Merge pull request #50 from matthewtownson/master

Tidying function names ready for v1.0

.travis.yml

@@ -8,9 +8,8 @@ branches:
   - v*
 
 python:
-- 2.7
 # - 3.4 Not in travis repositories
-- 3.5
+#- 3.5 Not in travis repositories
 - 3.6
 - 3.7
 

aotools/functions/_functions.py

@@ -1,6 +1,4 @@
 import numpy
-from . import pupil
-import warnings
 
 
 def gaussian2d(size, width, amplitude=1., cent=None):
@@ -40,84 +38,4 @@ def gaussian2d(size, width, amplitude=1., cent=None):
     image = amplitude * numpy.exp(
         -(((xCent - X) / xWidth) ** 2 + ((yCent - Y) / yWidth) ** 2) / 2)
 
-    return image
-
-
-def aziAvg(data):
-    """
-    Measure the azimuthal average of a 2d array
-
-    Args:
-        data (ndarray): A 2-d array of data
-
-    Returns:
-        ndarray: A 1-d vector of the azimuthal average
-    """
-    warnings.warn("This function will be removed in version 0.5, instead use aotools.image_processing.azimuthal_average",
-                  DeprecationWarning)
-
-    size = data.shape[0]
-    avg = numpy.empty(int(size / 2), dtype="float")
-    for i in range(int(size / 2)):
-        ring = pupil.circle(i + 1, size) - pupil.circle(i, size)
-        avg[i] = (ring * data).sum() / (ring.sum())
-
-    return avg
-
-
-def encircledEnergy(data,
-                    fraction=0.5, center=None,
-                    eeDiameter=True):
-    """
-        Return the encircled energy diameter for a given fraction
-        (default is ee50d).
-        Can also return the encircled energy function.
-        Translated and extended from YAO.
-
-        Parameters:
-            data : 2-d array
-            fraction : energy fraction for diameter calculation
-                default = 0.5
-            center : default = center of image
-            eeDiameter : toggle option for return.
-                If False returns two vectors: (x, ee(x))
-                Default = True
-        Returns:
-            Encircled energy diameter
-            or
-            2 vectors: diameters and encircled energies
-
-    """
-    warnings.warn(
-        "This function will be removed in version 0.5, instead use aotools.image_processing.encircled_energy",
-        DeprecationWarning)
-
-    dim = data.shape[0] // 2
-    if center is None:
-        center = [dim, dim]
-    xc = center[0]
-    yc = center[1]
-    e = 1.9
-    npt = 20
-    rad = numpy.linspace(0, dim**(1. / e), npt)**e
-    ee = numpy.empty(rad.shape)
-
-    for i in range(npt):
-        pup = pupil.circle(rad[i],
-                           int(dim) * 2,
-                           circle_centre=(xc, yc),
-                           origin='corner')
-        rad[i] = numpy.sqrt(numpy.sum(pup) * 4 / numpy.pi)  # diameter
-        ee[i] = numpy.sum(pup * data)
-
-    rad = numpy.append(0, rad)
-    ee = numpy.append(0, ee)
-    ee /= numpy.sum(data)
-    xi = numpy.linspace(0, dim, int(4 * dim))
-    yi = numpy.interp(xi, rad, ee)
-
-    if eeDiameter is False:
-        return xi, yi
-    else:
-        ee50d = float(xi[numpy.argmin(numpy.abs(yi - fraction))])
-        return ee50d
+    return image

aotools/functions/zernike.py

@@ -28,7 +28,7 @@ def phaseFromZernikes(zCoeffs, size, norm="noll"):
     return phase
 
 
-def zernike(j, N):
+def zernike_noll(j, N):
     """
      Creates the Zernike polynomial with mode index j,
      where j = 1 corresponds to piston.
@@ -143,7 +143,7 @@ def zernikeArray(J, N, norm="noll"):
         nJ = len(J)
         Zs = numpy.empty((nJ, N, N))
         for i in xrange(nJ):
-            Zs[i] = zernike(J[i], N)
+            Zs[i] = zernike_noll(J[i], N)
 
