CRM update

[ChaniaClare]

Pull Request To-Do List

Description of the changes:

I have done the following to make this PR ready for review:

• I have re-based my branch on the latest master branch
• I have added necessary documentation (if appropriate)
• I have commented my code, particularly in hard-to-understand areas
• I have assigned a reviewer to this PR

For the reviewer, make sure this PR meets these criteria before merging:

• New code has comments
• New code has tests (if appropriate)
• New code has documentation (if appropriate)
• New code has been reviewed by at least one other person

Summary by Sourcery

Enhance the Consumer-Resource Model (CRM) plotting utilities by adding a new function to visualize model dynamics with confidence intervals and true data points

New Features:

• Add a new plotting function plot_CRM_with_intervals that supports visualization of model trajectories with 95% credible intervals and optional true data overlay

Enhancements:

• Improve visualization capabilities for Consumer-Resource Model by adding support for confidence intervals
• Extend plotting functionality to include scatter points from true data for comparison

Chores:

• Minor formatting adjustment in the Bayesian inference code

[sourcery-ai]

Reviewer's Guide by Sourcery

This pull request introduces a new plotting function for visualizing CRM dynamics with credible intervals and modifies the shape of the sigma parameter in the run_inference function within infer_CRM_bayes.py.

No diagrams generated as the changes look simple and do not need a visual representation.

File-Level Changes

Change	Details	Files
Added a new function plot_CRM_with_intervals to visualize CRM dynamics with credible intervals.	Implemented plotting of median trajectories for species and resources. Implemented plotting of confidence intervals (ribbons) around the median trajectories. Added functionality to overlay true data points from a CSV file for comparison. Added labels, titles, and legends for clarity. Added functionality to save the plot to a file.	mimic/utilities/utilities.py
Modified the run_inference function in infer_CRM_bayes.py to adjust the shape of the sigma parameter.	Changed the shape of the 'sigma' parameter in the pm.HalfNormal distribution to (1,).	mimic/model_infer/infer_CRM_bayes.py

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[sourcery-ai]

Hey @ChaniaClare - I've reviewed your changes - here's some feedback:

Overall Comments:

• Consider extracting the plotting logic into a separate module to improve code organization.
• It might be helpful to add argument validation to the plotting functions.

Here's what I looked at during the review

• 🟢 General issues: all looks good
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟡 Complexity: 1 issue found
• 🟢 Documentation: all looks good

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[sourcery-ai]

issue (complexity): Consider extracting the repeated plotting patterns into helper functions to reduce code duplication and improve readability in the plot_CRM_with_intervals function, such as plotting trajectories with intervals and plotting true data points.

The new function introduces duplicated loops for plotting trajectories, intervals, and optional true data points. Consider extracting the repeated patterns into small helper functions. For example, create one helper to plot a series with its ribbon and another to plot true data points:

def plot_trajectory_with_interval(ax, times, data, lower, upper, label_prefix, linestyle='-', linewidth=2, color_offset=0):
    cmap = plt.cm.tab10
    for i in range(data.shape[1]):
        color = cmap(i + color_offset)
        ax.plot(times, data[:, i], label=f'{label_prefix} {i+1}', linestyle=linestyle, linewidth=linewidth, color=color)
        ax.fill_between(times, lower[:, i], upper[:, i], alpha=0.2, color=color)

def plot_true_data(ax, times, true_data, data_prefix, color_offset=0):
    cmap = plt.cm.tab10
    col_template = f'{data_prefix.lower()}_{{}}'
    for i in range(1, true_data.shape[1]+1):  # Adjust range if needed
        col_name = col_template.format(i)
        if col_name in true_data.columns:
            marker = 'o' if data_prefix.lower() == 'species' else 's'
            ax.scatter(times, true_data[col_name], marker=marker, s=30, 
                       color=cmap(i - 1 + color_offset), label=f'True {data_prefix} {i}')

# In plot_CRM_with_intervals, use:
def plot_CRM_with_intervals(observed_species, observed_resources, species_lower, species_upper,
                            resource_lower, resource_upper, times, filename=None):
    fig, ax = plt.subplots(figsize=(12, 8))
    plot_trajectory_with_interval(ax, times, observed_species, species_lower, species_upper, 'Species', linestyle='-', linewidth=2)
    plot_trajectory_with_interval(ax, times, observed_resources, resource_lower, resource_upper, 'Resource', linestyle='--', linewidth=2, color_offset=observed_species.shape[1])

    if filename:
        true_data = pd.read_csv(filename)
        true_times = true_data['time'].values
        plot_true_data(ax, true_times, true_data, 'species')
        plot_true_data(ax, true_times, true_data, 'resource', color_offset=observed_species.shape[1])

    ax.set_xlabel('Time', fontsize=14)
    ax.set_ylabel('Concentration', fontsize=14)
    ax.set_title('Consumer-Resource Model Dynamics with 95% Credible Intervals', fontsize=16)
    ax.legend(loc='best', fontsize=12)
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    if filename:
        plt.savefig(f"{filename.split('.')[0]}_with_intervals.png", dpi=300)
    plt.show()

This reduces duplication while keeping functionality intact.