def set_UVC(self, U, V, C=None):
self.u = ma.asarray(U).ravel()
self.v = ma.asarray(V).ravel()
if C is not None:
c = ma.asarray(C).ravel()
x,y,u,v,c = delete_masked_points(self.x.ravel(), self.y.ravel(),
self.u, self.v, c)
else:
x,y,u,v = delete_masked_points(self.x.ravel(), self.y.ravel(),
self.u, self.v)
magnitude = np.sqrt(u*u + v*v)
flags, barbs, halves, empty = self._find_tails(magnitude,
self.rounding, **self.barb_increments)
#Get the vertices for each of the barbs
plot_barbs = self._make_barbs(u, v, flags, barbs, halves, empty,
self._length, self._pivot, self.sizes, self.fill_empty, self.flip)
self.set_verts(plot_barbs)
#Set the color array
if C is not None:
self.set_array(c)
#Update the offsets in case the masked data changed
xy = np.hstack((x[:,np.newaxis], y[:,np.newaxis]))
self._offsets = xy
def recache(self, always=False):
if always or self._invalidx:
xconv = self.convert_xunits(self._xorig)
if ma.isMaskedArray(self._xorig):
x = ma.asarray(xconv, np.float_).filled(np.nan)
else:
x = np.asarray(xconv, np.float_)
x = x.ravel()
else:
x = self._x
if always or self._invalidy:
yconv = self.convert_yunits(self._yorig)
if ma.isMaskedArray(self._yorig):
y = ma.asarray(yconv, np.float_).filled(np.nan)
else:
y = np.asarray(yconv, np.float_)
y = y.ravel()
else:
y = self._y
if len(x) == 1 and len(y) > 1:
x = x * np.ones(y.shape, np.float_)
if len(y) == 1 and len(x) > 1:
y = y * np.ones(x.shape, np.float_)
if len(x) != len(y):
raise RuntimeError('xdata and ydata must be the same length')
self._xy = np.empty((len(x), 2), dtype=np.float_)
self._xy[:, 0] = x
self._xy[:, 1] = y
self._x = self._xy[:, 0] # just a view
self._y = self._xy[:, 1] # just a view
self._subslice = False
if (self.axes and len(x) > 1000 and self._is_sorted(x) and
self.axes.name == 'rectilinear' and
self.axes.get_xscale() == 'linear' and
self._markevery is None and
self.get_clip_on() is True):
self._subslice = True
nanmask = np.isnan(x)
if nanmask.any():
self._x_filled = self._x.copy()
indices = np.arange(len(x))
self._x_filled[nanmask] = np.interp(indices[nanmask],
indices[~nanmask], self._x[~nanmask])
else:
self._x_filled = self._x
if self._path is not None:
interpolation_steps = self._path._interpolation_steps
else:
interpolation_steps = 1
xy = STEP_LOOKUP_MAP[self._drawstyle](*self._xy.T)
self._path = Path(np.asarray(xy).T, None, interpolation_steps)
self._transformed_path = None
self._invalidx = False
self._invalidy = False
def __init__(self,*args,**kwargs):
TestCase.__init__(self,*args,**kwargs)
self.presidents = [nan, 87, 82, 75, 63, 50, 43, 32, 35, 60, 54, 55,
36, 39,nan,nan, 69, 57, 57, 51, 45, 37, 46, 39,
36, 24, 32, 23, 25, 32,nan, 32, 59, 74, 75, 60,
71, 61, 71, 57, 71, 68, 79, 73, 76, 71, 67, 75,
79, 62, 63, 57, 60, 49, 48, 52, 57, 62, 61, 66,
71, 62, 61, 57, 72, 83, 71, 78, 79, 71, 62, 74,
76, 64, 62, 57, 80, 73, 69, 69, 71, 64, 69, 62,
63, 46, 56, 44, 44, 52, 38, 46, 36, 49, 35, 44,
59, 65, 65, 56, 66, 53, 61, 52, 51, 48, 54, 49,
49, 61,nan,nan, 68, 44, 40, 27, 28, 25, 24, 24]
self.mdeaths = [2134,1863,1877,1877,1492,1249,1280,1131,1209,1492,1621,
1846,2103,2137,2153,1833,1403,1288,1186,1133,1053,1347,
1545,2066,2020,2750,2283,1479,1189,1160,1113, 970, 999,
1208,1467,2059,2240,1634,1722,1801,1246,1162,1087,1013,
959,1179,1229,1655,2019,2284,1942,1423,1340,1187,1098,
1004, 970,1140,1110,1812,2263,1820,1846,1531,1215,1075,
1056, 975, 940,1081,1294,1341]
self.fdeaths = [901, 689, 827, 677, 522, 406, 441, 393, 387, 582, 578,
666, 830, 752, 785, 664, 467, 438, 421, 412, 343, 440,
531, 771, 767,1141, 896, 532, 447, 420, 376, 330, 357,
445, 546, 764, 862, 660, 663, 643, 502, 392, 411, 348,
387, 385, 411, 638, 796, 853, 737, 546, 530, 446, 431,
362, 387, 430, 425, 679, 821, 785, 727, 612, 478, 429,
405, 379, 393, 411, 487, 574]
self.mdeaths = ma.asarray(self.mdeaths)
self.fdeaths = ma.asarray(self.fdeaths)
def recache(self, always=False):
if always or self._invalidx:
xconv = self.convert_xunits(self._xorig)
if ma.isMaskedArray(self._xorig):
x = ma.asarray(xconv, np.float_)
else:
x = np.asarray(xconv, np.float_)
x = x.ravel()
else:
x = self._x
if always or self._invalidy:
yconv = self.convert_yunits(self._yorig)
if ma.isMaskedArray(self._yorig):
y = ma.asarray(yconv, np.float_)
else:
y = np.asarray(yconv, np.float_)
y = y.ravel()
else:
y = self._y
if len(x) == 1 and len(y) > 1:
x = x * np.ones(y.shape, np.float_)
if len(y) == 1 and len(x) > 1:
y = y * np.ones(x.shape, np.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 = np.concatenate((x, y), 1)
self._x = self._xy[:, 0] # just a view
self._y = self._xy[:, 1] # just a view
self._subslice = False
if (
self.axes
and len(x) > 100
and self._is_sorted(x)
and self.axes.name == "rectilinear"
and self.axes.get_xscale() == "linear"
and self._markevery is None
):
self._subslice = True
if hasattr(self, "_path"):
interpolation_steps = self._path._interpolation_steps
else:
interpolation_steps = 1
self._path = Path(self._xy, None, interpolation_steps)
self._transformed_path = None
self._invalidx = False
self._invalidy = False
def _check(self, data):
array = biggus.NumpyArrayAdapter(data)
result = self.biggus_operator(array, axis=0).masked_array()
expected = self.numpy_masked_operator(data, axis=0)
if expected.ndim == 0:
if expected is np.ma.masked:
expected = ma.asarray(expected, dtype=array.dtype)
else:
expected = ma.asarray(expected)
np.testing.assert_array_equal(result.filled(), expected.filled())
np.testing.assert_array_equal(result.mask, expected.mask)
def __new__(cls, data=None, name=None, mask=None, fill_value=None,
dtype=None, shape=(), length=0,
description=None, unit=None, format=None, meta=None,
units=None, dtypes=None):
if dtypes is not None:
dtype = dtypes
warnings.warn("'dtypes' has been renamed to the singular 'dtype'.",
AstropyDeprecationWarning)
if units is not None:
unit = units
warnings.warn("'units' has been renamed to the singular 'unit'.",
AstropyDeprecationWarning)
if data is None:
dtype = (np.dtype(dtype).str, shape)
self_data = ma.zeros(length, dtype=dtype)
elif isinstance(data, (Column, MaskedColumn)):
self_data = ma.asarray(data.data, dtype=dtype)
if description is None:
description = data.description
if unit is None:
unit = unit or data.unit
if format is None:
format = data.format
if meta is None:
meta = deepcopy(data.meta)
if name is None:
name = data.name
elif isinstance(data, Quantity):
if unit is None:
self_data = ma.asarray(data, dtype=dtype)
unit = data.unit
else:
self_data = ma.asarray(data.to(unit), dtype=dtype)
else:
self_data = ma.asarray(data, dtype=dtype)
self = self_data.view(MaskedColumn)
if mask is None and hasattr(data, 'mask'):
mask = data.mask
if fill_value is None and hasattr(data, 'fill_value'):
fill_value = data.fill_value
self.mask = mask
self.fill_value = fill_value
self._name = name
self.unit = unit
self.format = format
self.description = description
self.parent_table = None
self.meta = meta
return self
def _mann_whitney_u(x, y=None):
"""
Calculate the Mann-Whitney-U test.
The Wilcoxon signed-rank test tests the null hypothesis that two related paired
samples come from the same distribution. In particular, it tests whether the
distribution of the differences x - y is symmetric about zero.
It is a non-parametric version of the paired T-test.
"""
# A significance-dict for a two-tailed test with 0.05 confidence
# from http://de.wikipedia.org/wiki/Wilcoxon-Mann-Whitney-Test
significance_table = \
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2],
[0, 0, 0, 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8],
[0, 0, 0, 0, 1, 2, 3, 4, 4, 5, 6, 7, 8, 9, 10, 11, 11, 12, 13, 14],
[0, 0, 0, 0, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 19, 20],
[0, 0, 0, 0, 0, 5, 6, 8, 10, 11, 13, 14, 16, 17, 19, 21, 22, 24, 25, 27],
[0, 0, 0, 0, 0, 0, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34],
[0, 0, 0, 0, 0, 0, 0, 13, 15, 17, 19, 22, 24, 26, 29, 31, 34, 36, 38, 41],
[0, 0, 0, 0, 0, 0, 0, 0, 17, 20, 23, 26, 28, 31, 34, 37, 39, 42, 45, 48],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 26, 29, 33, 36, 39, 42, 45, 48, 52, 55],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 33, 37, 40, 44, 47, 51, 55, 58, 62],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 41, 45, 49, 53, 57, 61, 65, 69],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 50, 54, 59, 63, 67, 72, 76],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 59, 64, 69, 74, 78, 83],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 70, 75, 80, 85, 90],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 75, 81, 86, 92, 98],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 87, 93, 99, 105],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 99, 106, 112],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 113, 119],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 127]]
U, p = stats.mannwhitneyu(x, y)
x = ma.asarray(x).compressed().view(numpy.ndarray)
y = ma.asarray(y).compressed().view(numpy.ndarray)
n1 = len(x)
n2 = len(y)
print n1, n2
if n1 > n2:
tmp = n2
n2 = n1
n1 = tmp
if n1 < 5 or n2 < 5:
return 10000, 10000
if n1 < 20 or n2 < 20:
print "WARNING: scipy.stat might not be accurate, p value is %f and significance according to table is %s" % \
(p, U <= significance_table[n1 - 1][n2 - 1])
return U, p
def test_masked_fill_value(self):
cubes = []
y = (0, 2)
cube = _make_cube((0, 2), y, 1)
cube.data = ma.asarray(cube.data)
cube.data.fill_value = 10
cubes.append(cube)
cube = _make_cube((2, 4), y, 1)
cube.data = ma.asarray(cube.data)
cube.data.fill_value = 20
cubes.append(cube)
result = concatenate(cubes)
self.assertEqual(len(result), 2)
def sim():
np.random.seed(1)
# Generate the time vector
dt = 0.05
N = int(30 // dt)
k = np.arange(N)
t = k * dt
# Instantiate the model
given = dict(
alpha=1, beta=-1, delta=0.2, gamma=0.3, omega=1,
g1=0, g2=0.1, x_meas_std=0.1,
x0=1, v0=0, x0_std=0.1, v0_std=0.1
)
q = GeneratedDTDuffing.pack('q', given)
c = GeneratedDTDuffing.pack('c', given)
params = dict(q=q, c=c, dt=dt)
sampled = dict(t=t)
model = GeneratedDTDuffing(params, sampled)
# Simulate the system
w = np.random.randn(N - 1, model.nw)
x = np.zeros((N, model.nx))
x[0] = stats.multivariate_normal.rvs(model.x0(), model.Px0())
for k in range(N - 1):
x[k + 1] = model.f(k, x[k]) + model.g(k, x[k]).dot(w[k])
# Sample the outputs
R = model.R()
v = np.random.multivariate_normal(np.zeros(model.ny), R, N)
y = ma.asarray(model.h(k, x) + v)
y[np.arange(N) % 4 != 0] = ma.masked
return model, t, x, y, q
def to_rgba(self, x, alpha=None, 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.
