Python numpy 模块，floor_divide() 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.floor_divide()。
def test_remainder_basic(self):
dt = np.typecodes['AllInteger'] + np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
if sg1 == -1 and dt1 in np.typecodes['UnsignedInteger']:
continue
if sg2 == -1 and dt2 in np.typecodes['UnsignedInteger']:
continue
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*71, dtype=dt1)
b = np.array(sg2*19, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
def test_float_remainder_exact(self):
# test that float results are exact for small integers. This also
# holds for the same integers scaled by powers of two.
nlst = list(range(-127, 0))
plst = list(range(1, 128))
dividend = nlst + [0] + plst
divisor = nlst + plst
arg = list(itertools.product(dividend, divisor))
tgt = list(divmod(*t) for t in arg)
a, b = np.array(arg, dtype=int).T
# convert exact integer results from Python to float so that
# signed zero can be used, it is checked.
tgtdiv, tgtrem = np.array(tgt, dtype=float).T
tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)
tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)
for dt in np.typecodes['Float']:
msg = 'dtype: %s' % (dt,)
fa = a.astype(dt)
fb = b.astype(dt)
div = np.floor_divide(fa, fb)
rem = np.remainder(fa, fb)
assert_equal(div, tgtdiv, err_msg=msg)
assert_equal(rem, tgtrem, err_msg=msg)
def test_float_remainder_roundoff(self):
# gh-6127
dt = np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*78*6e-8, dtype=dt1)
b = np.array(sg2*6e-8, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
# Equal assertion should hold when fmod is used
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
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 __ifloordiv__(self, other):
"""
Floor divide self by other in-place.
"""
other_data = getdata(other)
dom_mask = _DomainSafeDivide().__call__(self._data, other_data)
other_mask = getmask(other)
new_mask = mask_or(other_mask, dom_mask)
# The following 3 lines control the domain filling
if dom_mask.any():
(_, fval) = ufunc_fills[np.floor_divide]
other_data = np.where(dom_mask, fval, other_data)
self._mask |= new_mask
self._data.__ifloordiv__(np.where(self._mask, self.dtype.type(1),
other_data))
return self
def calc_l1_norm_itae(meas_values, desired_values, step_width):
"""
Calculate the L1-Norm of the ITAE (Integral of Time-multiplied Absolute
value of Error).
Args:
step_width (float): Time difference between measurements.
desired_values (array-like): Desired values.
meas_values (array-like): Measured values.
"""
def e_func(_t):
_idx = np.floor_divide(_t, step_width).astype(int)
e = t * np.abs(desired_values[_idx, ..., 0]
- meas_values[_idx, ..., 0])
return e
t = np.array([x * step_width for x in range(len(desired_values))])
err = e_func(t)
l1norm_itae = simps(err, t)
return l1norm_itae
def calc_l1_norm_abs(meas_values, desired_values, step_width):
"""
Calculate the L1-Norm of the absolute error.
Args:
step_width (float): Time difference between measurements.
desired_values (array-like): Desired values.
meas_values (array-like): Measured values.
"""
def e_func(_t):
_idx = np.floor_divide(_t, step_width).astype(int)
e = np.abs(desired_values[_idx, ..., 0]
- meas_values[_idx, ..., 0])
return e
t = np.array([x * step_width for x in range(len(desired_values))])
err = e_func(t)
l1norm_abs = simps(err, t)
return l1norm_abs
def test_remainder_basic(self):
dt = np.typecodes['AllInteger'] + np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
if sg1 == -1 and dt1 in np.typecodes['UnsignedInteger']:
continue
if sg2 == -1 and dt2 in np.typecodes['UnsignedInteger']:
continue
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*71, dtype=dt1)
b = np.array(sg2*19, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
def test_float_remainder_exact(self):
