semvec/pypi-packages huggingface dataset:
blip-alpha 0.0.19
A bayesian pipeline for detecting stochastic backgrounds with LISA.

#  BLIP: Bayesian LISA Pipeline



This is a bayesian pipeline for detecting stochastic backgrounds with LISA. BLIP stands for Bayesian LIsa Pipeline fully written in python


1) It is easier to maintain and run python code in virtual environments. Make a new virtualenv by doing

`python3 -m venv lisaenv`

2) Source it on linux or Mac by doing

`source lisaenv/bin/activate`

For Windows, source it by 

`activate`  while in `\lisawork\Scripts`


3) We need numpy, scipy for running this and matplotlib and chainconsumer are required for plotting. Install them all by doing

`pip install numpy scipy matplotlib chainconsumer`

4) We also need the healpy, the skymap package

`pip install healpy`

5) The sampler [dynesty](https://dynesty.readthedocs.io/en/latest/) is used for nested sampling. We get both the posteriors and bayesian evidence from it. The latter is the detection statistic. Install dynesty by doing

`pip install dynesty`

6) Some functionality also needs cython

`pip install cython`

7) You can change the parameters and the signal model in params.ini

To run do `python run_blip.py params.ini`

Posterior plots are automatically made in the output directory specified in params.ini


8) If you want to generate local documentation pages you also need sphinx

`pip install sphinx`

**Note**: The code is setup to work with python 3 and might not work with python2
More documentation at https://blip.readthedocs.io/en/latest/
dlsim 0.2.0
demo

# DLSim

DLSim is an open-source, cross-platform, lightweight, and fast Python traffic assignment tool 
adopted and modified from [ASU Trans+AI Lab](https://github.com/asu-trans-ai-lab/DTALite)

## Installation

DLSim has been published on [PyPI](https://pypi.org/project/dlsim/), and can be installed by using package manager [pip](https://pip.pypa.io/en/stable/) to install DLSim.

```bash
pip install dlsim
```
If you need a specific version of DLSim, say, 0.2.0,
```
$ pip install dlsim==0.2.0
```

If you want to test the latest features of DLSim, you can build the package from sources and install it offline, where **Python 3.x** is required.
```
# from the root directory of DLSim
$ python setup.py sdist bdist_wheel
$ cd dist
$ python -m pip install dlsim-version.tar.gz
``` 
## Usage

Find the shortest path (based on distance) and output it in the format of a sequence of node/link IDs.

```python
import dlsim as ds

network = ds.Network()

ds.g_ReadInputData(network.node_list, 
                   network.link_list, 
                   network.agent_list,
                   network.internal_node_seq_no_dict,
                   network.external_node_id_dict,
                   network.agent_td_list_dict,
                   network.zone_to_nodes_dict,network.node_seq_to_link_seq)
ds.network.allocate()

ds.g_find_shortest_path_for_agent(network)
ds.g_TrafficSimulation(network.node_list, network.link_list, 
                       network.agent_list, network.agent_td_list_dict)

ds.g_OutputFiles(network.link_list, 
                 network.agent_list, 
                 network.external_node_id_dict)
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.


## License
[License](https://github.com/asu-trans-ai-lab/DLSim)
aspectnlp 0.0.4
Aspect detection NLP toolkit is a Python package that perform NLP tasks based on aspect detection.
Keywords: aspectnlp

Aspect Detection NLP Toolkit
=============

Aspect Detection NLP toolkit (https://pypi.org/project/aspectnlp/) is a Python package that performs various NLP tasks based on aspect detection and aspect based sentiment analysis.

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

As this package requires cpu-pytorch, please install with the find link arguments
```
pip install aspect-x.x.x-py3-none-any.whl -f https://download.pytorch.org/whl/torch_stable.html
```
For nightly version please visit our github page: https://github.com/daishuanglu/aspectnlp

Usage
--------
To use this package, 
```python
from aspectnlp.aspect_detector import aspectDetector
from aspectnlp.aspect_sentiment import AspectSentimentScorer
from aspectnlp.vae_topic_model import VAETopic
from aspectnlp.w2v import fasttext_emb


corpus=[
    "While there's a decent menu, it shouldn't take ten minutes to get your drinks and 45 for a dessert pizza.",
    'Faster than old XBox, less crashing, definitely smoother experience.',
    'The product is really good but performance with things like sharing deals with partners is terrible and takes forever.',
    "Mostly still learning the tool. Things tend to change randomly from time to time... so that can be tricky. But its improving steadily!",
    "Office365 is improving, but there is a lot more we could do to make Office365 work for us and model best practices to our customers for how to use Excel.",
    "but it's not working now. Showing 'error loading accounts'.",
    "Changes in the new version of Chrome are in the right direction, but performance still feels mediocre at best. \
There are various places where it could prevent page-loading delays.",
    ]

asp_detector=aspectDetector()
sent_asp=asp_detector.detect(corpus,disp=True)


