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+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../external_models.html">Integrating external models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../external_models.html#running-an-external-model-on-the-command-line">Running an external model on the command line</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../external_models.html#running-an-external-model-in-python">Running an external model in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/wrapping_gluonts.html">Wrapping GluonTS models</a></li>
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+          <h1>All modules for which code is available</h1>
+<ul><li><a href="climate_health/api_types.html">climate_health.api_types</a></li>
+<li><a href="climate_health/spatio_temporal_data/temporal_dataclass.html">climate_health.spatio_temporal_data.temporal_dataclass</a></li>
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diff --git a/docs/_sources/api_docs.rst.txt b/docs/_sources/api_docs.rst.txt
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+API-docs
+========
+
diff --git a/docs/_sources/architecture_meeting.md.txt b/docs/_sources/architecture_meeting.md.txt
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+# Architecture Meeting for CHAP
+## Date: 2021-03-25
+- **Participants**: 
+  - [ ] @johndoe
+  - [ ] @janedoe
+  - [ ] @johndoe
+  - [ ] @janedoe
+- **Points of Discussion**:
+  - [ ] Point 1 
+  - [ ] Point 2
+  - [ ] Point 3
+  - mermaid
+  - C4
+- parse protocols into 
+- 
diff --git a/docs/_sources/external_models.rst.txt b/docs/_sources/external_models.rst.txt
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+Integrating external models
+------------------------------
+
+CHAP can run external models in two ways:
+
+- By specifying a path to a local code base
+- or by specifying a github URL to a git repo. The url needs to start with https://github.com/
+
+In either case, the directory or repo should be a valid MLproject directory, with an `MLproject` file. Se the specification in the `MLflow documentation <https://www.mlflow.org/docs/latest/projects.html#project-format>`_ for details. In addition, we require the following:
+
+- An entry point in the MLproject file called `train` with parameters `train_data` and `model`
+- An entry point in the MLproject file called `predict` with parameters `historic_data`, `future_data`, `model` and `out_file`
+
+These should contain commands that can be run to train a model and predict the future using that model. The model parameter should be used to save a model in the train step that can be read and used in the predict step. CHAP will provide all the data (the other parameters) when running a model.
+
+`Here is an example of a valid directory with an MLproject file <https://github.com/dhis2/chap-core/tree/dev/external_models/naive_python_model_with_mlproject_file>`_.
+
+
+The MLproject file can specify a docker image or Python virtual environment that will be used when running the commands.
+
+
+Running an external model on the command line
+----------------------------------------------
+
+External models can be run on the command line using the `chap evaluate` command. See `chap evaluate --help` for details.
+
+This example runs an auto ewars R model on public ISMIP data for Brazil using a public docker image with the R inla package. After running, a report file `report.pdf` should be made.
+
+.. code-block:: bash
+
+    chap evaluate --model-name https://github.com/sandvelab/chap_auto_ewars --dataset-name ISIMIP_dengue_harmonized --dataset-country brazil
+
+
+Running an external model in Python
+------------------------------------
+
+CHAP contains an API for loading models through Python. The following shows an example of loading and evaluating three different models by specifying paths/github urls, and evaluating those models:
+
+.. literalinclude :: ../scripts/external_model_example.py
+   :language: python
+
diff --git a/docs/_sources/history.rst.txt b/docs/_sources/history.rst.txt
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+.. include:: ../HISTORY.rst
diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
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+Climate health assessment platform (CHAP)
+==========================================
+
+CHAP is a platform for forecasting and for assessing forecasts of climate-sensitive health outcomes.
+In the early phase, the focus is on vector-borne diseases like malaria and dengue
+The platforms is to perform data parsing, data integration, forecasting based on any of multiple supported models, automatic brokering of compatible models for a given prediction context and robust forecast assessment and method comparison.
+
+The current version has basic data handling functionality in place, and is almost at a stage where it supports running a first external model (EWARS-Plus)
+
+
+.. toctree::
+   :maxdepth: 0
+   :caption: Contents:
+
+   installation
+   external_models
+   tutorials/wrapping_gluonts
+
+
+API documentation
+===================
+
+Data Fetching
+--------------
+
+Functionality for fetching data
+
+
+.. currentmodule:: climate_health.fetch
+
+.. autofunction:: gee_era5
+
+.. currentmodule:: climate_health.data
+
+.. autoclass:: DataSet
+    :members: from_period_observations, from_pandas, to_pandas
+
+.. autoclass:: PeriodObservation
+
+