pySpacell package¶
Submodules¶
pySpacell.pySpacell module¶
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class
pySpacell.pySpacell.
Spacell
(feature_file, image_label_file, column_objectnumber='ObjectNumber', column_x_y=['x', 'y'])¶ -
compute_per_image_analysis
(feature_columns, method, neighborhood_matrix_type, neighborhood_p0, neighborhood_p1, **kwargs)¶ Computes per image spatial analysis tests for the provided features for one neighborhood matrix.
Feature_columns: list of str features’ names from feature_table. All features will be tested on the same neighborhood matrix and with the same analysis method. Method: str can be - ‘assortativity’ or ‘ripley’ (for categorical features), - ‘moran’, ‘geary’, ‘getisord’ (global spatial autocorrelation for continuous features) Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Neighborhood_min_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_min_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’
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compute_per_object_analysis
(feature_columns, method, neighborhood_matrix_type, neighborhood_p0, neighborhood_p1, **kwargs)¶ Computes per object spatial analysis tests for the provided features for one neighborhood matrix.
Feature_columns: list of str features’ names from feature_table. All features will be tested on the same neighborhood matrices and with the same analysis method Method: str can be ‘moran’ or’getisord’ (local spatial autocorrelation for continuous features) Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Neighborhood_min_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_min_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’
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correlogram
(feature_columns, method, neighborhood_matrix_type, neighborhood_min_p0, neighborhood_max_p1, **kwargs)¶ Computes a serie of spatial analysis tests for the provided features. Gives one value for the image. The starting and ending neighborhood parameters, neighborhood_p0 and neighborhood_p1, are to be set. 3 modes are available to define the intermediary neighborhood parameters.
Feature_columns: list of str features’ names from feature_table. All features will be tested on the same neighborhood matrices and with the same analysis method Method: str can be - ‘assortativity’ or ‘ripley’ (for categorical features), - ‘moran’, ‘geary’, ‘getisord’ (global spatial autocorrelation for continuous features) Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Same ‘neighborhood_matrix_type’ for all the points in the correlogram. Neighborhood_min_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_min_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_step: int or float a step in terms of parameters ([p0, p0+step, p0+2step, …p1]) should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ one of neighborhood_step, nb_pairs_step or nb_test_points should be defined. Nb_pairs_step: int a step in terms of number of pairs for each test. Overlooked if neighborhood_step is provided. Nb_test_points: int a number of test points. Overlooked if neighborhood_step or nb_pairs_step are provided.
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get_neighborhood_matrix
(neighborhood_matrix_type, neighborhood_p0, neighborhood_p1, iterations='None', pysal_object=False, **kwargs)¶ Return the neighborhood matrix for specified parameters if already computed, thraw a ValueError otherwise. 3 modes are available:
- ‘k’ for k-nearest neighbors;
- ‘radius’ for neighbors at an euclidean distance;
- ‘network’ for neighbors from the object graph (touching objects are neighbors)
For each mode, an interval is requested to know which neighbors to include.
- Examples: ‘k’, 2, 4 -> 2nd, 3rd, and 4th-nearest neighbors
- ‘radius’, 50.75, 100.85 -> neighbors at an euclidean distance falling between 50.75 pixels and 100.85 pixels ‘network’, 0, 1 -> neighbors having boundaries touching
Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Neighborhood_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Pysal_object: bool if True, return a pysal.weights object if False, return a tuple (W, L) with W full numpy neighborhood matrix and L list of vertices’ ids
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get_suffix
(neighborhood_matrix_type, neighborhood_p0, neighborhood_p1, **kwargs)¶ Returns the suffix for the additionally computed features in the feature_table: local spatial-autocorrelation statistical tests’ outputs, and neighborhood computation’s number of neighbors for each object (‘radius’ or ‘network’ neighborhood matrix type) or distance of last neighbor (‘k’ neighborhood matrix type).
Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Neighborhood_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’
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matrix_types
= ['k', 'radius', 'network']¶
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precision_float_radius
= 3¶
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Module contents¶
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class
pySpacell.
Spacell
(feature_file, image_label_file, column_objectnumber='ObjectNumber', column_x_y=['x', 'y'])¶ -
compute_per_image_analysis
(feature_columns, method, neighborhood_matrix_type, neighborhood_p0, neighborhood_p1, **kwargs)¶ Computes per image spatial analysis tests for the provided features for one neighborhood matrix.
