semvec/pypi-packages huggingface dataset:
meow-base 1.0.5
A base framework for MEOW based implementations of workflows
License: MIT

# meow_base
Repository for base MEOW definitions, as well as a some example implementations. 

## Core definitons

The core MEOW defintions are found in **core/meow.py** These are parents classes for implementations to use, with certain attributes and functions enabling a certain amount of interchangeability within those classes.

The most import definitions are the BasePattern, and BaseRecipe, which define the conditions under which processing will take place, and the actuall processing to take place.

## MEOW Runner

The way to run a MEOW system is to create and MeowRunner instance, found in **core/runner.py**. This will take 1 or more Monitors, 1 or more Handlers, and 1 or more Conductors. In turn, these will listen for events, respond to events, and execute any analysis identified. Examples of how this can be run can be found in **tests/testRunner.py**

## Testing
Pytest unittests are provided within the 'tests directory, as well as a script **test_all.sh** for calling all test scripts from a single command. This can be run as:

    test_all.sh -s

or:

    SKIP_LONG=1 pytest . -W ignore::DeprecationWarning

This will skip the more time consuming tests if you just need quick feedback. Without the -s argument then all tests will be run. Individual test scripts can be started using:

    pytest test_runner.py::MeowTests -W ignore::DeprecationWarning

with individual tests runnable with:

    pytest test_runner.py::MeowTests::testMeowRunnerLinkedPythonExecution -W ignore::DeprecationWarning

to run locally, update your '~/.bashrc' file to inclue:

    # Manually added to get local testing of Python files working easier
    export PYTHONPATH=/home/patch/Documents/Research/Python

to generate a coverage report run:

    SKIP_LONG=1 pytest --cov --cov-report=html:../coverage . -W ignore::DeprecationWarning

## Benchmarking 
Benchmarks have been written for this library, but are included in the seperate https://github.com/PatchOfScotland/meow_benchmarks package
emosent-py 0.1.7
Python module to get Sentiment Rankings for Unicode Emojis.
License: MIT
Keywords: emoji,sentiment,analysis,ranking,emoticon,polarity

# emosent-py

emosent-py is a Python utility package to get Sentiment Rankings for Unicode Emojis.

Based on the research by Kralj Novak P, Smailović J, Sluban B, Mozetič I
(2015) on _Sentiment of Emojis_.

Journal Link:
<https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0144296>

CSV Data acquired from CLARIN repository,
Repository Link: <http://hdl.handle.net/11356/1048>

This project is inspired by [emoji-sentiment](https://github.com/dematerializer/emoji-sentiment), a similar utility written in JavaScript.

## Emoji Support

The complete listed of Emojis to Sentiment Ranking supported by this project
can be found at
[Emoji Sentiment Ranking v1.0](http://kt.ijs.si/data/Emoji_sentiment_ranking/).

## Installation

You can install emosent-py from using `pip`:

```bash
pip install emosent-py
```

## Usage

### Example 1

```python
from emosent import get_emoji_sentiment_rank
get_emoji_sentiment_rank('❤')
```

Output:

```python
{'unicode_codepoint': '0x2764',
 'occurrences': 8050,
 'position': 0.746943086,
 'negative': 355.0,
 'neutral': 1334.0,
 'positive': 6361.0,
 'unicode_name': 'HEAVY BLACK HEART',
 'unicode_block': 'Dingbats',
 'sentiment_score': 0.746}
```

### Example 2

```python
from emosent import get_emoji_sentiment_rank

# This function returns the emoji sentiment rank 
# mapped to the specified character.
get_emoji_sentiment_rank('😂')
```

Output:

```python
{'unicode_codepoint': '0x1f602',
 'occurrences': 14622,
 'position': 0.805100583,
 'negative': 3614.0,
 'neutral': 4163.0,
 'positive': 6845.0,
 'unicode_name': 'FACE WITH TEARS OF JOY',
 'unicode_block': 'Emoticons',
 'sentiment_score': 0.221}
```

### Example 3

```python
from emosent import get_emoji_sentiment_rank_multiple

# Parses the input text character by character and 
# extracts emoji sentiment ranks and their respective positions in the text.
get_emoji_sentiment_rank_multiple('well done buddy! 😁👏')
```

