semvec/pypi-packages huggingface dataset:
Omar8few4 0.1.8
a collection of convenience and wrapper functions supporting tasks frequently needed by scientists
Keywords: hpc,scientific computing

sci Package
===========

A Python 3 package or simply a collection of convenience and wrapper functions supporting tasks
frequently needed by scientists.

install
-------

| ``pip3 install sci`` or
| ``pip3 install --user --upgrade sci``

use the ``--user`` flag on shared computers where you do not have root access. It will install the
package in a place like ``~/.local/lib/python3.x/site-packages/sci`` . You can delete that folder
later to remove the package.

design goals
------------

1. Simplicity: wrap frequently used workflows into simple functions that make life easier for
   scientists. Most import statements should move into functions (except for frequently used ones
   like os, sys) to avoid confusion during autocomplete with VS Code and other IDEs.
2. Verbose but easily consumable docstrings: Docstrings are accessible via code autocomplete in IDEs
   such as VS Code or Atom and through automated documentation environments such as sphinx. Each
   docsting should start with an example use case for the function.
3. functional programming paradigm: While using classes is permitted we encourage writing fewer
   classes and more functions (perhaps with decorators)
4. Cross-platform: The code should work on Linux (RHEL and Debian based), Windows and Mac OS X.
5. Python 3 only compatibility, Python 2.x is legacy and we do not want to invest any extra time in
   it.

How to contribute your own code
-------------------------------

1. ``git clone git@github.com:FredHutch/sci-pkg.git``
2. create a new branch (eg : sci-yourname)
3. paste your function into module sci/new.py.
4. Make sure you add an example call in the first line of the doc string.
5. Add your test case to sci-pkg/sci/tests
assess-pps 0.1.1
Library for calculating time-depending metrics for pps

# Time-Dependent Metrics to Assess Performance Prediction Systems

### Installation
````
pip install assess_pps
````

### Get started
```Python
# Importing our library
from assess_pps import metrics as m

# for working with data we need pandas
import pandas as pd

# implementing csv files
ectel = pd.read_csv("D:/path")  # put your link here
ectelsys2 = pd.read_csv("D:/path")

# We use the file ectel.csv for testing.
prediction_time = 35  # till which week we should search the overall stability value
ectel_temp = ectel.loc[ectel.weeknumber <= prediction_time]  # dataframe with all data till predicted time
S = ectel_temp['idUser'].unique()  # list with student indexes
Y = [0, 1, 2]  # list of classes
x = 10  # x  earliest times of correct predictions

# Stability 
stability = m.Stability(ectel_temp, S)
print("Stability: ", stability)

# Accuracy
accuracy = m.Accuracy(ectel_temp, S)
print("Accuracy: ", accuracy)

# Earliness 
earliness = m.Earliness_Total(S, Y, x, ectel_temp)
print("Earliness: ", earliness)

# ESS 
# for ESS we need to know stability and earliness
ESS = m.ESS(stability, earliness)
print("ESS of average earliness: ", ESS)

# EAS 
# for EAS we need to know accuracy and earliness
EAS = m.EAS(accuracy, earliness)
print("EAS of average earliness: ", EAS)
```
molecular 0.2.5
A toolkit for molecular dynamics simulations


.. image:: molecular.svg
   :alt: molecular
   :height: 200

a package for molecular dynamics simulations and analysis

..
   master

.. image:: https://img.shields.io/pypi/v/molecular
   :target: https://pypi.org/project/molecular/

.. image:: https://img.shields.io/travis/com/lockhartlab/molecular/master
   :target: https://travis-ci.com/github/LockhartLab/molecular

.. image:: https://img.shields.io/codecov/c/github/lockhartlab/molecular/master
   :target: https://codecov.io/gh/LockhartLab/molecular

..
   *dev:*
   <nobr><img src="https://img.shields.io/travis/com/lockhartlab/molecular/dev"  alt="build-status-dev"/></nobr>
   <nobr><img src="https://img.shields.io/codecov/c/github/lockhartlab/molecular/dev" alt="coverage-dev"/></nobr>

note that this package is **still in development**
at this stage, I'm only building thing as they come to mind  
version 1.0 will be the first major release  
until then, consider this code heavily volatile

Note: portions of this code have Fortran backends that may need to be built prior to successful use of the code. We
have it on our to-do list to compile OS-specific wheels.

yet, install simply by calling `pip install molecular` or `pip install --pre molecular` for the latest development
release.

we endeavor to follow https://scikit-learn.org/stable/developers/performance.html

similar packages:
BioSimSpace
MDAnalysis
MDTraj