doc tweaks.

AUTHORS.rst

@@ -1,26 +1 @@
-Author:
-
-- `Ben Johnson (Harvard) <https://github.com/bd-j>`_
-
-Direct contributions to the code base:
-
-- `Gabe Brammer <https://github.com/gbrammer>`_
-- `James Guillochon <https://github.com/guillochon>`_
-- `Joel Leja <https://github.com/jrleja>`_
-- `John Moustakas <https://github.com/moustakas>`_
-- `Josh Speagle <https://github.com/joshspeagle>`_
-
-Comments, corrections & suggestions:
-
-- Antara Basu-Zych
-- Kevin Bundy
-- Phill Cargile
-- Charlie Conroy
-- Johnny Greco
-- Song Huang
-- Gourav Khullar
-- Sidney Lower
-- Dylan Nelson
-- Imad Pasha
-- Dan Weisz
-- Tom Zick
+The list of contributors can be found `on GitHub <https://github.com/bd-j/prospector/graphs/contributors>`_.

doc/installation.rst

@@ -1,7 +1,8 @@
 Installation
 ============
 
-|Codename| itself is is pure python.  To install a released version of just |Codename|, use ``pip``
+|Codename| itself is is pure python.  To install a released version of just
+|Codename|, use ``pip``
 
 .. code-block:: shell
 
@@ -52,7 +53,8 @@ environment, use the following procedure:
 Requirements
 ------------
 
-|Codename| works with Python3, and requires `numpy <http://www.numpy.org>`_ and `SciPy <http://www.scipy.org>`_
+|Codename| works with ``python>=3.9``, and requires `numpy
+<http://www.numpy.org>`_ and `SciPy <http://www.scipy.org>`_
 
 You will also need:
 

doc/quickstart.rst

@@ -19,8 +19,8 @@ ingrediants; for more realistic usage see :ref:`demo` or the :ref:`tutorial`.
 Build an observation
 --------------------
 
-First we'll get some data, using ``astroquery`` to get SDSS photometry of a galaxy.  We'll also
-get spectral data so we know the redshift.
+First we'll get some data, using ``astroquery`` to get SDSS photometry of a
+galaxy.  We'll also get spectral data so we know the redshift.
 
 .. code:: python
 
@@ -49,7 +49,7 @@ for this example we do *not* attempt to fit the spectrum at the same time.
     filters = load_filters([f"sdss_{b}0" for b in bands])
     maggies = np.array([10**(-0.4 * cat[0][f"cModelMag_{b}"]) for b in bands])
     magerr = np.array([cat[0][f"cModelMagErr_{b}"] for b in bands])
-    magerr = np.clip(magerr, 0.05, np.inf)
+    magerr = np.hypot(magerr, 0.05)
 
     obs = dict(wavelength=None, spectrum=None, unc=None, redshift=shdus[2].data[0]["z"],
                maggies=maggies, maggies_unc=magerr * maggies / 1.086, filters=filters)
@@ -78,7 +78,8 @@ should be replaced or adjusted depending on your particular science question.
     print(model)
 
 In principle we could also add noise models for the spectral and photometric
-data, but we'll make the default assumption of independent Gaussian noise for the moment.
+data, but we'll make the default assumption of independent Gaussian noise for
+the moment.
 
 .. code:: python
 

doc/requirements.txt

@@ -1,3 +1,4 @@
+python >= 3.9
 numpy >= 1.16
 scipy >= 1.1.0
 matplotlib >= 3.0

pyproject.toml

@@ -5,7 +5,7 @@ authors = [
     { name="Ben Johnson", email="benjamin.johnson@cfa.harvard.edu" },
 ]
 readme = "README.md"
-requires-python = ">=3.7"
+requires-python = ">=3.9"
 license = { text = "MIT License" }
 classifiers = [
     "Development Status :: 5 - Production/Stable",
@@ -21,7 +21,6 @@ dependencies = ["numpy", "h5py"]
 [project.optional-dependencies]
 test = ["pytest", "pytest-xdist"]
 
-
 [tool.setuptools]
 packages = ["prospect",
             "prospect.models", "prospect.sources",