Output:

```python
# Here, the emojis are found at positions 17 and 18 in the specified text.
[{'text_position': 17,
  'emoji_sentiment_rank': {'unicode_codepoint': '0x1f601',
   'occurrences': 2189,
   'position': 0.796151187,
   'negative': 278.0,
   'neutral': 648.0,
   'positive': 1263.0,
   'unicode_name': 'GRINNING FACE WITH SMILING EYES',
   'unicode_block': 'Emoticons',
   'sentiment_score': 0.45}},
 {'text_position': 18,
  'emoji_sentiment_rank': {'unicode_codepoint': '0x1f44f',
   'occurrences': 2336,
   'position': 0.787130164,
   'negative': 243.0,
   'neutral': 634.0,
   'positive': 1459.0,
   'unicode_name': 'CLAPPING HANDS SIGN',
   'unicode_block': 'Miscellaneous Symbols and Pictographs',
   'sentiment_score': 0.521}}]

```

## Versioning

We use [SemVer](http://semver.org/) for versioning. For the versions available,
see the
[tags on this repository](https://github.com/FintelLabs/emosent-py/tags).

## License

This project is licensed under the MIT License - see the
[LICENSE.txt](LICENSE.txt) file for more details.
cnos-connector 0.1.9
CnosDB Python Client
License: MIT
Requires Python: >=3.6,<4.0

# CnosDB Python Connector

CnosDB Python Connector repository contains the Python client library for the [CnosDB](https://github.com/cnosdb/cnosdb). cnosdb_connector adapted for PEP249.

## Installation

use pip install it from pypi, **Python 3.6** or later is required.

```
pip install cnos-connector
```

Then import the package:

```
import cnosdb_connector
```

## Getting Started

#### Query use SQL

```python
from cnosdb_connector import connect

conn = connect(url="http://127.0.0.1:31007/", user="root", password="")
resp = conn.execute("SHOW DATABASES")
print(resp)
```

#### Query use interface

```python
from cnosdb_connector import connect

conn = connect(url="http://127.0.0.1:31007/", user="root", password="")
conn.create_database("air")
resp = conn.list_database()
print(resp)
```

#### Query use PEP-249

```python
from cnosdb_connector import connect

conn = connect(url="http://127.0.0.1:31007/", user="root", password="")
cursor = conn.cursor()

cursor.execute("SHOW DATABASES")
resp = cursor.fetchall()
print(resp)
```

#### Query use pandas

```python
import pandas as pd
from cnosdb_connector import connect

conn = connect(url="http://127.0.0.1:31007/", user="root", password="")

resp = pd.read_sql("SHOW DATABASES", conn)
print(resp)
```

#### Write use LineProtocol

```python
from cnosdb_connector import connect

line0 = "test_insert,ta=a1,tb=b1 fa=1,fb=2 1"
line1 = "test_insert,ta=a1,tb=b1 fa=3,fb=4 2"
line2 = "test_insert,ta=a1,tb=b1 fa=5,fb=6 3"

conn = connect(url="http://127.0.0.1:31007/", user="root", password="")

conn.create_database_with_ttl("test_database", "100000d")
conn.switch_database("test_database")

conn.write_lines([line0, line1, line2])

resp = conn.execute("SELECT * FROM test_insert;")
print(resp)
```

#### Write use SQL

```python
from cnosdb_connector import connect

conn = connect(url="http://127.0.0.1:31007/", user="root", password="")

query = "insert test_insert(TIME, column1, column2, column3, column4, column5, column6, column7) values (100, -1234, 'hello', 1234, false, 1.2, 'beijing', 'shanghai'); "

conn.execute(query)

resp = conn.execute("SELECT * FROM test_insert;")
print(resp)
```

#### Write use CSV

```python
from cnosdb_connector import connect
import os

query = "CREATE TABLE test_insert(column1 BIGINT CODEC(DELTA),\
                                  column2 BOOLEAN,\
                                  column3 DOUBLE CODEC(GORILLA),\
                                  TAGS(column4));"

conn = connect(url="http://127.0.0.1:31007/", user="root", password="")
# table schema must same with csv file
conn.execute(query)

path = os.path.abspath("test.csv")
conn.write_csv("test_insert", path)

resp = conn.execute("SELECT * FROM test_insert;")
print(resp)
```

# License

CnosDB Python Connector use MIT License
graph-trackintel 0.0.7
Tools for creating, processing and analyzing location graphs
License: MIT
Requires Python: >=3.6

[![PyPI version](https://badge.fury.io/py/graph-trackintel.svg)](https://badge.fury.io/py/graph-trackintel)
[![codecov](https://codecov.io/gh/mie-lab/graph-trackintel/branch/main/graph/badge.svg?token=98I3U2WX2N)](https://codecov.io/gh/mie-lab/graph-trackintel)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
# graph-trackintel
Tools for building and preprocessing location graphs

## Installation:


This repo can then be installed in editable mode. In an activated virtual environment, cd into this folder and run
```
pip install -e .
```
This will execute the `setup.py` file and install required dependencies.