Python numpy.mintypecode() Examples
The following are 9
code examples of numpy.mintypecode().
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Example #1
| Source File: _util.py From lambda-packs with MIT License | 5 votes |
|
def _lazywhere(cond, arrays, f, fillvalue=None, f2=None):
"""
np.where(cond, x, fillvalue) always evaluates x even where cond is False.
This one only evaluates f(arr1[cond], arr2[cond], ...).
For example,
>>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])
>>> def f(a, b):
return a*b
>>> _lazywhere(a > 2, (a, b), f, np.nan)
array([ nan, nan, 21., 32.])
Notice it assumes that all `arrays` are of the same shape, or can be
broadcasted together.
"""
if fillvalue is None:
if f2 is None:
raise ValueError("One of (fillvalue, f2) must be given.")
else:
fillvalue = np.nan
else:
if f2 is not None:
raise ValueError("Only one of (fillvalue, f2) can be given.")
arrays = np.broadcast_arrays(*arrays)
temp = tuple(np.extract(cond, arr) for arr in arrays)
tcode = np.mintypecode([a.dtype.char for a in arrays])
out = _valarray(np.shape(arrays[0]), value=fillvalue, typecode=tcode)
np.place(out, cond, f(*temp))
if f2 is not None:
temp = tuple(np.extract(~cond, arr) for arr in arrays)
np.place(out, ~cond, f2(*temp))
return out
Example #2
| Source File: _util.py From lambda-packs with MIT License | 5 votes |
|
def _lazyselect(condlist, choicelist, arrays, default=0):
"""
Mimic `np.select(condlist, choicelist)`.
Notice it assumes that all `arrays` are of the same shape, or can be
broadcasted together.
All functions in `choicelist` must accept array arguments in the order
given in `arrays` and must return an array of the same shape as broadcasted
`arrays`.
Examples
--------
>>> x = np.arange(6)
>>> np.select([x <3, x > 3], [x**2, x**3], default=0)
array([ 0, 1, 4, 0, 64, 125])
>>> _lazyselect([x < 3, x > 3], [lambda x: x**2, lambda x: x**3], (x,))
array([ 0., 1., 4., 0., 64., 125.])
>>> a = -np.ones_like(x)
>>> _lazyselect([x < 3, x > 3],
... [lambda x, a: x**2, lambda x, a: a * x**3],
... (x, a), default=np.nan)
array([ 0., 1., 4., nan, -64., -125.])
"""
arrays = np.broadcast_arrays(*arrays)
tcode = np.mintypecode([a.dtype.char for a in arrays])
out = _valarray(np.shape(arrays[0]), value=default, typecode=tcode)
for index in range(len(condlist)):
func, cond = choicelist[index], condlist[index]
if np.all(cond is False):
continue
cond, _ = np.broadcast_arrays(cond, arrays[0])
temp = tuple(np.extract(cond, arr) for arr in arrays)
np.place(out, cond, func(*temp))
return out
Example #3
| Source File: scipy.py From vnpy_crypto with MIT License | 5 votes |
|
def _lazywhere(cond, arrays, f, fillvalue=None, f2=None):
"""
np.where(cond, x, fillvalue) always evaluates x even where cond is False.
This one only evaluates f(arr1[cond], arr2[cond], ...).
For example,
>>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])
>>> def f(a, b):
return a*b
>>> _lazywhere(a > 2, (a, b), f, np.nan)
array([ nan, nan, 21., 32.])
Notice it assumes that all `arrays` are of the same shape, or can be
broadcasted together.
"""
if fillvalue is None:
if f2 is None:
raise ValueError("One of (fillvalue, f2) must be given.")
else:
fillvalue = np.nan
else:
if f2 is not None:
raise ValueError("Only one of (fillvalue, f2) can be given.")
arrays = np.broadcast_arrays(*arrays)
temp = tuple(np.extract(cond, arr) for arr in arrays)
tcode = np.mintypecode([a.dtype.char for a in arrays])
out = _valarray(np.shape(arrays[0]), value=fillvalue, typecode=tcode)
np.place(out, cond, f(*temp))
if f2 is not None:
temp = tuple(np.extract(~cond, arr) for arr in arrays)
np.place(out, ~cond, f2(*temp))
return out
# Work around for complex chnges in gammaln in 1.0.0. loggamma introduced in 0.18.
Example #4
| Source File: filter_design.py From Computable with MIT License | 5 votes |
|
def lp2bp(b, a, wo=1.0, bw=1.0):
"""
Transform a lowpass filter prototype to a bandpass filter.
