " print(\"RGBA image\")\n", " elif chans == 3:\n", " print(\"RGB image\")\n", " return rgba\n", " else:\n", " print(\"Channel number is\",chans)\n", " sys.exit()\n", " else:\n", " assert chans == 4, 'RGBA image must have 4 channels.'\n", " #\n", " rgb = np.zeros((rows, cols, 3), dtype = 'float32')\n", " r, g, b, a = rgba[:,:,0], rgba[:,:,1], rgba[:,:,2], rgba[:,:,3]\n", " #\n", " a = np.asarray(a, dtype='float32')/255.0\n", " #\n", " R, G, B = background\n", " #\n", " rgb[:, :, 0] = r*a + (1.0 - a)*R\n", " rgb[:, :, 1] = g*a + (1.0 - a)*G\n", " rgb[:, :, 2] = b*a + (1.0 - a)*B\n", " #\n", " return np.asarray(rgb, dtype = np.uint8)\n", "#\n", "#\n", "#\n", "def rgb2grey(rgb, negative = 0, withHists = False):\n", " '''\n", " Convert RGB image to greyscale. Input RGB (and flag indicating negative required), output greyscale image.\n", " '''\n", " rows, cols, chans = rgb.shape\n", " #\n", " debugHere = False\n", " #\n", " if debugHere:\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running rgb2grey\")\n", " if chans == 4:\n", " print(\"RGBA image\")\n", " elif chans == 3:\n", " print(\"RGB image\")\n", " elif chans == 1:\n", " print(\"Greyscale image\")\n", " return rgb\n", " else:\n", " print(\"Channel number is\",chans)\n", " sys.exit()\n", " else:\n", " assert chans == 3, 'RGB image must have 3 channels.'\n", " #\n", " grey = np.zeros((rows, cols), dtype = 'float32')\n", " #\n", " r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]\n", " #\n", " grey[:, :] = (0.2125*(r*negative + (negative - 1.0)*r) + \n", " 0.7154*(g*negative + (negative - 1.0)*g) + \n", " 0.0721*(b*negative + (negative - 1.0)*b))\n", " if withHists:\n", " print(\" \")\n", " print(\"Max intensities red\",np.amax(r),\"blue\",np.amax(b),\"green\",np.amax(g))\n", " print(\" \")\n", " nBins = int(256/8)\n", " nBins = 256\n", " plt.figure(figsize = (10, 9))\n", " plt.subplot(4, 1, 1)\n", " plt.hist(np.ravel(r), bins = nBins, color = 'r')\n", " plt.yscale(\"log\")\n", " plt.xlim(-1, 256)\n", " plt.subplot(4, 1, 2)\n", " plt.hist(np.ravel(g), bins = nBins, color = 'g')\n", " plt.yscale(\"log\")\n", " plt.xlim(-1, 256)\n", " plt.subplot(4, 1, 3)\n", " plt.hist(np.ravel(b), bins = nBins, color = 'b')\n", " plt.yscale(\"log\")\n", " plt.xlim(-1, 256)\n", " plt.subplot(4, 1, 4)\n", " plt.hist(np.ravel(grey), bins = nBins, color = 'k')\n", " plt.yscale(\"log\")\n", " plt.xlim(-1, 256)\n", " plt.tight_layout()\n", " plt.show()\n", " #\n", " return np.asarray(grey, dtype = np.uint8)\n", "#\n", "#\n", "#\n", "def ceilDiv(a, b):\n", " '''\n", " Return a//b rounded up.\n", " '''\n", " ceiling = -(-a//b)\n", " return ceiling\n", "#\n", "#\n", "#\n", "def processImage(imgRaw, hotCut, withHists):\n", " #\n", " import sys\n", " import numpy as np\n", " import scipy.ndimage as ndimage\n", " import matplotlib.pyplot as plt\n", " %matplotlib inline\n", " # \n", " # Number of rows and columns. Depth is 3 for RGB, 4 for RGBA image. A is opacity (alpha)\n", " nRows = imgRaw.shape[0] \n", " nCols = imgRaw.shape[1]\n", " nDepth = imgRaw.shape[2]\n", " #\n", " nThresh = len(thresh)\n", " img = np.zeros((nRows, nCols))\n", " imgThr = np.zeros((nRows, nCols, nThresh))\n", " #\n", " # Determine image format and process accordingly\n", " if nDepth == 4:\n", " imgRGB = rgba2rgb(imgRaw)\n", " imgGrey = rgb2grey(imgRGB, 0, withHists)\n", " elif nDepth == 3:\n", " imgRGB = imgRaw\n", " imgGrey = rgb2grey(imgRGB, 1, withHists)\n", " elif nDepth == 1:\n", " imgRGB = imgRaw\n", " imgGrey = imgRaw\n", " else:\n", " print(\" \")\n", " print(\"Unexpected image depth\",nDepth)\n", " sys.stop()\n", " #\n", " # Remove \"hot\" pixels\n", " hotPixels = imgGrey > hotCut\n", " imgGrey[hotPixels] = 0\n", " #\n", " # Rescale \n", " imgGrey = np.asarray(255/hotCut*imgGrey, dtype = np.uint8)\n", " #\n", " if withHists:\n", " print(\" \")\n", " print(\"Maximum pixel value before removing hot pixels\",np.amax(imgGrey))\n", " print(\"Minimum pixel value \",np.amin(imgGrey))\n", " print(\"Type of raw image file is\",imgRaw.dtype)\n", " print(\"Type of greyscale image file is\",imgGrey.dtype)\n", " print(\"Number of rows\",nRows,\"of columns\",nCols,\"of pixels\",nRows*nCols,\"and depth\",nDepth)\n", " print(\"Hot pixel cut\",hotCut)\n", " print(\"Number of hot pixels\",np.sum(hotPixels))\n", " print(\"Maximum pixel value after removing hot pixels\",np.amax(imgGrey))\n", " print(\"Minimum pixel value \",np.amin(imgGrey))\n", " #\n", " return imgGrey, nRows, nCols\n", "#\n", "#\n", "#\n", "def findWheels(imgGrey, threshold):\n", " #\n", " if not hasattr(findWheels, \"firstCall\"):\n", " findWheels.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running findWheels\")\n", " if debug:\n", " findWheels.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running findWheels\")\n", " #\n", " # Create image with cluster threshold and run watershed algorithm\n", " clusImg = imgGrey > threshold\n", " if debug:\n", " plt.figure(figsize = (pltX, pltY))\n", " plt.imshow(clusImg);\n", " colDotsClus = ndimage.watershed_ift(clusImg.astype(np.uint8), markers)\n", " #\n", " # Remove \"isolated\" markers (i.e. markers in regions where no cluster found)\n", " colDotsClus[rMark, cMark] = colDotsClus[rMark + 1, cMark]\n", " #\n", " # Find the value of the marker of the last identified regions\n", " mMaxClus = np.amax(colDotsClus)\n", " #\n", " # Positions of marker values\n", " boolClus = colDotsClus >= mStart\n", " nFoundClus = len(np.unique(colDotsClus[boolClus]))\n", " rMarkerClus = np.zeros(nFoundClus)\n", " rMarkerClus = np.unique(colDotsClus[boolClus])\n", " #\n", " # Select the clusters, first pass (determine accepted number). Must exclude tiny clusters (noise) and any \n", " # really large clusters (background regions in image).\n", " nClus = 0\n", " cMarkerClus = np.zeros(nFoundClus)\n", " maxInDotClus = 0\n", " for nR in range(0, nFoundClus):\n", " boolClus = colDotsClus == rMarkerClus[nR]\n", " nHereCl = np.sum(boolClus)\n", " if nHereCl < minClusPixels or nHereCl > maxClusPixels:\n", " continue\n", " maxInDotClus = max(maxInDotClus, nHereCl)\n", " cMarkerClus[nClus] = rMarkerClus[nR]\n", " nClus += 1\n", " #\n", " # Information on clusters\n", " nInCluster = np.zeros(nClus).astype(int)\n", " iClusSum = np.zeros(nClus)\n", " #\n", " # Information on pixels in clusters\n", " lClus = np.sum(clusImg)\n", " #\n", " # Safe size of arrays would be number of pixels in image. Try to reduce size by using number of pixels in clusters\n", " # The factor lFact can be used to expand array sizes\n", " lFact = 4\n", " indexCl = np.zeros(lFact*lClus)\n", " cPixelsCl = np.zeros(lFact*lClus).astype(int)\n", " rPixelsCl = np.zeros(lFact*lClus).astype(int)\n", " iPixelsCl = np.zeros(lFact*lClus)\n", " #\n", " # Temporary information \n", " cPixelsHere = np.zeros(lClus).astype(int)\n", " rPixelsHere = np.zeros(lClus).astype(int)\n", " iPixelsHere = np.zeros(lClus)\n", " #\n", " # Find pixels in clusters, determine positions and plot \n", " if findWheels.firstCall:\n", " fig = plt.figure(figsize = (pltX, pltY))\n", " ax = fig.add_subplot(1, 1, 1)\n", " plt.title(\"Clusters with threshold \" + str(threshold), fontsize = 12)\n", " plt.xlabel('x pixel', fontsize = 12)\n", " plt.ylabel('y pixel', fontsize = 12)\n", " #\n", " # Figure control\n", " xOffMax = 2\n", " yOffMax = 2\n", " mSize = 0.001\n", " nCol = 0\n", " #\n", " nLastCl = 0\n", " #\n", " for nC in range(0, nClus):\n", " #\n", " # Clusters\n", " boolClus = colDotsClus == cMarkerClus[nC]\n", " nHereCl = np.sum(boolClus).astype(int)\n", " #\n", " nInCluster[nC] = nHereCl\n", " #\n", " # Indices of x and y pixels\n", " rPixelsHere, cPixelsHere = np.where(boolClus)\n", " #\n", " # Intensities in pixels\n", " iPixelsHere = imgGrey[rPixelsHere, cPixelsHere]\n", " iClusSum[nC] = np.sum(iPixelsHere)\n", " #\n", " indexCl[nLastCl:nLastCl + nHereCl] = nC*np.ones(nHereCl)\n", " cPixelsCl[nLastCl:nLastCl + nHereCl] = cPixelsHere[:]\n", " rPixelsCl[nLastCl:nLastCl + nHereCl] = rPixelsHere[:]\n", " iPixelsCl[nLastCl:nLastCl + nHereCl] = iPixelsHere[:]\n", " #\n", " if findWheels.firstCall:\n", " plt.scatter(cPixelsHere, rPixelsHere, s = mSize, c = colorTab[nCol], marker = 'o')\n", " rLab = np.amax(rPixelsHere[0:nHereCl]) + yOffMax\n", " cLab = np.amax(cPixelsHere[0:nHereCl]) + xOffMax\n", " plt.text(cLab, rLab, str(nC), color = colorTab[nCol])\n", " nCol = nCol + 1\n", " if nCol > nColTab - 1:\n", " nCol = 0\n", " #\n", " nLastCl = nLastCl + nHereCl\n", " #\n", " #\n", " if findWheels.firstCall:\n", " plt.xlim(-0.05*nCols, 1.05*nCols)\n", " plt.ylim(-0.05*nRows, 1.05*nRows)\n", " plt.grid(color = 'g')\n", " print(\" \")\n", " plt.show()\n", " #\n", " findWheels.firstCall = False\n", " #\n", " return nInCluster, rPixelsCl, cPixelsCl, iPixelsCl\n", "#\n", "#\n", "#\n", "def edgeFinderIn(picture, edgeWidth, useDiag = True):\n", " '''\n", " Return array containing pixels in edges (of width edgeWidth) of input (thresholded) image.\n", " The edges are inside the original image. The flag useDiag ensures \"corner\" pixels are selected.\n", " '''\n", " #\n", " shiftR = edgeWidth\n", " shiftC = edgeWidth\n", " nRows, nCols = picture.shape\n", " edges = np.full((nRows, nCols), False)\n", " imgShift = np.full((nRows, nCols), False)\n", " #\n", " # Right edge\n", " imgShift[0:nRows, 0:nCols - shiftC] = picture[0:nRows, shiftC:nCols]\n", " imgShift[0:nRows, nCols - shiftC:nCols] = False\n", " edges = np.logical_and(picture, np.logical_not(imgShift))\n", " #\n", " # Left edge \n", " imgShift[0:nRows, shiftC:nCols] = picture[0:nRows, 0:nCols - shiftC]\n", " imgShift[0:nRows, 0:shiftC] = False\n", " edges = np.logical_or(edges, np.logical_and(picture, np.logical_not(imgShift)))\n", " #\n", " # Lower edge \n", " imgShift[shiftR:nRows, 0:nCols] = picture[0:nRows - shiftR, 0:nCols]\n", " imgShift[0:shiftR, 0:nCols] = False \n", " edges = np.logical_or(edges, np.logical_and(picture, np.logical_not(imgShift)))\n", " #\n", " # Upper edge \n", " imgShift[0:nRows - shiftR, 0:nCols] = picture[shiftR:nRows, 0:nCols] \n", " imgShift[nRows - shiftR:nRows, 0:nCols] = False\n", " edges = np.logical_or(edges, np.logical_and(picture, np.logical_not(imgShift)))\n", " #\n", " if useDiag:\n", " #\n", " # Left upper edge\n", " imgShift[0:nRows, 0:nCols - shiftC] = picture[0:nRows, shiftC:nCols]\n", " imgShift[0:nRows, nCols - shiftC:nCols] = False\n", " imgShift[shiftR:nRows, 0:nCols] = imgShift[0:nRows - shiftR, 0:nCols]\n", " imgShift[0:shiftR, 0:nCols] = False \n", " edges = np.logical_or(edges, np.logical_and(picture, np.logical_not(imgShift)))\n", " #\n", " # Left lower edge\n", " imgShift[0:nRows, 0:nCols - shiftC] = picture[0:nRows, shiftC:nCols]\n", " imgShift[0:nRows, nCols - shiftC:nCols] = False\n", " imgShift[0:nRows - shiftR, 0:nCols] = imgShift[shiftR:nRows, 0:nCols] \n", " imgShift[nRows - shiftR:nRows, 0:nCols] = False\n", " edges = np.logical_or(edges, np.logical_and(picture, np.logical_not(imgShift)))\n", " #\n", " # Right upper edge \n", " imgShift[0:nRows, shiftC:nCols] = picture[0:nRows, 0:nCols - shiftC]\n", " imgShift[0:nRows, 0:shiftC] = False\n", " imgShift[shiftR:nRows, 0:nCols] = imgShift[0:nRows - shiftR, 0:nCols]\n", " imgShift[0:shiftR, 0:nCols] = False \n", " edges = np.logical_or(edges, np.logical_and(picture, np.logical_not(imgShift)))\n", " #\n", " # Right lower edge \n", " imgShift[0:nRows, shiftC:nCols] = picture[0:nRows, 0:nCols - shiftC]\n", " imgShift[0:nRows, 0:shiftC] = False\n", " imgShift[0:nRows - shiftR, 0:nCols] = imgShift[shiftR:nRows, 0:nCols] \n", " imgShift[nRows - shiftR:nRows, 0:nCols] = False\n", " edges = np.logical_or(edges, np.logical_and(picture, np.logical_not(imgShift)))\n", " #\n", " return edges\n", "#\n", "#\n", "#\n", "def expander(picture, edgeWidth, useDiag = True):\n", " '''\n", " Return array containing thresholded regions expanded by band of width edgeWidth. The flag useDiag ensures\n", " \"corner\" pixels are included correctly.\n", " '''\n", " #\n", " shiftR = 1\n", " shiftC = 1\n", " nRows, nCols = picture.shape\n", " edges = np.full((nRows, nCols), False)\n", " edgeSum = np.full((nRows, nCols), False)\n", " imgShift = np.full((nRows, nCols), False)\n", " #\n", " for nE in range(0, edgeWidth):\n", " #\n", " # Left edge\n", " imgShift[0:nRows, 0:nCols - shiftC] = picture[0:nRows, shiftC:nCols]\n", " imgShift[0:nRows, nCols - shiftC:nCols] = False\n", " edges = np.logical_and(np.logical_not(picture), imgShift)\n", " #\n", " # Right edge \n", " imgShift[0:nRows, shiftC:nCols] = picture[0:nRows, 0:nCols - shiftC]\n", " imgShift[0:nRows, 0:shiftC] = False\n", " edges = np.logical_or(edges, np.logical_and(np.logical_not(picture), imgShift))\n", " #\n", " # Upper edge \n", " imgShift[shiftR:nRows, 0:nCols] = picture[0:nRows - shiftR, 0:nCols]\n", " imgShift[0:shiftR, 0:nCols] = False \n", " edges = np.logical_or(edges, np.logical_and(np.logical_not(picture), imgShift))\n", " #\n", " # Lower edge \n", " imgShift[0:nRows - shiftR, 0:nCols] = picture[shiftR:nRows, 0:nCols] \n", " imgShift[nRows - shiftR:nRows, 0:nCols] = False\n", " edges = np.logical_or(edges, np.logical_and(np.logical_not(picture), imgShift))\n", " #\n", " if useDiag:\n", " #\n", " # Left upper edge\n", " imgShift[0:nRows, 0:nCols - shiftC] = picture[0:nRows, shiftC:nCols]\n", " imgShift[0:nRows, nCols - shiftC:nCols] = False\n", " imgShift[shiftR:nRows, 0:nCols] = imgShift[0:nRows - shiftR, 0:nCols]\n", " imgShift[0:shiftR, 0:nCols] = False \n", " edges = np.logical_or(edges, np.logical_and(np.logical_not(picture), imgShift))\n", " #\n", " # Left lower edge\n", " imgShift[0:nRows, 0:nCols - shiftC] = picture[0:nRows, shiftC:nCols]\n", " imgShift[0:nRows, nCols - shiftC:nCols] = False\n", " imgShift[0:nRows - shiftR, 0:nCols] = imgShift[shiftR:nRows, 0:nCols] \n", " imgShift[nRows - shiftR:nRows, 0:nCols] = False\n", " edges = np.logical_or(edges, np.logical_and(np.logical_not(picture), imgShift))\n", " #\n", " # Right upper edge \n", " imgShift[0:nRows, shiftC:nCols] = picture[0:nRows, 0:nCols - shiftC]\n", " imgShift[0:nRows, 0:shiftC] = False\n", " imgShift[shiftR:nRows, 0:nCols] = imgShift[0:nRows - shiftR, 0:nCols]\n", " imgShift[0:shiftR, 0:nCols] = False \n", " edges = np.logical_or(edges, np.logical_and(np.logical_not(picture), imgShift))\n", " #\n", " # Right lower edge \n", " imgShift[0:nRows, shiftC:nCols] = picture[0:nRows, 0:nCols - shiftC]\n", " imgShift[0:nRows, 0:shiftC] = False\n", " imgShift[0:nRows - shiftR, 0:nCols] = imgShift[shiftR:nRows, 0:nCols] \n", " imgShift[nRows - shiftR:nRows, 0:nCols] = False\n", " edges = np.logical_or(edges, np.logical_and(np.logical_not(picture), imgShift))\n", " #\n", " picture = np.logical_or(picture, edges)\n", " edgeSum = np.logical_or(edges, edgeSum)\n", " #\n", " return picture, edgeSum\n", "#\n", "#\n", "#\n", "def clusExpand(nInCluster, rPixelsCl, cPixelsCl, widthEx):\n", " #\n", " debugHere = False\n", " #\n", " if not hasattr(clusExpand, \"firstCall\"):\n", " clusExpand.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running clusExpand\")\n", " if debug:\n", " clusExpand.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running clusExpand\")\n", " #\n", " nClus = len(nInCluster)\n", " #\n", " # Temporary information \n", " lClus = np.amax(nInCluster)\n", " lFact = 1\n", " cPixelsHere = np.zeros(lFact*lClus).astype(int)\n", " rPixelsHere = np.zeros(lFact*lClus).astype(int)\n", " thisPic = np.zeros((nRows, nCols))\n", " thisPicEx = np.zeros((nRows, nCols))\n", " thisEdge = np.zeros((nRows, nCols))\n", " #\n", " # Information on expanded cluster\n", " nInClusEx = np.zeros(nClus).astype(int)\n", " iClusExSum = np.zeros(nClus)\n", " #\n", " # Information on pixels in expanded cluster\n", " lFact = 30\n", " indexClEx = np.zeros(lFact*lClus)\n", " cPixelsClEx = np.zeros(lFact*lClus).astype(int)\n", " rPixelsClEx = np.zeros(lFact*lClus).astype(int)\n", " iPixelsClEx = np.zeros(lFact*lClus)\n", " #\n", " # Information on edge\n", " nInEdge = np.zeros(nClus).astype(int)\n", " iEdgeSum = np.zeros(nClus)\n", " #\n", " # Information on pixels in expanded edge\n", " lFact = 30\n", " indexEdge = np.zeros(lFact*lClus)\n", " cPixelsEdge = np.zeros(lFact*lClus).astype(int)\n", " rPixelsEdge = np.zeros(lFact*lClus).astype(int)\n", " iPixelsEdge = np.zeros(lFact*lClus)\n", " #\n", " # Pictures before and after expanding and edges (all clusters)\n", " clusPic = np.zeros((nRows, nCols))\n", " clusPicEx = np.zeros((nRows, nCols))\n", " edgePic = np.zeros((nRows, nCols))\n", " #\n", " if clusExpand.firstCall:\n", " fig = plt.figure(figsize = (pltX, pltY))\n", " ax = fig.add_subplot(1, 1, 1)\n", " plt.title(\"Expanded clusters\", fontsize = 12)\n", " plt.xlabel('x pixel', fontsize = 12)\n", " plt.ylabel('y pixel', fontsize = 12)\n", " #\n", " # Figure control\n", " xOffMax = 2\n", " yOffMax = 2\n", " xOffMin = 30\n", " yOffMin = 30\n", " mSize = 0.001\n", " nCol = 0\n", " #\n", " nLastCl = 0\n", " nLastClEx = 0\n", " nLastEdge = 0\n", " for nC in range(0, nClus):\n", " #\n", " # Clusters\n", " nHereCl = nInCluster[nC]\n", " #\n", " # Indices of x and y pixels\n", " thisPic.fill(0) \n", " thisPic[rPixelsCl[nLastCl:nLastCl + nHereCl], cPixelsCl[nLastCl:nLastCl + nHereCl]] = 1\n", " clusPic += thisPic\n", " #\n", " nLastCl = nLastCl + nHereCl\n", " #\n", " # Expand cluster\n", " thisExp, thisEdge = expander(thisPic, widthEx, useDiag)\n", " nHereClEx = np.sum(thisExp).astype(int)\n", " nInClusEx[nC] = nHereClEx\n", " #\n", " # Indices of x and y pixels expanded cluster\n", " rPixelsHereEx, cPixelsHereEx = np.where(thisExp)\n", " thisPicEx.fill(0)\n", " thisPicEx[rPixelsHereEx, cPixelsHereEx] = 1\n", " clusPicEx += thisPicEx\n", " #\n", " # Intensities in pixels expanded cluster\n", " iPixelsHereEx = imgGrey[rPixelsHereEx, cPixelsHereEx]\n", " iClusExSum[nC] = np.sum(iPixelsHereEx)\n", " #\n", " indexClEx[nLastClEx:nLastClEx + nHereClEx] = nC*np.ones(nHereClEx)\n", " cPixelsClEx[nLastClEx:nLastClEx + nHereClEx] = cPixelsHereEx[:]\n", " rPixelsClEx[nLastClEx:nLastClEx + nHereClEx] = rPixelsHereEx[:]\n", " iPixelsClEx[nLastClEx:nLastClEx + nHereClEx] = iPixelsHereEx[:]\n", " #\n", " if clusExpand.firstCall:\n", " plt.scatter(cPixelsClEx[nLastClEx:nLastClEx + nHereClEx], rPixelsClEx[nLastClEx:nLastClEx + nHereClEx], \n", " s = mSize, c = colorTab[nCol], marker = 'o')\n", " rLab = np.amax(rPixelsClEx[nLastClEx:nLastClEx + nHereClEx]) + yOffMax\n", " cLab = np.amax(cPixelsClEx[nLastClEx:nLastClEx + nHereClEx]) + xOffMax\n", " plt.text(cLab, rLab, str(nC), color = colorTab[nCol])\n", " nCol = nCol + 1\n", " if nCol > nColTab - 1:\n", " nCol = 0 \n", " #\n", " nLastClEx = nLastClEx + nHereClEx\n", " #\n", " # Expanded edges\n", " nHereEdge = np.sum(thisEdge).astype(int)\n", " nInEdge[nC] = nHereEdge\n", " #\n", " # Indices of x and y pixels edge\n", " rPixelsHereEdge, cPixelsHereEdge = np.where(thisEdge)\n", " thisEdge.fill(0)\n", " thisEdge[rPixelsHereEdge, cPixelsHereEdge] = 1\n", " edgePic += thisEdge\n", " #\n", " # Intensities in pixels edge\n", " iPixelsHereEdge = imgGrey[rPixelsHereEdge, cPixelsHereEdge]\n", " iEdgeSum[nC] = np.sum(iPixelsHereEdge)\n", " #\n", " indexEdge[nLastEdge:nLastEdge + nHereEdge] = nC*np.ones(nHereEdge)\n", " cPixelsEdge[nLastEdge:nLastEdge + nHereEdge] = cPixelsHereEdge[:]\n", " rPixelsEdge[nLastEdge:nLastEdge + nHereEdge] = rPixelsHereEdge[:]\n", " iPixelsEdge[nLastEdge:nLastEdge + nHereEdge] = iPixelsHereEdge[:]\n", " nLastEdge = nLastEdge + nHereEdge\n", " #\n", " #\n", " if clusExpand.firstCall:\n", " plt.xlim(-0.05*nCols, 1.05*nCols)\n", " plt.ylim(-0.05*nRows, 1.05*nRows)\n", " plt.grid(color = 'g')\n", " print(\" \")\n", " plt.show()\n", " #\n", " clusExpand.firstCall = False\n", " #\n", " if debugHere:\n", " print(\" \")\n", " plt.figure(figsize = (pltX, pltY))\n", " plt.imshow(clusPic);\n", " plt.show()\n", " print(\" \")\n", " plt.figure(figsize = (pltX, pltY))\n", " plt.imshow(clusPicEx);\n", " plt.show()\n", " print(\" \")\n", " plt.figure(figsize = (pltX, pltY))\n", " plt.imshow(edgePic);\n", " plt.show()\n", " #\n", " return nInClusEx, rPixelsClEx, cPixelsClEx, iPixelsClEx, nInEdge, rPixelsEdge, cPixelsEdge, iPixelsEdge\n", "#\n", "#\n", "#\n", "def findRims(nInClus, rPixelsCl, cPixelsCl):\n", " #\n", " if not hasattr(findRims, \"firstCall\"):\n", " findRims.