Fix positional selection (#118)

* BUGFIX: when there was no cluster within 1 arcsec we selected ALL noise datapoints instead of the ones within 1 arcsec

* bump version

* Because of the positional selection we have to deal with empty lightcurves

* plot binned lightcurve only when it is not empty

docs/source/conf.py

@@ -23,7 +23,7 @@
 author = 'Jannis Necker'
 
 # The full version, including alpha/beta/rc tags
-release = 'v0.4.2.post1'
+release = 'v0.4.3'
 
 
 # -- General configuration ---------------------------------------------------

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "timewise"
-version = "0.4.2.post1"
+version = "0.4.3"
 description = "A small package to download infrared data from the WISE satellite"
 authors = ["Jannis Necker <[email protected]>"]
 license = "MIT"

timewise/__init__.py

@@ -2,4 +2,4 @@
 from timewise.wise_bigdata_desy_cluster import WISEDataDESYCluster
 from timewise.parent_sample_base import ParentSampleBase
 
-__version__ = "0.4.2.post1"
+__version__ = "0.4.3"

timewise/wise_data_base.py

@@ -1626,18 +1626,14 @@ def calculate_position_mask(lightcurve, ra, dec, whitelist_region, return_all=Fa
             logger.debug(f"Found {len(cluster_separations)} clusters")
 
             # if there is no cluster or no cluster within 1 arcsec,
-            # we select all noise datapoints within 1 arcsec if there are any
-            if (
-                    len(cluster_separations) == 0
-                    or min(cluster_separations) > np.radians(whitelist_region / 3600)
-            ):
+            # only the datapoints within 1 arcsec are selected as we did above
+            if len(cluster_separations) == 0:
                 logger.debug("No cluster found. Selecting all noise datapoints within 1 arcsec.")
-                if len(cluster_separations) > 0:
-                    logger.debug(f"Closest cluster is at {cluster_separations} arcsec")
-                noise_mask = cluster_res.labels_ == -1
-                selected_indices |= set(lightcurve.index[data_mask][noise_mask])
+            elif min(cluster_separations) > np.radians(whitelist_region / 3600):
+                logger.debug(f"Closest cluster is at {cluster_separations} arcsec")
 
             # if there is a cluster within 1 arcsec, we select all datapoints belonging to that cluster
+            # in addition to the datapoints within 1 arcsec
             else:
                 closest_label = cluster_separations.argmin()
                 selected_cluster_mask = cluster_res.labels_ == closest_label

timewise/wise_data_by_visit.py

@@ -93,6 +93,10 @@ def calculate_epochs(self, f, e, visit_mask, counts, remove_outliers, outlier_ma
         :return: the epoch
         :rtype: float
         """
+
+        if len(f) == 0:
+            return [], [], [], [], [], []
+
         u_lims = pd.isna(e)
         nan_mask = pd.isna(f)
 
@@ -516,13 +520,14 @@ def plot_diagnostic_binning(
         self.parent_sample.plot_cutout(ind=ind, ax=axs[0], which=which, arcsec=arcsec, **kwargs)
 
         # plot the lightcurve
-        self._plot_lc(
-            lightcurve=binned_lightcurve,
-            unbinned_lc=lightcurve[position_mask],
-            lum_key=lum_key,
-            ax=axs[-1],
-            save=False
-        )
+        if len(binned_lightcurve) > 0:
+            self._plot_lc(
+                lightcurve=binned_lightcurve,
+                unbinned_lc=lightcurve[position_mask],
+                lum_key=lum_key,
+                ax=axs[-1],
+                save=False
+            )
         self._plot_lc(
             unbinned_lc=lightcurve[~position_mask],
             lum_key=lum_key,