Python numpy 模块，fmax() 实例源码
我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.fmax()。
def imax(arrays, axis, ignore_nan = False):
"""
Maximum of a stream of arrays along an axis.
Parameters
----------
arrays : iterable
Arrays to be reduced.
axis : int or None, optional
Axis along which the maximum is found. The default
is to find the maximum along the 'stream axis', as if all arrays in ``array``
were stacked along a new dimension. If ``axis = None``, arrays in ``arrays`` are flattened
before reduction.
ignore_nan : bool, optional
If True, NaNs are ignored. Default is propagation of NaNs.
Yields
------
online_max : ndarray
Cumulative maximum.
"""
ufunc = np.fmax if ignore_nan else np.maximum
yield from ireduce_ufunc(arrays, ufunc, axis)
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.fmax.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), 9)
assert_equal(func(tmp2), 9)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def fea_plot(xg_model, feature, label, type = 'weight', max_num_features = None):
fig, AX = plt.subplots(nrows=1, ncols=2)
xgb.plot_importance(xg_model, xlabel=type, importance_type='weight', ax=AX[0], max_num_features=max_num_features)
fscore = xg_model.get_score(importance_type=type)
fscore = sorted(fscore.items(), key=itemgetter(1), reverse=True) # sort scores
fea_index = get_fea_index(fscore, max_num_features)
feature = feature[:, fea_index]
dimension = len(fea_index)
X = range(1, dimension+1)
Yp = np.mean(feature[np.where(label==1)[0]], axis=0)
Yn = np.mean(feature[np.where(label!=1)[0]], axis=0)
for i in range(0, dimension):
param = np.fmax(Yp[i], Yn[i])
Yp[i] /= param
Yn[i] /= param
p1 = AX[1].bar(X, +Yp, facecolor='#ff9999', edgecolor='white')
p2 = AX[1].bar(X, -Yn, facecolor='#9999ff', edgecolor='white')
AX[1].legend((p1,p2), ('Malware', 'Normal'))
AX[1].set_title('Comparison of selected features by their means')
AX[1].set_xlabel('Feature Index')
AX[1].set_ylabel('Mean Value')
AX[1].set_ylim(-1.1, 1.1)
plt.xticks(X, fea_index+1, rotation=80)
plt.suptitle('Feature Selection results')
def test_obj_value_points_correctly_class_far_from_hyperplane(self):
bias = 0.0
w = np.array([-1, 1, bias])
l = self.svm.l2reg
X = np.array([[5, 0.3], [1, -0.8], [1, 6], [-0.6, 3]])
Y = np.array([-1, -1, 1, 1])
# compute loss for all X -> 1-yi*(xi*w+b)
out = np.fmax(0, 1 - Y * (X.dot(w[0:-1]) + w[-1]))
expectedObj = 1.0
result, _ = self.svm._obj_func(w, X, Y, out)
self.assertAlmostEqual(expectedObj, result)
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.fmax.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), 9)
assert_equal(func(tmp2), 9)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.fmax.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), 9)
assert_equal(func(tmp2), 9)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.fmax.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), 9)
assert_equal(func(tmp2), 9)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.fmax.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), 9)
assert_equal(func(tmp2), 9)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.fmax.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), 9)
assert_equal(func(tmp2), 9)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def variability_prob(ndvi, ndsi, whiteness):
"""Use the probability of the spectral variability
to identify clouds over land.
Equation 15 (Zhu and Woodcock, 2012)
Parameters
----------
ndvi: ndarray
ndsi: ndarray
whiteness: ndarray
Output
------
ndarray :
probability of cloud over land based on variability
"""
ndi_max = np.fmax(np.absolute(ndvi), np.absolute(ndsi))
f_max = 1.0 - np.fmax(ndi_max, whiteness)
return f_max
# Eq 16, land_cloud_prob
# lCloud_Prob = lTemperature_Prob x Variability_Prob
def test_reduce(self):
dflt = np.typecodes['AllFloat']
dint = np.typecodes['AllInteger']
seq1 = np.arange(11)
seq2 = seq1[::-1]
func = np.fmax.reduce
for dt in dint:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
for dt in dflt:
tmp1 = seq1.astype(dt)
tmp2 = seq2.astype(dt)
assert_equal(func(tmp1), 10)
assert_equal(func(tmp2), 10)
tmp1[::2] = np.nan
tmp2[::2] = np.nan
assert_equal(func(tmp1), 9)
assert_equal(func(tmp2), 9)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_reduce_complex(self):
assert_equal(np.fmax.reduce([1, 2j]), 1)
assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([0, 0, nan])
assert_equal(np.fmax(arg1, arg2), out)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([0, 0, nan], dtype=np.complex)
assert_equal(np.fmax(arg1, arg2), out)
def _obj_func(self, w, X, Y, out):
"""
Computes primal value end gradient
Parameters
----------
w : {array-like} - hyperplane normal vector
X : {array-like, sparse matrix}, shape = [n_samples, n_features]
Training vector, where n_samples in the number of samples and
n_features is the number of features.
