notebooks/weighted_least_squares.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Outline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider a dataset (`ground_control_points.csv`, measured in eastings and northings) representing a set of ground control points. The coordinates of these points have also been measured using satellite imaging (`coordinates.csv`, as `x` and `y` coordinates).  \n",
    "\n",
    "A suitable model for transforming the data from the imaged coordinates into the ground coordinates is to be derived, and the best estimates of the corresponding parameters are to be determined.\n",
    "\n",
    "Candidate models are the **similarity** and **affine** transformations, the equations for which are given below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forming The Matrices ##\n",
    "### Similarity ###\n",
    "The similarity matrix, $A_{sim}$, is designed using the following equations:\n",
    "\\begin{equation}\n",
    "f_i(a, b, \\Delta{x})=ax_i - by_i + \\Delta{x}\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "f_i(a, b, \\Delta{y})=bx_i + ay_i + \\Delta{y}\n",
    "\\end{equation}\n",
    "\n",
    "Thus, our [design matrix](https://www.statlect.com/glossary/design-matrix#:~:text=A%20design%20matrix%20is%20a,each%20column%20to%20a%20characteristic.) requires four parameters: $E, N, x, y$. This results in a matrix with the following shape:\n",
    "\n",
    "\\begin{bmatrix}\n",
    "E_1& -N_1& 1& 0\\\\\\\n",
    "\\vdots& \\vdots& \\vdots& \\vdots\\\\\\\n",
    "E_n& -N_n& 1& 0\\\\\\\n",
    "N_1& E_1&  0& 1\\\\\\\n",
    "\\vdots& \\vdots& \\vdots& \\vdots\\\\\\\n",
    "N_n& E_n& 0& 1\n",
    "\\end{bmatrix}\n",
    "\n",
    "### Affine ###\n",
    "The affine matrix, $A_{aff}$, is designed using the following equations:\n",
    "\n",
    "\\begin{equation}\n",
    "f_i(a_0, a_1, a_2) = a_0 + a_1x_i + a_2y_i \\\\\\\n",
    "\\end{equation}\n",
    "\\begin{equation}\n",
    "f_i(b_0, b_1, b_2) = b_0 + b_1x_i + b_2y_i \\\\\\\n",
    "\\end{equation}\n",
    "\n",
    "This results in a matrix with the following shape:\n",
    "\n",
    "\\begin{bmatrix}\n",
    "1& E_1& N_1& 0& 0& 0 \\\\\\\n",
    "\\vdots& \\vdots& \\vdots& \\vdots& \\vdots& \\vdots\\\\\\\n",
    "1& E_n& N_n& 0& 0& 0 \\\\\\\n",
    "0& 0& 0& 1& E_1& N_1 \\\\\\\n",
    "\\vdots& \\vdots& \\vdots& \\vdots& \\vdots& \\vdots\\\\\\\n",
    "0& 0& 0& 1& E_n& N_n\n",
    "\\end{bmatrix}\n",
    "\n",
    "The $b$ vector is identical for both transforms, and is formed of all the $x$ components of the observations, followed by all the $y$ components of the observations.  \n",
    "The covariance matrix $C_b$ is usually diagonal, but in this case `statsmodels` requires a vector, of identical dimensions to $b$. It is formed of the square of the standard deviation of the $x$ values, for all $x$, followed by the square of the standard deviation of the $y$ values, for all $y$. Statsmodels requires it in the form of $1/C_b$.  It is converted into a diagonal weight matrix, **W**, in order to obtain the quality $(\\sigma)$ of the transformations."