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running findRims\")\n", " if debug:\n", " findRims.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running findRims\")\n", " #\n", " useDiag = True\n", " #\n", " nClus = len(nInClus)\n", " lClus = np.amax(nInClus).astype(int)\n", " #\n", " # Information on cluster edge\n", " nInClusEd = np.zeros(nClus).astype(int)\n", " iClusEdSum = np.zeros(nClus)\n", " #\n", " # Information on pixels in cluster edge\n", " lFact = 3\n", " indexClEd = np.zeros(lFact*lClus)\n", " cPixelsClEd = np.zeros(lFact*lClus)\n", " rPixelsClEd = np.zeros(lFact*lClus)\n", " iPixelsClEd = np.zeros(lFact*lClus)\n", " #\n", " # Pictures for display/edge finding\n", " thisPic = np.zeros((nRows, nCols))\n", " thisEdge = np.zeros((nRows, nCols))\n", " thisEdgePic = np.zeros((nRows, nCols))\n", " #\n", " # Edge width required\n", " width = 1\n", " #\n", " if findRims.firstCall:\n", " fig = plt.figure(figsize = (pltX, pltY))\n", " ax = fig.add_subplot(1, 1, 1)\n", " plt.title(\"Clusters and their edges\", fontsize = 12)\n", " plt.xlabel('x pixel', fontsize = 12)\n", " plt.ylabel('y pixel', fontsize = 12)\n", " #\n", " # Figure control\n", " xOffMax = 2\n", " yOffMax = 2\n", " xOffMin = 30\n", " yOffMin = 30\n", " nCol = 0\n", " mSize = 0.001\n", " #\n", " nLastCl = 0\n", " nLastClEd = 0\n", " for nC in range(0, nClus):\n", " #\n", " # Clusters\n", " nHereCl = nInClus[nC]\n", " #\n", " # Indices of x and y pixels\n", " thisPic.fill(0)\n", " thisPic[rPixelsCl[nLastCl:nLastCl + nHereCl], cPixelsCl[nLastCl:nLastCl + nHereCl]] = 1\n", " #\n", " if findRims.firstCall:\n", " plt.scatter(cPixelsCl[nLastCl:nLastCl + nHereCl], rPixelsCl[nLastCl:nLastCl + nHereCl],\n", " s = mSize, c = colorTab[nCol], marker = 'o')\n", " rLab = np.amax(rPixelsCl[nLastCl:nLastCl + nHereCl]) + yOffMax\n", " cLab = np.amax(cPixelsCl[nLastCl:nLastCl + nHereCl]) + xOffMax\n", " plt.text(cLab, rLab, str(nC), color = colorTab[nCol])\n", " #\n", " nCol = nCol + 1\n", " if nCol > nColTab - 1:\n", " nCol = 0\n", " #\n", " nLastCl = nLastCl + nHereCl\n", " #\n", " # Edges\n", " thisEdge = edgeFinderIn(thisPic, width, useDiag)\n", " nHereClEd = np.sum(thisEdge).astype(int)\n", " #\n", " nInClusEd[nC] = nHereClEd\n", " #\n", " # Indices of x and y pixels\n", " rPixelsHereEd, cPixelsHereEd = np.where(thisEdge)\n", " thisEdgePic[rPixelsHereEd, cPixelsHereEd] = 1\n", " #\n", " # Intensities in pixels\n", " iPixelsHereEd = imgGrey[rPixelsHereEd, cPixelsHereEd]\n", " iClusEdSum[nC] = np.sum(iPixelsHereEd)\n", " #\n", " indexClEd[nLastClEd:nLastClEd + nHereClEd] = nC*np.ones(nInClusEd[nC])\n", " cPixelsClEd[nLastClEd:nLastClEd + nHereClEd] = cPixelsHereEd[0:nHereClEd]\n", " rPixelsClEd[nLastClEd:nLastClEd + nHereClEd] = rPixelsHereEd[0:nHereClEd]\n", " iPixelsClEd[nLastClEd:nLastClEd + nHereClEd] = iPixelsHereEd[0:nHereClEd]\n", " #\n", " if findRims.firstCall:\n", " plt.scatter(cPixelsHereEd, rPixelsHereEd, s = mSize, c = 'k', marker = 'o')\n", " rLab = np.amin(rPixelsHereEd[0:nHereClEd]) - yOffMin\n", " cLab = np.amin(cPixelsHereEd[0:nHereClEd]) - xOffMin\n", " plt.text(cLab, rLab, str(nC), color = 'k')\n", " #\n", " nLastClEd = nLastClEd + nHereClEd\n", " #\n", " if findRims.firstCall:\n", " plt.xlim(-0.05*nCols, 1.05*nCols)\n", " plt.ylim(-0.05*nRows, 1.05*nRows)\n", " plt.grid(color = 'g')\n", " print(\" \")\n", " plt.show()\n", " #\n", " findRims.firstCall = False\n", " #\n", " return nInClusEd, rPixelsClEd, cPixelsClEd, iPixelsClEd\n", "#\n", "#\n", "#\n", "def selCometsThree(nInClus, rPixCl, cPixCl, nInClusEd, rPixClEd, cPixClEd,\n", " nInHeadL, rPixHdL, cPixHdL, nInHdEdL, rPixHdEdL, cPixHdEdL,\n", " nInHeadH, rPixHdH, cPixHdH, nInHdEdH, rPixHdEdH, cPixHdEdH):\n", " #\n", " UseLowHead = False\n", " #\n", " if not hasattr(selCometsThree, \"firstCall\"):\n", " selCometsThree.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running selCometsThree\")\n", " print(\"UseLowHead\",UseLowHead)\n", " #\n", " if debug:\n", " selCometsThree.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running selCometsThree\")\n", " print(\"UseLowHead\",UseLowHead)\n", " #\n", " nClus = len(nInClus)\n", " lClus = np.amax(nInClus)\n", " lClusEd = np.amax(nInClusEd)\n", " nHeadL = len(nInHeadL)\n", " lHeadL = np.amax(nInHeadL)\n", " nHeadH = len(nInHeadH)\n", " lHeadH = np.amax(nInHeadH)\n", " #\n", " minRimPnts0 = 100\n", " minWheelPnts0 = 500\n", " rMin0 = 5\n", " rMax0 = nRows - rMin0\n", " cMin0 = 5\n", " cMax0 = nCols - cMin0\n", " rWidMin0 = 20\n", " rWidMax0 = 200\n", " cWidMin0 = 50\n", " cWidMax0 = 500\n", " maxRtoW = 0.1\n", " #\n", " if selCometsThree.firstCall:\n", " #\n", " # Figure text positions\n", " rTextRow = 3\n", " rTextCol = 3\n", " wTextRow = 28\n", " wTextCol = 28\n", " #\n", " nCol = 0\n", " #\n", " fig = plt.figure(figsize=(7, 5))\n", " plt.title(\"Selected clusters\", fontsize = 12)\n", " plt.xlabel('Column', fontsize = 12)\n", " plt.ylabel('Row', fontsize = 12)\n", " #\n", " pnts_num = np.zeros(nClus).astype(int)\n", " pnts_row = np.zeros((nClus, lClus)).astype(int)\n", " pnts_col = np.zeros((nClus, lClus)).astype(int)\n", " mean_row = np.zeros(nClus)\n", " mean_col = np.zeros(nClus)\n", " #\n", " edgs_num = np.zeros(nClus).astype(int)\n", " edgs_row = np.zeros((nClus, lClusEd)).astype(int)\n", " edgs_col = np.zeros((nClus, lClusEd)).astype(int)\n", " #\n", " head_num = np.zeros(nHeadL).astype(int)\n", " head_row = np.zeros((nHeadL, lHeadL)).astype(int)\n", " head_col = np.zeros((nHeadL, lHeadL)).astype(int)\n", " #\n", " sumWheel = np.zeros(nClus)\n", " sumHead = np.zeros(nHeadL)\n", " #\n", " mean_row = np.zeros(nHeadL)\n", " mean_col = np.zeros(nHeadL)\n", " #\n", " iSelRim = 0\n", " nLastCl = 0\n", " nLastClEd = 0\n", " for nC in range(0, nClus):\n", " #\n", " # Clusters\n", " nHereCl = nInClus[nC]\n", " #\n", " # Cluster edges\n", " nHereClEd = nInClusEd[nC] \n", " rMinClEd = np.amin(rPixClEd[nLastClEd:nLastClEd + nHereClEd])\n", " cMinClEd = np.amin(cPixClEd[nLastClEd:nLastClEd + nHereClEd])\n", " rMaxClEd = np.amax(rPixClEd[nLastClEd:nLastClEd + nHereClEd])\n", " cMaxClEd = np.amax(cPixClEd[nLastClEd:nLastClEd + nHereClEd])\n", " rWidClEd = rMaxClEd - rMinClEd\n", " cWidClEd = cMaxClEd - cMinClEd \n", " #\n", " rimToWheel = nInClusEd[nC]/nInClus[nC]\n", " #\n", " if (nHereClEd > minRimPnts0 and nHereCl > minWheelPnts0 and\n", " rMinClEd > rMin0 and rMaxClEd < rMax0 and cMinClEd > cMin0 and cMaxClEd < cMax0 and \n", " rWidClEd > rWidMin0 and rWidClEd < rWidMax0 and cWidClEd > cWidMin0 and cWidClEd < cWidMax0 and\n", " rimToWheel < maxRtoW):\n", " #\n", " # Look at low threshold heads\n", " nHeadsInClusL = 0\n", " nLastHd = 0\n", " nLastHdEd = 0\n", " for nHL in range(0, nHeadL):\n", " #\n", " nHereHd = nInHeadL[nHL]\n", " #\n", " # Low head edges\n", " nHereHdEd = nInHdEdL[nHL]\n", " rMinHdEd = np.amin(rPixHdEdL[nLastHdEd:nLastHdEd + nHereHdEd])\n", " cMinHdEd = np.amin(cPixHdEdL[nLastHdEd:nLastHdEd + nHereHdEd])\n", " rMaxHdEd = np.amax(rPixHdEdL[nLastHdEd:nLastHdEd + nHereHdEd])\n", " cMaxHdEd = np.amax(cPixHdEdL[nLastHdEd:nLastHdEd + nHereHdEd])\n", " rWidHdEd = rMaxHdEd - rMinHdEd\n", " cWidHdEd = cMaxHdEd - cMinHdEd\n", " #\n", " # Find any low threshold head rims completely within the cluster rim\n", " if (rMinHdEd > rMinClEd and rMaxHdEd < rMaxClEd and \n", " cMinHdEd > cMinClEd and cMaxHdEd < cMaxClEd):\n", " #\n", " nHeadsInClusL += 1\n", " nLastHdSelL = nLastHd\n", " nLastHdEdSelL = nLastHdEd\n", " nHereHdSelL = nHereHd\n", " nHereHdEdSelL = nHereHdEd\n", " #\n", " nLastHd = nLastHd + nHereHd\n", " nLastHdEd = nLastHdEd + nHereHdEd\n", " #\n", " # End of loop over high heads \n", " #\n", " # Look at high threshold heads\n", " nHeadsInClusH = 0\n", " nLastHd = 0\n", " nLastHdEd = 0\n", " for nHH in range(0, nHeadH):\n", " #\n", " # High heads\n", " nHereHd = nInHeadH[nHH]\n", " #\n", " # High head edges\n", " nHereHdEd = nInHdEdH[nHH]\n", " rMinHdEd = np.amin(rPixHdEdH[nLastHdEd:nLastHdEd + nHereHdEd])\n", " cMinHdEd = np.amin(cPixHdEdH[nLastHdEd:nLastHdEd + nHereHdEd])\n", " rMaxHdEd = np.amax(rPixHdEdH[nLastHdEd:nLastHdEd + nHereHdEd])\n", " cMaxHdEd = np.amax(cPixHdEdH[nLastHdEd:nLastHdEd + nHereHdEd])\n", " rWidHdEd = rMaxHdEd - rMinHdEd\n", " cWidHdEd = cMaxHdEd - cMinHdEd\n", " #\n", " # Find any high threshold head rims completely within the cluster rim\n", " if (rMinHdEd > rMinClEd and rMaxHdEd < rMaxClEd and \n", " cMinHdEd > cMinClEd and cMaxHdEd < cMaxClEd):\n", " #\n", " nHeadsInClusH += 1\n", " nLastHdSelH = nLastHd\n", " nLastHdEdSelH = nLastHdEd\n", " nHereHdSelH = nHereHd\n", " nHereHdEdSelH = nHereHdEd\n", " #\n", " nLastHd = nLastHd + nHereHd\n", " nLastHdEd = nLastHdEd + nHereHdEd\n", " #\n", " # End of loop over high heads \n", " #\n", " if nHeadsInClusL == 1 and nHeadsInClusH == 1:\n", " # \n", " # This rim accepted\n", " pnts_num[iSelRim] = nHereCl\n", " pnts_row[iSelRim, 0:nHereCl] = rPixCl[nLastCl:nLastCl + nHereCl].astype(int)\n", " pnts_col[iSelRim, 0:nHereCl] = cPixCl[nLastCl:nLastCl + nHereCl].astype(int)\n", " #\n", " edgs_num[iSelRim] = nHereClEd\n", " edgs_row[iSelRim, 0:nHereClEd] = rPixClEd[nLastClEd:nLastClEd + nHereClEd].astype(int)\n", " edgs_col[iSelRim, 0:nHereClEd] = cPixClEd[nLastClEd:nLastClEd + nHereClEd].astype(int)\n", " #\n", " if UseLowHead:\n", " head_num[iSelRim] = nHereHdSelL\n", " head_row[iSelRim, 0:nHereHdSelL] = rPixHdL[nLastHdSelL:nLastHdSelL + nHereHdSelL].astype(int)\n", " head_col[iSelRim, 0:nHereHdSelL] = cPixHdL[nLastHdSelL:nLastHdSelL + nHereHdSelL].astype(int)\n", " else:\n", " head_num[iSelRim] = nHereHdSelH\n", " head_row[iSelRim, 0:nHereHdSelH] = rPixHdH[nLastHdSelH:nLastHdSelH + nHereHdSelH].astype(int)\n", " head_col[iSelRim, 0:nHereHdSelH] = cPixHdH[nLastHdSelH:nLastHdSelH + nHereHdSelH].astype(int)\n", " #\n", " # Sum cluster intensities\n", " sumWheel[iSelRim] = np.sum(imgGrey[pnts_row[iSelRim, 0:pnts_num[iSelRim]], pnts_col[iSelRim, 0:pnts_num[iSelRim]]])\n", " #\n", " # Head intensities and mean head position\n", " sumHead[iSelRim] = np.sum(imgGrey[head_row[iSelRim, 0:head_num[iSelRim]], head_col[iSelRim, 0:head_num[iSelRim]]])\n", " mean_row[iSelRim] = (np.sum(imgGrey[head_row[iSelRim, 0:head_num[iSelRim]], \n", " head_col[iSelRim, 0:head_num[iSelRim]]]*\n", " head_row[iSelRim, 0:head_num[iSelRim]])/sumHead[iSelRim])\n", " mean_col[iSelRim] = (np.sum(imgGrey[head_row[iSelRim, 0:head_num[iSelRim]], \n", " head_col[iSelRim, 0:head_num[iSelRim]]]*\n", " head_col[iSelRim, 0:head_num[iSelRim]])/sumHead[iSelRim])\n", " #\n", " if selCometsThree.firstCall:\n", " plt.plot(pnts_col[iSelRim, 0:pnts_num[iSelRim]], pnts_row[iSelRim, 0:pnts_num[iSelRim]],\n", " color = colorTab[nCol])\n", " plt.plot(mean_col[iSelRim], mean_row[iSelRim], marker = '+', color = colorTab[nCol])\n", " row_label = np.amax(pnts_row[iSelRim, 0:pnts_num[iSelRim]]) + rTextRow\n", " col_label = np.amax(pnts_col[iSelRim, 0:pnts_num[iSelRim]]) + rTextCol\n", " plt.text(col_label, row_label, str(nC), color = colorTab[nCol])\n", " nCol = nCol + 1\n", " if nCol > nColTab - 1:\n", " nCol = 0\n", " #\n", " iSelRim += 1\n", " #\n", " # End of if statement, rim accepted\n", " #\n", " # End of if statement, cluster in fiducial region \n", " nLastCl = nLastCl + nHereCl\n", " nLastClEd = nLastClEd + nHereClEd\n", " #\n", " # End of loop over clusters\n", " nRimOut = iSelRim\n", " #\n", " if selCometsThree.firstCall:\n", " plt.xlim(-0.05*nCols, 1.05*nCols)\n", " plt.ylim(-0.05*nRows, 1.05*nRows)\n", " plt.grid(color = 'g')\n", " print(\" \")\n", " plt.show()\n", " print(\" \")\n", " print(\"Number of selected clusters (nRimOut)\",nRimOut)\n", " #\n", " # Remove overlaps\n", " #\n", " overlap = np.zeros(nRimOut).astype(bool)\n", " #\n", " for rimA in range(0, nRimOut - 1):\n", " rowA = edgs_row[rimA, 0:edgs_num[rimA]]\n", " colA = edgs_col[rimA, 0:edgs_num[rimA]]\n", " for rimB in range(rimA + 1, nRimOut):\n", " rowB = edgs_row[rimB, 0:edgs_num[rimB]]\n", " colB = edgs_col[rimB, 0:edgs_num[rimB]]\n", " for nP in range(0, edgs_num[rimA]):\n", " dist2 = (rowA[nP] - rowB)**2 + (colA[nP] - colB)**2\n", " if np.amin(dist2) < 1:\n", " overlap[rimA] = True\n", " overlap[rimB] = True\n", " break\n", " #\n", " #\n", " #\n", " if selCometsThree.firstCall:\n", " print(\" \")\n", " print(\"Remove the overlaps:\",np.linspace(0, nRimOut - 1, nRimOut)[overlap])\n", " #\n", " nCol = 0\n", " #\n", " fig = plt.figure(figsize = (pltX, pltY))\n", " plt.title(\"Rims no overlaps\", fontsize = 12)\n", " plt.xlabel('Column', fontsize = 12)\n", " plt.ylabel('Row', fontsize = 12)\n", " #\n", " l_json = nRimOut - int(np.sum(overlap))\n", " #\n", " j_num = np.zeros(l_json).astype(int)\n", " j_row = np.zeros((l_json, lHeadL))\n", " j_col = np.zeros((l_json, lHeadL))\n", " #\n", " w_num = np.zeros(l_json).astype(int)\n", " w_row = np.zeros((l_json, lClus))\n", " w_col = np.zeros((l_json, lClus))\n", " wm_row = np.zeros(l_json)\n", " wm_col = np.zeros(l_json)\n", " #\n", " h_num = np.zeros(l_json).astype(int)\n", " h_row = np.zeros((l_json, lHeadL))\n", " h_col = np.zeros((l_json, lHeadL))\n", " #\n", " j_rim = 0\n", " for iRim in range(0, nRimOut):\n", " if overlap[iRim]:\n", " continue\n", " j_num[j_rim] = edgs_num[iRim]\n", " j_row[j_rim, 0:j_num[j_rim]] = edgs_row[iRim, 0:edgs_num[iRim]]\n", " j_col[j_rim, 0:j_num[j_rim]] = edgs_col[iRim, 0:edgs_num[iRim]]\n", " #\n", " w_num[j_rim] = pnts_num[iRim]\n", " w_row[j_rim, 0:w_num[j_rim]] = pnts_row[iRim, 0:pnts_num[iRim]]\n", " w_col[j_rim, 0:w_num[j_rim]] = pnts_col[iRim, 0:pnts_num[iRim]]\n", " wm_row[j_rim] = mean_row[iRim]\n", " wm_col[j_rim] = mean_col[iRim]\n", " #\n", " h_num[j_rim] = head_num[iRim]\n", " h_row[j_rim, 0:h_num[j_rim]] = head_row[iRim, 0:head_num[iRim]]\n", " h_col[j_rim, 0:h_num[j_rim]] = head_col[iRim, 0:head_num[iRim]]\n", " #\n", " if selCometsThree.firstCall:\n", " plt.plot(pnts_col[iRim, 0:pnts_num[iRim]], pnts_row[iRim, 0:pnts_num[iRim]],\n", " color = colorTab[nCol])\n", " plt.plot(mean_col[iRim], mean_row[iRim], marker = '+', color = colorTab[nCol])\n", " row_label = np.amax(pnts_row[iRim, 0:pnts_num[iRim]]) + rTextRow\n", " col_label = np.amax(pnts_col[iRim, 0:pnts_num[iRim]]) + rTextCol\n", " plt.text(col_label, row_label, str(iRim), color = colorTab[nCol])\n", " nCol += 1\n", " if nCol > nColTab - 1:\n", " nCol = 0\n", " j_rim += 1\n", " #\n", " n_rim = j_rim\n", " if selCometsThree.firstCall:\n", " plt.xlim(-0.05*nCols, 1.05*nCols)\n", " plt.ylim(-0.05*nRows, 1.05*nRows)\n", " plt.grid(color = 'g')\n", " print(\" \")\n", " plt.show()\n", " #\n", " fig = plt.figure(figsize = (pltX, pltY))\n", " plt.title(\"Final rims\", fontsize = 12)\n", " plt.xlabel('Column', fontsize = 12)\n", " plt.ylabel('Row', fontsize = 12)\n", " #\n", " for j_rim in range(0, n_rim):\n", " plt.plot(j_col[j_rim, 0:j_num[j_rim]], j_row[j_rim, 0:j_num[j_rim]],\n", " color = colorTab[nCol])\n", " plt.plot(wm_col[j_rim], wm_row[j_rim], marker = '+', color = colorTab[nCol])\n", " row_label = np.amax(j_row[j_rim, 0:j_num[j_rim]]) + rTextRow\n", " col_label = np.amax(j_col[j_rim, 0:j_num[j_rim]]) + rTextCol\n", " plt.text(col_label, row_label, str(j_rim), color = colorTab[nCol])\n", " nCol += 1\n", " if nCol > nColTab - 1:\n", " nCol = 0\n", " #\n", " plt.xlim(-0.05*nCols, 1.05*nCols)\n", " plt.ylim(-0.05*nRows, 1.05*nRows)\n", " plt.grid(color = 'g')\n", " print(\" \")\n", " plt.show()\n", " #\n", " selCometsThree.firstCall = False\n", " #\n", " return n_rim, w_num, w_row, w_col, wm_row, wm_col, head_num, head_row, head_col\n", "#\n", "#\n", "#\n", "def calcQuads(nWheels, pnts_num, pnts_row, pnts_col, mean_row, mean_col, head_num, head_row, head_col):\n", " #\n", " if not hasattr(calcQuads, \"firstCall\"):\n", " calcQuads.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running calcQuads\")\n", " if debug:\n", " calcQuads.firstCall = True\n", " print(\"------------------------------------------------------------------------------------\")\n", " print(\"Running calcQuads\")\n", " #\n", " lWheels = 10*np.amax(pnts_num)\n", " #\n", " pnts_num_UL = np.zeros(nWheels).astype(int)\n", " pnts_row_UL = np.zeros((nWheels, lWheels)).astype(int)\n", " pnts_col_UL = np.zeros((nWheels, lWheels)).astype(int)\n", " #\n", " pnts_num_UR = np.zeros(nWheels).astype(int)\n", " pnts_row_UR = np.zeros((nWheels, lWheels)).astype(int)\n", " pnts_col_UR = np.zeros((nWheels, lWheels)).astype(int)\n", " #\n", " pnts_num_LL = np.zeros(nWheels).astype(int)\n", " pnts_row_LL = np.zeros((nWheels, lWheels)).astype(int)\n", " pnts_col_LL = np.zeros((nWheels, lWheels)).astype(int)\n", " #\n", " pnts_num_LR = np.zeros(nWheels).astype(int)\n", " pnts_row_LR = np.zeros((nWheels, lWheels)).astype(int)\n", " pnts_col_LR = np.zeros((nWheels, lWheels)).astype(int)\n", " #\n", " head_num_UL = np.zeros(nWheels).astype(int)\n", " head_row_UL = np.zeros((nWheels, lWheels)).astype(int)\n", " head_col_UL = np.zeros((nWheels, lWheels)).astype(int)\n", " #\n", " head_num_UR = np.zeros(nWheels).astype(int)\n", " head_row_UR = np.zeros((nWheels, lWheels)).astype(int)\n", " head_col_UR = np.zeros((nWheels, lWheels)).astype(int)\n", " #\n", " head_num_LL = np.zeros(nWheels).astype(int)\n", " head_row_LL = np.zeros((nWheels, lWheels)).astype(int)\n", " head_col_LL = np.zeros((nWheels, lWheels)).astype(int)\n", " #\n", " head_num_LR = np.zeros(nWheels).astype(int)\n", " head_row_LR = np.zeros((nWheels, lWheels)).astype(int)\n", " head_col_LR = np.zeros((nWheels, lWheels)).astype(int)\n", " #\n", " sumULwheel = np.zeros(nWheels)\n", " sumURwheel = np.zeros(nWheels)\n", " sumLLwheel = np.zeros(nWheels)\n", " sumLRwheel = np.zeros(nWheels)\n", " #\n", " sumULhead = np.zeros(nWheels)\n", " sumURhead = np.zeros(nWheels)\n", " sumLLhead = np.zeros(nWheels)\n", " sumLRhead = np.zeros(nWheels)\n", " #\n", " if calcQuads.firstCall:\n", " #\n", " # Text positions\n", " wTextRow = 2\n", " wTextCol = 2\n", " #\n", " fig = plt.figure(figsize=(7, 5))\n", " plt.title(\"Wheel quadrants\", fontsize = 12)\n", " plt.xlabel('Column', fontsize = 12)\n", " plt.ylabel('Row', fontsize = 12)\n", " #\n", " nCol = 0\n", " #\n", " for nW in range(0, nWheels):\n", " #\n", " # Wheels\n", " colHere = pnts_col[nW, 0:pnts_num[nW]]\n", " rowHere = pnts_row[nW, 0:pnts_num[nW]]\n", " #\n", " boolUL = np.logical_and(colHere < mean_col[nW], rowHere > mean_row[nW]) \n", " pnts_num_UL[nW] = np.sum(boolUL)\n", " pnts_row_UL[nW, 0:pnts_num_UL[nW]] = rowHere[boolUL]\n", " pnts_col_UL[nW, 0:pnts_num_UL[nW]] = colHere[boolUL]\n", " #\n", " boolUR = np.logical_and(colHere > mean_col[nW], rowHere > mean_row[nW]) \n", " pnts_num_UR[nW] = np.sum(boolUR)\n", " pnts_row_UR[nW, 0:pnts_num_UR[nW]] = rowHere[boolUR]\n", " pnts_col_UR[nW, 0:pnts_num_UR[nW]] = colHere[boolUR]\n", " #\n", " boolLL = np.logical_and(colHere < mean_col[nW], rowHere < mean_row[nW])\n", " pnts_num_LL[nW] = np.sum(boolLL)\n", " pnts_row_LL[nW, 0:pnts_num_LL[nW]] = rowHere[boolLL]\n", " pnts_col_LL[nW, 0:pnts_num_LL[nW]] = colHere[boolLL]\n", " #\n", " boolLR = np.logical_and(colHere > mean_col[nW], rowHere < mean_row[nW]) \n", " pnts_num_LR[nW] = np.sum(boolLR)\n", " pnts_row_LR[nW, 0:pnts_num_LR[nW]] = rowHere[boolLR]\n", " pnts_col_LR[nW, 0:pnts_num_LR[nW]] = colHere[boolLR]\n", " #\n", " # Heads\n", " colHere = head_col[nW, 0:head_num[nW]]\n", " rowHere = head_row[nW, 0:head_num[nW]]\n", " #\n", " boolUL = np.logical_and(colHere < mean_col[nW], rowHere > mean_row[nW])\n", " head_num_UL[nW] = np.sum(boolUL)\n", " head_row_UL[nW, 0:head_num_UL[nW]] = rowHere[boolUL]\n", " head_col_UL[nW, 0:head_num_UL[nW]] = colHere[boolUL]\n", " #\n", " boolUR = np.logical_and(colHere > mean_col[nW], rowHere > mean_row[nW])\n", " head_num_UR[nW] = np.sum(boolUR)\n", " head_row_UR[nW, 0:head_num_UR[nW]] = rowHere[boolUR]\n", " head_col_UR[nW, 0:head_num_UR[nW]] = colHere[boolUR]\n", " #\n", " boolLL = np.logical_and(colHere < mean_col[nW], rowHere < mean_row[nW]) \n", " head_num_LL[nW] = np.sum(boolLL)\n", " head_row_LL[nW, 0:head_num_LL[nW]] = rowHere[boolLL]\n", " head_col_LL[nW, 0:head_num_LL[nW]] = colHere[boolLL]\n", " #\n", " boolLR = np.logical_and(colHere > mean_col[nW], rowHere < mean_row[nW]) \n", " head_num_LR[nW] = np.sum(boolLR)\n", " head_row_LR[nW, 0:head_num_LR[nW]] = rowHere[boolLR]\n", " head_col_LR[nW, 0:head_num_LR[nW]] = colHere[boolLR]\n", " #\n", " sumULwheel[nW] = np.sum(imgGrey[pnts_row_UL[nW, 0:pnts_num_UL[nW]], pnts_col_UL[nW, 0:pnts_num_UL[nW]]])\n", " sumURwheel[nW] = np.sum(imgGrey[pnts_row_UR[nW, 0:pnts_num_UR[nW]], pnts_col_UR[nW, 0:pnts_num_UR[nW]]])\n", " sumLLwheel[nW] = np.sum(imgGrey[pnts_row_LL[nW, 0:pnts_num_LL[nW]], pnts_col_LL[nW, 0:pnts_num_LL[nW]]])\n", " sumLRwheel[nW] = np.sum(imgGrey[pnts_row_LR[nW, 0:pnts_num_LR[nW]], pnts_col_LR[nW, 0:pnts_num_LR[nW]]])\n", " #\n", " sumULhead[nW] = np.sum(imgGrey[head_row_UL[nW, 0:head_num_UL[nW]], head_col_UL[nW, 0:head_num_UL[nW]]])\n", " sumURhead[nW] = np.sum(imgGrey[head_row_UR[nW, 0:head_num_UR[nW]], head_col_UR[nW, 0:head_num_UR[nW]]])\n", " sumLLhead[nW] = np.sum(imgGrey[head_row_LL[nW, 0:head_num_LL[nW]], head_col_LL[nW, 0:head_num_LL[nW]]])\n", " sumLRhead[nW] = np.sum(imgGrey[head_row_LR[nW, 0:head_num_LR[nW]], head_col_LR[nW, 0:head_num_LR[nW]]])\n", " #\n", " # Wheels\n", " if calcQuads.firstCall:\n", " size = 0.01\n", " plt.scatter(pnts_col_UL[nW, 0:pnts_num_UL[nW]], pnts_row_UL[nW, 0:pnts_num_UL[nW]], \n", " s = size, color = 'r')\n", " plt.scatter(pnts_col_UR[nW, 0:pnts_num_UR[nW]], pnts_row_UR[nW, 0:pnts_num_UR[nW]], \n", " s = size, color = 'b')\n", " plt.scatter(pnts_col_LL[nW, 0:pnts_num_LL[nW]], pnts_row_LL[nW, 0:pnts_num_LL[nW]], \n", " s = size, color = 'orange')\n", " plt.scatter(pnts_col_LR[nW, 0:pnts_num_LR[nW]], pnts_row_LR[nW, 0:pnts_num_LR[nW]], \n", " s = size, color = 'm')\n", " #\n", " # Heads\n", " plt.scatter(head_col_UL[nW, 0:head_num_UL[nW]], head_row_UL[nW, 0:head_num_UL[nW]], \n", " s = size, color = 'w')\n", " plt.scatter(head_col_UR[nW, 0:head_num_UR[nW]], head_row_UR[nW, 0:head_num_UR[nW]], \n", " s = size, color = 'c')\n", " plt.scatter(head_col_LL[nW, 0:head_num_LL[nW]], head_row_LL[nW, 0:head_num_LL[nW]], \n", " s = size, color = 'yellow')\n", " plt.scatter(head_col_LR[nW, 0:head_num_LR[nW]], head_row_LR[nW, 0:head_num_LR[nW]], \n", " s = size, color = 'g')\n", " #\n", " row_label = np.amax(pnts_row_UR[nW, 0:pnts_num[nW]]) + wTextRow\n", " col_label = np.amax(pnts_col_UR[nW, 0:pnts_num[nW]]) + wTextCol\n", " plt.text(col_label, row_label, str(nW), color = 'k')\n", " #\n", " if calcQuads.firstCall:\n", " plt.plot(mean_col, mean_row, marker = '+', linestyle = '', color = 'k')\n", " #\n", " plt.xlim(-0.05*nCols, 1.05*nCols)\n", " plt.ylim(-0.05*nRows, 1.05*nRows)\n", " plt.grid(color = 'g')\n", " print(\" \")\n", " plt.show()\n", " #\n", " calcQuads.firstCall = False\n", " #\n", " return sumULwheel, sumLLwheel, sumURwheel, sumLRwheel, sumULhead, sumLLhead, sumURhead, sumLRhead\n", "#\n", "then = now\n", "now = datetime.datetime.now()\n", "print(\" \")\n", "print(\"Date and time\",str(now))\n", "print(\"Time since last check is\",str(now - then))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Check directory contents " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Volume in drive C is OS\n", " Volume Serial Number is 8A8B-1A1F\n", "\n", " Directory of C:\\Users\\green\\OneDrive\\OneDocuments\\Liverpool\\LivDat\\ImageAnalysis\\Images\\MCdata\\pBreak090\n", "\n", "06/05/2021 15:49 .\n", "06/05/2021 15:49 ..\n", "23/04/2021 13:55 5,790,774 MCimage-pBreak090-000.bmp\n", "23/04/2021 13:55 5,790,774 MCimage-pBreak090-001.bmp\n", "23/04/2021 13:55 5,790,774 MCimage-pBreak090-002.bmp\n", "23/04/2021 13:55 5,790,774 MCimage-pBreak090-003.bmp\n", "23/04/2021 13:55 5,790,774 MCimage-pBreak090-004.bmp\n", "23/04/2021 13:55 5,790,774 MCimage-pBreak090-005.bmp\n", "23/04/2021 13:55 5,790,774 MCimage-pBreak090-006.bmp\n", "23/04/2021 13:55 5,790,774 MCimage-pBreak090-007.bmp\n", "23/04/2021 13:56 5,790,774 MCimage-pBreak090-008.bmp\n", "23/04/2021 13:56 5,790,774 MCimage-pBreak090-009.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-010.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-011.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-012.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-013.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-014.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-015.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-016.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-017.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-018.bmp\n", "06/05/2021 15:48 5,790,774 MCimage-pBreak090-019.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-020.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-021.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-022.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-023.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-024.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-025.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-026.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-027.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-028.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-029.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-030.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-031.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-032.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-033.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-034.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-035.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-036.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-037.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-038.bmp\n", "06/05/2021 15:49 5,790,774 MCimage-pBreak090-039.bmp\n", " 40 File(s) 231,630,960 bytes\n", " 2 Dir(s) 783,874,813,952 bytes free\n" ] } ], "source": [ "#!dir \"Images/ControlBatch\"\n", "#!dir \"Images/IrradiatedBatch\"\n", "!dir \"Images/MCdata/pBreak090\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Date and time 2021-06-03 14:06:55.567807\n", " \n", " \n", "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080/*.bmp\n", "Number found is 40\n", "Hottest allowed pixel intensity is 255\n", " \n", "Example image C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-000.bmp\n", " \n", "Max intensities red 255 blue 237 green 255\n", " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "Maximum pixel value before removing hot pixels 253\n", "Minimum pixel value 0\n", "Type of raw image file is uint8\n", "Type of greyscale image file is uint8\n", "Number of rows 1040 of columns 1392 of pixels 1447680 and depth 3\n", "Hot pixel cut 255\n", "Number of hot pixels 0\n", "Maximum pixel value after removing hot pixels 253\n", "Minimum pixel value 0\n", " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "Date and time 2021-06-03 14:07:03.001587\n", "Time since last check is 0:00:07.433780\n", "Time since noteboook start is 0:00:07.433780\n" ] } ], "source": [ "import datetime\n", "now = datetime.datetime.now()\n", "startNB = now\n", "print(\"Date and time \",str(now))\n", "print(\" \")\n", "#\n", "import os\n", "import sys\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import glob\n", "from PIL import Image\n", "import scipy\n", "import scipy.ndimage as ndimage\n", "import scipy.ndimage.filters as filters\n", "%matplotlib inline\n", "#\n", "path = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080/*.bmp\"\n", "images = (glob.glob(path))\n", "num_images = len(images)\n", "debug = False\n", "print(\" \")\n", "print(\"Look for images in directory \\n\",path)\n", "print(\"Number found is\",num_images)\n", "#\n", "# Set two thresholds for applying to image (lowest to highest)\n", "thresh = np.array([20, 120])\n", "#\n", "# Set cut on hottest allowed pixels\n", "hotCut = 255\n", "hotCut = min(hotCut, 255)\n", "print(\"Hottest allowed pixel intensity is\",hotCut)\n", "#\n", "# Check that ine of the images looks sensible!\n", "print(\" \")\n", "print(\"Example image\",images[0])\n", "imgRaw = plt.imread(images[0])\n", "withHists = True\n", "imgGrey, nRows, nCols = processImage(imgRaw, hotCut, withHists)\n", "print(\" \")\n", "pltX = nCols/200\n", "pltY = nRows/200\n", "plt.figure(figsize = (2*pltX, 2*pltY))\n", "plt.imshow(imgGrey, origin = 'lower', cmap = plt.get_cmap('afmhot'))\n", "plt.show()\n", "#\n", "then = now\n", "now = datetime.datetime.now()\n", "print(\" \")\n", "print(\"Date and time\",str(now))\n", "print(\"Time since last check is\",str(now - then))\n", "print(\"Time since noteboook start is\",str(now - startNB))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loop over all images in chosen directories and analyse" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Date and time 2021-06-03 14:07:03.077335\n", " \n", "List of image folders:\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090/\n", " \n", "Cluster ID using Watershed algorithm\n", "Cluster threshold 20.\n", "Head low threshold 80, high threshold 180.\n", "Min number of pixels in cluster 200, max number 200000.\n", "Min number of pixels in head 40, max number 20000.\n", "Width of expansion 9, useDiag = True\n", "Up-down asymmetry cut 0.9\n", " \n", "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-000.bmp read.\n", "------------------------------------------------------------------------------------\n", "Running findWheels\n", " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------------------------\n", "Running clusExpand\n", " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------------------------\n", "Running findRims\n", " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------------------------\n", "Running selCometsThree\n", "UseLowHead False\n", " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "Number of selected clusters (nRimOut) 7\n", " \n", "Remove the overlaps: []\n", " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------------------------\n", "Running calcQuads\n", " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000\\MCimage-pBreak000-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-026.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010\\MCimage-pBreak010-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-000.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020\\MCimage-pBreak020-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030\\MCimage-pBreak030-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040\\MCimage-pBreak040-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-021.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050\\MCimage-pBreak050-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060\\MCimage-pBreak060-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-018.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070\\MCimage-pBreak070-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-010.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080\\MCimage-pBreak080-039.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0oAAAJyCAYAAAD6hDCZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABpnUlEQVR4nO3de5xcdXn48c/DNQQUIcgdElSCgpcoWwrV/hILXqBSbLWCDQhKCSq2YlELghYvoD8VxP7qJRuloESRigpaUAFdvCBCgimXxoQIJAGWQLhDIAHy/P6YszCZ7G72MjPn7Ozn/XrNa+d7LjPPPPnmnHnO95wzkZlIkiRJkp6zUdkBSJIkSVLVWChJkiRJUgMLJUmSJElqYKEkSZIkSQ0slCRJkiSpgYWSJEmSJDWwUJIkSZKkBhZKkqSmiYg7IuKgfqbPiIi1EfFYRDwaEYsi4t1lxFiG4vPfWXYckqShs1CSJLXL3Zm5FfB84EPAnIjYq+SYKiMiNik7BknScyyUJEltlTWXAQ8ArxxouYj4r4i4JyIejohfRcQ+dfMOiYj/LUan7oqIDxfTb46IQ+uW2zQiVkbEtIiYEhEZEe+OiOUR8WBEvDci/iwiboyIhyLiP+rWPSYifhsRXyrm3RYRf1FMXx4R90bE0XXLbx4RX4yIZRGxIiK+HhFbRMSWwOXAzsWI2mMRsXNEnB4R34+ICyLiEeDkiFgVEZPqXnPfiLgvIjZtUvolSUNkoSRJaquI2Cgi/gbYDlgyyKKXA3sC2wM3AHPr5n0TOD4znwe8HPhFMf1bwJF1yx0C9Gbmgrppf1687uHAOcCpwEHAPsA7ImJ6w7I3ApOA7wAXAn8GvKR4n/+IiK2KZf8vMBWYVszfBfhEZj4OHEwxolY87i7WOQz4PvAC4CygB3hH3fsfCVyYmU8NkidJUgtYKEmS2mXniHgIeAL4IfAvmfmHgRbOzHMz89HMXA2cDrwqIrYuZj8F7B0Rz8/MBzPzhmL6BcAhEfH8on0U8O2Gl/50Zj6ZmT8HHge+m5n3ZuZdwK+BV9cte3tm/mdmPgN8D9gN+FRmri7WXwO8JCICOA74UGY+kJmPAmcCR2wgJ7/LzB9l5trMfAI4n6LQi4iNgXf2E78kqQ0slCRJ7XJ3Zr6A2jVK/w781UALRsTGEfG5iPhTcVraHcWs7Yq/b6M2WrQ0Iq6OiAMAipGa3wJvi4gXUBvJmcu6VtQ9f6Kf9laDLEtm9rf8C4GJwPziNL2HgJ8W0wezvKF9CbUC8EXAG4CHM/O6DbyGJKkFvHBUktRWmbk6Iv4VWBQRb83MH/Wz2D9QOy3tIGpF0tbAg0AUr3E9cFhx7c4HgIuojfZAbVTmH6nt435XjBS12kpqRdM+A7xfDrDeOtMz88mIuAiYCbwUR5MkqTSOKEmSmm3TiJhQ91jvoFxmrqF2Tc4nBniN5wGrgfupjdSc2TcjIjaLiJkRsXVx7c4jwDN16/4IeA3wQWrXLLVcZq4F5gBfiojtizh3iYg3FYusACbVnTo4mG8BxwB/Q+1UQklSCSyUJEnNdhm10ZW+x+kDLHcusHv9XerqfAtYCtwF/C9wbcP8o4A7itPy3kvdDRyKa30uBvYAfjDiTzF8/0rt5hTXFnFdCexVxPRH4LvAbcWpeTsP9CKZ+VtgLXBDZt7R8qglSf2KzIHOBpAkaWyKiE8AUzPzyA0uXEER8QvgO5n5jbJjkaTxymuUJEkdJSK2BY6lNuo05kTEn1E7dfCwsmORpPHMU+8kSR0jIo6jdie5yzPzV2XHM1wRcT61U/ZOLG4xLkkqiafeSZIkSVIDR5QkSZIkqcG4ukZpu+22yylTppQdBgC3PnAre267Z9lhjCvmvP3MefuZ8/Yz5+1nztvPnLefOW+f+fPnr8zM9X4gfFwVSlOmTGHevHllhwFAV3cX82ZVI5bxwpy3nzlvP3Pefua8/cx5+5nz9jPn7RMRS/ub7ql3kiRJktTAQkmSJEmSGlgoSZIkSVIDCyVJkiRJamChJEmSJEkNLJQkSZIkqYGFkiRJkiQ1sFCSJEmSpAYWSpIkSZLUwEJJkiRJkhpYKEmSWmfGjNpDkqQxxkJJkiRJkhpYKEmSJElSAwslSZIkSWpgoSRJkiRJDSyUJEmSJKmBhZIkSZIkNbBQkiRJkqQGpRZKEfHmiFgUEUsi4uR+5r80In4XEasj4sPDWVeSJEmSRqq0QikiNga+AhwM7A28MyL2bljsAeCfgS+OYF1JkiRJGpEyR5T2A5Zk5m2ZuQa4EDisfoHMvDczrweeGu66kiRJkjRSm5T43rsAy+vadwJ/3ux1I2IWMAtgwqQJdHV3DT/SFli4cmFlYhkvzHn7mfP2q1rOZ/cuAuD4CsXUbFXL+XhgztvPnLefOS9fmYVS9DMtm71uZnYD3QBdXV05b9a8Ib5Fa3V1d1GVWMYLc95+5rz9Kpfz78wAYN6snlLDaKXK5XwcMOftZ87bz5y3TxzfX2lR7ql3dwK71bV3Be5uw7qSJEmSNKgyC6XrgT0jYo+I2Aw4Ari0DetKkiRJ0qBKO/UuM5+OiA8APwM2Bs7NzFsi4r3F/K9HxI7APOD5wNqIOBHYOzMf6W/dUj6IJEmSpI5T5jVKZOZlwGUN075e9/weaqfVDWldSZIkSWqGUn9wVpIkSZKqyEJJkiRJkhpYKEmSJElSAwslSZIkSWpgoSRJkiRJDSyUJEmSJKmBhZIkSZIkNbBQkiRJkqQGFkqSJEmS1MBCSZIkSZIaWChJkiRJUgMLJUmSJElqYKEkSZIkSQ0slCRJkiSpgYWSJEmSJDWwUJIkSZKkBhZKkiRJktTAQkmSJEmSGlgoSZIkSVIDCyVJkiRJamChJEmSJEkNLJQkSZIkqYGFkiRJkiQ1sFCSJEmSpAalFkoR8eaIWBQRSyLi5H7mR0T8ezH/xoh4Td28OyLipohYEBHz2hu5JEmSpE62SVlvHBEbA18B3gDcCVwfEZdm5v/WLXYwsGfx+HPga8XfPq/PzJVtClmSJEnSOFHmiNJ+wJLMvC0z1wAXAoc1LHMY8K2suRZ4QUTs1O5AJUmSJI0vZRZKuwDL69p3FtOGukwCP4+I+RExq2VRSpIkSRp3Sjv1Doh+puUwlnltZt4dEdsDV0TEHzPzV+u9Sa2ImgUwYdIEurq7RhNz0yxcubAysYwX5rz9zHn7VS3ns3sXAXB8hWJqtqrlfDww5+1nztvPnJevzELpTmC3uvauwN1DXSYz+/7eGxE/pHYq33qFUmZ2A90AXV1dOW9WNe770NXdRVViGS/MefuZ8/arXM6/MwOAebN6Sg2jlSqX83HAnLefOW8/c94+cXx/YzPlnnp3PbBnROwREZsBRwCXNixzKfCu4u53+wMPZ2ZvRGwZEc8DiIgtgTcCN7czeEmSJEmdq7QRpcx8OiI+APwM2Bg4NzNviYj3FvO/DlwGHAIsAVYB7y5W3wH4YURA7TN8JzN/2uaPIEmSJKlDlXnqHZl5GbViqH7a1+ueJ3BCP+vdBryq5QFKkiRJGpdK/cFZSZIkSaoiCyVJkiRJamChJElqjblz4dpr4eqrYcqUWluSpDHCQkmS1Hxz58KsWbB6da29dGmtbbEkSRojLJQkSc136qmwatW601atqk2XJGkMsFCSJDXfsmXDmy5JUsVYKEmSmm/33Yc3XZKkirFQkiQ13xlnwMSJ606bOLE2XZKkMcBCSZLUfDNnQnc3bL55rT15cq09c2a5cUmSNESblB2AJKlDzZwJc+bUnvf0lBqKJEnD5YiSJEmSJDWwUJIkSZKkBhZKkiRJktTAQkmSJEmSGlgoSZIkSVIDCyVJkiRJamChJEmSJEkNLJQkSZIkqYGFkiRJkiQ1sFCSJEmSpAYWSpIkSZLUwEJJkiRJkhpYKEmSJElSAwslSZIkSWpgoSRJkiRJDUotlCLizRGxKCKWRMTJ/cyPiPj3Yv6NEfGaoa4rSZIkSSNVWqEUERsDXwEOBvYG3hkRezcsdjCwZ/GYBXxtGOtKkiRJ0oiUOaK0H7AkM2/LzDXAhcBhDcscBnwra64FXhAROw1x3UpbdNZszjuv9vypp2DGDLjgglp71apa+3vfq7UffrjW/sEPau2VK2vtH/+41r7nnlr7pz+ttZcvr7WvvLLWvu22Wvvqq4v3XlRrX3NNrX3zzbX29dfX2gsW1NoLFtTa119fa998c619zTW19qJFtfbVV9fat91Wa195Za29fHmt/dOf1tr33FNr//jHtfbKlbX2D35Qaz/8cK39ve/V2qtW1doXXFBrP/VUrX3eebV2nzlz4KCDnmt/9atw8MHPtb/8Zfibv3mu/cUvwtve9lz7c5+DI454rv3pT8ORRz7X/sQn4N3vfq59yikwa9Zz7Q9/GE444bn2iSfWHn1OOKG2TJ9Zs2qv0efd7669R58jj6zF0OeII2ox9nnb22qfoc/f/E3tM/Y5+OBaDvocdFAtR31mzKBtfW/1fbtUq++9/BRmvGBBR/e95d/7l2fbleh7vX/9bLudfc/tXmdv95Z85ezKbvfse53d99q5z336sa07uu+NBZuU+N67AMvr2ncCfz6EZXYZ4roARMQsaqNRTJg0ga7urtFF3SSrnjqb03tO5z/W/IR8ZmMW936Vpb/4Eeesupy1azbn1t5/55Qrv88XHr6CZ57YkiW9Z/ORn1/ImSt/ydOPbc2fej/Ph356AZ/s/TVPPTyJ23rP5J8uO4+tl/2ONQ/swO29n+L9//1Nnn/bday+bxfu6P0Es348m+ctuoEn75nM0t6P8Z5LvsJWN9/IE3e9mGW9H+VdP/wyW/7hf1m1fCrLe0/iHy4+i4nXLebxO/bmzt4Pcvh/fZ4trvkTj/3pldzVewJv+96ZTNhxKY8ufg139x7PYd/9FJu/8C4eWbgfvb3H8tdzP8Fm267g4ZsP4J7eY3jTtz/Gplvfz0P/85es6D2Sg771UTbZ6mEevOH13Nt7BDPO+xc23uJxHrj+DdzX+3Zed+4/s9Fmq7n/2oNZ2ftW9v/G+4mNn2HlNW/h/t5D6eo+HoD7fv1WHrzrDXR117ac9/7m7Ty8/C/p6v4gACuuOYJHl+7HUysX0tXdxT3XHsnjt7+Sru6PAnDPdUezavledHV/DIDeecfy5D2T6equbUnvvuF41jywA13dnwLgrgUn8PRjW3ND95kA3HnjB1m7ZnN+3/15AJbfVPui+pvuswFYdstH2Wiz1fR017asSxd+jE22epgrur8CwB2LPsFm963gsu7ZANx+66f45aNLuaT7mwDc9qcz+c2aRXy/+3wA/nT75/l93MiF3bUt7ZKlZ/OHa67j21tcCMCty7/MLb/5Nedu8n0AFt/1FW791RXMzh8BsKh3Nnf0/LgtfW/Jg09wR+/8yvS9mQ++iEfXPMpB3zqwrX2vq7vWJ9rR9x548sFnt3NV6Hv7P7SM+czn+O6utva9dm73bouFvH/h+yu53Wtn32vndu+xNZ/ki9d8sZLbvU7d564t9qHjve+1c5+7+P572bh3fsf2vbEgMrOcN474e+BNmfmPRfsoYL/M/Ke6Zf4b+Gxm/qZoXwV8FHjRhtbtT1dXV86bN68ln2e4urq7mDerGrGMF+a8/SqX877Doj09ZUbRUua8/SqX83HAnLefOW8/c94+ETE/M9cbTSlzROlOYLe69q7A3UNcZrMhrCtJkiRJI1LmNUrXA3tGxB4RsRlwBHBpwzKXAu8q7n63P/BwZvYOcV1JkiRJGpHSRpQy8+mI+ADwM2Bj4NzMvCUi3lvM/zpwGXAIsARYBbx7sHVL+BiSJEmSOlCZp96RmZdRK4bqp3297nkCJzSuN9C6kiRJktQMpf7grCRJkiRVkYWSJJVpxox1f6REkiRVgoWSJEmSJDWwUJIkSZKkBhZKkiRJktTAQkmSJEmSGlgoSZIkSVKDDRZKETEvIk6IiG3aEZAkSZIklW0oI0pHADsD10fEhRHxpoiIFsclSZIkSaXZYKGUmUsy81RgKvAd4FxgWUR8MiK2bXWAkiRJktRuQ7pGKSJeCZwFfAG4GHg78Ajwi9aFJkmSJEnl2GRDC0TEfOAh4JvAyZm5upj1+4h4bQtjkyRJkqRSDFooRcRGwMWZeWZ/8zPz71oSlSRJkiSVaNBT7zJzLfDmNsUiSZIkSZUwlGuUroiID0fEbhGxbd+j5ZFJkiRJUkk2eI0S8J7i7wl10xJ4UfPDkSRJkqTyDaVQellmPlk/ISImtCgeSZIkSSrdUE69u2aI0yRJkiSpIww4ohQROwK7AFtExKuBKGY9H5jYhtgkSZIkqRSDnXr3JuAYYFfg7LrpjwAfa2FMkiRJklSqAQulzDwfOD8i3paZF7cxJkmSJEkq1VCuUfptRHwzIi4HiIi9I+LYFsclSZIkSaUZSqH0n8DPgJ2L9mLgxFYFJEmSJEllG0qhtF1mXgSsBcjMp4FnWhqVJEmSJJVoKIXS4xExidqPzBIR+wMPj+ZNI2LbiLgiIm4t/m4zwHJvjohFEbEkIk6um356RNwVEQuKxyGjiUeSJEmS6g2lUPoX4FLgxRHxW+BbwD+N8n1PBq7KzD2Bq4r2OiJiY+ArwMHA3sA7I2LvukW+lJnTisdlo4xHkiRJkp412O3BAcjMGyJiOrAXtd9SWpSZT43yfQ8DZhTPzwd6gH9tWGY/YElm3gYQERcW6/3vKN9bktQuPT1lRyC13owZtb/2d6mjbLBQKkZ2DgGmFMu/MSLIzLMHXXFwO2RmL0Bm9kbE9v0sswuwvK59J/Dnde0PRMS7gHnASZn54ADxzwJmAUyYNIGu7q5RhN08C1curEws44U5b7+q5Xx27yIAju/gmKqW8/HAnLdf1XJexW1Ls1Ut5+OBOS9fZObgC0RcBjwJ3ERxQweAzPzkBta7Etixn1mnAudn5gvqln0wM9e5Tiki/h54U2b+Y9E+CtgvM/8pInYAVlK7burTwE6Z+Z5BPwjQ1dWV8+bN29BibdHV3cW8WdWIZbww5+1XuZxX8ahvk2OqXM7HAXPefpXLeRW3LU1WuZyPA+a8fSJifmauV5VucEQJ2DUzXzncN8zMgwYJZkVE7FSMJu0E3NvPYncCu9XHAdxdvPaKuteaA/xkuPGVasaM2tGnWWUHIkmSJKk/Q7mZw+UR8cYmv++lwNHF86OBS/pZ5npgz4jYIyI2A44o1qMorvr8LXBzk+OTJElSVc2Y8dxIntQiQxlRuhb4YURsBDxF7YYOmZnPH8X7fg64KCKOBZYBfw8QETsD38jMQzLz6Yj4ALUfu90YODczbynW/3xETKN26t0dwPGjiEWSJPVnHJxSJkkDGUqhdBZwAHBTbuiCpiHKzPuBA/uZfje1G0f0tS8D1rv1d2Ye1Yw4JEmSJA3BODxwMpRT724Fbm5WkSRJkiRJVTeUEaVeoCciLgdW900c5e3BJUmSJKmyhlIo3V48NisekiRJktTRNlgobej3kiRJkiSp02ywUIqILmo/Eju5fvmR/LaSJEmSJI0FQzn1bi7wEeAmYG1rw5EkSZKk8g2lULovMy9teSSSJEmSVBFDKZT+LSK+AVzFune9+0HLotLQjcN72kuSpA3w+4E0akMplN4NvBTYlOdOvUvAQkmSpKrxC7IkNcVQCqVXZeYrWh6J1B93+JIkSSrBRkNY5tqI2LvlkUiSJLXLjBnPHYzT2DJ3Llx7LVx9NUyZUmtLLTCUQul1wIKIWBQRN0bETRFxY6sDU5u5w1Cnc8cqSWPf3LkwaxasLi6bX7q01nabrhYYyql3b255FJLUSgPtWAFmziwvLknS8Jx6Kqxate60Vatq092eq8mGMqL0T8CWmbm0/tHqwCSVoFNHFgfbsap/ndoXJI1ty5YNb7o0CkMplP4IzImI30fEeyNi61YHJUlN5Y5VkjrD7rsPb7o0ChsslDLzG5n5WuBdwBTgxoj4TkS8vtXBSVJTuGOVhs/r+lRFZ5wBEyeuO23ixNp0qcmGMqJERGxM7beUXgqsBP4H+JeIuLCFsUlSc7hjlYbHC+ZVVTNnQnc3bL55rT15cq3t9UlqgQ0WShFxNrXT7w4BzszMfTPz/2bmocCrWx1gxymO0L1m8WMeoZPaxR2rNDxe1ze2dfpo4MyZsP/+MH063HGH2/J26PQ+NYCh3PXuZuC0zFzVz7z9mhxPZ6s7Qhfgnbekdpo5E+bMqT2vyg8Y9+14Vq+u7XjOOMNtgarB6/rGLu/yqWYbx31qKKfeLYLa9/qIODIizo6IyQCZ+XArg+s4HqFTPe8qNr55apOqzOv6xi6/a6jZxnGfGkqh9DVgVUS8CvgosBT4Vkuj6lRVPUJX1eHUqsYlNcM43vFoDPC6vrGrqt81wAOEY1WV+1SLDaVQejozEzgM+HJmfhl4XmvD6lBVPEJX1aPaVY1LapZxvOPRGOB1fWNXFb9raGwbx31qKIXSoxFxCnAU8N/FHfCGcm2TGlXxCF1Vj2pXNS6VoxOPQo7jHc+IdWI/qDIvmB+bqvhdQ2PbOO5TQymUDgdWA+/OzHuA1wJbtjSqTlV3hC6hGkfoqnpUu6pxSc0yjnc8klrI0UA12zjuU0P5wdl7gF8AfxMRdwCfBM4ZzZtGxLYRcUVE3Fr83WaA5c6NiHsj4uaRrF9JxRG6G6ZuNfojdM24hqeqR7WrGpfULFXe8Xh9oDS2ORqoZhunfWrAQikipkbEJyJiIfAfwHIgMvP1mfn/Rvm+JwNXZeaewFVFuz/nAW8exfqdq1nX8FT1qHZV42oWv4iOfc04DayKOx6vDxzb3LZIUtMMNqL0R+BA4NDMfF1RHD3TpPc9DDi/eH4+8Nb+FsrMXwEPjHT9jtasa3iqelS7qnHB6L8g+0VUVeb1gWOX2xZJaqrBbsrwNuAI4JcR8VPgQorfU2qCHTKzFyAzeyNi+1atHxGzgFkAEyZNoKu7a6QxN83s3kU8/tSqUcVy3dKl/Va5a5cuZb8RvO7syZsCm3L8SdvB41+C7i+NOLbZZy0C4PiT9hrxa7QiroUrFzbl3392b/H5RvhaPz7lJnZatWbdiatW0fvP7+HQx0f++ZphtJ+tUVVy3uzXqWpMMPqcN3vb0kzNzlWzNKufj1Yrti2dmvM3/f5+PvHbpWz2dHLPpM35j7fuzM/+fNKIX6+KearatmU85LzZqrJt6TMecr6ezBz0Qe3GDTOBnwCrqP2u0huHsN6VwM39PA4DHmpY9sFBXmcKcHPDtCGvX//Yd999sxKmT895U7ca3WtMnpwJ6z8mTx5xTDl9+uhiqvhr7Tu7Sf/+o40pov9/u4jmxDcazfy3ywrlvNmv08zXqlrOm71taaYm56pZmtbPR6sV25ZOzPkFF2ROnLhujiZOrE0fqSrmqUrblvGS8yarzLalTwfnHJiX/dQOQ7mZw+OZOTcz3wLsCixgCNcEZeZBmfnyfh6XACsiYieA4u+9Q6jp6o12/bGv06/h6WRVvVGF1zYI3LaMZVXdtkC1bu3u6aXtZ87LU6X/e2PQUG4P/qzMfCAzZ2fmX43yfS8Fji6eHw1c0ub1x74qX8OjwbXii2gnXzdlAddeblvGrvFS5M6Y8ewp3iPiz0+0Xyty3tNTe1SJRUnHGVah1ESfA94QEbcCbyjaRMTOEXFZ30IR8V3gd8BeEXFnRBw72PrjThXvmKUNq+IX0aoe7atqAdfpxZvblrGpituWKqryyFunMucaowa7mUPLZOb91O6o1zj9buCQuvY7h7O+1HJ9X5BXr659QT7jjJF9CZk5E+bMqT2vwhGxqh5hHayAK+vL30DFG5T/hXTGjNrFtrPKDUMlqtq2pYrOOKP2f7Z+29KJI29VYs41RpU1oiSNPVUd3WiGqh7tq2IBV9XRN2k8KA5WvWbxYyMfzR0vI29VOjVtPOS82WcajPYUUzWFhZLGh2ZscDr5C3JVr22oYgFXxeJtPOj00x07WbP+7eoOVgWM7mCVp5e2XyfnvJMPpI5zFkqqqdKRp6rq5C/IVT3aV8UCrorFW6fzS8jY1cx/u04+WKWxzb7ZsUq5Rkkwa/kqntdwZ5R3vOMdvP/972fVqlUccsgh661zzDHHcMwxx7By5Ure/va31yYuWFD7O2MG73vf+zj88MNZvnw5Rx111Hrrn3TSSRx66KEsWrSI448/fr35p512GgcddBALFizgxBNPXG/+mWeeyV/8xV9wzTXX8LGPfWy9+eeccw7Tpk3jygcf5DNLl65355fZs2ez11578eMf/5izzjprvfW//e1vs9tuu/G9732Pr33ta+t9vu9///tst912nHfeeZx33nnrrX/ZZZcxceJEvvrVr3LRRRetO3PBAs7aofb0i1/8Ij/5yU/Wmb3FFltw+eWXA/DpT3+aq666ap35kyZN4uLdd6/t5Bvcs9lmfPjII7ngggsAOPHEE1nQF3dh6tSpdHd3AzBr1iwWL168zmebNm0a55xzDgBHHnkkd9555zrrH3DAAXz2s58F4G1vexv333//OvMPXLqUj0+eDMDBBx/ME088sc78t7zlLXz4wx8u3m7Gep/h2b739a9zyE031Y76zpnz7LUO/fa9OgP1vUW9i5jxnRmj6nsH7r47H/nTn5jw1FM8ucMOfGGbbbiqLjao63tXXslnPvOZ9V7/2b63ciVn3Xnnen2z375Xp77vLd9iCz6y0UZMWLv22fm5xRbEGWf03/eAnuIgRL9978YbufyVrwQG6XsXXwzAKaecwu9+97t15u+6667P9b0lS/j1A+tuW/rte3UG7HtF/zzglFMG73sHHsjHP/5xYJR9b7Dt3umn9/sl5J5jj+XqTTYpfbsHbLjvDWe7V2dE2726bcugfW8o273i+Qb73kDbvZ//fMB/u89df/2wtnv/NcCPIbNs2cj63l138f5ddhnePrfOOtu9upz3ads+t019r2973mfQfS4DbPeKPG1x8MEb7ntD3e4NdZ9bZ9T73Ibt3n8P0jdHvN0DHnom+11/WN/3+umbI+57xWuds2BBU/reWOCIkppr7lxeu3Ahv3j4YS689loOXLGi7Iiap5/RjSc32og5e+xRUkDjw1U77MAN++wD06dzww9+wFU77FB6PF+YOpV7IlgL3LP55qz5ylfKH33rZAOM2m7fN0qh6mriv929fSPejRzNVcnsm50raj9GOz50dXXlvHnzyg6DE3fdlV8/0Mvz9vvL0b9Y35GCadNG/1qjdOCKFXxk8eJ1jrQ/udFGfGHq1NK/3LJgAY+ueXTUOT9wxQqOW7SI7TO5d/PNmbPHHuV/NmheP2hyf5rfO599d9p39C9Uxc9X0Zia0c/7XguoxLblwmuvZcd+vljfs/nmHLH//iVEtK6m9PMq9s0maOa/XdP3MR2a81apXD9vlibE1JLvP83anle0n9eP6lVBRMzPzK7G6Z56V5LFq9eyccNQ8Qtf+EJ22WUXnnnmGW666ab11tlxxx3Zcccdeeqpp7jllltqEx97rPZ3wQJ23nlntt9+e5588kn++Mc/rrf+rrvuynbbbceqVavWG4YGmDx5Mttssw2PPfYYS5YsWW/+HnvswdZbb83DDz/M7bffvt787z7xxDobCYAJa9dy9KJFnNXby9SpU5k4cSIrV65cb5gb4KUvfSkTJkzg3nvv5e67715v/j777MOmm27KPffcwz333LPe/Fe84hVsvPHG3HXXXdx3333rznzsMV68We3p8uXL1xtG32ijjXhlcfrT0qVLefDBB9eZv+mmm7LPPvtw1Q47MOf223nk6adhwgTo7YXeXjbffHNe9rKXAbBkyRIe6/t3KUycOJGpU6cCsHjxYlY1nIqy1VZb8ZKXvASAhQsXsrrhi8Xzn/98XvSiFwFwyy238NRTT60zf5snn2TyhAkA3Hjjjaxt+HeYNGnSs8PcjacoQF3fy+Smxx9/bmNY6Lfv1Rmo761as4oFKxaMvu89/TRbb7LJgH3vJS95CVtttRUPPvggS/s5PfLZvvfUU9y5evV6n29Efa/u/96gfY/aDgEG6HuPP84rt9wSGLzvAdx222088sgj68zv63sHrljBux9+mJ2BO3/1Kz41YQLf32yzkfe94vM9/7bbBu9722zD5OK0z1H1vUG2e3P22IMPL17MFnWvvQo4baONuPfee0vd7vXlboN9b0PbvTVruHvNmvX65oi2e3V9c9C+N8TtHgze92Dg7d6cPfZY7wtk37/dkiVLhrXdW/DUUyzdfHM+8cQT7ArcvfHG/Oeee3LVDjuMrO+tXs0um28+vH1unXW2e3U579OOvjek7V6T9rl92/M+I9ruFXna6MYbW973hrzdW7WK1WvXrvNvt8F9bsN2b8Hatev0zd5NNuHcl7yEq3bYYUTbvWO32IKPPPII22eusz3vM6zve/30zRH3veK1XvLYY6Pue2OFI0plmDGDfa/79XpHCUZ1jdK0aaVfo/TLX/2K6Kc/rQX+avr0cs7V77NgAWft8Az7Lnp05Ofq950vvfvu/O6RR9Y5olL6+dJ91yj19IzuOpG//MvaNUoNR4tGeo1S3xHIUfe9hx/mL7bemmvOPHN05+q//OW1a5QaPt+I+l7d/70RnatfePYapZ6ekZ+rf/DB6/1GSd/RzOV/+ZdtPVe/JdcoHXMMj3z966x6//vXG80te7t3zjnn8I/X/SOfe9HnRnedyN5787W7716vb47qGqVp00Z/jVIzrhOZO5f73/Uutlm7dp1/uxH3veJI+1tPOX10fa/vGqXLLhv9NUqveEVtYt2/X6ddo9Q4ojSqa5QOOKA61yjtsEPtAFrdv92It3tFHG857bQRb/cOXLGCU267jU3WrHl2WuPo1FC3e3d/8Yts9NGPrrfdHPU1Sj09HXeN0kAjShZKZZgxo7bBWfRoU14LqMYd66ZM6fdmB0yeXLsVaJk6Ned9mhVTkz9bV3cX82Y14f9cFT9flWKq8v+9Zqri/z2a0M/nzoVjj63dGW7y5JH/kHWfKuapyf/3mrI9r+L2oMKatj2vmqr92zVre953x8nGH/kdzR1tq7Tfa7KBCiVv5qDmqeKtnKXxoJNvXd/pvPX58PX0cPxJe5UdhTpBFX+frVnb81bcsnwc/pSMhZKap6q/xdPpqrihHw+qtMPwt53KM9ofs/b3V6RyVPUgRbO25x5AawoLJTVXJ//ydp8qfUGu6oa+2aqU8ypyNHfs8suMVI6qHqRo1vbcA2hNYaFUBk8bULNUdUOv9qobzU1wNHdDZsxY70eHS+OXGakcVT1I0azteVUPoI2xs2AslMY6j7SPb1Xd0Kv9itHcG6Zu1bmjuVVT7PBfs/ixke/wq/plptncVw1dlQr5TlblgxTN2J5X8XKIMXgWjIWSNJZVeUMvdbK6HX7AyHf4VfwyI40H4+EgRdUuhxiDZ8FYKKnzNeOob1WNhw29VEXN3OFX7cuMNB54kKL9xuBZMJuUHYDUUgMd9YXO2Bj2fYZm/gaLpA2r8g7fU9ykoZk5E+bMqT33/03r7b57/78RVeGzYBxRUmcbg8O8w+bRaKn9mn3aq9fwtJ85l9prDJ4FY6Gkzlblo75V5BcHdbpm3XFpDO7wJY0hnXiH5DF4uqOFkjqbNzuQ1KeZd1zyluzqM8ZudyyVaoydBWOhpM7mUV+NJ514BLLeaEc8m30qrrdk1xi83bEaeCaFBuHNHNR8Vdrg1N3sIFevJrzZgTR+eSqumm2w4tv9jNS/Kn1P3ABHlNT5POorCTwVV83X7OLb0/ikSrFQkiSND604FbfTT3fU4JpZfHsan1Q5pRRKEbFtRFwREbcWf7cZYLlzI+LeiLi5YfrpEXFXRCwoHoe0J3JJ0pg1Bu+4pIprZvE9Hn7OQu3nNVijUtaI0snAVZm5J3BV0e7PecCbB5j3pcycVjwua0GMkqROM8buuKSKa2bx7TV0UuWUdTOHw4AZxfPzgR7gXxsXysxfRcSUtkUlSZI0HDNnwpw5teejOXK/++610+36my6pFGUVSjtkZi9AZvZGxPYjeI0PRMS7gHnASZn5YH8LRcQsYBbAhEkT6OruGmnMTbVw5cLKxDIezO5dxONPrerYnM/uXQTA8RX7fFXr51XMU7NjqlrOq8icj31Vy3kz+tSbDgxOuyDYYk0+O+2JzYLPHBj8rAKftWo5Hw/MefkiMze81EheOOJKYMd+Zp0KnJ+ZL6hb9sHMHOg6pSnATzLz5XXTdgBWAgl8GtgpM9+zoZi6urpy3rx5w/kYLdPV3cW8WdWIZVyYMYP5vfPZd9GjZUfSGjNm1P5W7DzkyvXzKuapyTFVLudVZM7HvMrlvFl9au5cOPbY2g0dKvZzFpXL+ThgztsnIuZn5npVactGlDLzoEGCWREROxWjSTsB9w7ztVfUvdYc4Ccjj1TqAFX64i9JGplmncYnqSnKupnDpcDRxfOjgUuGs3JRXPX5W+DmgZaVJGkd3gVKkjQEZRVKnwPeEBG3Am8o2kTEzhHx7B3sIuK7wO+AvSLizog4tpj1+Yi4KSJuBF4PfKi94UuSJEnqZKXczCEz7wcO7Gf63cAhde13DrD+Ua2LTpIkSdJ4V9aIkiRJkiRVloWSJEmSJDUo63eUJKn9vIBfkiQNkYWSxoeeHo7v7sJfI5AkVZoHdKTK8NQ7SZIkSWpgoSRJkiRJDSyUJEmSJKmB1yhJUpm8HkGSpEpyREmSJEmSGlgoSZIkSVIDCyVJkiRJamChJEmSJEkNLJQkSZIkqYGFkiRJkiQ1sFCSJEmSpAYWSpIkSZLUwEJJkiRJkhpYKEmSJElSAwslSZIkSWpgoSRJkiRJDSyUJEmSJKmBhZIkSZIkNbBQkiRJkqQGFkqSJEmS1KCUQikito2IKyLi1uLvNv0ss1tE/DIiFkbELRHxweGsL0mSJEkjVdaI0snAVZm5J3BV0W70NHBSZr4M2B84ISL2Hsb6kiRJkjQiZRVKhwHnF8/PB97auEBm9mbmDcXzR4GFwC5DXV+SJEmSRqqsQmmHzOyFWkEEbD/YwhExBXg18PuRrC9JkiRJwxGZ2ZoXjrgS2LGfWacC52fmC+qWfTAz+73OKCK2Aq4GzsjMHxTTHhrG+rOAWQATJk3Yd58z9xnZB2qyhSsX8rLtXlZ2GOOKOW8/c95+5rz9zHn7VS3ns89aBMDxJ+1VciStU7WcjwfmvH3mHz9/fmZ2NU5vWaE0mIhYBMzIzN6I2Anoycz1ti4RsSnwE+BnmXn2cNdv1NXVlfPmzWveBxmFru4u5s2qRizjhTlvP3Pefua8/cx5+1Uu5zNm1P729JQZRUtVLufjgDlvn4jot1Aq69S7S4Gji+dHA5c0LhARAXwTWFhfJA11fUmSJEkaqbIKpc8Bb4iIW4E3FG0iYueIuKxY5rXAUcBfRcSC4nHIYOtLkiRJUjNsUsabZub9wIH9TL8bOKR4/hsghrO+JEmSJDVDWSNKkiRJklRZFkqSJEmS1MBCSZIkSZIaWChJkiRJUgMLJUmSJElqYKEkSZIkSQ0slCRJkiSpgYWSJEmSJDWwUJIkSZKkBhZKkiRJktTAQkmSJEmSGlgoSZIkSVIDCyVJkiRJamChJEmSJEkNLJQkSZIkqYGFkiRJkiQ1sFCSJEmSpAYWSpIkSZLUYJOyA5AkSRrTenrKjkBSCziiJEmSJEkNLJQkSZIkqYGFkiRJkiQ1sFCSJEmSpAYWSpIkSZLUwEJJkiRJkhqUUihFxLYRcUVE3Fr83aafZXaLiF9GxMKIuCUiPlg37/SIuCsiFhSPQ9r7CSRJkiR1srJGlE4GrsrMPYGrinajp4GTMvNlwP7ACRGxd938L2XmtOJxWetDliRJkjRelFUoHQacXzw/H3hr4wKZ2ZuZNxTPHwUWAru0K0BJkiRJ41dkZvvfNOKhzHxBXfvBzFzv9Lu6+VOAXwEvz8xHIuJ04BjgEWAetZGnBwdYdxYwC2DCpAn77nPmPk36FKOzcOVCXrbdy8oOY1wx5+1nztvPnLefOW8/c95+5rz9zHn7zD9+/vzM7Gqc3rJCKSKuBHbsZ9apwPlDLZQiYivgauCMzPxBMW0HYCWQwKeBnTLzPUOI6T5g6TA/SqtsR+0zqH3MefuZ8/Yz5+1nztvPnLefOW8/c94+kzPzhY0TN2nVu2XmQQPNi4gVEbFTZvZGxE7AvQMstylwMTC3r0gqXntF3TJzgJ8MMab1ElCWiJjXX+Wq1jHn7WfO28+ct585bz9z3n7mvP3MefnKukbpUuDo4vnRwCWNC0REAN8EFmbm2Q3zdqpr/i1wc4vilCRJkjQOlVUofQ54Q0TcCryhaBMRO0dE3x3sXgscBfxVP7cB/3xE3BQRNwKvBz7U5vglSZIkdbCWnXo3mMy8Hziwn+l3A4cUz38DxADrH9XSANuju+wAxiFz3n7mvP3MefuZ8/Yz5+1nztvPnJeslLveSZIkSVKVlXXqnSRJkiRVloWSJEmSJDWwUCpBRLw5IhZFxJKIOLnseMaDiLijuAHIgoiYV3Y8nSgizo2IeyPi5rpp20bEFRFxa/F3wB+W1vANkPPTI+Kufm6Co1GKiN0i4pcRsTAibomIDxbT7ectMkjO7ectEhETIuK6iPifIuefLKbbz1tkkJzbz0vmNUptFhEbA4up3e3vTuB64J2Z+b+lBtbhIuIOoCsz/eG2FomI/wM8BnwrM19eTPs88EBmfq44KLBNZv5rmXF2kgFyfjrwWGZ+sczYOlHx0xQ7ZeYNEfE8YD7wVuAY7OctMUjO34H9vCWKn2fZMjMfK37P8jfAB4G/w37eEoPk/M3Yz0vliFL77QcsyczbMnMNcCFwWMkxSaOWmb8CHmiYfBhwfvH8fGpfcNQkA+RcLZKZvZl5Q/H8UWAhsAv285YZJOdqkax5rGhuWjwS+3nLDJJzlcxCqf12AZbXte/EjX47JPDziJgfEbPKDmYc2SEze6H2hQfYvuR4xosPRMSNxal5nh7TAhExBXg18Hvs523RkHOwn7dMRGwcEQuAe4ErMtN+3mID5Bzs56WyUGq//n4byqMGrffazHwNcDBwQnHKktSJvga8GJgG9AJnlRpNB4qIrYCLgRMz85Gy4xkP+sm5/byFMvOZzJwG7ArsFxEvLzmkjjdAzu3nJbNQar87gd3q2rsCd5cUy7hR/JgxmXkv8ENqp0Cq9VYU1xj0XWtwb8nxdLzMXFHscNcCc7CvN1Vx/cDFwNzM/EEx2X7eQv3l3H7eHpn5ENBD7VoZ+3kb1Ofcfl4+C6X2ux7YMyL2iIjNgCOAS0uOqaNFxJbFRcBExJbAG4GbB19LTXIpcHTx/GjgkhJjGRf6vsgU/hb7etMUF1x/E1iYmWfXzbKft8hAObeft05EvDAiXlA83wI4CPgj9vOWGSjn9vPyede7EhS3dzwH2Bg4NzPPKDeizhYRL6I2igSwCfAdc958EfFdYAawHbAC+DfgR8BFwO7AMuDvM9ObDzTJADmfQe00jQTuAI7vu65AoxMRrwN+DdwErC0mf4zaNTP28xYYJOfvxH7eEhHxSmo3a9iY2gH1izLzUxExCft5SwyS829jPy+VhZIkSZIkNfDUO0mSJElqYKEkSZIkSQ0slCRJkiSpgYWSJEmSJDWwUJIkSZKkBhZKkqS2iIjHyo6hlSLi9Ij4cNlxSJKaw0JJkqSSRY37ZEmqEDfKkqS2iogZEXF1RFwUEYsj4nMRMTMirouImyLixcVyh0bE7yPiDxFxZUTsUEx/YURcERE3RMTsiFgaEdsV844sXmdBMW/jft7/joj4ZLH+TRHx0mL6OiNCEXFzREwpHn+MiG8U0+ZGxEER8duIuDUi9qt7+VdFxC+K6cfVvdZHIuL6iLgxIj5ZTJsSEQsj4qvADcBuLUi3JGmELJQkSWV4FfBB4BXAUcDUzNwP+AbwT8UyvwH2z8xXAxcCHy2m/xvwi8x8DfBDYHeAiHgZcDjw2sycBjwDzBzg/VcW638NGMrpci8Bvgy8Engp8A/A64p1P1a33CuBvwYOAD4RETtHxBuBPYH9gGnAvhHxf4rl9wK+lZmvzsylQ4hDktQmm5QdgCRpXLo+M3sBIuJPwM+L6TcBry+e7wp8LyJ2AjYDbi+mvw74W4DM/GlEPFhMPxDYF7g+IgC2AO4d4P1/UPydD/zdEOK9PTNvKuK9BbgqMzMibgKm1C13SWY+ATwREb+kVhy9Dngj8Idima2oFU7LgKWZee0Q3l+S1GYWSpKkMqyue762rr2W5/ZN/w84OzMvjYgZwOnF9BjgNQM4PzNPGcb7P1P3fk+z7pkWE4YZL0A2vE8WcX02M2evE2zEFODxIcQqSSqBp95Jkqpqa+Cu4vnRddN/A7wDoDitbZti+lXA2yNi+2LethExeRjvdwfwmmLd1wB7jCDmwyJiQkRMAmYA1wM/A94TEVsVr71LX4ySpOpyREmSVFWnA/8VEXcB1/Jc4fJJ4LsRcThwNdALPJqZKyPiNODnxR3kngJOAIZ67c/FwLsiYgG1AmfxCGK+DvhvatdNfToz7wbuLq6f+l1xSuBjwJHURrMkSRUVmY1nCUiSVF0RsTnwTGY+HREHAF8rbt4gSVLTOKIkSRprdgcuKkaN1gDHbWB5SZKGzRElSZIkSWrgzRwkSZIkqYGFkiRJkiQ1sFCSJEmSpAYWSpIkSZLUwEJJkiRJkhpYKEmSRi0iMiJe0jDt9Ii4oHg+IyLWRsRjxePOiLgoIv6snIjbr8jBnWXHIUkaGgslSVK73J2ZWwHPA/YH/gj8OiIOLDes6ogIf99QkirCQkmS1FZZc2dmfgL4BvB/B1o2Iv4rIu6JiIcj4lcRsU/dvEMi4n8j4tGIuCsiPlxMvzkiDq1bbtOIWBkR0yJiSjH69e6IWB4RD0bEeyPizyLixoh4KCL+o27dYyLitxHxpWLebRHxF8X05RFxb0QcXbf85hHxxYhYFhErIuLrEbFFRGwJXA7sXDeqtnMx6vb9iLggIh4BTo6IVRExqe41942I+yJi0yb9E0iShsBCSZJUph8ArykKif5cDuwJbA/cAMytm/dN4PjMfB7wcuAXxfRvAUfWLXcI0JuZC+qm/XnxuocD5wCnAgcB+wDviIjpDcveCEwCvgNcCPwZ8JLiff4jIrYqlv2/wFRgWjF/F+ATmfk4cDDFqFrxuLtY5zDg+8ALgLOAHuAdde9/JHBhZj41QI4kSS1goSRJKtPdQFArEtaTmedm5qOZuRo4HXhVRGxdzH4K2Dsinp+ZD2bmDcX0C4BDIuL5Rfso4NsNL/3pzHwyM38OPA58NzPvzcy7gF8Dr65b9vbM/M/MfAb4HrAb8KnMXF2svwZ4SUQEcBzwocx8IDMfBc4EjthADn6XmT/KzLWZ+QRwPkWhFxEbA+/sJ35JUotZKEmSmuEZoPHUsE2pFTOD2QVI4KHGGRGxcUR8LiL+VJyWdkcxa7vi79uojRYtjYirI+IAgGKk5rfA2yLiBdRGcuayrhV1z5/op73VIMuSmf0t/0JgIjC/OE3vIeCnxfTBLG9oX0KtAHwR8Abg4cy8bgOvIUlqMi8alSQ1wzJgCrCwbtoewOINrPe3wA3FqWmN/oHaaWkHUSuStgYepDYCRWZeDxxWXLvzAeAiaqM9UBuV+Udq+7nfFSNFrbaSWtG0zwDvlwOst870zHwyIi4CZgIvxdEkSSqFI0qSpGb4HnBaROwaERtFxEHAodSuvVlH1OwSEf9GrZj52ACv+TxgNXA/tZGaM+teY7OImBkRWxfX7jxCbVSrz4+A1wAfpHbNUstl5lpgDvCliNi+iHOXiHhTscgKYFLdqYOD+RZwDPA31E4llCS1mYWSJKkZPgVcA/yG2qjP54GZmXlz3TI7R8RjwGPA9cArgBnFdT79+RawFLgL+F/g2ob5RwF3FKflvZe6GzgU1/pcTG1U6wej+2jD8q/AEuDaIq4rgb2KmP4IfBe4rTg1b+eBXiQzfwuspTbadkfLo5YkrScyBzoTQJKksSsiPgFMzcwjN7hwBUXEL4DvZOY3yo5FksYjr1GSJHWciNgWOJbaqNOYExF/Ru3UwcPKjkWSxitPvZMkdZSIOI7aneQuz8xflR3PcEXE+dRO2TuxuMW4JKkEnnonSZIkSQ0cUZIkSZKkBuPqGqXtttsup0yZUnYYANz6wK3sue2eZYcxrpjz9jPn7WfO28+ct585bz9z3n7mvH3mz5+/MjPX+3HwcVUoTZkyhXnz5pUdBgBd3V3Mm1WNWMYLc95+5rz9zHn7mfP2M+ftZ87bz5y3T0Qs7W+6p95JkiRJUgMLJUmSJElqYKEkSZIkSQ0slCRJkiSpgYWSJEmSJDWwUJIkSZKkBhZKkiRJktTAQkmSJEmSGlgoSZIkSVIDCyVJkiRJamChJEmSNBozZtQekjqKhZIkSZIkNbBQkiRJkqQGFkqSJEmS1MBCSZIk9c9rbySNYxZKkiRJktTAQkmSJKnTOBoojZqFkiRJkiQ1sFCSJEmSpAYWSpIkSZLUwEJJkiRJkhpYKEmSJElSAwslSZIkSWpgoSRJkiRJDSyUJEmSJKlBqYVSRLw5IhZFxJKIOLmf+RER/17MvzEiXlM3746IuCkiFkTEvPZGLkmSJKmTbVLWG0fExsBXgDcAdwLXR8Slmfm/dYsdDOxZPP4c+Frxt8/rM3Nlm0KWJEmSNE6UOaK0H7AkM2/LzDXAhcBhDcscBnwra64FXhARO7U7UEmSxp25c+Haa+Hqq2HKlFpbksaR0kaUgF2A5XXtO1l3tGigZXYBeoEEfh4RCczOzO7+3iQiZgGzACZMmkBXd1dzoh+lhSsXViaW8cKct585bz9z3n6dmPM3/f5+TrtgKVusydqEpUt54j1H8ZlfnMbP/nxSucFRvZzP7l0EwPEdHFPVcj4emPPylVkoRT/TchjLvDYz746I7YErIuKPmfmr9RauFVDdAF1dXTlvVjUuZ+rq7qIqsYwX5rz9zHn7mfP268icnzkF1qy7S95iTXLGVckZ3yz/s1Yu59+ZAcC8WT2lhrGOJsdUuZyPA+a8feL4/kqOck+9uxPYra69K3D3UJfJzL6/9wI/pHYqnyRJGq1ly4Y3XZI6UJmF0vXAnhGxR0RsBhwBXNqwzKXAu4q73+0PPJyZvRGxZUQ8DyAitgTeCNzczuAlSepYu+8+vOmS1IFKK5Qy82ngA8DPgIXARZl5S0S8NyLeWyx2GXAbsASYA7y/mL4D8JuI+B/gOuC/M/Onbf0AkiR1qjPOgIkT1502cWJtuiSNE2Veo0RmXkatGKqf9vW65wmc0M96twGvanmAkiSNRzNn1v4eeyysXg2TJ9eKpL7pkjQOlFooSZKkipo5E+bMqT3v6Sk1FEkqQ5nXKEmSJElSJVkoSZIkSZ1oxozaQyNioTTW+R9AksozYwazz1pUdhSSpBawUJIkSZKkBhZKkiRJIzV3Llx7LVx9NUyZUmtL6ggWStJwebqjxgP7ubRhc+fCrFm1W6gDLF1aa1ssSR3BQkmSJGkkTj0VVq1ad9qqVbXpksY8CyVVm0e1JUlVtWzZ8KZLGlMslCRJkkZi992HN13SmGKhJOk5juBpPOj0ft7pn69KzjgDJk5cd9rEibXpZfIGEwL7QRNYKEmS1uXOdWiKPL1m8WPmabyaORO6u2HzzWvtyZNr7Zkzy4vJG0wI7AdNYqEkSXqOO9ehqctTgHkaz2bOhP33h+nT4Y47yi2SwBtMqMZ+0BQWSmOZR30lNZs716EZL3nq6ak9NHZ4gwmB/aBJLJTGKo/6SmoFd65DY55UVd5gQmA/aBILpbFqvBzNrBpH8dTp3LkOjXkqz4wZzD5rUdlRVFdVbzCh9rIfNIWF0ljl0cz2cxRP44E716ExT6qqKt5gQu1X5X4whu7MaaE0Vnk0s/0cxdN4UOWdazM0a1S4Lk8JnZcnjW1Vu8FElY2hL+3DZj8YNQulsarKRzObtdGp2mlujuJpvOjUnWuzR4WLPN0wdavR56lTt5vN5i3Z1aeTCxxVhoXSWDUejvpW7TQ3R/Gksa3TR4WruN1sJm/JLqnNLJTGsk496gvV/EJT5VG8TueRw7GrSv92nT4qXMXtZjN1+ucbD6q0PZCGwEJJ1VTFLzSdPoondbpOHxWu4nazmTr980mqHAsl1VTtKE9Vv9B08ihep1/b0GxV+z+jDev0UeGqbjehOf9fqvz5JHUkCyVVU6d/oamaTr+2QcPX01N7dJKqjgo36yBFp283O/3zdToPxpWjE7flbWShpOZqwa13gep8oelUnvs/PFXd4ftDnBtWtVHhZh6k6PTtprdkH7s8GKcxykJJzdOiW+9W5gtNJ6vyuf9VK0rc4ateTw/Hn7TXyNdv9kGKTt9uNvOW7GofD8ZpjNqk7ADGq0VnLWLGd2asM+0d73gH73//+1m1ahWHHHLIeuscc8wxHHPMMaxcuZK3v/3ttYkLFtT+zpjB+973Pg4//HCWL1/OUUcdtd76J510EoceeiiLFi3i+OOPf3b6gStWcNyiReyQSUyZwh2zZnHMz3++3vpnnnkmf/EXf8E111zDxz72sfXm/3zxYjbrZ0N4z7HHcsScOcyePZu99tqLH//4x5x11lnrrf/tb3+b3Xbbje9973t87WtfW+/zff/732e77bbjvPPO47zzzltv/csuu4yJEyfy1a9+lYsuumi9+fxD7c8Xv/hFfvKTn6wza4sttuDyyy8H4NOf/jRXXXXVOvMnTZrExRdfDMApt93G7x55ZJ3z7XfddVcuuOACAE488UQW9MVdmDp1Kt3d3QDMmjWLxYsXrzN/2rRpnHPOOQAceeSR3HnnnevMP+CAA/jsZz8LwNve9jbuv//+deYfuHQpH588GXp6OPjgg3niiSfWmf+Wt7yFD3/4wwDM6Oc6gUtf8AKe/+CD602/Z7PNOGLGjP77Xp2B+t6i3lo/H6jv9TnttNM46KCDWLBgASeeeOJzn2vFCj6yeDET1q6tTVi6lCff9S6+8JnPcNUOOzy73DnnnMO0adO48sor+cxnPrPe64+o79Wp73tvfu972bGvSOpT7PC/+vDD/fa9nuK0h1H3vVNO4Xe/+90689fpe0uW8OsHVvG8un/jlve9Aw/k4x//OED/fW/5cj68225A/31vRNu9OiPa7tVtVwbqe302tN3ry91I+94vly2r3eq6wdqlS/mrkW736j5f2/rehrZ7ixezeNWqdbabI+57Cxbw6JpVvPXTnx68721gu9fUvleX8z4j3e71GUrfG3S7t2oVe02c2JTt3nnnnffs9rzPhva59X3vX5Yu7f/I/LJlI+97xfNR73MXLuTO1avX+bcb9XavSX3v6cee7nf9kX7f69Pyvrehfe6TT7LbhAnrTa8iR5TGub4vojtmPvu7FLt/+tMcuGLFsF9r03vu6Xf69o1fKlU51/7N36x37v+TG23EnD32KCmimuNuv/25IqkwYe1ajrv99pIiGqQ/V2H0rdMtWPBcEdAhntl5536n39t3+pzWN20ai3ebuOHlVBkD9mdvxKGKi8wsO4a26erqynnz5pUdBieeeCLfvPyb7LvTvqN/sb4vDdOmjWj1C6+9dv2j48A9m2/OEfvvX9prPWuUn6/e/N75lch5SzQhpr6Rxe0zuXfzzZmzxx7rjNqMxGhz/ourr+73aM5a4K+mTx/x645GS/p5syxYwKNrHuV5+/1luXHUa+b/l2a9VpP/D4+mn683akrtIMUXpk4d+f+/Dt1G1Wva9rxZzPmgKt3Pq7iNKnRsP1+wgGlbbcU5DaPIZYqI+ZnZ1Ti91BGliHhzRCyKiCURcXI/8yMi/r2Yf2NEvGao644b06aNqsMOdHR8JKNAc/bYgyc3WrdLVWFUQkNz1Q47cMTzn89fbb01R+y//6iLpGYY6ChkmUfb7ecdYJTbzWa6aocd+MLUqdwTwVpqBfeovjxKFVTVfn7gihVc+Mgj/OLhh7nw2mtHdDaNhqcv51+6665qXHe8AaUVShGxMfAV4GBgb+CdEbF3w2IHA3sWj1nA14axbmX1nRfb6B3veAc9PT1cdtll/c4/5phj6Onp4fvf/36/89/3vvfR09PDt7/97X7nn3TSSfT09DB79uxnpw3ni+iZZ55JT08PZ555Zr/rHPrd7zLhW9/iiU037XdDOHv2bHp6ejjppJP6Xf/b3/42PT09vO997+t3/ve//316eno45phj+p1/2WWX0dPTwzve8Y5+5+910l709PTwlre8Zb15W2yxBT09PfT09HDggQeuN3/SpEnPzj/gfe9b70vWrrvu+uz8af18AZs6deqz86dOnbre/GnTpj07f9ddd11v/gEHHPDs/EmTJq03/8BttqGneI0ttthivflvectbnl2/P8/2vVe8ot/5reh79U477TR6enrW+78x1KLknHPOoaenh9NOO63f129m3+tvhx/f+AYfX7hwwL7Xl/tR970DDlhv/jp9b6ut1pvf8r534IHPzu+3702a9Gzf7E8Z2716A/W9Phva7p1zzjnsddJeo+p7H1+4kKtf+tJ+D1KMdrvXtr432HbvuOO4+NFH1/sy2vK+N9Tt3hjue4Nu96ZOpWfatNL2uY19r/5g3Ltf/3o+vnDh6PretGn0TJs28n3uccfx8WXL2DGTjYAdV6/mI4sXc+CKFSPqe/VF1yX/8z/0HHfc6Poe8KNn+j/rq/J9b4DtXn+Xe1T9ZkhljijtByzJzNsycw1wIXBYwzKHAd/KmmuBF0TETkNcV0PQ3xfRtRMmjPzo+MyZ/PZlL6vUqITGrr6i5MlNNwXgyaJddr9qHH175vDDS40HgLlz+cQ993D9E2s9MqrqKO4SOWnt2vW+jJbuS1/yR6PHqANuv/3Zu6F+4txzR9af+rkT30ivga0vADYCtn7oocoXAGXo77rjyt/9MDNLeQBvB75R1z4K+I+GZX4CvK6ufRXQNZR1+3vsu+++WRVbTZ2X//mftedr1mROn5757W/X2o8/XmtfeGGt/dBDtfbFF9fa991Xa196aa3d21trX355rb1sWa19xRW19p/+VGv39NTaf/xjrf3b3xbLf+6C7I2dci1kTp6ct59xQU6fnvmHP9TmX3ddbfmbbqq1f/vbWvuPf6y1e3pq7T/9qda+4pX/ktO3/kMuW1ZrX355bX5vb6196aW19n331doXX1xrP/RQrX3hhbX2449n5vTp+e2XfianT6/lKTPzP/+zNr9Pd3fmgQc+1/7KVzLf/Obn2ueck3nooZn7zq79+3/hC5l/93fPzf/sZzMPP/y59qc+lTlz5nPtj38885hjnmuffHLmccc91z7ppMz3v/+59gc/WHv0ef/7a8v0Oe642mv0OeaY2nv0mTmzFkOfww+vxdjn7/6u9hn6HHpo5jkv/vdnk/LmN9dy0OfAA2s56jN9eg7c9173xpy+9R+a1vde/pm/GbTv3XRTrX3ddbX2H/6Q/fe9rmMyp0/fcN+7otZuSt/LWl6a0ff6tKzvXXBB5sSJmfDcY+LEPPegC1rf9855rt1v35v6+WeTNmjfG8p2b+s/5KX71D7AaLd7Q+57Q9ju7Tt739H3vZedntO3/sPY63uFAbd7kyev2y+Lx31bTh5V39v6lVdvuO9taLv30s9kTp/enH3u1n/IK175L5nZhL736lk5fes/DH2fO1DfO+BvM6dPb9p2r28fWnbfu+CQC/LJTdbd3q3eZGJtO5jD2OdG9Ns3nyGGvd17eJvJ/b5WTp488u3e9Ol55YsnN/X73qi3e6/655y+9R9GvM9dO0DOMyLLBszLfmqHMm8P3t8dURvHGAdaZijr1l4gYha10/aYMGkCXd3rXadVilVPnc3pPafzH2t+Qj6zMYt7v8rSX/yIc1Zdzto1m3Nr779zypXf5wsPX8EzT2zJkt6z+cjPL+TMlb/k6ce25k+9n+dDP72AT/b+mqcensRtvWfyT5edx9bLfseaB3bg9t5P8f7//ibPv+06Vt+3C3f0foJZP57N8xbdwJP3TGZp78d4zyVfYaubb+SJVS9mwoTv8cEXnsa/f+xxVi3/b5b3vpR/uPgsJl63mMfv2Js7ez/I4f/1eba45k889qdXclfvCbzte2cyYcelPLr4NdzdezyHffdTbP7Cu3jP/Tvx6JpH+eu5f81m267g4ZsP4J7eY3jTtz/Gplvfz0P/85es6D2Sg771UTbZ6mEevOH13Nt7BDPO+xc23uJxHrj+DdzX+3Zed+4/M6f3Rm5/5CXMXzuf/b/xfmLjZ1h5zVu4v/dQurprt7y879dv5cG73kBX9wkA3Pubt/Pw8r+kq/uDAKy45ggeXbofT61cSFd3F/dceySP3/5Kuro/CsA91x3NquV70dVduwVm77xjefKeyXR1fwKAu284njUP7EBX96cAuGvBCTz92Nbc0F0bkr7zxg+yds3m/L778wAsv+lfAPhN99kALLvlo2y02Wp6ur8MwNKFH2OTrR7miu6vAHDHok+w2X0ruKy7NkR++62f4pePLuWS7m8CcNufzuQ3axbx/e7zAfjT7Z/n93EjF3bXbo66ZOnZTH1kOfN753N8dxe3Lv8yt/zm15y7Se10kcV3fYVbf3UFs/NHACzqnc0dPT/ut+99+Z5lPLrmUU658pSm9L0lDz7BHb3zB+57d72YZb0f5V0//DJb/uF/WbV8Kst7T1qv792y+hZWP7KQ91zynkH73iML96O391j+eu4nRt33NtpsNfdfezAre9866r7X1V3rE63qe6dd+B52WrWmYSOzioN+fSKnxNqW9r0/XHMd397iQoB++97+Dy1jPrW+OVjfG8p2b+M1p7PkwT/R1d01+u3eEPveULZ7t8VC3r/w/aPqe297aB8eXfMorzv3tWOq721ou/f5AW4Lve3jy/jGDXNG3PceW/NJvnjNFwftexva7h34yO3MXzuf48597aj3uZuuOZnF9y/m5O6uUfe9f75/Sx5d8yiH/9fhQ9rnDrTd+5+n/4e7NrmXD/30Q03Z7q0t9qFl970f/uIONn963ZGgzZ5eRe8/v4dDH//SkPe579tmU3Z6oGG7Cdy52Qv5j+v+37C2e1s9uLSfXl67zf/8zeePaLs3uXc+jz+1BfN75zfv+94ot3uz7t+OR9c8ymHfPWxE+9xLtnweuzz2yHp56t1mUw6tyPfz9fRXPbXjARwA/KyufQpwSsMys4F31rUXATsNZd3+HlUaUeo7MlMZ06eve7iyKq/VROa8ja9TaFrOK9qnKqPCR+kq2c+brCn9vKKfbdQGGFHKyZNH9