[Scipy-svn] r6818 - trunk/scipy/signal
scipy-svn at scipy.org
scipy-svn at scipy.org
Thu Sep 23 05:17:32 EDT 2010
Author: rgommers
Date: 2010-09-23 04:17:31 -0500 (Thu, 23 Sep 2010)
New Revision: 6818
Modified:
trunk/scipy/signal/signaltools.py
Log:
DOC: make signaltools docs conform to current documentation standard.
Modified: trunk/scipy/signal/signaltools.py
===================================================================
--- trunk/scipy/signal/signaltools.py 2010-09-23 09:17:07 UTC (rev 6817)
+++ trunk/scipy/signal/signaltools.py 2010-09-23 09:17:31 UTC (rev 6818)
@@ -302,7 +302,7 @@
return sigtools._order_filterND(a, domain, rank)
-def medfilt(volume,kernel_size=None):
+def medfilt(volume, kernel_size=None):
"""
Perform a median filter on an N-dimensional array.
@@ -345,7 +345,7 @@
return sigtools._order_filterND(volume,domain,order)
-def wiener(im,mysize=None,noise=None):
+def wiener(im, mysize=None, noise=None):
"""
Perform a Wiener filter on an N-dimensional array.
@@ -396,33 +396,41 @@
def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0, old_behavior=True):
"""Convolve two 2-dimensional arrays.
- Description:
+ Convolve `in1` and `in2` with output size determined by mode and boundary
+ conditions determined by `boundary` and `fillvalue`.
- Convolve in1 and in2 with output size determined by mode and boundary
- conditions determined by boundary and fillvalue.
+ Parameters
+ ----------
+ in1, in2 : ndarray
+ Two-dimensional input arrays to be convolved.
+ mode: str, optional
+ A string indicating the size of the output:
- Inputs:
+ ``valid`` : the output consists only of those elements that do not
+ rely on the zero-padding.
- in1 -- a 2-dimensional array.
- in2 -- a 2-dimensional array.
- mode -- a flag indicating the size of the output
- 'valid' (0): The output consists only of those elements that
- do not rely on the zero-padding.
- 'same' (1): The output is the same size as the input centered
- with respect to the 'full' output.
- 'full' (2): The output is the full discrete linear convolution
- of the inputs. (*Default*)
- boundary -- a flag indicating how to handle boundaries
- 'fill' : pad input arrays with fillvalue. (*Default*)
- 'wrap' : circular boundary conditions.
- 'symm' : symmetrical boundary conditions.
- fillvalue -- value to fill pad input arrays with (*Default* = 0)
+ ``same`` : the output is the same size as the largest input centered
+ with respect to the 'full' output.
- Outputs: (out,)
+ ``full`` : the output is the full discrete linear cross-correlation
+ of the inputs. (Default)
- out -- a 2-dimensional array containing a subset of the discrete linear
- convolution of in1 with in2.
+ boundary : str, optional
+ A flag indicating how to handle boundaries:
+ - 'fill' : pad input arrays with fillvalue. (default)
+ - 'wrap' : circular boundary conditions.
+ - 'symm' : symmetrical boundary conditions.
+
+ fillvalue : scalar, optional
+ Value to fill pad input arrays with. Default is 0.
+
+ Returns
+ -------
+ out : ndarray
+ A 2-dimensional array containing a subset of the discrete linear
+ convolution of `in1` with `in2`.
+
"""
if old_behavior:
warnings.warn(DeprecationWarning(_SWAP_INPUTS_DEPRECATION_MSG))
@@ -450,33 +458,41 @@
def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0, old_behavior=True):
"""Cross-correlate two 2-dimensional arrays.
- Description:
+ Cross correlate in1 and in2 with output size determined by mode and
+ boundary conditions determined by `boundary` and `fillvalue`.
- Cross correlate in1 and in2 with output size determined by mode
- and boundary conditions determined by boundary and fillvalue.
+ Parameters
+ ----------
+ in1, in2 : ndarray
+ Two-dimensional input arrays to be convolved.
+ mode: str, optional
+ A string indicating the size of the output:
- Inputs:
+ ``valid`` : the output consists only of those elements that do not
+ rely on the zero-padding.
- in1 -- a 2-dimensional array.
- in2 -- a 2-dimensional array.
- mode -- a flag indicating the size of the output
- 'valid' (0): The output consists only of those elements that
- do not rely on the zero-padding.
- 'same' (1): The output is the same size as the input centered
- with respect to the 'full' output.
- 'full' (2): The output is the full discrete linear convolution
- of the inputs. (*Default*)
- boundary -- a flag indicating how to handle boundaries
- 'fill' : pad input arrays with fillvalue. (*Default*)
- 'wrap' : circular boundary conditions.
- 'symm' : symmetrical boundary conditions.
- fillvalue -- value to fill pad input arrays with (*Default* = 0)
+ ``same`` : the output is the same size as the largest input centered
+ with respect to the 'full' output.
- Outputs: (out,)
+ ``full`` : the output is the full discrete linear cross-correlation
+ of the inputs. (Default)
- out -- a 2-dimensional array containing a subset of the discrete linear
- cross-correlation of in1 with in2.
+ boundary : str, optional
+ A flag indicating how to handle boundaries:
+ - 'fill' : pad input arrays with fillvalue. (default)
+ - 'wrap' : circular boundary conditions.
+ - 'symm' : symmetrical boundary conditions.
