semvec/pypi-packages huggingface dataset:
spaceboi 0.1.4
A radio satellite event tracker.
Requires Python: >=3.10

![spaceboi](cover.png)


# spaceboi

spaceboi gives you the satellite passes. Period.

A simple satellite pass predictor using TLE data from celestrak.org. It can be
run in a GUI mode or CLI mode. The GUI mode uses Qt to display the passes in a
table, and graph the current events. The CLI mode prints the passes to the
console. The passes can also be plotted using the plot mode.

# Install

Clone the repo and install the requirements

```bash
pip install -r requirements.txt
```

or 

Install from pypi
```
pip install spaceboi
```


# Configuration

The config file by default is stored in ~/.config/spaceboi/config.json. The GUI
settings will automatically be saved to this file. The config file can also be
manually specified using the --config option.

Example config.json
```json
{
    "urls": [
        "https://celestrak.org/NORAD/elements/gp.php?GROUP=weather&FORMAT=json",
        "https://celestrak.org/NORAD/elements/gp.php?GROUP=amateur&FORMAT=json",
    ],
    "lat": 40.7128,
    "lon": -74.006,
    "timezone": "America/New_York",
    "filter_enabled": true,
    "satellites": [
        "NOAA 15",
        "NOAA 18",
        "NOAA 19",
        "METEOR-M2 2",
        "METEOR-M2 3",
        "ISS (ZARYA)",
        "GOES 18"
    ],
    "min_alt": 10,
    "hours": 24,
    "mode": "gui",
    "config": "~/.config/spaceboi/config.json",
    "tle": "~/.local/share/spaceboi/TLE"
}
```

- urls: list of urls to fetch TLE data from. For now JSON format is supported
- lat: latitude of observer
- lon: longitude of observer
- timezone: timezone of observer
- filter_enabled: filter satellites by name
- satellites: list of satellites for the filter
- min_alt: minimum altitude to display
- hours: number of hours ahead to predict
- mode: the default mode to run the program in
- config: the path to the config file

# Usage

```bash
# A gui to view passes
python spaceboi.py --mode gui

# Print the passes
python spaceboi.py --mode cli

# Plot the passes
python spaceboi.py --mode plot
```

All config options can be used as command line arguments. For example:

```bash
python spaceboi.py --mode gui \
--lat 40.7128 --lon -74.006 --timezone "America/New_York" \
--filter_enabled true \
--satellites "NOAA 15" "NOAA 18" "NOAA 19" "METEOR-M2 2" "METEOR-M2 3" "ISS (ZARYA)" "GOES 18" \
--min_alt 10 --hours 24 --config "config.json"
```
QuantileRegression 0.1.5
QuantileRegression package based on SciPy optimization routines.
Requires Python: >=3.7
Keywords: quantile regression, quantile, regression, optimization, fit, fitting

# Quantile Regression 
  
This repository is for Python implementations of basic Quantile Regression routines. 

## References

### Articles, books

[RK1] Roger Koenker, 
[Quantile Regression](https://books.google.com/books/about/Quantile_Regression.html?id=hdkt7V4NXsgC), 
Cambridge University Press, 2005.

[RK2] Roger Koenker,
["Quantile Regression in R: a vignette"](https://cran.r-project.org/web/packages/quantreg/vignettes/rq.pdf),
(2006),
[CRAN](https://cran.r-project.org/).

[AA1] Anton Antonov,
["A monad for Quantile Regression workflows"](https://github.com/antononcube/MathematicaForPrediction/blob/master/MarkdownDocuments/A-monad-for-Quantile-Regression-workflows.md),
(2018),
[MathematicaForPrediction at GitHub](https://github.com/antononcube/MathematicaForPrediction).

### Packages, paclets

[RKp1] Roger Koenker,
[`quantreg`](https://cran.r-project.org/web/packages/quantreg/index.html),
[CRAN](https://cran.r-project.org/).

