Merge pull request #704 from ComputationalCryoEM/docs_fix_702

Fix Docstrings Formatting

src/aspire/apple/apple.py

@@ -47,7 +47,7 @@ def __init__(
             Journal of Structural Biology, Vol. 204, No. 2, 11.2018, p. 215-227.
 
         :param particle_size: Particle size (pixels) is required.
-        Remaining parameters generally have defaults based on particle size.
+            Remaining parameters generally have defaults based on particle size.
         :param min_particle_size:
         :param max_particle_size:
         :param query_image_size:
@@ -59,8 +59,8 @@ def __init__(
         :param model: One of svm/gaussian_mixture/gaussian_naive_bayes/xgboost/thunder_svm
         :param model_opts: Optional dictionary of svm model options. Defaults to None.
         :param n_processes: Concurrent processes to spawn.
-        May improve performance on very large machines.
-        Otherwise use default of 1.
+            May improve performance on very large machines.
+            Otherwise use default of 1.
         """
 
         self.particle_size = particle_size

src/aspire/apple/helper.py

@@ -138,7 +138,7 @@ def extract_references(cls, img, query_size, container_size):
             img: Micrograph image.
             query_size: Reference images must be of the same size of query images, i.e. (query_size x query_size).
             container_size: Containers are large regions used to select reference images. The size of each
-            region is (container_size x container_size)
+                region is (container_size x container_size)
 
         Returns:
             3D Matrix of reference images.  windows[0] is the first reference window.

src/aspire/basis/basis.py

@@ -22,7 +22,7 @@ def __init__(self, size, ell_max=None, dtype=np.float32):
 
         :param size: The size of the vectors for which to define the basis.
             Currently only square images and cubic volumes are supported.
-        :ell_max: The maximum order ell of the basis elements. If no input
+        :param ell_max: The maximum order ell of the basis elements. If no input
             (= None), it will be set to np.Inf and the basis includes all
             ell such that the resulting basis vectors are concentrated
             below the Nyquist frequency (default Inf).
@@ -162,7 +162,7 @@ def expand(self, x):
 
         :param x: An array whose last two or three dimensions are to be expanded
             the desired basis. These dimensions must equal `self.sz`.
-        :return : The coefficients of `v` expanded in the desired basis.
+        :return: The coefficients of `v` expanded in the desired basis.
             The last dimension of `v` is with size of `count` and the
             first dimensions of the return value correspond to
             those first dimensions of `x`.

src/aspire/basis/basis_utils.py

@@ -18,6 +18,7 @@
 def check_besselj_zeros(nu, z):
     """
     Sanity-check a sequence of estimated zeros of the Bessel function with order `nu`.
+
     :param nu: The real number order of the Bessel function.
     :param z: (Array-like) A sequence of postulated zeros.
     :return result: True or False.
@@ -54,7 +55,7 @@ def besselj_newton(nu, z0, max_iter=10):
     :param nu: The real number order of the Bessel function.
     :param z0: (Array-like) The initial guess(es) for the root-finding algorithm.
     :param max_iter: Maximum number of iterations for Newton-Raphson
-    (default: 10).
+        (default: 10).
     :return z: (Array-like) The estimated root(s).
     """
     z = z0
@@ -254,7 +255,7 @@ def all_besselj_zeros(ell, r):
     :param ell: The real number order of the Bessel function.
     :param r: The upper bound for zeros returned.
     :return n, r0: The number of zeros and the zeros themselves
-    as a NumPy array.
+        as a NumPy array.
     """
     k = 4
     # get the first 4 zeros
@@ -272,6 +273,7 @@ def all_besselj_zeros(ell, r):
 def unique_coords_nd(N, ndim, shifted=False, normalized=True, dtype=np.float32):
     """
     Generate unique polar coordinates from 2D or 3D rectangular coordinates.
+
     :param N: length size of a square or cube.
     :param ndim: number of dimension, 2 or 3.
     :param shifted: shifted half pixel or not for odd N.