# test that float results are exact for small integers. This also
# holds for the same integers scaled by powers of two.
nlst = list(range(-127, 0))
plst = list(range(1, 128))
dividend = nlst + [0] + plst
divisor = nlst + plst
arg = list(itertools.product(dividend, divisor))
tgt = list(divmod(*t) for t in arg)
a, b = np.array(arg, dtype=int).T
# convert exact integer results from Python to float so that
# signed zero can be used, it is checked.
tgtdiv, tgtrem = np.array(tgt, dtype=float).T
tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)
tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)
for dt in np.typecodes['Float']:
msg = 'dtype: %s' % (dt,)
fa = a.astype(dt)
fb = b.astype(dt)
div = np.floor_divide(fa, fb)
rem = np.remainder(fa, fb)
assert_equal(div, tgtdiv, err_msg=msg)
assert_equal(rem, tgtrem, err_msg=msg)
def test_float_remainder_roundoff(self):
# gh-6127
dt = np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*78*6e-8, dtype=dt1)
b = np.array(sg2*6e-8, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
# Equal assertion should hold when fmod is used
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
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 __ifloordiv__(self, other):
"""
Floor divide self by other in-place.
"""
other_data = getdata(other)
dom_mask = _DomainSafeDivide().__call__(self._data, other_data)
other_mask = getmask(other)
new_mask = mask_or(other_mask, dom_mask)
# The following 3 lines control the domain filling
if dom_mask.any():
(_, fval) = ufunc_fills[np.floor_divide]
other_data = np.where(dom_mask, fval, other_data)
self._mask |= new_mask
self._data.__ifloordiv__(np.where(self._mask, self.dtype.type(1),
other_data))
return self
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_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_remainder_basic(self):
dt = np.typecodes['AllInteger'] + np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
if sg1 == -1 and dt1 in np.typecodes['UnsignedInteger']:
continue
if sg2 == -1 and dt2 in np.typecodes['UnsignedInteger']:
continue
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*71, dtype=dt1)
b = np.array(sg2*19, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
def test_float_remainder_exact(self):
# test that float results are exact for small integers. This also
# holds for the same integers scaled by powers of two.
nlst = list(range(-127, 0))
plst = list(range(1, 128))
dividend = nlst + [0] + plst
divisor = nlst + plst
arg = list(itertools.product(dividend, divisor))
tgt = list(divmod(*t) for t in arg)
a, b = np.array(arg, dtype=int).T
# convert exact integer results from Python to float so that
# signed zero can be used, it is checked.
tgtdiv, tgtrem = np.array(tgt, dtype=float).T
tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)
tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)
for dt in np.typecodes['Float']:
msg = 'dtype: %s' % (dt,)
fa = a.astype(dt)
fb = b.astype(dt)
div = np.floor_divide(fa, fb)
rem = np.remainder(fa, fb)
assert_equal(div, tgtdiv, err_msg=msg)
assert_equal(rem, tgtrem, err_msg=msg)
def test_float_remainder_roundoff(self):
# gh-6127
dt = np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*78*6e-8, dtype=dt1)
b = np.array(sg2*6e-8, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
# Equal assertion should hold when fmod is used
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
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 __ifloordiv__(self, other):
"""
Floor divide self by other in-place.
"""
other_data = getdata(other)
dom_mask = _DomainSafeDivide().__call__(self._data, other_data)
other_mask = getmask(other)
new_mask = mask_or(other_mask, dom_mask)
# The following 3 lines control the domain filling
if dom_mask.any():
(_, fval) = ufunc_fills[np.floor_divide]
other_data = np.where(dom_mask, fval, other_data)
self._mask |= new_mask
self._data.__ifloordiv__(np.where(self._mask, self.dtype.type(1),
other_data))
return self
def test_remainder_basic(self):
dt = np.typecodes['AllInteger'] + np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
if sg1 == -1 and dt1 in np.typecodes['UnsignedInteger']:
continue
if sg2 == -1 and dt2 in np.typecodes['UnsignedInteger']:
continue
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*71, dtype=dt1)
b = np.array(sg2*19, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
def test_float_remainder_exact(self):
