Added info to index documentation page and fixed some format problems…

… in source docstrings

acalib/algorithms/__init__.py

@@ -1,7 +1,7 @@
 from .fellWalker import FellWalker
 from .clumpFind import ClumpFind
+from .gms import GMS
 from .indexing import Indexing
 from .stacking import Stacking
-from .gms import GMS
 
 __all__ = ['FellWalker','Indexing', 'ClumpFind', 'Stacking', 'GMS']

acalib/algorithms/algorithm.py

@@ -9,7 +9,7 @@ def __init__(self, params=None):
 
         Parameters
         ----------            
-            params: dict (default = None)
+            params : dict (default = None)
                 Dictionary with algorithm's parameters
         """
         self.config = dict()
@@ -32,7 +32,7 @@ def set_param(self, key, value):
             ----------     
             key : string
                 algorithm internal config dictionary key.
-            value: 
+            value :  
                 value to set.
         """
         self.config[key] = value

acalib/algorithms/gms.py

@@ -28,8 +28,9 @@ class GMS(Algorithm):
     
     Parameters
     ----------
-    params: dict (default = None)
-    Algorithm parameters, allowed keys: 
+    params : dict (default = None)
+        Algorithm parameters, allowed keys:
+         
         P : float (default = 0.05)
             Thresholding quantile for multiscale segmentation.
         PRECISION : float (default = 0.02)

acalib/algorithms/indexing.py

@@ -9,8 +9,9 @@ class Indexing(Algorithm):
 
     Parameters
     ----------
-    params: dict (default = None)
-    Algorithm parameters, allowed keys:    
+    params : dict (default = None)
+        Algorithm parameters, allowed keys:    
+        
         P : float (default = 0.05)
             Thresholding quantile for multiscale segmentation.
         PRECISION : float (default = 0.02)
@@ -48,7 +49,7 @@ def run(self, data):
 
             Returns
             -------
-            result: :class:`~acalib.Container` with the cube slices, segmentated images and region of interest tables for each scale analyzed.
+            :class:`~acalib.Container` with the cube slices, segmentated images and region of interest tables for each scale analyzed.
         """
 
         if data.wcs:

acalib/algorithms/stacking.py

@@ -19,14 +19,14 @@ def run(self, template_data, data_cont):
 
             Parameters
             ----------
-            template_data: (M,N) numpy.ndarray
+            template_data : (M,N) numpy.ndarray
                 Astronomical image.
-            data_cont: acalib.container
+            data_cont : acalib.container
                 An images set container
 
             Returns
             -------
-            result: (M,N) numpy.ndarray
+            result : (M,N) numpy.ndarray
                 Image stacked
         """
 

acalib/core/analysis.py

@@ -20,10 +20,10 @@ def rms(data, mask=None):
 
     Parameters
     ----------
-    data: (M,N,Z) numpy.ndarray or astropy.nddata.NDData
+    data : (M,N,Z) numpy.ndarray or astropy.nddata.NDData
         Astronomical data cube.
 
-    mask: numpy.ndarray (default = None)
+    mask : numpy.ndarray (default = None)
 
     Returns
     -------
@@ -42,22 +42,23 @@ def snr_estimation(data, mask=None, noise=None, points=1000, full_output=False):
     
     Parameters
     ---------- 
-    data: (M,N,Z) numpy.ndarray or astropy.nddata.NDData
+    data : (M,N,Z) numpy.ndarray or astropy.nddata.NDData
         Astronomical data cube.
 
-    mask: numpy.ndarray (default = None)
+    mask : numpy.ndarray (default = None)
 
-    noise: float (default=None)
+    noise : float (default=None)
         Noise level, if not given will use rms of the data.
     
-    points: (default=1000)
+    points : (default=1000)
 
-    full_output: boolean (default=False)
+    full_output : boolean (default=False)
         Gives verbose results if True
 
     Returns
-    "Signal to Noise Radio"
-    -------
+    --------
+
+    "Signal to Noise Radio" value
     
     """
     if noise is None:
@@ -90,12 +91,12 @@ def integrate(data, mask=None, axis=(0)):
    
     Parameters
     ----------    
-    data: (M,N,Z) numpy.ndarray or astropy.nddata.NDData
+    data : (M,N,Z) numpy.ndarray or astropy.nddata.NDData
         Astronomical data cube.
 
-    mask: numpy.ndarray (default = None)
+    mask : numpy.ndarray (default = None)
 
-    axis: (default=(0))
+    axis : int (default=(0))
     
     Returns
     -------
@@ -171,16 +172,18 @@ def spectra_sketch(data, samples, random_state=None):
     
     Parameters
     ----------
-    data: (M,N,Z) numpy.ndarray or astropy.nddata.NDData
+    data : (M,N,Z) numpy.ndarray or astropy.nddata.NDData
         Astronomical data cube.
 
-    samples: Number of pixel samples(int) used for the sketch.
+    samples : Number of pixel samples(int) used for the sketch.
 
