semvec/pypi-packages huggingface dataset:
charSn 0.0.7
functions to calculate group characters of Sn
Requires Python: >=3.7

# Characters_Sn
Functions for calculation group characters of the symmetric group Sn


This code was written by Lizzie Hernandez 
for Math 68 Algebraic Combinatorics course in Fall 2021 
at Darmouth College 

[GitHub Link] (https://github.com/lizziehv/math68-final)
[Google Colab Link] (https://colab.research.google.com/drive/1GMeXdseX97ZfMtfsO6Tvf5pijP4Zslc6?usp=sharing)
pjconf 1.0.0
A super simple JSON based configuration system.
Requires Python: >=3.6
Keywords: configuration,config,json

# pjconf

A simple configuration interface to use JSON files as configuration files.

## Installation

PJC is installed via pip.

```shell
pip install pjconf
```

## Usage

Loading configuration is straightforward, just import and point to your json file.

```python
import pjconf as pjc

# Load a json file as a configuration dictionary.
config = pjc.load_config('config.json')

# or, load a configuration file with an optional default set of configuration elsewhere
config = pjc.load_config('defaults.json', 'config.json', ignore_missing=True)
```

Configuration files are loaded in a cascading sequence, multiple positional filepaths are loaded and override/update the previous keys. Setting `ignore_missing=True` will allow for a flexible resolve of optional configuration; disabling it will raise a `FileNotFoundError` on the first file it can't find.

Accessing configuration uses a ``get()`` method, with a cool dot-recursive syntax, optional defaults and runtime type-casting.

Let's assume the following configuration file.

```json
{
    "opt1": true
    "opt2": 0.1
    "opt3": {
        "subopt1": "hello"
    } 
}
```

You can use the following syntax to access your configuration.

```python

# Access a simple option
opt1 = config.get('opt1')
# True

# Access a recursive (nested) option
subopt1 = config.get('opt3.subopt1')
# "hello"

# Try to access a missing option, but provide a default
missing_opt = config.get('missing', default=True)
# True

# Runtime type-casting
opt2asint = config.get('opt2', cast=int)
# 0
```

For the last example, the ``cast`` argument takes a callable with a single parameter - you can be as simple or as complicated (i.e. lambdas or defined functions) as you like.
jungle-test-cli 0.0.1
Jungle test CLI
Requires Python: >=3.7

## JUNGLE INTERVIEW TEST

The project follows the hexagonal architecture and the domain-driven design pattern.

### Technologies used
- Python: main application
- Golang: messaging
- Docker: containerization
- FastAPI: to create APIs for communication with the main application
- TimescaleDB: for timeseries data storage
- RabbitMQ: message broker
- gRPC: communication between services

### Available functionalities
- CRUD operations on customers' information
- Timeseries data ingestion (data ingested is passed to a message queue through a gRPC connection, implemented with Golang and RabbitMQ - this implementation can be found in `./infrastructure/messaging`)
- Raw timeseries data points retrieval from the database for an
asset, list of timeseries names and date range
- Deletion of datapoint
- Availability of timeseries data within a date range
- Statistics computation for a specific customer, timeseries and date
range

### Functionalities yet to be done
- Caching Statistics
- Storing configurations

### Getting started
- Clone this repo
- Switch to the repo's directory and setup the application by executing `make`. This is going to create default environment variables, build the docker containers used in the app and start the app in a detached mode (to stop: `make stop`. To start again: `make start` or `make startd` to start in a detached mode)
- In the same terminal, execute `make loadfixtures` to populate the database with the provided timeseries data files.
- Go to http://127.0.0.1:8000/redoc to view API documentation.

### CLI Usage
> `junglexcli --help` for help
> Note: Prompts are displayed for each required field without a flag option.

- Data ingestion: `junglexcli --ingest <PATH_TO_CSV>`. Please take note that the asset name for the timeseries datapoints will be the filename of the file.
- Saving timeseries to file: `junglexcli --datapoints_out <FOLDER_TO_SAVE_RECORD>` (e.g. `junglexcli --datapoints_out .` will save the result to the current directory).
- Compute stats: `junglexcli --stats <JSON_CONFIGURATION>`. To view an example configuration, issue `junglexcli --conf_example`.