{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "28763f6e-f67d-47f5-9c34-73309a3d6413",
   "metadata": {
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Statistical comparison of samples with stat_tool"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "102775f9-7a80-48a5-b202-4bab93ea3279",
   "metadata": {
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "## This notebook illustates how to perform statistical tests and other analyses for comparing samples with stat_tool.comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d3987a-58b5-4568-9ae1-b054ce159ce1",
   "metadata": {},
   "source": [
    "Load data sets (meri1.his to meri3.his)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c291d761-82a6-4f0f-a23f-25b2d1adc384",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running cmake --build & --install in /home/jdurand/devlp/Git/openalea/stat_tool/build\n",
      "Running cmake --build & --install in /home/jdurand/devlp/Git/openalea/sequence_analysis/build\n"
     ]
    }
   ],
   "source": [
    "from openalea.stat_tool import (\n",
    "    get_shared_data,\n",
    "    Histogram,\n",
    "    Vectors,\n",
    "    VectorDistance,\n",
    "    SelectVariable\n",
    ")\n",
    "\n",
    "from openalea.stat_tool.comparison import (\n",
    "    ComparisonTest, \n",
    "    Compare\n",
    ")\n",
    "\n",
    "from openalea.stat_tool.output import Plot\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "meri1 = Histogram(get_shared_data(\"meri1.his\"))\n",
    "meri2 = Histogram(get_shared_data(\"meri2.his\"))\n",
    "meri3 = Histogram(get_shared_data(\"meri3.his\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e671578-8217-4146-b3b3-3718f4d31be6",
   "metadata": {},
   "source": [
    "Exploratory analysis suggests that meri1 is globally larger than meri2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "82d27c9b-8703-45c6-a23d-d7f7025081de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Plot(meri1, meri2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a82674ec-06c5-4844-8c5c-04c9b5e397dc",
   "metadata": {},
   "source": [
    "## Statistical tests for comparing samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747491ed-30eb-4020-8636-c8574e29b050",
   "metadata": {},
   "source": [
    "**Student's test for comparing means in Gaussian samples**   \n",
    "Null hypothesis: sample 1 and sample 2 have the same means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5241e12e-5985-43af-8955-7718c1978033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "two-sided t-test (162 degrees of freedom)\n",
      "t-value: 1.87611   critical probability: 0.0624397\n",
      "reference t-value: 1.97472   reference critical probability: 0.05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(ComparisonTest(\"T\", meri1, meri2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90667b74-7c27-4221-a9f9-47202037e8fa",
   "metadata": {},
   "source": [
    "Alternative syntax, object-oriented:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97997531-6b72-489f-8b32-ce0fcd0e294c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "two-sided t-test (162 degrees of freedom)\n",
      "t-value: 1.87611   critical probability: 0.0624397\n",
      "reference t-value: 1.97472   reference critical probability: 0.05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(meri1.t_comparison(meri2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5c3562-6473-4dc5-b674-45c26354c747",
   "metadata": {},
   "source": [
    "show sample means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22b4355c-aa25-4421-8b6b-b251b2b549b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18.026315789473685, 16.707865168539325)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meri1.mean, meri2.mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9319cd-b612-4192-baec-b28b62660c1b",
   "metadata": {},
   "source": [
    "**Wilcoxon-Mann-Whitney nonparametric test for comparing means in two samples**   \n",
    "Null hypothesis: sample 1 and sample 2 have the same means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d367a799-3778-4818-8ed4-bb09487db30b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "two-sided Wilcoxon-Mann-Whitney test\n",
