Merge pull request #158 from OMS-NetZero/conda-forge

add conda-forge info

CHANGELOG.rst

@@ -1,6 +1,11 @@
 Changelog
 ---------
 
+master
+------
+
+(`#158 <https://github.com/OMS-NetZero/FAIR/pull/158>`_) Add `fair` to `conda-forge`
+
 v2.1.4
 ------
 

README.md

@@ -3,7 +3,7 @@
 [![Documentation Status](https://readthedocs.org/projects/fair/badge/?version=latest)](http://fair.readthedocs.io/en/latest/?badge=latest)
 [![image](https://zenodo.org/badge/DOI/10.5281/zenodo.1340643.svg)](https://doi.org/10.5281/zenodo.1340643)
 [![image](https://codecov.io/gh/OMS-NetZero/FAIR/branch/master/graph/badge.svg)](https://codecov.io/gh/OMS-NetZero/FAIR)
-[![image](https://img.shields.io/pypi/v/fair)](https://pypi.org/project/fair/) [![Anaconda-Server Badge](https://anaconda.org/chrisroadmap/fair/badges/version.svg)](https://anaconda.org/chrisroadmap/fair)
+[![image](https://img.shields.io/pypi/v/fair)](https://pypi.org/project/fair/) [![Anaconda-Server Badge](https://anaconda.org/conda-forge/fair/badges/version.svg)](https://anaconda.org/conda-forge/fair)
 
 # FaIR
 
@@ -18,13 +18,19 @@ forcing scenarios.
 
 ## Installation
 
-### From the Python Package Index
+### From anaconda (recommended)
 
-    pip install fair
+**NEW!** from v2.1.4, `fair` is available on `conda-forge`:
+
+    conda install -c conda-forge fair
+
+Older versions of `fair` (1.6.2+, 2.1.0-4) can be installed from the `chrisroadmap` channel:
 
-### From anaconda
+    conda install -c chrisroadmap fair==X.Y.Z
 
-    conda install -c chrisroadmap fair
+### From the Python Package Index
+
+    pip install fair
 
 ### From source
 
@@ -64,14 +70,13 @@ looks right.
 
 We have produced IPCC AR6 Working Group 1 consistent probabilistic
 ensembles to run with. The calibration data can be obtained
-[here](https://doi.org/10.5281/zenodo.7694879). These parameter sets are
+[here](https://doi.org/10.5281/zenodo.7112539). These parameter sets are
 calibrated to CMIP6 models, run in a large Monte Carlo ensemble, and
 constrained based on observed and assessed climate metrics. For an
 example of how to use this calibration data set with SSP emissions, see
 [this
 example](https://docs.fairmodel.net/en/latest/examples/calibrated_constrained_ensemble.html).
-If you\'re writing a paper using FaIR, you should use these. There\'ll
-be a paper on this at some point, for now please cite the Zenodo DOI.
+If you\'re writing a paper using FaIR, you should use these. A paper describing this method has been submitted, but for now please cite the Zenodo DOI.
 
 ## Citation
 

docs/examples/basic_run_example.rst

@@ -31,7 +31,7 @@ A run is initialised as follows:
 
 ::
 
-    f = FAIR()
+   f = FAIR()
 
 To this we need to add some information about the time horizon of our
 model, forcers we want to run with, their configuration (and the
@@ -40,38 +40,38 @@ options:
 
 ::
 
-    f.define_time(2000, 2050, 1)
-    f.define_scenarios(['abrupt', 'ramp'])
-    f.define_configs(['high', 'central', 'low'])
-    f.define_species(species, properties)
-    f.ghg_method='Myhre1998'
+   f.define_time(2000, 2050, 1)
+   f.define_scenarios(['abrupt', 'ramp'])
+   f.define_configs(['high', 'central', 'low'])
+   f.define_species(species, properties)
+   f.ghg_method='Myhre1998'
 
 We generate some variables: emissions, concentrations, forcing,
 temperature etc.:
 
 ::
 
-    f.allocate()
+   f.allocate()
 
 which creates ``xarray`` DataArrays that we can fill in:
 
 ::
 
-    fill(f.emissions, 40, scenario='abrupt', specie='CO2 FFI')
-    ...
+   fill(f.emissions, 40, scenario='abrupt', specie='CO2 FFI')
+   ...
 
 Finally, the model is run with
 
 ::
 
-    f.run()
+   f.run()
 
 Results are stored within the ``FAIR`` instance as ``xarray`` DataArrays
 or Dataset, and can be obtained such as
 
 ::
 
-    print(fair.temperature)
+   print(fair.temperature)
 
 Multiple ``scenarios`` and ``configs`` can be supplied in a ``FAIR``
 instance, and due to internal parallelisation is the fastest way to run
@@ -255,7 +255,7 @@ the properties; but
 -  ``type`` defines the species type such as CO2, an aerosol precursor,
    or volcanic forcing; there’s around 20 pre-defined types in FaIR.
    Some can only be defined once in a run, some can have multiple
-   instances (e.g. ``f-gas``). See ``fair.structure.species`` for a
+   instances (e.g. ``f-gas``). See ``fair.structure.species`` for a
    list.
 -  ``input_mode``: how the model should be driven with this ``specie``.
    Valid values are ``emissions``, ``concentration``, ``forcing`` or
@@ -464,8 +464,8 @@ future.
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 This defines how the model responds to a forcing: the default behaviour
-is the three-layer energy balance model as described in Cummins et al.
-(2020). The number of layers can be changed in ``run_control``.
+is the three-layer energy balance model as described in Cummins et
+al. (2020). The number of layers can be changed in ``run_control``.
 
 ``climate_configs`` is an ``xarray`` Dataset.
 

docs/examples/n-layer-ebm.rst

@@ -5,7 +5,7 @@ This notebook shows examples of extending the 3-layer energy balance
 model to general n.
 
 For the two and three layer cases we’ll take the MLE estimates from
-Cummins et al. (2020) for HadGEM2-ES, and we’ll use the GISS forcing.
+Cummins et al. (2020) for HadGEM2-ES, and we’ll use the GISS forcing.
 Where n > 3 the data is fake.
 
 .. code:: ipython3

docs/install.rst

@@ -1,31 +1,32 @@
 Installation
 ============
 
-From the Python Package Index (PyPI)
-------------------------------------
+`fair` requires `python` 3.8-3.11. `python` 3.12 will probably work but is not extensively tested yet.
+
+From anaconda (recommended)
+-------------
 
-Requires `python` 3.8, 3.9, 3.10 or 3.11. Probably the easiest way to get up and running.
 ::
 
-    pip install fair
+    conda install -c conda-forge fair
 
-From anaconda
--------------
 
-::
+From the Python Package Index (PyPI)
+------------------------------------
 
-    conda install -c chrisroadmap fair
+::
 
+    pip install fair
 
 From GitHub
 -----------
 
-The latest release can be obtained from https://github.com/OMS-NetZero/FAIR/releases as zip or tarball files, or the most current unreleased version can be cloned from https://github.com/OMS-NetZero/FAIR. 
+The latest release can be obtained from https://github.com/OMS-NetZero/FAIR/releases as zip or tarball files, or the most current unreleased version can be cloned from https://github.com/OMS-NetZero/FAIR.
 
 To install::
 
     pip install -e .[dev]
-    
+
 This will also give access to the notebooks in the `examples` directory, so you can experiment with the model using these as a starting point. For this to work, you should launch notebooks from the same directory in which you install `FAIR` locally.
 
 Guide for developers