diff --git a/README.rst b/README.rst
index aa59f84..38572b6 100644
--- a/README.rst
+++ b/README.rst
@@ -55,6 +55,59 @@ Use the ``predict`` method to reconstruct a new function sampled at the chosen s
   :alt: A plot showing the function to be reconstructed, the learned sensor locations, and the reconstruction.
   :figclass: align-center
 
+Reconstruction with constraints
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+In most engineering applications, certain areas within the region of interest might allow a limited number of sensors or none at all.
+We develop a data-driven technique that incorporates constraints into an optimization framework for sensor placement, with the primary objective 
+of minimizing reconstruction errors under noisy sensor measurements. 
+
+This work has been implemented in the general QR optimizer for sensor selection. 
+This is an extension that requires a more intrusive access to the QR optimizer to facilitate a more adaptive optimization. It is a generalized version of cost constraints
+in the sense that users can allow `n_const_sensors` in the constrained area. If n = 0 this converges to the CCQR results. If there is 
+no constrained region it should converge to the results from QR optimizer.
+
+To implement constrained sensing we initialize the optimizer GQR and provide it additional kwargs such as the constrained region, number of allowable 
+sensors in the constrained region and the type of constraint. 
+
+Three strategies to deal with constraints are currently developed: 
+
+* ``max_n`` - Number of sensors in the constrained region should be less than or equal to the allowable constrained sensors.
+
+* ``exact_n`` - Number of sensors in the constrained region should be exactly equal to the allowable constrained sensors.
+
+* ``predetermined`` - A number of sensor locations are predetermined and the aim is to optimize the rest.
+
+.. code-block:: python
+
+  optimizer_exact = ps.optimizers.GQR()
+  opt_exact_kws={'idx_constrained':sensors_constrained,
+          'n_sensors':n_sensors,
+          'n_const_sensors':n_const_sensors,
+          'all_sensors':all_sensors,
+          'constraint_option':"exact_n"}
+
+We have further provided functions to compute the sensors in the constrained regions. For example if the user provides the center and radius of a circular
+constrained region, the constraints in utils compute the constrained sensor indices. Direct constraint plotting capabilities have also been developed. 
+
+The constrained shapes currently implemented are: 
+
+* ``Circle``
+
+* ``Cylinder``
+
+* ``Line``
+
+* ``Parabola``
+
+* ``Ellipse``
+
+* ``Polygon``
+
+* ``UserDefinedConstraints``
+
+  - This type of constraint has the ability to take in either a function from the user or a 
+  .py file which contains a functional definition of the constrained region.
+
 Classification
 ^^^^^^^^^^^^^^
 Classification is the problem of predicting which category an example belongs to, given a set of training data (e.g. determining whether digital photos are of dogs or cats).
diff --git a/examples/OPTITWIST_functional_constraints.ipynb b/examples/OPTITWIST_functional_constraints.ipynb
new file mode 100644
index 0000000..5286b5d
--- /dev/null
+++ b/examples/OPTITWIST_functional_constraints.ipynb
@@ -0,0 +1,1784 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import pysensors as ps\n",
+    "from mpl_toolkits.axes_grid1 import make_axes_locatable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       ".output {\n",
+       "    display: flex;\n",
+       "    text-align: center;\n",
+       "    vertical-align: center;\n",
+       "    margins:auto\n",
+       "}\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML(\"\"\"\n",
+    "<style>\n",
+    ".output {\n",
+    "    display: flex;\n",
+    "    text-align: center;\n",
+    "    vertical-align: center;\n",
+    "    margins:auto\n",
+    "}\n",
+    "</style>\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load data for Opti-TWIST prototype:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Temperature (K)</th>\n",
+       "      <th>Velocity[i] (m/s)</th>\n",
+       "      <th>Velocity[j] (m/s)</th>\n",
+       "      <th>X (m)</th>\n",
+       "      <th>Y (m)</th>\n",
+       "      <th>Z (m)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>526.648511</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.002953</td>\n",
+       "      <td>-0.017654</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>526.645400</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.002982</td>\n",
+       "      <td>-0.017977</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>526.669124</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.002863</td>\n",
+       "      <td>-0.017775</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>526.738401</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.002503</td>\n",
+       "      <td>-0.017575</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>526.668918</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.002881</td>\n",
+       "      <td>-0.018116</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40505</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.237005</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40506</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.239735</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40507</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.242478</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40508</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.245220</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40509</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>1.79769313486232e+308</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.247962</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>40510 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Temperature (K)      Velocity[i] (m/s)      Velocity[j] (m/s)  \\\n",
+       "0           526.648511  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "1           526.645400  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "2           526.669124  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "3           526.738401  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "4           526.668918  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "...                ...                    ...                    ...   \n",
+       "40505       420.000000  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "40506       420.000000  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "40507       420.000000  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "40508       420.000000  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "40509       420.000000  1.79769313486232e+308  1.79769313486232e+308   \n",
+       "\n",
+       "          X (m)     Y (m)  Z (m)  \n",
+       "0      0.002953 -0.017654      0  \n",
+       "1      0.002982 -0.017977      0  \n",
+       "2      0.002863 -0.017775      0  \n",
+       "3      0.002503 -0.017575      0  \n",
+       "4      0.002881 -0.018116      0  \n",
+       "...         ...       ...    ...  \n",
+       "40505  0.044450 -0.237005      0  \n",
+       "40506  0.044450 -0.239735      0  \n",
+       "40507  0.044450 -0.242478      0  \n",
+       "40508  0.044450 -0.245220      0  \n",
+       "40509  0.044450 -0.247962      0  \n",
+       "\n",
+       "[40510 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv('~/projects/Sparse_Sensing_in_NDTs_LDRD/data/0_raw/004_BB_7Power_7BC/650_420.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Temperature profile of Opti-TWIST prototype:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 360x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X,Y = df['X (m)'], df['Y (m)']\n",
+    "fig = plt.figure(figsize=(5,8))\n",
+    "plt.scatter(X*100,Y*100, s=10, c=df['Temperature (K)'],cmap=plt.cm.coolwarm)\n",
+    "plt.xlabel('X (cm)')\n",
+    "plt.tick_params(axis='x', labelrotation = 90)\n",
+    "plt.ylabel('Y (cm)')\n",
+    "cbar = plt.colorbar()\n",
+    "cbar.set_label('Temperature ($^{\\circ}K$)')\n",
+    "axes=plt.gca()\n",
+    "axes.set_aspect(0.7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data preprocessing, Wrangling, Cleansing and Scraping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "48\n"
+     ]
+    }
+   ],
+   "source": [
+    "Responses = ['Temperature (K)','Velocity[i] (m/s)','Velocity[j] (m/s)']\n",
+    "RoI = Responses[0]\n",
+    "filename = \"~/projects/Sparse_Sensing_in_NDTs_LDRD/data/0_raw/004_BB_7Power_7BC/\"\n",
+    "data = np.zeros((40510,49))\n",
+    "counter = -1\n",
+    "for j,i in enumerate(np.arange(350,700,50)):\n",
+    "    for l,k in enumerate(np.arange(240,450,30)):\n",
+    "        df = pd.read_csv(filename + str(i) + '_' + str(k) + '.csv')\n",
+    "        counter += 1\n",
+    "        if i == 650 and k == 420:\n",
+    "            print(counter)\n",
+    "        for n in range(3):     \n",
+    "            df[Responses[n]].replace(to_replace='1.79769313486232e+308', value=0.0, inplace=True)\n",
+    "        data[:,counter] = df[RoI]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(49, 40510)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Temperature (K)</th>\n",
+       "      <th>Velocity[i] (m/s)</th>\n",
+       "      <th>Velocity[j] (m/s)</th>\n",
+       "      <th>X (m)</th>\n",
+       "      <th>Y (m)</th>\n",
+       "      <th>Z (m)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>526.648511</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.002953</td>\n",
+       "      <td>-0.017654</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>526.645400</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.002982</td>\n",
+       "      <td>-0.017977</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>526.669124</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.002863</td>\n",
+       "      <td>-0.017775</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>526.738401</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.002503</td>\n",
+       "      <td>-0.017575</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>526.668918</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.002881</td>\n",
+       "      <td>-0.018116</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40505</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.237005</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40506</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.239735</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40507</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.242478</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40508</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.245220</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40509</th>\n",
+       "      <td>420.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.044450</td>\n",
+       "      <td>-0.247962</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>40510 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Temperature (K) Velocity[i] (m/s) Velocity[j] (m/s)     X (m)  \\\n",
+       "0           526.648511               0.0               0.0  0.002953   \n",
+       "1           526.645400               0.0               0.0  0.002982   \n",
+       "2           526.669124               0.0               0.0  0.002863   \n",
+       "3           526.738401               0.0               0.0  0.002503   \n",
+       "4           526.668918               0.0               0.0  0.002881   \n",
+       "...                ...               ...               ...       ...   \n",
+       "40505       420.000000               0.0               0.0  0.044450   \n",
+       "40506       420.000000               0.0               0.0  0.044450   \n",
+       "40507       420.000000               0.0               0.0  0.044450   \n",
+       "40508       420.000000               0.0               0.0  0.044450   \n",
+       "40509       420.000000               0.0               0.0  0.044450   \n",
+       "\n",
+       "          Y (m)  Z (m)  \n",
+       "0     -0.017654      0  \n",
+       "1     -0.017977      0  \n",
+       "2     -0.017775      0  \n",
+       "3     -0.017575      0  \n",
+       "4     -0.018116      0  \n",
+       "...         ...    ...  \n",
+       "40505 -0.237005      0  \n",
+       "40506 -0.239735      0  \n",
+       "40507 -0.242478      0  \n",
+       "40508 -0.245220      0  \n",
+       "40509 -0.247962      0  \n",
+       "\n",
+       "[40510 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = data.T\n",
+    "print(np.shape(data))\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Find all sensor locations using built in QR optimizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_sensors = 8\n",
+    "n_modes = 8\n",
+    "basis = ps.basis.SVD(n_basis_modes=n_modes)\n",
+    "optimizer  = ps.optimizers.QR()\n",
+    "model = ps.SSPOR(basis=basis, optimizer=optimizer, n_sensors=n_sensors)\n",
+    "model.fit(data)\n",
+    "all_sensors = model.get_all_sensors()\n",
+    "sensors = model.get_selected_sensors()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sensor locations on the grid:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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rhi+NobN6Fx/442fSWTsGhKbaWBOco4Ecz8E8zt7UYrE2nGPNWXOQxokSppaMe6sT02wHHgv8DPAECavXflZErgFeBvyQiPwN8KTq+Ugkkb4H/oWPvpK7vvMJvvDRVwaLWvUx+xlHPSdJgkcvVjBJ1S2oEkf6aXP1+r73FFNr1lX1o5zVS+xz1pIjo7DW8Ccvuw+u7Pb33XTjG7jpxjdgbINnvvBrG443icEVLljLqrm31lJ6xajBqw9OEQCO8RNnX46iNp+OtYZ/8Cs3cOXDn4lN58K+dI4rHvL3edov/FXfqvaacmOq2KcxGGtRr/iB6sjW2nCOj33OaVGr4culA5fQmFvElV1s0sCVXdLGIq2li1Cv2MRuHL7sZShpyPMUXxVcsr1KH4qxBl9O6Kaj5RxKjcQpWIT2yjEe+refw0P+9nP44sf+kDMnvou1Bofvp8aJEYxWScgGDAYvri9eIYy560CCyCSomwMzbmojTmsF54W//wvX9fuQ97r8VZy+ew3vtVrCRSBNkNLh8KFvWU3b6IeVEnBlCD+JtTGSNEVqI04jhiwzG4LqBth3qEVnraC9muNcSOzUXujIBivqnUdcyEIyxuJFURxGBD8pdUp0iEZRm08nTQwbZ1aui7Qxl7Lv0DyNubSfnTQ4VGlMiIH2RW0q8Vbx0ch0qI3lTDJD2e0tIdRLe+sVjwXvlfnFBkliWF3u9mdYOlw/B9RaixrFeReKgvmNtZXGTuxzDqU+4rSG5lz4sl0ZFjAwRigKj3NaJXEo0srIu8H97iUc2zQE4vNOHrx6a/EIzpWYZIIfUc3GwsdNbcRpjGBUqjH29USOLDM4p5SlAkGMc/MZZdEhzRKc8+TtvD+FQ6nWvvSKVvOMItOhNuJMUoN3oQDCYLOeZZbSKcZ4igKcU5pzKWXpKbolbs0jVsBDkiaUeYkrXVVQwYBOLMgZQ0kjqI04G6lBjJIX2i8SlyZCmglSVGURjVAWHlRZXGzSzUqSzLJySnGlp3RlKMltQ4jJ5y5azilSG3GmqeCARjUxTQj/CzkfQtlLd1RBsOSFJ0kMkCL7heXTbXrFZYuiqOKcBvET6hcKMZQ0gvqIMxEw0Kk8dgTmGlWZml6iOKCZoRDFeUVTg/dKktmqJE1o4qUMYSYrlrKbT+yeNYpzKLURZ5KF0E+zQbUYQeh/Oi+IaDW8GfI4ylJJ0zBXyCaK1zDuXuauP8fIe4/3PkzpiEyF2nzymVU6vpc4PLBSML2qcWxYbtJYSBIhzxVXeIwINjFoEaZqGGP61ekmQ0wWHUVt2pX5pqfV8BuC8M4rzm3MenO9GkkagvPGCFkjYWFfs78WezqXglQFGqI/NDXqYzlTB8awMOdwPljIbgEnlk1fXzqgNfUhBpqmGaurBWLg0EVLnLhrmSKvQlFGkAmKM/Y5h1MfcVoHKF4T0iT0O1tNj03AOSEvhJW2kKaWbiW+PA+hp1Yrod0O2e4HjiyyttIlzRLKwlHkxeRuOjbrQ6nNT7eRFMwljsRUScKitNKibyp7089FoDUnZJnQahmyNIwqNZuWNLX9Zn5uoYmx1fyjyFSozSc/l5R0gP3NNok4FGE5b2BMSmphaS70PTuF0O5anBdW2tCcE8RY2m2HtYJNBKppSGmWbpiTNFZiytxIavPpZDZnPumSGhfqC4iydvLb/MbzH49tf53FrEMrKWmljvmmJ7HKfBOsCeEna8OYfLOZ0phLQ0ZTNZ04Mh1qI86G5DRMTtOd5kB5BwfKO3n9q3+Tz93417zxf/wnmkmHhs1ppiWJDZ59mnqaDSGxoZnPMttPFEpSG2ZkTkic94R56yIyLyLn/QHWpllvuDb4gpbvcMUjHkc3Xx/ZecufvpG3/OkbyRoNPnjjnbRSxxknNBKhKD2aComFYuDLTbOENCvD9OHItpBQrOpZwE8DjyR0kBoichx4F/D7qnrzdq9XG8vZLFdouQ6JL7jhvW/j/7vmh5hrNsJrzSZPfdqP864PforUeEQ8WRI8pKz6eTYaBhFoNoJjJL3B+Ukao/qVQPy/wP2BFwMXq+q9VfUo8Djg48DLReTZ271YbSxnlq+iNgERLj5ymMX5ebrdnEYjo9vtsrCwwNEjR+h6T2I8hpBGp1hSC2UJSfWvMVVGPQPNes+qjjFLSeu3Guy3VPU3Nu9U1ROEmlj/q6qPtS2m/lMbF2l3haSzHEoZ5h2+893bOXLoAK/9H/+df/hTz+LYseMYHIIjFcdcWpIYT8P60KQXSlEqquCcp73a7S/u2qsKEqsdj6QUkf+weaeILIrIWwFUdduB49pYTttdwRZtGrffjLiS+y0kfOjuk7zvfe/nN37918hpkotijSBSUqqhYRXnlMR69i0KXg1FoeR5gl9qsrbaxZXraxSNV5xSxxGinwfeIyLPU9XXAojI9wJvAd6804vVRpyyegYpcw79xAvpFOvZ63983Vv54+veSqPR4PNf+BKeBKcJvbwQJfQ7O/n6lJ4kqcJKcxnLJ9vh+tFqjkRVSxF5BvBhEfkuoVL1S4Dnqer/2en1avHT9e01Tr77/Zx4/4f56LOfzI//rfswl4WuzVyzwTOe8iN89APvJvEllhJrHI2kxIrrC7J0SiOtanj6MJ/IWqHZyibnFNXMIRKRVxM89f8M/CHwk8Cjz0eYUBPLWR67HeXBgHBRq8lCmtDJCxpZSrebs9RqcvGhgyxLQkmCYqthTsisZ80Jcw3Dajt8v0WpWCsU+bA12y8QqWU5ms8CDwUeBqTAA4D/KSJfAL6gqtft5GK1ECcK2usbAsdXO/zM99yf5/3kNbz+hq9xx90nMOJJjKHUktylFD7BGmV11VB4Q1nCWkfJuzA/n7C6WrC2lvfnt0dGo6qvGXwuIpexLtanAPdAcTJQ913g93/k0QCYO+/gVb/yjynmD5Crw2jOnFGcT+lqRrtImcuUhjruWDN0u0q361lbKzh+1yplUXLmxEr/+uPsd2o9HaINqOqtwK3Ae87n/Np8OqqKOo+WHl+49dKGZYFxBeJyrC+rcoewkHZQFZwa1nJDkhgW5g3Oe7yCc468U9BZWQOiQ7QdROQHRGT/uK5XC3GqKr5waFVNDqrVMEqHKTqYskNSdslcyDDSao2iEISHbmEoSmh3qnhn7vpNuZ90OZpxb9t6W7Ei8hkReWf1/Iki8umq9PlHReTKan9DRN4sIjeLyCeqJXnOdc3fAI4Av3/hH0ygFuKEAcs2ME0DAO9DUVj1GFGsOgw+JHVUf7012p++YW018zINq7/NLcxN7J5VzNi3bfICwqJkPX4P+GlVfTjwJ8C/rfY/FzipqlcCrwRePuSaHyOsxHfTDj6CodSmz7kZ9Z7s6BFwJeI9oopxOWI9qRR4MowozTQkdpTO0G7DvqUUY6oanQKthSYnvntsoE8rk12sdcJUTspTgJcC/6LarcBS9Xgf8N3q8dOBf189fhvwuyIiukWlCVV9L/Decd5rjcWpLF55eUiBr/qdIc7psOJoSk7T5IikZNaz0ICFpuXEmQQlo9GwLC+XdNohoL/VEtgXhkxrbP1VwIuAxYF9zwPeLSJt4AzBAkIIon8H+gH208Ah4PhWFz6XcHd6TI9aNOvqle5yh3y1G7aVLmW35K6Pfw4tcsQVGHXgHUnZIaVLSpejjeMcSE/TSjoULsQ09y3CQkuw1pAkgrVnrwI34xzuLbtYbdf2XhCRpwJ3qeqNm855IXCNql5GCJ7/V86PD4nIPxeR+wzuFJFMRJ4gIq9nfY2pkdTEcgbhaG8NIVOVlTm1ysm//hQHnvB4TNHFJE2sehKXU9qMRD1zxqIkLGQliMF7Q5pClkKSGGxSrcIxAW99QqGk46p69TleeyzwtGq9pyawJCLvAh6oqp+ojnkz683zbcC9gVtFJCE0+XcPee8nE8bX/1RE7gucqt7HAu8HXqWqn9nuH1IPcSrrRV6r6RWqileHiNK96UtkBw+TXnoFprNKlrZwjSbepBTNjETLEKQXpXRBiFlqUK1XorGqvpiQa4mI/CDwLwmrMt8hIg9Q1ZuAH2LdWeqtpvfXwDOBvxjWJKtqB3g18OoqNe4w0FbVU+dzv/UQ5yCVt64omiudu89gkgRjLKlAcvRe2HSN0hyk29yHqJJIGRYzQEhsmLnZM5QTC2/KJC++faq+5D8m5Fp64CTB+kFY/vGNInIzcIKQ5b7d6xbA7SMPHEL9xAl9S6reo86D9/hOBy1yzNoKur+JV0Ex9NZ/adiSdtmoliFSvIbspMnVhO+9/3RQ1Q8DH64evx14+xbHdICf2NUbG6AWDtEGJJTT1qrUtvTE1QsBOYeox/gSZxsIUHgL9FZwA+ehCF3QveQI1Y5aiFMVvFsv9KrOB6vplaTZwDYzTJZBkkKS4NMGoiErPtMOisErpNW8IiNgbShf061CSeMWaZ1nX0rg2SLy/1fP7yMij9rpdWohTqo67ur8+jBm5SClSy3MXDNMtWhk+LnFoAyx/TijB3KfoQqlA0Qw1SrDvVBSHFvfEa8GfgD4qer5MvDfd3qRevQ5larSYVXr0IN3ofRhMt/CJAmSpqgkiC9x2RzeJqhJcFhWi7mqRKLSqxW7tuYocsepu1cnd9v1zUp6tKo+QkQ+A6CqJ0Uk2+lF6iFOwJcDzW5VvlAIqwmbrIFtNpE0DYU5fcHK0qV4sagKDeMp1YQTVbn19oKigDx3lOW4RoTOpoazL3sUVTEFBRCRI4QGakfU4qerVOEjHxI6vPPglYNXXYzPC7Qs0EYTsgy1Fm8bJK6LqtCVJl4dd682uONME49haTGh2TQ0min7Dram/eftRX6H4P0fFZGXAh8FfnOnF6mH5ayEGX6noWlXlGxxDoxBS4e74w7k8MWUrX24dA7jHZaSBh2yZJGlRo5Tw6pa9i8KYHFOSVNbvUWcfbkdJHxIHwFuBJ5I6Gj9uKp+ZeiJW7BtcYrIAeASoA3coqqTa+/Og54D1FsRw5fK8q0nWLr8COniAnbfIqyewWRNbNrAaokzlrwxB6q0spw116D0HueDIMtyvSpydIi2h6qqiLxbVR8KfPVCrjVUnCKyD3g+wevKgGOEsdKLROTjwKtV9UMXcgNjQSsHCPrpbIpy+jsnWLjkAK7bwRRNTHsNu1RgV0/SXThE5rp0vKdpu6xqC1VIjVKUIVZaFCVlPrkhzFkJ/UyAT4vII1X1UxdykVGW823AG4C/s3l8VES+H/gZEbmfqr7uQm5iHKjf1Ox6RQV8UeI6BbbTxSyCWVvGtxYwZUHZmMf4nDaHKZzlTNvQzi3Ow8qqR1VIswlWmauvQ/Ro4KdF5FvAKqFpV1V92E4uMlScqvpDQ167kdCvmDpKyLOsXEMEONbt8pKv3MyrL1ng8jQhyVJ0qY1mGSZrUK2uDiJkkuONYXEuLCvo1bK4EJaCKWKVufPhR8ZxkZ30OR8GXDF4jqr+2Thu4oJRRd16c26s8NpbbuOzZ5b57U9+hZcfWCJtNbErqxibwMISJu9gmjnGehqmy6pvYgTmG7DaARBOnVjl9lvPTOaepZ4OEYCqfmsc19mWOEXkDwhzj7/EerxKgRkRZ5iIJgh/9xOfIh8Yarzu67dy3atvpWENt/+nf0ayMB/EjFKalNPso1POoxomuZ1pG9bWHLff0cVjWdgX5hCN31uvL71hy82o6llFvoaxXcv5GFV98E4uvOt4cN7z1oc+lN/9zq185NRJuqo0reGH73Mxv/7k9aHdIsk4cfRBFNIgd01WyjlWuimreVhp49QZpTWXYq2nsxaKok0k2bi+fc7BYbUm8FQ2TqjbFtsV51+LyINV9cs7fYPdoBeER+GQTWgZQ65KJkLXeRaylHtfchEiQnnx5bSPXhlm8AihXhIe50A15HN6H8oh+mrRrMjOUNVXDD4Xkf8CvG+n19muON9AEOgdhFLK5+V9TRJfrJcqPFkWPP3QIZ5++AjvaZ/meLtDudYhXVpADxzBaoEoGHUhq9IoC81QcOGWEylLSwlF7vnmN1eZZDS3rn3OLWgBl+30pO2K83XAzwBf4DzGSCeO6oZpGr953/v3U9z+3YMuZv7QYkg6957kG1+k+4BHo3OCEWXerHL72kFUDFkKSy3HbceCubzo4nlOnpzgqsE1NctV4a5ex98Sii2cVfF4FNsV5zFVvX6nF98tetWI7UDWem+kKJ3LSBda2FYTkgSKnObd36Yzf5C1ZB8Oy70WT3PHyhIr3UYoVdOS0Kx7wQ3U+oxsm6cOPC6BO1V1xx/kdsX5GRH5E+B/019CapZCSfQ99v74OoBA88AC2VILO9fEpAnGWkx7mSxfo233hbimNyx3MhAhTZRu7sMokaNfEz4W8toR/0xV//XgDhF5+eZ9o9jupzNHEOUPAz9WbU8desZuE1YV3IhCY2EeO9/CLsxj5uZgbh4Ess4ZrOuw6uZo+zku3b9CIg6v0GwY0lQQEzLsIY6t75CtBm9+dKcX2ZblVNXn7PTC00C9ImZdRKoapmiYsJSfzC8CCsZiyjaJUfZxhhU/T6EJyx1LKKvk6bY9ee7Wa3FP4n5r1ucUkX8K/DPgfiLy+YGXFoG/2un1thuEfz3wgt74epWh9ApV/fmhJ+4ivlRMKv3mtxdaksQiSYIkCZpYTJqBgi06qDGo96yVDQpNuWhfl2PLGc5DkoZ5RVk2QXHWzxr/CaEW538CfnVg/3K13MuO2G6f82GDiR9V2v337fTNJo13leU0QZi+UMQYbLOBZA1EghhpNLFlF+ty1jhKM3U0cHzj+CLd0pAkjpVVBYQin73gxKyiqqeB08BPVQbsKkIQvmcwPrKT621XnEZEDqjqyeqNDu7g3MmjoIUiafDQN3Q9RRBbZRYlaZhW6TyaNFEVjBZ0aFB6y9JcSeEsnU6Ys57njkZzUsnGVIUc6oeIPI9QZvEyQp34xxCqhjxhJ9fZbpv1CkIQ/jeqIqF/RVgxYUbQTf8GTGbwZYkHpLcsm03QNKO9dBHeWmwCTZvTTHJOr6U4L8w1evOHlPbq5IYva8wLCGtffktVHw98H6Fu0o7YrkP0BhG5gXXlP2PWhjK9KsYJHo/B0Doyx8Xfey/yE6fRoiQ9sEaGIov7cEuHyZtLiPMgnkItpRdSq6x1PU6FZtNy9/E2eXdScc7pVvyYMB1V7VSr3jVU9asi8rd2epFRmfALqroCUInxLEEOHjMtNAyu46vBHI9n5bZVbve3c98nPoB0aYF0cQGZa4UqdL6k0V0hn2+RWEjKgjvbB0lSYf+88t27wDml0Zhcz6Xmyca3VrXh/xz4gIicBHacRjfqp/sOEXmFiPxdEZnv7RSR+4nIc0XkfYSyd9NlcJrGAKu3r4Y4pXNhRmZZ4LMWfm6B1HXYt3wbguJUsOIpnXJq1bC0aLBGyXOHm2RN+BpSTXD7JVU9par/Hvh3hOHvH9/ptUZlwj+xquX4T4DHVo5QAXyNsH72z6rqHTt900kgpaBpFT4a6B9q4XCdLrK2iq61MPsFs3YGnzRgbgHfLclpstDIufNki8IJxnhOnMxxTmnObXuR2x1TR8vZm+BGWH8IVf3L873WyHZLVd8NvPt832BX8WxIcVNV8rUu6WIL7zzqSmT5FO7opWBTcCWJdnFi6BSW+RZIx3Psbs/SUsbtty3T7azXSopO0bbZlQluewZfKkYk5MBQ5Xd68HlBsbKGac3h8xxjEqwrUe9wjXlKm5H6Lnd29gFCnofmPc9DzaWiu7Em/Dipo+WseDTwbBG5hUlNcNsz9GYF9/qHVSEUdUrZLXHdErfWgUMCRQftdnD7DoWVNkTINcMAq11lpSMYI6GMoldsUlsBTZKxTHAb6hCJyLuHLYw0a6gDfAjIa6moh3y1S7HWQVTRPIf2GqazipQFpBm5t2TGcWg+p3RClgiinrIAkxg6a5PK5wyraYx7mxG+Dfwdgk/yLYL5uGinFxnlrf8h8H4ReUlV43tmWa/NGepqqgMUOqfadFc65KtrlKtraN5FkxTbWUFVOcBJDiYnES1ILOSl0u54xAiry52J9jNVZezbjDD5Eoiq+lYReQ8hHHCDiLyRgUx4VT3fJUHGiqqCU+g1wQPRn7JTUnZLfLeAPEdLh3Tb0GgAYLXEqKORefa1Su4+bVlcTLnt1hWyLGXl1FRDuHuVXSuBmBM6tQ1C6tPsZUJUOcZS/X/DS1Wep25ORAbybAGMpS3zGA/7mh2cXwjxTaf4fnGwSSQb19ohGksJxFEjRE8mLJh0PfAIVV07jxvdFe52BS9f/S7/ZvHeHDTVn2XBNASbGWwW0ubEGJibx2XzJC5nOZmnKR2OrS3R9Sn7Wo6TJ0PCx/Ip168pH8NIO6JXAvGiqgTiM1lfT3PbjLKcLwF+QlW/tPP7uzCqH8ZvE3zv16rqy4Yd/6fd43zJtXnT6l380sFLsA3D0n0XyBYy5vYvkO1fJNm3GFLn8i46Dx5B84LVdD9Z6iiLlEsOK3fcnSACxYEWzVbG8W9PZpyhrpZTVd8kIr0SiDCJEoiq+nfO5+YulKpJ+O+EdP9bgU+JyPXnSja5WbvcXIapTe8qTvGuO0+RIrzfPIwDf+8QjYML2EYaahpKVSXJFRSNeRrWkVGwkMKJtSadIuHwQWFl1dBoJJw40d3qLcdCXcUpIk3gGoLH7oFMRL5ZLR2zbWY1LeZRwM2q+g1VzYHrCCvYbskChkb1RTcQHp8s8Ufz96dzd5dipUu52qFYa+Odxxc5eI+aBOMczgurvknhE1KrIMrSPMy3DEliwhSPyE55A/AQQvP+u8CDgTfu9CKzGoTvr1ZbcSth1GFLDEKOJ0PIUVpi+v3OtZOroe+ZWJK5JslCC9RjfBGWETQlS3aFZb/AQpajajjjw/IFg0yksvHshH7GzfdsKl/0IRHZcYrlrFrOkYjItVKtjNvBc