change from pandas to NumPy data structure

Author: danehkar

README.rst

@@ -9,37 +9,21 @@ pyEQUIB Python Package
 .. image:: https://travis-ci.org/equib/pyEQUIB.svg?branch=master
     :target: https://travis-ci.org/equib/pyEQUIB
     :alt: Build Status
-    
+
 .. image:: https://ci.appveyor.com/api/projects/status/b3gw6vgf8s0vu8nv?svg=true
     :target: https://ci.appveyor.com/project/danehkar/pyequib
     :alt: Build Status
-    
-.. image:: https://coveralls.io/repos/github/equib/pyEQUIB/badge.svg?branch=master
+
+.. image:: https://coveralls.io/repos/github/equib/pyEQUIB/badge.svg?
     :target: https://coveralls.io/github/equib/pyEQUIB?branch=master
     :alt: Coverage Status
-    
+
 .. image:: https://img.shields.io/badge/license-GPL-blue.svg
     :target: https://github.com/equib/pyEQUIB/blob/master/LICENSE
     :alt: GitHub license
-    
-.. image:: https://img.shields.io/conda/vn/conda-forge/pyequib.svg
-    :target: https://anaconda.org/conda-forge/pyequib
-    :alt: Anaconda Cloud
-    
-.. image:: https://readthedocs.org/projects/pyequib/badge/?version=latest
-    :target: https://pyequib.readthedocs.io/en/latest/?badge=latest
-    :alt: Documentation Status
-    
+
 .. image:: https://img.shields.io/badge/python-2.7%2C%203.8-blue.svg
     :alt: Support Python versions 2.7 and 3.8
-    
-.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4287576.svg
-    :target: https://doi.org/10.5281/zenodo.4287576
-    :alt: Zenodo
-    
-.. image:: http://joss.theoj.org/papers/10.21105/joss.02798/status.svg
-    :target: https://doi.org/10.21105/joss.02798
-    :alt: JOSS
 
 
 Description
@@ -58,7 +42,7 @@ Installation
 ============
 
 Dependent Python Packages
--------------------------
+----------------------
 
  This package requires the following packages:
 
@@ -82,11 +66,6 @@ To install the stable version, you can use the preferred installer program (pip)
 
     $ pip install pyequib
 
-or you can install it from the cross-platform package manager *conda*:
-
-.. code-block::
-
-    $ conda install -c conda-forge pyequib
 
 How to Use
 ==========
@@ -185,7 +164,7 @@ The Documentation of the Python functions provides in detail in the *API Documen
         abb5007 = pyequib.calc_abundance(temperature=temperature, density=density, 
                                          line_flux=iobs5007, atomic_levels=levels5007,
                                          elj_data=o_iii_elj, omij_data=o_iii_omij, aij_data=o_iii_aij, 
-                                         h_i_aeff_data=hi_rc_data.aeff)
+                                         h_i_aeff_data=hi_rc_data['aeff'][0])
         print('N(O^2+)/N(H+):', abb5007)
 
       which gives::
@@ -308,7 +287,7 @@ The Documentation of the Python functions provides in detail in the *API Documen
         density=np.float64(5000.0)
         pyequib.print_ionic(temperature=temperature, density=density,
                     elj_data=o_iii_elj, omij_data=o_iii_omij, aij_data=o_iii_aij,
-                    h_i_aeff_data=hi_rc_data.aeff)
+                    h_i_aeff_data=hi_rc_data['aeff'][0])
 
       which gives::
       
@@ -914,49 +893,5 @@ For more information on how to use the API functions from the pyEQUIB libray, pl
 
 References
 ==========
-* Danehkar, A. (2020). pyEQUIB Python Package, an addendum to proEQUIB: IDL Library for Plasma Diagnostics and Abundance Analysis. *J. Open Source Softw.*, **5**, 2798. doi:`10.21105/joss.02798 <https://doi.org/10.21105/joss.02798>`_ ads:`2020JOSS....5.2798D <https://ui.adsabs.harvard.edu/abs/2020JOSS....5.2798D>`_.
-
-* Danehkar, A. (2018). proEQUIB: IDL Library for Plasma Diagnostics and Abundance Analysis. *J. Open Source Softw.*, **3**, 899. doi:`10.21105/joss.00899 <https://doi.org/10.21105/joss.00899>`_ ads:`2018JOSS....3..899D <https://ui.adsabs.harvard.edu/abs/2018JOSS....3..899D>`_.
-
-
-Citation
-========
-
-Using **pyEQUIB** in a scholarly publication? Please cite thess papers:
-
-.. code-block:: bibtex
-
-   @article{Danehkar2020,
-     author = {{Danehkar}, Ashkbiz},
-     title = {pyEQUIB Python Package, an addendum to proEQUIB: IDL Library for Plasma Diagnostics and Abundance Analysis},
-     journal = {Journal of Open Source Software},
-     volume = {5},
-     number = {55},
-     pages = {2798},
-     year = {2020},
-     doi = {10.21105/joss.02798}
-   }
-
-   @article{Danehkar2018,
-     author = {{Danehkar}, Ashkbiz},
-     title = {proEQUIB: IDL Library for Plasma Diagnostics and Abundance Analysis},
-     journal = {Journal of Open Source Software},
-     volume = {3},
-     number = {32},
-     pages = {899},
-     year = {2018},
-     doi = {10.21105/joss.00899}
-   }
-
-Learn More
-==========
+* Danehkar, A. (2018). proEQUIB: IDL Library for Plasma Diagnostics and Abundance Analysis. *J. Open Source Softw.*, **3**, 899. doi:`10.21105/joss.00899 <https://doi.org/10.21105/joss.00899>`_  ads:`2018JOSS....3..899D <https://ui.adsabs.harvard.edu/abs/2018JOSS....3..899D>`_.
 
