def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(npy.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(npy.float)
self.autoscale_None(val)
vmin, vmax = self.vmin, self.vmax
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin<=0:
raise ValueError("values must all be positive")
elif vmin==vmax:
return 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(npy.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (ma.log(val)-npy.log(vmin))/(npy.log(vmax)-npy.log(vmin))
if vtype == 'scalar':
result = result[0]
return result
def set_data(self, x, y, A):
A = ma.asarray(A)
if x is None:
x = npy.arange(0, A.shape[1] + 1, dtype=npy.float64)
else:
x = npy.asarray(x, npy.float64).ravel()
if y is None:
y = npy.arange(0, A.shape[0] + 1, dtype=npy.float64)
else:
y = npy.asarray(y, npy.float64).ravel()
if A.shape[:2] != (y.size - 1, x.size - 1):
print A.shape
print y.size
print x.size
raise ValueError("Axes don't match array shape")
if A.ndim not in [2, 3]:
raise ValueError("A must be 2D or 3D")
if A.ndim == 3 and A.shape[2] == 1:
A.shape = A.shape[:2]
self.is_grayscale = False
if A.ndim == 3:
if A.shape[2] in [3, 4]:
if (A[:, :, 0] == A[:, :, 1]).all() and (A[:, :, 0]
== A[:, :, 2]).all():
self.is_grayscale = True
else:
raise ValueError("3D arrays must have RGB or RGBA as last dim")
self._A = A
self._Ax = x
self._Ay = y
self.update_dict['array'] = True
def _check_xyz(self, args):
'''
For functions like contour, check that the dimensions
of the input arrays match; if x and y are 1D, convert
them to 2D using meshgrid.
Possible change: I think we should make and use an ArgumentError
Exception class (here and elsewhere).
'''
x = npy.asarray(args[0], dtype=npy.float64)
y = npy.asarray(args[1], dtype=npy.float64)
z = ma.asarray(args[2], dtype=npy.float64)
if z.ndim != 2:
raise TypeError("Input z must be a 2D array.")
else: Ny, Nx = z.shape
if x.shape == z.shape and y.shape == z.shape:
return x,y,z
if x.ndim != 1 or y.ndim != 1:
raise TypeError("Inputs x and y must be 1D or 2D.")
nx, = x.shape
ny, = y.shape
if nx != Nx or ny != Ny:
raise TypeError("Length of x must be number of columns in z,\n" +
"and length of y must be number of rows.")
x,y = npy.meshgrid(x,y)
return x,y,z
def to_rgba(self, x, alpha=1.0, bytes=False):
'''Return a normalized rgba array corresponding to x.
If x is already an rgb array, insert alpha; if it is
already rgba, return it unchanged.
If bytes is True, return rgba as 4 uint8s instead of 4 floats.
'''
try:
if x.ndim == 3:
if x.shape[2] == 3:
if x.dtype == npy.uint8:
alpha = npy.array(alpha*255, npy.uint8)
m, n = x.shape[:2]
xx = npy.empty(shape=(m,n,4), dtype = x.dtype)
xx[:,:,:3] = x
xx[:,:,3] = alpha
elif x.shape[2] == 4:
xx = x
else:
raise ValueError("third dimension must be 3 or 4")
if bytes and xx.dtype != npy.uint8:
xx = (xx * 255).astype(npy.uint8)
return xx
except AttributeError:
pass
x = ma.asarray(x)
x = self.norm(x)
x = self.cmap(x, alpha=alpha, bytes=bytes)
return x
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(npy.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(npy.float)
self.autoscale_None(val)
vmin, vmax = self.vmin, self.vmax
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin <= 0:
raise ValueError("values must all be positive")
elif vmin == vmax:
return 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(npy.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (ma.log(val) - npy.log(vmin)) / (npy.log(vmax) -
