semvec/pypi-packages huggingface dataset:
pybdsim 3.6.1
Python utilities for the Monte Carlo Particle accelerator code BDSIM.
Requires Python: >=3.6

# pybdsim #

A python package containing both utilities for preparing and analysing BDSIM input and output as well as controlling BDSIM.

## Authors ##

L. Nevay
A. Abramov
S. Alden
S. Boogert
M. Deniaud
C. Hernalsteens
F. Metzger
W. Parker
E. Ramoisiaux
W. Shields
J. Snuverink
R. Tesse
S. Walker


## Setup ##

pip install pybdsim

Or from source, from the main directory:

$ make install

or for development where the local copy of the repository is used and can
be reloaded with local changes:

$ make develop

Look in the Makefile for the various pip commands (e.g. for with a venv)


```
python
>>> import pybdsim
>>> d = pybdsim.Data.Load("output.root")
>>> eventTree = a.GetEventTree()
>>> for event in eventTree:
        print(event.Summary.nTracks)
>>> dh = pybdsim.Data.Load("analysis_histograms.root")
>>> pybdsim.Plot.Histogram1D(dh.histogramspy["Event/MergedHistograms/PlossHisto"])
```
autodistill-kosmos-2 0.1.1
Kosmos-2 base model for use with Autodistill.
Requires Python: >=3.7

<div align="center">
  <p>
    <a align="center" href="" target="_blank">
      <img
        width="850"
        src="https://media.roboflow.com/open-source/autodistill/autodistill-banner.png"
      >
    </a>
  </p>
</div>

# Autodistill Kosmos-2 Module

This repository contains the code supporting the Kosmos-2 base model for use with [Autodistill](https://github.com/autodistill/autodistill).

[Kosmos-2](https://github.com/microsoft/unilm/tree/master/kosmos-2), developed by Microsoft, is a multimodal language model that you can use for zero-shot object detection. You can use Kosmos-2 with autodistill for object detection.

Read the full [Autodistill documentation](https://autodistill.github.io/autodistill/).

Read the [Kosmos-2 Autodistill documentation](https://autodistill.github.io/autodistill/base_models/kosmos_2/).

## Installation

To use Kosmos-2 with autodistill, you need to install the following dependency:


```bash
pip3 install autodistill-kosmos-2
```

## Quickstart

```python
from autodistill_kosmos_2 import Kosmos2

# define an ontology to map class names to our Kosmos2 prompt
# the ontology dictionary has the format {caption: class}
# where caption is the prompt sent to the base model, and class is the label that will
# be saved for that caption in the generated annotations
# then, load the model
base_model = Kosmos2(
    ontology=CaptionOntology(
        {
            "person": "person",
            "a forklift": "forklift"
        }
    )
)

predictions = base_model.predict("./example.png")

base_model.label("./context_images", extension=".jpeg")
```


## License

This package is implemented using the [Transformers Kosmos-2 implementation](https://huggingface.co/microsoft/kosmos-2-patch14-224). The underlying Kosmos-2 model, developed by Microsoft, is licensed under an [MIT license](https://github.com/microsoft/unilm/blob/master/LICENSE).

## 🏆 Contributing

We love your input! Please see the core Autodistill [contributing guide](https://github.com/autodistill/autodistill/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
algtestprocess 0.1.6
A Python package for algtestprocess

# Algtest pyProcess

A tool for simple visualizations of gathered TPM and JavaCard smart card data collected by tpm2-algtest and JCAlgtest tools respectively.

## Getting started

### Dependencies

* Python 3 (see. [requirements.txt](./requirements.txt))

### Installing

* Setup virtual environment
```
python -m venv venv
source venv/bin/activate
```

* Install package using `pip` or run `setup.py`
```
pip install -e .
```

---
**NOTE**

If you are an user of fish shell, right command is `source venv/bin/activate.fish`

---

### Usage

#### pyprocess command

An entry point to the command line interface, which is used mostly to preprocess the datasets which are then analysed in jupyter notebooks.

```
Commands:
  tpm       A collection of commands for processing of results from tpm2-algtest
  javacard  NOT YET IMPLEMENTED
```

##### tpm commands

###### metadata-update

Command which is used to find all measurements on a given path, and group them by exact firmware. The output is `metadata.json` which contains these aggregated paths. Latter part of this file contains directory hashes, to prevent duplicate inclusion of the same measurement. An example of `metadata.json` file is shown below.

If you have already created a `metadata.json` file and just want to add more measurements, you may use `-i` option which takes path to old `metadata.json` file.
```
pyprocess tpm metadata-update ./results/tpmalgtest_results_2022
pyprocess tpm metadata-update -i metadata.json ./results/tpmalgtest_results_2023
```

An example of `metadata.json` file.

```
{
  "entries": {
    "STM 73.4.17568.4452": {
      "TPM name": "STM 73.4.17568.4452",
      "vendor": "STM",
      "title": "",
      "measurement paths": [
        "/home/tjaros/storage/research/tpm/tpmalgtest_results/out4/out",
        "/home/tjaros/storage/research/tpm/tpmalgtest_results/algtest_result_8d400ec7-122c-498f-b141-733d359ad78f_STM",
        "/home/tjaros/storage/research/tpm/tpmalgtest_results/algtest_result_9fdfdf94-3a4a-4cd3-9a92-86642f9f4f6c"
      ]
    },
    "AMD AMD 3.37.0.5": {
      "TPM name": "AMD AMD 3.37.0.5",
      "vendor": "AMD",
      "title": "",
      "measurement paths": [
        "/home/tjaros/storage/research/tpm/tpmalgtest_results/algtest_result_70de7ce0-de7c-42bc-9f86-708d8304ad48"
      ]
    },
  },
   "hashes": [
    "862ad83383339467dbd687f3ebb2d075",
    "7f78e96bd4fc47299cdbaa7cf1a1c1e3",
    "3d6f2c211f00245d2a7b2c6458b3626a",
    "2b3242fc08c73b608a227eea563f1531",
   ],
}
```

## License

This project is licensed under [MIT License](./LICENSE) - see the LICENSE file for details
servc-lake 1.2.1
Server implmentation of services for python
Requires Python: >=3.11

# servc-lake


## Requirements

As per usual, this package does not come bundled with any libraries to ensure full flexibility on dependencies and security vulnerabilities.

```
$ pip install servc servc-lake pyarrow [pandas]
```

## Environment Variables

**DATA_PATH** - the location to start writing files. Default: /tmp/datalake

### Parquet

```python
import pyarrow as pa
from servclake.parquet import Parquet

table = Parquet(
  {
    "name": "mytesttable",
    "schema": pa.schema(
        [
            pa.field("col1", pa.string(), nullable=False),
            pa.field("col2", pa.uint32(), nullable=False),
            pa.field("j3", pa.uint32(), nullable=False),
        ]
    ),
    "partition": ["col2", "col1"],
  }
)

# write data to table
table.write(pa.Table.from_pylist([{"col1": "a", "col2": 1, "j3": 1}]))

# read partition. reads partiion 1 of col2.
df = table.read([1])
```

### Delta Lake

```python
import pyarrow as pa
from servclake.delta import Delta

# create a deltalake instance
deltalake = Delta(
  "/datalake",
  [
    {
      "name": "mytesttable",
      "schema": pa.schema([pa.field("col", pa.string()), pa.field("value", pa.int32())]),
      "partition": "col",
    }
  ]
)

# delcare our dataframe
df = pa.Table.from_pylist(
    [
        {"col": "df1", "value": 1},
        {"col": "asd2", "value": 2},
        {"col": "asd3", "value": 3},
        {"col": "asd4", "value": 4},
        {"col": "asd5", "value": 5},
    ],
    schema=pa.schema([pa.field("col", pa.string()), pa.field("value", pa.int32())]),
)

# write dataframe to lake
table = self.lake.write("mytesttable", df, mode="append")
print(table.to_pandas())
```
industryts 0.0.7
Python library that provides functions to manipulate time series collected from industrial contexts
Requires Python: >=3.7

# Industry Time Series Library

## Time Series Manipulation for Industry Data

This repository hosts the **industry-ts**: _Industry Time Series_ Library --- a Python library that provides functions to manipulate time series collected from industrial contexts.

This project aims to address the necessity for open-source tools developed for solving problems with data collected from industrial process. 

The modules introduced provide a variety of functions that are particularly tailored to industry data, directed to working with its most common issues, such as discontinuities in process measurements and faulty sensors.

