semvec/pypi-packages huggingface dataset:
past-mtl-monitors 0.1.0
A library for creating past metric temporal logic monitors.
Requires Python: >=3.8,<4.0

Past MTL Monitors
=================
A library for creating past metric temporal logic monitors.

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<!-- markdown-toc start - Don't edit this section. Run M-x markdown-toc-generate-toc again -->
**Table of Contents**

- [Past MTL Monitors](#past-mtl-monitors)
- [Installation](#installation)
- [Usage](#usage)

<!-- markdown-toc end -->


# Installation

If you just need to use `past-mtl-monitors`, you can just run:

`$ pip install past-mtl-monitors`

For developers, note that this project uses the
[poetry](https://poetry.eustace.io/) python package/dependency
management tool. Please familarize yourself with it and then
run:

`$ poetry install`

# Usage

The primary entry point to the `past-mtl-monitors` package is the
`atom` function. This exposes a monitor factory which can be combined
with other monitor factories to create complex property monitors.

Under the hood, these monitor factories are just wrappers around
python coroutines that expect a `(time, val)` pair, where time is a
`float` and `val` is a mapping from strings to robustness values
(`float`).

**Note** `past-mtl-monitors` only implements a quantitative semantics
where a value greater than 0 implies sat and a value less than 0
implies unsat.

Thus if one would like to use Boolean semantics, use `1` for `True` and
`-1` for `False`.

```python
from past_mtl_monitors import atom

x, y, z = atom('x'), atom('y'), atom('z')

# Monitor that historically, x has been equal to y.
monitor = (x == y).hist().monitor()

#                    time         values
assert monitor.send((0    , {'x': 1, 'y': 1}))  ==  1   # sat
assert monitor.send((1.1  , {'x': 1, 'y': -1})) == -1   # unsat
assert monitor.send((1.5  , {'x': 1, 'y': 1}))  == -1   # unsat

monitor2 = x.once().monitor()  # Monitor's x's maximum value.
assert monitor2.send((0 , {'x': -10, 'y': 1})) == -10
assert monitor2.send((0 , {'x': 100, 'y': 2})) == 100
assert monitor2.send((0 , {'x': -100, 'y': -1})) == 100

# Monitor that x & y have been true since the last
# time that z held for 3 time units.
monitor3 = (x & y).since(z.hist(0, 3)).monitor()
```
geospark 1.3.1
GeoSpark Python
Requires Python: >=3.6

# geospark

This library is Python wrapper on GeoSpark library.

GeoSpark is a cluster computing system for processing large-scale spatial data. GeoSpark extends Apache Spark / SparkSQL with a set of out-of-the-box Spatial Resilient Distributed Datasets (SRDDs)/ SpatialSQL that efficiently load, process, and analyze large-scale spatial data across machines.

Python version supports:
<li> GeoSpark core </li>
<li> GeoSpark SQL </li>

Please follow tutorial on official GeoSpark website:
https://datasystemslab.github.io/GeoSpark/
sqlstate 0.0.2
A lightweight state framework for Python applications
Requires Python: >=3.6

# sqlstate
Super simple state manager for Python3

## Usage
### Define state
```python
import sqlstate
state = sqlstate.State('dog.tmp') # This creates a file called dog.tmp where your state will be saved.
```
### Store
```python
dogs = [{'name': 'Michael Clayton', 'breed': 'Labrador'}]
state.store(key='dogs', payload=dogs)
```
### Get
```python
dogs = state.get(key='dogs')
```
pyjn 0.0.1
JSON Ref Resolver for standard JSON Schema with Max and Min Schema Generation

# JSON Ref Resolver for standard JSON Schema with Max and Min Schema Generation

This is a python package having multiple utilities. 
[Github-flavored Markdown](https://github.com/deepstartup/pyjn/)

Warning : All the Files for ref-reslover should be placed under the same folder/dir.
1.pyjn.refsolver: input param (input file path) 
2.pyjn.maxjson:input param (json schema dict)
3.pyjn.minjson:input param (json schema dict)