+
+ fillvalue : scalar, optional
+ Value to fill pad input arrays with. Default is 0.
+
+ Returns
+ -------
+ out : ndarray
+ A 2-dimensional array containing a subset of the discrete linear
+ cross-correlation of `in1` with `in2`.
+
"""
if old_behavior:
warnings.warn(DeprecationWarning(_SWAP_INPUTS_DEPRECATION_MSG))
@@ -490,7 +506,7 @@
Median filter a 2-dimensional array.
Apply a median filter to the input array using a local window-size
- given by kernel_size (must be odd).
+ given by `kernel_size` (must be odd).
Parameters
----------
@@ -706,8 +722,8 @@
If M=len(b)-1 and N=len(a)-1. Then, the initial conditions are given
in the vectors x and y as::
- x = {x[-1],x[-2],...,x[-M]}
- y = {y[-1],y[-2],...,y[-N]}
+ x = {x[-1],x[-2],...,x[-M]}
+ y = {y[-1],y[-2],...,y[-N]}
If x is not given, its inital conditions are assumed zero.
If either vector is too short, then zeros are added
@@ -715,7 +731,7 @@
The output vector zi contains::
- zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]} where K=max(M,N).
+ zi = {z_0[-1], z_1[-1], ..., z_K-1[-1]} where K=max(M,N).
"""
N = np.size(a)-1
@@ -823,7 +839,7 @@
x = ifft(Xf*h, axis=axis)
return x
-def hilbert2(x,N=None):
+def hilbert2(x, N=None):
"""
Compute the '2-D' analytic signal of `x`
@@ -889,7 +905,7 @@
indx = argsort(p)
return take(p,indx,0), indx
-def unique_roots(p,tol=1e-3,rtype='min'):
+def unique_roots(p, tol=1e-3, rtype='min'):
"""
Determine unique roots and their multiplicities from a list of roots.
@@ -961,7 +977,7 @@
return array(pout), array(mult)
-def invres(r,p,k,tol=1e-3,rtype='avg'):
+def invres(r, p, k, tol=1e-3, rtype='avg'):
"""Compute b(s) and a(s) from partial fraction expansion: r,p,k
If M = len(b) and N = len(a)
@@ -1014,7 +1030,7 @@
b = b[1:]
return b, a
-def residue(b,a,tol=1e-3,rtype='avg'):
+def residue(b, a, tol=1e-3, rtype='avg'):
"""
Compute partial-fraction expansion of b(s) / a(s).
@@ -1085,7 +1101,7 @@
indx += sig
return r/rscale, p, k
-def residuez(b,a,tol=1e-3,rtype='avg'):
+def residuez(b, a, tol=1e-3, rtype='avg'):
"""Compute partial-fraction expansion of b(z) / a(z).
If M = len(b) and N = len(a)
@@ -1105,7 +1121,10 @@
-------------- + ------------------ + ... + ------------------
(1-p[i]z**(-1)) (1-p[i]z**(-1))**2 (1-p[i]z**(-1))**n
- See also: invresz, poly, polyval, unique_roots
+ See also
+ --------
+ invresz, poly, polyval, unique_roots
+
"""
b,a = map(asarray,(b,a))
gain = a[0]
@@ -1150,7 +1169,7 @@
indx += sig
return r/gain, p, k
-def invresz(r,p,k,tol=1e-3,rtype='avg'):
+def invresz(r, p, k, tol=1e-3, rtype='avg'):
"""Compute b(z) and a(z) from partial fraction expansion: r,p,k
If M = len(b) and N = len(a)
@@ -1170,7 +1189,10 @@
-------------- + ------------------ + ... + ------------------
(1-p[i]z**(-1)) (1-p[i]z**(-1))**2 (1-p[i]z**(-1))**n
- See also: residuez, poly, polyval, unique_roots
+ See also
+ --------
+ residuez, poly, polyval, unique_roots
+
"""
extra = asarray(k)
p, indx = cmplx_sort(p)
@@ -1201,13 +1223,13 @@
return b, a
-def resample(x,num,t=None,axis=0,window=None):
+def resample(x, num, t=None, axis=0, window=None):
"""
Resample `x` to `num` samples using Fourier method along the given axis.
The resampled signal starts at the same value as `x` but is sampled
- with a spacing of `len(x) / num * (spacing of x)`. Because a
- Fourier method is used, the signal is assumed periodic.
+ with a spacing of ``len(x) / num * (spacing of x)``. Because a
+ Fourier method is used, the signal is assumed to be periodic.
Parameters
----------
@@ -1226,7 +1248,6 @@
Returns
-------
-
resampled_x or (resampled_x, resampled_t)
Either the resampled array, or, if `t` was given, a tuple
containing the resampled array and the corresponding resampled
@@ -1373,7 +1394,7 @@
ret = transpose(ret,tuple(olddims))
return ret
-def lfilter_zi(b,a):
+def lfilter_zi(b, a):
#compute the zi state from the filter parameters. see [Gust96].
#Based on:
@@ -1398,7 +1419,7 @@
return array(zi_return)
-def filtfilt(b,a,x):
+def filtfilt(b, a, x):
b, a, x = map(asarray, [b, a, x])
# FIXME: For now only accepting 1d arrays
ntaps=max(len(a),len(b))
@@ -1460,7 +1481,9 @@
y : N-d array
the down-sampled signal
- See also: resample
+ See also
+ --------
+ resample
"""
if not isinstance(q, int):
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