      "standard normal value: 1.45438   critical probability: 0.145841\n",
      "reference standard normal value: 1.95996   reference critical probability: 0.05\n",
      "P(X1 < X2) = 0.400651   P(X1 = X2) = 0.0674157   P(X1 > X2) = 0.531934\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(ComparisonTest(\"W\", meri1, meri2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bf72baf9-71c6-4f59-bff8-56aeb96eea60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "two-sided Wilcoxon-Mann-Whitney test\n",
      "standard normal value: 1.45438   critical probability: 0.145841\n",
      "reference standard normal value: 1.95996   reference critical probability: 0.05\n",
      "P(X1 < X2) = 0.400651   P(X1 = X2) = 0.0674157   P(X1 > X2) = 0.531934\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(meri1.wmw_comparison(meri2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09b6292-31e9-45ce-8e4e-ddaa7e85192a",
   "metadata": {},
   "source": [
    "**Fisher's test for comparing variances in Gaussian samples**   \n",
    "Null hypothesis: sample 1 and sample 2 have the same variances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33c66680-4384-431d-8a56-0583742ee994",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-test (88 degrees of freedom, 75 degrees of freedom)\n",
      "F-value: 1.21097   critical probability: 0.19769\n",
      "reference F-value: 1.44897   reference critical probability: 0.05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(ComparisonTest(\"F\", meri1, meri2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f6b0fb1-82f2-4f08-aa07-c965a8b0a188",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-test (88 degrees of freedom, 75 degrees of freedom)\n",
      "F-value: 1.21097   critical probability: 0.19769\n",
      "reference F-value: 1.44897   reference critical probability: 0.05\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(meri1.f_comparison(meri2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab608e1-49e3-4ff7-90bd-5795a6fb709f",
   "metadata": {},
   "source": [
    "show sample standard deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8e420231-4501-46a2-9077-d8be68cda8ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(4.295652637137618), np.float64(4.727103300963237))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(meri1.variance), np.sqrt(meri2.variance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776ef21b-94e0-4341-8a15-45e40efc1923",
   "metadata": {},
   "source": [
    "## Using Compare to compare more than 2 samples.    \n",
    "This function prints each histogram separately, then using common rows and\n",
    "then prints dissimilarity measures between histograms. If the variables are numeric, \n",
    "an ANOVA is performed, as well as Fisher F-tests to compare variances.   \n",
    "The dissimilarity measure $d(h,h')$ between histograms $h$ and $h'$, seen as arrays of relative frequencies, \n",
    "globally writes as:\n",
    "$$\n",
    "d(h,h') = \\sum\\limits_k \\sum\\limits_{m > k} \\rho(h[k] * h'[m] - h[m] * h'[k], m-k)\n",
    "$$\n",
    "where \n",
    "$$\n",
    "\\rho(h[k] * h'[m] - h[m] * h'[k], m-k) = \n",
    "\\left\\lbrace \n",
    "\\begin{array}{l}\n",
    "|h[k] * h'[m] - h[m] * h'[k]| \\textrm{ if the variable is ordinal, with} d(h,h') = -d(h',h) \\\\\n",
    "h[k] * h'[m] - h[m] * h'[k] \\textrm{ if the variable is symbolic, with } d(h,h') = d(h',h)\\\\\n",
    "(m-k)(h[k] * h'[m] - h[m] * h'[k]) \\textrm{ if the variable is numeric, with }  d(h,h') = -d(h',h)\\\\\n",
    "\\end{array}\n",
    "\\right.\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e58f8289-fd18-4e96-9625-d1cdf6eed92d",
   "metadata": {},
   "outputs": [],
   "source": [
    "compare_numeric = Compare(meri1, meri2, meri3, \"NUMERIC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d9c2d96-1bd4-4c63-88ff-52e211b104b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frequency distribution 1 - sample size: 76\n",
      "mean: 18.0263   median: 18   mode: 17\n",
      "variance: 18.4526   standard deviation: 4.29565   lower quartile: 15   upper quartile: 21\n",
