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README: Update installation instructions

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- wget used to save the data to an `index.html?...` file

- while conda prefers `conda activate` nowadays
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Jonas Hörsch 2018-10-28 20:49:59 +01:00
parent 91b83930dd
commit 5adf2ccd6c
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@ -74,7 +74,7 @@ The python package requirements are curated in the conda [environment.yaml](envi
The environment can be installed and activated using The environment can be installed and activated using
```shell ```shell
.../pypsa-eur % conda env create -f environment.yaml .../pypsa-eur % conda env create -f environment.yaml
.../pypsa-eur % source activate pypsa-eur # or conda activate pypsa-eur on windows .../pypsa-eur % conda activate pypsa-eur # or source activate pypsa-eur on older linux installations
``` ```
**Note that activation is local to the currently open shell! After opening a new terminal window, one needs to reissue the second command!** **Note that activation is local to the currently open shell! After opening a new terminal window, one needs to reissue the second command!**
@ -83,12 +83,12 @@ The environment can be installed and activated using
Not all data dependencies are shipped with the git repository (since git is not suited for handling large changing files). Instead we provide two separate data bundles: Not all data dependencies are shipped with the git repository (since git is not suited for handling large changing files). Instead we provide two separate data bundles:
1. [pypsa-eur-data-bundle.tar.xz](https://vfs.fias.science/d/0a0ca1e2fb/files/?p=/pypsa-eur-data-bundle.tar.xz) contains common GIS datasets like NUTS3 shapes, EEZ shapes, CORINE Landcover, Natura 2000 and also electricity specific summary statistics like historic per country yearly totals of hydro generation, GDP and POP on NUTS3 levels and per-country load time-series. It should be extracted in the `data` subdirectory (so that all files are in the `data/bundle` subdirectory) 1. [pypsa-eur-data-bundle.tar.xz](https://vfs.fias.science/d/0a0ca1e2fb/files/?p=/pypsa-eur-data-bundle.tar.xz) contains common GIS datasets like NUTS3 shapes, EEZ shapes, CORINE Landcover, Natura 2000 and also electricity specific summary statistics like historic per country yearly totals of hydro generation, GDP and POP on NUTS3 levels and per-country load time-series. It should be extracted in the `data` subdirectory (so that all files are in the `data/bundle` subdirectory)
```shell ```shell
.../pypsa-eur/data % wget "https://vfs.fias.science/d/0a0ca1e2fb/files/?dl=1&p=/pypsa-eur-data-bundle.tar.xz" .../pypsa-eur/data % curl -OL "https://vfs.fias.science/d/0a0ca1e2fb/files/?dl=1&p=/pypsa-eur-data-bundle.tar.xz"
.../pypsa-eur/data % tar xJf pypsa-eur-data-bundle.tar.xz .../pypsa-eur/data % tar xJf pypsa-eur-data-bundle.tar.xz
``` ```
2. [pypsa-eur-cutouts.tar.xz](https://vfs.fias.science/d/0a0ca1e2fb/files/?p=/pypsa-eur-cutouts.tar.xz) are spatiotemporal subsets of the European weather data from the [ECMWF ERA5](https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation) reanalysis dataset and the [CMSAF SARAH-2](https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002) solar surface radiation dataset for the year 2013. They have been prepared by and are for use with the [atlite](https://github.com/FRESNA/atlite) tool. You can either generate them yourself using the `build_cutouts` snakemake rule or extract them directly in the `pypsa-eur` directory (extracting the bundle is recommended, since procuring the source weather data files for atlite is not properly documented at the moment): 2. [pypsa-eur-cutouts.tar.xz](https://vfs.fias.science/d/0a0ca1e2fb/files/?p=/pypsa-eur-cutouts.tar.xz) are spatiotemporal subsets of the European weather data from the [ECMWF ERA5](https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation) reanalysis dataset and the [CMSAF SARAH-2](https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002) solar surface radiation dataset for the year 2013. They have been prepared by and are for use with the [atlite](https://github.com/FRESNA/atlite) tool. You can either generate them yourself using the `build_cutouts` snakemake rule or extract them directly in the `pypsa-eur` directory (extracting the bundle is recommended, since procuring the source weather data files for atlite is not properly documented at the moment):
```shell ```shell
.../pypsa-eur % wget "https://vfs.fias.science/d/0a0ca1e2fb/files/?dl=1&p=/pypsa-eur-cutouts.tar.xz" .../pypsa-eur % curl -OL "https://vfs.fias.science/d/0a0ca1e2fb/files/?dl=1&p=/pypsa-eur-cutouts.tar.xz"
.../pypsa-eur % tar xJf pypsa-eur-cutouts.tar.xz .../pypsa-eur % tar xJf pypsa-eur-cutouts.tar.xz
``` ```