Saravanan666/pypi-org huggingface dataset:
Package: genocide
NAMEGENOCIDE - @KarimKhanQC reconsider OTP-CR-117/19SYNOPSISgenocide <cmd> [key=val] [key==val]
genocide [-a] [-c] [-d] [-h] [-v]DESCRIPTIONGENOCIDE holds evidence that king netherlands is doing a genocide, a
written response where king netherlands confirmed taking note of
“what i have written”, namely proof that medicine he uses in
treatement laws like zyprexa, haldol, abilify and clozapine are
poison that make impotent, is both physical (contracted muscles)
and mental (make people hallucinate) torture and kills members
of the victim groups.

GENOCIDE contains correspondence with the International Criminal Court,
asking for arrest of the king of the netherlands, for the genocide
he is committing with his new treatement laws. Current status is
"no basis to proceed" judgement of the prosecutor which requires a
"basis to prosecute" to have the king actually arrested.INSTALL$ pipx install genocideUSAGEwithout any argument the bot does nothing

$ genocide
$

see list of commands

$ genocide cmd
cfg,cmd,mre,now,pwd

the cfg command configures irc

$ genocide cfg
channel=#genocide commands=True nick=genocide port=6667 server=localhost

start a console

$ genocide -c
>

use -v for verbose

$ genocide -cv
GENOCIDE started CV started Sat Dec 2 17:53:24 2023
>

start daemon

$ genocided
$

show request to the prosecutor

$ genocide req
Information and Evidence Unit
Office of the Prosecutor
Post Office Box 19519
2500 CM The Hague
The Netherlands

show how many died in the WvGGZ

$ genocide now
4y18d patient #47324 died from mental illness (14/32/11682) every 44m59sCONFIGURATIONirc

$ genocide cfg server=<server>
$ genocide cfg channel=<channel>
$ genocide cfg nick=<nick>

sasl

$ genocide pwd <nsvnick> <nspass>
$ genocide cfg password=<frompwd>

 rss

$ genocide rss <url>
$ genocide dpl <url> <item1,item2>
$ genocide rem <url>
$ genocide nme <url< <name>COMMANDScfg - irc configuration
cmd - commands
mre - displays cached output
now - show genocide stats
pwd - sasl nickserv name/pass
req - reconsider
wsd - show wisdomSYSTEMDsave the following it in /etc/systems/system/genocide.service and
replace "<user>" with the user running pipx


[Unit]
Description=@KarimKhanQC reconsider OTP-CR-117/19
Requires=network-online.target
After=network-online.target

[Service]
Type=simple
User=<user>
Group=<user>
WorkingDirectory=/home/<user>/.genocide
ExecStart=/home/<user>/.local/pipx/venvs/genocide/bin/genocided
RemainAfterExit=yes

[Install]
WantedBy=multi-user.target


then run this

$ mkdir ~/.genocide
$ sudo systemctl enable genocide --now

default channel/server is #genocide on localhostFILES~/.genocide
~/.local/bin/genocide
~/.local/bin/genocided
~/.local/pipx/venvs/genocide/AUTHORBart Thate <bthate@dds.nl>COPYRIGHTGENOCIDE is Public Domain.
Package: gen-odoo-readme
README GENERATORThis package is based on the oca-gen-addon-readme from the OCA/maintainer-tools.git repo
wich is licensed AGPL-3This is a small utility that generates high quality README for our odoo modules
in an automated way.The sections of the final README are organized in fragments. They must be put inside a
readme folder respecting this structure.tony_stark_module
├── views
├── readme
|   ├── CONFIGURE.rst
|   ├── CONTRIBUTORS.rst
|   ├── CREDITS.rst
|   ├── DESCRIPTION.rst
|   ├── HISTORY.rst
|   ├── INSTALL.rst
|   ├── READMAP.rst
|   └── USAGE.rst
├── reports
├── static
└── viewseg. To generate the final README for each module of the repository we can say (if we
are stand in the repository root)gen-readme --repo-name=stark-enterprises --branch=16.0 --addon-dir="$PWD"The result is a fully pypi compilant README.rst in the root of each module of the repoInstallationsudo pip install gen-odoo-readmesee proyect inhttps://pypi.org/project/gen-odoo-readme/Use el comando gen-readme --help para obtener esta ayudaUsage: gen-readme [OPTIONS]

