cellnopt tutorial and data ploting

corneto/methods/signal/cellnopt_ilp.py

@@ -444,3 +444,127 @@ def plot_fitness(G, exp_list, P, measured_only=False):
                     axs[iexp - 1, imarker].set_ylabel(f"Experiment {iexp}")
 
     plt.show()
+
+def collect_field_into_matrix(experiments,field_name = 'input'):
+    """Collects the field_name values into matrix.
+
+    Collects the field_name values (input, inhibition etc) from a dictionary 
+    of experiments and returns them as a numpy array.
+
+    PARAMETERS:
+    - experiments: dictionary of experiments containing input values
+    - field_name: name of the field to collect (default: 'input')
+
+    RETURNS:
+    - input_matrix: numpy array of input values
+    - input_vars: list of unique input variable names
+
+    """
+    # Collect all unique input names
+    input_vars = set()
+    for exp in experiments.values():
+        # ensure field_name exists
+        if field_name not in exp:
+            raise ValueError('Field name (' + field_name + ')  not found in experiment')
+        input_vars.update(exp[field_name].keys())
+
+    input_vars = sorted(input_vars)  # Sorting to keep a consistent order
+    data = []
+
+    # Collect input values for each experiment
+    for exp in experiments.values():
+        row = [exp[field_name].get(var, 0) for var in input_vars]
+        data.append(row)
+
+    # Convert the data to a numpy array
+    input_matrix = np.array(data)
+
+    return input_matrix, input_vars
+
+def plot_data(exp_list):
+    """Plot the data.
+
+    PARAMETERS:
+    - exp_list: dictionary of experiments
+
+    """
+    import matplotlib.pyplot as plt
+
+    N_exps = len(exp_list)
+
+    # Ensure that all experiments have the input and output variables
+    for exp in exp_list.values():
+        if 'input' not in exp:
+            raise ValueError('Input not found in experiment')
+        if 'output' not in exp:
+            raise ValueError('Output not found in experiment')
+
+    # Collect the input and output variables
+    input_matrix, input_vars = collect_field_into_matrix(exp_list, 'input')
+    output_matrix, output_vars = collect_field_into_matrix(exp_list, 'output')
+
+    # Check if inhibition is present in any of the experiments
+    inhibition_present = any('inhibition' in exp for exp in exp_list.values())
+    if inhibition_present:
+        inhibition_matrix, inhibition_vars = collect_field_into_matrix(exp_list, 'inhibition')
+        perturbation_matrix = np.hstack((input_matrix, inhibition_matrix))
+        perturbation_vars = input_vars + inhibition_vars
+    else:
+        perturbation_matrix = input_matrix
+        perturbation_vars = input_vars
+
+    # Create the figure
+    # Set colors: input colors are blue, inhibition colors are red
+    perturbation_colors = ['blue'] * len(input_vars)
+    if inhibition_present:
+        perturbation_colors += ['red'] * len(inhibition_vars)
+
+    fig, axs = plt.subplots(N_exps - 1, len(output_vars) + 1, squeeze=False)
+
+    fig.tight_layout(pad=0.0)
+    # Adjust the space between subplots
+    plt.subplots_adjust(wspace=0.1, hspace=0.1)
+
+    for exp, iexp in zip(exp_list, range(N_exps)):
+        if iexp == 0:
+            continue
+
+        for imarker in range(len(output_vars)):
+            # output_names[imarker] is the name of the output node, find the position in the graph
+            imarker_name = output_vars[imarker]
+
+            axs[iexp - 1, imarker].plot(
+                [0, 10],
+                [
+                    exp_list["exp0"]["output"][imarker_name],
+                    exp_list[exp]["output"][imarker_name],
+                ],
+                "ro-",
+            )
+            axs[iexp - 1, imarker].set_ylim([-0.01, 1.1])
+
+            if iexp == 1:
+                axs[iexp - 1, imarker].set_title(imarker_name)
+            if iexp != N_exps - 1:
+                axs[iexp - 1, imarker].set_xticks([])
+            if imarker == 0:
+                axs[iexp - 1, imarker].set_ylabel(f"Exp. {iexp}")
+            else:
+                axs[iexp - 1, imarker].set_yticks([])
+
+        # Plot perturbation 
+        axs[iexp - 1, len(output_vars)].bar(
+            range(len(perturbation_vars)), perturbation_matrix[iexp], color=perturbation_colors
+        )
+        if iexp == N_exps - 1:
+            axs[iexp - 1, len(output_vars)].set_xticks(range(len(perturbation_vars)))
+            axs[iexp - 1, len(output_vars)].set_xticklabels(perturbation_vars, rotation=45)
+        else:
+            # No xtick label
+            axs[iexp - 1, len(output_vars)].set_xticks([])
+
+        axs[iexp - 1, len(output_vars)].set_ylim([-0.01, 1.1])
+        axs[iexp - 1, len(output_vars)].set_yticks([])
+        if iexp == 1:
+            axs[iexp - 1, len(output_vars)].set_title("Pert.")
+    plt.show()