numpy.core.function

module documentation

(source)

Undocumented

Function	`geomspace`	Return numbers spaced evenly on a log scale (a geometric progression).
Function	`linspace`	Return evenly spaced numbers over a specified interval.
Function	`logspace`	Return numbers spaced evenly on a log scale.
Variable	`array_function_dispatch`	Undocumented
Function	`_add_docstring`	Undocumented
Function	`_geomspace_dispatcher`	Undocumented
Function	`_linspace_dispatcher`	Undocumented
Function	`_logspace_dispatcher`	Undocumented
Function	`_needs_add_docstring`	Returns true if the only way to set the docstring of `obj` from python is via add_docstring.

@array_function_dispatch(_geomspace_dispatcher)
def geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0): (source) ¶

Return numbers spaced evenly on a log scale (a geometric progression).

This is similar to logspace, but with endpoints specified directly. Each output sample is a constant multiple of the previous.

Changed in version 1.16.0: Non-scalar start and stop are now supported.

See Also

logspace: Similar to geomspace, but with endpoints specified using log and base.
linspace: Similar to geomspace, but with arithmetic instead of geometric progression.
arange: Similar to linspace, with the step size specified instead of the number of samples.

how-to-partition

Notes

If the inputs or dtype are complex, the output will follow a logarithmic spiral in the complex plane. (There are an infinite number of spirals passing through two points; the output will follow the shortest such path.)

Examples

>>> np.geomspace(1, 1000, num=4)
array([    1.,    10.,   100.,  1000.])
>>> np.geomspace(1, 1000, num=3, endpoint=False)
array([   1.,   10.,  100.])
>>> np.geomspace(1, 1000, num=4, endpoint=False)
array([   1.        ,    5.62341325,   31.6227766 ,  177.827941  ])
>>> np.geomspace(1, 256, num=9)
array([   1.,    2.,    4.,    8.,   16.,   32.,   64.,  128.,  256.])

Note that the above may not produce exact integers:

>>> np.geomspace(1, 256, num=9, dtype=int)
array([  1,   2,   4,   7,  16,  32,  63, 127, 256])
>>> np.around(np.geomspace(1, 256, num=9)).astype(int)
array([  1,   2,   4,   8,  16,  32,  64, 128, 256])

Negative, decreasing, and complex inputs are allowed:

>>> np.geomspace(1000, 1, num=4)
array([1000.,  100.,   10.,    1.])
>>> np.geomspace(-1000, -1, num=4)
array([-1000.,  -100.,   -10.,    -1.])
>>> np.geomspace(1j, 1000j, num=4)  # Straight line
array([0.   +1.j, 0.  +10.j, 0. +100.j, 0.+1000.j])
>>> np.geomspace(-1+0j, 1+0j, num=5)  # Circle
array([-1.00000000e+00+1.22464680e-16j, -7.07106781e-01+7.07106781e-01j,
        6.12323400e-17+1.00000000e+00j,  7.07106781e-01+7.07106781e-01j,
        1.00000000e+00+0.00000000e+00j])

Graphical illustration of endpoint parameter:

>>> import matplotlib.pyplot as plt
>>> N = 10
>>> y = np.zeros(N)
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=True), y + 1, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.axis([0.5, 2000, 0, 3])
[0.5, 2000, 0, 3]
>>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
>>> plt.show()

Parameters
start:`array_like`	The starting value of the sequence.
stop:`array_like`	The final value of the sequence, unless `endpoint` is False. In that case, `num + 1` values are spaced over the interval in log-space, of which all but the last (a sequence of length `num`) are returned.
num:`integer`, optional	Number of samples to generate. Default is 50.
endpoint:`boolean`, optional	If true, `stop` is the last sample. Otherwise, it is not included. Default is True.
dtype:`dtype`	The type of the output array. If `dtype` is not given, the data type is inferred from `start` and `stop`. The inferred dtype will never be an integer; `float` is chosen even if the arguments would produce an array of integers.
axis:`int`, optional	The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. New in version 1.16.0.
Returns
`ndarray`	samples - `num` samples, equally spaced on a log scale.

@array_function_dispatch(_linspace_dispatcher)
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0): (source) ¶

Return evenly spaced numbers over a specified interval.

Returns num evenly spaced samples, calculated over the interval [start, stop].

The endpoint of the interval can optionally be excluded.

Changed in version 1.16.0: Non-scalar start and stop are now supported.

