semvec/pypi-packages huggingface dataset:
crosscorr 0.1.2
Calculate Binary Cross Correlation Functions
Keywords: Spectra Astronomy

# crosscorr
Easily calculate Cross Correlation Functions (CCFs) from different high resolution spectra. Includes masks from HARPS, ESPRESSO, and HPF. Uses Fortran for speed. The fortran code used here is originally derived from <a href='https://github.com/rabrahm/ceres'>ceres</a>. The notebooks/ directory shows an example on how to calculate CCFs with HPF spectra.

# Installation instructions
To install, run the following commands:
```
git clone https://github.com/TeHanHunter/crosscorr.git
cd crosscorr
python3 setup.py install
```
This should build the fortran code as well.

# More detailed installation instructions
Normally the setup.py should fully take care of the installation (including the fotran compilation), but listing instructions to build the fotran code in python just in case.

## Fortran installation instructions

In case the fortran code does not build, run the following commands:

### Linux

```
cd crosscorr/
f2py -c -m CCF_1d CCF_1d.f
f2py -c -m CCF_3d CCF_3d.f
f2py -c -m CCF_pix CCF_pix.f
```

## Mac
On mac you might need to try sudo (verified that this works on my mac).

```
cd crosscorr/
sudo f2py -c -m CCF_1d CCF_1d.f
sudo f2py -c -m CCF_3d CCF_3d.f
sudo f2py -c -m CCF_pix CCF_pix.f
```
elle 0.0.4
A namespace for finite element libraries targeted at research into neural network architectures.
Requires Python: >=3.6



A namespace for finite element libraries targeted at research into neural network architectures.


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[![Commits since latest release][gh-image]][gh-link]

[pypi-v-image]: https://img.shields.io/pypi/v/elle.svg
[pypi-v-link]: https://pypi.org/project/elle/

[travis-image]: https://api.travis-ci.org/claudioperez/elle.svg?branch=master
[travis-link]: https://travis-ci.org/claudioperez/elle

[gh-link]: https://github.com/claudioperez/elle/compare/v0.0.0...master
[gh-image]: https://img.shields.io/github/commits-since/claudioperez/elle/v0.0.0.svg
[![Documentation](https://img.shields.io/badge/documentation-blue)](https://www.claudioperez.xyz/pkgs/elle)

## Installation

`elle` is a namespace that contains several packages which can be installed independently


## Packages

```{rst}
.. rendre:: gallery
   :verbose:

```

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kaggle-submissions 0.0.1
A template Python module
Requires Python: >=3.9

<a href="https://www.islas.org.mx/"><img src="https://www.islas.org.mx/img/logo.svg" align="right" width="256" /></a>
[![codecov](https://codecov.io/gh/IslasGECI/kaggle/graph/badge.svg?token=RY807ST1T1)](https://codecov.io/gh/IslasGECI/kaggle)
![example branch
parameter](https://github.com/IslasGECI/kaggle/actions/workflows/actions.yml/badge.svg)
![licencia](https://img.shields.io/github/license/IslasGECI/kaggle)
![languages](https://img.shields.io/github/languages/top/IslasGECI/kaggle)
![commits](https://img.shields.io/github/commit-activity/y/IslasGECI/kaggle)
![PyPI - Version](https://img.shields.io/pypi/v/kaggle)

# Kaggle
Practice field for Kaggle's competitions.
ml-mixins 0.2.2
A collection of useful mixins for machine learning development code.
Requires Python: >=3.10

# ML Mixins

[![PyPI - Version](https://img.shields.io/pypi/v/ml-mixins)](https://pypi.org/project/ml-mixins/)
[![codecov](https://codecov.io/gh/mmcdermott/ML_mixins/graph/badge.svg?token=T2QNDROZ61)](https://codecov.io/gh/mmcdermott/ML_mixins)
[![tests](https://github.com/mmcdermott/ML_mixins/actions/workflows/tests.yaml/badge.svg)](https://github.com/mmcdermott/ML_mixins/actions/workflows/tests.yml)
[![code-quality](https://github.com/mmcdermott/ML_mixins/actions/workflows/code-quality-main.yaml/badge.svg)](https://github.com/mmcdermott/ML_mixins/actions/workflows/code-quality-main.yaml)
[![license](https://img.shields.io/badge/License-MIT-green.svg?labelColor=gray)](https://github.com/mmcdermott/ML_mixins#license)
[![PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/mmcdermott/ML_mixins/pulls)
[![contributors](https://img.shields.io/github/contributors/mmcdermott/ML_mixins.svg)](https://github.com/mmcdermott/ML_mixins/graphs/contributors)

This package contains some useful python mixin classes for use in ML / data science, including a mixin for
seeding (`SeedableMixin`), timing (`TimeableMixin`), and memory tracking (`MemTrackableMixin`).

## Installation

this package can be installed via [`pip`](https://pypi.org/project/ml-mixins/):

```
pip install ml-mixins
```

## Usage

You can use these mixins either by (1) Defining your classes to inherit from them, then leveraging their
included methods in your derived class, or (2) Adding them post-hoc to an existing class for use in secondary
applications such as benchmarking or debugging without overhead in production code. Below, we show how to use
each mixin directly first, then we show how to add them to an existing class at the end, as that process will
still leverage the same decorator methods and class member variables in the resulting modified classes.

### Mixin Documentation

#### `SeedableMixin`

```python
from mixins import SeedableMixin

class MyModel(SeedableMixin):
    ...

    @SeedableMixin.WithSeed
    def fit(self, X, y):
        # This function can now be called with a seed kwarg, or it will use a pseudo-random seed which will be
        # saved to a class member variable if a seed is not passed.
...
```

#### `TimeableMixin`

TODO

#### `MemTrackableMixin`

TODO

### Adding Mixins Post-Hoc

```python
from mixins import TimeableMixin, add_mixin


class MyModel:
    ...

    def fit(self, X, y): ...


# Add the mixin to the class
TimedModel = add_mixin(
    MyModel, TimeableMixin, decorate_methods={"fit": TimeableMixin.TimeAs}
)

# Now, the class `TimedModel` will have the same methods as `MyModel`, but with the added timing
# functionality:

model = TimedModel()
model.fit(X, y)
model._profile_durations()  # will print durations...
```
ncompasslib 0.0.1.post7
nCompass Tools Library
Requires Python: >=3.11

# ncompasslib

nCompass Library

A Python library providing immutable objects and traits for building robust applications.

## Installation:
```
pip install ncompasslib
```

## Basic Usage:

### 1. Creating an Immutable Object:
The Immutable class prevents attributes from being modified after they are first set.

```
from ncompasslib.immutable import Immutable

class MyClass(Immutable):
    def __init__(self):
        super().__init__()
        self.value = 42

obj = MyClass()
obj.value  # Returns 42
obj.value = 43  # Raises RuntimeError: Cannot change state once created
```

### 2. Using the Mutate Decorator:
When you need to modify an immutable object in a controlled way, use the mutate decorator.

```
from ncompasslib.immutable import Immutable, mutate

class Counter(Immutable):
    def __init__(self):
        super().__init__()
        self.count = 0
        
    @mutate
    def increment(self):
        self.count += 1
```

### 3. Creating Traits:
Traits are abstract base classes that are also immutable.

```
from ncompasslib.trait import Trait

class MyTrait(Trait):
    def __init__(self):
        super().__init__()
        self.trait_value = "example"
```

## Features:
- Immutable objects with controlled mutation
- Trait system for creating abstract interfaces
- Python 3.11+ support
- Comprehensive test suite

For more information, visit:
https://github.com/nCompass-tech/ncompasslib