IQM_Vis.UI package

Submodules

IQM_Vis.UI.custom_widgets module

Custom SubClasses of PyQt6 widget to extent to the required functionality

class IQM_Vis.UI.custom_widgets.ClickLabel(name='clickable label')

Bases: PyQt6.QtWidgets.QLabel

add a click mousePressEvent event to label (so can click on images)

clicked

mousePressEvent(self, ev: Optional[QMouseEvent])

class IQM_Vis.UI.custom_widgets.ProgressBar(*args, **kwargs)

Bases: PyQt6.QtWidgets.QProgressBar

IQM_Vis.UI.experiment_mode_2AFC module

create experiment window

class IQM_Vis.UI.experiment_mode_2AFC.make_experiment_2AFC(checked_transformation_params, data_store, image_display_size, rgb_brightness, display_brightness, default_save_dir='/home/matt/IQM-Vis', image_preprocessing='None', image_postprocessing='None', checked_metrics={})

Bases: PyQt6.QtWidgets.QMainWindow

calc_max_comparisons(num_images)

change_experiment_images(A_trans, B_trans)

click_completed()

clicked_image(image_name, widget_name)

closeEvent(self, a0: Optional[QCloseEvent])

experiment_layout()

setup

finish_experiment()

get_all_images()

save image name

get_metric_scores()

get IQM scores to save alongside the experiment for plotting/analysis purposes

get_single_transform_im(single_trans_dict)

get_trans_funcs()

get_unique_save_dir()

init_style(style='light', css_file=None)

partition(low, high)

given an unsorted partition of the array between low and high, order elements lower than a given pivot point to the left and higher to the right

quick_sort()

quit()

reset_clicked_image

reset_experiment()

save_experiment()

saved_experiment

setup_experiment()

show_all_images(tab='setup')

start_experiment()

swap_inds(i, j)

toggle_experiment()

class IQM_Vis.UI.experiment_mode_2AFC.reset_image_widget_to_black(time=0.1)

Bases: PyQt6.QtCore.QObject

change clicked image to black and pause

change_to_solid(data)

completed

stop()

IQM_Vis.UI.experiment_mode_2AFC.sort_list(list1, list2)

IQM_Vis.UI.experiment_mode_JND module

create experiment window JND

class IQM_Vis.UI.experiment_mode_JND.make_experiment_JND(checked_transformation_params, data_store, image_display_size, rgb_brightness, display_brightness, default_save_dir='/home/matt/IQM-Vis', dataset_name='dataset1', image_preprocessing='None', image_postprocessing='None', lower_im_num=1, upper_im_num=1, checked_metrics={})

Bases: PyQt6.QtWidgets.QMainWindow

https://www.verywellmind.com/what-is-the-just-noticeable-difference-2795306

closeEvent(self, a0: Optional[QCloseEvent])

experiment_layout()

setup

finish_experiment()

get_all_images()

get_metric_scores(calc=False)

get IQM scores to save alongside the experiment for plotting/analysis purposes

get_single_transform_im(single_trans_dict)

get_trans_funcs()

get_unique_save_dir()

get directory that is unique based on if it’s the same experiment or not

init_style(style='light', css_file=None)

quit()

reset_clicked_image

reset_experiment()

save_experiment()

saved_experiment

setup_experiment()

show_all_images(tab='setup')

start_experiment()

toggle_experiment()

user_decision(decision)

class IQM_Vis.UI.experiment_mode_JND.reset_image_widget_to_black(time=0.1)

Bases: PyQt6.QtCore.QObject

change clicked image to black and pause

change_to_solid(data)

completed

stop()

IQM_Vis.UI.images module

UI image functions

class IQM_Vis.UI.images.images(**kwargs)

Bases: object

change_data(ival, _redo_plots=True)

change_data_click_im(widget_ind, *args)

change_metric_correlations_graph(add=1)

change_metric_range_graph(add=1)

change_preview_images(ival)

change_to_data_num(ind)

change_to_specific_trans(trans_str)

completed_range_results(results)

data results sent from signal from thread worker

display_images()

display_metric_JND_plot()

display_metric_correlation_plot()

display_metric_images(metric_images, i)

display_metric_range_plot()

display_metrics(metrics, i)

display_metrics_graph(metrics, i)

display_metrics_text(metrics, i, disp_len=5)

display_radar_plots()

get_export_dir(i)

get_metrics_over_all_trans_with_init_values()

init_worker_thread()

set up thread for smoother range plot calculation

load_experiment_from_dir()

load_experiment_from_dir_JND()

load_human_experiment()

change the image dataset we are using

load_new_images_folder()

change the image dataset we are using

load_new_single_image()

change the image we are using

plot_metric_range_mlp(i)

plot_radar_graph(results, i)

plot_radar_mlp(i)

redo_plots(calc_range=False)

request_range_work

set_preview_images(preview_num)

set_save_dir_mpl(i=0)

stopped_range_worker(signal=False)

update_datastore_image_list(image_list, append_dataset=False)

view_correlation_instance

IQM_Vis.UI.images.message_on_plot(widget_axes, message)

