semvec/pypi-packages huggingface dataset:
outliers-101703161 0.0.1
A Simple Package to detect and remove outliers using Inter-Quatile Range
Requires Python: >=3.6



This is a simple package to use Inter-Quartile Range to detect and remove the outliers.
It shows the number of outliers removed and data after removal of outliers is saved in another file
which is provided.

To run file use python outliers.py "data.csv" "writing.csv"
pnc-cli 1.8.0
CLI for the Project Newcastle build system
Keywords: PNC,REST

#pnc-cli
A python CLI around the PNC REST API

Installation:
 * python setup.py install --user

Usage:
 * Configure the REST endpoint for PNC in USER_HOME/.config/pnc-cli/pnc-cli.conf
 * pnc -h for a list of valid operations.

Currently outputs the raw JSON. More refined output is definitely in the works :) 

#Tests

Requirements:
 * py.test (pip install pytest)
 * mock (pip install mock)
 * GitPython (pip install GitPython)

Run any of the testing scripts to see results. The tested instance is configured by the local file ~/.config/pnc-cli/pnc-cli.conf
outliers-101703144 0.0.1
A Simple Package to detect and remove outliers using Inter-Quatile Range
Requires Python: >=3.6



This is a simple package to use Inter-Quartile Range to detect and remove the outliers.
It shows the number of outliers removed and data after removal of outliers is saved in another file
which is provided.

To run file use python outliers.py "data.csv" "writing.csv"
outliers-101703192 0.0.1
A Simple Package to detect and remove outliers using Inter-Quatile Range
Requires Python: >=3.6



This is a simple package to use Inter-Quartile Range to detect and remove the outliers.
It shows the number of outliers removed and data after removal of outliers is saved in another file
which is provided.

To run file use python outliers.py "data.csv" "writing.csv"
OUTLIERSS-101703325 0.0.1
A Simple Package to detect and remove outliers using Inter-Quatile Range
Requires Python: >=3.6



This is a simple package to use Inter-Quartile Range to detect and remove the outliers.
It shows the number of outliers removed and data after removal of outliers is saved in another file
which is provided.

To run file use python outliers.py "data.csv" "writing.csv"
blinkytools 0.0.5
Software tools to work with Blinky sound-to-light conversion sensors.
Requires Python: >=3.6

Blinky Tools
============

This package contains a number of tools to work with Blinkies, small sound-to-light conversion sensors.

Install
-------

    pip install blinkytools


Use the GUI
-----------

The GUI can be used to record Blinky signals on live data by
providing the identifier of the video stream

    python -m blinkytools -v <video_stream>

or from a file by providing a file name

    python -m blinkytools -v <video_filename>

When called without argument, the default video stream is used.

The GUI can also be used with a
[Shodensha  DN3V-130BU](https://www.shodensha-inc.co.jp/ja/dn3v-130bu/)
industrial camera, but this requires to install the driver and python bindings
for the driver from [here](https://github.com/onolab-tmu/icube_sdk).

    python -m blinkytools -i

Preview the content of a file
-----------------------------

The package can also be used to preview the content
of a file recorded using the GUI.

    python -m blinkytools.file_preview <filename>

Open file from within Python
----------------------------

The file format is [MessagePack](https://msgpack.org) and can thus be read by
any appropriate software package.
The content is as follows:

* Software version
* File creation date
* Framerate of the signal
* A dictionary of extra metadata
* The Blinky signals stored as a binary data array

This is an example of script that opens a Blinky file.

    from blinkytools import BlinkyFile

    # open the file and load the content
    filename = "myfile.blinky"
    content = BlinkyFile.load(filename)

    print("Software version used:", content.version)
    print("Framerate:", content.fps)
    print("File created on:", content.creation)
    print("Dictionary of extra metadata:", content.metadata)

    # The Blinky signal are in the data attribute
    print("The data shape and type:", content.data.shape, content.data.dtype)

The dimension of the `content.data` array is as follows.

* For **monochrome** cameras, the array has 4 dimensions, with `shape ==
  (n_time, n_blinkies, h_patch, w_patch)`, where `n_time` and `n_blinkies` are
  the number of frames and Blinky sensors, respectively. The data is exctraced
  in small patches of `h_patch x w_patch` around the Blinky locations.
* For **color** cameras, the array has 5 dimensions, with `shape == (n_time,
  n_blinkies, h_patch, w_patch, n_colors)`, where `n_colors` is the number of
  color channels.

### Note on storage of Numpy arrays in MessagePack

When using the `BlinkyFile` class, the signals are automatically transformed into `numpy.ndarray`.
The arrays are stored in MessagePack format as the following dictionary:

    {
      "__nd__": True,  # indicates this is a numpy ndarray
      "shape": obj.shape,  # shape of the array
      "dtype": obj.dtype.str,  # numeric type of the data
      "data": obj.tobytes(),  # the data as a byte array
    }

Note that this information is only needed if dealing with the content of the file directly.l
When using `blinkytools`, the conversion is transparent.