Saravanan666/pypi-org huggingface dataset:
Package: tb3py
![alt text](https://github.com/usnistgov/tb3py/actions/workflows/main.yml/badge.svg)
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# TB3PYby Kevin F. Garrity and Kamal ChoudharyThis is a python wrapper for the
[ThreeBodyTB.jl](http://github.com/usnistgov/ThreeBodyTB.jl) Julia
package, which runs two- and three-body tight-binding calculations for
materials.Using the Julia version directly is the primary method for running the
code. However, this code is created to help users who might be more familiar
with python using the
[PyJulia](https://github.com/JuliaPy/pyjulia) interface.## InstallationFirst create a conda environment:
Install miniconda environment fromhttps://conda.io/miniconda.htmlBased on your system requirements, you’ll get a file something like ‘Miniconda3-latest-XYZ’.Now,` bashMiniconda3-latest-Linux-x86_64.sh(for linux) bashMiniconda3-latest-MacOSX-x86_64.sh(for Mac) `Download 32/64 bit python 3.6 miniconda exe and install (for windows)
Now, let’s make a conda environment, say “version”, choose other name as you like::` conda create--namemy_tb3 python=3.8 source activate my_tb3 conda install-cconda-forgejulia `Now, let’s install the package:` pip install requests git clonehttps://github.com/usnistgov/tb3py.gitcd tb3py python setup.py develop `Note that this can take a while and may use significant disk space. The code
will, if necessary, a) download & install Julia b) download & install
ThreeBodyTB.jl, and c) create a system image for fast loading.For main documentation of ThreeBodyTB.jl, see [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://pages.nist.gov/ThreeBodyTB.jl/)
This code is only the wrapper that downloads and installs that code.Alternate [conda install](https://anaconda.org/conda-forge/tb3py):` conda create--namemy_tb3 python=3.8 source activate my_tb3 conda install-cconda-forgetb3py `## ExamplesPredict total energy, electronic bandgap and bandstructure for a system using POSCAR file:` python tb3py/main.py--poscar_filetb3py/examples/POSCAR `Predict total energy, electronic bandgap and bandstructure for a system using cif file:` python tb3py/main.py--cif_filetb3py/examples/JVASP-1002.cif`There are several other examples provided to calculate total energies, electronic bandstructures, density of states, forces on atoms, vacancy and surface formation energies in the repo also.
More details and documentation will be available soon.## Performance TipsJulia can take advantage multiple threads. Try setting the environment variable below as appropriate for your machine.` JULIA_NUM_THREADS=8 export JULIA_NUM_THREADS `Note that despite using pre-compilation where possible, some
functions will run faster the second time you run them due to the
jit.Note that you must delete the system image if you want to update the
ThreeBodyTB.jl code and re-run the installation.
Package: tba-api-v3client
# Overview    Information and statistics about FIRST Robotics Competition teams and events.   # Authentication   All endpoints require an Auth Key to be passed in the header &#x60;X-TBA-Auth-Key&#x60;. If you do not have an auth key yet, you can obtain one from your [Account Page](/account).    A &#x60;User-Agent&#x60; header may need to be set to prevent a 403 Unauthorized error.  # noqa: E501
Package: tbase
tbase基于强化学习的交易算法Baselines(Pytorch&Tensorflow2实现). 旨在提供一个在中国股市上可复现的算法Baselines.支持环境： Python 3.5–3.7安装设置 tushare token(Tushare token注册申请):export TUSHARE_TOKEN=YOUR_TOKEN