   ]
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   "cell_type": "code",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels\n",
    "import statsmodels.formula.api as sm\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only execute the cell below if you have $\\LaTeX$ and the appropriate fonts installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   "outputs": [],
   "source": [
    "from matplotlib import rc\n",
    "# import matplotlib as mpl\n",
    "# mpl.rcParams['figure.dpi'] = 300\n",
    "rc('font', **{'family':'sans-serif',\n",
    "    'sans-serif':['Helvetica'],\n",
    "    'monospace': ['Inconsolata'],\n",
    "    'serif': ['Helvetica']})\n",
    "rc('text', **{'usetex': True})\n",
    "rc('text', **{'latex.preamble': '\\\\usepackage{sfmath}'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
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   "outputs": [],
   "source": [
    "# imaged observations\n",
    "coords = pd.read_csv('../data/coordinates.csv', index_col=0)\n",
    "# ground control points\n",
    "ground_control_points = pd.read_csv('../data/ground_control_points.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since the design matrices are formed of the easting and northing measurements and various arrangements of one and zero columns, it's easiest to create them by forming dicts which are given as input to the Pandas DataFrame constructor. In order to keep the functions flexible, they are not applied to DataFrames, but take iterables such as lists, tuples or numpy arrays as input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   "source": [
    "def affine_dict(points):\n",
    "    \"\"\"\n",
    "    Return a dict with the correct parameter arrangement for the affine design matrix\n",
    "    Takes an iterable of x, y point tuples as input \n",
    "    \"\"\"\n",
    "    zeros = np.zeros(len(points))\n",
    "    ones = np.ones(len(points))\n",
    "    return {\n",
    "        'a1x': np.concatenate(([point[0] for point in points], zeros)),\n",
    "        'a2y': np.concatenate(([point[1] for point in points], zeros)),\n",
    "        'b1x': np.concatenate((zeros, [point[0] for point in points])),\n",
    "        'b2y': np.concatenate((zeros, [point[1] for point in points])),\n",
    "        'a': np.concatenate((ones, zeros)),\n",
    "        'b': np.concatenate((zeros, ones))\n",
    "    }\n",
    "\n",
    "def similarity_dict(points):\n",
    "    \"\"\"\n",
    "    Return a dict with the correct parameter arrangement for the similarity design matrix\n",
    "    Takes an iterable of x, y point tuples as input\n",
    "    \"\"\"\n",
    "    zeros = np.zeros(len(points))\n",
    "    ones = np.ones(len(points))\n",
    "    return {\n",
    "        'a': np.concatenate(([point[0] for point in points], [point[1] for point in points])),\n",
    "        'b': np.concatenate((np.array([point[1] for point in points]) * -1, [point[0] for point in points])),\n",
    "        'Delta_x': np.concatenate((ones, zeros)),\n",
    "        'Delta_y': np.concatenate((zeros, ones))\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we could form our design matrices manually, by appending DataFrames:\n",
    "```python\n",
    "    dflen = len(ground_control_points)\n",
    "    zeros = np.zeros(dflen)\n",
    "    ones = np.ones(dflen)\n",
    "\n",
    "    # affine transform A matrix\n",
    "    affine_A = pd.DataFrame({\n",
    "        'a': ones,\n",
    "        'a1x': ground_control_points['E'],\n",
    "        'a2y': ground_control_points['N'],\n",
    "        'b': zeros,\n",
    "        'b1x': zeros,\n",
    "        'b2y': zeros,\n",
    "    }).append(pd.DataFrame({\n",
    "        'a': zeros,\n",
    "        'a1x': zeros,\n",
    "        'a2y': zeros,\n",
    "        'b': ones,\n",
    "        'b1x': ground_control_points['E'],\n",
    "        'b2y': ground_control_points['N'],\n",
    "        }), ignore_index=True\n",
    "    )\n",
    "    \n",
    "    # similarity transform A matrix\n",
    "    similarity_A = pd.DataFrame({\n",
    "        'a': ground_control_points['E'],\n",
    "        'b': ground_control_points['N'] * -1,\n",
    "        'Delta_x': ones,\n",
    "        'Delta_y': zeros,\n",
    "    }).append(pd.DataFrame({\n",
    "        'a': ground_control_points['N'],\n",
    "        'b': ground_control_points['E'],\n",
    "        'Delta_x': zeros,\n",
    "        'Delta_y': ones,\n",
    "        }), ignore_index=True\n",
    "    )\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Form Design Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a1x</th>\n",
