Documented functions with links to source

Program to examine helix classes to compute their collapsed (1D) and 2D power spectrum and width profile of helices

class segclassexam.SegClassExamPar

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_class_var_option
set_class_var_stack
set_compute_layer_line_correlation
set_layer_line_region
set_power_spectrum_reference

define_parameters_and_their_properties()

define_program_states()

set_class_var_option(feature_set)

set_class_var_stack(feature_set)

class segclassexam.SegClassExam(parset=None)

• Class that holds functions for examining segments from micrographs

• __init__ Function to interpret multi-input parameters

Methods

add_power_spectra_from_verticalized_stack(…)	Function to compute sum of in-planed rotated segments
apply_binfactor(binfactor, infilestack, …)	Function to reduce stack and modify pixelsize according to desired binfactor
cleanup(*files)	Function to clean up intermediate image files
collapse_power(addpowimg)	Function to project powerspectrum onto 1D plot to determine layer line position
compute_radii_for_fourier_mask(…)	>>> from spring.segment2d.segmentexam import SegmentExam
determine_width(infilestack, segsizepix, …)	Function to project width profile of segments
display_average_and_variance([twodavg, twodvar])	Function to add average and variance images to diagnostic output plot
display_power_spectra_enhanced_and_collapsed([…])	Function to visualize power spectra: sum of power spectra, enhanced sum and their collapsed 1D profile
enhance_power([avg_periodogram, pixelsize])	Function to visually enhance power spectrum by compensating for decay of amplitude
find_local_extrema(fits[, target, window])	Function from [SciPy-user] mailing list ‘Finding local minima of greater than a given depth’
generate_falloff_line(segment_size_in_pixel, …)	>>> from spring.segment2d.segmentexam import SegmentExam
make_oneoverres([arr, pixelsize])	Function to generate an array of resolution in reciprocal Angstrom
make_smooth_rectangular_mask(hel_width_pix, …)	>>> from spring.segment2d.segmentexam import SegmentExam
measure_peakdist([rowsaddimg, segsizepix, …])	Function to measure distance between two symmetrical peaks of 1D helix width projection
project_helix([seg])	Function to project image along helical axis by adding rows of image
project_normal_to_helix([seg])	Function to project image perpendicular to helical axis by adding columns of image
setup_fourxtwo([figno])	Function to setup 4 x 2 subplot grid for diagnostic output
split_quarters([addpowimgenh])	Function to split enhanced power spectrum (EMData object) into lower right quarter
visualize_power_avg_and_width_analysis(…)	Function to combine output of width and power spectra analysis into single summary sheet
visualize_widthprofile_and_histogram([…])	Function to add width profile to diagnostic output plot

add_smooth_gaussian_falloff_to_edge_of_binary_mask
add_up_power_spectra
add_width_histogram_next_to_width_profile
add_width_profile_from_avg_and_var
bin_image_stack_by_binfactor
check_maximum_class_number
collapse_periodograms
compute_avg_and_var_of_width_and_image
compute_power_correlations_with_rings
compute_radial_average_from_line
copy_database_and_filter_segment_ids
correlate_layer_lines_of_average_power_with_individual_segments
display_helix_width_and_normal_profile
enter_correlation_values_in_database
exam_classes
generate_radial_falloff_gradient
generate_rectangular_mask_with_linear_falloffs
generate_series_of_circular_masks_from_radii
generate_two_dee_cosine_falloff
insert_radial_falloff_gradient_into_corners_of_rectangular_mask
insure_mirror_symmetry_of_mask_parameters
limit_width_falloff_to_available_pixels_outside_binary_mask
make_binary_shape_mask
pad_image_to_current_size
perform_class_examination
prepare_segclassexam
print_figures
print_figures_and_finish
rename_plot_title_for_multiple_classes
resize_mask_to_segment_dimensions
window_image_to_current_sizes
write_avg_periodograms

display_helix_width_and_normal_profile(avg, var)

check_maximum_class_number(avgstack, classno_range)

prepare_segclassexam()

rename_plot_title_for_multiple_classes(infile, outfile, classno_start, classno_end, each_class_index, feature_set)

perform_class_examination(avgstack, varstack, classno_start, classno_end, power_img, power_enhanced_img)

print_figures(figures)

print_figures_and_finish(figures)

exam_classes()

segclassexam.main()

class segclassexam_mpi.SegClassExamMpi(parset=None)

