semvec/pypi-packages huggingface dataset:
slitherway 0.1.3
Python wrapper for the Flyway CLI
License: MIT
Requires Python: >=3.7,<4
Keywords: migrations,flyway

# Slitherway

[![PyPi version](https://badgen.net/pypi/v/slitherway/)](https://pypi.org/project/slitherway/)

Slitherway is a lightweight python wrapper around the [Flyway](https://flywaydb.org/) CLI.  
It allows you to run migrations directly from your python applications and tests

In order to use, you must have the Flyway CLI installed on your machine!

## Example

Using slitherway is simple

```py
from slitherway.commands import migrate
from slitherway.models import FlywayCommandArgs

args = FlywayCommandArgs(
    user=pg.POSTGRES_USER,
    password=pg.POSTGRES_PASSWORD,
    locations=["migrations"],
    url=f"jdbc:postgresql://localhost:{pg.get_exposed_port(5432)}/{pg.POSTGRES_DB}",
)

migrate(args)
```
muse-psf 0.0.3
muse_psf
License: MIT
Keywords: python adaptative optics muse psf

# muse-psf.0.1

This repository stores scripts for PSF fitting from IFS data using PampelMuse. (https://gitlab.gwdg.de/skamann/pampelmuse).

The main script is psf_fitting.py, which contains all the necessary steps to prepare the input catalogue necessary to run PampelMuse. This steps are described below: 

1. Scan directory: Scan the directory that contains your single datacubes and create a monitoring file to keep the track of the progress.
  
2. Make filter images from single datacubes: Calls esorex to create I-band images. Other filters are available in the "filters" folder.

3. Source extraction from filter images: Creates input catalogues for PampelMuse from the filter images.

4. Input catalogue for PampelMuse: Clip catalogue extracted in the previous step. 

5. Run PampelMuse: Calls the PampelMuse routines INITFIT/SINGLESRC, CUBEFIT, and POLYFIT.


DEPENDENCIES
------------

This script makes use of the following important libraries and softwares:

	+ Numpy.
	+ Astropy.
	+ Photutils.
	+ Esorex.
	+ SExtractor.
	+ Pampelmuse.

USAGE
-----

Before running the code, you must change the paths.py file, which contains the paths of the folders where you want to store the filter images, catalogues and psf files. If this folders is not in the library folder, the script creates them in the correspondent process. Also contains the instrument gain and the passband that you want to use to create the filter images.

After this is done, you must change the configuration files stored in the "config" folder. The default files and se.params are not necessary to change unless you want to use other configuration and obtain more or less parameters in the first catalogue.  
The files pampelmuse.json and se.config contain the information for the PSF fit, so you must change it with the parameters of the instrument and initial parameters of the PSF. 

After all this is done, you simply run:

		python psf_fitting.py <Datacubes Directory> <n° of files to process>

And the code will do all the steps sequentially one by one. When the script finish one of the process, it will ask you if you want to continue with the next one. If the answer is "no", then the process is stopped. 

OUTPUTS
-------

The code will generate the following outputs: 

1. Monitoring file: monitoring_file.txt stored in the main folder. 

2. Filter images: By default, the output images are stored in the "single_iband" folder.

3. SExtractor catalogue: Table in ASCII.ecsv format with the name “filename_filter.se.cat”.
The output table contains a lot of parameters like flux, magnitude, x and y positions, Alpha_J2000 (RA), Delta_J2000 (DEC), ellipticity, class_star (stellarity), etc. You can add or remove parameters changing the config/se.params file.

4. Input catalog for PampelMuse: These catalogues have the suffix ‘ppmuse_in.dat'.

5. psf.fits and prm.fits files: The parameter file (prm.fits) contains the parameters of the PSF and the psf file (psf.fits) contains the image of the PSF and the residuals.
charmed-traceback 0.1.2
Automatically apply traceback styling akin to Jetbrains Traceback highlighting, providing much easier debugging and log searching, as-well as feel more at home away from PyCharm
License: MIT
Requires Python: >=3.7.0,<4.0
Keywords: traceback,styling,developer

# Charmed Traceback
JetBrains style Tracebacks everywhere.<br>
Automatically apply traceback styling akin to Jetbrains Traceback highlighting, providing much easier debugging and log searching, as-well as feel more at home away from PyCharm