       "      <th>a2y</th>\n",
       "      <th>b1x</th>\n",
       "      <th>b2y</th>\n",
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       "      <th>0</th>\n",
       "      <td>501840.7579</td>\n",
       "      <td>185681.5922</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>502808.3026</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>502543.4900</td>\n",
       "      <td>185883.5300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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      "text/plain": [
       "           a1x          a2y  b1x  b2y    a    b\n",
       "0  501840.7579  185681.5922  0.0  0.0  1.0  0.0\n",
       "1  502808.3026  186667.6431  0.0  0.0  1.0  0.0\n",
       "2  502509.0600  186961.7100  0.0  0.0  1.0  0.0\n",
       "3  502543.4900  185883.5300  0.0  0.0  1.0  0.0\n",
       "4  502564.6500  186577.9900  0.0  0.0  1.0  0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affine_A = pd.DataFrame(\n",
    "    affine_dict(ground_control_points.values)\n",
    "    )\n",
    "\n",
    "similarity_A = pd.DataFrame(\n",
    "    similarity_dict(ground_control_points.values)\n",
    "    )\n",
    "\n",
    "affine_A.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Form b Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>-0.149588</td>\n",
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       "      <td>0.117489</td>\n",
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       "      <td>0.012923</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.059942</td>\n",
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      "text/plain": [
       "   coordinates\n",
       "0    -0.149588\n",
       "1     0.117489\n",
       "2     0.071546\n",
       "3     0.012923\n",
       "4     0.059942"
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     "metadata": {},
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   "source": [
    "b_vector = pd.DataFrame({\n",
    "    'coordinates': pd.concat([\n",
    "        coords['x_coordinates'],\n",
    "        coords['y_coordinates']],\n",
    "        ignore_index=True)\n",
    "    })\n",
    "\n",
    "b_vector.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join design matrix and b vector into single DataFrames, for easier manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
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   },
   "outputs": [],
   "source": [
    "affine_A = affine_A.join(b_vector)\n",
    "similarity_A = similarity_A.join(b_vector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a covariance vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.007911\n",
      "1    0.007911\n",
      "2    0.007911\n",
      "3    0.007911\n",
      "4    0.007911\n",
      "dtype: float64\n",
      "103    0.00751\n",
      "104    0.00751\n",
      "105    0.00751\n",
      "106    0.00751\n",
      "107    0.00751\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# we can safely divide by 2, because we're guaranteed an equal number of x and y coords\n",
    "half = int(len(b_vector) / 2)\n",
    "covariance = pd.Series(\n",
    "    [b_vector['coordinates'][:half].std() ** 2] * half +\n",
    "    [b_vector['coordinates'][half:].std() ** 2] * half\n",
    ")\n",
    "print(covariance.head())\n",
    "print(covariance.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carry out the regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>WLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>coordinates</td>   <th>  R-squared:         </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>WLS</td>       <th>  Adj. R-squared:    </th> <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>9.316e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 25 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>6.51e-236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:06:07</td>     <th>  Log-Likelihood:    </th> <td>  689.65</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   108</td>      <th>  AIC:               </th> <td>  -1367.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   102</td>      <th>  BIC:               </th> <td>  -1351.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -43.4359</td> <td>    0.037</td> <td>-1176.088</td> <td> 0.000</td> <td>  -43.509</td> <td>  -43.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a</th>         <td>  -75.2691</td> <td>    0.059</td> <td>-1279.011</td> <td> 0.000</td> <td>  -75.386</td> <td>  -75.