[AAp1] Anton Antonov,
[Quantile Regression WL paclet](https://github.com/antononcube/WL-QuantileRegression-paclet),
(2014-2023),
[GitHub/antononcube](https://github.com/antononcube).

[AAp2] Anton Antonov,
[Monadic Quantile Regression WL paclet](https://github.com/antononcube/WL-MonadicQuantileRegression-paclet),
(2018-2024),
[GitHub/antononcube](https://github.com/antononcube).

[AAp3] Anton Antonov,
[`QuantileRegression`](https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression),
(2019),
[Wolfram Function Repository](https://resources.wolframcloud.com/FunctionRepository/resources/QuantileRegression).

### Repositories

[AAr1] Anton Antonov,
[DSL::English::QuantileRegressionWorkflows in Raku](https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows),
(2020),
[GitHub/antononcube](https://github.com/antononcube/Raku-DSL-English-QuantileRegressionWorkflows).

### Videos

[AAv1] Anton Antonov,
["Boston useR! QuantileRegression Workflows 2019-04-18"](https://www.youtube.com/watch?v=a_Dk25xarvE),
(2019),
[Anton Antonov at YouTube](https://www.youtube.com/@AAA4Prediction).

[AAv2] Anton Antonov,
["useR! 2020: How to simplify Machine Learning workflows specifications"](https://www.youtube.com/watch?v=b9Uu7gRF5KY),
(2020),
[R Consortium at YouTube](https://www.youtube.com/channel/UC_R5smHVXRYGhZYDJsnXTwg).
jamurai 0.1.3
Jinja wrapper for file and directory transformation and injection

jamurai
=======

Jinja wrapper for file and directory transformation and injection

As a single content::

    import os
    import yaml
    import jamurai

    with open("/tmp/from.txt", "w") as from_file:
        from_file.write("{{ foo }}")

    content = {
        "source": "from.txt",
        "destination": "to.txt"
    }

    values = {
        "foo": "bar"
    }

    jamurai.build(content, values, "/tmp")

    with open("/tmp/to.txt", "r") as to_file:
        data = to_file.read()

    data
    # "bar"

For multiple content::

    machine = jamurai.Machine("/tmp")

    with open("/tmp/this.txt", "w") as from_file:
        from_file.write("{{ this }}")

    with open("/tmp/that.txt", "w") as from_file:
        from_file.write("{{ that }}")

    contents = [
        {
            "source": "this.txt",
            "destination": "these.txt"
        },
        {
            "source": "that.txt",
            "destination": "those.txt"
        }
    ]

    values = {
        "this": "yin",
        "that": "yang"
    }

    for content in contents:
        machine.build(content, values)

    with open("/tmp/these.txt", "r") as to_file:
        data = to_file.read()

    data
    # "yin"

    with open("/tmp/those.txt", "r") as to_file:
        data = to_file.read()

    data
    # "yang"

Look at the content docs at the 'CnC Forge https://github.com/gaf3/cnc-forge/blob/main/Output.md#content'_

The only difference is the base is the same direcctory unlike transforming from one repo to enother.
blahaj-radar 0.1.0
check blahaj stocks in türkiye!
Requires Python: >=3.12

# blahaj-radar
a simple stock checker for the ikea blahaj in türkiye ikea stores.

türkçe readme için [tıklayınız](README_tr.md)
## usage
this uses arguments. you can use it like this:
```bash
blahaj_radar --city <city> --lang <language>
```
or
```bash
blahaj_radar --lang <language>
```
you have to use the `--lang` argument. you can use `--city` to specify the city. if you don't specify the city, it will enter interactive mode.
## installation
you can install this using pip:
```bash
pip install blahaj-radar
```
or you can install it from source:
```bash
pip install git+https://github.com/sctech-tr/blahaj-radar.git
```
## list of cities
- IKEA ANKARA
- IKEA ANTALYA
- IKEA BAYRAMPAŞA
- IKEA BURSA
- IKEA İZMİR
- IKEA KARTAL
- IKEA ÜMRANİYE
- İNTERNET MAĞAZASI
- ADANA SİPARİŞ VE TESLİM NOKTASI
- BODRUM SİPARİŞ VE TESLİM NOKTASI
## what about other countries?
there is a tool for other countries here: [blahaj-radar](https://hazy.gay/blahaj)
## how does it work?
it uses the ikea website to check the stock. it uses the `requests` and `beautifulsoup4` libraries to do this.
## disclaimer
this is not affiliated with ikea in any way. this is just a fun project.
## license
GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later)
overscore 0.1.2
Double underscore access notation

