DOC: spelling and consistency fixes

deltasigma/_logsmooth.py

@@ -25,7 +25,7 @@
 
 
 def logsmooth(X, inBin, nbin=8, n=3):
- """Smooth the fft, and convert it to dB.
+ """Smooth the FFT and convert it to dB.
 
  **Parameters:**
 
@@ -43,8 +43,8 @@ def logsmooth(X, inBin, nbin=8, n=3):
  Around the location of the input signal and its harmonics (up to the
  third harmonic), don't average for n bins.
 
- The logsmooth algorithm uses nbin bins from 0 to 3*inBin,
- thereafter the bin sizes is increased by a factor of 1.1,
+ The ``logsmooth`` algorithm uses ``nbin`` bins from 0 to 3*inBin,
+ thereafter the bin sizes are increased by a factor 1.1,
  staying less than 2^10.
 
  For the :math:`n` sets of bins:
@@ -59,14 +59,14 @@ def logsmooth(X, inBin, nbin=8, n=3):
 
  **Returns:**
 
- f, p : tuple of 1d- ndarrays
+ f, p : tuple of 1d-arrays
  The bins and smoothed FFT, expressed in dB.
 
  .. seealso::
 
  * :func:`plotSpectrum`, convenience function to first call
  :func:`logsmooth` and then plot on a logarithmic x-axis its return
- value.
+ values.
 
  * :func:`circ_smooth`, smoothing algorithm suitable for linear
  x-axis plotting.

deltasigma/_scaleABCD.py

@@ -49,8 +49,8 @@ def scaleABCD(ABCD, nlev=2, f=0, xlim=1, ymax=None, umax=None, N_sim=1e5, N0=10)
  A vector or scalar specifying the limit for each state variable.
 
  ymax : scalar, optional
- The stability threshold. Inputs that yield quantizer inputs above ymax
- are considered to be beyond the stable range of the modulator.
+ The stability threshold. Inputs that yield quantizer inputs above
+ ``ymax`` are considered to be beyond the stable range of the modulator.
  If not provided, it will be set to :math:`n_{lev} + 5`
 
  umax : scalar, optional
@@ -68,9 +68,9 @@ def scaleABCD(ABCD, nlev=2, f=0, xlim=1, ymax=None, umax=None, N_sim=1e5, N0=10)
  specified limits.
 
  S : ndarray
- The diagonal scaling matrix S.
+ The diagonal scaling matrix.
 
- `S` is defined such that::
+ :math:`S` is defined such that::
 
  ABCDs = [[S*A*Sinv, S*B], [C*Sinv, D]]
  xs = S*x

deltasigma/_simulateDSM.py

@@ -73,7 +73,7 @@
  'CPython':True}
 
 def simulateDSM(u, arg2, nlev=2, x0=0.):
- """Simulate a Delta Sigma modulator
+ """Simulate a delta-sigma modulator.
 
  Compute the output of a general delta-sigma modulator with input ``u``,
  a structure described by ``ABCD``, an initial state ``x0`` (default zero) and
@@ -98,7 +98,7 @@ def simulateDSM(u, arg2, nlev=2, x0=0.):
  nlev : int or sequence or ndarray
  Number of levels in the quantizers. Set ``nlev`` to a scalar for a
  single quantizer modulator. Multiple quantizers are implied by
- making nlev an array.
+ making ``nlev`` an array.
  x0 : float or sequence or ndarray
  The initial status of the modulator. If ``x0`` is set to float, its
  value will be used for all the states. If it is set to a sequence of

deltasigma/_simulateQDSM.py

@@ -47,7 +47,7 @@
 
 
 def simulateQDSM(u, arg2, nlev=2, x0=None):
- """Simulate a quadrature Delta-Sigma modulator
+ """Simulate a quadrature delta-sigma modulator.
 