bKVy3kVX6uq2/PRatb2boCR+L6RqWFpdj+/4ILMzTd/9kyfEcXUCqPtU83MeZMxwIhSmdcoXQ/sGRF7RMRmwBHApQ3LXAq8q7j73f7Aw5nZO8R1JXWKnp7aQ/0b6LdI/I2SsaNT+/gZZ6z3G21MnFibLo1Es7Z3M2dCdzf03bxq8uRae+bM4cfUzH5eXNfH6tXVuuHB3LnPXhc24rvVNTPnbVLaqXeZ+XREfAD4GbAxcG5m3hIR7y3mfx24DDgEWAKsAt492LolfIzO0cwddCfu7KUqO+OM2o60/sJkv4yqCvq+AB17LKxeXftidMYZlf5ipIpr5vZu5kyYM6f2fDTfXZrZz/u5ycSzNzwo6/9NXfEGPFe8wfBjalbO26TMa5TIzMuoFUP1075e9zyBE4a6riSNS3U76Vy9mvDLqKpkjH0xKlXfUfvVq2tH7f1/vL6qFt/N6ufLlg1vejtUsXhrk1ILpXFrxgxm9y4qbjEhSU1Q7KRv6J3PvovuKDsaScPVzKP2na6qxXczYtl999q/fX/Ty1LF4q1NyrxGSZIkSTD4UXuNH1W8rm8cXwfriJKk51TpyJwkjRXN2HaO46P2qlPFU6nH8XWwjihpfJgxg9lnLSo7CkmS+jeOj9qrwcyZsP/+3DB1K7jjjvJPvRyDd6trFgslqQzNuM2mNJ74f0bNVMX+VMVTrqQ+RfHG9OnVKN7axEJJareBLtitwo5aY1tPD8eftFfZUTSf/2fUTFXtT80+at+pv801XnTq9nyMsVCS2s0LdqXh8f/M2FelL+1V7k/j9Ki9VFUWSu1WDPe/ZvFj1RnuV3t5wa40PP6fUTPZnyQNkYVSO9UN9wdUZ7hf7eUFu9Lw+H9GzWR/kjREFkrtVOXhfrWPF+xKw+P/GTWT/Un1qnRaqCrH31FqJ4f7Bev8RgKrV9cu2C37NxJUnhkzan/dUQ/M/zNqpqr3J7cFqqpm9c0x1MctlNpp991rp9v1N12t03dd2OrVtevCqrBDnDkT5sypPR9DGwypNP6fUTPZn8Y+/93UBp56104O97ef14VJkiRpBCyU2qnuNxISxtUvG5fG68IkSZI0Ap56127FcP8NvfPZd9EdZUfT+bwuTJIkSSPgiJI6m7eBlSRJ0ghYKKmzeV2YJEmSRsBCSZ3N68IkSZI0Al6jpM5X1evCvLWpJElSZTmiJI11xe9EcfXVtd+J8tbnkiRJo+aIkjSW1f1OFPDc70SBpxdK0mAc1Ze0AY4olaGnh+NP2qvsKNQJ/J0oSZKklrBQksYyfydKkiSpJSyUpLHM34mSJElqCQslaSzzd6IkSZJawkJJ40OnXhdW9ztRgL8TJUmS1CTe9U4a64rfiQK8i5M6m/1bktRGjihJkiRJUgMLJUmSJElqsMFCKSLmRcQJEbFNOwKSJEmSpLINZUTpCGBn4PqIuDAi3hQRMZo3jYhtI+KKiLi1+NtvERYRb46IRRGxJCJOrpt+ekTcFRELischo4lHkiRJkuptsFDKzCWZeSowFfgOcC6wLCI+GRHbjvB9Twauysw9gauK9joiYmPgK8DBwN7AOyNi77pFvpSZ04rHZSOMQ5IkSZLWM6RrlCLilcBZwBeAi4G3A48Avxjh+x4GnF88Px94az/L7AcsyczbMnMNcGGxniRJkiS11AZvDx4R84GHgG8CJ2fm6mLW7yPitSN83x0ysxcgM3sjYvt+ltkFWF7XvhP487r2ByLiXcA84KTMfHCA+GcBswAmTJpAV3fXCENuroUrF1YmlvGik3M+u3cRAMdX7PN1cs6bpdn/dlXLeVX7ZjNVLefjgTlvP3Pefua8fJGZA8+M2IhacXTmsF844kpgx35mnQqcn5kvqFv2wcxc5zqliPh74E2Z+Y9F+yhgv8z8p4jYAVgJJPBpYKfMfM+GYurq6sp58+YN96O0RFd3F/NmVSOW8aKjcz5jRu1vxX5npnI5r2KemhyTOW+/yuV8HDDn7WfO28+ct09EzM/M9arSQUeUMnNtRLwZGHahlJkHDRLMiojYqRhN2gm4t5/F7gR2q2vvCtxdvPaKuteaA/xkuPFJkiRJ0kCGco3SFRHx4YjYrbhb3bajuIlDn0uBo4vnRwOX9LPM9cCeEbFHRGxG7e57lwIUxVWfvwVuHmU8kiRJkvSsDV6jBPSd0nZC3bQEXjSK9/0ccFFEHAssA/4eICJ2Br6RmYdk5tMR8QHgZ8DGwLmZeUux/ucjYloRxx3A8aOIRZLUCnPnwrXXwurVMGUKnHEGzJxZdlSSJA3JUAqll2Xmk/UTImLCaN40M+8HDuxn+t3AIXXty4D1bv2dmUeN5v0lSS02dy7MmlUrkgCWLq21wWJJkjQmDOXUu2uGOE2SpJpTT4VVq9adtmpVbbokSWPAgCNKEbEjtVt0bxERrwaimPV8YGIbYpMkjVXLlg1vuiRJFTPYqXdvAo6hdre5s+umPwJ8rIUxSZLGut13r51u1990SZLGgAELpcw8Hzg/It6WmRe3MSZJ0lh3xhm1a5LqT7+bOLE2XZKkMWAo1yj9NiK+GRGXA0TE3sXd6iRJ6t/MmdDdDZtvXmtPnlxreyMHSdIYMZRC6T+p3aJ756K9GDixVQFJkjrEzJmw//4wfTrccYdFkiRpTBlKobRdZl4ErAXIzKeBZ1oalSRJkiSVaCiF0uMRMYnaj7sSEfsDD7c0KkmSJEkq0VB+cPZfgEuBF0fEb4EXAm9vaVSSJEmSVKINFkqZeUNETAf2ovZbSosy86mWRyZJkiRJJdlgoRQRGwOHAFOK5d8YEWTm2YOuKEmSJElj1FBOvfsx8CRwE8UNHSRJkiSpkw2lUNo1M1/Z8kgkSZIkqSKGcte7yyPijS2PRJIkSZIqYigjStcCP4yIjYCnqN3QITPz+S2NTJIkSZJKMpRC6SzgAOCmzMwWxyNJkiRJpRvKqXe3AjdbJEmSJEkaL4YyotQL9ETE5cDqvoneHlyqkJ6esiOQJEnqKEMplG4vHpsVD0mSJEnqaBsslDLzk+0IRJIkSZKqYoOFUkR0AacCk+uX97eVJEmSJHWqoZx6Nxf4CHATsLa14UiSJElS+YZSKN2XmZe2PBJJkiRJqoihFEr/FhHfAK5i3bve/aBlUUmSJElSiYZSKL0beCmwKc+depeAhZIkSZKkjjSUQulVmfmKlkciSZIkSRWx0RCWuTYi9m55JJIkSZJUEUMZUXodcHRE3E7tGqUA0tuDS5IkSepUQymU3tzyKCRJkiSpQoZy6t0/AVtm5tL6x2jeNCK2jYgrIuLW4u82Ayx3bkTcGxE3j2R9SZIkSRqJoRRKfwTmRMTvI+K9EbF1E973ZOCqzNyT2m3HTx5gufPof0RrqOtLkiRJ0rBtsFDKzG9k5muBdwFTgBsj4jsR8fpRvO9hwPnF8/OBtw7w3r8CHhjp+pIkSZI0EpGZG14oYmPgLdR+U2k34CJqN3l4PDOPGPabRjyUmS+oaz+YmQOdfjcF+ElmvnyE688CZgFMmDRh333O3Ge44bbEwpULedl2Lys7jHHFnLdf1XI++6xFABx/0l4lR/KcZsdkztuvajkfD8x5+5nz9jPn7TP/+PnzM7OrcfoGC6WIOBs4FPgF8M3MvK5u3qLM7HfvFxFXAjv2M+tU4Px2FUr1urq6ct68eRtarC26uruYN6sasYwX5rz9KpfzGTNqf3t6yoxiXU2OyZy3X+VyPg6Y8/Yz5+1nztsnIvotlIZy17ubgdMyc1U/8/YbaKXMPGiQYFZExE6Z2RsROwH3DiGOeqNdX5IkSZIGNJSbOSyi9ttJRMSREXF2REwGyMyHR/i+lwJHF8+PBi5p8/qSJEmSNKChFEpfA1ZFxKuAjwJLgW+N8n0/B7whIm4F3lC0iYidI+KyvoUi4rvA74C9IuLOiDh2sPUlSZIkqRmGcurd05mZEXEY8OXM/GZEHL3BtQaRmfcDB/Yz/W7gkLr2O4ezviRJkiQ1w1AKpUcj4hTgKOAvizvgDWU9SZIkSRqThnLq3eHAauDdmXkP8Fpgy5ZGJUmSJEkl2uDIUGbeExG/AP4hIi4AbgfOaXVgkiRJklSWAQuliJgKHAG8E7gf+B613116fZtikyRJkqRSDDai9Efg18ChmbkEICI+1JaoJEmSJKlEg12j9DbgHuCXETEnIg6k+D0lSZIkSepkAxZKmfnDzDwceCnQA3wI2CEivhYRb2xTfJIkSZLUdhu8611mPp6ZczPzLcCuwALg5FYHJkmSJEllGcrtwZ+VmQ9k5uzM/KtWBSRJkiRJZRtWoSRJkiRJ44GFkiRJkiQ1sFCSpLLMnQvXXgtXXw1TptTakiSpEiyUJI0PVStK5s6FWbNg9epae+nSWrvsuCRJEmChJGk8qGJRcuqpsGrVutNWrapNlyRJpbNQktT5qliULFs2vOmSJKmtLJQkdb4qFiW77z686ZIkqa0slCR1vioWJWecARMnrjtt4sTadEmSVDoLJUmdr4pFycyZ0N0Nm29ea0+eXGvPnFleTJIk6VmblB2AJLVcX/Fx7LG1GzpMnlwrksouSmbOhDlzas97ekoNRZIkrctCSdL4YFEiSZKGwVPvJEmSJKmBI0qSpNZx9E6SNEY5oiRJkiRJDSyUJEmSJKmBhZIkSZIkNbBQkiRJkqQGFkqSJEmS1MBCSZIkSZIaWChJkiRJUoNSCqWI2DYiroiIW4u/2wyw3LkRcW9E3Nww/fSIuCsiFhSPQ9oTuSRJkqTxoKwRpZOBqzJzT+Cqot2f84A3DzDvS5k5rXhc1oIYJUmSJI1TZRVKhwHnF8/PB97a30KZ+SvggTbFJEmSJElAeYXSDpnZC1D83X4Er/GBiLixOD2v31P3JEmSJGkkIjNb88IRVwI79jPrVOD8zHxB3bIPZuZA1ylNAX6SmS+vm7YDsBJI4NPATpn5ngHWnwXMApgwacK++5y5z4g+T7MtXLmQl233srLDGFfMeftVLeezz1oEwPEn7VVyJM9pdkxVy/l4YM7bz5y3nzlvP3PePvOPnz8/M7sap7esUBpMRCwCZmRmb0TsBPRkZr/fEvorlIYzv15XV1fOmzdv5IE3UVd3F/NmVSOW8cKct1/lcj5jRu1vT0+ZUayryTFVLufjgDlvP3Pefua8/cx5+0REv4VSWafeXQocXTw/GrhkOCsXxVWfvwVuHmhZSZIkSRqusgqlzwFviIhbgTcUbSJi54h49g52EfFd4HfAXhFxZ0QcW8z6fETcFBE3Aq8HPtTe8CVJkiR1sk3KeNPMvB84sJ/pdwOH1LXfOcD6R7UuOkmSJEnjXVkjSpIkSZJUWRZKkiRJktTAQkmSJEmSGlgoSZIkSVIDCyVJkiRJamChJEmSJEkNLJQkSZIkqYGFkiRJkiQ1sFCSJEmSpAYWSpIkSZLUwEJJkiRJkhpYKEmSJElSAwslSZIkSWpgoSRJkiRJDSyUJEmSJKmBhZIkSZIkNbBQkiRJkqQGFkqSJEmS1MBCSZIkSZIaWChJkiRJUgMLJUmSJElqYKEkSZIkSQ0slCRJkiSpgYWSJEmSJDWwUJIkSZKkBhZKkiRJktTAQkmSJEmSGlgoSZIkSVIDCyVJkiRJamChJEmSJEkNSimUImLbiLgiIm4t/m7TzzK7RcQvI2JhRNwSER8czvqSJEmSNFJljSidDFyVmXsCVxXtRk8DJ2Xmy4D9gRMiYu9hrC9JkiRJI1JWoXQYcH7x/HzgrY0LZGZvZt5QPH8UWAjsMtT1JUmSJGmkIjPb/6YRD2XmC+raD2bmgKfPRcQU4FfAyzPzkeGsHxGzgFkAEyZN2HefM/dpzocYpYUrF/Ky7V5Wdhjjijlvv6rlfPZZiwA4/qS9So7kOc2OqWo5Hw/MefuZ8/Yz5+1nzttn/vHz52dmV+P0lhVKEXElsGM/s04Fzh9GobMVcDVwRmb+oJg2rEKrT1dXV86bN29Yn6NVurq7mDerGrGMF+a8/SqX8xkzan97esqMYl1NjqlyOR8HzHn7mfP2M+ftZ87bJyL6LZQ2adUbZuZBgwSzIiJ2yszeiNgJuHeA5TYFLgbm9hVJhSGtL0nrqFKBJEmSKq2sa5QuBY4unh8NXNK4QEQE8E1gYWaePdz1JUmSJGmkyiqUPge8ISJuBd5QtImInSPismKZ1wJHAX8VEQuKxyGDrS9JkiRJzdCyU+8Gk5n3Awf2M/1u4JDi+W+AGM76kiRJktQMZY0oSZIkSVJlWShJkiRJUgMLJUmSJElqYKEkSZIkSQ0slCRJkiSpgYWSJEmSJDWwUJIkSZKkBhZKkiRJktTAQkmSJEmSGlgoSZIkSVIDCyVJkiRJamChJEmSJEkNLJQkSZIkqYGFkiRJkiQ1sFCSJEmSpAYWSpIkSZLUwEJJkiRJkhpsUnYAkjSu9fSUHYEkSeqHI0qSJEmS1MBCSZIkSZIaWChJkiRJUgMLJUmSJElqYKEkSZIkSQ0slCRJkiSpgYWSJEmSJDWwUJIkSZKkBhZKkiRJktTAQkmSJEmSGlgoSZIkSVKDyMyyY2ibiLgPWFp2HIXtgJVlBzHOmPP2M+ftZ87bz5y3nzlvP3Pefua8fSZn5gsbJ46rQqlKImJeZnaVHcd4Ys7bz5y3nzlvP3Pefua8/cx5+5nz8nnqnSRJkiQ1sFCSJEmSpAYWSuXpLjuAccict585bz9z3n7mvP3MefuZ8/Yz5yXzGiVJkiRJauCIkiRJkiQ1sFCSJEmSpAYWSiWIiDdHxKKIWBIRJ5cdz3gQEXdExE0RsSAi5pUdTyeKiHMj4t6IuLlu2rYRcUVE3Fr83abMGDvNADk/PSLuKvr6gog4pMwYO0lE7BYRv4yIhRFxS0R8sJhuP2+RQXJuP2+RiJgQEddFxP8UOf9kMd1+3iKD5Nx+XjKvUWqziNgYWAy8AbgTuB54Z2b+b6mBdbiIuAPoykx/uK1FIuL/AI8B38rMlxfTPg88kJmfKw4KbJOZ/1pmnJ1kgJyfDjyWmV8sM7ZOFBE7ATtl5g0R8TxgPvBW4Bjs5y0xSM7fgf28JSIigC0z87GI2BT4DfBB4O+wn7fEIDl/M/bzUjmi1H77AUsy87bMXANcCBxWckzSqGXmr4AHGiYfBpxfPD+f2hccNckAOVeLZGZvZt5QPH8UWAjsgv28ZQbJuVokax4rmpsWj8R+3jKD5Fwls1Bqv12A5XXtO3Gj3w4J/Dwi5kfErLKDGUd2yMxeqH3hAbYvOZ7x4gMRcWNxap6nx7RAREwBXg38Hvt5WzTkHOznLRMRG0fEAuBe4IrMtJ+32AA5B/t5qSyU2i/6meZRg9Z7bWa+BjgYOKE4ZUnqRF8DXgxMA3qBs0qNpgNFxFbAxcCJmflI2fGMB/3k3H7eQpn5TGZOA3YF9ouIl5ccUscbIOf285JZKLXfncBude1dgbtLimXcyMy7i7/3Aj+kdgqkWm9FcY1B37UG95YcT8fLzBXFDnctMAf7elMV1w9cDMzNzB8Uk+3nLdRfzu3n7ZGZDwE91K6VsZ+3QX3O7efls1Bqv+uBPSNij4jYDDgCuLTkmDpaRGxZXARMRGwJvBG4efC11CSXAkcXz48GLikxlnGh74tM4W+xrzdNccH1N4GFmXl23Sz7eYsMlHP7eetExAsj4gXF8y2Ag4A/Yj9vmYFybj8vn3e9K0Fxe8dzgI2BczPzjHIj6mwR8SJqo0gAmwDfMefNFxHfBWYA2wErgH8DfgRcBOwOLAP+PjO9+UCTDJDzGdRO00jgDuD4vusKNDoR8Trg18BNwNpi8seoXTNjP2+BQXL+TuznLRERr6R2s4aNqR1QvygzPxURk7Cft8QgOf829vNSWShJkiRJUgNPvZMkSZKkBhZKkiRJktTAQkmSJEmSGlgoSZIkSVIDCyVJkiRJamChJElqi4h4rOwYWikiTo+ID5cdhySpOSyUJEkqWdS4T5akCnGjLElqq4iYERFXR8RFEbE4Ij4XETMj4rqIuCkiXlwsd2hE/D4i/hARV0bEDsX0F0bEFRFxQ0TMjoilEbFdMe/I4nUWFPM27uf974iITxbr3xQRLy2mrzMiFBE3R8SU4vHHiPhGMW1uRBwUEb+NiFsjYr+6l39VRPyimH5c3Wt9JCKuj4gbI+KTxbQpEbEwIr4K3ADs1oJ0S5JGyEJJklSGVwEfBF4BHAVMzcz9gG8A/1Qs8xtg/8x8NXAh8NFi+r8Bv8jM1wA/BHYHiIiXAYcDr83MacAzwMwB3n9lsf7XgKGcLvcS4MvAK4GXAv8AvK5Y92N1y70S+GvgAOATEbFzRLwR2BPYD5gG7BsR/6dYfi/gW5n56sxcOoQ4JEltsknZAUiSxqXrM7MXICL+BPy8mH4T8Pri+a7A9yJiJ2Az4PZi+uuAvwXIzJ9GxIPF9AOBfYHrIwJgC+DeAd7/B8Xf+cDfDSHe2zPzpiLeW4CrMjMj4iZgSt1yl2TmE8ATEfFLasXR64A3An8oltmKWuG0DFiamdcO4f0lSW1moSRJKsPquudr69preW7f9P+AszPz0oiYAZxeTI8BXjOA8zPzlGG8/zN17/c0655pMWGY8QJkw/tkEddnM3P2OsFGTAEeH0KskqQSeOqdJKmqtgbuKp4fXTf9N8A7AIrT2rYppl8FvD0iti/mbRsRk4fxfncArynWfQ2wxwhiPiwiJkTEJGAGcD3wM+A9EbFV8dq79MUoSaouR5QkSVV1OvBfEXEXcC3PFS6fBL4bEYcDVwO9wKOZuTIiTgN+XtxB7ingBGCo1/5cDLwrIhZQK3AWjyDm64D/pnbd1Kcz827g7uL6qd8VpwQ+BhxJbTRLklRRkdl4loAkSdUVEZsDz2Tm0xFxAPC14uYNkiQ1jSNKkqSxZnfgomLUaA1w3AaWlyRp2BxRkiRJkqQG3sxBkiRJkhpYKEmSJElSAwslSZIkSWpgoSRJkiRJDSyUJEmSJKnB/wdv6cD+MSJgiAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-013.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090\\MCimage-pBreak090-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "Date and time 2021-06-03 14:49:15.092707\n", "Time since last check is 0:42:12.015372\n", "Time since notebook start is 0:42:19.524900\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import datetime\n", "now = datetime.datetime.now()\n", "print(\"Date and time \",str(now))\n", "#\n", "debug = False\n", "debugHere = False\n", "#\n", "pathDir00 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak000/\"\n", "pathDir01 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak010/\"\n", "pathDir02 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak020/\"\n", "pathDir03 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak030/\"\n", "pathDir04 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak040/\"\n", "pathDir05 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak050/\"\n", "pathDir06 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak060/\"\n", "pathDir07 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak070/\"\n", "pathDir08 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak080/\"\n", "pathDir09 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak090/\"\n", "#\n", "dirList = [pathDir00, pathDir01, pathDir02, pathDir03, pathDir04, pathDir05, pathDir06, pathDir07, pathDir08, pathDir09]\n", "#dirList = [pathDir08, pathDir09]\n", "#dirList = [pathDir06]\n", "print(\" \")\n", "print(\"List of image folders:\")\n", "for folder in dirList:\n", " print(folder)\n", "#\n", "pltX = nCols/200\n", "pltY = nRows/200\n", "#\n", "# Set three thresholds for applying to image (lowest to highest)\n", "thresh = np.array([20, 80, 180])\n", "nThresh = len(thresh)\n", "if nThresh != 3:\n", " print(\" \")\n", " print(\"nThresh =\",nThresh,\" not allowed. Must be 3. Stop program!\")\n", " sys.exit()\n", "\n", "imgThr = np.zeros((nRows, nCols, nThresh))\n", "#\n", "# Requirements on minimum and maximum number of pixels in wheels at the cluster threshold\n", "minClusPixels = 200\n", "maxClusPixels = 200000\n", "#\n", "# Requirements on minimum and maximum number of pixels in wheels at the head threshold(s)\n", "minHeadPixels = 40\n", "maxHeadPixels = 20000\n", "#\n", "print(\" \")\n", "print(\"Cluster ID using Watershed algorithm\")\n", "print(\"Cluster threshold\",thresh[0],\"\\b.\")\n", "print(\"Head low threshold\",thresh[1],\"\\b, high threshold\",thresh[2],\"\\b.\")\n", "print(\"Min number of pixels in cluster\",minClusPixels,\"\\b, max number\",maxClusPixels,\"\\b.\")\n", "print(\"Min number of pixels in head\",minHeadPixels,\"\\b, max number\",maxHeadPixels,\"\\b.\")\n", "#\n", "# Colour table for plots\n", "nColTab = 4\n", "colorTab = ['r', 'orange', 'b', 'c']\n", "#\n", "# Define grid of markers and background marker value:\n", "nStepCols = 3\n", "nStepRows = 3\n", "#\n", "# Ensure rMark + 1 (used later) doesn't cause overflow in number of image rows (i.e. rMark + 1 < nRows)\n", "if nRows%nStepRows > 1:\n", " rMarkTop = nRows\n", "else:\n", " rMarkTop = nRows - nStepRows\n", "rMark, cMark = np.ogrid[0:rMarkTop:nStepRows, 0:nCols:nStepCols]\n", "if debug:\n", " print(\" \")\n", " print(\"rMark.shape\",rMark.shape,\"cMark.shape\",cMark.shape)\n", "#\n", "mStart = 1\n", "markers = np.zeros((nRows, nCols)).astype(int)\n", "markers[rMark, cMark] = rMark/nStepRows + cMark*nRows/(nStepRows*nStepCols) + mStart\n", "if debugHere:\n", " plt.figure(figsize = (pltX, pltY))\n", " plt.imshow(markers, cmap = \"CMRmap\");\n", "#\n", "useDiag = True\n", "widthEx = 9\n", "aUDcut = 0.9\n", "print(\"Width of expansion\",widthEx,\"\\b, useDiag =\",useDiag)\n", "print(\"Up-down asymmetry cut\",aUDcut)\n", "#\n", "maxWheels = 24\n", "#\n", "num_dirs = len(dirList)\n", "#\n", "avgALR = np.zeros(num_dirs)\n", "errALR = np.zeros(num_dirs)\n", "avgAUD = np.zeros(num_dirs)\n", "errAUD = np.zeros(num_dirs)\n", "#\n", "print(\" \")\n", "for nd in range(0, num_dirs):\n", " path = dirList[nd] + \"*.bmp\"\n", " images = (glob.glob(path))\n", " num_images = len(images)\n", " asymmLR = np.zeros(num_images)\n", " asymmUD = np.zeros(num_images)\n", " aErrLR = np.zeros(num_images)\n", " aErrUD = np.zeros(num_images)\n", " print(\"Look for images in directory \\n\",path)\n", " print(\"Number found is\",num_images)\n", " useImage = np.zeros(num_images).astype(bool)\n", " for im in range(0, num_images):\n", " imgRaw = plt.imread(images[im])\n", " print(\"File \" + images[im] + \" read.\")\n", " if debugHere:\n", " plt.figure(figsize = (pltX, pltY))\n", " plt.imshow(imgRaw, origin = 'lower', cmap = plt.get_cmap('afmhot'))\n", " plt.show()\n", " #\n", " # Perform initial image prcsessing (convert to grey and apply hot pixel cut)\n", " withHists = False\n", " imgGrey, nRows, nCols = processImage(imgRaw, hotCut, withHists)\n", " #\n", " # Find wheels using cluster (lowest) threshold\n", " nInClus, rPixCl, cPixCl, iPixCl = findWheels(imgGrey, thresh[0])\n", " #\n", " # Find wheels using low head threshold\n", " nInHdL, rPixHdL, cPixHdL, iPixHdL = findWheels(imgGrey, thresh[1])\n", " #\n", " # Find wheels using high head threshold\n", " nInHdH, rPixHdH, cPixHdH, iPixHdH = findWheels(imgGrey, thresh[2])\n", " #\n", " # Expand clusters\n", " nInClEx, rPixClEx, cPixClEx, iPixClEx, nInClEd, rPixClEd, cPixClEd, iPixClEd = \\\n", " clusExpand(nInClus, rPixCl, cPixCl, widthEx)\n", " #\n", " # Find rims of expanded clusters\n", " nInClExEd, rPixClExEd, cPixClExEd, iPixClExEd = findRims(nInClEx, rPixClEx, cPixClEx)\n", " #\n", " # Find rims of low threshold head\n", " nInHdEdL, rPixHdEdL, cPixHdEdL, iPixHdEdL = findRims(nInHdL, rPixHdL, cPixHdL)\n", " #\n", " # Find rims of high threshold head\n", " nInHdEdH, rPixHdEdH, cPixHdEdH, iPixHdEdH = findRims(nInHdH, rPixHdH, cPixHdH)\n", " #\n", " # Select comets, three threshold analysis\n", " nWheels, pnts_num, pnts_row, pnts_col, mean_row, mean_col, head_num, head_row, head_col = \\\n", " selCometsThree(nInClEx, rPixClEx, cPixClEx, nInClExEd, rPixClExEd, cPixClExEd, \n", " nInHdL, rPixHdL, cPixHdL, nInHdEdL, rPixHdEdL, cPixHdEdL,\n", " nInHdH, rPixHdH, cPixHdH, nInHdEdH, rPixHdEdH, cPixHdEdH) \n", " #\n", " if nWheels > maxWheels:\n", " print(\"Maximum number of wheels exceeded.\")\n", " useImage[im] = False\n", " continue\n", " elif nWheels > 0:\n", " sumULwheel, sumLLwheel, sumURwheel, sumLRwheel, sumULhead, sumLLhead, sumURhead, sumLRhead = \\\n", " calcQuads(nWheels, pnts_num, pnts_row, pnts_col, mean_row, mean_col, head_num, head_row, head_col)\n", " #\n", " sumAllunsel = sumULwheel + sumLLwheel + sumURwheel + sumLRwheel\n", " aUDunsel = (sumULwheel + sumURwheel - sumLLwheel - sumLRwheel)/sumAllunsel\n", " boolAsel = abs(aUDunsel) < aUDcut\n", " nSel = np.sum(boolAsel)\n", " sumAll = sumULwheel[boolAsel] + sumLLwheel[boolAsel] + sumURwheel[boolAsel] + sumLRwheel[boolAsel]\n", " aUD = (sumULwheel[boolAsel] + sumURwheel[boolAsel] - sumLLwheel[boolAsel] - sumLRwheel[boolAsel])/sumAll\n", " aLR = (sumULwheel[boolAsel] + sumLLwheel[boolAsel] - sumURwheel[boolAsel] - sumLRwheel[boolAsel])/sumAll\n", " if nSel > 1:\n", " asymmUD[im] = np.mean(aUD)\n", " asymmLR[im] = np.mean(aLR)\n", " aErrUD[im] = np.std(aUD, ddof = 1)/np.sqrt(nSel)\n", " aErrLR[im] = np.std(aLR, ddof = 1)/np.sqrt(nSel)\n", " useImage[im] = True\n", " #elif nSel > 0:\n", " # asymmUD[im] = np.mean(aUD)\n", " # asymmLR[im] = np.mean(aLR)\n", " # aErrUD[im] = 0.02\n", " # aErrLR[im] = 0.02\n", " # useImage[im] = True\n", " else:\n", " print(\"No asymmetry (or asymmetry error) calculation possible.\")\n", " useImage[im] = False\n", " else:\n", " useImage[im] = False\n", " # End of loop over images\n", " #\n", " avgALR[nd] = np.sum(asymmLR[useImage]/aErrLR[useImage]**2)/np.sum(1/aErrLR[useImage]**2)\n", " errALR[nd] = np.sqrt(1/np.sum(1/aErrLR[useImage]**2))\n", " avgAUD[nd] = np.sum(asymmUD[useImage]/aErrUD[useImage]**2)/np.sum(1/aErrUD[useImage]**2)\n", " errAUD[nd] = np.sqrt(1/np.sum(1/aErrUD[useImage]**2))\n", " #\n", " xPoints = np.linspace(0, num_images - 1, num_images)\n", " yOnes = np.ones(num_images)\n", " #\n", " fig, axs = plt.subplots(2, figsize = (2*pltX, 2*pltY))\n", " axs[0].set_title(\"LR asymmetry\")\n", " axs[0].errorbar(xPoints[useImage], asymmLR[useImage], yerr = aErrLR[useImage], \n", " linestyle = '', color = 'r', marker = 'o')\n", " axs[0].plot(xPoints[useImage], np.zeros(num_images)[useImage], linestyle = \":\", color = 'b')\n", " axs[0].plot(xPoints[useImage], avgALR[nd]*yOnes[useImage], linestyle = '-', color = 'k')\n", " axs[0].plot(xPoints[useImage], (avgALR[nd] + errALR[nd])*yOnes[useImage], linestyle = '--', color = 'k')\n", " axs[0].plot(xPoints[useImage], (avgALR[nd] - errALR[nd])*yOnes[useImage], linestyle = '--', color = 'k')\n", " axs[0].set_xlabel(\"Image number\")\n", " axs[0].set_ylabel(\"Asymmetry\")\n", " axs[0].grid(color = 'g')\n", " #\n", " axs[1].set_title(\"UD asymmetry\")\n", " axs[1].errorbar(xPoints[useImage], asymmUD[useImage], yerr = aErrUD[useImage],\n", " linestyle = '', color = 'r', marker = 'o') \n", " axs[1].plot(xPoints[useImage], np.zeros(num_images)[useImage], linestyle = \":\", color = 'b')\n", " axs[1].plot(xPoints[useImage], avgAUD[nd]*yOnes[useImage], linestyle = '-', color = 'k')\n", " axs[1].plot(xPoints[useImage], (avgAUD[nd] + errAUD[nd])*yOnes[useImage], linestyle = '--', color = 'k')\n", " axs[1].plot(xPoints[useImage], (avgAUD[nd] - errAUD[nd])*yOnes[useImage], linestyle = '--', color = 'k')\n", " axs[1].set_xlabel(\"Image number\")\n", " axs[1].set_ylabel(\"Asymmetry\")\n", " axs[1].grid(color = 'g')\n", " #\n", " plt.tight_layout\n", " plt.show()\n", "#\n", "xPlot = 0.1*np.linspace(0, num_dirs - 1, num_dirs)\n", "yOnes = np.ones(num_dirs)\n", "#\n", "fig, axs = plt.subplots(2, figsize = (2*pltX, 2*pltY))\n", "axs[0].set_title(\"LR asymmetry summary\")\n", "axs[0].errorbar(xPlot, avgALR, yerr = errALR, linestyle = '', color = 'r', marker = 'o')\n", "axs[0].plot(xPlot, np.zeros(num_dirs), linestyle = \":\", color = 'b')\n", "axs[0].set_xlabel(\"p$_{Break}$\")\n", "axs[0].set_ylabel(\"Asymmetry\")\n", "axs[0].grid(color = 'g')\n", "#\n", "axs[1].set_title(\"UD asymmetry summary\")\n", "axs[1].errorbar(xPlot, avgAUD, yerr = errAUD, linestyle = '', color = 'r', marker = 'o') \n", "axs[1].plot(xPlot, np.zeros(num_dirs), linestyle = \":\", color = 'b')\n", "axs[1].set_xlabel(\"p$_{Break}$\")\n", "axs[1].set_ylabel(\"Asymmetry\")\n", "axs[1].grid(color = 'g')\n", "#\n", "then = now\n", "now = datetime.datetime.now()\n", "print(\" \")\n", "print(\"Date and time\",str(now))\n", "print(\"Time since last check is\",str(now - then))\n", "print(\"Time since notebook start is\",str(now - startNB))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit inverse asymmetry\n", "\n", "This is the function used to extract $p_{break}$ from a measured asymmetry. First plot the data (inverting $x$ and $y$ axes)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Date and time 2021-06-03 14:57:27.385009\n", " \n", "Index\t Asymmetry\t Error\t pBreak\t Error\n", "0\t -0.0011\t\t 0.0011\t 0.0000\t 0.0289\n", "1\t -0.0047\t\t 0.0013\t 0.1000\t 0.0289\n", "2\t -0.0205\t\t 0.0013\t 0.2000\t 0.0289\n", "3\t -0.0235\t\t 0.0014\t 0.3000\t 0.0289\n", "4\t -0.0380\t\t 0.0014\t 0.4000\t 0.0289\n", "5\t -0.0629\t\t 0.0019\t 0.5000\t 0.0289\n", "6\t -0.0820\t\t 0.0019\t 0.6000\t 0.0289\n", "7\t -0.0996\t\t 0.0019\t 0.7000\t 0.0289\n", "8\t -0.1171\t\t 0.0026\t 0.8000\t 0.0289\n", "9\t -0.1338\t\t 0.0016\t 0.9000\t 0.0289\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "Date and time 2021-06-03 14:57:27.685301\n", "Time since last check is 0:00:00.300292\n", "Time since notebook start is 0:50:32.117494\n" ] } ], "source": [ "import datetime\n", "now = datetime.datetime.now()\n", "print(\"Date and time \",str(now))\n", "#\n", "nPoints = len(xPlot)\n", "yData = xPlot\n", "xData = avgALR\n", "yError = 0.1/np.sqrt(12)*np.ones(len(xData)) \n", "xError = errALR\n", "print(\" \")\n", "print(\"Index\\t Asymmetry\\t Error\\t pBreak\\t Error\")\n", "for i in range(0, nPoints):\n", " print(f\"{i}\\t {xData[i]:.4f}\\t\\t {xError[i]:.4f}\\t {yData[i]:.4f}\\t {yError[i]:.4f}\")\n", "#\n", "# Plot data\n", "fig = plt.figure(figsize = (8, 6))\n", "plt.title('$p_{break}$ with asymmetry data')\n", "plt.xlabel('Asymmetry')\n", "plt.ylabel('$p_{break}$')\n", "plt.errorbar(xData, yData, xerr = xError, yerr = yError, fmt='r', \\\n", " linestyle = '', label = \"Data\") \n", "#plt.xlim(1.0, 7.0)\n", "#plt.ylim(0.0, 3.0)\n", "plt.grid(color = 'g')\n", "plt.legend()\n", "plt.show()\n", "#\n", "then = now\n", "now = datetime.datetime.now()\n", "print(\" \")\n", "print(\"Date and time\",str(now))\n", "print(\"Time since last check is\",str(now - then))\n", "print(\"Time since notebook start is\",str(now - startNB))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Date and time 2021-06-03 14:57:29.409397\n", " \n", "Fit quality:\n", "chisq per point = \n", " [3.27869 0.19965 0.27531 3.91395 2.81384 0.34016 1.11933 0.71039 0.04573\n", " 0.81908]\n", "chisq = 13.52, chisq/NDF = 1.93.