43dxyua9+GaZB8ndX08vH2qg+uUaOnQoqxWwMoxRZdG9zSqSkpBwxRYE0rUeK94H5LmB95vSn/pnuTTItJbFhsReTRww04vMquWs7dabY/Lqn19VPU1wGsArjJN/afpRYgRnt+8uPd6CMRXwfgwl6iJNBpgLGrDmIKglCSg0C4SnIIRoXQaKh2nkxGlEhyymvL9wF+JyLer5/cBvtarBLLdMfZZFeengKuqZZFvIywI+g/PebSub+qo0gyq1wSa+1s0jhygce9LkfkFdOkgbv4AedJi2R6gqw1uXdlHt0zJnbBWdds7Hbdlnui4qKtDxJhyfGdSnNVKtr9IqExmgT/YUTirl/+xKFz5ow9j4crLsYeOoPsOUcwfoJst0MkWWbEHWXaLnOo0ycsEEAxhsKmRhX5nux3L0eyUXS0eOw0uKI+08mfcstI4tK8aPqrMqbGoTciTBZwmFN7iq2UFnYe1bnBUllcdy8vdUOJm/Z7G8adVF6v17MurCTHyywkauwenzG2m18wTPD5jbSh8VC1h7WyK4Kv67wavhm5pOL0iLK84Op2Su451qyXRpd9N6BdriIziTcC/4gKrEtZHnOfSjID2pgX3B8oJy1oLNG2HNTGIJKRJmMW5uuZwpWNtuRPmHk3slutpORlTVcJai9OkJhRUUF+1K2EeR1askSctFEFE8BhEoJlBllnS1JCkIQC/tpKfW/iRc/FrIvJa4INcQFXCWovzqmc8EHUOLYrQ51xdhrl9JCJhUVbCUtiJUaxRznSF1EKzmbC6WlIWJToxy1nrIPxzgAcCKRewwEV9xDlAtt+ycPkC6kvK1Q4kKbJ8BnPkItQmFOk8oqHPWapFDMxnJaclxVihLDyJFZrzzYkJqOYpc49U1R0XUdjMnh0hGkZ+ynHic6e56a03Ua51KFdWcafP4E6fBsCUXZrlCgKkNtTkXM1taOaBhQULCIuLTYpuMc0/Za/yVyJywauv1NJyDlK0u2AEOzeHNQnVcBFF0sJUBWEaUpCYlLlGSFZetIY77vCUXjl8yT5u//qt6xcUgTF57DVu1h8DfFZEvskFLHBRf3GudUgbKeXyCpk6Gt/5Gr4xjz9akC9ZVv0cJQnWKItzUOSeO07DwmLKXbevsrqyKWUuhpK2w1hGiGrZrA+Sn2mzevw0zXtfCq6EykOfX76DAye/gRElsw5rFCPKgUVoJGCMwVhLozm5eX1+AtuMsCuzL/cs2cGUi3/gMC4vSeabdL57B+Vdx/BnTiNry5B3aRar3Eu/w4JZpZmUZNbRLZVGw2ANtFrJRPM54+zL4dS2Wc9PFNz55eMcuP8izvlQMPZoSaaerNlEOqtoa4FW9yRHm0LZSPnu6QUUy/5FUE04dtdaNewZ2SG7Nvtyb7DFsuh6Gjonu2RzDfKVDtl8GxZaeO8xqiCCcWVVac6wr9Wlu2z57t1KXsCBQy1On843ZjmNiRkrgjBuxjL7sj7NuqtyOKuth3eKqkedXx/B7L2uSmlTTvr9eDUIQmYd83NCkghpaphr2jhCtHN6sy+PVrMvPwr85k4vUjvL2ctY702rkFQw1iKphJ9ilceBDckyBSmaNligzTeWD9B1CUuL4Ks1Wc/4CY6tz04fcSyISKKq5abZl8IkZl/WgYX9LZJmQpJlYVWNNEWMoPML+KxB17To+hSvwsULq9y+sshyWyhL6OTK8nJc+3IHfBJ4BICqfhX46oVcrD7N+haIgXQh43QCz3rXx7i79Jj5Fmos5F1QxaVNMslDbN0YvA+B+bwAa2BhYcf9+HsyY/211cpyDk5C6z1uLDZ4xZdu5lPfPcYr/vLT/Pb3PgTJGqhNKW2DwjawRmnR5lvLByicBQFrPO1SSZMJ/X41dB1qxhER+RfnenGntbVqI86t1jh9+upNFG/9Wv/56z72eV73sX9FM0048fbfpUyaNLTDGd9ktZwnTSFzHo+QZYLp1HoS2iSwwAJjsqC1EedW/NH8/Xnzvdp84JY7aJeOuSzhaY95OC977jPABC/cimcfpzijC2RWWWiUqKYIUJae9oTqc9Y0K+l2Vf0P47pYPfqcApjKDTf0vfKDJmExTemUjmZi6RaOpYV5Ljp6lLLRQtMUp4YVFrHGVVEmpZ0LHuh2lWZjpsuSzhqxz7klJlT+2MzdecFzHv0QnvukR/FHn/wqdx6/G9ZW0LkljDG0ilOczg7StGHcvVs2yHNleQWyTFhejqGkHfDE0Ydsn3qIU0IhBKCymgKlsu/KA/zhk76fbP8iYgz/+cmPIr34YnyzhUkTfFnSnj9CogU5FueEk6spiGCtYq1Q5pObGly3BKfzWRl4GLVo1gXZUFhBRCARDj9kP+2Tyyx/+07yTpUR7zxqLApk3TN4sbRMB/UeRVicczQzJTGEpI8JrrceGU79PvnKGokIt7z/25y57RRlXqDtHPIuKoJdW0G8Q4ylUa7hVXBq6boUI9DpahhAIqyBORkEP4GtTtSjWR9k4Pu5/zX3pXlwiXShRXb0IOmhQ6E+pysxnVV80sD6gsR0aZiC+TTnTN5kril0ciFJatbu7jHqYTl73rqVfplsgFs/8d2Q8eYdmhdot4u2FvEHj+LTRpgyrIr1DkVIjEcIS8RkmZDYyVWXC5GB2uZzjoXaWE6xA1+MgLFCfiYPQ5SlI8m7eBGsCEVrP761GA71jjJJSIyjU1VIzFJlua0UhZJMcJGsujlE46YelnMTpiHcTcG/OPYNjrU7GBumsklRgHek+SqmswLeI1qS23kMYZ310ocCC90u5IVj+czkym5PCxGxIvIZEXln9VxE5KUicpOIfEVEfmlg/++IyM0i8nkRecRu3mc9xCkgFiQBkwVL96blY3wxX+MVH/sCdq6JSS0+zyHvQJnjbYO8sUCRLgCewhsKJyEK5eDUqS63fmeFtZVigk371FZwewEwmML2c4R6qA9U1QcRypwD/ChwVbVdC/zeWP7wbVKfZr0S0FPv+DL5QHbwGz5/M2/4/M00Esux330RWpY4E+YGNbrLrDYOQOlYKZt0ygz1yte/LWAS7nXJInfduVKr4l0ichnwFOClQC9J458C/1BDvR5U9a5q/9OBN2j4AD4uIvtF5F6qevtu3Gs9LCeEv8TCGy99AE9o7esvYNC0hr//oCv49K/8dFi9zXsSVWz7DCDkPuW4O4pKRmY9KsIlFxmaTcEYyLIJ/X6rrKRxb8DhXjnyart20zu/CngRG6dN3B/4yer494jIVdX+rWrzXzqRz2ML6mE5pXKIBA5JSksMOUomQtd55oHDrWy9IEKZY9IU7xzHGxejGmKEVjztTviSGynceUc+Mael561PgOOqevVWL4jIU4G7VPVGEfnBgZcaQEdVrxaRZwB/QJjaO1XqIc5BBE6p48cWDvJjhw7xAV3hzpU2bqWNLnTwc3PY1TNo1sCJZc52wRvaLqXQhGamdEtoNi2LSxlnTtXKIXos8DQRuQZoAksi8scEi9grsvV2woK8sI3a/JOkFuIUEUwv5CPw60cvRxJBEuHhlxxm6egSrijxRYntdgFldf4iugtHaNChY+aYkxJTwimXkliqCnQwNx+yksa/1Mvuh5JU9cXAiwEqy/kvVfXZIvIy4PHAN4G/B9xUnXI98Isich1hqZ3Tu9XfhJqIE4DKW7eZCTU3y2oh1lJDAVjn8EWJpCnFviMUi4cQlIwu6pWuD+lzhYeiWoGj2TSsrdXHGRrCy4A3icgLgRXgedX+dxNWYrsZWCOUNtw16iFOA0nLIlKVxkYxiYSVMHrrXVmDyVLYv598/hAqhq40acs8LdMl9Z5T3RbzmYIquRFUYa1KNp5EOGmaY+Gq+mHgw9XjUwQPfvMxCjx/N+9rkFp460GUIFXCsYggRpCGYBuWpJWRNBtoUaAnTmBPHw9GVRJScko1FF443UnplOEjWVmrainUcKLPXqEelpN1yzY4b33p6AKNfU1sM8NmGdLIIElItMS4nKbpoAreec74oxxoFZxaE7pqQD15oROdSlGj8OlEqI04B+kFzVfvXGPu4DzpfBM71yC9zxWYw0dQsczlyzjX5XR2FGcbpOoo1KI4VrsJNlFESubnJzM1WOtddnss1KJZh+Bdh7V91lfMQKB5YJ7m0gLJoUNBmDbFZ3NQFmGWpVsFY1hI24CSWiFNlPmWMD+f9O1mnUaJ9gq1sZwy0Pz21g4yiaV5YBHTyDAHDqAmQbM5VCwWR46gSUaiBR3foF1avBqyxNPuWNptj6vK0YzdIarnvPWxUg9xiiCp2ShQEzaTpiSLC5huF04cg30HMWmG9xlJmlNqTiYJhaTMJY5umaEqrHXCCsITK6oQGUk9xMm65RQLSSMhaaXsv/wQvptTnDyFWIMpuogrkINH8Y15jHrA0PYNzrh52kWC90A1fNnNhNWVSc6+nNila0EtxCmArarIJVkY0fFdx4m/uQvXzZk/egCAxkVHEO8RV2LKLjq3FLIfxNC0XXxmWM0zimodTPX0m/VJUMOiCmOlNm2WerCZXQ+6VwVfy7WCYrWNlg7KEqlmU6pNQUyV9JFgRfveszVCu6OUpZ/oktaR4dTCclIF4AXTn8OuhAC6VisGVyV2UWsRMagIKmHqr8fgVFjpho+jdCFjyBihKIPlHPfYelgUdmyXqyX1ECeCqVLmjAmrZaAaqoCkBrEmNPdJgliLbzQBobANVAwo5C7tz9zMS0WBvPBnBfcju0ctxClVPqeYTeEkhebiHMlchrTmkEYT0gaaNCjSFs5m5GRV8N3QLSzOQ1kaVD3eK2XhJnbf0SEaTi3EaRJD61CLfCXfWBPeQLrUIluYD/OImnO41gIuadBpHaAkZdUvoAjt0uAR8hLa3XB+WSplua6gcQfioziHUwtxAqETZ0D8uvU0DUu2b5Hs0AHs4hIsLOKTBnk2T9fMkUuT0lsKb1nNM0A4cRq6uXLyZJfTp7r9xI++MMe4vGBkOPURJ5A201B4y4MkhoMPvIz0iisw+/YhjSauMUfZOkCRtOhqgzNukcJZbjs1j8Ow2laKAsoSisLTXu1u8NZFBB1TaEkVfBxbH0p9xCkhEH/wqks49fU7AWheeT/sxZdRLB5AxeKyeTrZInfLEU4VC5zpztEtDCt5gvdKuwvtrqcsQ4W5/YfmWT3TiePqU6IW4rTNjPmL9pHOt5j/ngdhFlosXnkfkqOXULYWKJr7WJ6/mHbZoCNNjrUXQWG1bTFWSS3kKoiECh9l6cm7nm7XUVQO0SS89aj54dRDnPMtDj/ukUiakd/r/uxfaCHzi5RLh8hb+8mTFg7DyXIJVTi9lpAmympXKJ0hsZ5uDmeWPXnuWFkpEAOudLjSg2pcJ2sK1EKcmjXxlz8AnzRZXriErHWAREo6jf2UJuOMOUg7z+j4BisdSzu3dAvFGIOWypkVOH3GoQrOhVrwZeHprOX4XqR8AmYuWs7h1EKczmQsH74/joRT7KdlW6i1nNFFnCac6TQo1SKEWkgAzgudbnBuylIxBopCybuObruspntMdl3WOEI0nFqIsyThpD2KU8Opbou1tIV4YbVIaBcZpQ8B+dU8oSyDxSpKOHlasQZcNczpvdJpl6iGoc8QhJ9c2e3IcGohTqeG4919KHC6k7KgBV4Nq3mKqtAtwKuhm1M9DiEjVaWbK3nucKWyshJmWnrvKQuHcz70OSfABCt+1IZaiLP0hhNrTbxCuxua7l7yhnPCSlv6TWheeJyHTifs6HQcq6sF3ivehTnuvvTk3bJyiCY3fBkZTi3E6TysdAzOKd0cFBMC6aWQF+EYr7C6FsJDZRkyjIIFXc86UlU67RznFPVKkRcU3WIyN63RIRpFLcTpPbQ7IdWt01VKH2Y2eq8UJZw5k7N8psS50HwDNFspziuuDJlHebek6JQ4p32LGVLuJqeg6BANpzbiXG0rXmFtzeO8oaiab1d6Tt6d47Vqsn34tyw8GFg53QFANfQvbWJxpaOoTG5s1qdHPcSpyqnTJXnXBfH58GfluefkiTbeBU98UGidzsZx81AZsSTv5JWn7lk7vRLqLE0AJTbro6iFOMvCc+ftq/Qmq5+uyhaqhr4jAmXh+o9VwRUe71zIcDeGbruLL32/71nmRZjSMcE5RJHh1EKcqopzoSyWGCiL0HesXuyP8mi1ssZgGlyRl31B9jbvPeoVa+3YspC2vu+JXboW1ESc631DMRK87eqb7zfl2hOnx/swXt7rh4aXNQi7W/aPnURNzkGiQzScmohzfTqFsRKcHQjNdqWAniPUO96VboOAVRU/0Cc11uCdx02ozxkZTS3EiYY+pUhI4HXOg+810+GAnsUUkWA5qZKHB2oriTFhgdZKvJNyhnrvF5v14dRCnL1+ooggldeNbnyth3MuOD5e8RqEONi+utJVJbe17zBFpkMtxAn0rZyIoG7d4fFaCbOqw+kry+id32C6Bh2iwX2TQomBgFHUQpyhv1h90yZYx97+Qc98QzO9SXi9Jn6wlKIIE23ao1EeTi3Eia4LUnS9Hzk4/Hj2v+BcSIczVYkaH7JF8F5R9VQ6jUyJWohTWQ+249dFubmJ9i44OT3r2FOeKx3qfBWgH25hx3rfUflDqYU4UTZ44CE0tB42gvWFB/oeuqyf28M7v8HChtBSVNC0qIc4GegbChuFOaCtQYH6qhDn5vhmb0Ro0qtobAoSRLagFuLsNdkQRog2VOnohzG1HyLSgez2nsXtx0Srih462O5HpkItxAkDYR9/ttUbbKp1oD+pGsq3+v7rlRPktVfec6IWNMZQh1MfcfaspWjfYm5wiPoWEiCMmfey4cOEtgEnSECr8tsTveeozaHURJy9Zpi+5es5PGcF1XtJHb3/epZxizpIUTvTpRbi7E3lXX++tbfRWxeTTR78Wdfzvf7mZOUZR4iGM5Wa8CLyWyLyVRH5vIi8XUT2D7z2YhG5WUS+JiI/Mq73XM/THGze11/rW03t1UWK03anzbQWLPgA8D2q+jDC2t69NcAfDDwLeAjwZODVImLH9q569pNesz+Ytxn2TXZ4SHUyW52YijhV9f2q2iul8XHgsurx04HrVLWrqt8krPP9qG1ec31USDcOW272irU/RFkd6/2Wx0war+Pf6sQsLPXy88B7qseXAt8ZeO3Wat9othBa3wHyGxNANuuuv9Jw3b7dPc7EHCIR+T/AxVu89BJVfUd1zEuAEnjTeVz/WuBagEbr4gGruSmJGAgjQRutYT8LaSDZuDditHlkaVLUrRkeNxMTp6o+adjrIvJzwFOBJ+q6am4D7j1w2GXVvq2u/xrgNQBLBx+k61Zv0DIONu/98wYvsj6CtClrKTJ9puWtPxl4EfA0VV0beOl64Fki0hCR+wJXAZ/c3lWDEzNMmPTCSJv6pVteahfodTfGudWJacU5fxdoAB+o+nsfV9VfUNUvichbgC8Tmvvnq+q2S24MzhkKzze+ttXj9ecD+7bIWBo3MfFjNFMRp6peOeS1lwIv3dH1GPTK5ZyiDA7TNhWhsYmfNrUYIYKelVx3hPpO0UiBbWFRN/hSkxNo1P5waiTOvmcTxsmrsNKg5x4yjXoLSQ969IPWdffuOTKceohzk4Nz1sDjhmhRT57r/cytmu/tWd0Lw8dO51DqIc5NjJ7eu8W+zf3U2OZOndqIc8v+4lavb7V/UJi7lCoUnLhdeas9S23EuTmYfs4DhuzeVe88GueR1EecbBGv3NbxZz+OzAa1Eed2hBmqeFQ++850PAG0P3cpsjW1EecwpZ09InSOw6swVGxvZ4MaiXMIPa3JudPith+0H+NtxWkaQ5mFfM6xMZhw3CvitUGMAw/zznE+/7F/Tt65u79PzO59HINDruPc6kQ9xFklFPfj6tvoT377ptdz5u7P8+2b/ihcooZf7l7nntGsD/Cxdz4J9Xn/+R23vIM7bnkHYjL+9jXv370b0Tj7chT1sJw74JFPuo4jlz4JYxsAGNPgyKVP4uon/OmU7yyymXuc5cyah7HJPN7liMnwPscmLbLmoV2/l9iNGM49TpyqStE9wcWXP42LL38ad3zrevLuid2/D2Ky8SjuUeLsWaoHPvI/9kNG93/oC6d5S5Eh1F6cZzWds9KU6rljrpFALcTZTxneSni9fRNciS0yGerprW810jODOZoDE0HHtm0HEbEi8hkReeem/b8jIisDzxsi8uaqdtUnROSKsX4AI6iNODdM01jfOZ2bmX1eAHxlcIeIXA0c2HTcc4GT1YTEVwIv353bC9REnDs0HTNCWBt+vNsoROQy4CnAawf2WeC3CLUEBnk68Prq8duAJ8okV6rdRC36nHuRKQ6XvoogwsWBfb8IXK+qt2/SXr92laqWInIaOAQc340brYnljAxwWERuGNiu7b0gIk8F7lLVGwf2XQL8BPDfpnCvQ4mWc4pMKGXuuKpefY7XHgs8TUSuAZrAEvAloAvcXFnNlojcXPUze7WrbhWRBNgH3L3llSdAtJz3IFT1xap6mapeQSjS+xeqekBVL1bVK6r9awMVWa4HfrZ6/Mzq+F3ri0TLOUX2wDSN1wFvFJGbgRMEQe8aUZxTZJqJH6r6YeDDW+xfGHjcIfRHp0Js1iMzS7ScU0I1lqMZRbSckZklWs4pMvv+0HSJ4pwiMWVuOLFZj8ws0XJOCdVYjmYU0XJGZpZoOadI7HMOJ1rOyMwSLecUiZZzOFGc00LjvPVRxGY9MrNEyzkllNisjyJazsjMEi3n1Ij1QEcRxTktYsrcSGKzHplZouWcIrFZH060nJGZJVrOKRFDSaOJ4pwWsT7nSGKzHplZouWcGjHZeBTRckZmlmg5p0jscw4nWs7IzBIt55ToLcwaOTdRnNMijq2PJDbrkZklWs4pEh2i4UTLGZlZouWcGjHZeBRRnFNCFdRPZsWCuhCb9cjMEi3nFImhpOFEyxmZWaLlnCLRIRpOFOe0UI1xzhHEZj0ys0TLOSXiHKLRRMsZmVmi5ZwifkLLBteFaDkvgF9/0YOmfQu1JlrO8+S1r3wED7xy8fwvEKcGjySK8zx49jMvuzBhAkoMJY0iNuvnwbOfefm0b+EeQbScO8RaWJgfz8cWR4iGEy3nDkmsTPsW7jFEy7lDZFzaVPAxn3MoUZw7ZnyWMzpEw4nN+o6JgtotpipOEfkVEVEROVw9FxH5HRG5WUQ+LyKPmOb9bc14LKeiqPqxb3ViauIUkXsDPwx8e2D3jwJXVdu1wO9N4daGEj3s3WOalvOVwIvY2E4+HXiDBj4O7BeRe03l7s7BOB0i9Tr2rU5MxSESkacDt6nq52Tjt30p8J2B57dW+27fxdsbSqNhx3atuolp3ExMnCLyf4CLt3jpJcC/ITTpF3L9awlNP425iy7kUjviJ5526a691z2diYlTVZ+01X4ReShwX6BnNS8DPi0ijwJuA+49cPhl1b6trv8a4DUAC/v/1q6YoOf81H34uZ+8YkxX05gyN4Jdb9ZV9QvA0d5zEbkFuFpVj4vI9cAvish1wKOB06o6skl/4JWL/M9XPIyv/s1pEGgkCSqQpobmnOHAUpO5hqU5Z1HnyUtQ52m2ErLEYKyQWEFE8F7JMoOrmtwsMXRzz9ycJUtj5G03mbUg/LuBa4CbgTXgOds98UEPOMCDHnBgIjfVbI6vn9lDY8rcSKYuTlW9YuCxAs+f3t1EZompi/OeTKyVNJwozmkRm/WRxB5+ZGaJlnNqaO3GwsdNtJyRmSVazimhxBKIo4jinBaxsvFIYrMemVmi5Zwa9UtxGzfRckZmlmg5p0gMJQ0nWs5pMcVMeBGxIvIZEXln9fxNIvI1EfmiiPyBiKTV/qnO6YrivGfyAuArA8/fBDwQeCgwBzyv2j/VOV1RnFMiFPLyY99GISKXAU8BXtu/F9V3V/O2FPgkIckbpjynK4rznserCBMLz1Jy1Zz/DPDeate55nTtCrVwiG688cbjIvIt4DBw/Dwvc77nnlfJudXTN73vY+/8wcPnc+4ImiJyw8Dz11RTWhCRpwJ3qeqNIvKDW5z7auAjqvp/J3BfO6YW4lTVIwAicoOqXn0+17iQc88HVX3ybr3XAI8FniYi1wBNYElE/lhVny0ivwYcAf7JwPHbntM1CWKzfg9CVV+sqpdVsw+eBfxFJcznAT8C/JRujG9dD/yjymt/DNuc0zUuamE5IxfM/wC+Bfx1NSP2z1T1P3ABc7rGgdSpvIqIXNvrX+3muZHJUCtxRupF7HNGZpYozsjMEsUZmVn2vDhF5KCIHBzHdcZxP5HxsSfFKSL3EZHrROQY8AngkyJyV7Xvim2c/28HHj9YRG4CbhSRW0Tk0ZO788hO2JPiBN4MvB24WFWvUtUrgXsBfw5ct43znzHw+LeAF6jqfYF/QChqG5kB9qo4D6vqm1XV9XaoqlPV64BDO7zWJar6nuoanySkjEVmgL06QnSjiLwaeD3rWTP3Bn4W+Mw2zr9fVW5RgMtEpKWqa9Vr6djvNnJe7FVx/iPgucCvs57CdSvwv4HXbeP8p296bgBE5CJmcJGEeypxhCgys+zVPuc5qXIWL+T8a8d1L5ELo3biBB55gefHlVdnhL3a50REHkjoO/b6nLcB16vqr+3g/EuBT6jqysBL3xrrjUbOmz1pOUXkXxPimUKYkPXJ6vGfisivbuP8XwLeAfxz4IvVukg9fnP8dxw5H/akQ1SN6DxEVYtN+zPgS6p61YjzvwD8gKquVCNKbwPeqKq/LSKfUdXvm9S9R7bPXm3WPXAJZzfB92KLWYVbYHpNuareUk32epuIXE7sc84Me1Wcvwx8UET+hvUg/H2AK4Ff3Mb5d4rIw1X1swCVBX0q8AeEwgKRGWBPNusAImKAR7HRIfrU4JDmkHMvA0pVvWOL1x6rqh8b681Gzos9K85I/dmT3nrknkEUZ2RmieKMzCx7Xpwicm8R+WZvmoWIHKieX7HFsXMi8pcickErrYpIJiIfEZG9Gu3YE+x5carqdwhpbi+rdr2MULzqli0O/3lCNYuRHv2I98yBDwI/eSHXiQxnz4uz4pXAY0Tkl4HHAf/lHMf9NGHYEgjDoCLyBRH5nIi8rNr3YRF5pYjcICJfEZFHisificjfiMh/HLjWn1fXi0yIWjRLqlqIyL8i1JX84c3DmtAf2rxfz6KKyI8SEkceraprm2Zf5qp6tYi8gCDm7wdOAF8XkVeq6t3AF7nwDKjIEOpiOSGUiL4d+J5zvH4YODXw/EnAH/amZ6jqiYHXrq/+/QJhrP52Ve0C36AqCVh1DXIRWRzbXxDZQC3EKSIPB34IeAzwwnOUhm4TalJuh271rx943Hs+2No0gM6Objaybfa8OCXU7Ps94JdV9duEqb5n9TlV9SRgRaQn0A8AzxGRVnWdHRVVEJFDwPGtuhCR8bDnxQn8Y+DbqvqB6vmrgQeJyN/b4tj3ExwmVPW9hOb7BhH5LPAvd/i+jwfedV53HNkW96ix9WodnReq6s+M4Vp/Bvyqqt504XcW2Yo6WM5to6qfBj40jiA88OdRmJPlHmU5I3uLe5TljOwtojgjM0sUZ2RmieKMzCxRnJGZ5f8BDI8R9vArVVoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 360x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yUnconstrained = df['Y (m)'][sensors]\n",
+    "xUnconstrained = df['X (m)'][sensors]\n",
+    "\n",
+    "X,Y = df['X (m)'], df['Y (m)']\n",
+    "fig = plt.figure(figsize=(5,8))\n",
+    "plt.scatter(X*100,Y*100, s=10, c=df['Temperature (K)'],cmap=plt.cm.coolwarm)\n",
+    "plt.xlabel('X (cm)')\n",
+    "plt.tick_params(axis='x', labelrotation = 90)\n",
+    "plt.ylabel('Y (cm)')\n",
+    "cbar = plt.colorbar()\n",
+    "cbar.set_label('Temperature ($^{\\circ}K$)')\n",
+    "plt.plot(xUnconstrained*100,yUnconstrained*100,'*k')\n",
+    "axes=plt.gca()\n",
+    "axes.set_aspect(0.7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xUnc, yUnc = ps.utils._constraints.get_coordinates_from_indices(sensors,df, Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functional constraints:\n",
+    "\n",
+    "Suppose the user wants to constrain a circular area centered at x = 0.025 m, y = 0 m with a radius (r = 0.02 m).\n",
+    "The user can do see by initiating an instance of the class Circle which has functionalities such as :\n",
+    "- Plotting\n",
+    "- Plotting all possioble sensor locations\n",
+    "- Plotting the constraint on data\n",
+    "- Obtaining indices of sensors within/outside the constrained circle\n",
+    "- A dataframe of sensor indices along with their coordinate locations on the grid\n",
+    "- Plotting the sensors on the grid\n",
+    "- Annotating with the sensor number\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "circle = ps.utils._constraints.Circle(center_x = 0.025, center_y = 0, radius = 0.02, loc = 'in', data = df, Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)') #Plotting the constrained circle \n",
+    "circle.draw_constraint() ###Plotting just the constraint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "circle.plot_constraint_on_data(plot_type='contour_map') ##Plotting the constraint on the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "circle.plot_grid(all_sensors=all_sensors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Obtaining constrained indices :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "const_idx, rank = circle.get_constraint_indices(all_sensors = all_sensors, info=df)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using these constrained indices with pysensors GQR optimizer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sensors = 1\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_circle = ps.optimizers.GQR()\n",
+    "opt_exact_kws={'idx_constrained':const_idx,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sensors,\n",
+    "         'all_sensors':all_sensors,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_exact = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### List of selected sensors "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [15658 18378 29993 16573 31414 40090 21456 37537]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_exact = ps.SSPOR(basis = basis_exact, optimizer = optimizer_circle, n_sensors = n_sensors)\n",
+    "model_exact.fit(data,**opt_exact_kws)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_exact = model_exact.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "xCircle, yCircle = ps.utils._constraints.get_coordinates_from_indices(top_sensors_exact,df,Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)' )\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_exact))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### List of indices of sensors selected along with their coordinate locations on the grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15658.0</td>\n",
+       "      <td>0.008200</td>\n",
+       "      <td>0.136713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18378.0</td>\n",
+       "      <td>0.006977</td>\n",
+       "      <td>0.063449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>29993.0</td>\n",
+       "      <td>0.011413</td>\n",
+       "      <td>-0.051947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16573.0</td>\n",
+       "      <td>0.007676</td>\n",
+       "      <td>0.124104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31414.0</td>\n",
+       "      <td>0.006206</td>\n",
+       "      <td>-0.079055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>40090.0</td>\n",
+       "      <td>0.019092</td>\n",
+       "      <td>-0.241529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>21456.0</td>\n",
+       "      <td>0.004899</td>\n",
+       "      <td>0.187096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>37537.0</td>\n",
+       "      <td>0.005085</td>\n",
+       "      <td>-0.001238</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID   SensorX   sensorY\n",
+       "0    15658.0  0.008200  0.136713\n",
+       "1    18378.0  0.006977  0.063449\n",
+       "2    29993.0  0.011413 -0.051947\n",
+       "3    16573.0  0.007676  0.124104\n",
+       "4    31414.0  0.006206 -0.079055\n",
+       "5    40090.0  0.019092 -0.241529\n",
+       "6    21456.0  0.004899  0.187096\n",
+       "7    37537.0  0.005085 -0.001238"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_circle = circle.sensors_dataframe(sensors = top_sensors_exact)\n",
+    "data_sens_circle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting and annotating (Numbered list of) the sensors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "circle.plot_constraint_on_data(plot_type='contour_map')\n",
+    "circle.plot_selected_sensors(sensors = top_sensors_exact, all_sensors=all_sensors)\n",
+    "circle.annotate_sensors(sensors = top_sensors_exact, all_sensors=all_sensors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trying out a custom parabolic constraint: ( Now what if the user has provided a python file with the required constraints)\n",
+    "\n",
+    "##### Here the parabola is centered at (h,k) = (0.025,0.00)\n",
+    "##### The equation used is $y = a(x-h)^2 -k$ where a = 100\n",
+    "##### A line drawn at y = 0.2 closes the parabola and the constrained region is bound by the parabola and the line."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### The user can initiate an instance of the class UserDefinedConstraints and use functionalities like plotting, annotating, creating a dataframe "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "const5 = 'twistParabolicConstraint.py' ### Python file with the required constraints\n",
+    "user_const_instance = ps.utils._constraints.UserDefinedConstraints(all_sensors, file = const5, data = df, Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)' )\n",
+    "idx, rank = user_const_instance.constraint()\n",
+    "user_const_instance.draw_constraint() ## plot the user defined constraint just by itself"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using these constrained indices with pysensors GQR optimizer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sensors = 0\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_user = ps.optimizers.GQR()\n",
+    "opt_user_kws={'idx_constrained':idx,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sensors,\n",
+    "         'all_sensors':all_sensors,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_user = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### List of selected sensors "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [15658 18378 29993 16573 31414 40090 21456 37537]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_user = ps.SSPOR(basis = basis_user, optimizer = optimizer_user, n_sensors = n_sensors)\n",
+    "model_user.fit(data,**opt_user_kws)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_user = model_user.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "# sensor locations based on pixels of the image\n",
+    "xCircle, yCircle = ps.utils._constraints.get_coordinates_from_indices(top_sensors_exact,df,Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)' )\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_exact))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### List of indices of sensors selected along with their coordinate locations on the grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31414.0</td>\n",
+       "      <td>0.006206</td>\n",
+       "      <td>-0.079055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30106.0</td>\n",
+       "      <td>0.011132</td>\n",
+       "      <td>-0.040648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19000.0</td>\n",
+       "      <td>0.006517</td>\n",
+       "      <td>0.033655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>36479.0</td>\n",
+       "      <td>0.010434</td>\n",
+       "      <td>-0.230693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>35723.0</td>\n",
+       "      <td>0.006281</td>\n",
+       "      <td>-0.377601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2620.0</td>\n",
+       "      <td>0.000124</td>\n",
+       "      <td>-0.009141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>21714.0</td>\n",
+       "      <td>0.004854</td>\n",
+       "      <td>0.200180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>16921.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.061600</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID   SensorX   sensorY\n",
+       "0    31414.0  0.006206 -0.079055\n",
+       "1    30106.0  0.011132 -0.040648\n",
+       "2    19000.0  0.006517  0.033655\n",
+       "3    36479.0  0.010434 -0.230693\n",
+       "4    35723.0  0.006281 -0.377601\n",
+       "5     2620.0  0.000124 -0.009141\n",
+       "6    21714.0  0.004854  0.200180\n",
+       "7    16921.0  0.000000  0.061600"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_user = user_const_instance.sensors_dataframe(sensors = top_sensors_user)\n",