     # Else, cast to int and create up to that number
     except TypeError:
@@ -154,7 +154,7 @@ def zernikeArray(J, N, norm="noll"):
         Zs = numpy.empty((maxJ, N, N))
 
         for j in xrange(1, maxJ+1):
-            Zs[j-1] = zernike(j, N)
+            Zs[j-1] = zernike_noll(j, N)
 
 
     if norm=="p2v":

aotools/image_processing/__init__.py

@@ -1,4 +1,3 @@
 from .centroiders import *
-from ._image_processing import *
 from .contrast import *
 from .psf import *

aotools/image_processing/centroiders.py

@@ -41,7 +41,7 @@ def correlation_centroid(im, ref, threshold=0., padding=1):
         # Correlate frame with reference image
         corr = cross_correlate(im[frame], ref, padding=padding)
 
-        cx, cy = centreOfGravity(corr, threshold=threshold)
+        cx, cy = centre_of_gravity(corr, threshold=threshold)
 
         cy -= float(ny) / 2. * (float(padding) - 1)
         cx -= float(nx) / 2. * (float(padding) - 1)
@@ -51,20 +51,16 @@ def correlation_centroid(im, ref, threshold=0., padding=1):
     return centroids
 
 
-def centreOfGravity(img, threshold=0, minThreshold=0, **kwargs):
+def centre_of_gravity(img, threshold=0, min_threshold=0, **kwargs):
     """
     Centroids an image, or an array of images.
     Centroids over the last 2 dimensions.
     Sets all values under "threshold*max_value" to zero before centroiding
     Origin at 0,0 index of img.
 
-    The value under which pixels are set to 0
-     is max(threshold*max_value, minThreshold)
-
     Parameters:
         img (ndarray): ([n, ]y, x) 2d or greater rank array of imgs to centroid
         threshold (float): Percentage of max value under which pixels set to 0
-        minThreshold (float): Absolute max value under which pixels set to 0
 
     Returns:
         ndarray: Array of centroid values (2[, n])
@@ -73,10 +69,10 @@ def centreOfGravity(img, threshold=0, minThreshold=0, **kwargs):
 
     if threshold != 0:
         if len(img.shape) == 2:
-            thres = numpy.max((threshold*img.max(), minThreshold))
+            thres = numpy.max((threshold*img.max(), min_threshold))
             img = numpy.where(img > thres, img - thres, 0)
         else:
-            thres = numpy.maximum(threshold*img.max(-1).max(-1), [minThreshold]*img.shape[0])
+            thres = numpy.maximum(threshold*img.max(-1).max(-1), [min_threshold]*img.shape[0])
             img_temp = (img.T - thres).T
             zero_coords = numpy.where(img_temp < 0)
             img[zero_coords] = 0
@@ -94,7 +90,7 @@ def centreOfGravity(img, threshold=0, minThreshold=0, **kwargs):
     return numpy.array([x_centroid, y_centroid])
 
 
-def brightestPxl(img, threshold, **kwargs):
+def brightest_pixel(img, threshold, **kwargs):
     """
     Centroids using brightest Pixel Algorithm
     (A. G. Basden et al,  MNRAS, 2011)
@@ -124,7 +120,7 @@ def brightestPxl(img, threshold, **kwargs):
         img[:]  = (img.T - pxlValues).T
         img = img.clip(0, img.max(), out=img)
 
-    return centreOfGravity(img)
+    return centre_of_gravity(img)
 
 
 def cross_correlate(x, y, padding=1):

aotools/turbulence/slopecovariance.py

@@ -15,13 +15,11 @@
 
 """
 