\n", " \n", "Parameters returned by fit:\n", "a = 0.0455 +- 0.0204\n", "b = -8.6912 +- 0.8243\n", "c = -18.7061 +- 6.1060\n", " \n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "Date and time 2021-06-03 14:57:30.109809\n", "Time since last check is 0:00:00.700412\n", "Time since notebook start is 0:50:34.542002\n" ] } ], "source": [ "import datetime\n", "now = datetime.datetime.now()\n", "print(\"Date and time \",str(now))\n", "#\n", "from scipy.optimize import least_squares\n", "#\n", "goodPoints = np.logical_not(np.isnan(avgALR))\n", "yData = xPlot[goodPoints]\n", "xData = avgALR[goodPoints]\n", "yError = 0.1/np.sqrt(12)*np.ones(len(avgALR))[goodPoints] \n", "xError = errALR[goodPoints]\n", "nPoints = len(xPlot[goodPoints])\n", "#\n", "def fitLine(p, x):\n", " '''\n", " Quadratic\n", " '''\n", " f = p[0] + p[1]*x + p[2]*x**2\n", " return f\n", "#\n", "def fitLineDiff(p, x):\n", " '''\n", " Differential of fitLine\n", " '''\n", " df = p[1] + 2*p[2]*x\n", " return df\n", "#\n", "def fitChi(p, x, y, xerr, yerr):\n", " '''\n", " Cost function\n", " '''\n", " e = (y - fitLine(p, x))/(np.sqrt(yerr**2 + fitLineDiff(p, x)**2*xerr**2))\n", " return e\n", "#\n", "def fitLineErr(p, pe, x):\n", " '''\n", " Quadratic function error given errors on fitted parameters\n", " '''\n", " fe = np.sqrt((pe[0])**2 + (x*pe[1])**2 + (x**2*pe[2])**2)\n", " return fe\n", "#\n", "# Set initial values of fit parameters, run fit\n", "pInit = [0.1, -10.0, -20.0]\n", "out = least_squares(fitChi, pInit, args=(xData, yData, xError, yError))\n", "#\n", "fitOK = out.success\n", "#\n", "# Test if fit failed\n", "if not fitOK:\n", " print(\" \")\n", " print(\"Fit failed\")\n", "else:\n", " #\n", " # get output\n", " pFinal = out.x\n", " aVal = pFinal[0]\n", " bVal = pFinal[1]\n", " cVal = pFinal[2]\n", " #\n", " # Calculate chis**2 per point, summed chi**2 and chi**2/NDF\n", " chisqArr = fitChi(pFinal, xData, yData, xError, yError)**2\n", " chisq = np.sum(chisqArr)\n", " NDF = nPoints - 3\n", " redchisq = chisq/NDF\n", "#\n", " np.set_printoptions(precision = 5)\n", " print(\" \")\n", " print(\"Fit quality:\")\n", " print(\"chisq per point = \\n\",chisqArr)\n", " print(\"chisq = {:5.2f}, chisq/NDF = {:5.2f}.\".format(chisq, redchisq))\n", " #\n", " # Compute covariance\n", " jMat = out.jac\n", " jMat2 = np.dot(jMat.T, jMat)\n", " detJmat2 = np.linalg.det(jMat2)\n", " #\n", " if detJmat2 < 1E-32:\n", " print(\"Value of determinat detJmat2\",detJmat2)\n", " print(\"Matrix singular, error calculation failed.\")\n", " print(\" \")\n", " print(\"Parameters returned by fit:\")\n", " print(\"a = {:5.3f}\".format(aVal))\n", " print(\"b = {:5.3f}\".format(bVal))\n", " print(\"c = {:5.3f}\".format(cVal))\n", " print(\" \")\n", " aErr = 0.0\n", " bErr = 0.0\n", " cErr = 0.0\n", " else:\n", " covar = np.linalg.inv(jMat2)\n", " aErr = np.sqrt(covar[0, 0])\n", " bErr = np.sqrt(covar[1, 1])\n", " cErr = np.sqrt(covar[2, 2])\n", " #\n", " print(\" \")\n", " print(\"Parameters returned by fit:\")\n", " print(\"a = {:6.4f} +- {:6.4f}\".format(aVal, aErr))\n", " print(\"b = {:6.4f} +- {:6.4f}\".format(bVal, bErr))\n", " print(\"c = {:6.4f} +- {:6.4f}\".format(cVal, cErr))\n", " print(\" \")\n", " #\n", " # Calculate fitted function values\n", " fitData = fitLine(pFinal, xData)\n", " pError = np.zeros(3)\n", " pError[0] = aErr\n", " pError[1] = bErr\n", " pError[2] = cErr\n", " xErrPlot = np.linspace(-0.14, 0, 50)\n", " fitDataErr = fitLine(pFinal, xErrPlot)\n", " fitErr = fitLineErr(pFinal, pError, xErrPlot)\n", " #\n", " # Plot data\n", " fig = plt.figure(figsize = (8, 6))\n", " plt.title('$p_{break}$ with asymmetry data and fit')\n", " plt.xlabel('Asymmetry')\n", " plt.ylabel('$p_{break}$')\n", " plt.errorbar(xData, yData, xerr = xError, yerr = yError, fmt='r', \\\n", " linestyle = '', label = \"Data\") \n", " plt.plot(xErrPlot, fitDataErr, color = 'b', linestyle = '-', label = \"Fit\")\n", " plt.plot(xErrPlot, fitDataErr + fitErr, color = 'b', linestyle = '--')\n", " plt.plot(xErrPlot, fitDataErr - fitErr, color = 'b', linestyle = '--')\n", " #plt.xlim(1.0, 7.0)\n", " #plt.ylim(0.0, 3.0)\n", " plt.grid(color = 'g')\n", " plt.legend()\n", " plt.show()\n", "#\n", "then = now\n", "now = datetime.datetime.now()\n", "print(\" \")\n", "print(\"Date and time\",str(now))\n", "print(\"Time since last check is\",str(now - then))\n", "print(\"Time since notebook start is\",str(now - startNB))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Determine asymmetry test images\n", "\n", "Use parameters set in above cells for image analysis so above function is appropriate!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Date and time 2021-05-07 13:12:31.812337\n", " \n", "Cluster ID using Watershed algorithm\n", "Cluster threshold 20.\n", "Head low threshold 80, high threshold 180.\n", "Min number of pixels in cluster 200, max number 200000.\n", "Min number of pixels in head 40, max number 20000.\n", "Width of expansion 9, useDiag = True\n", "Up-down asymmetry cut 0.9\n", " \n", "List of image folders:\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075/\n", "C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085/\n", " \n", "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-034.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045\\MCimage-pBreak045-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-012.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055\\MCimage-pBreak055-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-019.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-036.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-037.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065\\MCimage-pBreak065-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075/*.bmp\n", "Number found is 39\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-034.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-036.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075\\MCimage-pBreak075-039.bmp read.\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Look for images in directory \n", " C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085/*.bmp\n", "Number found is 40\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-000.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-001.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-002.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-003.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-004.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-005.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-006.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-007.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-008.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-009.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-010.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-011.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-012.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-013.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-014.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-015.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-016.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-017.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-018.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-019.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-020.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-021.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-022.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-023.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-024.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-025.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-026.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-027.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-028.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-029.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-030.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-031.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-032.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-033.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-034.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-035.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-036.bmp read.\n", "No asymmetry (or asymmetry error) calculation possible.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-037.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-038.bmp read.\n", "File C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085\\MCimage-pBreak085-039.bmp read.\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0oAAAJyCAYAAAD6hDCZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABqGklEQVR4nO3de5hdVXn48e9LuISAIBK5BhKUi4hihClCtXUsqEBFbLWKBhSlTWyxlYq1ChZvBa0VxVbFBLWgRJEfaEWLN9CgVaMkmHIxBBAIAQIxXAKTCbm+vz/2HnKyM5PM5dzmzPfzPOeZWftyzjtr1tnnvHutvXZkJpIkSZKkjbZpdQCSJEmS1G5MlCRJkiSpwkRJkiRJkipMlCRJkiSpwkRJkiRJkipMlCRJkiSpwkRJkiRJkipMlCRJdRMR90bEcf0s746IDRHRExFPRsSiiHh7K2JshfLvv7/VcUiSBs9ESZLULA9m5s7ALsA/ApdExCEtjqltRMS2rY5BkrSRiZIkqamycC3wKHD4QNtFxP+LiIciYkVE/CwiDqtZd2JE/K7snXogIt5bLr81Ik6q2W67iFgeEVMjYkpEZES8PSKWRMRjEfHOiPijiLg5Ih6PiM/V7Ht6RPwiIj5Trrs7Iv64XL4kIpZFxNtqtt8hIj4VEfdFxMMR8cWI2DEidgK+D+xT9qj1RMQ+EfHhiLgqIi6PiCeA90dEb0TsXvOcR0bEHyJiuzpVvyRpkEyUJElNFRHbRMRrgYnAXVvY9PvAQcAewE3A7Jp1XwZmZOYzgBcAPymXfxU4tWa7E4GlmbmgZtlLyud9E3ARcC5wHHAY8MaIeHll25uB3YGvA1cAfwQcWL7O5yJi53LbfwMOBqaW6/cFzsvMlcAJlD1q5ePBcp+TgauAZwIXAnOAN9a8/qnAFZm5dgv1JElqABMlSVKz7BMRjwOrgG8D78nM3w60cWZ+JTOfzMzVwIeBF0XEruXqtcDzI2KXzHwsM28ql18OnBgRu5Tl04CvVZ76Y5n5VGb+CFgJfCMzl2XmA8DPgRfXbHtPZv5XZq4HvgnsB3w0M1eX+68BDoyIAP4G+MfMfDQznwQuAE7ZSp38KjP/OzM3ZOYq4DLKRC8ixgFv7id+SVITmChJkprlwcx8JsU1Sv8B/NlAG0bEuIj4RET8vhyWdm+5amL58/UUvUWLI+KGiDgGoOyp+QXw+oh4JkVPzmw29XDN76v6Ke+8hW3JzP62fzYwAZhfDtN7HPhBuXxLllTK36FIAJ8DvBJYkZm/2cpzSJIawAtHJUlNlZmrI+KfgUUR8brM/O9+NnsLxbC04yiSpF2Bx4Aon+NG4OTy2p13AVdS9PZA0Svz1xSfcb8qe4oabTlF0nTYAK+XA+y3yfLMfCoirgSmAc/D3iRJahl7lCRJ9bZdRIyveWx2Ui4z11Bck3PeAM/xDGA18AhFT80FfSsiYvuImBYRu5bX7jwBrK/Z97+BI4B3U1yz1HCZuQG4BPhMROxRxrlvRLy63ORhYPeaoYNb8lXgdOC1FEMJJUktYKIkSaq3ayl6V/oeHx5gu68A+9fOUlfjq8Bi4AHgd8DcyvrTgHvLYXnvpGYCh/Jan6uBA4BvDfuvGLp/ppicYm4Z13XAIWVMtwPfAO4uh+btM9CTZOYvgA3ATZl5b8OjliT1KzIHGg0gSdLoFBHnAQdn5qlb3bgNRcRPgK9n5pdaHYskjVVeoyRJ6igR8SzgDIpep1EnIv6IYujgya2ORZLGMofeSZI6RkT8DcVMct/PzJ+1Op6hiojLKIbsnVVOMS5JahGH3kmSJElShT1KkiRJklQxpq5RmjhxYk6ZMqXVYQBw56N3ctCzDmp1GGOCdd0c1nPzWNfNYT03j3XdHNZz81jXzVGvep4/f/7yzNzsBuFjKlGaMmUK8+bNa3UYAHTN6mLe9PaIpdNZ181hPTePdd0c1nPzWNfNYT03j3XdHPWq54hY3N9yh95JkiRJUoWJkiRJkiRVmChJkiRJUoWJkiRJkiRVmChJkiRJUoWJkiRJkiRVmChJkiRJUkVLE6WIOD4iFkXEXRHx/n7WR0T8R7n+5og4YrD7SpIkSdJwtSxRiohxwOeBE4DnA2+OiOdXNjsBOKh8TAcuHsK+kiRJkjQsrexROgq4KzPvzsw1wBXAyZVtTga+moW5wDMjYu9B7tvWFl04k0svLX5fuxa6u+Hyy4tyb29R/uY3i/KKFUX5W98qysuXF+XvfrcoP/RQUf7BD4rykiVF+brrivLddxflG24oX3tRUf7lL4vyrbcW5RtvLMoLFhTlBQuK8o03FuVbby3Kv/xlUV60qCjfcENRvvvuonzddUV5yZKi/IMfFOWHHirK3/1uUV6+vCh/61tFecWKovzNbxbl3t6ifPnlRXnt2qJ86aVFuc8ll8Bxx20sf+ELcMIJG8sPX38Kr33txvKnPgWvf/3G8ic+AaecsrH8sY/BqaduLJ93Hrz97RvLH/gATJ++sfze98KZZ24sn3VW8ehz5pnFNn2mTy+eo8/b3168Rp9TTy1i6HPKKUWMfV7/+uJv6PPa18JnP7uxfMIJRR30Oe64oo76dHdj22tS2/vsZ7HtjeK2t+jCmba9UqPb3j1f/qhtb0FR9rjncW+stL3RIDKzNS8c8Qbg+Mz867J8GvCSzHxXzTbfAz6Rmf9blq8H/hmYsrV9a55jOkVvFON3H3/kYRcc1tC/a7B++4lPs9+f/oSJf/w9cv047rjoC0x86X+z+9HfZ8OaHbjzP/+DZ//pVTzrj37M+lU7cdcXPs0er7iC3Y74Ket6duX3Mz/JnsddzjNf9HPWrtidu790AXu9+lJ2fcGvWPPontzzXx9l7xO/zC6H/obVf9iXe796HvucNJNnHHwTTz00mcWzz2Hf132enZ97M6seeC73XfE+Jr3+s+w05Xf0LjmYJVeezX5vvJAJ+93Bynufz/1Xv5v9T/kkO+77e3p+fzgP/PeZTJ52AeP3WsyTdxzBg9+dwZS3fpQdnv0ATyw8iqXXnsEBbz+P7Z/1MCtuPYaHfng6z/nrc9hu10d4/P/+hIevO5Xnzngf2+68gsduegXLfnoKB/7dexi340oevfGV/OFnb+Cgv/8Httl+NY/MPYHlv3gdB5/1d8S49Sz/5Wt45FcnccjZMwD4w89fx2PzXsnB/1gcOZfNeQMrbv4TDvqHdwNw8zWvYMKSkzjwzPcA8NCPTmXl3Yfz3He+ryj/4G30LjmE5/zNOQAs/Z8zeOqhyRxwRnEkffCaGax5dE+mnP5RAB749pms69mVyaddAMD9V72bDWt2YP+3fBKAJd8sXme/N30agPu+/j622X41k95QHFkXf+0ctt15Bfv+xecBuPfSop72ee1MoPjCMH6vxez9518G4O5LLmDCfovY6/jLAPj9Fz/JTs+5mb1eVRxp7/r8p3nG837DnsdeAcCd//FZdj385+zRfRUAd3zm8+zW9WOe/Sf/DRRf/nY/5rt1b3sP7vUVnrtNt22vpu09fP0pPHn7UXVve70nnMahEw+17TX4uHfHFX/N8077vG2vCce9BV94L3tOfsy21+Dj3p1PzWWv+/7OtteE497yQz/OoRMPte1t4bhXDwuXL+TQiYeO+Hnmz5g/PzO7qstbmSj9FfDqSrJzVGb+fc02/wN8vJIovQ94ztb27U9XV1fOmzevIX/PUHXN6mLe9PaIpdNZ181hPTePdd0c1nPzWNfNYT03j3XdHPWq54joN1HadsTPPHz3A/vVlCcBDw5ym+0Hsa8kSZIkDUsrr1G6ETgoIg6IiO2BU4BrKttcA7y1nP3uaGBFZi4d5L6SJEmSNCwt61HKzHUR8S7gh8A44CuZeVtEvLNc/0XgWuBE4C6gF3j7lvZtwZ8hSZIkqQO1cugdmXktRTJUu+yLNb8ncGZ1v4H2lSRJkqR6aOkNZyVJkiSpHZkoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZI0GnR3Fw9JUlOYKEmSJElShYmSJEmSJFWYKEmSJNWbQyWlUc9ESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqWpIoRcSzIuLHEXFn+XO3AbY7PiIWRcRdEfH+muUfjogHImJB+TixedFLkiRJ6nSt6lF6P3B9Zh4EXF+WNxER44DPAycAzwfeHBHPr9nkM5k5tXxc24ygJUmSJI0NrUqUTgYuK3+/DHhdP9scBdyVmXdn5hrginI/SZIkSWqobVv0untm5lKAzFwaEXv0s82+wJKa8v3AS2rK74qItwLzgLMz87H+XigipgPTAcbvPp6uWV31iH/EFi5f2DaxdDrrujms5+axrpuj3ep55tJFAMxoo5jqpd3quh7a8f/VifXcrqzr5mh0PUdmNuaJI64D9upn1bnAZZn5zJptH8vMTa5Tioi/Al6dmX9dlk8DjsrMv4+IPYHlQAIfA/bOzHdsLaaurq6cN2/ecP+kuuqa1cW86e0RS6ezrpvDem4e67o52q6eu7uLn3PmtDKKhmi7uq6HNvx/dWQ9tynrujnqVc8RMT8zN8u4GtajlJnHbSGYhyNi77I3aW9gWT+b3Q/sV1OeBDxYPvfDNc91CfC9+kQtSZIkSa27Ruka4G3l728DvtPPNjcCB0XEARGxPXBKuR9lctXnL4BbGxirJEmSpDGmVdcofQK4MiLOAO4D/gogIvYBvpSZJ2bmuoh4F/BDYBzwlcy8rdz/kxExlWLo3b3AjCbHL0mSJKmDtSRRysxHgGP7Wf4gcGJN+Vpgs6m/M/O0hgYoSZIkaUxr1dA7SZIkSWpbJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEntqru7eEhqOhMlSZIkSaowUZIkSZKkChMlSZIkSaowUZIkSZKkChMlSZIkSaowUZIkSZKkChMlSZIkSaowUZIkqd3Nng1z58INN8CUKUVZktRQJkqSpM7RiTfnnD0bpk+H1auL8uLFRdlkSZIaykRJ7akTv+xI0nCcey709m66rLe3WC5JahgTJUmS2tl99w1tuTqHQy6lljJRkiSpne2//9CWqzM45FJqORMlSZLa2fnnw4QJmy6bMKFYrs7lkEup5UyUJElqZ9OmwaxZsMMORXny5KI8bVpr41JjOeRSarltWx2AJEnaimnT4JJLit/nzGlpKGqS/fcvhtv1t1xSU9ijJEmS1G4ccim1nImSJElSu3HIpdRyDr2TJEmqp75pvVevLqb1Pv/84SU4DrlUJ+q7T+YoaNNb7VGKiHkRcWZE7NaMgCRJkkYtp/WWOsZght6dAuwD3BgRV0TEqyMiGhyXpLGou3vjmSZJGo2c1lvqGFtNlDLzrsw8FzgY+DrwFeC+iPhIRDyr0QFKkiSNGk7rLXWMQU3mEBGHAxcC/w5cDbwBeAL4SeNCkyRtprubmRcuanUUkgYy0PTdTustjTqDuUZpPvAZ4Ebg8Mz8h8z8dWZeCNzd6AAlSZJGDaf1ljrGFme9i4htgKsz84L+1mfmXzYkKkmSpNGob3a7M84oJnSYPHn4s95JaqktJkqZuSEijgf6TZQkSZJU4bTeUkcYzDVKP46I90bEfhHxrL5HwyOTJEmSpBYZzA1n31H+PLNmWQLPqX84kiRJkupmFN3gtd0MJlE6NDOfql0QEeMbFI8kSZL6+OVWapnBDL375SCXSZLUOrNnw9y5cMMNMGVKUZYkaZgG7FGKiL2AfYEdI+LFQJSrdgEmDLSfBqG7m5lLF8H0VgciSW1ipENDZs+G6dOLWcYAFi8uyuBsY5KkYdnS0LtXA6cDk4BP1yx/AjingTFJkjQ0554Lvb2bLuvtLZabKI0NXochqc4GTJQy8zLgsoh4fWZe3cSYJEkamvvuG9pySZK2YjDXKP0iIr4cEd8HiIjnR8QZDY5LY5nXGUgaqv33H9pySZK2YjCJ0n8BPwT2Kct3AGc1KiC1QHf3xiELrTbQdQadkiy1U11LneT882FC5fLZCROK5ZIkDcNgEqWJmXklsAEgM9cB60fyouVNa38cEXeWP3cbYLuvRMSyiLh1OPtrFNrSdQaSNJBp02DWLNhhh6I8eXJR9vqkxvHEj6QON5hEaWVE7E5xk1ki4mhgxQhf9/3A9Zl5EHB9We7PpcDxI9hfo43XGUgarmnT4Oij4eUvh3vvNUmSpHYzyi6vGEyi9B7gGuC5EfEL4KvA34/wdU8GLit/vwx4XX8bZebPgEeHu79GIa8zkCRJ6jyj8PKKLU0PDkBm3hQRLwcOobiX0qLMXDvC190zM5eWz780IvZo1P4RMZ3yjkXjdx9P16yu4cZcNzOXLmLl2t62iAWKeABmtEE8rz42+ODlwY5r8ullq7YP/vXY4IfDjG/h8oXWdYPNvHARn17bSxcj+7s6tX7q5dW/foTzfrGYI9YlS3ffgc+9bh9++JLdWx1WXdTrf1/PNtROxw5ov/dHu9V1J9dPvbRbm+5k7VTX7dIWv/uBW9i7d82mC3t7WfoP7+CklZ8Z1nM2up4jM7e8QcQ44M+BKdQkVpn56YH2Kfe7Dtirn1XnApdl5jNrtn0sMwe6TmkK8L3MfEHNsscHu3+trq6unDdv3tY2a7zubuYvnc+Ri55sdSSFdrv3xOzZcMYZxRmHyZOLi7FHMISma1YX86a3wf8d2q+u66VebbpT66ce+s7E1V7DN2FC51yHU6//fR3bUFsdO6D93h/tVtcdXD/10nZtuoO1VV23S1vcZhvoL++IgA0bhvWU9arniJifmZtlXFvtUQK+CzwF3EI5ocNgZOZxWwjm4YjYu+wN2htYNtjnLY10f7WzadPgkkuK31v9pu507XLw1NZ5Q1VJ0mi2//7FcLv+lrepwVyjNCkz/zIzP5SZH+l7jPB1rwHeVv7+NuA7Td5fY0V3NzMvXNTqKKSRzxDmRCejkzPDNccou0BcGpNG4W0cBpMofT8iXlXn1/0E8MqIuBN4ZVkmIvaJiGv7NoqIbwC/Ag6JiPtrbnTb7/6SRjG/6GyZE51I/RuFF4hLY9IovI3DYIbezQW+HRHbAGspJnTIzNxluC+amY8Ax/az/EHgxJrym4eyv6RRaqAvOtDWB9CmOv/8/q9RauMzcepgfSc2Vq8uTmyM8FrSEXFYqjR6jLLLKwbTo3QhcAwwITN3ycxnjCRJkqTNeKPhras5E5cwKs7EqUO1Ww+Ow1IlNchgEqU7gVtza9PjSdJw+UVncMobqt508M7eUFWt024nNhyWOnp5Dd/oMgb/X4MZercUmBMR3wdW9y3c2vTgkjRoo3AmHGnMarcTGw5LldQgg+lRuge4HtgeeEbNQ5KevlbhiDt6hj8JwyicCUcas9qtB2cUXiAuaXTYao9SHaYCl9Spaq5VCBj+JAx929bxRsOSGqQde3BG2QXikkaHrSZKEdEFnAtMrt0+Mw9vYFySRoN6zjbVrl90vCmvtClPbEgaIwZzjdJs4J+AW4ANjQ1HalN+We5fu12rIKk52vXEhiTV0WCuUfpDZl6Tmfdk5uK+R8Mjk0aqHtfOaMva7VoFSaNPdzczL1zU6ig0FGNw9jONTYNJlD4UEV+KiDdHxF/2PRoemTQSA107Y7JUX07CIKnTmARIKg0mUXo7MBU4HjipfLymgTF1Nns5mqPd7vMBG+9kf8MNnfO/9yao0ugye3Zx/Nlmm845DtVTJx6nJQ3bYK5RelFmvrDhkYwF9ZohTFvXbtfODHQnexj9//vyWoWbls7nyEX3tjoaSQPpOw71nUTqpONQPXTycbod9SWlq1cXSakTgqgNRWZueYOIS4DPZObvmhNS43R1deW8efNaF8CUKf3eVPO+CA7fZRee/exns++++7J+/XpuueWWzbbba6+92GuvvVi7di233XbbZuv32Wcf9thjD5566iluv/32zdZPmjSJiRMn0tvbyx133AHAG9as4bxVq5gEPLTddnz5uc/lOzvtxF133bXZ/gcccAC77rorK1as4J577tls/YEHHsjOO+/MY489xuJ+/s6DDz6YCRMmsHz5cu6///7N1j/vec9j/PjxLFu2jAfL+Nh556fXH3bYYWy33XY89NBDPPTQQ5vt/8IXvpBx48bxwAMPcP3vf8/+/bTth3bYgZfuuy+PPPLIJsu32WYbDj+8mMhx8eLFPPbYY5us327lSg7baSeYOpW7776bJ554YpP1O+ywA4ceeigAd911Fz09PZusv+XJJ9lvw+ZzofT973feeWcOPPBAABYuXMjq1as32W6XXXbhOc95DgC33XYba9eu3WT9brvtxuTJkwG4+eab2VB5rd1335399tsPgAULFmxcUcb57AMOGFnbW7OGHVnNdof/0aDbXq3Jkyez22670TN/PnetWrXJ/x2a3PYefHDTlT09HDZhAtsdccSg2t4f/vCHzdZPnToVFixgyerVPNJ3r5fSVtvedttx2GGHARRt78EHWZ/rGfeMXYGtt70JEyZw8MEHA3DHHXfQW+lpbVnbKz193Pvtb7ll5crN/veNOO7Verrt9fRsdtx7cs2THH7I4SNre+vXM2HcOJZPmjT0tkfluNcXX00dDartAUuWLOGn99wz4HHxmL333nrbqx73ens5dMIEmDp1ZG1vwQJu7lnBhp133WT9kNteX3xl/Qy67ZXHvZufeGLA+nne+PGbLd9q21u7lonbbUfvwQcPue1BY457T655kmdsX9wKc0htr3rc6+nhhTvtxLgXv3hQba/6mftXa9fyn089xfia40Uv8A877shV22+/9bY3Co57y7ZdxpF7HzmotldV9+NeX/2U741ht73yeQ6cOnVkn7kbNnD0LrtwUT/rhqprVhfzpo/8u31EzM/MrurywfQovQx4W0TcA6wGAkinBx+GAXozJmXS09PD9ttv//Qbp/qmB1ixYsXTb5z+1j/xxBPssccerF69ut/1Tz75JBMnTmTVqlX09PRwyoYNfDaTncr1+6xdyz/dcQdP7LUXC/rZf+XKley6666sXLmy3+fv7e1l55135sknn+x3/apVq5gwYcKA61evXs348eN54okn6Fm/vlhYs93atWvZbrvtWLFiRb/7r1+/nnHjxrFixQrOAWbC038bwFPbbMMlBxzA48uWbbZ/RDz9++OPP77Z+nE1B8HHH398s4PuU089tcn62jLAvv0kSbDxf79u3bqnl61YsWKTMrDJQfiJJ55gfV/91MTfd9B+8sknqZ4AGTdu3NNfGDb528rn2X7FipG1vW22YcdtGXTb62/9brvtRu/69cX/vrJNU9tedf369azdsIHtynrYWtvrb32fx9eto6fygbvVtjdu3Cbre9evJ4Eot9ta21uzZs0m62vLQOvaXunp415mv//7eh/3+lu/22670dvbu9n6Dblh5G1vhx2YMG7c8NoeleNeP8fFQbe9xx9n0gAnRvdYvXpwba963KscF0fS9lZugPXV+h9q26vUz6DbXnnc21L99FTeF30xbbHtjRvHxO22G1bbg8Yc9zbkBnrWFL8Pqe31c1xcn8k4Bj4u9umvbX1w/XqqqecE4F9WreLSNWu23vZGwXGPicXvg2l7VXU/7lXeG8Nue+XzjPgzt58TD+1qMInS8Q2PYqzYf/9+e5SW7bADLzv6aN74xjfyd3/3d/T29nLiiSdutt3pp5/O6aefzvLly3nDG96w2fq//du/5U1vehNLlizhtNNO22z92WefzUknncSiRYuYMWMGn5o7l50qZ1HGb9jA+Znc+LKXbbb/BRdcwB//8R/zy1/+knPOOWez9RdddBFTp07luuuu41//9V83Wz9z5kwOOeQQvvvd73LhhRdutv5rX/sa++23H9/85je5eMaMYmF5RgrgqquuYuLEiVx66aVceumlm+1/7bXXMmHCBL7whS9w5ZVX8umHH+ZvFi1ij0yW7bADe335y/zLtGns+KlP8b3vfW+TfXfccUe+//3vA/Cxj32M66+/fpP1u992G1cfdhjMmcMHPvABfvWrX22yftKkSVx++eUAnHXWWZudQXps/nx27+dg0fe/nzp1KhdddBEAp5566mZnYI455hg+/vGPA/D6179+s7Nzxx57LP/yL/8CwAknnMCqVas2Wf+a17yG9773vQB0116kXMb5xg9/eGRtb9kyDly/hD2uuWZQba/qgx/8IMcddxwLuro46667Nvm/Q5Pb3sUXb7pywQKuOuwwJs6ZM+i2VzVnzhzo7uZTS5bwvfKLW5+ttr3dd+fqq68GKNrexRcXZ4WPKt6jW2t7Bx98MLNmzQJg+vTpm53ZHnHbW7yYfyn+yKG1vdLTx70/+RNOvOWWzf739T7uVT3d9hYs4Kyzztpk3fyl87n0vy4dWdvr7eWQCRP47tlnD73tUTnu9cVXU0eDanvApz71KZZ98IPsVTnmA2wzeTLvOeOMrbe9IR73Bt32urs58aZf0HvESzdZ37DjXqn6mbts7tx+6ycnTeJlz33uZsu32vaWL+ekiRNZNHPmkNseNOa4N3/pfI7c+0hgiG2vetxbsIBrX/hCJsyZM6i2V/3M3e+GGzbbHmA/4GUve1nz2h6N+8y9YpcrmDN9zqDaXlXdj3t99VMeO4bd9srnuejrXx/ZZ+5TT7HfaEmWMnOLD+BTwPO3tt1oeBx55JHZUpdfnjlhQiZsfEyYUCxvhYhNY+l7RLQmnlovf3nxqMPzzDt457o8z4jiabf/fZ9Oq+dGqGMdtVVd10s9/q7LL8/cYYfifTF5cuvfF6UjZ47wM6Pef1cHH4faok3Xu37a8Hg24jbdZ6R/2+TJ/X//mDy5PvG1gbrVdT204edYvd4b9apnYF72kzsMZta724FLIuLXEfHOiNh1q3uof+02Q5j3wGmemv890Pr/vdQuBrqAfrTPNtaOf1ffcWjyZIjwOFTlcbp5vLWERomtJkqZ+aXMfCnwVmAKcHNEfD0iXtHo4DrStGlw9NHcdPDOcO+9rT0Ae6BqrvJ/z8tf3vr/PTgN7mg1Zw4zzj6k1VHUTztO5V8Pjfi75swpHiMxbVpx/NmwoT2OQ+1m2jR46qmif8P6aZx6J6Xe+2p0qcexrEkG06NERIwDnlc+lgP/B7wnIq5oYGxqNM+ejV3teLa73vzgHB3abSr/eunUv0uql3Y7edipPCk6IltNlCLi0xTD704ELsjMIzPz3zLzJODFjQ5QDdauB6pRdLZhVKr32e5O6+WoNz+oBtauQ4C7u5l54aLh79+uf5eksWMsnBRtsMH0KN1KcdPZGZn5m8q6oxoQk6RG82x387TrB1W79Lh16hDgTv27JG3dSE+01EunDm1uosEkSoso7p1ERJwaEZ+OiMkAmbmikcFplJk9G8aPLy4S9qx5Y430S65nu5vHD6ot69QhwJ36d0kaPTwpOmKDSZQuBnoj4kXA+4DFwFcbGpVGn3Y9a14PnThsyrPdzdPJH1T1em+06xDgkerUv6ueyjZ0xB09nXN8ldqFJ0VHbDCJ0rpyfvGTgc9m5meBZzQ2LI06nXrWvFMTQM92N0+nflB16ntDzVPThgJsQ1K9eVJ0xAaTKD0ZER8ATgP+p5wBb9vGhqVRp13Pmo90koFOTQDBs93N0qkfVJ383lBz2IakxvKk6IgNJlF6E7AaeHtmPgS8FNipoVFp9OnUs+btmAB24lDATtapH1Tt+N7Q6GIbkhqvXidFx+h3j8HccPYh4CfAayPiXuAjwEWNDUujTqeeNW+3BLDThzu127TwXoMzsHZ7b2j0sQ1Jo0Onf/fYggETpYg4OCLOi4iFwOeAJUBk5isy8z+bFqFGh76z5pMnF7PedcpZ83ZLAB2q0jxj+INhUNrtvaHRxzakdjs5pv6N4e8eW+pRuh04FjgpM19WJkfrmxOWRqVp04qz5Rs2dM5Z83YbNuVQleYZwx8Mg9Ju7w2NPjVtKME2JLWrMfzdY0uTMrweOAX4aUT8ALiC8n5K0pgybRpccknxe6vPfO2/f9Gz0d9y1dcY/mAYtHZ6b2h0KtvQTUvnc+Sie1sdjaT+jOHvHgP2KGXmtzPzTcDzgDnAPwJ7RsTFEfGqJsUnqZZDVZrH6yckSRrT3z0GM5nDysycnZmvASYBC4D3NzowSf1wuFPzjOEPBkka0Bid/WxMG8PfPYZ0P6TMfBSYWT4ktYLDnZqj7wPgjDOKCR0mTy6SpDHwwSBJ/Rpokhvw2Njpxuh3j8HcR0mSxqZOnNZbkobLSW40xgypR0nSCNXrLMwYOpsjSWoTTnKjMcZESZIkqd468YTWGJ79TGOTQ+8kScPnDSOlsaMdJ7lxcgk1kD1KkiQ1ikmkOkm7TXLj5BJqMHuUWmHOHGacfUiro5AkSRqadprkxskl1GD2KMkznpIkafRxcgk1mD1KkiRJGn0GmkTCySVUJyZKo1l3d/GQJEkaa9pxcgl1FBMlSZIkjT7TpsGsWbDDDkV58uSi7EQOqhOvUZIGo1Ov4+rUv0vS6DFnDjNmdTGv1XF0uu5uZi5dBNNbHUidTZsGl1xS/O5nmurMHiVJkiRJqrBHSZKawTOdkiSNKvYoSZIkSVJFSxKliHhWRPw4Iu4sf+42wHZfiYhlEXFrZfmHI+KBiFhQPk5sTuSSpI43ezbMncsRd/TAlClFWZI05rSqR+n9wPWZeRBwfVnuz6XA8QOs+0xmTi0f1zYgRklSs8yZ0x7DE2fPhunTYfVqAmDx4qJssiRJY06rEqWTgcvK3y8DXtffRpn5M+DRJsUkSWND2WPCDTfYY1J17rnQ27vpst7eYrkkaUxp1WQOe2bmUoDMXBoRewzjOd4VEW8F5gFnZ+Zj/W0UEdMpJ8Mcv/t4umZ1DTfmulq4fOGIY5m5dBEAM9rkb2pX9ahrbV271XO93h/t+D4bSV2/+teP8MHLF7PjmiwWLF7Mqnecxr/+5IP88CW71zHK0ek3ixf3ewZxw+LFHNVGbaDTtNvxoxPNXLqIlWt761LP7XZcbMd46lXX9eDn4fBFZjbmiSOuA/bqZ9W5wGWZ+cyabR/LzIGuU5oCfC8zX1CzbE9gOZDAx4C9M/MdW4upq6sr581rjzs1dM3qYt70EcbS3V38bIfhKm2sLnWtrWq7eu7g98eI6nrKlGI4WdXkyXDvvSMJqzNYPy3RdsePTtTdzfyl8zly0ZN1eS6gfY6vbRhP3eq6HupVP+1Wz9Tv2BER8zNzs4yrYT1KmXncFoJ5OCL2LnuT9gaWDfG5H655rkuA7w0/UkkaQ+67b2jLx5rzzy+uSaodfjdhQrFckjSmtOoapWuAt5W/vw34zlB2LpOrPn8B3DrQtpKkGvvvP7TlY820aTBrFuywAwlFT9KsWcVyabRyJkdpWFqVKH0CeGVE3Am8siwTEftExNMz2EXEN4BfAYdExP0RcUa56pMRcUtE3Ay8AvjH5oYvSaPU+ecXPSS17DHZ1LRpcPTR3HTwzsVwO5MkjWbO5NhcJqUdpSWTOWTmI8Cx/Sx/EDixpvzmAfY/rXHRSVIH6/vSf8YZsHp10WNy/vkmA1Kn2tJMjr7v62ugpBQ6o67b6NqkZmlVj5IkqVXKHhNe/nJ7TKRO53WJzePtBTqOiZIkSVKn8rrE5jEp7TgmSpIkSZ3K6xKbx6S045goSZIkdSpncmyedk1K58wZk9cX1UNLJnOQJElSk0ybBpdcwk1L53PkontH/nzt9qW7XeKpmSwnV68mnCxn1DNRkiRJkuqh3kmpWsqhd5IkSZJUYaIkSZIkSRUmSpIkSZJUYaIkqfPMng1z58INN8CUKUVZkiRpCEyUJHWW2bNh+nRYvbooL15clE2WJEnSEJgoSeos554Lvb2bLuvtLZZLkiQNkomSpM5y331DWy5JktQPEyVJnWX//Ye2XJIkqR8mSpI6y/nnw4QJmy6bMKFYLklj1Zw5zDj7kFZHIY0qJkqSOsu0aTBrFuywQ1GePLkoT5vW2rgkSdKosm2rA5Ckups2DS65pPh9zpyWhiJJkkYne5RGK+8TI0mSJDWMidJo5H1iJEmSpIYyURqNvE+MJEmS1FAmSqOR94mRJEmSGspEaTTyPjGSJElSQ5kojUbeJ0aSJElqKBOl0cj7xEiSJEkN5X2URivvEyNJkiQ1jD1KkiRJklRhoiRJUtWcOcw4+5BWRyFJaiGH3knSWOSQXUmStsgeJUmSJEmqMFGSJEmSpAoTJUmSJEmqMFGSJEmSpAoTJUmSJEmqMFGSJEmSpAoTJUmSJEmqMFGSJEmSpAoTJUmSJEmqMFGSJEmSpAoTJUmSJEmqMFGSJEmS6mXOHGacfUiro1AdmChJkiRJUoWJkiRJkiRVmChJkiRJUoWJkiRJkiRVmChJkiRJUoWJkiRJkiRVtCRRiohnRcSPI+LO8udu/WyzX0T8NCIWRsRtEfHuoewvSZIkScPVqh6l9wPXZ+ZBwPVluWodcHZmHgocDZwZEc8fwv6SJEmSNCytSpROBi4rf78MeF11g8xcmpk3lb8/CSwE9h3s/pIkSZI0XJGZzX/RiMcz85k15ccyc8DhcxExBfgZ8ILMfGIo+0fEdGA6wPjdxx952AWH1eePGKGFyxdy6MRDR/QcMy9cBODdn7eiHnWtrWu3eu7k90e71XWnsp6bx7puDuu5eazr5qhXPc+fMX9+ZnZVlzcsUYqI64C9+ll1LnDZEBKdnYEbgPMz81vlsiElWn26urpy3rx5Q/o7GqVrVhfzpo8wlu7u4uecOSMNp6PVpa61VW1Xzx38/mi7uu5Q1nPzWNfNYT03j3XdHPWq54joN1HadsTPPIDMPG4LwTwcEXtn5tKI2BtYNsB22wFXA7P7kqTSoPaXJEmSpOFo1TVK1wBvK39/G/Cd6gYREcCXgYWZ+emh7i9JkiRJw9WqROkTwCsj4k7glWWZiNgnIq4tt3kpcBrwZxGxoHycuKX9JUmSJKkeGjb0bksy8xHg2H6WPwicWP7+v0AMZf8xpwOvvZAkSZLaQat6lCRJkiSpbZkoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVKFiZIkSZIkVZgoSZIkSVJFS244K0kN5w2ZJUnSCNijJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVBGZ2eoYmiYi/gAsbnUcpYnA8lYHMUZY181hPTePdd0c1nPzWNfNYT03j3XdHPWq58mZ+ezqwjGVKLWTiJiXmV2tjmMssK6bw3puHuu6Oazn5rGum8N6bh7rujkaXc8OvZMkSZKkChMlSZIkSaowUWqdWa0OYAyxrpvDem4e67o5rOfmsa6bw3puHuu6ORpaz16jJEmSJEkV9ihJkiRJUoWJkiRJkiRVmCi1QEQcHxGLIuKuiHh/q+PpVBFxb0TcEhELImJeq+PpJBHxlYhYFhG31ix7VkT8OCLuLH/u1soYO8UAdf3hiHigbNsLIuLEVsbYCSJiv4j4aUQsjIjbIuLd5XLbdR1toZ5t03UWEeMj4jcR8X9lXX+kXG6brqMt1LNtukEiYlxE/DYivleWG9amvUapySJiHHAH8ErgfuBG4M2Z+buWBtaBIuJeoCszveFbnUXEnwI9wFcz8wXlsk8Cj2bmJ8oTALtl5j+3Ms5OMEBdfxjoycxPtTK2ThIRewN7Z+ZNEfEMYD7wOuB0bNd1s4V6fiO26bqKiAB2ysyeiNgO+F/g3cBfYpuumy3U8/HYphsiIt4DdAG7ZOZrGvn9wx6l5jsKuCsz787MNcAVwMktjkkaksz8GfBoZfHJwGXl75dRfPnRCA1Q16qzzFyamTeVvz8JLAT2xXZdV1uoZ9VZFnrK4nblI7FN19UW6lkNEBGTgD8HvlSzuGFt2kSp+fYFltSU78cPiUZJ4EcRMT8iprc6mDFgz8xcCsWXIWCPFsfT6d4VETeXQ/McOlNHETEFeDHwa2zXDVOpZ7BN1105RGkBsAz4cWbaphtggHoG23QjXAS8D9hQs6xhbdpEqfmin2WeeWiMl2bmEcAJwJnlECapE1wMPBeYCiwFLmxpNB0kInYGrgbOyswnWh1Pp+qnnm3TDZCZ6zNzKjAJOCoiXtDikDrSAPVsm66ziHgNsCwz5zfrNU2Umu9+YL+a8iTgwRbF0tEy88Hy5zLg2xTDHtU4D5fXH/Rdh7CsxfF0rMx8uPxg3gBcgm27LsrrC64GZmfmt8rFtus666+ebdONlZmPA3MorpuxTTdIbT3bphvipcBry2vQrwD+LCIup4Ft2kSp+W4EDoqIAyJie+AU4JoWx9RxImKn8kJhImIn4FXArVveSyN0DfC28ve3Ad9pYSwdre8DofQX2LZHrLwg+8vAwsz8dM0q23UdDVTPtun6i4hnR8Qzy993BI4Dbsc2XVcD1bNtuv4y8wOZOSkzp1B8f/5JZp5KA9v0tvV6Ig1OZq6LiHcBPwTGAV/JzNtaHFYn2hP4dvGZzLbA1zPzB60NqXNExDeAbmBiRNwPfAj4BHBlRJwB3Af8Vesi7BwD1HV3REylGLZ7LzCjVfF1kJcCpwG3lNcaAJyD7breBqrnN9um625v4LJytt1tgCsz83sR8Sts0/U0UD1/zTbdNA07Tjs9uCRJkiRVOPROkiRJkipMlCRJkiSpwkRJkiRJkipMlCRJkiSpwkRJkiRJkipMlCRJTRERPa2OoZEi4sMR8d5WxyFJqg8TJUmSWiwKfiZLUhvxoCxJaqqI6I6IGyLiyoi4IyI+ERHTIuI3EXFLRDy33O6kiPh1RPw2Iq6LiD3L5c+OiB9HxE0RMTMiFkfExHLdqeXzLCjXjevn9e+NiI+U+98SEc8rl2/SIxQRt0bElPJxe0R8qVw2OyKOi4hfRMSdEXFUzdO/KCJ+Ui7/m5rn+qeIuDEibo6Ij5TLpkTEwoj4AnATsF8DqluSNEwmSpKkVngR8G7ghcBpwMGZeRTwJeDvy23+Fzg6M18MXAG8r1z+IeAnmXkE8G1gf4CIOBR4E/DSzJwKrAemDfD6y8v9LwYGM1zuQOCzwOHA84C3AC8r9z2nZrvDgT8HjgHOi4h9IuJVwEHAUcBU4MiI+NNy+0OAr2bmizNz8SDikCQ1ybatDkCSNCbdmJlLASLi98CPyuW3AK8of58EfDMi9ga2B+4pl78M+AuAzPxBRDxWLj8WOBK4MSIAdgSWDfD63yp/zgf+chDx3pOZt5Tx3gZcn5kZEbcAU2q2+05mrgJWRcRPKZKjlwGvAn5bbrMzReJ0H7A4M+cO4vUlSU1moiRJaoXVNb9vqClvYONn038Cn87MayKiG/hwuTwGeM4ALsvMDwzh9dfXvN46Nh1pMX6I8QJk5XWyjOvjmTlzk2AjpgArBxGrJKkFHHonSWpXuwIPlL+/rWb5/wJvBCiHte1WLr8eeENE7FGue1ZETB7C690LHFHuewRwwDBiPjkixkfE7kA3cCPwQ+AdEbFz+dz79sUoSWpf9ihJktrVh4H/FxEPAHPZmLh8BPhGRLwJuAFYCjyZmcsj4oPAj8oZ5NYCZwKDvfbnauCtEbGAIsG5Yxgx/wb4H4rrpj6WmQ8CD5bXT/2qHBLYA5xK0ZslSWpTkVkdJSBJUvuKiB2A9Zm5LiKOAS4uJ2+QJKlu7FGSJI02+wNXlr1Ga4C/2cr2kiQNmT1KkiRJklThZA6SJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEkasYjIiDiwsuzDEXF5+Xt3RGyIiJ7ycX9EXBkRf9SaiJuvrIP7Wx2HJGlwTJQkSc3yYGbuDDwDOBq4Hfh5RBzb2rDaR0R4f0NJahMmSpKkpsrC/Zl5HvAl4N8G2jYi/l9EPBQRKyLiZxFxWM26EyPidxHxZEQ8EBHvLZffGhEn1Wy3XUQsj4ipETGl7P16e0QsiYjHIuKdEfFHEXFzRDweEZ+r2ff0iPhFRHymXHd3RPxxuXxJRCyLiLfVbL9DRHwqIu6LiIcj4osRsWNE7AR8H9inpldtn7LX7aqIuDwingDeHxG9EbF7zXMeGRF/iIjt6vQvkCQNgomSJKmVvgUcUSYS/fk+cBCwB3ATMLtm3ZeBGZn5DOAFwE/K5V8FTq3Z7kRgaWYuqFn2kvJ53wRcBJwLHAccBrwxIl5e2fZmYHfg68AVwB8BB5av87mI2Lnc9t+Ag4Gp5fp9gfMycyVwAmWvWvl4sNznZOAq4JnAhcAc4I01r38qcEVmrh2gjiRJDWCiJElqpQeBoEgSNpOZX8nMJzNzNfBh4EURsWu5ei3w/IjYJTMfy8ybyuWXAydGxC5l+TTga5Wn/lhmPpWZPwJWAt/IzGWZ+QDwc+DFNdvek5n/lZnrgW8C+wEfzczV5f5rgAMjIoC/Af4xMx/NzCeBC4BTtlIHv8rM/87MDZm5CriMMtGLiHHAm/uJX5LUYCZKkqR6WA9Uh4ZtR5HMbMm+QAKPV1dExLiI+ERE/L4clnZvuWpi+fP1FL1FiyPihog4BqDsqfkF8PqIeCZFT85sNvVwze+r+invvIVtycz+tn82MAGYXw7Texz4Qbl8S5ZUyt+hSACfA7wSWJGZv9nKc0iS6syLRiVJ9XAfMAVYWLPsAOCOrez3F8BN5dC0qrdQDEs7jiJJ2hV4jKIHisy8ETi5vHbnXcCVFL09UPTK/DXF59yvyp6iRltOkTQdNsDr5QD7bbI8M5+KiCuBacDzsDdJklrCHiVJUj18E/hgREyKiG0i4jjgJIprbzYRhX0j4kMUycw5AzznM4DVwCMUPTUX1DzH9hExLSJ2La/deYKiV6vPfwNHAO+muGap4TJzA3AJ8JmI2KOMc9+IeHW5ycPA7jVDB7fkq8DpwGsphhJKkprMREmSVA8fBX4J/C9Fr88ngWmZeWvNNvtERA/QA9wIvBDoLq/z6c9XgcXAA8DvgLmV9acB95bD8t5JzQQO5bU+V1P0an1rZH/akPwzcBcwt4zrOuCQMqbbgW8Ad5dD8/YZ6Eky8xfABoretnsbHrUkaTOROdBIAEmSRq+IOA84ODNP3erGbSgifgJ8PTO/1OpYJGks8holSVLHiYhnAWdQ9DqNOhHxRxRDB09udSySNFY59E6S1FEi4m8oZpL7fmb+rNXxDFVEXEYxZO+scopxSVILtHToXUQcD3wWGAd8KTM/UVkf5foTgV7g9L77ZETEP1JcBJzALcDbM/OpJoYvSZIkqUO1rEepvIne5ynub/F84M0R8fzKZidQ3Dn9IGA6cHG5777APwBdmfkCikRrazf0kyRJkqRBaeU1SkcBd2Xm3QARcQXFWOzf1WxzMvDVLLq95kbEMyNi73LdtsCOEbGWYtrYB7f2ghMnTswpU6bU8U8YvjsfvZODnnVQq8MYE6zr5rCem8e6bg7ruXms6+awnpvHum6OetXz/Pnzl2fmZjcHb2WitC+b3o38fuAlg9hm38ycFxGforjB4SrgRwNNLxsR0yl6oxi/+/jyt9Zbt3zdxnvLq6Gs6+awnpvHum4O67l5rOvmsJ6bx7pujrrV8wwW97e4lYlS9LOsesFUv9tExG4UvU0HAI8D/y8iTs3MzW7Kl5mzgFkAXV1dOW/6vBEFXS9ds7pol1g6nXXdHNZz81jXzWE9N4913RzWc/NY181Rr3qOGf2lHK2d9e5+YL+a8iQ2Hz430DbHAfdk5h/KO7J/C/jjBsYqSZIkaQxpZaJ0I3BQRBwQEdtTTMZwTWWba4C3RuFoYEVmLqUYcnd0REwoZ8Y7FljYzOAlSZIkda6WDb3LzHUR8S7ghxSz1n0lM2+LiHeW678IXEsxNfhdFNODv71c9+uIuAq4CVgH/JZyeJ0kSZIkjVQrr1EiM6+lSIZql32x5vcEzhxg3w8BH2pogJIkSZLGpFYOvZMkSZKktmSiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEnSWNLdXTwkSVtkoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJklRhoiRJkiRJFSZKkiRJUr10dzPzwkWtjkJ1YKIkSZIkSRUmSpIkSZJU0dJEKSKOj4hFEXFXRLy/n/UREf9Rrr85Io6oWffMiLgqIm6PiIURcUxzo5ckSZLUqVqWKEXEOODzwAnA84E3R8TzK5udABxUPqYDF9es+yzwg8x8HvAiYGHDg5YkSZI0JrSyR+ko4K7MvDsz1wBXACdXtjkZ+GoW5gLPjIi9I2IX4E+BLwNk5prMfLyJsUuSJEnqYNu28LX3BZbUlO8HXjKIbfYF1gF/AP4rIl4EzAfenZkrqy8SEdMpeqMYv/t4umZ11e0PGImFyxe2TSydzrpuDuu5eazr5ujUep65tJiNa0Yb/W2dWtftxnpujplLF7Fyba913QSNbtOtTJSin2U5yG22BY4A/j4zfx0RnwXeD/zLZhtnzgJmAXR1deW86fNGFHS9dM3qol1i6XTWdXNYz81jXTdHx9bz17sBmDd9TkvDqNWxdd1mrOcm+Xo385fOt66boF5tOmb0l3K0dujd/cB+NeVJwIOD3OZ+4P7M/HW5/CqKxEmSJEmSRqyVidKNwEERcUBEbA+cAlxT2eYa4K3l7HdHAysyc2lmPgQsiYhDyu2OBX7XtMglSZIkdbSWDb3LzHUR8S7gh8A44CuZeVtEvLNc/0XgWuBE4C6gF3h7zVP8PTC7TLLurqyTJEmSpGFr5TVKZOa1FMlQ7bIv1vyewJkD7LsA8Co5SZIkSXXX0hvOSpIkSVI7MlGSJEmSpAoTJUmSJEmqMFEazbq7i4ckSZKkujJRkiRJkqQKEyVJkiRJqjBRkiRJkqQKEyVJnclr+CRJ0giYKEmSJEnasjF4AtJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJEqRW6u5l54aJWRyFJkiRpACZKkiRJklRhoqTOZu+dJEmShsFESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZLUObq7i4ckSSNkoiRJ0mhgEihJTWWiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVGGiJEmSJEkVJkqSJEmSVNHSRCkijo+IRRFxV0S8v5/1ERH/Ua6/OSKOqKwfFxG/jYjvNS9qSZIkqR+zZ8PcuRxxRw9MmVKUNWq1LFGKiHHA54ETgOcDb46I51c2OwE4qHxMBy6urH83sLDBoUqSJElbNns2TJ8Oq1cTAIsXF2WTpVGrlT1KRwF3ZebdmbkGuAI4ubLNycBXszAXeGZE7A0QEZOAPwe+1MygJUmSpM2cey709m66rLe3WK5RqZWJ0r7Akpry/eWywW5zEfA+YEOD4pPUbN3dxUOSpNHmvvuGtlxtb9sWvnb0sywHs01EvAZYlpnzI6J7iy8SMZ1i2B7jdx9P16yuYYRaXzOXLmLl2t4RxfLqXz/Ceb9YzPbrkod234HPvW4ffviS3esYZWeoR11rcBYuXzjiep65dBEAM+rw/6rnc7WbetR1p6rn/73d6rlef1s7vjfara47lfXcWN/dbTv2fnTNZsuX7rYdJ3VAvY/FY0dkVnOT5oiIY4APZ+ary/IHADLz4zXbzATmZOY3yvIioBv4B+A0YB0wHtgF+FZmnrql1+zq6sp58+bV/48Zqu5u5i+dz5GLnhze/n1jYGu7dydMgFmzYNq0+sTYKUZa1xq0rlldzJs+wvdXX2/SnDkjDae+z9Vm6lLXnaqO//e2q+d6/W1t+N5ou7ruUNZzg3X697MOPnZExPzM3CzjauXQuxuBgyLigIjYHjgFuKayzTXAW8vZ744GVmTm0sz8QGZOyswp5X4/2VqS1FEcAytJktRepk0rkqIddiiGSE2e3DlJ0hjVsqF3mbkuIt4F/BAYB3wlM2+LiHeW678IXAucCNwF9AJvb1W8bcUxsJIkSe1n2jS45BJuWjqfIxfd2+poNEKtvEaJzLyWIhmqXfbFmt8TOHMrzzEHmNOA8NrX/vsXU072t1ySJEnSiLX0hrMapvPPL8a81powoVguSZIkacRMlEajmjGwgGNgJUmSpDpr6dA7jUA5BhZoq9lHJEltbPZsmDsXVq+GKVOKkQieZJOkftmjJEnSWNA3dfHq1UV58eKiPHt2a+OSpDZloiRJ6gx9vSU33FD0lpgAbMpbS0jSkJgoSZJGP3tLts5bS0jSkJgoSZJGP3tLtm6gW0h4awlJ6peJkiRp9LO3ZOu8tYQkDYmJkiRp9LO3ZOu8tYQkDYmJkiRp9LO3ZHCmTYOjj4aXvxzuvdckSZK2YKuJUkTMi4gzI2K3ZgQkSdKQ2VsiSaqzwdxw9hTg7cCNETEP+C/gR5mZDY1MkqSh8EbckqQ62mqPUmbelZnnAgcDXwe+AtwXER+JiGc1OkBJkiRJarZBXaMUEYcDFwL/DlwNvAF4AvhJ40KTJDVMd3fxkCRJ/drq0LuImA88DnwZeH9mlnfz49cR8dIGxiZJkiSp1WbPhrlzi5t6T5lSTJQzBq4B3WKPUkRsA1ydmcdm5tdrkiQAMvMvGxqdNBLlm/qIO3qKN/Xs2a2OSJIkaXSZPRumTy+SJIDFi4vyGPhetcVEKTM3AMc3KRapfmre1AFj6k0tSZJUN+eeC729my7r7S2Wd7jBXKP044h4b0TsFxHP6ns0PDJpJMbwm1qSJKlu7rtvaMs7yGCmB39H+fPMmmUJPKf+4Uh1Mobf1GpTfRMnOG21JGk02X//YmROf8s73GB6lA7NzANqH8DzGx2YxriRzsg10Jt3DLypJUmS6ub882HChE2XTZhQLO9wg0mUfjnIZVL7GMNvakmSpLqZNg1mzYIddijKkycX5TEw692AQ+8iYi9gX2DHiHgxFNfEA7sAEwbaT2oLfW/eM84gV68mJk8eM1NZSpK0me5uZi5dBNNbHYhGpWnT4JJLit/H0BDyLV2j9GrgdGAS8Oma5U8A5zQwJqk+yjf1TUvnc+Sie1sdjSRJquW1m2pzAyZKmXkZcFlEvD4zr25iTJIkSZLUUoO5RukXEfHliPg+QEQ8PyLOaHBckiRJgzfSSYAkqWIwidJ/AT8E9inLdwBnNSogSZIkSWq1wSRKEzPzSmADQGauA9Y3NCpJkiRJaqHBJEorI2J3ipvMEhFHAysaGpUkSVIrOIRPUmlLs971eQ9wDfDciPgF8GzgDQ2NSpIkSZJaaKuJUmbeFBEvBw6huJfSosxc2/DIJEmSJHWWUTQt/FYTpYgYB5wITCm3f1VEkJmf3uKOktrfKDpYSZIkNdNght59F3gKuIVyQgdJkiRJ6mSDSZQmZebhDY9krJg9G+bO5YjVq2HKFDj/fJg2rdVRSVJnsHdUklQng5n17vsR8aqGRzIWzJ4N06fD6tUEwOLFRXn27FZHJkmSJKnGYBKlucC3I2JVRDwREU9GxBONDqwjnXsu9PZuuqy3t1guSaORUylLkjrUYBKlC4FjgAmZuUtmPiMzd6nHi0fE8RGxKCLuioj397M+IuI/yvU3R8QR5fL9IuKnEbEwIm6LiHfXI56Gu+++oS2XJEmS1BKDuUbpTuDWzMx6vnA5m97ngVcC9wM3RsQ1mfm7ms1OAA4qHy8BLi5/rgPOLqcufwYwPyJ+XNm3/ey/fzHcrr/lw+FYfEmSJKkhBpMoLQXmRMT3gdV9C+swPfhRwF2ZeTdARFwBnAzUJjsnA18tk7S5EfHMiNg7M5eWcZGZT0bEQmDfyr7t5/zzi2uSaoffTZhQLJckSZLUNgaTKN1TPrYvH/WyL7Ckpnw/RW/R1rbZlzJJAoiIKcCLgV/39yIRMR2YDjB+9/F0zeoaadwj8upT9uC8ry5m+3XJQ8/ans+9bg9+uPIzMOszLY2r3cxcugiAGSP8f81cuoiVa3tb/n9vV/WqZ4CFyxeOuJ7rGU89n6seOrWu262e66ke9VxPnfw/s003Xrt9Hr76149w3i/K70O778DnXrcPP3zJ7q0Oqy7ara7rpR3fYw0/TmdmSx7AXwFfqimfBvxnZZv/AV5WU74eOLKmvDMwH/jLwbzmkUcemW3h5S/PeQfv3Ooo2tvLX1486vA81vUW1KueM/PImXV4f9Urnssvz9xhh0zInDy5KLdap9Z1Hf+udlOXeq6nDv6f2aaboJ0+Dy+/PHPChOIY3feYMKE9jtX10E51XU9t+B6r13EamJf95A5bncwhIroi4tsRcVM5ocLNEXFzHXK0+4H9asqTgAcHu01EbAdcDczOzG/VIR5JnaBmGn7Aafglqd04C7BGicHMejcb+C/g9cBJNY+RuhE4KCIOiIjtgVOAayrbXAO8tZz97mhgRWYujYgAvgwszJFfKyWpk/gBrHro7mbmhYtaHYXUmZwFWKPEYK5R+kNmVhOYEcvMdRHxLuCHwDjgK5l5W0S8s1z/ReBa4ETgLqAXeHu5+0sphurdEhELymXnZOa19Y5T0ijjB7Aktbd6zwIsNchgEqUPRcSXKK4Pqp31bsTD3crE5trKsi/W/J7Amf3s979AjPT1JXWgen8A991M1en4Jak+nAVYo8Rght69HZgKHM/GYXevaWBMkjR8559ffODWavUH8OzZMHcu3HADTJni9VKSxrZp02DWLNhhh6I8eXJRnjattXHVy5w5zDj7kFZHoToYTI/SizLzhQ2PRNLY1pdMrF5dJBPnnz+8D82+fc44o3iuyZOH/1z1MNDkErWxStJYM20aXHJJ8bs99mpTg+lRmhsRz294JJLGrnrPVDdtGhx9NLz85XDvva1NSJxcQpKkUWkwidLLgAURsaicGvyWOk0PLo093d0br3nRRp2cTDi5hCRJo9Jght4d3/AoJI1tnZxMOLuTJEmj0mB6lP4e2CkzF9c+Gh2YpDFkoKShE5IJJ5cY2+xFlqRRazCJ0u3AJRHx64h4Z0Ts2uigJI0x7ZhM1Eu7ze5U7+vBpHZg8i+pAbaaKGXmlzLzpcBbgSnAzRHx9Yh4RaODkzRGtFsyUW9OLiE1jsm/pAYZTI8SETEOeF75WA78H/CeiLiigbFJGkvaKZnoZJ18PZjGJpP/5nNIqcaIrSZKEfFpiuF3JwIXZOaRmflvmXkS8OJGByipQRyqMjZ18vVgGpvqmfx7XJRUYzA9SrdS3HR2Rmb+prLuqAbEJKnRHKoydnXy9WAam+qV/Nf7uGivizTqDSZRWgQEQEScGhGfjojJAJm5opHBSWoQh6qMXZ1+PZjGnnol/x4XpcYbZb22g0mULgZ6I+JFwPuAxcBXGxqVpMbyOpWxzevB1Enqlfx7XJQaaxSOZhlMorQuMxM4GfhsZn4WeEZjw5LaTKcNofA6FWl0GWVnYZuuHsm/x0WpsUZhr+1gEqUnI+IDwGnA/5Qz4G3b2LAkNZTXqUijxyg8CzsqeVyUGmsU9toOJlF6E7AaeHtmPgS8FNipoVFJaiyvU5FGj1F4FnZIuruZeeGiVkfhcVFqtFHYa7vVnqHMfCgifgK8JSIuB+4BLmp0YJIabNo0uOSS4vc5c0b2XN3dzFy6CKaPOCpJVaPwLOyoVc/jorbOOh5bzj+/6A2vPfHT5r22A/YoRcTBEXFeRCwEPgcsASIzX5GZ/9m0CKVO4TUGkoZjFJ6FlaTNjMJe2y0NvbsdOBY4KTNfViZH65sTllQnc+Yw4+xDWh2F1xhIGj6vnZHUKUbZrKtbSpReDzwE/DQiLomIYynvpyRpiDr9GgNJjTMKz8JKUicYMFHKzG9n5puA5wFzgH8E9oyIiyPiVU2KT+oMXmOgTuRw0uYZZWdhJakTbHXWu8xcmZmzM/M1wCRgAfD+RgcmdZROvsag/LJ8xB09flkeSxxOKknqcIOZHvxpmfloZs7MzD9rVEBSR6r3NQbtcgPcmi/LAX5ZHkscTiqNHp7QkoZlSImSpGHq1GsM/LI8djmcVBodPKElDZuJktqnd6LTdeI1Bn5ZHrs6eTip1Ek8oSUNm4mSpOHzy/LY5ZTV0ujgCS3Vy5w5Y+4mwSZKkobPL8tjV6cOJwWv51Bn8YSWNGwmSpKGr+bLckJnfVkGp7/emk4cTur1HKqHdjp2eEJLGjYTJUkjU35ZvungnTvnyzI4/fVY5fUczdWJvXftduzo9BNaUgOZKElSf/zCPDZ5PUfzdGrvXTseOzr1hJbUYCZKktQfvzCPTV7P0TztmFDUQ72PHc5MK7XMtq0OYKyavqSXZ1QOfG984xv5u7/7O3p7eznxxBM32+f000/n9NNPZ/ny5bzhDW/YbP3f/u3f8qY3vYklS5Zw2mmnbbb+7LPP5qSTTmLRokXMmDFj44oFCwD44HXXcdxxx7FgwQLOOuuszfa/4IIL+OM//mN++ctfcs4552y2/qKLLmLq1Klcd911/Ou//utm62fOnMkhhxzCd7/7XS688MLN1n/ta19jv/3245vf/CYXlzHVfjhcddVVTJw4kUsvvZRLL710s/2vvfZaJkyYwBe+8AWuvPLKp5cvWrqI7q93M6ecqeVTn/oU3/ve9zbZd8cdd+T73/8+AB/72Me4/vrrN1m/+223cfVhhwHwgQ98gF/96lebrJ80aRKXX345AGeddRYL+uIvHXzwwcyaNQuA6XfcwR2V//3UqVO56KKLADj11FO5//77N1l/zDHH8PGPfxyA17/+9TxSqZ9jjz2Wf/mXfwHghBNOYNWqVZvs/5rXvIb3vve95S41r10+zxu/8IWRtb1lyzgQht72Sh/84AeLttfTw1l33bXZl4Jhtb2aOhpS27v4YgCu2H579uobOlPjoe2355RKfAO1vT5Pt70lS/heZd+ttr3dd+fqq68GNra9vjYNQ2x706dzxx13bFI/U886a2ht75FHNll/7OLF/MvkycAQ216pZce90tNtrzzuHbvjjvzTNtswfsOGjRtNmMAdp5/O9H7i3+pxr7eXQyZMGFLbq7XJca+f4+Kg217tca98nh1POGHIba/WpIULufzQQ2HOnKG1vdJPFy8uepIqNixezJ91d2+97VWPe5X6GXbbK5/n9EsvHVbbG+yxo9r2qp4+7q1YwTn33LPZcXHIn7kLFvDkmo3fPYbU9qqfuQsWcO0LX8gEGFrbKw3nuFdr2Me90pA/c7fW9vo57rFL8ftoOO5VNfz73lNPsd/48Zstb0f2KElSPy454ACe2mbTQ+S67bfnkgMOaFFEpQULOHhJ79a307Bcv+ee/PvBB/NQBBuAp/bcE2bNYvmrXtXq0DrOY894Rr/Ll/XNpDhK9XfseGqbbVp/7OhkCxZsPDkm1VFkZqtjaJqurq6cN29eq8OA7m7mL53PkYuebHUkhb6zHfWYG79ez1XHmLpmdTFv+gj/7+32d7Xb89Bm9Vyv55o9G844o7goe/LkYpaoVo/tr9fxow3bUFtpt3qu53O1y/P0XaNUO/xuwoThTzTQLn8X1PfYUce/q27fPdrtfd9u8VCnz8RO1W7fPYCImJ+ZXdXlLe1RiojjI2JRRNwVEe/vZ31ExH+U62+OiCMGu68kjVgnTn8ttYtOno3NY4fUEVrWoxQR44A7gFcC9wM3Am/OzN/VbHMi8PfAicBLgM9m5ksGs29/2qVH6axJk/j5o0t5xlF/0upQCn3d1VOnts9z1TGm+Uvnc+TeR47sSdrt72q356HN6rmez1XPmOphwQKeXPPkyI8fnVo/9dJu9VzP52rD52mruu7g56lLPZfPBbTP+77d4qFOn4mdasECpu68MxdVrg0bjk7uUToKuCsz787MNcAVwMmVbU4GvpqFucAzI2LvQe7b1u5YfTgPPVT8nlm8xx9+uChv2FCUly0ryuvWFeXly4vy2rVFue/awjVrivKjjxbl1auL8mOPFeWnnirKjz9elHt7i/ITTxTllevHs6DnQJ4se+N7eor1PT1F+ckni/LKlUX5iSeKct9oiccfL8pPPVWUH1v3DBYs2HgLiUcfLdavWVOUH3mkKK9dW5SXLy/K69YV5WXLYEHPgWzIonk+/HCxvi+nf+ihTYciL10K//d/G8sPPgg337yxvOaxPbj11o3lJUvgtts2lu+7D35Xk2IvXgwLF24s33sv3N67ccaru++G2utCf/97