Merge pull request #29 from matthewtownson/master

Tests and bug fixing

aotools/functions/_functions.py

@@ -1,4 +1,5 @@
 import numpy
+from . import pupil
 
 
 def gaussian2d(size, width, amplitude=1., cent=None):
@@ -7,31 +8,31 @@ def gaussian2d(size, width, amplitude=1., cent=None):
 
 
     Args:
-        size (tuple, float): Dimensions of Array to place gaussian
+        size (tuple, float): Dimensions of Array to place gaussian (y, x)
         width (tuple, float): Width of distribution.
-                                Accepts tuple for x and y values.
+                                Accepts tuple for x and y values in order (y, x).
         amplitude (float): Amplitude of guassian distribution
-        cent (tuple): Centre of distribution on grid.
+        cent (tuple): Centre of distribution on grid in order (y, x).
     '''
 
     try:
-        xSize = size[0]
-        ySize = size[1]
+        ySize = size[0]
+        xSize = size[1]
     except (TypeError, IndexError):
         xSize = ySize = size
 
     try:
-        xWidth = float(width[0])
-        yWidth = float(width[1])
+        yWidth = float(width[0])
+        xWidth = float(width[1])
     except (TypeError, IndexError):
         xWidth = yWidth = float(width)
 
     if not cent:
-        xCent = size[0] / 2.
-        yCent = size[1] / 2.
+        xCent = xSize/2.
+        yCent = ySize/2.
     else:
-        xCent = cent[0]
-        yCent = cent[1]
+        yCent = cent[0]
+        xCent = cent[1]
 
     X, Y = numpy.meshgrid(range(0, xSize), range(0, ySize))
 
@@ -55,7 +56,7 @@ def aziAvg(data):
     size = data.shape[0]
     avg = numpy.empty(size / 2, dtype="float")
     for i in range(size / 2):
-        ring = circle(i + 1, size) - circle(i, size)
+        ring = pupil.circle(i + 1, size) - pupil.circle(i, size)
         avg[i] = (ring * data).sum() / (ring.sum())
 
     return avg

aotools/image_processing/_image_processing.py

@@ -17,7 +17,7 @@ def r0fromSlopes(slopes, wavelength, subapDiam):
 
     r0 = ((0.162 * (wavelength ** 2) * subapDiam ** (-1. / 3)) / slopeVar) ** (3. / 5)
 
-    r0 = r0.mean()
+    r0 = float(r0.mean())
 
     return r0
 
@@ -50,7 +50,7 @@ def image_contrast(image):
 
     contrast = (image.max() - image.min()) / (image.max() + image.min())
 
-    return contrast
+    return float(contrast)
 
 
 def rms_contrast(image):
@@ -66,4 +66,4 @@ def rms_contrast(image):
 
     image /= image.max()
 
-    return image.std()
+    return float(image.std())

aotools/turbulence/slopecovariance.py

@@ -481,53 +481,3 @@ def create_tomographic_covariance_reconstructor(covariance_matrix, n_onaxis_suba
     tomo_recon = cov_onoff.dot(icov_offoff)
 