# Installation
### Through pip:<br>
`pip install charmed-traceback`
### Build with poetry:<br>
`pip install Poetry`<br>
`poetry build`

# Usage
### Charmed Traceback can be executed as a module:
`python -m charmed_traceback somefile.py`
### Charmed Traceback also works well within a script or even directly in the interpreter REPL. Standard usage will re-stylize the output, unless it's being redirected to a pipe:
```python
import charmed_traceback
charmed_traceback.add_hook()
```
### If want to retain the charmed style even when stderr is being piped, tack on an always=True argument:
```python
import charmed_traceback
charmed_traceback.add_hook(always=True)
```
### There are also a couple of convenience imports, which get the footprint down to one line:
```python
# Same as add_hook()
import charmed_traceback.auto

# Same as add_hook(always=True)
import charmed_traceback.always
```
### When implementing with a project that alternates between having access to development dependencies and not having those dependencies, it would be most convenient to provide a context manager to use this Charmed Tracebacks:
```python
import contextlib

def dev_charm_traceback_handler() -> None:
    with contextlib.suppress(ImportError):
        import charmed_traceback.auto

# This call can be left in a project regaurdless of the presence of Charmed Traceback package.
dev_charm_traceback_handler()
```
hf-model-s-cpu 0.1.1
model-s served from hf spaces using torch+cpu
License: MIT
Requires Python: >=3.8.3,<4.0.0

# model-s
[![pytest](https://github.com/ffreemt/model-s-cpu/actions/workflows/routine-tests.yml/badge.svg)](https://github.com/ffreemt/model-s-cpu/actions)[![python](https://img.shields.io/static/v1?label=python+&message=3.8%2B&color=blue)](https://www.python.org/downloads/)[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)[![PyPI version](https://badge.fury.io/py/hf_model_s_cpu.svg)](https://badge.fury.io/py/hf_model_s_cpu)

model-s served from hf spaces for torch cpu

## Install it

```shell
pip install git+https://github.com/ffreemt/model-s-cpu
# poetry add git+https://github.com/ffreemt/model-s-cpu
# git clone https://github.com/ffreemt/model-s-cpu && cd model-s-cpu
```

## Install `sentence-transformers`
Simply
```bash
pip install sentence-transformers
```
Or
```bash
pip install --no-cache-dir torch==1.8.0+cpu -f https://download.pytorch.org/whl/torch_stable.html  # adjust according to os/python
pip install transformers tqdm numpy scikit-learn nltk sentencepiece pillow
pip install --no-deps sentence-transformers
```

## Use it
```python
from hf_model_s_cpu import model_s

```
slap.core.semver 0.1.0dev1
Provides a semver specifier to represent version constraints compatible with `packaging`.
License: MIT
Requires Python: >=3.6,<4.0

# slap.core.semver

A semver version constraint specifier compatible with [`packaging`][0].

  [0]: https://packaging.pypa.io/
  [1]: https://docs.npmjs.com/about-semantic-versioning#using-semantic-versioning-to-specify-update-types-your-package-can-accept

## Usage

```py
from slap.core.semver.specifier import SemverSpecifier

spec = SemverSpecifier('1.x')
assert str(spec.version_selector.canonical) == "^1.0.0"
assert spec.contains("1.2.3")
assert not spec.contains("0.3.0")
```

The `SemverSpecifier` constructor will raise `packaging.specifier.InvalidSpecifier` if the
specifier string cannot be parsed.

## Behaviour

The version ranges supported by `SemverSpecifier` are [inspired by NPM][1]. Below is a table to
illustrate the behaviour:

| Release | Example |
| ------- | ------- |
| Patch release | `1.0`, `1.0.x`, `~1.0.4` |
| Minor release | `1.0`, `1.x`, `^1.0.4` |
| Major release | `*` or `x` |
| Other | `x.0.4` |

In addition to the NPM semver version ranges, Python version epochs, pre releases, post releases,
dev releases and local versions are supported. To list some examples: `2!^1.0.4`, `~1.0.3.post1`,
`^1.0.4.dev1+gdeadbeef`.

> Note: The remainder fields are not supported in the `x` placeholder format.

## Compatibility

Requires Python 3.6 or higher.