uPPOjeW77ioefe68s9gGgKlTuWPCVO6+e+P6228vXqPPwoVFDH1+97tNJ0u67TZYsnqPp8u33gq1x5qbby7qoM///V9RR30WLGBj2yNY0HNg69reyqL8dNtbvyMLeg4cftt7jE3b3rpdht72FhT1AGXb6zmQLC85H2rbu/9+Rt72bt9YvvtuuGPVfk+Xh9T2KNrtJm2vd/+ht70lG8u33gr3r3720+Uhtb1WH/eqba963Fs/gTtWHz78ttd33MvtgGG2vQWV417PgU/X5Yjb3uo9h972qse9VZOeLg+57VWOe/euOWToba963Fs98enyiNpejmNBz4Ejb3vrdgaG0faqx711O7Gg58Cht73qcS+L+buG1fYWbKzLpUvh/3qe+3S5Kce9rX3mrtr36fJQ296wjnvD/cxt9+PeSD9zB/t9L8cxWrRy1rt9gZqmx/0UvUZb22bfQe4LQERMB6YDjN99PF2zNksWm27mTiu4YfvLeezoOfT88ffI9eNY/9AXeOqY/6bn6O+zYc0OrH/4P3jqpVfR80c/Zv2qnVj/h0+z6mVX0HPET1nXsyvrl3+SVX96OT0v+jlrV+zO+kcuYFX3pfS84FeseXRP1j/6UVb92ZfZ7tDfsPoP+7L+sfNYddxMtj34Jp56aDLrZ59D7ys/zzbPvZl/und/PrnsQnpffQkx5Xf0LjmY9VeeTe8JF8J+d9B77/NZf/W76T3xk+S+v6f394ez/r/PpPc1F7Bhr8WsuuMI1n93BitP+iiff2w9v145lX/e6cOsPPk81j7rYVbdegzrf3g6K//iHNbs+gir/u9PWH/dqax8/fvYducVrLrpFaz/6Sms/Kv3MG7HlbzqG/vwvttvZ+8nHuOhW37FJ15wIl/Z8EF6Tvk7Ytx6nvrla1j/q5PoeUsxk8tTP38d6+e9kp63nFmU57yB9Tf/CT1veTcAec1fsm7JSfS85T0ArP7Rqay7+3B63vI+ANb84G2sW3IIPW8pZnZZ8z9nsO6hyfS85byifM0MjnpsRz60598y4y09rP32mazr2ZWet1wAwNqr3s2GNTvQ85ZPFuVvFq/T86ZPF+Wvv49ttl9Nzxs+W5S/dg658wp6/uLzAKy79DzWPOthel47syh/+aOs2WsxPX/+5aJ8yQWs2W8RPcdfVpS/+ElOWnI9pz3rP5jxlh7Wff7TrH7eb+g59goA1i//LE8d/nN6uq8qyg9/nqe6fkzPn/x3UV46k6eO/m7R9t50MOsvqk/b27DXjazcZqchtb1VDzyX9Ve8j95Xf5aY8jvec88BXPiHf6P3hIuH3PbWPfsBVi08ivXXnsHKk8/jc4+u55crj+ScnT446Lb31I2vZP3P3kDPG/+BbbZfzVNzT+C5i1/FJ/d4LWe+pWfIbW/19aew7vajRtT21j26Jz1v+WjRdr59Jsc8AWc96695z1t6RtT2Zi5dz0ceeh/zXrBqSG1v9XNupudVxWxT6z7/aV639Hu8ebeLmfGWnqG1vRYf96ptr3rce/c9B/GZdR+h98SLh9z2ao97H1r3RiZuu4w3/emLh9z21v/idZsc955737FcuOeJzHhLz4ja3syl67n00VOYPfmlQ2p71ePenz65in/e8+ziuDiC497Mpev5wLpzue3wJ4bU9qrHvb/8w1X81TO/VLTFEbS9C+8fz3vWfobHX3bNiNre3/Wey5ETfsHbXrP7kNpe9bj3rrsP5fPLP0Tvay4aUturHvc+uv71bLfhMd7+sj8acturHvcOvv+lXLjna4u22ITj3tY+c49d+Rhn7/H+oi024DN3a22vv+PehkNvpGdiz6g67o3kM3co3/fOX/9adh63oi7fyRcuX9jQ7/atHHr3V8CrM/Ovy/JpwFGZ+fc12/wP8PHM/N+yfD3wPuA5W9u3P+0y9I7ubo78zc836/5u+fTgV1018ukizzqL6x57jH/dbbfN1g9qiuaf/Yx173gH2/adjqCYLejfDz6Yv/35z4c1PXhf93ddpgefM6c9pirti68cZjDs6cFL9Wh7/77i3/n2Cd8e2VSlXV3F9OCV4RMjmh586tRhTQ/+tAULuOqww5j4i18Mue31qes0uRdfvMnwmRFPD3766fWZHnzOnI6YHvxp5TCl/7zu5yObJrdvevCzzx759OB98dW8P0Y0Pfgxx7R0evCnj3vd3Zx40y/oPeKlm6wf8hTNffGV9TPi6cEvumhkbW/5ck6aOJFFM2eObIrmF7+4mB68clwc3vTgG48ddZke/Oc/b4/pwfviK+uoHaYHv2KXK5g3fd7oOu6VmjY9+CiYzKGVPUr3A/vVlCcBDw5ym+0Hsa9Go3PP3SRJAhi/YQN/c889LQpIkjQqtNH1KZI6Qyt7lLalmJDhWOABigkZ3pKZt9Vs8+fAu9g4mcN/ZOZRg9m3P+3Uo+T04APYZpuNA6NrRWwcQD1EbTdtdT20Wzy0YT2301TB9dRu01a3W/3US7vVcz2fqw2fpyM/E9twmnGnB28epwffglE0PXjLepQyc11EvAv4ITAO+Epm3hYR7yzXfxG4liJJugvoBd6+pX1b8GeMfrNnw9y5xUF4ypTW3ydm//03vaqydrkkSY3WLl+2++4z1Xel/OLFRRmcblxqklYOvSMzr6VIhmqXfbHm9wTOHOy+GqJ2PAiff37/NyA8//zWxCNJUiuce+6mn4VQlM8910RJapKW3nBWLbalg3Cr1NyAEOisGxBKkjRYtXNUD2Z5s/SNRLnhhmIkyuzZrY1Ho8+cOe3Tc7sVLe1RUou160F42jS45JLi91HyRpIkqa7acSh6O45EkRrIHqWxbKCD7XAPwp5lkiSpPs4/vxh6XqvVQ9HbcSSK1EAmSmNZPQ/CA51lMlmSJGno2nEoeruORJEaxERpLKvnQdizTNLoMorGiI9a9rIPzpw5zDj7kFZH0Z6mTYOjj4aXvxzuvbf1w9vqPRJFanNeozTW1et6IM8ySdJG7XwthwmyhsuZaTXG2KOk+vAskyRtZC+7OrHXth2HA0oNZKKk+mjHi04lqVXsZVenarfhgFIDmSipPjzLJEkb2csuNYfXAqqBTJRUP55lkqSCvexS4znjrhrMREmSpHqzl11qPK8FVIM5610rzJnDjFldzGt1HJKkxqnXrKKS+ue1gGowe5SkrXH8sySpVTpx9rx68VpANZiJkrQljn+WJKk9eS2gGsxESdoSxz9LW1avs93d3cVDkgbLawHVYF6jJG2J45+by+ElkqSh8FpANZA9StKWOP5ZkiRpTDJRkrbE8c/S2DRnDjPOPqTVUUiSWsihd9KW9I1zPuOMYkKHyZOLJMnxz2OHQzkkSaNZ3/Wffp4NmYmStDXtNv65b7ry1auL6cpN3CRJkurOoXeqL+/30FhOVy6pHXh/OUljgImSTG5GE6crl9RqnrCRNEaYKEmjidOVS2o1T9hIGiNMlKTRxOnKx7ZyuNMRd/Q43Emt4wkbafRwmOyImChJo4nTlY9dNcOdAhzupNbxhI00OjhMdsRMlKTRZNo0mDULdtihKE+eXJSd9a7zOdxJ7cITNtLo4OfGiDk9uDTatNt05WoOhzupXXh/OWl08HNjxEyUJGk02H//YthEf8ulZvOEjdT+/NwYMYfeSdJo4HAnSdJQ+LkxYiZKkjQa1FyfluD1aZKkLfO65hFz6J0kjRblcKebls7nyEX3tjoaSVK7c5jsiNijJEmS1OnmzGHG2Ye0OgppVLFHSZIkSaOXPSVqEBMlSZ3JD05JkjQCLRl6FxHPiogfR8Sd5c/dBtju+IhYFBF3RcT7a5b/e0TcHhE3R8S3I+KZTQtekiRJGi3mzPHk4TC16hql9wPXZ+ZBwPVleRMRMQ74PHAC8HzgzRHx/HL1j4EXZObhwB3AB5oStSRJkqQxoVWJ0snAZeXvlwGv62ebo4C7MvPuzFwDXFHuR2b+KDPXldvNBSY1NlxJUsPMng1z58INN8CUKUVZkqQWa1WitGdmLgUof+7Rzzb7AktqyveXy6reAXy/7hFKkhpv9myYPh1Wry7KixcXZZMlSVKLNWwyh4i4Dtirn1XnDvYp+lmWldc4F1gHDPiJGhHTgekA43cfT9esrkG+fGMtXL6wbWJpRzOXLgJgRh3qqB51Xc946qHd4gHbdLPMXLqIlWt7O6auv/uBW9i7d82mC3t7WfoP7+CklZ9pTVCldjt2tNv7vt2O09q6etVzu7XFdmSbbo5G13Nk5ta3qveLRiwCujNzaUTsDczJzEMq2xwDfDgzX12WPwCQmR8vy28D3gkcm5m9g3ndrq6unDdvXh3/kuHrmtXFvOntEUtb6u4uftbh4sO61HUd46mLdosH23TTdHczf+l8jlz0ZKsjqY9ttoH+PociYMOG5sdTo+2OHe32vm+347S2qm713G5tsQ3ZppujXvUcEfMzc7OMq1VD764B3lb+/jbgO/1scyNwUEQcEBHbA6eU+xERxwP/DLx2sEmSJKkN7b//0JZLktQkrUqUPgG8MiLuBF5ZlomIfSLiWoBysoZ3AT8EFgJXZuZt5f6fA54B/DgiFkTEF5v9B0iS6uD882HChE2XTZhQLJfUnpxuWmNES244m5mPAMf2s/xB4MSa8rXAtf1sd2BDA5QkNce0acXPM84oJnSYPLlIkvqWS5LUIi1JlCRJetq0aXDJJcXvnqWWJLWJVg29kyRJkqS2ZaIkSZIkSRUmSpIkSZJUYaIkSZIkSRUmSpIkSZJUYaIkSZIkSRUmSpIkSZJU4X2UJEkaDbzHlCQ1lYmSJEmNYnIjSaOWQ+8kSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqnMxBkiQNnRNVSOpw9ihJkiRJUoWJkiRJkiRVOPROGgyHmEiSJI0p9ihJkiRJUoWJkiSNJnPmMOPsQ1odhSRJHc+hd2pPDnWTxhbf85KkNmOPkiRJkiRV2KMkjUaefZckSWooe5QkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqSIys9UxNE1E/AFY3Oo4ShOB5a0OYoywrpvDem4e67o5rOfmsa6bw3puHuu6OepVz5Mz89nVhWMqUWonETEvM7taHcdYYF03h/XcPNZ1c1jPzWNdN4f13DzWdXM0up4deidJkiRJFSZKkiRJklRhotQ6s1odwBhiXTeH9dw81nVzWM/NY103h/XcPNZ1czS0nr1GSZIkSZIq7FGSJEmSpAoTJUmSJEmqMFFqgYg4PiIWRcRdEfH+VsfTqSLi3oi4JSIWRMS8VsfTSSLiKxGxLCJurVn2rIj4cUTcWf7crZUxdooB6vrDEfFA2bYXRMSJrYyxE0TEfhHx04hYGBG3RcS7y+W26zraQj3bpussIsZHxG8i4v/Kuv5Iudw2XUdbqGfbdINExLiI+G1EfK8sN6xNe41Sk0XEOOAO4JXA/cCNwJsz83ctDawDRcS9QFdmesO3OouIPwV6gK9m5gvKZZ8EHs3MT5QnAHbLzH9uZZydYIC6/jDQk5mfamVsnSQi9gb2zsybIuIZwHzgdcDp2K7rZgv1/EZs03UVEQHslJk9EbEd8L/Au4G/xDZdN1uo5+OxTTdERLwH6AJ2yczXNPL7hz1KzXcUcFdm3p2Za4ArgJNbHJM0JJn5M+DRyuKTgcvK3y+j+PKjERqgrlVnmbk0M28qf38SWAjsi+26rrZQz6qzLPSUxe3KR2Kbrqst1LMaICImAX8OfKlmccPatIlS8+0LLKkp348fEo2SwI8iYn5ETG91MGPAnpm5FIovQ8AeLY6n070rIm4uh+Y5dKaOImIK8GLg19iuG6ZSz2CbrrtyiNICYBnw48y0TTfAAPUMtulGuAh4H7ChZlnD2rSJUvNFP8s889AYL83MI4ATgDPLIUxSJ7gYeC4wFVgKXNjSaDpIROwMXA2clZlPtDqeTtVPPdumGyAz12fmVGAScFREvKDFIXWkAerZNl1nEfEaYFlmzm/Wa5ooNd/9wH415UnAgy2KpaNl5oPlz2XAtymGPapxHi6vP+i7DmFZi+PpWJn5cPnBvAG4BNt2XZTXF1wNzM7Mb5WLbdd11l8926YbKzMfB+ZQXDdjm26Q2nq2TTfES4HXltegXwH8WURcTgPbtIlS890IHBQRB0TE9sApwDUtjqnjRMRO5YXCRMROwKuAW7e8l0boGuBt5e9vA77Twlg6Wt8HQukvsG2PWHlB9peBhZn56ZpVtus6GqiebdP1FxHPjohnlr/vCBwH3I5tuq4GqmfbdP1l5gcyc1JmTqH4/vyTzDyVBrbpbev1RBqczFwXEe8CfgiMA76Smbe1OKxOtCfw7eIzmW2Br2fmD1obUueIiG8A3cDEiLgf+BDwCeDKiDgDuA/4q9ZF2DkGqOvuiJhKMWz3XmBGq+LrIC8FTgNuKa81ADgH23W9DVTPb7ZN193ewGXlbLvbAFdm5vci4lfYputpoHr+mm26aRp2nHZ6cEmSJEmqcOidJEmSJFWYKEmSJElShYmSJEmSJFWYKEmSJElShYmSJEmSJFWYKEmSmiIielodQyNFxIcj4r2tjkOSVB8mSpIktVgU/EyWpDbiQVmS1FQR0R0RN0TElRFxR0R8IiKmRcRvIuKWiHhuud1JEfHriPhtRFwXEXuWy58dET+OiJsiYmZELI6IieW6U8vnWVCuG9fP698bER8p978lIp5XLt+kRygibo2IKeXj9oj4UrlsdkQcFxG/iIg7I+Komqd/UUT8pFz+NzXP9U8RcWNE3BwRHymXTYmIhRHxBeAmYL8GVLckaZhMlCRJrfAi4N3AC4HTgIMz8yjgS8Dfl9v8L3B0Zr4YuAJ4X7n8Q8BPMvMI4NvA/gARcSjwJuClmTkVWA9MG+D1l5f7XwwMZrjcgcBngcOB5wFvAV5W7ntOzXaHA38OHAOcFxH7RMSrgIOAo4CpwJER8afl9ocAX83MF2fm4kHEIUlqkm1bHYAkaUy6MTOXAkTE74EflctvAV5R/j4J+GZE7A1sD9xTLn8Z8BcAmfmDiHisXH4scCRwY0QA7AgsG+D1v1X+nA/85SDivSczbynjvQ24PjMzIm4BptRs953MXAWsioifUiRHLwNeBfy23GZnisTpPmBxZs4dxOtLkprMREmS1Aqra37fUFPewMbPpv8EPp2Z10REN/DhcnkM8JwBXJaZHxjC66+veb11bDrSYvwQ4wXIyutkGdfHM3PmJsFGTAFWDiJWSVILOPROktSudgUeKH9/W83y/wXeCFAOa9utXH498IaI2KNc96yImDyE17sXOKLc9wjggGHEfHJEjI+I3YFu4Ebgh8A7ImLn8rn37YtRktS+7FGSJLWrDwP/LyIeAOayMXH5CPCNiHgTcAOwFHgyM5dHxAeBH5UzyK0FzgQGe+3P1cBbI2IBRYJzxzBi/g3wPxTXTX0sMx8EHiyvn/pVOSSwBziVojdLktSmIrM6SkCSpPYVETsA6zNzXUQcA1xcTt4gSVLd2KMkSRpt9geuLHuN1gB/s5XtJUkaMnuUJEmSJKnCyRwkSZIkqcJESZIkSZIqTJQkSZIkqcJESZIkSZIqTJQkSZIkqeL/A2svklXO9LsxAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " \n", "Folder\t Average LR asymmetry\t Error\n", "0\t -0.0564\t\t 0.0020\n", "1\t -0.0738\t\t 0.0019\n", "2\t -0.0918\t\t 0.0021\n", "3\t -0.1156\t\t 0.0019\n", "4\t -0.1168\t\t 0.0004\n", " \n", "Date and time 2021-05-07 13:23:39.229151\n", "Time since last check is 0:11:07.416814\n", "Time since notebook start is 0:34:16.523539\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import datetime\n", "now = datetime.datetime.now()\n", "print(\"Date and time \",str(now))\n", "#\n", "debug = False\n", "#\n", "print(\" \")\n", "print(\"Cluster ID using Watershed algorithm\")\n", "print(\"Cluster threshold\",thresh[0],\"\\b.\")\n", "print(\"Head low threshold\",thresh[1],\"\\b, high threshold\",thresh[2],\"\\b.\")\n", "print(\"Min number of pixels in cluster\",minClusPixels,\"\\b, max number\",maxClusPixels,\"\\b.\")\n", "print(\"Min number of pixels in head\",minHeadPixels,\"\\b, max number\",maxHeadPixels,\"\\b.\")\n", "#\n", "# Colour table for plots\n", "nColTab = 4\n", "colorTab = ['r', 'orange', 'b', 'c']\n", "#\n", "if debug:\n", " print(\" \")\n", " print(\"rMark.shape\",rMark.shape,\"cMark.shape\",cMark.shape)\n", " plt.figure(figsize = (pltX, pltY))\n", " plt.imshow(markers, cmap = \"CMRmap\");\n", "#\n", "print(\"Width of expansion\",widthEx,\"\\b, useDiag =\",useDiag)\n", "print(\"Up-down asymmetry cut\",aUDcut)\n", "#\n", "#\n", "pathDirT00 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak045/\"\n", "pathDirT01 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak055/\"\n", "pathDirT02 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak065/\"\n", "pathDirT03 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak075/\"\n", "pathDirT04 = \"C:/Users/green/OneDrive/OneDocuments/Liverpool/LivDat/ImageAnalysis/Images/MCdata/pBreak085/\"\n", "#\n", "dirListT = [pathDirT00, pathDirT01, pathDirT02, pathDirT03, pathDirT04]\n", "print(\" \")\n", "print(\"List of image folders:\")\n", "for folder in dirListT:\n", " print(folder)\n", "#\n", "num_dirsT = len(dirListT)\n", "avgALRT = np.zeros(num_dirsT)\n", "errALRT = np.zeros(num_dirsT)\n", "avgAUDT = np.zeros(num_dirsT)\n", "errAUDT = np.zeros(num_dirsT)\n", "#\n", "print(\" \")\n", "for nd in range(0, num_dirsT):\n", " path = dirListT[nd] + \"*.bmp\"\n", " imagesT = (glob.glob(path))\n", " num_imagesT = len(imagesT)\n", " asymmLRT = np.zeros(num_imagesT)\n", " asymmUDT = np.zeros(num_imagesT)\n", " aErrLRT = np.zeros(num_imagesT)\n", " aErrUDT = np.zeros(num_imagesT)\n", " print(\"Look for images in directory \\n\",path)\n", " print(\"Number found is\",num_imagesT)\n", " useImageT = np.zeros(num_imagesT).astype(bool)\n", " for im in range(0, num_imagesT):\n", " imgRaw = plt.imread(imagesT[im])\n", " print(\"File \" + imagesT[im] + \" read.\")\n", " if debug:\n", " plt.figure(figsize = (pltX, pltY))\n", " plt.imshow(imgRaw, origin = 'lower', cmap = plt.get_cmap('afmhot'))\n", " plt.show()\n", " #\n", " # Perform initial image prcsessing (convert to grey and apply hot pixel cut)\n", " withHists = False\n", " imgGrey, nRows, nCols = processImage(imgRaw, hotCut, withHists)\n", " #\n", " # Find wheels using cluster (lowest) threshold\n", " nInClus, rPixCl, cPixCl, iPixCl = findWheels(imgGrey, thresh[0])\n", " #\n", " # Find wheels using low head threshold\n", " nInHdL, rPixHdL, cPixHdL, iPixHdL = findWheels(imgGrey, thresh[1])\n", " #\n", " # Find wheels using high head threshold\n", " nInHdH, rPixHdH, cPixHdH, iPixHdH = findWheels(imgGrey, thresh[2])\n", " #\n", " # Expand clusters\n", " nInClEx, rPixClEx, cPixClEx, iPixClEx, nInClEd, rPixClEd, cPixClEd, iPixClEd = \\\n", " clusExpand(nInClus, rPixCl, cPixCl, widthEx)\n", " #\n", " # Find rims of expanded clusters\n", " nInClExEd, rPixClExEd, cPixClExEd, iPixClExEd = findRims(nInClEx, rPixClEx, cPixClEx)\n", " #\n", " # Find rims of low threshold head\n", " nInHdEdL, rPixHdEdL, cPixHdEdL, iPixHdEdL = findRims(nInHdL, rPixHdL, cPixHdL)\n", " #\n", " # Find rims of high threshold head\n", " nInHdEdH, rPixHdEdH, cPixHdEdH, iPixHdEdH = findRims(nInHdH, rPixHdH, cPixHdH)\n", " #\n", " # Select comets, three threshold analysis\n", " nWheels, pnts_num, pnts_row, pnts_col, mean_row, mean_col, head_num, head_row, head_col = \\\n", " selCometsThree(nInClEx, rPixClEx, cPixClEx, nInClExEd, rPixClExEd, cPixClExEd, \n", " nInHdL, rPixHdL, cPixHdL, nInHdEdL, rPixHdEdL, cPixHdEdL,\n", " nInHdH, rPixHdH, cPixHdH, nInHdEdH, rPixHdEdH, cPixHdEdH) \n", " #\n", " if nWheels > maxWheels:\n", " print(\"Maximum number of wheels exceeded.\")\n", " useImageT[im] = False\n", " continue\n", " elif nWheels > 0:\n", " sumULwheel, sumLLwheel, sumURwheel, sumLRwheel, sumULhead, sumLLhead, sumURhead, sumLRhead = \\\n", " calcQuads(nWheels, pnts_num, pnts_row, pnts_col, mean_row, mean_col, head_num, head_row, head_col)\n", " #\n", " sumAllunsel = sumULwheel + sumLLwheel + sumURwheel + sumLRwheel\n", " aUDunsel = (sumULwheel + sumURwheel - sumLLwheel - sumLRwheel)/sumAllunsel\n", " boolAsel = abs(aUDunsel) < aUDcut\n", " nSel = np.sum(boolAsel)\n", " sumAll = sumULwheel[boolAsel] + sumLLwheel[boolAsel] + sumURwheel[boolAsel] + sumLRwheel[boolAsel]\n", " aUD = (sumULwheel[boolAsel] + sumURwheel[boolAsel] - sumLLwheel[boolAsel] - sumLRwheel[boolAsel])/sumAll\n", " aLR = (sumULwheel[boolAsel] + sumLLwheel[boolAsel] - sumURwheel[boolAsel] - sumLRwheel[boolAsel])/sumAll\n", " if nSel > 1:\n", " asymmUDT[im] = np.mean(aUD)\n", " asymmLRT[im] = np.mean(aLR)\n", " aErrUDT[im] = np.std(aUD, ddof = 1)/np.sqrt(nSel)\n", " aErrLRT[im] = np.std(aLR, ddof = 1)/np.sqrt(nSel)\n", " useImageT[im] = True\n", " #elif nSel > 0:\n", " # asymmUDT[im] = np.mean(aUD)\n", " # asymmLRT[im] = np.mean(aLR)\n", " # aErrUDT[im] = 0.02\n", " # aErrLRT[im] = 0.02\n", " # useImageT[im] = True\n", " else:\n", " print(\"No asymmetry (or asymmetry error) calculation possible.\")\n", " useImageT[im] = False\n", " else:\n", " useImageT[im] = False\n", " #\n", " avgALRT[nd] = np.sum(asymmLRT[useImageT]/aErrLRT[useImageT]**2)/np.sum(1/aErrLRT[useImageT]**2)\n", " errALRT[nd] = np.sqrt(1/np.sum(1/aErrLRT[useImageT]**2))\n", " avgAUDT[nd] = np.sum(asymmUDT[useImageT]/aErrUDT[useImageT]**2)/np.sum(1/aErrUDT[useImageT]**2)\n", " errAUDT[nd] = np.sqrt(1/np.sum(1/aErrUDT[useImageT]**2))\n", " #\n", " xPointsT = np.linspace(0, num_imagesT - 1, num_imagesT)\n", " yOnesT = np.ones(num_imagesT)\n", " #\n", " fig, axs = plt.subplots(2, figsize = (2*pltX, 2*pltY))\n", " axs[0].set_title(\"LR asymmetry\")\n", " axs[0].errorbar(xPointsT[useImageT], asymmLRT[useImageT], yerr = aErrLRT[useImageT], \n", " linestyle = '', color = 'r', marker = 'o')\n", " axs[0].plot(xPointsT[useImageT], np.zeros(num_imagesT)[useImageT], linestyle = \":\", color = 'b')\n", " axs[0].plot(xPointsT[useImageT], avgALRT[nd]*yOnesT[useImageT], linestyle = '-', color = 'k')\n", " axs[0].plot(xPointsT[useImageT], (avgALRT[nd] + errALRT[nd])*yOnesT[useImageT], linestyle = '--', color = 'k')\n", " axs[0].plot(xPointsT[useImageT], (avgALRT[nd] - errALRT[nd])*yOnesT[useImageT], linestyle = '--', color = 'k')\n", " axs[0].set_xlabel(\"Image number\")\n", " axs[0].set_ylabel(\"Asymmetry\")\n", " axs[0].grid(color = 'g')\n", " #\n", " axs[1].set_title(\"UD asymmetry\")\n", " axs[1].errorbar(xPointsT[useImageT], asymmUDT[useImageT], yerr = aErrUDT[useImageT],\n", " linestyle = '', color = 'r', marker = 'o') \n", " axs[1].plot(xPointsT[useImageT], np.zeros(num_imagesT)[useImageT], linestyle = \":\", color = 'b')\n", " axs[1].plot(xPointsT[useImageT], avgAUDT[nd]*yOnesT[useImageT], linestyle = '-', color = 'k')\n", " axs[1].plot(xPointsT[useImageT], (avgAUDT[nd] + errAUDT[nd])*yOnesT[useImageT], linestyle = '--', color = 'k')\n", " axs[1].plot(xPointsT[useImageT], (avgAUDT[nd] - errAUDT[nd])*yOnesT[useImageT], linestyle = '--', color = 'k')\n", " axs[1].set_xlabel(\"Image number\")\n", " axs[1].set_ylabel(\"Asymmetry\")\n", " axs[1].grid(color = 'g')\n", " #\n", " plt.tight_layout\n", " plt.show()\n", "#\n", "print(\" \")\n", "print(\"Folder\\t Average LR asymmetry\\t Error\")\n", "for nd in range(0, num_dirsT):\n", " print(f\"{nd}\\t {avgALRT[nd]:.4f}\\t\\t {errALRT[nd]:.4f}\")\n", "#\n", "xPlotT = np.linspace(0, num_dirsT - 1, num_dirsT)\n", "yOnesT = np.ones(num_dirsT)\n", "#\n", "fig, axs = plt.subplots(2, figsize = (2*pltX, 2*pltY))\n", "axs[0].set_title(\"LR asymmetry summary\")\n", "axs[0].errorbar(xPlotT, avgALRT, yerr = errALRT, linestyle = '', color = 'r', marker = 'o')\n", "axs[0].plot(xPlotT, np.zeros(num_dirsT), linestyle = \":\", color = 'b')\n", "axs[0].set_xlabel(\"Folder\")\n", "axs[0].set_ylabel(\"Asymmetry\")\n", "axs[0].grid(color = 'g')\n", "#\n", "axs[1].set_title(\"UD asymmetry summary\")\n", "axs[1].errorbar(xPlotT, avgAUDT, yerr = errAUDT, linestyle = '', color = 'r', marker = 'o') \n", "axs[1].plot(xPlotT, np.zeros(num_dirsT), linestyle = \":\", color = 'b')\n", "axs[1].set_xlabel(\"Folder\")\n", "axs[1].set_ylabel(\"Asymmetry\")\n", "axs[1].grid(color = 'g')\n", "#\n", "then = now\n", "now = datetime.datetime.now()\n", "print(\" \")\n", "print(\"Date and time\",str(now))\n", "print(\"Time since last check is\",str(now - then))\n", "print(\"Time since notebook start is\",str(now - startNB))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate $p_{break}$\n", "\n", "Using asymmetry value, determine $p_{break}$." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Date and time 2021-05-07 13:23:39.472619\n", " \n", "Folder\t LR asymmetry\t Error\t pBreak\t Error\t MC truth\n", "0\t -0.056\t\t 0.002\t 0.476\t 0.056\t 0.450\n", "1\t -0.074\t\t 0.002\t 0.585\t 0.073\t 0.550\n", "2\t -0.092\t\t 0.002\t 0.686\t 0.094\t 0.650\n", "3\t -0.116\t\t 0.002\t 0.800\t 0.127\t 0.750\n", "4\t -0.117\t\t 0.000\t 0.805\t 0.129\t 0.850\n", " \n", "Date and time 2021-05-07 13:23:39.491552\n", "Time since last check is 0:00:00.018933\n", "Time since notebook start is 0:34:16.785940\n", " \n", "Date and time 2021-05-07 13:23:39.492538\n", "Time since last check is 0:00:00.000986\n", "Time since notebook start is 0:34:16.786926\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import datetime\n", "now = datetime.datetime.now()\n", "print(\"Date and time \",str(now))\n", "#\n", "def invAsymm(A, Ae, a, ae, b, be, c, ce, incFuncErr):\n", " #\n", " p = a + b*A + c*A**2\n", " if incFuncErr:\n", " #\n", " # Error including uncertainties in coefficients in inverting function\n", " pe = np.sqrt(((b + 2*A*c)*Ae)**2 + (ae)**2 + (A*be)**2 + (A**2*ce)**2)\n", " else:\n", " #\n", " # Error assuming coefficients in inverting function perfectly known\n", " pe = np.abs((b + 2*A*c)*Ae)\n", " #\n", " return p, pe\n", "#\n", "incFuncErr = True \n", "#\n", "pB, pBerr = invAsymm(avgALRT, errALRT, aVal, aErr, bVal, bErr, cVal, cErr, incFuncErr)\n", "MCtruth = [0.45, 0.55, 0.65, 0.75, 0.85]\n", "print(\" \")\n", "print(f\"Folder\\t LR asymmetry\\t Error\\t pBreak\\t Error\\t MC truth\")\n", "for nd in range(0, num_dirsT):\n", " print(f\"{nd}\\t {avgALRT[nd]:.3f}\\t\\t {errALRT[nd]:.3f}\\t {pB[nd]:.3f}\\t {pBerr[nd]:.3f}\\t {MCtruth[nd]:.3f}\")\n", "#\n", "fig, axs = plt.subplots(1, figsize = (pltX, pltY))\n", "axs.set_title(\"$p_{break}$ extraction\")\n", "axs.errorbar(xPlotT, pB, yerr = pBerr, linestyle = '', color = 'r', marker = 'o')\n", "axs.plot(xPlotT, MCtruth, linestyle = ':', marker = '^', color = 'b')\n", "axs.set_xlabel(\"Folder\")\n", "axs.set_ylabel(\"$p_{break}$\")\n", "axs.grid(color = 'g')\n", "#\n", "then = now\n", "now = datetime.datetime.now()\n", "print(\" \")\n", "print(\"Date and time\",str(now))\n", "print(\"Time since last check is\",str(now - then))\n", "print(\"Time since notebook start is\",str(now - startNB))\n", "#\n", "then = now\n", "now = datetime.datetime.now()\n", "print(\" \")\n", "print(\"Date and time\",str(now))\n", "print(\"Time since last check is\",str(now - then))\n", "print(\"Time since notebook start is\",str(now - startNB))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }