semvec/pypi-packages huggingface dataset:
eden-simulator 0.2.3
Standalone Python wheels for the EDEN neural simulator
Requires Python: >=3.2
Keywords: simulator,simulation,HPC,neuroscience,NeuroML

# EDEN Python bindings + wheels

EDEN is a high-performance [NeuroML]( http://neuroml.org )-based neural simulator written in C++.
This wheel package contains Python bindings to use the simulator, as well as `win32`, `win_amd64`, `macosx_x86_64` and `manylinux1` standalone builds of the simulator.

## Installing

EDEN can be installed with one `pip` command:
```
pip install eden-simulator
```

The only non-`pip` requirement is for the GNU C compiler, Intel C compiler or Clang to be installed and available on `PATH` at run time.

## Usage

EDEN directly runs NeuroML v2 models of neural networks. 

The `eden_simulator` package exposes the `runEden` method, which takes the LEMS simulation file of the NeuroML model to be run as parameter:
```python
import eden_simulator
results = eden_simulator.runEden('<LEMS simulation file>.xml')
```
This interface returns the recorded trajectories specified in the simulation files in a Python dictionary, same as [`pyNeuroML`](https://pypi.org/project/pyNeuroML/) does with other simulation backends.

Thread-level parallelism can also be controlled with the `threads` keyword argument.

If other command-line arguments should be added, they can be specified as a list of strings in the optional parameter `extra_cmdline_args`.  
In case a specific instance of the EDEN executable is preferred, it can be selected with the optional parameter `executable_path`.  
Both options can be combined into a fully custom command to be run from Python: it can be passed as a list of strings in the optional parameter `full_cmdline`.

The recorded trajectories are also available relative to the working directory, as specified in the LEMS file.
Some `.gen.c` and `.gen.so` temporary files may also be generated in the working directory, these can be removed after the simulation is run.

For more information about the NeuroML model format, refer to http://neuroml.org .

The program's source code is available on GitLab: https://gitlab.com/neurocomputing-lab/Inferior_OliveEMC/eden .

This software is released as open-source, under the GNU General Public License version 3.

If you have any questions, suggestions or issues with running EDEN, please [submit a ticket]( https://gitlab.com/neurocomputing-lab/Inferior_OliveEMC/eden/-/issues ) on GitLab, or contact the developer at s.panagiotou@erasmusmc.nl .
wordpredict 0.2.3
word predictor
Requires Python: >=3.9,<4.0

# wordpredict

This is a library that predicts words for ambiguous input.

## Installation

```bash
pip install wordpredict
```

## How to use

### code

```python
import pandas as pd
from wordpredict import WordPredict


corpus = pd.read_csv(
    "./unigram_freq.csv",
    header=0,
    keep_default_na=False,
).values
wp = WordPredict(corpus[:, 0], corpus[:, 1])

print("start user input")

input = ["e", "f", "g", "h"]
print(wp.update(input))
input = ["e", "f", "g", "h"]
print(wp.update(input))
input = ["i", "j", "k", "l"]
print(wp.update(input))

print("reset user input")
wp.reset()

input = ["e", "f", "g", "h"]
print(wp.update(input))
input = ["m", "n", "o", "p"]
print(wp.update(input))
```

### output

```
start user input
['for', 'e', 'from', 'he', 'has', 'have']
['he', 'get', 'here', 'her', 'help', 'few']
['help', 'held', 'felt', 'hell', 'hello', 'helps']
reset user input
['for', 'e', 'from', 'he', 'has', 'have']
['for', 'home', 'go', 'how', 'good', 'end']
```

### corpus

e.g., https://www.kaggle.com/datasets/rtatman/english-word-frequency

## execution time

```python
%%timeit

import pandas as pd
from wordpredict import WordPredict


corpus = pd.read_csv(
    "./unigram_freq.csv",
    header=0,
    keep_default_na=False,
).values
wp = WordPredict(corpus[:, 0], corpus[:, 1])
```

1.42 s ± 83.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

```python
%%timeit

input = ["e", "f", "g", "h"]
wp.update(input)
input = ["e", "f", "g", "h"]
wp.update(input)
input = ["i", "j", "k", "l"]
wp.update(input)
```

8.34 ms ± 315 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

## note

autocomple was implemented with reference to https://doi.org/10.1145/3173574.3173755
IQS-algorithm 1.0.0
The IQS is an iterative approach for optimizing short keyword queries given a prototype document through interaction with an opaque search engine such as Twitter.
Requires Python: >=3.6

# Iterative Query Selection (IQS)

## Python Package Overview

* Performs the IQS algorithm on various queries by providing a simple API for accessing all its functionality.
* Modify the quality of the search results from Twitter by setting different parameters.
* Designed for users with technical background.
* Download the package using pip install IQS-algorithm

## Check out the IQS algorithm web platform in the following link: [IQS Web](https://iqs.cs.bgu.ac.il/)

The platform can help you explore the IQS algorithm benefits:
* Search and retrieve data from Twitter's website using the IQS algorithm.
* Displays results from the experiment described in the academic paper. In the experiment, the IQS algorithm is compared to another algorithm in the field (ALMIK) on a specific dataset.
* Presenting the academic paper with Q&A