Saravanan666/pypi-org huggingface dataset:
Package: dxnesici
DX-NES-ICIDX-NES-ICI[1] is a Natural Evolution Strategy (NES) for Mixed-Integer Black-Box Optimization (MI-BBO).
DX-NES-ICI reportedly improves the performance of DX-NES-IC [2], one of the most promising continuous BBO methods, on MI-BBO problems.
Simultaneously, DX-NES-ICI outperforms CMA-ES w. Margin [3], one of the most leading MI-BBO methods.Getting StartedPrerequisitesYou needNumPyandSciPythat are the packages for scientific computing.InstallingPlease install via pip.$pipinstalldxnesiciUsageProblem settingSet the number of dimensions (dim), dimensions of continuous variables (dim_co), and dimensions of integer variables (dim_int).
Then, set objective function and the domains of integer variables, where
the decision variables must be arranged in such a way as to concatenate a (dim_co)-dimensional continuous vector and a (dim_int)-dimensional integer vector.
Note that the number of elements of the domain in an integer variable must be greater than or equal to 2.dim=20dim_co=dim//2dim_int=dim//2domain_int=[list(range(-10,11))for_inrange(dim_int)]defn_int_tablet(x):xbar=np.array(x)xbar[dim_co:]=np.round(xbar[dim_co:])xbar[:dim_co]*=100returnnp.sum(xbar**2)The hyperparameters of DX-NES-ICISet initial mean vector (m), initial step size (sigma), and population size (lamb).
Note that population size should be an even number.
Set the minimum marginal probability (margin).
1.0 / (dim * lamb) [3] is the recommended value of minimum marginal probability.m=np.ones([dim,1])*2.sigma=1.0lamb=6margin=1.0/(dim*lamb)Running DX-NES-ICIPass variables to construct DXNESICI.
You must pass the maximal number of evaluations and a target evaluation value to run the optimizer.
Return values are a success flag, the best solution in the last generation, and the best evaluation value in the last generation.dxnesici=DXNESICI(dim_co,domain_int,n_int_tablet,m,sigma,lamb,margin)success,x_best,f_best=dxnesici.optimize(dim*1e4,1e-10)Version HistoryVersion 1.0.3 (2023-12-09)Update README.Version 1.0.2 (2023-12-09)Fixed a bug in the return value of the best solution.Fixed misleading implementations of functions in sample programs. Note that in these sample programs, benchmark functions return exactly the same values before and after this fix.Add stdout of #Eval when DX-NES-ICI finish.Version 1.0.1 (2023-4-20)First implementation.ReferenceKoki Ikeda and Isao Ono. 2023. Natural Evolution Strategy for Mixed-Integer Black-Box Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’23). 8 pages.https://doi.org/10.1145/3583131.3590518Masahiro Nomura, Nobuyuki Sakai, Nobusumi Fukushima, and Isao Ono. 2021. Distance-weighted Exponential Natural Evolution Strategy for Implicitly Constrained Black-Box Function Optimization. In IEEE Congress on Evolutionary Computation (CEC ’21). 1099–1106.https://doi.org/10.1109/CEC45853.2021.9504865Ryoki Hamano, Shota Saito, Masahiro Nomura, and Shinichi Shirakawa. 2022. CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’22). 639–647.https://doi.org/10.1145/3512290.3528827
Package: dxp
DXPData analysis and exploration library.UsageData PopulationDXP provides a library of commonly used functions,
and an executable with sane defaults
that will read in a CSV file for input,
and output another CSV file populated with values
from your custompop.pyfile.Plotting DataDXP uses Matplotlib to create plots of linear regressions
from an input file. Refer todplt -hfor
more information about usage.
Package: dx-profiler
Waffle ProfilerDx 인퍼런스 분석 툴Dx에 App을 설치하고 영상을 인퍼런스한 후 로그를 남긴다.남긴 로그와 영상 파일을 읽어와 화면에 인퍼런스 결과를 보여주는 기능도 제공한다.사용법설치$pipinstalldx-profilersudo 등록dx-profiler는 dx쪽에 영상을 복사하는 과정에서 sudo 권한이 필요하다.이를 위해 dx-profiler를 sudo에서 실행할 수 있도록 secure_path에 추가해준다.예시)$whichdx-profiler
/home/yoon/.cache/pypoetry/virtualenvs/dx-profiler-MxXHGb83-py3.10/bin/dx-profiler
$sudovisudo
...

