def test_mark_greaterthan(self):
y = [ 0, -1, 0, 0, 0, 1, 1, -1, -1, -1,
1, 1, 0, 0, 0, 0, 1, 1, 0, 0]
klust = Cluster(y,0)
assert_equal(klust.starts, [ 0, 1, 2, 5, 7, 10, 12, 16, 18])
assert_equal(klust.mark_greaterthan(3),
[1,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,1,1,1])
def test_mark_greaterthan(self):
y = [0, -1, 0, 0, 0, 1, 1, -1, -1, -1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]
klust = Cluster(y, 0)
assert_equal(klust.starts, [0, 1, 2, 5, 7, 10, 12, 16, 18])
assert_equal(
klust.mark_greaterthan(3),
[1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1])
def test_grouped_slices(self):
"Make sure we're not massing w/ .uniques ..."
y = [0, -1, 0, 0, 0, 1, 1, -1, -1, -1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0]
klust = Cluster(y, 0)
uniques_before = klust.uniques
klust.grouped_slices()
uniques_after = klust.uniques
assert_equal(uniques_before, uniques_after)
def test_grouped_slices(self):
"Make sure we're not massing w/ .uniques ..."
y = [ 0, -1, 0, 0, 0, 1, 1, -1, -1, -1,
1, 1, 0, 0, 0, 0, 1, 1, 0, 0]
klust = Cluster(y,0)
uniques_before = klust.uniques
klust.grouped_slices()
uniques_after = klust.uniques
assert_equal(uniques_before, uniques_after)
def test_cluster(self):
sequence = [0, 0, 1, 2, 2, 2, 3, 4, 3, 4, 4, 4]
klust = Cluster(sequence, 0)
assert_equal(klust.clustered, [[
0,
0,
], [
1,
], [
2,
2,
2,
], [
3,
], [
4,
], [
3,
], [
4,
4,
4,
]])
assert_equal(klust.uniques, [0, 1, 2, 3, 4, 3, 4])
assert_equal(len(klust.uniques), len(klust.clustered))
assert_equal(klust.slices, [
slice(0, 2),
slice(2, 3),
slice(3, 6),
slice(6, 7),
slice(7, 8),
slice(8, 9),
slice(9, 12)
])
#
x = [
1.8, 1.3, 2.4, 1.2, 2.5, 3.9, 1., 3.8, 4.2, 3.3, 1.2, 0.2, 0.9,
2.7, 2.4, 2.8, 2.7, 4.7, 4.2, 0.4
]
klust = Cluster(x, 1)
assert_equal(klust.starts, [0, 2, 3, 4, 5, 6, 7, 10, 13, 17, 19])
assert_equal(Cluster(x, 1.5).starts, [0, 6, 7, 10, 13, 17, 19])
assert_equal(Cluster(x, 2.5).starts, [0, 6, 7, 19])
assert_equal(
Cluster(x, 2.5, np.greater).starts,
[0, 1, 2, 3, 4, 5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18])
def plot_enso_background(self, ensoindices=None, lag=0, **optinfo):
"""
Plots colored stripes in the background of plot to represent ENSO phases.
Parameters
----------
ensophases: {array-like}, optional
Array of ENSO indices (``+1`` for El Niño, ``0`` for Neutral and
``-1`` for La Niña episodes).
If None, the ENSO indices of the underlying series are used instead.
"""
if ensoindices is None:
series = self._series
if series is None or not hasattr(series, 'ensoindices') or \
series.ensoindices is None:
raise ValueError("Undefined ENSO indices!")
ensoindices = series.ensoindices
if self.xdata is None:
errmsg = "Unable to retrieve the dates of the current plot!"
raise ValueError(errmsg)
#
dates = self.xdata
clust_indices = Cluster(ensoindices.filled(0), 0)
_dates = np.empty(len(dates) + 1, int)
_dates[:-1] = dates
_dates[-1] = dates[-2]
episodes = dict([(k, zip(_dates[v[:, 0]], _dates[v[:, 1]]))
for (k, v) in clust_indices.grouped_limits().items()])
#
colors = ENSOcolors['polygons']
for (key, idx) in {'C': -1, 'N': 0, 'W': +1}.iteritems():
colors[idx] = colors[key]
#
trans = blended_transform_factory(self.transData, self.transAxes)
for (k, lim) in episodes.iteritems():
_bbc = BrokenBarHCollection(
[(x + lag, y - x) for (x, y) in lim],
(0, 1),
facecolors=colors[k],
edgecolors=colors[k],
)
_bbc.set_alpha(0.2)
_bbc.set_transform(trans)
self.add_collection(_bbc)
def plot_enso_background(self, ensoindices=None, lag=0, **optinfo):
"""
Plots colored stripes in the background of plot to represent ENSO phases.
Parameters
----------
ensophases: {array-like}, optional
Array of ENSO indices (``+1`` for El Niño, ``0`` for Neutral and
``-1`` for La Niña episodes).
If None, the ENSO indices of the underlying series are used instead.
"""
if ensoindices is None:
series = self._series
if series is None or not hasattr(series, 'ensoindices') or \
series.ensoindices is None:
raise ValueError("Undefined ENSO indices!")
ensoindices = series.ensoindices
if self.xdata is None:
errmsg = "Unable to retrieve the dates of the current plot!"
raise ValueError(errmsg)
#
dates = self.xdata
clust_indices = Cluster(ensoindices.filled(0), 0)
_dates = np.empty(len(dates) + 1, int)
_dates[:-1] = dates
_dates[-1] = dates[-2]
episodes = dict([(k, zip(_dates[v[:, 0]], _dates[v[:, 1]]))
for (k, v) in clust_indices.grouped_limits().items()])
#
colors = ENSOcolors['polygons']
for (key, idx) in {'C':-1, 'N':0, 'W':+1}.iteritems():
colors[idx] = colors[key]
#
trans = blended_transform_factory(self.transData, self.transAxes)
for (k, lim) in episodes.iteritems():
_bbc = BrokenBarHCollection([(x + lag, y - x) for (x, y) in lim],
(0, 1),
facecolors=colors[k],
edgecolors=colors[k],)
_bbc.set_alpha(0.2)
_bbc.set_transform(trans)
self.add_collection(_bbc)
