Saravanan666/pypi-org huggingface dataset:
Package: tir-cli
TIR CLIStable Release : version - 0.1.1The  TIR  Command  Line  Interface is a unified tool to manage all your tir services
A command line tool developed by tir Networks LtdGetting startedJust Login to yourE2E myAccountand get your access/project tokens.
For more on access token referE2E TokensAdd this to your system and you are all set to go.RequirementsThe tir-cli package works on Python versions:3.x and greater
setuptools 45.2.0 or greater
requests 2.22.0 or greaterNoticesAttention!
We recommend that all customers regularly monitor theTIR docswebsite for any important update bulletins related to tir-cli.InstallationInstallation of the TIR CLI and its dependencies use a range of packaging features provided by pip and setuptools. To ensure smooth installation, it’s recommended to use:pip: 21.0 or greater
requests: 2.22.0 or greater
setuptools: 45.2.0 or greaterThe safest way to install the tir CLI is to use pip in a virtualenv:
pip install tir-cliConfigurationBefore using the tir CLI, you need to configure your tir credentials/Tokens
This can be done by running the following commands to add token,more:tir config add      ->   To add projectFurther set default project by using :
tir config set      ->   To set default project for systemBasic CommandsAn tir CLI command has the following structure:tir <command> <subcommand> [options and parameters]For example, to list buckets, the command would be:tir datasets listTo view help/man documentation, use one of the following:tir --helpTo get the version of the tir CLI:tir --version/-vTo get the package info of the tir CLI:tir --infoTo turn on debugging :tir --debug  <command> <subcommand>To get  arguments info :tir <command> <subcommand> --helpSupportFor support/help referTIR CLI docsor visittir NetworksAuthors and acknowledgmentAman MishraProject status
Package: tirds
tirdsis a command-line tool to backup and restore Google Cloud Datastore Entities via local.tirdsstands forTotte IRete DaSu(DataStore).You can back up Google Cloud Datastore entities, and restore them. Backups are saved to Google Cloud Storage. For details, seeBacking Up and Restoring.But if you move a backup to another bucket, you will not be able to restore it. Because thebackup_infofile that is metadata of the backup includes the bucket name.tirdsdownloads a backup containsbackup_infoand blob files from the specified bucket, and uploads it to another bucket after replacing the bucket name in thebackup_info.InstallationTo install, usepip.$pipinstalltirdsUsage$tirds-hDownload backupDownload a backup from Google Cloud Storage.$tirdsdownload--outOUTDIR--key-fileKEYFILEhandleYou can gethandlefrom datastore admin.KEYFILEis a path to the private key file for Service Account.Upload backupUpload a backup to Google Cloud Storage after replacing the bucket name.$tirdsupload--key-fileKEYFILEsrcdirbucketsrcdiris a path to the backup.bucketis a bucket name that is the upload destination and to be replaced with original bucket name.
Package: tirem
Name:Python API for TIREMAuthor:Idan MiaraPython API forTIREMThis Python API is part ofRFModel.Notice of Non-Affiliation and DisclaimerThis repository is not affiliated, associated, authorized, endorsed by, or in any way officially connected withALION,
or any of its subsidiaries or its affiliates.The namesALIONandTIREMas well as related names, marks, emblems and images are registered trademarks of their respective owners.In case I’ve made a mistake and you believe you’ve found any copyrighted material here please let me know and I’ll take care of that.TIREMTheTIREMPython API package is a Python/Cython API wrapper for theTIREMmodel byALION.TheTIREMmodel itself is not included.You have to obtain a license fromALIONin order to be able to use this Python API.You would need to install the licensedTIREMDLL files.
You can do it by simply copy the DLL files into thetiremfolder in yoursite-packagesor by installing thetirem-binwheel that you may build.Buildingtiremwheel> build-tirem-pyd-clean.bat
> build-tirem-wheel.batTIREM-BINAfter obtaining aTIREMlicense you may use the following instructions in order to build thetirem-binwheel for you own use. This wheel shall be covered by the same license you got withTIREM.Installing this wheel will simply copy the DLL into theTIREMfolder in yoursite-packagesBuildingtirem-binwheel:Copy the TIREM DLL files tosrctiremand run:> build-tirem-bin-wheel.batLicenseThis library is released under the MIT license, hence allowing commercial
use of the library. Please refer to theLICENSEfile.The license for this library covers all the files in this repository.
