Implemented module for background estimation

utils_bge.py

@@ -0,0 +1,58 @@
+import scipy.optimize as sp_opt
+import pandas as pd
+
+
+
+def construct_mask(x, subinterval_list):
+
+    """ constructs a mask of length len(x) that indicates whether the entries of x lie within the subintervals,
+    speficified in the subinterval_list.
+
+    Parameters:
+    x (array_like):
+    subinterval_list (list of two-element tuples):
+
+    Returns:
+    mask (Bool array_like):
+    
+    Usage:
+
+    x = np.array([0.0 0.25 0.5 0.75 1.5 2.0 2.5 3.0 3.5 4.0])
+    subinterval_list = [(0.25,0.75),(2.5,3.5)]
+    mask = contruct_mask(x,subinterval_list)     
+    
+    """
+
+    mask = x < 0.9*x.min()
+    for subinterval in subinterval_list:
+        mask = mask | ((x >= subinterval[0]) & (x <= subinterval[1]))
+
+    return mask
+
+
+def estimate_background(x,y,mask,method: str):
+
+    """fits a background model based on the values of x and y indicated by a mask using a method, among available options.
+    
+    Parameters:
+    x,y (array_like, e.g., np.array, pd.Series): 
+    mask (Bool array_like):
+    method (str): 
+
+    Returns:
+    y_bg (array_like): values of the fitted model at x, or similarly the obtained background estimate
+    
+    """
+
+    if method == 'linear':
+        def linear_model(x,m,b):
+            return (m*x) + b
+        
+        popt, pcov = sp_opt.curve_fit(linear_model,x[mask],y[mask])
+
+        y_bg = linear_model(x,*popt)
+
+    else:
+        raise ValueError("Parameter 'method' can only be set as 'linear'. Future code releases may include more options. ")
+    
+    return y_bg