Y : array-like, shape = [n_samples]
Target vector relative to X
out: loss function values
Returns
-------
(obj,grad) : tuple, obj - function value, grad - gradient
"""
l2reg = self.l2reg
# we remember bias, to recover it after gradient computation
bias = w[-1]
# set bias to zero, don't penalize b
w[-1] = 0
max_out = np.fmax(0, out)
obj = np.sum(max_out ** 2) / 2 + l2reg * w.dot(w) / 2
grad = l2reg * w - np.append([np.dot(max_out * Y, X)], [np.sum(max_out * Y)])
w[-1] = bias
return (obj, grad)
def test_obj_value_points_correctly_class_close_to_hyperplane(self):
bias = 0.0
w = np.array([-1, 1, bias])
l = self.svm.l2reg
X = np.array([[0.5, 0.3], [1, 0.8], [1, 1.4], [0.6, 0.9]])
Y = np.array([-1, -1, 1, 1])
# compute loss for all X -> 1-yi*(xi*w+b)
out = np.fmax(0, 1 - Y * (X.dot(w[0:-1]) + w[-1]))
expectedObj = 2.0650000000000004
result, _ = self.svm._obj_func(w, X, Y, out)
self.assertAlmostEqual(expectedObj, result)
def test_obj_grad_points(self):
bias = 0.0
w = np.array([-1, 1, bias])
l = self.svm.l2reg
X = np.array([[0.5, 0.3], [1, 0.8], [1, 1.4], [0.6, 0.9]])
Y = np.array([-1, -1, 1, 1])
# compute loss for all X -> 1-yi*(xi*w+b)
out = np.fmax(0, 1 - Y * (X.dot(w[0:-1]) + w[-1]))
expected = np.array([-0.82, 0.41, 0.3])
(obj, grad) = self.svm._obj_func(w, X, Y, out)
np.testing.assert_array_almost_equal(expected, grad)
def _scale_cosine_similarity(x, metric='cosine', inverse=False):
""" Given a cosine similarity on L2 normalized data,
appriximately convert it to Jaccard similarity, and/or
normalize it to the [0, 1] interval
Parameters
----------
x : {float, ndarray}
the cosine similarity value
metric : str
the conversion to apply one of ['cosine', 'jaccard']
inverse : bool
perform the inverse de-normalization operation
"""
valid_metrics = ['cosine', 'jaccard', 'cosine_norm', 'jaccard_norm',
'cosine-positive']
if metric not in valid_metrics:
raise ValueError('metric {} not supported, must be in {}'
.format(metric, valid_metrics))
if metric == 'cosine':
return x
elif metric == 'cosine-positive':
if isinstance(x, (int, float)):
return max(x, 0.0)
else:
return np.fmax(x, 0.0)
if metric.startswith('jaccard'):
if not inverse:
x = cosine2jaccard_similarity(x)
else:
x = jaccard2cosine_similarity(x)
if metric.endswith('norm'):
x = _normalize_similarity(x, metric=metric.split('_')[0],
inverse=inverse)
return x
def test_reduce_complex(self):
assert_equal(np.fmax.reduce([1, 2j]), 1)
assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([0, 0, nan])
assert_equal(np.fmax(arg1, arg2), out)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([0, 0, nan], dtype=np.complex)
assert_equal(np.fmax(arg1, arg2), out)
def forward_cpu(self, inputs):
x, = inputs
# y = log(1 + exp(beta * x)) / beta
bx = self.beta * x
y = (numpy.fmax(bx, 0) +
numpy.log1p(numpy.exp(-numpy.fabs(bx)))) * self.beta_inv
return utils.force_array(y.astype(x.dtype)),
def test_reduce_complex(self):
assert_equal(np.fmax.reduce([1, 2j]), 1)
assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([0, 0, nan])
assert_equal(np.fmax(arg1, arg2), out)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([0, 0, nan], dtype=np.complex)
assert_equal(np.fmax(arg1, arg2), out)
def twoFactorGaussianExample(inds,t_max=1.0,tau_max=3.0,b0=0.0759,b1=-0.0439,k=0.4454,a2=0.5,s1=0.02,s2=0.01,K=0.5,verbose=False):
'''
Compute the two factor Gaussian Example in Beck-Tempone-Szepessy-Zouraris
'''
f0 = lambda tau: b0+b1*np.exp(-1.0*k*tau)
