From ef2db7a41df878ed2770d5986df380aff134499f Mon Sep 17 00:00:00 2001 From: Wenliang Dai Date: Mon, 23 Jul 2018 23:53:59 +0100 Subject: [PATCH] fix accuracy.p bug --- train_multi.py | 2 +- train_multi_human.py | 2 +- visualisation.ipynb | 197 ++++++++++++++++++++++++++++++++++++++++--- 3 files changed, 186 insertions(+), 15 deletions(-) diff --git a/train_multi.py b/train_multi.py index 34530f0..ae1306a 100644 --- a/train_multi.py +++ b/train_multi.py @@ -220,7 +220,7 @@ def main(args): totalclasswise_gtpixels_test[1] = totalclasswise_gtpixels_test[1].reshape((-1, n_classes[1])) totalclasswise_predpixels_test[1] = totalclasswise_predpixels_test[1].reshape((-1, n_classes[1])) - if isinstance(avg_pixel_acc, np.ndarray): + if isinstance(avg_pixel_acc, list): avg_pixel_acc[0] = np.vstack((avg_pixel_acc[0], np.sum(totalclasswise_pixel_acc[0], axis=1) / np.sum(totalclasswise_gtpixels[0], axis=1))) mean_class_acc[0] = np.vstack((mean_class_acc[0], np.mean(totalclasswise_pixel_acc[0] / totalclasswise_gtpixels[0], axis=1))) mIoU[0] = np.vstack((mIoU[0], np.mean(totalclasswise_pixel_acc[0] / (totalclasswise_gtpixels[0] + totalclasswise_predpixels[0] - totalclasswise_pixel_acc[0]), axis=1))) diff --git a/train_multi_human.py b/train_multi_human.py index 18eaa27..ec45db9 100644 --- a/train_multi_human.py +++ b/train_multi_human.py @@ -222,7 +222,7 @@ def main(args): totalclasswise_gtpixels_test[1] = totalclasswise_gtpixels_test[1].reshape((-1, n_classes[1])) totalclasswise_predpixels_test[1] = totalclasswise_predpixels_test[1].reshape((-1, n_classes[1])) - if isinstance(avg_pixel_acc, np.ndarray): + if isinstance(avg_pixel_acc, list): avg_pixel_acc[0] = np.vstack((avg_pixel_acc[0], np.sum(totalclasswise_pixel_acc[0], axis=1) / np.sum(totalclasswise_gtpixels[0], axis=1))) mean_class_acc[0] = np.vstack((mean_class_acc[0], np.mean(totalclasswise_pixel_acc[0] / totalclasswise_gtpixels[0], axis=1))) mIoU[0] = np.vstack((mIoU[0], np.mean(totalclasswise_pixel_acc[0] / (totalclasswise_gtpixels[0] + totalclasswise_predpixels[0] - totalclasswise_pixel_acc[0]), axis=1))) diff --git a/visualisation.ipynb b/visualisation.ipynb index 3fe4a16..4332341 100644 --- a/visualisation.ipynb +++ b/visualisation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -71,6 +71,104 @@ " " ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def draw_multi(folder, loss_range=[1, 10]):\n", + " losses = pickle.load(open(os.path.join(folder, \"saved_loss.p\"), \"rb\"))\n", + " x = np.squeeze(np.asarray(losses[\"X\"]))\n", + " # task 1\n", + " train_loss = np.squeeze(np.asarray(losses[\"Y1\"]))\n", + " val_loss = np.squeeze(np.asarray(losses[\"Y1_test\"]))\n", + " \n", + " plt.figure()\n", + " plt.grid()\n", + " axes = plt.gca()\n", + " axes.set_ylim(loss_range)\n", + " plt.title('Task 1 - Loss')\n", + " plt.plot(x, train_loss, label='train')\n", + " plt.plot(x, val_loss, label='val')\n", + " plt.legend()\n", + " \n", + " # task 2\n", + " train_loss = np.squeeze(np.asarray(losses[\"Y2\"]))\n", + " val_loss = np.squeeze(np.asarray(losses[\"Y2_test\"]))\n", + " \n", + " plt.figure()\n", + " plt.grid()\n", + " axes = plt.gca()\n", + " axes.set_ylim(loss_range)\n", + " plt.title('Task 2 - Loss')\n", + " plt.plot(x, train_loss, label='train')\n", + " plt.plot(x, val_loss, label='val')\n", + " plt.legend()\n", + " \n", + " \n", + " accuracy = pickle.load(open(os.path.join(folder, \"saved_accuracy.p\"), \"rb\"))\n", + " print(accuracy)\n", + " x2 = np.squeeze(np.asarray(accuracy[\"X\"]))\n", + " P1 = accuracy[\"P1\"]\n", + " M1 = accuracy[\"M1\"]\n", + " I1 = accuracy[\"I1\"]\n", + " P1test = accuracy[\"P1_test\"]\n", + " M1test = accuracy[\"M1_test\"]\n", + " I1test = accuracy[\"I1_test\"]\n", + " \n", + " P2 = accuracy[\"P2\"]\n", + " M2 = accuracy[\"M2\"]\n", + " I2 = accuracy[\"I2\"]\n", + " P2test = accuracy[\"P2_test\"]\n", + " M2test = accuracy[\"M2_test\"]\n", + " I2test = accuracy[\"I2_test\"]\n", + " \n", + " plt.figure()\n", + " plt.grid()\n", + " plt.title('Task 1 - Avg pixel accuracy')\n", + " plt.plot(x2, P1, label='train')\n", + " plt.plot(x2, P1test, label='val')\n", + " plt.legend()\n", + " \n", + " plt.figure()\n", + " plt.grid()\n", + " plt.title('Task 2 - Avg pixel accuracy')\n", + " plt.plot(x2, P2, label='train')\n", + " plt.plot(x2, P2test, label='val')\n", + " plt.legend()\n", + " \n", + " plt.figure()\n", + " plt.grid()\n", + " plt.title('Task 1 - Mean class accuracy')\n", + " plt.plot(x2, M1, label='train')\n", + " plt.plot(x2, M1test, label='val')\n", + " plt.legend()\n", + " \n", + " plt.figure()\n", + " plt.grid()\n", + " plt.title('Task 2 - Mean class accuracy')\n", + " plt.plot(x2, M2, label='train')\n", + " plt.plot(x2, M2test, label='val')\n", + " plt.legend()\n", + " \n", + " plt.figure()\n", + " plt.grid()\n", + " plt.title('Task 1 - mIoU')\n", + " plt.plot(x2, I1, label='train')\n", + " plt.plot(x2, I1test, label='val')\n", + " plt.legend() \n", + " \n", + " plt.figure()\n", + " plt.grid()\n", + " plt.title('Task 2 - mIoU')\n", + " plt.plot(x2, I2, label='train')\n", + " plt.plot(x2, I2test, label='val')\n", + " plt.legend() " + ] + }, { "cell_type": "code", "execution_count": 3, @@ -195,43 +293,116 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'X': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87], 'P1': array([0.8434854], dtype=float32), 'P2': array([0.7939454], dtype=float32), 'M1': array([0.5781161], dtype=float32), 'M2': array([0.4087556], dtype=float32), 'I1': array([0.43401355], dtype=float32), 'I2': array([0.29565144], dtype=float32), 'P1_test': array([0.85259295], dtype=float32), 'P2_test': array([0.80015576], dtype=float32), 'M1_test': array([0.51651907], dtype=float32), 'M2_test': array([0.38447192], dtype=float32), 'I1_test': array([0.3725513], dtype=float32), 'I2_test': array([0.2792721], dtype=float32)}\n" + ] + }, + { + "ename": "ValueError", + "evalue": "x and y must have same first dimension, but have shapes (87,) and (1,)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m 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\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 241\u001b[0m raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 242\u001b[0;31m \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m raise ValueError(\"x and y can be no greater than 2-D, but have \"\n", + "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (87,) and (1,)" + ] + }, + { + "data": { + "image/png": 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KrM28eG3LmCBZKaU8oUX23AWICPbj+iFt+WLFTrbl6UidSilVpcUGfoBbhqXh6+Pg1R83\nN3ZWlFKqyWjRgT8uLJCr01P4aHE2uwvKGjs7SinVJLScwH+kAtZNh13Lq62eeHY7Kg289tOWRsqY\nUko1LS0n8IsDPrkNlrxdbXVKVDDjerfmvfk72F9c3kiZU0qppqPlBH6HAxJ6wa5lJ2y6I6M9fzSv\nMO/jFxohY0op1bS0nMAP0Lov7F5li31cdGwF1/hm4rPpW9buOthImVNKqaahhQX+PlBRCnnrq6/P\nWYIDQ4KjgIc+WkHFkcrGyZ9SSjUBLSvwJ/axrzuXVl+fswiADiGlrMwp4PXZWxs4Y0op1XS0rMAf\n3QH8Q2HnceX82TbwB5fv57xu8fxjxga25OoAbkop79SyAn9NFbzGQPZCAORwMX8Zm0aAr4OHP16h\nUzMqpbxSywr8cGIFb/52KM6FpHQAYh0F/PGibizcdoD3FuxoxIwqpVTjaIGB/7gKXmcxD13G2tei\nXK7on8zQDtH8/at17DmoPXqVUt6l5QX+oxW8zuKe7EXgGwTtMuxy8V5EhL+M70n5kUoen9ZgI0Ur\npVST0PIC/9EKXmfLnuyFtvgnrLVdLtoLQGpMCPeO7shXq3bz7erdjZRZpZRqeC0v8DsckNjbVvBW\nHILdKyA5HUJiADka+AFuG96OLglhPPr5agrLDjdenpVSqgG1vMAPtrhn9yrIWQJHyiF5APj4QXAU\nFB8L/H4+Dv52WU/2FJbxyCcrKTt8pBEzrZRSDaNlBv6qCt5l79rl5AH2NSSu2hM/QN82kfzm/M58\nuWIXV74yl6z9JQ2cWaWUalh1Bn4RmSwie0VkVS3bJ4jIChFZKSJzRKS3y7ZtzvXLRGSRJzN+UlUV\nvCs/gvBkCE+0y6GxJwR+gDszOvDajels21fMRf83m5nrT0yjlFIthTtP/G8CY06yfSswwhjTE/gz\nMOm47SONMX2MMemnlsVTUFXBW1Fqy/erhMZXK+pxdW63eP539zASIwL5f28u5OtVuxoos0op1bDq\nDPzGmB+B/SfZPscYc8C5OA9I9lDeTl1VBS9UD/whcVCUW+tuqTEhfHrnUPqktOKBD5azbreO5KmU\nanl8PXy8W4CvXJYN8K2IGOBVY8zxvwaOEpGJwESA+Ph4MjMz3TphUVFRjWnbH4kmBViS68tB5/aU\nvYW0P1zMT999xRHfoFqPeWO7Sv60p5IbXp3NY0OCCPUXt/LS1NR2bZRem5PRa1OzFnVdjDF1/gGp\nwKo60owE1gLRLuuSnK9xwHLgbHfO179/f+OumTNn1rwha6Ex715lzOGyY+uWvGPMY+HG7NtcPe2P\nzxmz4PVqqxZv3286/m66mfDaPHO44ojb+WlKar02Sq/NSei1qVlTvy7AIuNGfDXGeKZVj4j0Al4H\nxhlj9rncVHKcr3uBT4GBnjifW5LT4br3wTfg2LrQePt6fHHP/Fdg9gt2QDenfm0ieXJ8D2ZvyuO+\n95exWUfzVEq1EKdd1CMibYBPgBuMMRtc1ocADmNMofP9ecATp3u+0xIaa19dK3hLD0DRHvv+wFaI\nand001UDUsjOL+WVzM18sWIXwzrEcOOQtpzbLR6R5ln8o5RS7jTnnALMBTqLSLaI3CIivxSRXzqT\nPApEAy8f12wzHpgtIsuBBcCXxpivz8BncF9InH11bdKZu+HY+y2zTtjlgXM7Mee3o/jN+Z3ZnFvE\nxLcX87tPV+mQzkqpZqvOJ35jzLV1bL8VuLWG9VuA3ifu0YhCYuyra+CvGsXTLxi2ZEL6L07YLSY0\ngLtGduD2s9vx3IwN/DtzM0cqK3nqsl44HPrkr5RqXjzdqqdp8/GD4OjqRT2568E3ELpeDBtnQGWl\nbQ5aA18fBw+d3xk/h/DiD5s4UglPX9ELHw3+SqlmpGUO2XAyxw/bkLseYjpC+1FQuh/2rDzp7iLC\nA+d15v7Rnfh4STZ3v7eE/cXlZzjTSinlOd4X+I8ftiFvPcR0hrQRdnlLpluHuXd0R357QRdmrNnD\nOc9l8vHi7KomrEop1aR5X+APiTtW1FNeDPk7ILaLHc8ntovbgR/g9hHt+eKeYaTFhPDrD5cz4fX5\nrNmpvX2VUk2b9wX+0Phj7fjzNtrX2E72NW0EbJ9rx/F3U5eEcD765Vn85dIerMopYOyLP3H724tY\nvbPAwxlXSinP8MLAHwuHi+FQEeQ5m3LGdLav7TLswG5ZC+p1SIdDmDCoLT89PIr7RndkzuZ9XPji\nbO54ZzG7C3ROX6VU0+J9gb+qLX/xXshdBw7fY522UoeC+NSruMdVRJAf943uxOyHR3HvOR35Yd1e\nzv3HLD5YlKXl/0qpJsO7mnMChFZ14sq1LXqi2oGvv10XGAFJ/WDrLOCPp3yKiCA/7j+3E+P7JvHw\nxyt46KMV/G/5Trq3jiDrQAnZ+0sICfDlH1f3IT488PQ/k1JK1YP3PfGHuj7xr4eYTtW3t8uAnMVQ\ndvpl9GkxIUy9bTBPjOvOku0H+M/sLazOKSA8yI/lWflc99o8cgvdr09QSilP8L4n/qqinoIc2L8F\nuo2rvj1tBPz4DGz7GbqMPe3TORzCjUNSuWZAG3wccrSz14Kt+7lp8gKue20eUycOJjo0oI4jKaWU