pip install tbase --upgradeExamplepython3 -m tbase.run --alg ddpgFeatures(In progress)可以完美复现训练过程,How?加速多进程CPU并行: 多进程运行独立的Enviroment进行探索多进程单GPU并行多进程多GPU并行分布式多机多卡并行: 参考menger支持算法:单Agent(Double Dueling)DQNDDPGTD3(Twin Delayed Deep Deterministic Policy Gradients)A2C-ACKTRA2CACKTRPPOPPO2GAILOPD多AgentMADDPG自定义NetLSTM-MLPLSTM_Merge_MLPMLPLSTMCNNCNN-MLP训练例如 ddpgpython3 -m tbase.run --alg ddpg --num_env 1 --gamma 0.53 --seed 9 --print_action默认参数:scenario: "average", 平均分仓操作codes: "000001.SZ", 平安银行indexs: "000001.SH,399001.SZ",000001.SH:沪指, 399001.SZ: 深指start: "20190101", 训练开始时间end: "201901231", 训练结束时间max_iter_num: "500", 训练轮数num_env: "1", 并行进程数, 当num_env=1时，不使用多进程方式运行seed: "None", 系统随机种子print_action: "False", 随机打印action的值，方便查看action分布状况reward_fn: "daily_return_with_chl_penalty", env reward functionrun_id: "1", 运行序号, 方便查看相同参数多次运行结果差异debug: "False", debug模式，打印具体的交易信息eval: "False", 回测评估，为True时，不训练，只加载模型进行回测其他参数Defalut policy net setting(actor)Defalut value net setting(critic)运行tensorboardtensorboard --logdir=/tmp/tbase/tensorboard可以在http://localhost:6006中查看训练的loss, reward ,portfolio, time等指标评估&&回测训练周期内的评估指标回测: 模型在训练周期外(评估周期)的评估指标# lookback=10， 若测试从20200101开始, 则eval_start往前推10个交易日
python -m tbase.run --alg ddpg --eval --eval_start 20191218 --eval_end 20200223 --seed 9滑动窗口更新模型, 在评估周期内，每隔一个窗口T，重新训练一次模型，当T>评估周期时，等价于固定模型评估指标绝对收益率(Absolute Return)额外收益率(Excess Return)相对于"买入持有"策略相对于基线策略比如"沪深300"最大回撤: 在选定周期内任一历史时点往后推，净值走到最低点时的收益率回撤幅度的最大值夏普比率: 投资组合每承受一单位总风险，会产生多少的超额报酬年化收益率待优化考虑到部署到线上的方便性, 更换tensorflow实现参数配置管理: yml使用cpprb作业replay buffer测试: 提升 pytest 覆盖率(coveralls.io)模型分享，评估，部署(wandb)模型榜单排名performance: 参考评估指标(去除绝对收益率)大规模训练: 参考menger分布式, evnviroment explorer, 方便无限scale多个buffersamplemodel update & inference监控: explorer, buffer, learner, 吞吐量，以方便定位bottle neckstock2vector由于计算资源有限，为所有的算法跑完A股中所有的股票，需要花费大量的时间，希望有空闲计算资源的朋友，可以跑一下模型，将实验结果分享出来线上交流方式QQ群: 477860214
Package: tbas-py
tbas_pythonTðŸ…±ASA set of tools for working withTBASpip install tbas-pyto install. Requires Python 3.6+echo "+>++>+++D" | tbasto run the headless interpreterIncludes a TK-based graphical debugger w/ breakpoints:echo "+>++>+++D" | tbas-guito run the UItbas/badge_io.py - Implementations of various IO modestbas/buffer.py - Implements the FIFO/FILO buffertbas/corpus.py - Functions that generate TBAS programs to do useful thingstbas/datatypes.py - Contains macros used for type hintingtbas/interpreter.py - User interface and drivertbas/machine.py - Contains class that tracks machine statetests/tests.py - unit teststbas/ui.py - main UI classtbas/util.py - Helper functions
Package: tbats
BATS and TBATS time series forecastingPackage provides BATS and TBATS time series forecasting methods described in:De Livera, A.M., Hyndman, R.J., & Snyder, R. D. (2011), Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106(496), 1513-1527.InstallationFrom pypi:pipinstalltbatsImport via:fromtbatsimportBATS,TBATSMinimal working example:fromtbatsimportTBATSimportnumpyasnp# required on windows for multi-processing,# see https://docs.python.org/2/library/multiprocessing.html#windowsif__name__=='__main__':np.random.seed(2342)t=np.array(range(0,160))y=5*np.sin(t*2*np.pi/7)+2*np.cos(t*2*np.pi/30.5)+\((t/20)**1.5+np.random.normal(size=160)*t/50)+10# Create estimatorestimator=TBATS(seasonal_periods=[14,30.5])# Fit modelfitted_model=estimator.fit(y)# Forecast 14 steps aheady_forecasted=fitted_model.forecast(steps=14)# Summarize fitted modelprint(fitted_model.summary())Reading model details# Time series analysisprint(fitted_model.y_hat)# in sample predictionprint(fitted_model.resid)# in sample residualsprint(fitted_model.aic)# Reading model parametersprint(fitted_model.params.alpha)print(fitted_model.params.beta)print(fitted_model.params.x0)print(fitted_model.params.components.use_box_cox)print(fitted_model.params.components.seasonal_harmonics)Seeexamplesdirectory for more details.TroubleshootingBATS and TBATS tries multitude of models under the hood andmay appear