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High-level description

This file contains unit tests for the compute_morans_i function, which calculates Moran’s I statistic for spatial autocorrelation in a phylogenetic tree. The tests cover various scenarios, including single and multiple variables, custom weight matrices, and error handling.

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References

This file references the following symbols:

  • cassiopeia.tl.compute_morans_i: The function being tested, which calculates Moran’s I statistic.
  • cassiopeia.data.CassiopeiaTree: A class representing a phylogenetic tree.
  • cassiopeia.mixins.AutocorrelationError: An exception class for errors related to autocorrelation calculations.

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Symbols

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TestAutocorrelation

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Description

This class contains unit tests for the compute_morans_i function. It sets up a basic tree structure and example data for testing.

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Inputs

This class does not take any inputs.

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Outputs

This class does not return any outputs. It uses the unittest framework to run assertions and report test results.

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Internal Logic

The class defines several test methods, each testing a specific aspect of the compute_morans_i function:

  • test_simple_moran_single_variable: Tests the function with a single variable and compares the result to a known value.
  • test_moran_bivariate: Tests the function with multiple variables and compares the resulting correlation matrix to expected values.
  • test_moran_custom_weights: Tests the function with a user-provided weight matrix.
  • test_moran_exceptions: Tests various error conditions, such as invalid input data types and mismatched tree leaves.

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setUp

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Description

This method sets up the test environment by creating a basic tree structure and example data.

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Inputs

This method does not take any inputs.

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Outputs

This method does not explicitly return any outputs. It sets the self.basic_tree and self.X attributes, which are used by the test methods.

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Error Handling

The test_moran_exceptions method specifically tests for various error conditions that can occur in the compute_morans_i function, such as:

  • Passing non-numerical data in the X argument.
  • Providing a weight matrix (W) that does not have the same leaves as the tree.
  • Not providing any data for calculating autocorrelations.
  • Providing a weight matrix with incorrect leaves.

These error conditions are handled by raising AutocorrelationError exceptions with informative messages.