\nZ3jfE3/VsA075oI5ArGdq29PHgA+AbDtJ4+e1t/XUa2H78C0KP5zczpZB0qY8Pp8ffJXSjUY7wv8\nPn4QFAXbZtvl44t6/AIhZSBs9Wzgr8lZ7WN4/cYBbM0r5uynZ/L4tNU62btS6ozzvsAPti1/SR4g\nJwZ+sMU9e1ZCSa0zTnrMsI4xfPGrYYztmcg787aT8Wwm97+/jL0HtRmoUurM8NLA7xyXv1UK+Aef\nuD1tuH2t+lVwhnWMD+O5q3rz40Mj+cVZqUxfuYvRz2szUKXUmeGdgb+qgje2S83bW/ezwzR7uJy/\nLq1bBfGHi7rx1b3D6ZIQzkMfreDGyQvYtFdn/1JKeY53Bv6qJp01FfOAbdffZnCDlPPXpF1sKFMn\nDubPzmago5+fxZWvzOGjxdmUlFc0Sp6UUi2Hdwf+41v0uEodDrlrT5yf1xNK8+2YQCfhcAg3DEkl\n8zcjeeSCLuQVlfPgh8sZ9NfveWXWZsoOH/F8vpRSXsFLA79z0vWYkwT+tLPt65ko7vnpOXjzQnsD\nqENsWAC/HNGeH349gvcnDmZAahRPfbWO0c/P4osVO7UOQClVb24FfhGZLCJ7RWRVLdtFRF4UkU0i\nskJE+rlsu0lENjr/bvJUxk9LlwvhvCchOb32NIl9wD/szAT+bT/ZPgS7V7i9i4gwqF00k28ewDu3\nDCI0wJe731vK8Kdn8sAHy5iyYAdbcrUuQClVN3ef+N8Expxk+wVAR+ffRODfACISBTwGDAIGAo+J\nSOSpZtZjAiPgrF+Bw6f2ND6+0HbIycv5578K3/2pfucuOwi7ltv3Va/uMgb2rmNYxxi+vGc4z17Z\nm+6tw5m1PpfffrKSUc/N4pGPV1B0SOsBlFK1c2vIBmPMjyKSepIk44D/GlvuME9EWolIIpABzDDG\n7AcQkRnYG8iU08l0g0kdDhu/hYO77EQtrlZ+BF89ZN/3vwkiU907ZtZ8MJX2fX0D/9J3YNrdcMdc\nfOK7cUX/ZK7on4wxhq15xby/MItJP23h5815PHdln/odWynlNTw1Vk8SkOWynO1cV9v6E4jIROyv\nBeLj48nMzHTrxEVFRW6nra/QwmDSgTVfTWJv/Iij68MOrqfv0t9TFNaBsMLNbP/sr2xLu86tY7bb\n/B7J4kt+q+4EbJ7Lwnrkvd/iFwkHNnz3FjuTLjhh+5BgiB4YyGsryrj61bmMSDQUls8kzF8ngz/e\nmfzeNHd6bWrWkq5LkxmkzRgzCZgEkJ6ebjIyMtzaLzMzE3fT1lvlcFj9BN2OrKFbrxshKg0KsuG1\niRDRmvDbvoVPbiM192dSb/r3yYuOqmx6EpL7E9V+FGQ+RcaQdAgIrXu/vWsh004c0ymkkE61fOYM\n4LqxFfxt+lrem7+DhfsOcevwdtw6PI2wQD+3P3pLd0a/N82cXpuataTr4qnAnwOkuCwnO9flYGOR\n6/pMD53zzHP4QK+rYcEk2PA1tGoDCJSXwI2fQ0g09L0ePvqFHcO//aiTH6+8GHYuhbPusZXHGNiz\nyvYZqMuSt8HhB4m9IXvRSZOGBvjyl0t70s0vl9n5Efzz+428NXcbY3smcm63eM5qH02Arxs3KaVU\ni+Sp5pzTgBudrXsGAwXGmF3AN8B5IhLprNQ9z7mu+bjgabhrAYx91gZdDFz5BsR1tdu7XAhBkbb8\nvS5Z86Gyws70ldjbrnOnnL+iHFZMhc4XQKfz7ZSRbswXkBTq4N/X9+d/dw9jaPsYPluawy/eWEi/\nJ2bwwAfL2LFPB4RTyhu59cQvIlOwT+4xIpKNbanjB2CMeQWYDowFNgElwC+c2/aLyJ+Bhc5DPVFV\n0dtsiNiOXrGdYeBtJ273DYCeV8HiN+2gbsFRtR9r2892aseUQeAfaoeOcCfwb/gKSvZBvxudxUkG\ncpZA+5FufYSeyRG8NKEfZYePMHfzPr5ds5tPl+bwv+U7mTCoLb8a1UHnA1DKi7jbqufaOrYb4K5a\ntk0GJtc/a81I3+thwauw6uOabw5Vtv9sn/QDwuxyYm/Yuazu4y99B8Ja26KkQ4V2Xc4itwN/lUA/\nH0Z2iWNklzjuG92JF77byNvztvPhoizG9U1ifJ8k0ttG4nBoZbBSLVmTqdxt1hJ7QUIvWPp27YH/\ncKmd0nHQ7S779YbNP9htfkE173dwJ2z6DoY9YJ/2g1rZMYayF59WluPDA/nbZT25ZVgaL83cxKdL\ncnhv/g5aRwRyXvcEeqdE0DMpgrSY0GoTyCilmj