semvec/pypi-packages huggingface dataset:
ikalogic-cs8000 1.0.8
Python API for CS8000 series RF switches
License: MIT
Requires Python: >=3.8
Keywords: cs8000, rf-switch, serial, python, api, ikalogic

# CS8000 Python API

Python library for controlling [CS8000 series RF switches](https://ikalogic.com/rf-switches/cs8216/intro/).

## Installation

```bash
pip install ikalogic-cs8000
```

## Quick Start

```python
from ikalogic import cs8000

# Create instance and connect
sw = cs8000.CS8000("CS8216")
sw.open()  # Auto-detect first available device

# Switch connections
sw.prepare(1, 1)  # Connect COM1 to CH1
sw.prepare(2, 0)  # Disconnect COM2
sw.commit()
# Close connection
sw.close()
```

## Documentation
Full documentation is available at: https://ikalogic.com/kb/cs8000-api/cs8000_home/
dtreg 1.1.2
Interact with Data Type Registries and Create Machine Actionable Data
License: MIT
Requires Python: >=3.8
Keywords: data type registry, schema, JSON-LD

# dtreg
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**100% AI-free: we did not use any AI technologies in developing this
package.**

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://knowledgeloom.tib.eu/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://knowledgeloom.tib.eu/pages/help).