F = lambda x: np.exp(-1.0*x)
G = lambda x: np.fmax(np.exp(-1.0*x)-K)
Psi = lambda x: 1.0*x
U = lambda x: 0.0*x
d1 = lambda s: s1*s1*s
d20 = lambda s: np.exp(-0.5*a2*s)
d2 = lambda s: 2*s2*s2/a2*d20(s)*(1.0-d20(s))
drift = lambda s: d1(s)+d2(s)
v1 = lambda s: s1*np.ones(np.shape(s))
v2 = lambda s: s2*d20(s)
vols = [v1,v2]
identifierString = 'Evaluating the Two Factor Gaussian example.\n'
identifierString += 's1: %f, s2: %f, b0: %f, tau_max: %f, t_max: %f\n'%(s1,s2,b0,tau_max,t_max)
identifierString += 'k: %f, a2: %f, K: %f, b1: %f'%(k,a2,K,b1)
return multiLevelHjmModel(inds,F,G,U,Psi,drift,vols,f0,t_max=t_max,tau_max=tau_max,identifierString=identifierString,verbose=verbose)
def test_reduce_complex(self):
assert_equal(np.fmax.reduce([1, 2j]), 1)
assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([0, 0, nan])
assert_equal(np.fmax(arg1, arg2), out)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([0, 0, nan], dtype=np.complex)
assert_equal(np.fmax(arg1, arg2), out)
def test_reduce_complex(self):
assert_equal(np.fmax.reduce([1, 2j]), 1)
assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([0, 0, nan])
assert_equal(np.fmax(arg1, arg2), out)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([0, 0, nan], dtype=np.complex)
assert_equal(np.fmax(arg1, arg2), out)
def test_reduce_complex(self):
assert_equal(np.fmax.reduce([1, 2j]), 1)
assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([0, 0, nan])
assert_equal(np.fmax(arg1, arg2), out)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([0, 0, nan], dtype=np.complex)
assert_equal(np.fmax(arg1, arg2), out)
def clip_to_window(boxlist, window):
"""Clip bounding boxes to a window.
This op clips input bounding boxes (represented by bounding box
corners) to a window, optionally filtering out boxes that do not
overlap at all with the window.
Args:
boxlist: BoxList holding M_in boxes
window: a numpy array of shape [4] representing the
[y_min, x_min, y_max, x_max] window to which the op
should clip boxes.
Returns:
a BoxList holding M_out boxes where M_out <= M_in
"""
y_min, x_min, y_max, x_max = np.array_split(boxlist.get(), 4, axis=1)
win_y_min = window[0]
win_x_min = window[1]
win_y_max = window[2]
win_x_max = window[3]
y_min_clipped = np.fmax(np.fmin(y_min, win_y_max), win_y_min)
y_max_clipped = np.fmax(np.fmin(y_max, win_y_max), win_y_min)
x_min_clipped = np.fmax(np.fmin(x_min, win_x_max), win_x_min)
x_max_clipped = np.fmax(np.fmin(x_max, win_x_max), win_x_min)
clipped = np_box_list.BoxList(
np.hstack([y_min_clipped, x_min_clipped, y_max_clipped, x_max_clipped]))
clipped = _copy_extra_fields(clipped, boxlist)
areas = area(clipped)
nonzero_area_indices = np.reshape(np.nonzero(np.greater(areas, 0.0)),
[-1]).astype(np.int32)
return gather(clipped, nonzero_area_indices)
def test_reduce_complex(self):
assert_equal(np.fmax.reduce([1, 2j]), 1)
assert_equal(np.fmax.reduce([1+3j, 2j]), 1+3j)
def test_float_nans(self):
nan = np.nan
arg1 = np.array([0, nan, nan])
arg2 = np.array([nan, 0, nan])
out = np.array([0, 0, nan])
assert_equal(np.fmax(arg1, arg2), out)
def test_complex_nans(self):
nan = np.nan
for cnan in [complex(nan, 0), complex(0, nan), complex(nan, nan)]:
arg1 = np.array([0, cnan, cnan], dtype=np.complex)
arg2 = np.array([cnan, 0, cnan], dtype=np.complex)
out = np.array([0, 0, nan], dtype=np.complex)
assert_equal(np.fmax(arg1, arg2), out)
def load_contour_data(fpath, normalize=True):
""" Load contour data from vamp output csv file.
