semvec/pypi-packages huggingface dataset:
springpy 1.0.0
Distance Matrix Visualizer in Python
Requires Python: >=3.4

﻿# Springpy

### Springpy visualises a distance matrix.
While similar to the "Spring Function" in [qgraph](https://github.com/SachaEpskamp/qgraph) in R and [networkx](https://networkx.org/) in Python, **springpy** provides a simple way to visualize a distance matrix using the Fruchterman Reingold method. **Springpy** allows you to create a spring graph or animate a distance matrix in just one line of code.

## Download
```sh
pip3 install springpy
```

## Usage
```py
import springpy as sp
matrix = [[0         , 0.00638545,  0.28778769],
          [0.00638545, 0         ,  0.21402251],
          [0.28778769, 0.21402251,  0         ]]

# Show a matplotlib animation
sp.animate(matrix)

# Save to video (Default Name:"spring_result.mp4")
sp.animate(matrix, save=True)

# Show a matplotlib graph
sp.graph(matrix)

# Save the graph(Default Name:"spring_result.jpg")
sp.graph(matrix, save=True)
```
Input: A [distance matrix](https://www.wikipedia.org/wiki/Distance_matrix) embedded into a 2D Python List.

### Demonstration:
Data: [dmatrix.csv](https://github.com/kiwirafe/springpy/blob/main/example/dmatrix.csv)  
Code: [example.py](https://github.com/kiwirafe/springpy/blob/main/example/example.py)

Result(animation & video):

https://user-images.githubusercontent.com/30309285/230230490-a8efb0eb-b12b-4ed2-9f08-1aefd3099c97.mp4

Result(graph):
![springpy_result](https://user-images.githubusercontent.com/30309285/233753442-ec1ed362-64dd-4dd4-a9ea-7c59c5f7c27d.jpg)

### Change Default Values
#### For Calculation
```py
import springpy as sp

# Width of the graph, default 100px
sp.w = 100
# Height of the graph, default 100px
sp.h = 100
# Cool down delta, default 0.975
sp.delta = 0.975
# The eplison, if the change is smaller than this value, then stop iterating, default 5
sp.eplison = 10
# Max iteration, the maximum number of iterations, default 500
sp.MaxIter = 500
```

#### For sp.animate:
```py
sp.animate(self, matrix, save=False, interval=100, VideoName="springpy_result.mp4"):
"""
:param save: whether to save the video, default False
:param interval: the interval between frames, default 100
:param VideoName: the name of the video for the result, default "springpy_result.gif". Note that this name must include the suffix(ie .gif)
""" 
```

#### For sp.graph:
```py
sp.graph(self, matrix, save=False, ImageName = "springpy_result.jpg"):
"""
:param save: whether to save the graph, default False
:param VideoName: the name of the graph for the result, default "springpy_result.jpg". Note that this name must include the suffix(ie .jpg or .png)
""" 
```

## Links
- [kiwirafe.com](kiwirafe.com)
- [Bibliography](https://github.com/kiwirafe/springpy/blob/main/Bib.md)
- [Example](https://github.com/kiwirafe/springpy/tree/main/example)
entx-py 1.0.2
The python file encryption library.
Requires Python: >=3.10

# EntX-Py - Encrypt and Decrypt your python files!

EntXpy allows you to encrypt and decrypt your python files with the EntX library, with encrypted files requiring a password to run/decrypt. All encrypted files have the extension .entxpy.

## The FileClient object
To perform operations on a file, you must initialise a FileClient object, with the file path and the password you wish to use.

```
from entxpy import FileClient

path = "path to .py / .entxpy file"
password = "your password"

my_client = FileClient(password, path)
```

## Encrypting a .py file
```
from entxpy import FileClient

path = "path to .py/ file"
password = "your password"

my_client = FileClient(password, path)

my_client.pack()
```

## Decrypting a .entxpy file
```
from entxpy import FileClient

path = "path to entxpy file"
password = "your password"

my_client = FileClient(password, path)

my_client.unpack()
```

## Running a .py / .entxpy file
```
from entxpy import FileClient

path = "path to .py / .entxpy file"
password = "your password"

my_client = FileClient(password, path)

my_client.run()
```

## Exceptions:

### entxpy.errors.InvalidPath
Raised when the path provided does not lead to a valid file

### entxpy.errors.InvalidOperation
Raised when an operation is being performed on an incorrect file, i.e running FileClient.unpack() on a .py file.
celldancer 1.1.7
Study RNA velocity through neural network.
Requires Python: >=3.7.6

cellDancer - Estimating Cell-dependent RNA Velocity
===========================================================================================

**cellDancer** is a modularized, parallelized, and scalable tool based on a deep learning framework for the RNA velocity analysis of scRNA-seq. Our website of tutorials is available at `cellDancer Website <https://guangyuwanglab2021.github.io/cellDancer_website/>`_.


cellDancer's key applications
========================================================
* Estimate cell-specific RNA velocity for each gene.
* Derive cell fates in embedding space.
* Estimate pseudotime for each cell in embedding space.