Return an analog band-pass filter with center frequency `wo` and
bandwidth `bw` from an analog low-pass filter prototype with unity
cutoff frequency, in transfer function ('ba') representation.
"""
a, b = map(atleast_1d, (a, b))
D = len(a) - 1
N = len(b) - 1
artype = mintypecode((a, b))
ma = max([N, D])
Np = N + ma
Dp = D + ma
bprime = numpy.zeros(Np + 1, artype)
aprime = numpy.zeros(Dp + 1, artype)
wosq = wo * wo
for j in range(Np + 1):
val = 0.0
for i in range(0, N + 1):
for k in range(0, i + 1):
if ma - i + 2 * k == j:
val += comb(i, k) * b[N - i] * (wosq) ** (i - k) / bw ** i
bprime[Np - j] = val
for j in range(Dp + 1):
val = 0.0
for i in range(0, D + 1):
for k in range(0, i + 1):
if ma - i + 2 * k == j:
val += comb(i, k) * a[D - i] * (wosq) ** (i - k) / bw ** i
aprime[Dp - j] = val
return normalize(bprime, aprime)
Example #5
| Source File: filter_design.py From Computable with MIT License | 5 votes |
|
def lp2bs(b, a, wo=1.0, bw=1.0):
"""
Transform a lowpass filter prototype to a highpass filter.
Return an analog band-stop filter with center frequency `wo` and
bandwidth `bw` from an analog low-pass filter prototype with unity
cutoff frequency, in transfer function ('ba') representation.
"""
a, b = map(atleast_1d, (a, b))
D = len(a) - 1
N = len(b) - 1
artype = mintypecode((a, b))
M = max([N, D])
Np = M + M
Dp = M + M
bprime = numpy.zeros(Np + 1, artype)
aprime = numpy.zeros(Dp + 1, artype)
wosq = wo * wo
for j in range(Np + 1):
val = 0.0
for i in range(0, N + 1):
for k in range(0, M - i + 1):
if i + 2 * k == j:
val += (comb(M - i, k) * b[N - i] *
(wosq) ** (M - i - k) * bw ** i)
bprime[Np - j] = val
for j in range(Dp + 1):
val = 0.0
for i in range(0, D + 1):
for k in range(0, M - i + 1):
if i + 2 * k == j:
val += (comb(M - i, k) * a[D - i] *
(wosq) ** (M - i - k) * bw ** i)
aprime[Dp - j] = val
return normalize(bprime, aprime)
Example #6
| Source File: _util.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def _lazywhere(cond, arrays, f, fillvalue=None, f2=None):
"""
np.where(cond, x, fillvalue) always evaluates x even where cond is False.
This one only evaluates f(arr1[cond], arr2[cond], ...).
For example,
>>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])
>>> def f(a, b):
return a*b
>>> _lazywhere(a > 2, (a, b), f, np.nan)
array([ nan, nan, 21., 32.])
Notice it assumes that all `arrays` are of the same shape, or can be
broadcasted together.
"""
if fillvalue is None:
if f2 is None:
raise ValueError("One of (fillvalue, f2) must be given.")
else:
fillvalue = np.nan
else:
if f2 is not None:
raise ValueError("Only one of (fillvalue, f2) can be given.")
arrays = np.broadcast_arrays(*arrays)
temp = tuple(np.extract(cond, arr) for arr in arrays)
tcode = np.mintypecode([a.dtype.char for a in arrays])
out = _valarray(np.shape(arrays[0]), value=fillvalue, typecode=tcode)
np.place(out, cond, f(*temp))
if f2 is not None:
temp = tuple(np.extract(~cond, arr) for arr in arrays)
np.place(out, ~cond, f2(*temp))
return out
Example #7
| Source File: _util.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def _lazyselect(condlist, choicelist, arrays, default=0):
"""
Mimic `np.select(condlist, choicelist)`.