#Data Returns in JSON format:
Example : 
import pyjn
pyjn=pyjn()
json_pathtest='C:/Users/Entity.json'
print(pyjn.refsolver(json_pathtest))
aryansindhi-101703110-outlier-cmd 1.0.3
A small package that removes outliers from a pandas dataframe
Requires Python: >=3.6

This Python package is created to remove outlier rows from a dataset.
To use thi dataset you ned to create a pandas dataframe(you can use 
pd.read_csv() to do it), and to to use this package your dataset
should have only numerical values and if you have any strings
then you can use one hot encoding or simple label encoder to do that,
but make sure before using it that it is all done.
Now this function takes one argument that is the dataframe.
To use it you have to write:

from Aryan_Sindhi_101703110_outlier_removal import outlier
df = outlier.rem_out(data) 

and your df variable will now contain the new data which
will not contain outlier rows and this function will also
print the number of rows deleted!
THANKS!
Aryan Sindhi
101703110
COE-5
Thapar Institute of Engineering and Technology
reconer 0.1.0
RecoNER, Debug annotated Named Entity Recognition (NER) data for inconsitencies and get insights on improving the quality of your data.
Requires Python: >=3.6

<p align="center">
    <em>RecoNER, Debug annotated Named Entity Recognition (NER) data for inconsitencies and get insights on improving the quality of your data.</em>
</p>
<p align="center">
<a href="https://dev.azure.com/kabirkhan1137/reconer/_build" target="_blank">
    <img src="https://dev.azure.com/kabirkhan1137/reconer/_apis/build/status/kabirkhan.reconer?branchName=master" alt="Build Status">
</a>
<a href="https://codecov.io/gh/kabirkhan/reconer">
  <img src="https://codecov.io/gh/kabirkhan/reconer/branch/master/graph/badge.svg" alt="Coverage"/>
</a>
<a href="https://pypi.org/project/reconer" target="_blank">
    <img src="https://badge.fury.io/py/reconer.svg" alt="Package version">
</a>
</p>

---

**Documentation**: <a href="https://kabirkhan.github.io/reconer" target="_blank">https://kabirkhan.github.io/reconer</a>

**Source Code**: <a href="https://github.com/kabirkhan/reconer" target="_blank">https://github.com/kabirkhan/reconer</a>

---

RecoNER is a library to help you fix your annotated NER data and identify examples that are hardest for your model to predict so you can strategically prioritize the examples you annotate. 

The key features are:

* **Data Validation and Cleanup**: Easily Validate the format of your NER data. Filter overlapping Entity Annotations, fix missing properties.
* **Model Insights**: Analyze how well your model does on your Dataset. Identify the top errors your model is making so you can prioritize data collection and correction strategically.
* **Model Insights**: Analyze how well your model does on your Dataset. Identify the top errors your model is making so you can prioritize data collection and correction strategically.
* **Dataset Management**: `reconer` provides a `Dataset` class to manage the train/dev/test split of your data and apply the same functions across all splits in your data + a concatenation of all examples. Operate inplace to consistently transform your data.
* **Serializable Dataset**: Serialize and Deserialize your data to and from JSON to the `reconer` type system. 
* **Type Hints**: Comprehensive Typing system based on Python 3.6+ Type Hints

## Requirements

Python 3.6+

Python 3.6+

`RecoNER` is built on a few comprehensive, high-performing packages.

* <a href="https://spacy.io" class="external-link" target="_blank">spaCy</a>
* <a href="https://pydantic-docs.helpmanual.io/" class="external-link" target="_blank">Pydantic (Type system and JSON Serialization)</a>
* <a href="https://typer.tiangolo.com" class="external-link" target="_blank">Typer (CLI)</a>.


## Installation

<div class="termy">

```console
$ pip install reconer
---> 100%
Successfully installed reconer
```

</div>

## License

This project is licensed under the terms of the MIT license.