semvec/pypi-packages huggingface dataset:
burger-shop-reminder 1.0.0
A daily reminder for a random burger shop with images
Requires Python: >=3.6

# ハンバーガーショップリマインダーシステム
## 概要
このシステムは、毎日午前9時に世界中のハンバーガーショップをランダムに選び、画像付きでリマインダーを提供するPythonアプリケーション。
  
  以下の機能を備えている：

+ ランダムにハンバーガーショップを選択
+ 選択したショップの詳細と画像を表示
+ 画像をローカルファイルに保存
## 使用技術

  + random: ハンバーガーショップをランダムに選択するために使用
  + requests: ハンバーガーショップの画像をインターネットからダウンロードするために使用
  + PIL (Pillow): 画像の処理および表示のために使用
  + apscheduler: スケジューリングを管理し、毎日決まった時間にタスクを実行するために使用
  + matplotlib: 画像の表示および保存のために使用
# コード説明
## ライブラリのインポート
```
import random
import requests
from PIL import Image
from io import BytesIO
from apscheduler.schedulers.blocking import BlockingScheduler
import matplotlib
matplotlib.use('Agg')  # GUIを使わないバックエンドを使用
import matplotlib.pyplot as plt
```
必要なライブラリをインポートする。matplotlibのバックエンドをAggに設定することで、GUIを使わずに画像を処理する。

## ハンバーガーショップのリスト
```
burger_shops = [
    {"name": "In-N-Out Burger", "location": "USA", "image_url": "https://via.placeholder.com/150"},
    {"name": "Shake Shack", "location": "USA", "image_url": "https://via.placeholder.com/150"},
    {"name": "Burger King", "location": "Worldwide", "image_url": "https://via.placeholder.com/150"},
    {"name": "McDonald's", "location": "Worldwide", "image_url": "https://via.placeholder.com/150"},
    {"name": "Five Guys", "location": "USA", "image_url": "https://via.placeholder.com/150"}
    # 他のハンバーガー店を追加
]
```
サンプルとして5つのハンバーガーショップをリストに追加している。

## リマインダーファンクション
```
def notify_burger_shop():
    # ランダムにハンバーガー店を選ぶ
    shop = random.choice(burger_shops)
    name = shop["name"]
    location = shop["location"]
    image_url = shop["image_url"]
    
    print(f"Selected shop: {name} in {location}")
    print(f"Image URL: {image_url}")
    
    try:
        # 画像をダウンロードして表示
        response = requests.get(image_url)
        response.raise_for_status()  # HTTPエラーが発生した場合は例外を発生させる
        img = Image.open(BytesIO(response.content))
        
        # 画像を表示
        plt.imshow(img)
        plt.title(f"{name} in {location}")
        plt.axis('off')  # 軸を非表示
        plt.savefig(f"{name}.png")  # 画像をファイルに保存
        plt.close()  # プロットを閉じる
        print(f"Image saved as {name}.png")
    except requests.exceptions.RequestException as e:
        print(f"Error downloading image: {e}")
    except Exception as e:
        print(f"Error displaying image: {e}")
    
    # 通知メッセージを作成
    print(f"Today's recommended burger shop: {name} in {location}")
```
この関数は、ランダムに選んだハンバーガーショップの情報を表示し、画像をダウンロードして保存する。
## スケジューラの設定
```
scheduler = BlockingScheduler()
# 毎日午前9時に実行するように設定
scheduler.add_job(notify_burger_shop, 'cron', hour=9, minute=0)

try:
    print("Burger shop reminder started.")
    scheduler.start()
except (KeyboardInterrupt, SystemExit):
    print("Burger shop reminder stopped.")

```
apschedulerを使用して、毎日午前9時にnotify_burger_shop関数が実行されるようにスケジュールする。

## 実行方法
このスクリプトを実行することで、システムが起動し、毎日午前9時にランダムなハンバーガーショップの情報と画像が表示される。
```
python your_script.py
```
このシステムを実行するには、必要なライブラリをインストールする必要がある。
```
pip install requests pillow apscheduler matplotlib
```
## 注意点
+ 画像URLはサンプルのため、実際のURLに置き換える。
+ スクリプトを終了するには、Ctrl+Cを使用する。
sparkit-aviad.klein 0.1.0
A set of utilities and an intuitive API for constructing SQL queries programmatically
Requires Python: >=3.11

# Spar-kit - Python style API for heavy SQL users

Spark-kit provides a set of utilities and an intuitive API for constructing SQL queries programmatically, making it easier to build, read, and maintain complex SQL statements.

## Installation

```sh
pip install sparkit
```

## Usage

Sparkit was built borrowing from its inspiring packages to write SQL from left to right. Although a query might look like this:
```sql
SELECT id -- starting from what columns we want
FROM db.schema.table1 -- but we should start from where we want them
```

### Simplest entry point `table` and `col`
Sparkit allows you to start from sources:

```python
from sparkit.sql import table, col
from sparkit.column import Column

t = table("db.schema.table1")
print(t.sql)
# Output: SELECT * FROM db.schema.table1

t = table("db.schema.customers").select(col("id"), col("name"))
print(t.sql)
# Output: SELECT id, name FROM db.schema.customers

# declaring columns as objects
customer_id: Column = col("id")
```

### Operators and literals

All the basic python operators can be used on the `Column` object: `==,!=,>,>=,<.<=,&,|,+,-,...`

```python
from sparkit.sql import table, col, lit

query = table("db.schema.customers").select(
        col("id"), 
        (col("first_name") == lit("john")).as_("is_john"),
        (col("last_name") == lit("doe")).as_("is_doe"),
        (col("age") - col("years_since_joined")).as_("age when joined")
    )
print(query.sql)
# Output: SELECT id, first_name = 'john' AS is_john, last_name = 'doe' AS is_doe, age - years_since_joined AS `age when joined` FROM db.schema.customers
```