Defaultssecure_path="/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin:/home/yoon/.cache/pypoetry/virtualenvs/dx-profiler-MxXHGb83-py3.10/bin"...인퍼런스autocare_docker 레포를 clone한 후 dx-batch 브랜치로 checkout한다.docker compose up -d 로 dx 도커를 실행시킨 후 dx-profiler를 실행한다.필요한 값영상 파일 (-F)앱 (-A)로그 (-O)예시)$sudodx-profilerinference-F/mnt/hdd/videos/test_videos/helmet.mp4--app~/Downloads/aff0d790-8e15-47f4-874c-7320dc3673f9.zip-Olog.txt성공적으로 실행되면 영상 인퍼런스가 끝날때 까지 Waiting for EOS...를 출력하며
종료되면 Done.을 출력하고 종료된다.영상확인생성한 로그 기반으로 영상을 재생한다.예시)$sudodx-profilerprofile-Flog.txt
Package: dx-punch
FeaturesDesign according to Eurocode provisions (EN-1992-1)Evaluation of basic control perimeters (§6.4.2)Support for columns of arbitrary polygonal shape.Support for drop panels.Automatic classification of columns into 'internal', 'edge',
and 'corner'.Evaluation of design shear stress (§6.4.3) for biaxial eccentricity
of all classes of columns, using three methods:Analytical via numerical evaluation of the perimeter modulus W1
(6.40) in both directions.Approximate through Eq. (6.43).Simplified through §6.4.3(6).Design checks according to §6.4.4.Support for declaration of soil-pressure on raft-slabs:UniformDiscrete pressure fieldEvaluation of a code-compliant shear-cage layout according to
provisions of §6.4.5.Not yet supported:Consideration of openings in the evaluation of basic control perimeters
(§6.4.2(3)).Favourable effect of in-plane normal stresses in Eq. (6.47).Evaluation of radial layout of shear reinforcement (Fig. 6.22A).APIWe use thedxstack of packages (dx-utilities,dx-base,dx-eurocode)
that enables:Geometric representation of structural elements usingshapely.Geometric operations and linear algebra utilities withnumpyandmathutils.Use ofdx_utilities.fieldsfor various representations.Visualization of results usingmatplotlib.Support for tabular reports usingpandas.ContributeSource code lives inhttps://gitlab.com/d-e/dx-punch.Public APISee thedocumentation pages.
Package: dxpy
dxpy: DNAnexus Python APIAPI DocumentationBuildingFrom the dx-toolkit root directory:python3 -m pip install -e src/pythonDebuggingSet the_DX_DEBUGenvironment variable to a positive integer before
running a dxpy-based program (such asdx) to display the input and
output of each API call. Supported values are 1, 2, and 3 with
increasing numbers producing successively more verbose output.Example:$ _DX_DEBUG=1 dx lsPython coding styleConform toPEP-8.Relax the line length requirement to 120 characters per line, where you judge readability not to be compromised.Relax other PEP-8 requirements at your discretion if it simplifies code or is needed to follow conventions
established elsewhere at DNAnexus.Document your code in a format usable bySphinx Autodoc.Runpylint -Eon your code before checking it in.Do not introduce module import-time side effects.Do not add module-level attributes into the API unless you are absolutely certain they will remain constants. For
example, do not declare an attributedxpy.foo(dxpy._foois OK), or any other non-private variable in the
global scope of any module. This is because unless the value is a constant, it may need to be updated by an
initialization method, which may need to run lazily to avoid side effects at module load time. Instead, use
accessor methods that can perform the updates at call time:_foo=Nonedefget_foo():initialize()return_fooOther useful resources:Google Python style guidePython version compatibilitydxpy is supported on Python 3 (3.8+)Convention for Python scripts that are also modulesSome scripts, such as format converters, are useful both as standalone executables and as importable modules.We have the following convention for these scripts:Install the script intosrc/python/dxpy/scriptswith a name likedx_useful_script.py. This will allow
importing withimport dxpy.scripts.dx_useful_script.Include in the script a top-level function calledmain(), which should be the entry point processor, and
conclude the script with the following stanza:if__name__=='__main__':main()The dxpy installation process (invoked throughsetup.pyor withmake -C src pythonat the top level)
will find the script and install a launcher for it into the executable path automatically. This is done using theentry_pointsfacility of setuptools/distribute.Note: the install script will replace underscores in the name of your module with dashes in the name of the launcher
script.Typically, when called on the command line,main()will first parse the command line arguments (sys.argv). However,
when imported as a module, the arguments need to instead be passed as inputs to a function. The following is a
suggestion for how to accommodate both styles simultaneously with just one entry point (main):defmain(**kwargs):iflen(kwargs)==0:kwargs=vars(arg_parser.parse_args(sys.argv[1:]))...if__name__=='__main__':main()
Package: dxr-cmd
A small command-line tool for searchingDXR.InstallDXR-cmd is written in Python and can be installed usingpip, which comes
included with most recent versions of Python:pip install dxr-cmdUsageAdds a command calleddxr:Run a search against DXR.

