Process_EELS_-Python-

Process EELS systematically: align, normalize, PCA, denoise_LLR, plot, find peak.

The main script is 'Process_EELS.py', which includes three classes:

1. Line.
   The instance of the class is an object describing a line of EELS.
   Main function: slice, plot, find_zlp, align, yshift, spline, integrate, find_peak, denoise_LLR.

2. Lines.
   The instance of the class is an object describing a list of EELS lines.
   Main function: add, delete, slice, slice_display, align, normalize, denoise_LLR, PCA, make_plot, find_peak, subtract background signals.

3. Map.
   The Child Class of Lines.
   Additional function: normalize_map,sum_in_an_area, mapping_slice_display, PCA_plot

Examples using these three classes for systematically analyze EELS data are presented:

1. 'Process_EELS_Line.ipynb'
   Examples of the basic functions based on Line Class:
   1: slice_data
   2: yshift_data
   3: spline
   4: find_zlp_max
   5: denoise_LLR
   6: find_peak

2. 'Process_EELS_Lines.ipynb'
   Examples of the further analysis based on Lines Class:
   1: Plot multiple lines.
   2: Validation test of the manual background subtraction by finding peaks.
   3: Background subtraction by raw and subtrate data.

3. 'Process_EELS_Map.ipynb'
   Examples of the EELS mapping analysis based on Map Class:
   1: Initial process (align, and normalize).
   2: Slice display.
   3: Decomposition by PCA (or NMF).
   4: Summming over all the pixels.

Any feedback is welcome. Please let me know if you have any questions on bugs or any suggestions to expand the functionality.

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