engine package¶

Subpackages¶

engine.tests package

Submodules¶

engine.agglomerative module¶

engine.aggregation_api module¶

engine.annotate module¶

engine.blob_clustering module¶

engine.classification module¶

class engine.classification.Classification(environment)¶

class engine.classification.VoteCount(environment, param_dict)¶: Bases: engine.classification.Classification

engine.classification.findsubsets(S, m)¶

engine.clustering module¶

class engine.clustering.Cluster(shape, project, additional_params)¶: Bases: object

engine.clustering.chunk_it(seq, num)¶

engine.clustering.identity_mapping(markings)¶

engine.clustering.index(a, x)¶: Locate the leftmost value exactly equal to x

engine.csv_output module¶

engine.folger module¶

engine.gorongosa_aggregation module¶

class engine.gorongosa_aggregation.GorongosaSurvey¶: Bases: engine.survey_aggregation.Survey

engine.helper_functions module¶

helper functions for extracting necessary parameters for different types of markings also for dimensionality reduction

exception engine.helper_functions.EmptyPolygon¶: Bases: exceptions.Exception

exception engine.helper_functions.InvalidMarking(pt)¶: Bases: exceptions.Exception

engine.helper_functions.csv_string(string)¶: remove or replace all characters which might cause problems in a csv template :param str: :return:

engine.helper_functions.hesse_line_reduction(line_segments)¶: use if we want to cluster based on Hesse normal form - but want to retain the original values :param line_segment: :return:

engine.helper_functions.rectangle_reduction(list_of_rectangles)¶: reduce rectangles to a 2-d marking,ie. their center :param list_of_rectangles: :return:

engine.helper_functions.relevant_bezier_params(marking, image_dimensions)¶

engine.helper_functions.relevant_circle_params(marking, image_dimensions)¶

engine.helper_functions.relevant_ellipse_params(marking, image_dimensions)¶

engine.helper_functions.relevant_line_params(marking, image_dimensions)¶

engine.helper_functions.relevant_point_params(marking, image_dimensions)¶

engine.helper_functions.relevant_polygon_params(marking, image_dimensions)¶

engine.helper_functions.relevant_rectangle_params(marking, image_dimensions)¶

engine.helper_functions.relevant_text_params(marking, image_dimensions)¶: extract the relevant params from the the transcription marking note that the text is the last item - which means we can treat the results pretty much like a line segment - which it mostly is :param marking: :param image_dimensions: :return:

engine.helper_functions.text_line_reduction(line_segments)¶: use if we want to cluster based on Hesse normal form - but want to retain the original values :param line_segment: :return:

engine.helper_functions.warning(*objs)¶

engine.heuristic_lcs module¶

engine.job_runner module¶

engine.jobs module¶

engine.json_transcription module¶

engine.json_transcription.json_dump(project)¶

engine.kdtree_multiclick_correct module¶

engine.latex_transcription module¶

engine.load_redis module¶

engine.load_redis.configure_redis(environment)¶

engine.multiClickCorrect module¶

class engine.multiClickCorrect.MultiClickCorrect(dist_threshold=inf, overlap_threshold=inf, min_cluster_size=0)¶

engine.panoptes_ibcc module¶

engine.rectangle_clustering module¶

engine.simplified_transcription module¶

engine.string_agglomeration module¶

engine.survey_aggregation module¶

class engine.survey_aggregation.Survey¶

engine.text_aggregation module¶

engine.text_clustering module¶

class engine.text_clustering.TextClustering(shape, project, param_dict)¶: Bases: engine.clustering.Cluster

engine.text_clustering.levenshtein(a, b)¶: Calculates the Levenshtein distance between a and b.

engine.web_api module¶

Module contents¶