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a1x</th>       <td>    0.0002</td> <td> 1.64e-07</td> <td> 1306.384</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a2y</th>       <td>  6.12e-05</td> <td> 1.52e-07</td> <td>  402.923</td> <td> 0.000</td> <td> 6.09e-05</td> <td> 6.15e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b</th>         <td>   31.8332</td> <td>    0.058</td> <td>  549.436</td> <td> 0.000</td> <td>   31.718</td> <td>   31.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b1x</th>       <td>-6.055e-05</td> <td> 1.59e-07</td> <td> -380.059</td> <td> 0.000</td> <td>-6.09e-05</td> <td>-6.02e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b2y</th>       <td>    0.0002</td> <td> 1.48e-07</td> <td> 1522.644</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>132.905</td> <th>  Durbin-Watson:     </th> <td>   2.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>2298.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-4.513</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>23.718</td>  <th>  Cond. No.          </th> <td>4.03e+21</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 1.27e-28. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &   coordinates    & \\textbf{  R-squared:         } &     1.000   \\\\\n",
       "\\textbf{Model:}            &       WLS        & \\textbf{  Adj. R-squared:    } &     1.000   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } & 9.316e+05   \\\\\n",
       "\\textbf{Date:}             & Mon, 25 Mar 2024 & \\textbf{  Prob (F-statistic):} & 6.51e-236   \\\\\n",
       "\\textbf{Time:}             &     10:06:07     & \\textbf{  Log-Likelihood:    } &    689.65   \\\\\n",
       "\\textbf{No. Observations:} &         108      & \\textbf{  AIC:               } &    -1367.   \\\\\n",
       "\\textbf{Df Residuals:}     &         102      & \\textbf{  BIC:               } &    -1351.   \\\\\n",
       "\\textbf{Df Model:}         &           5      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &     -43.4359  &        0.037     & -1176.088  &         0.000        &      -43.509    &      -43.363     \\\\\n",
       "\\textbf{a}         &     -75.2691  &        0.059     & -1279.011  &         0.000        &      -75.386    &      -75.152     \\\\\n",
       "\\textbf{a1x}       &       0.0002  &     1.64e-07     &  1306.384  &         0.000        &        0.000    &        0.000     \\\\\n",
       "\\textbf{a2y}       &     6.12e-05  &     1.52e-07     &   402.923  &         0.000        &     6.09e-05    &     6.15e-05     \\\\\n",
       "\\textbf{b}         &      31.8332  &        0.058     &   549.436  &         0.000        &       31.718    &       31.948     \\\\\n",
       "\\textbf{b1x}       &   -6.055e-05  &     1.59e-07     &  -380.059  &         0.000        &    -6.09e-05    &    -6.02e-05     \\\\\n",
       "\\textbf{b2y}       &       0.0002  &     1.48e-07     &  1522.644  &         0.000        &        0.000    &        0.000     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 132.905 & \\textbf{  Durbin-Watson:     } &    2.064  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000 & \\textbf{  Jarque-Bera (JB):  } & 2298.174  \\\\\n",
       "\\textbf{Skew:}          &  -4.513 & \\textbf{  Prob(JB):          } &     0.00  \\\\\n",
       "\\textbf{Kurtosis:}      &  23.718 & \\textbf{  Cond. No.          } & 4.03e+21  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{WLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The smallest eigenvalue is 1.27e-28. This might indicate that there are \\newline\n",
       " strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            WLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            coordinates   R-squared:                       1.000\n",
       "Model:                            WLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 9.316e+05\n",
       "Date:                Mon, 25 Mar 2024   Prob (F-statistic):          6.51e-236\n",
       "Time:                        10:06:07   Log-Likelihood:                 689.65\n",
       "No. Observations:                 108   AIC:                            -1367.\n",
       "Df Residuals:                     102   BIC:                            -1351.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -43.4359      0.037  -1176.088      0.000     -43.509     -43.363\n",
       "a            -75.2691      0.059  -1279.011      0.000     -75.386     -75.152\n",
       "a1x            0.0002   1.64e-07   1306.384      0.000       0.000       0.000\n",
       "a2y          6.12e-05   1.52e-07    402.923      0.000    6.09e-05    6.15e-05\n",