src/aspire/basis/fb_2d.py

@@ -242,8 +242,8 @@ def _evaluate_t(self, v):
             must equal `self.sz`.
         :return: The evaluation of the coefficient array `v` in the dual basis
             of `basis`. This is an array of vectors whose last dimension equals
-             `self.count` and whose first dimensions correspond to
-             first dimensions of `v`.
+            `self.count` and whose first dimensions correspond to
+            first dimensions of `v`.
         """
         v = v.asnumpy().T  # RCOPT
 

src/aspire/basis/fb_3d.py

@@ -26,7 +26,7 @@ def __init__(self, size, ell_max=None, dtype=np.float32):
         :param size: The size of the vectors for which to define the basis.
             May be a 3-tuple or an integer, in which case a cubic basis is assumed.
             Currently only cubic images are supported.
-        :ell_max: The maximum order ell of the basis elements. If no input
+        :param ell_max: The maximum order ell of the basis elements. If no input
             (= None), it will be set to np.Inf and the basis includes all
             ell such that the resulting basis vectors are concentrated
             below the Nyquist frequency (default Inf).
@@ -144,6 +144,7 @@ def basis_norm_3d(self, ell, k):
     def _evaluate(self, v):
         """
         Evaluate coefficients in standard 3D coordinate basis from those in FB basis
+
         :param v: A coefficient vector (or an array of coefficient vectors) to
             be evaluated. The first dimension must equal `self.count`.
         :return: The evaluation of the coefficient vector(s) `v` for this basis.

src/aspire/basis/ffb_2d.py

@@ -186,7 +186,7 @@ def _evaluate_t(self, x):
         Evaluate coefficient in FB basis from those in standard 2D coordinate basis
 
         :param x: The Image instance representing coefficient array in the
-        standard 2D coordinate basis to be evaluated.
+            standard 2D coordinate basis to be evaluated.
         :return v: The evaluation of the coefficient array `v` in the FB basis.
             This is an array of vectors whose last dimension equals `self.count`
             and whose first dimension correspond to `x.n_images`.

src/aspire/basis/fpswf_2d.py

@@ -112,7 +112,7 @@ def evaluate_t(self, images):
         Evaluate coefficient vectors in PSWF basis using the fast method.
 
         :param images: Image stack in the standard 2D coordinate basis.
-        :return : Coefficient array in the PSWF basis.
+        :return: Coefficient array in the PSWF basis.
         """
 
         if not isinstance(images, Image):

src/aspire/basis/fspca.py

@@ -38,11 +38,11 @@ def __init__(
         :param src: Source instance
         :param basis: Optional Fourier Bessel Basis (usually FFBBasis2D)
         :param components: Optionally assign number of principal components
-        to use for the FSPCA basis.
-        Default value of `None` will use `self.basis.count`.
+            to use for the FSPCA basis.
+            Default value of `None` will use `self.basis.count`.
         :param noise_var: Optionally assign noise variance.
-        Default value of `None` will estimate noise with WhiteNoiseEstimator.
-        Use 0 when using clean images so cov2d skips applying noisy covar coeffs..
+            Default value of `None` will estimate noise with WhiteNoiseEstimator.
+            Use 0 when using clean images so cov2d skips applying noisy covar coeffs..
         :param batch_size: Batch size for computing basis coefficients.
             `batch_size` is also passed to BatchedRotCov2D.
         """
@@ -311,7 +311,7 @@ def expand_from_image_basis(self, x):
         basis vectors, described by an eigenvector in FSPCA.
 
         :param x:  The Image instance representing a stack of images in the
-        standard 2D coordinate basis to be evaluated.
+            standard 2D coordinate basis to be evaluated.
         :return: Stack of coefs in the FSPCABasis.
         """
         fb_coefs = self.basis.evaluate_t(x)
@@ -325,7 +325,7 @@ def expand(self, x):
         basis vectors, described by an eigenvector in FSPCA.
 
         :param x:  Coefs representing a stack in the
-        Fourier Bessel basis.
+            Fourier Bessel basis.
         :return: Stack of coefs in the FSPCABasis.
         """
 
@@ -342,7 +342,7 @@ def evaluate_to_image_basis(self, c):
 
         :param c:  Stack of coefs in the FSPCABasis to be evaluated.
         :return: The Image instance representing a stack of images in the
-        standard 2D coordinate basis..
+            standard 2D coordinate basis..
         """
         c_fb = self.evaluate(c)
 

src/aspire/basis/polar_2d.py

@@ -102,10 +102,10 @@ def _evaluate_t(self, x):
         Evaluate coefficient in polar Fourier grid from those in standard 2D coordinate basis
 
