Saravanan666/pypi-org huggingface dataset:
Package: geyser-cli
Geyser-cligeyser-cliis an unofficial command line utility for checking the universal
vault(s) of a set of addresses participating in Ampleforth's Geyser v2.Installationpip install geyser-cliSetupEthereum nodegeyser-clineeds to talk to an Ethereum node. You can either use a local one
or a hosted service.
Anyway, set the node's URL as environment variable$PROVIDER.Example:$ export PROVIDER=https://mainnet.infura.io/v3/<INFURA-PROJECT-ID>CommandsChecking the available commands with$ geyser --helpoutputs:Usage: geyser [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  claimed    Get the already claimed tokens from the user's vault(s)
  locked     Get the tokens locked in the user's vault(s)
  positions  Get the user's LP positions held in their vault(s)
  status     Get the status of the user's vault(s)Thestatuscommand is the combined output of all other commands.With it, you can check the current status for each vault held by set of
addresses.Example:$ geyser status                            \
gov.merkleplant.eth                        \
0xa308de214e01c365834e3344c1088b0d2b97559cOutput:gov.merkleplant.eth

No vault(s) found...

0xa308DE214e01c365834e3344C1088B0D2B97559c

Vault 0xa899bede339275dbd9f2c5a98e915be7f030f26f:

Locked tokens:

Ticker                             Balance
-------------------------------  ---------
ubAAMPL                            0.025
SLP                                0
UNI-V2                             0.00016
BAL-REBASING-SMART-V1-AMPL-USDC   15

Claimed tokens:

Ticker      Balance
--------  ---------
AMPL        7.85485

In Geyser Beehive:

Ticker      Balance
--------  ---------
AMPL      953.419
WETH        0.29912

In Geyser Pescadero:

Ticker      Balance
--------  ---------
AMPL       16.4279
WETH        0.00514

In Geyser Old Faithful:

Ticker      Balance
--------  ---------
AMPL        2.99358
USDC        5.91427Note that the output contains colors and other stylings not shown above.SupportIf there are any question, don't hesitate to ask!You can reach me at pascal [at] merkleplant.xyz or in the official Ampleforth
Discord.ContributionsAny kind of contribution is highly welcome!AcknowledgmentThis project is heavily inspired byuniswap-python.
Package: gf180
gf180 0.0.3GlobalFoundries 180nm MCU based onGoogle open source PDKThis is a pure python implementation of the PDK.InstallationInstallation for new python usersIf you don't have python installed on your system you candownload the gdsfactory installerthat includes python3, miniconda and all gdsfactory plugins.Installation for new gdsfactory usersIf you already have python installed. Open Anaconda Prompt and then install the ubcpdk using pip.pip install gf180 --upgradeThen you need to restart Klayout to make sure the new technology installed appears.Installation for developersFor developers you need togit clonethe GitHub repository, fork it, git add, git commit, git push and merge request your changes.git clone https://github.com/gdsfactory/gf180.git
cd gf180
make installDocumentationRun notebooks ongdsfactory docs
Package: gf2lib
gf2libgf2lib是一个操作有限域GF(2)多项式和GF(2)矩阵的库gf2lib.gf2poly：GF(2)多项式gf2lib.gf2matrix：GF(2)矩阵一、 快速开始（1） 通过pip安装pipinstallgf2lib（2）直接拷贝位于src中的源代码二、 demo更多参考样例见GitHub中的example文件夹，gf2lib（1） gf2polyfromgf2lib.gf2polyimportGF2Polyir_poly=0x11b# p(x)=x^8+x^4+x^3+x+1a=GF2Poly(0b101,ir_poly)# x^2+1b=GF2Poly(0b010,ir_poly)# xprint(a.value)# 0b101print(a.ir_poly)# 0b100011011print(a)# 0b101c=a+b# 加法d=a*b# 乘法e=a**4# 幂f=a/b# 除法g=a%b# 模h=a.inverse()# 求逆（2） gf2matrixfromgf2lib.gf2matriximportGF2MatrixM=GF2Matrix([[0,1,0,0],[0,0,1,0],[0,0,0,1],[1,0,0,0]])M=M*M# 乘法M=M.inverse()# 求逆print(M)# [[0, 0, 1, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 1, 0, 0]]gf2libgf2lib is a library for manipulating GF (2) polynomials and GF (2) matrices over finite fieldsgf2lib.gf2poly: GF (2) polynomialgf2lib.gf2matrix: GF (2) matrix1. Quick Start(1) Install via pippipinstallgf2lib(2) Directly copy the source code in src2. DemoFor more reference examples, see the example folder in GitHub.gf2lib
Package: gfa
# GNSS Field Analysis (GFA)My name is Vicente Yañez and I’m a geologist interested in computer science. GFA is a compilation of python’s functions that I wrote for my internship in [Centro Sismológico Nacional](http://www.sismologia.cl/) (CSN), during my undergraduate thesis and that I am still developing it.GFA have the functions to calculate vectors from a gnss time series data with the trajectory model of [Bevis & Brown, 2014](https://link.springer.com/article/10.1007/s00190-013-0685-5) and with this, obtain a velocity field that represent the cortical deformation. Also, for the calculation of the velocity field, GFA has the distance-weighed function from [Cardozo & Allmendinger](http://www.sciencedirect.com/science/article/pii/S0098300408002410).After, from the velocity field this program include the tools to calculate the velocity tensor, and from this, the vorticity and stretching tensors (from the equations of [Davis & Titus, 2011](http://www.joshuadavis.us/teaching/2013fcomps/davistitus2011.pdf)).I am sorry if you read any misspells o grammatical error. English is my second lenguage and my skills in it is far from perfect.## Collaborators
Francisco García. Francisco is a geologist who is doing his doctoral thesis in Universidad de Concepción about the connection of sismicity and volcanic eruptions in the southern Andes. You can contact him to his emailfranciscogarcia@udec.clAlso you can contact me to my mailvicenteyanez@protonmail.com# InstallAll the dependences necessary for run GFA are installable via pip. Also, the use of virtualev is recommended.` sudo pip3 install virtualenv (if you don't have it installed) mkdir your_project cd your_project virtualenv your_project . your_project/bin/activate sudo pip3 install gfa `If everything is alright, you can continue in the [How to start page](https://github.com/VicenteYanez/GFA/wiki/How-to-start)
Package: gfagraphs
GFAGraphs - A Python GFA libraryThis Python library aims to be an abstraction layer for GFA file format.
Two implementations are proposed:a legacy one, which will remain until deprecated, ingfagraphs/a new, cleaner and faster implementation, inpgGraphs/[!WARNING]Moving forward, features will only be added topgGraphs, whilegfagraphswill remain as it, until full migration is done inpancattool.Package pgGraphsThe package is organized in 4 modules:pgGraphs.abstractionscontains abstractions over GFA formatspgGraphs.gfaparsercontains an abstract class for parsing and saving GFA file formatpgGraphs.graphcontains the mainGraphclass that represents a GFA graph in memory, and edit functionspgGraphs.networkxconains an abstract class for visualization and modelization of GFA in a NetworkX object[!NOTE]Want to contribute? Feel free to open a PR on an issue about a missing, buggy or incomplete feature!
Package: gfa-logging
This library provide a logging class to record data or debug your application.To install:python3 setup.pyUsage example for two loggers:from gfa_logging import MsgLogger

msg_logger = MsgLogger()
data_logger = MsgLogger(name='data_logger', file_name='data.csv',
                        header="sensorID,value,timestamp")

