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## Project description

Scientific software development often relies on stochasticity, e.g. for Monte Carlo integration or simulating the Ising model. Testing non-deterministic code is difficult. This package offers a bootstrap test to validate stochastic algorithms, including multiple hypothesis correction for vector statistics. It can be installed by running `pip install pytest-bootstrap`

.

## Example

Suppose we want to implement the expected value of log-normal distribution with location parameter `\mu`

and scale parameter `\sigma`

.

```
>>> import numpy as np
>>>
>>> def lognormal_expectation(mu, sigma):
... return np.exp(mu + sigma ** 2 / 2)
>>>
>>> def lognormal_expectation_wrong(mu, sigma):
... return np.exp(mu + sigma ** 2)
```

We can validate our implementation by simulating from a lognormal distribution and comparing with the bootstrapped mean.

```
>>> from pytest_bootstrap import bootstrap_test
>>>
>>> mu = -1
>>> sigma = 1
>>> reference = lognormal_expectation(mu, sigma)
>>> x = np.exp(np.random.normal(mu, sigma, 1000))
>>> result = bootstrap_test(x, np.mean, reference)
```

This returns a summary of the test, such as the bootstrapped statistics.

```
>>> result.keys()
dict_keys(['alpha', 'alpha_corrected', 'reference', 'lower', 'upper',
'z_score', 'median', 'iqr', 'tol', 'statistics'])
```

Comparing with our incorrect implementation reveals the bug.

```
>>> reference_wrong = lognormal_expectation_wrong(mu, sigma)
>>> result = bootstrap_test(x, np.mean, reference_wrong)
Traceback (most recent call last):
...
pytest_bootstrap.BootstrapTestError: the reference value 1.0 lies outside
the 1 - (alpha = 0.01) interval ...
```

Visualising the bootstrapped distribution using `pytest_bootstrap.result_hist`

can help identify discrepancies between the bootstrapped statistics and the theoretical reference value. Note that you need to install matplotlib separately or install pytest-bootstrap using `pip install pytest-bootstrap[plot]`

.

```
.. plot:: examples/lognormal.py
:caption: Histogram of bootstrapped means reveals the erroneous implementation of the log-normal mean.
```

A comprehensive set of examples can be found in the tests.

## Interface

```
.. automodule:: pytest_bootstrap
:members:
```

## Project details

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