python - Using Matplotlib with Django -


i have built simple web application using django reads csv file , allows users plot graphs selecting x & y attributes.

the frontend makes use of ajax calls call backend methods plot graphs using python's matplotlib. issue arises when plots called asynchronously, causing race condition: different charts plotted same figure.

in attempts overcome problem, assign random "id" each user can call matplotlib figure number -- each user plots on different figures.

import matplotlib import pandas pd matplotlib.use('agg')  #cm array containing 2 confusion matrices generated http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html def plot_confusion_matrix(user_id, cm, classes, path,                           normalize=false,                           title='confusion matrix',                           cmap=plt.cm.blues):     id = user_id + random.randint(1, 10000)     fig = plt.figure(id)     axis1 = fig.add_subplot(121)     title1 = title + " (train)"     title2 =title + " (test)"     def plot_cm(cm, title):         plt.imshow(cm, interpolation='nearest', cmap=cmap)         plt.title(title)         #plt.colorbar()         tick_marks = np.arange(len(classes))         plt.xticks(tick_marks, classes, rotation=45)         plt.yticks(tick_marks, classes)         thresh = cm.max() / 2.         i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):             plt.text(j, i, cm[i, j],                      horizontalalignment="center",                      color="white" if cm[i, j] > thresh else "black")          plt.tight_layout()         plt.ylabel('true label')         plt.xlabel('predicted label')     plot_cm(cm=cm[0], title=title1)     axis2 = fig.add_subplot(122)     plot_cm(cm=cm[1], title=title2)     plt.tight_layout()     fig.savefig(path)     plt.close(id)

however, not solve problem -- when user plots 3 graphs @ 1 time graphs overlap on each other.

ok, try plot directly axis create this:

def plot_confusion_matrix(user_id, cm, classes, path,                           normalize=false,                           title='confusion matrix',                           cmap=plt.cm.blues):     id = user_id + random.randint(1, 10000)     fig = plt.figure(id)     axis1 = fig.add_subplot(121)     title1 = title + " (train)"     title2 =title + " (test)"     def plot_cm(cm, title, ax):         ax.imshow(cm, interpolation='nearest', cmap=cmap)         ax.set_title(title)         #plt.colorbar()         tick_marks = np.arange(len(classes))         ax.set_xticks(tick_marks, classes, rotation=45)         ax.set_yticks(tick_marks, classes)         thresh = cm.max() / 2.         i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):             ax.text(j, i, cm[i, j],                      horizontalalignment="center",                      color="white" if cm[i, j] > thresh else "black")          plt.tight_layout()         ax.set_ylabel('true label')         ax.set_xlabel('predicted label')     plot_cm(cm=cm[0], title=title1, axis1)     axis2 = fig.add_subplot(122)     plot_cm(cm=cm[1], title=title2, axis2)     plt.tight_layout()     plt.savefig(path)     plt.close(id)

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