slow when fittingto long time series. In order to speed it up you can start with constrained model search space. It is recommended to run it without Box-Cox transformation and ARMA errors modelling that are the slowest model elements:# Create estimatorestimator=TBATS(seasonal_periods=[14,30.5],use_arma_errors=False,# shall try only models without ARMAuse_box_cox=False# will not use Box-Cox)fitted_model=estimator.fit(y)In some environment configurations parallel computation of models freezes. Reason for this is unclear yet. Ifthe process appears to be stuckyou can try running it on a single core:estimator=TBATS(seasonal_periods=[14,30.5],n_jobs=1)fitted_model=estimator.fit(y)For ContributorsBuilding package:pipinstall-e.[dev]Unit and integration tests:pytesttest/R forecast package comparison tests. Those DO NOT RUN with default test command, you need R and forecast package installed:pytesttest_R/Comparison to R implementationPython implementation is meant to be as much as possible equivalent to R implementation in forecast package.BATS in Rhttps://www.rdocumentation.org/packages/forecast/versions/8.4/topics/batsTBATS in R:https://www.rdocumentation.org/packages/forecast/versions/8.4/topics/tbats
Package: tb-cli
The team b cliThis is a cli platform to help a developer manage multiple repositories and other
common automation tasks.Installationtb can be installed in your local Python 3.7 environment or it can be downloaded as a single binary for your operating
system.Python installpipinstalltb-cliOperating system-specific binary installVisithttps://bitbucket.org/mrdon/tb/downloads/and download the release for your
operating system.Untar the file and you will find a single 'tb' binary.Place it somewhere in your pathUpon first execution of a repo command like 'tb repo',
it will ask you some questions about your environmentDevelopmentThis project includes a number of helpers in theMakefileto streamline common development tasks.To install it in development mode, runmakeThis will not only install tb in a local virtualenv, but also build the 'tb' binary in dist/Environment setupThe following demonstrates setting up and working with a development environment:# create a virtualenv for developmentmakevirtualenvsourceenv/bin/activate# run tb cli applicationtb--help# run pytest / coveragemaketestReleasing to BitbucketThe tb release process uses Bitbucket pipelines to release the version and release the
linux binary (OSX has to be released manually).To run a release:Find the commit you want to release.  For the latest commit, you'd
visit:https://bitbucket.org/mrdon/tb/commits/masterFind the "Run pipeline" link on the right side and click "Run".  This will update
the CHANGES.MD file with the release info, tag the repository, build the binary, upload
the binary to Bitbucket, then finally change the version to the next dev version.Build and upload the OSX releaseYou must create a Bitbucket app token that has the ability to write to your
repositories.  Remember this, along with your Bitbucket user name, as it will be needed
in the last stepGet access to an OSX machine and run the next steps on that machine.Clone the tb repository and checkout the tag that was created previouslyRun the osx release command:make dist-osx-uploadContributorsPull requests, issues and comments welcome. For pull requests:Add tests for new features and bug fixesFollow the existing styleSeparate unrelated changes into multiple pull requestsSee the existing issues for things to start contributing.For bigger changes, make sure you start a discussion first by creating
an issue and explaining the intended change.LicenseCopyright (c) 2019 Don Brown, Atlassian and others.
Apache 2.0 licensed, seeLICENSE.txtfile.