# test that float results are exact for small integers. This also
# holds for the same integers scaled by powers of two.
nlst = list(range(-127, 0))
plst = list(range(1, 128))
dividend = nlst + [0] + plst
divisor = nlst + plst
arg = list(itertools.product(dividend, divisor))
tgt = list(divmod(*t) for t in arg)
a, b = np.array(arg, dtype=int).T
# convert exact integer results from Python to float so that
# signed zero can be used, it is checked.
tgtdiv, tgtrem = np.array(tgt, dtype=float).T
tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)
tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)
for dt in np.typecodes['Float']:
msg = 'dtype: %s' % (dt,)
fa = a.astype(dt)
fb = b.astype(dt)
div = np.floor_divide(fa, fb)
rem = np.remainder(fa, fb)
assert_equal(div, tgtdiv, err_msg=msg)
assert_equal(rem, tgtrem, err_msg=msg)
def test_float_remainder_roundoff(self):
# gh-6127
dt = np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*78*6e-8, dtype=dt1)
b = np.array(sg2*6e-8, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
# Equal assertion should hold when fmod is used
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
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 __ifloordiv__(self, other):
"""
Floor divide self by other in-place.
"""
other_data = getdata(other)
dom_mask = _DomainSafeDivide().__call__(self._data, other_data)
other_mask = getmask(other)
new_mask = mask_or(other_mask, dom_mask)
# The following 3 lines control the domain filling
if dom_mask.any():
(_, fval) = ufunc_fills[np.floor_divide]
other_data = np.where(dom_mask, fval, other_data)
self._mask |= new_mask
self._data.__ifloordiv__(np.where(self._mask, self.dtype.type(1),
other_data))
return self
def test_remainder_basic(self):
dt = np.typecodes['AllInteger'] + np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
if sg1 == -1 and dt1 in np.typecodes['UnsignedInteger']:
continue
if sg2 == -1 and dt2 in np.typecodes['UnsignedInteger']:
continue
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*71, dtype=dt1)
b = np.array(sg2*19, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
def test_float_remainder_exact(self):
# test that float results are exact for small integers. This also
# holds for the same integers scaled by powers of two.
nlst = list(range(-127, 0))
plst = list(range(1, 128))
dividend = nlst + [0] + plst
divisor = nlst + plst
arg = list(itertools.product(dividend, divisor))
tgt = list(divmod(*t) for t in arg)
a, b = np.array(arg, dtype=int).T
# convert exact integer results from Python to float so that
# signed zero can be used, it is checked.
tgtdiv, tgtrem = np.array(tgt, dtype=float).T
tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)
tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)
for dt in np.typecodes['Float']:
msg = 'dtype: %s' % (dt,)
fa = a.astype(dt)
fb = b.astype(dt)
div = np.floor_divide(fa, fb)
rem = np.remainder(fa, fb)
assert_equal(div, tgtdiv, err_msg=msg)
assert_equal(rem, tgtrem, err_msg=msg)
def test_float_remainder_roundoff(self):
# gh-6127
dt = np.typecodes['Float']
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
fmt = 'dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (dt1, dt2, sg1, sg2)
a = np.array(sg1*78*6e-8, dtype=dt1)
b = np.array(sg2*6e-8, dtype=dt2)
div = np.floor_divide(a, b)
rem = np.remainder(a, b)
# Equal assertion should hold when fmod is used
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
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 __ifloordiv__(self, other):
"""
Floor divide self by other in-place.
"""
other_data = getdata(other)
dom_mask = _DomainSafeDivide().__call__(self._data, other_data)
other_mask = getmask(other)
new_mask = mask_or(other_mask, dom_mask)
# The following 3 lines control the domain filling
if dom_mask.any():
(_, fval) = ufunc_fills[np.floor_divide]
other_data = np.where(dom_mask, fval, other_data)
self._mask |= new_mask
self._data.__ifloordiv__(np.where(self._mask, self.dtype.type(1),
other_data))
return self
def _window_slicing_sequence(self, X, window, shape_1X, y=None, stride=1):
""" Slicing procedure for sequences (aka shape_1X = [.., 1]).