Copyrighted material byALION, such asTIREMDLL files (or otherALIONmaterial)
are covered by a different license, given byALION, and are not part of this repository.
Package: tiresia
tiresiaInstallationpip install tiresiaDescriptiontiresiais just a wrapper aroundscikit-learnGridSearchCV. The idea is to simplify the model testing workflow. With tiresia you can choose which models to test and to exclude and provide aparam_gridfor the ones you want to explore deeper, while keeping the parameters of the less interesting ones on default.Examplefromtiresia.predictorimportAutoPredictorfromsklearn.datasetsimportmake_regression,make_classificationfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportroc_auc_score,r2_scoretest_type="regressor"iftest_type=="classifier":train,target=make_classification(5000,20)eliftest_type=="regressor":train,target=make_regression(5000,20)x_train,x_test,y_train,y_test=train_test_split(train,target,test_size=0.3)autopred=AutoPredictor(estimator_type=test_type,scoring=r2_score,greater_is_better=True)autopred.fit(x_train,y_train,x_test,y_test)predictions=autopred.predictionsresults=autopred.resultsprint(results)REGRESSORr2_score18LassoCV1.00012HuberRegressor1.00023LinearSVR1.00022LinearRegression1.00021LassoLarsIC1.00020LassoLarsCV1.00029RANSACRegressor1.00017Lasso1.00016LarsCV1.00015Lars1.00014KernelRidge1.00028PassiveAggressiveRegressor1.00031Ridge1.00032RidgeCV1.00033SGDRegressor1.00035TransformedTargetRegressor1.0002BayesianRidge1.00024MLPRegressor0.9986ElasticNetCV0.99211HistGradientBoostingRegressor0.95710GradientBoostingRegressor0.9255ElasticNet0.8818ExtraTreesRegressor0.85327OrthogonalMatchingPursuitCV0.82730RandomForestRegressor0.8241BaggingRegressor0.7940AdaBoostRegressor0.75136TweedieRegressor0.74313KNeighborsRegressor0.6863DecisionTreeRegressor0.48626OrthogonalMatchingPursuit0.3877ExtraTreeRegressor0.38634SVR0.35719LassoLars0.35325NuSVR0.2859GaussianProcessRegressor0.0384DummyRegressor-0.003
Package: tiresias
Tiresias: Predicting Security Events Through Deep LearningThis code was implemented as part of the IEEE S&PDeepCASE: Semi-Supervised Contextual Analysis of Security Events[1] paper.
We provide a Pytorch implementation ofTiresias: Predicting Security Events Through Deep Learning(CCS'18).
We ask people tociteboth works when using the software for academic research papers.IntroductionWith the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (eg. whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias xspace, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias xspace on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias xspace are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task.DocumentationWe provide an extensive documentation including installation instructions and reference attiresias.readthedocs.ioReferences[1]van Ede, T., Aghakhani, H., Spahn, N., Bortolameotti, R., Cova, M., Continella, A., van Steen, M., Peter, A., Kruegel, C. & Vigna, G. (2022, May). DeepCASE: Semi-Supervised Contextual Analysis of Security Events. In 2022 Proceedings of the IEEE Symposium on Security and Privacy (S&P). IEEE.[2]Shen, Y., Mariconti, E., Vervier, P. A., & Stringhini, G. (2018). Tiresias: Predicting security events through deep learning. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS) (pp. 592-605).BibtexDeepCASE@inproceedings{vanede2020deepcase,
  title={{DeepCASE: Semi-Supervised Contextual Analysis of Security Events}},
  author={van Ede, Thijs and Aghakhani, Hojjat and Spahn, Noah and Bortolameotti, Riccardo and Cova, Marco and Continella, Andrea and van Steen, Maarten and Peter, Andreas and Kruegel, Christopher and Vigna, Giovanni},
  booktitle={Proceedings of the IEEE Symposium on Security and Privacy (S&P)},
  year={2022},
  organization={IEEE}
}Tiresias@inproceedings{shen2018tiresias,
  title={Tiresias: Predicting security events through deep learning},
  author={Shen, Yun and Mariconti, Enrico and Vervier, Pierre Antoine and Stringhini, Gianluca},
  booktitle={Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security},
  pages={592--605},
  year={2018}
}
Package: tirf-ob
TIRF Object Cropping [tirf_ob]Python package for cropping images based on input coordinates.InstallationStable releaseTo install Semantic_Label_Creator, run this command in your terminal:$pipinstalltirf_obThis is the preferred method to install tirf_ob , as it will always install the most recent stable release.From sourcesThe sources for tirf_ob can be downloaded from theGithub repo.You can either clone the public repository:$gitclonegit://github.com/RRobert92/tirf_ob$pythonsetup.pyinstallor install is with pip:$pipinstalltirf_obUsageTo use tirf_ob with terminal:crop_tirf -dir_img C:/... -dir_csv C:/... -o C:/.../output -m 256

string [-dir_img] Directory of the image that should be cropped.