      "coefficient of skewness: -0.370952   coefficient of kurtosis: -0.0181747\n",
      "mean absolute deviation: 3.3705   coefficient of concentration: 0.132789\n",
      "information: -207.685 (-2.7327)\n",
      "\n",
      "frequency distribution 2 - sample size: 89\n",
      "mean: 16.7079   median: 17   mode: 17\n",
      "variance: 22.3455   standard deviation: 4.7271   lower quartile: 15   upper quartile: 20\n",
      "coefficient of skewness: -0.977759   coefficient of kurtosis: 0.540819\n",
      "mean absolute deviation: 3.55208   coefficient of concentration: 0.151923\n",
      "information: -242.014 (-2.71926)\n",
      "\n",
      "frequency distribution 3 - sample size: 120\n",
      "mean: 12.9417   median: 14   mode: 15\n",
      "variance: 14.9125   standard deviation: 3.86168   lower quartile: 10   upper quartile: 16\n",
      "coefficient of skewness: -0.563081   coefficient of kurtosis: -0.649252\n",
      "mean absolute deviation: 3.14347   coefficient of concentration: 0.165223\n",
      "information: -307.094 (-2.55912)\n",
      "\n",
      "   | frequency distribution 1 | frequency distribution 2 | frequency distribution 3 | cumulative distribution 1 function | cumulative distribution 2 function | cumulative distribution 3 function\n",
      " 0   0   0   0          0          0          0\n",
      " 1   0   0   0          0          0          0\n",
      " 2   0   0   0          0          0          0\n",
      " 3   0   0   0          0          0          0\n",
      " 4   0   1   0          0   0.011236          0\n",
      " 5   0   2   4          0  0.0337079  0.0333333\n",
      " 6   1   4   8  0.0131579  0.0786517        0.1\n",
      " 7   1   1   6  0.0263158  0.0898876       0.15\n",
      " 8   0   1   5  0.0263158   0.101124   0.191667\n",
      " 9   0   0   3  0.0263158   0.101124   0.216667\n",
      "10   1   0   5  0.0394737   0.101124   0.258333\n",
      "11   3   1   5  0.0789474    0.11236        0.3\n",
      "12   2   3   5   0.105263   0.146067   0.341667\n",
      "13   2   3  12   0.131579   0.179775   0.441667\n",
      "14   4   6  15   0.184211   0.247191   0.566667\n",
      "15   6   5  21   0.263158   0.303371   0.741667\n",
      "16   5   7  14   0.328947   0.382022   0.858333\n",
      "17   9  12   6   0.447368   0.516854   0.908333\n",
      "18   7  10   7   0.539474   0.629213   0.966667\n",
      "19   8   5   3   0.644737   0.685393   0.991667\n",
      "20   4   9   0   0.697368   0.786517   0.991667\n",
      "21   6   9   1   0.776316    0.88764          1\n",
      "22   4   4       0.828947   0.932584           \n",
      "23   4   4       0.881579   0.977528           \n",
      "24   7   1       0.973684   0.988764           \n",
      "25   0   1       0.973684          1           \n",
      "26   1           0.986842                      \n",
      "27   1                  1                      \n",
      "\n",
      "dissimilarities between frequency distributions\n",
      "\n",
      "            | frequency distribution 1 | frequency distribution 2 | frequency distribution 3\n",
      "frequency distribution 1          0  -1.31845  -5.08465\n",
      "frequency distribution 2    1.31845         0   -3.7662\n",
      "frequency distribution 3    5.08465    3.7662         0\n",
      "\n",
      "analysis of variance\n",
      "\n",
      "source of variation | degrees of freedom | sum of squares | mean square\n",
      "between samples    2  1400.11  700.055\n",
      "within samples   282  5124.94  18.1736\n",
      "total            284  6525.05  22.9755\n",
      "\n",
      "F-test (2 degrees of freedom, 282 degrees of freedom)\n",
      "F-value: 38.5205   critical probability: 1.62112e-15\n",
      "reference F-value: 3.02778   reference critical probability: 0.05\n",
      "reference F-value: 4.6812   reference critical probability: 0.01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(compare_numeric)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42731995-e491-400b-8bf1-cf1da32f730e",
   "metadata": {},
   "source": [