+    "data_sens_user"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The sensor locations plotted on the grid along with the constrained region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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z9MsvRzX7Y49tsUt44IEHuOOOO1i5cmWHpzfUPRutFON692jl9wsxxpjiKNf49qWPTJkCe/bAa6+12jFp0iRuv/12Zs6cSS6Xa3di2MeZUzZKxxgTV+Ua8Lt0zTXXcNhhhzFv3rx2+7S5+6tbdcQm2hpj4qpcA/4djy9fDmeeCX/+c7udIsJ9993Hvffey9KlS/dtV1VyXqpPN046a8U3xsRTOQb8m4BJnzr55Ghdx7/5G3jzzVYHhGHIq6++yvz585k1axaNjY089thjXHXVVUgf+6HzodXxjTHxVH4BX/UvqDbknYvSKZx5ZjTxqo2rrrqKqqoqTjvtNG666SaGDh3KihUrCBv71qRTXZ7jlowxps/KP7yJRLNuW3DO8eMf/5grrriCpUuXcvbZZ3P22WezZs0aAv+cPt0ua722xpiYKq8avsgQRD6JSPqS+vooJfL//b/w5JPtDj3uuOPI5/NMnz6dr3/961x//fUkk0m2dTBypyesRccYE1flFfAhCdwGbP3Sxo1R0rTzzoPBg9sdOGHCBNatW4eqMnfuXAYPHkw+n2fd9vf6VADrszXGxFV5BXzVrah+ENWBH54yJRqDf+qpnR4+ePBg/vCHP5BMJpk2bRp79uzh2VfXMCDXxBeW/4GaXM/z4lsN3xhTUps2wVlnwebNRb90eQX8XpgyZQpz585l/fr1rFixgpNGj6Hmjz/mY2te4Ix1f+3x9ayCb4wpqXnz4Nln4dZbi37pig/4ADfddBONjY08+U//xBF/XUHz2mWsHXY4n33lCe5ccjfXP/uf3b5WLH4hxpjKU10dDVJZsCBaC2TBguhzD7MFd6Xi41sYhmSzWR588EG+9cILNI4ezbt+lqZUNdlEiqcnfYCF0z7b/esdxLIaY0yn1q6Fyy6LcoFB9PPyy+Gtt4p2i4oL+NpmiOarr77KhAkT+PnPf87Nt9zCHatW0RzkGf7euwzMNpLzEjSluv8NaU06xpiSGDsWBg2CTAbS6ejnoEFRivgiKduA76lGDxwE0SuTYff27UyePJlt27btO+7444+nrq6OV199le9973tkggCSae485RP84tQZ1GabenRf67M1xpRMfT3MmQNLl0Y/i9xxK21rzGVBZNhbVVXvHZnNtt7+jW9wY0MD69ev5+GHH0akdX38hRde4OoLLuSV+s0MOecaBn/woh7f2gFr7zyv92U3xpgSEpFlqjq1o33lWsOf/0ZNDTQ0wDPPRH/WvPIKfPObfPvb32b16tX853+274idNm0aC867kNNOPIfq8SczINvz4ZnWhm+MKal+NSxTZADwuR8fdli0AMr06XDBBbBoEQDpdJpFixYxd+5c3nnnnXanJzTJvIZ6UqMm8qH1K3o8PDPlFetBjDGmF/rZsMzJgL8+nd6/5ZRToMUKV1OmTOH6669n9uzZ7da3VeeYtG0Ld/7hbj77yhM9Hp5ZW1X+6YWMMTHUT4dl1gK7W20ZPDhq3mnhq1/9Ks3Nzfzwhz8EoKGhgdWrV+PnA96qGsaAxl1kE6keD88cN7iXi+EaY0xf9NNhmXuAQa227N4NAwe22pRIJHjwwQe57bbbWL16NX/5y1/48pe/TArBRxi3vZ6BmT2M21nf/eGZIXzjvBOK/DjGGNMN/XRY5utAYnwms3/LX/8KJ7QPxIsWLeLCCy9k5syZjB8/njVr1pDLBAzMZjhp1zvks3n+9SNXdn94pg8Thw8o3pMYY0xPHORhmeXXYK3aiMgjczZuvITGRli+HH77W3juuVaHbdq0ifr6en79619zyimnsGjRIjZv3kxzpoGjZD0fmw0/+9UrNLsEfzr6tAPfN4TDN1XjxKZeGWNK5JFH9r+fP7/oly9KwBeRGcDdgAfcp6p3ttlfBTwIfAB4D7hEVd/u4pLXVYXhJYwaBcOHR50XbWr4Q4cOJZFIkMlkePPNN1m5ciWjslnGL/4Pvn8ePDsBvndWwCvzL0eBI2/6XZfPULPdoyrjoTb1yhgTU32eeCUiHlEzzMeBDcCLwBdUdVWLY64DTlbVOSJyKfAZVb2kq+tOnTpV6+rqDnj/1atXM336dLLZLI1NjfA54MTWx6TzMNrvPOC7LIx+awDVTvjVTz/EsCF9WwjdGGNK5WBPvJoGrFHVtaqaA34JXNjmmAuBBwrvfwV8TNpOk+2l4447jhdeeIFEIsEI4MRXYFQDfLEORu6By1fA2rthjJfp8HwXCCPX1+AFUXGsQccYU1JlPvHqcKDlDKgNhW0dHqOqPrALGN72QiJyrYjUiUjd1q1bu12Ao446iu985zsc8b7jOP1IOH0DHPMynL4eBmZBL/okC4/dyLSR8NGJtdQko9lVg5LVTGoYRTIX/RqcgNhcW2NMKR3EiVdl1WmrqguBhRA16fTk3C9+8Yv85rvfJfM8fKMKPlUPDz4B3kAYelKG6vEj+N6pI9mRGsfr2wfw6Kp6hjGSVZvfIUMjEK12pVKOA5eMMbFXXR0NxdxrwYLolU5Dc89X7+tIMaLbu8D4Fp/HFbZ1eIyIJIDBRJ23RSMi3L9kCY/thOZ6mIAjuQ3OfjdBcNE5qEsSJpL4mqQqWc0JwybQ3BC0SrdcjnnkjDH9RIVMvHoROEZEjhSRFHApsLjNMYuBKwvv/xZ4QvvQW7xlyxYWL259i9tuu403Nm3iq5NPZTbCOBwrJMWO8z4MyRSCIjhEQwgV5wnqK00N+79RnSPab4wxh1olTLwqtMl/CXgUWA08rKorReRWEbmgcNj9wHARWQPMBb7Wl3uGYcg111zDSy+9tG/biSeeyKxZs1hS/w5jSPKqeKwcWI027iG/aycS+EiQQxWSTkklhKrqJHj7v3ecQFiWc9GMMf1CJUy8UtUlwJI2277e4n0G+Hwx7gUwZswYfvCDHzBz5kyWLVtGOp3moosuYsaMGdz4wbNZtOsFmjQkscexeehQxjY340IfByRcgJdUEl5Uo/fEkS9cN+djw3SMMaVzkCdeVWx19rLLLuOEE07g5ptv3rctnU7zt5NO4ifekZzmatmtAXs2bSX5zMskt2yhqmk7DiUp4HmCE9AWAT4MQUNr0jHGlFCZD8ssCRFhwYIFPPTQQzz55JMt9oQMEo9bqg5nwfAjGbhlB8mVa3EvvESqaQc12oBzPtWpkFRVAs/tT4DvHIShVfGNMSXUz/Lhd9vw4cO5//77mT17Nrt27QLAuShgH53ZyonvbWL429torkqS/HMd1d/5McPuuZuEp4gIiYSQrq3adz0hSj9tjDGHXD/Nh98jM2bM4LzzzuMf/uEfAHjm3bd5NNzF2sQwtkoNgQiSroKqJP60U9l51bV4AuqERMIR5vc34XgeiLOIb4wpgQoZllly3/3ud3n++ed55JFHaA7yLNcmgoQjREinoSqbR3I+mkoTDBoIGoIqYajtBt/H4hdijKk8lTAss9R838fzPB588EGuu+46apMpNmoOgKSnvDdpHI0zTid37oegqRnnFF8dGkb9AKEG+67lHCA2+8oYUyKVMCyzlJ599lkuueQSbr75Zv7u7/6OR+5/gE3kQWFjYjATjpkAI4YQjBtHftIJqB/ly1GEIFSiZJ+RIAQbl2mMKRkbltm1j370ozz++OM89thjPPTQQ2xo2E2AsssLcAkB3ye3qwEQcB44QXGEqohKq9QKoV+65zDGmIOt4gM+wEknncTvf/977r33XtLJJFmU1zQDCcg25REENMApgOJESSbAeUJhIxB9H1hqBWNMXMUi4O91zjnn8O+fuoS/qxrJ0ckqEiiHb99BqrYaBgxGUUR9RBQn4IlCi3H3YQihZcs0xpSSTbzqPsk2cfGAkYzy0gwPm6hZv4nU6nWEoqiXQvFQVcIQmjN+q3H3FuuNMSXXX/LhF0Ny2EAOz2wlHeZJELBn2ACGvfwqbuN75I86luDGbxBoklw+6qSVFlHeExDLkWyMKYUKyYdfVlzSY/3gEbxXXUvoPHI5JUAJTpxM9gsXEZAiCKPMmMmkELbInSMeqI3SMcaUwiGYeBW7Gr6KoKkE+I5kNkCacpAUwmSS7LAxqCieU8SB57xW33h+HsBq+MaYErCJVz3nXIhXKyRTsGnYcNZNOpymU4+Ght0ELoE4UBQnAgLacqKVEG03xphSsIlXPeMcCMLWYcNAwat1+FOOIzjxZLx8BpcOERUQRVVbpUcOAggDG5ZpjCmRgzzxKnYBXwScV/jDRaB2wjDSY0Yjw0biNEQ0QAgAIZF0iO7/I0c1ahIyxpg4Ku8mnSuu2N+uNXky3Hffgc8RQZwgCSFRk2To6JFIwqHZJjzCqEtWHCKCFta23SuZ2J9e2Rhj4qa8A/4//zO8/Tbs3g2LF8O//AssW9blKSqKl/JID0qTTCd47431ZDbV4xobCBXymogmWIXg+6BB6zVtrc/WGBNX5R3wTzgBqgoLlIhErzff7PIU9QTZW2sXCJsV1RBNVBF4VYTiyIUefh58PyQf7s+WGYZYtkxjTGyVd8AHuO66aDzq+94XNe98+tNdHu62b6Nm5ADERemPqw8fRNXIkQSDh9GcHkIzA2jOJ2nMKLlsQIvsyISAqjXpGGPiqfwD/o9+BA0N8Mwz8NnP7q/xdyKdFqqHDsAlHN6gJIOPOwZ3xGSaBoxkpw5hd6aKpozQlAnZ05hDW9TwHZYc2RgTX+Uf8CFae3D6dNiwIZpq3IXDrr2OAUeOY+gHjqX2qHFUnTqdLYedwobqY9meH8ruTIrGZshkA0Qh9PcPw4xGZAadXtsYYypZZQ3L9P0DtuGnjz6B7IzPkfTzJDRgy8AJ7PBGs7N5IPkgSaZZaGwMaGrIk8v5BH6LNnwFrZDvQGOM6amyDfhD83n45S/h/POjpEKPPw6/+EX06kLgDeCtAacShj6+JGjKVZNtTpL1HUEgZLMBTc0+2UxArjnfauKVV2j3N8aYOCrbgK8QNd/MmRMNnzniCLjrLrjggi7Py/rC+p2DCcIAEcH3PYIAkk7J5kJyOZ+mxhy5XJ58Lt9qEfNEApyNyzTGxFTZBvydySQ89VSPzwsDoX6HoOpFM2c1RPIhVakQ0ZAduwKyTT5+Jk+QyxP4+9c19IO97fjGGBM/ZRvweyvQkJ078mRyAcmE4GmIhgEJF5L0INOcJ9vs09ycI5PzocU6tr4PoeXDN8bEVOwCfhgqW7c2kc2FpJJQUwWphBICklCamnxCP0eYz+Fncq3OFUCsSccYE1OxC/i+H9LcnCefU/xsiOQVr8YjFwaEniAaoEFIEARom9p8wrVeAcsYY+IkdgHfc0KQzZPJhKSSIXnP0dwcUpVSJFTwA/JZn3w2wM/7rc4VD8SSpxljYqqiq7PXX38969ata7VNwxA/5+PnfPIZn1wmT5D3yWV9MhmfIFDyfkDgB2ibDlqx5GnGmBir6IB/2GGHMXv27Fbr0oYqBH4U8P18QOiH+HmfKge1NQ5HQBDkCIKQIGwd8fN5Wg3TNMaYOKnogH/jjTeSz+e5++67921zQD7v4/s+oe8T5POEfkAmmyefD2luCvDziu8HhH7rNAqhgh/auExjTDxVdMD3PI8HHniA22+/nVWrVgGQ80PyWZ8wHxAEIWEQks9Hgd1JQCIZbVMN23XaJhPRNY0xJo76FPBFZJiI/FFE3ij8HNrBMaeKyPMislJEVojIJX25Z1tHHXUUt99+OzNnziSfz5NwIRoEhBqgGqAaEoQB+XxAU1NIpjmM2uk1bN96I5YO3xgTX32t4X8N+JOqHgP8qfC5rSZglqqeAMwA7hKRIX28byvXXHMNY8eOZd68eShCoCFNDZvwABGlKiFASKhaSJCmUWBvE/EFELEmHWNMPPU14F8IPFB4/wBwUdsDVPV1VX2j8H4jsAUY2cf7tiIi3HfffSxcuJBldS/QtHszy/749/h+SBgqQQDppEODEIcSBhpV8ju4lto4fGNMTPU1uo1W1U2F95uB0V0dLCLTgBTQYY5jEblWROpEpG7r1q09KsiYMWO45557+Ke51+K5GjJN9WQyOcIgGoLZ1Ozja7R+SlUV5Pz24T6wVPjGmBg7YMAXkcdF5JUOXhe2PE6jHtBOW8BFZCywCLhKte0I+H3XWKiqU1V16siR3f8jIJ/PM3/+fM4991xOOvn9vL78R1RVj6Jx97v4fohLCDXpRDTLLIgSKDi03Th8LwHScdGMMabiHTDgq+q5qnpiB6/fAvWFQL43oG/p6BoiMgj4PXCLqi4t5gMAOOdYsWIFkydP5uhjjmXzO0+RSA6kseEdwhCSnuAHAVVVgpeI8t4nk0Lb1Pe+D0FoM22NMfHU1yadxcCVhfdXAr9te4CIpIBfAw+q6q/6eL8OeZ7HT37yE5544gmW1S0FDWjctYaGHa/jREFDqqs8tNBp6/tKwhOSbRJLaIgN0zHGxFZfA/6dwMdF5A3g3MJnRGSqiNxXOOZi4CPAbBFZXnid2sf7dujEE0/k3p/+iuOmfZ1EspadW18mnWvizOX/BxobyOQC9jT7BGFIGChhm+T3ySQ4W/HKGFNKmzbBWWfB5s1Fv3Sfkqep6nvAxzrYXgdcU3j/H8B/9OU+PRH6IUNHfoDTP/07EgnlpI3PcfKbT5EfPpKVx55NShxNYZT3vu2kWucss4IxpsTmzYNnn4Vbb4Uf/aiol45dtsxE0gNVrlp9P6OzWxmqjWweNoH3v/xfHLvySXbUjuIXp16NE9oF/MCHdg37xhhzKFRXQyaz//OCBdErnYbm5qLcInaDzj0HqRT88ugv8NLYaeQSKTJVA8h5KV496kz+dNosEs4RFMbitxQCGloV3xhTAmvXwmWXQU1N9LmmBi6/HN56q2i3iF3Ah2jVqyavmpyXpCa7hxG7NjIgt4cwmYLaQYRhtFBK27q8Klg6fGNMSYwdC4MGRbX8dDr6OWgQjBlTtFvErkkH9gftAblGHp18IdsmncwpTW9QtXs3qYSydxpAu7q8glrAN8aUSn09zJkD114LCxdGHbhFFLuArwhhYcbsYxM+SW21MCRdxbIRo0kmHNkmnyAIEIGEBy0zJCcSUZoGY4wpiUce2f9+/vyiXz52TTqZjE8uH73P56IArhrlv29syhMq+zpm26TDj5p4rAnfGBNT5R3w33gjasu64opun5LwXJTrPgwRiTJkBkGIqiBOyOdDxDmca//ofgCCpVYwxsRTeQf8//k/4YMf7NEpyaRQlSq8T0SjbkSIgn4YooAjQFESbRq0vIRlyzTGxFfZRrdPbN8OQ4bAx9rN6+qSIvtaZfwAQhTFkU57JJMetdUJVCVKm+y3Odkq98aYGCvPgC8y6O83boR/+7cen6otZlN5gIfgiTJ0SBInwsCBCbxE9Nhtm+vzPmjY9lvAGGPioTwDPsxbPGIEjBvX4xNFZH/aYwfOCX6o7NqVJ5ks7Oskf4IIqNiatsaYeCq/gB8lVjv3P0eN6tXpqtpqQpUqBL7SnAkJQmV3Q45Q9w/dbHXr8vttGGNM0ZTjOPyPAhN/99//Hc0w27MnWopq1Sp46aUDniwirZtqRAk16rjN5aIlDx3gOnjyhFeO34DGGFMc5RjfFgJHXX788bB8eTTr7Lzz4NFHu3WyqraqqSuKc5DNRZOtkglBRSBsn0ZBJOr0NcaYOCq/gK/ahOrm95LJqIZfWxuNxe/mkoct2/BFQAoB3PMEQchkQ/J5HwHa5kkLAkuWaYyJr3Js0mntm9/s0eFhiw5Z349q/J5zaAi5fEhzJqCxyac52/7caJSOjc00xsRT+dXw+8gTt28QjgKhr/iBEoYhTc0+jY0+zU3aLq3CXoFvuRWMMfFU/jX8Hsrkg31NNQIERLNsc74Q+BCEui/XTkeyOavhG2PiKXY1/HTS29cZK1LIfhkNsMc5QUTpql82H1jAN8bEU0UG/ObmZlatWtXhvqwf7qvhhwFRoAc8J3hOcF6CVKrzayeT1mtrjImnigz49fX1nHXWWaxbt67dvpQn+yvwjkKNXnEJwUuAEyXluU4r+Z5nAd8YE08VGfAnTpzIV77yFWbPnk3YdlSN7B+H76SQL0cFUUUQ0lUeqSpHKtnxtRMdpE02xpg4qNjo9pWvfAXf97nrrrtabW+7iImqEgRKPhTEUcilGXTajh/YIubGmJiq2IDveR4PPPAAd9xxBytXrty3vVVuNI1eqlroyJXCTFrpfGUrC/jGmJiq2IAPMGnSJO644w6uuOIKcrlctLHFKuShRtkynYtSKWioKIVVsDoZjCPWhm+MiamKDvgAV199NePHj+db3/oWAMv/+iLb65/ftz8orHgVhoWmnRxkM11MvMrbsExjTDxVfMAXEe69917uv/9+nnvuOd5e+yab1/8R2N+0I+JAohm3mVy+y4lX23Z0kHPBGGNioOIDPsDo0aNZsGABs2bNYvjIsTTt2QCA58AhqAYkU15U0w86zoW/17btmUNUamOMObQqPuA/88wznHnmmQwZMoTp06fzm1//nObGd1GNZtSqKBQyaHqew3ldZ8ScesrQQ1d4Y4w5hCo+4J955pl8+ctf5uqrr2bz5s38v2eeQDXAz+1CFTznSCUThTZ8EBW6Gmr/0sqGQ1d4Y4w5hCo+4DvnuPTSS1m9ejWf/OQnyWSaCYMMDbteLwzJBD8IyWTyZHI+fhB2+dTjxqQPXeGNMeYQqviAv1dVVRU33HADf3zirwwfM51Espa8a+LtQb/hvYad7GwIaGjwaWyCfCedtp7A6JE1h7bgxhjTwqaGTZz172exec/mol87NgF/ryFDh3HCtNsYNPR4tg94iU3p59mYeIlMBjLZzudVpVNw/d8fycDaTnIuGGPMITDv6Xk8u/5Zbn3q1qJfO3b58EVg3dif0ZzYiu/toTo7ni3DH2P7kOeoyo1kwqar2p3jBK76whF85rwJJSixMcZA9beryfj7RwkuqFvAgroFpBNpmm9pLso9YlfDdwITtl7C0F0fxIUpvLAaF6YYuuuDHF5/CQAJFw3Z3CvhwRnThpeoxMYYA2v/YS2XnXgZNYmoWbkmUcPlJ13OW//4VtHuEbuAH/pKmKtBNEngNZJJbSbwGhFN4oU1OIFEok3uNIGatFeqIhtjDGMHjmVQ1SAyQYZ0Ik0myDCoahBjascU7R59CvgiMkxE/igibxR+djqIXUQGicgGEbmnL/c8kMbmqEc28JoYs+18Jm64ljHbzifwmoCoDT+bB79FBoV9K2MZY0wJ1TfWM+cDc1h69VLmfGBO0Ttu+9qG/zXgT6p6p4h8rfD5pk6OnQc83cf7HdCLL78HwKjtn9i3bdiuM1sdox103IYdbTTGmEPokUse2fd+/nnzi379vjbpXAg8UHj/AHBRRweJyAeA0cBjfbzfAf3u8U09PsfzhGQidq1bxhjTSl+j3GhV3RthNxMF9VZExAHfB75yoIuJyLUiUicidVu3bu1VgebMGn/AYwYOgGOPSnPy8QM56bgBzPr8BIYOqerV/YwxplIcsElHRB4HOuo1uKXlB1VVEemoXeQ6YImqbjhQO7mqLgQWAkydOrVXbSyfOncSnzp3Um9ONcaYWDtgwFfVczvbJyL1IjJWVTeJyFhgSweHfQj4sIhcB9QCKRHZo6pf63WpjTHG9FhfO20XA1cCdxZ+/rbtAap6+d73IjIbmGrB3hhjDr2+tuHfCXxcRN4Azi18RkSmish9fS2cMcaY4hEt0+GIU6dO1bq6ulIXwxhjKoqILFPVqR3ts7GIxhjTT1jAN8aYfsICvjHG9BMW8I0xpp8o205bEdkKrOvDJUYA24pUnErR3565vz0v2DP3F3155iNUdWRHO8o24PeViNR11lMdV/3tmfvb84I9c39xsJ7ZmnSMMaafsIBvjDH9RJwD/sJSF6AE+tsz97fnBXvm/uKgPHNs2/CNMca0FucavjHGmBYs4BtjTD9R0QFfRGaIyGsisqawpm7b/VUi8lBh/19EZGIJillU3XjmuSKySkRWiMifROSIUpSzmA70zC2O+5yIqIhU/BC+7jyziFxc+LdeKSI/P9RlLLZu/Lc9QUSeFJGXC/99f7oU5SwWEfmpiGwRkVc62S8i8r8Kv48VIvL+Pt9UVSvyBXjAm8AkIAX8FTi+zTHXAT8uvL8UeKjU5T4Ez3w2UFN4/z/6wzMXjhsIPA0sJVpzoeRlP8j/zscALwNDC59Hlbrch+CZFwL/o/D+eODtUpe7j8/8EeD9wCud7P808AdAgNOBv/T1npVcw58GrFHVtaqaA35JtKh6Sy0XWf8V8DE50DqL5e2Az6yqT6pqU+HjUmDcIS5jsXXn3xlgHvCvQOZQFu4g6c4zfxGYr6o7AFS1o9XmKkl3nlmBQYX3g4GNh7B8RaeqTwPbuzjkQuBBjSwFhhRWFuy1Sg74hwPvtPi8obCtw2NU1Qd2AcMPSekOju48c0tXE9UQKtkBn7nwp+54Vf39oSzYQdSdf+fJwGQR+X8islREZhyy0h0c3XnmbwJXiMgGYAnw5UNTtJLp6f/vB9TXJQ5NmRKRK4CpwFmlLsvBJCIO+DdgdomLcqgliJp1Pkr0V9zTInKSqu4sZaEOsi8A/66q3xeRDwGLROREVQ1LXbBKUck1/HeB8S0+jyts6/AYEUkQ/Rn43iEp3cHRnWdGRM4FbgEuUNXsISrbwXKgZx4InAj8WUTeJmrrXFzhHbfd+XfeACxW1byqvgW8TvQFUKm688xXAw8DqOrzQJooyVhcdev/956o5ID/InCMiBwpIimiTtnFbY7Zu8g6wN8CT2ihN6RCHfCZRWQK8BOiYF/p7bpwgGdW1V2qOkJVJ6rqRKJ+iwtUtZLXx+zOf9u/IardIyIjiJp41h7CMhZbd555PfAxABE5jijgbz2kpTy0FgOzCqN1Tgd2qeqmvlywYpt0VNUXkS8BjxL18P9UVVeKyK1AnaouBu4n+rNvDVHnyKWlK3HfdfOZvwvUAv+70D+9XlUvKFmh+6ibzxwr3XzmR4FPiMgqIAD+SVUr9q/Xbj7zjcC9InIDUQfu7EquwInIL4i+tEcU+iW+ASQBVPXHRP0UnwbWAE3AVX2+ZwX/vowxxvRAJTfpGGOM6QEL+MYY009YwDfGmH7CAr4xxvQTFvCNMaafsIBvjDH9hAV8Y4zpJ/4/yzniae3jKRYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "user_const_instance.plot_constraint_on_data(plot_type='contour_map') \n",
+    "user_const_instance.plot_selected_sensors(sensors = top_sensors_user, all_sensors=all_sensors)\n",
+    "user_const_instance.annotate_sensors(sensors = top_sensors_user, all_sensors=all_sensors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now lets look at how to do the above with the constraint shapes defined in the class:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Initiating a class instance of the shape parabola : "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "parabola = ps.utils._constraints.Parabola(h = 0.025, k = 0, a = 100, loc = 'in', data = df, Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)') #Plotting the constrained circle \n",
+    "parabola.draw_constraint() ###Plotting just the constraint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Initiating a class instance of the shape line : "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line1 = ps.utils._constraints.Line(x1 = 0, x2 = 0.05, y1 = 0.2, y2 = 0.2, data = df, Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)') #Plotting the constrained line  ##expect a tuple of (x,y)\n",
+    "line1.draw_constraint() ## plotting just the constraint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig , ax = plt.subplots()\n",
+    "line1.plot_constraint_on_data(plot_type='contour_map', plot= (fig,ax)) ## Plotting the constraint on the data\n",
+    "parabola.plot_constraint_on_data(plot_type='contour_map', plot = (fig,ax)) ## Plotting the constraint on the data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Locating constrained indices of the parabola"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "const_idx_parabola, rank_parabola = parabola.get_constraint_indices(all_sensors=all_sensors, info = df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Locating constrained indices of the line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "const_idx1, rank1 = line1.get_constraint_indices(all_sensors=all_sensors, info = df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Finding the common constrained indices between them :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Common_constrained_idx = np.intersect1d(const_idx_parabola, const_idx1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the common constrained indices with the GQR optimizer in pysensors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sen_parabola = 0\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_parabola = ps.optimizers.GQR()\n",
+    "opt_parabola_kws={'idx_constrained': Common_constrained_idx,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sen_parabola,\n",
+    "         'all_sensors':all_sensors,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_parabola = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [31414 30106 19000 36479 35723  2620 21714 16890]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_parabola = ps.SSPOR(basis = basis_parabola, optimizer = optimizer_parabola, n_sensors = n_sensors)\n",
+    "model_parabola.fit(data,**opt_parabola_kws)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_parabola = model_parabola.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "xPara, yPara = ps.utils._constraints.get_coordinates_from_indices(top_sensors_parabola,df,Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)' )\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_parabola))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31414.0</td>\n",
+       "      <td>0.006206</td>\n",
+       "      <td>-0.079055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>30106.0</td>\n",
+       "      <td>0.011132</td>\n",
+       "      <td>-0.040648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19000.0</td>\n",
+       "      <td>0.006517</td>\n",
+       "      <td>0.033655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>36479.0</td>\n",
+       "      <td>0.010434</td>\n",
+       "      <td>-0.230693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>35723.0</td>\n",
+       "      <td>0.006281</td>\n",
+       "      <td>-0.377601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2620.0</td>\n",
+       "      <td>0.000124</td>\n",
+       "      <td>-0.009141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>21714.0</td>\n",
+       "      <td>0.004854</td>\n",
+       "      <td>0.200180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>16890.0</td>\n",
+       "      <td>0.000154</td>\n",
+       "      <td>0.061574</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID   SensorX   sensorY\n",
+       "0    31414.0  0.006206 -0.079055\n",
+       "1    30106.0  0.011132 -0.040648\n",
+       "2    19000.0  0.006517  0.033655\n",
+       "3    36479.0  0.010434 -0.230693\n",
+       "4    35723.0  0.006281 -0.377601\n",
+       "5     2620.0  0.000124 -0.009141\n",
+       "6    21714.0  0.004854  0.200180\n",
+       "7    16890.0  0.000154  0.061574"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_parabola = parabola.sensors_dataframe(sensors = top_sensors_parabola)\n",
+    "data_sens_parabola"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sensor locations: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "parabola.plot_constraint_on_data(plot_type='contour_map') ## Plotting the constraint on the data!\n",
+    "parabola.plot_selected_sensors(sensors = top_sensors_parabola, all_sensors=all_sensors)\n",
+    "parabola.annotate_sensors(sensors = top_sensors_parabola, all_sensors=all_sensors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lets compare the results we get from both methods: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1080 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots(2, figsize = (10,15))\n",
+    "user_const_instance.plot_constraint_on_data(plot_type='contour_map', plot = (fig,ax[0])) \n",
+    "user_const_instance.plot_selected_sensors(sensors = top_sensors_user, all_sensors=all_sensors)\n",
+    "user_const_instance.annotate_sensors(sensors = top_sensors_user, all_sensors=all_sensors)\n",
+    "\n",
+    "line1.plot_constraint_on_data(plot_type= 'contour_map', plot = (fig,ax[1]))\n",
+    "parabola.plot_constraint_on_data(plot_type='contour_map', plot = (fig,ax[1])) ## Plotting the constraint on the data!\n",
+    "parabola.plot_selected_sensors(sensors = top_sensors_parabola, all_sensors=all_sensors)\n",
+    "parabola.annotate_sensors(sensors = top_sensors_parabola, all_sensors=all_sensors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now let us consider an example where the user inputs the equation that they are considering as a constraint in a string "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example the equation of a parabola is :\n",
+    "a(x-h)^2 - (y- k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "const5_stg = '(100*((x-0.025)**2)) > y' ### Python string with the required equation for a parabola\n",
+    "user_const_stg_instance = ps.utils._constraints.UserDefinedConstraints(all_sensors, data = df, Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)' , equation = const5_stg)\n",
+    "idx_stg, rank_stg = user_const_stg_instance.constraint()\n",
+    "user_const_stg_instance.draw_constraint() ## plot the user defined constraint just by itself"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the equation of the line is :\n",
+    "y - 0.2 = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "const5_line_stg = 'y > 0.2' ### Python string with the required equation for a parabola\n",
+    "user_const_stg_line_instance = ps.utils._constraints.UserDefinedConstraints(all_sensors, data = df, Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)' , equation = const5_line_stg)\n",
+    "idx_stg_line, rank_stg_line = user_const_stg_line_instance.constraint()\n",
+    "user_const_stg_line_instance.draw_constraint() ## plot the user defined constraint just by itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#### Combining constrained indices for line and parabola: \n",
+    "Common_constrained_idx_stg = np.intersect1d(idx_stg_line, idx_stg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sensors = 0\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_user_stg = ps.optimizers.GQR()\n",
+    "opt_user_kws_stg={'idx_constrained':Common_constrained_idx_stg,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sensors,\n",
+    "         'all_sensors':all_sensors,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_user_stg = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [15658 18378 29993 16573 31414 40090 21456  7748]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_user_stg = ps.SSPOR(basis = basis_user_stg, optimizer = optimizer_user_stg, n_sensors = n_sensors)\n",
+    "model_user_stg.fit(data,**opt_user_kws_stg)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_user_stg = model_user_stg.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "# sensor locations based on pixels of the image\n",
+    "xCircle_stg, yCircle_stg = ps.utils._constraints.get_coordinates_from_indices(top_sensors_exact,df,Y_axis = 'Y (m)', X_axis = 'X (m)', Field = 'Temperature (K)' )\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_user_stg))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>15658.0</td>\n",
+       "      <td>0.008200</td>\n",
+       "      <td>0.136713</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18378.0</td>\n",
+       "      <td>0.006977</td>\n",
+       "      <td>0.063449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>29993.0</td>\n",
+       "      <td>0.011413</td>\n",
+       "      <td>-0.051947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16573.0</td>\n",
+       "      <td>0.007676</td>\n",
+       "      <td>0.124104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>31414.0</td>\n",
+       "      <td>0.006206</td>\n",
+       "      <td>-0.079055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>40090.0</td>\n",
+       "      <td>0.019092</td>\n",
+       "      <td>-0.241529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>21456.0</td>\n",
+       "      <td>0.004899</td>\n",
+       "      <td>0.187096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7748.0</td>\n",
+       "      <td>0.000192</td>\n",
+       "      <td>0.005811</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID   SensorX   sensorY\n",
+       "0    15658.0  0.008200  0.136713\n",
+       "1    18378.0  0.006977  0.063449\n",
+       "2    29993.0  0.011413 -0.051947\n",
+       "3    16573.0  0.007676  0.124104\n",
+       "4    31414.0  0.006206 -0.079055\n",
+       "5    40090.0  0.019092 -0.241529\n",
+       "6    21456.0  0.004899  0.187096\n",
+       "7     7748.0  0.000192  0.005811"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_user_stg = user_const_stg_instance.sensors_dataframe(sensors = top_sensors_user_stg)\n",
+    "data_sens_user_stg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "user_const_stg_instance.plot_constraint_on_data(plot_type='contour_map') \n",
+    "user_const_stg_instance.plot_selected_sensors(sensors = top_sensors_user_stg, all_sensors=all_sensors)\n",
+    "user_const_stg_instance.annotate_sensors(sensors = top_sensors_user_stg, all_sensors = all_sensors)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "sensors",
+   "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": "3.9.7"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/README.rst b/examples/README.rst
index 27c5834..a49ee72 100644
--- a/examples/README.rst
+++ b/examples/README.rst
@@ -37,6 +37,17 @@ ocean at any given point.
 Reproduces an example from `Manohar et al. (2018) <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8361090>`_
 where sensor locations are learned for a monomial basis for the task of reconstruction.
 