:param X: np.array
Array containing the input samples.
Must be of shape [n_samples, data] where data is a 1D array.
:param window: int
Size of the window to use for slicing.
:param shape_1X: list or np.array
Shape of a single sample [n_lines, n_col].
:param y: np.array (default=None)
Target values.
:param stride: int (default=1)
Step used when slicing the data.
:return: np.array and np.array
Arrays containing the sliced sequences and target values (empty if 'y' is None).
"""
if shape_1X[1] < window:
raise ValueError('window must be smaller than the sequence dimension')
len_iter = np.floor_divide((shape_1X[1] - window), stride) + 1
iter_array = np.arange(0, stride*len_iter, stride)
ind_1X = np.arange(np.prod(shape_1X))
inds_to_take = [ind_1X[i:i+window] for i in iter_array]
sliced_sqce = np.take(X, inds_to_take, axis=1).reshape(-1, window)
if y is not None:
sliced_target = np.repeat(y, len_iter)
elif y is None:
sliced_target = None
return sliced_sqce, sliced_target
def lab2npy(input_path, out, frame_rate= 0.01):
"""
frame_rate : sampling rate, typically 10 ms (or 0.01s)
"""
name=os.path.basename(input_path)
df=pd.read_table(input_path, sep=" ", header=None)
df.columns=["onset", "offset", "phone", "score"]
#onset and offset are in 100* nanosecond. Need to be put in second
df["onset"]=df["onset"]*10**(-7)
df["offset"]=df["offset"]*10**(-7)
phones=['a{}'.format(i) for i in range(1, 49)]
list_feats=[]
R=np.empty(1)
R[0]=frame_rate
for i in range(len(df)):
on=df["onset"].iloc[i]
off=df["offset"].iloc[i]
nb_frame=int(np.floor_divide((off-on),R[0]))
for ff in range(nb_frame):
one_hot=np.empty(len(phones))
for j in range(len(phones)):
if df["phone"][i]==phones[j]:
one_hot[j]=1
else:
one_hot[j]=0
list_feats.append(one_hot)
arr_feats = np.array(list_feats)
directory=out + "/npy/"
try:
os.stat(directory)
except:
os.mkdir(directory)
np.save(directory + "/"+ name.split(".")[0], arr=arr_feats, allow_pickle=False)
def test_floor_division_complex(self):
# check that implementation is correct
msg = "Complex floor division implementation check"
x = np.array([.9 + 1j, -.1 + 1j, .9 + .5*1j, .9 + 2.*1j], dtype=np.complex128)
y = np.array([0., -1., 0., 0.], dtype=np.complex128)
assert_equal(np.floor_divide(x**2, x), y, err_msg=msg)
# check overflow, underflow
msg = "Complex floor division overflow/underflow check"
x = np.array([1.e+110, 1.e-110], dtype=np.complex128)
y = np.floor_divide(x**2, x)
assert_equal(y, [1.e+110, 0], err_msg=msg)
def __floordiv__(self, other):
"""
Divide other into self, and return a new masked array.
"""
if self._delegate_binop(other):
return NotImplemented
return floor_divide(self, other)
def __rfloordiv__(self, other):
"""
Divide self into other, and return a new masked array.
"""
return floor_divide(other, self)
def int_div(x1: Number = 1.0, x2: Number = 2.0) -> Int:
return np.floor_divide(x1, x2)
def __ifloordiv__(self, other):
return floor_divide(self, other, self)
def generate_op(self, op, out, x, y):
self.append("np.floor_divide({}, {}, out={})", x, y, out)
def vox_to_pos(self, vox):
return np.floor_divide(vox - self.MOVE_GRID_OFFSET, self.MOVE_DELTA).astype(np.int64)
def get_output_margin(model_config):
return np.floor_divide(model_config.input_fov_shape - model_config.output_fov_shape, 2)
def __init__(self, volume, shape, label_margin=None):
self.volume = volume
self.shape = shape
self.margin = np.floor_divide(self.shape, 2).astype(np.int64)
if label_margin is None:
label_margin = np.zeros(3, dtype=np.int64)
self.label_margin = label_margin
self.skip_blank_sections = True
self.ctr_min = self.margin
self.ctr_max = (np.array(self.volume.shape) - self.margin - 1).astype(np.int64)
self.random = np.random.RandomState(CONFIG.random_seed)
