def generate_polycollection(ax, x, y1, y2):
"""
Accessory function that creates new PolyCollection class instance representing
new correct field-of-view. We use its output to assign new *path* to the old
one instance. Algorithm has been taken from the matplotlib' sources for the
fill_between() function and used because there is no easy way to update polygon
with new data
input:
same as for the fill_between() function
returns:
matplotlib.collections.PolyCollection instance
"""
# i.e. where = outer_circle >= fov_circle
where = y2 >= y1
kwargs = {'facecolor': colors['fov_outer'], 'alpha': 0.25}
# Convert the arrays so we can work with them
x = np.ma.masked_invalid(ax.convert_xunits(x))
y1 = np.ma.masked_invalid(ax.convert_yunits(y1))
y2 = np.ma.masked_invalid(ax.convert_yunits(y2))
where = where & ~functools.reduce(np.logical_or,
map(np.ma.getmask, [x, y1, y2]))
x, y1, y2 = np.broadcast_arrays(np.atleast_1d(x), y1, y2)
polys = []
for ind0, ind1 in contiguous_regions(where):
xslice = x[ind0:ind1]
y1slice = y1[ind0:ind1]
y2slice = y2[ind0:ind1]
if not len(xslice):
continue
N = len(xslice)
X = np.zeros((2 * N + 2, 2), float)
# the purpose of the next two lines is for when y2 is a
# scalar like 0 and we want the fill to go all the way
# down to 0 even if none of the y1 sample points do
start = xslice[0], y2slice[0]
end = xslice[-1], y2slice[-1]
X[0] = start
X[N + 1] = end
X[1:N + 1, 0] = xslice
X[1:N + 1, 1] = y1slice
X[N + 2:, 0] = xslice[::-1]
X[N + 2:, 1] = y2slice[::-1]
polys.append(X)
return mcoll.PolyCollection(polys, **kwargs)
def test_contiguous_regions():
a, b, c = 3, 4, 5
# Starts and ends with True
mask = [True]*a + [False]*b + [True]*c
expected = [(0, a), (a+b, a+b+c)]
assert cbook.contiguous_regions(mask) == expected
d, e = 6, 7
# Starts with True ends with False
mask = mask + [False]*e
assert cbook.contiguous_regions(mask) == expected
# Starts with False ends with True
mask = [False]*d + mask[:-e]
expected = [(d, d+a), (d+a+b, d+a+b+c)]
assert cbook.contiguous_regions(mask) == expected
# Starts and ends with False
mask = mask + [False]*e
assert cbook.contiguous_regions(mask) == expected
# No True in mask
assert cbook.contiguous_regions([False]*5) == []
# Empty mask
assert cbook.contiguous_regions([]) == []
def test_contiguous_regions():
a, b, c = 3, 4, 5
# Starts and ends with True
mask = [True] * a + [False] * b + [True] * c
expected = [(0, a), (a + b, a + b + c)]
assert cbook.contiguous_regions(mask) == expected
d, e = 6, 7
# Starts with True ends with False
mask = mask + [False] * e
assert cbook.contiguous_regions(mask) == expected
# Starts with False ends with True
mask = [False] * d + mask[:-e]
expected = [(d, d + a), (d + a + b, d + a + b + c)]
assert cbook.contiguous_regions(mask) == expected
# Starts and ends with False
mask = mask + [False] * e
assert cbook.contiguous_regions(mask) == expected
# No True in mask
assert cbook.contiguous_regions([False] * 5) == []
# Empty mask
assert cbook.contiguous_regions([]) == []
def fill_gradient(self,
canvas,
X,
percentiles,
color=Tango.colorsHex['mediumBlue'],
label=None,
**kwargs):
ax = canvas
plots = []
if 'edgecolors' not in kwargs:
kwargs['edgecolors'] = 'none'
if 'facecolors' in kwargs:
color = kwargs.pop('facecolors')
if 'array' in kwargs:
array = kwargs.pop('array')
else:
array = 1. - np.abs(np.linspace(-.97, .97, len(percentiles) - 1))
if 'alpha' in kwargs:
alpha = kwargs.pop('alpha')
else:
alpha = .8
if 'cmap' in kwargs:
cmap = kwargs.pop('cmap')
else:
cmap = LinearSegmentedColormap.from_list('WhToColor',
(color, color),
N=array.size)
cmap._init()
cmap._lut[:-3, -1] = alpha * array
kwargs['facecolors'] = [cmap(i) for i in np.linspace(0, 1, cmap.N)]
# pop where from kwargs
where = kwargs.pop('where') if 'where' in kwargs else None
# pop interpolate, which we actually do not do here!
if 'interpolate' in kwargs: kwargs.pop('interpolate')
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
from itertools import tee
#try:
# from itertools import izip as zip
#except ImportError:
# pass
a, b = tee(iterable)
next(b, None)
return zip(a, b)
polycol = []
for y1, y2 in pairwise(percentiles):
try:
from matplotlib.cbook import contiguous_regions
except ImportError:
from matplotlib.mlab import contiguous_regions
# Handle united data, such as dates
ax._process_unit_info(xdata=X, ydata=y1)
ax._process_unit_info(ydata=y2)
# Convert the arrays so we can work with them
from numpy import ma
x = ma.masked_invalid(ax.convert_xunits(X))
y1 = ma.masked_invalid(ax.convert_yunits(y1))
y2 = ma.masked_invalid(ax.convert_yunits(y2))
if y1.ndim == 0:
y1 = np.ones_like(x) * y1
if y2.ndim == 0:
y2 = np.ones_like(x) * y2
if where is None:
where = np.ones(len(x), np.bool)
else:
where = np.asarray(where, np.bool)
if not (x.shape == y1.shape == y2.shape == where.shape):
raise ValueError("Argument dimensions are incompatible")
from functools import reduce
mask = reduce(ma.mask_or, [ma.getmask(a) for a in (x, y1, y2)])
if mask is not ma.nomask:
where &= ~mask
polys = []
for ind0, ind1 in contiguous_regions(where):
xslice = x[ind0:ind1]
y1slice = y1[ind0:ind1]
y2slice = y2[ind0:ind1]
if not len(xslice):
continue
N = len(xslice)
p = np.zeros((2 * N + 2, 2), np.float)
# the purpose of the next two lines is for when y2 is a
# scalar like 0 and we want the fill to go all the way
# down to 0 even if none of the y1 sample points do
start = xslice[0], y2slice[0]
end = xslice[-1], y2slice[-1]
p[0] = start
p[N + 1] = end
p[1:N + 1, 0] = xslice
p[1:N + 1, 1] = y1slice
p[N + 2:, 0] = xslice[::-1]
p[N + 2:, 1] = y2slice[::-1]
polys.append(p)
polycol.extend(polys)
from matplotlib.collections import PolyCollection
if 'zorder' not in kwargs:
kwargs['zorder'] = 0
plots.append(PolyCollection(polycol, label=label, **kwargs))
ax.add_collection(plots[-1], autolim=True)
ax.autoscale_view()
return plots
