Fixing the tests

      - utc_properties: reference/utc_properties.md

      - deterministic doublet simulation: reference/doublet_simulation_deterministic.md

      - stochastic doublet simulation: reference/doublet_simulation_stochastic.md

      - assess optimal chunk size: reference/doublet_simulation_stochastic.md

      - assess optimal chunk size: reference/assess_optimal_chunk_size.md

from os import path

from pathlib import Path

import timeit

from pathlib import Path

import matplotlib.pyplot as plt

import numpy as np

import xarray as xr

import matplotlib.pyplot as plt

from pythermogis.doublet_simulation.deterministic_doublet import calculate_doublet_performance

def assess_optimal_chunk_size(n_simulations: int = 1000, chunk_step_size: int = 100, plot_outfile : str | Path = None):

    """

    This runs the same set of doublet simulations using different chunk sizes and prints the results to the terminal to show find which chunk size is optimal.

    It runs each chunk size 3 times and takes the average of their times to find the time taken for that chunk size.

    The chunks which are tested go from 1 -> n_simulations with steps of chunk_step_size.

    Parameters

    ----------

        coords={"sample": np.arange(n_simulations)}

    )

    n_attempts = 2 # do the same operation n_attempts and take an average of their times.

    n_attempts = 3 # do the same operation n_attempts and take an average of their times.

    sample_chunks = np.arange(1, n_simulations + 2, chunk_step_size)

    # run in series

    output_data_path = Path(path.dirname(__file__), "resources") / "test_output" / "parallelization"

    output_data_path.mkdir(parents=True, exist_ok=True)

    assess_optimal_chunk_size(n_simulations = 1000, chunk_step_size=100, plot_outfile = output_data_path / "parallelization.png")

    assess_optimal_chunk_size(n_simulations = 100, chunk_step_size=50, plot_outfile = output_data_path / "parallelization.png")

    assert output_data_path.exists()