-==================  =============================================
-**Documentation**   https://pyequib.readthedocs.io/
-**Repository**      https://github.com/equib/pyEQUIB
-**Issues & Ideas**  https://github.com/equib/pyEQUIB/issues
-**Conda-Forge**     https://anaconda.org/conda-forge/pyequib
-**PyPI**            https://pypi.org/project/pyequib/
-**DOI**             `10.21105/joss.02798 <https://doi.org/10.21105/joss.02798>`_
-**Archive**         `10.5281/zenodo.4287576 <https://doi.org/10.5281/zenodo.4287576>`_
-==================  =============================================

examples/cel_abundance_example.py

@@ -41,13 +41,12 @@
 print('Emissivity(O III 5007):', emis)
 
 abb5007 = pyequib.calc_abundance(temperature=temperature, density=density, line_flux=iobs5007, atomic_levels=levels5007,
-                         elj_data=o_iii_elj, omij_data=o_iii_omij, aij_data=o_iii_aij, h_i_aeff_data=hi_rc_data.aeff)
+                         elj_data=o_iii_elj, omij_data=o_iii_omij, aij_data=o_iii_aij, h_i_aeff_data=hi_rc_data['aeff'][0])
 print('N(O^2+)/N(H+):', abb5007)
 
 nlj = pyequib.calc_populations(temperature=temperature, density=density, elj_data=o_iii_elj, omij_data=o_iii_omij, aij_data=o_iii_aij)
 print('Atomic Level Populations:', nlj)
 
-
 n_crit = pyequib.calc_crit_density(temperature=temperature, elj_data=o_iii_elj, omij_data=o_iii_omij, aij_data=o_iii_aij)
 print('Critical Densities:', n_crit)
 
@@ -58,7 +57,7 @@
 
 pyequib.print_ionic(temperature=temperature, density=density,
             elj_data=o_iii_elj, omij_data=o_iii_omij, aij_data=o_iii_aij,
-            h_i_aeff_data=hi_rc_data.aeff)
+            h_i_aeff_data=hi_rc_data['aeff'][0])
 #
 # --- End MAIN program. ---------------
 

examples/cel_diagnostics_example.py

@@ -21,7 +21,6 @@
 atom_aij_file = os.path.join(base_dir,data_dir, 'AtomAij.fits')
 atom_rc_sh95_file = os.path.join(base_dir,data_rc_dir, 'rc_SH95.fits')
 
-
 atom = 'h'
 ion = 'ii' # H I Rec
 hi_rc_data = atomneb.read_aeff_sh95(atom_rc_sh95_file, atom, ion)
@@ -30,7 +29,7 @@
 ion = 'ii'
 s_ii_elj = atomneb.read_elj(atom_elj_file, atom, ion, level_num=5) # read Energy Levels (Ej)
 s_ii_omij = atomneb.read_omij(atom_omij_file, atom, ion) # read Collision Strengths (Omegaij)
-s_ii_aij = atomneb.read_aij(atom_aij_file, atom, ion) # read Transition Probabilities (Aij)\
+s_ii_aij = atomneb.read_aij(atom_aij_file, atom, ion) # read Transition Probabilities (Aij)
 
 upper_levels = '1,2,1,3/'
 lower_levels = '1,5/'
@@ -62,6 +61,6 @@
 
 pyequib.print_ionic(temperature=temperature, density=density,
             elj_data=s_ii_elj, omij_data=s_ii_omij, aij_data=s_ii_aij,
-            h_i_aeff_data=hi_rc_data.aeff)
+            h_i_aeff_data=hi_rc_data['aeff'][0])
 
 # --- End MAIN program. ---------------

examples/orl_abundance_example.py

@@ -63,9 +63,9 @@
 ion = 'iii' # Ne II
 ne_ii_rc_data = atomneb.read_aeff_collection(atom_rc_all_file, atom, ion)
 
-h_i_aeff_data = h_i_rc_data.aeff
-he_i_aeff_data = he_i_rc_data.aeff
-he_ii_aeff_data = he_ii_rc_data.aeff
+h_i_aeff_data = h_i_rc_data['aeff'][0]
+he_i_aeff_data = he_i_rc_data['aeff'][0]
+he_ii_aeff_data = he_ii_rc_data['aeff'][0]
 
 temperature = np.float64(10000.0)
 density = np.float64(5000.0)