# If the volume has a mask channel, further limit ctr_min and
# ctr_max to lie inside a margin in the AABB of the mask.
if self.volume.mask_data is not None:
mask_min, mask_max = self.volume.mask_bounds
mask_min = self.volume.local_coord_to_world(mask_min)
mask_max = self.volume.local_coord_to_world(mask_max)
self.ctr_min = np.maximum(self.ctr_min, mask_min + self.label_margin)
self.ctr_max = np.minimum(self.ctr_max, mask_max - self.label_margin - 1)
if np.any(self.ctr_min >= self.ctr_max):
raise ValueError('Cannot generate subvolume bounds: bounds ({}, {}) too small for shape ({})'.format(
np.array_str(self.ctr_min), np.array_str(self.ctr_max), np.array_str(self.shape)))
def __init__(self, parent, partitioning, partition_index):
super(PartitionedVolume, self).__init__(
parent,
parent.resolution,
image_data=parent.image_data,
label_data=parent.label_data,
mask_data=parent.mask_data)
self.partitioning = np.asarray(partitioning)
self.partition_index = np.asarray(partition_index)
partition_shape = np.floor_divide(np.array(self.parent.shape), self.partitioning)
self.bounds = ((np.multiply(partition_shape, self.partition_index)).astype(np.int64),
(np.multiply(partition_shape, self.partition_index + 1)).astype(np.int64))
def shape(self):
return tuple(np.floor_divide(np.array(self.parent.shape), self.scale))
def test_floor_division_complex(self):
# check that implementation is correct
msg = "Complex floor division implementation check"
x = np.array([.9 + 1j, -.1 + 1j, .9 + .5*1j, .9 + 2.*1j], dtype=np.complex128)
y = np.array([0., -1., 0., 0.], dtype=np.complex128)
assert_equal(np.floor_divide(x**2, x), y, err_msg=msg)
# check overflow, underflow
msg = "Complex floor division overflow/underflow check"
x = np.array([1.e+110, 1.e-110], dtype=np.complex128)
y = np.floor_divide(x**2, x)
assert_equal(y, [1.e+110, 0], err_msg=msg)
def __floordiv__(self, other):
"""
Divide other into self, and return a new masked array.
"""
if self._delegate_binop(other):
return NotImplemented
return floor_divide(self, other)
def __rfloordiv__(self, other):
"""
Divide self into other, and return a new masked array.
"""
return floor_divide(other, self)
def __ifloordiv__(self, other):
""" See __div__. """
oth = sanitize_units_mul(self, other)
np.floor_divide(self, oth, out=self)
return self
def plot_minute_histogram(splits):
def plot(ax, detections):
bins = np.floor_divide(detections['timestamp'], 60).astype('int64')
counts = np.bincount(bins)
ax.plot(counts)
ax.set_xlabel('Minute')
ax.set_ylabel('Count')
fig = plt.figure()
plot_rxtx_matrix(fig, splits, plot)
fig.suptitle('Histogram of number of detections per minute')
def test_floor_division_complex(self):
# check that implementation is correct
msg = "Complex floor division implementation check"
x = np.array([.9 + 1j, -.1 + 1j, .9 + .5*1j, .9 + 2.*1j], dtype=np.complex128)
y = np.array([0., -1., 0., 0.], dtype=np.complex128)
assert_equal(np.floor_divide(x**2, x), y, err_msg=msg)
# check overflow, underflow
msg = "Complex floor division overflow/underflow check"
x = np.array([1.e+110, 1.e-110], dtype=np.complex128)
y = np.floor_divide(x**2, x)
assert_equal(y, [1.e+110, 0], err_msg=msg)