spectra

Various routines for analysis and preprocessing of spectral data.

Prerequisites

1. numpy: basic data storage and manipulation
2. scipy: signal processing
3. lmfit: non-linear fitting
4. matplotlib: plotting
5. pandas: tables

Running

# add to $PYTHONPATH or add to system path
import sys
sys.path.append('spectra/')  # relative or absolute path to dir

import spectra as sp

# call appropriate functions
sp.get_xy('')

Contents

array_help

• find_nearest
• find_nearest_index
• find_nearest_tolerance
• copy_range
• copy_range_array
• copy_range_yarray
• sort_by_list
• sort_array_column
• split_columns

background

*See (https://github.com/charlesll/rampy)[rampy] for better functions

calibrate

• neon_peaks
• calibrate
• calibrate_x_data2
• find_laser_wavelength
• find_best_offset
• find_best_offset2

convert

• wl2wn
• wl2rwn
• wn2wl
• rwn2wn
• rwn2wl
• absorption
• nm2ev
• nm2ev_xy
• nm2ev_xyz

file_io

• path
• rpath
• assure_path_exists
• make_fname
• write2col
• getxy
• clean_file
• load_folder
• quick_load_xy
• list_all_files
• write_json
• read_json

filters

• smooth_data
• butter_lp_filter
• butter_lowpass
• butter_lowpass_filter
• wicker
• savgol_filter
• butterworth_bandpass
• resample

fitting

• fit_peaks
• split_and_fit
• fit_data
• fit_data_bg
• output_results
• fit_peak_table
• batch_fit_single_peak
• line_fit
• exponential_fit_offset
• exponential_fit
• poly_fit
• build_model
• set_parameters
• build_model_d
• build_model_dd

misc

• crop
• generate_spectrum
• activity_to_intensity
• find_common
• remove_absorption_jumps

normalize

• normalize
• normalize_msc
• normalize_pq
• normalize_2pt
• normalize_fs

peaks

• find_peaks
• gaussian
• lorentzian
• voigt
• guess_peak_width
• find_fwhm
• lorentzian_d
• lorentzian_dd
• gaussian_d
• gaussian_dd

plot

• plot_xy
• fit_plot_single
• plot_peak_fit
• plot_components

read_files

• data_details
• read_cary
• read_craic
• read_nicolet
• read_horiba
• read_renishaw

Todo

1. Background search Better background search
2. Rebinning spectra (is this useful?)
3. Normalizing
4. Dealing with a batch of spectra
5. Better data extraction function
6. Working with different peak shapes
7. Error estimates