npy.log(vmin))
if vtype == 'scalar':
result = result[0]
return result
def set_data(self, x, y, A):
A = ma.asarray(A)
if x is None:
x = npy.arange(0, A.shape[1]+1, dtype=npy.float64)
else:
x = npy.asarray(x, npy.float64).ravel()
if y is None:
y = npy.arange(0, A.shape[0]+1, dtype=npy.float64)
else:
y = npy.asarray(y, npy.float64).ravel()
if A.shape[:2] != (y.size-1, x.size-1):
print A.shape
print y.size
print x.size
raise ValueError("Axes don't match array shape")
if A.ndim not in [2, 3]:
raise ValueError("A must be 2D or 3D")
if A.ndim == 3 and A.shape[2] == 1:
A.shape = A.shape[:2]
self.is_grayscale = False
if A.ndim == 3:
if A.shape[2] in [3, 4]:
if (A[:,:,0] == A[:,:,1]).all() and (A[:,:,0] == A[:,:,2]).all():
self.is_grayscale = True
else:
raise ValueError("3D arrays must have RGB or RGBA as last dim")
self._A = A
self._Ax = x
self._Ay = y
self.update_dict['array'] = True
def _make_verts(self, U, V):
uv = ma.asarray(U+V*1j)
a = ma.absolute(uv)
if self.scale is None:
sn = max(10, math.sqrt(self.N))
scale = 1.8 * a.mean() * sn / self.span # crude auto-scaling
self.scale = scale
length = a/(self.scale*self.width)
X, Y = self._h_arrows(length)
# There seems to be a ma bug such that indexing
# a masked array with one element converts it to
# an ndarray.
theta = npy.angle(ma.asarray(uv[..., npy.newaxis]).filled(0))
xy = (X+Y*1j) * npy.exp(1j*theta)*self.width
xy = xy[:,:,npy.newaxis]
XY = ma.concatenate((xy.real, xy.imag), axis=2)
return XY
def _make_verts(self, U, V):
uv = ma.asarray(U + V * 1j)
a = ma.absolute(uv)
if self.scale is None:
sn = max(10, math.sqrt(self.N))
scale = 1.8 * a.mean() * sn / self.span # crude auto-scaling
self.scale = scale
length = a / (self.scale * self.width)
X, Y = self._h_arrows(length)
# There seems to be a ma bug such that indexing
# a masked array with one element converts it to
# an ndarray.
theta = npy.angle(ma.asarray(uv[..., npy.newaxis]).filled(0))
xy = (X + Y * 1j) * npy.exp(1j * theta) * self.width
xy = xy[:, :, npy.newaxis]
XY = ma.concatenate((xy.real, xy.imag), axis=2)
return XY
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin, vmax = self.vmin, self.vmax
if cbook.iterable(value):
val = ma.asarray(value)
return vmin * ma.power((vmax / vmin), val)
else:
return vmin * pow((vmax / vmin), value)
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin, vmax = self.vmin, self.vmax
if cbook.iterable(value):
val = ma.asarray(value)
return vmin * ma.power((vmax/vmin), val)
else:
return vmin * pow((vmax/vmin), value)
def _parse_args(self, *args):
X, Y, U, V, C = [None] * 5
args = list(args)
if len(args) == 3 or len(args) == 5:
C = ma.asarray(args.pop(-1)).ravel()
V = ma.asarray(args.pop(-1))
U = ma.asarray(args.pop(-1))
nn = npy.shape(U)
nc = nn[0]
nr = 1
if len(nn) > 1:
nr = nn[1]
if len(args) == 2:
X, Y = [npy.array(a).ravel() for a in args]
if len(X) == nc and len(Y) == nr:
X, Y = [a.ravel() for a in npy.meshgrid(X, Y)]
else:
indexgrid = npy.meshgrid(npy.arange(nc), npy.arange(nr))
X, Y = [npy.ravel(a) for a in indexgrid]
return X, Y, U, V, C
def _parse_args(self, *args):
X, Y, U, V, C = [None]*5
args = list(args)
if len(args) == 3 or len(args) == 5:
C = ma.asarray(args.pop(-1)).ravel()
V = ma.asarray(args.pop(-1))
U = ma.asarray(args.pop(-1))
nn = npy.shape(U)
nc = nn[0]
nr = 1
if len(nn) > 1:
nr = nn[1]
if len(args) == 2:
X, Y = [npy.array(a).ravel() for a in args]