Initializes DataFrame to have all future columns.
Parameters
----------
fpath : str
Path to vamp output csv file.
Returns
-------
contour_data : DataFrame
Pandas data frame with all contour data.
"""
try:
contour_data = pd.read_csv(fpath, header=None, index_col=None,
delimiter=',').astype(float)
del contour_data[0] # all zeros
del contour_data[1] # just an unnecessary index
headers = contour_data.columns.values.astype('str')
headers[0:12] = ['onset', 'offset', 'duration', 'pitch mean', 'pitch std',
'salience mean', 'salience std', 'salience tot',
'vibrato', 'vib rate', 'vib extent', 'vib coverage']
contour_data.columns = headers
except:
contour_data = loadpickle(fpath)
# trying to load with pickle
# Check if there is any column with all nans... it should not be considered
df = contour_data.isnull().all()
if np.where(df)[0]:
contour_data = contour_data.drop(contour_data.columns[np.where(df)[0][0]], axis=1)
# To ensure the contour has a duration > 0
contour_data['duration'] = np.fmax(contour_data['duration'].values,0.001)
contour_data.num_end_cols = 0
contour_data['overlap'] = -1 # overlaps are unset
contour_data['labels'] = -1 # all labels are unset
contour_data['melodiness'] = ""
contour_data['mel prob'] = -1
contour_data.num_end_cols = 4
if normalize:
contour_data = normalize_features(contour_data)
return contour_data
def test_datetime_minmax(self):
# The metadata of the result should become the GCD
# of the operand metadata
a = np.array('1999-03-12T13', dtype='M8[2m]')
b = np.array('1999-03-12T12', dtype='M8[s]')
assert_equal(np.minimum(a, b), b)
assert_equal(np.minimum(a, b).dtype, np.dtype('M8[s]'))
assert_equal(np.fmin(a, b), b)
assert_equal(np.fmin(a, b).dtype, np.dtype('M8[s]'))
assert_equal(np.maximum(a, b), a)
assert_equal(np.maximum(a, b).dtype, np.dtype('M8[s]'))
assert_equal(np.fmax(a, b), a)
assert_equal(np.fmax(a, b).dtype, np.dtype('M8[s]'))
# Viewed as integers, the comparison is opposite because
# of the units chosen
assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))
# Interaction with NaT
a = np.array('1999-03-12T13', dtype='M8[2m]')
dtnat = np.array('NaT', dtype='M8[h]')
assert_equal(np.minimum(a, dtnat), a)
assert_equal(np.minimum(dtnat, a), a)
assert_equal(np.maximum(a, dtnat), a)
assert_equal(np.maximum(dtnat, a), a)
# Also do timedelta
a = np.array(3, dtype='m8[h]')
b = np.array(3*3600 - 3, dtype='m8[s]')
assert_equal(np.minimum(a, b), b)
assert_equal(np.minimum(a, b).dtype, np.dtype('m8[s]'))
assert_equal(np.fmin(a, b), b)
assert_equal(np.fmin(a, b).dtype, np.dtype('m8[s]'))
assert_equal(np.maximum(a, b), a)
assert_equal(np.maximum(a, b).dtype, np.dtype('m8[s]'))
assert_equal(np.fmax(a, b), a)
assert_equal(np.fmax(a, b).dtype, np.dtype('m8[s]'))
# Viewed as integers, the comparison is opposite because
# of the units chosen
assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))
# should raise between datetime and timedelta
#
# TODO: Allowing unsafe casting by
# default in ufuncs strikes again... :(
a = np.array(3, dtype='m8[h]')
b = np.array('1999-03-12T12', dtype='M8[s]')
#assert_raises(TypeError, np.minimum, a, b)
#assert_raises(TypeError, np.maximum, a, b)
#assert_raises(TypeError, np.fmin, a, b)
#assert_raises(TypeError, np.fmax, a, b)
assert_raises(TypeError, np.minimum, a, b, casting='same_kind')
assert_raises(TypeError, np.maximum, a, b, casting='same_kind')
assert_raises(TypeError, np.fmin, a, b, casting='same_kind')
assert_raises(TypeError, np.fmax, a, b, casting='same_kind')
def test_datetime_minmax(self):
# The metadata of the result should become the GCD
# of the operand metadata
a = np.array('1999-03-12T13', dtype='M8[2m]')
b = np.array('1999-03-12T12', dtype='M8[s]')
assert_equal(np.minimum(a, b), b)
