Functions | |
| def | get_test_data_path (filename) |
| def | get_output_dir () |
| def | graph () |
| def | test_initializer (graph) |
| def | test_initializer2 (graph) |
| def | test_number_of_nodes (graph) |
| def | test_number_of_edges (graph) |
| def | test_get_nodes (graph) |
| def | test_topological_sort (graph) |
| def | test_has_soma_segment (graph) |
| def | test_is_soma_missing (graph) |
| def | test_remove_soma_segment (graph) |
| def | test_set_nodes (graph) |
| def | test_preprocess (graph) |
| def | test_read_from_fileUGX (graph) |
| def | test_write_to_ugx (graph) |
| def | test_save_preprocess (graph) |
| def | test_splitedges (graph) |
| def | test_splitedgesN (graph) |
| def | test_get_trunks (graph) |
| def | test_assemble_trunks (graph) |
| def | test_assemble_trunks2 (graph) |
| def | test_assemble_linear_resample (graph) |
| def | test_assemble_cubic_resample (graph) |
| def | test_swc_to_ugx (graph) |
| def | test_ugx_to_swc (graph) |
| def | test_generate_refinements_cubic (graph) |
| def | test_generate_refinements_linear (graph) |
Variables | |
| float | delta = 2.0 |
Detailed Description
Test suite for the Python bindings of the NeuronGraph C++ library.
This module contains unit tests that verify the functionality of the Python bindings
for the NeuronGraph C++ library. It tests various operations including file I/O,
graph manipulation, and analysis functions.
Key Test Categories:
- Basic graph operations (node/edge counts, topology)
- File I/O (SWC and UGX formats)
- Graph preprocessing and manipulation
- Trunk extraction and reassembly
- Resampling and refinement operations
- File format conversion
Dependencies:
pytest: Test framework
rich: Enhanced terminal output formatting
python_package.neurongraph: The Python bindings being tested
Usage:
To run all tests:
pytest test_bindings.py -v
To run a specific test:
pytest test_bindings.py::test_function_name -v
Notes:
- Test data files are stored in the test/data directory
- Output files are written to test/output
- The test fixture 'graph' provides a pre-loaded NeuronGraph instance
Function Documentation
◆ get_output_dir()
| def python_package.test.test_bindings.get_output_dir | ( | ) |
Construct the full path to the output directory.
The output directory is used to store temporary files generated by
the test suite. It is located in the parent directory of the directory
containing this script.
Returns:
str: The full path to the output directory.
Here is the caller graph for this function:
◆ get_test_data_path()
| def python_package.test.test_bindings.get_test_data_path | ( | filename | ) |
Construct the full path to the test data file given its filename.
Args:
filename (str): The name of the test data file.
Returns:
str: The full path to the test data file located in the same directory as this script.
Here is the caller graph for this function:
◆ graph()
| def python_package.test.test_bindings.graph | ( | ) |
A fixture providing a neuron graph object from a test data file.
This fixture provides a neuron graph object that has been initialized with data
from a file in the test data directory. The fixture is scoped to the module,
meaning that it is created once per module (i.e., once per test suite) and is
reused across all tests in the module.
The neuron graph object is created by calling the default constructor and
then calling the readFromFile method with a file in the test data directory.
The file is located in the same directory as the current script, and the full
path is determined using the get_test_data_path function.
Returns:
A neuron graph object (ng.NeuronGraph) that has been initialized with data
from the test data file.
Here is the call graph for this function:
◆ test_assemble_cubic_resample()
| def python_package.test.test_bindings.test_assemble_cubic_resample | ( | graph | ) |
Test that the assembleTrunks() method correctly reassembles a neuron graph
from its cubically resampled trunks.
This test first retrieves the trunks from the neuron graph, then performs
cubic spline resampling on the trunks. The resampled trunks are then
reassembled into a neuron graph using the assembleTrunks() method. The test
verifies that the number of nodes in the assembled graph is different from
the original, indicating that the cubic resampling process changed the
number of nodes in the graph.
Args:
graph: A fixture providing a neuron graph object.
Here is the call graph for this function:
◆ test_assemble_linear_resample()
| def python_package.test.test_bindings.test_assemble_linear_resample | ( | graph | ) |
Test that the assembleTrunks() method correctly reassembles a neuron graph from its linearly resampled trunks. This test first reads the number of nodes from the SWC file, then splits the nodes into trunks, linearly resamples the trunks, and assembles the trunks back into a neuron graph using the assembleTrunks() method. It then reads the number of nodes from the assembled graph and verifies that the two numbers are different, ensuring that the linear resampling changed the number of nodes in the graph.
Here is the call graph for this function:
◆ test_assemble_trunks()
| def python_package.test.test_bindings.test_assemble_trunks | ( | graph | ) |
Test that the assembleTrunks() method correctly reassembles a neuron graph from its trunks. This test first reads the number of nodes from the SWC file, then splits the nodes into trunks, reassembles the trunks back into a neuron graph using the assembleTrunks() method, and verifies that the two numbers are equal.