if len(X) == nc and len(Y) == nr:
X, Y = [a.ravel() for a in npy.meshgrid(X, Y)]
else:
indexgrid = npy.meshgrid(npy.arange(nc), npy.arange(nr))
X, Y = [npy.ravel(a) for a in indexgrid]
return X, Y, U, V, C
def set_data(self, A, shape=None):
"""
Set the image array
ACCEPTS: numpy/PIL Image A"""
# check if data is PIL Image without importing Image
if hasattr(A, 'getpixel'):
X = pil_to_array(A)
else:
X = ma.asarray(A) # assume array
self._A = X
self._imcache = None
def set_data(self, A, shape=None):
"""
Set the image array
ACCEPTS: numpy/PIL Image A"""
# check if data is PIL Image without importing Image
if hasattr(A,'getpixel'):
X = pil_to_array(A)
else:
X = ma.asarray(A) # assume array
self._A = X
self._imcache =None
def recache(self):
#if self.axes is None: print 'recache no axes'
#else: print 'recache units', self.axes.xaxis.units, self.axes.yaxis.units
if ma.isMaskedArray(self._xorig) or ma.isMaskedArray(self._yorig):
x = ma.asarray(self.convert_xunits(self._xorig), float)
y = ma.asarray(self.convert_yunits(self._yorig), float)
x = ma.ravel(x)
y = ma.ravel(y)
else:
x = npy.asarray(self.convert_xunits(self._xorig), float)
y = npy.asarray(self.convert_yunits(self._yorig), float)
x = npy.ravel(x)
y = npy.ravel(y)
if len(x)==1 and len(y)>1:
x = x * npy.ones(y.shape, float)
if len(y)==1 and len(x)>1:
y = y * npy.ones(x.shape, float)
if len(x) != len(y):
raise RuntimeError('xdata and ydata must be the same length')
x = x.reshape((len(x), 1))
y = y.reshape((len(y), 1))
if ma.isMaskedArray(x) or ma.isMaskedArray(y):
self._xy = ma.concatenate((x, y), 1)
else:
self._xy = npy.concatenate((x, y), 1)
self._x = self._xy[:, 0] # just a view
self._y = self._xy[:, 1] # just a view
# Masked arrays are now handled by the Path class itself
self._path = Path(self._xy)
self._transformed_path = TransformedPath(self._path, self.get_transform())
self._invalid = False
def __call__(self, X, alpha=1.0, bytes=False):
"""
X is either a scalar or an array (of any dimension).
If scalar, a tuple of rgba values is returned, otherwise
an array with the new shape = oldshape+(4,). If the X-values
are integers, then they are used as indices into the array.
If they are floating point, then they must be in the
interval (0.0, 1.0).
Alpha must be a scalar.
If bytes is False, the rgba values will be floats on a
0-1 scale; if True, they will be uint8, 0-255.
"""
if not self._isinit: self._init()
alpha = min(alpha, 1.0) # alpha must be between 0 and 1
alpha = max(alpha, 0.0)
self._lut[:-3, -1] = alpha
mask_bad = None
if not cbook.iterable(X):
vtype = 'scalar'
xa = npy.array([X])
else:
vtype = 'array'
xma = ma.asarray(X)
xa = xma.filled(0)
mask_bad = ma.getmask(xma)
if xa.dtype.char in npy.typecodes['Float']:
npy.putmask(xa, xa == 1.0,
0.9999999) #Treat 1.0 as slightly less than 1.
xa = (xa * self.N).astype(int)
# Set the over-range indices before the under-range;
# otherwise the under-range values get converted to over-range.
npy.putmask(xa, xa > self.N - 1, self._i_over)
npy.putmask(xa, xa < 0, self._i_under)
if mask_bad is not None and mask_bad.shape == xa.shape:
npy.putmask(xa, mask_bad, self._i_bad)
if bytes:
lut = (self._lut * 255).astype(npy.uint8)
else:
lut = self._lut
rgba = lut[xa]
if vtype == 'scalar':
rgba = tuple(rgba[0, :])
return rgba
def __call__(self, X, alpha=1.0, bytes=False):
"""
X is either a scalar or an array (of any dimension).
