| Name | dtreg JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | Interact with Data Type Registries and Create Machine Actionable Data |
| upload_time | 2025-02-15 11:35:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
data type registry
schema
json-ld
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# dtreg
<!-- badges: start -->
[](https://badge.fury.io/py/dtreg)
[](https://coveralls.io/github/OlgaLezhnina/dtreg_py?branch=master)
<!-- badges: end -->
The goal of dtreg is to help users interact with various data type registries (DTRs) and create machine-readable data.
Currently, we support the [ePIC](https://fc4e-t4-3.github.io/) and [ORKG](https://orkg.org/) DTRs.
* First, load a DTR schema (an ePIC datatype or an ORKG template) as a Python object.
* Then, create a new instance of the schema by filling in the relevant fields.
* Finally, write the instance as a machine-readable JSON-LD file.
## Installation
```sh
## install from PyPi:
pip install dtreg
```
## Example
This example shows you how to work with a DTR schema.
You need to know the schema identifier; see the [help page](https://reborn.orkg.org/pages/help).
```python
## import functions from the dtreg
from dtreg.load_datatype import load_datatype
from dtreg.to_jsonld import to_jsonld
## import pandas for a dataframe
import pandas as pd
## load the schema with the known identifier
dt = load_datatype("https://doi.org/21.T11969/aff130c76e68ead3862e")
## look at the schemata you might need to use
dt.__dict__.keys()
## check available fields for your schema
dt.data_item.prop_list
## create your instance by filling the fields of your choice
## see the help page to know more about the fields
my_label = "my results"
my_df = pd.DataFrame({'A': [1], 'B': [2]})
my_df.name = "dataframe_name"
url_1 = dt.url(label = "URL_1")
url_2 = dt.url(label = "URL_2")
my_inst = dt.data_item(label=my_label,
has_expression=[url_1, url_2],
source_table=my_df)
## write the instance in JSON-LD format as a string
my_json = to_jsonld(my_inst)
## the result can be saved as a JSON file
with open('my_file.json', 'w') as f:
f.write(my_json)
```
For more information, please see the [help page](https://reborn.orkg.org/pages/help).
Raw data
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"description": "# dtreg\n<!-- badges: start -->\n[](https://badge.fury.io/py/dtreg)\n[](https://coveralls.io/github/OlgaLezhnina/dtreg_py?branch=master)\n\n\n<!-- badges: end -->\n\nThe goal of dtreg is to help users interact with various data type registries (DTRs) and create machine-readable data. \nCurrently, we support the [ePIC](https://fc4e-t4-3.github.io/) and [ORKG](https://orkg.org/) DTRs.\n* First, load a DTR schema (an ePIC datatype or an ORKG template) as a Python object.\n* Then, create a new instance of the schema by filling in the relevant fields.\n* Finally, write the instance as a machine-readable JSON-LD file. \n## Installation\n\n```sh\n## install from PyPi:\npip install dtreg\n```\n\n## Example\n\nThis example shows you how to work with a DTR schema.\nYou need to know the schema identifier; see the [help page](https://reborn.orkg.org/pages/help).\n\n```python\n## import functions from the dtreg\nfrom dtreg.load_datatype import load_datatype\nfrom dtreg.to_jsonld import to_jsonld\n## import pandas for a dataframe\nimport pandas as pd\n## load the schema with the known identifier\ndt = load_datatype(\"https://doi.org/21.T11969/aff130c76e68ead3862e\")\n## look at the schemata you might need to use\ndt.__dict__.keys() \n## check available fields for your schema\ndt.data_item.prop_list \n## create your instance by filling the fields of your choice\n## see the help page to know more about the fields\nmy_label = \"my results\"\nmy_df = pd.DataFrame({'A': [1], 'B': [2]})\nmy_df.name = \"dataframe_name\"\nurl_1 = dt.url(label = \"URL_1\")\nurl_2 = dt.url(label = \"URL_2\")\nmy_inst = dt.data_item(label=my_label,\n has_expression=[url_1, url_2],\n source_table=my_df)\n## write the instance in JSON-LD format as a string\nmy_json = to_jsonld(my_inst) \n\n## the result can be saved as a JSON file\nwith open('my_file.json', 'w') as f:\n f.write(my_json)\n\n```\nFor more information, please see the [help page](https://reborn.orkg.org/pages/help).\n",
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