'''
if alpha is None:
_alpha = 1.0
else:
_alpha = alpha
try:
if x.ndim == 3:
if x.shape[2] == 3:
if x.dtype == np.uint8:
_alpha = np.array(_alpha*255, np.uint8)
m, n = x.shape[:2]
xx = np.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 != np.uint8:
xx = (xx * 255).astype(np.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(np.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(np.float)
self.autoscale_None(val)
vmin, vmax = self.vmin, self.vmax
cmin, cmax = self.cmin * vmin, self.cmax * vmax
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin == vmax:
result = 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = 0. * val + 0.5
result[val > cmax] = (ma.log10(val[val > cmax]) - ma.log10(cmax)) / (np.log10(vmax) - np.log10(cmax)) / 2. + 0.5
result[val < cmin] = -(ma.log10(-val[val < cmin]) - ma.log10(-cmin)) / (np.log10(-vmin) - np.log10(-cmin)) / 2. + 0.5
if vtype == 'scalar':
result = result[0]
return result
def bodytrack(threadstuple, serv):
#serv_M = ma.asarray([[0, 0, 0, 0, 6983544, 91465, 23131612, 171783],
# [340208, 21308, 230049, 18250, 83687, 35901, 0, 0],
# [0, 0, 0, 0, 0, 0, 22032093, 103155]])
serv_M = ma.asarray(serv)
rout = [
[[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 1, 1],
[0, 0, 1, 0]],
[[39, 1, 0, 0],
[0, 4, 1, 0],
[1, 0, 1, 0],
[0, 0, 0, 0]],
[[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 1]]
]
qt = list(islice(cycle((1,0)), serv_M.shape[1]))
routL = map (lambda x: rout_insert_inter(normalizeRowWise(np.asarray(x))), rout)
res = mva_multiclass (routL, 1./serv_M.T, threadstuple, qt)
cont = (res[0][1::2] - serv_M.T[1::2]).T
print "%.0f %.0f %.0f" % (cont[1,0], cont[0,2], cont[2,3])
return res
def __call__(self, value, clip=None, midpoint=None):
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(np.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==vmax:
return 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val-vmin) * (1.0/(vmax-vmin))
#result = (ma.arcsinh(val)-np.arcsinh(vmin))/(np.arcsinh(vmax)-np.arcsinh(vmin))
result = result**(1./self.nthroot)
if vtype == 'scalar':
result = result[0]
return result
def process_value(value):
"""
Homogenize the input *value* for easy and efficient normalization.
*value* can be a scalar or sequence.
Returns *result*, *is_scalar*, where *result* is a
masked array matching *value*. Float dtypes are preserved;
integer types with two bytes or smaller are converted to
np.float32, and larger types are converted to np.float.
Preserving float32 when possible, and using in-place operations,
can greatly improve speed for large arrays.
Experimental; we may want to add an option to force the
use of float32.
"""
if cbook.iterable(value):
is_scalar = False
result = ma.asarray(value)
if result.dtype.kind == 'f':
if isinstance(value, np.ndarray):
result = result.copy()
elif result.dtype.itemsize > 2:
result = result.astype(np.float)
else:
result = result.astype(np.float32)
else:
is_scalar = True
result = ma.array([value]).astype(np.float)
return result, is_scalar
def smooth(self, Y):
'''Run UKS
Params
------
Y : [T, n_dim_state] array
Y[t] = t obs
If Y is a masked array and any Y[t] is masked, obs is assumed missing and ignored.