       "b             31.8332      0.058    549.436      0.000      31.718      31.948\n",
       "b1x        -6.055e-05   1.59e-07   -380.059      0.000   -6.09e-05   -6.02e-05\n",
       "b2y            0.0002   1.48e-07   1522.644      0.000       0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                      132.905   Durbin-Watson:                   2.064\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2298.174\n",
       "Skew:                          -4.513   Prob(JB):                         0.00\n",
       "Kurtosis:                      23.718   Cond. No.                     4.03e+21\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.27e-28. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affine_results = sm.wls(\n",
    "    formula='coordinates ~ a + a1x + a2y + b + b1x + b2y',\n",
    "    data=affine_A,\n",
    "    weights=1 / covariance).fit()\n",
    "affine_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>WLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>coordinates</td>   <th>  R-squared:         </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>WLS</td>       <th>  Adj. R-squared:    </th> <td>   0.999</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>5.503e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 25 Mar 2024</td> <th>  Prob (F-statistic):</th> <td>2.91e-166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:06:11</td>     <th>  Log-Likelihood:    </th> <td>  508.28</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   108</td>      <th>  AIC:               </th> <td>  -1009.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   104</td>      <th>  BIC:               </th> <td>  -997.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  -44.2930</td> <td>    0.141</td> <td> -313.207</td> <td> 0.000</td> <td>  -44.573</td> <td>  -44.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>a</th>         <td>    0.0002</td> <td> 5.61e-07</td> <td>  392.234</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>b</th>         <td>-5.932e-05</td> <td> 5.62e-07</td> <td> -105.497</td> <td> 0.000</td> <td>-6.04e-05</td> <td>-5.82e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Delta_x</th>   <td>  -77.3361</td> <td>    0.225</td> <td> -343.520</td> <td> 0.000</td> <td>  -77.783</td> <td>  -76.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Delta_y</th>   <td>   33.0430</td> <td>    0.225</td> <td>  147.046</td> <td> 0.000</td> <td>   32.597</td> <td>   33.489</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 3.832</td> <th>  Durbin-Watson:     </th> <td>   2.442</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.147</td> <th>  Jarque-Bera (JB):  </th> <td>   2.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.016</td> <th>  Prob(JB):          </th> <td>   0.342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.310</td> <th>  Cond. No.          </th> <td>5.56e+21</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 6.67e-29. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &   coordinates    & \\textbf{  R-squared:         } &     0.999   \\\\\n",
       "\\textbf{Model:}            &       WLS        & \\textbf{  Adj. R-squared:    } &     0.999   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } & 5.503e+04   \\\\\n",
       "\\textbf{Date:}             & Mon, 25 Mar 2024 & \\textbf{  Prob (F-statistic):} & 2.91e-166   \\\\\n",
       "\\textbf{Time:}             &     10:06:11     & \\textbf{  Log-Likelihood:    } &    508.28   \\\\\n",
       "\\textbf{No. Observations:} &         108      & \\textbf{  AIC:               } &    -1009.   \\\\\n",
       "\\textbf{Df Residuals:}     &         104      & \\textbf{  BIC:               } &    -997.8   \\\\\n",
       "\\textbf{Df Model:}         &           3      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &     -44.2930  &        0.141     &  -313.207  &         0.000        &      -44.573    &      -44.013     \\\\\n",
       "\\textbf{a}         &       0.0002  &     5.61e-07     &   392.234  &         0.000        &        0.000    &        0.000     \\\\\n",
       "\\textbf{b}         &   -5.932e-05  &     5.62e-07     &  -105.497  &         0.000        &    -6.04e-05    &    -5.82e-05     \\\\\n",
       "\\textbf{Delta\\_x}  &     -77.3361  &        0.225     &  -343.520  &         0.000        &      -77.783    &      -76.890     \\\\\n",
       "\\textbf{Delta\\_y}  &      33.0430  &        0.225     &   147.046  &         0.000        &       32.597    &       33.489     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       &  3.832 & \\textbf{  Durbin-Watson:     } &    2.442  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.147 & \\textbf{  Jarque-Bera (JB):  } &    2.145  \\\\\n",
