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[source]

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()[source]
define_program_states()[source]
set_class_var_option(feature_set)[source]
set_class_var_stack(feature_set)[source]
class segclassexam.SegClassExam(parset=None)[source]
  • 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)[source]
check_maximum_class_number(avgstack, classno_range)[source]
prepare_segclassexam()[source]
rename_plot_title_for_multiple_classes(infile, outfile, classno_start, classno_end, each_class_index, feature_set)[source]
perform_class_examination(avgstack, varstack, classno_start, classno_end, power_img, power_enhanced_img)[source]
print_figures(figures)[source]
print_figures_and_finish(figures)[source]
exam_classes()[source]
segclassexam.main()[source]
class segclassexam_mpi.SegClassExamMpi(parset=None)[source]

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()[source]
collect_powers_and_print_figures_and_finish_mpi(figures)[source]
get_local_power_stacks()[source]
exam_classes()[source]
segclassexam_mpi.main()[source]