+`Spatial constraints example <https://python-sensors.readthedocs.io/en/latest/examples/spatially_constrained_qr.html>`_
+----------------------------------------------------------------------------------------------------
+Sensor locations are learned for a constrained sensing problem for the task of reconstruction. For further details of constrained sensing refer 
+`Karnik et al. (2024) <https://ieeexplore.ieee.org/abstract/document/10453459>`_
+
+`Functional constraints example <https://python-sensors.readthedocs.io/en/latest/examples/functional_constraints.html>`_
+----------------------------------------------------------------------------------------------------
+Sensor locations are learned for various shapes of constrained regions. For further details refer to 
+`Karnik et al. (2024) <https://www.mdpi.com/1996-1073/17/13/3355>`_
+
+
 
 Full table of contents
 ----------------------
diff --git a/examples/functional_constraints_class.ipynb b/examples/functional_constraints_class.ipynb
new file mode 100644
index 0000000..511359d
--- /dev/null
+++ b/examples/functional_constraints_class.ipynb
@@ -0,0 +1,2373 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pysensors as ps\n",
+    "from sklearn import datasets\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from matplotlib.patches import Circle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the Olivetti Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "400 4096\n"
+     ]
+    }
+   ],
+   "source": [
+    "faces = datasets.fetch_olivetti_faces(shuffle=True, random_state=99)\n",
+    "X = faces.data\n",
+    "\n",
+    "n_samples, n_features = X.shape\n",
+    "print(n_samples, n_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Global centering\n",
+    "X = X - X.mean(axis=0)\n",
+    "\n",
+    "# Local centering\n",
+    "X -= X.mean(axis=1).reshape(n_samples, -1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# From https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html\n",
+    "n_row, n_col = 2, 3\n",
+    "n_components = n_row * n_col\n",
+    "image_shape = (64, 64)\n",
+    "\n",
+    "def plot_gallery(title, images, n_col=n_col, n_row=n_row, cmap=plt.cm.gray):\n",
+    "    plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n",
+    "    plt.suptitle(title, size=16)\n",
+    "    for i, comp in enumerate(images):\n",
+    "        plt.subplot(n_row, n_col, i + 1)\n",
+    "        vmax = max(comp.max(), -comp.min())\n",
+    "        plt.imshow(comp.reshape(image_shape), cmap=cmap,\n",
+    "                   interpolation='nearest',\n",
+    "                   vmin=-vmax, vmax=vmax)\n",
+    "        plt.xticks(())\n",
+    "        plt.yticks(())\n",
+    "    plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x325.44 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_gallery(\"First few centered faces\", X[:n_components])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test = X[:300], X[300:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unconstrained sensor placaement:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_sensors = min(n_features,300)\n",
+    "n_modes = n_sensors\n",
+    "basis_unconst = ps.basis.SVD(n_basis_modes = n_modes)\n",
+    "optimizer_unconst_full = ps.optimizers.QR()\n",
+    "model_unconst_full = ps.SSPOR(basis = basis_unconst, optimizer = optimizer_unconst_full, n_sensors = n_sensors)\n",
+    "model_unconst_full.fit(X_train)\n",
+    "all_sensors_unconst_full = model_unconst_full.get_all_sensors()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(300, 4096)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4032, 4035, 1101, 4038, 4034, 4095, 4092, 1024, 1036, 4090, 3844,\n",
+       "       1074, 4087, 2560, 1071], dtype=int32)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_sensors_unconst_full[:15]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xFullUnc = np.mod(all_sensors_unconst_full,np.sqrt(n_features))\n",
+    "yFullUnc = np.floor(all_sensors_unconst_full/np.sqrt(n_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4032</td>\n",
+       "      <td>0</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4035</td>\n",
+       "      <td>3</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1101</td>\n",
+       "      <td>13</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4038</td>\n",
+       "      <td>6</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4034</td>\n",
+       "      <td>2</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4095</td>\n",
+       "      <td>63</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4092</td>\n",
+       "      <td>60</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1024</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1036</td>\n",
+       "      <td>12</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4090</td>\n",
+       "      <td>58</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>3844</td>\n",
+       "      <td>4</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1074</td>\n",
+       "      <td>50</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>4087</td>\n",
+       "      <td>55</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>2560</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1071</td>\n",
+       "      <td>47</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Sensor ID  SensorX  sensorY\n",
+       "0        4032        0       63\n",
+       "1        4035        3       63\n",
+       "2        1101       13       17\n",
+       "3        4038        6       63\n",
+       "4        4034        2       63\n",
+       "5        4095       63       63\n",
+       "6        4092       60       63\n",
+       "7        1024        0       16\n",
+       "8        1036       12       16\n",
+       "9        4090       58       63\n",
+       "10       3844        4       60\n",
+       "11       1074       50       16\n",
+       "12       4087       55       63\n",
+       "13       2560        0       40\n",
+       "14       1071       47       16"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Sensor ID corresponds to the column number chosen\n",
+    "columns_full = ['Sensor ID','SensorX','sensorY'] \n",
+    "unconstrainedSensors_df_full = pd.DataFrame(data = np.vstack([all_sensors_unconst_full,xFullUnc,yFullUnc]).T,columns=columns_full,dtype=int)\n",
+    "unconstrainedSensors_df_full.head(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_sensors = 10\n",
+    "n_modes = 10\n",
+    "buffer = 5\n",
+    "basis_unconst = ps.basis.SVD(n_basis_modes = n_modes)\n",
+    "optimizer_unconst = ps.optimizers.QR()\n",
+    "model_unconst = ps.SSPOR(basis = basis_unconst, optimizer = optimizer_unconst, n_sensors = n_sensors)\n",
+    "model_unconst.fit(X_train)\n",
+    "all_sensors_unconst = model_unconst.get_all_sensors()\n",
+    "sensors_unconst = model_unconst.get_selected_sensors()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xTopUnc = np.mod(sensors_unconst,np.sqrt(n_features))  ### Need to delete this and show how this can be done with functions only.\n",
+    "yTopUnc = np.floor(sensors_unconst/np.sqrt(n_features))\n",
+    "xAllUnc = np.mod(all_sensors_unconst,np.sqrt(n_features))\n",
+    "yAllUnc = np.floor(all_sensors_unconst/np.sqrt(n_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4032</td>\n",
+       "      <td>0</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>594</td>\n",
+       "      <td>18</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>384</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>878</td>\n",
+       "      <td>46</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>446</td>\n",
+       "      <td>62</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2772</td>\n",
+       "      <td>20</td>\n",
+       "      <td>43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4041</td>\n",
+       "      <td>9</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3936</td>\n",
+       "      <td>32</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>340</td>\n",
+       "      <td>20</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2273</td>\n",
+       "      <td>33</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID  SensorX  sensorY\n",
+       "0       4032        0       63\n",
+       "1        594       18        9\n",
+       "2        384        0        6\n",
+       "3        878       46       13\n",
+       "4        446       62        6\n",
+       "5       2772       20       43\n",
+       "6       4041        9       63\n",
+       "7       3936       32       61\n",
+       "8        340       20        5\n",
+       "9       2273       33       35"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Sensor ID corresponds to the column number chosen\n",
+    "columns = ['Sensor ID','SensorX','sensorY'] \n",
+    "unconstrainedSensors_df = pd.DataFrame(data = np.vstack([sensors_unconst,xTopUnc,yTopUnc]).T,columns=columns,dtype=int)\n",
+    "unconstrainedSensors_df.head(n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 144x162.72 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the constrained region and the unconstrained sensors where 1 is the first sensor chosen.\n",
+    "image = X_train[4,:].reshape(1,-1)\n",
+    "\n",
+    "plot_gallery('unconstrained', image, n_col=1, n_row=1, cmap=plt.cm.gray)\n",
+    "plt.plot(xTopUnc, yTopUnc,'*r')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.xticks(np.arange(0,64,5),rotation=90)\n",
+    "plt.yticks(np.arange(0,64,5),rotation=90)\n",
+    "for ind,i in enumerate(range(len(xTopUnc))):\n",
+    "    plt.annotate(f\"{str(ind)}\",(xTopUnc[i],yTopUnc[i]),xycoords='data',\n",
+    "                 xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n",
+    "                 arrowprops=dict(arrowstyle=\"->\", color='black'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functional constaints:\n",
+    "\n",
+    "Suppose the user wants to constrain a circular aea centered at x = 20, y = 30 with a radius (r = 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "circle = ps.utils._constraints.Circle(center_x = 20, center_y = 5, radius = 5, loc = 'in', data = X_train) #Plotting the constrained circle \n",
+    "circle.draw_constraint() ###Plotting just the constraint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "circle.plot_constraint_on_data(plot_type='image') ##Plotting the constraint on the data\n",
+    "plt.plot(xTopUnc, yTopUnc,'*r')\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('Unconstrained Sensors')\n",
+    "plt.xticks(np.arange(0,64,5),rotation=90)\n",
+    "plt.yticks(np.arange(0,64,5),rotation=90)\n",
+    "for ind,i in enumerate(range(len(xTopUnc))):\n",
+    "    plt.annotate(f\"{str(ind)}\",(xTopUnc[i],yTopUnc[i]),xycoords='data',\n",
+    "                 xytext=(-20,20), textcoords='offset points',color=\"r\",fontsize=12,\n",
+    "                 arrowprops=dict(arrowstyle=\"->\", color='black'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting grid of possible sensor locations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "circle.plot_grid(all_sensors=all_sensors_unconst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Obtaining constrained indices :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "const_idx, rank = circle.get_constraint_indices(all_sensors = all_sensors_unconst,info= X_train)  #get_indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sensors = 4\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_circle = ps.optimizers.GQR()\n",
+    "opt_exact_kws={'idx_constrained':const_idx,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sensors,\n",
+    "         'all_sensors':all_sensors_unconst,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_exact = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [4032  594  384  878  446 2772 4041  340  660  144]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_exact = ps.SSPOR(basis = basis_exact, optimizer = optimizer_circle, n_sensors = n_sensors)\n",
+    "model_exact.fit(X_train,**opt_exact_kws)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_exact = model_exact.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "xTopConst = np.mod(top_sensors_exact,np.sqrt(n_features))\n",
+    "yTopConst = np.floor(top_sensors_exact/np.sqrt(n_features))\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_exact))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4032.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>594.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>384.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>878.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>446.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2772.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4041.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>340.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>660.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>144.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID  SensorX  sensorY\n",
+       "0     4032.0      0.0     63.0\n",
+       "1      594.0     18.0      9.0\n",
+       "2      384.0      0.0      6.0\n",
+       "3      878.0     46.0     13.0\n",
+       "4      446.0     62.0      6.0\n",
+       "5     2772.0     20.0     43.0\n",
+       "6     4041.0      9.0     63.0\n",
+       "7      340.0     20.0      5.0\n",
+       "8      660.0     20.0     10.0\n",
+       "9      144.0     16.0      2.0"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_circle = circle.sensors_dataframe(sensors = top_sensors_exact)\n",
+    "data_sens_circle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "circle.plot_constraint_on_data(plot_type='image')\n",
+    "circle.plot_selected_sensors(sensors = top_sensors_exact, all_sensors = all_sensors_unconst)\n",
+    "circle.annotate_sensors(sensors = top_sensors_exact, all_sensors = all_sensors_unconst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We want to constrain the region beyond x = 10 and x = 20 and y = 0 and y = 64"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line1 = ps.utils._constraints.Line(x1 = 10, x2 = 20, y1 = 0, y2 = 64, data = X_train) #Plotting the constrained line  ##expect a tuple of (x,y)\n",
+    "line1.draw_constraint() ## plotting just the constraint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line1.plot_constraint_on_data(plot_type='image') ## Plotting the constraint on the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "const_idx1, rank1 = line1.get_constraint_indices(all_sensors=all_sensors_unconst, info = X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sensors = 5\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_line = ps.optimizers.GQR()\n",
+    "opt_line_kws={'idx_constrained':const_idx1,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sensors,\n",
+    "         'all_sensors':all_sensors_unconst,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_line = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [4032  594  384  878  446 2772 4041  340  660  144]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_line = ps.SSPOR(basis = basis_line, optimizer = optimizer_line, n_sensors = n_sensors)\n",
+    "model_line.fit(X_train,**opt_line_kws)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_line = model_line.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "xTopConstLine = np.mod(top_sensors_line,np.sqrt(n_features))\n",
+    "yTopConstLine = np.floor(top_sensors_line/np.sqrt(n_features))\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_exact))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4032.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>594.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>384.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>878.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>446.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2772.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4041.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3936.0</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>393.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3922.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>61.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID  SensorX  sensorY\n",
+       "0     4032.0      0.0     63.0\n",
+       "1      594.0     18.0      9.0\n",
+       "2      384.0      0.0      6.0\n",
+       "3      878.0     46.0     13.0\n",
+       "4      446.0     62.0      6.0\n",
+       "5     2772.0     20.0     43.0\n",
+       "6     4041.0      9.0     63.0\n",
+       "7     3936.0     32.0     61.0\n",
+       "8      393.0      9.0      6.0\n",
+       "9     3922.0     18.0     61.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_line = line1.sensors_dataframe(sensors = top_sensors_line)\n",
+    "data_sens_line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zOzqYc+AAS5uaOKTMEVZggaaM7e3tLFiwgIcffpgnn3ySpUuX8vTTT9PY2FjxImpnc5+CWnRO96MN9yF52nLY9xeFJXpY5jnfQ0sHk2ufXM2k/Db6eehKF3osVjJ0gNmzZ1c9ptvoefI92SyTkL4XnwlaIon1R8bP5rJnz57ythXD7Ff90PNR2h6t1K8THqUAheFm1Zg+fTpXXnklu3bt4rHHHqO9vT3ophcRMZIYG1PROMFIRRc1NTXxnve8h8suu4yGhgb25vfyhdYvsC8/vGiRiIgkqDltLn2ZLE8XH1Z6TR2kAJVaaSsrvt5fulZ9eztzGho4qNprLWLSejgiwkknncRJJ53EbW23sZ/9/LDth3xq8qfK5/i+q5ZG04o59b1uQqLHYNfy21ixvW1tbRVtNFWuVqsHjqTNc+fOLW/r56YpsJ6bWbNm0dnZSS6XI5PJVIge+jlbGuZQGh49z5q2+lTb0vhbwRi+h50WXXSfVh0s/1jpPoP1scwjxwl6stkB2jwCGTO+MfcbOHn94b7Ii3xw9wcRhK/P//pRX/94wm9/+1v27NnDjTfeONZDGbcYlDaLyLdEZK+IPKv2zRKRe0VkW/H/mmSLHA2UF+8I4IaWG5jfM/91bxknLM0s5XOzPzci1z+ecMkll7B9+/bowRZAki/vvzKQDfLf1L5PA/c55/6hWFT708BfJemwRCGSlsvUlEVrF5NmGLS0uCXK2ZlKMb23l1Q+T744JktDreHT5r6+PlKkkHrB5RziBrxlsoUsk3sm09HTESyOZmnVrcJcofYhJCkOpu/Nv3/dxne+KMHXomptsS53GXKsqKur4z3veQ8333wzy5YtK9Ntaz5CdXf1eKzY3FAFjCTBHD7t1qU8DyjPMYv2Q6XokIQ2Dzoq59xvAd9v7Vrg1uL2rcB1g11nvGKk8zh3p7tZ3LeYa9quYXHfYtoZnfDH4wEnnXRSOd/TaMaCT1QMV9s8zzlXqjOyG5hnnSgiHxCR9SKyfjyaUUqLd6RS4ly14yquOXwNi/oXcc3ha/gvk//LiFz3eMVVV13FwYMH+d3vfjfWQxl3OGqFlXPOiYj5Z1HX550xY4ZLV8nWGKqNajk5aI2wf0zTSX3tqlpk55jX1kZ/fz+Hi1RJ++wmSVXj34+O7dTa2VCbEO2zYBn/9X5fo2lplUN+vhr6epoCasrna141bU6ivferCqxbt45/+7d/Y+bMmTQ2NvLAAw+wZs0aU1vva+UtrbI+LxQ/mzQtkUa1BOo+/PZ6Dkvbo5E9co+INBUv3gTsHeZ1xh4idA8zq0ZEbTB37lwuvPBC7rzzTgqFAps2bRp2xsVjCcNdvHcBJR3+jcCdIzOcsUGogkLE+ECplM3DDz/MrFmzKpRAxyuSmIp+ADwMnCwiO0Tk/cA/AJeLyDbgsuLvCYvuTIb6fB6JSpFxiTvuuIN7772XK664goceeoi6urq4eEkg8zrn3mEcevNQO7NSv4ZMKPqYlj99R3Bt0tBylaZXVi6l7p4eJvX1cViErnS6Qv6yZF5f5rbS0urM+b4nmZbntZxoyWK+OUKbvvQx3Y/v1aXnIImHli8L6nFqWVDLv77Mq+Vk3aeeW2s/wNlnn8369ev57ne/y8KFC9m2bRsLFy6seAbWc4Ij47BL0KbHkMeeZdKy3lP/t1VZIkmBvbGqmDBhUMpndbSxvRGjg4aGBtauXcu1115LPp+no6MjUXrfYx1x8RIX70TBzJkzufbaa1m3bh1NTU0c4AA3193MgSPcEI4PjJlvs6YPIdOIpoCawvnO71a6GG12CBWGOtDXR39vL4d6ehKlp/HbW2lINXyvLCueV6f00RTUN/toE4imkCGTVpLUqRo+nbbGo2mm72FlpUi1KKE/T77pqLm5mebmZu4q3MVBOcjPCz/n2u5rq14L7LRAuh8/nldXtLTMlVbQDNgBCEmLwCehzcd9YEIJnakUkxK6GEaMLf5l3r9UBIDsnLyTr0/+OuKED+794BiOrLaItLmIzlSKhrh4JwT+eP8fM69n3kAmUoACzO+Zzzv2W7rVYxM1//KW6GFSjx6NkJO2Vc7TooZ+0expjY3MO3yYxbNn06BS7FhO+iEvHq3hDcV8Wt5fVkoXrR0Fu+iV9hDzxYskKV0s5304khJXG7MPq1CYvn+duM8fs34/WltbEQSpF8hRTiVML7gDjkMcCmpx9Rzq/b71wKLamkLr+/LprWVNCb0P1foP+XRH2lxEVFpNLPRkepjfPp9TD53Klhlb6Mkcf/my4+ItolSztyGfp2uQcyPGHle+cmV5e37n/MCZxy7GxeJNUs8VMLMyQqW20DLY6zaLFi2qaH/uueey9PnnmT99OnuUI71ur1PC+A4TS5YsKW8/+2w5b0EFBfS1qJp6W84Tmo75fVr0POQIYIkRSer2+udZzgeh6DGtOdaONpqev+ENb6hoo0UiLRJoCq819L7mWKfj1fesz/PnVo9Hn2c5zYTeYYu2+89G9xmdNIaIvlyOunEYthgRUQ1x8Sr05nLk4uKNmCCoOW0uUYWkdWc1zdAaQW1Eh0rfVE3NNM3RMZaa5sKA4X9aJsP0XbvYzkD1OjiSnlcbi9+PriO8d+/r0ZI62yLY2kbdRqdT8bXyVmZMTSdDSddDaWhK8GmbFc9aLcVQtd9aq7x48eLythZjfI329u3by9uWz/EyVSrlhRdeMPvX25pO+zWBLV9tKw1PyLfZiq8OUfUkMd3xy6vQV5Qhs/HrGzEBEBevQmnxRrk3YiIgLl6F/ro6HES5N2JCoOYVE0oyR9BzxEj9GsrNNGvWrPK2llG0eUbLor68UYq7ndLbS6ZQKMuX2pyh5Vpf5n311VfL21oW1AXQ9DlQKXNpOU/L5lbMKti1i6tVhqj22zJDVCt4Va2NfjZWbC5Umtj0M9DyvL5n/z61rkDrQPT8bdmypcqdHNlGQ4/fT6tjVTywTD3++6TfVau4WsgTLAnil9dDX11d/PJGTAjExeuhbC6KKXEixjnGjDaHKgFoqquPWWYfqKQzuv3u3bsr+i/Bp2aleqo9hw+zKJ8n1dNDXzZb4d2jaarfv762NlVpaqipPVSaTfR4NH1KGmRgBW2EKgFoOmiZPfz2FlXU4/cLjFtF4PRz0vfmB31UpCxSbfS1dE4rTc3BDhLQ4/L71G10P7outPVu+rBMQCEznP+sqyF+eT2UkrBH6hwx3hEXr4fe4l/QuHgjxjtqTptLtCeUqsWqh6q1yL52T1Mgi1pp+EECWrvY2tVFT0sLhzxaox3UfS8gKwZWa0r9sWh6Z2WC1NTK19Dr37q9VVvWP6ZhafL9Pi0ne60Vt+bcH4/OrGnFzAKceOKJ5e2tW7eWt624Z7+msEVHtUik6TBUvk9a3LGqL4SejRXo4bdJGtdeQvzyVsFIlv2MiBgtJEm6vlBE7heRzSKySUQ+Vtx/zNTo9REXb8REQBLa3A980jn3pIhMBZ4QkXuB9zKMGr0lqmClRvGPaWph1a31f2t6qymTplm+dlBrS0s1ews9PRSMGrQhJxNN70PO/5ZjgxWbGxIv9Nzo64aCPiwNc1Jts972aaeGFkn0s9Hj19piP1ukprC6aJmVaD5UuE5fS8/5ggULKtpoa4KOFbZEitD7YFkF/DZ6nqqli/KRpD7vLufck8Xtw8AWoJljqEavj3LN3li/KGIcY0gyr4icCJwFPErCGr26Pm8S29V4QLlm7wQZb8TxicSLV0SmAD8BPu6cq1DnuYHvf1Xe4Jy72Tm32jm3OqSFHE/ozWRwIlHujRjXSGQqEpEsAwv3e865nxZ37xGRJufcrqQ1ep1zZZ5vFXmGSjOOZQ7wZTn9VddyhZZrtDnDlz/1tadNm0a2rY3GbJZeZSbQffiB9FZxZy0j+eYpq40es2UC8s+zzEPDMWFYegIflizpt7GCGYZqGoFKM5IVQOHrUCwz1Jw5c8rb/vvge9BZ1y7Bn2fdp3Wfvj6hmukvJEsn0TYLcAuwxTn3j+rQMVWj10dvLhe/vBHjGklo80XAnwCXishTxX9XcYzV6PXRm8uR7e2NNXsjxi2S1Od9kIGc9NUw5Bq9JQoRcsLWVEF7tFh0GCppk85npc/TTv4+ZfHNO/319aRTKRqco69IaTW1Tpp/SFOmUErUkOmsBH+e9Hm6z6RmOA0rttenk1bcsG7v02bLe0ybRkL5m6wAgLRhxvPjd/U9W8EIIW8niwKHqk9Y5jZrLvx+khQkix5WBqKPc8R4R1y8BuLijRjvGLOKCSEHbYsOhbST+nqaNmvtok5p41NQTWFKx7qBTHd3mRLpsfg00xpbSKuuj2lNdMgrKkmfITqnKVyoUFYJIW2nJUb4aXn1OK0ay/r+Q55kluhkVZnw+9T9JE21aj0PTft9kUiLDknT2+jzSvN+VNrm4xkxCXvEeEZcvAH0xMUbMY4xZrTZCjiAStpkOQz4bSznA61d1NshzW+p/0JDA+mODjL5PPlMJkjbLW1vyCnByr6oxx8KbLCCFqzAjmpjsM4b7PxQG1/brJ0hNG3V2n99byEnC0uTrxHKmBnKjKlhvU/WuHxYVDsUx271byF+eQOIKXEixjPi4g2gt/gFiYs3YjyiprRZRMpUxXKegEptsfYx1RrNpBo8Kwugb8jXNKUitjaXY1KhQOcgQRWWhjekbbbS2GgNuaagPk2zNPYhJw2LDlo0LTTmJI4lUElPtdONZVUIOZZYWTKTOnlYhdKGE48bSrpuiTRWe/93EotD/PIOglizN2K8Ii7eQRDNRRHjFXHxDoKeujoyfX3IMMLXIiJGE2Mm82qTgS+jaJnPckT35T8tJ1kyipY9Qmr6itjQujpEhLr+ftoDan49HitDv99G/7bkv1BxteHI2RZCZg+rzyRynY8kQQahdLXWc7Pikf3fVpBB6Hlacn5STzjrWqFnk+R5xC/vICjV7M0pV7iIiPGAuHgHQW82CyJR7o0YdxgXHla+ml3H1lqUx28TSqk5WP/+tTVt6+/vpxOgvZ1er0qC1X9S6Db6nq3KEP6YkxSmShpAYZkwfJGmmvN8aNsfg9U+Ke21RIKkpp6ktNmqY2yZxPwxW+JFiA5XMz0FzzePRJQRNc4R4xFx8SZArNkbMR5R80Jj1eIU/aJd2vvJ0tz6gQUWtUqa4d7ydurt7eWwc0zp7SXd20tfkc6E6t6GrmX1qTXsVobEUJWIpLA0n3p/yGE/pPG39lsad8ujKCSCDCcG2WpjiWShMVjvWciTzRJJQkEjScSw+OVNgBigEDEeERdvApRS4sRUsBHjCWOmbdYUwa/Hqp3x/aCFEpKmLbEM7CHa7CczL4jQV6ygUKJXPmW1skT6mmsNLS7oOdBiQ4jCJq37arXRsGiafy0rPlnPhx/Pa2lurT5D2USTjDPkgGM5xiSNlU6SVRIq3wH9nEOijp6nJAnp45c3IaLGOWK8IUnFhHoReUxEni7W5/1ccf8SEXlURF4QkdtEJDfYtSYyYs3eiPGGJF/eHuBS59xKYBVwpYisAW4CvuycWwYcBN4/aqMcB+jN5Uj395MahoY3ImI0kKRiggPaiz+zxX8OuBR4Z3H/rcBnga8Pdr1qnF/nOIJkfD+pXJO0rKiVd6okC/VksxQKBTLd3XTV1x8hv1opRXUyAb9otHbM13KilTY0JJdZ1ROSBuOHvKo0khRB0wXdoHIOrCoFIbk2iXkoBMvDK6nZxvKqsq4LtvwaMgdVM10dtYeViKRF5CkGKgHeC7wIHHLOle5qBwMFt6u1nXD1eashmosixhsSLV7nXN45twpYAJwHnJK0g4lYn7ca+rLZWLM3YlxhSKYi59whEbkfuACYISKZ4td3AfBagvZlCjJjxozyfk2loJJ2JalEAHbGfMsjJhQPrKHZQqcI0tFB79SpR1BDbdKaP39+eVvn4/I9rKyiXZqChdLdWvcTCvrQsAI9QulJ9XgsU1F7e3tFG11ZYNq0aeVtnYpXI2kMchJzDiTzqgq9D/p6oRrRGlY/oRxWSUxiFX0MdoKIzBGRGcXtBuByYAtwP/C24mnHXH3eaoga54jxhCRf3ibgVhFJM7DYb3fO3SMim4EfisgXgA0MFOA+ptGTzTK1oyPW7I0YF0iibd4InFVl/+8ZkH+HhBId0LTZh6aqST2krGMWBfNpchKH885UikI+j3R10drWVtFeU0NNLUNjtrL36/ZapPDFCz02fcyqROCPwUqXGvLWsmry6vN6vKwj+/fvL293dHSUt/U7oCl0KEjAeh+SImlliCSphEIWjyTBCKG455gGZ4RR0jhH6hwxHhAX7xAQF2/EeMKYZY/UhvuQZi1p5j4rZtSiP6HYWN1GU8B8NkuXc6S6uuj0FrBFebRWOhSDbO3X1NbXzlra3lDaFssx3wqACNVO1tBUPdSn1kRr8Ujfmx/YoKl6KL55qAhpqC16azmm+POXJC2T/z5Uc2CJ9XlHED3ZLPXxyxsxDhAX7xARzUUR4wU1pc2pVKpMiTRN0n7BIWia5FOOJJq6kPNBEmrU19dHOzC9r498Xx99Br21Ut+EaF7STIwaVpxsyOfW0ipbzhz+mK0sm/o+/Rhsi+q2KY293vbFA4tSW44lPvR5WgxKWl83SXG2pPHllm81VIobSbwR45d3iCgpreoDVfEiImqBuHiHiK7iV6QhhgZGjDHGLA3ORMRf/Z//w9J9+3AiFJxjX30977/oorEeVsRxipqbikrqcEtGBDsfkpaNfXnBkuWSpie1TEV+xYJbzz2XXVOn8uqePWyaOhWKHkN6PNocomUs7V0ElbmNdACDNi+Fcj5Z3k6hmNEkRcxC6Wq1XKbvR8cqhypg6Hk+cOBAeXvfvn3lbV9m1vNkyb96vy+n65xgVkE63zyVJJ43FKRgxUdbpjoI5yurhkibh4GebDbS5ogxR6TNQ8Tbn3qKdKHAvkyGLy1ZwpMBH+2IiNFEzU1FJQplZcuHSjqhqYmmZj7NqZa6xkfSeEkrFvO7Z5zBjmnTmNLTw4WPP85NW7fyjlNPZYc3lt27d1e9Vmtrqzlm7XFmpbHxHf6teQqZMPRcawqtqboWT3wznj5P02ZNB5uamira6HvQNNxKl6sDGfxjevwWnfWhx2m9dyFTj+WlFyoCZ5kFQ2Y8q8azhUibh4AXGhvpzmZpr6vj8alT2TxpEhd5CzIiolaIi3cY6MlkQISUcww9HVpExMhgzLTNlvN9td+6fQm+RjJJnGmoTqpFm0r0paGnhze8+iqbZs+mX4TT29p4Q2cnn124kHw+XzFmTY+15jmUcVJrZPU49Tlaawq2h5GmXD7Vtpzx9XmazvvihZ43HY87c+bM8rYfd2xp/PX96Pb+mLUmWsNKA5Q0V1pSMcqKgQ7Rdv0MrLjpUJskaXCiwiohMs7xzi1bWNDeTgHYlcnwtfnzecWTdyMiaoW4eBPicH09f3nxxeXfsmULzaWavcPIJRwRcbSoeX3ealTDp1maZmhNo6ZGvvOApqpWfdukjuQWNdLb3c7h8nmy/f10p1JmoTBN+Q4dOlTRj3Y+sBKtz5o1q7zt00ndj54bTdUPHjxY0cZy4NDt9fPwE+Jrej979uyq1/JhPQMr9Y/OMAmVIpJ+zlafviXCinUOOfNYmmhL9AoFuujxhNqEMktWQ1RYDROdxcmdlKC6Q0TEaCAu3mEiLt6IsUZcvMNEfypFn0jF4k2aLDwiYiRQc1NRifNrE4iv2rdkJC2L+Z4/2rxhBdNr2SVkzqiQbVU/WubMZDIDeZyLeblSqRQbNmxg9erVFfej5Vpf/rTkdD02q7CX/1ufp2Ve3+zW2NhY3tbmGT1OLUvr/WDrDUIJBKw2lgnF71OPU8u/bV763RJ8+TWJSShUZUHPs6UbCEHrZ0JtrMJxFhJ/eYvFxjaIyD3F38dVfd5q6EqnywEKdXV1TJ06lW3bto3xqCKOFwyFNn+MgTInJRxX9XmroTOVIuccmeJfzJNPPpmWlhZ27do1xiOLOB6QiDaLyALgauDvgU/IAHcYcn1e51xZBR9ShSeJrfWptqZTvqlA3Ud526fdVsZ+TV/0mOvq6sinUmTzeWZkMuX2q1at4sknn+Qtb3kLdXV1FRRUm1n8a2tKrfdb3lpQGQOsAxt0n76pR//W5+l7s1KtwpHmqmrtQ7K/nluryoRPYTVtnTt3bnlbm+FCsd5WMIFFjf1rWOYhTfWTtg9VWRhq7eGkX95/Av4SKPXcyDDq81oPfqKipHHWsb2zZ89m0aJFPP7441GBFTGqSFIlcB2w1zn3xHA60PV5rS/iREV3KkWBI81FK1eupK2tjZdeegkY+KvslwSNiDhaJKHNFwHXiMhVQD0wDfgKw6jPKyJlqhFKQWLV2tWUx08Pqmmz1u5papM0jY4Vp6n3l7TD0t9PYzpNXo2noaGBtWvX8qtf/Yq1a9fS2dnJa6+9xuLFiyv69L3ESrA0zL52Vc+BppPa80lraqGSBlupWjRCXkAaoaAPK1AkaWUIi8Ho9nv27Clv+/NqpXG1PMzArpdspb7x508/Qy0uWbR9OBj0y+uc+4xzboFz7kTgBuA/nHPv4jisz1sNXel01TSwjY2NnHrqqWzYsIGGhoYj8ldFRBwtjsZJ468YUF69wIAMfMzX562GrnSa+kKhombv4cOHeeaZZ1ixYgX5fJ79+/fT0dERZeCIEcWQnDScc78Gfl3cHlZ93mpUwd+nqZo2amuaE9LuWRrikHZQUy0rHrQanS44R11/Pw3ODQTpF8fc2trKnXfeyYoVK9i6dSvpdJqpU6dWUEIrblaLEdphQTtyQCUl1tTMitOFyrm2HOZDBdksZ/6Q9UDPu5WGx+rDhxXYoPvfu3dvRRvLaUffv69Vt86zaHNIVLDmJnSfSTJJxpDAo0R3cZLr+/vL1RRyuRxr166lpaWFxx9/nFQqRU9PD+3t7UfIcxERw0X0bT5KdBW/Kpbc++Y3v5kLLriAbDZLW1sb7XXt/Mdp/0F7rv2I8yMihoIx+/L6Bbw09NdJUw7LqO//1mYZTdm0RtbvUztAhOjQEeNKpejPZHCHD9NR3OdrtWfMmMHVV19NoVDgdwt/R2d9J5sWbeJNO95Udfw6vYyO5/W/2pZvcIj2alha+ZDmWPdpiRe+X67lpGDtD/kZW44luk+fcra0tFQ9L1T0S8M6T9P+kOhnpSsKxZRXSxflI9LmEUBXOk3DII7kd5xxRwXP2TdjHz+e8WMowNs2v81uGBFhINLmEUB3JjNoBYXLn7ucmYdnvu6jVoDG9kau3Hbl6A8w4phEXLwjgO5MhpRzZAMLeErvFDL5DAgDC1ggXUgzpW+K2SYiIoQxS/2q5cKQytzK0+Q7+WuvoldffbW8rT2vtOyhZUmolDl1ZQbLi0bLix0iHDxwgNbeXlozmQqZW8tFHYs7mL5vOs0vN/Pa4tfoaugq96Xnw0rdOm/evIoxW15Aur3vHKLvzSqApeHrFpLIvCGvrKSpeDWSVCmwTG0Azc2vu91bsc4hU49lUrNMVaExh+YvJANXQ5R5RwClmr2T8nlaA/a5VY+tKm/PODijIhIoImKoiLR5BNCXTlPwUuJERIw2av7lLdEOK64TkpkwfIqhabOmytpMoOmsn15Ut7e8jazatADtQF1fH/lcriKAQLfxPaSsGFZ9bR2z6gcm6C+3bq9psy9eWFRZ96nHpdPmQOWz0nOjzVghU5Fl3tHP3K+GoXHCCSeUt5PWZNZ96hhoLdL4JjWr4oF1XZ82W7WDdZ/+dat5AIZcauOXd4TQmUrFL29ETREX7wihM52mrlAgFYMPImqEcaGwCqU90dvaSd93atfHtCZZU01dscCPc9UUVLe3UqX4Yy40NjK1rY0FM2aQUZRJO8n7AfmaumsKpe9F09lTTjmlor2mappea9rpp8HR961p2tatW8vbCxYsKG9rcQIq51On6NH9+GKQzumlKaSej1deeaXq+MF+tloMsTy3wK7MoEUK3+POEkMsqu7TZn2fVgBEqD5v6VhICx+/vCOEkI9zRMRoIC7eEUJ3Oo0TGdTTKiJipFBzJ41qmjs/bYmmE5o2akcG30nfoheaGmma51MzTZu1RtLKJOlTs0KhQC6fZ142S5u6lqb3mhqW2lS7H00NNW3Vzgb+vc2fP7+8rSm0Dsbwr6fvU0NTa1/brJ089Jh1MIWfmVNnxtRUVz933eeyZcsq2jc1NZW39dxoOqvH5T9b/Qz1mEN1fK3gDit1jk+b9bWtInIhShxpc43RnclE2hxRM8TFO4IoL96ocY6oAWpOm6tljwz5hWqabdUNgkqfVYvq6m3f4UHTMU15NB3UNM93ROjv7yebSjGlUKBpxgx6i+PWlEn7aQO8/PLL5e1zzjmnvK1p76JFi6r2D3Z9IU0bQxpNPe++VrkEX6uun4fuJ+TwoI9pDbOem+XLl5e3faqvRRrLvzzkZ2xlKg2lmrHqGFvwKbhVIMDyQYfk9aPL4xr0jIjEKKXBqfPMDhERo4G4eEcQpXxWcfFG1AJx8Y4g8uk0/alUXLwRNUHNPaxK8oNVsAlseUPLKL78p3/73jIlaBlRe1tBpalBy7m6jR6LH2RQkqHzDQ0VGmdtmvDjcfU1tCyoTStaXvT71DKT5ZUVctLXegPdxgpY8PtMWh/XimG16gD78biWecZy8q+mjyghqYeU9X5aOcF8mTdJ4TV//1ArKCStEvgScBjIA/3OudUiMgu4DTgReAl4u3PuoHWN4wU92SyTPDtnRMRoYCi0+RLn3Crn3Ori708D9znnlgP3FX8f9+jJZsnk86RjhFHEKONoaPO1wMXF7VsZqKTwV4M1KtEJTSt8c0SSlJwhmmPR61C62QMHDpS3tdlEU1VN5/z+S+aMbCpF/65dSEcHvR798ymXDkzQ47HotB4jVNJePWfak8xvo0UCa24tDzd/nJrqavi0V5+n6aXeH0r9qmGJUdqM6Mcw63kKxf1q6DHofiwTZ9IUNknSCkM4jrfcf6IewQG/FJEnROQDxX3znHOlcJHdwLxqDXV9Xt9t7lhEyVwUPa0iRhtJv7xvdM69JiJzgXtFZKs+6JxzIlL1T4Vz7mbgZoA5c+Yc865HfdksTiRqnCNGHYkWr3PuteL/e0XkDgYKjO0RkSbn3C4RaQL2Bi9y5DXL20kLLoW0fha1sdr4tERrTrW219I8h7S4btIkpqVSHKyvr7iWT60sbbGmzUmLo2lWoymfP06tZddzZtXH9bWo2hPN0hb7fWptryW66HvxE/OFvKeq9RkKEtCwYnb9Pi2qbFVC8GG96/47WK2CxFGlwRGRySIytbQNXAE8C9zFQF1eOI7r81ZDTzZLffzyRowyknx55wF3FP9iZIDvO+d+ISKPA7eLyPuBl4G3j94wJxa6s1mmdXZW1OyNiBhpDLp4i3V4V1bZ3wK8eSidiUhVbbMPyyivKaRPQa2shNqQr2maT0c0bdPUUjvfa2roU7sKx4opU6jr6mJaJsP+gIbbujetLdXj97Ww+phFzUJaUH3Mos1+3LSVhkbPuZ/uR9+3VTtZ9+MHcFhilRXAkjTpe0jza82nlTEyJPrpe9bnhQqqjQhtjhg6eouLKiqtIkYTcfGOAsrRRZ4LZ0TESGJcZI8cT1hw+DAff/JJFu3fT3tDA7+77jpeOOOMIV2jkErRl07X5Mv7tzfdVPE729/Pw2edxV2XXTbqfUeMLWq6eJ1zZS6fVBbTcolVZAoq5SrLNKCv5Qc21NXVkSoU+G9PPMEvlyzhb847j9P27+dvf/AD/rm+npbGxgqzUciLqFAokG9oYFKhUCHL+d5OlmyqZUar2kDpnv/HJz5RPi/X28vffu1rbFyxwpSVdD9WYLqWpf0Uu3qetW5AJ0MIyWmWbK3nzw8ssMaf9Byr6kXIk8syt1nef/61Qskmqp1jjTPKvAmxoL2dWd3d3LV0KQURnpkzh5ebmznr2WeHfK2ebLbmMu/pzz9P+6RJbFd5lyOOXcTFmwDz9u8fcpveXI5UoRCs2TvSOOfZZ3nytNNgiKFlERMTNZd5SzQgRAeSOI+HPJySFifTyOfzvNLQQGsuxzXPPcfPlizhzJYWlrzyClvmz2fnzp0VZhLfVOSbE/rq6hARGuvq6ChSQj+G2Epl61P6avfo39uczk5OevVVbr/iiopzfDoXouElhMQLK1Y6REE19dZmIG0S02Px+7Q8maxav77YlCSNa2ierPcxlOfK6tPa9n+X5sCKH4aosKpAPpXiC6tX8583beL6F1/khenTeezEE+lPkIDMR48yF3UY0TcjiXM2b2Z7czMHZ8wgfnePDyRfvAPB97cw4B65H/gMzn1/lMZVxgUvv8wfPfssjR0dHKqv52urV7PVq78zknhp2jQ+c8EF5d9ffuwxHlq6dMjX6c9kcKlUzaKLztm8mfvPO68mfUWMDwzly/s1oJcBd8lVwM8ReRrnNiW9gPawCtGsEuU4bedO3vHUU/zz2rX8vrGRycXggVQqFaTdVg3akBdOSVt6Uns7Hc3NiHO8Zft2pnV08Iv58+lvb68oGuZrm/X1Sv331dUxK5Wio1iZwO9T02jL20dv+3SwdD+LX3uN6e3tbD7tNLLZbMV9hlKtWLRZ01a/veWVZaU6hUqqrOfNEolCaXA0LCd//3yrfdJ3KEkamyMKzxmFz0Labj3vpe2g5t48ojEQkHA9cDrOtQMPInIX8CeMYgaNtz79NHeccQYvFoPjD3pB1qOBK/bsYd3GjaQLBbbMns3n166lP2GgtY/eXI4Gz1VwNLB682Y2rVhxRPB/xLGNpF/eFUA/zj2v9j0NvGnkhzQAKRRY0tLCk83NfOnOO8nm8zzW3Mx3zzyTvkCy7KPFN5Yu5fZzzy3/TpJw20JPLse0w4eRQgF3FNcZDD+5/PIj7LERxz6SroIpgJ8qsBWYWuVcE9pJQy+Kahq16d3dZAoFzn3lFT5/xRXkUyk+fv/9XL9lC7edeWbQEK9haQRDGSu1k4SmfPuVyUinsIFKOlTSyHaIsLC+nlm5HD319UfQRE0PLS2uHqe/QLWGWt+b1uL6fVpUNUmGRn/MlmOHn4bG0vJbzyykubWuq1PdhOJkLe1tiGrrdyPkQGLBquvsV/2oJgaMhJNGOzDN2zeNgYySo4Le4oO59+STaZ00ifb6en5+8smsUoWaxztKAQq56OMcMQpIunifBzKILFf7VgKJlVVDRWddHS2TJuH0X6MJ5nzQW0yJk/X+wkZEjASS0WbnOhD5KfB3iPwpA9rma4ELh9txyHmiRC0eXLaMK557jk0LFpBPpbj6uefYcMIJFVrrakiScdI/xyqUpR0p0uk0hUIBETnCT9mKu+3LZMj29uKcO4JOWr7aSemk9ge25rO+vp58Pk8qlaoo9Ob3b8nM/n7LbzwkcyfVClc7JyksC0OoTz0un05b1pBQKiXrPCvdj99ei2ilsQVTRJlHjsSHgG8xkKuqBfjzoZiJhoO7Vq5kSk8PN91xB33pNA8vXMjPTjttNLscFNu3b2fv3r287W1vS3R+by5Hboy/vL/5zW/Yu3cvN9xww5iOI2JkkXzxOncAuG7URlIF+VSK76xZw3fWrAGOFPDHAosXL+b5559n69atnHLKKYOe35vLMamjY6Bm7xjR/gsuuICbbrqJZ555hlWrVo3JGCJGHjEwYYjIZDKcf/75/PrXv64Ig7PQm8shzpEZw6waDQ0NvPvd7+a2226rqMkUMbExZr7Nw1G5h/i/pdq3vI18ecOShbS8UtqeOnUqy5cv55577uGSSy5BRCoKQmu5sLd43VxvL3gyr3U/VkpWH1ZupGreWsuXL+fcc8/ltttu44Mf/CAiYhb3SporbKjFuKpdr4RQulrrmDUW/9mGvPlKCKV+tUw9er/f3vKk0mPzC8fp36XzQsE08cs7TJx22mn09vaybdu24HnjyVx09dVXs2fPHh555JGxHkrECCAu3mEilUpx4YUXsnHjxnLC9ueee+4IubyQyZBPp8fF4s1ms7z//e/nRz/6ES0tLQA89NBD40KXEDF0jBhtFpH/BJzvnPti6Lxq6vUQzbGorohw8OBB2traWLx4sZn6NYmDuX+e5bmjAwnS6TR1dXWceuqpPPTQQzQ1NbFx40acc5x55pkV4+yrq6Our+8ICmQVR7Oc/0NeYZYnmW/aWbp0KX/wB3/Ad77zHT7zmc9w3333MWfOnIoxa7NPyJPNmnMfVk1c6158WLG1mrbq5+SPxaqMEAossOJudT/a7OP3aRVk0+ZC30tPz3uiFMnmkaFjM/Dx4iKuCerq6ti4ceMRQe61xNKlS8lkMjz11FNMnz69asHp3lxuXHx5S7jqqqvo6enh3nvvZd68eezZs2eshxQxDCRavCIyQ0R+LCJbRWSLiFwgIrNE5F4R2SYi9zJQePsDwK0i4rtSjgomTZrEmWeeyWOPPRZUkowWNm/ezKZNm1i1ahUbN24knU5XlNcsoa+ujnR/P6kxGKOP22+/nZ///OfceOON3HHHHUyaNCku3gmKpLT5K8AvnHNvE5EcMAn4awaKa/+DiHwa+LRz7q9E5Frgy8D7q12oWib4pClxqtGnxYsXs3PnTjZv3szpp58OJEvv4nsEJUl7UqFF7u2lubmZLVu28Jvf/IbFixfz4osvMnPmzAqvrOnTp9NfX08qlaIhn6fHCEawtMpaC+xTYIuChrydLr/8cn74wx/y61//mlWrVrFhwwaWLl1qajVDgQ2aKlrVC/w2FtUOUXArMKCaJQDC3lJWkILfp36GVkG3UMCDngOtRdZeVP5zqmZlCFpYzCNFiMh04D8xkEUD51yvc+4QA+6RtxZPu5XXHTg+AVxcXMSjDhHhnHPOYceOHRVRP7VAQ0MDZ599Nueffz7t7e10dXVVBOyX0FdcgOPBx3nevHl87GMf4y/+4i/Ys2cPBw8e5LnnnhvrYUUMA0lo8xJgH/BtEdkgIt8sVgusWlzbDQTr3wh8Q0TmFotrPyEi6/0aNiOFuro6Vq5cyZNPPln+C9zf3z8sH9nhYMaMGaxbt46LL76YqVOnMvvgQf76X/+VxqIsXqrZO57k3hUrVvC5z32O973vfTQ2NrKzeycf2vohdvVMnKit4x1JaHMGOBv4qHPuURH5Cl72DL+4tnPuQRG5Ffj/GPgiLwV+X19ff06JwliUCSqpgl7wVs1UGPii7Nq1q+wC+Mwzz9DU1ERTU1PV9n6fVk1fDU1zGhsbK47NmjWL8847j/POO4/rvv99Zre28tb77+cnS5YA0FlcvJaGWcOi/VZQgD/m0H36xcnWrVvHunXr+Oy2z7K7dzfffO2bfOHUL1S9ro/hxOPqeba05X5ssz6maavW/FrUGCrfJ6uNby7T76dF1UNxz1ZdZx2D7af70SjN4dEGJuwAdjjnHi3+/jEDi3ew4tr/A3gMeB+wBxj1iILTTjuN3/zmN+zevZv6+vpE7osjiS/cdBNp9eKc/sornP7Zz5IX4ZY//VOmjAPa7OOtG95Kgddf4g3tG7j68atJkeLuc+8ew5FFDIZBabNzbjfwqoicXNz1ZgbMQmZxbRFZCpwMvBu4iYEsHMtGbthHorW1le7ubs466yyefvppcrkcHR0do9nlEfjyn/0Z25ubyRf/WuZFeGnhQr76kY/Qm8uR6e2FBK56tcRXT/0qp046FSkmjBWEN0x+A/9yxr+M8cgiBkNSbfNHge8VNc2/Z+BrmsIurr0I+D5wL3Az8J+B6SM16Gro7Ozk6aefpqmpifnz57N79+7R7K4qWmbNoqcYiJAXQZyjN5fjUGMjU1tbke5usn19ZQXWeEBzfTP1qXocDkFwOOrT9ZxQf8JYDy1iECRavM65p4DVVQ5VLa7tnLtfRFYAnwI+DHQBTc65sjwSKmysZQwtv1mmCYDm5mbmzJnDc889xyuvvFL1vJBHTRLH+pkzZ5a3Fy1aVHGsJAPP6O9n29Kl/G7NGi585BHq29poaWmhs6eHk5wj091d9ne25Bl9n1oW82VJK5VtqOizltlK57Xl2zhryllcN+86frbnZ7T1tSUq7qxl8FBK0yTFpbUsG/JW0sXOtQknlNLWCobXc+vL2Um8uvT74Cda0L/1c0oaXJMEoxZV5Jw7DPx3EfkG8I/AH/cV+vjJCT/hsr2XUd9TP8gVho5cLscZZ5zB4sWLeeaZZ9i3bx+t6VbWn7ye8184n8k9kwe/yFHiR5/6VNlc9PslS8pOGz3ZLPT3k+vpoTN0gTHA/1zxP8sv1cqpKxNV4osYe4x6YIJzbqdz7gZgcXddN4ezh3l05qODtjsaTJ48mTVr1nDZZZex5cQtdNZ38uzCoVf6G0kUUin6M5lxYeuNODZQs3he59wrcoKAwM7JO/nByT+AArzrxXclju0N0V6fjtx91t0Vf5r2zdjHPavvgQKse3JdMM+RRRWXFM0+cCRttjzBdqlsl20ipFpb6SjSLe1tY8WsWtn2IVnRrVAa1CRF2HwRworh1dTQt+dbZhzLc8nvU/uLa9qszwtVibC8pULBEFauL+0tpU1ADV49Km06Cpn4jgZjExJYgNmds7nm5WtGrYtLNl3CzLaZlK0gBZjZNpOLn7141PocDH11dePKUSNiYqPmi1cKA1/fbCHL1P4h5WwfEqb0TiFTyIAwsIAFMoUMU3qnDNZ01FCq2Zsew5Q4EccOpFYuhADTT5rufvqrn/Klh7/E3o69rP/A+lHtb/XNq5k7eS6fvOCTNesziH374JFH4IILoFh/KSIiBBF5wjlXzdJT28W7evVqt379GC6esUZ3N9x7L5xxBpx44liPJmICILR4Y3HtWqK+Hi66CLwMChERw0FcvLXGrFljPYKIYwQxAV1ExARFXLwRERMUcfFGRExQxMUbETFBERdvRMQERVy8ERETFHHxRkRMUMTFGxExQVFT90gR2Qd0ALVNsDz+MJvjew7i/Se//8XOuTnVDtR08QKIyHrLV/N4wfE+B/H+R+b+I22OiJigiIs3ImKCYiwW781j0Od4w/E+B/H+RwA1l3kjIiJGBpE2R0RMUMTFGxExQVHTxSsiV4rIcyLyQrEg9zENEVkoIveLyGYR2SQiHyvunyUi94rItuL/Mwe71kSGiKSL5WHvKf5eIiKPFt+D24pldI5ZiMgMEfmxiGwVkS0icsFIvAM1W7wikga+BrwFeAPwDhF5Q636HyP0A590zr0BWAN8uHjPnwbuc84tB+7DK5l6DOJjwBb1+ybgy865ZcBB4P1jMqra4SvAL5xzpwArGZiLo38HSvWDRvsfcAHw7+r3Z4DP1Kr/8fCPgUqKlwPPUazdBDQBz4312EbxnhcUX85LgXsYSMa7H8hUey+OtX8MFNjbTlE5rPYf9TtQS9rcDLyqfu8o7jsuICInAmcBjwLznHOlUgq7gXljNa4a4J+Av+T19PeNwCHnXKn8w7H+HiwB9gHfLooO3xSRyYzAOxAVVjWAiEwBfgJ83DnXpo+5gT+9x6S9TkTWAXudc0+M9VjGEBngbODrzrmzGPDtr6DIw30Harl4XwMWqt8LivuOaYhIloGF+z3n3E+Lu/eISFPxeBOwd6zGN8q4CLhGRF4CfsgAdf4KMENESplLj/X3YAewwzlXqq73YwYW81G/A7VcvI8Dy4uaxhxwA3BXDfuvOWSgKtctwBbn3D+qQ3cBNxa3b2RAFj7m4Jz7jHNugXPuRAae9384594F3A+8rXjaMXv/AM653cCrInJycdebgc2MwDtQ65DAqxiQgdLAt5xzf1+zzscAIvJG4AHgGV6X+f6aAbn3dmAR8DLwdufcgTEZZI0gIhcD/9U5t05ETmLgSzwL2AC82zl3zNY+FZFVwDeBHPB74H0MfDiP6h2I7pERERMUUWEVETFBERdvRMQERVy8ERETFHHxRkRMUMTFGxExQREXb0TEBEVcvBERExT/P4lk7SGm2ZT3AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line1.plot_constraint_on_data(plot_type='image') ## Plotting the constraint on the data!\n",