Here is the call graph for this function:
◆ test_assemble_trunks2()
| def python_package.test.test_bindings.test_assemble_trunks2 | ( | graph | ) |
Test that the assembleTrunks() method correctly reassembles a neuron graph
from its trunks when the trunks have been renumbered.
This test first reads the number of nodes from the SWC file, then splits the
nodes into trunks, renumbers the trunks to start at 1, reassembles the trunks
back into a neuron graph using the assembleTrunks() method, and verifies
that the two numbers are equal.
Parameters
----------
graph : NeuronGraph
The neuron graph to test
Here is the call graph for this function:
◆ test_generate_refinements_cubic()
| def python_package.test.test_bindings.test_generate_refinements_cubic | ( | graph | ) |
Test that the generateRefinements() method correctly generates cubic refinements.
Verifies that the generateRefinements() method produces the expected number of
refinements using cubic interpolation. This is done by checking that each refined
node set has at least as many nodes as the original set.
Args:
graph: A fixture providing a neuron graph object.
◆ test_generate_refinements_linear()
| def python_package.test.test_bindings.test_generate_refinements_linear | ( | graph | ) |
Test that the generateRefinements() method correctly generates linear refinements.
Verifies that the generateRefinements() method produces the expected number of
refinements using linear interpolation. This is done by checking that each refined
node set has at least as many nodes as the original set.
Args:
graph: A fixture providing a neuron graph object.
◆ test_get_nodes()
| def python_package.test.test_bindings.test_get_nodes | ( | graph | ) |
Test that the getNodes() method returns a dictionary of SWCNode objects.
Verifies that the getNodes() method returns a dictionary where the keys are
integers and the values are SWCNode objects.
Args:
graph: A fixture providing a neuron graph object.
◆ test_get_trunks()
| def python_package.test.test_bindings.test_get_trunks | ( | graph | ) |
Test that the getTrunks() method returns a non-empty collection of trunks.
Verifies that the getTrunks() method correctly extracts trunk segments from
a neuron graph and that the number of trunks is greater than zero.
Args:
graph: A fixture providing a neuron graph object.
◆ test_has_soma_segment()
| def python_package.test.test_bindings.test_has_soma_segment | ( | graph | ) |
Test that the hasSomaSegment() method correctly identifies neurons with a soma segment.
Verifies that the hasSomaSegment() method correctly identifies neurons with a
soma segment. This is done by calling the method on the test data neuron
and verifying that it returns True.
Args:
graph: A fixture providing a neuron graph object.
◆ test_initializer()
| def python_package.test.test_bindings.test_initializer | ( | graph | ) |
Test the default initialization of a NeuronGraph object.
This test verifies that a newly created NeuronGraph object has
zero nodes immediately after initialization. It checks the initial
state of the graph to ensure that no nodes are present.
Args:
graph: A fixture providing a neuron graph object.
◆ test_initializer2()
| def python_package.test.test_bindings.test_initializer2 | ( | graph | ) |
Test the initialization of a NeuronGraph object with a file path.
This test verifies that a newly created NeuronGraph object can be initialized
with a file path and that the object is populated with data from the file.
Args:
graph: A fixture providing a neuron graph object.
Here is the call graph for this function:
◆ test_is_soma_missing()
| def python_package.test.test_bindings.test_is_soma_missing | ( | graph | ) |
Test that the isSomaMissing() method correctly identifies neurons that are missing a soma.
Verifies that the isSomaMissing() method correctly identifies neurons that
are missing a soma. This is done by calling the method on the test data
neuron and verifying that it returns False.
Args:
graph: A fixture providing a neuron graph object.
◆ test_number_of_edges()
| def python_package.test.test_bindings.test_number_of_edges | ( | graph | ) |
Test that the graph has the correct number of edges.
Verifies that the numberOfEdges() method returns the correct number of edges
in the graph. The correct number of edges is determined by the number of
edges in the test data file.
Args:
graph: A fixture providing a neuron graph object.
◆ test_number_of_nodes()
| def python_package.test.test_bindings.test_number_of_nodes | ( | graph | ) |
Test that the graph has the correct number of nodes.
Verifies that the numberOfNodes() method returns the correct number of nodes
in the graph. The correct number of nodes is determined by the number of
nodes in the test data file.
Args:
graph: A fixture providing a neuron graph object.
◆ test_preprocess()
| def python_package.test.test_bindings.test_preprocess | ( | graph | ) |
Test that the preprocess() method correctly reduces the number of nodes.
Verifies that the preprocess() method correctly reduces the number of nodes
in the neuron graph. This is done by calling the method on the test data
neuron and verifying that the number of nodes decreases.
Args:
graph: A fixture providing a neuron graph object.
