semvec/pypi-packages huggingface dataset:
convectors 0.1.3
End-to-end NLP package for seamless integration of Pandas Series, DataFrame and Keras model

Convectors: build end-to-end NLP pipelines
==========

Inspired by the Keras syntax, Convectors allows you to build NLP pipelines by adding different processing Layers.
Fully compatible with pandas and Keras, it can either process list or pandas series on the fly, or apply processing to a whole DataFrame by using columns as inputs and outputs. Tensorflow's Keras models can be added as a layer, embedded and saved within a larger end-to-end NLP model.


```
pip install convectors
```

Simple classification example
=====

In this basic example, we create an NLP pipeline for a sequence classification task:

```python
from convectors import load_model
from convectors.layers import Argmax, Keras, Sequence, Tokenize
from sklearn.datasets import fetch_20newsgroups
from tensorflow.keras.layers import LSTM, Dense, Embedding
from tensorflow.keras.models import Sequential

# load data
training_set = fetch_20newsgroups(subset="train")
testing_set = fetch_20newsgroups(subset="test")

# create encoder model
encoder = Tokenize(stopwords=["en"])
encoder += Sequence(max_features=20000, pad=True, maxlen=200)

# get and transform training data
X_train = encoder(training_set.data)  # fit and transform
y_train = training_set.target  # get training data

# infer number of features and classes
N_FEATURES = encoder["Sequence"].n_features + 1
N_CLASSES = y_train.max() + 1
EMBEDDING_DIM = 32

# create keras model and fit
model = Sequential()
model.add(Embedding(N_FEATURES, EMBEDDING_DIM, mask_zero=True))
model.add(LSTM(32, activation="tanh", return_sequences=False))
model.add(Dense(32, activation="tanh"))
model.add(Dense(N_CLASSES, activation="softmax"))
model.compile("nadam", "sparse_categorical_crossentropy", metrics=["accuracy"])
model.fit(X_train, y_train, epochs=1, batch_size=800)

# once learned, add Keras model
encoder += Keras(model=model, trained=True)
encoder += Argmax()
encoder.verbose = False  # turn verbosity off

# for model persistence:
encoder.save("model.p")
encoder = load_model("model.p")

# predict for new data
y_pred = encoder(testing_set.data)
y_true = testing_set.target
# print accuracy
print((y_pred == y_true).mean())
```
nkdfu 0.2
DFU tool for updating Nitrokeys' firmware
Requires Python: >=3.6

# Nitrokey DFU tool - nkdfu

`nkdfu` is a Python DFU tool for updating Nitrokeys' firmware. Currently supports Nitrokey Pro only.

Based on [python-dfu] project, which brings implementation of USB DFU 1.1 spec.

## Call

```
$ nkdfu FIRMWARE_PATH <flags>
```

See `nkdfu --help` for details.

## Installation

### From Repository

```bash
$ pip3 install nkdfu
```


### Directly from releases
It is possible to install it directly from the releases page:

```bash
$ pip3 install https://github.com/Nitrokey/nkdfu/releases/download/v0.1/nkdfu-0.1-py3-none-any.whl
```

## License
License follows upstream - GPLv2. See [LICENSE] for details.


[LICENSE]: ./LICENSE
[python-dfu]: https://github.com/vpelletier/python-dfu
stormbot 2.1.1
XMPP bot

Stormbot
========

Hi, my name is Stormbot and I'm here to execute your deepest desire.
I'll listen to all the stupid things you say on your jabber room and tries to
serve you.


Curriculum Vitae
================

Work Experience
---------------

I've been working for my past master for at least 3 days.
Here is a sample of the things he have been ordering me::

    <master>   | stormbot: say "I'm here to serve you"

    <master>   | stormbot: fortune
    <stormbot> | Il faut pas respirer la compote, ça fait tousser.

    <master>   | stormbot: music

Personnal Skills
----------------

I'm able to:

* play some music for you.
* repeat out loud all the secret you will tell me
* say some random dumb things
* explain you all the above as long as you don't fully understand it

Moreover I'm able te easily learn new things to do.