    "An abbreviated version can be used for variable type: N, O or S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "878e4c94-60a8-4ffb-97c5-78107764158a",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert(compare_numeric == Compare(meri1, meri2, meri3, \"N\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8ff2522-372e-4c1e-b5a8-462803f3364b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frequency distribution 1 - sample size: 76\n",
      "mean: 18.0263   median: 18   mode: 17\n",
      "variance: 18.4526   standard deviation: 4.29565   lower quartile: 15   upper quartile: 21\n",
      "coefficient of skewness: -0.370952   coefficient of kurtosis: -0.0181747\n",
      "mean absolute deviation: 3.3705   coefficient of concentration: 0.132789\n",
      "information: -207.685 (-2.7327)\n",
      "\n",
      "frequency distribution 2 - sample size: 89\n",
      "mean: 16.7079   median: 17   mode: 17\n",
      "variance: 22.3455   standard deviation: 4.7271   lower quartile: 15   upper quartile: 20\n",
      "coefficient of skewness: -0.977759   coefficient of kurtosis: 0.540819\n",
      "mean absolute deviation: 3.55208   coefficient of concentration: 0.151923\n",
      "information: -242.014 (-2.71926)\n",
      "\n",
      "frequency distribution 3 - sample size: 120\n",
      "mean: 12.9417   median: 14   mode: 15\n",
      "variance: 14.9125   standard deviation: 3.86168   lower quartile: 10   upper quartile: 16\n",
      "coefficient of skewness: -0.563081   coefficient of kurtosis: -0.649252\n",
      "mean absolute deviation: 3.14347   coefficient of concentration: 0.165223\n",
      "information: -307.094 (-2.55912)\n",
      "\n",
      "   | frequency distribution 1 | frequency distribution 2 | frequency distribution 3 | cumulative distribution 1 function | cumulative distribution 2 function | cumulative distribution 3 function\n",
      " 0   0   0   0          0          0          0\n",
      " 1   0   0   0          0          0          0\n",
      " 2   0   0   0          0          0          0\n",
      " 3   0   0   0          0          0          0\n",
      " 4   0   1   0          0   0.011236          0\n",
      " 5   0   2   4          0  0.0337079  0.0333333\n",
      " 6   1   4   8  0.0131579  0.0786517        0.1\n",
      " 7   1   1   6  0.0263158  0.0898876       0.15\n",
      " 8   0   1   5  0.0263158   0.101124   0.191667\n",
      " 9   0   0   3  0.0263158   0.101124   0.216667\n",
      "10   1   0   5  0.0394737   0.101124   0.258333\n",
      "11   3   1   5  0.0789474    0.11236        0.3\n",
      "12   2   3   5   0.105263   0.146067   0.341667\n",
      "13   2   3  12   0.131579   0.179775   0.441667\n",
      "14   4   6  15   0.184211   0.247191   0.566667\n",
      "15   6   5  21   0.263158   0.303371   0.741667\n",
      "16   5   7  14   0.328947   0.382022   0.858333\n",
      "17   9  12   6   0.447368   0.516854   0.908333\n",
      "18   7  10   7   0.539474   0.629213   0.966667\n",
      "19   8   5   3   0.644737   0.685393   0.991667\n",
      "20   4   9   0   0.697368   0.786517   0.991667\n",
      "21   6   9   1   0.776316    0.88764          1\n",
      "22   4   4       0.828947   0.932584           \n",
      "23   4   4       0.881579   0.977528           \n",
      "24   7   1       0.973684   0.988764           \n",
      "25   0   1       0.973684          1           \n",
      "26   1           0.986842                      \n",
      "27   1                  1                      \n",
      "\n",
      "dissimilarities between frequency distributions\n",
      "\n",
      "            | frequency distribution 1 | frequency distribution 2 | frequency distribution 3\n",
      "frequency distribution 1           0  -0.131283  -0.629496\n",
      "frequency distribution 2    0.131283          0  -0.520599\n",
      "frequency distribution 3    0.629496   0.520599          0\n",
      "\n",
      "Kruskal-Wallis test\n",
      "chi-square test (2 degrees of freedom)\n",
      "chi-square value: 69.844   critical probability: 6.81649e-16\n",
      "reference chi-square value: 5.99146   reference critical probability: 0.05\n",