Usage: dxr [options] <query>...

Options:
  --case-insensitive  Perform a case-insensitive search (searches are
                      case-sensitive by default).
  -h --help           Show this screen.
  --limit=LIMIT       Maximum number of matches [default: 50]
  --no-highlight      Disable syntax highlighting.
  --pager=PROGRAM     Direct output through PROGRAM.
  --server=DOMAIN     DXR instance to send the search request to.
                      [default: https://dxr.mozilla.org]
  --style=STYLE       Name of Pygments style for syntax highlighting.
                      [default: paraiso-dark]
  --tree=TREE         Code tree to search against.
                      [default: mozilla-central]
  -v --version        Show program version.Developer SetupUse avirtualenv!Install the package in development mode:$ ./setup.py developLicenseThis software is licensed under theMIT License. For more information, see
theLICENSEfile.
Package: dxsp
Swap made easyTrade on any blockchainswith uniswap based router, 0x protocol and kwenta sdk.How to use itfrom dxsp import DexSwap
    dex = DexSwap()
    #BUY 1% to SWAP with BITCOIN
    #demo_tx = await dex.submit_order({
        "action": "BUY",
        "instrument": "BTC",
        "quantity": 1,
    })Documentation
Package: dxter
dexterdata exploration terserWhat is dexter?dexteris a lightweight Python package built on top ofnumpyandpandasthat allows fast data exploration
for multiple structured table files in a folder. It's a high-level tool suitable for a first contact with a dataset
composed of multiple dataframes.FeaturesImporting multiple table files withreadm_csv()Saving DataFrames and Names with theFrameMapclassApplying pandas methods to multiple DataFrames at onceInstallingNot available for installing yet, but available for download and import at:https://github.com/igormagalhaesr/dexterUsageImporting dexter:importdexterasdxtReading multiple dataframes in a folder:dataframes=dxt.readm_csv(./folder/)Displaying results:dataframes.display()Out:col1col2col30value1$31value2$52value3$7firstsecondthird0one17%1two19%2three23%Names and Framesnames=dataframes.namesframes=dataframes.framesMultiple Dataframes Typesdataframes.dtypes()Multiple Missing Valuesdataframes.multiple_missing()Multiple Descriptionsdataframes.describe()For more concrete examples, check thenotebookContributingFork it (https://github.com/igormagalhaesr/dexter)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull RequestDependenciesNumPyPandasIPythonLicenseDistributed under theBSD 3license. SeeLICENSE.txtfor more information.ContactIgor Magalhaes –@igormagalhaesr–igormagalhaesr@gmail.comgithub.com/igormagalhaesr
Package: dxt-explorer
DXT Explorer is an interactive web-based log analysis tool to visualize Darshan DXT logs and help understand the I/O behavior of applications. Our tool adds an interactive component to Darshan trace analysis that can aid researchers, developers, and end-users to visually inspect their applications' I/O behavior, zoom-in on areas of interest and have a clear picture of where is the I/O problem.DocumentationYou can find our complete documentation atdxt-explorer.readthedocs.io.CitationYou can find more information about DXT Explorer in our PDSW'21 paper. If you use DXT in your experiments, please consider citing:@inproceedings{dxt-explorer,
   title = {{I/O Bottleneck Detection and Tuning: Connecting the Dots using Interactive Log Analysis}},
   author = {Bez, Jean Luca and Tang, Houjun and Xie, Bing and Williams-Young, David and Latham, Rob and Ross, Rob and Oral, Sarp and Byna, Suren},
   booktitle = {2021 IEEE/ACM Sixth International Parallel Data Systems Workshop (PDSW)},
   year = {2021},
   volume = {},
   number = {},
   pages = {15-22},
   doi = {10.1109/PDSW54622.2021.00008}
}DXT Explorer Copyright (c) 2021, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office atIPO@lbl.gov.NOTICE.  This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights.  As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.
Package: dx-utilities
dx-utilitiespackageFeaturesGeometry module on top ofshapely.Representations of physical fields.Linear algebra and numerical integration.Utilities on top ofnumpyandmathutils.Vector.Decorators.Exception classes with error-code.Data-structures.Book-keeping of physical constants and manipulation of units.Testing and printing utilities.Sample usage>>> from dx_utilities.geometry import PlanarShape
>>> rectangle = PlanarShape.new(shape='rectangle', bx=1.0, by=2.0)
>>> rectangle.area
2.0Public APISee thedocumentation pages.ContributeSource code lives inhttps://gitlab.com/d-e/dx-utilities.
Package: dxutils
Titledxutils -- Utils for DepXThis is a set of utilities which started as tools for internal use of our department.
At that time it was Department X (10) - of computational materials science.
The current name is different but the package name stucked. It turns out that the tools
may be os some use for other researchers and can be used in other programs.
They are centered around solid state computational physics.InstallationYou can install the package from pypi. It has several dependencies and requires python 3.5 or later.To install from pypi (better use some virtual environment using e.g.:python3 -m venv venv && . venv/bin/activate):pip install dxutilsUsageThe package provides a number of utility functions organized in the library you can use in your python programs and a command line programgen_alamodefor generating ALAMODE input files from the DFT structural data. The default format is VASP 5+ POSCAR file but you can use any format which is known to the ASE library used for reading and transforming the data. Get help ongen_alamode:$ gen_alamode --help
Usage: gen_alamode [OPTIONS] [ACTION]

