Clean-up of the docs (#767)

RELEASE_NOTES.md

@@ -16,7 +16,7 @@ When importing an xlsx file created with pyam < 2.0, which has an "exclude" colu
 
 - [#763](https://github.com/IAMconsortium/pyam/pull/763) Implement a fix against carrying over unused levels when initializing from an indexed pandas object
 - [#759](https://github.com/IAMconsortium/pyam/pull/759) Excise "exclude" column from meta and add a own attribute
-- [#747](https://github.com/IAMconsortium/pyam/pull/747) Drop support for Python 3.7 #747
+- [#747](https://github.com/IAMconsortium/pyam/pull/747) Drop support for Python 3.7
 
 # Release v1.9.0
 

docs/R_tutorials/pyam_R_tutorial.ipynb

@@ -456,7 +456,7 @@
     "## Query data from an IIASA data resource\n",
     "\n",
     "The IIASA *Energy, Climate, and Environment* Program hosts a suite of **Scenario Explorer** instances and related infrastructure to support analysis of integrated-assessment pathways in IPCC reports and model comparison projects. \n",
-    "High-profile use cases include the [IAMC 1.5°C Scenario Explorer hosted by IIASA](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer) supporting the *IPCC Special Report on Global Warming of 1.5°C* (SR15) and the Horizon 2020 project [CD-LINKS](https://data.ene.iiasa.ac.at/cd-links).\n",
+    "High-profile use cases include the [IAMC 1.5°C Scenario Explorer hosted by IIASA](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer) supporting the *IPCC Special Report on Global Warming of 1.5°C* (SR15) and the Horizon 2020 project [CD-LINKS](https://data.ece.iiasa.ac.at/cd-links).\n",
     "\n",
     "The **pyam** package can retrieve scenario data directly from any **Scenario Explorer** database instance\n",
     "hosted by IIASA for use in your processing and analysis workflows."

docs/data.rst

@@ -28,7 +28,7 @@ of any scenario data provided in this format.
 .. figure:: _static/iamc_template.png
 
    Illustrative example of IAMC-format timeseries data |br|
-   via the `IAMC 1.5°C Scenario Explorer`_ (:cite:`Huppmann:2019:scenario-data`)
+   via the `IAMC 1.5°C Scenario Explorer`_
 
 .. _`IAMC 1.5°C Scenario Explorer`: https://data.ece.iiasa.ac.at/iamc-1.5c-explorer
 

docs/examples/plot_sankey.py

@@ -18,9 +18,9 @@
 #
 # .. _SR15: http://ipcc.ch/sr15/
 #
-# .. _`IAMC 1.5°C Scenario Explorer and Data hosted by IIASA` : https://data.ene.iiasa.ac.at/iamc-1.5c-explorer
+# .. _`IAMC 1.5°C Scenario Explorer and Data hosted by IIASA` : https://data.ece.iiasa.ac.at/iamc-1.5c-explorer
 #
-# .. _License : https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/license
+# .. _License : https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/#/license
 #
 # If you haven't cloned the **pyam** GitHub repository to your machine,
 # you can download the data file from

docs/index.rst

@@ -84,9 +84,9 @@ a timeseries format for *yearly data* developed and used by the
 .. figure:: _static/iamc_template.png
 
    Illustrative example of IAMC-format timeseries data |br|
-   via the `IAMC 1.5°C Scenario Explorer`_ (:cite:`Huppmann:2019:scenario-data`)
+   via the `IAMC 1.5°C Scenario Explorer`_
 
-.. _`IAMC 1.5°C Scenario Explorer`: https://data.ene.iiasa.ac.at/iamc-1.5c-explorer
+.. _`IAMC 1.5°C Scenario Explorer`: https://data.ece.iiasa.ac.at/iamc-1.5c-explorer
 
