semvec/pypi-packages huggingface dataset:
rs3-api 0.1.0
A wrapper for Runescape 3 API to make it easier to work.
Requires Python: >=3.7
Keywords: runescape3,rs3,runescape,api

# Runescape 3 API Wrapper

## Under construction


## Hiscores

```python
from rs3_api.hiscores import Hiscore

hiscore = Hiscore()

## User hiscores
user = hiscore.user('zezima')
print(user)
print(user.account_type)
print(user.skills)
print(user.minigames)

archived_seasonal_events = user.get_season(archived=True)
print(archived_seasonal_events)

## Seasonal events
print(hiscore.seasonal_events.all)
print(hiscore.seasonal_events.current)

## Clan Hiscores
clan = hiscore.clan('atlantis')
print(clan.members)
print(clan.get_member('oieeusougoku'))
evalseg 2.0.8
Medical Image Segmentation Evaluation

[![pypi](https://img.shields.io/pypi/v/evalseg.svg)](https://pypi.org/project/evalseg)
[![codecov](https://codecov.io/gh/modaresimr/evalseg/branch/main/graph/badge.svg?token=evalseg_token_here)](https://codecov.io/gh/modaresimr/evalseg)
[![CI](https://github.com/modaresimr/evalseg/actions/workflows/main.yml/badge.svg)](https://github.com/modaresimr/evalseg/actions/workflows/main.yml)

# Medical Image Segmentation Evaluation

This project is intended to evaluate Medical Segmentation approaches from multiple prespective.
![graphic_abstract](https://user-images.githubusercontent.com/9498182/200766838-d133d84f-2805-4818-b98c-a470debdb6ee.png)

![graphic-abstract](https://user-images.githubusercontent.com/9498182/197723220-760ad148-e7a7-4bd6-bacb-805111141dcc.png)

![3d](https://user-images.githubusercontent.com/9498182/200767060-946e7184-7d3c-447f-8df1-5edc195e4c0f.png)

### What is included on this repository?

- 📃 Documentation
- 🐋 A simple [Containerfile](Containerfile) to build a container image for your project.  
  `Containerfile` is a more open standard for building container images than Dockerfile, you can use buildah or docker with this file.
- 🧪 Testing structure using [pytest](https://docs.pytest.org/en/latest/)
- ✅ Code linting using [flake8](https://flake8.pycqa.org/en/latest/)
- 📊 Code coverage reports using [codecov](https://about.codecov.io/sign-up/)
- 🛳️ Automatic release to [PyPI](https://pypi.org) using [twine](https://twine.readthedocs.io/en/latest/) and github actions.
- 🎯 Entry points to execute your program using `python -m <evalseg>` or `$ evalseg` with basic CLI argument parsing.
- 🔄 Continuous integration using [Github Actions](.github/workflows/) with jobs to lint, test and release your project on Linux, Mac and Windows environments.

---

# evalseg

## Install it from PyPI

```bash
pip install evalseg
```

```bash
pip install git+https://github.com/modaresimr/evalseg
```

## Usage

```py
comming soon
```

```bash
$ python -m evalseg
#or
$ evalseg
```

## Development

Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.
customersegmentationrfm 0.0.1
Customer Segmentation: RFM Anlaysis
Requires Python: >=3.6


# Customer Segmentation: RFM 
Customer segmentation and RFM analysis


Recency, frequency, and monetary value (RFM) analysis is a technique used in marketing to determine which customers are the most valuable. RFM is useful for estimating a company's revenue from both existing and potential customers by determining which customers are most likely to make repeat purchases of the company's items.

This package does the data processing for the inputted data, conducts RFM Analysis and provides ability to visualize the results and identify the most valueable customer segments. 

### Example Usage 

I used Online Retail Store data to illustrate functionality of the package. 
<br>
Here is the data I used for further analysis

<img width="950" alt="Screen Shot 2022-12-16 at 21 35 35" src="https://user-images.githubusercontent.com/103374702/208156103-d18e972d-4094-4403-9237-61dda6112fd0.png">

The package helps to investigate the data, find anomalies and understand its' overall structure and feauture distributions. 

