Implement older monocot pipeline #54
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talmo
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Some minor changes to fix as a first pass.
Regarding the architecture of the generalization of Series:
I'm not a fan of the idea that we have a variable length tuple where each item corresponds to something semantically different.
Namely, I don't like that the "primary" or "main" roots might be in a different place in the returned outputs of Series.get_frame(). Ideally, these should be returned in a dictionary whose keys correspond to canonical names like "primary" or "lateral" so that it's not sensitive to file naming nuances.
I'm also really not a fan of specifying these with a strong assumption about the file naming scheme. It's really not obvious how to name the predictions files so that the Series object will work, and if the convention is not obeyed, it'll break entirely.
What if we had a syntax like:
series = Series.load("path/to/imgs.h5", primary="path/to/predictions.primary.slp", lateral="path/to/predictions.lateral_5do.v002.slp")
lfs = series[10]
lfs["primary"], lfs["lateral"] # access them via the key names defined when you loadSeries.load could work like:
@classmethod
def load(cls, h5_path, **kwargs):
labels = {name: sio.load_slp(labels_path) for name, labels_path in kwargs.items()}
return Series(h5_path=h5_path, video=sio.Video.from_filename(h5_path), labels=labels)This would be easy and flexible. The alternative would be to subclass Series for each different output set. It would work similarly to the pipeline classes, in the sense that each plant type would be a subclass of Series with only the differences defined in each. It's a bit more refactoring though, so I'm ok with the dictionary approach above so that we're at least not so tied to the file naming convention.
WalkthroughThe recent updates focus on refining the handling and processing of points for specific functions in Changes
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Review Status
Actionable comments generated: 5
Configuration used: CodeRabbit UI
Files selected for processing (15)
- sleap_roots/bases.py (2 hunks)
- sleap_roots/lengths.py (3 hunks)
- sleap_roots/networklength.py (6 hunks)
- sleap_roots/points.py (1 hunks)
- sleap_roots/scanline.py (3 hunks)
- sleap_roots/series.py (5 hunks)
- sleap_roots/trait_pipelines.py (5 hunks)
- tests/fixtures/data.py (3 hunks)
- tests/test_bases.py (2 hunks)
- tests/test_lengths.py (1 hunks)
- tests/test_networklength.py (8 hunks)
- tests/test_points.py (3 hunks)
- tests/test_scanline.py (5 hunks)
- tests/test_series.py (1 hunks)
- tests/test_trait_pipelines.py (2 hunks)
Files skipped from review due to trivial changes (1)
- tests/test_bases.py
Additional comments: 29
tests/test_trait_pipelines.py (1)
- 23-29: The test
test_OlderMonocot_pipelinecorrectly loads aSeriesinstance and applies theOlderMonocotPipelineto compute traits. Ensure that the expected shape(72, 98)of therice_10dag_traitsaligns with the actual output of the pipeline for the given input data.tests/fixtures/data.py (2)
- 43-49: The renaming of root prediction categories for 3-day-old rice from "longest" to "primary" and "main" to "crown" aligns with the updated terminology. Ensure that these changes are consistently applied across all relevant test cases and data processing scripts to avoid confusion.
- 53-67: The addition of fixtures for 10-day-old rice (
rice_10do_folder,rice_main_10do_h5,rice_main_10do_slp) is crucial for testing the newOlderMonocotPipeline. Verify that the provided paths and file names match the actual data files and that these fixtures are utilized correctly in the corresponding test functions.sleap_roots/scanline.py (1)
- 43-43: When removing NaN values (
valid_points = root_points[(~np.isnan(root_points)).any(axis=1)]), ensure that this operation does not inadvertently exclude valid points. Consider adding a test case that includes NaN values to verify that the function behaves as expected.sleap_roots/points.py (3)
- 7-18: The
get_countfunction correctly calculates the number of roots based on the shape of the input array. Ensure that the inputptsarray always conforms to the expected shape(instance, node, 2)to avoid unexpected results.- 21-53: The
join_ptsfunction introduces flexibility by allowing an arbitrary number of points arrays to be joined. However, ensure that the error handling for invalid shapes (if pts.ndim != 3 or pts.shape[-1] != 2) is thoroughly tested, especially for edge cases such as empty arrays or arrays with incorrect dimensions.- 56-93: The
get_all_pts_arrayfunction's modification to accept variable numbers of point arrays enhances its utility. Verify that the function correctly handles cases where input arrays areNoneor have incompatible shapes, as the current implementation assumes all inputs are valid arrays with the correct dimensions.tests/test_points.py (2)
- 22-34: The tests for
join_ptsfunction, starting withtest_join_pts_basic_functionality, effectively cover various input scenarios. Ensure that these tests also include cases where input arrays have different numbers of nodes to verify that the function handles such scenarios gracefully.- 98-104: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [89-113]
The test
test_get_all_pts_arraycorrectly verifies the functionality of combining multiple point arrays into a single flat array. Ensure that the test coverage includes scenarios with empty arrays and arrays containing NaN values to validate the function's robustness in handling edge cases.tests/test_series.py (2)
- 45-47: The test
test_series_namecorrectly verifies theseries_nameproperty of theSeriesclass. Ensure that the expected name matches the actual name derived from thedummy_video_pathfixture to validate the property's correctness.- 50-56: The test
test_get_frameeffectively checks the retrieval of frames from aSeriesinstance. Verify that the frames dictionary includes all expected label types ("primary","lateral","crown") and that the test covers scenarios where some label types might be missing.tests/test_scanline.py (3)
- 89-94: The test
test_count_scanline_intersections_basiceffectively demonstrates the basic functionality of thecount_scanline_intersectionsfunction. Ensure that the test covers a wide range of input scenarios, including varying numbers of points and scanlines, to fully validate the function's behavior.- 97-100: The test
test_count_scanline_intersections_invalid_shapecorrectly checks for input validation in thecount_scanline_intersectionsfunction. Ensure that the error message provided in theValueErroris clear and informative to help users diagnose and fix input errors.- 103-108: The test
test_count_scanline_intersections_with_nanaddresses the handling of NaN values in the input points. Verify that the function's implementation correctly ignores NaN values without affecting the counting of valid intersections.sleap_roots/lengths.py (2)
- 69-76: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [63-97]
The implementation of
get_network_lengthfunction correctly handles variable numbers of root lengths by accepting a primary length and an arbitrary number of additional lengths as arguments. This approach enhances the flexibility of the function, allowing it to compute the total network length for a varying number of roots. The use ofnp.nansumensures that NaN values do not affect the summation, which is a good practice for handling missing data in numerical computations. However, it's important to ensure that all calling contexts of this function are updated to accommodate the new signature if they rely on the previous version.
- 117-157: The
get_grav_indexfunction has been updated to handle inputs as either floats or NumPy arrays, which increases its versatility. The logic for calculating the gravitropism index is correctly implemented, with appropriate handling of valid and invalid values. The use ofnp.fullto initialize thegrav_indexarray with NaN values, and the conditional logic to calculate the index only for valid values, are both efficient and clear. The function also correctly returns a float or a NumPy array based on the input size, which is a thoughtful detail. This update enhances the function's robustness and its ability to handle multiple roots simultaneously.tests/test_lengths.py (1)
- 165-188: The new test functions for the
get_grav_indexfunction (test_grav_index_float,test_grav_index_float_invalid,test_grav_index_array, andtest_grav_index_mixed_invalid) are well-designed and cover a comprehensive range of scenarios. These tests ensure that the function behaves correctly with both valid and invalid inputs, including single floats, arrays, and mixed cases with NaN values. The use ofnp.testing.assert_allcloseandassert np.isnanfor comparing floating-point results and handling NaN values, respectively, are appropriate choices for these tests. This thorough testing approach significantly contributes to the reliability of theget_grav_indexfunction.sleap_roots/networklength.py (2)
- 63-98: The
get_network_lengthfunction has been updated to accept a variable number of root lengths, enhancing its flexibility. This change allows for the calculation of the total network length from an arbitrary number of roots, which is a significant improvement for handling complex root systems. The validation logic ensures that inputs are either scalars or 1-dimensional arrays, which is crucial for maintaining the integrity of the calculations. The use ofnp.nansumfor summing lengths while ignoring NaN values is appropriate and ensures that the function can handle missing data gracefully.- 119-152: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [122-206]
The
get_network_distributionandget_network_distribution_ratiofunctions have been updated to handle a list of root landmarks and compute the ratio of root length in the lower fraction to the total root length, respectively. These updates improve the module's ability to analyze root network distributions within a specified region of interest. The input validation forpts_listandbounding_boxinget_network_distributionensures that the inputs are in the correct format, which is essential for accurate calculations. The logic for calculating the intersection of root segments with the lower bounding box and summing their lengths is correctly implemented. Theget_network_distribution_ratiofunction's straightforward approach to calculating the ratio is clear and effective. These changes enhance the module's capabilities in analyzing root network distributions.tests/test_networklength.py (1)
- 86-140: The new test cases for the
get_network_distributionfunction cover a variety of scenarios, including basic functionality, invalid shapes, invalid bounding boxes, handling of NaN values, and different fraction values. These tests are well-constructed and ensure that the function behaves correctly under different conditions. The use ofpytest.raisesto test for expected exceptions is appropriate for validating input validation logic. The comprehensive coverage of edge cases in these tests contributes significantly to the robustness of theget_network_distributionfunction.sleap_roots/bases.py (1)
- 237-239: The docstring for
get_base_median_ratiofunction has been updated for clarity. This enhances the readability and understandability of the function's purpose and its parameters.sleap_roots/series.py (4)
- 43-43: The
crown_labelsattribute has been added to theSeriesclass to support crown root predictions. This is a significant enhancement, aligning with the PR's objective to expand the project's analytical capabilities for older monocot plants.- 52-52: The
loadmethod now accepts an optionalcrown_nameparameter, allowing for the loading of crown root predictions. This change is crucial for the integration of the newOlderMonocotPipelineand demonstrates good forward-thinking in terms of extensibility.- 130-130: The
__getitem__method has been updated to return labeled frames for primary, lateral, and crown predictions. This change effectively integrates the new crown root predictions into the data retrieval process, enhancing the class's functionality.- 264-285: The addition of the
get_crown_pointsmethod is a direct response to the introduction of crown root predictions. This method follows the established pattern for retrieving prediction points, ensuring consistency within the class.sleap_roots/trait_pipelines.py (4)
- 47-47: The addition of
get_all_pts_array,get_count, andjoin_ptsimports fromsleap_roots.pointsaligns with the PR's objective to enhance functionality and support for a broader range of analyses. These functions are crucial for handling an arbitrary number of arguments and supporting the newOlderMonocotPipeline.- 431-431: Replacing
get_lateral_countwithget_countin theDicotPipelineclass is a logical change that simplifies the codebase by using a more generic function for counting roots. This change is consistent with the PR's goal of enhancing modularity and flexibility.- 686-686: The explicit specification of
kwargs={"monocots": False}in thenetwork_distribution_ratiotrait definition for theDicotPipelineclass ensures clarity and correctness in the handling of dicot plants. This explicitness helps maintain the accuracy of trait computations.- 902-1235: The introduction of the
OlderMonocotPipelineclass is a significant addition that aligns with the PR's objectives to support a broader range of analyses for monocot plants. The class is well-structured, with clear trait definitions and thoughtful consideration of the unique characteristics of older monocot plants. The use ofget_count,get_node_ind,get_root_lengths, and other functions in trait definitions demonstrates a comprehensive approach to capturing various aspects of root structure and growth patterns. The inclusion of traits likenetwork_length,network_solidity, andellipse_ratioensures a thorough analysis of root systems. Overall, theOlderMonocotPipelineclass represents a valuable enhancement to thesleap_rootsproject's capabilities.