### Joins:
```python
t1 = table("db.schema.table1").as_("t1")
t2 = table("db.schema.table2").as_("t2")
inner = t1.join(t2, col("t1.id") == col("t2.id"))
print(inner.sql)
# Output: SELECT * FROM db.schema.table1 AS t1 INNER JOIN db.schema.table2 AS t2 ON t1.id = t2.id
```

## Inspiration and rationale

We wished to create a left-to-right API to write the huge SQL queries that sometimes dominate python code, without working with SQLAlchemy or spark which is a pain on its own

 - [Spark](https://spark.apache.org/examples.html)
 - [Polars](https://docs.pola.rs/)

## SQL flavour

Version 0.1.0 was built over BigQuery syntax, with the aim of supporting more flavours in future versions.

## Contributing

Please be aware of the package dependency structure:
![dependency structure](/sparkit/module%20relationship.png)

## License

This project is licensed under the MIT License. See the LICENSE file.

## Contact
For any inquiries, please contact [aviad.klein@gmail.com](mailto:aviad.klein@gmail.com) - don't hope for high SLA...
airflow-markdown-extension 0.1.1
A custom superfence for pymdown-extensions which render Airflow DAGs.
Requires Python: >=3.9
Keywords: DAG, airflow, markdown, mkdocs, superfence


# Airflow Markdown Extension

A custom superfence for pymdown-extensions which render Airflow DAGs.

This is currently a PoC / very experimental project (no tests, no support for task group and dynamic tasks, ...). Not recommended for production use.
kalman-detector 0.5.0
Detecting Fast Radio Bursts with Spectral Structures
Requires Python: >=3.9

# kalman_detector

[![GitHub CI](https://github.com/pravirkr/kalman_detector/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/pravirkr/kalman_detector/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/pravirkr/kalman_detector/branch/main/graph/badge.svg)](https://codecov.io/gh/pravirkr/kalman_detector)
[![License](https://img.shields.io/github/license/pravirkr/kalman_detector)](https://github.com/pravirkr/kalman_detector/blob/main/LICENSE)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

A Python Implementation of a Kalman filter detector for detecting smoothly variying signals hidden in gaussian noise, such as Fast Radio Bursts (FRBs).

The detection statistic is designed to process `I(f)`, a sequence of observed "amplitudes" (where `f` is an arbitrary indexed parameter), and decide between the following hypotheses:

```markdown
H0: I(f) = N(f)         Pure gaussian noise
H1: I(f) = A(f) + N(f)  A(f) is a smooth gaussian process with an unknown smoothness parameter. 
```

## Installation

The quickest way to install the package is to use [pip](https://pip.pypa.io):

```bash
pip install -U kalman_detector
```

## Usage

```python
from kalman_detector.main import KalmanDetector

kalman = KalmanDetector(spectrum_std)
kalman.prepare_fits(ntrials=10000)
kalman.get_significance(spectrum)
```

### Example

An example script demonstrating how to use the Kalman detector can be found in the [examples](examples/frb_kalman.py) directory.

|           |           |
| --------- | --------- |
| ![Kalman demo for FRB171003](examples/frb171003_kalman.png) | ![Kalman demo for FRB171004](examples/frb171004_kalman.png) |

### Efficiency

An example efficiency plot can be generates using:

```python
python -m kalman_detector.efficiency
```

## Citation

Please cite [Kumar, Zackay & Law (2024)](https://ui.adsabs.harvard.edu/abs/2024ApJ...960..128K) if you find this code useful in your research.
The BibTeX entry for the paper is:

```bash
@ARTICLE{2024ApJ...960..128K,
       author = {{Kumar}, Pravir and {Zackay}, Barak and {Law}, Casey J.},
        title = "{Detecting Fast Radio Bursts with Spectral Structure Using the Continuous Forward Algorithm}",
      journal = {\apj},
     keywords = {Radio astronomy, Radio transient sources, Astronomy data analysis, Astrostatistics techniques, Interstellar scintillation, 1338, 2008, 1858, 1886, 855, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2024,
        month = jan,
       volume = {960},
       number = {2},
          eid = {128},
        pages = {128},
          doi = {10.3847/1538-4357/ad0964},
archivePrefix = {arXiv},
       eprint = {2306.07914},
 primaryClass = {astro-ph.HE},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024ApJ...960..128K},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
py-missing-releases-librmo 0.1.1
Uses information stored in librmo to discover missing album releases from your music artists
Requires Python: >=3.11.5

# Py-Missing-Releases-LibRMO

With a media library set up by LibRMO you can use this application to query its database and return a list of missing releases from the artists you have

# Usage

Will take some time to acquire the responses from MusicBrainz (1s per existing release and then 1s per artist), but results are cached in MusicBrainzAPI.

```python
import pprint

from py_missing_releases_librmo import MissingReleases

mr = MissingReleases()
result = mr.execute()
pprint.pprint(result[::10])
pprint.pprint(f"Missing albums: {len(result)}")
```

```
[Release(name='Down to Earth',
         artist=AlbumArtist(name='Ozzy Osbourne',
                            mb_id='8aa5b65a-5b3c-4029-92bf-47a544356934'),
         mb_id='c76e9935-1cef-3339-930f-cf1914fd1037',
         url=''),
 Release(name='Cavalry in Thousands',
         artist=AlbumArtist(name='Tengger Cavalry',
                            mb_id='71c53622-5e7d-44da-90d3-30ea2c89ad0e'),
         mb_id='f0af6659-cef4-4397-aa28-5773d04c96fb',
         url=''),
...
...
'Missing albums: 519'
```