Returns
-------
smoothed_state_means : [T, n_dim_state] array
filtered_state_means[t] = mean of t state distribution | [0, T-1] obs
smoothed_state_covariances : [T, n_dim_state, n_dim_state] array
filtered_state_covariances[t] = covariance of t state distribution | [0, T-1] obs
'''
Y = ma.asarray(Y)
(transition_functions, observation_functions,
transition_covariance, observation_covariance,
initial_state_mean, initial_state_covariance) = (
self._initialize_parameters()
)
(filtered_state_means, filtered_state_covariances) = self.filter(Y)
(smoothed_state_means, smoothed_state_covariances) = (
additive_unscented_smoother(
filtered_state_means, filtered_state_covariances,
transition_functions, transition_covariance
)
)
return (smoothed_state_means, smoothed_state_covariances)
def detect_contour(img, level):
"""Returns list of vertices of contours at a given level
Arguments:
img (array): the image array
level (number): the level at which to create the contour
Returns:
(list of nx2 arrays): list of list of vertices of the different contours
Note:
The contour detection is based on matplotlib's QuadContourGenerator
"""
#parameter
mask = None;
corner_mask = True;
nchunk = 0;
#prepare image data
z = ma.asarray(img, dtype=np.float64);
ny, nx = z.shape;
x, y = np.meshgrid(np.arange(nx), np.arange(ny));
#find contour
contour_generator = _contour.QuadContourGenerator(x, y, z.filled(), mask, corner_mask, nchunk)
vertices = contour_generator.create_contour(level);
return vertices;
def set_data(self, x, y, A):
A = ma.asarray(A)
if x is None:
x = np.arange(0, A.shape[1] + 1, dtype=np.float64)
else:
x = np.asarray(x, np.float64).ravel()
if y is None:
y = np.arange(0, A.shape[0] + 1, dtype=np.float64)
else:
y = np.asarray(y, np.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 __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(np.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(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (ma.log(val)-np.log(vmin))/(np.log(vmax)-np.log(vmin))
if vtype == 'scalar':
result = result[0]
return result
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 = self.ax.convert_xunits( args[0] )
y = self.ax.convert_yunits( args[1] )
x = np.asarray(x, dtype=np.float64)
y = np.asarray(y, dtype=np.float64)
z = ma.asarray(args[2], dtype=np.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 = np.meshgrid(x,y)
return x,y,z
def set_data(self, x, y, A):
x = np.asarray(x, np.float32)
y = np.asarray(y, np.float32)
A = ma.asarray(A)
if len(x.shape) != 1 or len(y.shape) != 1\
or A.shape[0:2] != (y.shape[0], x.shape[0]):
raise TypeError("Axes don't match array shape")
if len(A.shape) not in [2, 3]:
raise TypeError("Can only plot 2D or 3D data")
if len(A.shape) == 3 and A.shape[2] not in [1, 3, 4]:
raise TypeError(
"3D arrays must have three (RGB) or "
"four (RGBA) color components"
)
if len(A.shape) == 3 and A.shape[2] == 1:
A.shape = A.shape[0:2]
if len(A.shape) == 2:
if A.dtype != np.uint8:
A = (self.cmap(self.norm(A)) * 255).astype(np.uint8)
else:
A = np.repeat(A[:, :, np.newaxis], 4, 2)
A[:, :, 3] = 255
else:
if A.dtype != np.uint8:
A = (255 * A).astype(np.uint8)
if A.shape[2] == 3:
B = np.zeros(tuple(list(A.shape[0:2]) + [4]), np.uint8)
B[:, :, 0:3] = A
B[:, :, 3] = 255
A = B
self._A = A
self._Ax = x
self._Ay = y
self._imcache = None
def _expected(self, transpose=False):
data = self.data
if transpose:
data = self.data.T
# Expected raster weights per target grid cell.
# This is the (fractional) source cell contribution
# to each target cell (out of 255)
weights = np.array([[[63, 127, 127], # top left hand cell (tlhc)
[127, 255, 255]],
[[127, 127, 63], # top right hand cell (trhc)
[255, 255, 127]],
[[127, 255, 255], # bottom left hand cell (blhc)
[63, 127, 127]],
[[255, 255, 127], # bottom right hand cell (brhc)
[127, 127, 63]]], dtype=np.uint8)
weights = weights / 255
# Expected source points per target grid cell.
tmp = data[1:-1, 1:-1]
shape = (-1, 2, 3)
cells = [tmp[slice(0, 2), slice(0, 3)].reshape(shape), # tlhc
tmp[slice(0, 2), slice(3, None)].reshape(shape), # trhc
tmp[slice(2, None), slice(0, 3)].reshape(shape), # blhc
tmp[slice(2, None), slice(3, None)].reshape(shape)] # brhc
cells = ma.vstack(cells)
# Expected fractional weighted result.
num = (cells * weights).sum(axis=(1, 2))
dom = weights.sum(axis=(1, 2))
expected = num / dom
expected = ma.asarray(expected.reshape(2, 2))
if transpose:
expected = expected.T
return expected
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(numpy.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(numpy.float)
if self.staticrange is None:
self.autoscale_None(val)
vmin, vmax = self.vmin, self.vmax
else:
self.vmin, self.vmax = None, None
self.autoscale_None(val)
vmin, vmax = self.vmax - self.staticrange, self.vmax
if vmin > vmax:
raise ValueError("minvalue must be less than or equal to maxvalue")
elif vmin==vmax:
result = 0.0 * val
else:
vmin = float(vmin)
vmax = float(vmax)
rmin = float(self.rmin)
rmax = float(self.rmax)
if clip:
mask = ma.getmask(val)
val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val-vmin) * ((rmax-rmin) / (vmax-vmin)) + rmin
if vtype == 'scalar':
result = result[0]
return result
def _check(self, data):
array = biggus.NumpyArrayAdapter(data)
result = std(array, axis=0, ddof=0).masked_array()
expected = ma.std(data, axis=0, ddof=0)
if expected.ndim == 0:
expected = ma.asarray(expected)
np.testing.assert_array_equal(result.filled(), expected.filled())
np.testing.assert_array_equal(result.mask, expected.mask)
def test_concat_masked_2x2d(self):
cubes = []
y = (0, 2)
cube = _make_cube((0, 2), y, 1)
cube.data = ma.asarray(cube.data)
cube.data[(0, 1), (0, 1)] = ma.masked
cubes.append(cube)
cube = _make_cube((2, 4), y, 2)
cube.data = ma.asarray(cube.data)
cube.data[(0, 1), (1, 0)] = ma.masked
cubes.append(cube)
result = concatenate(cubes)
self.assertCML(result, ("concatenate", "concat_masked_2x2d.cml"))
self.assertEqual(len(result), 1)
self.assertEqual(result[0].shape, (2, 4))
mask = np.array([[True, False, False, True], [False, True, True, False]], dtype=np.bool)
self.assertArrayEqual(result[0].data.mask, mask)
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 = np.angle(ma.asarray(uv[..., np.newaxis]).filled(0))
xy = (X+Y*1j) * np.exp(1j*theta)*self.width
xy = xy[:,:,np.newaxis]
XY = ma.concatenate((xy.real, xy.imag), axis=2)
return XY
def to_rgba(self, x, alpha=None, bytes=False):
"""
Return a normalized rgba array corresponding to *x*.
In the normal case, *x* is a 1-D or 2-D sequence of scalars, and
the corresponding ndarray of rgba values will be returned,
based on the norm and colormap set for this ScalarMappable.
There is one special case, for handling images that are already
rgb or rgba, such as might have been read from an image file.
If *x* is an ndarray with 3 dimensions,
and the last dimension is either 3 or 4, then it will be
treated as an rgb or rgba array, and no mapping will be done.