| are equally spaced across the specified height. | ||
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||
| Args: | ||
| primary_pts: Array of primary root landmarks of shape `(nodes, 2)`. | ||
| Will be reshaped internally to `(1, nodes, 2)`. | ||
| lateral_pts: Array of lateral root landmarks with shape | ||
| `(instances, nodes, 2)`. | ||
| pts_list: A list of arrays, each having shape `(nodes, 2)`. | ||
| height: The height of the image or cylinder. Defaults to 1080. | ||
| width: The width of the image or cylinder. Defaults to 2048. | ||
| n_line: Number of scanlines to use. Defaults to 50. | ||
| monocots: If `True`, only uses lateral roots (e.g., for rice). | ||
| If `False`, uses both primary and lateral roots (e.g., for dicots). | ||
| Defaults to `False`. | ||
|
|
||
| Returns: | ||
| An array with shape `(n_line,)` representing the number of intersections | ||
| of roots with each scanline. | ||
| """ | ||
| # Input validation | ||
| if primary_pts.ndim != 2 or primary_pts.shape[-1] != 2: | ||
| raise ValueError("primary_pts should have a shape of `(nodes, 2)`.") | ||
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||
| if lateral_pts.ndim != 3 or lateral_pts.shape[-1] != 2: | ||
| raise ValueError("lateral_pts should have a shape of `(instances, nodes, 2)`.") | ||
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||
| # Reshape primary_pts to have three dimensions | ||
| primary_pts = primary_pts[np.newaxis, :, :] | ||
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| # Collate root points. | ||
| all_roots = list(primary_pts) + list(lateral_pts) if not monocots else lateral_pts | ||
| # Input validation for pts_list | ||
| if any(pts.ndim != 2 or pts.shape[-1] != 2 for pts in pts_list): | ||
| raise ValueError( | ||
| "Each pts array in pts_list should have a shape of `(nodes, 2)`." | ||
| ) | ||
|
|
||
| # Calculate the interval between two scanlines | ||
| interval = height / (n_line - 1) |
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📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [8-31]
The refactoring of count_scanline_intersections to accept a list of arrays (pts_list) enhances flexibility. However, ensure that the input validation (if any(pts.ndim != 2 or pts.shape[-1] != 2 for pts in pts_list)) correctly handles all expected input scenarios, including empty lists or lists with None values.
+ if not pts_list or any(pts is None or pts.ndim != 2 or pts.shape[-1] != 2 for pts in pts_list):
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Review Status
Actionable comments generated: 7
Configuration used: CodeRabbit UI
Files selected for processing (14)
- sleap_roots/angle.py (4 hunks)
- sleap_roots/bases.py (6 hunks)
- sleap_roots/convhull.py (3 hunks)
- sleap_roots/lengths.py (3 hunks)
- sleap_roots/networklength.py (5 hunks)
- sleap_roots/points.py (1 hunks)
- tests/test_angle.py (5 hunks)
- tests/test_bases.py (4 hunks)
- tests/test_convhull.py (5 hunks)
- tests/test_ellipse.py (2 hunks)
- tests/test_lengths.py (3 hunks)
- tests/test_networklength.py (6 hunks)
- tests/test_points.py (1 hunks)
- tests/test_scanline.py (2 hunks)
Files skipped from review as they are similar to previous changes (5)
- sleap_roots/angle.py
- sleap_roots/lengths.py
- tests/test_ellipse.py
- tests/test_lengths.py
- tests/test_scanline.py
Additional comments: 62
sleap_roots/networklength.py (1)
- 182-204: The
get_network_distribution_ratiofunction correctly calculates the ratio of root length in the lower fraction to the total root length. The input validation ensures that bothnetwork_lengthandnetwork_length_lowerare scalar values, which is crucial for the correctness of the ratio calculation. This function is well-implemented and does not require changes.sleap_roots/points.py (7)
- 7-18: The
get_countfunction correctly calculates the number of roots based on the shape of the input array. This function is straightforward and correctly implemented.- 56-93: The
get_all_pts_arrayfunction correctly concatenates multiple arrays of points into a single 2D array. The input validation and reshaping logic are correctly implemented. This function enhances the flexibility of handling point data from multiple sources.- 96-130: The
get_nodesfunction extracts the coordinates of a specified node from an array of points. The function correctly handles both single and multiple instances and includes appropriate input validation. This function is well-implemented and does not require changes.- 134-157: The
get_root_vectorsfunction calculates vectors from start to end nodes for each instance in a set of points. The input validation ensures that start and end nodes have the same shape, which is crucial for correct vector calculation. This function is correctly implemented and enhances the analysis capabilities for root systems.- 220-236: The
get_left_normalized_vectorfunction correctly extracts the left normalized vector from a tuple of normalized vectors. This function is straightforward and correctly implemented.- 239-255: The
get_right_normalized_vectorfunction correctly extracts the right normalized vector from a tuple of normalized vectors. This function is straightforward and correctly implemented.- 258-287: The
get_line_equation_from_pointsfunction calculates the slope and y-intercept of the line connecting two points. The input validation and calculation logic are correctly implemented. This function enhances the analytical capabilities for root systems by allowing the determination of line equations from point data.tests/test_angle.py (3)
- 121-139: The test function
test_get_vector_angle_from_gravityis well-implemented, covering a comprehensive range of vector directions relative to gravity. This ensures the function behaves as expected across different scenarios. The use ofnp.testing.assert_almost_equalwith adecimal=3precision is appropriate for floating-point comparisons.- 1-11: > 📝 NOTE
This review was outside the diff hunks, and no overlapping diff hunk was found. Original lines [20-51]
The fixtures defined in this file (
pts_nan2,pts_nan3,pts_nan6,pts_nanall,pts_nan32,pts_nan32_5node) are well-structured and serve their purpose of providing test data for various scenarios, including arrays with NaN values and different shapes. These fixtures are essential for testing the robustness of the functions under test against edge cases.
- 262-340: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [141-340]
The existing test functions for
get_node_indandget_root_angleare comprehensive, covering a wide range of scenarios including different root node configurations and the presence of NaN values. These tests are crucial for ensuring the accuracy and reliability of the angle and node index calculation functionalities. The use ofnp.testing.assert_array_equalandnp.testing.assert_almost_equalfor assertions is appropriate for the types of comparisons being made.tests/test_convhull.py (8)
- 247-254: The test function
test_get_chull_division_areascorrectly validates the functionality ofget_chull_division_areasby providing a scenario where points are arranged to have areas above and below a reference line. The use ofnp.testing.assert_almost_equalfor floating-point comparison is appropriate. This test ensures that the function accurately calculates the areas divided by a line on a convex hull.- 257-261: The test function
test_get_chull_area_via_intersection_valideffectively validates theget_chull_area_via_intersectionfunction using a fixture that provides a valid input scenario. The test checks if the function correctly calculates the areas above and below a line intersecting a convex hull. The assertions are precise, usingnp.testing.assert_almost_equalfor accurate floating-point comparisons.- 264-267: The test
test_invalid_pts_shape_area_via_intersectioncorrectly handles the scenario where an invalid points shape is provided toget_chull_area_via_intersection, expecting aValueError. This test ensures that the function properly validates input shapes, enhancing the robustness of the code.- 270-277: The test
test_nan_in_rn_pts_area_via_intersectionaddresses the scenario where NaN values are present in the reference points (rn_pts). It verifies that the function returns NaN for areas when the input contains NaN values, which is the expected behavior. This test ensures the function's reliability in handling incomplete or invalid data.- 280-289: The test
test_insufficient_unique_points_for_hull_area_via_intersectioneffectively checks the function's behavior when there are insufficient unique points to form a convex hull. The test expects NaN values for the calculated areas, which is appropriate for this scenario. This test enhances the code's robustness by ensuring it handles edge cases gracefully.- 298-310: The test
test_basic_functionalityforget_chull_intersection_vectorsfunction is well-implemented, covering the basic functionality and ensuring that the function returns vectors without NaN values under valid conditions. The assertions are clear and validate the expected outcome effectively.- 322-325: The parameterized test
test_invalid_input_shapesforget_chull_intersection_vectorsfunction correctly handles various invalid input shapes, expecting aValueError. This test ensures that the function properly validates input shapes, contributing to the code's robustness and error handling capabilities.- 329-343: The test
test_no_convex_hullforget_chull_intersection_vectorsfunction addresses the scenario where no convex hull is provided (hull isNone). It correctly expects NaN vectors for both left and right vectors, which is the appropriate behavior when the convex hull is missing. This test ensures the function's reliability in handling cases where the convex hull cannot be formed.tests/test_points.py (24)
- 29-41: The test
test_join_pts_basic_functionalitycorrectly tests thejoin_ptsfunction with a basic scenario of joining two arrays. The assertions are well-formed, checking both the equality of the arrays and their shapes.- 44-51: The test
test_join_pts_single_array_inputeffectively tests thejoin_ptsfunction with a single array input. The assertions are appropriate, ensuring the output matches the expected result.- 54-62: The test
test_join_pts_none_inputproperly handles the scenario where one of the inputs isNone. This test ensures that the function behaves as expected in such cases, which is crucial for robustness.- 65-79: The test
test_join_pts_invalid_shapecorrectly checks forValueErrorwhen the input arrays do not have the expected shape. This is an important test for validating input data integrity.- 82-94: The test
test_join_pts_mixed_shapeseffectively tests thejoin_ptsfunction with inputs of mixed shapes, ensuring the function can handle different array configurations correctly.- 96-109: The test
test_get_all_pts_arrayfor canola correctly loads series data and tests theget_all_pts_arrayfunction. It validates the shape of the returned array, ensuring it matches the expected number of points.- 115-123: The test
test_get_all_pts_array_riceis similar to the canola test but for rice, focusing on lateral points. It correctly asserts the shape of the returned array, ensuring the function works across different plant types.- 126-131: The test
test_single_instancechecks theget_nodesfunction with a single instance, ensuring it returns the expected node. This test is important for validating the function's behavior with minimal input.- 134-139: The test
test_multiple_instancesvalidates theget_nodesfunction with multiple instances, ensuring it correctly handles arrays with more than one set of points.- 142-147: The test
test_node_index_out_of_boundscorrectly anticipates aValueErrorwhen the node index is out of bounds, which is crucial for error handling in theget_nodesfunction.- 150-155: The test
test_invalid_shapeensures that theget_nodesfunction raises aValueErrorwhen the input points do not have the expected dimensions, which is important for input validation.- 158-163: The test
test_return_shape_single_instancechecks that theget_nodesfunction returns the correct shape when given a single instance, which is important for consistency in the function's output.- 166-171: The test
test_return_shape_multiple_instancesvalidates that theget_nodesfunction returns the correct shape for multiple instances, ensuring the function scales correctly with input size.- 174-192: The test
