# MDAnalysisData
[](https://www.mdanalysis.org/MDAnalysisData/)
[](http://makeapullrequest.com)
[](https://zenodo.org/badge/latestdoi/147885122)
Access to data for workshops and extended tests of MDAnalysis.
Data sets are stored at external stable URLs (e.g., on
[figshare](https://figshare.com/), [zenodo](https://zenodo.org/), or
[DataDryad](https://www.datadryad.org/)) and this package provides a
simple interface to download, cache, and access data sets.
## Installation
To use, install the package
```bash
pip install --upgrade MDAnalysisData
```
## Accessing data sets
Import the datasets and access your data set of choice:
```python
from MDAnalysisData import datasets
adk = datasets.fetch_adk_equilibrium()
```
The returned object contains attributes with the paths to topology and
trajectory files so that you can use it directly with, for instance, [MDAnalysis](https://www.mdanalysis.org):
```python
import MDAnalysis as mda
u = mda.Universe(adk.topology, adk.trajectory)
```
The metadata object also contains a `DESCR` attribute with a
description of the data set, including relevant citations:
```python
print(adk.DESCR)
```
## Managing data
Data are locally stored in the **data directory** `~/MDAnalysis_data`
(i.e., in the user's home directory). This location can be changed by
setting the environment variable `MDANALYSIS_DATA`, for instance
```bash
export MDANALYSIS_DATA=/tmp/MDAnalysis_data
```
The location of the data directory can be obtained with
```python
MDAnalysisData.base.get_data_home()
```
If the data directory is removed then data are downloaded again. Data
file integrity is checked with a SHA256 checksum when the file is
downloaded.
The data directory can we wiped with the function
```python
MDAnalysisData.base.clear_data_home()
```
## Credits
This package is modelled after
[sklearn.datasets](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets). It
uses code from `sklearn.datasets` (under the [BSD 3-clause
license](https://github.com/scikit-learn/scikit-learn/blob/master/COPYING)).
No data are included; please see the `DESCR` attribute for each data
set for authorship, citation, and license information for the data.
[](https://www.mdanalysis.org/MDAnalysisData/)
[](http://makeapullrequest.com)
[](https://zenodo.org/badge/latestdoi/147885122)
Access to data for workshops and extended tests of MDAnalysis.
Data sets are stored at external stable URLs (e.g., on
[figshare](https://figshare.com/), [zenodo](https://zenodo.org/), or
[DataDryad](https://www.datadryad.org/)) and this package provides a
simple interface to download, cache, and access data sets.
## Installation
To use, install the package
```bash
pip install --upgrade MDAnalysisData
```
## Accessing data sets
Import the datasets and access your data set of choice:
```python
from MDAnalysisData import datasets
adk = datasets.fetch_adk_equilibrium()
```
The returned object contains attributes with the paths to topology and
trajectory files so that you can use it directly with, for instance, [MDAnalysis](https://www.mdanalysis.org):
```python
import MDAnalysis as mda
u = mda.Universe(adk.topology, adk.trajectory)
```
The metadata object also contains a `DESCR` attribute with a
description of the data set, including relevant citations:
```python
print(adk.DESCR)
```
## Managing data
Data are locally stored in the **data directory** `~/MDAnalysis_data`
(i.e., in the user's home directory). This location can be changed by
setting the environment variable `MDANALYSIS_DATA`, for instance
```bash
export MDANALYSIS_DATA=/tmp/MDAnalysis_data
```
The location of the data directory can be obtained with
```python
MDAnalysisData.base.get_data_home()
```
If the data directory is removed then data are downloaded again. Data
file integrity is checked with a SHA256 checksum when the file is
downloaded.
The data directory can we wiped with the function
```python
MDAnalysisData.base.clear_data_home()
```
## Credits
This package is modelled after
[sklearn.datasets](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets). It
uses code from `sklearn.datasets` (under the [BSD 3-clause
license](https://github.com/scikit-learn/scikit-learn/blob/master/COPYING)).
No data are included; please see the `DESCR` attribute for each data
set for authorship, citation, and license information for the data.
Metadata
Release files for MDAnalysisData 0.2.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| MDAnalysisData-0.2.2.tar.gz | 49.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| MDAnalysisData-0.2.2-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 79.9 kB
Release files / MDAnalysisData-0.2.2.tar.gz
| Download URL | MDAnalysisData-0.2.2.tar.gz |
|---|---|
| Size | 49.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
|
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.10.0 pkginfo/1.4.2 requests/2.19.1 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/3.6.5
|
Release files / MDAnalysisData-0.2.2-py2.py3-none-any.whl
| Download URL | MDAnalysisData-0.2.2-py2.py3-none-any.whl |
|---|---|
| Size | 30.5 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
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|
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BLAKE2b-256 checksum How to use checksums |
ff4234fbcb6da7525fd9215236c3aa096614843f1909e889e1111eaa71b8f5ca
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.10.0 pkginfo/1.4.2 requests/2.19.1 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.23.4 CPython/3.6.5
|