 Subannual time resolution
 ^^^^^^^^^^^^^^^^^^^^^^^^^

docs/tutorials/iiasa_dbs.ipynb

@@ -7,7 +7,7 @@
     "# Read directly from IIASA data resources\n",
     "\n",
     "The IIASA *Energy, Climate, and Environment* Program hosts a suite of **Scenario Explorer** instances and related infrastructure to support analysis of integrated-assessment pathways in IPCC reports and model comparison projects. \n",
-    "High-profile use cases include the [IAMC 1.5°C Scenario Explorer hosted by IIASA](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer) supporting the *IPCC Special Report on Global Warming of 1.5°C* (SR15) and the Horizon 2020 project [CD-LINKS](https://data.ene.iiasa.ac.at/cd-links).\n",
+    "High-profile use cases include the [IAMC 1.5°C Scenario Explorer hosted by IIASA](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer) supporting the *IPCC Special Report on Global Warming of 1.5°C* (SR15) and the Horizon 2020 project [CD-LINKS](https://data.ece.iiasa.ac.at/cd-links).\n",
     "\n",
     "IIASA's [modeling platform infrastructure](http://software.ene.iiasa.ac.at/ixmp-server) and the Scenario Explorer UI is not only a great resource on its own, but it also allows the underlying datasets to be directly queried.\n",
     "**pyam** takes advantage of this ability to allow you to easily pull data and work with it in your Python data processing and analysis workflow."
@@ -61,7 +61,7 @@
    "metadata": {},
    "source": [
     "In this example, we will be retrieving data from the *IAMC 1.5°C Scenario Explorer hosted by IIASA*\n",
-    "([link](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer)),\n",
+    "([link](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer)),\n",
     "which provides the quantitative scenario ensemble underpinning\n",
     "the *IPCC Special Report on Global Warming of 1.5C* (SR15).\n",
     "\n",

docs/tutorials/pyam_first_steps.ipynb

@@ -47,9 +47,9 @@
     "\n",
     "The timeseries data used in this tutorial is a partial snapshot of the scenario ensemble\n",
     "compiled for the IPCC's *Special Report on Global Warming of 1.5°C* ([SR15](http://ipcc.ch/sr15/)).\n",
-    "The complete scenario ensemble data is publicly available from the [IAMC 1.5°C Scenario Explorer and Data hosted by IIASA](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).  \n",
+    "The complete scenario ensemble data is publicly available from the [IAMC 1.5°C Scenario Explorer and Data hosted by IIASA](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer).  \n",
     "\n",
-    "Please read the [License](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/license) page of the IAMC 1.5°C Scenario Explorer before using the full scenario data for scientific analysis or other work.\n",
+    "Please read the [License](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/#/license) page of the IAMC 1.5°C Scenario Explorer before using the full scenario data for scientific analysis or other work.\n",
     "\n",
     "<img style=\"float: right; margin: 10px;\" src=\"_static/cdlinks_logo.png\">\n",
     "\n",
@@ -61,7 +61,7 @@
     " - the \"Faster Transition Scenario\" from the IEA's [World Energy Outlook 2017](https://www.oecd-ilibrary.org/energy/world-energy-outlook-2017_weo-2017-en),\n",
     " - the \"1.0\" scenario submitted by the GENeSYS-MOD team ([Löffler et al., 2017](https://doi.org/10.3390/en10101468))\n",
     "\n",
-    "Please refer to the [About](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer/#/about) page of the *IAMC 1.5°C Scenario Explorer* for references and additional information.\n",
+    "Please refer to the [About](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer/#/about) page of the *IAMC 1.5°C Scenario Explorer* for references and additional information.\n",
     "\n",
     "<div class=\"alert alert-warning\">\n",
     "\n",
@@ -75,7 +75,7 @@
     "> D. Huppmann, E. Kriegler, V. Krey, K. Riahi, J. Rogelj, K. Calvin, F. Humpenoeder, A. Popp, S. K. Rose, J. Weyant, et al.  \n",
     "> *IAMC 1.5°C Scenario Explorer and Data hosted by IIASA* (release 2.0)  \n",
     "> Integrated Assessment Modeling Consortium & International Institute for Applied Systems Analysis, 2019.  \n",
-    "> doi: [10.5281/zenodo.3363345](https://doi.org/10.5281/zenodo.3363345) | url: [data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer)\n",
+    "> doi: [10.5281/zenodo.3363345](https://doi.org/10.5281/zenodo.3363345) | url: [data.ece.iiasa.ac.at/iamc-1.5c-explorer](https://data.ece.iiasa.ac.at/iamc-1.5c-explorer)\n",
     "\n",
     "***"
    ]