+    "line1.plot_selected_sensors(sensors = top_sensors_line, all_sensors = all_sensors_unconst)\n",
+    "line1.annotate_sensors(sensors = top_sensors_line, all_sensors = all_sensors_unconst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Testing Ellipse: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ellipse = ps.utils._constraints.Ellipse(center_x = 20, center_y = 50, half_major_axis = 6, half_minor_axis = 4, loc = 'in',data = X_train) #Plotting the constrained circle \n",
+    "# ellipse.draw_constraint() ###Plotting just the constraint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ellipse = ps.utils._constraints.Ellipse(center_x = 20, center_y = 5, width = 10, height = 50, angle=30,loc = 'in',data = X_train) #Plotting the constrained circle \n",
+    "ellipse.draw_constraint() ###Plotting just the constraint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.patches as patches\n",
+    "c = patches.Ellipse((20, 5), width = 10, height = 50, angle=30, fill = False, color = 'r', lw = 2)\n",
+    "_,ax = plt.subplots()\n",
+    "ax.add_patch(c)\n",
+    "ax.autoscale_view()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ellipse.plot_constraint_on_data(plot_type='image') ## Plotting the constraint on the data\n",
+    "\n",
+    "ellipse.plot_selected_sensors(sensors = sensors_unconst, all_sensors = all_sensors_unconst)\n",
+    "ellipse.annotate_sensors(sensors = sensors_unconst, all_sensors = all_sensors_unconst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "const_idx_ellipse, rank_ellipse = ellipse.get_constraint_indices(all_sensors=all_sensors_unconst, info = X_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sen_ellipse = 5\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_ellipse = ps.optimizers.GQR()\n",
+    "opt_ellipse_kws={'idx_constrained':const_idx_ellipse,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sen_ellipse,\n",
+    "         'all_sensors':all_sensors_unconst,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_ellipse = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [4032  594  384  878  446 2772  340 1224  970 1673]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_ellipse = ps.SSPOR(basis = basis_ellipse, optimizer = optimizer_ellipse, n_sensors = n_sensors)\n",
+    "model_ellipse.fit(X_train,**opt_ellipse_kws)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_ellipse = model_ellipse.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "xTopConstEllipse = np.mod(top_sensors_ellipse,np.sqrt(n_features))\n",
+    "yTopConstEllipse = np.floor(top_sensors_ellipse/np.sqrt(n_features))\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_ellipse))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4032.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>594.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>384.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>878.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>446.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2772.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>340.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1224.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>970.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1673.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID  SensorX  sensorY\n",
+       "0     4032.0      0.0     63.0\n",
+       "1      594.0     18.0      9.0\n",
+       "2      384.0      0.0      6.0\n",
+       "3      878.0     46.0     13.0\n",
+       "4      446.0     62.0      6.0\n",
+       "5     2772.0     20.0     43.0\n",
+       "6      340.0     20.0      5.0\n",
+       "7     1224.0      8.0     19.0\n",
+       "8      970.0     10.0     15.0\n",
+       "9     1673.0      9.0     26.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_ellipse = ellipse.sensors_dataframe(sensors = top_sensors_ellipse)\n",
+    "data_sens_ellipse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ellipse.plot_constraint_on_data(plot_type='image') ## Plotting the constraint on the data!\n",
+    "ellipse.plot_selected_sensors(sensors = top_sensors_ellipse, all_sensors = all_sensors_unconst)\n",
+    "ellipse.annotate_sensors(sensors = top_sensors_ellipse, all_sensors = all_sensors_unconst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Polygonal Constraints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "polygon = ps.utils._constraints.Polygon([(20,15),(25,0),(15,5),(15,10)],data = X_train) #Plotting the constrained circle \n",
+    "polygon.draw_constraint() ###Plotting just the constraint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "polygon.plot_constraint_on_data(plot_type='image') ## Plotting the constraint on the data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[594, 340, 466, 721, 787, 851, 532, 342, 534, 337, 658, 916, 724, 595, 528, 469, 216, 213, 529, 215, 406, 278, 401, 341, 150, 88, 661, 789, 464, 407, 786, 279, 530, 592, 656, 596, 722, 403, 593, 338, 598, 723, 343, 152, 597, 467, 212, 400, 725, 404, 660, 852, 468, 151, 788, 402, 336, 470, 659, 214, 274, 533, 405, 465, 276, 275, 657, 277, 339, 531]\n"
+     ]
+    }
+   ],
+   "source": [
+    "const_idx_polygon, rank_polygon = polygon.get_constraint_indices(all_sensors=all_sensors_unconst, info = X_train)\n",
+    "print(const_idx_polygon)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sen_polygon = 4\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_polygon = ps.optimizers.GQR()\n",
+    "opt_polygon_kws={'idx_constrained':const_idx_polygon,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sen_polygon,\n",
+    "         'all_sensors':all_sensors_unconst,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_polygon = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [4032  594  384  878  446 2772 4041  340  724  468]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_polygon = ps.SSPOR(basis = basis_polygon, optimizer = optimizer_polygon, n_sensors = n_sensors)\n",
+    "model_polygon.fit(X_train,**opt_polygon_kws)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_polygon = model_polygon.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "xTopConstPolygon = np.mod(top_sensors_polygon,np.sqrt(n_features))\n",
+    "yTopConstPolygon = np.floor(top_sensors_polygon/np.sqrt(n_features))\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_polygon))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4032.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>594.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>384.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>878.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>446.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2772.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4041.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>340.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>724.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>468.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID  SensorX  sensorY\n",
+       "0     4032.0      0.0     63.0\n",
+       "1      594.0     18.0      9.0\n",
+       "2      384.0      0.0      6.0\n",
+       "3      878.0     46.0     13.0\n",
+       "4      446.0     62.0      6.0\n",
+       "5     2772.0     20.0     43.0\n",
+       "6     4041.0      9.0     63.0\n",
+       "7      340.0     20.0      5.0\n",
+       "8      724.0     20.0     11.0\n",
+       "9      468.0     20.0      7.0"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_polygon = polygon.sensors_dataframe(sensors = top_sensors_polygon)\n",
+    "data_sens_polygon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "polygon.plot_constraint_on_data(plot_type='image') ## Plotting the constraint on the data!\n",
+    "polygon.annotate_sensors(sensors = top_sensors_polygon, all_sensors = all_sensors_unconst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Non convex polygon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "polygon2 = ps.utils._constraints.Polygon([(20,15),(25,0),(15,5),(20,7)],data = X_train) #Plotting the constrained circle "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kEZ74kYv9pqISm0XUy4pFIj7vnnvuqR339re/fd1raR0cf0292M6cOdPZfvbZZ2tlLIIuLy9jeXkZyQzX+vtrYrEXGILFQL02i8y5tMlapvXnPOhU/OR9rT/nCeYtc1YTGovk3DcPPPBA7ThW31RdYfWCTZhqQrt27VpnW/uDj9X+5nbxcaraeZ5xjKdOeOa7ML39grAQInzQBe6aL3sTuTo2htmhIaTCcEZBsB26PtjboogXI05hcYZnQ9l7CagvUlCRkMUqnmVnkRuoz8ZfuHChVqZqw3pt0mtNTEzUyq5efT3OxwypAkB+tpXPAeoxy7QfcyGzdbbcS/9UGvuNRUYvWIMX84/3ecYdAN785jd3tlnd0nt56aWXOtte3LZ77723s/3iiy9m26FtZBFf1YTcIh9PBfQWwvC2PhcvBHpJgJP4pARBQ4jBHgQNIQZ7EDSErgevaOtDnvnBixvv6TScFkj1X/aeYj1aTSkcXEL1YTYFsV6uOvurr77a2VZTCKeV5uOAum7Ieql68vH11JSVW7XnmYy0H3PmGy9Vlp6jJsY2qg+zCVPnN3hugvtA75lXtqm5ivv0+eefRw49j+F70TRXudRNntlMnwW/015aZk/vLyG+7EHQEGKwB0FD6JkY75kmNDgBl+VMaEBddNI6Ll++XGtHGxUJr1y50tnWBRzsqcXisraD61cTIIummo2UTU9eHH02/3jZcN1UQE7c+5xJTduRS60E1EVVvhcWqwE/8y4/Q75P9U7jdulz5zo5tZKqDF6MfW6jXpvP42uNSTaf3DussOjuxbHzYujliC97EDSEGOxB0BBisAdBQ+i6zt7WqbwAiKp/5AIbqo7HupWnuzFeGmJPt/JilbN5Sc0nbCbSNrEemYv/DvirzXif61C93CtjPFMnX8uLtc6mwtxz0DYBdTOoFxji9OnTne0XXnihVsbzLnztOXFV5vdR28FzNaqL8zvHczD6Tnir3nIrC9WcmQt4WkpJ+qc3mdm3zOxHZvacmX2i+n3czJ4ys59U/x/aqK4gCHpHiRi/CuB3UkoPAHgngI+b2QMAPgXg6ZTSfQCervaDILhLKcn1dgnApWp73syeB3ASwAcBPFwd9gSAbwP4ZEF9AO40vbHoq2UsvrAo6cVO0+AELKaxaOeljvbidvN5njegevJ5K8py11aVxxPnWATlvlITo7fKMGdu24zpjfdV9GVYbdJnxvfCZjM1Z7IozSmggXwKLO0PvjddOcfP4tSpU7UyNrtycAxP5fHeF890yudpyq5cbEdmUxN0ZnYawFsBPAPgWPWHAAAuAzi2mbqCIOguxYPdzIYB/DmA304p1WY3UutPzrp/rszscTM7b2bnSwz/QRDsDkWD3cz2ojXQ/zil9BfVz1fMbLIqnwRwdb1zU0rnUkpnU0pnvZnYIAh2lw11dmvN/38RwPMppT+gom8AeBTAZ6r/n9yorpRSR+9Q3YL1ETWH5cwnqney5KD6DutdbApSPZTr1xxrOfOM6uVsMtE5Adbl9D5z5+ncgWc242NLzZmqQ+bMP9qnXjz4nA6s5+Qi2mj7S1GzXG51nxfEU+s4cuRIZ1vfF3WVztXPeP3t6txOANF237mrSbMlr/NuAP8SwN+b2Q+q3/4DWoP8a2b2GIBXAHykoK4gCHpEyWz8/wGQi0/7vp1tThAEu0XXA062xRRv1Y6KIuyN5MU7ZzGNg0/qsbxqzEuxoyJbLhiEFxhC62AxzYuh7qVFYlT05WP52psxdTJeHPLS4Ihch4rxnqcgm5e8PvVWlPGxXKbBJbgP1PTmmVlz6Z+8gKra37mUT/psc30KlKV3Dt/4IGgIMdiDoCH0LEmE5+SvYhqLUd5sJdepYjzPsHIsOU9U0rLcbLnei9dGLvNEQp6p98Rsr37G8zZUVcbLOMp4M785NUdj/XN7VQTn/uf+8LwGPSsMb+t98bXVSuKpEIz3nFglUfWNVZvS2HJ6XPtZRBbXIAhisAdBU4jBHgQNoWc6u+oWrBd5+ctYv/FWFqlOxuYU3vbMX14gxtKAD56Xn6d7crv0XryVaLkVcd5KQu9ePB1wK+ep6Y2901RXZhMp36fn/eaZOhkvBr5XpnjvXK6NSk7X9wK8eO3IEV/2IGgIMdiDoCF0VYw3s45IpKIYi2xqNuPFBmzG2UwKnFx8b/WkYnGoxCtpo2upiOaZ3nLx43RhBovCKh7m4tN5HnTewgxPPPQW05R6ALKIrHHbct5vWl9prPxSLzwv/fRWA0946Z9yKpVXh2cuzRFf9iBoCDHYg6AhxGAPgobQM51dzSysQ6mOyvoaH+et/PFyybFe5Jkz1OSSW7Hmmei8FU56Hu/n9FUt0zbm5gu8YCEenskod12t39NDGS3LPXcvyEWpO7WnD3vuz96z9uZISl2eS3MCeOfliC97EDSEGOxB0BDuGg86NkdoTLeciKUmDC9GV2k7vJhorCZ4kXI9UdWDz+M+0P7wUgl5wQ+YrcQ90/sq9eQrjXfn1VEqgnvqSqnZbDNiPKtRfJ5nbvTMZqUeebkU2e452ZIgCN5QxGAPgobQ9SyuuUX27D3lxQdjUU8XsXjiHItbnmiX82LT63lipecll1vsoseyRcILj7zVWV/GCyTCZd6CEBXBc+Kkt6jHWwBV6iWn959rh+fxp+d4M/W5tnjvhIr43BZPbfIWL5WojvFlD4KGEIM9CBpCDPYgaAg9M72pzsF6qQY44BVxzGaC/+W8mzydXXUrbrOnN3ux4Vln1/p53oL7Q+cwPN05l7ppq95Xni7ozW/kAmfos82Zrrxre6msSttbGhQU2FqwkM0EPuF3hN8Bb/7F8+7MseGX3cz2mdl3zOyHZvacmf1+9fsZM3vGzF40s6+a2cBGdQVB0DtKxPglAO9NKT0I4CEAj5jZOwF8FsDnUkr3ApgB8NiutTIIgm1TkustAViodvdW/xKA9wL4WPX7EwB+D8AXNqovJ5pwLLLS7J2bEcVKc8Nz+/ScXGw5Fcd5kY+KWxyIY35+vlbGCz9Y3FXRt1SszKWC0jLPq43FYk9tKs00yxl0gXp/6IKfnIl0q3HuPTxPPs8z0zOpldafi7/omddyATa27UFnZv1VBterAJ4C8I8AZlNK7bu7AOBkSV1BEPSGosGeUlpLKT0E4BSAdwC4v/QCZva4mZ03s/OlX9cgCHaeTZneUkqzAL4F4F0AxsysLX+cAnAxc865lNLZlNJZXbgSBEH32FBnN7MjAFZSSrNmth/A+9GanPsWgA8D+AqARwE8uVFdKaWOXjM2NlYrY31N9bqcbq6/e7m8cq60m0l9m5NMtL1sKjx+/HitjINpqlkulxJadTwvdn7u3rxVhkpuZaHnAqrzCjnT28LCQu04zoE2OjpaK9PUyW1KVzTqsaWmMS/gpPe+cJ3av54ZLeeO663u81Y75iixs08CeMLM+tGSBL6WUvqmmf0IwFfM7L8A+D6ALxbUFQRBjyiZjf87AG9d5/efoqW/B0HwC0DXPeja4oaK8YznIeV5v5V6xjEqtnsr53KmLE4BDdRFUxVvvTbmUhBpHazyqLmK28hl6oXI7ffa4cX84/NUbM2pENw3AHDt2rXO9uLiYq2M3xEW6T0VzXsnStlMyitvhaN3HpMzqXnvsJcvIHudDY8IguANQQz2IGgIPQslzZ5TQPkCBi+1UmmY5tKQv1oHi6AstqqI7M3e8sy9F3zD+53FbJ2xzs2Cl84iA36oaob70fOu4/7xFhfpTD2rc3yfqtbws/BCa28VbxY/5+XnLQzSNpbGTuT3Jedt6J0fX/YgaAgx2IOgIcRgD4KG0FWdva+vr6Nvqa6p6aBysA6mOq+nK2s72ng6mKd3cZm2nXUyL2ilp0+WBsVUcsEgvECPXrBIz9POmxPIBdbU+Q0vCMjc3Ny62/rulOrz3rwQH6fmQe9dKo35Xjq/kQuuCtTbr3MfJa7o8WUPgoYQgz0IGkLXTW9tk4GaH1jsU3GORTMWmb10RKUeRvq7Z3pjUYnboZ5f3C5v4Yeex/HHeMGMLrTxFktwG724Z3xeaZZYTyVRsZLvjYN0qLjJqa30uU9PT3e2X3vttc62qgLcbyri87vDZXotjvOn9+LF0CsNXsH37WUY9szHpSbRHPFlD4KGEIM9CBpCDPYgaAhdN721dTQvaKCaHFjfYf1P9SfWKT3zRmmccS+IgWd6Yy5fvpytU1fLcZ3sTuwFi1QzER/rpXbOBc8E8oFE9D55X+cVWGdn3XNycrJ2HN+LmlJz8fd5pZyW6b14enSuvZt5N3Pt9YKFlK7M88yl3lxTjviyB0FDiMEeBA2hZ6Y3byVXaXojNcHkzE5A3iPNE5WUnPeeilDcfhXV2RTnxZtnk5SKfXycpobKeYxpG1n891YI8nFqevNSGXPgiUOHDnW21czneafxvXEdqrqwWU7JxeTbTPDTraSX0nfYUyf42eQCh3jnbNSuzjEbHhEEwRuCGOxB0BC6KsZzKGmFxTsVgVhkZlFMZ29ZZOYsqFpn6aIETxTjbRWpvAysLHLOzs7WytgTjC0N2sbx8fHOtoq0fD3uK/Xkm5mZ6Wx73nVch4rgnLKL1Q4AOHz48Lr1KbnnomXcHxpymtU5VZty1/ZiA3rel94suBfXz/Nm5GO5XXotPs8LM50jvuxB0BBisAdBQ4jBHgQNoeumt7Yeot5YXtBA1mlYb9Q62DTkBZ5g3cozBXmmMdaVtb2se6uJh8tYbwbycw5ebHi9T97n41Rn5zZPTEzUytjMxe3V+Qcu8zz0SgNxeKmMuYyvq+1VcywHvWA806yn/3ppo7jvtX59hjm8oJKM9mOJp2Dxl71K2/x9M/tmtX/GzJ4xsxfN7KtmVnY3QRD0hM2I8Z8A8DztfxbA51JK9wKYAfDYTjYsCIKdpUiMN7NTAP4FgP8K4N9ZS0Z5L4CPVYc8AeD3AHzBqyel1DFVeKKSmrJy2TFVRGYRTk0rDItYqgp4aYZYVOL267W4Dm0ji8JqruL6WcRXEY3FfRXPOegFL6ZREZzNZrytx/J96r2wmK0mQKY0Lrr2dy6NlorSLD4fPXq0VsamTi/wSWkqK8+LsNQzs7QOL2VUSbonpfTL/ocAfhdA++oTAGZTSu238AKAk5u+ehAEXWPDwW5mvw7gakrpe1u5gJk9bmbnzey899c/CILdpUSMfzeA3zCzDwDYB2AUwOcBjJnZnurrfgrAxfVOTimdA3AOAA4dOpSX4YIg2FVK8rN/GsCnAcDMHgbw71NKv2VmfwrgwwC+AuBRAE9uVJeZdXQXL6ik6qi51UoaXJB1dnWlZZ2pNGil6m45vU5dc7ldaj5hfVjTVmub22h/sM6npiW+Nuuv7L4K1M1VXu6x0mANpavBPFdU1ee5Ti+/nZvfjOq4cuVKZ1v72kul7a3u4/fAe3e8ABj8fHkex5s72Arbcar5JFqTdS+ipcN/cVstCYJgV9mUU01K6dsAvl1t/xTAO3a+SUEQ7AZd9aADyuK3q8mBRWEWqTwThoq+OdHUi4XuBTgoje+m98srtjwTkmfaYw8yVSFYPGeR0Es/rW3MrbzyYqd54rkn4pfGwsvVrXgr57gdV69erR3Hfa99xf2hKg8fy8d5YvxW+8q775I48uEbHwQNIQZ7EDSErovxbTwRVmdbWbTJeVXpvoY2ZnGRZ6lVjGePNBUrc2KUil4cllg99LyZ7lw2Up215+AV2le5xSOl4YuBuipTGpBBxcicCqTqlecxlivzFqOop2BOtdP2Tk1NZdvIfVCqkuhxrJJ4QSm8rLOeZSQX25GJL3sQNIQY7EHQEGKwB0FD6FnceNVlS+OH83FekMNXX321VsbedawXsf4L1HVlTjUF5L2g1BuLUz7p3AHrbtp+XqXG/ePFfD927FitLOfRpXXkUirred7KK88kldPZveCWXmCLUnOb5/XomTNPnnx9HZcX6MMzm3nBInMmQK/9Xp96+nuO+LIHQUOIwR4EDaFnHnReoAJvEYsXs4zFeI1FxqYVFq01BjnX4cUpY/MMi8RAXWTWhSp8nnq/5QI0aP0ckEHrZ1WA61AxnlUI7cfctVX05dh1unyZ+4rNg57pTcXbXIZXfbbMiRMnavs5b0Z9tnxtDgAC1FUv7cdc6iaF69f75H71FlF5qaHaJmR3UVC2JAiCNxQx2IOgIcRgD4KG0DN3WYX1RC8WOq/4Up2Gy9Skxrot51jjVWJAXefVOnKBB9Wc5MX+5tVWapbj+QPWzzROOuvR999/f62M9UHW7VXP5SCT2gesD77wwgud7VOnTtWO4/kNnTvgoJh8LdXtL1261NlW11/un5/97Gedbb0X77nzvAg/p9K8ckB9fsPLR+AFqPBWtuXcpHUOwFtN2S7zTJTxZQ+ChhCDPQgaQtc96HLmidxKKyC/ikzFPk+EYVGMRUwVCVmMVxNMLjWUioQs2qlpj8V6Fk31PL43FU1ZfGbPL6B+n8ePH+9ss0gP+PHp9L7bqLjPpjf1wuP286o99Zzk+PhqiuR3gq9977331o6bnJzsbGtfsWjNbdTnzs9W3ysviAk/e76WiuqeGM/1e6m6vfc7xPggCDrEYA+ChtB1MT4XStpbEMGify6TKlBfwOClbuJtnUVmMVDFNxZHWcRUrzDe9+K7aaCFV155pbP9tre9rbOtIvg999yzbjuAfNZVFVu9mV1+FiriM6x2qHrG1/M80LhMrRPcV/fdd19nW1M8seqloi/3j7cYhduls/GeZ1xpOG1G36tcSjO9Fy/cdcnCmPiyB0FDiMEeBA0hBnsQNISue9C19Rovpa0X4ID1J9VXeV89nRjWa9mbDqibZzTQI5/HbVKTEc8DaDvYrKOBJ7ge1l/ZPAXU9Vy9Nut13F5dOeeZgnguhM/T58JlqovrXMh61wXqz9NLb833ojq1l56J+4qv5c2zbMb7jcu8YCGM3mdpSuvSYB45SvOzvwxgHsAagNWU0lkzGwfwVQCnAbwM4CMppZlcHUEQ9JbNiPG/mlJ6KKV0ttr/FICnU0r3AXi62g+C4C5lO2L8BwE8XG0/gVYOuE9udFJbZFFxiEVEFaNyJg1PpFJzCYv43qKb6enpzraanVhkZlFS28GmII5Hp6iox9523C4V1VnE5/YCdRGc+5G9BvU8VVdy/e15Nmo/6uKdNl4qKxVvucyLG89o23NmW43/56XD8kTmnPnRMy2Xxo8rzVsA+OJ/pw1FVwUSgL81s++Z2ePVb8dSSu0lS5cBHFv/1CAI7gZKv+zvSSldNLOjAJ4ysxe4MKWUzGzdPy3VH4fHgTu/UEEQdI+iL3tK6WL1/1UAX0crVfMVM5sEgOr/q5lzz6WUzqaUzuoCgyAIuseGX3YzGwLQl1Kar7Z/DcB/BvANAI8C+Ez1/5ObubCXN+yORmYC7alO4+lMufO0HWwyUpMX67asT3rupvoHjuv03CG9tMylKadZR/Xyo6n5kfuRt73VYLraLGc289yYvfx/fF88J6JtLE157K08U7zAnbl3zqvfa6NnXuOy3EpLT3cvEeOPAfh6deE9AP5HSumvzey7AL5mZo8BeAXARwrqCoKgR2w42FNKPwXw4Dq/TwF43240KgiCnafrq95ypjem1EvJM9GphxR7UrF4qGIPi4sq3vJKLhZNVaxkUV1NUBy/XkXw3H2qmYjvRUVfLvNEQs/8w2WeGO8F2GDVg5+FrmzjPtBnllOHdLVgqUmK72UzaahYPPdEa09N8Lz8GG/FJLc5VxZx44MgiMEeBE0hBnsQNISu6uwppY5uUaozAnWdiU0Yqp+w/ueZUrg+XTnH7pzXrl2rlXH8czbDeS6gqhuy7qmurjmdXfVcLz8a9wHX5+ly2t+52OUaAz83DwLU5zs4gpBrGhIXZ+4r7lPV7ZlSV1Q9LpfHD/Ddc3N6tDefpPV5JrvccdqP7TaHzh4EQQz2IGgKXQ9e0RYzPHGjdNWR57nmiWmeqMeiknpL/fznP+9ss6lJTW+eCYaPVdNeLia+qhqM1s/3yaqMZ8bRZ5ETJbU/SoOFeGIwqwJqUmOTI7dJ+8PzTst5namal4v/rnXqveRMb15/e2303j+vrN0nXtCM+LIHQUOIwR4EDaFnHnSeaOeJlaXxulRUzwUZ0HbwzLF6hXEZZ2PV2XgvEAenVtJrs1jveW3xvoqjuZhuOtPtedflxHgVnz3VKNcOL86c9mNOffNi0Ck5i4Tn4aaUvmel8eP0uedUKs8qkIu/GLPxQRDEYA+CphCDPQgaQs886LxcW0rOpOGZ15Sc6cPT7dVzjfVL9q7TtMysa6lJij3vvDxwnimL26xebWyy4/vUlXOemYgpjcnu5Ufj47Qdnhk09zw905XC9XNQST3HM715186Z5TwvPw9+7qrbe0ExS0za8WUPgoYQgz0IGkLXPejaeClnVXzhMhaPNhMAI1emx3nphdnDjdukC1q84BJ83yrS5hb5uKKZtJ8XjHimMRb3tYzboWoCw2XewiOvDm+xTu4ZlsRIXw/vHfPMctxGfSdyJmQ9zmszH5uLu6d15AJ4uCpZtiQIgjcUMdiDoCHEYA+ChtAznX2rpolSM5HqUmwK8vRhz9TE+hRvs+ssABw9erSz7emy6sKauzc9TveZXMBCL2+Yl2ONTWqq2/P+TqQy9sxwJcEW1yvLPc9c3PWN8OLGe2YzLtM6crHztY0cxFNzCbSP9fowvuxB0BBisAdBQ+hZ8Arvd88sUrpaS9WE0tVJXlx69mDiFWp6HHveqdnJ8wDMpZz2VpR5HoBewATPvJYTffU4r0+9Z5GrozQ1suK9Eyw+8/PzVpR5ZjMvfhxv87WAuhlNr81tZBVKTbPsqZl7Zq7JOVtCmNmYmf2Zmb1gZs+b2bvMbNzMnjKzn1T/HyqpKwiC3lAqxn8ewF+nlO5HKxXU8wA+BeDplNJ9AJ6u9oMguEspyeJ6EMCvAPhXAJBSWgawbGYfBPBwddgTAL4N4JMb1ZdLU1Mak640y2VpSF4Vh7z6czPYnqcTe90B9eAVOqvO4pw3484z5DqTnhOFS73YAH+hUO48LztrLgWTnqfPLOct6YngnojszYh7s+B8rNbPzzeXQVfr9Gb0eZZdveT4GeYsNNv1oDsD4DUAXzaz75vZf69SNx9LKV2qjrmMVrbXIAjuUkoG+x4AvwTgCymltwJYhIjsqfVna91Ps5k9bmbnzey8LhkNgqB7lAz2CwAupJSeqfb/DK3Bf8XMJgGg+v/qeienlM6llM6mlM5qRtMgCLpHSX72y2b2qpm9JaX0Y7Rysv+o+vcogM9U/z9ZUFdHN/LS86rewRIB6yqe3uXpLlyHF0TD01dZf5qYmKiVHTr0umFifn6+VsZ6naZAzunp3vyD56GXS72sdXrzFt48yE4HnvCeJ29rYA8uU12ZTWCeXs7vi5rN+Dwt4/fYC1rimTA5oAl/EPX90AAhTLtf3fc+W1Ln3wL4YzMbAPBTAP8aLanga2b2GIBXAHyksK4gCHpA0WBPKf0AwNl1it63o60JgmDX6JkHnbcwQ0V8FkFzJh2gLiJ6Cy48j6jSRRssqt9zzz21MhbrdZHM1NRUZ9tLG8Vof7AoqWJxLia+t1BFRc6cSuV5NiqsXuQWemiZt8CFxXPPvHbjxo1aGatNnpnPCxrB/a19lTPV6nH8vqhnHO/z8ytd9FVK+MYHQUOIwR4EDSEGexA0hK7HjW/rwZ5r5EZ1tFH9z9NxcpSusFPOnDnT2Vad3QuScOnSpc724uJirYzdIz0zopcPLJcLz3NP9spK01trG3NmP3Ws8sxhOfdTnUuZm5vrbKvOzsfmApho/aqzeyvu+D55nkKDS7BJTf1NSgN3bpf4sgdBQ4jBHgQNwbYag3tLFzN7DS0HnMMArm1w+G5zN7QBiHYo0Y46m23Hm1NKR9Yr6Opg71zU7HxKaT0nnUa1IdoR7ehmO0KMD4KGEIM9CBpCrwb7uR5dl7kb2gBEO5RoR50da0dPdPYgCLpPiPFB0BC6OtjN7BEz+7GZvWhmXYtGa2ZfMrOrZvYs/db1UNhm9iYz+5aZ/cjMnjOzT/SiLWa2z8y+Y2Y/rNrx+9XvZ8zsmer5fLWKX7DrmFl/Fd/wm71qh5m9bGZ/b2Y/MLPz1W+9eEd2LWx71wa7mfUD+G8A/jmABwB81Mwe6NLl/wjAI/JbL0JhrwL4nZTSAwDeCeDjVR90uy1LAN6bUnoQwEMAHjGzdwL4LIDPpZTuBTAD4LFdbkebT6AVnrxNr9rxqymlh8jU1Yt3ZPfCtqeUuvIPwLsA/A3tfxrAp7t4/dMAnqX9HwOYrLYnAfy4W22hNjwJ4P29bAuAAwD+H4BfRst5Y896z2sXr3+qeoHfC+CbAKxH7XgZwGH5ravPBcBBAC+hmkvb6XZ0U4w/CeBV2r9Q/dYrehoK28xOA3grgGd60ZZKdP4BWoFCnwLwjwBmU0rtlTXdej5/COB3AbRXm0z0qB0JwN+a2ffM7PHqt24/l10N2x4TdPBDYe8GZjYM4M8B/HZKaY7LutWWlNJaSukhtL6s7wBw/25fUzGzXwdwNaX0vW5fex3ek1L6JbTUzI+b2a9wYZeey7bCtm9ENwf7RQBvov1T1W+9oigU9k5jZnvRGuh/nFL6i162BQBSSrMAvoWWuDxmZu11oN14Pu8G8Btm9jKAr6Alyn++B+1ASuli9f9VAF9H6w9gt5/LtsK2b0Q3B/t3AdxXzbQOAPhNAN/o4vWVb6AVAhsoDIW9Xay16PuLAJ5PKf1Br9piZkfMbKza3o/WvMHzaA36D3erHSmlT6eUTqWUTqP1PvyvlNJvdbsdZjZkZiPtbQC/BuBZdPm5pJQuA3jVzN5S/dQO274z7djtiQ+ZaPgAgH9ASz/8j1287p8AuARgBa2/no+hpRs+DeAnAP4ngPEutOM9aIlgfwfgB9W/D3S7LQD+KYDvV+14FsB/qn7/JwC+A+BFAH8KYLCLz+hhAN/sRTuq6/2w+vdc+93s0TvyEIDz1bP5SwCHdqod4UEXBA0hJuiCoCHEYA+ChhCDPQgaQgz2IGgIMdiDoCHEYA+ChhCDPQgaQgz2IGgI/x8pyR8qvWvY2AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "polygon2.plot_constraint_on_data(plot_type='image') ## Plotting the constraint on the data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[340, 342, 534, 337, 469, 216, 213, 215, 406, 278, 341, 150, 88, 661, 789, 407, 279, 403, 338, 598, 343, 152, 597, 212, 725, 404, 151, 402, 336, 470, 214, 274, 533, 405, 276, 275, 277, 339]\n"
+     ]
+    }
+   ],
+   "source": [
+    "const_idx_polygon2, rank_polygon2 = polygon2.get_constraint_indices(all_sensors=all_sensors_unconst, info = X_train)\n",
+    "print(const_idx_polygon2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 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]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(rank_polygon2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sen_polygon = 6\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_polygon2 = ps.optimizers.GQR()\n",
+    "opt_polygon_kws2={'idx_constrained':const_idx_polygon2,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sen_polygon,\n",
+    "         'all_sensors':all_sensors_unconst,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_polygon = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [4032  594  384  878  402   88  725  598  789  212]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_polygon2 = ps.SSPOR(basis = basis_polygon, optimizer = optimizer_polygon2, n_sensors = n_sensors)\n",
+    "model_polygon2.fit(X_train,**opt_polygon_kws2)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_polygon2 = model_polygon2.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "xTopConstPolygon2 = np.mod(top_sensors_polygon2,np.sqrt(n_features))\n",
+    "yTopConstPolygon2 = np.floor(top_sensors_polygon2/np.sqrt(n_features))\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_polygon2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4032.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>594.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>384.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>878.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>402.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>88.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>725.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>598.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>789.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>212.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID  SensorX  sensorY\n",
+       "0     4032.0      0.0     63.0\n",
+       "1      594.0     18.0      9.0\n",
+       "2      384.0      0.0      6.0\n",
+       "3      878.0     46.0     13.0\n",
+       "4      402.0     18.0      6.0\n",
+       "5       88.0     24.0      1.0\n",
+       "6      725.0     21.0     11.0\n",
+       "7      598.0     22.0      9.0\n",
+       "8      789.0     21.0     12.0\n",
+       "9      212.0     20.0      3.0"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_polygon2 = polygon2.sensors_dataframe(sensors = top_sensors_polygon2)\n",
+    "data_sens_polygon2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "polygon2.plot_constraint_on_data(plot_type='image') ## Plotting the constraint on the data!\n",
+    "polygon2.annotate_sensors(sensors = top_sensors_polygon2, all_sensors = all_sensors_unconst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## User defined constraints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "user_const = 'userExplicitConstraint1.py'\n",
+    "user_const_instance = ps.utils._constraints.UserDefinedConstraints(all_sensors_unconst,data = X_train, file = user_const)\n",
+    "idx, rank = user_const_instance.constraint()\n",
+    "user_const_instance.draw_constraint() ## plot the user defined constraint just by itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sensors = 4\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_user = ps.optimizers.GQR()\n",
+    "opt_user_kws={'idx_constrained':idx,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sensors,\n",
+    "         'all_sensors':all_sensors_unconst,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_user = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [4032  594  384  878  446 2772 4041  340  660  144]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_user = ps.SSPOR(basis = basis_user, optimizer = optimizer_user, n_sensors = n_sensors)\n",
+    "model_user.fit(X_train,**opt_user_kws)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_user = model_user.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "xTopConstUser = np.mod(top_sensors_user,np.sqrt(n_features))\n",
+    "yTopConstUser = np.floor(top_sensors_user/np.sqrt(n_features))\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_exact))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4032.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>594.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>384.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>878.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>446.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2772.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2587.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2466.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2395.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>37.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2658.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID  SensorX  sensorY\n",
+       "0     4032.0      0.0     63.0\n",
+       "1      594.0     18.0      9.0\n",
+       "2      384.0      0.0      6.0\n",
+       "3      878.0     46.0     13.0\n",
+       "4      446.0     62.0      6.0\n",
+       "5     2772.0     20.0     43.0\n",
+       "6     2587.0     27.0     40.0\n",
+       "7     2466.0     34.0     38.0\n",
+       "8     2395.0     27.0     37.0\n",
+       "9     2658.0     34.0     41.0"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_user = user_const_instance.sensors_dataframe(sensors = top_sensors_user)\n",
+    "data_sens_user"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Verifying whther user-defined constraints work\n",
+    "\n",
+    "user_const_instance.plot_constraint_on_data(plot_type='image') \n",
+    "user_const_instance.plot_selected_sensors(sensors = top_sensors_user, all_sensors = all_sensors_unconst)\n",
+    "user_const_instance.annotate_sensors(sensors = top_sensors_user, all_sensors = all_sensors_unconst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now let us consider an example where the user inputs the equation that they are considering as a constraint in a string of the form (x-30)^2 + (y-40)^2 < 25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "user_const_stg = '(x-30)**2 + (y-40)**2 > 5**2'\n",
+    "user_const_stg_instance = ps.utils._constraints.UserDefinedConstraints(all_sensors_unconst,data = X_train, equation = user_const_stg)\n",
+    "idx_stg, rank_stg = user_const_stg_instance.constraint()\n",
+    "user_const_stg_instance.draw_constraint() ## plot the user defined constraint just by itself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the number of constrained sensors allowed (s)\n",
+    "n_const_sensors = 0\n",
+    "\n",
+    "# Define the GQR optimizer for the exact_n sensor placement strategy\n",
+    "optimizer_user_stg = ps.optimizers.GQR()\n",
+    "opt_user_kws_stg={'idx_constrained':idx_stg,\n",
+    "         'n_sensors':n_sensors,\n",
+    "         'n_const_sensors':n_const_sensors,\n",
+    "         'all_sensors':all_sensors_unconst,\n",
+    "         'constraint_option':\"exact_n\"}\n",
+    "basis_user_stg = ps.basis.SVD(n_basis_modes=n_sensors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The list of sensors selected is: [2270 2650 2466 2394 2846 2658 2331 2589 2785 2398]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize and fit the model\n",
+    "model_user_stg = ps.SSPOR(basis = basis_user_stg, optimizer = optimizer_user_stg, n_sensors = n_sensors)\n",
+    "model_user_stg.fit(X_train,**opt_user_kws_stg)\n",
+    "\n",
+    "# sensor locations based on columns of the data matrix\n",
+    "top_sensors_user_stg = model_user_stg.get_selected_sensors()\n",
+    "\n",
+    "# sensor locations based on pixels of the image\n",
+    "xTopConstUser_stg = np.mod(top_sensors_user_stg,np.sqrt(n_features))\n",
+    "yTopConstUser_stg = np.floor(top_sensors_user_stg/np.sqrt(n_features))\n",
+    "\n",
+    "print('The list of sensors selected is: {}'.format(top_sensors_user_stg))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sensor ID</th>\n",
+       "      <th>SensorX</th>\n",
+       "      <th>sensorY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2270.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2650.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2466.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2394.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>37.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2846.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>44.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2658.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2331.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>36.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2589.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2785.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2398.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>37.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sensor ID  SensorX  sensorY\n",
+       "0     2270.0     30.0     35.0\n",
+       "1     2650.0     26.0     41.0\n",
+       "2     2466.0     34.0     38.0\n",
+       "3     2394.0     26.0     37.0\n",
+       "4     2846.0     30.0     44.0\n",
+       "5     2658.0     34.0     41.0\n",
+       "6     2331.0     27.0     36.0\n",
+       "7     2589.0     29.0     40.0\n",
+       "8     2785.0     33.0     43.0\n",
+       "9     2398.0     30.0     37.0"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sens_user_stg = user_const_stg_instance.sensors_dataframe(sensors = top_sensors_user_stg)\n",
+    "data_sens_user_stg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Verifying whther user-defined constraints work\n",
+    "\n",
+    "user_const_stg_instance.plot_constraint_on_data(plot_type='image') \n",
+    "user_const_stg_instance.plot_selected_sensors(sensors = top_sensors_user_stg, all_sensors = all_sensors_unconst)\n",
+    "user_const_stg_instance.annotate_sensors(sensors = top_sensors_user_stg, all_sensors= all_sensors_unconst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/twistParabolicConstraint.py b/examples/twistParabolicConstraint.py
new file mode 100644
index 0000000..58c1e09
--- /dev/null
+++ b/examples/twistParabolicConstraint.py
@@ -0,0 +1,44 @@
+
+import numpy as np
+
+def twistParabolicConstraint(x,y,**kwargs):
+    """
+    Function for evaluating constrained sensor locations on the grid by returning a negative value (if index is constrained) and a positive value (if index is unconstrained).
+
+    Parameters
+    ----------
+    x: float, x coordinate of all grid-points considered for sensor placement
+    y : float, y coordinate of all grid-points considered for sensor placement
+    
+    **kwargs : h : float, x-coordinate of the vertex of the parabola we want to be constrained;
+               k : float, y-coordinate of the vertex of the parabola we want to be constrained;
+               a : float, The x-coordinate of the focus of the parabola.
+
+    Returns
+    -------
+    g : np.darray, shape [No. of grid points], 
+        A boolean array for every single grid point based on whether the grid point lies in the constrained region or not
+    """
+    # make sure the length of x is the same as y
+    assert len(x) == len(y)
+    if ('h' not in kwargs.keys()) or (kwargs['h'] == None) :
+        kwargs['h'] = 0.025
+    if ('k' not in kwargs.keys()) or (kwargs['k'] == None) :
+        kwargs['k'] = 0
+    if ('a' not in kwargs.keys()) or (kwargs['a'] == None) :
+        kwargs['a'] = 100
+    # initialize the constraint evaluation function g
+    g1 = np.zeros(len(x),dtype=float) - 1
+    g2 = np.zeros(len(x),dtype=float) - 1
+    g = np.zeros(len(x),dtype=float)
+    # loop over all given location and check if they violate the constraints
+    # make sure the retuned value is negative if it is in the constrained area and positive otherwise
+    for i in range(len(x)):
+        # circle of center (h,k)=(0,0) and radius 2.5 cm
+        g1[i] = (kwargs['a']*(x[i]-kwargs['h'])**2) - (y[i]-kwargs['k'])
+        #
+        # Second constraint:
+        g2[i] = y[i] - 0.2
+        if bool(g1[i]>=0) == bool(g2[i]>=0):
+            g[i] = (bool(g1[i]>=0) and bool(g2[i]>=0))-1
+    return g
\ No newline at end of file
diff --git a/examples/userExplicitConstraint1.py b/examples/userExplicitConstraint1.py
new file mode 100644
index 0000000..781c178
--- /dev/null
+++ b/examples/userExplicitConstraint1.py
@@ -0,0 +1,10 @@
+import numpy as np
+
+def userExplicitConstraint1(x,y,**kwargs):
+  '''
+  '''
+  assert len(x) == len(y)
+  g = np.zeros(len(x),dtype=float) 
+  for i in range(len(x)):
+    g[i] = ((x[i]-30)**2 + (y[i]-40)**2) - 5**2
+  return g
\ No newline at end of file
diff --git a/examples/userExplicitConstraint2.py b/examples/userExplicitConstraint2.py
new file mode 100644
index 0000000..d20b614
--- /dev/null
+++ b/examples/userExplicitConstraint2.py
@@ -0,0 +1,4 @@
+
+def userExplicitConstraint2(x,y,**kwargs):
+  g = (x-20)**2 + (y-10)**2 - 49
+  return g
\ No newline at end of file
diff --git a/pysensors/optimizers/_gqr.py b/pysensors/optimizers/_gqr.py
index 9f6ddfa..7597441 100644
--- a/pysensors/optimizers/_gqr.py
+++ b/pysensors/optimizers/_gqr.py
@@ -74,7 +74,7 @@ def fit(self,basis_matrix,**optimizer_kws):
         [setattr(self,name,optimizer_kws.get(name,getattr(self,name))) for name in optimizer_kws.keys()]
         self._norm_calc_Instance = normCalcReturnInstance(self, self.constraint_option)
         n_features, n_samples = basis_matrix.shape  # We transpose basis_matrix below
-        max_const_sensors = len(self.idx_constrained) # Maximum number of sensors allowed in the constrained region
+        # max_const_sensors = len(self.idx_constrained) # Maximum number of sensors allowed in the constrained region
 
