[Scipy-svn] r4917 - trunk/scipy/signal
scipy-svn@scip...
scipy-svn@scip...
Sun Nov 2 05:32:28 CST 2008
Author: stefan
Date: 2008-11-02 05:32:19 -0600 (Sun, 02 Nov 2008)
New Revision: 4917
Modified:
trunk/scipy/signal/signaltools.py
Log:
Wrap long lines.
Modified: trunk/scipy/signal/signaltools.py
===================================================================
--- trunk/scipy/signal/signaltools.py 2008-11-02 10:11:28 UTC (rev 4916)
+++ trunk/scipy/signal/signaltools.py 2008-11-02 11:32:19 UTC (rev 4917)
@@ -17,14 +17,17 @@
from scipy.misc import factorial
_modedict = {'valid':0, 'same':1, 'full':2}
-_boundarydict = {'fill':0, 'pad':0, 'wrap':2, 'circular':2, 'symm':1, 'symmetric':1, 'reflect':4}
+_boundarydict = {'fill':0, 'pad':0, 'wrap':2, 'circular':2, 'symm':1,
+ 'symmetric':1, 'reflect':4}
+
def _valfrommode(mode):
try:
val = _modedict[mode]
except KeyError:
if mode not in [0,1,2]:
- raise ValueError, "Acceptable mode flags are 'valid' (0), 'same' (1), or 'full' (2)."
+ raise ValueError, "Acceptable mode flags are 'valid' (0)," \
+ "'same' (1), or 'full' (2)."
val = mode
return val
@@ -33,7 +36,8 @@
val = _boundarydict[boundary] << 2
except KeyError:
if val not in [0,1,2] :
- raise ValueError, "Acceptable boundary flags are 'fill', 'wrap' (or 'circular'), \n and 'symm' (or 'symmetric')."
+ raise ValueError, "Acceptable boundary flags are 'fill', 'wrap'" \
+ " (or 'circular'), \n and 'symm' (or 'symmetric')."
val = boundary << 2
return val
@@ -185,7 +189,8 @@
size = domain.shape
for k in range(len(size)):
if (size[k] % 2) != 1:
- raise ValueError, "Each dimension of domain argument should have an odd number of elements."
+ raise ValueError, "Each dimension of domain argument " \
+ "should have an odd number of elements."
return sigtools._order_filterND(a, domain, rank)
@@ -692,7 +697,8 @@
a = [0.2156, 0.4160, 0.2781, 0.0836, 0.0069]
n = arange(0,M)
fac = n*2*pi/(M-1.0)
- w = a[0] - a[1]*cos(fac) + a[2]*cos(2*fac) - a[3]*cos(3*fac) + a[4]*cos(4*fac)
+ w = a[0] - a[1]*cos(fac) + a[2]*cos(2*fac) - a[3]*cos(3*fac) + \
+ a[4]*cos(4*fac)
if not sym and not odd:
w = w[:-1]
return w
@@ -1339,17 +1345,19 @@
"""Resample to num samples using Fourier method along the given axis.
The resampled signal starts at the same value of 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 periodic.
- Window controls a Fourier-domain window that tapers the Fourier spectrum
- before zero-padding to aleviate ringing in the resampled values for
- sampled signals you didn't intend to be interpreted as band-limited.
+ Window controls a Fourier-domain window that tapers the Fourier
+ spectrum before zero-padding to aleviate ringing in the resampled
+ values for sampled signals you didn't intend to be interpreted as
+ band-limited.
- If window is a string then use the named window. If window is a float, then
- it represents a value of beta for a kaiser window. If window is a tuple,
- then the first component is a string representing the window, and the next
- arguments are parameters for that window.
+ If window is a string then use the named window. If window is a
+ float, then it represents a value of beta for a kaiser window. If
+ window is a tuple, then the first component is a string
+ representing the window, and the next arguments are parameters for
+ that window.
Possible windows are:
'blackman' ('black', 'blk')
@@ -1361,12 +1369,14 @@
'general gauss' ('general', 'ggs') # requires two parameters
(power, width)
- The first sample of the returned vector is the same as the first sample of the
- input vector, the spacing between samples is changed from dx to
+ The first sample of the returned vector is the same as the first
+ sample of the input vector, the spacing between samples is changed
+ from dx to
+
dx * len(x) / num
If t is not None, then it represents the old sample positions, and the new
- sample positions will be returned as well as the new samples.
+ sample positions will be returned as well as the new samples.
"""
x = asarray(x)
X = fft(x,axis=axis)
@@ -1420,14 +1430,16 @@
N = dshape[axis]
bp = sort(unique(r_[0,bp,N]))
if any(bp > N):
- raise ValueError, "Breakpoints must be less than length of data along given axis."
+ raise ValueError, "Breakpoints must be less than length " \
+ "of data along given axis."
Nreg = len(bp) - 1
# Restructure data so that axis is along first dimension and
# all other dimensions are collapsed into second dimension
rnk = len(dshape)
if axis < 0: axis = axis + rnk
newdims = r_[axis,0:axis,axis+1:rnk]
- newdata = reshape(transpose(data,tuple(newdims)),(N,prod(dshape,axis=0)/N))
+ newdata = reshape(transpose(data, tuple(newdims)),
+ (N, prod(dshape, axis=0)/N))
newdata = newdata.copy() # make sure we have a copy
if newdata.dtype.char not in 'dfDF':
newdata = newdata.astype(dtype)
@@ -1457,7 +1469,8 @@
#x must be bigger than edge
if x.size < edge:
- raise ValueError, "Input vector needs to be bigger than 3 * max(len(a),len(b)."
+ raise ValueError, "Input vector needs to be bigger than " \
+ "3 * max(len(a),len(b)."
if len(a) < ntaps:
a=r_[a,zeros(len(b)-len(a))]
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