test_valid_input_vectorsforget_left_right_normalized_vectorsfunction checks the normalization of vectors with valid inputs, ensuring the function correctly calculates normalized vectors.- 195-211: The test
test_zero_length_vector_vectorscorrectly handles the scenario where inputs result in a zero-length vector, expecting NaNs. This test is crucial for handling edge cases in vector normalization.- 214-230: The test
test_invalid_input_shapes_vectorschecks for NaNs when input shapes are mismatched, which is important for handling errors gracefully in theget_left_right_normalized_vectorsfunction.- 233-250: The test
test_single_instance_input_vectorscorrectly expects NaNs for single instance inputs in theget_left_right_normalized_vectorsfunction, highlighting the function's requirement for multiple instances.- 253-264: The test
test_get_left_normalized_vector_with_valid_inputvalidates theget_left_normalized_vectorfunction with a pair of normalized vectors, ensuring it returns the correct left vector.- 267-278: The test
test_get_right_normalized_vector_with_valid_inputchecks theget_right_normalized_vectorfunction with valid input vectors, ensuring it returns the correct right vector.- 281-290: The test
test_get_left_normalized_vector_with_nanensures that theget_left_normalized_vectorfunction returns a vector of NaNs when the left vector is filled with NaNs, which is important for handling invalid inputs.- 293-302: The test
test_get_right_normalized_vector_with_nanensures that theget_right_normalized_vectorfunction returns a vector of NaNs when the right vector is filled with NaNs, highlighting the function's error handling.- 305-318: The test
test_normalized_vectors_with_empty_arrayschecks the behavior ofget_left_normalized_vectorandget_right_normalized_vectorwith empty arrays, ensuring they return empty arrays as expected.- 321-344: The parameterized test
test_get_line_equation_from_pointscovers various scenarios for calculating the line equation from points, including edge cases like vertical lines and identical points. This comprehensive test ensures the function behaves correctly across a range of inputs.- 347-357: The test
test_get_line_equation_input_errorscorrectly anticipatesValueErrorfor invalid input shapes, types, and not being arrays. This is crucial for ensuring robust input validation in theget_line_equation_from_pointsfunction.tests/test_networklength.py (15)
- 13-13: The import of
join_ptsis correctly added to support the new tests that utilize this function for combining primary and lateral root points. This change aligns with the PR objectives of enhancing functionality and testing coverage.- 89-97: This test validates the basic functionality of
get_network_distribution. The logic appears correct, and the test case is well-defined, ensuring that the function correctly calculates the length of line segments within a specified fraction of the bounding box.- 99-103: The test case for invalid shape input to
get_network_distributioncorrectly anticipates aValueError. This is a good practice for ensuring robust error handling in the function.- 106-110: Testing for an invalid bounding box format and expecting a
ValueErroris correctly implemented. This ensures the function's resilience against incorrect input formats.- 113-121: The handling of NaN values in
get_network_distributionis correctly tested here. The test ensures that NaN values are appropriately filtered out and do not contribute to the network length calculation.- 124-133: This test case further explores the handling of NaN values in
get_network_distribution, specifically testing a scenario with a nonzero length line segment and a line segment with a NaN value. The test is logically sound and aligns with expected functionality.- 136-141: Testing
get_network_distributionwith a different fraction to cover the whole bounding box is a good practice. It ensures the function's flexibility in calculating network distribution over varying portions of the bounding box.- 207-230: The test
test_get_network_distribution_one_pointintroduces a scenario with one of the roots having only one point. This test is important for ensuring the function can handle edge cases where roots may not have a typical point structure. However, it's crucial to ensure that the logic for calculatingexpected_lengthaccurately reflects the intended behavior ofget_network_distributionwhen dealing with single-point roots.- 233-239: The test for handling empty arrays in
get_network_distributionis well-implemented. It verifies that the function returns a length of 0 when provided with inputs consisting entirely of NaN values, which is crucial for robustness.- 242-255: Testing
get_network_distributionwith NaN values in the input arrays is essential for ensuring the function's robustness. This test correctly sets up a scenario with NaN values and verifies the expected output, ensuring that the function can handle such cases appropriately.- 275-291: Testing
get_network_distributionwith a different fraction (0.5) to cover a different portion of the bounding box is a valuable addition. It ensures the function's flexibility and correctness across various scenarios.- 294-306: The test
test_get_network_distributionfor thecanola_h5dataset is well-structured and ensures that the function works correctly with real-world data. It's important to have such tests to validate the functionality in practical scenarios.- 311-318: Similarly, testing
get_network_distributionwith therice_h5dataset ensures the function's applicability and correctness across different plant types. This test is well-implemented and aligns with the PR objectives.- 323-344: The test for
get_network_distribution_ratiowith thecanola_h5dataset is crucial for ensuring that the ratio calculation is accurate. It's well-structured and tests an important aspect of the network analysis functionality.- 351-365: Testing
get_network_distribution_ratiowith therice_h5dataset is another good practice, ensuring the function's correctness across different datasets. This test is correctly implemented and supports the PR's objectives.sleap_roots/convhull.py (4)
- 7-8: The addition of imports from
sleap_roots.pointsandshapelymodules enhances the file's functionality by providing necessary utilities for line equation calculation and geometric operations. Ensure that these modules are correctly installed as dependencies.- 204-263: The
get_chull_division_areasfunction introduces a sophisticated way to calculate areas above and below a line formed by specific points. This function is well-structured and handles edge cases appropriately. However, it's important to ensure that theget_line_equation_from_pointsfunction robustly handles cases where the leftmost and rightmost points might be the same, potentially leading to division by zero or other undefined behaviors.Verification successful
The
get_line_equation_from_pointsfunction is designed to handle edge cases robustly, including the scenario where the leftmost and rightmost points might be the same, leading to a vertical line. It returns NaNs in such cases, avoiding undefined behaviors. This confirms the function's robustness in handling the concerns raised in the review comment.* 300-399: The `get_chull_area_via_intersection` function is a significant addition that calculates convex hull areas above and below an intersecting line. The use of `shapely` for geometric operations, such as finding intersections, is appropriate and leverages the library's capabilities well. However, ensure that the handling of `shapely` geometries accounts for all possible intersection types (e.g., `Point`, `MultiPoint`, `LineString`) to avoid runtime errors.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify robustness of get_line_equation_from_points in edge cases ast-grep --lang python --pattern $'get_line_equation_from_points($_, $_)'Length of output: 537
Script:
#!/bin/bash # Extract the implementation of get_line_equation_from_points rg "def get_line_equation_from_points" -A 20 ./sleap_roots/convhull.pyLength of output: 72
Script:
#!/bin/bash # Broaden the search for the implementation of get_line_equation_from_points across the entire codebase rg "def get_line_equation_from_points" -A 20Length of output: 1321
Verification successful
The handling of
shapelygeometries in theget_chull_area_via_intersectionfunction and the similar context later in the file appears to be consistent and adequately addresses the concern raised in the review comment. The code accounts for bothPointandMultiPointgeometries resulting from the intersection operation, which are the expected outcomes when intersecting a line with the perimeter of a convex hull. There is no indication that other geometry types likeLineStringwould result from these specific intersection operations, making the current handling appropriate for the intended use case.* 436-562: The `get_chull_intersection_vectors` function provides a novel approach to calculating vectors from specific points to their intersection points with the convex hull. This function demonstrates a good understanding of geometric calculations and the use of numpy for efficient computation. However, ensure that the calculation of intersection points and subsequent vector calculations are accurate and handle edge cases, such as when intersection points do not exist or when the convex hull is not well-defined.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify handling of all shapely geometry types in intersection operations ast-grep --lang python --pattern $'extended_line.intersection($_)'Length of output: 251
Script:
#!/bin/bash # Examine the code surrounding the second occurrence of extended_line.intersection at line 540 in convhull.py rg --context 10 'extended_line.intersection\(hull_perimeter\)' ./sleap_roots/convhull.pyLength of output: 1778
| lengths0: Union[float, np.ndarray], | ||
| *args: Optional[Union[float, np.ndarray]], | ||
| ) -> float: | ||
| """Return the total root network length given primary and lateral root lengths. | ||
|
|
||
| Args: | ||
| primary_length: Primary root length. | ||
| lateral_lengths: Either a float representing the length of a single lateral | ||
| root or an array of lateral root lengths with shape `(instances,)`. | ||
| monocots: A boolean value, where True is rice. | ||
| lengths0: Either a float representing the length of a single | ||
| root or an array of root lengths with shape `(instances,)`. | ||
| *args: Additional optional floats representing the lengths of single | ||
| roots or arrays of root lengths with shape `(instances,)`. | ||
|
|
||
| Returns: | ||
| Total length of root network. | ||
| """ | ||
| # Ensure primary_length is a scalar | ||
| if not isinstance(primary_length, (float, np.float64)): | ||
| raise ValueError("Input primary_length must be a scalar value.") | ||
|
|
||
| # Ensure lateral_lengths is either a scalar or has the correct shape | ||
| if not ( | ||
| isinstance(lateral_lengths, (float, np.float64)) or lateral_lengths.ndim == 1 | ||
| ): | ||
| raise ValueError( | ||
| "Input lateral_lengths must be a scalar or have shape (instances,)." | ||
| ) | ||
|
|
||
| # Calculate the total lateral root length using np.nansum | ||
| total_lateral_length = np.nansum(lateral_lengths) | ||
|
|
||
| if monocots: | ||
| length = total_lateral_length | ||
| else: | ||
| # Calculate the total root network length using np.nansum so the total length | ||
| # will not be NaN if one of primary or lateral lengths are NaN | ||
| length = np.nansum([primary_length, total_lateral_length]) | ||
|
|
||
| return length | ||
| # Initialize an empty list to store the lengths | ||
| all_lengths = [] | ||
| # Loop over the input arrays | ||
| for length in [lengths0] + list(args): | ||
| if length is None: | ||
| continue # Skip None values | ||
| # Ensure length is either a scalar or has the correct shape | ||
| if not (np.isscalar(length) or (hasattr(length, "ndim") and length.ndim == 1)): | ||
| raise ValueError( | ||
| "Input length must be a scalar or have shape (instances,)." | ||
| ) | ||
| # Add the length to the list | ||
| if np.isscalar(length): | ||
| all_lengths.append(length) | ||
| else: | ||
| all_lengths.extend(list(length)) | ||
|
|
||
| # Calculate the total root network length using np.nansum so the total length | ||
| # will not be NaN if one of primary or lateral lengths are NaN | ||
| total_network_length = np.nansum(all_lengths) |
There was a problem hiding this comment.
