Merge branch 'main' into image_deconvolution

.github/workflows/docs.yml

@@ -0,0 +1,31 @@
+name: docs
+
+on: [push, pull_request, workflow_dispatch]
+
+permissions:
+  contents: write
+
+jobs:
+  docs:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - name: Install dependencies
+        run: |
+          sudo apt install pandoc
+          pip install sphinx sphinx_rtd_theme nbsphinx mock
+      - name: Sphinx build
+        run: |
+          make -C docs html
+      - name: Deploy to GitHub Pages
+        uses: peaceiris/actions-gh-pages@v3
+        if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/docs' }}
+        with:
+          publish_branch: gh-pages
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: docs/_build/html
+          force_orphan: true
+

README.md

@@ -1,26 +1,10 @@
 # cosipy
-The COSI high-level data analysis tools
 
-## Installation from source
+The cosipy library is [COSI](https://cosi.ssl.berkeley.edu)'s high-level analysis software.
 
-Meant for developers. Currently the only option.
+The main repository is hosted in https://github.com/cositools/cosipy
 
-1. Clone the repository into your local machine
+For the cosipy installation and usage instructions please refer to the main [cosipy documentation](https://cositools.github.io/cosipy/).
 
-```
-git clone [email protected]:cositools/cosipy.git
-```
 
-2. Move to the cosipy folder
 
-```
-cd cosipy
-```
-
-3. Create a new conda environment. Optional but highly encouranged.
-
-4. Install it:
-
-```
-pip install -e .
-```

cosipy/response/FullDetectorResponse.py

@@ -835,7 +835,7 @@ def get_point_source_response(self,
             Effective time spent by the source at each pixel location in spacecraft coordinates
         coord : :py:class:`astropy.coordinates.SkyCoord`
             Source coordinate
-        scatt_map : :py:cass:`SpacecraftAttitudeMap`
+        scatt_map : :py:class:`SpacecraftAttitudeMap`
             Spacecraft attitude map
         
         Returns

cosipy/threeml/COSILike.py

@@ -34,36 +34,34 @@
 logger = logging.getLogger(__name__)
 
 class COSILike(PluginPrototype):
-
+    """
+    COSI 3ML plugin.
+
+    Parameters
+    ----------
+    name : str
+        Plugin name e.g. "cosi". Needs to have a distinct name with respect to other plugins in the same analysis
+    dr : str
+        Path to full detector response
+    data : histpy.Histogram
+        Binned data. Note: Eventually this should be a cosipy data class
+    bkg : histpy.Histogram
+        Binned background model. Note: Eventually this should be a cosipy data class
+    sc_orientation : cosipy.spacecraftfile.SpacecraftFile
+        Contains the information of the orientation: timestamps (astropy.Time) and attitudes (scoord.Attitude) that describe
+        the spacecraft for the duration of the data included in the analysis
+    nuisance_param : astromodels.core.parameter.Parameter, optional
+        Background parameter
+    coordsys : str, optional
+        Coordinate system ('galactic' or 'spacecraftframe') to perform fit in, which should match coordinate system of data 
+        and background. This only needs to be specified if the binned data and background do not have a coordinate system 
+        attached to them
+    precomputed_psr_file : str, optional
+        Full path to precomputed point source response in Galactic coordinates
+    """
     def __init__(self, name, dr, data, bkg, sc_orientation, 
                  nuisance_param=None, coordsys=None, precomputed_psr_file=None, **kwargs):
 
-        """
-        COSI 3ML plugin
-        
-        Parameters
-        ----------
-        name : str
-            Plugin name e.g. "cosi". Needs to have a distinct name with respect to other plugins in the same analysis
-        dr : Path
-            Path to full detector response
-        data: histpy.Histogram
-            Binned data. Note: Eventually this should be a cosipy data class
-        bkg: histpy.Histogram
-            Binned background model. Note: Eventually this should be a cosipy data class
-        sc_orientation: cosipy.spacecraftfile.SpacecraftFile
-            Contains the information of the orientation: timestamps (astropy.Time) and attitudes (scoord.Attitude) that describe
-            the spacecraft for the duration of the data included in the analysis
-        nuisance_param: astromodels.core.parameter.Parameter
-            Background parameter (optional)
-        coordsys: str
-            Coordinate system ('galactic' or 'spacecraftframe') to perform fit in, which should match coordinate system of data 
-            and background. This only needs to be specified if the binned data and background do not have a coordinate system 
-            attached to them
-        precomputed_psr_file: str
-            Full path to precomputed point source response in Galactic coordinates (optional) 
-        """
-
         # create the hash for the nuisance parameters. We have none for now.
         self._nuisance_parameters = collections.OrderedDict()
 
@@ -121,13 +119,13 @@ def __init__(self, name, dr, data, bkg, sc_orientation,
             print("--> done")
 
     def set_model(self, model):
-
         """
         Set the model to be used in the joint minimization.
         