-    random_state: (default=None)
+    random_state : (default=None)
     
-    Returns: 
-     spectra: (array) 
-     slices:  (list)
+    Returns
+    -------
+
+     spectra (array) and slices (list)
+
     """
     # Specific for a FREQ,DEC,RA order
     if random_state is not None:
@@ -392,10 +395,19 @@ def _kernel_shift(back, kernel, x, y):
 @support_nddata
 def vel_stacking(data,data_slice, wcs=None, mask=None,uncertainty=None, meta=None, unit=None):
     """
-       Create an image collapsing the frecuency axis
-        :param data_slice: Sector to be collapsed
-        :type data_slice: slice
-        :returns: image (NDData): 2D-Array with the stacked cube.
+    Create an image collapsing the frecuency axis
+   
+    Parameters
+    ----------
+    data : numpy.ndarray
+        Astronomical 2D image 
+
+    slice : slice object
+        Sector to be collapsed
+    
+    Returns
+    -------
+    image (NDData): 2D-Array with the stacked cube.
 
     """
     if len(data.shape) != 3:

acalib/core/models.py

@@ -8,13 +8,13 @@ def gaussian_function(mu, P, feat, peak):
 
     Parameters
     ----------
-    mu: numpy.ndarray
+    mu : numpy.ndarray
         Centers of gaussians array.
-    P: numpy.ndarray
+    P : numpy.ndarray
         Precision matrix.
-    feat: numpy.ndarray.
+    feat : numpy.ndarray.
         Features matrix.
-    peak: float
+    peak : float
         Peak value of the resulting evaluation.
 
     Returns
@@ -39,14 +39,14 @@ def create_mould(P, delta):
 
     Parameters
     ----------
-    P: numpy.ndarray
+    P : numpy.ndarray
         Precision matrix.
-    delta: list or numpy.ndarray
+    delta : list or numpy.ndarray
         Delta values used to generate the mould.
 
     Returns
     -------
-    result: numpy.ndarray
+    result : numpy.ndarray
         Mould matrix.
     """
     n = len(delta)

acalib/core/transform.py

@@ -17,15 +17,15 @@ def scale(inputCont, majorAxisTemplate):
 
     Parameters
     ----------
-    inputCont: acalib.Container
+    inputCont : acalib.Container
         Container with the images to be scaled.
 
-    mayorAxisTemplate: float
+    mayorAxisTemplate : float
         Axis respect the scale will be performed on all the images of the container.
 
     Returns
     -------
-    result: list
+    result : list
         List with the scaled images. 
     """
     scaledData = []
@@ -43,14 +43,14 @@ def rotate(data, angle):
 
     Parameters
     ----------
-    data: list
+    data : list
         List of (M,N,Z) numpy.ndarray images.
-    angle: float
+    angle : float
         Rotation reference angle that will be applied to all the images.
 
     Returns
     -------
-    result: tuple
+    result : tuple
         Tuple with the list of rotated images and the list of rotation angles applied to each one.  
     """
     rotatedData = []
@@ -81,15 +81,15 @@ def crop_and_align(data, angles):
 
     Parameters
     ----------
-    data: list
+    data : list
         List of astronomical data cubes (numpy.ndarray).
         
-    angles: list
+    angles : list
         List of angles (float) to perform alignment.
 
     Returns
     -------
-    result: list
+    result : list
         List of *aligned* astronomical data cubes (numpy.ndarray).
     """
     alignedData = []
@@ -121,12 +121,12 @@ def standarize(data):
 
     Parameters
     ----------
-    data: numpy.ndarray or astropy.nddata.NDData
+    data : numpy.ndarray or astropy.nddata.NDData
         Astronomical data cube.
 
     Returns
     -------
-    result: tuple
+    result : tuple
         Tuple containing the standarized numpy.ndarray or astropy.nddata.NDData cube, the factor scale y_fact and the shift y_min.
     """
     y_min = data.min()
@@ -142,16 +142,16 @@ def unstandarize(data, a, b):
 
     Parameters
     ----------
-    data: numpy.ndarray or astropy.nddata.NDData
+    data : numpy.ndarray or astropy.nddata.NDData
         Astronomical data cube.
-    a: float
+    a : float
         Scale value.
-    b: float
+    b : float
         Shift value.
 
     Returns
     ------- 
-    result: numpy.ndarray or astropy.nddata.NDData
+    result : numpy.ndarray or astropy.nddata.NDData
         Unstandarized astronomical cube.
     """
     return a*data+b
@@ -163,13 +163,13 @@ def add(data, flux, lower, upper):
 
     Parameters
     ----------
-    data: numpy.ndarray or astropy.nddata.NDData
+    data : numpy.ndarray or astropy.nddata.NDData
         Astronomical data cube.
-    flux: numpy.ndarray
+    flux : numpy.ndarray
         Flux added to the cube.
-    lower: float
+    lower : float
         Lower bound of the sub-cube to which flux will be added. 
-    upper: float
+    upper : float
         Upper bound of the sub-cube to which flux will be added.
     """
 
@@ -183,14 +183,14 @@ def denoise(data, threshold):
 
     Parameters
     ----------
-    data: numpy.ndarray or astropy.nddata.NDData
+    data : numpy.ndarray or astropy.nddata.NDData
         Astronomical data cube.
-    threshold: float
+    threshold : float
         Threshold value used for denoising.
 
     Returns
     -------
-    result: numpy.ndarray
+    result : numpy.ndarray
         Denoised (thresholded) astronomical data cube.
     """
 
@@ -206,12 +206,12 @@ def fits_props(img):
 
     Parameters
     ----------
-    img: numpy.ndarray
+    img : numpy.ndarray
         Astronomical data cube.
 
     Returns
     -------
-    result: dict
+    result : dict
         Dictionary with properties of the image: *centroid*, *major*, *minor*, *ratio*, *angle*, *area*, *img*, *clr*, *label*, *orig*.
     """
     flt = threshold_otsu(img)