Bugfixes to sourcefinder internals for Python-2 compatibility.

* Fixed tuple-unpacking as function arguments - it appears
  Python-3 is a bit more flexible in this regard.
* Fixed incorrect assignment of array.data instead of array
* Refactored validator declarations a bit.
* Added a mask-shape check to the Island constructor.

Author: timstaley

docs/source/notebooks/gaussian_parameterizations.ipynb

@@ -184,9 +184,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from fastimgproto.sourcefind.fit import Gaussian2dFit\n",
+    "from fastimgproto.sourcefind.fit import Gaussian2dParams\n",
     "import numpy as np\n",
-    "import astropy.units as u"
+    "import astropy.units as u\n",
+    "import attr"
    ]
   },
   {
@@ -204,7 +205,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "g1 = Gaussian2dFit(x_centre=0,\n",
+    "g1 = Gaussian2dParams(x_centre=0,\n",
     "                   y_centre=0,\n",
     "                   amplitude=1,\n",
     "                   semimajor=1.5,\n",
@@ -239,7 +240,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "g1.theta = -np.pi/4\n",
+    "g1 = attr.evolve(g1, theta = -np.pi/4)\n",
     "g1.correlation"
    ]
   },
@@ -249,8 +250,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "g1.theta = np.pi/4\n",
-    "g1.semimajor = 1000\n",
+    "g1 = attr.evolve(g1, theta = np.pi/4, semimajor=1000)\n",
     "g1.correlation"
    ]
   },
@@ -260,7 +260,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "g1.semimajor = 1.0001\n",
+    "g1 = attr.evolve(g1, semimajor=1.0001)\n",
     "g1.correlation"
    ]
   }

docs/source/notebooks/simulate_and_image.ipynb

@@ -241,8 +241,8 @@
     "ax1.set_xlim(axlims)\n",
     "ax1.set_ylim(axlims)\n",
     "for src in sfimage.islands:\n",
-    "    ax1.axvline(src.xbar, ls=':')\n",
-    "    ax1.axhline(src.ybar, ls=':')"
+    "    ax1.axvline(src.params.moments_fit.x_centre, ls=':')\n",
+    "    ax1.axhline(src.params.moments_fit.y_centre, ls=':')"
    ]
   }
  ],

docs/source/notebooks/sourcefinding.ipynb

@@ -185,9 +185,10 @@
    "outputs": [],
    "source": [
     "plt.imshow(island.data)\n",
-    "plt.xlim(island.extremum_x_idx-10,island.extremum_x_idx+10,)\n",
-    "plt.ylim(island.extremum_y_idx-10,island.extremum_y_idx+10,)\n",
-    "plt.scatter(island.xbar,island.ybar, marker='*', s=200, c='y',)"
+    "plt.xlim(island.extremum.index.x-10,island.extremum.index.x+10,)\n",
+    "plt.ylim(island.extremum.index.y-10,island.extremum.index.y+10,)\n",
+    "moments_fit = island.params.moments_fit\n",
+    "plt.scatter(moments_fit.x_centre,moments_fit.y_centre, marker='*', s=200, c='y',)"
    ]
   },
   {
@@ -201,7 +202,8 @@
     "print()\n",
     "print(\"Island barycentres:\")\n",
     "for i in sfimage.islands:\n",
-    "    print(i.xbar, i.ybar)"
+    "    moments = i.params.moments_fit\n",
+    "    print(moments.x_centre, moments.y_centre)"
    ]
   }
  ],

setup.py

@@ -6,7 +6,7 @@
 
 install_requires = [
     'astropy',
-    'attrs>=16.3.0',
+    'attrs>=17.2.0',
     'click',
     'drive-casa>=0.7.6',
     'pandas',

src/fastimgproto/sourcefind/image.py

@@ -8,15 +8,8 @@
 from scipy import ndimage
 from scipy.optimize import OptimizeResult, least_squares
 
-from fastimgproto.sourcefind.fit import (
-    Gaussian2dParams,
-    gaussian2d,
-    gaussian2d_jac,
-)
-from fastimgproto.utils import (
-    nonzero_bounding_slice_2d,
-    positive_negative_sign_validator,
-)
+from fastimgproto.sourcefind.fit import Gaussian2dParams, gaussian2d
+from fastimgproto.utils import nonzero_bounding_slice_2d
 
 _STRUCTURE_ELEMENT = np.array([[1, 1, 1],
                                [1, 1, 1],
@@ -98,7 +91,12 @@ class IslandParams(object):
     """
 
     # Required for initialization
-    sign = attrib(validator=positive_negative_sign_validator)
+    sign = attrib()
+    @sign.validator
+    def check_sign(instance, attribute, value):
+        if value not in (-1, 1):
+            raise ValueError("'sign' should be +1 or -1")
+
     extremum = attrib(validator=attr.validators.instance_of(Pixel))
 