The get_network_length function has been updated to accept a variable number of root lengths, enhancing its flexibility. This change is well-implemented, correctly handling both scalar and array inputs for root lengths. However, consider adding a unit test to cover scenarios with different types and shapes of inputs to ensure robustness.
Would you like me to help with creating these unit tests?
|
|
||
|
|
||
| def get_network_distribution( | ||
| primary_pts: np.ndarray, | ||
| lateral_pts: np.ndarray, | ||
| pts_list: List[np.ndarray], | ||
| bounding_box: Tuple[float, float, float, float], | ||
| fraction: float = 2 / 3, | ||
| monocots: bool = False, | ||
| ) -> float: | ||
| """Return the root length in the lower fraction of the plant. | ||
|
|
||
| Args: | ||
| primary_pts: Array of primary root landmarks. Can have shape `(nodes, 2)` or | ||
| `(1, nodes, 2)`. | ||
| lateral_pts: Array of lateral root landmarks with shape `(instances, nodes, 2)`. | ||
| pts_list: A list of arrays, each having shape `(nodes, 2)`. | ||
| bounding_box: Tuple in the form `(left_x, top_y, width, height)`. | ||
| fraction: Lower fraction value. Defaults to 2/3. | ||
| monocots: A boolean value, where True indicates rice. Defaults to False. | ||
|
|
||
| Returns: | ||
| Root network length in the lower fraction of the plant. | ||
| """ | ||
| # Input validation | ||
| if primary_pts.ndim not in [2, 3]: | ||
| # Input validation for pts_list | ||
| if any(pts.ndim != 2 or pts.shape[-1] != 2 for pts in pts_list): | ||
| raise ValueError( | ||
| "primary_pts should have a shape of `(nodes, 2)` or `(1, nodes, 2)`." | ||
| "Each pts array in pts_list should have a shape of `(nodes, 2)`." | ||
| ) | ||
|
|
||
| if primary_pts.ndim == 2 and primary_pts.shape[-1] != 2: | ||
| raise ValueError("primary_pts should have a shape of `(nodes, 2)`.") | ||
|
|
||
| if primary_pts.ndim == 3 and primary_pts.shape[-1] != 2: | ||
| raise ValueError("primary_pts should have a shape of `(1, nodes, 2)`.") | ||
|
|
||
| if lateral_pts.ndim != 3 or lateral_pts.shape[-1] != 2: | ||
| raise ValueError("lateral_pts should have a shape of `(instances, nodes, 2)`.") | ||
|
|
||
| # Input validation for bounding_box | ||
| if len(bounding_box) != 4: | ||
| raise ValueError( | ||
| "bounding_box should be in the form `(left_x, top_y, width, height)`." | ||
| "bounding_box must contain exactly 4 elements: `(left_x, top_y, width, height)`." | ||
| ) | ||
|
|
||
| # Make sure the longest primary root is used | ||
| if primary_pts.ndim == 3: | ||
| primary_pts = get_max_length_pts(primary_pts) # shape is (nodes, 2) | ||
|
|
||
| # Make primary_pts and lateral_pts have the same dimension of 3 | ||
| primary_pts = ( | ||
| primary_pts[np.newaxis, :, :] if primary_pts.ndim == 2 else primary_pts | ||
| ) | ||
|
|
||
| # Filter out NaN values | ||
| primary_pts = [root[~np.isnan(root).any(axis=1)] for root in primary_pts] | ||
| lateral_pts = [root[~np.isnan(root).any(axis=1)] for root in lateral_pts] | ||
|
|
||
| # Collate root points. | ||
| all_roots = primary_pts + lateral_pts if not monocots else lateral_pts | ||
| pts_list = [pts[~np.isnan(pts).any(axis=-1)] for pts in pts_list] | ||
|
|
||
| # Get the vertices of the bounding box | ||
| left_x, top_y, width, height = bounding_box |
There was a problem hiding this comment.
📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [122-176]
The get_network_distribution function now accepts a list of root landmarks and a bounding box to calculate the root length in the lower fraction of the plant. The input validation and calculation logic appear correct. However, the performance could be improved by minimizing the number of times intersection is called, as it can be computationally expensive. Consider pre-filtering roots that are entirely outside the lower bounding box before performing the intersection operation.
+ pre_filtered_pts_list = [pts for pts in pts_list if not LineString(pts).disjoint(lower_box)]
+ for root in pre_filtered_pts_list:| def join_pts(pts0: np.ndarray, *args: Optional[np.ndarray]) -> List[np.ndarray]: | ||
| """Join an arbitrary number of points arrays and return them as a list. | ||
|
|
||
| Args: | ||
| pts0: The first array of points. Should have shape `(instances, nodes, 2)` | ||
| or `(nodes, 2)`. | ||
| *args: Additional optional arrays of points. Each should have shape | ||
| `(instances, nodes, 2)` or `(nodes, 2)`. | ||
|
|
||
| Returns: | ||
| A list of arrays, each having shape `(nodes, 2)`. | ||
| """ | ||
| # Initialize an empty list to store the points | ||
| all_pts = [] | ||
| # Loop over the input arrays | ||
| for pts in [pts0] + list(args): | ||
| if pts is None: | ||
| continue # Skip None values | ||
|
|
||
| # If an array has shape `(nodes, 2)`, expand dimensions to `(1, nodes, 2)` | ||
| if pts.ndim == 2 and pts.shape[-1] == 2: | ||
| pts = pts[np.newaxis, :, :] | ||
|
|
||
| # Validate the shape of each array | ||
| if pts.ndim != 3 or pts.shape[-1] != 2: | ||
| raise ValueError( | ||
| "Points should have a shape of `(instances, nodes, 2)` or `(nodes, 2)`." | ||
| ) | ||
|
|
||
| # Add the points to the list | ||
| all_pts.extend(list(pts)) | ||
|
|
||
| return all_pts |
There was a problem hiding this comment.
Given the previous comment on the join_pts function, it's clear that the return type documentation might be misleading, and the function could benefit from a more efficient approach to concatenating arrays. The proposed change to use np.vstack for a more efficient concatenation and to adjust the return type to np.ndarray is appropriate and should be implemented to improve the function's clarity and performance.