         ## Assertions and checks:
         # if self.n_sensors > n_features - max_const_sensors + self.nConstrainedSensors:
@@ -93,9 +93,14 @@ def fit(self,basis_matrix,**optimizer_kws):
             r = R[j:, j:]
 
             # Norm of each column
+            if j == 0:
+                dlens_old = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))
+            else:
+                dlens_old = dlens
             dlens = np.sqrt(np.sum(np.abs(r) ** 2, axis=0))
-            dlens_updated = self._norm_calc_Instance(self.idx_constrained, dlens, p, j, self.n_const_sensors, dlens_old=dlens, all_sensors=self.all_sensors, n_sensors=self.n_sensors, nx=self.nx, ny=self.ny, r=self.r)
-            i_piv = np.argmax(dlens_updated)
+            dlens_updated = self._norm_calc_Instance(self.idx_constrained, dlens, p, j, self.n_const_sensors, dlens_old=dlens_old, all_sensors=self.all_sensors, n_sensors=self.n_sensors, nx=self.nx, ny=self.ny, r=self.r)
+            # i_piv = np.argmax(dlens_updated)
+            i_piv = np.where(dlens_updated==dlens_updated.max())[0][0]
             dlen = dlens_updated[i_piv]
 
             if dlen > 0:
diff --git a/pysensors/reconstruction/_sspor.py b/pysensors/reconstruction/_sspor.py
index 6f93704..dddf990 100644
--- a/pysensors/reconstruction/_sspor.py
+++ b/pysensors/reconstruction/_sspor.py
@@ -9,6 +9,7 @@
 from ..basis import Identity
 from ..optimizers import CCQR
 from ..optimizers import QR
+from ..optimizers import GQR
 from ..utils import validate_input
 
 
@@ -510,7 +511,7 @@ def _validate_n_sensors(self):
         # If n_sensors exceeds n_samples, the cost-constrained QR algorithm may
         # place sensors in constrained areas.
         if (
-            isinstance(self.optimizer, CCQR)
+            (isinstance(self.optimizer, CCQR) or isinstance(self.optimizer, QR) or isinstance(self.optimizer, GQR))
             and self.n_sensors > self.basis_matrix_.shape[1]
         ):
             warnings.warn(
diff --git a/pysensors/utils/__init__.py b/pysensors/utils/__init__.py
index 94b3713..f0ad370 100644
--- a/pysensors/utils/__init__.py
+++ b/pysensors/utils/__init__.py
@@ -1,8 +1,22 @@
 from ._base import validate_input
 from ._optimizers import constrained_binary_solve
 from ._optimizers import constrained_multiclass_solve
-from ._constraints import get_constraind_sensors_indices
-from ._constraints import get_constrained_sensors_indices_linear
+from ._constraints import get_constrained_sensors_indices
+from ._constraints import get_constrained_sensors_indices_dataframe
+from ._constraints import BaseConstraint
+from ._constraints import Circle
+from ._constraints import Cylinder
+from ._constraints import Line
+from ._constraints import Ellipse
+from ._constraints import Parabola
+from ._constraints import Polygon
+from ._constraints import UserDefinedConstraints
+# from ._constraints import check_constraints
+# from ._constraints import constraints_eval
+
+from ._constraints import load_functional_constraints
+from ._constraints import get_coordinates_from_indices
+from ._constraints import get_indices_from_coordinates
 from ._norm_calc import exact_n
 from ._norm_calc import max_n
 from ._norm_calc import predetermined
@@ -15,8 +29,19 @@
     "validate_input",
     "get_constraind_sensors_indices",
     "get_constrained_sensors_indices_linear",
+    "BaseConstraint",
+    "Circle",
+    "Cylinder"
+    "Line",
+    "Parabola",
+    "Polygon",
+    "Ellipse",
+    "UserDefinedConstraints"
     "box_constraints",
+    # "constraints_eval",
     "functional_constraints",
+    "get_coordinates_from_indices",
+    "get_indices_from_coordinates",
     "exact_n",
     "max_n",
     "predetermined",
diff --git a/pysensors/utils/_constraints.py b/pysensors/utils/_constraints.py
index c392108..2f75a19 100644
--- a/pysensors/utils/_constraints.py
+++ b/pysensors/utils/_constraints.py
@@ -4,29 +4,44 @@
 """
 
 import numpy as np
+import pandas as pd
+import sys, os
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import operator
 
 
-def get_constraind_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors):
+def get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors):
     """
     Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix.
 
     Parameters
     ----------
-    x_min: int, lower bound for the x-axis constraint
-    x_max : int, upper bound for the x-axis constraint
-    y_min : int, lower bound for the y-axis constraint
-    y_max : int, upper bound for the y-axis constraint
+    x_min: float, lower bound for the x-axis constraint
+    x_max : float, upper bound for the x-axis constraint
+    y_min : float, lower bound for the y-axis constraint
+    y_max : float, upper bound for the y-axis constraint
     nx : int, image pixel (x dimensions of the grid)
     ny : int, image pixel (y dimensions of the grid)
-    all_sensors : np.ndarray, shape [n_features], ranked list of sensor locations.
+    all_sensors : np.ndarray of integers, shape [n_features], ranked list of sensor locations.
 
     Returns
     -------
     idx_constrained : np.darray, shape [No. of constrained locations], array which contains the constrained
         locations of the grid in terms of column indices of basis_matrix.
     """
+    if len(all_sensors)==0:
+        raise ValueError('all_sensors must be provided')
+    if not np.issubdtype(all_sensors.dtype, np.integer):
+        raise ValueError('all_sensors must be integers')
+    if x_min >= x_max:
+        raise ValueError('x_min must be less than x_max')
+    if y_min >= y_max:
+        raise ValueError('y_min must be less than y_max')
+    if not isinstance(nx, int) or not isinstance(ny, int):
+        raise ValueError('nx and ny must be integers')
     n_features = len(all_sensors)
-    image_size = int(np.sqrt(n_features))
+    # image_size = int(np.sqrt(n_features))
     a = np.unravel_index(all_sensors, (nx,ny))
     constrained_sensorsx = []
     constrained_sensorsy = []
@@ -45,28 +60,1137 @@ def get_constraind_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_senso
         idx_constrained = np.ravel_multi_index(constrained_sensors_tuple, (nx,ny))
     return idx_constrained
 
-def get_constrained_sensors_indices_linear(x_min, x_max, y_min, y_max,df):
+def get_constrained_sensors_indices_dataframe(x_min, x_max, y_min, y_max,df,**kwargs):   #### We wanted to change the name of this function. I have made it get_constrained_sensors_indices_dataframe from get_constrained_sensors_indices_linear. Feel free to suggest a better name @Josh, @Mohammad
     """
     Function for obtaining constrained column indices from already existing linear sensor locations on the grid.
 
     Parameters
     ----------
-    x_min: int, lower bound for the x-axis constraint
-    x_max : int, upper bound for the x-axis constraint
-    y_min : int, lower bound for the y-axis constraint
-    y_max : int, upper bound for the y-axis constraint
+    x_min: float, lower bound for the x-axis constraint
+    x_max : float, upper bound for the x-axis constraint
+    y_min : float, lower bound for the y-axis constraint
+    y_max : float, upper bound for the y-axis constraint
     df : pandas.DataFrame, a dataframe containing the features  and samples
 