IQM_Vis.UI.layout module

UI create layout

class IQM_Vis.UI.layout.layout(**kwargs)

Bases: PyQt6.QtWidgets.QMainWindow

init_layout()

init_style(style='light', css_file=None)

IQM_Vis.UI.main module

main entry point to initialise the UI

class IQM_Vis.UI.main.make_app(app, data_stores: list, transformations: dict, metrics_info_format='graph', metrics_avg_graph=False, metric_range_graph=True, metric_params: dict = {}, image_display_size=300, default_save_dir='/home/matt/IQM-Vis', default_dataset_name='dataset1', restrict_options=None, num_steps_range=11, num_step_experiment=6, num_images_scroll_show=7, test=False)

Bases: IQM_Vis.UI.widgets.widgets, IQM_Vis.UI.layout.layout, IQM_Vis.UI.images.images

change_save_folder()

change the save folder we are using

clear_all_cache_data()

closeEvent(self, a0: Optional[QCloseEvent])

construct_UI()

get_menu_checkboxes()

list all trans/metrics in the menu drop downs

load_all_metric_images()

load_all_metrics()

load_all_transforms()

make_menu()

make_status_bar()

quit()

reset_correlation_data()

IQM_Vis.UI.main.set_checked_menu_from_iterable(main_menu, iterable, name, action_store, connect_func, restrict_options=None)

IQM_Vis.UI.threads module

thread and signal classes to makethe UI smoother useful info about PyQt6 threads: https://www.pythontutorial.net/pyqt/pyqt-qthread/

class IQM_Vis.UI.threads.get_range_results_worker

Bases: PyQt6.QtCore.QObject

completed

current_image

do_work(data)

progress

stop()

stopped

IQM_Vis.UI.utils module

IQM_Vis.UI.utils.add_layout_to_tab(tab, layout, name)

IQM_Vis.UI.widgets module

UI create widgets

class IQM_Vis.UI.widgets.custom_float_validator(parent=None)

Bases: PyQt6.QtGui.QValidator

validate(self, a0: Optional[str], a1: int)

IQM_Vis.UI.widgets.get_float_validator()

IQM_Vis.UI.widgets.is_almost_float(input_string)

IQM_Vis.UI.widgets.is_float(input_string)

IQM_Vis.UI.widgets.make_float_from_text(txt)

class IQM_Vis.UI.widgets.widgets

Bases: object

JND_dataset_range_lower(txt)

change the dataset_name we are using

JND_dataset_range_upper(txt)

change the dataset_name we are using

change_dataset_name(txt)

change the dataset_name we are using

change_display_im_display_brightness()

change_display_im_rgb_brightness()

change_display_im_size()

change_graph_size()

change_human_scores_after_exp_2AFC(path)

change_human_scores_after_exp_JND(path)

change_num_steps()

change_plot_lims(state)

change_post_processing(*args)

change_pre_processing(*args)

change_text_exp_trans(trans)

change colour of text when checkbox is pressed

change_text_export_trans(trans)

change colour of text when checkbox is pressed

colour_lineedit(widget, txt)

disable_settings_button()

display_slider_num(key, param_group, disp_len=5)

edit_slider_vals(sliders_dict, key, info_item)

enable_settings_button()

export_trans_images()

generic_value_change(key, param_group)

init_widgets(**kwargs)

create all the widgets we need and init params

launch_experiment_2AFC()

Launch the 2 alternate forced choice experiment

launch_experiment_JND()

launch the Just Noticable difference experiment

make_slider_range(sliders_dict, key, _min, _max, num_steps=None)

open_mlp_new(mpl_canvas)

reset_slider_group(param_group, redo_plots=False, display_images=True)

reset_sliders()

set_image_name_text()

update_image_settings()

button to apply new image settings

update_progress(v)

update_status_bar(v, time=None)

Module contents