semvec/pypi-packages huggingface dataset:
icon-uxarray 0.1.8
Awesome icon_uxarray created by jcanton

# icon_uxarray

[![codecov](https://codecov.io/gh/jcanton/icon_uxarray/branch/main/graph/badge.svg?token=icon_uxarray_token_here)](https://codecov.io/gh/jcanton/icon_uxarray)
[![CI](https://github.com/jcanton/icon_uxarray/actions/workflows/main.yml/badge.svg)](https://github.com/jcanton/icon_uxarray/actions/workflows/main.yml)

Awesome icon_uxarray created by jcanton

## Install it from PyPI

```bash
pip install icon-uxarray
```

## Usage

Convert an icon grid to be UGRID-compatible

```py
from icon_uxarray import icon_grid_2_ugrid
ugrid_fname = icon_grid_2_ugrid(iconGrid_fname='some_icon_grid.nc')
```

then use it for plotting

```py
import uxarray as ux
import holoviews as hv
import panel as pn

# open dataset
uxds = ux.open_dataset(grid_fname, data_fname)

# static plot
hvplot = uxds['temp'].isel(time=0).isel(height=0).plot()
server = pn.panel(hvplot).show()

# interactive plot
def sliders_plot(itime, iheight):
    return uxds['temp'].isel(time=itime).isel(height=iheight).plot()
torus = hv.DynamicMap(sliders_plot, kdims=['time', 'height'])
hvplot = torus.redim.range(time=(0, len(uxds.time)), height=(0, len(uxds.height)))
server = pn.panel(hvplot).show()
```

## Development

Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.
scipion-em-modelangelo 3.1.1
Scipion plugin for modelangelo.
Keywords: scipion cryoem imageprocessing modelling modelangelo scipion-3.0

====================
Modelangelo   plugin
====================

This plugin provide a wrapper for `Model angelo <https://github.com/3dem/model-angelo>`_ which allows *de novo* atomic modelling


Installation
------------

a) Stable version

.. code-block::

    scipion3 installp -p scipion-em-modelangelo

b) Developer's version

    * download repository

    .. code-block::

        git clone https://github.com/scipion-em/scipion-em-modelangelo.git

    * install

    .. code-block::

        scipion3 installp -p path_to_scipion-em-modelangelo --devel

Modelangelo will be installed automatically with the plugin using a Conda environment.


Configuration variables
.......................

There are some variables related to the model-angelo installation. If you have installed
model-angelo within Scipion, you may define `MODEL_ANGELO_ENV_ACTIVATION` for specifying
how to activate the environment. This variable with be used together with the general
conda activation to generate the final model-angelo command. For example:

.. code-block::

    MODEL_ANGELO_ENV_ACTIVATION = conda activate model_angelo

If this variable is not defined, a default value will be provided that will work if the
latest version is installed.

If model-angelo is installed already outside Scipion, one could define `MODEL_ANGELO_ACTIVATION`.
This variable will provide an activation (or load) command that can be anything and the Scipion
conda activate will not be prepended. For example (loading model-angelo as a module):

.. code-block::

    MODEL_ANGELO_ACTIVATION = module load model-angelo/main

If you need to use CUDA different from the one used during Scipion installation (defined by *CUDA_LIB*), you can add *MODEL_ANGELO_CUDA_LIB* variable to the config file.

Protocols
---------

* Model builder

Tests
-----

* scipion3 tests modelangelo.tests.tests_model_angelo.TestModelAngel
utils2p 1.0.1
Basic utility functions for 2 photon image data generated using ThorImage and ThorSync.

utils2p
=======

.. image:: https://travis-ci.com/NeLy-EPFL/utils2p.svg?token=snip9q1Tczja5zRZ7RGJ&branch=master
.. image:: https://codecov.io/gh/NeLy-EPFL/utils2p/branch/master/graph/badge.svg?token=Y5TJHHYYFT
  :target: https://codecov.io/gh/NeLy-EPFL/utils2p
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.5501119.svg
  :target: https://doi.org/10.5281/zenodo.5501119

.. contents Topics

Overview
--------
This module provides utility functions necessary for loading and processing
2-photon imaging data acquired with ThorLabs microscopes. It includes function
to read files generated by ThorImage and ThorSync.

Documentation
-------------
The full documentation is hosted on `github pages <https://nely-epfl.github.io/utils2p/>`_.

Installation
------------
Note that you will need an installation of `numpy <https://numpy.org/>`_ to install this package.
You can install numpy with the following command:

.. code-block:: shell

    pip install numpy

The package can be installed using `pip <https://pypi.org/project/pip/>`_ with:

.. code-block:: shell

    pip install utils2p

If you need the latest version or you want to make changes to the code, you should install
utils2p from source by following the steps below.

To do so first create a copy of the source code on your machine by cloning the repository:

.. code-block:: shell

    git clone https://github.com/NeLy-EPFL/utils2p.git

Then install it using the following command:

.. code-block:: shell

    pip install -e utils2p

Dependencies
------------
- python >3.6
- `pip <https://pypi.org/project/pip/>`_
- `numpy <https://numpy.org/>`_
- `h5py <https://www.h5py.org/>`_
- `scipy <https://scipy.org/>`_