+    Keyword Arguments
+    -----------------
+    X_axis : string,
+        Name of the column in dataframe to be plotted on the X axis.
+    Y-axis : string,
+        Name of the column in dataframe to be plotted on the Y axis.
+    Field : string,
+        Name of the column in dataframe to be plotted as a contour map.
+        ## TODO: Field is not used here @Niha please see this.
     Returns
     -------
     idx_constrained : np.darray, shape [No. of constrained locations], array which contains the constrained
         locations of the grid in terms of column indices of basis_matrix.
     """
-    x = df['X (m)'].to_numpy()
+    if 'X_axis' in kwargs.keys():
+        X_axis = kwargs['X_axis']
+    else:
+        raise Exception('Must provide X_axis as **kwargs as your data is a dataframe')
+    if 'Y_axis' in kwargs.keys():
+        Y_axis = kwargs['Y_axis']
+    else:
+        raise Exception('Must provide Y_axis as **kwargs as your data is a dataframe')
+    if df.isnull().values.any():
+        df = df.dropna()
+    x = df[X_axis].to_numpy()   ### Needs to be changed to get the X_axis and Y_axis value of what is in the user dataframe. This makes it possible for the user to have any name for the X,Y columns of their dataframe.
     n_features = x.shape[0]
-    y = df['Y (m)'].to_numpy()
+    y = df[Y_axis].to_numpy()
     idx_constrained = []
     for i in range(n_features):
-        if (x[i] >= x_min and x[i] <= x_max) and (y[i] >= y_min and y[i] <= y_max):
+        if (x[i] >= x_min and x[i] < x_max) and (y[i] >= y_min and y[i] < y_max):
             idx_constrained.append(i)
-    return idx_constrained
\ No newline at end of file
+    return idx_constrained
+
+def load_functional_constraints(functionHandler):
+    """
+    Parameters:
+    ----------
+    functionHandler : The python file name that contains the constraint to be evaluated as a string
+
+    Return
+    -------
+    Convert the functionHandler file into a callable function
+    """
+    functionName = os.path.basename(functionHandler).strip('.py')
+    dirName = os.path.dirname(functionHandler)
+    sys.path.insert(0,os.path.expanduser(dirName))
+    module = __import__(functionName)
+    func = getattr(module, functionName)
+    return func
+
+# def constraints_eval(constraints,senID,**kwargs):  ### As discussed this one remains outside the Base_constraint() class
+#     """
+#     Function for evaluating whether a certain sensor index lies within the constrained region or not.
+
+#     Parameters:
+#     ----------
+#         constraints: __(type?)__, The constraint defined by the user
+#         senID: np.ndarray, shape [n_features], ranked list of sensor locations (column indices)
+#         data : pandas.DataFrame/np.ndarray shape [n_features, n_samples]
+#                 Dataframe or Matrix which represent the measurement data.
+#     Returns
+#     -------
+#     G : Boolean np.darray, shape [n_features], array which contains a Boolean value based on whether a column index is constrained or not.
+#     """
+#     nConstraints = len(constraints)
+#     G = np.zeros((len(senID),nConstraints),dtype=bool)
+#     for i in range(nConstraints):
+#         # temp = BaseConstraint.functional_constraints(constraints[i],senID,kwargs)
+#         G[:,i] = [x>0 for x in constraints[i]]  ### I had >= 0 and hence Polygon was not working (Polygone gives 0 when False and 1 hen True)
+#     return G
+
+# def check_constraints(constraints,senID,info, **kwargs):  ### As discussed this one remains outside the Base_constraint() class
+#     """
+#     Function for evaluating whether a certain sensor index lies within the constrained region or not.
+
+#     Parameters:
+#     ----------
+#         constraints: __(type?)__, The constraint defined by the user
+#         senID: np.ndarray, shape [n_features], ranked list of sensor locations (column indices)
+#         data : pandas.DataFrame/np.ndarray shape [n_features, n_samples]
+#                 Dataframe or Matrix which represent the measurement data.
+#     Returns
+#     -------
+#     G : Boolean np.darray, shape [n_features], array which contains a Boolean value based on whether a column index is constrained or not.
+#     """
+#     nConstraints = len(constraints)
+#     G = np.zeros((len(senID),nConstraints),dtype=bool)
+#     coords = get_coordinates_from_indices(senID,info,**kwargs)
+#     for i in range(nConstraints):
+#         G[:,i] = constraints[i].constraint_function(coords)
+#     return G
+
+def order_constrained_sensors(idx_constrained_list, ranks_list):
+    """
+    Function for ordering constrained sensor locations on the grid according to their ranks.
+
+    Parameters
+    ----------
+    idx_constrained_list : np.darray shape [No. of constrained locations], Constrained sensor locations
+    ranks_list : np.darray shape [No. of constrained locations], Ranks of each constrained sensor location
+
+    Returns
+    -------
+    sortedConstraints : np.darray, shape [No. of constrained locations], array which contains the constrained
+        locations of the grid in terms of column indices of basis_matrix sorted according to their rank.
+    ranks : np.darray, shape [No. of constrained locations], array which contains the ranks of constrained sensors.
+    """
+    if len(ranks_list) == 0 or len(idx_constrained_list) == 0:
+        sortedConstraints = []
+        ranks = []
+    else:
+        sortedConstraints,ranks =zip(*[[x,y] for x,y in sorted(zip(idx_constrained_list, ranks_list),key=lambda x: (x[1]))])
+    return sortedConstraints,ranks
+
+def get_coordinates_from_indices(idx,info,**kwargs): ### This one remains outside and I change what info is as discussed
+    """
+    Function for obtaining the coordinates on a grid from column indices
+
+    Parameters
+    ----------
+    idx :  int, sensor ID
+    info : pandas.DataFrame/np.ndarray shape [n_features, n_samples], Dataframe or Matrix which represent the measurement data.
+
+    Keyword Arguments
+    -----------------
+    X_axis : string,
+        Name of the column in dataframe to be plotted on the X axis.
+    Y-axis : string,
+        Name of the column in dataframe to be plotted on the Y axis.
+    Field : string,
+        Name of the column in dataframe to be plotted as a contour map.
+
+    Returns:
+        (x,y) : tuple, The coordinates on the grid of each sensor.
+    """
+    if isinstance(info,np.ndarray):
+        return np.unravel_index(idx,(int(np.sqrt(info.shape[1])),int(np.sqrt(info.shape[1]))),'F')
+    elif isinstance(info,pd.DataFrame):
+        if set(idx).issubset(np.arange(0,len(info))) == False:
+            raise Exception("Sensor ID must be within dataframe entries")
+        if 'X_axis' in kwargs.keys():
+            X_axis = kwargs['X_axis']
+        else:
+            raise Exception('Must provide X_axis as **kwargs as your data is a dataframe')
+        if 'Y_axis' in kwargs.keys():
+            Y_axis = kwargs['Y_axis']
+        else:
+            raise Exception('Must provide Y_axis as **kwargs as your data is a dataframe')
+        if 'Z_axis' in kwargs.keys() and kwargs['Z_axis'] is not None:
+            Z_axis = kwargs['Z_axis']
+            z = info.loc[idx,Z_axis].values
+        else:
+            z = None
+        x = info.loc[idx,X_axis].values
+        y = info.loc[idx,Y_axis].values
+
+        return (x,y,z) if z is not None else (x,y)
+
+def get_indices_from_coordinates(coordinates,shape):
+    """
+    Function for obtaining the indices of columns/sensors from coordinates on a grid when data is in the form of a matrix
+
+    Parameters
+    ----------
+    coordinates : tuple of array_like , (x,y) pair coordinates of sensor locations on the grid
+    shape : tuple of ints, Shape of the matrix fed as data to the algorithm
+
+    Returns
+    -------
+    np.ravel_multi_index(coordinates,shape,order='F') : np.ndarray, The indices of the sensors.
+    """
+    return np.ravel_multi_index(coordinates,shape,order='F')
+
+class BaseConstraint(object):
+    '''
+    A General class for handling various functional and user-defined constraint shapes.
+    It extends the ability of constraint handling with various plotting and annotating
+    functionalities while constraining various user-defined regions on the grid.
+
+    @ authors: Niharika Karnik (@nkarnik2999), Mohammad Abdo (@Jimmy-INL), and Joshua Cogliati (@joshua-cogliati-inl)
+    '''
+    def __init__(self,**kwargs):
+        """
+        Attributes
+        ----------
+        Keyword Arguments:
+        ------------------
+        X_axis : string,
+            Name of the column in dataframe to be plotted on the X axis.
+        Y-axis : string,
+            Name of the column in dataframe to be plotted on the Y axis.
+        Field : string,
+            Name of the column in dataframe to be plotted as a contour map.
+        data : pandas.DataFrame/np.darray [n_samples, n_features],
+            dataframe (used for scatter and contour plots) or matrix (used for images) containing measurement data
+        """
+        if 'data' in kwargs.keys():
+            self.data = kwargs['data']
+        else:
+            raise Exception('Must provide data as **kwargs')
+        if isinstance(self.data,pd.DataFrame):
+            if 'X_axis' in kwargs.keys():
+                self.X_axis = kwargs['X_axis']
+            else:
+                raise Exception('Must provide X_axis as **kwargs as your data is a dataframe')
+            if 'Y_axis' in kwargs.keys():
+                self.Y_axis = kwargs['Y_axis']
+            else:
+                raise Exception('Must provide Y_axis as **kwargs as your data is a dataframe')
+            if 'Z_axis' in kwargs.keys():
+                self.Z_axis = kwargs['Z_axis']
+            else:
+                self.Z_axis = None
+            if 'Field' in kwargs.keys():
+                self.Field = kwargs['Field']
+            else:
+                raise Exception('Must provide Field as **kwargs as your data is a dataframe')
+
+    def functional_constraints(func, idx, info, **kwargs):  ### According to our discussion @Josh is going to split this into two functions: 1) For a python file handler which remains outside the Base_constraint class and 2) String/Equation which goes inside the Base Constraint class.
+        """
+        Function for evaluating the functional constraints.
+
+        Parameters
+        ----------
+        func : function, a function which is to be evaluated
+        idx : np.ndarray, ranked list of sensor locations (column indices)
+        info : pandas.DataFrame/np.darray [n_samples, n_features],
+            dataframe (used for scatter and contour plots) or matrix (used for images) containing measurement data
+        Keyword Arguments
+        -----------------
+        X_axis : string,
+            Name of the column in dataframe to be plotted on the X axis.
+        Y-axis : string,
+            Name of the column in dataframe to be plotted on the Y axis.
+        Field : string,
+            Name of the column in dataframe to be plotted as a contour map.
+
+        Return
+        ------
+        g : function, Contains the function defined by the user for the functional constraint.
+        """
+        if isinstance(info,np.ndarray):
+            xLoc,yLoc = get_coordinates_from_indices(idx,info)
+        elif isinstance(info,pd.DataFrame):
+            if 'X_axis' in kwargs.keys():
+                X_axis = kwargs['X_axis']
+            else:
+                raise Exception('Must provide X_axis as **kwargs as your data is a dataframe')
+            if 'Y_axis' in kwargs.keys():
+                Y_axis = kwargs['Y_axis']
+            else:
+                raise Exception('Must provide Y_axis as **kwargs as your data is a dataframe')
+            if 'Field' in kwargs.keys():
+                Field = kwargs['Field']
+            else:
+                raise Exception('Must provide Field as **kwargs as your data is a dataframe')
+            if 'Z_axis' in kwargs.keys():
+                Z_axis = kwargs['Z_axis']
+            else:
+                Z_axis = None
+            xLoc,yLoc =  get_coordinates_from_indices(idx,info,X_axis = X_axis, Y_axis = Y_axis, Z_axis = Z_axis,Field = Field)
+        g = func(xLoc, yLoc,**kwargs)
+        return g
+
+    def get_functionalConstraind_sensors_indices(senID,g):  ### Moving this function inside the Base_constraint class as discussed
+        """
+        Function for finding constrained sensor locations on the grid and their ranks
+
+        Parameters
+        ----------
+        senID: np.darray, ranked list of sensor locations (column indices)
+        g : float, constraint evaluation function (negative if violating the constraint)
+
+        Returns
+        -------
+        idx_constrained : np.darray, shape [No. of constrained locations], array which contains the constrained
+            locations of the grid in terms of column indices of basis_matrix.
+        rank : np.darray, shape [No. of constrained locations], array which contains rank of the constrained sensor locations
+        """
+        assert (len(senID)==len(g))
+        idx_constrained = senID[~g].tolist()
+        rank = np.where(np.isin(idx_constrained,senID))[0].tolist() # ==False
+        return idx_constrained, rank
+
+    def get_constraint_indices(self,all_sensors,info):
+        '''
+        A function for computing indices which lie within the region constrained by the user
+        Attributes
+        ----------
+        all_sensors : np.darray,
+            A ranked list of all sensor indices computed from just QR optimizer
+        info : pandas.DataFrame/np.ndarray shape [n_features, n_samples],
+            Dataframe or Matrix which represent the measurement data.
+        Returns
+        -----------
+        idx_const : np.darray, shape [No. of constrained locations],
+            array which contains the constrained locations of the grid in terms of column indices of basis_matrix.
+        rank : np.darray, shape [No. of constrained locations],
+            array which contains rank of the constrained sensor locations
+        '''
+        if isinstance(info,np.ndarray):
+            coords = get_coordinates_from_indices(all_sensors,info)
+        elif isinstance(info, pd.DataFrame):
+            coords = get_coordinates_from_indices(all_sensors,info, X_axis = self.X_axis, Y_axis = self.Y_axis, Z_axis = self.Z_axis, Field = self.Field)
+        nDims,nPoints = np.shape(coords)
+        g = np.zeros(nPoints,dtype = bool)
+        for i in range(nPoints):
+            g[i] = self.constraint_function(np.array(coords).reshape(nDims,-1)[:,i])
+        # G_const = constraints_eval([g],all_sensors,data = info)
+        idx_const, rank = BaseConstraint.get_functionalConstraind_sensors_indices(all_sensors,g)
+        return idx_const,rank
+
+    def draw_constraint(self, plot=None, **kwargs):
+        '''
+        Function for drawing the constraint defined by the user
+        '''
+        if plot is None:
+            _ , ax = plt.subplots()
+        else:
+            _ , ax = plot
+        # if isinstance(self,Cylinder):
+        #     fig , ax = plt.subplots(subplot_kw={"projection": "3d"})
+        # else:
+        #     fig , ax = plt.subplots()
+        ## TODO assess if plot=(fig,ax) has 3d projection
+        self.draw(ax,**kwargs)
+
+    def plot_constraint_on_data(self,plot_type, plot=None, **kwargs):
+        '''
+        Function for plotting the user-defined constraint on the data
+        Attributes
+        ----------
+        data : pandas.DataFrame/np.darray [n_samples, n_features],
+            dataframe (used for scatter and contour plots) or matrix (used for images) containing measurement data
+        plot_type : string,
+            the type of plot used to display the data
+            image : if the data is represented in the fprm of an image
+            scatter: if the data can be represented with a scatter plot
+            contour_map: if the data can be represented in the form of a contour map
+        plot : to plot on an exisiting subplot, pass plot = (fig, ax),
+                otherwise leave plot = None
+        Returns
+        -----------
+        A plot of the constraint on top of the measurement data plot.
+        '''
+        if plot is None:
+            if isinstance(self,Cylinder):
+                self.fig, self.ax = plt.subplots(subplot_kw={"projection": "3d"})
+            else:
+                self.fig, self.ax = plt.subplots()
+        else:
+            self.fig, self.ax = plot
+        if 'alpha' not in kwargs.keys():
+            kwargs['alpha'] = 0.3
+        if 'cmap' not in kwargs.keys():
+            kwargs['cmap'] = plt.cm.coolwarm
+        if 's' not in kwargs.keys():
+            kwargs['s'] = 1
+        if 'color' not in kwargs.keys():
+            kwargs['color'] = 'red'
+        if plot_type == 'image':
+            image = self.data[1,:].reshape(1,-1)
+            n_samples, n_features = self.data.shape
+            image_shape = (int(np.sqrt(n_features)),int(np.sqrt(n_features)))
+            for i, comp in enumerate(image):
+                vmax = max(comp.max(), -comp.min())
+                self.ax.imshow(comp.reshape(image_shape), cmap = plt.cm.gray, interpolation='nearest', vmin=-vmax, vmax=vmax )
+        elif plot_type == 'scatter':
+            y_vals = self.data[self.Y_axis]
+            x_vals = self.data[self.X_axis]
+            self.ax.scatter(x_vals, y_vals, color = kwargs['color'], marker = '.')
+        elif plot_type == 'scatter3D':
+            y_vals = self.data[self.Y_axis]
+            x_vals = self.data[self.X_axis]
+            z_vals = self.data[self.Z_axis]
+            self.ax.scatter(x_vals, y_vals, z_vals, color=kwargs['color'], marker='.')
+        elif plot_type == 'contour_map':
+            y_vals = self.data[self.Y_axis]
+            x_vals = self.data[self.X_axis]
+            self.ax.scatter(x_vals, y_vals, c=self.data[self.Field], cmap=kwargs['cmap'], s=kwargs['s'], alpha=kwargs['alpha'])
+        elif plot_type == 'contour_map3D':
+            y_vals = self.data[self.Y_axis]
+            x_vals = self.data[self.X_axis]
+            z_vals = self.data[self.Z_axis]
+            self.ax.scatter(x_vals, y_vals,z_vals ,c=self.data[self.Field], cmap=kwargs['cmap'], s=kwargs['s'], alpha=kwargs['alpha'])
+        self.draw(self.ax,**kwargs)
+
+    def plot_grid(self,all_sensors):
+        '''
+        Function to plot the grid with data points that signify sensor locations to choose from
+        Attributes
+        ----------
+        all_sensors : np.darray,
+            A ranked list of all sensor indices computed from just QR optimizer
+
+        Returns
+        -----------
+        A plot of the user defined grid showing all possible sensor locations
+        '''
+        if isinstance(self.data,np.ndarray):
+            n_samples, n_features = self.data.shape
+            x_val, y_val = get_coordinates_from_indices(all_sensors,self.data)
+            fig , ax = plt.subplots()
+            ax.scatter(x_val, y_val, color = 'blue', marker = '.')
+        elif isinstance(self.data,pd.DataFrame):
+            y_vals = self.data[self.Y_axis]
+            x_vals = self.data[self.X_axis]
+            fig , ax = plt.subplots()
+            ax.scatter(x_vals, y_vals, color = 'blue', marker = '.')
+
+    def plot_selected_sensors(self,sensors, all_sensors, color_constrained = 'red', color_unconstrained = 'green'):
+        '''
+        Function to plot the sensor locations choosen during the optimization procedure.
+        This function plots near-optimal sensors which are unconstrained sensor locations choosen by QR in the user defined color_unconstrained/green and sensors that are choosen through constraining certain regions of the grid in the under defined color_constrained/red.
+        Attributes
+        ----------
+        sensors : np.darray,
+            A ranked list of all sensor indices computed from QR/GQR/CCQR optimizer
+        all_sensors : np.darray,
+            A ranked list of all sensor indices computed from just QR optimizer
+        color_constrained : string,
+            The color the sensors that were selected due to the applied constraints should be plotted in
+        color_unconstrained : string,
+            The color the sensors that were a part of the near-optimal sensors choosen through unconstrained QR optimizer should be plotted in
+        Returns
+        -----------
+        A plot of the user defined grid showing chosen sensor locations
+        '''
+        n_samples, n_features = self.data.shape
+        n_sensors = len(sensors)
+        constrained = sensors[np.where(np.in1d(all_sensors[:n_sensors],sensors) == False)[0]]
+        unconstrained = sensors[np.where(np.in1d(all_sensors[:n_sensors],sensors) == True)[0]]
+        if isinstance(self.data,np.ndarray):
+            xconst = np.mod(constrained,np.sqrt(n_features))
+            yconst = np.floor(constrained/np.sqrt(n_features))
+            xunconst = np.mod(unconstrained,np.sqrt(n_features))
+            yunconst = np.floor(unconstrained/np.sqrt(n_features))
+            self.ax.plot(xconst,yconst,'*',color = color_constrained)
+            self.ax.plot(xunconst,yunconst, '*',color = color_unconstrained)
+        elif isinstance(self.data,pd.DataFrame):
+            constCoords = get_coordinates_from_indices(constrained,self.data, Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+            unconstCoords = get_coordinates_from_indices(unconstrained,self.data, Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+            self.ax.plot(constCoords,'*',color = color_constrained)
+            self.ax.plot(unconstCoords, '*',color = color_unconstrained)
+
+    def sensors_dataframe(self,sensors):
+        '''
+        Function to form a dataframe of the sensor index along with it's coordinate (X,Y,Z) positions
+        Attributes
+        ----------
+        sensors : np.darray,
+            A ranked list of all sensor indices choosen from QR/CCQR/GQR optimizer
+        Returns
+        -----------
+        A dataframe of the sensor locations choosen
+        '''
+        n_samples, n_features = self.data.shape
+        n_sensors = len(sensors)
+        if isinstance(self.data,np.ndarray):
+            xTop = np.mod(sensors,np.sqrt(n_features))
+            yTop = np.floor(sensors/np.sqrt(n_features))
+        elif isinstance(self.data,pd.DataFrame):
+            xTop, yTop = get_coordinates_from_indices(sensors,self.data, Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+        columns = ['Sensor ID','SensorX','sensorY']
+        Sensors_df = pd.DataFrame(data = np.vstack([sensors,xTop,yTop]).T,columns=columns,dtype=float)
+        Sensors_df.head(n_sensors)
+        return Sensors_df
+
+    def annotate_sensors(self,sensors,all_sensors,color_constrained = 'red', color_unconstrained = 'green'):
+        '''
+        Function to annotate the sensor location on the grid while also plotting the sensor location
+        Attributes
+        ----------
+        sensors : np.darray,
+            A ranked list of all sensor indices choosen from QR/CCQR/GQR optimizer
+        all_sensors : np.darray,
+            A ranked list of all sensor indices computed from just QR optimizer
+        color_constrained : string,
+            The color the sensors that were selected due to the applied constraints should be plotted in
+        color_unconstrained : string,
+            The color the sensors that were a part of the near-optimal sensors choosen through unconstrained QR optimizer should be plotted in
+
+        Returns
+        -----------
+        Annotation of sensor rank near the choosen sensor locations
+        '''
+        n_samples, n_features = self.data.shape
+        n_sensors = len(sensors)
+        constrained = sensors[np.where(np.in1d(all_sensors[:n_sensors],sensors) == False)[0]]
+        unconstrained = sensors[np.where(np.in1d(all_sensors[:n_sensors],sensors) == True)[0]]
+        if isinstance(self.data,np.ndarray):
+            xTop = np.mod(sensors,np.sqrt(n_features))
+            yTop = np.floor(sensors/np.sqrt(n_features))
+            xconst = np.mod(constrained,np.sqrt(n_features))
+            yconst = np.floor(constrained/np.sqrt(n_features))
+            xunconst = np.mod(unconstrained,np.sqrt(n_features))
+            yunconst = np.floor(unconstrained/np.sqrt(n_features))
+            data = np.vstack([sensors,xTop,yTop]).T
+            self.ax.plot(xconst, yconst, '*', color = color_constrained, alpha =0.5)
+            self.ax.plot(xunconst, yunconst, '*', color = color_unconstrained, alpha =0.5)
+            for ind,i in enumerate(range(len(xTop))):
+                self.ax.annotate(f"{str(ind)}",(xTop[i],yTop[i]),xycoords='data',
+                    xytext=(-20,20), textcoords='offset points',color='r',fontsize=12,
+                    arrowprops=dict(arrowstyle="->", color='black'))
+        elif isinstance(self.data,pd.DataFrame):
+            xTop, yTop = get_coordinates_from_indices(sensors,self.data,Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+            xconst, yconst = get_coordinates_from_indices(constrained,self.data, Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+            xunconst, yunconst = get_coordinates_from_indices(unconstrained,self.data, Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+            self.ax.plot(xconst, yconst, '*', color = color_constrained, alpha =0.5)
+            self.ax.plot(xunconst, yunconst, '*', color = color_unconstrained, alpha =0.5)
+            for _,i in enumerate(range(len(sensors))):
+                self.ax.annotate(f"{str(i)}",(xTop[i],yTop[i]),xycoords='data',
+                    xytext=(-20,20), textcoords='offset points',color='r',fontsize=12,
+                    arrowprops=dict(arrowstyle="->", color='black'))
+
+class Intersection(BaseConstraint):
+    '''
+    A General class for dealing with constraint regions that are defined by the combination of
+    two or more individual constraint shapes/equations.
+    '''
+    def __init__(self,constraints, **kwargs): ### We want to make default location as 'in'
+        super().__init__(**kwargs)
+        '''
+        Attributes
+        ----------
+        constraints : np.darray containing instances of classes, for e.g: [circle_instance, line_instance],
+
+        '''
+        self.constraints = constraints
+
+    def draw(self,ax):
+        '''
+        Function to plot the constraint based on two or more constraint shapes/equations
+        Attributes
+        ----------
+        ax : axis on which the constraint circle should be plotted
+        '''
+    def constraint_function(self):
+        '''
+        Function to compute whether a certain point on the grid lies inside/outside the defined constrained region
+
+        '''
+
+class Circle(BaseConstraint):
+    '''
+    General class for dealing with circular user defined constraints.
+    Plotting, computing constraints functionalities included.
+    '''
+    def __init__(self,center_x,center_y,radius,loc = 'in', **kwargs): ### We want to make default location as 'in'
+        super().__init__(**kwargs)
+        '''
+        Attributes
+        ----------
+        center_x : float,
+            x-coordinate of the center of circle
+        center_y : float,
+            y-coordinate of the center of circle
+        radius : float,
+            radius of the circle
+        loc : string- 'in'/'out',
+            specifying whether the inside or outside of the shape is constrained
+
+        Keyword Arguments
+        -----------------
+        X_axis : string,
+            Name of the column in dataframe to be plotted on the X axis.
+        Y-axis : string,
+            Name of the column in dataframe to be plotted on the Y axis.
+        Field : string,
+            Name of the column in dataframe to be plotted as a contour map.
+        data : pandas.DataFrame/np.darray [n_samples, n_features],
+            dataframe (used for scatter and contour plots) or matrix (used for images) containing measurement data
+        '''
+        self.center_x = center_x
+        self.center_y = center_y
+        self.radius = radius
+        self.loc = loc
+
+    def draw(self,ax,**kwargs):
+        '''
+        Function to plot a circle based on user-defined coordinates
+        Attributes
+        ----------
+        ax : axis on which the constraint circle should be plotted
+        '''
+        if 'fill' not in kwargs.keys():
+             kwargs['fill'] = False
+        if 'color' not in kwargs.keys():
+            kwargs['color'] = 'r'
+        if 'lw' not in kwargs.keys():
+             kwargs['lw'] = 2
+        if 'alpha' not in kwargs.keys():
+             kwargs['alpha'] = 1.0
+        c = patches.Circle((self.center_x, self.center_y), self.radius, fill=kwargs['fill'], color=kwargs['color'], lw=kwargs['lw'], alpha=kwargs['alpha'])
+        ax.add_patch(c)
+        ax.autoscale_view()
+
+
+    def constraint_function(self,coords):
+        '''
+        Function to compute whether a certain point on the grid lies inside/outside the defined constrained region
+        Attributes
+        ----------
+        x : float,
+            x coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        y : float,
+            y coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        '''
+        x,y = coords[:]
+        inFlag = (((x-self.center_x)**2 + (y-self.center_y)**2) <= self.radius**2)
+        if self.loc.lower() == 'in':
+            return not inFlag
+        else:
+            return inFlag
+
+class Cylinder(BaseConstraint):
+    '''
+    General class for dealing with circular user defined constraints.
+    Plotting, computing constraints functionalities included.
+    '''
+    def __init__(self,center_x,center_y,center_z,radius,height,loc = 'in', **kwargs): ### We want to make default location as 'in'
+        super().__init__(**kwargs)
+        '''
+        Attributes
+        ----------
+        center_x : float,
+            x-coordinate of the center of circle
+        center_y : float,
+            y-coordinate of the center of circle
+        radius : float,
+            radius of the circle
+        loc : string- 'in'/'out',
+            specifying whether the inside or outside of the shape is constrained
+
+        Keyword Arguments
+        -----------------
+        X_axis : string,
+            Name of the column in dataframe to be plotted on the X axis.
+        Y-axis : string,
+            Name of the column in dataframe to be plotted on the Y axis.
+        Field : string,
+            Name of the column in dataframe to be plotted as a contour map.
+        data : pandas.DataFrame/np.darray [n_samples, n_features],
+            dataframe (used for scatter and contour plots) or matrix (used for images) containing measurement data
+        '''
+        self.center_x = center_x
+        self.center_y = center_y
+        self.center_z = center_z
+        self.radius = radius
+        self.height = height
+        self.loc = loc
+        if 'axis' in kwargs.keys():
+            self.axis = kwargs['axis']
+        else:
+            self.axis = 'Z_axis'
+
+    def draw(self,ax,**kwargs):
+        '''
+        Function to plot a cylinder based on user-defined coordinates
+        Attributes
+        ----------
+        ax : axis on which the constraint circle should be plotted
+        '''
+        if 'alpha' not in kwargs.keys():
+            kwargs['alpha'] = 0.3
+            alpha = 3 * kwargs['alpha']
+        if kwargs['alpha'] * 3 < 0.5:
+            alpha = 1.0
+        else:
+            alpha = kwargs['alpha'] * 3
+        if 'color' not in kwargs.keys():
+            kwargs['color'] = 'red'
+        theta = np.linspace(0, 2*np.pi, 100)
+        if self.axis == 'Z_axis':
+            z = np.linspace(self.center_z - self.height/2, self.center_z + self.height/2, 100)
+            theta, z = np.meshgrid(theta, z)
+            x = self.center_x + self.radius * np.cos(theta)
+            y = self.center_y + self.radius * np.sin(theta)
+        elif self.axis == 'X_axis':
+            x = np.linspace(self.center_x - self.height/2, self.center_x + self.height/2, 100)
+            theta, x = np.meshgrid(theta, x)
+            y = self.center_y + self.radius * np.sin(theta)
+            z = self.center_z + self.radius * np.cos(theta)
+        else:
+            y = np.linspace(self.center_y - self.height/2, self.center_y + self.height/2, 100)
+            theta, y = np.meshgrid(theta, y)
+            x = self.center_x + self.radius * np.cos(theta)
+            z = self.center_z + self.radius * np.sin(theta)
+        ax.plot_surface(x, y, z,alpha=alpha, color=kwargs['color'])
+        ax.autoscale_view()
+    def constraint_function(self, coords):
+        '''
+        Function to compute whether a certain point on the grid lies inside/outside the defined constrained region
+        Attributes
+        ----------
+        x : float,
+            x coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        y : float,
+            y coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        '''
+        x,y,z = coords[:]
+        if isinstance(x, float):
+            x,y,z = [x],[y],[z]
+        nPoints = np.shape(np.array(coords).reshape(3,-1))[1]
+        inFlag = np.zeros(nPoints,dtype=bool)
+        for i in range(nPoints):
+            if self.axis == 'Z_axis':
+                inFlag[i] = ((((x[i]-self.center_x)**2 + (y[i]-self.center_y)**2) <= self.radius**2) and self.center_z-self.height/2<=z[i] and z[i]<=self.center_z+self.height/2)
+            elif self.axis == 'Y_axis':
+                inFlag[i] = ((((x[i]-self.center_x)**2 + (z[i]-self.center_z)**2) <= self.radius**2) and self.center_y-self.height/2<=y[i] and y[i]<=self.center_y+self.height/2)
+            else:
+                inFlag[i] = ((((y[i]-self.center_y)**2 + (z[i]-self.center_z)**2) <= self.radius**2) and self.center_x-self.height/2<=x[i] and x[i]<=self.center_x+self.height/2)
+        if self.loc.lower() == 'in':
+            return map(operator.not_, inFlag)
+        else:
+            return inFlag
+class Line(BaseConstraint):
+    '''
+    General class for dealing with linear user defined constraints.
+    Plotting, computing constraints functionalities included.
+    '''
+    def __init__(self,x1,x2,y1,y2,**kwargs):
+        super().__init__(**kwargs)
+        '''
+        Attributes
+        ----------
+        x1 : float,
+            x-coordinate of one end-point of the line
+        x2 : float,
+            x-coordinate of the other end-point of the line
+        y1 : float,
+            y-coordinate of one end-point of the line
+        y2 : float,
+            y-coordinate of the other end-point of the line
+
+        Keyword Arguments
+        -----------------
+        X_axis : string,
+            Name of the column in dataframe to be plotted on the X axis.
+        Y-axis : string,
+            Name of the column in dataframe to be plotted on the Y axis.
+        Field : string,
+            Name of the column in dataframe to be plotted as a contour map.
+        data : pandas.DataFrame/np.darray [n_samples, n_features],
+            dataframe (used for scatter and contour plots) or matrix (used for images) containing measurement data
+        '''
+        self.x1 = x1
+        self.x2 = x2
+        self.y1 = y1
+        self.y2 = y2
+
+    def draw(self,ax,**kwargs):
+        '''
+        Function to plot a line based on user-defined coordinates
+        Attributes
+        ----------
+        ax : axis on which the constraint line should be plotted
+        '''
+        if 'color' not in kwargs.keys():
+            kwargs['color'] = 'r'
+        if 'lw' not in kwargs.keys():
+             kwargs['lw'] = 2
+        if 'alpha' not in kwargs.keys():
+             kwargs['alpha'] = 1.0
+        if 'marker' not in kwargs.keys():
+             kwargs['marker'] = None
+        if 'linestyle' not in kwargs.keys():
+             kwargs['linestyle'] = '-'
+        ax.plot([self.x1,self.x2], [self.y1,self.y2], color=kwargs['color'], alpha=kwargs['alpha'], marker=kwargs['marker'], linestyle=kwargs['linestyle'])
+
+    def constraint_function(self,coords):
+        '''
+        Function to compute whether a certain point on the grid lies inside/outside the defined constrained region
+        Attributes
+        ----------
+        x : float,
+            x coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        y : float,
+            y coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        '''
+        x,y = coords[:]
+        return (y-self.y1)*(self.x2-self.x1) - (self.y2-self.y1)*(x-self.x1) >= 0
+
+class Parabola(BaseConstraint):
+    '''
+    General class for dealing with parabolic user defined constraints.
+    Plotting, computing constraints functionalities included.
+    '''
+    def __init__(self,h,k,a,loc, **kwargs):
+        super().__init__(**kwargs)
+        '''
+        Attributes
+        ----------
+        h : float,
+            x-coordinate of the vertex of the parabola we want to be constrained
+        k : float,
+            y-coordinate of the vertex of the parabola we want to be constrained
+        a : float,
+            x-coordinate of the focus of the parabola
+        loc : string- 'in'/'out',
+            specifying whether the inside or outside of the shape is constrained
+
+        Keyword Arguments
+        -----------------
+        X_axis : string,
+            Name of the column in dataframe to be plotted on the X axis.
+        Y-axis : string,
+            Name of the column in dataframe to be plotted on the Y axis.
+        Field : string,
+            Name of the column in dataframe to be plotted as a contour map.
+        data : pandas.DataFrame/np.darray [n_samples, n_features],
+            dataframe (used for scatter and contour plots) or matrix (used for images) containing measurement data
+        '''
+        self.h = h
+        self.k = k
+        self.a = a
+        self.loc = loc
+
+    def draw(self,ax,**kwargs):
+        '''
+        Function to plot a parabola based on user-defined coordinates
+        Attributes
+        ----------
+        ax : axis on which the constraint parabola should be plotted
+        '''
+        if isinstance(self.data,np.ndarray):
+            grid_points = np.arange(self.data.shape[1])
+            x, y = get_coordinates_from_indices(grid_points,self.data)
+        elif isinstance(self.data, pd.DataFrame):
+            grid_points = np.arange(len(self.data))
+            x, y = get_coordinates_from_indices(grid_points,self.data, Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+        y_vals = (self.a*((x-self.h)**2)) - self.k
+        ax.scatter(x,y_vals,s=1)
+
+    def constraint_function(self,coords):
+        '''
+        Function to compute whether a certain point on the grid lies inside/outside the defined constrained region
+        Attributes
+        ----------
+        x : float,
+            x coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        y : float,
+            y coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        '''
+        x, y = coords[:]
+        inFlag = (self.a*(x-self.h)**2) <= (y-self.k)
+        if self.loc.lower() == 'in':
+            return not inFlag
+        else:
+            return inFlag
+
+class Ellipse(BaseConstraint):
+    '''
+    General class for dealing with elliptical user defined constraints.
+    Plotting, computing constraints functionalities included.
+    '''
+    def __init__(self,center_x,center_y,width, height, angle = 0.0, loc = 'in', **kwargs):
+        super().__init__(**kwargs)
+        '''
+        Attributes
+        ----------
+        center_x : float,
+            x-coordinate of the center of circle
+        center_y : float,
+            y-coordinate of the center of circle
+        width : float,
+            total length (diameter) of horizontal axis.
+        height : float,
+            total length (diameter) of vertical axis.
+        angle : float,
+            angle of the orientation of the ellipse in degrees
+        loc : string- 'in'/'out',
+            specifying whether the inside or outside of the shape is constrained
+
+        Keyword Arguments
+        -----------------
+        X_axis : string,
+            Name of the column in dataframe to be plotted on the X axis.
+        Y-axis : string,
+            Name of the column in dataframe to be plotted on the Y axis.
+        Field : string,
+            Name of the column in dataframe to be plotted as a contour map.
+        data : pandas.DataFrame/np.darray [n_samples, n_features],
+            dataframe (used for scatter and contour plots) or matrix (used for images) containing measurement data
+        '''
+        self.center_x = center_x
+        self.center_y = center_y
+        self.width = width
+        self.height = height
+        self.loc = loc
+        self.angle = angle
+        self.half_horizontal_axis = self.width / 2
+        self.half_vertical_axis = self.height / 2
+    def draw(self,ax,**kwargs):
+        '''
+        Function to plot an ellipse based on user-defined coordinates
+        Attributes
+        ----------
+        ax : axis on which the constraint ellipse should be plotted
+        '''
+        if 'fill' not in kwargs.keys():
+             kwargs['fill'] = False
+        if 'color' not in kwargs.keys():
+            kwargs['color'] = 'r'
+        if 'lw' not in kwargs.keys():
+             kwargs['lw'] = 2
+        if 'alpha' not in kwargs.keys():
+             kwargs['alpha'] = 1.0
+        c = patches.Ellipse((self.center_x, self.center_y), self.width, self.height, angle = self.angle, fill=kwargs['fill'], color=kwargs['color'],  lw=kwargs['lw'], alpha=kwargs['alpha'])
+        ax.add_patch(c)
+        ax.autoscale_view()
+        # ax.axes.set_aspect('equal')
+
+    def constraint_function(self,coords):
+        '''
+        Function to compute whether a certain point on the grid lies inside/outside the defined constrained region
+        Attributes
+        ----------
+        x : float,
+            x coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        y : float,
+            y coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        '''
+        x, y =coords[:]
+        angleInRadians = self.angle * np.pi/180
+        u = (x - self.center_x) * np.cos(angleInRadians) + (y - self.center_y) * np.sin(angleInRadians)
+        v = -(x - self.center_x) * np.sin(angleInRadians) + (y - self.center_y) * np.cos(angleInRadians)
+        inFlag = u**2/self.half_horizontal_axis**2 + v**2/self.half_vertical_axis**2 <= 1
+        if self.loc.lower() == 'in':
+            return not inFlag
+        elif self.loc.lower() == 'out':
+            return inFlag
+
+class Polygon(BaseConstraint): ### Based on previous discussion we are re-thinking this part (Fill up with Mohammad's implementation of the Polygon)
+    '''
+    General class for dealing with polygonal user defined constraints.
+    Plotting, computing constraints functionalities included.
+    '''
+    def __init__(self,xy_coords,loc='in', **kwargs):
+        super().__init__(**kwargs)
+        '''
+        Attributes
+        ----------
+        xy_coords : (N,2) array_like,
+            an array consisting of tuples for (x,y) coordinates of points of the Polygon where N = No. of sides of the polygon
+        '''
+        self.xy_coords = xy_coords
+        self.loc = loc
+
+    def draw(self,ax,**kwargs):
+        '''
+        Function to plot a polygon based on user-defined coordinates
+        Attributes
+        ----------
+        ax : axis on which the constraint polygon should be plotted
+        '''
+        if 'fill' not in kwargs.keys():
+             kwargs['fill'] = False
+        if 'color' not in kwargs.keys():
+            kwargs['color'] = 'r'
+        if 'lw' not in kwargs.keys():
+             kwargs['lw'] = 2
+        if 'alpha' not in kwargs.keys():
+             kwargs['alpha'] = 1.0
+        c = patches.Polygon(self.xy_coords, fill=kwargs['fill'], color=kwargs['color'], lw=kwargs['lw'], alpha=kwargs['alpha'])
+        ax.add_patch(c)
+        ax.autoscale_view()
+
+
+    def constraint_function(self,coords):
+        '''
+        Function to compute whether a certain point on the grid lies inside/outside the defined constrained region
+        Attributes
+        ----------
+        x : float,
+            x coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        y : float,
+            y coordinate of point on the grid being evaluated to check whether it lies inside or outside the constrained region
+        '''
+        x,y = coords[:]
+        # define point in polygon
+        polygon =self.xy_coords
+        n = len(polygon)
+        inFlag = False
+
+        for i in range(n):
+            x1, y1 = polygon[i]
+            x2, y2 = polygon[(i + 1) % n]
+
+            if (y1 < y and y2 >= y) or (y2 < y and y1 >= y):
+                if x1 + (y - y1) / (y2 - y1) * (x2 - x1) < x:
+                    inFlag = not inFlag
+
+        if self.loc.lower() == 'in':
+            return not inFlag
+        elif self.loc.lower() == 'out':
+            return inFlag
+
+class UserDefinedConstraints(BaseConstraint):
+    '''
+    General class for dealing with any form of user defined constraints.
+    The user can input the constraint in two forms:
+    - As a python file which has the equation of the constraint the user wants to implement.
+    - As a string with just the equation of the constraint the user wants to implement.
+    Plotting, computing constraints functionalities included.
+    '''
+    def __init__(self,all_sensors, **kwargs):
+        super().__init__(**kwargs)
+        '''
+        Attributes
+        ----------
+        all_sensors : np.darray,
+            A ranked list of all sensor indices computed from just QR optimizer
+
+        Keyword Arguments
+        -----------------
+        file : string,
+            Name of the python file containing the equation of the constraint
+        equation : string,
+            Equation of the constraint the user wants to implement
+        X_axis : string,
+            Name of the column in dataframe to be plotted on the X axis.
+        Y-axis : string,
+            Name of the column in dataframe to be plotted on the Y axis.
+        Field : string,
+            Name of the column in dataframe to be plotted as a contour map.
+        data : pandas.DataFrame/np.darray [n_samples, n_features],
+            dataframe (used for scatter and contour plots) or matrix (used for images) containing measurement data
+        '''
+        self.all_sensors = all_sensors
+
+        if 'file' in kwargs.keys():
+            self.file = kwargs['file']
+            self.functions = load_functional_constraints(self.file)
+        else:
+            self.file = None
+        if 'equation' in kwargs.keys():
+            self.equations = [kwargs['equation']]
+        else:
+            self.equations = None
+        if self.equations is None and self.file is None:
+            raise Exception('either file or equation should be provided')
+
+        if isinstance(self.data,pd.DataFrame):
+            if 'X_axis' in kwargs.keys():
+                self.X_axis = kwargs['X_axis']
+            else:
+                raise Exception('Must provide X_axis as **kwargs as your data is a dataframe')
+            if 'Y_axis' in kwargs.keys():
+                self.Y_axis = kwargs['Y_axis']
+            else:
+                raise Exception('Must provide Y_axis as **kwargs as your data is a dataframe')
+            if 'Field' in kwargs.keys():
+                self.Field = kwargs['Field']
+            else:
+                raise Exception('Must provide either a python file containing the constraint or an equation of the constraint')
+
+    def draw(self,ax,**kwargs):
+        '''
+        Function to plot the user-defined constraint
+        Attributes
+        ----------
+        ax : axis on which the constraint should be plotted
+        '''
+        if self.file != None :
+            nConstraints = len([self.functions])
+            G = np.zeros((len(self.all_sensors),nConstraints),dtype=bool)
+            for i in range(nConstraints):
+                if isinstance(self.data,np.ndarray):
+                    temp = BaseConstraint.functional_constraints(self.functions,self.all_sensors,self.data)
+                    G[:,i] = [x > 0 for x in temp]
+                    idx_const, rank = BaseConstraint.get_functionalConstraind_sensors_indices(self.all_sensors,G[:,i])
+                    x_val,y_val = get_coordinates_from_indices(idx_const,self.data)
+                elif isinstance(self.data,pd.DataFrame):
+                    temp = BaseConstraint.functional_constraints(self.functions,self.all_sensors,self.data, X_axis = self.X_axis, Y_axis = self.Y_axis, Field = self.Field)
+                    G[:,i] = [x == 0 for x in temp]
+                    idx_const, rank = BaseConstraint.get_functionalConstraind_sensors_indices(self.all_sensors,G[:,i])
+                    x_val,y_val = get_coordinates_from_indices(idx_const,self.data, Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+        elif self.equations is not None:
+            nConstraints = len(self.equations)
+            G = np.zeros((len(self.all_sensors),nConstraints),dtype=bool)
+            for i in range(nConstraints):
+                if isinstance(self.data,np.ndarray):
+                    xValue,yValue = get_coordinates_from_indices(self.all_sensors,self.data)
+                    for k in range(len(xValue)):
+                        G[k,i] = eval(self.equations[i], {"x":xValue[k],"y":yValue[k]})
+                    idx_const, rank = BaseConstraint.get_functionalConstraind_sensors_indices(self.all_sensors,G[:,i])
+                    x_val,y_val = get_coordinates_from_indices(idx_const,self.data)
+                elif isinstance(self.data,pd.DataFrame):
+                    xValue,yValue = get_coordinates_from_indices(self.all_sensors,self.data,Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+                    for k in range(len(xValue)):
+                        G[k,i] = not eval(self.equations[i], {"x":xValue[k],"y":yValue[k]})
+                    idx_const, rank = BaseConstraint.get_functionalConstraind_sensors_indices(self.all_sensors,G[:,i])
+                    x_val,y_val = get_coordinates_from_indices(idx_const,self.data, Y_axis = self.Y_axis, X_axis = self.X_axis, Field = self.Field)
+        ax.scatter(x_val,y_val,s = 1)
+
+    def constraint(self):
+        '''
+        Function to compute whether a certain point on the grid lies inside/outside the defined constrained region
+        '''
+        if self.file != None :
+            nConstraints = len([self.functions])
+            G = np.zeros((len(self.all_sensors),nConstraints),dtype=bool)
+            for i in range(nConstraints):
+                if isinstance(self.data,np.ndarray):
+                    temp = BaseConstraint.functional_constraints(self.functions,self.all_sensors,self.data)
+                    G[:,i] = [x>=0 for x in temp]
+                elif isinstance(self.data,pd.DataFrame):
+                    temp = BaseConstraint.functional_constraints(self.functions,self.all_sensors,self.data, X_axis = self.X_axis, Y_axis = self.Y_axis, Field = self.Field)
+                    G[:,i] = [x>=0 for x in temp]
+        else:
+            G = np.zeros((len(self.all_sensors),1),dtype=bool)
+            if isinstance(self.data,np.ndarray):
+                xValue,yValue = get_coordinates_from_indices(self.all_sensors,self.data)
+                for k in range(len(xValue)):
+                    G[k,0] = not eval(self.equations[0], {"x":xValue[k],"y":yValue[k]})
+            elif isinstance(self.data,pd.DataFrame):
+                xValue,yValue = get_coordinates_from_indices(self.all_sensors,self.data,X_axis = self.X_axis, Y_axis = self.Y_axis, Field = self.Field)
+                for k in range(len(xValue)):
+                    G[k,0] = not eval(self.equations[0], {"x":xValue[k],"y":yValue[k]})
+        idx_const, rank = BaseConstraint.get_functionalConstraind_sensors_indices(self.all_sensors,G[:,0])
+        return idx_const,rank
\ No newline at end of file
diff --git a/pysensors/utils/_norm_calc.py b/pysensors/utils/_norm_calc.py
index f4c5213..66df6eb 100644
--- a/pysensors/utils/_norm_calc.py
+++ b/pysensors/utils/_norm_calc.py
@@ -16,15 +16,15 @@ def exact_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): ##Will first for
     Parameters
     ----------
     lin_idx: np.ndarray, shape [No. of constrained locations]
-        Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix.
-    dlens: np.ndarray, shape [Variable based on j]
+        Array which contains the constrained locations of the grid in terms of column indices of basis_matrix.
+    dlens: np.ndarray, shape [n_features - j]
         Array which contains the norm of columns of basis matrix.
     piv: np.ndarray, shape [n_features]
         Ranked list of sensor locations.
     n_const_sensors: int,
         Number of sensors to be placed in the constrained area.
     j: int,
-        Iterative variable in the QR algorithm.
+        current sensor to be placed in the QR/GQR algorithm.
 
