5505-public/dima
2
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Moved to src/ folder.

Browse Source
This commit is contained in:
florez_j
2024-03-18 13:42:30 +01:00
parent 07a532b20b
commit 23d0923c93
2 changed files with 0 additions and 112 deletions
Download Patch File Download Diff File Expand all files Collapse all files

54
napp_plotlib.py
View File

@ -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()

58
utils_bge.py
View File

@ -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