```
rfm = CustomerSegmentation(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount')
rfm.exploratory_analysis(df)
```
<img width="427" alt="Screen Shot 2022-12-16 at 21 51 39" src="https://user-images.githubusercontent.com/103374702/208158781-15f6ded7-32f8-4265-b1c9-25332575e2fe.png">

<img width="717" alt="Screen Shot 2022-12-16 at 21 40 42" src="https://user-images.githubusercontent.com/103374702/208156895-d08d225b-f1d0-4630-9e1a-cbceebe3f2be.png">
With the help of this function we can get descriptive statistics, and important information about the data, with which we are going to work. 

The package enables to find the RFM scores, segment the customers based on their scores. 

```
df_scores = rfm.produce_rfm_dateset(df)
df_scores.head()
```

<img width="357" alt="Screen Shot 2022-12-16 at 21 42 02" src="https://user-images.githubusercontent.com/103374702/208157136-f05636f7-5374-4f18-9d62-65679cdb4af2.png">

```
df_scores = rfm.calculate_rfm_score(df_values)
rfm_table = rfm.find_segments(df_scores)
segment_table = rfm.find_segment_df(rfm.rfm_table)
segment_table.head()
```

<img width="263" alt="Screen Shot 2022-12-16 at 21 44 51" src="https://user-images.githubusercontent.com/103374702/208157664-ea9cd6d2-b523-49d7-83cb-4e0e450c4aec.png">


We can also plot the distribution of segments, and visually identify the ones with highest number of customers

```
rfm.plot_segment_distribution()
```

<img width="661" alt="Screen Shot 2022-12-16 at 21 46 10" src="https://user-images.githubusercontent.com/103374702/208157860-7ed6a27d-c36a-4960-80eb-96849df7da73.png">


```
rfm.find_customers('Champions')
```

<img width="572" alt="Screen Shot 2022-12-16 at 21 47 20" src="https://user-images.githubusercontent.com/103374702/208158073-ce6912bc-5617-49a9-b2a7-b3db44293f29.png">
ma-rfm-cs 0.0.1
Customer Segmentation: RFM Anlaysis
Requires Python: >=3.6


# Customer Segmentation: RFM 
Customer segmentation and RFM analysis


Recency, frequency, and monetary value (RFM) analysis is a technique used in marketing to determine which customers are the most valuable. RFM is useful for estimating a company's revenue from both existing and potential customers by determining which customers are most likely to make repeat purchases of the company's items.

This package does the data processing for the inputted data, conducts RFM Analysis and provides ability to visualize the results and identify the most valueable customer segments. 

### Example Usage 

I used Online Retail Store data to illustrate functionality of the package. 
<br>
Here is the data I used for further analysis

<img width="950" alt="Screen Shot 2022-12-16 at 21 35 35" src="https://user-images.githubusercontent.com/103374702/208156103-d18e972d-4094-4403-9237-61dda6112fd0.png">

The package helps to investigate the data, find anomalies and understand its' overall structure and feauture distributions. 

```
rfm = CustomerSegmentation(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount')
rfm.exploratory_analysis(df)
```
<img width="427" alt="Screen Shot 2022-12-16 at 21 51 39" src="https://user-images.githubusercontent.com/103374702/208158781-15f6ded7-32f8-4265-b1c9-25332575e2fe.png">

<img width="717" alt="Screen Shot 2022-12-16 at 21 40 42" src="https://user-images.githubusercontent.com/103374702/208156895-d08d225b-f1d0-4630-9e1a-cbceebe3f2be.png">
With the help of this function we can get descriptive statistics, and important information about the data, with which we are going to work. 

The package enables to find the RFM scores, segment the customers based on their scores. 

```
df_scores = rfm.produce_rfm_dateset(df)
df_scores.head()
```

<img width="357" alt="Screen Shot 2022-12-16 at 21 42 02" src="https://user-images.githubusercontent.com/103374702/208157136-f05636f7-5374-4f18-9d62-65679cdb4af2.png">

```
df_scores = rfm.calculate_rfm_score(df_values)
rfm_table = rfm.find_segments(df_scores)
segment_table = rfm.find_segment_df(rfm.rfm_table)
segment_table.head()
```

<img width="263" alt="Screen Shot 2022-12-16 at 21 44 51" src="https://user-images.githubusercontent.com/103374702/208157664-ea9cd6d2-b523-49d7-83cb-4e0e450c4aec.png">


We can also plot the distribution of segments, and visually identify the ones with highest number of customers

