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Analysis & visualization of energy & climate scenarios

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In https://github.com/znicholls/silicone/pull/72, @Rlamboll has added a feature to do bulk aggregation within an `IamDataFrame`. This is a convenience function, but means you could quickly specify to calculate a bunch of...

We observed recently that there are situations where we want `append()` to overwrite existing data. At the moment, an error is raised if data is recalculated. It should be extended...

good first issue

## Description A [comment](https://github.com/IAMconsortium/pyam/issues/335#issuecomment-591372494) by @gidden in #335 raises a thorny question that deserves its own issue for discussion (I think): Should any variable in a scenario (or scenario ensemble)...

In https://github.com/IAMconsortium/pyam/pull/315, the aggregation tests were removed. They should be added back in future in a way which makes the intent of the tests clearer. Testing code in master before...

At a workshop of IPCC TG-Data this week, @aspinuso presented on "Data-Intensive and Reproducible Science", using a `pyam` tutorial as an example for a workflow with detailed provenance tracking: https://github.com/aspinuso/pyam-binder/blob/master/pyam.ipynb...

question

The recipe for building the release on `conda-forge` should include installing and running the unit tests. Issue where this was first raised: https://github.com/conda-forge/pyam-feedstock/pull/11

Using the `stack_plot()` feature and experiencing some strange behaviour, I went to the `aggregating_variables_and_plotting_with_negative_values` tutorial notebook (#198) to see check whether I was doing something wrong. I noticed that the...

If you install first from source and then from pip, the pip version will overtake the source version (even if source is more up-to-date than pip). Solution: conform to ixmp...

As part of the extension to include `datetime` as time format in addition to yearly data (see #167), the function `read_files()` was simplified so that it only reads one file....

df here is queried of all iamc15 data as so ``` df = pyam.read_iiasa_iamc15( model='*', scenario='*', variable=['Emissions|CO2', 'Primary Energy|Coal', 'AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED'], region='World' ) ``` ![image](https://user-images.githubusercontent.com/1392657/48190574-60bb4600-e343-11e8-8c3b-837ed9ad5874.png)

plotting