Updates to docstrings

.readthedocs.yml

@@ -17,6 +17,7 @@ sphinx:
 formats: all
 
 # Optionally set the version of Python and requirements required to build your docs
+# Currently (Nov 2021), readthedocs does not support Python later than 3.8
 python:
   version: 3.8
   system_packages: true

docs/api_ref_local/descriptions.rst

@@ -14,4 +14,5 @@ generated instance of the ModelDescription (or SimpleModelDescription) class.
 
 
 .. automodule:: pyimfit.descriptions
+      :imported-members:
       :members:

docs/conf.py

@@ -22,7 +22,7 @@
 # -- Project information -----------------------------------------------------
 
 project = 'PyImfit'
-copyright = '2020-2021, Peter Erwin'
+copyright = '2019-2021, Peter Erwin'
 author = 'Peter Erwin'
 
 # The short X.Y version

docs/howto_generate_docs.txt

@@ -34,9 +34,10 @@ while editing them.
 2. Update rST files if needed: index.rst; api_ref/api_index.rst, other
 api-related .rst files in that directory.
 
-3. IF the Jupyter notebook file has been updated, generate a markdown version
+3. IF one or more the Jupyter notebook files have been updated, generate a markdown versions
 with
    $ jupyter nbconvert pyimfit_emcee.ipynb --to markdown
+   $ jupyter nbconvert pyimfit_bootstrap_BtoT.ipynb --to markdown
 
 4. Generate rST versions of the Markdown files (by running convert_md_to_rst.sh
 script), generate the Sphinx HTML files, extract the apidoc-generated bits of

docs/pyimfit_bootstrap_BtoT.ipynb

@@ -464,18 +464,11 @@
     "hist(b2t_values, bins=np.arange(0.14,0.2,0.0025));xlabel(r\"$B/T$\");ylabel(r\"$N$\")\n",
     "axvline(GetBtoT(imfit_fitter, p_bestfit), color='k')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -489,7 +482,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.6"
   },
   "toc": {
    "colors": {

docs/pyimfit_bootstrap_BtoT.md

@@ -244,10 +244,7 @@ axvline(GetBtoT(imfit_fitter, p_bestfit), color='k')
 
 
 
+
 ![png](pyimfit_bootstrap_BtoT_files/pyimfit_bootstrap_BtoT_37_1.png)
+
 
-
-
-```python
-
-```

docs/pyimfit_bootstrap_BtoT.rst

@@ -256,5 +256,3 @@ A histogram of the :math:`B/T` values (vertical line = best-fit value):
    :alt: png
 
    png
-
-.. code:: python

pyimfit/descriptions.py

@@ -316,14 +316,12 @@ def dict_to_FunctionDescription(cls, inputDict):
         Parameters
         ----------
         inputDict : dict
-            dict describing the function
-            Examples:
+            dict describing the function. Examples:
                 fDict = {'name': "Gaussian", 'label': "blob", 'parameters': p}
                 where p is a dict containing parameter names and values, e.g.
                 p = {'PA': 0.0, 'ell': 0.5, 'I_0': 100.0, 'sigma': 10.0}
                 OR
-                p = {'PA': [0.0, "fixed"], 'ell': [0.5, 0.1,0.8], 'I_0': [100.0, 10.0,1e3],
-                    'sigma': [10.0, 5.0,20.0]}
+                p = {'PA': [0.0, "fixed"], 'ell': [0.5, 0.1,0.8], 'I_0': [100.0, 10.0,1e3], 'sigma': [10.0, 5.0,20.0]}
 
         Returns
         -------
@@ -439,8 +437,7 @@ def getFunctionAsDict(self):
                 where p is a dict containing parameter names and values, e.g.
                 p = {'PA': 0.0, 'ell': 0.5, 'I_0': 100.0, 'sigma': 10.0}
                 OR
-                p = {'PA': [0.0, "fixed"], 'ell': [0.5, 0.1,0.8], 'I_0': [100.0, 10.0,1e3],
-                    'sigma': [10.0, 5.0,20.0]}
+                p = {'PA': [0.0, "fixed"], 'ell': [0.5, 0.1,0.8], 'I_0': [100.0, 10.0,1e3], 'sigma': [10.0, 5.0,20.0]}
         """
         # FIXME: write code for this!
         paramsDict = { param.name: param.getParamInfoList() for param in self._parameters }
@@ -567,6 +564,7 @@ def dict_to_FunctionSetDescription(cls, inputDict: dict):
             dict describing the function set
             {'X0': val-or-list, 'Y0': val-or-list, 'function_list': [list of dicts describing functions]}
             {'label': str, 'X0': val-or-list, 'Y0': val-or-list, 'function_list': [list of dicts describing functions]}
+
                 where "val-or-list" for X0 and Y0 is one of
                     value
                     [value, "fixed"]
@@ -886,6 +884,7 @@ def dict_to_ModelDescription(cls, inputDict: dict):
         inputDict : dict
             dict describing the model, with one required entry -- "function_sets" --
             and one optional entry -- "options"
+
                 "function_sets" : list of dict, each one specifying a function set,
                 suitable as input to FunctionSetDescription.dict_to_FunctionSetDescription()