8N/J7S9wb46jeQ+XdI6AFR7atP5J69CI6UQ9th\nx/ZJ7G178O5ZA8n9az7usndtu/++1x9bl5QOm2bYDl1yekG5Q1wo/7i6D3+5tIIZa/bw+bKdTFmw\ngzfn2L4GIf4+3HZ2O+4Z1VF/CSjVQmjg95ReV8KiyZD512PrgqJg1B+g/822mAep3oKntbOT1a5l\nNQf+fZth/iTbkSwq7dj65P6w/D3I3wGRbT2S/WB/X8b1SWJcnyQqjlSyObeYlTkFfL92Dy98t5Fl\nWfm8cHUfWgX7e+R8SqnGo4HfU4Ii4a55toJ3/1bYvxmW/Be+fAAWvwFHKiChpy2qqRKRYverqYI3\nbyO8eZH9RXDB36tvS3KOMZSzyGOB35Wvj4POCWF0Tgjj8n5JvLdgB49PW81F/zebF6/tS+/kVlr8\no1QzpoHf04Kj7F9yf+h5Jaz5DL75AxzMhkF3VE8rYot7jg/8e9fBWxcDBm76AuK7Vd8e3x18A23x\nUY/Lz+jHEREmDGpLt8Rw7nx3CZe9PAc/HyGpVRApUcGc3TGWq9JTiAjWzmFKNRca+M8kEeh+KXQ8\nH1Z+aNvgHy+xN8z7t22r7+sPO+bB1Am2IvemL2qeM8DHz3YAq6Mjlyf1bRPJl/cM5+tVu9mxv4Ts\nAyVsyS3mL9PX8vyMDVzeP4mbz0qlQ1xYg+VJKXVqNPA3BP9gO5BbTRJ720rf7IWw+lNY+LqdC/j6\nTyCmY+3HTE6HBa8du2E0gKgQf64b1KbautU7C3jz5218sCibd+btYFiHGG46K5VRXeK0OEipJkoD\nf2NLdFbwvj0ejhy2zT1H/bHu8XuS+sORf9khH5L6nTztGdS9dQTPXNmbRy7owpQFO3hn3g5u++8i\nUqKC6BQXxv6Scg4Ul1Np4M6M9lyVnqKtg5RqZN45A1dTEplmK3mj2sMtM2xFrjuDtlVNIpNTS3v+\n3A2EFG3zWDbrEh0awN2jOvLTwyN56bp+pEQGs6ugjBB/X3oltyIm1J9HPlnJNZPmsWlvYYPlSyl1\nIn3ib2wOB9w131bWujO6Z5WIFDvkw465J3YaqyiHd6+gV0kRjL2hfsd1dagIXhoIIx6uvaiqyt61\n8POL+F38Ty7slciFvarPX2CM4cNF2fxl+lou+OdPXJWewtieiQxMi8LPR58/lGpI+j+uKfAPqX9w\nFoFu42D1Z5C7ofq2Ze9A/nYCyvfBpu9PPV9rp8HBHFj9Sd1pF71h+xbU8gtERLhqQArf/3oE4/sk\n8fGSbCa8Pp8Bf/mOBz9crtNGKtWANPA3ZyMetjeNGY8eW3e4DGY9A8kDKPeLgKX/PfXjL3vPvm6f\na4eiPpnNzhtM9oKTJosJDeAIrL2kAAAgAElEQVSZK3uz9I/n8cr1/RnVJY6vV+3m/Bd+5Kmv1lGs\n00YqdcZp4G/OQmNh+K/t6J1bMu26xW9C4U4451H2xI+E9V9BUW79j31gu50Uvs1ZcOQQ7Jhz8rT7\nNtn3WScP/FWC/H0Y0yOB56/qw8wHMxjXJ4lXZm1m1HOZ/HfuNnbml9Y/z0opt2jgb+4G/dJOEPPN\n7+3InT89Z4d4SDubXYmj7fj/K6bW/7jLpwICF/8TfAJg0w+1p93s3Na6rw38xtTrVLFhATx7ZW8+\nufMsEsIDefTz1Zz11A+c949Z/HX6WuZszuPwkcr6fwalVI20cre58wuE0X+ys4C9fSkU74Wr3wag\nJCQFkgfaoSOG3O3+gG7G2PL6tLMhthO0HXIsuNdk8w8QnmQHkvvy13BgW/WxhdzUr00kn901lM25\nRWSuzyVzfS5v/ryNST9uISzAl+GdYjirfQydE8LoFBemvYWVOkUa+FuC7pfa3r/ZC6DD6OoDwfW7\nAab9yj6Jtxnk3vF2zLXBO+O3drn9KFuPcHAnhLeunvZIBWyZBd0uthPMgO2MdgqBH2wlcIe4MDrE\nhXHr8HYUH6pg9qY8Zq7byw/r9jJ95e6jaePDAzivWwLXDWpD18TwUzqfUt5IA39LIAJjn4ZP74Bz\nHqu+rful8NUjtpLX3cC/7F07Q1jXi+1yVeDfPBP6TqieducSOFQA7c+BuG52v6z50Ouq0/9cQEiA\nL+d3T+D87gkYY8jJL2XjniI27ClkRU4B7y/K4u152+nbphWX9UsmvW0kHeNC8dUmokrVSgN/S9G6\nrx0d9HgBYdDjMlj1CYx56tjsX7UpL4bVn0P38bbFEEBcd9tnYPMPJwb+zT8AAu0ybJPUpP5uV/DW\nl