semvec/pypi-packages huggingface dataset:
scipion-em-janni 1.0.1
Plugin to use the denoising functionality of Sphire-Janni program within the Scipion framework.
Keywords: scipion cryoem imageprocessing denoise scipion-2.0

=======================
JANNI Scipion Plugin
=======================

This plugin allows to use the denoising functionality of Sphire-Janni software
within Scipion framework. It installs Janni and downloads the generic pre-trained
model.

NOTE: this version is not streamified and does not process in batches.

`JANNI`_ (Just Another Noise 2 Noise Implementation) implements a neural network
denoising tool described.

=====
Setup
=====

It is assumed that Scipion is currently installed. If not, follow the instructions `here`_.

Janni plugin requires to have Conda (Anaconda or Miniconda) installed and not initialized in
the shell.

- **Install this plugin in user mode:**

    It can be installed in user mode via Scipion main window (**Configuration** >
    **Plugins**) or using the command line:

.. code-block::

    scipion installp -p local/path/to/scipion-em-janni

- **Install this plugin in developer mode:**

    Using the command line:

.. code-block::

    scipion installp -p local/path/to/scipion-em-janni --devel

JANNI integration
------------------

The following steps presuppose that you have Anaconda or Miniconda installed on
your computer.
In ``$SCIPION_HOME/config/scipion.conf`` (Option View > Show Hidden Files must be enabled) set
**CONDA_ACTIVATION_CMD** variable in the Packages section.

For example:

::

 CONDA_ACTIVATION_CMD = . ~/anaconda3/etc/profile.d/conda.sh

Running JANNI tests
----------------------
To check that everything is properly installed and configured, you might want
to run some tests:

.. code-block::

   scipion test --grep janni --run

.. _JANNI: https://sphire.mpg.de/wiki/doku.php?id=janni
.. _here: https://scipion-em.github.io/docs/docs/scipion-modes/how-to-install.html
pyRinexpro 0.1.3
Python client for the rinexpro API

# pyRinexpro
pyRinexpro is a Python client for the [rinexpro](https://rinexpro.com/en/home/) API.

## Install
pyRinexpro can be install using pip

```
pip install pyRinexpro
```
## Getting Started
The first step is to import the pyRinexpro module and to instantiate a pyRinexpro.Client using your API key

```python
>>> import pyRinexpro as rnx
>>> client = rnx.Client(API_KEY)
```
From the client you can create a pyRinexpro.Session by uploading a session file
```python
>>> session = client.uploadSession("your_session_file")
```
Once the session preprocessing is completed, which should take about 20 seconds, you can create and run a pyRinexpro.Process from this session
```python
>>> process = session.newProcess('ppp', mode='static')
```
Once the process is completed you can get the resultat as a pyRinexpro.Log object
```python
>>> log = process.getLog()
```
Trajectory can be transfom into a pandas.DataFrame object
```python
>>> log.trajectory.df()
```
Results can be exported to geojson, kml and kmz
```python
>>> log.to_geojson('new_file.json')
>>> log.to_kml('new_file.kml')
>>> log.to_kmz('new_file.kmz')
```
## Documentation
Documentation can be found [here](https://pyrinexpro.readthedocs.io/en/latest/)