         :param x: The Image instance representing coefficient array in the
-        standard 2D coordinate basis to be evaluated.
+            standard 2D coordinate basis to be evaluated.
         :return v: The evaluation of the coefficient array `v` in the polar
-        Fourier grid. This is an array of vectors whose first dimension
-        corresponds to x.n_images, and last dimension equals `self.count`.
+            Fourier grid. This is an array of vectors whose first dimension
+            corresponds to x.n_images, and last dimension equals `self.count`.
         """
         nimgs = x.n_images
 

src/aspire/basis/pswf_2d.py

@@ -155,7 +155,7 @@ def evaluate_t(self, images):
 
         :param images: coefficient array in the standard 2D coordinate basis
             to be evaluated.
-        :return : The evaluation of the coefficient array in the PSWF basis.
+        :return: The evaluation of the coefficient array in the PSWF basis.
         """
 
         if not isinstance(images, Image):
@@ -174,7 +174,7 @@ def evaluate(self, coefficients):
         Evaluate coefficients in standard 2D coordinate basis from those in PSWF basis
 
         :param coeffcients: A coefficient vector (or an array of coefficient
-        vectors) in PSWF basis to be evaluated. (n_image, count)
+            vectors) in PSWF basis to be evaluated. (n_image, count)
         :return : Image in standard 2D coordinate basis.
 
         """

src/aspire/classification/averager2d.py

@@ -27,8 +27,8 @@ def __init__(self, composite_basis, src, num_procs=1, dtype=None):
         :param composite_basis:  Basis to be used during class average composition (eg FFB2D)
         :param src: Source of original images.
         :param num_procs: Number of processes to use.
-        `None` will attempt computing a suggestion based on machine resources.
-        Note some underlying code may already use threading.
+            `None` will attempt computing a suggestion based on machine resources.
+            Note some underlying code may already use threading.
         :param dtype: Numpy dtype to be used during alignment.
         """
 
@@ -83,9 +83,9 @@ def average(
 
         :param classes: class indices, refering to src. (n_classes, n_nbor).
         :param reflections: Bool representing whether to reflect image in `classes`.
-        (n_clases, n_nbor)
+            (n_clases, n_nbor)
         :param coefs: Optional basis coefs (could avoid recomputing).
-        (n_classes, coef_count)
+            (n_classes, coef_count)
         :return: Stack of synthetic class average images as Image instance.
         """
 
@@ -95,9 +95,9 @@ def _cls_images(self, cls, src=None):
         preserving the class/nbor order.
 
         :param cls: An iterable (0/1-D array or list) that holds the indices of images to align.
-        In class averaging, this would be a class.
+            In class averaging, this would be a class.
         :param src: Optionally override the src, for example, if you want to use a different
-        source for a certain operation (ie alignment).
+            source for a certain operation (ie alignment).
         """
         src = src or self.src
 
@@ -124,7 +124,7 @@ def __init__(
         :param src: Source of original images.
         :param alignment_basis: Optional, basis to be used only during alignment (eg FSPCA).
         :param num_procs: Number of processes to use.
-        Note some underlying code may already use threading.
+            Note some underlying code may already use threading.
         :param dtype: Numpy dtype to be used during alignment.
         """
 
@@ -258,7 +258,7 @@ def __init__(
         """
         See AligningAverager2D, adds:
 
-        :params n_angles: Number of brute force rotations to attempt, defaults 360.
+        :param n_angles: Number of brute force rotations to attempt, defaults 360.
         """
         super().__init__(
             composite_basis, src, alignment_basis, num_procs=num_procs, dtype=dtype
@@ -488,10 +488,10 @@ def __init__(
         :param composite_basis:  Basis to be used during class average composition.
         :param src: Source of original images.
         :param alignment_src: Optional, source to be used during class average alignment.
-        Must be the same resolution as `src`.
+            Must be the same resolution as `src`.
         :param num_procs: Number of processes to use.
-        `None` will attempt computing a suggestion based on machine resources.
-        Note some underlying code may already use threading.
+            `None` will attempt computing a suggestion based on machine resources.
+            Note some underlying code may already use threading.
         :param dtype: Numpy dtype to be used during alignment.
         """
 
@@ -644,19 +644,19 @@ def __init__(
     ):
         """
         :param alignment_basis: Basis to be used during alignment.
-        For current implementation of ReddyChatterjiAverager2D this should be `None`.
-        Instead see `alignment_src`.
+            For current implementation of ReddyChatterjiAverager2D this should be `None`.
+            Instead see `alignment_src`.
         :param src: Source of original images.
         :param composite_basis:  Basis to be used during class average composition.
         :param alignment_src: Optional, source to be used during class average alignment.
-        Must be the same resolution as `src`.
+            Must be the same resolution as `src`.
         :param radius: Brute force translation search radius.
-        Defaults to src.L//8.
+            Defaults to src.L//8.
         :param dtype: Numpy dtype to be used during alignment.
 
