Relocated scripts

src/napp_plotlib.py

@@ -0,0 +1,54 @@
+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()
+
+
+

src/utils_bge.py

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+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