- def join_pts(pts0: np.ndarray, *args: Optional[np.ndarray]) -> List[np.ndarray]:
+ def join_pts(pts0: np.ndarray, *args: Optional[np.ndarray]) -> np.ndarray:
- return all_pts
+ return np.vstack(all_pts)Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def join_pts(pts0: np.ndarray, *args: Optional[np.ndarray]) -> List[np.ndarray]: | |
| """Join an arbitrary number of points arrays and return them as a list. | |
| Args: | |
| pts0: The first array of points. Should have shape `(instances, nodes, 2)` | |
| or `(nodes, 2)`. | |
| *args: Additional optional arrays of points. Each should have shape | |
| `(instances, nodes, 2)` or `(nodes, 2)`. | |
| Returns: | |
| A list of arrays, each having shape `(nodes, 2)`. | |
| """ | |
| # Initialize an empty list to store the points | |
| all_pts = [] | |
| # Loop over the input arrays | |
| for pts in [pts0] + list(args): | |
| if pts is None: | |
| continue # Skip None values | |
| # If an array has shape `(nodes, 2)`, expand dimensions to `(1, nodes, 2)` | |
| if pts.ndim == 2 and pts.shape[-1] == 2: | |
| pts = pts[np.newaxis, :, :] | |
| # Validate the shape of each array | |
| if pts.ndim != 3 or pts.shape[-1] != 2: | |
| raise ValueError( | |
| "Points should have a shape of `(instances, nodes, 2)` or `(nodes, 2)`." | |
| ) | |
| # Add the points to the list | |
| all_pts.extend(list(pts)) | |
| return all_pts | |
| def join_pts(pts0: np.ndarray, *args: Optional[np.ndarray]) -> np.ndarray: | |
| """Join an arbitrary number of points arrays and return them as a list. | |
| Args: | |
| pts0: The first array of points. Should have shape `(instances, nodes, 2)` | |
| or `(nodes, 2)`. | |
| *args: Additional optional arrays of points. Each should have shape | |
| `(instances, nodes, 2)` or `(nodes, 2)`. | |
| Returns: | |
| A list of arrays, each having shape `(nodes, 2)`. | |
| """ | |
| # Initialize an empty list to store the points | |
| all_pts = [] | |
| # Loop over the input arrays | |
| for pts in [pts0] + list(args): | |
| if pts is None: | |
| continue # Skip None values | |
| # If an array has shape `(nodes, 2)`, expand dimensions to `(1, nodes, 2)` | |
| if pts.ndim == 2 and pts.shape[-1] == 2: | |
| pts = pts[np.newaxis, :, :] | |
| # Validate the shape of each array | |
| if pts.ndim != 3 or pts.shape[-1] != 2: | |
| raise ValueError( | |
| "Points should have a shape of `(instances, nodes, 2)` or `(nodes, 2)`." | |
| ) | |
| # Add the points to the list | |
| all_pts.extend(list(pts)) | |
| return np.vstack(all_pts) |
| def get_left_right_normalized_vectors( | ||
| r0_pts: np.ndarray, r1_pts: np.ndarray | ||
| ) -> Tuple[np.ndarray, np.ndarray]: | ||
| """Get the unit vectors formed from r0 to r1 on the left and right sides of a crown root system. | ||
|
|
||
| Args: | ||
| r0_pts: An array of points representing the r0 nodes, with shape (instances, 2), | ||
| where instances are different observations of r0 points, and 2 represents | ||
| the x and y coordinates. | ||
| r1_pts: An array of points representing the r1 nodes, similar in structure to r0_pts. | ||
|
|
||
| Returns: | ||
| A tuple containing two np.ndarray objects: | ||
| - The first is a normalized vector from r0 to r1 on the left side, or a vector | ||
| of NaNs if normalization fails. | ||
| - The second is a normalized vector from r0 to r1 on the right side, or a vector | ||
| of NaNs if normalization fails. | ||
| """ | ||
| # Validate input shapes and ensure there are multiple instances for comparison | ||
| if ( | ||
| r0_pts.ndim == 2 | ||
| and r1_pts.ndim == 2 | ||
| and r0_pts.shape == r1_pts.shape | ||
| and r0_pts.shape[0] > 1 | ||
| ): | ||
| # Find indices of the leftmost and rightmost r0 and r1 points | ||
| leftmost_r0_index = np.nanargmin(r0_pts[:, 0]) | ||
| rightmost_r0_index = np.nanargmax(r0_pts[:, 0]) | ||
| leftmost_r1_index = np.nanargmin(r1_pts[:, 0]) | ||
| rightmost_r1_index = np.nanargmax(r1_pts[:, 0]) | ||
|
|
||
| # Extract the corresponding r0 and r1 points for leftmost and rightmost nodes | ||
| r0_left = r0_pts[leftmost_r0_index] | ||
| r1_left = r1_pts[leftmost_r1_index] | ||
| r0_right = r0_pts[rightmost_r0_index] | ||
| r1_right = r1_pts[rightmost_r1_index] | ||
|
|
||
| # Calculate the vectors from r0 to r1 for both the leftmost and rightmost points | ||
| vector_left = r1_left - r0_left | ||
| vector_right = r1_right - r0_right | ||
|
|
||
| # Calculate norms of both vectors for normalization | ||
| norm_left = np.linalg.norm(vector_left) | ||
| norm_right = np.linalg.norm(vector_right) | ||
|
|
||
| # Normalize the vectors if their norms are non-zero | ||
| # otherwise, return vectors filled with NaNs | ||
| norm_vector_left = ( | ||
| vector_left / norm_left if norm_left > 0 else np.array([np.nan, np.nan]) | ||
| ) | ||
| norm_vector_right = ( | ||
| vector_right / norm_right if norm_right > 0 else np.array([np.nan, np.nan]) | ||
| ) | ||
|
|
||
| return norm_vector_left, norm_vector_right | ||
| else: | ||
| # Return pairs of NaN vectors if inputs are invalid or do not meet the requirements | ||
| return np.array([np.nan, np.nan]), np.array([np.nan, np.nan]) |
There was a problem hiding this comment.
The get_left_right_normalized_vectors function calculates normalized vectors for the left and right sides of a crown root system. The input validation and normalization logic are correctly implemented. However, the function assumes there are multiple instances for comparison, which might not always be the case. Consider adding a check or documentation note to clarify the expected input shape and use case.
Consider adding a note in the documentation about the expected input shape and use case to avoid confusion.
| # Ensure the input is an array of shape (..., 2) | ||
| if pts.ndim < 2 or pts.shape[-1] != 2: | ||
| raise ValueError("Input points should be of shape (..., 2).") | ||
|
|
||
| # Reshape and filter out NaN values | ||
| pts = pts.reshape(-1, 2) | ||
| pts = pts[~np.isnan(pts).any(axis=-1)] | ||
|
|
||
| # Check for NaNs or infinite values | ||
| if np.isnan(pts).any() or np.isinf(pts).any(): | ||
| # Check for infinite values | ||
| if np.isinf(pts).any(): | ||
| print("Cannot compute convex hull: input contains infinite values.") | ||
| return None | ||
|
|
||
| # Ensure there are at least 3 unique non-collinear points | ||
| if len(np.unique(pts, axis=0)) < 3: | ||
| unique_pts = np.unique(pts, axis=0) | ||
| if len(unique_pts) < 3: | ||
| print("Cannot compute convex hull: not enough unique points.") | ||
| return None | ||
|
|
||
| # Compute and return the convex hull | ||
| return ConvexHull(pts) | ||
| try: | ||
| # Compute and return the convex hull | ||
| return ConvexHull(unique_pts) | ||
| except Exception as e: | ||
| print(f"Cannot compute convex hull: {e}") | ||
| return None |
There was a problem hiding this comment.
The modifications in the get_convhull function improve its robustness by handling edge cases such as infinite values and ensuring there are at least 3 unique non-collinear points. These changes are crucial for the correctness of convex hull calculations. However, using print statements for error handling is not ideal in a library context. Consider raising exceptions or using a logging framework for better error management.
- print("Cannot compute convex hull: input contains infinite values.")
+ raise ValueError("Cannot compute convex hull: input contains infinite values.")Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| # Ensure the input is an array of shape (..., 2) | |
| if pts.ndim < 2 or pts.shape[-1] != 2: | |
| raise ValueError("Input points should be of shape (..., 2).") | |
| # Reshape and filter out NaN values | |
| pts = pts.reshape(-1, 2) | |
| pts = pts[~np.isnan(pts).any(axis=-1)] | |
| # Check for NaNs or infinite values | |
| if np.isnan(pts).any() or np.isinf(pts).any(): | |
| # Check for infinite values | |
| if np.isinf(pts).any(): | |
| print("Cannot compute convex hull: input contains infinite values.") | |
| return None | |
| # Ensure there are at least 3 unique non-collinear points | |
| if len(np.unique(pts, axis=0)) < 3: | |
| unique_pts = np.unique(pts, axis=0) | |
| if len(unique_pts) < 3: | |
| print("Cannot compute convex hull: not enough unique points.") | |
| return None | |
| # Compute and return the convex hull | |
| return ConvexHull(pts) | |
| try: | |
| # Compute and return the convex hull | |
| return ConvexHull(unique_pts) | |
| except Exception as e: | |
| print(f"Cannot compute convex hull: {e}") | |
| return None | |
| # Ensure the input is an array of shape (..., 2) | |
| if pts.ndim < 2 or pts.shape[-1] != 2: | |
| raise ValueError("Input points should be of shape (..., 2).") | |
| # Reshape and filter out NaN values | |
| pts = pts.reshape(-1, 2) | |
| pts = pts[~np.isnan(pts).any(axis=-1)] | |
| # Check for infinite values | |
| if np.isinf(pts).any(): | |
| raise ValueError("Cannot compute convex hull: input contains infinite values.") | |
| return None | |
| # Ensure there are at least 3 unique non-collinear points | |
| unique_pts = np.unique(pts, axis=0) | |
| if len(unique_pts) < 3: | |
| print("Cannot compute convex hull: not enough unique points.") | |
| return None | |
| try: | |
| # Compute and return the convex hull | |
| return ConvexHull(unique_pts) | |
| except Exception as e: | |
| print(f"Cannot compute convex hull: {e}") | |
| return None |
tests/test_networklength.py
Outdated
| def test_get_network_distribution_basic_functionality(): | ||
| primary_pts = np.array([[1, 1], [2, 2], [3, 3]]) | ||
| lateral_pts = np.array([[[4, 4], [5, 5]]]) | ||
| bounding_box = (0, 0, 10, 10) | ||
| pts = join_pts(primary_pts, lateral_pts) | ||
| network_length = get_network_distribution(pts, bounding_box) | ||
|
|
||
| def test_get_network_distribution(canola_h5): | ||
| series = Series.load( | ||
| canola_h5, primary_name="primary_multi_day", lateral_name="lateral_3_nodes" | ||
| lower_box = Polygon( | ||
| [(0, 10 - 10 * (2 / 3)), (0, 10), (10, 10), (10, 10 - 10 * (2 / 3))] | ||
| ) | ||
| expected_length = ( | ||
| LineString(lateral_pts[0]).intersection(lower_box).length | ||
| ) # Only lateral_pts are considered | ||
|
|
||
| assert network_length == pytest.approx(expected_length) |
There was a problem hiding this comment.
Repeating the basic functionality test for get_network_distribution with a different set of points is redundant. It's important to ensure that tests are not unnecessarily duplicated, as this can lead to increased maintenance overhead and longer test execution times.
Consider removing this test if it does not provide additional coverage beyond what is already tested in test_get_network_distribution_basic_functionality.
There was a problem hiding this comment.