assert_equal(np.minimum(a, b).dtype, np.dtype('M8[s]'))
assert_equal(np.fmin(a, b), b)
assert_equal(np.fmin(a, b).dtype, np.dtype('M8[s]'))
assert_equal(np.maximum(a, b), a)
assert_equal(np.maximum(a, b).dtype, np.dtype('M8[s]'))
assert_equal(np.fmax(a, b), a)
assert_equal(np.fmax(a, b).dtype, np.dtype('M8[s]'))
# Viewed as integers, the comparison is opposite because
# of the units chosen
assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))
# Interaction with NaT
a = np.array('1999-03-12T13', dtype='M8[2m]')
dtnat = np.array('NaT', dtype='M8[h]')
assert_equal(np.minimum(a, dtnat), a)
assert_equal(np.minimum(dtnat, a), a)
assert_equal(np.maximum(a, dtnat), a)
assert_equal(np.maximum(dtnat, a), a)
# Also do timedelta
a = np.array(3, dtype='m8[h]')
b = np.array(3*3600 - 3, dtype='m8[s]')
assert_equal(np.minimum(a, b), b)
assert_equal(np.minimum(a, b).dtype, np.dtype('m8[s]'))
assert_equal(np.fmin(a, b), b)
assert_equal(np.fmin(a, b).dtype, np.dtype('m8[s]'))
assert_equal(np.maximum(a, b), a)
assert_equal(np.maximum(a, b).dtype, np.dtype('m8[s]'))
assert_equal(np.fmax(a, b), a)
assert_equal(np.fmax(a, b).dtype, np.dtype('m8[s]'))
# Viewed as integers, the comparison is opposite because
# of the units chosen
assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))
# should raise between datetime and timedelta
#
# TODO: Allowing unsafe casting by
# default in ufuncs strikes again... :(
a = np.array(3, dtype='m8[h]')
b = np.array('1999-03-12T12', dtype='M8[s]')
#assert_raises(TypeError, np.minimum, a, b)
#assert_raises(TypeError, np.maximum, a, b)
#assert_raises(TypeError, np.fmin, a, b)
#assert_raises(TypeError, np.fmax, a, b)
assert_raises(TypeError, np.minimum, a, b, casting='same_kind')
assert_raises(TypeError, np.maximum, a, b, casting='same_kind')
assert_raises(TypeError, np.fmin, a, b, casting='same_kind')
assert_raises(TypeError, np.fmax, a, b, casting='same_kind')
def gen_anscombe_forward(signal, gauss_std, gauss_mean = 0, poisson_multi = 1):
"""
Applies the generalized Anscombe variance-stabilization transform
assuming a mixed Poisson-Gaussian noise model as:
signal = poisson_multi*Poisson{signal0} + Gauss{gauss_mean, gauss_std},
where Poisson{} and Gauss{} are generalized descriptions of Poisson and
Gaussian noise.
Parameters
----------
signal : ndarray
Noisy signal (1-,2-,3D)
gauss_std : float, int
Standard deviation of Gaussian noise
poisson_multi : float or int, optional (default = 1)
Effectively a multiplier that scales the effect of the Poisson
noise
gauss_mean : float or int, optional (default = 0)
Mean Gaussian noise level
Returns
-------
fsignal : ndarray (matched to signal shape)
"Anscombe-transformed" signal with an approximate unity standard \
deviation/variance (~ 1)
Note
----
This software is a direct translation (with minor alterations) of the
original MATLAB software created by Alessandro Foi and Markku Mäkitalo
(Tampere University of Technology - 2011-2012). Please cite the references
below if using this software. http://www.cs.tut.fi/~foi/
References
----------
[1] J.L. Starck, F. Murtagh, and A. Bijaoui, Image Processing and
Data Analysis, Cambridge University Press, Cambridge, 1998)
"""
SMALL_VAL = 1
fsignal = 2/poisson_multi * _np.sqrt(_np.fmax(SMALL_VAL,poisson_multi*signal +
(3/8)*poisson_multi**2 +
gauss_std**2 -
poisson_multi*gauss_mean))
# fsignal = _ne.evaluate('2/poisson_multi * sqrt(where(poisson_multi*signal + (3/8)*poisson_multi**2 +\
# gauss_std**2 - poisson_multi*gauss_mean > SMALL_VAL,\
# poisson_multi*signal + (3/8)*poisson_multi**2 +\
# gauss_std**2 - poisson_multi*gauss_mean, SMALL_VAL))')
#fsignal = 2/poisson_multi * _np.sqrt(_np.fmax(SMALL_VAL,fsignal))
return fsignal