If the last dimension is 3, the *alpha* kwarg (defaulting to 1)
will be used to fill in the transparency. If the last dimension
is 4, the *alpha* kwarg is ignored; it does not
replace the pre-existing alpha. A ValueError will be raised
if the third dimension is other than 3 or 4.
In either case, if *bytes* is *False* (default), the rgba
array will be floats in the 0-1 range; if it is *True*,
the returned rgba array will be uint8 in the 0 to 255 range.
Note: this method assumes the input is well-behaved; it does
not check for anomalies such as *x* being a masked rgba
array, or being an integer type other than uint8, or being
a floating point rgba array with values outside the 0-1 range.
"""
# First check for special case, image input:
try:
if x.ndim == 3:
if x.shape[2] == 3:
if alpha is None:
alpha = 1
if x.dtype == np.uint8:
alpha = np.uint8(alpha * 255)
m, n = x.shape[:2]
xx = np.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 != np.uint8:
xx = (xx * 255).astype(np.uint8)
if not bytes and xx.dtype == np.uint8:
xx = xx.astype(float) / 255
return xx
except AttributeError:
# e.g., x is not an ndarray; so try mapping it
pass
# This is the normal case, mapping a scalar array:
x = ma.asarray(x)
x = self.norm(x)
x = self.cmap(x, alpha=alpha, bytes=bytes)
return x
def RhoMask(RomsNC, latbounds, lonbounds) :
"""
defines mask on rho points only, without time dimension
"""
Rholats = (RomsNC.variables['lat_rho'][:] >= latbounds[0])*(RomsNC.variables['lat_rho'][:] <= latbounds[1])
Rholons = (RomsNC.variables['lon_rho'][:] >= lonbounds[0])*(RomsNC.variables['lon_rho'][:] <= lonbounds[1])
Rho_Mask = ma.asarray(np.invert(Rholats*Rholons))
return Rho_Mask
def test_masked_and_unmasked(self):
cubes = []
y = (0, 2)
cube = _make_cube((2, 4), y, 2)
cube.data = ma.asarray(cube.data)
cubes.append(cube)
cubes.append(_make_cube((0, 2), y, 1))
result = concatenate(cubes)
self.assertEqual(len(result), 1)
def test_concat_masked_2x2d(self):
cubes = []
y = (0, 2)
cube = _make_cube((0, 2), y, 1)
cube.data = ma.asarray(cube.data)
cube.data[(0, 1), (0, 1)] = ma.masked
cubes.append(cube)
cube = _make_cube((2, 4), y, 2)
cube.data = ma.asarray(cube.data)
cube.data[(0, 1), (1, 0)] = ma.masked
cubes.append(cube)
result = concatenate(cubes)
self.assertCML(result, ('concatenate', 'concat_masked_2x2d.cml'))
self.assertEqual(len(result), 1)
self.assertEqual(result[0].shape, (2, 4))
mask = np.array([[True, False, False, True],
[False, True, True, False]], dtype=np.bool)
self.assertArrayEqual(result[0].data.mask, mask)
def test_masked_vs_unmasked(self):
cubes = []
y = (0, 2)
cubes.append(_make_cube((0, 2), y, 1))
cube = _make_cube((2, 4), y, 2)
cube.data = ma.asarray(cube.data)
cubes.append(cube)
result = concatenate(cubes)
self.assertEqual(len(result), 2)
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 _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)
if self.angles == 'xy':
theta = self._angles(U, V).filled(0)[:,np.newaxis]
elif self.angles == 'uv':
theta = np.angle(ma.asarray(uv[..., np.newaxis]).filled(0))
else:
theta = ma.asarray(self.angles*np.pi/180.0).filled(0)
xy = (X+Y*1j) * np.exp(1j*theta)*self.width
xy = xy[:,:,np.newaxis]
XY = ma.concatenate((xy.real, xy.imag), axis=2)
return XY
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))
V = ma.asarray(args.pop(-1))
U = ma.asarray(args.pop(-1))
if U.ndim == 1:
nr, nc = 1, U.shape[0]
else:
nr, nc = U.shape
if len(args) == 2: # remaining after removing U,V,C
X, Y = [np.array(a).ravel() for a in args]
if len(X) == nc and len(Y) == nr:
X, Y = [a.ravel() for a in np.meshgrid(X, Y)]
else:
indexgrid = np.meshgrid(np.arange(nc), np.arange(nr))
X, Y = [np.ravel(a) for a in indexgrid]
return X, Y, U, V, C
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)
if self.angles == 'xy':
theta = self._angles(U, V).filled(0)[:, np.newaxis]
elif self.angles == 'uv':
theta = np.angle(ma.asarray(uv[..., np.newaxis]).filled(0))
else:
theta = ma.asarray(self.angles * np.pi / 180.0).filled(0)
xy = (X + Y * 1j) * np.exp(1j * theta) * self.width
xy = xy[:, :, np.newaxis]
XY = ma.concatenate((xy.real, xy.imag), axis=2)
return XY
def unmasked_data_as_1d_array(array):
array = ma.asarray(array)
if array.ndim == 0:
if array.mask:
data = np.array([])
else:
data = np.array([array.data])
else:
data = array.data[~ma.getmaskarray(array)]
return data
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 __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 = np.array([X])
else:
vtype = 'array'
xma = ma.asarray(X)
xa = xma.filled(0)
mask_bad = ma.getmask(xma)
if xa.dtype.char in np.typecodes['Float']:
np.putmask(xa, xa == 1.0,
0.9999999) #Treat 1.0 as slightly less than 1.