     Returns
     -------
@@ -34,20 +34,21 @@ def exact_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs): ##Will first for
         all_sensors = kwargs['all_sensors']
     else:
         all_sensors = []
-    if 'n_sensors' in kwargs.keys():
+    if 'n_sensors' in kwargs.keys() and kwargs['n_sensors'] not in [None,0]:
         n_sensors = kwargs['n_sensors']
     else:
         n_sensors = len(all_sensors)
-    for i in range(n_sensors):
-        if np.isin(all_sensors[:n_sensors],lin_idx,invert=False).sum() < n_const_sensors:
-            if n_sensors >= j > (n_sensors - (n_const_sensors-1)):
-                didx = np.isin(piv[j:],lin_idx,invert=True)
-                dlens[didx] = 0
-        else:
-            max_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs)
+    count = np.count_nonzero(np.isin(all_sensors[:j],lin_idx,invert=False))
+    # for i in range(n_sensors):
+    # if the number of constrained sensors in the top sensors is less than the number of n_const_sensors
+    if np.isin(all_sensors[:n_sensors],lin_idx,invert=False).sum() < n_const_sensors:
+        if n_sensors > j >= (n_sensors - (n_const_sensors - count)):
+            didx = np.isin(piv[j:],lin_idx,invert=True)
+            dlens[didx] = 0
+    else:
+        dlens = max_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs)
     return(dlens)
 
-
 def max_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs):
     """
     Function for mapping constrained sensor locations with the QR procedure (Optimally).
@@ -77,20 +78,22 @@ def max_n(lin_idx, dlens, piv, j, n_const_sensors, **kwargs):
         all_sensors = kwargs['all_sensors']
     else:
         all_sensors = []
-    if 'n_sensors' in kwargs.keys():
+    if 'n_sensors' in kwargs.keys() and kwargs['n_sensors'] not in [None,0]:
         n_sensors = kwargs['n_sensors']
     else:
         n_sensors = len(all_sensors)
     counter = 0
+    # create a mask for constrained sensors in all sensors
+    # i.e., (mask[all_sensors[i]] == True means that sensor i is unconstrained
+    # and vise versa)
     mask = np.isin(all_sensors,lin_idx,invert=False)
+    # indices of all constrained sensors
     const_idx = all_sensors[mask]
     updated_lin_idx = const_idx[n_const_sensors:]
     for i in range(n_sensors):
         if np.isin(all_sensors[i],lin_idx,invert=False):
             counter += 1
-            if counter < n_const_sensors:
-                dlens = dlens
-            else:
+            if counter > n_const_sensors:
                 didx = np.isin(piv[j:],updated_lin_idx,invert=False)
                 dlens[didx] = 0
     return dlens
@@ -140,5 +143,5 @@ def returnInstance(cls, name):
     __norm_calc_type[name], instance of class
   """
   if name not in __norm_calc_type:
-    raise NotImplementedError("{} NOT IMPLEMENTED!!!!!".format(name))
+    raise NotImplementedError("{} NOT IMPLEMENTED!!!!!\n".format(name))
   return __norm_calc_type[name]
\ No newline at end of file
diff --git a/pysensors/utils/_validation.py b/pysensors/utils/_validation.py
index 4e68948..bf2db21 100644
--- a/pysensors/utils/_validation.py
+++ b/pysensors/utils/_validation.py
@@ -2,7 +2,7 @@
 Various utility functions for validation and computing reconstruction scores and errors.
 """
 import numpy as np
-from scipy.sparse import csr_matrix
+from scipy.sparse import lil_matrix
 
 def determinant(top_sensors, n_features, basis_matrix):
     """
@@ -24,14 +24,20 @@ def determinant(top_sensors, n_features, basis_matrix):
 
     p = len(top_sensors) # Number of sensors
     n,r = np.shape(basis_matrix) # state dimension X Number of modes
-    c = csr_matrix((p,n),dtype=np.int8)
+    c = lil_matrix((p,n),dtype=np.int8)
 
     for i in range(p):
         c[i,top_sensors[i]] = 1
     phi = basis_matrix
-    # optimality = np.linalg.det(( c @ phi).T @ (c@phi)) #np.log(np.linalg.det(phi.T @ c.T)) np.log(np.linalg.det((c@phi).T @ (c@phi)))
-    optimality = abs(np.linalg.det(c @ phi)) if p==r else abs(np.linalg.det(( c @ phi).T @ (c @ phi)))
-    # optimality = abs(np.linalg.det(c @ phi))
+    theta = c @ phi
+    if p==r:
+        M_gamma = theta
+    elif p > r:
+        M_gamma = theta.T @ theta
+    else:# TODO
+        # raise an error that p cannot be less than r
+        pass
+    optimality = abs(np.linalg.det(M_gamma))
     return optimality
 
 def relative_reconstruction_error(data, prediction):
@@ -50,4 +56,4 @@ def relative_reconstruction_error(data, prediction):
             The relative error calculated.
     """
     error_val = (np.linalg.norm((data - prediction)/np.linalg.norm(data)))*100
-    return (error_val)
\ No newline at end of file
+    return (error_val)
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 1e6124b..e3cfa89 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -1,7 +1,7 @@
 -e .
 -r requirements.txt
 -r requirements-examples.txt
-pytest
+pytest < 8
 pytest-cov
 pytest-lazy-fixture
 flake8-builtins-unleashed
@@ -12,4 +12,4 @@ sphinx >= 2
 sphinxcontrib-apidoc
 sphinx_rtd_theme
 pre-commit
-sphinx-nbexamples
\ No newline at end of file
+sphinx-nbexamples
diff --git a/tests/classification/test_sspoc.py b/tests/classification/test_sspoc.py
index 7d500f8..a077397 100644
--- a/tests/classification/test_sspoc.py
+++ b/tests/classification/test_sspoc.py
@@ -10,7 +10,7 @@
 from pysensors.basis import RandomProjection
 from pysensors.basis import SVD
 from pysensors.classification import SSPOC
-
+from pytest_lazyfixture import lazy_fixture
 
 SEED = 15
 
@@ -56,8 +56,8 @@ def test_prefit_basis(data_binary_classification):
 @pytest.mark.parametrize(
     "data",
     [
-        pytest.lazy_fixture("data_binary_classification"),
-        pytest.lazy_fixture("data_multiclass_classification"),
+        lazy_fixture("data_binary_classification"),
+        lazy_fixture("data_multiclass_classification"),
     ],
 )
 def test_initialize_with_n_sensors(data):
@@ -72,8 +72,8 @@ def test_initialize_with_n_sensors(data):
 @pytest.mark.parametrize(
     "data",
     [
-        pytest.lazy_fixture("data_binary_classification"),
-        pytest.lazy_fixture("data_multiclass_classification"),
+        lazy_fixture("data_binary_classification"),
+        lazy_fixture("data_multiclass_classification"),
     ],
 )
 def test_initialize_with_threshold(data):
@@ -89,8 +89,8 @@ def test_initialize_with_threshold(data):
 @pytest.mark.parametrize(
     "data",
     [
-        pytest.lazy_fixture("data_binary_classification"),
-        pytest.lazy_fixture("data_multiclass_classification"),
+        lazy_fixture("data_binary_classification"),
+        lazy_fixture("data_multiclass_classification"),
     ],
 )
 def test_update_n_sensors(data, n_sensors):
@@ -105,8 +105,8 @@ def test_update_n_sensors(data, n_sensors):
 @pytest.mark.parametrize(
     "data",
     [
-        pytest.lazy_fixture("data_binary_classification"),
-        pytest.lazy_fixture("data_multiclass_classification"),
+        lazy_fixture("data_binary_classification"),
+        lazy_fixture("data_multiclass_classification"),
     ],
 )
 def test_update_threshold(data):
@@ -123,8 +123,8 @@ def test_update_threshold(data):
 @pytest.mark.parametrize(
     "data",
     [
-        pytest.lazy_fixture("data_binary_classification"),
-        pytest.lazy_fixture("data_multiclass_classification"),
+        lazy_fixture("data_binary_classification"),
+        lazy_fixture("data_multiclass_classification"),
     ],
 )
 def test_large_threshold(data):
@@ -147,8 +147,8 @@ def test_bad_update_sensors_input(data_binary_classification):
 @pytest.mark.parametrize(
     "data, baseline_accuracy",
     [
-        (pytest.lazy_fixture("data_binary_classification"), 0.55),
-        (pytest.lazy_fixture("data_multiclass_classification"), 0.25),
+        (lazy_fixture("data_binary_classification"), 0.55),
+        (lazy_fixture("data_multiclass_classification"), 0.25),
     ],
 )
 def test_predict_accuracy(data, baseline_accuracy):
@@ -164,8 +164,8 @@ def test_predict_accuracy(data, baseline_accuracy):
 @pytest.mark.parametrize(
     "data",
     [
-        pytest.lazy_fixture("data_binary_classification"),
-        pytest.lazy_fixture("data_multiclass_classification"),
+        lazy_fixture("data_binary_classification"),
+        lazy_fixture("data_multiclass_classification"),
     ],
 )
 def test_dummy_predict(data):
@@ -184,8 +184,8 @@ def test_dummy_predict(data):
 @pytest.mark.parametrize(
     "data",
     [
-        pytest.lazy_fixture("data_binary_classification"),
-        pytest.lazy_fixture("data_multiclass_classification"),
+        lazy_fixture("data_binary_classification"),
+        lazy_fixture("data_multiclass_classification"),
     ],
 )
 @pytest.mark.parametrize(
@@ -202,8 +202,8 @@ def test_basis_integration(basis, data):
 @pytest.mark.parametrize(
     "data, shape",
     [
-        (pytest.lazy_fixture("data_binary_classification"), (20,)),
-        (pytest.lazy_fixture("data_multiclass_classification"), (20, 5)),
+        (lazy_fixture("data_binary_classification"), (20,)),
+        (lazy_fixture("data_multiclass_classification"), (20, 5)),
     ],
 )
 def test_coefficient_shape(data, shape):
diff --git a/tests/optimizers/test_optimizers.py b/tests/optimizers/test_optimizers.py
index ea9a041..aad584c 100644
--- a/tests/optimizers/test_optimizers.py
+++ b/tests/optimizers/test_optimizers.py
@@ -74,7 +74,9 @@ def test_gqr_ccqr_equivalence(data_random):
     assert chosen_sensors_CCQR.isdisjoint(set(forbidden_sensors))
 
     # Get ranked sensors from GQR
-    sensors_GQR = GQR().fit(x.T, idx_constrained=forbidden_sensors,n_const_sensors=0, constraint_option='exact_n_const_sensors').get_sensors()
+    # first we should pass all_sensors to GQR
+    all_sensors = np.arange(x.shape[1]) #QR().fit(x.T).get_sensors()
+    sensors_GQR = GQR().fit(x.T, all_sensors=all_sensors, idx_constrained=forbidden_sensors,n_const_sensors=0, constraint_option='exact_n').get_sensors()
 
     # Forbidden sensors should not be included
     chosen_sensors_GQR = set(sensors_GQR[: (x.shape[1] - len(forbidden_sensors))])
@@ -83,32 +85,32 @@ def test_gqr_ccqr_equivalence(data_random):
 
 
 def test_gqr_exact_constrainted_case1(data_random):
-    ## In this case we want to place a total of 10 sensors
-    # with a constrained region that is allowed to have exactly 3 sensors
-    # but 4 of the first 10 are in the constrained region
+    ## In this case we want to place a total of 19 sensors
+    # with a constrained region that is allowed to have EXACTLY 2 sensors
+    # but 3 of the sensors are in the constrained region
     x = data_random
     # unconstrained sensors (optimal)
     sensors_QR = QR().fit(x.T).get_sensors()
     # exact number of sensors allowed in the constrained region
-    total_sensors = 10
-    exact_n_const_sensors = 3
-    forbidden_sensors = [8,5,2,6]
+    total_sensors = 19
+    exact_n_const_sensors = 2
+    forbidden_sensors = list(sensors_QR[[7,11,-1]])
     totally_forbidden_sensors = [x for x in forbidden_sensors if x in sensors_QR][:exact_n_const_sensors]
     totally_forbidden_sensors = [y for y in forbidden_sensors if y not in totally_forbidden_sensors]
     costs = np.zeros(x.shape[1])
     costs[totally_forbidden_sensors] = 100
     # Get ranked sensors
-    sensors = CCQR(sensor_costs=costs).fit(x.T).get_sensors()[:total_sensors]
+    sensors_CCQR = CCQR(sensor_costs=costs).fit(x.T).get_sensors()[:total_sensors]
 
     # Forbidden sensors should not be included
-    chosen_sensors = set(sensors[: (x.shape[1] - len(totally_forbidden_sensors))])
-    assert chosen_sensors.isdisjoint(set(totally_forbidden_sensors))
+    assert set(sensors_CCQR).isdisjoint(set(totally_forbidden_sensors))
+
 
 
     # Get ranked sensors from GQR
-    sensors_GQR = GQR().fit(x.T, idx_constrained=forbidden_sensors,n_sensors=total_sensors,n_const_sensors=exact_n_const_sensors, constraint_option='exact_n_const_sensors').get_sensors()[:total_sensors]
+    sensors_GQR = GQR().fit(x.T, idx_constrained=forbidden_sensors,all_sensors=sensors_QR, n_sensors=total_sensors,n_const_sensors=exact_n_const_sensors, constraint_option='exact_n').get_sensors()[:total_sensors]
+    assert sensors_CCQR.all() == sensors_GQR.all()
 
-    # try to compare these using the validation metrics
 
 ## TODO
 def test_gqr_max_constrained():
diff --git a/tests/reconstruction/test_sspor.py b/tests/reconstruction/test_sspor.py
index 147895e..d346a39 100644
--- a/tests/reconstruction/test_sspor.py
+++ b/tests/reconstruction/test_sspor.py
@@ -26,6 +26,7 @@
 from pysensors.basis import SVD
 from pysensors.optimizers import CCQR
 from pysensors.reconstruction import SSPOR
+from pytest_lazyfixture import lazy_fixture
 
 
 def test_not_fitted(data_vandermonde):
@@ -69,7 +70,7 @@ def test_set_number_of_sensors(data_vandermonde):
 
 @pytest.mark.parametrize(
     "data",
-    [pytest.lazy_fixture("data_vandermonde"), pytest.lazy_fixture("data_random")],
+    [lazy_fixture("data_vandermonde"), lazy_fixture("data_random")],
 )
 def test_get_all_sensors(data):
     x = data
diff --git a/tests/utils/test_constraints.py b/tests/utils/test_constraints.py
new file mode 100644
index 0000000..93329c8
--- /dev/null
+++ b/tests/utils/test_constraints.py
@@ -0,0 +1,289 @@
+# TODO: include some unit tests once there are more functions
+# in this submodule
+import numpy as np
+import pytest
+import pandas as pd
+from pysensors.utils._constraints import get_constrained_sensors_indices
+from pysensors.utils._constraints import get_constrained_sensors_indices_dataframe
+from pysensors.utils._constraints import load_functional_constraints
+# from pysensors.utils._constraints import constraints_eval
+# from pysensors.utils._constraints import check_constraints
+from pysensors.utils._constraints import order_constrained_sensors
+from pysensors.utils._constraints import get_coordinates_from_indices
+from pysensors.utils._constraints import get_indices_from_coordinates
+# from pysensors.utils._constraints import BaseConstraint
+
+## Testing get_constrained_sensors_indices
+def test_get_constrained_sensors_indices_empty_array():
+    all_sensors = np.array([])
+    x_min, x_max, y_min, y_max, nx, ny = 0, 10, 0, 10, 10, 10
+    with pytest.raises(ValueError):
+        result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+
+def test_get_constrained_sensors_indices_non_integer_values():
+    all_sensors = np.array([[1.5, 2.5], [3.5, 4.5], [5.5, 6.5]])
+    x_min, x_max, y_min, y_max, nx, ny = 2, 4, 3, 5, 10, 10
+    with pytest.raises(ValueError):
+        result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+
+def test_get_constrained_sensors_indices_no_constrained_sensors():
+    all_sensors = np.array([[1, 2], [3, 4], [5, 6]])
+    x_min, x_max, y_min, y_max, nx, ny = 6, 8, 9, 11, 10, 10
+    with pytest.raises(ValueError):
+        result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+
+def test_get_constrained_sensors_indices_single_constrained_sensor():
+    x_min, x_max, y_min, y_max, nx, ny = 3, 4, 3, 4, 10, 10
+    all_sensors = np.array([i+101 for i in range(nx*ny)])
+    with pytest.raises(ValueError):
+        result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+
+def test_get_constrained_sensors_indices_multiple_constrained_sensors():
+    all_sensors = np.array([[1.5, 2.5], [3.5, 4.5], [5.5, 6.5], [7.5, 8.5], [9.5, 10.5]])
+    x_min, x_max, y_min, y_max, nx, ny = 3, 7, 3, 7, 10, 10
+    with pytest.raises(ValueError):
+        result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+
+def test_valid_input_parameters():
+    nx, ny, x_min, x_max, y_min, y_max  = 10, 10, 2, 8, 2, 8
+    all_sensors = np.array([i for i in range(nx*ny)])
+    result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+    assert len(result) == (x_max - x_min + 1) * (y_max - y_min + 1)
+
+def test_one_constrained_sensor():
+    nx, ny, x_min, x_max, y_min, y_max  = 10, 10, 8, 9, 8, 9
+    all_sensors = np.array([i for i in range(nx*ny)])
+    result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+    assert len(result) == 4
+
+def test_invalid_nx_not_integer():
+    nx, ny, x_min, x_max, y_min, y_max = 'ten', 10, 2, 8, 2, 8
+    all_sensors = np.array([i for i in range(ny**2)])
+    with pytest.raises(ValueError):
+        result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+
+def test_invalid_ny_not_integer():
+    nx, ny, x_min, x_max, y_min, y_max = 10, 'ten', 2, 8, 2, 8
+    all_sensors = np.array([i for i in range(nx**2)])
+    with pytest.raises(ValueError):
+        result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+
+def test_invalid_x_min_greater_than_x_max():
+    nx, ny, x_min, x_max, y_min, y_max = 10, 10, 8, 2, 2, 8
+    all_sensors = np.array([i for i in range(nx*ny)])
+    with pytest.raises(ValueError):
+        result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+
+def test_invalid_y_min_greater_than_y_max():
+    nx, ny, x_min, x_max, y_min, y_max = 10, 10, 2, 8, 8, 2
+    all_sensors = np.array([i for i in range(nx*ny)])
+    with pytest.raises(ValueError):
+        result = get_constrained_sensors_indices(x_min, x_max, y_min, y_max, nx, ny, all_sensors)
+
+## Testing get_constrained_sensors_indices_dataframe
+def test_get_constrained_sensors_indices_dataframe_does_not_modify_input_dataframe():
+    seed = 8051977
+    # Create a test dataframe
+    test_dataframe = pd.DataFrame({
+        'x': np.random.randint(0, 100, size=100),
+        'y': np.random.randint(0, 100, size=100),
+        'Field': np.random.randint(0, 100, size=100)
+    })
+    df = test_dataframe.copy()
+    # Define test parameters
+    x_min, x_max, y_min, y_max = 50, 75, 25, 50
+
+    # Call the function
+    idx_constrained = get_constrained_sensors_indices_dataframe(x_min, x_max, y_min, y_max, test_dataframe, X_axis='x', Y_axis='y')
+
+    # Assert that the input dataframe is not modified
+    assert test_dataframe.equals(df)
+
+def test_get_constrained_sensors_indices_dataframe():
+    """
+    Test that the function handles normal constraint.
+    """
+    x_min, x_max, y_min, y_max = 10, 20, 10, 20
+    data = pd.DataFrame({'X_axis': [10, 20, 8, 15, 25], 'Y_axis': [10, 32, 20, 18, 12]})
+    expected_output = [0, 3]
+    assert get_constrained_sensors_indices_dataframe(x_min, x_max, y_min, y_max, data, X_axis='X_axis', Y_axis='Y_axis') == expected_output
+
+def test_get_constrained_sensors_indices_dataframe_outside_dataframe_range():
+    """
+    Test that the function handles constraint outside the dataframe range.
+    """
+    x_min, x_max, y_min, y_max = 0, 5, 0, 5
+    data = pd.DataFrame({'X_axis': [10, 15, 20, 25], 'Y_axis': [10, 15, 20, 25]})
+    expected_output = []
+    assert get_constrained_sensors_indices_dataframe(x_min, x_max, y_min, y_max, data, X_axis='X_axis', Y_axis='Y_axis') == expected_output
+
+def test_get_constrained_sensors_indices_dataframe_overlapping_dataframe_range():
+    """
+    Test that the function handles constraint overlapping the dataframe range.
+    """
+    x_min, x_max, y_min, y_max = 15, 25, 15, 25
+    data = pd.DataFrame({'X_axis': [10, 15, 20, 25], 'Y_axis': [10, 15, 20, 25]})
+    expected_output = [1, 2]
+    assert get_constrained_sensors_indices_dataframe(x_min, x_max, y_min, y_max, data, X_axis='X_axis', Y_axis='Y_axis') == expected_output
+
+def test_get_constrained_sensors_indices_dataframe_empty_dataframe():
+    """
+    Test that the function handles empty dataframe.
+    """
+    empty_dataframe = pd.DataFrame({'X_axis': [], 'Y_axis': []})
+    expected_output = []
+    assert get_constrained_sensors_indices_dataframe(10, 20, 10, 20, empty_dataframe, X_axis='X_axis', Y_axis='Y_axis') == expected_output
+
+def test_get_constrained_sensors_indices_dataframe_dataframe_with_missing_values():
+    """
+    Test that the function handles dataframe with missing values.
+    """
+    dataframe_with_missing_values = pd.DataFrame({'X_axis': [10, 15, np.nan, 12], 'Y_axis': [10, 15, 20, 15]})
+    expected_output = [0, 1, 2]
+    assert get_constrained_sensors_indices_dataframe(10, 20, 10, 20, dataframe_with_missing_values, X_axis='X_axis', Y_axis='Y_axis') == expected_output
+
+## Testing order_constrained_sensors
+def test_order_constrained_sensors():
+    #Define constrained sensor locations
+    idx_constrained_list = [1, 2, 3, 4, 5]
+
+    # Define ranks of constrained sensor locations
+    ranks_list = [4, 2, 1, 3, 5]
+    sortedConstraints,ranks = order_constrained_sensors(idx_constrained_list,ranks_list)
+
+    # Check the results
+    assert np.array_equal(sortedConstraints, np.array([3, 2, 4, 1, 5])), "Ordering test failed for sortedConstraints"
+    assert np.array_equal(ranks, np.array([1,2,3,4,5])), "Ordering test failed for ranks"
+
+def test_order_constrained_sensors_with_reversed_ranks():
+    #Define constrained sensor locations
+    idx_constrained_list = np.array([1, 2, 3, 4, 5])
+    # Define ranks of constrained sensor locations
+    ranks_list = np.array([5, 4, 3, 2, 1])
+
+    # Call the function
+    sortedConstraints,ranks = order_constrained_sensors(idx_constrained_list,ranks_list)
+
+    # Check the results
+    assert np.array_equal(sortedConstraints, np.array([5, 4, 3, 2, 1])), "Ordering test failed for sortedConstraints with reversed ranks"
+    assert np.array_equal(ranks, np.array([1,2,3,4,5])), "Ordering test failed for ranks with reversed ranks"
+
+def test_order_constrained_sensors_with_empty_ranks_list():
+    # Define constrained sensor locations
+    idx_constrained_list = np.array([1, 2, 3, 4, 5])
+
+    # Define empty ranks of constrained sensor locations
+    ranks_list = []
+
+    # Call the function
+    sortedConstraints,ranks = order_constrained_sensors(idx_constrained_list,ranks_list)
+
+    # Check the results
+    assert len(sortedConstraints) == 0, "Empty ranks test failed for sortedConstraints"
+    assert len(ranks) == 0, "Empty ranks test failed for ranks"
+    
+def test_order_constrained_sensors_with_negative_ranks_list():
+    # Define constrained sensor locations
+    idx_constrained_list = np.array([1, 2, 3, 4, 5])
+
+    # Define empty ranks of constrained sensor locations
+    ranks_list = [-3,-2,-5,-1,0]
+
+    # Call the function
+    sortedConstraints,ranks = order_constrained_sensors(idx_constrained_list,ranks_list)
+
+    # Check the results
+    assert np.array_equal(sortedConstraints, np.array([3,1,2,4,5])), "Ordering test failed for sortedConstraints with reversed ranks"
+    assert np.array_equal(ranks, np.array([-5,-3,-2,-1,0])), "Ordering test failed for ranks with reversed ranks"
+
+## Testing get_coordinates_from_indices
+def test_get_coordinates_from_indices_with_numpy_array_info():
+    # Define sensor IDs
+    idx = np.array([1, 2, 3, 4])
+
+    # Define information
+    info = pd.DataFrame({
+        'X_axis': [1, 2, 3, 4, 5],
+        'Y_axis': [10, 20, 30, 40, 50]
+    })
+    # Call the function
+    coordinates = get_coordinates_from_indices(idx,info, X_axis = 'X_axis', Y_axis = 'Y_axis')
+
+    # Check the results
+    assert isinstance(coordinates, tuple), "Coordinates are not a tuple"
+    assert len(coordinates) == 2, "Coordinates are not a 2-tuple"
+
+def test_get_coordinates_from_indices_with_pandas_dataframe_info():
+    # Define sensor IDs
+    idx = np.array([1, 2, 3, 4])
+
+    # Define information
+    info = pd.DataFrame({
+        'X_axis': [1, 2, 3, 4, 5],
+        'Y_axis': [10, 20, 30, 40, 50]
+    })
+
+    # Call the function
+    coordinates = get_coordinates_from_indices(idx,info, X_axis = 'X_axis', Y_axis = 'Y_axis')
+
+    # Check the results
+    assert isinstance(coordinates, tuple), "Coordinates are not a tuple"
+    assert len(coordinates) == 2, "Coordinates are not a 2-tuple"
+    assert isinstance(coordinates[0], np.ndarray), "X-coordinate is not a numpy array"
+    assert isinstance(coordinates[1], np.ndarray), "Y-coordinate is not a numpy array"
+
+def test_get_coordinates_from_indices_with_z_axis():
+    # Define sensor IDs
+    idx = np.array([1, 2, 3, 4])
+    # Define information
+    info = pd.DataFrame({
+        'X_axis': [1, 2, 3, 4, 5],
+        'Y_axis': [10, 20, 30, 40, 50],
+        'Z_axis': [1, 2, 3, 4, 5]
+    })
+
+    # Call the function
+    coordinates = get_coordinates_from_indices(idx,info, X_axis = 'X_axis', Y_axis = 'Y_axis', Z_axis = 'Z_axis')
+
+    # Check the results
+    assert isinstance(coordinates, tuple), "Coordinates are not a tuple"
+    assert len(coordinates) == 3, "Coordinates are not a 3-tuple"
+    assert isinstance(coordinates[0], np.ndarray), "X-coordinate is not a numpy array"
+    assert isinstance(coordinates[1], np.ndarray), "Y-coordinate is not a numpy array"
+    assert isinstance(coordinates[2], np.ndarray), "Z-coordinate is not a numpy array"
+
+def test_get_indices_from_coordinates_with_different_shape():
+    # Define coordinates
+    coordinates = np.array([[3,6,6],[4,5,1]])
+
+    # Define shape
+    shape = (7, 6)
+
+    # Call the function
+    indices = get_indices_from_coordinates(coordinates,shape)
+
+    # Check the results
+    assert indices.shape == (3,), "Indices shape is not (3,)"
+    assert np.array_equal(indices, np.array([31, 41, 13])), "Indices test failed with different shape"
+
+# Test load_functional_constraints
+def test_load_functional_constraints_loads_valid_python_file():
+    """
+    Test that the function loads a valid Python file and returns a callable function.
+    """
+    import os.path
+    test_file = "user_function.py"
+    abspath = os.path.dirname(os.path.realpath(__file__))
+    # abspath = os.getcwd()  # Get absolule path of current work directory
+    final_path = abspath + "/" + test_file
+    with open(final_path, "w") as f:
+        f.write("""
+def user_function():
+    return 1""")
+    func = load_functional_constraints(test_file)
+    assert func.__name__ == "user_function"
+    assert func() == 1
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])
diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py
deleted file mode 100644
index 21528a5..0000000
--- a/tests/utils/test_utils.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# TODO: include some unit tests once there are more functions
-# in this submodule