semvec/pypi-packages huggingface dataset:
findimport 0.1
This is a simple library to find the recursive import link of source code
License: MIT
Keywords: Module Finder,Import Finder,Module Tracker,Import Tracker

# Python Find imported Module
pip3 install findimport

# Example:
# findownimportfile
if the source file in /mnt/c/workspace/example/source_code.py

## Input:
python3 -m findimport  --source source_code.py --path /mnt/c/workspace/ or /mnt/c/workspace/example

## Output:
/mnt/c/workspace/example/own_import_find.txt

## Input:
python3 -m findimport  --source source_code.py --path /mnt/c/workspace/ or /mnt/c/workspace/example --save_text /mnt/c/workspace/imported_file.txt
## Ouput:
/mnt/c/workspace/imported_file.txt



$ python3.7
>> import findimport
>> import_list = findimport.find_own_import(source,path)
>> print(import_list)
 ['/mnt/c/workspace/example/other_code.py',...]

# findownimportfile
python3 -m findimport  --source source_code.py --path 'dummystring' --find_all_import


S$ python3.7
>> import findimport
>> import_list = findimport.find_all_import(source)
sightingdb 0.0.6
The Python Client Library for SightingDB
License: MIT

********************************
SightingDB Python Client Library
********************************

SightingDB is a fast database to perform Sightings. It allows to count how many time someone has seen something, when was the first time, when was the last time etc.

This client library is an abstraction from the REST API.

Examples
--------

To write 3 values:

.. code-block:: python

		import sightingdb
		con = sightingdb.connection(host="localhost", apikey="changeme")
		writer = sightingdb.writer(con)
		writer.add("/key/namespace1", "pypi.org")
		writer.add("/key/namespace1", "pypi.org")
		writer.add("/key/namespace2", "example.com")
		writer.commit()

We are counting, so pypi.org will have a count of 2.

To read 2 values, it is almost like writing but ``writer`` became ``reader`` and we ``fetch`` data to iterate on:

.. code-block:: python

		import sightingdb
		con = sightingdb.connection(host="localhost", apikey="changeme")
		reader = sightingdb.reader(con)
		reader.add("/key/namespace1", "pypi.org")
		reader.add("/key/namespace2", "example.com")
		for i in reader.fetch():
		    print(str(i))

And you will be all set to read all the values.
svd2vec 0.3.3
A library that converts words to vectors using PMI and SVD
License: MIT

# SVD2vec

SVD2vec is a python library for representing documents words as vectors. Vectors are created using the PMI (Pointwise Mutual Information) and the SVD (Singular Value Decomposition).

This library implements recommendations from "Improving Distributional Similarity with Lessons Learned from Word Embeddings" (Omer Levy, Yoav Goldberg, and Ido Dagan). This papers suggests that traditional methods like PMI and SVD can be as good as word2vec by appling the same hyperparameters.

Documentation can be found at [https://valentinp72.github.io/svd2vec/index.html](https://valentinp72.github.io/svd2vec/index.html)

### Installation

```shell
pip install svd2vec
```

### Example

```shell
wget http://mattmahoney.net/dc/text8.zip -O text8.gz
gzip -d text8.gz -f
```

```python
# Building
>>> from svd2vec import svd2vec
>>> documents = [open("text8", "r").read().split(" ")]
>>> svd = svd2vec(documents, window=2, min_count=100)
```

```python
# I/O
>>> svd.save("svd.bin")
>>> svd = svd2vec.load("svd.bin")
```

```python
# Similarities
>>> svd.similarity("bad", "good")
# 0.4156516999158368
>>> svd.similarity("monday", "friday")
# 0.839529117681973
```

```python
# Most similar words
>>> svd.most_similar(positive=["january"], topn=2)
# [('february', 0.6854849518368631), ('october', 0.6653385092683669)]
>>> svd.most_similar(positive=['moscow', 'france'], negative=['paris'], topn=4)
# [('russia', 0.6221746629754187), ('ussr', 0.6024809889985986), ('soviet', 0.5794180517326273), ('bolsheviks', 0.5365123080505297)]
```

```python
# Analogies
>>> svd.analogy("paris", "france", "berlin")
# [('germany', 0.6977716641680641), ...]
>>> svd.analogy("road", "cars", "rail")
# [('trains', 0.7532519174901262), ...]
>>> svd.analogy("cow", "cows", "pig")
# [('pigs', 0.6944101149919422), ...]
>>> svd.analogy("man", "men", "woman")
# [('women', 0.7471792753875327), ...]
```

Using [Gensim](https://pypi.org/project/gensim/) you can load a `svd2vec` model using it's `word2vec` representation:
```python
>>> from gensim.models.keyedvectors import Word2VecKeyedVectors
>>> svd.save_word2vec_format("svd_word2vec_format.txt")
>>> keyed_vector = Word2VecKeyedVectors.load_word2vec_format("svd_word2vec_format.txt")
>>> keyed_vector.similarity("good", "bad")
# 0.54922897
```

---

[Improving Distributional Similarity with Lessons Learned from Word Embeddings](https://www.mitpressjournals.org/doi/abs/10.1162/tacl_a_00134) <br>
**Omer Levy**, **Yoav Goldberg**, and **Ido Dagan** <br>
Transactions of the Association for Computational Linguistics 2015 Vol. 3, 211-225 <br>
batterytender 1.2.0
Python API for talking to the Deltran Battery Tender API
License: MIT
Keywords: deltran battery tender batterytender

===================================================
Python API for Deltran Battery Tender WiFi products
===================================================


Installation
============

.. code-block:: bash

    pip install batterytender


Usage
=====

Module
------

You can import the module as ``batterytender``.

.. code-block:: python

    import batterytender

    email = 'XXXXXXXXXXXXXXXXXXXX'
    password = 'XXXXXXXXXXXXXXXXXXXX'

    bt = batterytender.BatteryTender(email, password)

    for monitor in bt.monitors:
        print('Monitor id: {}'.format(monitor.device_id))
        print('    Name: {}'.format(monitor.name))
        print('    Date: {}'.format(monitor.date))
        print('    Voltage: {}'.format(monitor.voltage))


        # Historical readings
        print('    History:')
        for history in monitor.history:
            print('        Date: {}'.format(history['date']))
            print('        Voltage: {}'.format(history['voltage']))


        # The latest reading is also available in the same format as
        # historical reading via the `.current` property
        print('    Current:')
        print('        Date: {}'.format(monitor.current['date']))
        print('        Voltage: {}'.format(monitor.current['voltage']))