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")
def test_filters():
"""Test the ``Filter*`` classes."""
from clair.diagram import FilterInterval, FilterContains
print "Start"
#Create a test data frame
data = pd.DataFrame({"id": ["foo-" + str(i) for i in range(10)],
"str_list": None,
"float_val": np.linspace(2.1, 21, 10),
"time": pd.date_range(start="2000-1-1", periods=10,
freq="D")})
str_lists =[["foo"], ["bar"], ["foo", "bar"], ["baz"], None,
["foo"], ["bar"], ["foo", "bar"], ["baz"], ["boum"]]
for i, l in enumerate(str_lists):
data["str_list"][i] = l
data.set_index("id", drop=False, inplace=True)
print data
print "\nSelect string lists that contain string 'foo'."
contains_foo = FilterContains("str_list", "foo")
data_foo = contains_foo.filter(data)
print data_foo
assert all(data_foo["id"] == ["foo-0", "foo-2", "foo-5", "foo-7"])
print "\nSelect strings that contain the substring '2'."
contains_2 = FilterContains("id", "2")
data_2 = contains_2.filter(data)
print data_2
assert all(data_2["id"] == ["foo-2"])
print "\nSelect values between 3 and 9."
between_3_9 = FilterInterval("float_val", 3, 9)
data_3_9 = between_3_9.filter(data)
print data_3_9
#TODO: Bug report to Pandas because of inconsistent behavior.
# print data_3_9["float_val"] == [4.2, 6.3, 8.4]
# print data_3_9["id"] == ["foo-1", "foo-2", "foo-3"]
# print data_3_9["float_val"]
# assert all(data_3_9["float_val"] == [4.2, 6.3, 8.4])
assert all(data_3_9["id"] == ["foo-1", "foo-2", "foo-3"])
print "\nExclude values between 3 and 9."
exclude_3_9 = FilterInterval("float_val", 3., 9., inside=False)
data_no_3_9 = exclude_3_9.filter(data)
print data_no_3_9
#Parameter ``inside``:
# * True vs. False must partition data frame into disjoint sets.
# * Both options together must cover all elements of data frame.
assert len(data_no_3_9) == 7
assert len(data_3_9) == 3
all_data = data_3_9.combine_first(data_no_3_9)
assert all(all_data["float_val"] == data["float_val"])
print "\nSelect select dates between 2000-1-3 and 2000-1-6 " \
"(excluding last date)."
contains_3dates = FilterInterval("time",
pd.Timestamp("2000-1-3"),
pd.Timestamp("2000-1-6"))
data_3dates = contains_3dates.filter(data)
print data_3dates
assert all(data_3dates["id"] == ["foo-2", "foo-3", "foo-4"])
print "\nTest converting filters to strings."
print contains_foo
print exclude_3_9
print contains_3dates