Merge pull request #12 from soar-telescope/fix_error_on_incomplete_data

- Now the program will not crash and will return None, I'm not fully convinced this is the best solution for this but and idea is described in #13

docs/conf.py

@@ -44,6 +44,7 @@
     'matplotlib.sphinxext.plot_directive',
 ]
 
+autoclass_content = 'both'
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
 
@@ -55,7 +56,7 @@
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'test_', 'tests']
 
 
 # -- Options for HTML output -------------------------------------------------

docs/source/modules.rst

@@ -0,0 +1,7 @@
+triplespec_focus
+================
+
+.. toctree::
+   :maxdepth: 4
+
+   triplespec_focus

docs/source/triplespec_focus.rst

@@ -0,0 +1,45 @@
+triplespec\_focus package
+=========================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   triplespec_focus.tests
+
+Submodules
+----------
+
+triplespec\_focus.entrypoints module
+------------------------------------
+
+.. automodule:: triplespec_focus.entrypoints
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+triplespec\_focus.triplespec\_focus module
+------------------------------------------
+
+.. automodule:: triplespec_focus.triplespec_focus
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+triplespec\_focus.utils module
+------------------------------
+
+.. automodule:: triplespec_focus.utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: triplespec_focus
+   :members:
+   :undoc-members:
+   :show-inheritance:

setup.cfg

@@ -49,6 +49,9 @@ docs =
     sphinx
     sphinx-rtd-theme
 
+[coverage:run]
+omit=*/tests/*,*__init__.py
+
 [options.entry_points]
 triplespec_focus = triplespec_focus.run_triplespec_focus
 
@@ -71,6 +74,6 @@ triplespec_focus = triplespec_focus.run_triplespec_focus
 # W504 line break after binary operator
 # W505 doc line too long
 [flake8]
-max-line-length = 100
-max-doc-length = 79
+max-line-length = 120
+max-doc-length = 120
 ignore = E126,E127,E128,E201,E202,E203,E221,E225,E226,E227,E231,E251,E501,E731,E741,W503,W504,W505

tox.ini

@@ -19,7 +19,7 @@ setenv =
     NPY_LAPACK_ORDER=
 
 # Pass through the following environment variables which may be needed for the CI
-passenv = HOME WINDIR LC_ALL LC_CTYPE CC CI
+passenv = HOME,WINDIR,LC_ALL,LC_CTYPE,CC,CI
 
 # Run the tests in a temporary directory to make sure that we don't import
 # this package from the source tree
@@ -66,7 +66,6 @@ extras =
     alldeps: all
 
 commands =
-    pip freeze
     !cov: pytest --pyargs triplespec_focus {toxinidir}/docs {posargs}
     cov: pytest --pyargs triplespec_focus {toxinidir}/docs --cov triplespec_focus --cov-config={toxinidir}/setup.cfg {posargs}
 
@@ -75,15 +74,13 @@ changedir = docs
 description = invoke sphinx-build to build the HTML docs
 extras = docs
 commands =
-    pip freeze
     sphinx-build -W -b html . _build/html
 
 [testenv:linkcheck]
 changedir = docs
 description = check the links in the HTML docs
 extras = docs
 commands =
-    pip freeze
     sphinx-build -W -b linkcheck . _build/html
 
 [testenv:codestyle]

triplespec_focus/tests/test_triplespec_focus.py

@@ -70,6 +70,16 @@ def test_get_focus_with_data(self):
         self.assertIsInstance(self.tspec_focus.fitted_model, Model)
         self.assertIsInstance(results, list)
 
+    def test_get_focus_with_fwhm_monotonically_increasing(self):
+        focus_values = [-1300, -1200, -1100, -1000, -900, -800, -700]
+        fwhm_values = [1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7]
+        data = {'focus': focus_values, 'fwhm': fwhm_values}
+        df = DataFrame(data=data)
+        self.assertIsNone(self.tspec_focus.best_focus)
+        self.assertIsNone(self.tspec_focus.fitted_model)
+        results = self.tspec_focus.get_best_focus(df=df, x_axis_size=2000)
+        self.assertIsNone(results)
+
     def test_detect_sources(self):
         ccd = CCDData.read(self.test_file_name, unit='adu')
         self.tspec_focus.show_mask = True
@@ -89,5 +99,3 @@ def test_call_with_file_list(self):
         results = self.tspec_focus(file_list=sorted(data_path.glob(pattern='*.fits')))
         self.assertIsInstance(results, dict)
         self.assertEqual(len(results), 11)
-
-

triplespec_focus/triplespec_focus.py

@@ -16,8 +16,8 @@
 from pandas import DataFrame
 from pandas import concat
 from pathlib import Path
-from photutils import DAOStarFinder
-from photutils import CircularAperture
+from photutils.detection import DAOStarFinder
+from photutils.aperture import CircularAperture
 
 from scipy import optimize
 from typing import List, Union
@@ -59,7 +59,8 @@ def __init__(self,
             n_brightest (int): Number of the brightest sources to use for measuring source statistics. Default 5.
             saturation (float): Data value at which the detector saturates. Default 40000.
             plot_results (bool): If set to True will display information plots at the end. Default False.
-            debug_plots (bool): If set to True will display several plots useful for debugging or viewing the process. Default False.
+            debug_plots (bool): If set to True will display several plots useful for debugging or viewing the process.
+              Default False.
         """
 