msg_logger.logger.info('_____ Started _____')
data_logger.logger.info("281868,28.5,1540476278")
Package: gfapy
GfapyThe Graphical Fragment Assembly (GFA) are formats for the representation
of sequence graphs, including assembly, variation and splicing graphs.
Two versions of GFA have been defined (GFA1 and GFA2) and several sequence
analysis programs have been adopting the formats as an interchange format,
which allow to easily combine different sequence analysis tools.This library implements the GFA1 and GFA2 specification
described athttps://github.com/GFA-spec/GFA-spec/blob/master/GFA-spec.md.
It allows to create a Gfa object from a file in the GFA format
or from scratch, to enumerate the graph elements (segments, links,
containments, paths and header lines), to traverse the graph (by
traversing all links outgoing from or incoming to a segment), to search for
elements (e.g. which links connect two segments) and to manipulate the
graph (e.g. to eliminate a link or a segment or to duplicate a segment
distributing the read counts evenly on the copies).The GFA format can be easily extended by users by defining own custom
tags and record types. In Gfapy, it is easy to write extensions modules,
which allow to define custom record types and datatypes for the parsing
and validation of custom fields. The custom lines can be connected, using
references, to each other and to lines of the standard record types.RequirementsGfapy has been written for Python 3 and tested using Python version 3.7.
It does not require any additional Python packages or other software.InstallationGfapy is distributed as a Python package and can be installed using
the Python package manager pip, as well as conda (in the Bioconda channel).
It is also available as a package in some Linux distributions (Debian, Ubuntu).The following command installs the current stable version from the Python
Packages index:pip install gfapyIf you would like to install the current development version from Github,
use the following command:pip install -e git+https://github.com/ggonnella/gfapy.git#egg=gfapyAlternatively it is possible to install gfapy using conda. Gfapy is
included in the Bioconda (https://bioconda.github.io/) channel:conda install -c bioconda gfapyUsageIf you installed gfapy as described above, you can import it in your script
using the conventional Python syntax:>>> import gfapyDocumentationThe documentation, including this introduction to Gfapy, a user manual
and the API documentation is hosted on the ReadTheDocs server,
at the URLhttp://gfapy.readthedocs.io/en/latest/and it can be
downloaded as PDF from the URLhttps://github.com/ggonnella/gfapy/blob/master/manual/gfapy-manual.pdf.ReferencesGiorgio Gonnella and Stefan Kurtz “GfaPy: a flexible and extensible software
library for handling sequence graphs in Python”, Bioinformatics (2017) btx398https://doi.org/10.1093/bioinformatics/btx398
Package: gfat
Generador Ficheros Agencia TributariaGenerador Ficheros Agencia TributariaFree software: MIT licenseDocumentation:https://gfat.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2018-01-17)First release on PyPI.
Package: gfc
gfc::A Geodesy and GNSS data processing librarygfc (Geodesy Foundation Class )
Descriptiongfc is a python library that contains some fundamental utilities and algorithms for geodetic data processing.
Now, it has the ability to process GNSS (Global Navigation Satellite System) and SLR (Satellite Laser Ranging) data.InstallationDownload using pip
pip install gfcInstructions for use
...
Package: gfeat
gfeatPython genomic features extractor from raw files (Fasta, GTF and VCF).gfeatis a convenient tool for extracting diverse DNA features for genomic modeling and analysis. It allows to get,
for example, transcript Kozak sequence and GC content, genome codon pair bias and etc. It is also possible to inject
SNPs using a VCF file, get position-type variant matrices and investigate sequences with different combinations
of heterozygous variants (when homozygous always stay injected in the sequence).You can find several short usage examples in the Tutorial section of the documentation.Free software: MIT licenseDocumentation:https://i12g-gagneurweb.in.tum.de/public/docs/gfeatFeaturesGFGenome:GFGenome.get_consensus_Kozak_seq(seq=False)Consensus Kozak sequenceGFGenome.get_Kozak_matrix()Kozak sequence matrix (rows - transcripts)GFGenome.get_consensus_stop_codon_context(seq=False)Consensus stop codon context sequenceGFGenome.get_stop_codon_context_matrix()Stop codon context matrix (rows - transcripts)GFGenome.get_codon_pair_bias()Codon pair biasGFGenome.get_nucleobase_mutation_table()Nucleobase mutation matrix (table with positions of variants and their type in Kozak sequence and stop codon context)GFTranscript:GFTranscript.codon_counts()Coding sequence codon countGFTranscript.utr5_motif_counts(pattern)5’UTR motif countGFTranscript.utr3_motif_counts(pattern)3’UTR motif countGFTranscript.codon_usage()Coding sequence codon usageGFTranscript.gc_content(region)Coding sequence, 5’UTR or 3’UTR G and C contentGFTranscript.get_Kozak_seq()Kozak sequenceGFTranscript.get_stop_codon_context()Stop codon contextGFTranscript.get_codon_pairs_frequency()Coding sequence codon pair frequencyUpstream AUG:UpstreamAUG.predict_on_sample(seq)Predict on sample (get all AUGs with the information whether they are in-frame or not and whether they have a stop codon or not)UpstreamAUG.predict_on_sample_with_pos(seq)Predict on sample with position (predict on sample plus positions of AUGs)UpstreamAUG.predict_on_sample_with_pos_pandas()Predict on sample with position and appending to the passed dictionaryUpstreamAUG.predict_on_batch(seq_list)Predict on batch5’UTR class:FivePrimeUTRSeq(data, reverse_complement_bool, contig=None, strand=None)An object contains list of all 5’UTR sequences, their positions, exons, positions of exons and corresponding transcriptsAuxiliary functions:VCFMutator.mutate_sequence(interval, fasta=None, seq_whole=None)Mutate sequenceVCFMutator.mutate_codon_context(intervals, seqs, column_names)Mutate codon contextreverse_complement(dna)Reverse complementPCA_with_standard_sample_deviation_scaling(df, n_comp=2)Pca with scalingCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.gfeatis based
onpyensemblpackage and can be partially viewed as its extension.History0.1.0 (2018-09-27)First release on PyPI.1.0.0 (2018-09-28)First stable release on PyPI.