# Topic modeling based on aspect words
corpus=[i['aspect'] for i in sent_asp]
topic_model=VAETopic(corpus,n_topics=10)
topic_model.fit()
top_words,topics,topic_ids=topic_model.get_top_words_and_topics(disp=True)

# Aspect based on aspect words
analyzer=AspectSentimentScorer()
for sentId,sample in enumerate(sent_asp):
    content = list(analyzer.generateInputContent(sample))
    sentiment, scores = analyzer.scoreABSA(content)
    sent_asp[sentId]['sentiment']=sentiment
    sent_asp[sentId]['score']=scores
    sent_asp[sentId]['compound']=[analyzer.lin2compound(s) for s in scores]
    
for content in sent_asp:
    print(content)

# aspect fuzzy matcher
word_list=['menu','sales','account','User Interface','search','Note','telephony','portal','food']
fastext= fasttext_emb()
for i,v in enumerate(fastext.w2v(word_list)):
    print(word_list[i],':',v)
```


Key Features Supported with Aspect NLP toolkit
--------
* Aspect detection
* Aspect based sentiment analysis
* Text relevance
* Keyword extraction
* Topic summarization
* sentiment analysis
idf-updater 1.0.0
A library for updating IDF files to newer versions

This package is used to update singular IDF's, lists of IDF's, or directories of IDF's to newer versions. Built
for Linux and MacOS as windows has the IDFVersionUpdater.exe functionality from EnergyPlus.

Will find local EnergyPlus conversion files. Currently supports version updating from 8.0 --> newest EnergyPlus Version

'update_idf' can be passed a folder containing idf files, a path to a singular idf, or
a list of paths to idf files.

Install requires EnergyPlus installed in default location on computer.

Documentation can be found here: https://idf-updater.readthedocs.io/en/latest/
blip-test 0.0.1
A bayesian pipeline for detecting stochastic backgrounds with LISA.

#  BLIP: Bayesian LISA Pipeline



This is a bayesian pipeline for detecting stochastic backgrounds with LISA. BLIP stands for Bayesian LIsa Pipeline fully written in python


1) It is easier to maintain and run python code in virtual environments. Make a new virtualenv by doing

`python3 -m venv lisaenv`

2) Source it on linux or Mac by doing

`source lisaenv/bin/activate`

For Windows, source it by 

`activate`  while in `\lisawork\Scripts`


3) We need numpy, scipy for running this and matplotlib and chainconsumer are required for plotting. Install them all by doing

`pip install numpy scipy matplotlib chainconsumer`

4) We also need the healpy, the skymap package

`pip install healpy`

5) The sampler [dynesty](https://dynesty.readthedocs.io/en/latest/) is used for nested sampling. We get both the posteriors and bayesian evidence from it. The latter is the detection statistic. Install dynesty by doing

`pip install dynesty`

6) Some functionality also needs cython

`pip install cython`

7) You can change the parameters and the signal model in params.ini

To run do `python run_blip.py params.ini`

Posterior plots are automatically made in the output directory specified in params.ini


8) If you want to generate local documentation pages you also need sphinx

`pip install sphinx`

**Note**: The code is setup to work with python 3 and might not work with python2
More documentation at https://blip.readthedocs.io/en/latest/
pdfmailmerge 0.1.1
Easy command line mailmerge csv -> pdf tool.
Requires Python: >=3.7.1,<4.0.0

# pdf-mailmerge


Mailmerge to a pdf or html file. Adds csv data to an html jinja template.
Can be configured to generate filenames and subdirectories as well as sort order from columns in the csv.

## Install

```
pip install pdf-mailmerge
```

## Usage

Requires a `config.yml` file in your directory containing:

```
data: Some.csv

template: "Some_jinja.html"

identifier_columns:
  - "col1"
  - "col2"

filename_columns:
  - "col3"
  - "col4"

output_folders:
  - "col1"
  - "col5"

sort:
  - "col2"
  - "col3"
```

#### Required Configuration Fields

`data`: csv file with the data you wish to mailmerge

`template`: jinja template being used for the mailmerge

`identifier_columns`: columns used to construct a primary key for the data. If a column already contains the primary key, list it but only it. A mailmerge document will be created for every unique value or unique value combination of the identifier columns.

`filename_columns`: columns used to construct the filename for the document. Name will be "col1_col2_" etc.

#### Optional Configuration Fields

`output_folders`: columns used to construct output subdirectories. All files placed in either `html` or `pdf` directory. Additional folder nesting levels based on order of output folders. Sample file path `pdf/col1/col5`. 

`sort`: columns used to sort documents within their output folder. If multiple options, sort precedence given in order of list.