semvec/pypi-packages huggingface dataset:
jageocoder-converter 2.0.3
Dictionary generator for jageocoder.
Requires Python: >=3.8.1,<4.0.0

# jageocoder-converter

This package is a CUI application for creating an address dictionary for use with the address geocoder [jageocoder](https://github.com/t-sagara/jageocoder).
It can be used when you want to create a dictionary that includes only some prefectures.

For more detailed information, please refer to
[the Japanese document](doc/index.rst).

### Prerequisites

Requires Python 3.7 or later.

### Installing

Install the package using `pip install jageocoder-converter`.

## Uninstalling

Remove the directory containing the database.
Then, do `pip uninstall jageocoder-converter`.

## Run

Show help with the following command.

```sh
python -m jageocoder_converter -h
``` 

To create a dictionary, run the following command.

```sh
python -m jageocoder_converter
```

During the process, it downloads the location reference information
needed to create the dictionary from the web. Before downloading,
you will be prompted with a link to the terms of use.
Be sure to read them, and enter a capital 'Y'.

**More examples**

To create a dictionary, including only 東京都,
without records from 住居表示住所, run the following command.

```sh
python -m jageocoder_converter --no-jusho 13
```

You may create a dictionary in the specified directory.

```sh
python -m jageocoder_converter --db-dir=oazadb --no-gaiku --no-jusho
```

If you already understand the terms of use, etc., and want to process it
in the background, you can specify the quiet option. It may be useful
to add a debug option to check the progress in the log file.

```sh
nohup python -m jageocoder_converter --quiet --debug &
```

## Authors

* Takeshi SAGARA - [Info-proto Co.,Ltd.](https://www.info-proto.com/)

## License

This project is licensed under [the MIT License](https://opensource.org/licenses/mit-license.php).

This is not the scope of the dictionary data license.
Please follow the terms of use of the respective dictionary data.
rugpi 0.0.0
This package has been reserved for Rugpi.
Requires Python: >=3.12,<4.0

This crate name has been reserved for [Rugpi](http://rugpi.io).

While not released on crates.io, Rugpi consists of crates with these names. To prevent malicious actors from taking them, we reserve them. Later, we may also use them to publish some Rugpi-related libraries.
slurm-view 0.0.1
View a SLURM cluster and inspect nodes and jobs.
Requires Python: >=3.10
Keywords: XNAT,TUI

# Slurm Viewer
## Introduction
View the nodes in a slurm cluster and view the queue.

## Settings
```json
{
  "debug": "True",
  "server": "SERVER or NONE",
  "nodelist": ["List of nodes to view"],
  "node_columns": [
    "short_name",
    "gpu_tot",
    "gpu_alloc",
    "gpu_avail",
    "gpu_type",
    "gpu_mem",
    "cpu_tot",
    "cpu_alloc",
    "cpu_avail",
    "real_memory",
    "alloc_mem",
    "freemem"
  ],
  "queue_columns": [
    "nodelist",
    "user",
    "job_id",
    "command",
    "submit_time",
    "start_delay",
    "min_memory"
  ]
}
```
amz-ppc-optimizer 1.2.1
Python package for optimizing Amazon advertising campaigns

# Amazon PPC Optimizer

This project is a Python script that optimizes Amazon PPC campaigns by analyzing search term reports and adjusting campaign keywords and bids to improve advertising performance.

## Requirements

Before running the script, make sure you have the following prerequisites installed:

- Python 3.x
- Required Python packages (specified in `requirements.txt`)

You can install the required packages by running:

```bash
pip install amz-ppc-optimizer
```

## Usage
Data Preparation
- Bulk sheet report containing campaign data
- Search term report for sponsored products

Sample usage
```python
    sheet_handler = AmzSheetHandler()
    sheet_handler.read_bulk_sheet_report(filename="amz-ppc-bulk-file-name.xlsx")

    keyword_optimizer = ApexOptimizer(sheet_handler.sponsored_prod_camp, desired_acos=0.3, min_bid=0.734)
    keyword_optimizer.optimize_spa_keywords(exclude_dynamic_bids=False)

    datagram = keyword_optimizer.datasheet

    search_termed = ""
    if optimize_search_terms is True:
        sheet_handler.read_search_terms_report(filename="Sponsored Products Search term report.xlsx")
        search_terms_optimizer = SearchTermOptimizer(sheet_handler.sponsored_product_search_terms)

        # Get profitable and unprofitable search terms based on ACOS value
        profitable_st = search_terms_optimizer.filter_profitable_search_terms(desired_acos=0.3)
        unprofitable_st = search_terms_optimizer.filter_unprofitable_search_terms(desired_acos=0.3)

        # Add profitable search terms to exact campaigns
        datagram = search_terms_optimizer.add_search_terms(datagram, profitable_st, 1)
        datagram = search_terms_optimizer.add_search_terms(datagram, unprofitable_st, 0.6)

        search_termed = "ST_"

    filename = "Sponsored_Products_Campaigns_" + search_termed + str(datetime.datetime.utcnow().date()) + ".xlsx"
    sheet_handler.write_data_file(filename, datagram, "Sponsored Products Campaigns")
```

### Optimization Process
The script reads the campaign data from data.xlsx and optimizes keywords in sponsored product campaigns.
It also reads the search term report from Sponsored Products Search term report.xlsx.
Profitable and unprofitable search terms are determined based on the specified ACOS (Advertising Cost of Sales) threshold.
Profitable search terms are added to exact match campaigns, and bids are adjusted.
Unprofitable search terms are added with reduced bids to maintain visibility but control costs.


## Configuration
You can configure the optimization settings in the script:

Adjust the desired_acos value to set your desired ACOS threshold for filtering profitable and unprofitable search terms.

## License
This project is licensed under the MIT License - see the LICENSE file for details.