Feature_columns: list of str features’ names from feature_table. All features will be tested on the same neighborhood matrix and with the same analysis method. Method: str can be - ‘assortativity’ or ‘ripley’ (for categorical features), - ‘moran’, ‘geary’, ‘getisord’ (global spatial autocorrelation for continuous features) Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Neighborhood_min_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_min_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’
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compute_per_object_analysis
(feature_columns, method, neighborhood_matrix_type, neighborhood_p0, neighborhood_p1, **kwargs)¶ Computes per object spatial analysis tests for the provided features for one neighborhood matrix.
Feature_columns: list of str features’ names from feature_table. All features will be tested on the same neighborhood matrices and with the same analysis method Method: str can be ‘moran’ or’getisord’ (local spatial autocorrelation for continuous features) Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Neighborhood_min_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_min_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’
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correlogram
(feature_columns, method, neighborhood_matrix_type, neighborhood_min_p0, neighborhood_max_p1, **kwargs)¶ Computes a serie of spatial analysis tests for the provided features. Gives one value for the image. The starting and ending neighborhood parameters, neighborhood_p0 and neighborhood_p1, are to be set. 3 modes are available to define the intermediary neighborhood parameters.
Feature_columns: list of str features’ names from feature_table. All features will be tested on the same neighborhood matrices and with the same analysis method Method: str can be - ‘assortativity’ or ‘ripley’ (for categorical features), - ‘moran’, ‘geary’, ‘getisord’ (global spatial autocorrelation for continuous features) Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Same ‘neighborhood_matrix_type’ for all the points in the correlogram. Neighborhood_min_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_min_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_step: int or float a step in terms of parameters ([p0, p0+step, p0+2step, …p1]) should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ one of neighborhood_step, nb_pairs_step or nb_test_points should be defined. Nb_pairs_step: int a step in terms of number of pairs for each test. Overlooked if neighborhood_step is provided. Nb_test_points: int a number of test points. Overlooked if neighborhood_step or nb_pairs_step are provided.
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get_neighborhood_matrix
(neighborhood_matrix_type, neighborhood_p0, neighborhood_p1, iterations='None', pysal_object=False, **kwargs)¶ Return the neighborhood matrix for specified parameters if already computed, thraw a ValueError otherwise. 3 modes are available:
- ‘k’ for k-nearest neighbors;
- ‘radius’ for neighbors at an euclidean distance;
- ‘network’ for neighbors from the object graph (touching objects are neighbors)
For each mode, an interval is requested to know which neighbors to include.
- Examples: ‘k’, 2, 4 -> 2nd, 3rd, and 4th-nearest neighbors
- ‘radius’, 50.75, 100.85 -> neighbors at an euclidean distance falling between 50.75 pixels and 100.85 pixels ‘network’, 0, 1 -> neighbors having boundaries touching
Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Neighborhood_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Pysal_object: bool if True, return a pysal.weights object if False, return a tuple (W, L) with W full numpy neighborhood matrix and L list of vertices’ ids
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get_suffix
(neighborhood_matrix_type, neighborhood_p0, neighborhood_p1, **kwargs)¶ Returns the suffix for the additionally computed features in the feature_table: local spatial-autocorrelation statistical tests’ outputs, and neighborhood computation’s number of neighbors for each object (‘radius’ or ‘network’ neighborhood matrix type) or distance of last neighbor (‘k’ neighborhood matrix type).
Neighborhood_matrix_type: str should be ‘k’, ‘radius’, or ‘network’ Neighborhood_p0: int or float minimum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’ Neighborhood_p1: int or float maximum bound for the neighborhood. should be int for ‘k’ or ‘network’. Can be int or float for ‘radius’
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class
pySpacell.pySpacell._neighborhood_matrix.
NeighborhoodMatrixComputation
¶
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class
pySpacell.pySpacell._assortativity.
Assortativity
¶
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class
pySpacell.pySpacell._ripley.
Ripley
¶
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class
pySpacell.pySpacell._ripley.
RipleyObject
(classes, count_classes, K, K_cross, K_cross_star, permutations, lambda_i)¶
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class
pySpacell.pySpacell._spatial_autocorrelation.
SpatialAutocorrelation
¶
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class
pySpacell.pySpacell._visualization.
Visualization
¶