# scipy/scipy

DOC: Add backquotes to indicate variables in docstrings

1 parent 19bdf23 commit a0f4eb4476a22648df09f51016a85890cd0a1fea endolith committed Sep 29, 2012
Showing with 22 additions and 21 deletions.
1. +2 −2 scipy/signal/ltisys.py
2. +17 −16 scipy/signal/signaltools.py
3. +2 −2 scipy/signal/wavelets.py
4. +1 −1 scipy/signal/windows.py
4 scipy/signal/ltisys.py
 @@ -402,10 +402,10 @@ def lsim2(system, U=None, T=None, X0=None, **kwargs): Notes ----- - This function uses :func:`scipy.integrate.odeint` to solve the + This function uses `scipy.integrate.odeint` to solve the system's differential equations. Additional keyword arguments given to `lsim2` are passed on to `odeint`. See the documentation - for :func:`scipy.integrate.odeint` for the full list of arguments. + for `scipy.integrate.odeint` for the full list of arguments. """ if isinstance(system, lti):
33 scipy/signal/signaltools.py
 @@ -56,8 +56,8 @@ def correlate(in1, in2, mode='full'): """ Cross-correlate two N-dimensional arrays. - Cross-correlate `in1` and `in2` with the output size determined by the mode - argument. + Cross-correlate `in1` and `in2` with the output size determined by the + `mode` argument. Parameters ---------- @@ -132,7 +132,7 @@ def _centered(arr, newsize): def fftconvolve(in1, in2, mode="full"): - """Convolve two N-dimensional arrays using FFT. See :func:`convolve`. + """Convolve two N-dimensional arrays using FFT. See `convolve`. """ s1 = array(in1.shape) @@ -166,7 +166,7 @@ def convolve(in1, in2, mode='full'): """ Convolve two N-dimensional arrays. - Convolve `in1` and `in2` with output size determined by mode. + Convolve `in1` and `in2` with output size determined by `mode`. Parameters ---------- @@ -283,7 +283,7 @@ def medfilt(volume, kernel_size=None): Perform a median filter on an N-dimensional array. Apply a median filter to the input array using a local window-size - given by kernel_size. + given by `kernel_size`. Parameters ---------- @@ -373,7 +373,7 @@ def wiener(im, mysize=None, noise=None): def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0): """Convolve two 2-dimensional arrays. - Convolve `in1` and `in2` with output size determined by mode and boundary + Convolve `in1` and `in2` with output size determined by `mode` and boundary conditions determined by `boundary` and `fillvalue`. Parameters @@ -386,7 +386,7 @@ def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0): ``valid`` : the output consists only of those elements that do not rely on the zero-padding. - ``same`` : the output is the same size as ``in1`` centered + ``same`` : the output is the same size as `in1` centered with respect to the 'full' output. ``full`` : the output is the full discrete linear cross-correlation @@ -425,7 +425,7 @@ def convolve2d(in1, in2, mode='full', boundary='fill', fillvalue=0): def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0): """Cross-correlate two 2-dimensional arrays. - Cross correlate in1 and in2 with output size determined by mode and + Cross correlate `in1` and `in2` with output size determined by `mode` and boundary conditions determined by `boundary` and `fillvalue`. Parameters @@ -438,7 +438,7 @@ def correlate2d(in1, in2, mode='full', boundary='fill', fillvalue=0): ``valid`` : the output consists only of those elements that do not rely on the zero-padding. - ``same`` : the output is the same size as ``in1`` centered + ``same`` : the output is the same size as `in1` centered with respect to the 'full' output. ``full`` : the output is the full discrete linear cross-correlation @@ -471,7 +471,7 @@ def medfilt2d(input, kernel_size=3): """ Median filter a 2-dimensional array. - Apply a median filter to the input array using a local window-size + Apply a median filter to the `input` array using a local window-size given by `kernel_size` (must be odd). Parameters @@ -646,7 +646,7 @@ def lfiltic(b, a, y, x=None): def deconvolve(signal, divisor): - """Deconvolves divisor out of signal. + """Deconvolves `divisor` out of `signal`. """ num = atleast_1d(signal) @@ -1306,7 +1306,7 @@ def lfilter_zi(b, a): Parameters ---------- b, a : array_like (1-D) - The IIR filter coefficients. See `scipy.signal.lfilter` for more + The IIR filter coefficients. See `lfilter` for more information. Returns @@ -1435,7 +1435,7 @@ def filtfilt(b, a, x, axis=-1, padtype='odd', padlen=None): and even extensions have the corresponding symmetry about the end point of the data. The constant extension extends the data with the values at end points. On both the forward and backwards passes, the - initial condition of the filter is found by using lfilter_zi and + initial condition of the filter is found by using `lfilter_zi` and scaling it by the end point of the extended data. Parameters @@ -1569,10 +1569,11 @@ def filtfilt(b, a, x, axis=-1, padtype='odd', padlen=None): def decimate(x, q, n=None, ftype='iir', axis=-1): - """Downsample the signal x by an integer factor q, using an order n filter. + """Downsample the signal `x` by an integer factor `q`, using an order `n` + filter. - By default an order 8 Chebyshev type I filter is used. A 30 point FIR - filter with hamming window is used if ftype is 'fir'. + By default, an order 8 Chebyshev type I filter is used. A 30 point FIR + filter with hamming window is used if `ftype` is 'fir'. Parameters ----------
4 scipy/signal/wavelets.py
 @@ -326,8 +326,8 @@ def cwt(data, wavelet, widths): The first argument is the number of points that the returned vector will have (len(wavelet(width,length)) == length). The second is a width parameter, defining the size of the wavelet - (e.g. standard deviation of a gaussian). See - :func:`scipy.signal.ricker`, which satisfies these requirements. + (e.g. standard deviation of a gaussian). See `ricker`, which + satisfies these requirements. widths : sequence Widths to use for transform.
2 scipy/signal/windows.py
 @@ -1032,7 +1032,7 @@ def general_gaussian(M, p, sig, sym=True): Number of points in the output window. If zero or less, an empty array is returned. p : float - Shape parameter. p = 1 is identical to :func:`~gaussian`, p = 0.5 is + Shape parameter. p = 1 is identical to `gaussian`, p = 0.5 is the same shape as the Laplace distribution. sig : float The standard deviation, sigma.