def test_extended_breaks():
x = np.arange(100)
limits = min(x), max(x)
for n in (5, 7, 10, 13, 31):
breaks = extended_breaks(n=n)
assert len(breaks(limits)) <= n + 1
# Reverse limits
breaks = extended_breaks(n=7)
npt.assert_array_equal(breaks((0, 6)), breaks((6, 0)))
# Infinite limits
limits = float('-inf'), float('inf')
breaks = extended_breaks(n=5)
assert len(breaks(limits)) == 0
# Zero range discrete
limits = [1, 1]
assert len(breaks(limits)) == 1
assert breaks(limits)[0] == limits[1]
# Zero range continuous
limits = [np.pi, np.pi]
assert len(breaks(limits)) == 1
assert breaks(limits)[0] == limits[1]
def test_extended_breaks():
x = np.arange(100)
limits = min(x), max(x)
for n in (5, 7, 10, 13, 31):
breaks = extended_breaks(n=n)
assert len(breaks(limits)) <= n+1
# Reverse limits
breaks = extended_breaks(n=7)
npt.assert_array_equal(breaks((0, 6)), breaks((6, 0)))
# Infinite limits
limits = float('-inf'), float('inf')
breaks = extended_breaks(n=5)
assert len(breaks(limits)) == 0
# Zero range discrete
limits = [1, 1]
assert len(breaks(limits)) == 1
assert breaks(limits)[0] == limits[1]
# Zero range continuous
limits = [np.pi, np.pi]
assert len(breaks(limits)) == 1
assert breaks(limits)[0] == limits[1]
def contour_lines(X, Y, Z, levels):
# Preparation of values and the creating of contours is
# adapted from MPL with some adjustments.
X = np.asarray(X, dtype=np.float64)
Y = np.asarray(Y, dtype=np.float64)
Z = np.asarray(Z, dtype=np.float64)
zmin, zmax = Z.min(), Z.max()
mask = None
corner_mask = False
nchunk = 0
contour_generator = _contour.QuadContourGenerator(X, Y, Z, mask,
corner_mask, nchunk)
if isinstance(levels, int):
levels = extended_breaks(n=levels)((zmin, zmax))
# The counter_generator gives us a list of vertices that
# represent all the contour lines at that level. There
# may be 0, 1 or more vertices at a level. Each one of
# these we call a piece, and it represented as an nx2 array.
#
# We want x-y values that describe *all* the contour lines
# in tidy format. Therefore each x-y vertex has a
# corresponding level and piece id.
segments = []
piece_ids = []
level_values = []
start_pid = 1
for level in levels:
vertices = contour_generator.create_contour(level)
for pid, piece in enumerate(vertices, start=start_pid):
n = len(piece)
segments.append(piece)
piece_ids.append(np.repeat(pid, n))
level_values.append(np.repeat(level, n))
start_pid = pid + 1
# Collapse the info and make it fit for dataframe columns
if segments:
x, y = np.vstack(segments).T
piece = np.hstack(piece_ids)
level = np.hstack(level_values)
else:
x, y = [], []
piece = []
level = []
data = pd.DataFrame({
'x': x,
'y': y,
'level': level,
'piece': piece,
})
return data
def contour_lines(X, Y, Z, levels):
# Preparation of values and the creating of contours is
# adapted from MPL with some adjustments.
X = np.asarray(X, dtype=np.float64)
Y = np.asarray(Y, dtype=np.float64)
Z = np.asarray(Z, dtype=np.float64)
zmin, zmax = Z.min(), Z.max()
mask = None
corner_mask = False
nchunk = 0
contour_generator = _contour.QuadContourGenerator(
X, Y, Z, mask, corner_mask, nchunk)
if isinstance(levels, int):
levels = extended_breaks(n=levels)((zmin, zmax))
# The counter_generator gives us a list of vertices that
# represent all the contour lines at that level. There
# may be 0, 1 or more vertices at a level. Each one of
# these we call a piece, and it represented as an nx2 array.
#
# We want x-y values that describe *all* the contour lines
# in tidy format. Therefore each x-y vertex has a
# corresponding level and piece id.
segments = []
piece_ids = []
level_values = []
start_pid = 1
for level in levels:
vertices = contour_generator.create_contour(level)
for pid, piece in enumerate(vertices, start=start_pid):
n = len(piece)
segments.append(piece)
piece_ids.append(np.repeat(pid, n))
level_values.append(np.repeat(level, n))
start_pid = pid + 1
# Collapse the info and make it fit for dataframe columns
x, y = np.vstack(segments).T
piece = np.hstack(piece_ids)
level = np.hstack(level_values)
data = pd.DataFrame({
'x': x,
'y': y,
'level': level,
'piece': piece,
})
return data
def train(self, scale):
# Do nothing if scales are inappropriate
if set(scale.aesthetics) & {'color', 'colour', 'fill'} == 0:
warn("colorbar guide needs color or fill scales.")
return None
if not issubclass(scale.__class__, scale_continuous):
warn("colorbar guide needs continuous scales")
return None
# value = breaks (numeric) is used for determining the
# position of ticks
limits = scale.limits
breaks = scale.get_breaks(strict=True)
breaks = np.asarray(breaks)
breaks = breaks[~np.isnan(breaks)]
if not len(breaks):
return None
self.key = pd.DataFrame({
scale.aesthetics[0]: scale.map(breaks),
'label': scale.get_labels(breaks),
'value': breaks
})
bar = extended_breaks(n=self.nbin, Q=[1, 2, 5, 10])(limits)
# discard locations in bar not in scale.limits
bar = bar.compress((limits[0] <= bar) & (bar <= limits[1]))
self.bar = pd.DataFrame({'color': scale.map(bar), 'value': bar})
labels = ' '.join(six.text_type(x) for x in self.key['label'])
info = '\n'.join([
self.title, labels, ' '.join(self.bar['color'].tolist()),
self.__class__.__name__
])
self.hash = hashlib.md5(info.encode('utf-8')).hexdigest()
return self