         :param num_procs: Number of processes to use.
-        `None` will attempt computing a suggestion based on machine resources.
-        Note some underlying code may already use threading.
+            `None` will attempt computing a suggestion based on machine resources.
+            Note some underlying code may already use threading.
         :param dtype: Numpy dtype to be used during alignment.
         """
 

src/aspire/classification/rir_class2d.py

@@ -55,13 +55,13 @@ def __init__(
         for Viewing Direction Classification in Cryo-EM. (2014)
 
         :param src: Source instance.  Note it is possible to use one `source` for classification (ie CWF),
-        and a different `source` for stacking in the `averager`.
+            and a different `source` for stacking in the `averager`.
         :param pca_basis: Optional FSPCA Basis instance
         :param fspca_components: Optinally set number of components (top eigvals) to keep from full FSCPA.
-        Default value of None will infer from `pca_basis` when provided, otherwise defaults to 400.
+            Default value of None will infer from `pca_basis` when provided, otherwise defaults to 400.
         :param alpha: Amplitude Power Scale, default 1/3 (eq 20 from  RIIR paper).
         :param sample_n: Threshold for random sampling of bispectrum coefs. Default 4000,
-        high values such as 50000 reduce random sampling.
+            high values such as 50000 reduce random sampling.
         :param n_nbor: Number of nearest neighbors to compute.
         :param n_classes: Number of class averages to return.
         :param bispectrum_freq_cutoff: Truncate (zero) high k frequecies above (int) value, defaults off (None).
@@ -70,7 +70,7 @@ def __init__(
         :param bispectrum_implementation: See `bispectrum`.
         :param selector: A ClassSelector subclass. Defaults to RandomClassSelector.
         :param num_procs: Number of processes to use.
-        `None` will attempt computing a suggestion based on machine resources.
+            `None` will attempt computing a suggestion based on machine resources.
         :param batch_size: Chunk size (typically number of images) for batched methods.
         :param averager: An Averager2D subclass. Defaults to BFSReddyChatterjiAverager2D.
         :param dtype: Optional dtype, otherwise taken from src.
@@ -281,9 +281,9 @@ def nn_classification(self, coef_b, coef_b_r):
         :param coef_b:
         :param coef_b_r:
         :returns:  Tuple of classes, refl, dists where
-        classes is an integer array of indices representing image ids,
-        refl is a bool array representing reflections (True is refl),
-        and distances is an array of distances as returned by NN implementation.
+            classes is an integer array of indices representing image ids,
+            refl is a bool array representing reflections (True is refl),
+            and distances is an array of distances as returned by NN implementation.
         """
         # _nn_classification is assigned during initialization.
         return self._nn_classification(coef_b, coef_b_r)

src/aspire/covariance/covar.py

@@ -141,6 +141,7 @@ def cb(xk):
     def apply_kernel(self, coeff, kernel=None, packed=False):
         """
         Applies the kernel represented by convolution
+
         :param coeff: The volume matrix (6 dimensions) to be convolved (but see the `packed` argument below).
         :param kernel: a Kernel object. If None, the kernel for this Estimator is used.
         :param packed: whether the `coeff` matrix represents an isometrically mapped packed vector,
@@ -165,7 +166,7 @@ def src_backward(self, mean_vol, noise_variance, shrink_method=None):
         Apply adjoint mapping to source
 
         :return: The sum of the outer products of the mean-subtracted images in `src`, corrected by the expected noise
-        contribution and expressed as coefficients of `basis`.
+            contribution and expressed as coefficients of `basis`.
         """
         covar_b = np.zeros(
             (self.src.L, self.src.L, self.src.L, self.src.L, self.src.L, self.src.L),
@@ -192,6 +193,7 @@ def src_backward(self, mean_vol, noise_variance, shrink_method=None):
     def _shrink(self, covar_b_coeff, noise_variance, method=None):
         """
         Shrink covariance matrix
+
         :param covar_b_coeff: Outer products of the mean-subtracted images
         :param noise_variance: Noise variance
         :param method: One of None/'frobenius_norm'/'operator_norm'/'soft_threshold'