{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Normal Score Transform" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import sys\n", "ppath = \"../..\"\n", "\n", "if ppath not in sys.path:\n", " sys.path.append(ppath)\n", "\n", "import pygeostatistics as pygs\n", "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read Spatial Data:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(85, 4)\n" ] }, { "data": { "text/plain": [ "array([14.6515, 14.5093, 14.0639, 15.1084, 13.919 , 13.1304, 14.5724,\n", " 15.0814, 13.91 , 13.4024, 14.9395, 15.2159, 14.5777, 14.2483,\n", " 14.4281, 15.2606, 16.1859, 14.2079, 16.9583, 13.8354])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pygeostatistics.gslib_reader import SpatialData\n", "\n", "data = SpatialData('../../testData/test.gslib')\n", "\n", "print(data.df.shape)\n", "\n", "vr = data.vr['por']\n", "vr[:20]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup Normal Score Transform:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "from pygeostatistics.normal_score_transform import NormalScoreTransform\n", "\n", "nst = NormalScoreTransform(\n", " vr, np.ones_like(vr),\n", " 0, 30,\n", " 0, 1,\n", " 15, 1)\n", "nst.create_transform_func()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tranform Data to Normal Score (forward):" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.49005024e-08, -1.47987098e-01, -7.40785459e-01, 4.57851945e-01,\n", " -9.51843262e-01, -1.73719397e+00, -1.18233857e-01, 3.30295806e-01,\n", " -9.99304789e-01, -1.42921943e+00, 2.07916558e-01, 6.28904221e-01,\n", " -8.85849411e-02, -5.24400524e-01, -2.07916558e-01, 6.65262904e-01,\n", " 1.88950998e+00, -5.58547494e-01, 2.51912447e+00, -1.10170891e+00])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "normal_score = nst.transform(vr)\n", "\n", "normal_score[:20]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Transform Normal Score to Data (backward):" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([14.6515, 14.5093, 14.0639, 15.1084, 13.919 , 13.1304, 14.5724,\n", " 15.0814, 13.91 , 13.4024, 14.9395, 15.2159, 14.5777, 14.2483,\n", " 14.4281, 15.2606, 16.1859, 14.2079, 16.9583, 13.8354])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vr_back = nst.back_transform(normal_score)\n", "\n", "vr_back[:20]" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAGeCAYAAACadcoEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XucHHWd7//XO5MBJhCcYBJ1AiGKkD3ckbDcvEACBhBwRBfMhhUvR3R/C+4+wFFzyBF08QSNctHVdWFFlkOICuIocT3BGwb5AQoMENhDxHDNBOjhErlkIGHmc/7omtCZzEx3J91d3dXv5+MxD6arqrs+PUPeU/2tb31KEYGZmWXXuLQLMDOz6nLQm5llnIPezCzjHPRmZhnnoDczyzgHvZlZxjnozSpM0qOSjkm7DrMhDnprSJL+VtKdkl6S9KSkX0h6Z9p1lUPSUZLWpF2HZZ+D3hqOpHOAS4H/BbwJmA58B3j/CNuOr211ZvXHQW8NRdIbgC8D/xARN0TEyxGxMSJujIguSRdIul7SNZJeAD4qaXtJl0pam3xdKmn75PUmS1omaZ2k5yTdImlcsu7zknolvShplaQ5yfKhffwwWXe3pANGqXfEfUvaEfgF0JF8KnlJUkdNfojWdBz01mgOB3YAfjLGNu8HrgfagSXAecBhwIHAAcBfAwuTbc8F1gBTyH86+B9ASJoJnAUcEhETgbnAo8P2cR2wC3At0C2pdYRaRtx3RLwMHA+sjYidkq+1pf8YzErnoLdG80bgmYh4bYxtbouI7ogYjIh+YD7w5YjIRUQf8CXg75JtNwJvAXZPPhncEvkGUAPA9sDekloj4tGIWF2wj7si4vqI2AhcTP6Pz2Ej1DLWvs1qwkFvjeZZYHKRsfcnhj3uAB4rePxYsgxgMfBn4CZJD0v6AkBE/Bn4J+ACICfpB8OGVjbtIyIGyX8qGGnoZax9m9WEg94azW3AK0DnGNsMb8m6Fti94PH0ZBkR8WJEnBsRbwNOAs4ZGouPiGsj4p3JcwP4asFr7Db0TTKmv+vQa5a67xHqNKsKB701lIj4C/BF4NuSOiVNkNQq6XhJXxvlaUuBhZKmSJqcPP8aAEknSnq7JAEvkB+yGZA0U9Ls5KTtK0B/sm7IwZJOST5Z/BPwKnB7OfsGngbemJxgNqsaB701nIi4GDiH/AnVPvLDKGcB3aM85ULgTuA+YCVwd7IMYE/gV8BL5D8tfCcibiY/Pn8R8AzwFDCV/InaIT8FTgOeJz/mfkoyXl/yviPiQfJ/CB5OZv14SMeqQr7xiFl5JF0AvD0iTk+7FrNS+IjezCzjHPRmZhnnoRszs4zzEb2ZWcY56M3MMs5Bb2aWcQ56M7OMc9CbmWWcg97MLOMc9GZmGeegNzPLOAe9mVnGOejNzDLOQW9mlnEOejOzjHPQm5llnIPezCzjHPTWECTdLOm/V/g1Z0iK5L6vDSG5l22PpBclfSbteoZLfp5vT7sO21zD/A9uVg8kzQAeAVoj4rUUSvgccHNEHJTCvq1B+Yjetpry/P/QMFX+hLA78MDWPLGRPrlYZfkfqW1G0hckXT9s2WWSvpl8f7Okr0i6FVgPvK3M539U0sPJ0MMjkuaXUd4ekv4g6S+Sfippl4J9XCfpqWTdCkn7FKxrk/QNSY8l638vqW2E9/5BSY9K2neMGlYk/10n6SVJhyfv6VZJl0h6DrhA0h6SfiPpWUnPSFoiqb1gX49K+qyk+5Kafihph2TdZEnLJK2T9JykWySNk/Qb4GjgX5J97yXpDZKultSXvL+FQ398R6mrcNm65HdxRLL8CUk5SWcU1Lm9pK9LelzS05K+W/izk9Ql6UlJayV9vIzfpdVSRPjLX5u+yB8xrgd2Th63AE8ChyWPbwYeB/YhP/TXWurzgR2BF4CZybq3APuUWNfNQC+wb/I6PwauKVj/cWAisD1wKXBPwbpvJ8+fltRzRLLdDCCS9/Ex4M/A24vUsek5Bcs+CrwGnJ28VhvwduDYZD9TyP+BuLTgOY8CfwA6gF2A/wt8Olm3CPgu0Jp8vYvXb/t5M/DfC17nauCnyXufAfwJ+MQYdQ0t+1jys7gw+X1+O6n1vcCLwE7Ja1wK/CypcSJwI7AoWXcc8HTB7+Ta5Gcz5s/QXyn8u067AH/V3xfwe+AjyffHAqsL1t0MfHlrnp+EwTrgg0BbmTXdDFxU8HhvYAPQMsK27UngvIH8p9Z+4IARthsK7c8C/wXsWkIdowX940We1wn0FDx+FDi94PHXgO8m3385Ce8tArMw6JOgfhXYu2D9p8iP4Y9YV7LsoYLH+yXv500Fy54FDgQEvAzsUbDucOCR5Psrh/1O9nLQ1+eXh25sJNcC85Lv/zZ5XOiJrXl+RLwMnAZ8GnhS0s8l/VUZdRXu9zHyR7uTJbVIukjSakkvkA9RgMnJ1w7A6jFetwv4dkSsKaOWsWpD0lRJP5DUm9R0TVJLoacKvl8P7JR8v5j8p4ubkqGVL4yyz8nAduR/FkMeI//JZcS6Ek8XfN8PEBHDl+1E/pPIBOCuZJhnHfB/kuWQ/zQy/HdidchBbyO5DjhK0q7AB9gy6GNrnx8RyyPiWPLDNg8CV5RR124F308HNgLPkP9j8n7gGPJH8TOSbZSsfwXYY4zXfS+wUNIHS6hhtPc+fPmiZNn+EbEzcHpST/EdRLwYEedGxNuAk4BzJM0ZYdNnyP8Mdi9YNp38EFexekvxDPnQ3yci2pOvN0TE0B+kJ9nyd2J1yEFvW4iIPvJDBN8n/zH9/1bi+ZLeJOlkSTuSH3J4CRgo46VPl7S3pAnkhzeuj4gB8mPHr5IfcpgA/K+CWgbJDzFcLKkjOfo/XNL2Ba/7APnx5m9LOrlIDX3AIMNOQo9gIvn3t07SNPKfGkoi6URJb5ck8uc0Bhjh55S89x8BX5E0UdLuwDnkPz1ss+RndwVwiaSpSW3TJM1NNvkR8NGC38n5ldivVZ6D3kZzLfkj5OFH89vy/HHAucBa4DngPcD/ByDpXZJeKvKa/xu4ivyQxw7A0AVDV5MfNuglP9Z++7DnfRZYCfwx2e9XGfb/fkTcC5wIXCHp+NEKiIj1wFeAW5PhjMNG2fRLwDuAvwA/B24o8t4K7Qn8ivwfituA70TEzaNsezb5cfSHyZ8buZb8H7ZK+Tz5YaTbkyGoXwEzASLiF+RP1v4m2eY3FdyvVdDQmXwzM8soH9GbmWWcg95sGEnzkwuShn9t1RWpZmnz0I2ZWcb5iN7MLOMapsnR5MmTY8aMGWmXYWZWN+66665nImJKse0aJuhnzJjBnXfemXYZZmZ1Q1JJVyN76MbMLOMc9GZmGeegNzPLOAe9mVnGOejNzDLOQW9mlnEOejOzjHPQm5llnIPezCzjqhr0kq6UlJN0f8GyH0q6J/l6VNI91azBzKzZVbsFwlXAv5C/AxAAEXHa0PeSvkH+DjxmZlYlVQ36iFghacZI65L7YZ4KzK5mDWZmzS7NpmbvAp6OiIdG20DSmcCZANOn+wbzZtb4unt6Wbx8FWvX9dMRr9D1N7PonLV7VfeZ5snYecDSsTaIiMsjYlZEzJoypWgnTjOzutbd08uCG1bSu66fAHq1Awt+8gDdPb1V3W8qQS9pPHAK8MM09m9mlobFy1fRv3Fgs2X9A8Hi5auqut+0juiPAR6MiDUp7d/MrObWrusva3mlVHt65VLgNmCmpDWSPpGs+jBFhm3MzLKmY8LIkdvR3lbV/VZ71s28UZZ/tJr7NTOrK6+8AjvsQNdJ+7Pghvvo3zi4aVVbawtdc2dWdfe+MtbMrJpyOTjkELj0UjoPmsaiU/ZnWnsbAqa1t7HolP3oPGhaVUtomHvGmpk1nFwO5syB1ath//0B6DxoWtWDfTgf0ZuZVUNhyC9bBrPTuzbUQW9mVmkbNsAxx9RFyIOHbszMKm+77eCss+Dtb0895MFBb2ZWOX198Kc/wZFHwplnpl3NJg56M7NK6OvLH70/9RQ88gjstFPaFW3ioDcz21ZDIT80Jl9HIQ8+GWtmtm2Gh3wdjMkP56A3M9sW3/pWXYc8OOjNzLbN+efDHXfUbciDg97MrHy5HHzgA7BmDbS0wH77pV3RmHwy1sysHIVXvD78MOy6a9oVFeUjejOzUg1va/Dud6ddUUkc9GZmpejrq5veNeVy0JuZlUKCiRMbLuTBY/RmZmN79tn8BVCTJ8Ott+YDv8H4iN7MbDS5HBx1FJxxRv5xA4Y8OOjNzEZWeOK1jhqUbQ0HvZnZcHV005BKcNCbmRWKgA99KDMhDz4Za2a2OQkuuQReeAGOPjrtairCR/RmZpAfrrn88vz3Bx+cmZAHH9GbmW0+Jj93Luy+e9oVVZSP6M2suQ0/8ZqxkAcHvZk1s4zNrhmNg97MmteKFfn7u2Y45MFj9GbWjAYHYdy4/DTKd78bpk5Nu6KqctCbWeZ19/SyePkq1q7rp2PidnTdvpTOsz8M731v5kMeHPRmlnHdPb0suGEl/RsHAOh9cQML9nof5KAz5dpqxWP0ZpZpi5ev2hTyQ/pbd2DxmuY5znXQm1mmrV3XX9byLHLQm1mmdbS3lbU8ixz0ZpZduRxdB7+RttaWzRa3tbbQNXdmSkXVXvMMUplZc0kuhuocHISr/w+Lf/lQftZNextdc2fSedC0tCusGQe9mWVP4RWvN95I58G70XnwbmlXlRoP3ZhZtgwLeebMSbui1DnozSxbPvtZh/wwDnozy5bLLoNf/cohX8BBb2aNL5eDs8+GV16BSZPgiCPSrqiu+GSsmTWkzfrXrH+erltW0blyJRxySNql1R0HvZk1nC3610yYxIIT/hHGdzRN/5pyeOjGzBpKd08v5/7o3i371wzm+9rYlhz0ZtYwho7kByJGXN9M/WvK4aA3s4YxUifKQs3Uv6YcDnozaxhjHbE3W/+acjjozawx5HJ0rH9+xFUtEotO2a+p+teUw0FvZvUvaWvQteJq2oalVltrC9849QCH/Bg8vdLM6ttrr+Xv7bp6NZ3LLoNJM1+fP9+EnSi3hoPezOrb+PHQ1QVveQvMnk0nONjL5KA3s7qx2dWuE7eja/dBOk+fC/Pnp11aQ6vqGL2kKyXlJN0/bPnZklZJekDS16pZg5k1hqE58r3r+gmg98UNLOh5me7//6G0S2t41T4ZexVwXOECSUcD7wf2j4h9gK9XuQYzawAjzZHvb92exSueSKmi7Khq0EfECuC5YYv/HrgoIl5NtslVswYzq3/dPb30jjJH3le7brs0xuj3At4l6SvAK8BnI+KPI20o6UzgTIDp06fXrkIzq4nunl4u+NkDrOvfOOo2vtp126Uxj348MAk4DOgCfiRJI20YEZdHxKyImDVlypRa1mhmVTY0Jj9WyPtq18pII+jXADdE3h+AQWByCnWYWYqK9a0BfLVrhaQR9N3AbABJewHbAc+kUIeZpWSsMfkh09rbHPIVUtUxeklLgaOAyZLWAOcDVwJXJlMuNwBnRIzSc9TMMmdoyGYsHrKprKoGfUTMG2XV6dXcr5nVr2JDNpMmtHL+Sfv4aL6CfGWsmdXUWNMlLz3tQAd8Fbh7pZnVzjPP0PGG7Udc5TH56vERvZlVzRa9a1ZcTdfkySx429zNhm88Jl9dPqI3s6oYsXfNvp1w/PEsOmU/prW3IfJH8p5GWV0+ojezilvYvZJrbn98i+X9rTuweM14bj19moO9hnxEb2YVNVrID3Hvmtpz0JtZRS0ZI+TBvWvS4KA3s4rp7uml2NWPPulaew56M6uIUq54bW9r9dh8Cnwy1sy2ydAUymK9awAuOHmfGlRkwznozWyrDR3FF+tCCXD6YdN9NJ8SB72ZbbVSWg23SHzj1AMc8inyGL2ZbbViUyXbWlsc8nXAQW9mW61j4najrvMVr/XDQzdmVrLhvWuOXnkzP572Dvpbd9i0TVtriwO+zviI3sxKMlLvmh9PewcffOsE962pcz6iN7OSjHTitb91B3770vbc+oXZKVVlpfARvZmVZLQTr+5dU/8c9GZWktFOvLp3Tf1z0JtZcbkcXSuupu21DZst9g1DGoOD3szGlsvBnDl03vJjFh24o0+8NiCfjDWz0SUhz+rVsGwZnbNn05l2TVY2H9Gb2egWLtwU8sz2zJpG5aA3s9FdfDH89rcO+QbnoDezzeVy8MlPwksvwU47waGHpl2RbSMHvZm9bmhMfskSuP/+tKuxCnHQm1le4YnXG2+Eww5LuyKrEAe9mW0Z8nPmpF2RVZCnV5o1oc26ULa30XXAznT29zvkM8pBb9Zkunt66bruXjYOBgC96/rpuuUV+MGv6Zy1e8rVWTV46MasySy44b5NIT9k42Bwwc9XpVSRVZuD3qyJLOxeSf/GwRHXrevfWONqrFYc9GZNorunlyW3P552GZYCB71ZE+ju6eXcH91LjLHNpAmtNavHastBb5ZxQ7cAHIixYh7OP2mfGlVkteZZN2YZNnQkXyzkTz9sutsNZ5iD3iyjSjmSFzD/sOlc2Llf7QqzmnPQm2XM0MVQvUXu5doi8Y1TD/CRfBNw0JtlyPCLoUbT1triu0M1EZ+MNcuQC372QNGQb5Ec8k3GQW+WIcUuemprbfFwTRMqOnQj6ZSx1kfEDZUrx8zKVdigbCzT2tvomjvTId+EShmjPyn571TgCOA3yeOjgZsBB71ZSrp7ejn3unsZKDJcM2lCK7d+wbcDbFZFgz4iPgYgaRmwd0Q8mTx+C/Dt6pZnZmM57ycri4Z8a4t8MVSTK2fWzYyhkE88DexV4XrMrETzr7iNlzcMjLpekO817+GapldO0N8saTmwFAjgw8Bvq1KVmY2qu6eXc354DyP3oHzdIxe9ryb1WP0rOegj4ixJHwDenSy6PCJ+Up2yzGwkpYa8alKNNYpyL5i6G3gxIn4laYKkiRHxYjUKM7MtLbjhvqIhD/m2BmZDSp5HL+mTwPXAvyWLpgHd1SjKzLbU3dM76k1DCp3u3jU2TDkXTP0DcCTwAkBEPER+yqWZ1cDi5cVv9XfkHrs45G0L5QT9qxGxYeiBpPEw5n0MkHSlpJyk+wuWXSCpV9I9ydcJ5Zdt1nyKXRC159QdWfLJw2tUjTWScoL+d5L+B9Am6VjgOuDGIs+5CjhuhOWXRMSBydd/llGDWdPqaG8bdd3ph03nl+ccVbtirKGUE/RfAPqAlcCngP8EFo71hIhYATy31dWZGfT1waJFdB27J22tLZutEh6Tt+LKmV45CFyRfG2rsyR9BLgTODcinh9pI0lnAmcCTJ/uWQTWHAp713RM3I6uFVfTecuP6ezshFP2e32dL4ayEimK3GJs04bSicA/A7uT/wMhICJi5yLPmwEsi4h9k8dvAp4hP77/z8BbIuLjxfY/a9asuPPOO0uq1axRjdS7pmXwNb6xfxudp783xcqsHkm6KyJmFduunKGbS4EzgDdGxM4RMbFYyI8kIp6OiIGCTwh/Xe5rmGXVSL1rBsaN57w/lTJ73mxk5QT9E8D9UepHgFEkzdCGfAC4f7RtzZpJd0/vqL1rxuppY1ZMOVfGfg74T0m/A14dWhgRF4/2BElLgaOAyZLWAOcDR0k6kPzQzaPkT+yaNbWF3StZcvvjaZdhGVVO0H8FeAnYAdiulCdExLwRFn+vjH2aZV53Ty9Lbn98zItS3LvGtkU5Qb9LRPhskFmFLV6+auwrD3HvGts25YzR/0qSg96swopd8ep58ratyjmi/wfgc5JeBTZS4vRKMxvDyy/Tsf55eidM2mKVgEtOO9Dz5G2blXREL0nAPhExLiLatmV6pZkVmDCBrkl/oW3Yv0SRH65xyFsllBT0yZRK32TErFJyObj/fpDo/NI/sOhvDmRaexsCprW3cclpB3q4xiqmnKGb2yUdEhF/rFo1Zs0gl4M5c+Avf4GHHoLtt6fzoGk+ereqKSfojwY+Jekx4GVeH6PfvyqVmWXRUMivXg3LlsH226ddkTWBcoL++KpVYZZhC7tXsvSOJxiIoGVwgHm7vocLL7sMZs9OuzRrEuV0r3xM0gHAu5JFt0TEvdUpyywbFnav5JqCK14HxrVwzQHHwwtTuDDFuqy5lHPP2H8ElpC/feBU4BpJZ1erMLMsWHrHE2UtN6uGcoZuPgEcGhEvA0j6KnAb8K1qFGaWBQOj9AAcbblZNZRzZayAwhZ6A7gFh9nocjlaBkfuOtki/9Ox2inniP77wB2ShubTd+IGZWabGX7i9W3PPsFDU2Zssd28Q3erfXHWtIoe0Ut6K2xqR/wx8veAfR74WERcWt3yzBrH0InXoWGZgXEtPDRlBntO3XHTEXyL5N41VnOlHNFfDxws6dcRMQe4u8o1mTWk0U6wPty3ntWLTqhxNWavKyXox0k6H9hL0jnDV4514xGzrOvu6eVLNz7A8+s3jrqNT7xa2koJ+g+TH48fD0ysbjlmjWP4HPnR+MSrpa1o0EfEKuCrku6LiF/UoCazutfd01tSyINPvFr6ypleuZeknZX3PUl3+0Yk1qwWL19VdBufeLV6Uc70yo9HxGWS5gJTyM/A+T5wU1UqM6tjxe4KNa29jVu/4F42Vh/KCfqhgcYTgO9HxL3JDUnMmkJ3Ty+Ll69i7bp+xkljnmTtmjuzhpWZja2coL9L0k3AW4EFkiYCg9Upy6y+dPf0suCGlfRvzF/pOlbIn+47Q1mdKbfXzYHAwxGxXtIbyQ/fmGXe4uWrNoV8IQFDkT9pQivnn7SPQ97qTjltigclPQ3sLamcPxBmDW+sMflHL3pfDSsxK1/JgZ10qzwN+C9eb24WwIoq1GVWVzombkfvixu2XN7elkI1ZuUp58i8E5gZEa9WqxizelLYoGwcwbiBAQZbXv8n09ba4pOu1hDKCfqHgVbAQW+Z1t3Ty+euv5cNA6+fcB1E0DKettZxvLJxkI72NrrmzvR4vDWEcoJ+PXCPpF9TEPYR8ZmKV2WWku6eXrquu5eNgyPPqtnwWvCIx+StwZQT9D9Lvswya/HyVaOGPLhBmTWmcmbd/Ec1CzFL2/wrbqO3yBWvblBmjaicWTd7AouAvYEdhpZHxNuqUJdZTc2/4jZuXf1c0e3coMwaUTlNzb4P/CvwGnA0cDXwv6tRlFktdff0lhTyR+6xixuUWUMqJ+jbIuLXgCLisYi4AHDXJmtoQ60NxjJpQiuXnnYgSz55eI2qMqusck7GviJpHPCQpLOAXmBqdcoyq43RWhsU6vmiu3FbYyvniP6fgAnAZ4CDgdOBM6pRlFmtFGs3fOQeu9SoErPqKemIXlILcGpEdAEv4WZm1sAKr3glAkaZSXPkHrt4uMYyoaSgj4gBSQdLUoQnElvjOvbim3ko9/LrC0YI+bbWFhadsp+verXMKGeMvgf4qaTrgE3/UiLihopXZVYFC7tXbh7ywwjc2sAyqZyg3wV4ls1n2gTgoLe6V8rNvN3awLKqnKD/94i4tXCBpCMrXI9ZRS3sXsm1dzzOGF0NAF/xatlWzqybb5W4zKwuLOxeyTW3Fw958BWvlm1Fj+glHQ4cAUyRdE7Bqp2BlmoVZratlt7xREnb7Tl1R1/xaplWytDNdsBOybYTC5a/AHyoGkWZbYvunl4WL19VUqfJ0w+b7pC3zCsa9BHxO+B3kq6KiMdG207StyLi7IpWZ1amoZYGxa529RRKayYlj9GPFfIJn5i11JXS0qCtdZxD3ppKObNuzOpad0/vmP3kWyTmHbqbh2qs6TjoLROKdaGc1t7GrV9ws1VrTpUMek9EtlR09/Ry7o/uHfXka1trC11zZ9a4KrP6Uc4dpmZExKPDlh0SEX9MHl5WycLMSlHKnaE8Hm/NrpwLpm6QtOlfi6T3AFcOPY6IqypYl1lRC7tXFg35ae1tDnlreuUE/aeAbklvlnQC+SP4E8Z6gqQrJeUk3T/Cus9KCkmTyyvZLK/YBVEesjHLK2d65R/J33TkJuAC4NiIKHbp4VXAccMXStoNOBYYu8uU2RjGuiCqRfKQjVmilBYIN5LvUjlkAvAX4HuSiIiTR3tuRKyQNGOEVZcAnwN+Wla1ZkNyOVoGBxgYN3IXjm+ceoBD3ixRysnYr1dyh5JOBnoj4l4V6Rgo6UzgTIDp06dXsgxrQIV3hmqJQd727BM8NGXGFtsduccuDnmzAqW2QEDSW4EnI+KV5HEb8KZydiZpAnAeUNLdliPicuBygFmzZvnOVk1sqBPlkAGN46EpM9hz6o483Lc+H/6+IMpsROXMo7+OfBfLIQPJskPKeI09gLcCQ0fzuwJ3S/rriHiqjNexJjHUoGy0K14f7lvP6kVjzgkwa3rlBP34iNgw9CAiNkjarpydRcRKYOrQY0mPArMi4plyXseaQ3dPL13X3cvGMRrKl9Kh0qzZlTO9si8ZXwdA0vuBMQNa0lLgNmCmpDWSPrF1ZVozuuBnD4wZ8uA7Q5mVopwj+k8DSyT9C/l2B08AHxnrCRExr8j6GWXs35rMuv6NRbfxnaHMiis56CNiNXCYpJ0ARcSL1SvLbGw+8WpWurKamkl6H7APsMPQ1MiI+HIV6rImVTiFcjSTJrTS88WSJm6ZGeU1Nfsu+Yuljgb+nfxtBP9QpbqsCQ2fQjmS1hZx/kn71Kgis2wo52TsERHxEeD5iPgScDjgAVKrmLF614h8g7LFH/IVr2blKmfoZmgi83pJHcCz5OfEm221oXnya9f1M9b8mkcuel/NajLLmnKCfpmkduBrwF3Jsn+vfEnWLEq9kbenUJptm3KC/uvA3wPvIj83/hbgX6tRlDWHUm7kDZ5Cabatygn6/wBeBL6ZPJ4HXA2cWumirDmsHeNG3uAplGaVUk7Qz4yIAwoe/1bSvZUuyJpHx8Tt6H1xwxbLfSNvs8oqZ9ZNj6TDhh5IOhS4tfIlWVPI5ehacTVtG1/ZbLHvCmVWeaXceGQl+RuPtAIfkfR48nh34L+qW55lUi4Hc+bQuXo1/N3fsXjNeNau66ejvY2uuTM9fdKswkoZujmx6lVYc/nlL+Hhh2HZMjpnz6Yz7XrMMq6UG488VotCrAlEgATz58PRR0NHR9oVmTWFcsbozbZeLgdHHAG33JJ/7JA3q5mympqZlWOYUskSAAAMoklEQVT+Fbdx6+rn8g8iOHLGiSzZWLz1sJlVlo/orSo2C3kAiVt3P5D5q9vSK8qsSTnorSo2C/kSlptZ9TjozcwyzkFvlZfLpV2BmRVw0FtlJRdDHdn3pxFXH7nHLjUuyMwc9FY5ScizejVL/nb/LUL9yD12YcknD0+pOLPm5emVVhkFIc+NN8KcOSyZk3ZRZgY+ordK+cxnNgt5M6sfDnqrjG9+E266ySFvVocc9Lb1cjno6oING2DqVHjnO9OuyMxG4KC3rTM0Jv/tb8MDD6RdjZmNwUFv5Ss88bpsGRx0UNoVmdkYPOvGSrKweyVL73iCgQhaBgeYt+t7uPCyy2C2b/lnVu8c9FbUwu6VXHP745seD4xr4ZoDjocXpnBhinWZWWk8dGNFLb3jibKWm1l9cdBbUQMRZS03s/rioRsbUXdPL4uXr2Ltuv7XbwE4TMsIy8ys/jjobQvdPb0suGEl/RsH8gtGCfR5h+5Ww6rMbGs56G0Li5evej3kR9AiMe/Q3biwc78aVmVmW8tBb1tYu65/xOUCHrnofbUtxsy2mU/G2uZyOTraRh6q6Wj3/V7NGpGD3l6XXPHa9fPv0Na6+f8aba0tdM2dmVJhZrYtPHRjeQVtDTqXXQaTZm6addPR3kbX3Jl0HjQt7SrNbCs46G3L3jWzZ9MJDnazjPDQjeU7UBaEvJlli4Pe4ItfhD/8wSFvllEO+maVy0FnJzzxBLS0wL77pl2RmVWJx+ibUeGY/COPwG6+wtUsyxz0TaKwd03H+ufpanlLfnbNu9+ddmlmVmUO+iYwvHdN74RJLDjhH2HSTDpTrs3Mqs9j9E1gpN41/YP55WaWfQ76JjBa75rRlptZtjjos66vj471z4+4yr1rzJqDgz7L+vpg9my6VlxN27DftHvXmDUPn4zNqiTk3bvGzKoa9JKuBE4EchGxb7Lsn4H3A4NADvhoRKytZh1NJwL+5m/cu8bMgOoP3VwFHDds2eKI2D8iDgSWAV+scg3NR4KLL4af/9xtDcysukEfESuA54Yte6Hg4Y5AVLOGppLLwb/9W/77d7wDjj463XrMrC6kMkYv6SvAR4C/AE6jSihsazB3LsyYkXZFZlYnUpl1ExHnRcRuwBLgrNG2k3SmpDsl3dnX11e7AhvN8H7yDnkzK5D29MprgQ+OtjIiLo+IWRExa8qUKTUsq4GMcNMQM7NCNQ96SXsWPDwZeLDWNWTK73+f70DpkDezUVR7euVS4ChgsqQ1wPnACZJmkp9e+Rjw6WrWkFmDgzBuHJxyCrzznTB1atoVmVmdqmrQR8S8ERZ/r5r7bAq5HLzvfXDhhfkTrw55MxuDr4xtEFv0k984ic7W1rTLMrMG4KBvAO4nb2bbIu1ZN1YC95M3s23hoG8A7idvZtvCQV/vcjk6dhp5LN795M2sFA76epZcDNX1u/+grXXzX5X7yZtZqRz09argitfOBZ9g0Sn7M629DQHT2ttYdMp+bjtsZiXxrJt6NEJbA/eTN7Ot5SP6etTV5d41ZlYxDvp6dNll8OtfO+TNrCIc9PUil4OzzoL+fmhvh8MPT7siM8sIj9GnaIu2Br//E5333w+HHJJ2aWaWIQ76lIza1mB8h9samFlFeegmJW5rYGa14qBPidsamFmtOOjT8NJLdLTvMOIqtzUws0pz0NdaLgeHH07Xy/9FW2vLZqvc1sDMqsFBX0uFbQ2O2ptFp+zntgZmVnWedVMrhW0NbrwR5sxxWwMzqwkf0dfCa6/Be9+7WcibmdWKj+hrYfx4+Pzn8zfxdsibWY056Kspl4P77oNjjoF589KuxsyalIO+WobG5NesgUceyfevMTNLgYO+Gob3k3fIm1mKfDK20ka4aYiZWZoc9JV25ZUOeTOrKw76Svv85+Huux3yZlY3HPSVkMvB8cfDn/8MEvzVX6VdkZnZJg76bTU0Jv+73+Vn2JiZ1RkH/bYYfuL1qKPSrsjMbAsO+q3V1+fZNWbWEBz0W2v77eHNb3bIm1nd8wVT5errgwkTYOed4aab8idfzczqmI/oy5HL5Y/eTz01/9ghb2YNwEf0pSo88XrZZWlXY2ZWMh/Rl8JtDcysgTnoSzF/vkPezBqWh25K8c1vwlNPwdFHp12JmVnZfEQ/mr4+uOQSiID/9t8c8mbWsHxEP5K+vvwQzerVcOKJsOeeaVdkZrbVfEQ/XGHIL1vmkDezhuegLzQ85H3i1cwyoOmHbrp7elm8fBVr1/XTsT10bbcrncsuc8ibWWY0ddB39/Sy4IaV9G8cAKD3VVhw/Gdg0kw6U67NzKxSmnroZvHyVZtCfkj/a4MsXr4qpYrMzCqvqYN+7br+spabmTWi5g36vj461j8/4qqO9rYaF2NmVj3NGfQvvgizZ9O14mrahv0E2lpb6Jo7M526zMyqoDlPxu60E5x0Ep3HHAOTZr4+66a9ja65M+k8aFraFZqZVYwiIu0aSjJr1qy48847t+1FcjlYtw722qsyRZmZpUjSXRExq9h2zXNEP9RqeP16ePBBaG1NuyIzs5qo6hi9pCsl5STdX7BssaQHJd0n6SeS2qtZA7B5P/krrnDIm1lTqfbJ2KuA44Yt+yWwb0TsD/wJWFDVCnzTEDNrclUN+ohYATw3bNlNEfFa8vB2YNdq1sD//J8OeTNramlPr/w48IvRVko6U9Kdku7s6+vbuj1cfDH89rcOeTNrWqkFvaTzgNeAJaNtExGXR8SsiJg1ZcqUrdvRjjvCoYdu3XPNzDIglVk3ks4ATgTmRKPM7zQza1A1D3pJxwGfB94TEetrvX8zs2ZT7emVS4HbgJmS1kj6BPAvwETgl5LukfTdatZgZtbsqnpEHxHzRlj8vWru08zMNpf2rBszM6syB72ZWcY56M3MMs5Bb2aWcQ56M7OMc9CbmWWcg97MLOMc9GZmGeegNzPLuIa5Z6ykPuCxrXz6ZOCZCpZTz/xes6uZ3q/fa2l2j4iirX0bJui3haQ7S7mBbhb4vWZXM71fv9fK8tCNmVnGOejNzDKuWYL+8rQLqCG/1+xqpvfr91pBTTFGb2bWzJrliN7MrGk56M3MMi5TQS/pSkk5SfcXLFss6UFJ90n6iaT2NGuspFHe7z8n7/UeSTdJ6kizxkoZ6b0WrPuspJA0OY3aKm2U3+sFknqT3+s9kk5Is8ZKGu13K+lsSaskPSDpa2nVV0mj/G5/WPB7fVTSPZXeb6aCHrgKOG7Ysl8C+0bE/sCfgAW1LqqKrmLL97s4IvaPiAOBZcAXa15VdVzFlu8VSbsBxwKP17qgKrqKEd4rcElEHJh8/WeNa6qmqxj2fiUdDbwf2D8i9gG+nkJd1XAVw95rRJw29HsFfgzcUOmdZiroI2IF8NywZTdFxGvJw9uBXWteWJWM8n5fKHi4I5CJs+0jvdfEJcDnyMj7hDHfayaN8n7/HrgoIl5NtsnVvLAqGOt3K0nAqcDSSu83U0Ffgo8Dv0i7iGqT9BVJTwDzyc4R/RYknQz0RsS9addSI2clw3JXSpqUdjFVthfwLkl3SPqdpEPSLqgG3gU8HREPVfqFmyboJZ0HvAYsSbuWaouI8yJiN/Lv9ay066kGSROA88jwH7Jh/hXYAzgQeBL4RrrlVN14YBJwGNAF/Cg54s2yeVThaB6aJOglnQGcCMyP5rpw4Frgg2kXUSV7AG8F7pX0KPkhubslvTnVqqokIp6OiIGIGASuAP467ZqqbA1wQ+T9ARgk3/wrkySNB04BfliN18980Es6Dvg8cHJErE+7nmqTtGfBw5OBB9OqpZoiYmVETI2IGRExg3wwvCMinkq5tKqQ9JaChx8Atph9lDHdwGwASXsB25HtbpbHAA9GxJpqvPj4arxoWiQtBY4CJktaA5xPfpbN9sAvk09+t0fEp1MrsoJGeb8nSJpJ/gjoMSCz7zUivpduVdUxyu/1KEkHkj/p/CjwqdQKrLBR3u+VwJXJNMQNwBlZ+DQ+xv/HH6ZKwzbgFghmZpmX+aEbM7Nm56A3M8s4B72ZWcY56M3MMs5Bb2aWcQ56M7OMc9CbbQNJLWnXYFZMpi6YMqs0SV8FHouI7ySPLwBeJN9S40nyvWf2Tq1AsxL4iN5sbD8ATit4fCrQR77XzHkR4ZC3uucjerMxRESPpKnJnbqmAM+Tv8nJHyLikXSrMyuNg96suOuBDwFvJn+ED/ByeuWYlcdBb1bcD8i3Bp4MvAeYmW45ZuXxGL1ZERHxADCR/N2snky7HrNyuXulmVnG+YjezCzjHPRmZhnnoDczyzgHvZlZxjnozcwyzkFvZpZxDnozs4z7f1YioabHzV1dAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "sc = ax.scatter(vr, vr_back, zorder=10)\n", "line, = ax.plot(np.arange(12,18), np.arange(12,18), linestyle='--', color='red', zorder=1)\n", "info = ax.set( \n", " xlabel='vr', ylabel='back_transformed', aspect=1, title=\"vr vs. back_transformed\"\n", " )\n", "fig.set(figwidth=6, figheight=6)\n", "sup = fig.suptitle(\"Crossplot\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot Data and their cumaltive histograms:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(ncols=2, nrows=3)\n", "\n", "freq = stats.cumfreq(vr, numbins=20)\n", "x = freq.lowerlimit + np.linspace(\n", " 0, freq.binsize*freq.cumcount.size, freq.cumcount.size)\n", "\n", "axes[0][0].hist(vr, bins=20)\n", "axes[0][0].set(title=\"Orginal Data\")\n", "\n", "axes[0][1].bar(x, freq.cumcount, width=freq.binsize)\n", "axes[0][1].set(title=\"Cumulative Histogram\")\n", "\n", "freq2 = stats.cumfreq(normal_score, numbins=20)\n", "\n", "x2 = freq2.lowerlimit + np.linspace(\n", " 0, freq2.binsize*freq2.cumcount.size, freq2.cumcount.size)\n", "axes[1][0].hist(normal_score, bins=20)\n", "axes[1][0].set(title=\"Normal Score\")\n", "\n", "axes[1][1].bar(x2, freq2.cumcount, width=freq2.binsize)\n", "axes[1][1].set(title=\"Cumulative Histogram\")\n", "\n", "freq3 = stats.cumfreq(vr_back, numbins=20)\n", "\n", "x3 = freq3.lowerlimit + np.linspace(\n", " 0, freq3.binsize*freq3.cumcount.size, freq3.cumcount.size)\n", "axes[2][0].hist(vr_back, bins=20)\n", "axes[2][0].set(title=\"Back-transformed Data\")\n", "\n", "axes[2][1].bar(x3, freq3.cumcount, width=freq3.binsize)\n", "axes[2][1].set(title=\"Cumulative Histogram\")\n", "\n", "size = fig.set(figheight=8, figwidth=8)\n", "plt.tight_layout()" ] }, { "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": "3.6.6" } }, "nbformat": 4, "nbformat_minor": 2 }