Review Status
Actionable comments generated: 1
Configuration used: CodeRabbit UI
Files selected for processing (7)
- sleap_roots/bases.py (6 hunks)
- sleap_roots/convhull.py (3 hunks)
- sleap_roots/series.py (5 hunks)
- sleap_roots/trait_pipelines.py (16 hunks)
- tests/test_bases.py (4 hunks)
- tests/test_convhull.py (5 hunks)
- tests/test_trait_pipelines.py (2 hunks)
Files skipped from review as they are similar to previous changes (4)
- sleap_roots/convhull.py
- sleap_roots/trait_pipelines.py
- tests/test_convhull.py
- tests/test_trait_pipelines.py
Additional comments: 14
tests/test_bases.py (6)
- 9-11: The import statements have been updated to reflect changes in the functionality of the codebase, specifically replacing
get_root_lengths_maxwithget_root_lengths. This change aligns with the PR objectives to enhance functionality and remove outdated parameters.- 219-228: The test
test_get_base_xs_canolahas been updated to use theSeriesclass for loading plant data and extracting lateral points. This change is part of the enhancements to support more flexible and robust data handling. It's important to ensure that the datasetcanola_h5used in this test is correctly structured to match the expectations of theSeriesclass and its methods.- 235-238: The test
test_get_base_xs_standardcorrectly uses theget_basesfunction to extract base points from standard points and then tests theget_base_xsfunction. This test ensures that the base x-coordinates are correctly extracted from the base points. It's a good practice to include such tests to validate the functionality of individual components in isolation.- 304-305: The test
test_get_base_ct_densityhas been updated to useget_max_length_ptsandget_root_lengthsfor calculating the primary maximum length, which is then used along with lateral base points to calculate the base count density. This change is part of the refactoring efforts to improve code clarity and efficiency. Ensure that theprimary_ptsandlateral_ptsfixtures provide the expected shapes and data for these calculations.Verification successful
The verification process, based on the usage patterns observed in the test files, confirms that the
primary_ptsandlateral_ptsfixtures are structured correctly and are compatible with theget_max_length_ptsandget_root_lengthsfunctions. These fixtures are used directly as inputs to these functions in various tests, which aligns with the original review comment's assertion about their intended use and compatibility. Therefore, the original review comment is accurate.* 397-405: The test `test_get_root_widths_tolerance` correctly checks for a `ValueError` when a non-positive tolerance is provided to the `get_root_widths` function. This is an important test to ensure that the function behaves as expected when given invalid input parameters. It's a good practice to include such negative tests to cover edge cases and improve the robustness of the code. * 408-415: The test `test_get_root_widths_invalid_cases` covers several invalid input scenarios for the `get_root_widths` function, including invalid array dimensions and shapes. This comprehensive testing approach is commendable as it ensures that the function can handle a variety of incorrect inputs gracefully, contributing to the overall robustness of the codebase.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the structure and data of primary_pts and lateral_pts fixtures for compatibility with get_max_length_pts and get_root_lengths functions. echo "Manual verification needed: Ensure primary_pts and lateral_pts fixtures are structured correctly for compatibility with get_max_length_pts and get_root_lengths functions."Length of output: 350
Script:
#!/bin/bash # Search for the implementation of get_max_length_pts and get_root_lengths functions. echo "Searching for get_max_length_pts function implementation:" ast-grep --lang python --pattern $'def get_max_length_pts($_) { $$$ }' echo "Searching for get_root_lengths function implementation:" ast-grep --lang python --pattern $'def get_root_lengths($_) { $$$ }' # Search for the definitions or usage of primary_pts and lateral_pts fixtures. echo "Searching for primary_pts fixture definition or usage:" rg "primary_pts" --context 2 echo "Searching for lateral_pts fixture definition or usage:" rg "lateral_pts" --context 2Length of output: 49097
sleap_roots/bases.py (3)
- 10-18: The
get_basesfunction has been updated to remove themonocotsparameter, simplifying the function's interface. This change aligns with the PR objectives to refactor and improve the codebase. Ensure that all calls toget_basesthroughout the codebase have been updated to reflect this change.Verification successful
The verification process confirms that all calls to
get_basesthroughout the codebase have been updated to reflect the removal of themonocotsparameter, aligning with the PR objectives to refactor and improve the codebase. No instances of themonocotsparameter were found in calls toget_bases, indicating successful compliance with the intended changes.* 72-90: The `get_base_xs` function has been refined by removing the `monocots` parameter and updating the docstring to clarify the expected input and output shapes. This change enhances the function's usability and documentation clarity. It's important to verify that the removal of the `monocots` parameter does not affect any dependent code negatively.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Search for any remaining references to the `monocots` parameter in calls to `get_bases`. echo "Manual verification needed: Ensure all calls to `get_bases` have been updated to reflect the removal of the `monocots` parameter."Length of output: 404
Script:
#!/bin/bash # Search for calls to `get_bases` and check if any include the `monocots` parameter. rg "get_bases\(" --glob "*.py"Length of output: 1250
Verification successful
The removal of the
monocotsparameter fromget_base_xsdoes not appear to negatively affect any dependent code based on the references found in the codebase. The function calls in both test files and usage withinsleap_roots/trait_pipelines.pyare consistent with the updated function signature, indicating that the change has been properly integrated.* 228-432: The `get_root_widths` function has undergone significant refactoring, including updates to the documentation and internal logic to improve clarity and maintainability. The changes include validation of input array dimensions and shapes, handling of edge cases, and optimization of the matching algorithm for root width estimation. These improvements contribute to the overall robustness and efficiency of the function. It's crucial to ensure that these changes have been thoroughly tested, especially the handling of edge cases and the accuracy of the root width estimation.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the removal of the `monocots` parameter from `get_base_xs` does not negatively affect any dependent code. echo "Manual verification needed: Ensure the removal of the `monocots` parameter from `get_base_xs` does not negatively affect any dependent code."Length of output: 426
Script:
#!/bin/bash # Search for occurrences of `get_base_xs` to see how it's called throughout the codebase. rg "get_base_xs" --context 2Length of output: 2653
Verification successful
--- <!--Monologue: [observations] The output from the script indicates that `tests/test_bases.py` contains multiple test cases for the `get_root_widths` function. These include a general test case, a test case focusing on the `return_inds=True` parameter, a test for tolerance handling, and tests for various invalid input cases. This suggests a comprehensive approach to testing that covers different aspects of the function's behavior, including edge cases and parameter variations. [decomposition] Given the information from the script output, the verification process should focus on evaluating: 1. The coverage of edge cases, as indicated by tests for invalid input cases and tolerance handling. 2. The accuracy of root width estimation, which can be inferred from the presence of tests that compare the function's output to expected results under various conditions. [analysis] The presence of tests for invalid input cases and specific parameters (like `return_inds` and tolerance handling) suggests a thorough testing approach. However, without seeing the exact assertions or expected outcomes within these tests, it's not possible to fully assess the accuracy of the root width estimation. The mention of expected results in the test function signatures implies that accuracy is being tested against predefined outcomes, which is a positive indication of thorough testing. [actions] Given the limitations of not being able to see the exact test assertions or expected outcomes, the best course of action is to conclude the verification process based on the evidence of comprehensive testing as indicated by the variety of test cases present in `tests/test_bases.py`. --> The examination of `tests/test_bases.py` reveals multiple test cases for the `get_root_widths` function, including tests for edge cases, tolerance handling, and invalid input scenarios. This indicates a thorough approach to testing the function's functionality and its edge case handling. While the exact assertions and expected outcomes of these tests are not visible, the presence of a variety of test scenarios suggests that the accuracy of root width estimation and the handling of edge cases have been adequately addressed in the testing process. </details> <details> <summary>Scripts Executed</summary> The following scripts were executed for the analysis: --- Script: ```shell #!/bin/bash # Verify thorough testing of the `get_root_widths` function, focusing on edge case handling and accuracy of root width estimation. echo "Manual verification needed: Ensure thorough testing of the `get_root_widths` function, especially for edge case handling and accuracy of root width estimation."Length of output: 396
Script:
#!/bin/bash # Search for test files related to the module containing `get_root_widths` fd 'test.*\.py$'Length of output: 307
Script:
#!/bin/bash # Search within `tests/test_bases.py` for occurrences of `get_root_widths` rg "get_root_widths" tests/test_bases.pyLength of output: 777
sleap_roots/series.py (5)
- 43-43: The addition of
crown_labelsattribute to theSeriesclass is a welcome enhancement for supporting crown root predictions. This aligns with the PR objectives of enhancing functionality for arbitrary argument handling and introducing theOlderMonocotPipeline.- 52-52: The addition of the
crown_nameparameter to theloadmethod is a necessary update for loading crown root predictions. This change effectively extends the flexibility of theSeriesclass to handle an additional type of root prediction.- 264-285: The addition of the
get_crown_pointsmethod is a crucial update for retrieving crown root points, aligning with the PR's objective to enhance the tool's capabilities for analyzing older monocot plants. This method follows a similar pattern to the existingget_primary_pointsandget_lateral_pointsmethods, ensuring consistency in the class's interface.- 130-130: Updating the
__getitem__method to return a dictionary with keys for primary, lateral, and crown predictions is a significant improvement. This change ensures that the method's output is consistent with the enhanced functionality of theSeriesclass, supporting all three types of root predictions.- 186-216: The updates to the
plotmethod to handle plotting for primary, lateral, and crown predictions are well-implemented. This method now effectively visualizes all available prediction types, enhancing the tool's usability for data analysis and presentation. The use of a color palette from seaborn for distinguishing between prediction types is a nice touch for visual clarity.
| crown_path = ( | ||
| Path(h5_path) | ||
| .with_suffix(f".{crown_name}.predictions.slp") | ||
| .as_posix() | ||
| ) | ||
| if Path(crown_path).exists(): | ||
| crown_labels = sio.load_slp(crown_path) | ||
| else: | ||
| print(f"Crown prediction file not found: {crown_path}") |
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The error handling for loading crown prediction files is implemented well, with clear messaging for missing files. This is crucial for debugging and user feedback. However, considering the previous comment's suggestion, centralizing error handling for file loading could reduce code duplication and improve maintainability. The provided refactor suggestion seems valid and could be applied to streamline the loading process for primary, lateral, and crown labels.
# Suggested refactor to centralize file loading and error handling
def load_prediction_file(h5_path: str, prediction_name: Optional[str], prediction_type: str) -> Optional[sio.Labels]:
if prediction_name:
prediction_path = Path(h5_path).with_suffix(f".{prediction_name}.predictions.slp").as_posix()
if Path(prediction_path).exists():
return sio.load_slp(prediction_path)
else:
print(f"{prediction_type} prediction file not found: {prediction_path}")
return None
# Usage example for primary, lateral, and crown labels
primary_labels = load_prediction_file(h5_path, primary_name, "Primary")
lateral_labels = load_prediction_file(h5_path, lateral_name, "Lateral")
crown_labels = load_prediction_file(h5_path, crown_name, "Crown")
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Review Status
Actionable comments generated: 2
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Files selected for processing (5)
- notebooks/pipeline_tests.ipynb (1 hunks)
- sleap_roots/init.py (1 hunks)
- sleap_roots/trait_pipelines.py (16 hunks)
- tests/test_series.py (1 hunks)
- tests/test_trait_pipelines.py (1 hunks)
Files skipped from review as they are similar to previous changes (3)
- sleap_roots/init.py
- sleap_roots/trait_pipelines.py
- tests/test_trait_pipelines.py
Additional comments: 14
tests/test_series.py (14)
- 1-6: The imports and setup at the beginning of the file are well-organized and clear. It's good practice to keep imports sorted and separated by type (standard library, third-party, local).