## Table of Contents

- [Industry Time Series Library](#industry-time-series-library)
  - [Time Series Manipulation for Industry Data](#time-series-manipulation-for-industry-data)
  - [Table of Contents](#table-of-contents)
  - [Main Features](#main-features)
  - [How to use it](#how-to-use-it)
  - [Documentation](#documentation)

## Main Features

* **Data Generation**: Generate synthetic data from well defined stochastic processes for testing and benchmarking purposes.
* **Modelling**: Fit time series models to data.
* **Preprocessing**: Preprocess time series with filtering, feature engineering and other techniques.

## How to use it

The package is available in [PyPI](https://pypi.org/project/industryts/), and can be installed with pip:

```bash
pip install industryts
```

Alternatively, to use the library, you can clone the repository and install it with pip:

```bash
git clone https://github.com/Industry-Time-Series/industry-ts.git
cd industry-ts
git checkout packaging
pip install .
```

## Documentation

The official documentation is hosted on https://industry-time-series.github.io/industry-ts/
scPECA-test 1.1
PECA2 gene regulatory network construction for single-cell data

# scPECA
## Introduction
This is a python version of PECA2 gene regulatory 
network construction software designed for single-cell data.
It has a faster running speed and a lower memory footprint.

## Installation
```commandline
pip install scPECA
```
Or install via Github
```commandline
git clone https://github.com/zhangjiahao1234/scPECA.git
cd scPECA
python setup.py install
```

## Run scPECA
### Input
scPECA requires to input the paired (sc)RNA-seq and (sc)ATAC-seq data,
and it provides data pre-processing in some formats. 
(Pre-processing can also be done manually by the user)
#### Format 1
Paired bulk RNA-seq and ATAC-seq count data

sample_name_RNA.txt

<table>
    <tr>
  		<td>gene1</td> 
        <td>10</td>
    </tr>
    <tr>
        <td>gene2</td> 
        <td>3</td>
    </tr>
</table>

sample_name_ATAC.txt


<table>
    <tr>
  		<td>chr1_10000_10100</td> 
        <td>1</td>
    </tr>
    <tr>
        <td>chr1_20000_20100 </td> 
        <td>0</td>
    </tr>
</table>

#### Format 2
scRNA-seq count data and scATAC-seq count data within the same cluster
without meta information

sample_name_scRNA.csv

|        | barcode1 | barcode2 |
|--------|----------|----------|
| gene1  | 1        | 19       |
| gene2  | 3        | 6        |

sample_name_scATAC.csv

|                  | barcode1 | barcode2 |
|------------------|----------|----------|
| chr1_10000_10100 | 1        | 0        |
| chr1_20000_20100 | 0        | 1        |

#### Format 3
scRNA-seq count data and scATAC-seq count data 
with meta information

sample_name_scRNA.csv

|        | barcode1 | barcode2 |
|--------|----------|----------|
| gene1  | 1        | 19       |
| gene2  | 3        | 6        |

sample_name_scRNA_meta.csv

<table>
    <tr>
  		<td>barcode1</td> 
        <td>celltype1</td>
    </tr>
    <tr>
        <td>barcode2</td> 
        <td>celltype2</td>
    </tr>
</table>

sample_name_scATAC.csv

|                  | barcode1 | barcode2 |
|------------------|----------|----------|
| chr1_10000_10100 | 1        | 0        |
| chr1_20000_20100 | 0        | 1        |

sample_name_scATAC_meta.csv

<table>
    <tr>
  		<td>barcode1</td> 
        <td>celltype1</td>
    </tr>
    <tr>
        <td>barcode2</td> 
        <td>celltype2</td>
    </tr>
</table>

### Run example

```commandline
import scPECA
import os

data_path = os.path.join(os.path.dirname(scPECA.__file__), 'Cones') # demo data path
demo = scPECA.scPECA(data_path, 'Cones', 'hg38') # Create a scPECA class
demo.RNA_process(2) # Format 2 RNA data processing
demo.ATAC_process(2) # Format 2 ATAC data processing
demo.network('Cones', 20, 0) # PECA2 GRN construction
```

The details of each function can be viewed in python.

### Output 
sample_name_network.txt

## System & Software Requirements
System: linux

Linux software: Homer

Python package: pybedtools, ismember, scipy, numpy_groupies, 

## Considerations

1. RNA data preprocessing currently supports only hg38 and mm10 genomes.
2. pybedtools may not support the latest version of 
python and will require the creation of a new environment.