>> 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')
[]
>>> plt.plot(x2, y + 0.5, 'o')
[]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
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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).
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
.. versionchanged:: 1.25.0
Non-scalar 'base` is now supported
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,
stop, or base 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.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
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.
:ref:`how-to-partition`
Notes
-----
If base is a scalar, 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. ])
>>> np.logspace(2.0, 3.0, num=4, base=[2.0, 3.0], axis=-1)
array([[ 4. , 5.0396842 , 6.34960421, 8. ],
[ 9. , 12.98024613, 18.72075441, 27. ]])
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')
[]
>>> plt.plot(x2, y + 0.5, 'o')
[]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
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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.
.. versionchanged:: 1.16.0
Non-scalar `start` and `stop` are now supported.
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.
.. versionadded:: 1.16.0
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
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.
:ref:`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')
[]
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
[]
>>> 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()
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Add documentation to an existing object, typically one defined in C
The purpose is to allow easier editing of the docstrings without requiring
a re-compile. This exists primarily for internal use within numpy itself.
Parameters
----------
place : str
The absolute name of the module to import from
obj : str
The name of the object to add documentation to, typically a class or
function name
doc : {str, Tuple[str, str], List[Tuple[str, str]]}
If a string, the documentation to apply to `obj`
If a tuple, then the first element is interpreted as an attribute of
`obj` and the second as the docstring to apply - ``(method, docstring)``
If a list, then each element of the list should be a tuple of length
two - ``[(method1, docstring1), (method2, docstring2), ...]``
warn_on_python : bool
If True, the default, emit `UserWarning` if this is used to attach
documentation to a pure-python object.
Notes
-----
This routine never raises an error if the docstring can't be written, but
will raise an error if the object being documented does not exist.
This routine cannot modify read-only docstrings, as appear
in new-style classes or built-in functions. Because this
routine never raises an error the caller must check manually
that the docstrings were changed.
Since this function grabs the ``char *`` from a c-level str object and puts
it into the ``tp_doc`` slot of the type of `obj`, it violates a number of
C-API best-practices, by:
- modifying a `PyTypeObject` after calling `PyType_Ready`
- calling `Py_INCREF` on the str and losing the reference, so the str
will never be released
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