What's new
========================================================
cellDancer is updated to v1.1.7

* Added progress bar for adata_to_df_with_embed() and adata_to_raw().
* Added try except to catch genes with low quality in velocity().

Installation
========================================================
cellDancer requires Python version >= 3.7.6 to run.

To run cellDancer locally, create an `conda <https://docs.conda.io/en/latest>`_ or `Anaconda <https://www.anaconda.com/>`_ environment as ``conda create -n cellDancer python==3.7.6``, and activate the new environment with ``conda activate cellDancer``. cellDancer could be installed with ``pip install celldancer``.

To install cellDancer from source code, run:
``pip install 'your_path/Source Code/cellDancer'``.

For M1 Mac users if you encountered a problem while installing bezier. Please refer to the following link:
https://bezier.readthedocs.io/en/2021.2.12/#installing

If any other dependency could not be installed with ``pip install celldancer``, try ``pip install --no-deps celldancer``. Then install the dependencies by ``pip install -r requirements.txt``.
markit-processing 0.0.1
Code for post processing data
Requires Python: >=3.7

# markit-processing

Code for post processing data from [mvig-robotflow/rfmarkit-gateway](https://github.com/mvig-robotflow/rfmarkit-gateway) and [mvig-robotflow/rfmarkit-rs-recorder](https://github.com/mvig-robotflow/rfmarkit-rs-recorder)
markit-rs-recorder 2.0.0
Realsense remote recorder
Requires Python: >=3.6

# RealsenseRecorder

A set of Python scripts to operate Intel Realsense Cameras. By using multi-thread techniques, this script can record color + depth from up to 4 Realsense Cameras (2 x L515 and 2 x D435)

## Installation

### From Source

```shell
git clone https://github.com/mvig-robotflow/rfimarkit-rs-recorder
cd rfimarkit-rs-recorder
python setup.py
```

### From PyPi

```shell
python -m pip install markit-rs-recorder
```

## Usage

### Script Usage

```python
import io

import cv2
import numpy as np
import yaml
from markit_rs_recorder.common import new_realsense_camera_system_from_config, RealsenseSystemModel

cfg_str = """
realsense:
  cameras:
  - color:
    - exposure: -1
      format: rs.format.bgra8
      fps: 30
      height: 1080
      width: 1920
    depth: [] # Do not user depth 
    endpoint: {}
    imu: []
    product_id: 0B64 # 0B64 for L515
    product_line: L500 # Currently supported models are L500(L515) and D400(D435)
    ref: 1
    sn: f0220485 # SN of target Camera, can get from RealSenseViewer
  system:
    base_dir: ./realsense_data
    frame_queue_size: 100
    interactive: false
    interval_ms: 0
    use_bag: false
"""


def main():
    cfg = yaml.load(io.StringIO(cfg_str), yaml.SafeLoader)
    sys = new_realsense_camera_system_from_config(RealsenseSystemModel, cfg['realsense'], None)
    print(sys.cameras)
    cam = sys.cameras[0]
    cam.open()
    cam.start()
    mtx = np.array(cam.intrinsics_matrix)
    while True:
        color_image, depth_image, ts, sys_ts, frame_counter = cam.get_frames()
        cv2.imshow("frame", color_image)
        key = cv2.waitKey(1)
        if key == 27:
            print('esc break...')
            cv2.destroyAllWindows()
            break


main()
```

### Command Line Usage

To Create and persist record configuration:

```shell
python -m markit_rs_recorder configure
```

To launch a remote record station that supports REST API

```shell
python -m markit_rs_recorder serve
```

To launch a remote record station that is calibrated 
```shell
python -m markit_rs_recorder serve --calibration=path/to/calibration
```

> Note: The calibration.json file is expected to be in the `path/to/calibration` directory. The calibration file can be generated by running `python -m realsense_recorder calibrate`

To run calibration

```shell
python -m markit_rs_recorder calibrate
```

To run post-processing

```shell
python -m markit_rs_recorder post_process --base_dir path/to/recording
```