  This function computes the output of a quadrature delta-sigma modulator
  corresponding to an input :math:`u`, with a description of the modulator, an

deltasigma/_simulateSNR.py

@@ -102,15 +102,15 @@ def simulateSNR(arg1, osr, amp=None, f0=0, nlev=2, f=None, k=13,
  f = \\frac{1}{4\ \mathrm{OSR}}
 
  k : int, optional
- The number of samples used to compute the FFT is set by the integer `k`
- - default value 13 - through:
+ The number of samples used to compute the FFT is set by the integer
+ ``k`` - default value 13 - through the relationship:
 
  .. math::
 
  N_{samples} = 2^k
 
  quadrature : boolean, optional
- Whether the delta sigma modulator is a quadrature modulator or not.
+ Whether the delta-sigma modulator is a quadrature modulator or not.
  Defaults to ``False``.
 
  **Returns:**
@@ -121,10 +121,12 @@ def simulateSNR(arg1, osr, amp=None, f0=0, nlev=2, f=None, k=13,
  amp : ndarray
  The amplitudes corresponding to the SNR values.
 
+ .. seealso:: :func:`predictSNR`.
+
  .. rubric:: Example:
 
  Compare the SNR vs input amplitude curve for a fifth-order modulator, as 
- determined by the describing function method (:func:`predictSNR`) with
+ computed by the describing function method (:func:`predictSNR`) with
  that determined by simulation (:func:`simulateSNR`).::
 
  import pylab as plt

deltasigma/_synthesizeNTF.py

@@ -44,11 +44,12 @@ def synthesizeNTF(order=3, osr=64, opt=0, H_inf=1.5, f0=0.0):
  **Parameters:**
 
  order : int, optional
- the order of the modulator, defaults to 3
+ the order of the modulator, defaults to 3.
  osr : float, optional
- the oversampling ratio, defaults to 64
+ the oversampling ratio, defaults to 64.
  opt : int or list of floats, optional
- flag for optimized zeros, defaults to 0
+ flag to select which algorithm is to be used to place the zeros,
+ defaults to 0.
 
  * 0 -> not optimized,
  * 1 -> optimized,
@@ -57,13 +58,14 @@ def synthesizeNTF(order=3, osr=64, opt=0, H_inf=1.5, f0=0.0):
  * 4 -> same as 3, but with at least one zero at band-center
  * [z] -> zero locations in complex form
 
- H_inf : real, optional
- max allowed peak value of the NTF. Defaults to 1.5
- f0 : real, optional
- center frequency for BP modulators, or 0 for LP modulators.
+ H_inf : float, optional
+ maximum allowed value of the absolute value of the NTF. Defaults to 1.5.
+ f0 : float, optional
+ center frequency for band-pass modulators, or 0 for low-pass modulators.
  Defaults to 0.
- 1 corresponds to the sampling frequency, so that 0.5 is the
- maximum value. Value 0 specifies an LP modulator.
+ The frequency is normalized throughout the ``deltasigma`` package. The
+ normalization is: 1 corresponds to the sampling frequency. Therefore 0.5
+ is the maximum value that can be assigned to ``f0``.
 
  **Returns:**
 
@@ -73,35 +75,36 @@ def synthesizeNTF(order=3, osr=64, opt=0, H_inf=1.5, f0=0.0):
  **Raises:**
 
  ValueError
- * 'Error. f0 must be less than 0.5' if f0 is out of range
+ * 'Error. ``f0`` must be less than 0.5', if ``f0`` is out of range.
 
  * 'Order must be even for a bandpass modulator.' if the order is
- incompatible with the modulator type.
+ odd and therefore incompatible with the modulator type.
 
- * 'The opt vector must be of length xxx' if opt is used to explicitly
- pass the NTF zeros and these are in the wrong number.
+ * 'The ``opt`` vector must be of length xxx' if ``opt`` is used to
+ explicitly pass the NTF zeros and these are in the wrong number.
 