overscore
=========

Library for double underscore access notation

Overscore provides a way to retrieve (and store) multi-dimensional data using a single string
with double underscores.

Inspired by Django, the access string can be used as a routine argument or a URL parameter,
allowing for complex access within simple contexts::

    import overscore

    data = {
        "things": {
            "a": {
                "b": [
                    {
                        "1": "yep"
                    }
                ]
            }
        }
    }

    overscore.get(data, "things__a__b__0____1")
    # "yep"

All keys/indexes are separated by double underscores. Extra underscores dictate how to
parse that place in the path.

.. list-table:: Underscores and Behavior
    :header-rows: 1

    * - Underscores
      - Following
      - Meaning
      - Example
      - Equivalent
    * - 2
      - letters and numbers
      - key
      - a__b
      - ["a"]["b"]
    * - 2
      - numbers
      - index
      - a__1
      - ["a"][1]
    * - 3
      - numbers
      - negative index
      - a___2
      - ["a"][-2]
    * - 4
      - numbers
      - numerical key
      - a____3
      - ["a"]["3"]
    * - 5
      - numbers
      - neagtive numerical key
      - a_____4
      - ["a"]["-4"]
myfirstpkg2412 0.1.1
A sample Python package hhhhh 2024年12月07日21:48:15
Requires Python: >=3.6

# File Management Utility

This Python script provides a utility to find and optionally delete files based on their suffixes in specified directories. It uses `os`, `glob`, and `colorama` modules for file management and terminal text coloring.

---

## Features

1. **Find Files**  
   - Search for files with specific suffixes in multiple directories.
   - Display the file names along with their sizes in a formatted and colorized output.

2. **Delete Files**  
   - Delete files with specific suffixes in multiple directories.
   - Provides confirmation messages after successful deletion.

---

## Requirements

- Python 3.6 or later
- Install the `colorama` library for colorized terminal output:
  ```bash
  pip install colorama
pd-dwi 1.1.4
Physiologically-Decomposed Diffusion-Weighted MRI machine-learning model for predicting response to neoadjuvant chemotherapy in invasive breast cancer
Requires Python: <3.9,>=3.8.1

# PD-DWI

PD-DWI is a physiologically-decomposed Diffusion-Weighted MRI machine-learning model for predicting response to neoadjuvant chemotherapy in invasive breast cancer.

PD-DWI was developed by [TCML](https://tcml-bme.github.io/) group as part of [BMMR2 challenge](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=89096426) using [ACRIN-6698](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=50135447) dataset.

This work was accepted to MICCAI 2022 (to be held during Sept 18-22 in Singapore). 

**If you publish any work which uses this package, please cite the following publication:** 
```
M. Gilad, M. Freiman. PD-DWI: Predicting response to neoadjuvant chemotherapy in invasive breast cancer with Physiologically-Decomposed Diffusion-Weighted MRI machine-learning model. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 to be held during Sept 18-22 in Singapore. 
```
A pre-print version of our paper is available at https://arxiv.org/abs/2206.05695

## Installation

PD-DWI model can be installed directly from Github:

```
pip install git+https://github.com/TechnionComputationalMRILab/PD-DWI.git
```

## Usage

PD-DWI can be used in a Python script or via command line.

To explore all CLI options and syntax requirements run `pd-dwi --help` in your terminal.

## Contact

Please contact us on ms.maya.gilad@gmail.com