bug fix for numpy 2.0 and single exp obs

README.md

@@ -21,6 +21,11 @@ Python environment. The general approach is as follows:
    template files and underlying methodology that is used in the IDL SolarSoft
    environment.
 
+## Citing EISPAC
+
+If you use EISPAC in your reaserch, please consider citing the
+[JOSS paper](https://joss.theoj.org/papers/10.21105/joss.04914).
+
 ## Getting Started
 
 * Install using PIP (recommended) or by manually downloading this repo.
@@ -120,8 +125,5 @@ Here, in no particular order, is a list of some things that may be added in futu
 * Expanded documentation
 * More unit and integration tests
 * More detailed logging (with option to send all log information to a file)
-* Scripts for quickly viewing data and spectra fits
-* Scripts and routines for creating new fit templates
 * Consider adding a subclass of `NDCubeSequence` which can hold multiple spectral windows
 * Consider storing the output fit parameters in another `NDCube`
-* Restructure project to use the Sunpy affiliated package template?

docs/guide/06-acknowledge.rst

@@ -1,6 +1,34 @@
-Acknowledgments
-===============
+Acknowledgments and Citation
+============================
 
 This work was sponsored by NASA’s Hinode project. Hinode is a Japanese
 mission developed and launched by ISAS/JAXA, with NAOJ as domestic
 partner and NASA and STFC (UK) as international partners.
+
+Citing EISPAC
+-------------
+
+If you use EISPAC in a research project, please cite the code in any 
+publications or presentations of that work. This can be as simple as adding
+"this research used version X.Y.Z of the of EISPAC software (citation)" to
+your methods, software, or acknowledgements section.
+
+Below is the bibtex code for the current EISPAC paper.
+
+.. code:: bibtex
+
+    @ARTICLE{Weberg2023,
+        author = {{Weberg}, Micah and {Warren}, Harry and {Crump}, Nicholas and {Barnes}, Will},
+            title = "{EISPAC - The EIS Python Analysis Code}",
+        journal = {The Journal of Open Source Software},
+        keywords = {Python, astronomy, solar physics, spectroscopy},
+            year = 2023,
+            month = may,
+        volume = {8},
+        number = {85},
+            eid = {4914},
+            pages = {4914},
+            doi = {10.21105/joss.04914},
+        adsurl = {https://ui.adsabs.harvard.edu/abs/2023JOSS....8.4914W},
+        adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+    }

eispac/core/read_cube.py

@@ -343,9 +343,12 @@ def read_cube(filename=None, window=0, exp_set='sum', apply_radcal=True, radcal=
 
     ### (7) Calculate average exposure time and cadence
     avg_exptime = np.mean(meta['duration'])
-    diff_date_obs = np.diff(meta['date_obs'].astype('datetime64'))
-    diff_date_obs = diff_date_obs / np.timedelta64(1, 's')
-    avg_cad = np.mean(np.abs(diff_date_obs))
+    if index['nexp'] > 1:
+        diff_date_obs = np.diff(meta['date_obs'].astype('datetime64'))
+        diff_date_obs = diff_date_obs / np.timedelta64(1, 's')
+        avg_cad = np.mean(np.abs(diff_date_obs))
+    else:
+        avg_cad = avg_exptime # default for single exposure obs
 
     ### (8) Create a new header dict updated values
     # Note: the order of axes here is the same as the original fits index

eispac/core/read_fit.py

@@ -123,8 +123,11 @@ def read_fit(filename, verbose=False):
     # Restore the fit function
     fit_result.fit_func = getattr(fit_fns, fit_result.func_name)
 
-    # Make sure the .fit['Line_ids'] is ALWAYS an array (for code consistency)
+    # Make sure certain metadata are ALWAYS kept as arrays (for code consistency)
     fit_result.fit['line_ids'] = np.atleast_1d(fit_result.fit['line_ids'])
+    for META_KEY in ['date_obs', 'duration']:
+        if META_KEY in fit_result.meta.keys(): # need check for very old files
+            fit_result.meta[META_KEY] = np.atleast_1d(fit_result.meta[META_KEY])
 
     # If version number is missing, try to guess it
     if 'eispac_version' not in top_key_list:

eispac/core/save_fit.py

@@ -90,9 +90,9 @@ def walk_and_save(hdf5_group, data_obj, data_key, print_str, verbose=False):
         if verbose:
             print('   ', print_str)
         temp_data = data_obj
-        if isinstance(data_obj, (np.ndarray, np.str_)):
-            if str(data_obj.dtype).startswith('U', 1):
-                temp_data = data_obj.astype(np.string_)
+        if hasattr(data_obj, 'dtype'):
+            if 'U' in str(data_obj.dtype):
+                temp_data = data_obj.astype(np.bytes_)
         hdf5_group.create_dataset(str(data_key), data=temp_data)
 
 # function to save fit dictionary