Make me work
============

::

    pip install stormbot
    stormbot --help

Plugins
=======

Here are some official plugins:

* fortune_
* say_
* music_
* role_
* yocto-alert_

.. _fortune: https://pypi.org/project/stormbot-fortune
.. _role: https://pypi.org/project/stormbot-role
.. _music: https://pypi.org/project/stormbot-music
.. _say: https://pypi.org/project/stormbot-say
.. _yocto-alert: https://pypi.org/project/stormbot-yocto-alert

Plugin installation is as easy as::

    pip install stormbot-fortune
    stormbot --plugins stormbot_fortune.fortune

License
=======

I'm a free bot consequently I've chosen to offer myself under the following conditions::

    Permission to use, copy, modify, and distribute this software for any
    purpose with or without fee is hereby granted, provided that the above
    copyright notice and this permission notice appear in all copies.

    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
    WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
    MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
    ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
    WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
    ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
    OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
PyIpsm 0.0.3
PyIpsm is a python package to communicate with Petroleum Experts(Petex) software - Prosper, MBAL and GAP - through openserver using Python. This Python package eliminates the the need for writing VBA code to interface with Petex software.
Requires Python: >=3.9

# PyIpsm

PyIpsm is a python package to communicate with Petroleum Experts(Petex) software - Prosper, MBAL and GAP - through openserver using Python.  This Python package eliminates the the need for writing VBA code to interface with Petex software.

## Quick start

```bash
$ pip install PyIpsm
```

Pyipsm requires [Pywin32](https://pypi.org/project/pywin32/) installed. If Pywin32 is not installed, install it using pip and then pip install Pyipsm.

```bash
$ pip install Pywin32
$ pip install PyIpsm
```

## Example session:

```python
# Import Pyipsm
>>> import PyIpsm.ipsm as Ipsm

# Provide the full path to Petex executable.
>>> prosper_exe = "C:\\Program Files\\Petroleum Experts\\IPM 12\\prosper.exe"

# Establish openserver connection...
>>> openserver_file = "PX32.OpenServer.1"
>>> open_server = Ipsm.OpenServer(openserver_file)

# Start executables...
>>> prosper = Ipsm.Program(prosper_exe, open_server)

# Do prosper command...
>>> open_file_command = "PROSPER.OPENFILE=C:\\Apps\\src\\Src\\IPSM\\Openserver\\Openserver\\Code\\Test\\Test_v2.Out"
>>> prosper_error = prosper.commands.doSlow(open_file_command)
>>> prosper_error = prosper.commands.do("PROSPER.SHUTDOWN")
>>> print(f"Prosper error: {prosper_error.number}")

```

## Contributing

Contributions are invited and welcome.

## License

MIT
dubhub-instrument-psycopg2 0.34b2
DubHub fork of OpenTelemetry psycopg2 instrumentation
Requires Python: >=3.7

OpenTelemetry Psycopg Instrumentation
=====================================

|pypi|

.. |pypi| image:: https://badge.fury.io/py/opentelemetry-instrumentation-psycopg2.svg
   :target: https://pypi.org/project/opentelemetry-instrumentation-psycopg2/

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

::

    pip install opentelemetry-instrumentation-psycopg2


References
----------
* `OpenTelemetry Psycopg Instrumentation <https://opentelemetry-python-contrib.readthedocs.io/en/latest/instrumentation/psycopg2/psycopg2.html>`_
* `OpenTelemetry Project <https://opentelemetry.io/>`_
* `OpenTelemetry Python Examples <https://github.com/open-telemetry/opentelemetry-python/tree/main/docs/examples>`_
kodexa-spatialkit 1.0.23b0
Kodexa Spatial Toolkit

# Spatial Kit

![Build](https://github.com/kodexa-ai/kodexa-spatialkit/workflows/Python%20Package%20Using%20Anaconda/badge.svg)

A collection of functions and utility to support working with spatial content in Kodexa.

You can simply install the package with:

    pip install kodexa-spatialkit


# Development Environment

If you are looking to work on Spatial Kit, you will need to have a development environment.

First ensure you have Anaconda installed, then run 

     conda env create -f environment.yml 

Once created you can activate the environment with:

    conda activate kodexa_spatialkit

Then install the dependencies:

    pip install -r requirements.txt


(C) Copyright [2020] Kodexa, Inc