Methods

add_power_spectra_from_verticalized_stack(…)	Function to compute sum of in-planed rotated segments
apply_binfactor(binfactor, infilestack, …)	Function to reduce stack and modify pixelsize according to desired binfactor
cleanup(*files)	Function to clean up intermediate image files
collapse_power(addpowimg)	Function to project powerspectrum onto 1D plot to determine layer line position
compute_radii_for_fourier_mask(…)	>>> from spring.segment2d.segmentexam import SegmentExam
determine_width(infilestack, segsizepix, …)	Function to project width profile of segments
display_average_and_variance([twodavg, twodvar])	Function to add average and variance images to diagnostic output plot
display_power_spectra_enhanced_and_collapsed([…])	Function to visualize power spectra: sum of power spectra, enhanced sum and their collapsed 1D profile
enhance_power([avg_periodogram, pixelsize])	Function to visually enhance power spectrum by compensating for decay of amplitude
find_local_extrema(fits[, target, window])	Function from [SciPy-user] mailing list ‘Finding local minima of greater than a given depth’
generate_falloff_line(segment_size_in_pixel, …)	>>> from spring.segment2d.segmentexam import SegmentExam
make_oneoverres([arr, pixelsize])	Function to generate an array of resolution in reciprocal Angstrom
make_smooth_rectangular_mask(hel_width_pix, …)	>>> from spring.segment2d.segmentexam import SegmentExam
measure_peakdist([rowsaddimg, segsizepix, …])	Function to measure distance between two symmetrical peaks of 1D helix width projection
project_helix([seg])	Function to project image along helical axis by adding rows of image
project_normal_to_helix([seg])	Function to project image perpendicular to helical axis by adding columns of image
setup_fourxtwo([figno])	Function to setup 4 x 2 subplot grid for diagnostic output
split_quarters([addpowimgenh])	Function to split enhanced power spectrum (EMData object) into lower right quarter
visualize_power_avg_and_width_analysis(…)	Function to combine output of width and power spectra analysis into single summary sheet
visualize_widthprofile_and_histogram([…])	Function to add width profile to diagnostic output plot

add_smooth_gaussian_falloff_to_edge_of_binary_mask
add_up_power_spectra
add_width_histogram_next_to_width_profile
add_width_profile_from_avg_and_var
bin_image_stack_by_binfactor
check_maximum_class_number
collapse_periodograms
collect_powers_and_print_figures_and_finish_mpi
compute_avg_and_var_of_width_and_image
compute_power_correlations_with_rings
compute_radial_average_from_line
copy_database_and_filter_segment_ids
correlate_layer_lines_of_average_power_with_individual_segments
display_helix_width_and_normal_profile
enter_correlation_values_in_database
exam_classes
generate_radial_falloff_gradient
generate_rectangular_mask_with_linear_falloffs
generate_series_of_circular_masks_from_radii
generate_two_dee_cosine_falloff
get_local_power_stacks
insert_radial_falloff_gradient_into_corners_of_rectangular_mask
insure_mirror_symmetry_of_mask_parameters
limit_width_falloff_to_available_pixels_outside_binary_mask
make_binary_shape_mask
pad_image_to_current_size
perform_class_examination
prepare_segclassexam
prepare_segclassexam_mpi
print_figures
print_figures_and_finish
rename_plot_title_for_multiple_classes
resize_mask_to_segment_dimensions
window_image_to_current_sizes
write_avg_periodograms

prepare_segclassexam_mpi()

collect_powers_and_print_figures_and_finish_mpi(figures)

get_local_power_stacks()

exam_classes()

segclassexam_mpi.main()