Merge pull request #766 from dirac-institute/small_fixes

Small numpy changes

src/kbmod/analysis/plotting.py

@@ -477,7 +477,7 @@ def plot_time_series(values, times=None, indices=None, ax=None, figure=None, tit
     title : `str` or None, optional
         Title of the plot. `None` by default.
     """
-    y_values = np.array(values)
+    y_values = np.asarray(values)
 
     # If no axes were given, create a new figure.
     if ax is None:
@@ -486,15 +486,15 @@ def plot_time_series(values, times=None, indices=None, ax=None, figure=None, tit
 
     # If no valid indices are given, use them all.
     if indices is None:
-        indices = np.array([True] * len(values), dtype=bool)
+        indices = np.full(len(values), True, dtype=bool)
     else:
-        indices = np.array(indices, dtype=bool)
+        indices = np.asarray(indices, dtype=bool)
 
     # If the times are not given, then use linear spacing.
     if times is None:
         x_values = np.linspace(0, len(values) - 1, len(values), dtype=int)
     else:
-        x_values = np.array(times)
+        x_values = np.asarray(times)
 
     # Plot the data with the curve in blue, the valid points as blue dots,
     # and the invalid indices as smaller red dots.

src/kbmod/reprojection.py

@@ -627,7 +627,7 @@ def _get_first_psf_at_time(work_unit, time):
         If the time is not found in list of observation times in the work_unit,
         raise an error.
     """
-    obstimes = np.array(work_unit.get_all_obstimes())
+    obstimes = np.asarray(work_unit.get_all_obstimes())
 
     # if the time isn't in the list of times, raise an error.
     if time not in obstimes:

src/kbmod/results.py

@@ -532,7 +532,7 @@ def filter_rows(self, rows, label=""):
             return
 
         # Check if we are dealing with a mask of a list of indices.
-        rows = np.array(rows)
+        rows = np.asarray(rows)
         if rows.dtype == bool:
             if len(rows) != len(self.table):
                 raise ValueError(
@@ -627,7 +627,7 @@ def revert_filter(self, label=None, add_column=None):
 
         # If we don't have the tracking column yet, add it.
         if add_column is not None and add_column not in self.table.colnames:
-            self.table[add_column] = np.array([""] * len(self.table), dtype=str)
+            self.table[add_column] = np.full(len(self.table), "", dtype=str)
 
         # Make a list of tables to merge.
         table_list = [self.table]
@@ -748,7 +748,7 @@ def write_column(self, colname, filename):
             raise KeyError(f"Column {colname} missing from data.")
 
         # Save the column.
-        data = np.array(self.table[colname])
+        data = np.asarray(self.table[colname])
         np.save(filename, data, allow_pickle=False)
 
     def load_column(self, filename, colname):

src/kbmod/run_search.py

@@ -237,7 +237,7 @@ def run_search(self, config, stack, trj_generator=None, wcs=None, extra_meta=Non
                 "cluster_type": config["cluster_type"],
                 "cluster_eps": config["cluster_eps"],
                 "cluster_v_scale": config["cluster_v_scale"],
-                "times": np.array(mjds),
+                "times": np.asarray(mjds),
             }
             apply_clustering(keep, cluster_params)
             cluster_timer.stop()

src/kbmod/trajectory_explorer.py

@@ -85,8 +85,8 @@ def evaluate_linear_trajectory(self, x, y, vx, vy):
         result = Results.from_trajectories([trj])
 
         # Get the psi and phi curves and do the sigma_g filtering.
-        psi_curve = np.array([self.search.get_psi_curves(trj)])
-        phi_curve = np.array([self.search.get_phi_curves(trj)])
+        psi_curve = np.asarray([self.search.get_psi_curves(trj)])
+        phi_curve = np.asarray([self.search.get_phi_curves(trj)])
         obs_valid = np.full(psi_curve.shape, True)
         result.add_psi_phi_data(psi_curve, phi_curve, obs_valid)
 

src/kbmod/trajectory_utils.py

@@ -123,7 +123,7 @@ def trajectory_predict_skypos(trj, wcs, times):
     result : `astropy.coordinates.SkyCoord`
         A SkyCoord with the transformed locations.
     """
-    dt = np.array(times)
+    dt = np.asarray(times)
     dt -= dt[0]
 
     # Predict locations in pixel space.