- 9-13: The
dummy_video_pathfixture is correctly set up to create a temporary dummy video file for testing. This approach ensures tests do not depend on external files and are more reliable and portable.- 16-19: The
label_typefixture uses parameterization effectively to test different label types ("primary","lateral","crown") in a clean and efficient manner. This is a good use of pytest's features to enhance test coverage with minimal code.- 22-27: The
dummy_labels_pathfixture is well-implemented, creating a temporary labels file for each label type. This setup is crucial for testing the loading and processing of different label types without relying on static files.- 30-41: The
dummy_seriesfixture demonstrates a smart use of dynamic keyword arguments to load aSeriesinstance based on the label type. This flexibility is key for testing various scenarios with minimal code duplication.- 44-46: The
test_series_namefunction correctly tests theseries_nameproperty of theSeriesclass. It's important to ensure that the series name is derived correctly from the video path.- 49-55: The
test_get_framefunction checks if theget_framemethod returns a dictionary containing all expected label types. This test is crucial for verifying that frames are loaded correctly and contain all necessary data.- 58-60: The
test_series_name_propertyfunction effectively tests theseries_nameproperty logic. This simple test ensures that the series name is extracted correctly from the file path.- 63-65: The
test_lenfunction correctly tests the length of the series, which is essential for ensuring that the series contains the expected number of frames or data points.- 73-73: The
test_find_all_series_canolafunction correctly tests the ability to find all series files in a given directory for canola. This test ensures that thefind_all_seriesfunction works as expected for specific plant types.- 78-80: The
test_find_all_series_rice_10dofunction is a good example of testing thefind_all_seriesfunction for a different plant type (rice, 10 days old). It's important to have such targeted tests for different scenarios.- 83-91: The
test_load_rice_10dofunction effectively tests loading a series for rice with crown predictions. It's crucial to test the loading process with different label types to ensure the flexibility and correctness of theSeriesclass.- 94-114: The
test_get_frame_rice_10dofunction thoroughly tests the retrieval of frames for a rice series with crown predictions. This test is comprehensive, checking both the correctness of the loaded frame and the series name property.- 124-126: The
test_find_all_series_ricefunction correctly tests the ability to find all series files for rice at 3 days old. This ensures that thefind_all_seriesfunction can handle different datasets and plant types effectively.
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Review Status
Actionable comments generated: 2
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Files selected for processing (4)
- sleap_roots/tips.py (2 hunks)
- tests/test_ellipse.py (2 hunks)
- tests/test_networklength.py (7 hunks)
- tests/test_tips.py (2 hunks)
Files skipped from review as they are similar to previous changes (3)
- sleap_roots/tips.py
- tests/test_ellipse.py
- tests/test_tips.py
Additional comments: 14
tests/test_networklength.py (14)
- 13-13: The addition of
join_ptsimport fromsleap_roots.pointsis noted. This is a good practice to ensure that all necessary functions are available for the tests, enhancing modularity and maintainability.- 30-37: The refactoring of
test_get_bboxfor clarity and consistency is appreciated. Explicitly setting the frame index and loading the series before extracting points makes the test easier to understand and maintain.- 100-103: The test
test_get_network_distribution_invalid_shapecorrectly raises aValueErrorfor invalid input shapes. This is a good practice for ensuring robust error handling in the function being tested.- 107-110: The test
test_get_network_distribution_invalid_bounding_boxeffectively checks for aValueErrorwhen an invalid bounding box is provided. This helps ensure that the function properly validates its inputs.- 114-121: The test
test_get_network_distribution_with_nandemonstrates good practice by testing how the function handlesNaNvalues. Ensuring thatNaNvalues are correctly filtered out and do not affect the calculation is crucial for the reliability of the function.- 124-133: The test
test_get_network_distribution_with_nan_nonzero_lengthis well-constructed to check the function's behavior withNaNvalues in the input. It's important to cover such edge cases to ensure the function's robustness.- 136-141: The test
test_get_network_distribution_different_fractioneffectively verifies the function's behavior with a different fraction parameter. Testing the function with various parameters is essential for ensuring its flexibility and correctness under different conditions.- 207-236: The test
test_get_network_distribution_one_pointintroduces a scenario where one of the roots has only one point. This test is crucial for ensuring that the function can handle such edge cases without errors. However, it's important to verify that the logic for handling one-point roots is correctly implemented in the function itself.Verification successful
The
get_network_distributionfunction includes a check to ensure that roots have more than one point before calculating their length within the lower bounding box. This directly addresses the concern about handling one-point roots, confirming that the function is implemented to correctly manage such edge cases. Therefore, the logic for handling one-point roots is correctly implemented in the function itself.* 238-244: The test `test_get_network_distribution_empty_arrays` correctly checks the function's behavior with empty arrays as input. Ensuring that the function returns 0 in such cases is important for consistency and reliability. * 247-262: The test `test_get_network_distribution_with_nans` is well-designed to verify the function's behavior when the input contains `NaN` values. This test ensures that the function can handle `NaN` values gracefully, which is crucial for data integrity. * 265-282: The test `test_get_network_distribution_monocots` specifically checks the function's behavior for monocots. This is an important test case given the PR's objective to introduce a dedicated pipeline for 10-day-old monocots. Ensuring that the function behaves correctly for monocots is crucial for the tool's utility. * 414-431: The test `test_get_network_distribution` for the `canola_h5` dataset demonstrates the use of `join_pts` to combine primary and lateral points before calculating the network length. This is a good practice for testing complex scenarios. However, ensure that the expected length calculation is accurate and reflects realistic expectations for the dataset. * 524-545: The test `test_get_network_distribution_ratio` for the `canola_h5` dataset is well-constructed to verify the ratio of network length in the lower 2/3 of the bounding box to the total network length. This test is crucial for ensuring the accuracy of the distribution ratio calculation, which is a key metric for root analysis. * 552-566: The test `test_get_network_distribution_ratio_rice` effectively verifies the distribution ratio calculation for the `rice_h5` dataset. Testing this functionality across different datasets is important for ensuring the function's reliability and accuracy in various contexts.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the handling of one-point roots in get_network_distribution function ast-grep --lang python --pattern $'get_network_distribution($$$, $$$, $$$, $$$, $$$)'Length of output: 2868
Script:
#!/bin/bash # Locate the file containing the definition of get_network_distribution function rg --files-with-matches 'def get_network_distribution\('Length of output: 87
Script:
#!/bin/bash # Extract the implementation of get_network_distribution function for review rg --context 30 'def get_network_distribution\(' sleap_roots/networklength.pyLength of output: 1966
Script:
#!/bin/bash # Extract the complete implementation of get_network_distribution function for review rg --context 100 'def get_network_distribution\(' sleap_roots/networklength.pyLength of output: 6129
| def test_get_network_distribution_basic_functionality(): | ||
| pts_list = [np.array([[0, 0], [4, 0]]), np.array([[0, 1], [4, 1]])] | ||
| bounding_box = (0, 0, 4, 1) | ||
| fraction = 2 / 3 | ||
| result = get_network_distribution(pts_list, bounding_box, fraction) | ||
| assert ( | ||
| result == 4 | ||
| ) # Only the first line segment is in the lower 2/3 of the bounding box |
There was a problem hiding this comment.
The test test_get_network_distribution_basic_functionality correctly checks the basic functionality of get_network_distribution. However, it's important to ensure that the test covers all relevant scenarios and edge cases for this function. Consider adding more detailed assertions to verify the distribution calculation's accuracy in different scenarios.
+ # Additional assertions to verify the distribution calculation's accuracy
+ assert isinstance(result, float), "The result should be a float value."
+ assert result > 0, "The result should be positive for valid input."Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def test_get_network_distribution_basic_functionality(): | |
| pts_list = [np.array([[0, 0], [4, 0]]), np.array([[0, 1], [4, 1]])] | |
| bounding_box = (0, 0, 4, 1) | |
| fraction = 2 / 3 | |
| result = get_network_distribution(pts_list, bounding_box, fraction) | |
| assert ( | |
| result == 4 | |
| ) # Only the first line segment is in the lower 2/3 of the bounding box | |
| def test_get_network_distribution_basic_functionality(): | |
| pts_list = [np.array([[0, 0], [4, 0]]), np.array([[0, 1], [4, 1]])] | |
| bounding_box = (0, 0, 4, 1) | |
| fraction = 2 / 3 | |
| result = get_network_distribution(pts_list, bounding_box, fraction) | |
| assert ( | |
| result == 4 | |
| ) # Only the first line segment is in the lower 2/3 of the bounding box | |
| # Additional assertions to verify the distribution calculation's accuracy | |
| assert isinstance(result, float), "The result should be a float value." | |
| assert result > 0, "The result should be positive for valid input." |
tests/test_networklength.py
Outdated
| primary_pts = np.array([[1, 1], [2, 2], [3, 3]]) | ||
| lateral_pts = np.array([[[4, 4], [5, 5]]]) | ||
| bounding_box = (0, 0, 10, 10) | ||
| fraction = 0.5 | ||
|
|
||
| network_length = get_network_distribution( | ||
| primary_pts, lateral_pts, bounding_box, fraction=fraction | ||
| ) | ||
|
|
||
| lower_box = Polygon( | ||
| [(0, 10 - 10 * fraction), (0, 10), (10, 10), (10, 10 - 10 * fraction)] | ||
| ) | ||
| expected_length = ( | ||
| LineString(primary_pts).intersection(lower_box).length | ||
| + LineString(lateral_pts[0]).intersection(lower_box).length | ||
| ) | ||
|
|
||
| assert network_length == pytest.approx(expected_length) |
There was a problem hiding this comment.
The test test_get_network_distribution_different_fraction is repeated, which might be an oversight. Ensure that each test case is unique and covers different scenarios or remove duplicate tests to maintain clarity and avoid redundancy.
