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# parameter_estimators_test.py

## High-level description

This file contains unit tests for the `parameter_estimators.py` module in Cassiopeia. The tests cover functions that estimate parameters related to mutation rates and missing data in lineage tracing data.

## Code Structure

The `TestCassiopeiaTree` class contains multiple test methods, each testing different aspects of the parameter estimation functions. The tests are structured around two example trees: `self.discrete_tree` and `self.continuous_tree`, representing discrete and continuous character data respectively.

## Symbols

### `TestCassiopeiaTree`

#### Description

A class containing unit tests for parameter estimation functions.

#### Inputs

None

#### Outputs

None

#### Internal Logic

The class uses the `unittest` framework to define and run tests. It sets up two example trees (`self.discrete_tree` and `self.continuous_tree`) in the `setUp` method, which are then used by the individual test methods.

***

### `test_proportions`

#### Description

Tests the `get_proportion_of_mutation` and `get_proportion_of_missing_data` functions.

#### Inputs

Uses `self.discrete_tree` and `self.continuous_tree`

#### Outputs

None

#### Internal Logic

* Calculates the proportion of mutations and missing data for both trees using the functions being tested.
* Asserts that the calculated proportions match the expected values.

***

### `test_estimate_mutation_rate`

#### Description

Tests the `estimate_mutation_rate` function.

#### Inputs

Uses `self.discrete_tree` and `self.continuous_tree`

#### Outputs

None

#### Internal Logic

* Estimates the mutation rate for both trees using the function being tested, with different combinations of the `continuous` and `assume_root_implicit_branch` parameters.
* Asserts that the estimated mutation rates are close to the expected values.

***

### `test_estimate_missing_data_bad_cases`

#### Description

Tests the error handling of the `estimate_missing_data_rates` function.

#### Inputs

Uses `self.discrete_tree` and `self.continuous_tree`

#### Outputs

None

#### Internal Logic

* Defines several scenarios where the `estimate_missing_data_rates` function should raise either a `ParameterEstimateError` or a `ParameterEstimateWarning`.
* Uses `assertRaises` to verify that the expected exceptions are raised in each scenario.

***

### `test_estimate_stochastic_missing_data_probability`

#### Description

Tests the `estimate_missing_data_rates` function for estimating the stochastic missing data probability.

#### Inputs

Uses `self.discrete_tree` and `self.continuous_tree`

#### Outputs

None

#### Internal Logic

* Estimates the stochastic missing data probability for both trees using the function being tested, with different combinations of the `continuous`, `assume_root_implicit_branch`, and `heritable_missing_rate` parameters.
* Asserts that the estimated probabilities are close to the expected values.

***

### `test_estimate_heritable_missing_data_rate`

#### Description

Tests the `estimate_missing_data_rates` function for estimating the heritable missing data rate.

#### Inputs

Uses `self.discrete_tree` and `self.continuous_tree`

#### Outputs

None

#### Internal Logic

* Estimates the heritable missing data rate for both trees using the function being tested, with different combinations of the `continuous`, `assume_root_implicit_branch`, and `stochastic_missing_probability` parameters.
* Asserts that the estimated rates are close to the expected values.

## Dependencies

* unittest
* networkx
* numpy
* pandas
* cassiopeia
* cassiopeia.tools.parameter\_estimators
* cassiopeia.mixins

## Error Handling

The tests in this file primarily focus on verifying the correct error handling of the parameter estimation functions. They check for scenarios where `ParameterEstimateError` and `ParameterEstimateWarning` exceptions should be raised due to invalid input parameters or estimated values.