If scalar, a tuple of rgba values is returned, otherwise
an array with the new shape = oldshape+(4,). If the X-values
are integers, then they are used as indices into the array.
If they are floating point, then they must be in the
interval (0.0, 1.0).
Alpha must be a scalar.
If bytes is False, the rgba values will be floats on a
0-1 scale; if True, they will be uint8, 0-255.
"""
if not self._isinit: self._init()
alpha = min(alpha, 1.0) # alpha must be between 0 and 1
alpha = max(alpha, 0.0)
self._lut[:-3, -1] = alpha
mask_bad = None
if not cbook.iterable(X):
vtype = 'scalar'
xa = npy.array([X])
else:
vtype = 'array'
xma = ma.asarray(X)
xa = xma.filled(0)
mask_bad = ma.getmask(xma)
if xa.dtype.char in npy.typecodes['Float']:
npy.putmask(xa, xa==1.0, 0.9999999) #Treat 1.0 as slightly less than 1.
xa = (xa * self.N).astype(int)
# Set the over-range indices before the under-range;
# otherwise the under-range values get converted to over-range.
npy.putmask(xa, xa>self.N-1, self._i_over)
npy.putmask(xa, xa<0, self._i_under)
if mask_bad is not None and mask_bad.shape == xa.shape:
npy.putmask(xa, mask_bad, self._i_bad)
if bytes:
lut = (self._lut * 255).astype(npy.uint8)
else:
lut = self._lut
rgba = lut[xa]
if vtype == 'scalar':
rgba = tuple(rgba[0,:])
return rgba
def _contour_args(self, *args):
if self.filled: fn = 'contourf'
else: fn = 'contour'
Nargs = len(args)
if Nargs <= 2:
z = ma.asarray(args[0], dtype=npy.float64)
x, y = self._initialize_x_y(z)
elif Nargs <=4:
x,y,z = self._check_xyz(args[:3])
else:
raise TypeError("Too many arguments to %s; see help(%s)" % (fn,fn))
self.zmax = ma.maximum(z)
self.zmin = ma.minimum(z)
if self.logscale and self.zmin <= 0:
z = ma.masked_where(z <= 0, z)
warnings.warn('Log scale: values of z <=0 have been masked')
self.zmin = z.min()
self._auto = False
if self.levels is None:
if Nargs == 1 or Nargs == 3:
lev = self._autolev(z, 7)
else: # 2 or 4 args
level_arg = args[-1]
try:
if type(level_arg) == int:
lev = self._autolev(z, level_arg)
else:
lev = npy.asarray(level_arg).astype(npy.float64)
except:
raise TypeError(
"Last %s arg must give levels; see help(%s)" % (fn,fn))
if self.filled and len(lev) < 2:
raise ValueError("Filled contours require at least 2 levels.")
# Workaround for cntr.c bug wrt masked interior regions:
#if filled:
# z = ma.masked_array(z.filled(-1e38))
# It's not clear this is any better than the original bug.
self.levels = lev
#if self._auto and self.extend in ('both', 'min', 'max'):
# raise TypeError("Auto level selection is inconsistent "
# + "with use of 'extend' kwarg")
self._levels = list(self.levels)
if self.extend in ('both', 'min'):
self._levels.insert(0, min(self.levels[0],self.zmin) - 1)
if self.extend in ('both', 'max'):
self._levels.append(max(self.levels[-1],self.zmax) + 1)
self._levels = npy.asarray(self._levels)
self.vmin = npy.amin(self.levels) # alternative would be self.layers
self.vmax = npy.amax(self.levels)
if self.extend in ('both', 'min'):
self.vmin = 2 * self.levels[0] - self.levels[1]
if self.extend in ('both', 'max'):
self.vmax = 2 * self.levels[-1] - self.levels[-2]
self.layers = self._levels # contour: a line is a thin layer
if self.filled:
self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])
if self.extend in ('both', 'min'):
self.layers[0] = 0.5 * (self.vmin + self._levels[1])
if self.extend in ('both', 'max'):
self.layers[-1] = 0.5 * (self.vmax + self._levels[-2])
return (x, y, z)