# The following clip is fast, and prevents possible
# conversion of large positive values to negative integers.
if NP_CLIP_OUT:
np.clip(xa * self.N, -1, self.N, out=xa)
else:
xa = np.clip(xa * self.N, -1, self.N)
xa = xa.astype(int)
# Set the over-range indices before the under-range;
# otherwise the under-range values get converted to over-range.
np.putmask(xa, xa > self.N - 1, self._i_over)
np.putmask(xa, xa < 0, self._i_under)
if mask_bad is not None and mask_bad.shape == xa.shape:
np.putmask(xa, mask_bad, self._i_bad)
if bytes:
lut = (self._lut * 255).astype(np.uint8)
else:
lut = self._lut
rgba = np.empty(shape=xa.shape + (4, ), dtype=lut.dtype)
lut.take(xa, axis=0, mode='clip', out=rgba)
# twice as fast as lut[xa];
# using the clip or wrap mode and providing an
# output array speeds it up a little more.
if vtype == 'scalar':
rgba = tuple(rgba[0, :])
return rgba
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 = np.shape(U)
nc = nn[0]
nr = 1
if len(nn) > 1:
nr = nn[1]
if len(args) == 2: # remaining after removing U,V,C
X, Y = [np.array(a).ravel() for a in args]
if len(X) == nc and len(Y) == nr:
X, Y = [a.ravel() for a in np.meshgrid(X, Y)]
else:
indexgrid = np.meshgrid(np.arange(nc), np.arange(nr))
X, Y = [np.ravel(a) for a in indexgrid]
return X, Y, U, V, C
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
gamma = self.gamma
vmin, vmax = self.vmin, self.vmax
if cbook.iterable(value):
val = ma.asarray(value)
return ma.power(value, 1. / gamma) * (vmax - vmin) + vmin
else:
return pow(value, 1. / gamma) * (vmax - vmin) + vmin
def inverse(self, value):
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin = float(self.vmin)
vmax = float(self.vmax)
if cbook.iterable(value):
val = ma.asarray(value)
return vmin + val * (vmax - vmin)
else:
return vmin + value * (vmax - vmin)
def test_neural_network(data, model=None):
y = asarray(data)
x = arange(len(data))
y_train, y_test, x_train, x_test = train_test_split(y, x, test_size=0.33)
guesser = Predictioner(model_input=model)
guesser.update_input(train_x=x_train, train_y=y_train)
history = guesser.fit_model(verbose=0)
eva = guesser.evaluate(y_test, x_test)
guesser.predict(x)
guesser.visualize()
return eva[0]
def sample(self, n_timesteps, initial_state=None, random_state=None):
'''Sample from model defined by the Unscented Kalman Filter
Parameters
----------
n_timesteps : int
number of time steps
initial_state : optional, [n_dim_state] array
initial state. If unspecified, will be sampled from initial state
distribution.
random_state : optional, int or Random
random number generator
'''
(transition_functions, observation_functions, transition_covariance,
observation_covariance, initial_state_mean,
initial_state_covariance) = (self._initialize_parameters())
n_dim_state = transition_covariance.shape[-1]
n_dim_obs = observation_covariance.shape[-1]
# logic for instantiating rng
if random_state is None:
rng = check_random_state(self.random_state)
else:
rng = check_random_state(random_state)
# logic for selecting initial state
if initial_state is None:
initial_state = rng.multivariate_normal(initial_state_mean,
initial_state_covariance)
# logic for generating samples
x = np.zeros((n_timesteps, n_dim_state))
z = np.zeros((n_timesteps, n_dim_obs))
for t in range(n_timesteps):
if t == 0:
x[0] = initial_state
else:
transition_function = (_last_dims(transition_functions,
t - 1,
ndims=1)[0])
transition_noise = (rng.multivariate_normal(
np.zeros(n_dim_state),
transition_covariance.newbyteorder('=')))
x[t] = transition_function(x[t - 1], transition_noise)
observation_function = (_last_dims(observation_functions,
t,
ndims=1)[0])
observation_noise = (rng.multivariate_normal(
np.zeros(n_dim_obs), observation_covariance.newbyteorder('=')))
z[t] = observation_function(x[t], observation_noise)
return (x, ma.asarray(z))
def __init__(self, *members, **kwargs):
ArrayChain.__init__(self, *members, **kwargs)
self.combined = np.empty(self.shape)
# adapted copy-paste from ArrayChain.__init__
if not members[1:]:
members = members[0]
kwargs = dict(zip(members[0::2], members[1::2]))
#
if any([isinstance(v, ma.MaskedArray) for v in kwargs.values()]):
self.combined = ma.asarray(self.combined)
for k in kwargs:
self[k] = kwargs[k]
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)
val = val * (self._upper - self._lower) + self._lower
return self.invfn(val)
else:
value = value * (self._upper - self._lower) + self._lower
return self.invfn(value)
def generate_nearly_covered_modis_lst(area, doy, lat1, lat2, lon1, lon2):
in_path = os.path.join("Data", "MOD11A1", "3km")
out_path = get_out_path(
os.path.join("Data", "MOD11A1", "3km_nearly_overlapped"))
log_file = os.path.join(out_path, 'log.txt')
sys.stdout = Logger(log_file)
print(area, doy)
if doy + ".nc" in os.listdir(
os.path.join("Data", "Sentinel", "usa_filtered")):
fh_sentinel = Dataset(
os.path.join("Data", "Sentinel", "usa_filtered", doy + ".nc"), "r")
elif doy + ".nc" in os.listdir(
os.path.join("Data", "Sentinel", "usa_filtered_real_gap")):
fh_sentinel = Dataset(
os.path.join("Data", "Sentinel", "usa_filtered_real_gap",
doy + ".nc"), "r")
lat_indices, lon_indices = select_area(lat1, lat2, lon1, lon2, "M03")
lats, lons = get_lat_lon("M03")
assert (len(lats) != 0 and len(lons) != 0)
lats = lats[lat_indices[0]:lat_indices[1]]
lons = lons[lon_indices[0]:lon_indices[1]]
i_lat_start, i_lat_end, i_lon_start, i_lon_end = match_lat_lon(
fh_sentinel.variables["lat"][:], fh_sentinel.variables["lon"][:], lats,
lons)
sm_mask = ~ma.getmaskarray(
fh_sentinel.variables["soil_moisture"][i_lat_start:i_lat_end + 1,
i_lon_start:i_lon_end + 1])
if np.sum(sm_mask) > 0:
s_day = datetime.datetime.strptime(doy, "%Y%m%d").date()
n_days = 0
lst_day_ref = []
while True:
hist_day = str(s_day + datetime.timedelta(days=-n_days)).replace(
"-", "")
print(hist_day, )
fh = Dataset(os.path.join(in_path, hist_day + ".nc"), "r")
lst_day_ref.append(
fh.variables["LST_Day"][i_lat_start:i_lat_end + 1,
i_lon_start:i_lon_end + 1])
fh.close()
lst_day_ref_mask = ~np.all(
ma.getmaskarray(ma.asarray(lst_day_ref)), axis=0)
overlap = np.logical_and(sm_mask, lst_day_ref_mask)
if np.sum(overlap) / float(np.sum(sm_mask)) > 0.95:
modis_lst_fill_missing_by_most_recent_values(doy, area, n_days)
break
n_days += 1
def inverse(self, value):
# ORIGINAL MATPLOTLIB CODE
if not self.scaled():
raise ValueError("Not invertible until scaled")
vmin, vmax = self.vmin, self.vmax
# CUSTOM APLPY CODE
if cbook.iterable(value):
val = ma.asarray(value)
else:
val = value
if self.stretch == 'Linear':
pass
elif self.stretch == 'Log':
val = (ma.power(10., val * ma.log10(self.midpoint)) -
1.) / (self.midpoint - 1.)