  Generates gen/opt/phon/dos file depending on the ACTION (default: opt). The
  default values of parameters are enclosed in parethesis.

Options:
  -o, --order INTEGER  Approximation order (1)
  -p, --prefix TEXT    Prefix used in calculations (CRYST)
  -n, --name PATH      Supercell POSCAR file (SPOSCAR)
  -s, --scale FLOAT    Scale of the unit cell (1.0)
  -e, --evec INTEGER   Print eigenvectors (1)
  -m, --msd INTEGER    Print mean squere displacement (1)
  --c1 TEXT            First order interaction cutoff (None)
  --c2 TEXT            Second order interaction cutoff (10)
  --c3 TEXT            Third order interaction cutoff (10)
  -k, --kpath PATH     File with reciprocal space path
  -g, --grid TEXT      k-grid for dos calculation (10x10x10)
  -d, --ndat INTEGER   Number of data points used in fitting (All)
  -f, --dfset TEXT     Name of the DFSET file (DFSET)
  -t, --tmax INTEGER   Max temperature (1000)
  -c, --charge TEXT    Name of the Born effective charges file (<prefix>.born)
  -b, --born INTEGER   If non-zero use info from <prefix>.born as Born
                       effective charges. Use <born> = [1,2,3] value to select
                       method of non-analytic correction.
  --help               Show this message and exit.
Package: dxz
DXZdxz is a Python package that implements the dxz algorithm that is described in the paperDancing with Decision Diagrams: A Combined Approach to Exact Cover. The algorithms returns all solutions to a given exact cover problem.InstallationUse the package managerpipto install dxz.pipinstalldxzThe Algorithmdxz is an algorithm for solving an exact cover problem. It build azddthat represents all solutions for a given problem. This module converts the zdd to to a list of lists of ints. Each list in the list represents one solution.For more details about dxz read the paperDancing with Decision Diagrams: A Combined Approach to Exact Cover.Usageimportdxzrows=8columns=5# A flatten matrix of boolean values. Row 0 is 1,0,0,1,1 row 2 is 0,1,1,0,0 and so on.matrix=[1,0,0,1,1,# 00,1,1,0,0,# 10,0,0,1,1,# 20,1,0,1,0,# 31,0,1,0,0,# 40,0,0,0,1,# 50,0,0,1,0,# 61,0,0,0,0,# 7]# Get all solutions to the exact cover problem.solutions=dxz.dxz_solve(rows,columns,matrix,0)print(solutions)# [#   [1,0],#   [1,7,2],#   [1,7,6,5],#   [3,4,5]# ]# One solutions is row 1 and 2 another solution is row 1, 7 and 2.# Get only the number of solutionsnumber_of_solutions=dxz.dxz_count(rows,columns,matrix)# Get only 3 solutions.solutions=dxz.dxz_solve(rows,columns,matrix,3)ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.LicenseMIT
Package: dyalog-jupyter-kernel
Jupyter kernel for Dyalog APLThis is aJupyterkernel forDyalog APL. Thewikihas more details.For complete installation instructions from scratch on Windows, seeWINDOWS.mdin this repository.If you're a kernel developer, seeDEVELOPMENT.mdin this repository.Do you have any pre-made notebook documents?A collection of ready-made notebooks is availablehere.InstallationPre-requisites:Python version 3.8 or later. Python can be installed in several ways. We recommend the standard download fromhttps://www.python.org/downloads/.Jupyter: see theofficial documentationfor installation instructions.Installing the Dyalog kernelThis kernel is installable via Python's package installer,pip. It comes bundled with Python, but does occasionally need updating.In your terminal, run the follwing command:pipinstalldyalog-jupyter-kernel[==2.0.1]After successfully installing the kernel module itself, you need to register it with your Jupyter installation, using the following terminal command:python-m'dyalog_kernel'installYou should now be able to see thedyalog_aplkernel listed:jupyterkernelspeclist