```
rfm.plot_segment_distribution()
```

<img width="661" alt="Screen Shot 2022-12-16 at 21 46 10" src="https://user-images.githubusercontent.com/103374702/208157860-7ed6a27d-c36a-4960-80eb-96849df7da73.png">


```
rfm.find_customers('Champions')
```

<img width="572" alt="Screen Shot 2022-12-16 at 21 47 20" src="https://user-images.githubusercontent.com/103374702/208158073-ce6912bc-5617-49a9-b2a7-b3db44293f29.png">
replenigo 0.1.0
Script that utilizes Sbanken's Open Banking API to refill accounts to specified balances
Requires Python: >=3.10,<4.0

# replenigo

[![PyPI](https://img.shields.io/pypi/v/replenigo)](https://pypi.org/project/replenigo/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/replenigo)
[![PyPI - License](https://img.shields.io/pypi/l/replenigo)](https://github.com/mathiazom/replenigo/blob/main/LICENSE)

Script that utilizes Sbanken's Open Banking API to refill accounts to specified balances

## Usage

> I recommend you to check out Sbanken's [developer portal](https://sbanken.no/bruke/utviklerportalen/) and read the [documentation](https://github.com/Sbanken/api-examples#swagger) for the Sbanken API before using this app

1. Retrieve credentials required to use the API at https://sbanken.no/bruke/utviklerportalen/

2. Install `replenigo`
```shell
pip install replenigo
```

3. Use [`replenigo.template.yaml`](replenigo.template.yaml) to create your own `replenigo.yaml` config file with required credentials, source account and refill parameters. Some defaults are provide (see [`replenigo.defaults.yml`](replenigo/replenigo.defaults.yaml)), if not specified in `replenigo.yaml`.
4. See below for use of the `replenigo` CLI

# `replenigo`

Script that utilizes Sbanken's Open Banking API to refill accounts to specified balances

**Usage**:

```console
$ replenigo [OPTIONS] COMMAND [ARGS]...
```

**Options**:

* `--install-completion`: Install completion for the current shell.
* `--show-completion`: Show completion for the current shell, to copy it or customize the installation.
* `--help`: Show this message and exit.

**Commands**:

* `refill`: Refill accounts to specified balances

## `replenigo refill`

Refill accounts to specified balances

**Usage**:

```console
$ replenigo refill [OPTIONS]
```

**Options**:

* `-c, --config-file FILENAME`: Configurations file  [default: replenigo.yaml]
* `-r, --reverse-if-above-goal / -R, --no-reverse-if-above-goal`: If refill goal has been exceeded, decide if the surplus should be transferred back to the source account
* `-m, --transfer-message TEXT`: Message to be displayed in the bank transfer (max 30 chars)
* `-p, --write-checkpoints / -P, --no-checkpoints`: Enable or disable "checkpointing", which creates an empty checkpoint file on each successful refill
* `-d, --checkpoints-dir DIRECTORY`: Directory to store checkpoint files
* `--help`: Show this message and exit.
cs-rfm 0.0.1
Customer Segmentation: RFM Anlaysis
Requires Python: >=3.6


# Customer Segmentation: RFM 
Customer segmentation and RFM analysis


Recency, frequency, and monetary value (RFM) analysis is a technique used in marketing to determine which customers are the most valuable. RFM is useful for estimating a company's revenue from both existing and potential customers by determining which customers are most likely to make repeat purchases of the company's items.

This package does the data processing for the inputted data, conducts RFM Analysis and provides ability to visualize the results and identify the most valueable customer segments. 

### Example Usage 

I used Online Retail Store data to illustrate functionality of the package. 
<br>
Here is the data I used for further analysis

<img width="950" alt="Screen Shot 2022-12-16 at 21 35 35" src="https://user-images.githubusercontent.com/103374702/208156103-d18e972d-4094-4403-9237-61dda6112fd0.png">

The package helps to investigate the data, find anomalies and understand its' overall structure and feauture distributions. 

```
rfm = CustomerSegmentation(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount')
rfm.exploratory_analysis(df)
```
<img width="427" alt="Screen Shot 2022-12-16 at 21 51 39" src="https://user-images.githubusercontent.com/103374702/208158781-15f6ded7-32f8-4265-b1c9-25332575e2fe.png">

<img width="717" alt="Screen Shot 2022-12-16 at 21 40 42" src="https://user-images.githubusercontent.com/103374702/208156895-d08d225b-f1d0-4630-9e1a-cbceebe3f2be.png">
With the help of this function we can get descriptive statistics, and important information about the data, with which we are going to work. 

The package enables to find the RFM scores, segment the customers based on their scores. 