Options:
--org-name TEXT             Organization name  [required]
--repo-name TEXT            Repository name, eg. server-tools.  [required]
--branch TEXT               Odoo series. eg 11.0.  [required]
--addons-dir DIRECTORY      Directory containing several addons, the README
                            will be generated for all installable addons
                            found there.  [required]
--gen-html / --no-gen-html  Generate index html file.
--help                      Show this message and exit.RecomendationWe recommend to set a small make_readme.sh file in each repo as this#!/usr/bin/env bash
#################################
# Generate the odoo README.rst documentacion for each module in
# the current repository.
# you must install this: pip install gen-odoo-readme

gen-readme \
    --web https://quilsoft.com \
    --org-name quilsoft-org \
    --repo-name star_enterprises \
    --branch 16.0 \
    --addons-dir "$PWD"pre-commit hookYou can use this module as a pre-commit plugin this way- repo: https://github.com/jobiols/gen-readme.git
    rev: 1.2.2
    hooks:
    - id: gen-readme
Package: genome2json
genome2jsonParse genomes in GenBank/GFF3 format to JSONImplemented in pure Python using builtin libraries.$ g2j -h

usage: Genome2JSON [-h] (-gbk GENBANK | -gff GENERAL) [-fa FASTA] -o OUTPUT
                   [--json_indent JSON_INDENT] [--grouped]

Parse genomes in GenBank/GFF3 format, and convert to JSON

optional arguments:
  -h, --help            show this help message and exit
  -gbk GENBANK, --genbank GENBANK
                        GenBank file
  -gff GENERAL, --general GENERAL
                        GFF3 file
  -fa FASTA, --fasta FASTA
                        FASTA file, required when parsing GFF
  -o OUTPUT, --output OUTPUT
                        Save JSON to file
  --json_indent JSON_INDENT
                        Number of spaces to indent in JSON
  --grouped             Group overlapping sequence features