Package: tbcm
Ten Bit Color MapsFeatures1024 colors in each sequential colormapPerceptually uniform - each color is same perceptual distance from the previous color (before rounding)Raw color data rounded so they can be used in a byte tensor (see pytorch example below)All maps start at blackInstallationpipinstalltbcmUsageWith matplotlibfromtbcmimporttb_inferno,tb_oleronthen to use tb_inferno, do:importmatplotlib.pyplotaspltimportnumpyasnpx,y=np.meshgrid(np.linspace(-1,1,15),np.linspace(-1,1,15))z=np.cos(x*np.pi)*np.sin(y*np.pi)fig=plt.figure(figsize=(9,4))ax1=fig.add_subplot(121,projection='3d')ax1.plot_surface(x,y,z,rstride=1,cstride=1,cmap=tb_inferno)ax2=fig.add_subplot(122)cf=ax2.contourf(x,y,z,51,vmin=-1,vmax=1,cmap=tb_inferno)cbar=fig.colorbar(cf)result:and the same with tb_oleron is:fig=plt.figure(figsize=(9,4))ax1=fig.add_subplot(121,projection='3d')ax1.plot_surface(x,y,z,rstride=1,cstride=1,cmap=tb_oleron)ax2=fig.add_subplot(122)cf=ax2.contourf(x,y,z,51,vmin=-1,vmax=1,cmap=tb_oleron)cbar=fig.colorbar(cf)result:With pytorchfromtbcmimporttb_inferno_data,tb_oleron_dataimporttorchtb_inferno_cm=torch.tensor(tb_inferno_data,dtype=torch.uint8)tb_oleron_cm=torch.tensor(tb_oleron_data,dtype=torch.uint8)then to use tb_inferno, do:fromPILimportImagez=torch.cos(torch.linspace(-1,1,256).view(1,-1)*torch.pi)*torch.sin(torch.linspace(-1,1,256).view(-1,1)*torch.pi)z.add_(1).div_(2)h,w=z.shapeidx=z.view(-1).mul(1023).round().clamp(0,1023).long()img=tb_inferno_cm[idx].view(h,w,3)Image.fromarray(img.numpy())result:and the same with tb_oleron:h,w=z.shapeidx=z.view(-1).mul(1023).round().clamp(0,1023).long()img=tb_oleron_cm[idx].view(h,w,3)Image.fromarray(img.numpy())result:
Package: tbcontrol
A Python library for solving textbook control problemsThe tbcontrol package collects functions useful to solve the kinds of problems encountered in undergraduate process control textbooks. It is the distributable part of a larger project to develop Jupyter notebooks for dynamics and control.Therepositorycollects notebooks for the subjects CPN321 (Process Dynamics), and CPB421 (Process Control) at the Chemical Engineering department of the University of Pretoria.You can experiment with the notebooks without installing anything using this link:The links below will allow you to view the notebooks using the notebook viewer. You can also click on the "Launch Binder" badge above to launch a notebook server which will allow you to run all the code online.View in Notebook ViewerView on GitHubWe use Travis CI to check every update made to the repository. Note that we only test on the version of Python specified inenvironment.yml.
Package: tbcp-devops
TBCP - DevOps Python Moduleready-2-use and user-friendly🦄 About 🦄Minimum Viable Product:What is what we want?DevOps stuff for working with files, folders, project structures, sementic versioning, Git management and so on🚀 Getting Started 🚀Project LinksHomepageDocumentationRepositoryIssues✋ Prerequisites ✋💪 Installation 💪😏 Development 😏🤓 Linting 🤓🧐 Testing 🧐🤩 Building 🤩🥳 Publishing 🥳😅 Support 😅Don't be shy!You are also welcome to open apost in the issue registarfor simple questions.⭐️ Features ⭐️SmokeandUnit-testedmodulesSecurity-first production-readyconfigurationsby defaultExtensivedocumentation😎 Built With 😎🏆 Acknowledgements 🏆Thanks for these awesome resources that were used during the development of theBootcamp: DevOps Python Module:📑 Changelog 📑SeeCHANGELOGfor more information.📋 Roadmap 📋In the Initialization sectionCreate Nodejs and Python ProjectsAutomatic creation of Gitlab ProjectsAutomate Versioningin Python Projectsin Node.js Projectswith NPMwith YarnSecret ManagementLog MessengerParse Error messages and search on SEBash HistorySee theopen issuesfor a list of proposed features (and known issues).🤝 Contribute 🤝Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make aregreatly appreciated.Please read thecontribution guidelinesfirst.Give us a star, it's really important! 😅Fork the Project: (git clone https://gitlab.com/the-bootcamp-project/packages/devops_python.git)Create your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request📜 License 📜SeeLICENSEfor more information.💌 Contact 💌Bootcamp contributors-contributors@bootcamp-project.com
