@@ -168,7 +168,7 @@ def rot90(m, k=1, axes=(0, 1)):
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Array of two or more dimensions.
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k : integer
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Number of times the array is rotated by 90 degrees.
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- axes: (2,) array_like
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+ axes : (2,) array_like
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The array is rotated in the plane defined by the axes.
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Axes must be different.
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@@ -1656,7 +1656,7 @@ def unwrap(p, discont=None, axis=-1, *, period=2*pi):
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larger than ``period/2``.
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axis : int, optional
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Axis along which unwrap will operate, default is the last axis.
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- period: float, optional
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+ period : float, optional
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Size of the range over which the input wraps. By default, it is
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``2 pi``.
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@@ -2974,15 +2974,14 @@ def bartlett(M):
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\\ frac{M-1}{2} - \\ left|n - \\ frac{M-1}{2}\\ right|
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\\ right)
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- Most references to the Bartlett window come from the signal
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- processing literature, where it is used as one of many windowing
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- functions for smoothing values. Note that convolution with this
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- window produces linear interpolation. It is also known as an
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- apodization (which means"removing the foot", i.e. smoothing
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- discontinuities at the beginning and end of the sampled signal) or
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- tapering function. The fourier transform of the Bartlett is the product
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- of two sinc functions.
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- Note the excellent discussion in Kanasewich.
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+ Most references to the Bartlett window come from the signal processing
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+ literature, where it is used as one of many windowing functions for
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+ smoothing values. Note that convolution with this window produces linear
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+ interpolation. It is also known as an apodization (which means "removing
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+ the foot", i.e. smoothing discontinuities at the beginning and end of the
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+ sampled signal) or tapering function. The Fourier transform of the
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+ Bartlett window is the product of two sinc functions. Note the excellent
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+ discussion in Kanasewich [2]_.
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References
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----------
@@ -3075,7 +3074,7 @@ def hanning(M):
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-----
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The Hanning window is defined as
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- .. math:: w(n) = 0.5 - 0.5cos \\ left(\\ frac{2\\ pi{n}}{M-1}\\ right)
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+ .. math:: w(n) = 0.5 - 0.5 \\ cos \\ left(\\ frac{2\\ pi{n}}{M-1}\\ right)
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\\ qquad 0 \\ leq n \\ leq M-1
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The Hanning was named for Julius von Hann, an Austrian meteorologist.
@@ -3179,7 +3178,7 @@ def hamming(M):
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-----
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The Hamming window is defined as
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- .. math:: w(n) = 0.54 - 0.46cos \\ left(\\ frac{2\\ pi{n}}{M-1}\\ right)
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+ .. math:: w(n) = 0.54 - 0.46 \\ cos \\ left(\\ frac{2\\ pi{n}}{M-1}\\ right)
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\\ qquad 0 \\ leq n \\ leq M-1
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The Hamming was named for R. W. Hamming, an associate of J. W. Tukey
@@ -4731,10 +4730,10 @@ def trapz(y, x=None, dx=1.0, axis=-1):
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Returns
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-------
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trapz : float or ndarray
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- Definite integral of 'y' = n-dimensional array as approximated along
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- a single axis by the trapezoidal rule. If 'y' is a 1-dimensional array,
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- then the result is a float. If 'n' is greater than 1, then the result
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- is an 'n-1' dimensional array.
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+ Definite integral of `y` = n-dimensional array as approximated along
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+ a single axis by the trapezoidal rule. If `y` is a 1-dimensional array,
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+ then the result is a float. If `n` is greater than 1, then the result
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+ is an `n`-1 dimensional array.
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See Also
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--------
@@ -4865,9 +4864,9 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
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Returns
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-------
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X1, X2,..., XN : ndarray
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- For vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` ,
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- return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij'
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- or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy'
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+ For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``,
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+ returns ``(N1, N2, N3,..., Nn)`` shaped arrays if indexing='ij'
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+ or ``(N2, N1, N3,..., Nn)`` shaped arrays if indexing='xy'
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with the elements of `xi` repeated to fill the matrix along
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the first dimension for `x1`, the second for `x2` and so on.
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@@ -5016,7 +5015,7 @@ def delete(arr, obj, axis=None):
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>>> mask[[0,2,4]] = False
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>>> result = arr[mask,...]
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- Is equivalent to `np.delete(arr, [0,2,4], axis=0)`, but allows further
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+ Is equivalent to `` np.delete(arr, [0,2,4], axis=0)` `, but allows further
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use of `mask`.
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Examples
@@ -5207,9 +5206,9 @@ def insert(arr, obj, values, axis=None):
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Notes
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-----
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- Note that for higher dimensional inserts `obj=0` behaves very different
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- from `obj=[0]` just like `arr[:,0,:] = values` is different from
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- `arr[:,[0],:] = values`.
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+ Note that for higher dimensional inserts `` obj=0` ` behaves very different
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+ from `` obj=[0]`` just like `` arr[:,0,:] = values` ` is different from
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+ `` arr[:,[0],:] = values` `.
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Examples
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--------
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