-import time
 import multiprocessing
 
 import numpy
 import scipy.special
 
-from ..functions import circle
 
 class CovarianceMatrix(object):
     """

appveyor.yml

@@ -2,12 +2,15 @@ build: false
 
 environment:
   matrix:
-    - PYTHON_VERSION: 2.7
-      MINICONDA: C:\Miniconda-x64
-
     - PYTHON_VERSION: 3.5
       MINICONDA: C:\Miniconda35-x64
 
+    - PYTHON_VERSION: 3.6
+        MINICONDA: C:\Miniconda36-x64
+
+    - PYTHON_VERSION: 3.7
+        MINICONDA: C:\Miniconda37-x64
+
 
 branches:
   only:

doc/source/conf.py

@@ -80,17 +80,20 @@ def __getattr__(cls, name):
 
 # General information about the project.
 project = u'AOtools'
-copyright = u'2016, Centre for Advanced Instrumentation'
-author = u'Centre for Advanced Instrumentation'
+copyright = u'2019, The Authors'
+author = u'AOtools'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = u'0.1'
+full_version = aotools.__version__
+version_parts = full_version.split('.')
+print(version_parts)
+version = u'0.5'
 # The full version, including alpha/beta/rc tags.
-release = u'0.1.0'
+release = aotools.__version__  # '0.1.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

test/test_centroiders.py

@@ -3,33 +3,33 @@
 from nose.tools import raises
 
 
-def test_centreOfGravity_single():
+def test_centre_of_gravity_single():
     img = numpy.random.random((10, 10))
-    com = image_processing.centreOfGravity(img, 0.1)
+    com = image_processing.centre_of_gravity(img, 0.1)
     assert(com.shape[0]) == 2
 
 
-def test_centreOfGravity_many():
+def test_centre_of_gravity_many():
     img = numpy.random.random((5, 10, 10))
-    com = image_processing.centreOfGravity(img, 0.1)
+    com = image_processing.centre_of_gravity(img, 0.1)
     assert(com.shape[0] == 2)
     assert(com.shape[1] == 5)
 
-def test_centreOfGravity_value():
+def test_centre_of_gravity_value():
     img = numpy.zeros((1, 5, 5))
     img[0, 1:3, 2:4] = 1.
-    centroid = image_processing.centreOfGravity(img)
+    centroid = image_processing.centre_of_gravity(img)
     numpy.testing.assert_almost_equal(centroid, numpy.array([[2.5], [1.5]]))
 
-def test_brightestPxl_single():
+def test_brightest_pixel_single():
     img = numpy.random.random((10, 10))
-    com = image_processing.brightestPxl(img, 0.3)
+    com = image_processing.brightest_pixel(img, 0.3)
     assert(com.shape[0] == 2)
 
 
 def test_brightestPxl_many():
     img = numpy.random.random((5, 10, 10))
-    com = image_processing.brightestPxl(img, 0.1)
+    com = image_processing.brightest_pixel(img, 0.1)
     assert(com.shape[0] == 2)
     assert(com.shape[1] == 5)
 

test/test_functions.py

@@ -11,26 +11,3 @@ def test_gaussian2d_2d():
     gaussian = functions.gaussian2d((10, 8), (3, 2), 10., (4, 3))
     print(gaussian.shape)
     assert gaussian.shape == (10, 8)
-
-
-def test_encircledEnergy():
-    data = numpy.random.rand(32, 32)
-    ee50d = functions.encircledEnergy(data)
-    print(ee50d)
-    assert type(ee50d) == float
-
-
-def test_encircledEnergy_func():
-    data = numpy.random.rand(32, 32)
-    x, y = functions.encircledEnergy(data, eeDiameter=False)
-    print(y.min(), y.max())
-    assert len(x) == len(y)
-    assert numpy.max(y) <= 1
-    assert numpy.min(y) >= 0
-
-
-def test_aziAvg():
-    data = numpy.random.rand(32, 32)
-    azi = functions.aziAvg(data)
-    print(azi.shape)
-    assert azi.shape == (16,)