Small corrections in _constraints

Author: niharika2999

pysensors/utils/_constraints.py

@@ -0,0 +1,128 @@
+
+"""
+Various utility functions for mapping constrained sensors locations with the column indices for class GQR.
+"""
+
+import numpy as np
+
+
+def get_constraind_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
+        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.
+
+        Returns
+        -------
+        idx_constrained : np.darray, shape [No. of constrained locations]
+            Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix.
+    """
+    n_features = len(all_sensors)
+    image_size = int(np.sqrt(n_features))
+    a = np.unravel_index(all_sensors, (nx,ny))
+    constrained_sensorsx = []
+    constrained_sensorsy = []
+    for i in range(n_features):
+        if (a[0][i] >= x_min and a[0][i] <= x_max) and (a[1][i] >= y_min and a[1][i] <= y_max):  
+            constrained_sensorsx.append(a[0][i])
+            constrained_sensorsy.append(a[1][i])
+
+    constrained_sensorsx = np.array(constrained_sensorsx)
+    constrained_sensorsy = np.array(constrained_sensorsy)
+    constrained_sensors_array = np.stack((constrained_sensorsy, constrained_sensorsx), axis=1)
+    constrained_sensors_tuple = np.transpose(constrained_sensors_array)
+    if len(constrained_sensorsx) == 0: ##Check to handle condition when number of sensors in the constrained region = 0
+        idx_constrained = []
+    else:
+        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):
+    """
+    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
+        df : pandas.DataFrame
+            A dataframe containing the features  and samples
+        
+        Returns
+        -------
+        idx_constrained : np.darray, shape [No. of constrained locations]
+            Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix.
+    """
+    x = df['X (m)'].to_numpy()
+    n_features = x.shape[0]
+    y = df['Y (m)'].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):
+            idx_constrained.append(i)
+    return idx_constrained
+
+def box_constraints(position,lower_bound,upper_bound,):
+    """
+    Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. ##TODO : BETTER DEFINITION
+
+    Parameters
+        ----------
+        position: ##TODO: FILL
+            
+        lower_bound : ##TODO: FILL
+           
+        upper_bound : ##TODO: FILL
+        
+        Returns
+        -------
+        idx_constrained : np.darray, shape [No. of constrained locations]       ##TODO: CHECK IF CORRECT
+            Array which contains the constrained locationsof the grid in terms of column indices of basis_matrix.
+    """
+    for i,xi in enumerate(position):
+        f1 = position[i] - lower_bound[i]
+        f2 = upper_bound[i] - position [i]
+    return +1 if (f1 and f2 > 0) else -1
+
+def functional_constraints(position, func_response,func_input, free_term):
+    """
+    Function for mapping constrained sensor locations on the grid with the column indices of the basis_matrix. ##TODO: BETTER DEFINITION
+
+    Parameters
+        ----------
+        position: ##TODO : FILL
+            
+        func_response : ##TODO : FILL
+            
+        func_input: ##TODO : FILL
+            
+        free_term : ##TODO : FILL
+        
+        Returns
+        -------
+        g : ##TODO : FILL
+            
+    """
+    g = func_response + func_input + free_term
+    return g