Package: tbcp-gitpy
GitPy - Working with Git in Pythonready-2-use user-friendly Python module🦄 About 🦄Minimum Viable Product:What is what we want?Provide a user-friendly Python module for the use of Git.What are the goals?Work with Git ConfigWork with Git RepositoriesCheck if Git is installedCheck if CWD is a Git RepoLoad Git Repo by PathGet Infos from Git RepoInit new Git RepoWork with Git FilesWork with Git CommitsWork with Git TreesWork with Git TagsList TagsCreate TagDelete TagWork with Git BranchesList local branches of a repoCreate new local branchoptionally push to remoteRemove local branchoptionally remove from remoteCheckout specific branchCheckoutmainbranchRenamemastertomainWork with Git RemotesWork with Git HooksWork with Git Flows🚀 Getting Started 🚀Project LinksHomepageDocumentationRepositoryIssues✋ Prerequisites ✋💪 Installation 💪😏 Development 😏🤓 Linting 🤓🧐 Testing 🧐🤩 Building 🤩🥳 Publishing 🥳😅 Support 😅Don't be shy!You are also welcome to open apost in the issue registarfor simple questions.⭐️ Features ⭐️SmokeandUnit-testedmodulesSecurity-first production-readyconfigurationsby defaultExtensivedocumentation😎 Built With 😎🏆 Acknowledgements 🏆Thanks for these awesome resources that were used during the development of theBootcamp: ESLint & Prettier Configuration:Library:GitPythonHow to:RTFM.page - GitPython📑 Changelog 📑SeeCHANGELOGfor more information.📋 Roadmap 📋In the Initialization sectionCreate Nodejs and Python ProjectsAutomatic creation of Gitlab ProjectsAutomate Versioningin Python Projectsin Node.js Projectswith NPMwith YarnSecret ManagementLog MessengerParse Error messages and search on SEBash HistorySee theopen issuesfor a list of proposed features (and known issues).🤝 Contribute 🤝Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make aregreatly appreciated.Please read thecontribution guidelinesfirst.Give us a star, it's really important! 😅Fork the Project: (git clone https://gitlab.com/the-bootcamp-project/packages/gitpy.git)Create your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request📜 License 📜SeeLICENSEfor more information.💌 Contact 💌Bootcamp contributors-contributors@bootcamp-project.com
Package: tbc-video-export
TBC-Video-ExportCross-platform tool for exporting S-Video and CVBS-type TBC files to standard video files created by the decode projects:VHS-Decode (Tape Decoding)/LaserDisc-Decode/CVBS-Decode (Composite Decoding)/HiFi-Decode (FM Audio Decoding)To playback a 4FSC TBC file on physical playout systems, check:FL2K TBC PlayerInstallationWindows, Linux and macOSYou can find the latest self-contained binaries on thereleasespage.PyPIpipx install tbc-video-exportBasic UsageWindowstbc-video-export.exe input.tbcLinuxtbc-video-export.AppImage input.tbcmacOS[!TIP]
You can add the application to yourPATHby creating a symlink:ln -s /Applications/tbc-video-export.app/Contents/MacOS/tbc-video-export /usr/local/bin/tbc-video-export.tbc-video-export input.tbcor (without symlink)/Applications/tbc-video-export.app/Contents/MacOS/tbc-video-export input.tbcPyPI[!IMPORTANT]
Usepipx ensurepathto add the application to yourPATH. If you install viapipyou need to do this manually.tbc-video-export input.tbcHelp--help- List all available arguments.--list-profiles- List all FFmpeg profiles.--profile- Define FFmpeg profile.--dump-default-config- Dump the FFmpeg profiles config totbc-video-export.json, allowing the creation and editing of profiles.Readout TerminalCVBS (Combined)S-Video (Y + C)FeaturesFFmpeg Encoding ProfilesAutomatic CVBS/S-Video input detectionClear processing readout screen and loggingAutomatedld-toolinteractionsChecksums for Audio and Video streamsAdvanced audio input options...Internal functioninput.tbc+[input_chroma.tbc]+input.json⟶ld-process-*⟶ld-chroma-decoder⟶YUV stream⟶FFmpeg⟶Output Video [w/audio and metadata]Changelog & Development NotesThis has a dedicated Wiki PageCreditsJuniorIsAJitterbug- DevelopmentHarry Munday- Project Management (documentation, testing, direction, icon)DisclaimerThis is my first major Python project. If you have any comments, suggestions, or improvements, feel free to create an issue, do a pull request, or contactJitterbugorHarryon theDomesday86discord.