   [-default] None
string [-dir_csv] Directory of the .csv file with coordinates.
   [-default] None
string [-o]   Output directory to the folder where all of cropped image is stored.
   [-default] None
int    [-m]  Size of corpping area in pixels.
   [-default] 256
Package: tir-framework
TIR - Test Interface RobotTIR is a Python module used to create test scripts for web interfaces. With it, you can easily create and execute test suites and test cases for any supported web interface systems, such as Protheus Webapp.Current Supported TechnologiesProtheus WebappAPWTable of ContentsDocumentationInstallationConfigUsageDockerSamplesContact UsContributingDocumentationOur documentation can be found here:TIR DocumentationTIR Technical DocumentationThis project has a docs folder withSphinxfiles.Ourcreate_docs.cmdscript handles the installation of dependencies and creates the offline documentation on the doc_files/build/html folder.InstallationThe installation is pretty simple. All you need as a requirement is Python 3.7.9 (Mozilla Firefox) installed in your system.There are three ways of installing TIR:1. Installing and Upgrade from PyPITIR can be installed via pip fromPypipipinstalltir_framework--upgrade2. via Terminal(Deprecated For The Branch Master)You can install TIR via the terminal. Make sure your Python and Git are installed and run this command:pipinstallgit+https://github.com/totvs/tir.git--upgradeIt will install the last release of TIR in the active Python instance.ConfigThe environment must be configured through aconfig.jsonfile.
You can find one to be used as a base in this repository. To select your file,
you can either put it in your workspace or pass its path as a parameter of any of our classes' initialization.Config optionsHere you can find all the supported keys:Config.json keysCustom config pathJust pass the path as a parameter in your script:Protheus WebApp Class example:#To use a custom path for your config.jsontest_helper=Webapp("C:\PATH_HERE\config.json")UsageAfter the module is installed, you could just import it into your Test Case.
See the following example:Protheus WebApp ClassSamplesWe have a repository with different samples of TIR scripts:TIR Script SamplesContactGitterContributingTo contribute be sure to follow theContributionguidelines.Also, it's important to understand the chosenarchitecture.
Package: tirf-trajectory
TIRF Trajectory介绍该软件用来处理tirf实验采集的带噪声的FRET强度信号，目前可以自动对进行FRET强度等级进行分类和截断。安装安装miniconda. 国内用户可以使用清华镜像下载安装文件, 国内软件源镜像的设置可以参考清华tuna的说明.以Linux为例，假设miniconda安装在默认目录，打开命令行界面，按照以下命令行安装运行环境（只需运行一次，即完成安装）。source~/miniconda3/bin/activatecdpath/to/proj/dir/contains/environment_rt.yml
condaenvcreate-fenvironment_rt.yml运行以后每次使用之前，先自行下面的操作source~/miniconda3/bin/activatecdpath/to/proj/dir/notebooks
condaactivatetirf_traj
jupyterlab--ip=0.0.0.0打开浏览器输入http://10.0.0.3:8888/lab(假设运行上一步命令机器的ip为10.0.0.3)，或http://127.0.0.1:8888/lab(假设运行上一步命令的机器就是打开浏览器的机器)。处于安全考虑，不建议再未做进一步设置的前提下在公网可访问的情况下开启jupyter。在浏览器中打开tirfdata.ipynb,依次执行即可，请特别注意文本标注的cell中的提示。此外需要注意的事项，目前版本如需正常显示图像，需要保证你的浏览器可以正常连接以下网址。unpkg.comcdn.bokeh.orgcdn.holoviz.org在浏览器中打开tdp.ipynb,可画TDP图
Package: tirt
the simulation of Thurstone Item Response Theory, include fixed forced
test and adaptive forced test.