  **Warns:**
 
- * 'Creating a lowpass ntf.' if the center frequency is different
- from zero, but so low that a low pass modulator must be designed.
+ * 'Creating a low-pass NTF.', if the center frequency is different
+ from zero, but so close to 0 that a low-pass modulator must be
+ designed.
 
- * 'Unable to achieve specified H_inf ...' if the desired H_inf
+ * 'Unable to achieve specified ``H_inf`` ...', if the desired ``H_inf``
  cannot be achieved.
 
- * 'Iteration limit exceeded' if the routine converges too slowly.
+ * 'Iteration limit exceeded', if the routine converges too slowly.
 
  **Notes:**
 
- This is actually a wrapper function which calls the appropriate version
- of synthesizeNTF, based on the module control flag `optimize_NTF` which
+ This is actually a wrapper function which calls the appropriate version of
+ ``synthesizeNTF``, based on the module control flag ``optimize_NTF`` which
  determines whether to use optimization tools.
 
- Parameter ``H_inf`` is used to enforce the Lee stability criterion.
+ The parameter ``H_inf`` is used to enforce the Lee stability criterion.
 
  **Example:**
 
- Fifth-order lowpass modulator; zeros optimized for an oversampling ratio of 32.::
+ Fifth-order low-pass modulator; zeros optimized for an oversampling ratio of 32.::
 
  from deltasigma import *
  H = synthesizeNTF(5, 32, 1)
@@ -122,12 +125,12 @@ def synthesizeNTF(order=3, osr=64, opt=0, H_inf=1.5, f0=0.0):
  .. seealso::
 
  :func:`clans` : Closed-Loop Analysis of Noise-Shaper.
-  An alternative method for selecting NTFs based on the 1-norm of the
-  impulse response of the NTF
+ An alternative method for selecting NTFs based on the 1-norm of the
+ impulse response of the NTF.
 
  :func:`synthesizeChebyshevNTF()` : Select a type-2 highpass Chebyshev NTF.
- This function does a better job than synthesizeNTF if osr
- or H_inf is low.
+ This function does a better job than :func:`synthesizeNTF` if ``osr``
+ or ``H_inf`` is low.
 
  """
  if f0 > 0.5:

deltasigma/_synthesizeQNTF.py

@@ -68,7 +68,7 @@ def synthesizeQNTF(order=4, OSR=64, f0=0., NG=-60, ING=-20, n_im=None):
 
  .. note::
 
- From the Matlab Delta-Sigma Toobox:
+ From the MATLAB Delta-Sigma Toolbox:
  ALPHA VERSION:
  This function uses an experimental ad-hoc method that is
  neither optimal nor robust.

deltasigma/_undbm.py

@@ -39,7 +39,7 @@ def undbm(p, z=50):
  **Returns:**
 
  Vrms : scalar or sequence
-  The RMS voltage corresponding to p
+ The RMS voltage corresponding to ``p``.
 
  .. seealso:: :func:`undbp`, :func:`undbv`, :func:`dbm`, :func:`db`
 

deltasigma/_undbp.py

@@ -39,7 +39,7 @@ def undbp(x):
  **Returns:**
 
  Prms : scalar or sequence
- The RMS power corresponding to x.
+ The RMS power corresponding to ``x``.
 
  .. seealso:: :func:`undbm`, :func:`undbv`, :func:`dbp`, :func:`db`
 

deltasigma/_undbv.py

@@ -39,7 +39,7 @@ def undbv(x):
  **Returns:**
 
  Vrms : scalar or sequence
-  The RMS voltage corresponding to x.
+ The RMS voltage corresponding to ``x``.
 
  .. seealso:: :func:`undbm`, :func:`undbp`, :func:`dbv`, :func:`db`
 

deltasigma/_zinc.py

@@ -32,7 +32,7 @@ def zinc(f, m=64, n=1):
  **Parameters:**
 
  f : ndarray
- The frequencies f at which the magnitude response is evaluated.
+ The frequencies at which the magnitude response is evaluated.
 
  m : int, optional
  The order of the comb filters.