Package: tbee
Python module to build up and solve Tight-Binding models.TBEE is written in fully vectorized Numpy.TBEE is composed of the following classes:latticesystemplotpropagationsaveTBEE main features:Complex lattice structures.Complex-valued onsite energies and hoppings.Hermitian and non-Hermitian Tight-Binding Hamiltonians.Sublattices.Hoppings defined by their type, tags, and angles.Any type of hoppings:Neighbors hoppings,Next-neighbors hoppings,Next-next-neighbors hoppings,etc..Implementation of onsite energies and hopping patterns:Dimerization defects.Magnetic field.Strain.Hopping disorder.Onsite disorder.Time propagationTBEE is available athttps://github.com/cpoli/tbeeTo use TBEE:Install Python3.4 or Python3.5 and three additional packages:numpy 1.10
scipy 0.16
matplotlib 1.5
Seehttps://cpoli.github.io/python-doc.htmlfor Python installation details and to install a github repository (for mac).Examples are available athttps://github.com/cpoli/tbee/tree/master/examples
Package: tbev
TensorBoard Embedding VisualizerAs the name suggests, this tool is made for visualizing high dimensional embeddings into 3D space using TensorBoard Projector tool. It performs PCA and also has an option for T-SNE for doing dimensionality reduction. All you have to do is provide you embeddings data and its corresponding labels and optionally images(paths) in pickle format as a dictionary. The structure of this dictionary is explained below.Getting StartedRequirementPython3Tensorflow > 1.4InstallInstall this tool using pip with the following command.pip install tbevUpgrade to the latest version by using the--upgradeflagThis will be installed as a command line tool and you can simply runtbevin you command line to get started.UsageUsage:
    tbev demo 
    tbev <pickle_file> [--logdir=<path>]

Options:
    -h --h    Show help screen
    --logdir=<path>  Location to store log files [default: ./logs/]tbev <pickle_file> [--logdir=]
You will need to pass your embeddings in form of a pickle file. The pickle file should contain a dictionary in the following format.{
    "embedding":2D matrix of shape [m embeddings, n dimensions],
    "labels":{
        "label1": List of shape (m,), A label for each embedding,
        "label2": List of shape (m,), A label for each embedding,
        You can put as many labels as you want.
    },
    "sprite_paths":(Optional) List of shape (m,), A path to image to be shown in tensorboard.
}Save this dictionary into a pickle file and use the commandtbev <pickle_file> [--logdir=<path>]This will create a ./logs folder by default where it will store the checkpoints file. You can optionally mention your own name for storing the logs using the --logdir option. If everything works out well. It will start the tensorboard server for you.TensorboardAfter the Tensorboard is started, it will show you the local URL to view the tensorboard. By default it islocalhost:6006.
Package: tbevents
# tbeventstbevents is a library that makes it possible to create an event-drive system in Python.A messaging service such as Rabbit is used to transit events.A [series of articles on Medium](https://medium.com/@eskelsen/uma-arquitectura-simples-e-eficiente-para-sistemas-event-driven-em-python-parte-i-5eb59336d858) describes its use#### Changelog:##### 28/11/2021
- Now, by default, new topic aren’t announced in new_event_topic_created/event_store.
To announce, you must specify announce_new_topic=True on TbWorker declaration, like that:w = TbWorker(“example_worker_1”, announce_new_topic=True)- Now you can declare a prefix to Rabbit Exchange names creating an environment variable:export RBMQ_PREFIX=”my_prefix/”