模拟瑟斯顿项目反应理论，包括固定测验和自适应测验。瑟斯顿IRT模型简介和应用瑟斯顿IRT模型主要应用于迫选式非认知测验（人格测验，动机测验，兴趣测验等）。固定测验模拟模拟100个被试，30个维度，每个维度10个陈述，每道题3个陈述，所以下面这个陈述总共有100题from tirt import SimFixedTirt

fixed_tirt = SimFixedTirt(subject_nums=100, trait_size=30, items_size_per_dim=10)
theta_list = fixed_tirt.sim()
score_list = fixed_tirt.scores

for i, theta in enumerate(theta_list):
    print score_list[i]
    print theta自适应测验模拟模拟1个被试，题库600道题，30个维度，首先随机抽10题，第二阶段抽合适的题40道题，总共50道题from tirt import SimAdaptiveTirt

sat = SimAdaptiveTirt(subject_nums=1, item_size=600, trait_size=30, max_sec_item_size=40)
sat.sim()

for key, value in sat.thetas.items():
    print sat.scores[key]
    print value一致性迫选测验通常都没有测谎量表（迫选测验本身抗作假），而衡量被试是否认真作答有更好的一致性分数from tirt import irt_consistency_score, sim_scores, BayesProbitModel, gen_item_dict, SimFixedTirt
from tirt.utils import random_params

# 生成试题字典
item_dict = gen_item_dict(30, 10, block_size=3)
# 生成试题参数
a, b = random_params(item_dict, 30, block_size=3)
# 生成随机得分
scores = sim_scores(30, 10, 10)

for score in scores:
    model = BayesProbitModel(a, b, score=score)
    # 打印一致性
    print irt_consistency_score(model)

model = SimFixedTirt(trait_size=30, items_size_per_dim=10, subject_nums=100, model='bayes_probit')
model.sim()
print model.get_consistency_scores()
Package: tis-but-a-scratch
Tis But a ScratchCode to support the physical modeling of a robotic arm.  The arm is driven by
two motors: one at the base and one at tne "elbow." The base experiences the
greatest torque and inertia because it must move both the base arm, the fore-arm
motor, and the fore-arm with its claw attachment.Computing the Moment of InertiaNote that the moment of inertia for an arm with joints is a little tricky to
calculate. But there's a theorem that helps.  The Parallel Axis Theorem
states that if you know the moment of inertia about the centroid of an
object, then the moment of inertia about an axis parallel to that centroid
can be computed as follows:Whereis the moment about the new axis,is the moment
about the centroid,is the centroid, andis the cross-sectional
area of the object.Using this theorem, we can conclude that the contribution
of the base arm segment (closest to the robot) remains constant because its
centroid is a fixed distance from the axis.  Only the forearm's contribution
to the total momentum affecting the base drive motor varies.  And it does so
in an easily predictable way.  We just need to know the cross-sectional area
of the part and its centroid distance from the elbow to compute the inertia
contributed by the fore-arm.
