semvec/pypi-packages huggingface dataset:
NEMSpy 0.1.6
Python wrapper for the NOAA Environmental Modeling System
License: MIT
Requires Python: >=3.8

# NEMSpy
### Python wrapper for the NOAA Environmental Modeling System

[![tests](https://github.com/noaa-ocs-modeling/NEMSpy/workflows/tests/badge.svg)](https://github.com/noaa-ocs-modeling/NEMSpy/actions)

This repository implements the [National Unified Operational Prediction Capability (NUOPC)](https://www.earthsystemcog.org/projects/nuopc/).

#### Related Repositories:
- https://github.com/NOAA-EMC/NEMS
- https://github.com/esmf-org/esmf
django-migration-scripts 0.1.2
Add migration scripts seamlessly
License: MIT

PyPI: https://pypi.org/project/django-migration-scripts

### Installation

```bash
pip install django_migration_scripts
```

### Usage

Add to INSTALLED_APPS in settings.py

```python
INSTALLED_APPS = [
######,

'django_migration_scripts',

#####
]
```

Then run the script from shell

```python
python manage.py addscript <app_name>
```

This adds a script in the app in the <app>/scripts folder. Add your script there

### Running the script

```python
python manage.py runscripts
```
struct-lmm 0.3.2
Linear mixed model to study multivariate genotype-environment interactions
License: MIT
Keywords: lmm,gwas,environment

# Struct-LMM

Structured Linear Mixed Model (StructLMM) is a computationally efficient method to
test for and characterize loci that interact with multiple environments [1].

This a standalone module that implements the basic functionalities of StructLMM.
However, we recommend using StructLMM via
[LIMIX2](https://limix.readthedocs.io/en/2.0.x/index.html) as this additionally
implements:

- Multiple methods for GWAS;
- Methods to characterize GxE at specific variants;
- Command line interface.

## Install

From terminal, it can be installed using [pip](https://pypi.org/pypi/pip):

```bash
pip install struct-lmm
```

## Usage

```python
>>> from numpy import ones, concatenate
>>> from numpy.random import RandomState
>>>
>>> from struct_lmm import StructLMM
>>>
>>> random = RandomState(1)
>>> n = 20
>>> k = 4
>>> y = random.randn(n, 1)
>>> E = random.randn(n, k)
>>> M = ones((n, 1))
>>> x = 1.0 * (random.rand(n, 1) < 0.2)
>>>
>>> lmm = StructLMM(y, M, E)
>>> lmm.fit(verbose=False)
>>> # Association test
>>> pv = lmm.score_2dof_assoc(x)
>>> print(pv)
0.8470017313426488
>>> # Association test
>>> pv, rho = lmm.score_2dof_assoc(x, return_rho=True)
>>> print(pv)
0.8470017313426488
>>> print(rho)
0
>>> M = concatenate([M, x], axis=1)
>>> lmm = StructLMM(y, M, E)
>>> lmm.fit(verbose=False)
>>> # Interaction test
>>> pv = lmm.score_2dof_inter(x)
>>> print(pv)
0.6781100453132024
```

## Problems

If you encounter any problem, please, consider submitting a [new issue](https://github.com/limix/struct-lmm/issues/new).

## Authors

- [Danilo Horta](https://github.com/horta)
- [Francesco Paolo Casale](https://github.com/fpcasale)
- [Oliver Stegle](https://github.com/ostegle)
- [Rachel Moore](https://github.com/rm18)

## License

This project is licensed under the [MIT License](https://raw.githubusercontent.com/limix/struct-lmm/master/LICENSE.md).

[1] Moore, R., Casale, F. P., Bonder, M. J., Horta, D., Franke, L., Barroso, I., &
    Stegle, O. (2018). [A linear mixed-model approach to study multivariate
    gene–environment interactions](https://www.nature.com/articles/s41588-018-0271-0) (p. 1). Nature Publishing Group.
circulation-import 0.0.1
Set of tools facilitating the process of importing book collections into SimplyE's Circulation Manager by using SFTP protocol
License: MIT
Requires Python: >=3.7,<4.0

# circulation-import
Set of tools facilitating the process of importing book collections into SimplyE's Circulation Manager by using SFTP protocol.

## Architecture

circulation-import consists of two parts:
- **client** responsible for uploading content to the SFTP server, waiting for a report, downloading it and converting it to CSV format
- **server** responsible for watching for new book collections, importing them into CM using its **directory_import** script and uploading a report to the SFTP server
  
Picture below illustrates the architecture of the solution:
  ![circulation-import architecture](docs/01-circulation-import-architecture.png "circulation-import architecture")

Another picture below contains a sequence diagram 
  ![Import workflow](docs/02-Import-workflow.png "Import workflow")


## Usage
1. Update all the submodules:
```bash
git submodule update --remote --recursive
cd circulation-lcp-test
git submodule update --remote --recursive
cd ..
```

2. Run the LCP testbed:
```bash
docker-compose --file circulation-lcp-test/docker-compose.yml --env-file circulation-lcp-test/.env up -d
```

3. Follow the instructions in LCP testbed's [README.md file](circulation-lcp-test/README.md) to set it up

4. Run the server:
```bash
docker-compose --file circulation-lcp-test/docker-compose.yml --file docker-compose.yml --env-file circulation-lcp-test/.env up -d
```

5. Create and activate a virtual environment:
```bash
python -m venv .venv
source .venv/bin/activate
```

3. Install *circulation-import* from PyPi:
```bash
pip install circulation-import
```

7. Run the client:
```bash
python -m circulation-import client import \
    --collection-name=lcp \
    --data-source-name=data_source_1 \
    --books-directory=./circulation-lcp-test/lcp-collection/collection \
    --covers-directory=./circulation-lcp-test/lcp-collection/collection \
    --reports-directory=./reports \
    --metadata-file=./circulation-lcp-test/lcp-collection/collection/onix.xml \
    --metadata-format=onix \
    --configuration-file=./configuration/client-configuration.yml \
    --logging-configuration-file=./configuration/logging.yml
```

8. Go to [reports](./reports) folder and find a report in CSV format