```
df_scores = rfm.produce_rfm_dateset(df)
df_scores.head()
```

<img width="357" alt="Screen Shot 2022-12-16 at 21 42 02" src="https://user-images.githubusercontent.com/103374702/208157136-f05636f7-5374-4f18-9d62-65679cdb4af2.png">

```
df_scores = rfm.calculate_rfm_score(df_values)
rfm_table = rfm.find_segments(df_scores)
segment_table = rfm.find_segment_df(rfm.rfm_table)
segment_table.head()
```

<img width="263" alt="Screen Shot 2022-12-16 at 21 44 51" src="https://user-images.githubusercontent.com/103374702/208157664-ea9cd6d2-b523-49d7-83cb-4e0e450c4aec.png">


We can also plot the distribution of segments, and visually identify the ones with highest number of customers

```
rfm.plot_segment_distribution()
```

<img width="661" alt="Screen Shot 2022-12-16 at 21 46 10" src="https://user-images.githubusercontent.com/103374702/208157860-7ed6a27d-c36a-4960-80eb-96849df7da73.png">


```
rfm.find_customers('Champions')
```

<img width="572" alt="Screen Shot 2022-12-16 at 21 47 20" src="https://user-images.githubusercontent.com/103374702/208158073-ce6912bc-5617-49a9-b2a7-b3db44293f29.png">
astronomer-nova 0.1.3
Automate migration from Airflow 1 to Airflow 2
Requires Python: <4,>=3.9
Keywords: Airflow,Migration

# Astronomer Nova
### (Formerly Airflow V2 Upgrader)

This project aims to automate common tasks when upgrading DAGs from Airflow 1 to Airflow 2,
making use of [RedBaron](https://redbaron.readthedocs.io/en/latest/) to parse and manipulate the Python syntax tree.

# Features

- Automatically upgrade DAG source code from Airflow 1 to Airflow 2
- Scan Dags and report changes (Coming Soon)

# Installation
You can install the Astronomer Nova CLI through Pip

```shell
pip install astronomer-nova
```

The simplest way to get started with Astronomer Nova is by using the astro cli.
Simply add `astronomer-nova` to your list of dependencies in `requirements.txt`.
Then you can run it with the following commands:

```shell
astro dev start
astro dev bash
nova
```

## Installation from sources

To install astronomer-nova in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):

```shell
pip install --editable .[lint,test]
```

To build from sources:

```
python -m build
```

OR

```
python -m pip wheel .
```

## Running tests:

```shell
python -m pytest
```

# How to Use

```shell
nova dags/ dags/upgraded/
```

For more information, you can access the help menu with:

```shell
nova --help
```

# How it works

Astronomer Nova performs the upgrade by:

1. Building an import map for classes in `airflow.hooks`, `airflow.operators`, `airflow.sensors`, and `airflow.providers`, which maps the classes to the current Airflow version import path.
2. Modifying Python files in a DAG directory to update
   - Imports
   - DAG access controls
   - XCOM push
   - XCOM pull
3. Writing updated DAG to either the same DAG directory or a parameter output directory with `_upgraded` added to the filename.

The script is configured to run against `./dags` and to write output to `./dags/upgraded` by default.

## Caveats

In order to load the Airflow import map, Airflow provider packages must be installed. There are often cross-dependencies within Airflow providers (eg `airflow.operators.s3_to_hive_operator` required the Hive provider), so make sure the `requirements.txt` file includes all necessary .

The script currently tries to correct imports that do not need to be changed going from Airflow 1 to 2, and logging does not include which files are currently being acted against.

The code uses Astronomer-centric opinions, like removing DAG-level RBAC. In the future, I'd rather have these decisions configurable at runtime, as well as generating per-DAG reports on the DAGs that can be used for further remediation, as well as understanding of DAG requirements, like the use of Connections and Variables.


# Contribute

- Issue Tracker: https://github.com/astronomer/astronomer-nova/issues
- Source Code: https://github.com/astronomer/astronomer-nova

# Contact

cabella@astronomer.io