Prepping for RTD

.rtd-environment.yml

@@ -10,5 +10,6 @@ dependencies:
     - matplotlib
     - numpy
     - sphinx-astropy
+    - h5py
 #   - pip:
 #       - dependency_from_pip

.travis.yml

@@ -38,7 +38,7 @@ env:
         # List runtime dependencies for the package that are available as conda
         # packages here.
         - CONDA_DEPENDENCIES=''
-        - CONDA_DEPENDENCIES_DOC='sphinx-astropy'
+        - CONDA_DEPENDENCIES_DOC='sphinx-astropy h5py'
 
         # List other runtime dependencies for the package that are available as
         # pip packages here.

MANIFEST.in

@@ -8,8 +8,6 @@ include arcesetc/tests/coveragerc
 recursive-include arcesetc *.pyx *.c *.pxd
 recursive-include docs *
 recursive-include licenses *
-recursive-include cextern *
-recursive-include scripts *
 
 prune build
 prune docs/_build

arcesetc/__init__.py

@@ -20,5 +20,5 @@ class UnsupportedPythonError(Exception):
 
 if not _ASTROPY_SETUP_:
     # For egg_info test builds to pass, put package imports here.
-    from .utils import *
     from .plots import *
+    from .utils import *

arcesetc/plots.py

@@ -1,12 +1,11 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import astropy.units as u
+from .utils import (closest_target, archive, scale_flux,
+                    get_closest_order, matrix_row_to_spectrum,
+                    sn_to_exp_time)
 
-from .utils import (_closest_target, available_sptypes, archive,
-                    _get_closest_order, _matrix_row_to_spectrum, _scale_flux,
-                    _sn_to_exp_time)
-
-__all__ = ['plot_order_counts', 'plot_order_sn', 'available_sptypes']
+__all__ = ['plot_order_counts', 'plot_order_sn']
 
 
 @u.quantity_input(exp_time=u.s, wavelength=u.Angstrom)
@@ -49,21 +48,21 @@ def plot_order_counts(sptype, wavelength, V, exp_time=None,
         Exposure time input, or computed to achieve S/N ratio
         ``signal_to_noise`` at wavelength ``wavelength``
     """
-    target, closest_spectral_type = _closest_target(sptype)
+    target, closest_spectral_type = closest_target(sptype)
 
     matrix = archive[target][:]
 
-    closest_order = _get_closest_order(matrix, wavelength)
-    wave, flux = _matrix_row_to_spectrum(matrix, closest_order)
-    flux *= _scale_flux(archive[target], V)
+    closest_order = get_closest_order(matrix, wavelength)
+    wave, flux = matrix_row_to_spectrum(matrix, closest_order)
+    flux *= scale_flux(archive[target], V)
 
     fig, ax = plt.subplots()
 
     if exp_time is not None and signal_to_noise is None:
         flux *= exp_time.to(u.s).value
 
     elif exp_time is None and signal_to_noise is not None:
-        exp_time = _sn_to_exp_time(wave, flux, wavelength, signal_to_noise)
+        exp_time = sn_to_exp_time(wave, flux, wavelength, signal_to_noise)
         flux *= exp_time.value
     else:
         raise ValueError("Supply either the `exp_time` or the "
@@ -120,20 +119,20 @@ def plot_order_sn(sptype, wavelength, V, exp_time=None, signal_to_noise=None,
         Exposure time input, or computed to achieve S/N ratio
         ``signal_to_noise`` at wavelength ``wavelength``
     """
-    target, closest_spectral_type = _closest_target(sptype)
+    target, closest_spectral_type = closest_target(sptype)
 
     matrix = archive[target][:]
 
-    closest_order = _get_closest_order(matrix, wavelength)
-    wave, flux = _matrix_row_to_spectrum(matrix, closest_order)
-    flux *= _scale_flux(archive[target], V)
+    closest_order = get_closest_order(matrix, wavelength)
+    wave, flux = matrix_row_to_spectrum(matrix, closest_order)
+    flux *= scale_flux(archive[target], V)
 
     fig, ax = plt.subplots()
 
     if exp_time is not None:
         flux *= exp_time.to(u.s).value
     elif exp_time is None and signal_to_noise is not None:
-        exp_time = _sn_to_exp_time(wave, flux, wavelength, signal_to_noise)
+        exp_time = sn_to_exp_time(wave, flux, wavelength, signal_to_noise)
         flux *= exp_time.value
     else:
         raise ValueError("Supply either the `exp_time` or the "

arcesetc/utils.py

@@ -1,4 +1,3 @@
-
 import numpy as np
 from json import load
 import os
@@ -18,7 +17,7 @@
 temps = np.array([sptype_to_temp[key] for key in spectral_types])
 
 
-def _closest_sptype(sptype):
+def closest_sptype(sptype):
     """
     Return closest spectral type in the archive.
 
