def action_plot(self):
max_range = 6.28
if 'range' in request.GET:
max_range = float(request.GET['range'])
# import things
import numpy as np
import matplotlib.pyplot as plt
# determine x, sin(x) and cos(x)
x = np.arange(0, max_range, 0.1)
y1 = np.sin(x)
y2 = np.cos(x)
# make figure
fig = plt.figure()
fig.subplots_adjust(hspace = 0.5, wspace = 0.5)
fig.suptitle('The legendary sine and cosine curves')
# first subplot
ax = fig.add_subplot(2, 1, 1)
ax.plot(x, y1, 'b')
ax.plot(x, y1, 'ro')
ax.set_title ('y = sin(x)')
ax.set_xlabel('x')
ax.set_ylabel('y')
# second subplot
ax = fig.add_subplot(2, 1, 2)
ax.plot(x, y2, 'b')
ax.plot(x, y2, 'ro')
ax.set_title ('y = cos(x)')
ax.set_xlabel('x')
ax.set_ylabel('y')
# make canvas
return draw_matplotlib_figure(fig)
tup = tup + ([dimension_mean] * len(xx.ravel()) , )
Z = classifier.predict(np.c_[tup])
Z = Z.reshape(xx.shape)
ax.contourf(xx, yy, Z)
# scatter-plot the data
ax.scatter(x, y, c=target, cmap=plt.cm.gist_rainbow)
ax.set_title (first_dimension + ' vs ' + second_dimension + ' ('+caption+')')
ax.set_xlabel(first_dimension)
ax.set_ylabel(second_dimension)
subplot_index += 1
second_dimension_index += 1
first_dimension_index += 1
second_dimension_start_index += 1
# make canvas
file_name = 'classification/plot_'+str(np.random.randint(10000))+str(time.time())+'.png'
plot_url = draw_matplotlib_figure(fig,file_name,'example')
except Exception, e:
result['success'] = False
result['message'] = 'Unexpected error while creating plot : '+ e.message
if not result['success']:
return json.dumps(result)
# initiate false positive, false negative, true positive, and true negative
training_false_positive = {}
training_false_negative = {}
training_true_positive = {}
training_true_negative = {}
testing_false_positive = {}
testing_false_negative = {}
testing_true_positive = {}
testing_true_negative = {}