     # Optional
@@ -141,6 +139,11 @@ class Island(object):
                          validator=attr.validators.instance_of(np.ndarray))
     mask = attrib(cmp=False,
                   validator=attr.validators.instance_of(np.ndarray))
+    @mask.validator
+    def check_mask_shape(instance, attribute, value):
+        if not value.shape == instance.parent_data.shape:
+            raise ValueError("Mask-shape does not match parent-shape")
+
     params = attrib(validator=attr.validators.instance_of(IslandParams))
 
     # xbar = attrib(default=None)
@@ -158,7 +161,7 @@ def data(self):
         Return a MaskedArray view of the parent-array.
         """
         return np.ma.MaskedArray(
-            self.parent_data.data,
+            self.parent_data,
             mask=self.mask,
         )
 

src/fastimgproto/utils.py

@@ -27,15 +27,6 @@ def reset_progress_bar(progress_bar, total, description="Doing something"):
     return
 
 
-
-def positive_negative_sign_validator(instance, attribute, value):
-    """
-    Attrs validator for verifying ``value in (-1,1)``.
-    """
-    if value not in (-1, 1):
-        raise ValueError("'sign' should be +1 or -1")
-
-
 def nonzero_bounding_slice_2d(input):
     """
     Get slices defining the bounding box for any nonzero / True-valued subarray.

tests/test_sourcefind/test_detection.py

@@ -2,6 +2,7 @@
 
 import attr
 import numpy as np
+import pytest
 import scipy.ndimage
 from pytest import approx
 
@@ -15,6 +16,10 @@
 )
 from fastimgproto.sourcefind.fit import Gaussian2dParams
 from fastimgproto.sourcefind.image import (
+    Island,
+    IslandParams,
+    Pixel,
+    PixelIndex,
     SourceFindImage,
     estimate_rms,
     extremum_pixel_index,
@@ -41,6 +46,26 @@ def test_rms_estimation():
     assert np.abs((rms_est - rms) / rms) < 0.05
 
 
+def test_island_constructor_validation():
+    img = np.zeros((ydim, xdim), dtype=np.float_)
+    bright_pixel  = Pixel(index=PixelIndex(y=int(ydim / 2), x=int(xdim / 2)),
+                       value = 1.0)
+    island_params = IslandParams(sign=1,
+                                 extremum = bright_pixel)
+
+    bad_mask = np.ones((2,2), dtype=bool)
+    with pytest.raises(ValueError):
+        island = Island(parent_data = img,
+                        mask=bad_mask,
+                        params = island_params)
+
+    mask = np.ones_like(img, dtype=bool)
+    mask[attr.astuple(bright_pixel.index)] = False
+    island = Island(parent_data = img,
+                        mask=mask,
+                        params = island_params)
+
+
 def test_basic_source_detection():
     """
     We use a flat background (rather than noisy) to avoid random-noise fluctuations
@@ -50,7 +75,7 @@ def test_basic_source_detection():
     start adding secondary sources and check we get multiple finds as
     expected...
     """
-    img = np.zeros((ydim, xdim))
+    img = np.zeros((ydim, xdim), dtype=np.float_)
     add_gaussian2d_to_image(bright_src, img)
 
     sf = SourceFindImage(img, detection_n_sigma=4,
@@ -127,7 +152,6 @@ def test_negative_source_detection():
     neg_island = negative_islands[0]
     pos_island = positive_islands[0]
 
-
     min_pixel_index = extremum_pixel_index(img, -1)
     assert attr.astuple(neg_island.extremum.index) == min_pixel_index
 
@@ -137,7 +161,7 @@ def test_negative_source_detection():
     # Sanity check that the island masks look sensible
     # Check that the mask==False regions are disjoint - taking the boolean OR
     # on both masks should result in a fully `True` mask-array.
-    assert (np.logical_or(neg_island.data.mask,pos_island.data.mask).all())
+    assert (np.logical_or(neg_island.data.mask, pos_island.data.mask).all())
 
     # And that the true/false regions look sensible for the extremum pixels:
     assert neg_island.data.mask[min_pixel_index] == False

tests/test_sourcefind/test_fitting.py

@@ -54,13 +54,13 @@ def test_gauss2d_func_and_jacobian():
     xy_pairs = np.column_stack((x_samples, y_samples))
 
     looped_g2d = np.array(
-        [gaussian2d(*tuple(pair), *attr.astuple(profile))
+        [gaussian2d(*tuple(list(pair) + list(attr.astuple(profile))))
          for pair in xy_pairs])
     vectored_g2d = gaussian2d(x_samples, y_samples, *attr.astuple(profile))
     assert (looped_g2d == vectored_g2d).all()
 
     looped_jac = np.array(
-        [gaussian2d_jac(*tuple(pair), *attr.astuple(profile))
+        [gaussian2d_jac(*tuple(list(pair) + list(attr.astuple(profile))))
          for pair in xy_pairs])
     vectored_jac = gaussian2d_jac(x_samples, y_samples, *attr.astuple(profile))