Availablekernels:python3C:\Users\stefa\work\Jupyter\py312\share\jupyter\kernels\python3dyalog_aplC:\Users\stefa\AppData\Roaming\jupyter\kernels\dyalog_aplStart Jupyter:jupyterlab# or jupyter notebookand you should now see Dyalog APL amongst your kernels. Click on the Dyalog APL icon.
Package: dyatel
# DYATELA system daemon makes periodic checks and actions to achieve a specified number of errors.Awailable resource:- http## Installpip install dyatel## Example config file```---- task:- check:name: prodtesttype: httpurl: https://microservice.net/api/1.1/method: getpause: 5expectation:status: 200exception:count: 3action:command: /usr/bin/supervisorctl restart apppause: 30```## Systemd Unit```[Unit]Description=Simplified simple zebra serviceAfter=syslog.target[Service]Type=simpleUser=rootGroup=rootWorkingDirectory=/usr/localExecStart=/usr/local/bin/dyatel.pyStandardOutput=syslogStandardError=syslog[Install]WantedBy=multi-user.target```
Package: dyban
dybanThis package is intended to be used for Network Reconstruction of Dynamic Bayesian Networks.To test the algorithm on the Yeast data set run the bash script.
Example to run a Non-Homogeneous Dynamic Bayesian Networksh yeast_pipeline.sh -m nh-dbnWhere -m denotes the method to use'h-dbn' -> Homogeneous Dynamic Bayesian Network'nh-dbn' -> Non-Homonegeneous Dynamic Bayesian Network'seq-dbn' -> Sequentially Coupled Dynamic Bayesian Network'glob-dbn' -> Globally Coupled Dynamic Bayesian NetworkThis will be the readme of the package.Github-flavored Markdownfor reference.Run this command to install the package locallypip install .Orpip install -e .To be able to edit the source code and (hot-reload) updates?To run the python profiler use the bash script:sh algorithm_profiling.shIn order to be able to see the profiler results you need to have 'kcachegrind'sudo apt-get install -y kcachegrind
Package: dycco
Dycco is a(nother) Python port ofDocco, the original quick-and-dirty,
hundred-line-long, literate-programming-style documentation generator. For an
example and more information, seeits self-generated docs.This port of Docco has fewer features than the pimary Python port,Pycco.
For instance, Dycco can generate documentation for Python files and nothing
else. It was written mostly as a reason to play with Python’s AST.You should probably usePyccoinstead.InstallationUsepipto install:pip install dyccoUsageCommand Line UsageJust passdyccoa list of files and it will generate documentation for each
of them. By default, the generated documentation is put in adocs/subdirectory:$ dycco my_python_file.pyDycco can generate docs for multiple files at once:$ dycco my_package/*.pyAnd you can control the output location:$ dycco --output-dir=/path/to/docs my_package/*.pyAll command line options are given below:$ dycco --helpOutputs:usage: dycco [-h] [-o OUTPUT_DIR] source_file [source_file ...]

Literate-style documentation generator.

positional arguments:
  source_file           Source files to document

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT_DIR, --output-dir OUTPUT_DIR
                        Output directory (will be created if necessary)Library UsageDycco can also be used as a plain old Python library:>>> import dycco
>>> dycco.document('my_python_file.py', 'my_output_dir')CreditsDycco is just a simple re-implementation ofDocco, with some inspiration and
template code from its primary Python portPycco.