Documented functions with links to source

Program to evaluate the performance of scanner by correlating adjacent rows and lines with each other from a pure noise image.

class scanrowcolcorr.ScanRowColCorrPar

Class to initiate default dictionary with input parameters including help and range values and status dictionary

Methods

define_parameters_and_their_properties
define_program_states
set_mic_area

define_parameters_and_their_properties()

define_program_states()

set_mic_area(feature_set)

class scanrowcolcorr.ScanRowColCorr(parset=None)

• Class that holds all functions required for computing row-to-row and column-to-column cross-correlation

• __init__ Function to read in the entered parameter dictionary and load micrograph

1. Usage: ScanRowColCorr(pardict)

2. Input: pardict = OrderedDict of program parameters

Methods

evalcolcc([area])	Function to evaluate cross-correlation of adjacent columns
evalrowcc([area])	Function to evaluate cross-correlation of adjacent rows
visrowcolcc([row, ccrow, col, cccol])	Function to visualize results of row-to-row and column-to-column cross-correlation using matplotlib

perform_scanrowcolcorr

evalrowcc(area=None)

• Function to evaluate cross-correlation of adjacent rows

1. Usage: rows, ccrows, output2 = evalrowcc(area)

2. Input: area = area in percent to included in analysis of input micrograph, i.e. exclude label

3. Output: list of rows, list of cross-correlation

evalcolcc(area=None)

• Function to evaluate cross-correlation of adjacent columns

1. Usage: cols, cccols, output2 = evalrowcc(area)

2. Input: area = area in percent to included in analysis of input micrograph, i.e. exclude label

3. Output: list of columns, list of cross-correlation

visrowcolcc(row=None, ccrow=None, col=None, cccol=None)

• Function to visualize results of row-to-row and column-to-column cross-correlation using matplotlib

1. Usage: output = visrowcolcc(row, ccrow, col, cccol)

2. Input: row = list of rows, ccrow = list of row-to-row cross-correlation, col = list of columns, cccol = list of column-to-column correlation

3. Output: output plot to saved to PDF, SVG, or PNG, TIF, JPG format

perform_scanrowcolcorr()

scanrowcolcorr.main()