elif self.stretch == 'Sqrt':
val = val * val
elif self.stretch == 'Arcsinh':
val = self.midpoint * \
ma.sinh(val * ma.arcsinh(1. / self.midpoint))
elif self.stretch == 'Arccosh':
val = self.midpoint * \
ma.cosh(val * ma.arccosh(1. / self.midpoint))
elif self.stretch == 'Power':
val = ma.power(val, (1. / self.exponent))
elif self.stretch == 'Exp':
val = 1. / np.exp(val)
else:
raise Exception("Unknown stretch in APLpyNormalize: %s" %
self.stretch)
return vmin + val * (vmax - vmin)
def filter_auxfiles(data, isets, auxf, auxpar, auxconds):
if auxf:
data = ma.asarray(data)
for f in range(len(auxf)):
catlg = cl.ClumpCatalog.from_file([auxf[f]])
size = np.shape(catlg.clumps)[0]
npars = len(auxpar[f])
tmparr = np.zeros((npars, size))
for i in range(size):
for j in range(npars):
tmparr[j, i] = catlg.clumps[i].record[auxpar[f][j]]
tmparr = ma.asarray(tmparr)
fcond = auxconds[f]
col = 0
while fcond:
cond = fcond.pop(0)
cut = fcond.pop(0)
if cond == ">":
mask_aux = ma.masked_greater(tmparr[col, :], cut)
elif cond == "=":
mask_aux = ma.masked_equal(tmparr[col, :], cut)
elif cond == "<":
mask_aux = ma.masked_less(tmparr[col, :], cut)
elif cond == "!=":
mask_aux = ma.masked_not_equal(tmparr[col, :], cut)
col += 1
tmparr.mask = mask_aux.mask
if np.shape(tmparr.mask):
data.mask = tmparr.mask
else:
data = data.data
return data
def dotd(A, B, out=None):
"""Diagonal of :math:`\mathrm A\mathrm B^\intercal`.
If ``A`` is :math:`n\times p` and ``B`` is :math:`p\times n`, it is done in :math:`O(pn)`.
Args:
A (array_like): Left matrix.
B (array_like): Right matrix.
out (:class:`numpy.ndarray`, optional): copy result to.
Returns:
:class:`numpy.ndarray`: Resulting diagonal.
"""
A = ma.asarray(A, float)
B = ma.asarray(B, float)
if A.ndim == 1 and B.ndim == 1:
if out is None:
return ma.dot(A, B)
return ma.dot(A, B, out)
if out is None:
out = ma.empty((A.shape[0], ), float)
out[:] = ma.sum(A * B.T, axis=1)
return out
def estimate_kdp_vulpiani(psidp,dr,windsize = 7, band = 'C', interpolate = False):
masked = psidp.mask
pool = mp.Pool(processes = mp.cpu_count(),maxtasksperchild=1)
func=partial(kdp_estimation_vulpiani,dr = dr, band = band, windsize = windsize, interpolate = interpolate)
all_psidp = list(psidp)
list_est = pool.map(func,all_psidp)
kdp = np.zeros(psidp.shape)*np.nan
phidp_rec = np.zeros(psidp.shape)*np.nan
for i,l in enumerate(list_est):
kdp[i,0:len(l[0])] = l[0]
phidp_rec[i,0:len(l[1])] = l[1]
kdp = ma.asarray(kdp)
kdp.mask = masked
phidp_rec = ma.asarray(phidp_rec)
phidp_rec.mask = masked
pool.close()
return kdp, phidp_rec
def dotd(A, B):
r"""Diagonal of :math:`\mathrm A\mathrm B^\intercal`.
If ``A`` is :math:`n\times p` and ``B`` is :math:`p\times n`, it is done in
:math:`O(pn)`.
Args:
A (array_like): Left matrix.
B (array_like): Right matrix.
Returns:
:class:`numpy.ndarray`: Resulting diagonal.
"""
A = asarray(A, float)
B = asarray(B, float)
if A.ndim == 1 and B.ndim == 1:
return dot(A, B)
out = empty((A.shape[0],), float)
out[:] = sum(A * B.T, axis=1)
return out
def msokalsneath(u, v):
r"""
Computes the Sokal-Sneath dissimilarity between two boolean vectors
``u`` and ``v``,
.. math::
\frac{R}
{c_{TT} + R}
where :math:`c_{ij}` is the number of occurrences of
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n` and :math:`R = 2(c_{TF} + c_{FT})`.
Parameters
----------
u : ndarray
An :math:`n`-dimensional vector.
v : ndarray
An :math:`n`-dimensional vector.
