def test_PlotterScatter():
"""
Test class ``PlotterLine``.
TODO: assertions. Look at Matplotlib tests.
http://matplotlib.org/devel/testing.html
"""
from clair.diagram import PlotterScatter, FilterContains
print("Start")
#Create a data frame with random prices and times
prices = create_test_prices()
prices = FilterContains("product", "foo", True).filter(prices)
prices = prices.sort("time")
#Create the objects for Matplotlib
fig = plt.figure()
#Test line plot
ax = fig.add_subplot(2, 1, 1)
graph = PlotterScatter()
graph.plot(ax, prices["time"], prices["price"])
#Test scatter plot
ax = fig.add_subplot(2, 1, 2)
graph = PlotterScatter(saturation=0.5, marker="p", color="orange",
markersize=7)
graph.plot(ax, prices["time"], prices["price"])
fig.autofmt_xdate()
plt.show()
print("End")
def test_PlotterLine():
"""
Test class ``PlotterLine``.
TODO: assertions. Look at Matplotlib tests.
http://matplotlib.org/devel/testing.html
"""
from clair.diagram import PlotterLine, FilterContains, PlotterScatter
print("Start")
#Create a data frame with random prices and times
prices = create_test_prices()
prices = FilterContains("product", "foo", True).filter(prices)
prices = prices.sort("time")
#Create the objects for Matplotlib
fig = plt.figure()
#Test line plot
ax = fig.add_subplot(3, 1, 1)
graph = PlotterLine()
graph.plot(ax, prices["time"], prices["price"])
#Compute average and standard deviation
monthly = pd.TimeGrouper(freq="M")
prices_t = prices.set_index("time")
prices_s = prices_t.groupby(monthly).aggregate([np.mean, np.std])
# print prices_s
#Test limit display: filled
ax = fig.add_subplot(3, 1, 2)
graph_mean = PlotterLine(markersize=7, fill_limits=True)
graph_mean.plot(ax, prices_s.index,
prices_s["price"]["mean"], prices_s["price"]["std"],)
# #Plot scatter plot on top, to see how they look together
scatter = PlotterScatter(saturation=0.5)
scatter.plot(ax, prices["time"], prices["price"])
#Test limit display: lines
ax = fig.add_subplot(3, 1, 3)
graph_mean = PlotterLine(markersize=7, fill_limits=False)
graph_mean.plot(ax, prices_s.index,
prices_s["price"]["mean"], prices_s["price"]["std"],)
# #Plot scatter plot on top, to see how they look together
scatter = PlotterScatter(saturation=0.5)
scatter.plot(ax, prices["time"], prices["price"])
fig.autofmt_xdate()
plt.show()
print("End")