def test_odd_length_filter(self):
start = datetime(1951, 3, 31)
end = datetime(1958, 12, 31)
x = self.data[0]
res = convolution_filter(x, [.75, .5, .3, .2, .1])
expected = self.expected.conv2_odd
assert_almost_equal(res.values.squeeze(), expected)
assert res.index[0] == start
assert res.index[-1] == end
res = convolution_filter(x, [.75, .5, .3, .2, .1], nsides=1)
expected = self.expected.conv1_odd
assert_almost_equal(res.values.squeeze(), expected)
assert res.index[0] == start
assert res.index[-1] == end
# with no NAs
# not a stable filter
res = recursive_filter(x, [.75, .5, .3, .2, .1],
init=[150, 100, 125, 135, 145])
expected = self.expected.recurse_odd
# only have 12 characters in R and this blows up and gets big
assert_almost_equal(res.values.squeeze(), expected, 4)
assert res.index[0] == start
assert res.index[-1] == end
def test_pandas2d(self):
start = datetime(1951, 3, 31)
end = datetime(1958, 12, 31)
x = pd.concat((self.data[0], self.data[0]), axis=1)
res = convolution_filter(x, [[.75, .75], [.25, .25]])
assert res.index[0] == start
assert res.index[-1] == end
def test_convolution(self):
x = self.data.values.squeeze()
res = convolution_filter(x, [.75, .25])
expected = self.expected.conv2
assert_almost_equal(res, expected)
res = convolution_filter(x, [.75, .25], nsides=1)
expected = self.expected.conv1
assert_almost_equal(res, expected)
x = self.datana.values.squeeze()
res = convolution_filter(x, [.75, .25])
expected = self.expected.conv2_na
assert_almost_equal(res, expected)
res = convolution_filter(x, [.75, .25], nsides=1)
expected = self.expected.conv1_na
assert_almost_equal(res, expected)
def test_convolution2d(self):
x = self.data.values
res = convolution_filter(x, [[.75], [.25]])
expected = self.expected.conv2
assert_almost_equal(res, expected[:, None])
res = convolution_filter(np.c_[x, x], [[.75, .75], [.25, .25]])
assert_almost_equal(res, np.c_[expected, expected])
res = convolution_filter(x, [[.75], [.25]], nsides=1)
expected = self.expected.conv1
assert_almost_equal(res, expected[:, None])
x = self.datana.values
res = convolution_filter(x, [[.75], [.25]])
expected = self.expected.conv2_na
assert_almost_equal(res, expected[:, None])
res = convolution_filter(x, [[.75], [.25]], nsides=1)
expected = self.expected.conv1_na
assert_almost_equal(res, expected[:, None])
def test_pandas(self):
start = datetime(1951, 3, 31)
end = datetime(1958, 12, 31)
x = self.data[0]
res = convolution_filter(x, [.75, .25])
assert res.index[0] == start
assert res.index[-1] == end
res = convolution_filter(x, [.75, .25], nsides=1)
assert res.index[0] == start
# with no nan-padding q1 if not
assert res.index[-1] == end
res = recursive_filter(x, [.75, .25])
assert res.index[0] == start
assert res.index[-1] == end
x = self.datana
res = recursive_filter(x, [.75, .25])
assert res.index[0] == start
assert res.index[-1] == end