       "\\textbf{Skew:}          & -0.016 & \\textbf{  Prob(JB):          } &    0.342  \\\\\n",
       "\\textbf{Kurtosis:}      &  2.310 & \\textbf{  Cond. No.          } & 5.56e+21  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{WLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The smallest eigenvalue is 6.67e-29. This might indicate that there are \\newline\n",
       " strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            WLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            coordinates   R-squared:                       0.999\n",
       "Model:                            WLS   Adj. R-squared:                  0.999\n",
       "Method:                 Least Squares   F-statistic:                 5.503e+04\n",
       "Date:                Mon, 25 Mar 2024   Prob (F-statistic):          2.91e-166\n",
       "Time:                        10:06:11   Log-Likelihood:                 508.28\n",
       "No. Observations:                 108   AIC:                            -1009.\n",
       "Df Residuals:                     104   BIC:                            -997.8\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -44.2930      0.141   -313.207      0.000     -44.573     -44.013\n",
       "a              0.0002   5.61e-07    392.234      0.000       0.000       0.000\n",
       "b          -5.932e-05   5.62e-07   -105.497      0.000   -6.04e-05   -5.82e-05\n",
       "Delta_x      -77.3361      0.225   -343.520      0.000     -77.783     -76.890\n",
       "Delta_y       33.0430      0.225    147.046      0.000      32.597      33.489\n",
       "==============================================================================\n",
       "Omnibus:                        3.832   Durbin-Watson:                   2.442\n",
       "Prob(Omnibus):                  0.147   Jarque-Bera (JB):                2.145\n",
       "Skew:                          -0.016   Prob(JB):                        0.342\n",
       "Kurtosis:                       2.310   Cond. No.                     5.56e+21\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 6.67e-29. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "similarity_results = sm.wls(\n",
    "    formula='coordinates ~ a + b + Delta_x + Delta_y',\n",
    "    data=similarity_A,\n",
    "    weights=1 / covariance).fit()\n",
    "similarity_results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Out-of-band Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def random_point(df):\n",
    "    \"\"\" return a random point from the uniform distribution using min & max from the df \"\"\"\n",
    "    x = np.random.uniform(\n",
    "        df['E'].min(), df['E'].max())\n",
    "    y = np.random.uniform(\n",
    "        df['N'].min(), df['N'].max())\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# get 10 random points\n",
    "rpoints = [random_point(ground_control_points) for x in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# predict transformed coordinates\n",
    "predicted_affine = list(affine_results.predict(exog=affine_dict(rpoints)))\n",
    "predicted_similarity = list(similarity_results.predict(exog=similarity_dict(rpoints)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 728x728 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 717,
       "width": 695
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "fig = plt.figure(figsize=(7.28, 7.28))\n",
    "ax = fig.add_subplot(111, aspect='equal', facecolor='none')\n",
    "orig = plt.scatter(\n",
    "    coords['x_coordinates'],\n",
    "    coords['y_coordinates'],\n",
    "    color='#33CCFF',\n",
    "    edgecolor='#333333',\n",
    "    marker='o',\n",
    "    lw=1,\n",
    "    s=80,\n",
    "    alpha=1.0)\n",
    "# the predict() method generates points used to plot updated observations\n",
    "aff = plt.scatter(\n",
    "    affine_results.predict()[:half],\n",
    "    affine_results.predict()[half:],\n",
    "    marker='o',\n",
    "    color='#006600',\n",
    "    edgecolor='#333333',\n",
    "    lw=1,\n",
    "    s=25,\n",
    "    alpha=0.75)\n",
    "sim = plt.scatter(\n",
    "    similarity_results.predict()[:half],\n",
    "    similarity_results.predict()[half:],\n",
    "    marker='o',\n",
    "    color='#CC00CC',\n",
    "    edgecolor='#333333',\n",
    "    lw=1,\n",
    "    s=25,\n",
    "    alpha=0.75)\n",
    "\n",
    "pred_aff = plt.scatter(\n",
    "    predicted_affine[:len(rpoints)],\n",
    "    predicted_affine[len(rpoints):],\n",