@@ -35,7 +34,7 @@ def _closest_sptype(sptype):
     return spectral_types[np.argmin(np.abs(sptype_to_temp[sptype] - temps))]
 
 
-def _closest_target(sptype):
+def closest_target(sptype):
     """
     Return target with the closest spectral type in the archive.
 
@@ -51,7 +50,7 @@ def _closest_target(sptype):
     closest_spectral_type : str
         Closest spectral type available in the archive
     """
-    closest_spectral_type = _closest_sptype(sptype)
+    closest_spectral_type = closest_sptype(sptype)
     return sptypes[closest_spectral_type], closest_spectral_type
 
 
@@ -67,7 +66,7 @@ def available_sptypes():
     return sorted(spectral_types)
 
 
-def _get_closest_order(matrix, wavelength):
+def get_closest_order(matrix, wavelength):
     """
     Return the spectral order index closest to wavelength ``wavelength``.
 
@@ -86,7 +85,7 @@ def _get_closest_order(matrix, wavelength):
     return np.argmin(np.abs(matrix[:, 0] - wavelength.to(u.Angstrom).value))
 
 
-def _matrix_row_to_spectrum(matrix, closest_order):
+def matrix_row_to_spectrum(matrix, closest_order):
     """
     Given a ``matrix`` from the archive and a spectral order index
     ``closest_order``, return the spectrum (wavelength and flux).
@@ -113,7 +112,7 @@ def _matrix_row_to_spectrum(matrix, closest_order):
     return wave, flux
 
 
-def _scale_flux(dataset, V):
+def scale_flux(dataset, V):
     """
     Parameters
     ----------
@@ -127,7 +126,7 @@ def _scale_flux(dataset, V):
     return magnitude_scaling
 
 
-def _sn_to_exp_time(wave, flux, wavelength, signal_to_noise):
+def sn_to_exp_time(wave, flux, wavelength, signal_to_noise):
     """
     Calculate the required exposure time to achieve signal-to-noise ratio
     ``signal_to_noise`` given the count rates ``flux`` as a function of
@@ -182,13 +181,13 @@ def signal_to_noise_to_exp_time(sptype, wavelength, V, signal_to_noise):
         Exposure time input, or computed to achieve S/N ratio
         ``signal_to_noise`` at wavelength ``wavelength``
     """
-    target, closest_spectral_type = _closest_target(sptype)
+    target, closest_spectral_type = closest_target(sptype)
 
     matrix = archive[target][:]
 
-    closest_order = _get_closest_order(matrix, wavelength)
-    wave, flux = _matrix_row_to_spectrum(matrix, closest_order)
-    flux *= _scale_flux(archive[target], V)
+    closest_order = get_closest_order(matrix, wavelength)
+    wave, flux = matrix_row_to_spectrum(matrix, closest_order)
+    flux *= scale_flux(archive[target], V)
 
-    exp_time = _sn_to_exp_time(wave, flux, wavelength, signal_to_noise)
+    exp_time = sn_to_exp_time(wave, flux, wavelength, signal_to_noise)
     return exp_time

setup.cfg

@@ -33,10 +33,10 @@ description = Exposure time calculator for APO/ARCES
 long_description = Calculate S/N and exposure times for stellar spectroscopy with the ARC Echelle Spectrograph (ARCES) on the 3.5 m Telescope at Apache Point Observatory
 author = Brett Morris & Trevor Dorn-Wallenstein
 author_email = brettmorris21@gmail.com
-license = Other
-url = https://github.com/bmorris3/ARCES-ETC
+license = MIT
+url = https://github.com/bmorris3/arcesetc
 edit_on_github = False
-github_project = bmorris3/ARCES-ETC
+github_project = bmorris3/arcesetc
 # install_requires should be formatted as a comma-separated list, e.g.:
 # install_requires = astropy, scipy, matplotlib
 install_requires = astropy