Package: g-feature-pipeline
Feature PipelineCheck out thisMedium articlefor more details about this module.Create Environment File~/energy-forecasting$cp.env.default.envThe commandcp .env.default .envis used to create a copy of the.env.defaultfile and name it.env. In many projects, the.envfile is
used to store environment variables that the application needs to run. The.env.defaultfile is usually a template that includes all the environment
variables that the application expects, but with default values. By copying it
to.env, you can customize these values for your own environment.Set Up the ML_PIPELINE_ROOT_DIR Variable~/energy-forecasting$exportML_PIPELINE_ROOT_DIR=$(pwd)The commandexport ML_PIPELINE_ROOT_DIR=$(pwd)is setting the value of theML_PIPELINE_ROOT_DIRenvironment variable to the current directory. In this
context,$(pwd)is a command substitution that gets replaced with the output
of thepwdcommand, which prints the path of the current directory. Theexportcommand then makes this variable available to child processes of the
current shell.In essence,ML_PIPELINE_ROOT_DIRis an environment variable that is set to the
path of the current directory. This can be useful for scripts or programs that
need to reference the root directory of the ML pipeline, as they can simply
refer toML_PIPELINE_ROOT_DIRinstead of needing to know the exact path.Install for DevelopmentCreate virtual environment:~/energy-forecasting$cdfeature-pipeline&&rmpoetry.lock
~/energy-forecasting/feature-pipeline$bash../scripts/devops/virtual_environment/poetry_install.sh
~/energy-forecasting/feature-pipeline$source.venv/bin/activateWe first navigate to thefeature-pipelinedirectory and remove thepoetry.lockfile. This step is essential if we intend to add new
dependencies to thepyproject.tomlfile, as it ensures that Poetry
accurately resolves and installs the latest compatible versions of all
dependencies.We then execute thepoetry_install.shscript. This script
is responsible for creating the virtual environment and installing the
project dependencies. Importantly, it also includes steps to resolve
potential issues related to the macOS arm64 architecture.Finally, we activate the virtual environment. This step provides an isolated
workspace for our project, preventing conflicts between the project's
dependencies and those installed globally on the system.Check theSet Up Additional ToolsandUsagesections to
seehow to set uptheadditional toolsandcredentialsyou need to
run this project.Usage for DevelopmentTo start the ETL pipeline run:~/energy-forecasting/feature-pipeline$python-mfeature_pipeline.pipelineTo create a new feature view run:~/energy-forecasting/feature-pipeline$python-mfeature_pipeline.feature_viewNOTE:Be careful to set theML_PIPELINE_ROOT_DIRvariable as explained in
thissection.
Package: gfe-fhir
fhirGFEPython Boilerplate contains all the boilerplate you need to create a Python package.Free software: LGPL 3.0Documentation:https://fhirgfe.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2018-07-20)First release on PyPI.
Package: gff2bed
gff2bedOverviewGFF3andBEDare common formats for storing the coordinates of genomic features such as genes. GFF3 format is more versatile, but BED format is simpler and enjoys a rich ecosystem of utilities such asbedtools. For this reason, it is often convenient to store genomic features in GFF3 format and convert them to BED format for genome arithmetic.This module provides two convenience functions to streamline converting data from GFF3 to BED format for bioinformatics analysis:parse(), which reads data from a GFF3 file, andconvert(), which converts GFF3-formatted data to BED-formatted data that can be passed on e.g. topybedtools.DocumentationSee full online documentation athttp://salk-tm.gitlab.io/gff2bedInstallationWithcondagff2bedis available frombioconda, and can be installed withcondacondainstall-cbiocondagff2bedWithpipgff2bedis available fromPyPI, and can be installed withpippipinstallgff2bedTutorialTo follow this tutorial, first ensure you have the following modules installed
in addition togff2bed:urllib3pandaspybedtoolsThis tutorial will involve working with some files on disk, so we'll make a
temporary directory for easy cleanup later.fromtempfileimportTemporaryDirectorytemp_dir=TemporaryDirectory()Next, download an example GFF3 fileimporturllib3importshutilimportos.pathGFF3_URL='https://gitlab.com/salk-tm/gff2bed/-/raw/main/test/data/ColCEN_AT1G01010-20_TAIR10.gff3.gz'GFF3_FILE=os.path.join(temp_dir.name,'ColCEN_AT1G01010-20_TAIR10.gff3.gz')http=urllib3.PoolManager()withhttp.request('GET',GFF3_URL,preload_content=False)asr,open(GFF3_FILE,'wb')asdest_file:shutil.copyfileobj(r,dest_file)To read the GFF3 file into a Pandas data frame without converting to BED, usegff2bed.parse()importpandasaspdimportgff2bedgff_data=pd.DataFrame(gff2bed.parse(GFF3_FILE))gff_data.head()0     1      2  3                                                  4
0  Chr1  7489   9757  +  {'ID': 'AT1G01010', 'Note': 'protein_coding_ge...
1  Chr1  9786  12596  -  {'ID': 'AT1G01020', 'Note': 'protein_coding_ge...Note:The implementation ofgff2bedfollows a philosophy of simplicity. It depends on nothing but the built-in python libraries, and it includes nothing but theparse()andconvert()functions. Typically when applyinggff2bedin practice, you will use it in conjunction with other modules such aspandasorpybedtools.To create a data frame of BED formatted data, pass the stream togff2bed.convert()before passing topd.DataFrame()bed_data=pd.DataFrame(gff2bed.convert(gff2bed.parse(GFF3_FILE)))bed_data.head()0     1      2          3  4  5
0  Chr1  7488   9757  AT1G01010  0  +
1  Chr1  9785  12596  AT1G01020  0  -You can similarly create aBedToolwithpybedtoolsfrompybedtoolsimportBedToolbed_data=BedTool(gff2bed.convert(gff2bed.parse(GFF3_FILE))).saveas()bed_data.head()Chr1    7488    9757    AT1G01010       0       +
 Chr1   9785    12596   AT1G01020       0       -To complete the tutorial, clean up the temporary directorytemp_dir.cleanup()
Package: gfFermentation
gfFermentation - Script Grainfather fermenter controlers.This package allows for reading/controlling a Grainfather Conical fermenter or fermentation device. Currently reading and setting of target temperature/pausing and status is supported.InstallgfFermentation is available on pippip install gffermentationUsagefrom gfFermentation.grainfather import Grainfather