pyam/core.py

@@ -615,18 +615,17 @@ def pivot_table(
         Parameters
         ----------
         index : str or list of str
-            rows for Pivot table
+            Rows for Pivot table
         columns : str or list of str
-            columns for Pivot table
-        values : str, default 'value'
-            dataframe column to aggregate or count
-        aggfunc : str or function, default 'count'
-            function used for aggregation,
-            accepts 'count', 'mean', and 'sum'
-        fill_value : scalar, default None
-            value to replace missing values with
-        style : str, default None
-            output style for pivot table formatting
+            Columns for Pivot table
+        values : str, optional
+            Dataframe column to aggregate or count
+        aggfunc : str or function, optional
+            Function used for aggregation, accepts 'count', 'mean', and 'sum'
+        fill_value : scalar, optional
+            Value to replace missing values
+        style : str, optional
+            Output style for pivot table formatting,
             accepts 'highlight_not_max', 'heatmap'
         """
         index = [index] if isstr(index) else index
@@ -768,7 +767,7 @@ def as_pandas(self, meta_cols=True):
 
         Parameters
         ----------
-        meta_cols : list, default None
+        meta_cols : list, optional
             join `data` with all `meta` columns if True (default)
             or only with columns in list, or return copy of `data` if False
         """
@@ -1449,7 +1448,7 @@ def check_aggregate(
         ----------
         variable : str or list of str
             Variable(s) checked for matching aggregation of sub-categories.
-        components : list of str, default None
+        components : list of str, optional
             List of variables to aggregate, defaults to sub-categories of `variable`.
         method : func or str, optional
             Method to use for aggregation,
@@ -2865,15 +2864,15 @@ def filter_by_meta(data, df, join_meta=False, **kwargs):
 
     Parameters
     ----------
-    data : pandas.DataFrame
-        :class:`pandas.DataFrame` to which meta columns are to be joined,
+    data : :class:`pandas.DataFrame`
+        Data to which meta columns are to be joined,
         index or columns must include `['model', 'scenario']`
-    df : IamDataFrame
+    df : :class:`IamDataFrame`
         IamDataFrame from which meta columns are filtered and joined (optional)
-    join_meta : bool, default False
+    join_meta : bool, optional
         join selected columns from `df.meta` on `data`
     kwargs
-        meta columns to be filtered/joined, where `col=...` applies filters
+        Meta columns to be filtered/joined, where `col=...` applies filters
         with the given arguments (using :meth:`utils.pattern_match`).
         Using `col=None` joins the column without filtering (setting col
         to nan if `(model, scenario)` not in `df.meta.index`)
@@ -2923,7 +2922,7 @@ def compare(
     ----------
     left, right : IamDataFrames
         two :class:`IamDataFrame` instances to be compared
-    left_label, right_label : str, default `left`, `right`
+    left_label, right_label : str, optional
         column names of the returned :class:`pandas.DataFrame`
     drop_close : bool, optional
         remove all data where `left` and `right` are close

pyam/utils.py

@@ -522,8 +522,8 @@ def find_depth(data, s="", level=None):
         IAMC-style variables
     s : str, default ''
         remove leading `s` from any variable in `data`
-    level : int or str, default None
-        if None, return depth (number of ``|``); else, return list of booleans
+    level : int or str, optional
+        If None, return depth (number of ``|``); else, return list of booleans
         whether depth satisfies the condition (equality if level is int,
         >= if ``.+``,  <= if ``.-``)
     """