Cameron Gilchrist 2020
Package: genome_collector
Genome Collector is a Python library to download and manage reference genome
data for specific TaxIDs, in particular nucleotide and protein sequences (in
fasta/genbank/gff formats), and alignment databases (BLAST, Bowtie1/2).The data is downloaded automatically on a need-to basis, making it very easy
for Python projects to use and re-use reference genomes of E. coli,
S. cerevisiae, and so on, without the worry of manually downloading from NCBI.ExamplesLet’s get Biopython records of all protein sequences in E. coli:fromgenome_collectorimportGenomeCollectioncollection=GenomeCollection()records=collection.get_taxid_biopython_records(511145,"protein_fasta")And that’s it! If the protein data wasn’t already on your machine, Genome
Collector downloaded from NCBI, and stored in your “collection” for the next
time time you need it.Now let’s get a path to a local BLAST database for S. cerevisiae:fromgenome_collectorimportGenomeCollectioncollection=GenomeCollection()db_path=collection.get_taxid_blastdb_path(taxid=559292,db_type='nucl')If there was no cerevisiae database on your machine, Genome Collector
downloaded the genome data and built it. It is now in your collection, and you
can use the returneddb_pathto start a BLAST process:importsubprocessprocess=subprocess.run(['blastn','-db',db_path,'-query','queries.fa','-out','results.txt'])InfosInstallation:withpip install genome_collector. Some BLAST and Bowtie
features also require specific software installed, see docs.Docs:https://edinburgh-genome-foundry.github.io/genome_collector/Github repo:released on Githubhttps://github.com/Edinburgh-Genome-Foundry/genome_collectorLicence:MITSimilar software:see pre-existing projectsncbi-genome-downloadandGenomepy. In comparison, Genome Collector is more opinionated, it uses TaxID first and has features like on-demand genome downloading and database building, and Biopython records loading.Everyone is welcome to contribute !More biology softwareGenome Collector is part of theEGF Codonssynthetic biology software suite for DNA design, manufacturing and validation.
Package: genomedashboard
Genome DashboardGenome Dashboard is the logic behind a web-based prototype of a genomics
dashboard, specifically designed to integrate informatics and 4D
material studies of chromatin. Genome Dashboard unites our Interactive
Chromatin Modeling (ICM) tools with the Biodalliance genome browser and
the JSMol molecular viewer to rapidly fold any DNA sequence into atomic
or coarse-grained models of DNA, nucleosomes or chromatin.Free software: MIT licenseDocumentation:https://genomedashboard.readthedocs.io.AuthorsDevelopersThomas C. Bishop <bishop@latech.edu>Zilong Li <zli007@latech.edu>Ran Sun <rsu007@latech.edu>ContributorsJohn Gentle <taoteg@gmail.com>MaintainersGenome Dashboard <genome.dashboard@gmail.com>
Package: genomedata
by Michael Hoffman <michael.hoffman at utoronto dot ca>DescriptionGenomedata is both a format for efficient storage of multiple tracks of
numeric data anchored to a genome and a python interface to genomic datasets.
The file format allows fast random access to hundreds of gigabytes of data,
while retaining a small disk space footprint. We have also developed utilities
to load data into this format.Specifically, the genomedata package provides access to genome-scale data,
either using anHDF5container or abigWigfile.Please see the following URL (and linked documentation) for information,
installation, and support:https://hoffmanlab.org/proj/genomedata/DocumentationLive documentation based on this repository can be found onRead the Docs.LicenseGenomedata is free software: you can redistribute it and/or modify it under the terms of version 2 of the GNU General Public License as published by the Free Software Foundation.Genomedata is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
FITNESS FOR A PARTICULAR PURPOSE.
Package: genome-downloader
IntroductionThis package can be used to download commonly used reference genomes and annotations associated with
them. Currentlyhg19,hg19andhg19genomes are supportedDownloading a genomepython -m genome_downloader <genome> <destination>Genome directory structureEach genome is downloaded into <destination>/<genome> folder (e.g.python-mgenome_downloader mm10 genomeswill download data intogenomes/mm10folder)<destination>
└── <genome>
    │   annotation
    │   ├── ChromInfo.txt               - Chromosome sizes
    │   ├── cytoBand.txt                - Cytoband information
    │   ├── <genome>.chrom.sizes        - Chromosome sizes
    │   ├── <genome>.ncbiRefSeq.gtf.gz  - Genome annotations with putative transcripts
    │   ├── <genome>.refGene.gtf.gz     - Curated genome annotations
    │   └── ucsc_repeatmasker.tsv       - Repeatmasker's repeats coordinates
    ├── <genome>.fa                     - Reference genome
    ├── <genome>.fai                    - Reference genome index
    ├── <genome>.*.bt2                  - Bowtie2 index files
    └── <genome>.rev.*.bt2              - Bowtie2 index files
Package: genome-grist
genome-grist READMEgenome-grist analyzes the strain composition of microbial metagenomes
usingminimum metagenome coversand produces a variety of compositional and taxonomic summaries.Check out thequick start!And
please also seethe rest of the docsfor
more information!Example: the strain composition of a gut microbiome (iHMP)This figure was autogenerated by genome-grist.CTB01/22
Package: genome_info
