Suppress WCS warnings

CHANGELOG.md

@@ -13,6 +13,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Significant rewrite of the SPICE kernel file management, this rewrite is required
   so that future work can enable writing SPICE kernel files.
 
+### Fixed
+
+- Astropy WCS warnings are now suppressed.
+
 ### Removed
 
 - Removed support for SPK Files of type 3, these should be a trivial change from type 2

docs/tutorials/kona.rst

@@ -15,6 +15,7 @@ First we do some setup, including importing many needed packages.
     import matplotlib.pyplot as plt
     import numpy as np
     import datetime
+    import warnings
 
     import astropy
     from astropy.io import fits
@@ -55,7 +56,9 @@ calculated from the corners of the frame.
     # Build the corner position of the FOV in RA/DEC, and build those into vectors
     # The WCS will raise a warning because the FITs files produced by WISE use an
     # old format.
-    frame_wcs = WCS(frame.header, relax=True)
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore")
+        frame_wcs = WCS(frame.header)
     corners = []
     dx, dy = frame_wcs.pixel_shape
     for x, y in zip([0, 0, dx, dx], [0, dy, dy, 0]):

src/neospy/irsa.py

@@ -5,6 +5,7 @@
 import time
 import logging
 import numpy as np
+import warnings
 import pandas as pd  # type: ignore
 import matplotlib.pyplot as plt  # type: ignore
 from xml.etree import ElementTree
@@ -194,7 +195,11 @@ def plot_fits_image(fit, vmin=-3, vmax=7, cmap="bone_r"):
         Color map to use for the plot.
     """
     data = np.nan_to_num(fit.data)
-    wcs = WCS(fit.header, relax=True)
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore")
+        wcs = WCS(fit.header)
+
     if not plt.get_fignums():
         plt.figure(dpi=120, figsize=(6, 6), facecolor="w")
     ax = plt.subplot(projection=wcs)

src/neospy/wise.py

@@ -498,7 +498,10 @@ def plot_frames(
         except OSError:
             continue
         data = np.nan_to_num(fit.data)
-        wcs = WCS(fit.header, relax=True)
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore")
+            wcs = WCS(fit.header)
+
         ax = plt.subplot(2, 2, band, projection=wcs)
 
         data_no_bkg = data - np.median(data)