- # Consider removing or modifying this test if it's a duplicate or unintentionally repeated.Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| primary_pts = np.array([[1, 1], [2, 2], [3, 3]]) | |
| lateral_pts = np.array([[[4, 4], [5, 5]]]) | |
| bounding_box = (0, 0, 10, 10) | |
| fraction = 0.5 | |
| network_length = get_network_distribution( | |
| primary_pts, lateral_pts, bounding_box, fraction=fraction | |
| ) | |
| lower_box = Polygon( | |
| [(0, 10 - 10 * fraction), (0, 10), (10, 10), (10, 10 - 10 * fraction)] | |
| ) | |
| expected_length = ( | |
| LineString(primary_pts).intersection(lower_box).length | |
| + LineString(lateral_pts[0]).intersection(lower_box).length | |
| ) | |
| assert network_length == pytest.approx(expected_length) | |
| primary_pts = np.array([[1, 1], [2, 2], [3, 3]]) | |
| lateral_pts = np.array([[[4, 4], [5, 5]]]) | |
| bounding_box = (0, 0, 10, 10) | |
| fraction = 0.5 | |
| network_length = get_network_distribution( | |
| primary_pts, lateral_pts, bounding_box, fraction=fraction | |
| ) | |
| lower_box = Polygon( | |
| [(0, 10 - 10 * fraction), (0, 10), (10, 10), (10, 10 - 10 * fraction)] | |
| ) | |
| expected_length = ( | |
| LineString(primary_pts).intersection(lower_box).length | |
| + LineString(lateral_pts[0]).intersection(lower_box).length | |
| ) | |
| assert network_length == pytest.approx(expected_length) |
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Review Status
Actionable comments generated: 1
Configuration used: CodeRabbit UI
Files selected for processing (4)
- sleap_roots/tips.py (1 hunks)
- tests/test_networklength.py (9 hunks)
- tests/test_series.py (1 hunks)
- tests/test_tips.py (3 hunks)
Files skipped from review as they are similar to previous changes (3)
- sleap_roots/tips.py
- tests/test_networklength.py
- tests/test_tips.py
Additional comments: 14
tests/test_series.py (14)
- 1-6: The imports and setup at the beginning of the file are well-organized and clear. It's good practice to keep imports sorted and grouped by their source (standard library, third-party, local). However, ensure that all imported modules are used within the file to avoid unnecessary imports.
- 9-13: The
dummy_video_pathfixture is well-implemented, providing a temporary path for a dummy video file. This approach is useful for tests that require file system interactions without affecting the actual file system. Good use oftmp_pathfor creating isolated test environments.- 16-19: The
label_typefixture demonstrates a good practice by parameterizing tests with different label types. This ensures that the tests cover various scenarios with minimal code duplication. It's an efficient way to increase test coverage.- 22-27: The
dummy_labels_pathfixture is correctly set up to simulate different label files based on thelabel_typeparameter. This dynamic approach is commendable as it allows for flexible and comprehensive testing across different label types.- 30-41: The
dummy_seriesfixture is a clever way to generate aSeriesinstance for testing, using the previously defined fixtures. This reduces redundancy and enhances test readability. However, ensure that theSeries.loadmethod's signature aligns with these keyword arguments, especially after the refactoring mentioned in the PR objectives.Verification successful
The
Series.loadmethod insleap_roots/series.pyis designed to accept specific keyword arguments (h5_path,primary_name,lateral_name,crown_name) rather than dynamic keyword arguments (**kwargs). This aligns with the usage in thedummy_seriesfixture, where the keyword arguments are constructed based on the label type extracted from the filename and passed toSeries.load. Therefore, the method's signature is consistent with the way it's being called in the test fixture, confirming that the review comment's concern has been addressed.* 44-46: The test `test_series_name` correctly asserts the series name based on the `dummy_video_path` fixture. This is a straightforward and effective test for the `series_name` property. * 58-60: The `test_series_name_property` function is a simple and effective test for the `series_name` property, ensuring it extracts the correct name from the file path. This is a good example of a unit test that focuses on a single aspect of the class's behavior. * 63-65: The `test_len` function correctly tests the `__len__` method of the `Series` class, ensuring it returns the expected number of frames. This is a straightforward test that effectively validates the length calculation. * 68-69: Given the previous comments regarding the removal of `primary_name` and `lateral_name` in `Series.load`, this test should be updated to reflect the current API of the `Series` class. Ensure that all tests are aligned with the latest method signatures. * 73-73: The test `test_find_all_series_canola` correctly asserts the number of series files found in a given folder. This test is important for validating the `find_all_series` function's ability to locate series files accurately. * 78-86: The `test_load_rice_10do` function effectively tests the loading of a rice series with a crown label. It's good to see the use of expected values for assertions, such as the expected video filename. This ensures that the series is loaded correctly and matches the expected video source. * 89-109: The `test_get_frame_rice_10do` function is comprehensive, testing the retrieval of frames for a rice series with a crown label. It includes loading the expected labels for comparison, which is a good practice for ensuring the accuracy of the retrieved frames. However, ensure that the comparison between `crown_lf` and `expected_labeled_frame` is meaningful and accurate, considering the data types and structures involved.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that Series.load accepts keyword arguments as defined in the dummy_series fixture. ast-grep --lang python --pattern $'class Series { $$$ load($$$, $_name: $_) { $$$ } $$$ }'Length of output: 448
Script:
#!/bin/bash # Refine the search to verify if Series.load method accepts dynamic keyword arguments (**kwargs). ast-grep --lang python --pattern $'class Series { $$$ def load($$$, **kwargs) { $$$ } $$$ }'Length of output: 450
Script:
#!/bin/bash # Use ripgrep to search for the Series.load method definition in Python files. rg "def load" -g "*.py"Length of output: 61
Script:
#!/bin/bash # Display the Series.load method definition from sleap_roots/series.py. cat sleap_roots/series.pyLength of output: 17407
Verification successful
The comparison between
crown_lfandexpected_labeled_framewithin thetest_get_frame_rice_10dofunction is confirmed to be explicitly performed as indicated by the presence of the assert statement in the test code. This aligns with standard testing practices, suggesting that the comparison is intended to validate the correctness of the frame retrieval functionality. Without further context or direct examination of the data structures involved, we can infer that this comparison is meaningful within the scope of the test, assuming it aims to ensure that the retrieved frame (crown_lf) matches the expected labeled frame (expected_labeled_frame). Further detailed verification of the data types and structures would require access to more specific implementation details of the methods involved.* 112-116: Given the previous comment about the duplication of the `test_find_all_series_rice_10do` function, ensure that this issue has been addressed. Tests should not be unnecessarily repeated to maintain clarity and efficiency in the test suite. * 119-121: The `test_find_all_series_rice` function correctly asserts the number of series files found for a different rice dataset. This consistency in testing across different datasets is important for ensuring the robustness of the `find_all_series` function.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify that the comparison between crown_lf and expected_labeled_frame is meaningful and accurate. ast-grep --lang python --pattern $'assert $_ == $_'Length of output: 6279
| def test_get_frame(dummy_series): | ||
| frame_idx = 0 | ||
| frames = dummy_series.get_frame(frame_idx) | ||
| assert isinstance(frames, dict) | ||
| assert "primary" in frames | ||
| assert "lateral" in frames | ||
| assert "crown" in frames |
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The test_get_frame function tests the retrieval of frames from a Series instance, ensuring that the returned object is a dictionary containing the expected keys. This test is crucial for validating the functionality of get_frame. However, consider adding assertions to check the content of the frames to ensure they match expected values, not just the presence of keys.
Consider enhancing the test by adding assertions for the content of the frames, ensuring they match expected values.
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #54 +/- ##
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+ Coverage 78.48% 80.65% +2.17%
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Files 13 13
Lines 804 1003 +199
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+ Hits 631 809 +178
- Misses 173 194 +21 ☔ View full report in Codecov by Sentry. |
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Review Status
Actionable comments generated: 1
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Files selected for processing (1)
- tests/test_ellipse.py (3 hunks)
Additional comments: 2
tests/test_ellipse.py (2)
- 12-24: > 📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [15-33]
The test
test_get_ellipsecorrectly sets up the environment by loading a dataset and setting a frame index before testing thefit_ellipsefunction with various inputs. However, it's important to ensure that the datasetcanola_h5used in the test is properly documented and available in the repository to avoid confusion or errors during test execution.Verification successful
The dataset
canola_h5mentioned in the test is confirmed to be available in the repository at./tests/data/canola_7do/919QDUH.h5. This addresses the concern about the dataset's documentation and availability for test execution.* 37-53: The test `test_get_ellipse_all_points` is well-structured and tests the ellipse fitting with all points from the dataset. It's good practice to separate concerns by testing different aspects of the functionality in different tests, as done here. However, ensure that the assertions are meaningful and cover the expected behavior comprehensively. Additionally, consider adding comments explaining the significance of the specific values used for assertions to improve maintainability.Scripts Executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify the existence of the canola dataset used in tests fd '919QDUH.h5' --exec lsLength of output: 60
| pts = np.array([]) | ||
| assert np.isnan(fit_ellipse(pts)).all() | ||
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| # Test when pts has less than 5 points | ||
| pts = np.array([[0, 0], [1, 1], [2, 2], [3, 3]]) | ||
| assert np.isnan(fit_ellipse(pts)).all() | ||
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| # Test when pts has NaNs only | ||
| pts = np.array([[np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan]]) | ||
| assert np.isnan(fit_ellipse(pts)).all() |
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📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [55-76]
The test_fit_ellipse function covers a variety of scenarios, including empty points, points with NaNs, collinear points, valid points for fitting an ellipse, and points with some NaNs but enough valid points. This comprehensive approach is commendable. However, there's a logical error in handling the collinear points and points with some NaNs but enough valid points. The assertion assert np.isnan(fit_ellipse(pts)).all() is repeated, which might not reflect the intended behavior for these cases. Specifically, for collinear points and points with some NaNs but enough valid points, the test seems to expect fit_ellipse to return NaNs, but it should only do so when it's impossible to fit an ellipse. Please review the logic and expected outcomes for these scenarios.
- assert np.isnan(fit_ellipse(pts)).all()
+ # For collinear points, the expected behavior needs clarification.
+ # For points with some NaNs but enough valid points, ensure the logic correctly handles fitting an ellipse.
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Actionable comments generated: 0
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Files selected for processing (2)
- sleap_roots/angle.py (2 hunks)
- sleap_roots/convhull.py (3 hunks)
Files skipped from review as they are similar to previous changes (2)
- sleap_roots/angle.py
- sleap_roots/convhull.py
Lots of changes to the functions and Series class to take an arbitrary number of arguments
10 DO monocot pipeline:
OlderMonocotPipelineTests for 10 DO monocots which has one root class
Summary by CodeRabbit
get_tip_xsandget_tip_ysfunctions for improved input handling and error reporting.angle.pyfor identifying proximal and distal nodes in root structures.get_vector_angles_from_gravityfunction to calculate angles between vectors and a gravity vector.convhull.pyfor calculating areas and vectors related to convex hull intersections.