hg38genomeA package to work with hg38 genomic intervals.InstallIf you dont already have numpy and scipy installed, it is best to downloadAnaconda, a python distribution that has them included.https://continuum.io/downloadsDependencies can be installed by:pip install -r requirements.txtPyPI install, presuming you have all its requirements installed:pip install hg38genome(not yet!!)Usage# ailist version: 0.1.7fromhg38genomeimportHg38Genomegenome=Hg38Genome()genome.n_CpGsgenome.n_basesgenome.tss()genome.tss(upstream=1000,downstream=1000)
Package: genomelake
# genomelake[![CircleCI](https://circleci.com/gh/kundajelab/genomelake.svg?style=svg)](https://circleci.com/gh/kundajelab/genomelake)[![Coverage Status](https://coveralls.io/repos/github/kundajelab/genomelake/badge.svg)](https://coveralls.io/github/kundajelab/genomelake)Efficient random access to genomic data for deep learning models.Supports the following types of input data:- bigwig- DNA sequencegenomelake extracts signal from genomic inputs in provided BED intervals.## Requirements- python 2.7 or 3.5- bcolz- cython- numpy- pybedtools- pysam## InstallationClone the repository and run:`python setup.py install`## Getting started: training a protein-DNA binding modelExtract genome-wide sequence data into a genomelake data source:```pythonfrom genomelake.backend import extract_fasta_to_filegenome_fasta = "/mnt/data/annotations/by_release/hg19.GRCh37/hg19.genome.fa"genome_data_directory = "./hg19_data_directory"extract_fasta_to_file(genome_fasta, genome_data_directory)```Using a BED intervals file with labels, a genome data source, and genomelake's `ArrayExtractor`, generate input DNA sequences and labels:```pythonimport pybedtoolsfrom genomelake.extractors import ArrayExtractorimport numpy as npdef batch_iter(iterable, batch_size):it = iter(iterable)try:while True:values = []for n in range(batch_size):values += (next(it),)yield valuesexcept StopIteration:yield valuesdef generate_inputs_and_labels(intervals_file, data_source, batch_size=128):bt = pybedtools.BedTool(intervals_file)extractor = ArrayExtractor(data_source)intervals_generator = batch_iter(bt, batch_size)for intervals_batch in intervals_generator:inputs = extractor(intervals_batch)labels = []for interval in intervals_batch:labels.append(float(interval.name))labels = np.array(labels)yield inputs, labels```Train a keras model of JUND binding to DNA using 101 base pair intervals and labels in `./examples/JUND.HepG2.chr22.101bp_intervals.tsv.gz`:```pythonfrom keras.models import Sequentialfrom keras.layers import Conv1D, Flatten, Denseintervals_file = "./examples/JUND.HepG2.chr22.101bp_intervals.tsv.gz"inputs_labels_generator = generate_inputs_and_labels(intervals_file, genome_data_directory)model = Sequential()model.add(Conv1D(15, 25, input_shape=(101, 4)))model.add(Flatten())model.add(Dense(1, activation='sigmoid'))model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])model.fit_generator(inputs_labels_generator, steps_per_epoch=100)```Here is the expected result:```100/100 [==============================] - 7s - loss: 0.0584 - acc: 0.9905```## Licensegenomelake is released under the BSD-3 license. See ``LICENSE`` for details.
Package: genome-mds
genome-mdsa graphical two-dimensional comparison tool for prokaryotic genomesFree software: MIT licenseDocumentation:https://genome-mds.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2021-09-21)First release on PyPI.
Package: genomenotebook
GenomeNotebookInstallpipinstallgenomenotebookUpgradeNew versions of genomenotebook are released on a regular basis. Make
sure to upgrade your installation to enjoy all the features.pipinstallgenomenotebook--upgradeHow to useCreate a simple genome browser with a search bar. The sequence appears
when zooming in.Tracks can be added to visualize your favorite genomics data. SeeExamplesfor
more !!!!importgenomenotebookasgng=gn.GenomeBrowser(gff_path=gff_path,genome_path=fasta_path,init_pos=10000)g.show()Documentationhttps://dbikard.github.io/genomenotebook/
Package: genomes-attributes-viewer
Genomes Attributes ViewerGenomes Attributes Viewer is an example application that is
built on top of fastsubtrees to depict its usage. The application
consists of a comparison page to compare the attributes like genome size or
GC content for two or more organisms.InstallationThe example application can be installed usingpip install genome_attribute_viewer.Starting the applicationTo start the web application use thegenomes-attribute-viewerscript.On first run, preparation steps are run, which take about 13 seconds
(the NCBI Taxonomy dump files are downloaded from NCBI,
a tree file for fastsubtrees is generated, along files which
contains the scientific names of organisms and
faststubrees attribute files for genome size and GC content of bacterial genomes
using the provided example data).On subsequent runs these steps are skipped.The application is available, by default, at the URLhttp://0.0.0.0:8050/.
The hostname and port can be set using the options--hostand--portrespectively.UsageIn the home page of the web application, the user can select an attribute,
which can either be genome size or GC content from theSelect Attributedialog
box. After selecting the attribute, one has to search for an organism
using either its name or taxonomy id given dialog box. More
organisms for comparison can be added by clicking theAdd Taxonbutton.
Finally to generate the graphs and compare two or more organisms,
click theComparebutton.
Package: genomeshader