Changed in version 1.20.0: Values are rounded towards -inf instead of 0 when an integer dtype is specified. The old behavior can still be obtained with np.linspace(start, stop, num).astype(int)

See Also

arange: Similar to linspace, but uses a step size (instead of the number of samples).
geomspace: Similar to linspace, but with numbers spaced evenly on a log scale (a geometric progression).
logspace: Similar to geomspace, but with the end points specified as logarithms.

how-to-partition

Examples

>>> np.linspace(2.0, 3.0, num=5)
array([2.  , 2.25, 2.5 , 2.75, 3.  ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. ,  2.2,  2.4,  2.6,  2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)

Graphical illustration:

>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()

Parameters
start:`array_like`	The starting value of the sequence.
stop:`array_like`	The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of `num + 1` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False.
num:`int`, optional	Number of samples to generate. Default is 50. Must be non-negative.
endpoint:`bool`, optional	If True, `stop` is the last sample. Otherwise, it is not included. Default is True.
retstep:`bool`, optional	If True, return (`samples`, `step`), where `step` is the spacing between samples.
dtype:`dtype`, optional	The type of the output array. If `dtype` is not given, the data type is inferred from `start` and `stop`. The inferred dtype will never be an integer; `float` is chosen even if the arguments would produce an array of integers. New in version 1.9.0.
axis:`int`, optional	The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. New in version 1.16.0.
Returns
samples: `ndarray` - There are `num` equally spaced samples in the closed interval `[start, stop]` or the half-open interval `[start, stop)` (depending on whether `endpoint` is True or False). step: `float`, optional - Only returned if `retstep` is True Size of spacing between samples.

@array_function_dispatch(_logspace_dispatcher)
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0): (source) ¶

Return numbers spaced evenly on a log scale.

In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below).

Changed in version 1.16.0: Non-scalar start and stop are now supported.

See Also

arange: Similar to linspace, with the step size specified instead of the number of samples. Note that, when used with a float endpoint, the endpoint may or may not be included.
linspace: Similar to logspace, but with the samples uniformly distributed in linear space, instead of log space.
geomspace: Similar to logspace, but with endpoints specified directly.

how-to-partition

Notes

Logspace is equivalent to the code

>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
... # doctest: +SKIP
>>> power(base, y).astype(dtype)
... # doctest: +SKIP

Examples

>>> np.logspace(2.0, 3.0, num=4)
array([ 100.        ,  215.443469  ,  464.15888336, 1000.        ])
>>> np.logspace(2.0, 3.0, num=4, endpoint=False)
array([100.        ,  177.827941  ,  316.22776602,  562.34132519])
>>> np.logspace(2.0, 3.0, num=4, base=2.0)
array([4.        ,  5.0396842 ,  6.34960421,  8.        ])

Graphical illustration:

>>> import matplotlib.pyplot as plt
>>> N = 10
>>> x1 = np.logspace(0.1, 1, N, endpoint=True)
>>> x2 = np.logspace(0.1, 1, N, endpoint=False)
>>> y = np.zeros(N)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()

Parameters
start:`array_like`	`base ** start` is the starting value of the sequence.
stop:`array_like`	`base ** stop` is the final value of the sequence, unless `endpoint` is False. In that case, `num + 1` values are spaced over the interval in log-space, of which all but the last (a sequence of length `num`) are returned.
num:`integer`, optional	Number of samples to generate. Default is 50.
endpoint:`boolean`, optional	If true, `stop` is the last sample. Otherwise, it is not included. Default is True.
base:`array_like`, optional	The base of the log space. The step size between the elements in `ln(samples) / ln(base)` (or `log_base(samples)`) is uniform. Default is 10.0.
dtype:`dtype`	The type of the output array. If `dtype` is not given, the data type is inferred from `start` and `stop`. The inferred type will never be an integer; `float` is chosen even if the arguments would produce an array of integers.
axis:`int`, optional	The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. New in version 1.16.0.
Returns
`ndarray`	samples - `num` samples, equally spaced on a log scale.

array_function_dispatch = (source) ¶

Undocumented

def _add_docstring(obj, doc, warn_on_python): (source) ¶

Undocumented

def _geomspace_dispatcher(start, stop, num=None, endpoint=None, dtype=None, axis=None): (source) ¶

Undocumented

def _linspace_dispatcher(start, stop, num=None, endpoint=None, retstep=None, dtype=None, axis=None): (source) ¶

Undocumented

def _logspace_dispatcher(start, stop, num=None, endpoint=None, base=None, dtype=None, axis=None): (source) ¶

Undocumented

def _needs_add_docstring(obj): (source) ¶

Returns true if the only way to set the docstring of obj from python is via add_docstring.

This function errs on the side of being overly conservative.

numpy.core.function_base

`numpy.core.function_base`