From 23d0923c93fcefad982856cb8f92ef46f0e274df Mon Sep 17 00:00:00 2001
From: Florez Ospina Juan Felipe <juan.florez-ospina@psi.ch>
Date: Mon, 18 Mar 2024 13:42:30 +0100
Subject: [PATCH] Moved to src/ folder.

---
 napp_plotlib.py | 54 ---------------------------------------------
 utils_bge.py    | 58 -------------------------------------------------
 2 files changed, 112 deletions(-)
 delete mode 100644 napp_plotlib.py
 delete mode 100644 utils_bge.py

diff --git a/napp_plotlib.py b/napp_plotlib.py
deleted file mode 100644
index c22ca93..0000000
--- a/napp_plotlib.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-
-def plot_image(dataframe,filter):
-
-    for meas_idx in dataframe.loc[filter,:].index:
-        meas = dataframe.loc[meas_idx,:] # pandas Series    
-        fig = plt.figure()
-        ax = plt.gca()
-        rows, cols = meas['image'].shape
-        scientaEkin_eV = meas['scientaEkin_eV'].flatten()
-        x_min, x_max = np.min(scientaEkin_eV), np.max(scientaEkin_eV)
-        y_min, y_max = 0, rows
-        ax.imshow(meas['image'],extent = [x_min,x_max,y_min,y_max])
-        ax.set_xlabel('scientaEkin_eV')
-        ax.set_ylabel('Replicates')
-        ax.set_title(meas['name'][0] + '\n' + meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
-
-def plot_spectra(dataframe,filter):
-
-    """ plot_spectra plots XPS spectra associated to 'dataframe' after row reduced by 'filter'. 
-    When more than one row are specified by the 'filter' input, indivial spectrum are superimposed
-    on the same plot.
-
-    Parameters:
-    dataframe (pandas.DataFrame): table with heterogenous entries obtained by read_hdf5_as_dataframe.py.
-    filter (binaray array): binary indexing array with same number of entries as rows in dataframe.
-
-    """
-    fig = plt.figure()
-    ax = plt.gca()
-
-    for meas_idx in dataframe.loc[filter,:].index:
-        meas = dataframe.loc[meas_idx,:] # pandas Series   
-
-        rows, cols = meas['image'].shape
-        bindingEnergy_eV = meas['bindingEnergy_eV'].flatten()
-        spectrum_countsPerSecond = meas['spectrum_countsPerSecond'].flatten()
-        x_min, x_max = np.min(bindingEnergy_eV), np.max(bindingEnergy_eV)
-        y_min, y_max = 0, rows
-        #for i in range(cols):
-        #ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'][0])
-        ax.plot(bindingEnergy_eV, spectrum_countsPerSecond,label = meas['name'])
-
-    ax.set_xlabel('bindingEnergy_eV')
-    ax.set_ylabel('counts Per Second')
-    ax.set_title('\n'+meas['sample']+ '\n' + 'PE spectra')
-    #ax.set_title('\n'+meas['sample'][0]+ '\n' + 'PE spectra')
-    #ax.set_title(meas['name'][0] + '\n'+meas['sample'][0]+ '\n' + meas['lastModifiedDatestr'][0])
-    ax.legend()
-
-
-
diff --git a/utils_bge.py b/utils_bge.py
deleted file mode 100644
index c350ebf..0000000
--- a/utils_bge.py
+++ /dev/null
@@ -1,58 +0,0 @@
-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 < 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