         self.best_fwhm = None
@@ -113,12 +114,15 @@ def __call__(self,
             file_list (List, None): Optional file list with files to be used to obtain best focus. Default None.
             source_fwhm (float): Full width at half maximum to use for source detection and statistics.
             det_threshold (float): Number of standard deviation above median to use as detection threshold. Default 5.0.
-            mask_threshold (float): Number of standard deviation below median to use as a threshold for masking values. Default 1.
+            mask_threshold (float): Number of standard deviation below median to use as a threshold for masking values.
+              Default 1.
             n_brightest (int): Number of the brightest sources to use for measuring source statistics. Default 5.
             saturation_level (float): Data value at which the detector saturates. Default 40000.
-            show_mask (bool): If set to True will display masked values in red when debug_plots is also True: Default False.
+            show_mask (bool): If set to True will display masked values in red when debug_plots is also True:
+              Default False.
             plot_results (bool): If set to True will display information plots at the end. Default False.
-            debug_plots (bool): If set to True will display several plots useful for debugging or viewing the process. Default False.
+            debug_plots (bool): If set to True will display several plots useful for debugging or viewing the process.
+              Default False.
             print_all_data (bool): If set to True will print the entire dataset at the end.
 
         Returns:
@@ -200,17 +204,20 @@ def __call__(self,
         for star_id in star_ids:
             star_phot = self.sources_df[self.sources_df['id'] == star_id]
             interpolated_data = self.get_best_focus(df=star_phot)
-            all_stars_photometry.append([star_phot, interpolated_data, self.best_focus])
-            all_focus.append(self.best_focus)
-            all_fwhm.append(self.best_fwhm)
+            if interpolated_data:
+                all_stars_photometry.append([star_phot, interpolated_data, self.best_focus])
+                if self.best_focus and self.best_fwhm:
+                    all_focus.append(self.best_focus)
+                    all_fwhm.append(self.best_fwhm)
 
         mean_focus = np.mean(all_focus)
         median_focus = np.median(all_focus)
         focus_std = np.std(all_focus)
         mean_fwhm = np.mean(all_fwhm)
 
         best_image_overall = CCDData.read(self.best_image_overall, unit='adu')
-        self.best_image_fwhm = self.sources_df[self.sources_df['filename'] == os.path.basename(self.best_image_overall)]['fwhm'].mean()
+        self.best_image_fwhm = self.sources_df[self.sources_df['filename'] == os.path.basename(
+            self.best_image_overall)]['fwhm'].mean()
 
         focus_data = []
         fwhm_data = []
@@ -276,7 +283,8 @@ def detect_sources(self, ccd: CCDData, debug_plots: bool = False) -> QTable:
 
         Args:
             ccd (CCDData): An image with point sources.
-            debug_plots (bool): If set to True will display the image with the sources. Default False.
+            debug_plots (bool): If set to True will display the image with the sources.
+              Default False.
 
         Returns:
             An Astropy's QTable containing ids, centroids, focus value and image name.
@@ -337,11 +345,13 @@ def get_best_focus(self, df: DataFrame, x_axis_size: int = 2000) -> List[np.ndar
         """Obtains the best focus for a single source
 
         Args:
-            df (DataFrame): Pandas DataFrame containing at least a 'focus' and a 'fwhm' column. The data should belong to a single source.
+            df (DataFrame): Pandas DataFrame containing at least a 'focus' and a 'fwhm' column.
+              The data should belong to a single source.
             x_axis_size (int): Size of the x-axis used to sample the fitted model. Is not an interpolation size.
 
         Returns:
-            A list with the x-axis and the sampled data using the fitted model
+            A list with the x-axis and the sampled data using the fitted model, None if it is not possible to find the
+              focus.
 
         """
         focus_start = df['focus'].min()
@@ -353,11 +363,20 @@ def get_best_focus(self, df: DataFrame, x_axis_size: int = 2000) -> List[np.ndar
         modeled_data = self.fitted_model(x_axis)
         index_of_minimum = np.argmin(modeled_data)
         middle_point = x_axis[index_of_minimum]
-
-        self.best_focus = optimize.brent(self.fitted_model, brack=(focus_start, middle_point, focus_end))
-        self.best_fwhm = modeled_data[index_of_minimum]
-
-        return [x_axis, modeled_data]
+        if middle_point == focus_start or middle_point == focus_end:
+            self.log.warning("The focus vs FWHM curve does not seem to have a V or U shape. Trying by forcing the "
+                             "mean focus as the middle point for Brent's optimization bracket definition.")
+            middle_point = (focus_start + focus_end) / 2.
+
+        self.log.debug(f"Brent optimization bracket, Start (xa): {focus_start} Middle (xb): {middle_point} End (xc): {focus_end}")
+
+        try:
+            self.best_focus = optimize.brent(self.fitted_model, brack=(focus_start, middle_point, focus_end))
+            self.best_fwhm = modeled_data[index_of_minimum]
+            self.log.info(f"Found best focus at {self.best_focus} with a fwhm of {self.best_fwhm}")
+            return [x_axis, modeled_data]
+        except ValueError as error:
+            self.log.error(error)
 
 
 if __name__ == '__main__':

triplespec_focus/utils.py

@@ -15,7 +15,7 @@
 from pandas import DataFrame
 from typing import Union, List
 
-from photutils import CircularAperture, CircularAnnulus, ApertureStats
+from photutils.aperture import CircularAperture, CircularAnnulus, ApertureStats
 
 log = logging.getLogger(__name__)