         Parameters
         ----------
-        model: astromodels.core.model.Model; any model supported by astromodels
+        model : astromodels.core.model.Model
+            Any model supported by astromodels
         """
 
         # Get point sources and extended sources from model: 
@@ -194,7 +192,7 @@ def set_model(self, model):
                 print("... Calculating point source responses ...")
 
                 self._psr = {}
-                self._source_location = {} # Shoule the poition information be in the point source response? (HY)
+                self._source_location = {} # Should the poition information be in the point source response? (HY)
 
                 for name, source in point_sources.items():
                     coord = source.position.sky_coord
@@ -240,20 +238,19 @@ def set_model(self, model):
             # Convolve with spectrum
             # See also the Detector Response and Source Injector tutorials
             spectrum = source.spectrum.main.shape
-
-            total_expectation = self._psr[name].get_expectation(spectrum).project(['Em', 'Phi', 'PsiChi'])
-            # should it be like self._psr[name].get_expectation(spectrum) (without 'project')? (HY)
+
+            total_expectation = self._psr[name].get_expectation(spectrum)
+
+            # Save expected counts for source:
+            self._expected_counts[name] = copy.deepcopy(total_expectation)
 
             # Need to check if self._signal type is dense (i.e. 'Quantity') or sparse (i.e. 'COO').
             if type(total_expectation.contents) == u.quantity.Quantity:
-                total_expectation = total_expectation.contents.value
+                total_expectation = total_expectation.project(['Em', 'Phi', 'PsiChi']).contents.value
             elif type(total_expectation.contents) == COO:
-                total_expectation = total_expectation.contents.todense() 
+                total_expectation = total_expectation.project(['Em', 'Phi', 'PsiChi']).contents.todense() 
             else:
                 raise RuntimeError("Expectation is an unknown object")
-
-            # Save expected counts for source:
-            self._expected_counts[name] = copy.deepcopy(total_expectation)
 
             # Add source to signal and update source counter:
             if self.src_counter == 0:
@@ -266,13 +263,13 @@ def set_model(self, model):
         self._model = model
 
     def get_log_like(self):
-
         """
-        Return the value of the log-likelihood.
+        Calculate the log-likelihood.
         
         Returns
         ----------
-        log_like: float
+        log_like : float
+            Value of the log-likelihood
         """
 
         # Recompute the expectation if any parameter in the model changed
@@ -309,25 +306,25 @@ def get_log_like(self):
         return log_like
 
     def inner_fit(self):
-
         """
-        This fits nuisance parameters.
+        Required for 3ML fit.
         """
 
         return self.get_log_like()
 
     def _get_dwell_time_map(self, coord):
-
         """
-        Get the dwell time map of the source in the spacecraft frame.
+        Get the dwell time map of the source in the inertial (spacecraft) frame.
         
         Parameters
         ----------
-        coord: astropy.coordinates.SkyCoord; the coordinate of the target source.
+        coord : astropy.coordinates.SkyCoord
+            Coordinates of the target source
         
         Returns
         -------
-        dwell_time_map: mhealpy.containers.healpix_map.HealpixMap
+        dwell_time_map : mhealpy.containers.healpix_map.HealpixMap
+            Dwell time map
         """
 
         self._sc_orientation.get_target_in_sc_frame(target_name = self._name, target_coord = coord)
@@ -336,32 +333,26 @@ def _get_dwell_time_map(self, coord):
         return dwell_time_map
 
     def _get_scatt_map(self):
-
         """
         Get the spacecraft attitude map of the source in the inertial (spacecraft) frame.
         
-        Parameters
-        ----------
-        nside: int; resolution of scatt map
-        coordsys: BaseFrameRepresentation or str; coordinate system of map
-        
         Returns
         -------
-        scatt_map: cosipy.spacecraftfile.scatt_map.SpacecraftAttitudeMap
+        scatt_map : cosipy.spacecraftfile.scatt_map.SpacecraftAttitudeMap
         """
 
         scatt_map = self._sc_orientation.get_scatt_map(nside = self._dr.nside * 2, coordsys = 'galactic')
 
         return scatt_map
 
     def set_inner_minimization(self, flag: bool):
-
         """
-        Turn on the minimization of the internal COSI parameters.
+        Turn on the minimization of the internal COSI (nuisance) parameters.
         
         Parameters
         ----------
-        flag: bool; turns on and off the minimization  of the internal parameters
+        flag : bool
+            Turns on and off the minimization  of the internal parameters
         """
 
         self._fit_nuisance_params: bool = bool(flag)

cosipy/ts_map/fast_norm_fit.py

@@ -240,4 +240,4 @@ def solve(self, data, bkg, unit_excess):
             #Assumed to be a numerical error
             ts = 0
 
-        return (ts, norm, norm_err, failed)
+        return (ts, norm, norm_err, failed, iteration)