username = ''    # Your username
password = ''    # Your password

gfIO = Grainfather(username, password) # Login to Grainfather (tokens also supported)

for controller in gfIO.controllers:
    print(controller)
Package: gffpandas
gffpandasGFF annotations in panda dataframesFree software: ISC licenseDocumentation:https://gffpandas.readthedocs.io.About gffpandasgffpandas is a Python library, which can be used to work with annotation data. It facilitates the work with gff3 files in regard to filter desired annotation entries of the gff3 file. A gff3 file contains information about the location and attributes of genomic features. The gffpandas library is an easy to use and time-saving library.This gffpandas library is an alternative to gffutils or bcbio-gff, but it is inspired by the Python library Pandas. This means that the data frame structure is used to work with the annotation data. With gffpandas it is possible to filter a gff3 file by different functions. One big adventage is that several filter functions can be combined so that the required annotation entries can be selected. Furthermore, can the filtered annotation data be safed again as gff3 file or as csv or tsv file.For the documentation seegffpandas_repository.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.2.0 (2019-03-13)filter_feature_of_type for several features possible now1.1.0 (2019-03-13)get_feature_by_attribute for several values possible now1.0.1 (2019-03-06)adding type annotation1.0.0 (2019-03-05)changing GitHub url0.1.2 (2018-09-07)Removing of small text errors0.1.1 (2018-09-07)First release on PyPI0.1.0 (2017-07-11)Start of the project gffpandas
Package: gffpandas-jb
gffpandasGFF annotations in panda dataframesFree software: ISC licenseDocumentation:https://gffpandas.readthedocs.io.About gffpandasgffpandas is a Python library, which can be used to work with annotation data. It facilitates the work with gff3 files in regard to filter desired annotation entries of the gff3 file. A gff3 file contains information about the location and attributes of genomic features. The gffpandas library is an easy to use and time-saving library.This gffpandas library is an alternative to gffutils or bcbio-gff, but it is inspired by the Python library Pandas. This means that the data frame structure is used to work with the annotation data. With gffpandas it is possible to filter a gff3 file by different functions. One big adventage is that several filter functions can be combined so that the required annotation entries can be selected. Furthermore, can the filtered annotation data be safed again as gff3 file or as csv or tsv file.For the documentation seegffpandas_repository.This fork of the  gffpandas repository fixes a few minor problems with parsing
GFFs with features that end with an unnecessary semicolon.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.2.1 (2020-10-02)Bug fix: Removed pytest-runner from setup requirements1.2.0 (2019-03-13)filter_feature_of_type for several features possible now1.1.0 (2019-03-13)get_feature_by_attribute for several values possible now1.0.1 (2019-03-06)adding type annotation1.0.0 (2019-03-05)changing GitHub url0.1.2 (2018-09-07)Removing of small text errors0.1.1 (2018-09-07)First release on PyPI0.1.0 (2017-07-11)Start of the project gffpandas