Notice it assumes that all `arrays` are of the same shape, or can be
broadcasted together.
All functions in `choicelist` must accept array arguments in the order
given in `arrays` and must return an array of the same shape as broadcasted
`arrays`.
Examples
--------
>>> x = np.arange(6)
>>> np.select([x <3, x > 3], [x**2, x**3], default=0)
array([ 0, 1, 4, 0, 64, 125])
>>> _lazyselect([x < 3, x > 3], [lambda x: x**2, lambda x: x**3], (x,))
array([ 0., 1., 4., 0., 64., 125.])
>>> a = -np.ones_like(x)
>>> _lazyselect([x < 3, x > 3],
... [lambda x, a: x**2, lambda x, a: a * x**3],
... (x, a), default=np.nan)
array([ 0., 1., 4., nan, -64., -125.])
"""
arrays = np.broadcast_arrays(*arrays)
tcode = np.mintypecode([a.dtype.char for a in arrays])
out = _valarray(np.shape(arrays[0]), value=default, typecode=tcode)
for index in range(len(condlist)):
func, cond = choicelist[index], condlist[index]
if np.all(cond is False):
continue
cond, _ = np.broadcast_arrays(cond, arrays[0])
temp = tuple(np.extract(cond, arr) for arr in arrays)
np.place(out, cond, func(*temp))
return out
Example #8
| Source File: _util.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _lazywhere(cond, arrays, f, fillvalue=None, f2=None):
"""
np.where(cond, x, fillvalue) always evaluates x even where cond is False.
This one only evaluates f(arr1[cond], arr2[cond], ...).
For example,
>>> a, b = np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])
>>> def f(a, b):
return a*b
>>> _lazywhere(a > 2, (a, b), f, np.nan)
array([ nan, nan, 21., 32.])
Notice it assumes that all `arrays` are of the same shape, or can be
broadcasted together.
"""
if fillvalue is None:
if f2 is None:
raise ValueError("One of (fillvalue, f2) must be given.")
else:
fillvalue = np.nan
else:
if f2 is not None:
raise ValueError("Only one of (fillvalue, f2) can be given.")
arrays = np.broadcast_arrays(*arrays)
temp = tuple(np.extract(cond, arr) for arr in arrays)
tcode = np.mintypecode([a.dtype.char for a in arrays])
out = _valarray(np.shape(arrays[0]), value=fillvalue, typecode=tcode)
np.place(out, cond, f(*temp))
if f2 is not None:
temp = tuple(np.extract(~cond, arr) for arr in arrays)
np.place(out, ~cond, f2(*temp))
return out
Example #9
| Source File: _util.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _lazyselect(condlist, choicelist, arrays, default=0):
"""
Mimic `np.select(condlist, choicelist)`.
Notice it assumes that all `arrays` are of the same shape, or can be
broadcasted together.
All functions in `choicelist` must accept array arguments in the order
given in `arrays` and must return an array of the same shape as broadcasted
`arrays`.
Examples
--------
>>> x = np.arange(6)
>>> np.select([x <3, x > 3], [x**2, x**3], default=0)
array([ 0, 1, 4, 0, 64, 125])
>>> _lazyselect([x < 3, x > 3], [lambda x: x**2, lambda x: x**3], (x,))
array([ 0., 1., 4., 0., 64., 125.])
>>> a = -np.ones_like(x)
>>> _lazyselect([x < 3, x > 3],
... [lambda x, a: x**2, lambda x, a: a * x**3],
... (x, a), default=np.nan)
array([ 0., 1., 4., nan, -64., -125.])
"""
arrays = np.broadcast_arrays(*arrays)
tcode = np.mintypecode([a.dtype.char for a in arrays])
out = _valarray(np.shape(arrays[0]), value=default, typecode=tcode)
for index in range(len(condlist)):
func, cond = choicelist[index], condlist[index]
if np.all(cond is False):
continue
cond, _ = np.broadcast_arrays(cond, arrays[0])
temp = tuple(np.extract(cond, arr) for arr in arrays)
np.place(out, cond, func(*temp))
return out
