Adding information in the readme and in the tests

print(results)

```

### plotting the results of a pythermoigs simulation

It is expected that the user knows how to plot using matplotlib and xarrays, such that they can make plots of the inputs and outputs to suit their needs.

That being said, a plotting function is provided which will produce a plot of a results DataSet, provided it either:

 - has dimensions = ["p_value"] and processes more than 1 p_value

 - has dimensions = ["p_value", "x", "y"], in which case the results will be plotted as maps

Check out the definition of `plot_doublet_performance_results` for more details.

```python

from pythermogis import calculate_doublet_performance, instantiate_utc_properties_builder, plot_doublet_performance_results

import xarray as xr

input_data = xr.Dataset({

    "thickness_mean": ((), 300),

    "thickness_sd": ((), 50),

    "ntg": ((), 0.5),

    "porosity": ((), 0.5),

    "depth": ((), 5000),

    "ln_permeability_mean": ((), 5),

    "ln_permeability_sd": ((), 0.5),

})

utc_properties = instantiate_utc_properties_builder().setUseHeatPump(True).build()

results = calculate_doublet_performance(input_data, utc_properties=utc_properties)

plot_doublet_performance_results(results, outfile = "path/to/outfile")

```

## ThermoGIS Scenario Configuration Files

If you have a valid configuration file, you can parse a utc_properties class using the method: `instantiate_utc_properties_from_xml`.

from thermogis_classes.doublet import *

from thermogis_classes.utc_properties import *

from plotting.plot_doublet_performance_results import plot_doublet_performance_results

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import math

import xarray as xr

from pygridsio import plot_grid

import matplotlib.pyplot as plt

from pathlib import Path

def plot_doublet_performance_results(results: xr.Dataset, outfile: Path | str = None, title: str =None) -> None:

    """

    This will make plots of the results, with two modes;

    1. If the results DataSet has a dimension of [p_value] line plots of all variables with p_value as the x-axis will be made

    2. If he dimensions are [p_value, x, y] then maps of all variables will be made with the variables along the row of the plot and p_value along the column

    if no outfile is provided the plots will be shown using plt.show()

    :param title:

    :param outfile:

    :param results:

    :return:

    """

    dims = list(results.dims)

    if dims == ['p_value'] and len(results.p_value) > 1:

        plot_maps = False

    elif set(dims) == {'p_value', 'x', 'y'}:

        plot_maps = True

    else:

        raise TypeError(f"Results Dataset has dims: {results.dims}.plot_doublet_performance_results() only accepts:\n1. a results DataSet with dims=[p_value] (where their is more than 1 p_value)\n2. or dims=[p_value, x, y]\nInstead, write a custom plotting function for your dataset.")

    if plot_maps:

        _plot_doublet_performance_results_maps(results, outfile = outfile, title = title)

    else:

        _plot_doublet_performance_results_pvalue(results, outfile = outfile, title = title)

def _plot_doublet_performance_results_pvalue(results: xr.Dataset, outfile: Path | str = None, title: str = None) -> None:

    """

    Given a results DataSet with the dimensions of p_value (and *only* p_value) then make a plot of every variable in that results dataset varying as a function of p_value

    :param results:

    :param outfile:

    :param title:

    :return:

    """

    n_variables = len(results.variables)

    rounded_up = int(math.ceil(n_variables / 5) * 5)

    ncols = 5

    nrows = rounded_up / 5

    fig, axes = plt.subplots(nrows=int(nrows), ncols=int(ncols), figsize=(5 * ncols, 5 * nrows), sharex=True)

    axes_flat = axes.flatten()

    [results[variable].plot(ax=axes_flat[i_variable], x = "p_value") for i_variable, variable in enumerate(results.variables)]

    [ax.set_visible(False) for ax in axes_flat[n_variables:]]   # set unused axes to invisible

    plt.suptitle(title)

    if outfile is not None:

        plt.savefig(outfile)

        plt.close()

        return

    plt.show()

def _plot_doublet_performance_results_maps(results: xr.Dataset, outfile: Path | str = None, title: str = None) -> None:

    """

    Given a results DataSet with the dimensions of p_value, x, y then make a map plot of every variable in that results dataset with variables changing per-row and p_value changing per-column

    :param results:

    :param outfile:

    :param title:

    :return:

    """

    n_variables = len(results.variables) - 3 # don't plot the p_value, x or y variables

    n_p_values = len(results.p_value)

    fig, axes = plt.subplots(nrows=n_variables, ncols=n_p_values, figsize=(5 * n_p_values, 5 * n_variables), sharex=True, sharey=True)

    for i_p_value, p_value in enumerate(results.p_value):

        results_p_value = results.isel(p_value=i_p_value)

        for i_variable, variable in enumerate(results.variables):

            if variable in ["p_value", "x", "y"]: continue

            plot_grid(results_p_value[variable], axes=axes[i_variable - 3, i_p_value])

    plt.suptitle(title)

    if outfile is not None:

        plt.savefig(outfile)

        plt.close()

        return

    plt.show()

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from thermogis_classes.doublet import *

from thermogis_classes.utc_properties import *

from plotting.plot_doublet_performance_results import plot_doublet_performance_results

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