    "    lw=.8,\n",
    "    s=25,\n",
    "    color='none',\n",
    "    edgecolor='#006600',\n",
    "    hatch='......',\n",
    "    alpha=1.)\n",
    "\n",
    "pred_sim = plt.scatter(\n",
    "    predicted_similarity[:len(rpoints)],\n",
    "    predicted_similarity[len(rpoints):],\n",
    "    lw=.8,\n",
    "    s=25,\n",
    "    color='none',\n",
    "    edgecolor='#CC00CC',\n",
    "    hatch='......',\n",
    "    alpha=1.) \n",
    "\n",
    "ax.set_xlabel('x Coordinates')\n",
    "ax.set_ylabel('y Coordinates')\n",
    "plt.title(\"Imaged Points and Transformations (WLS)\")\n",
    "leg = plt.legend(\n",
    "    (orig, aff, sim, pred_aff, pred_sim),\n",
    "    ('Original observations',\n",
    "    r'Affine transform ($\\sigma$: %0.4f)' % np.sqrt(affine_results.mse_resid),\n",
    "    r'Similarity transform ($\\sigma$: %0.4f)' % np.sqrt(similarity_results.scale),\n",
    "    r'Predicted affine transform',\n",
    "    r'Predicted similarity transform'),\n",
    "    loc='upper left',\n",
    "    scatterpoints=1,\n",
    "    fontsize=9)\n",
    "leg.get_frame().set_alpha(0.5)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "plt.tight_layout()\n",
    "ax.grid(False)\n",
    "plt.savefig('../WLS.png', format=\"png\", bbox_inches='tight', transparent=True, dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's clear from the sigma-nought values and by visual inspection of the plot above that the affine transform is the more accurate model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manually Calculate Out-of-band Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using statsmodels to predict out-of-band values is useful, but in certain cases we may wish to calculate them 'manually'; that is, by using a transformation matrix to transform given eastings and northings. The parameters returned by our `fit()` method allow us to accomplish this, once we've arranged our input points in the correct shape.\n",
    "\n",
    "The transformation matrix for the affine transform is as follows:\n",
    "\n",
    "\\begin{align}\n",
    "\\begin{bmatrix}\n",
    "    x'& \\dots& x'_{n}\\\\\\\n",
    "    y'& \\dots& y'_{n}\\\\\\\n",
    "    1& \\dots& 1_{n}\n",
    "\\end{bmatrix}\n",
    "=\n",
    "\\begin{bmatrix}\n",
    "    a1x& a2y& a&\\\\\\\n",
    "    b1x& b2y& b&\\\\\\\n",
    "    0& 0& 1\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "    x& \\dots& x_{n}\\\\\\\n",
    "    y& \\dots& y_{n}\\\\\\\n",
    "    1& \\dots& 1_{n}\n",
    "\\end{bmatrix}\n",
    "\\end{align}\n",
    "\n",
    "([Goshtasby, A.A., 2012. Image Registration: Principles, Tools and Methods. p348]((https://books.google.co.uk/books?id=VthiwQOT6t4C&pg=PA347&lpg=PA347&dq=affine+transform+a+a1x+a2y&source=bl&ots=8WOI4xglcn&sig=iQ3VpSD7xzJ6XIa7ZWhgFm-Izto&hl=en&sa=X&ei=oseOVKbsJofD7gaC6IDgDQ&ved=0CD4Q6AEwBg#v=onepage&q=affine%20transform%20a%20a1x%20a2y&f=false)))\n",
    "\n",
    "\n",
    "To which we add the intercept we've calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def affine_oob(params, points):\n",
    "    \"\"\"\n",
    "    Calculate out-of-band points using least-squares params\n",
    "    inputs:\n",
    "        Array of estimated params (intercept, a, a1x a2y, b, b1x, b2y)\n",
    "        List or array of point tuples\n",
    "    returns list of affine-transformed point tuples\n",
    "    \"\"\"\n",
    "    p = params.params\n",
    "    # form affine matrix\n",
    "    tmatrix = np.vstack([\n",
    "        np.array([p.iloc[2], p.iloc[3], p.iloc[1]]),\n",
    "        np.array([p.iloc[5], p.iloc[6], p.iloc[4]]),\n",
    "        np.array([0, 0, 1])\n",
    "    ])\n",
    "    # form param matrix\n",
    "    orig = np.vstack([(point[0], point[1], 1) for point in points]).T\n",
    "    dot_result = np.dot(tmatrix, orig)\n",
    "    # add intercept\n",
    "    with_intercept = [dot_result[0] + p.iloc[0], dot_result[1] + p.iloc[0]]\n",
    "    return list(zip(with_intercept[0], with_intercept[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.041208349635269315, -0.1326337462002625)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# second result from statsmodels prediction\n",
    "predicted_affine[1], predicted_affine[11] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.041208349635255104, -0.1326337462002698)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = affine_oob(affine_results, rpoints)\n",
    "# second manually-calculated point\n",
    "res[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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