Returns
-------
d : double
The Sokal-Sneath dissimilarity between vectors ``u`` and ``v``.
"""
u = ma.asarray(u, order='c')
v = ma.asarray(v, order='c')
if u.dtype == np.bool:
ntt = (u & v).sum()
else:
ntt = (u * v).sum()
(nft, ntf) = _nbool_correspond_ft_tf(u, v)
denom = ntt + 2.0 * (ntf + nft)
if denom == 0:
raise ValueError('Sokal-Sneath dissimilarity is not defined for '
'vectors that are entirely false.')
return float(2.0 * (ntf + nft)) / denom
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 = np.asarray(self.convert_xunits(self._xorig), float)
y = np.asarray(self.convert_yunits(self._yorig), float)
x = np.ravel(x)
y = np.ravel(y)
if len(x) == 1 and len(y) > 1:
x = x * np.ones(y.shape, float)
if len(y) == 1 and len(x) > 1:
y = y * np.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 = np.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, value, clip=None):
method = self.stretch
exponent = self.exponent
midpoint = self.midpoint
if clip is None:
clip = self.clip
if cbook.iterable(value):
vtype = 'array'
val = ma.asarray(value).astype(np.float)
else:
vtype = 'scalar'
val = ma.array([value]).astype(np.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 == vmax:
return 0.0 * val
else:
if clip:
mask = ma.getmask(val)
val = ma.array(np.clip(val.filled(vmax), vmin, vmax),
mask=mask)
result = (val - vmin) * (1.0 / (vmax - vmin))
negative = result < 0.
if self.stretch == 'linear':
pass
elif self.stretch == 'log':
result = ma.log10(result * (self.midpoint - 1.) + 1.) \
/ ma.log10(self.midpoint)
elif self.stretch == 'sqrt':
result = ma.sqrt(result)
elif self.stretch == 'arcsinh':
result = ma.arcsinh(result / self.midpoint) \
/ ma.arcsinh(1. / self.midpoint)
elif self.stretch == 'power':
result = ma.power(result, exponent)
else:
raise Exception("Unknown stretch in APLpyNormalize: %s" %
self.stretch)
result[negative] = -np.inf
if vtype == 'scalar':
result = result[0]
return result
def msokalmichener(u, v):
r"""
Computes the Sokal-Michener dissimilarity between two boolean vectors
``u`` and ``v``, which is defined as
.. math::
\frac{R}
{S + R}
where :math:`c_{ij}` is the number of occurrences of
:math:`\mathtt{u[k]} = i` and :math:`\mathtt{v[k]} = j` for
:math:`k < n`, :math:`R = 2 * (c_{TF} + c_{FT})` and
:math:`S = c_{FF} + c_{TT}`.
Parameters
----------
u : ndarray
An :math:`n`-dimensional vector.
v : ndarray
An :math:`n`-dimensional vector.
Returns
-------
d : double
The Sokal-Michener dissimilarity between vectors ``u`` and ``v``.
"""
u = ma.asarray(u, order='c')
v = ma.asarray(v, order='c')
if u.dtype == np.bool:
ntt = (u & v).sum()
nff = (~u & ~v).sum()
else:
ntt = (u * v).sum()
nff = ((1.0 - u) * (1.0 - v)).sum()
(nft, ntf) = _nbool_correspond_ft_tf(u, v)
return float(2.0 * (ntf + nft)) / float(ntt + nff + 2.0 * (ntf + nft))
def mcityblock(u, v):
r"""
Computes the Manhattan distance between two n-vectors u and v,
which is defined as
.. math::
\sum_i {(u_i-v_i)}.
Parameters
----------
u : ndarray
An :math:`n`-dimensional vector.
v : ndarray
An :math:`n`-dimensional vector.
Returns
-------
d : double
The City Block distance between vectors ``u`` and ``v``.
"""
u = ma.asarray(u, order='c')
v = ma.asarray(v, order='c')
return abs(u - v).sum()
def mchebyshev(u, v):
r"""
Computes the Chebyshev distance between two n-vectors u and v,
which is defined as
.. math::
\max_i {|u_i-v_i|}.
Parameters
----------
u : ndarray
An :math:`n`-dimensional vector.
v : ndarray
An :math:`n`-dimensional vector.
Returns
-------
d : double
The Chebyshev distance between vectors ``u`` and ``v``.
"""
u = ma.asarray(u, order='c')
v = ma.asarray(v, order='c')
return max(abs(u - v))
def mbraycurtis(u, v):
r"""
Computes the Bray-Curtis distance between two n-vectors ``u`` and
``v``, which is defined as
.. math::
\sum{|u_i-v_i|} / \sum{|u_i+v_i|}.
Parameters
----------
u : ndarray
An :math:`n`-dimensional vector.
v : ndarray
An :math:`n`-dimensional vector.
Returns
-------
d : double
The Bray-Curtis distance between vectors ``u`` and ``v``.
"""
u = ma.asarray(u, order='c')
v = ma.asarray(v, order='c')
return abs(u - v).sum() / abs(u + v).sum()
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
self._rgbacache = None
self._oldxslice = None
self._oldyslice = None
def test_regrid_ok_src_masked(self):
self.data = ma.asarray(self.data)
self.data[2, 3] = ma.masked # tlhc 1x masked of 6x src cells
self.data[2, 4] = ma.masked # trhc 2x masked of 6x src cells
self.data[2, 5] = ma.masked
self.data[3, 2] = ma.masked # blhc 3x masked of 6x src cells
self.data[3, 3] = ma.masked
self.data[4, 3] = ma.masked
self.data[3, 4] = ma.masked # brhc 4x masked of 6x src cells
self.data[3, 5] = ma.masked
self.data[4, 4] = ma.masked
self.data[4, 5] = ma.masked
result = agg(self.data, self.sx_points, self.sx_bounds, self.sy_points,
self.sy_bounds, self.sx_dim, self.sy_dim, self.gx_bounds,
self.gy_bounds)
assert_array_equal(result, self._expected())
