<< demo/views.py>>
import matplotlib.pyplot as pltimport numpy as np
import mpld3
def plot_test1(request):
context = {}
fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))
N = 100
"""
Demo about using matplotlib and mpld3 to rendor charts
"""
scatter = ax.scatter(np.random.normal(size=N),
np.random.normal(size=N),
c=np.random.random(size=N),
s=1000 * np.random.random(size=N),
alpha=0.3,
cmap=plt.cm.jet)
ax.grid(color='white', linestyle='solid')
ax.set_title("Scatter Plot (with tooltips!)", size=20)
labels = ['point {0}'.format(i + 1) for i in range(N)]
tooltip = mpld3.plugins.PointLabelTooltip(scatter, labels=labels)
mpld3.plugins.connect(fig, tooltip)
#figure = mpld3.fig_to_html(fig)
figure = json.dumps(mpld3.fig_to_dict(fig))
context.update({ 'figure' : figure })
"""
Demo about using tensorflow to predict the result
"""
num = np.random.randint(100)
prediction = predict_service.predict(num)
context.update({ 'num' : num })
context.update({ 'prediction' : prediction })
return render(request, 'demo/demo.html', context)
<<demo/demo.html>>
<script type="text/javascript" src="http://d3js.org/d3.v3.min.js"></script><script type="text/javascript" src="http://mpld3.github.io/js/mpld3.v0.2.js"></script>
<style>
/* Move down content because we have a fixed navbar that is 50px tall */
body {
padding-top: 50px;
padding-bottom: 20px;
}
</style>
<html>
<div id="fig01"></div>
<script type="text/javascript">
figure = {{ figure|safe }};
mpld3.draw_figure("fig01", figure);
</script>
</html>