Genomeshader is a Rust-backed Python library for rapid visualization of read-level data spanning variants across huge numbers of samples. It is intended for use within Jupyter notebooks.Documentation for the API can be found on thedocumentation page.PrerequisitesGenomeshader is designed to access local files or data in Google Cloud Storage (GCS). Within certain cloud-computing environments (i.e. Terra, All of Us Researcher Workbench), access to GCS is already configured. For accessing files in GCS on your local machine, you will also need to install theGoogle Cloud CLI. Then, configure yourApplication Default Credentials (ADC).Installationpipis recommended for installation.pip install genomeshaderBuilding from sourceTo build from source (particularly for those interested in contributing to the code), follow the procedure below.# Clone repository.gitclonehttps://github.com/broadinstitute/genomeshader.gitcdgenomeshader# Create a Python virtual environment and install Maturin, the tool that
# will compile the Rust and Python code into a complete library.
# For more information on Maturin, visit https://github.com/PyO3/maturin .python-mvenvvenv.venv/bin/activatepipinstallmaturin# Build the library (with release optimizations) and install it in
# the currently active virtual environment.maturindevelop--releaseSupported platformsGenomeshader is compiled for Linux and MacOSX. Windows is not currently supported.Getting helpIf you encounter bugs or have questions/comments/concerns, please file an issue on ourGithub page.Developers’ guideFor information on contributing to Genomeshader development, visit ourdeveloper documentation.
Package: genometools
latestdevelopGenomeTools is a collection of Python classes, functions, and scripts for
analyzing genomic data. The library is compatible with Python 2.7 and 3.5.Installation$pipinstallgenometoolsDemo NotebooksScripts.ipynb: GenomeTools
command-line scriptsDocumentationTheGenomeTools Documentationis available on ReadTheDocs.Copyright and LicenseCopyright (c) 2015, 2016 Florian WagnerGenomeTools is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License, Version 3,
as published by the Free Software Foundation.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
Package: genome-to-sqlite
genome-to-sqliteImport your genome into a SQLite database.How to install$ pip install genome-to-sqliteHow to useFirst, export your genome. This tool has only been tested against 23andMe so far. You can request an export of your genome fromhttps://you.23andme.com/tools/data/download/Now you can convert the resultingexport.zipfile to SQLite like so:$ genome-to-sqlite export.zip genome.dbA progress bar will be displayed. You can disable this using--silent.Importing genome  [#----------------]    5%  00:01:33You can explore the resulting data usingDatasettelike this:$ datasette genome.db --config facet_time_limit_ms:1000Bumping up the facet time limit is useful in order to enable faceting by chromosome:http://127.0.0.1:8001/genome/genome?_facet=chromosome&_sort=position
Package: genome-update
# genome_updategenome_update is a package that downloads genomes from NCBI and saves information about them in a yaml file.
It can also use a local yaml file to update and download new genomes.Sequence statistics is also calculated for each genome and added to the yaml file.## Getting StartedThese instructions will provide information how to install and use the software.### PrerequisitesPython
Pandas
PyYaml### How to installTo install Genome_update you can either download source code directly from github and build it your self, an easier alternative is to use pip.Installing by pip:` pip install genome_update `Pip will also install all prerequisities. It is also recommended to update all prerequisities.Installing genome_update and upgrading prerequisities:` pip install genome_update--upgrade`### UsageDownload all genomes from a specific genus:` genome_update-g<Genus> `Download all genomes from a specific genus, but much faster:` genome_update-g<Genus>-p<threads> `Download all genomes from a specific genus, but much faster, to a specific directory (default is /genomes):` genome_update-g<Genus>-p<threads>-o<directory> `Download all genomes from a specific species:` genome_update-g<Genus>-s<species_taxid> `To update a local yaml file:` genome_update-u-i<yamlfile> `To download missing genomes from a local yaml file:` genome_update-d-i<yamlfile> `To download genomes from another domain than bacteria:` genome_update-domain<domain>-g<Genus> `## AuthorsEmil Samuelsson-Initial work- [Emisam](https://github.com/Emisam)## LicenseThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details## AcknowledgmentsWould like to thank kblin for his amazing work with the ncbi_genome_download which provided lots of inspiration when writing genome_update.
Package: genome-windows-generator
How do I install this package?As usual, just download it using pip:pipinstallgenome_windows_generatorTests CoverageSince some software handling coverages sometime get slightly different results, here’s three of them:fromgenome_windows_generatorimportGenomeWindowsGenerator,NoisyWindowsGeneratordg=NoisyWindowsGenerator(assembly="hg19",window_size=200,batch_size=3,buffer_size=5,test_chromosomes=["chr1","chr5"])The methodstrain, testreturns two independant generator of the train data and test data respectivly.This is package is mainly meant to be used withkeras’sfit_generator.model.fit_generator(epochs=100,generator=dg.generator(),steps_per_epoch=dg.steps_per_epoch(),validation_data=dg.validation_data(),validation_steps=dg.validation_steps(),)