      "reference chi-square value: 9.21034   reference critical probability: 0.01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(Compare(meri1, meri2, meri3, \"ORDINAL\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c5e9cccb-758d-47ba-9ce2-9c745980c154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frequency distribution 1 - sample size: 76\n",
      "information: -207.685 (-2.7327)\n",
      "\n",
      "frequency distribution 2 - sample size: 89\n",
      "information: -242.014 (-2.71926)\n",
      "\n",
      "frequency distribution 3 - sample size: 120\n",
      "information: -307.094 (-2.55912)\n",
      "\n",
      "   | frequency distribution 1 | frequency distribution 2 | frequency distribution 3 | cumulative distribution 1 function | cumulative distribution 2 function | cumulative distribution 3 function\n",
      " 0   0   0   0          0          0          0\n",
      " 1   0   0   0          0          0          0\n",
      " 2   0   0   0          0          0          0\n",
      " 3   0   0   0          0          0          0\n",
      " 4   0   1   0          0   0.011236          0\n",
      " 5   0   2   4          0  0.0337079  0.0333333\n",
      " 6   1   4   8  0.0131579  0.0786517        0.1\n",
      " 7   1   1   6  0.0263158  0.0898876       0.15\n",
      " 8   0   1   5  0.0263158   0.101124   0.191667\n",
      " 9   0   0   3  0.0263158   0.101124   0.216667\n",
      "10   1   0   5  0.0394737   0.101124   0.258333\n",
      "11   3   1   5  0.0789474    0.11236        0.3\n",
      "12   2   3   5   0.105263   0.146067   0.341667\n",
      "13   2   3  12   0.131579   0.179775   0.441667\n",
      "14   4   6  15   0.184211   0.247191   0.566667\n",
      "15   6   5  21   0.263158   0.303371   0.741667\n",
      "16   5   7  14   0.328947   0.382022   0.858333\n",
      "17   9  12   6   0.447368   0.516854   0.908333\n",
      "18   7  10   7   0.539474   0.629213   0.966667\n",
      "19   8   5   3   0.644737   0.685393   0.991667\n",
      "20   4   9   0   0.697368   0.786517   0.991667\n",
      "21   6   9   1   0.776316    0.88764          1\n",
      "22   4   4       0.828947   0.932584           \n",
      "23   4   4       0.881579   0.977528           \n",
      "24   7   1       0.973684   0.988764           \n",
      "25   0   1       0.973684          1           \n",
      "26   1           0.986842                      \n",
      "27   1                  1                      \n",
      "\n",
      "dissimilarities between frequency distributions\n",
      "\n",
      "            | frequency distribution 1 | frequency distribution 2 | frequency distribution 3\n",
      "frequency distribution 1          0  0.343288  0.670724\n",
      "frequency distribution 2   0.343288         0  0.621348\n",
      "frequency distribution 3   0.670724  0.621348         0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(Compare(meri1, meri2, meri3, \"SYMBOLIC\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d254af6d-278e-4e10-8bb7-f588a5a85579",
   "metadata": {},
   "source": [
    "## Using Compare to standardize vectors in distance computations\n",
    "\n",
    "The type VECTOR_DISTANCE implements standardization procedures. The objective\n",
    "of standardization is to avoid the dependence on the variable type (chosen among\n",
    "symbolic, ordinal, numeric and circular) and, for numeric variables, on the choice of\n",
    "the measurement units by converting the original variables to unitless variables. \n",
    "This can be used for hierarchical clustering, see clustering.ipynb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f77fa21-b867-499c-9a72-07313bbaccc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<openalea.stat_tool._stat_tool._DistanceMatrix at 0x75fa08ff5770>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec10 = Vectors(get_shared_data(\"chene_sessile.vec\"))\n",
    "# Discard variables 1, 3 and 6\n",
    "vec15 = SelectVariable(vec10, [1, 3, 6], Mode=\"Reject\")\n",
    "matrix10 = Compare(vec15, VectorDistance(\"N\", \"N\", \"N\"))\n",
    "matrix10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "353dbbc1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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