# pydplace
A Python library to curate [D-PLACE](https://d-place.org) data.
[](https://github.com/D-PLACE/pydplace/actions?query=workflow%3Atests)
[](https://pypi.org/project/pydplace)
To install `pydplace` run
```
pip install pydplace
```
## Usage
### Bootstrapping a `pydplace`-curated dataset
`pydplace` provides a `cldfbench` dataset template to create the skeleton of files and directories for a
D-PLACE dataset, to be run with [cldfbench new](https://github.com/cldf/cldfbench/#creating-a-skeleton-for-a-new-dataset-directory).
Running
```shell
cldfbench new --template dplace_dataset
```
will create a dataset skeleton looking as follows
```shell
$ tree testtree/
```
### Implementing CLDF creation
Implementing CLDF creation means - as for any other `cldfbench`-curated dataset - filling in the
`cmd_makecldf` method of the `Dataset` subclass in `cldfbench_<id>.py`.
### Running CLDF creation
With `cmd_makecldf` implemented, CLDF creation can be triggered running
```shell
cldfbench makecldf cldfbench_<id>.py
```
The resulting CLDF dataset can be validated running
```shell
pytest
```
### Release workflow
```shell
cldfbench makecldf --glottolog-version v4.8 --with-cldfreadme cldfbench_<id>.py
pytest
cldfbench zenodo --communities dplace cldfbench_<id>.py
cldfbench cldfviz.map cldf --pacific-centered --format png --width 20 --output map.png --with-ocean --no-legend
cldfbench readme cldfbench_<id>.py
dplace check cldfbench_<id>.py
git commit -a -m"release vX.Y"
git push origin
```
Then create a release on GitHub, thereby pushing the repos to Zenodo.
### Using the datasets
```shell
$ csvgrep -c Var_ID -m AnnualMeanTemperature cldf/data.csv | csvstat -c Value
4. "Value"
Type of data: Number
Contains null values: False
Unique values: 1649
Smallest value: -19,45
Largest value: 29,153
Sum: 32.700,717
Mean: 16,449
Median: 19,721
StDev: 9,684
Most common values: 14,392 (9x)
21,66 (6x)
6,96 (6x)
23,335 (5x)
21,619 (5x)
Row count: 1988
```
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"description": "# pydplace\n\nA Python library to curate [D-PLACE](https://d-place.org) data.\n\n[](https://github.com/D-PLACE/pydplace/actions?query=workflow%3Atests)\n[](https://pypi.org/project/pydplace)\n\n\nTo install `pydplace` run\n\n```\npip install pydplace\n```\n\n## Usage\n\n### Bootstrapping a `pydplace`-curated dataset\n\n`pydplace` provides a `cldfbench` dataset template to create the skeleton of files and directories for a\nD-PLACE dataset, to be run with [cldfbench new](https://github.com/cldf/cldfbench/#creating-a-skeleton-for-a-new-dataset-directory).\n\nRunning\n\n```shell\ncldfbench new --template dplace_dataset \n```\n\nwill create a dataset skeleton looking as follows\n```shell\n$ tree testtree/\n```\n\n\n### Implementing CLDF creation\n\nImplementing CLDF creation means - as for any other `cldfbench`-curated dataset - filling in the\n`cmd_makecldf` method of the `Dataset` subclass in `cldfbench_<id>.py`.\n\n\n### Running CLDF creation\n\nWith `cmd_makecldf` implemented, CLDF creation can be triggered running\n```shell\ncldfbench makecldf cldfbench_<id>.py\n```\n\nThe resulting CLDF dataset can be validated running\n```shell\npytest\n```\n\n\n### Release workflow\n\n```shell\ncldfbench makecldf --glottolog-version v4.8 --with-cldfreadme cldfbench_<id>.py\npytest\ncldfbench zenodo --communities dplace cldfbench_<id>.py\ncldfbench cldfviz.map cldf --pacific-centered --format png --width 20 --output map.png --with-ocean --no-legend\ncldfbench readme cldfbench_<id>.py\ndplace check cldfbench_<id>.py\ngit commit -a -m\"release vX.Y\"\ngit push origin\n```\n\nThen create a release on GitHub, thereby pushing the repos to Zenodo.\n\n\n### Using the datasets\n\n```shell\n$ csvgrep -c Var_ID -m AnnualMeanTemperature cldf/data.csv | csvstat -c Value\n 4. \"Value\"\n\n\tType of data: Number\n\tContains null values: False\n\tUnique values: 1649\n\tSmallest value: -19,45\n\tLargest value: 29,153\n\tSum: 32.700,717\n\tMean: 16,449\n\tMedian: 19,721\n\tStDev: 9,684\n\tMost common values: 14,392 (9x)\n\t 21,66 (6x)\n\t 6,96 (6x)\n\t 23,335 (5x)\n\t 21,619 (5x)\n\nRow count: 1988\n```\n",
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