Loop in Matplotlib in a single subplot-CodePudding

I'm currently working on Principal Components Analysis and I would like to plot a correlation circle, 3 in my case because I've got 3 PCA.

The code is ok but I would like to show the result in a subplot (1 row, 3 columns) because now I have 3 consecutive figures.

When I try to initilaze the Matplotlib figure with fig, ax = plt.subplots(1,3) it returns 3 "grid" of subplots of 1x3 with 1 circle inside each. So instead of that I would like 1 "grid" with my 3 circle in 3 columns on the same row.

My code :

pcs = pca.components_
def display_circles(pcs, n_comp, pca, axis_ranks, labels=None, label_rotation=0, lims=None):
    # Initialise the matplotlib figure
    fig, ax = plt.subplots(1,3)

    # For each factorial plane
    for d1, d2 in axis_ranks: 
        if d2 < n_comp:

            # Determine the limits of the chart
            if lims is not None :
                xmin, xmax, ymin, ymax = lims
            elif pcs.shape[1] < 30 :
                xmin, xmax, ymin, ymax = -1, 1, -1, 1
            else :
                xmin, xmax, ymin, ymax = min(pcs[d1,:]), max(pcs[d1,:]), min(pcs[d2,:]), max(pcs[d2,:])

            # Add arrows
            plt.quiver(np.zeros(pcs.shape[1]), np.zeros(pcs.shape[1]), pcs[d1,:], pcs[d2,:], angles='xy', scale_units='xy', scale=1, color="grey")
            
            # Display variable names
            if labels is not None:  
                for i,(x, y) in enumerate(pcs[[d1,d2]].T):
                    if x >= xmin and x <= xmax and y >= ymin and y <= ymax :
                        plt.text(x, y, labels[i], fontsize='10', ha='center', va='center', rotation=label_rotation, color="blue", alpha=0.5)
            
            # Display circle
            circle = plt.Circle((0,0), 1, facecolor='none', edgecolor='b')
            plt.gca().add_artist(circle)

            # Label the axes, with the percentage of variance explained
            plt.xlabel('PC{} ({}%)'.format(d1 1, round(100*pca.explained_variance_ratio_[d1],1)))
            plt.ylabel('PC{} ({}%)'.format(d2 1, round(100*pca.explained_variance_ratio_[d2],1)))

            plt.title("Correlation Circle (PC{} and PC{})".format(d1 1, d2 1))
            plt.show(block=False)

display_circles(pcs, num_components, pca, [(0,1), (1,2), (0,2)], labels = header)

Thanks for the help !!

CodePudding user response：

Sadly, you didn't provide data, so this answer is only going to explain what you can do to achieve your goal.

With fig, ax = plt.subplots(1,3) you created 3 different axis that are stored inside ax. So, ax[0] refers to the first axis on the left, ax[1] refers to the center axis, ax[2] refers to the axis on the right. We use them to target the correct axis.

Since you have 3 PCA, we need an index to target the correct axis. So, change for d1, d2 in axis_ranks: to for k, (d1, d2) in enumerate(axis_ranks):. Now we can use the index k to target the correct axis.

Next, you need to replace plt. with ax[i].. But we need to be careful though, as some methods are going to have a different name:

pcs = pca.components_
def display_circles(pcs, n_comp, pca, axis_ranks, labels=None, label_rotation=0, lims=None):
    # Initialise the matplotlib figure
    fig, ax = plt.subplots(1,3)

    # For each factorial plane
    for k, (d1, d2) in enumerate(axis_ranks): 
        if d2 < n_comp:

            # Determine the limits of the chart
            if lims is not None :
                xmin, xmax, ymin, ymax = lims
            elif pcs.shape[1] < 30 :
                xmin, xmax, ymin, ymax = -1, 1, -1, 1
            else :
                xmin, xmax, ymin, ymax = min(pcs[d1,:]), max(pcs[d1,:]), min(pcs[d2,:]), max(pcs[d2,:])

            # Add arrows
            ax[k].quiver(np.zeros(pcs.shape[1]), np.zeros(pcs.shape[1]), pcs[d1,:], pcs[d2,:], angles='xy', scale_units='xy', scale=1, color="grey")
            
            # Display variable names
            if labels is not None:  
                for i,(x, y) in enumerate(pcs[[d1,d2]].T):
                    if x >= xmin and x <= xmax and y >= ymin and y <= ymax :
                        ax[k].text(x, y, labels[i], fontsize='10', ha='center', va='center', rotation=label_rotation, color="blue", alpha=0.5)
            
            # Display circle
            circle = plt.Circle((0,0), 1, facecolor='none', edgecolor='b')
            ax[k].add_artist(circle)

            # Label the axes, with the percentage of variance explained
            ax[k].set_xlabel('PC{} ({}%)'.format(d1 1, round(100*pca.explained_variance_ratio_[d1],1)))
            ax[k].set_ylabel('PC{} ({}%)'.format(d2 1, round(100*pca.explained_variance_ratio_[d2],1)))

            ax[k].set_title("Correlation Circle (PC{} and PC{})".format(d1 1, d2 1))

display_circles(pcs, num_components, pca, [(0,1), (1,2), (0,2)], labels = header)