4iQHBlMcmQwI7vEAXCguJyPl2Tz3oId/PGzVQAE+/vQIymCszvGMKZHgo4fpNRx3J1zdwzwT8AH\neN0Y89Rx2/8BVM0DGAzEGWNaObcdAVY6t+0wxlziiYyreuh/s50d7H/3waWvgs9J/tlXfwrlhdD7\numPrHA47zeOm76Cy0i5X2fS9HTKiaq7hlIG2gvlQkXs9kE9TZIg/tw5vxy3D0tiaV8yK7AKWZeWz\nZMcBnv12A89+u4H2sSGM7BxH2+hgkiKDaN0qiANllRQdqiDYz0eHkFBep87ALyI+wEvAuUA2sFBE\nphlj1lSlMcbc75L+V0Bfl0OUGmP6eC7Lqt6S02H04/Dd47ZYaPwrNQf/7XNh+kN23oA2Q6pvaz8K\nVrwPu5fbXxcApfl2ToDhvz6WLmWQnTFs5xJbOdxARIR2saG0iw1lfN8kAPYcLOPb1bv5evVu/jt3\nO+XHtwzK/AaAmFB/xvVJ4rpBbWgfe+ZvVko1Nnee+AcCm4wxWwBEZCowDlhTS/prgcdq2aYay7D7\nbWud7/9kly99tXpv4ayF8O4VtvJ2wsfVn+oB2jl/0G3+4Vjg3zrLBvn25xxLVzWGUNb8Bg38NYkP\nD+SGIancMCSVykpDXtEhsvNL2ZlfysJlq0lKbUfxoSNs3FvIf+du4z+ztzKkXTQTR7RjZOe4Rs27\nUmeSmDraXIvIFcAYY8ytzuUbgEHGmLtrSNsWmAckG2OOONdVAMuACuApY8xntZxnIjARID4+vv/U\nqe61PS8qKiI0VJ/SalLTtWmz/SPabX2bA616si86ncKwToCh58onKfePYFmfv1AeEF3j8dIX3keF\nbxCrevyeCt9gOm14hbi9P/Lz0HcwjmPPEAMW3EVZYAIre/3xTH6803L8tSk4ZPgp5zAzd1Swr8zQ\nO9aH67r4Ex/ifZXE+n+qZk39uowcOXKxMSbdnbSeDvwPY4P+r1zWJRljckSkHfADcI4xZvPJzpme\nnm4WLXJvkpHMzEwyMjLcSuttar028/4Nc1+Ggh3H1rVqC7+YDhHJtR9wxmPw8wv2vTgDYqcL4Nr3\nqqf7/C5Y9yU8tPW0J4M/U2q7NuUVlbw5Zyv//G4jh48YJgy2xT9Bfj4E+vmQEBFAl4RwQgJabrsI\n/T9Vs6Z+XUTE7cDvzrc3B0hxWU52rqvJNcBdriuMMTnO1y0ikokt/z9p4Fdn2OA77F/hHlsWv28T\ndL8MIpJOvt/wByC2C5Tuh9IDUFYAfa47MV3KIFj6jj3uySaTaYL8fR1MPLs94/sk8fev1/PGz9tO\nSCMCadEhdE4IIzrUn7BAP8ICfene2rYkkiZ6s1OqijuBfyHQUUTSsAH/GuCE/+0i0gWIBOa6rIsE\nSowxh0QkBhgKPO2JjCsPCIuHzhe4nz4wAvpcW3e65IH2NWt+swv8VeLCA3nuqt48Ma47xeUVlJVX\nUnK4guz9pazeeZA1uwpYt7uQ/JJyCssqqKi0v5z7pLTiwfM6M7RDtN4AVJNVZ+A3xlSIyN3AN9jm\nnJONMatF5AlgkTFmmjPpNcBUU73sqCvwqohUYscFesq1NZBqoWI6QWAr23eg93UnVhQ3hPIS5xwH\np3fukADfasU6XRLCGd0tvloaYwylh48wbdlOXvx+I9f/Zz6D0qJ44NxODGp3Yn1JQelhwgN99cag\nGo1bBZXGmOnA9OPWPXrc8uM17DcH6Hka+VPNkcMBIx6Cb34H3z0G5/25Yc+fvwNeGWabmQ6994yf\nTkQI9vflmoFtuLRfEu8vzOJfP2zi6knzGN4xhgfO7USn+DC+XLGLqQt3sGRHPud0ieOf1/YltAXX\nFaimS7916swYfCfs3wJzXoTIVBhwy4lpDhXB4jfsZDLx3Wyz0HYjICjy1M9rDHz5oK1/WPQGnHVP\ng1YwB/j6cOOQVK5KT+Gdedt5OXMzl748h0A/B2WHK+kQF8r1g9swZUEWV/x7Dq/flE5yZHCD5U8p\n0MCvzhQRGPN3yM+C6Q/aoaPbn2N7BZfsh5UfwryXbSVxQi8b/Jf817YWSuhpJ6Fv3cf2GUjsc2Lw\nzlpgf1Gk/7/qFcyrP4GN39gOaDvmwo55dnTRBhbo58Otw9txzcA2/HfuNnbllzG+b2v6tYlERDi/\newJ3vruE8S/9zO/GduVAyWE27S1ka14xfdtE8ouzUokLD2zwfCvvoIFfnTk+vnDFZHjjAnjvKuc4\n/S5VQJ0usMUxKQPsKJ85i+wQEFnzYc3nsOQtmy55AJz7ZxvAKyth7v/B90/Ym8Rnd0DZQRj8S3sT\n+ephe7O47n14rqsdXtpTgT/zKTu3cIdz7E2srlZQQGiAL3dmdDhh/fCOsXx651BufWshD3ywHIBW\nwX4kRwbxyqzN/OenrVzWL4lL+yZRdKiCvYWH2HvwEJEhfnRJCKdzQhgRQTostTo1GvjVmRUQChM+\nggWvgvjYlkGBEXZ8n/jux9L5+NrhpKuGlDYG8rfb8YF+fBbeGAOdL4TKw7DxW+h6CVz4PHxxH3z9\nsJ2EJn+7/TVx/cf2HN0usb8kLnga/IJO73NsngmZfwP/MFjj7IOY2AeunXLiUNVu6hAXyhf3DGfd\nroOkxoQQHeKPiLAtr5jXZ2/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vLtH0TI4iPMQmhjPGn1ng9wdn/whWvgyfPeZ0IY3tejitx1hY8zqUFRIen8E3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\njeyvqWPNznLntaucTzbtpeZ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-67, -66, -66, -67, -66, -67, -69, -66, -65, -65, -65, -65, -66, -65, -65, -65, -65, -65, -65, -66, -64, -65, -65, -65, -65, -66, -65, -64, -62, -66, -66, -73, -72, -64, -65, -63, -65, -62, -62, -62, -62, -62, -63, -62, -62, -62, -62, -62, -63, -73, -73, -63, -63, -63, -63, -63, -63, -63, -63, -63, -63, -64, -64, -65, -65, -64, -62, -64, -64, -63, -67, -67, -71, -71, -64, -64, -64, -64, -64, -59, -59, -67, -67, -67, -68, -63, -67, -62, -59, -59, -58, -57, -56, -58, -60, -56, -56, -58, -60, -56, -60, -61, -62, -62, -59, -59, -53, -59, -59, -60, -59, -59, -60, -60, -59, -60, -61, -60, -61, -62, -61, -58, -59, -92, -55, -56, -55, -56, -56, -55, -56, -58, -58, -58, -59, -59, -58, -56, -56, -56, -61, -61, -60, -58, -59, -59, -63, -62, -62, -58, -58, -56, -59, -62, -62, -62, -66, -59, -59, -62, -62, -64, -60, -62, -64, -65, -57, -59, -62, -61, -62, -61, -65, -65, -57, -57, -59, -65, -64, -64, -59, -60, -59, -59, -64, -59, -61, -51, -68, -68, -66, -66, -66, -68, -67, -66, -66, -65, -65, -59, -54, -54, -62, -62, -62, -62, -62, -64, -62, -66, -65, -59, -67, -61, -59, -56, -64, -56, -65, -54, -52, -47, -53, -59, -52, -54, -50]" + "a = np.array([1])" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([ 74., 910., 2518., 179., 57., 4., 0.]),\n", - " array([-80., -70., -60., -50., -40., -30., -20., -10.]),\n", - " )" + "array([3])" ] }, - "execution_count": 66, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" - }, + } + ], + "source": [ + "a + [2]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
" + "False" ] }, + "execution_count": 17, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "plt.hist(data, range=[-80,-10], bins=7)" + "isinstance(0, list)" ] }, {