     return tomo_recon
-
-if __name__ == "__main__":
-
-    N = 1
-    threads = 20
-
-    n_wfs = 6
-    telescope_diameter = 4.2
-    nx_subaps = 10
-
-    n_layers = 3
-    layer_altitudes = numpy.linspace(0, 20000, 35)
-    layer_r0s = [1] * n_layers
-    layer_L0s = [25.] * n_layers
-
-    asterism_radius = 60
-
-    subap_diameters = [telescope_diameter / nx_subaps] * n_wfs
-    pupil_masks = [circle(nx_subaps/2., nx_subaps)] * n_wfs
-    gs_altitudes = [90000] * n_wfs
-    gs_positions = [
-            [asterism_radius, 0], 
-            [numpy.sin(numpy.pi/3.) * asterism_radius, numpy.cos(numpy.pi/3.) * asterism_radius], 
-            [numpy.sin(numpy.pi/3.) * asterism_radius, -numpy.cos(numpy.pi/3.) * asterism_radius],
-            [-numpy.sin(numpy.pi/3.) * asterism_radius, numpy.cos(numpy.pi/3.) * asterism_radius], 
-            [-numpy.sin(numpy.pi/3.) * asterism_radius, -numpy.cos(numpy.pi/3.) * asterism_radius], 
-            [-asterism_radius, 0]]
-    wfs_magnifications = [1.] * n_wfs
-    pupil_offsets = [[0, 0]] * n_wfs
-    wfs_rotations = [0] * n_wfs
-    wfs_wavelengths = [550e-9] * n_wfs
-
-    t1 = time.time()
-    for i in range(N):
-        print("\nIteration {}".format(i))
-        cov_mat = CovarianceMatrix(n_wfs, pupil_masks, telescope_diameter, subap_diameters, gs_altitudes, gs_positions, wfs_wavelengths,
-        n_layers, layer_altitudes, layer_r0s, layer_L0s, wfs_magnifications, pupil_offsets, wfs_rotations, threads)
-        cov_mat.make_covariance_matrix()
-
-    t2 = time.time()
-
-    time_taken = (t2 - t1)/N
-    covmat_per_sec = 1./time_taken
-    print("Time for 1 Covariance Matrix: {}s".format(time_taken))
-    print("Covariance Matrics per second: {} cps".format(covmat_per_sec))
-
-
-    # from matplotlib import pyplot
-    # pyplot.imshow(cov_mat.covariance_matrix)
-    # pyplot.show()

test/test_functions.py

@@ -0,0 +1,13 @@
+from aotools import functions
+import numpy
+
+
+def test_gaussian2d():
+    gaussian = functions.gaussian2d(10, 3, 10.)
+    assert gaussian.shape == (10, 10)
+
+
+def test_gaussian2d_2d():
+    gaussian = functions.gaussian2d((10, 8), (3, 2), 10., (4, 3))
+    print(gaussian.shape)
+    assert gaussian.shape == (10, 8)

test/test_image_processing.py

@@ -0,0 +1,30 @@
+from aotools import image_processing
+import numpy
+
+
+def test_r0fromSlopes():
+    slopes = numpy.random.random((2, 100, 2))
+    wavelength = 500e-9
+    subapDiam = 0.5
+    r0 = image_processing.r0fromSlopes(slopes, wavelength, subapDiam)
+    print(type(r0))
+
+
+def test_slopeVarfromR0():
+    r0 = 0.1
+    wavelength = 500e-9
+    subapDiam = 0.5
+    variance = image_processing.slopeVarfromR0(r0, wavelength, subapDiam)
+    assert type(variance) == float
+
+
+def test_image_contrast():
+    image = numpy.random.random((20, 20))
+    contrast = image_processing.image_contrast(image)
+    assert type(contrast) == float
+
+
+def test_rms_contrast():
+    image = numpy.random.random((20, 20))
+    contrast = image_processing.rms_contrast(image)
+    assert type(contrast) == float

test/test_slopecovariance.py

@@ -155,7 +155,7 @@ def test_slopecovmat_makecovmat_multithreaded():
 
 if __name__ == "__main__":
     N = 1
-    threads = 2
+    threads = 20
 
     n_wfs = 6
     telescope_diameter = 8.
@@ -198,6 +198,6 @@ def test_slopecovmat_makecovmat_multithreaded():
     print("Time for 1 Covariance Matrix: {}s".format(time_taken))
     print("Covariance Matrics per second: {} cps".format(covmat_per_sec))
 
-    from matplotlib import pyplot
-    pyplot.imshow(cov_mat.covariance_matrix)
-    pyplot.show()
+    # from matplotlib import pyplot
+    # pyplot.imshow(cov_mat.covariance_matrix)
+    # pyplot.show()