◆ test_read_from_fileUGX()
| def python_package.test.test_bindings.test_read_from_fileUGX | ( | graph | ) |
Test that the readFromFileUGX() method correctly reads neuron data from a UGX file.
This test first loads neuron data from an SWC file and checks the number of nodes.
Then, it creates a new NeuronGraph instance and reads neuron data from a UGX file.
It verifies that the number of nodes in the new graph is equal to the number of nodes
in the original graph, ensuring that the UGX file is read correctly.
Args:
graph: A fixture providing a neuron graph object.
Here is the call graph for this function:
◆ test_remove_soma_segment()
| def python_package.test.test_bindings.test_remove_soma_segment | ( | graph | ) |
Test that the removeSomaSegment() method correctly removes the soma segment.
Verifies that the removeSomaSegment() method correctly removes the soma
segment from the neuron graph. This is done by calling the method on the
test data neuron and verifying that the number of nodes decreases.
Args:
graph: A fixture providing a neuron graph object.
◆ test_save_preprocess()
| def python_package.test.test_bindings.test_save_preprocess | ( | graph | ) |
Test that the preprocess() method correctly reduces the number of nodes and can write the resulting neuron data to an SWC file.
Verifies that the preprocess() method correctly reduces the number of nodes
in the neuron graph. This is done by calling the method on the test data
neuron and verifying that the number of nodes decreases. The resulting
neuron data is written to an SWC file.
Args:
graph: A fixture providing a neuron graph object.
Here is the call graph for this function:
◆ test_set_nodes()
| def python_package.test.test_bindings.test_set_nodes | ( | graph | ) |
Test that the setNodes() method correctly replaces the nodes in the graph.
Verifies that the setNodes() method correctly replaces the nodes in the
graph. This is done by calling the method on the test data neuron and
verifying that the number of nodes does not change.
Args:
graph: A fixture providing a neuron graph object.
◆ test_splitedges()
| def python_package.test.test_bindings.test_splitedges | ( | graph | ) |
Test that the splitEdges() method correctly splits neuron edges once.
Verifies that the splitEdges() method correctly splits neuron edges once.
This is done by calling the method on the test data neuron, writing the
resulting neuron data to an SWC file, and verifying that the number of
nodes increases.
Args:
graph: A fixture providing a neuron graph object.
Here is the call graph for this function:
◆ test_splitedgesN()
| def python_package.test.test_bindings.test_splitedgesN | ( | graph | ) |
Test that the splitEdgesN() method correctly splits neuron edges multiple times.
Verifies that the splitEdgesN() method correctly splits neuron edges multiple
times. This is done by calling the method on the test data neuron N times,
writing the resulting neuron data to a UGX file each time, and verifying that
the number of nodes increases each time.
Args:
graph: A fixture providing a neuron graph object.
Here is the call graph for this function:
◆ test_swc_to_ugx()
| def python_package.test.test_bindings.test_swc_to_ugx | ( | graph | ) |
Test that the swc2ugx() method correctly converts a SWC file to a UGX file. This test first reads the number of nodes from the SWC file, then converts the SWC file to a UGX file using the swc2ugx() method. It then reads the number of nodes from the UGX file and verifies that the two numbers are equal, ensuring that the UGX file is correctly converted from the SWC file.
Here is the call graph for this function:
◆ test_topological_sort()
| def python_package.test.test_bindings.test_topological_sort | ( | graph | ) |
Test that the topologicalSort() method correctly sorts the nodes.
Verifies that the topologicalSort() method correctly sorts the nodes in
the graph. This is done by checking the result of the isTopologicallySorted()
method before and after calling the topologicalSort() method.
Args:
graph: A fixture providing a neuron graph object.
◆ test_ugx_to_swc()
| def python_package.test.test_bindings.test_ugx_to_swc | ( | graph | ) |
Test that the ugx2swc() method correctly reads neuron data from a UGX file.
This test first reads neuron data from a UGX file and checks the number of nodes.
Then, it creates a new NeuronGraph instance and reads neuron data from the same UGX
file. It verifies that the number of nodes in the new graph is equal to the number
of nodes in the original graph, ensuring that the UGX file is read correctly.
Args:
graph: A fixture providing a neuron graph object.
Here is the call graph for this function:
◆ test_write_to_ugx()
| def python_package.test.test_bindings.test_write_to_ugx | ( | graph | ) |
Test that the writeToFileUGX() method correctly writes neuron data to a UGX file.
Verifies that the writeToFileUGX() method correctly writes neuron data to a
UGX file. This is done by calling the method on the test data neuron,
writing the neuron data to a UGX file, then reading the neuron data from
the same UGX file. It verifies that the number of nodes in the new graph
is equal to the number of nodes in the original graph, ensuring that the
UGX file is written correctly.
Args:
graph: A fixture providing a neuron graph object.
Here is the call graph for this function:
Variable Documentation
◆ delta
| float python_package.test.test_bindings.delta = 2.0 |
