Docs

nifty5/domains/log_rg_space.py

@@ -24,7 +24,7 @@ from .structured_domain import StructuredDomain
 
 
 class LogRGSpace(StructuredDomain):
-    """Represents a logarithmic Cartesian grid.
+    '''Represents a logarithmic Cartesian grid.
 
     Parameters
     ----------
@@ -38,7 +38,7 @@ class LogRGSpace(StructuredDomain):
     harmonic : bool, optional
         Whether the space represents a grid in position or harmonic space.
         Default: False.
-    """
+    '''
     _needed_for_hash = ['_shape', '_bindistances', '_t_0', '_harmonic']
 
     def __init__(self, shape, bindistances, t_0, harmonic=False):
@@ -76,6 +76,7 @@ class LogRGSpace(StructuredDomain):
 
     @property
     def t_0(self):
+        """np.ndarray : array of coordinates of pixel ndim*(1,)."""
         return np.array(self._t_0)
 
     def __repr__(self):
@@ -83,22 +84,32 @@ class LogRGSpace(StructuredDomain):
             self.shape, self.harmonic))
 
     def get_default_codomain(self):
+        """Returns a :class:`LogRGSpace` object representing the (position or
+        harmonic) partner domain of `self`, depending on `self.harmonic`. The
+        `bindistances` are transformed and `t_0` stays the same.
+
+        Returns
+        -------
+        LogRGSpace
+            The parter domain
+        """
         codomain_bindistances = 1./(self.bindistances*self.shape)
         return LogRGSpace(self.shape, codomain_bindistances, self._t_0, True)
 
     def get_k_length_array(self):
-        """Produces array of distances to origin of the space.
+        """Generates array of distances to origin of the space.
 
         Returns
         -------
-        numpy.ndarray(numpy.float64) with shape self.shape 
-            Distances to origin of the space.
-            If any index of the array is zero then the distance
-            is np.nan if self.harmonic True.
+        numpy.ndarray
+            Distances to origin of the space. If any index of the array is
+            zero then the distance is np.nan if self.harmonic True.
+            The dtype is float64, the shape is `self.shape`.
 
         Raises
         ------
-        NotImplementedError: if self.harmonic is False
+        NotImplementedError
+            If `self.harmonic` is False.
         """
         if not self.harmonic:
             raise NotImplementedError
@@ -106,14 +117,15 @@ class LogRGSpace(StructuredDomain):
         return Field.from_global_data(self, np.linalg.norm(ks, axis=0))
 
     def get_k_array(self):
-        """Produces coordinates of the space.
+        """Generates coordinates of the space.
 
         Returns
         -------
-        numpy.ndarray(numpy.float64) with shape (len(self.shape),) + self.shape 
-            Coordinates of the space.
-            If one index of the array is zero the corresponding coordinate is
-            -np.inf (np.nan) if self.harmonic is False (True).
+        numpy.ndarray
+            Coordinates of the space. If one index of the array is zero the
+            corresponding coordinate is -np.inf (np.nan) if self.harmonic is
+            False (True).
+            The dtype is float64 and shape: `(len(self.shape),) + self.shape`.
         """
         ndim = len(self.shape)
         k_array = np.zeros((ndim,) + self.shape)

nifty5/domains/structured_domain.py

@@ -50,7 +50,7 @@ class StructuredDomain(Domain):
 
     @property
     def total_volume(self):
-        """float : Total domain volume
+        """float : Total domain volume.
 
         Returns the sum over all the domain's pixel volumes.
         """
@@ -63,7 +63,7 @@ class StructuredDomain(Domain):
         raise NotImplementedError
 
     def get_k_length_array(self):
-        """k vector lengths, if applicable,
+        """k vector lengths, if applicable.
 
         Returns the length of the k vector for every pixel.
         This method is only implemented for harmonic domains.

nifty5/library/dynamic_operator.py

@@ -147,7 +147,7 @@ def dynamic_operator(domain,
     Parameters
     ----------
     domain : RGSpace
-        The position space in which the Green's function should be constructed.
+        The position space in which the Green's function shall be constructed.
     harmonic_padding : None, int, list of int
         Amount of central padding in harmonic space in pixels. If None the
         field is not padded at all.
@@ -158,9 +158,9 @@ def dynamic_operator(domain,
     key : String
         key for dynamics encoding parameter.
     causal : boolean
-        Whether or not the Green's function should be causal in time.
+        Whether or not the Green's function shall be causal in time.
     minimum_phase: boolean
-        Whether or not the Green's function should be a minimum phase filter.
+        Whether or not the Green's function shall be a minimum phase filter.
 
     Returns
     -------
@@ -203,8 +203,8 @@ def dynamic_lightcone_operator(domain,
     Parameters
     ----------
     domain : RGSpace
-        The position space in which the Green's function should be constructed.
-        Dim > 1 required.
+        The position space in which the Green's function shall be constructed.
+        It needs to have at least two dimensions.
     harmonic_padding : None, int, list of int
         Amount of central padding in harmonic space in pixels. If None the
         field is not padded at all.
@@ -221,9 +221,9 @@ def dynamic_lightcone_operator(domain,
     quant : float
         Quantization of the light cone in pixels.
     causal : boolean
-        Whether or not the Green's function should be causal in time.
+        Whether or not the Green's function shall be causal in time.
     minimum_phase: boolean
-        Whether or not the Green's function should be a minimum phase filter.
+        Whether or not the Green's function shall be a minimum phase filter.
 
     Returns
     -------

nifty5/library/smooth_linear_amplitude.py

@@ -51,13 +51,13 @@ def CepstrumOperator(target, a, k0):
       that the sqrt-cepstrum is essentially proportional to 1/k**2).
 
     - A field which is symmetric around the pixel in the middle of the space.
-      This is result of the :class:`SymmetrizingOperator` and needed in order to
-      decouple the degrees of freedom at the beginning and the end of the
+      This is result of the :class:`SymmetrizingOperator` and needed in order
+      to decouple the degrees of freedom at the beginning and the end of the
       amplitude whenever :class:`CepstrumOperator` is used as in
       :class:`SLAmplitude`.
 
-    The prior on the zero mode, or zero subspaces in the case of dim > 1,
-    is the integral of the prior of all other modes along the corresponding
+    The prior on the zero mode (or zero subspaces for more than one dimensions)
+    is the integral of the prior over all other modes along the corresponding
     axis.
 
     Parameters
@@ -79,9 +79,11 @@ def CepstrumOperator(target, a, k0):
         raise ValueError
     if len(target) > 1 or target[0].harmonic:
         raise TypeError
-    if isinstance(k0, float):
-        k0 = (k0, )*len(target.shape)
-    elif len(k0) != len(target.shape):
+    if isinstance(k0, (float, int)):
+        k0 = np.array([k0]*len(target.shape))
+    else:
+        k0 = np.array(k0)
+    if len(k0) != len(target.shape):
         raise ValueError
     if np.any(np.array(k0) <= 0):
         raise ValueError
@@ -98,7 +100,7 @@ def CepstrumOperator(target, a, k0):
     no_zero_modes = (slice(1, None),)*dim
     ks = q_array[(slice(None),) + no_zero_modes]
     cepstrum_field = np.zeros(shape)
-    cepstrum_field[no_zero_modes] = _ceps_kernel(dom, ks, a, k0)
+    cepstrum_field[no_zero_modes] = _ceps_kernel(ks, a, k0)
     # Fill zero-mode subspaces
     for i in range(dim):
         fst_dims = (slice(None),)*i