Package: tisdk
钛旗云 SDK 使用说明Python 版（V1.0）说明该SDK需使用python3以上版本安装 SDK通过pip一键完成安装pipinstalltisdk-U使用 SDK 调试接口安装完成后, 可直接在命令行使用tireq命令调试接口tirequsernamesecret_keymethodurl[foo:bar,foo2:bar2...]tireq命令的参数依次是:用户名 (由钛旗云系统分配)密钥 (由钛旗云系统分配)请求方法名 (getpostput...)请求 url请求体参数 (参数名和参数值以:相隔, 多对参数之间以,相隔)非必须参考示例如下tireqmyusername4fbe6e7084ec4d05post/api/xxx/v1name1:value1在代码中调用 SDK 发起请求参考Python代码如下importtisdkusername='myusername'secret_key='4fbe6e7084ec4d05'method='post'url='/api/xxx/v1'data={'name1':'value1'}# 直接调用 ti_request 方法发起请求# 每次发起请求时都传入`用户名`和`密钥`tisdk.ti_request(username,secret_key,method,url,data)# 以`用户名`和`密钥`初始化 Ti 对象# 之后调用 Ti.request 方法发起请求, 不需每次传`用户名`和`密钥`ti=tisdk.Ti(username,secret_key)ti.request(method,url,data)JavaSDKV1.0下载说明通过我司提供的 jar 包工具类，可直接生成请求需要的签名和数据的加密或者直接发起请求jar 包依赖第三方库 fastjson(1.2以上) 和 httpclient(4.5以上)<dependency><groupId>org.apache.httpcomponents</groupId><artifactId>httpclient</artifactId><version>4.5.9</version></dependency><dependency><groupId>com.alibaba</groupId><artifactId>fastjson</artifactId><version>1.2.58</version></dependency>jar 包工具类说明AesUtils: Aes 加解密工具类HmacSha256: Hmac256 生成工具类RequestUtil： api 请求工具类Ti: 钛旗云 sdk 工具类在代码中调用 SDK 发起请求参考Java代码如下importcom.alibaba.fastjson.JSON;importcom.kq300061.tisdk.AesUtils;importcom.kq300061.tisdk.Ti;importjava.util.HashMap;importjava.util.Map;publicclassReqTest{publicstaticvoidmain(String[]args){try{Stringusername="username";Stringsecret="secret";Stringurl="https://XX//api/xxx/v1";HashMap<String,Object>reqbody=newHashMap<String,Object>(){{put("name","abc");put("phone","cdf");put("idCard","123");}};// httpsStringresp=newTi(username,secret).doHttpsPost(url,reqbody);// 或者使用以下这种方式// Ti TiRequest = new Ti(username, secret);// String resp = TiRequest.doHttpsPost(url, reqbody);System.out.println(resp);}catch(Exceptione){e.printStackTrace();}}}
Package: tiskitpy
TiSKitPyRoutines for time series data processingUses the obspy seismological Trace, Stream (data) and Inventory (metadata)
classes, but should work for non-seismology datasets as wellDocumentationClassesCleanRotator: rotate data to minimize noise on vertical channelDataCleaner: Transfer_Function-based data cleaningDecimator: Decimate time series and update metadata with the decimator's
responseSpectralDensity: Calculate and manipulate spectral density functions.TimeSpans: Specify time spans to be removed, kept, zeroed, etc.ResponseFunctions: Frequency response functions for a given input channel.FunctionsFIR_corr: transform zero-phase data to minimum phase (only works for
LCHEAPO loggers, need to update to calculate/work for any
zero-phase filter)readMSEED: read in MSEED data, including if the file is too big (> 2 GB)
for obspy's read() functionrptransient: calculate and remove periodic transient (VERY manual!).Based on Matlab code by E Wielandt, used in Deen et al., 2017PetersonNoiseModel: return the Peterson High and Low Noise ModelsInstallationFirst, installobspyusing the instructions on their webpage.
Then, in the pip/conda environment that contains obspy...From this repositoryClone or download this repository, then from within the main repository directory, run:pip install .You can also install in editable mode (for developers), with:pip install -e .UsingpipTypepip install tiskitpy
Package: tisp
Download the TSP instances.Extract them and put them in a folder calledtsp.When installed into your environment (for example using Poetry), you can do:tisp <path to tsp .dat>Alternatively, when using it as a library, you can do:fromtispimportclicli()Then run the script. By default it looks for thegr48instance.To run a specific instance, do (the path is interpreted relatively):fromtispimportsolve_from_pathsolve_from_path("gr96.dat")Or, when you have an instance already loaded, with the costs a simple cost matrix of typelist[list[float]]:fromtispimportdo_branch_and_bound# inst = ... load instance heredo_branch_and_bound(inst)It only works for fully connected instances. An example is:3
0 20 5
20 0 3
5 3 0"3" is the size of the instance, the rest indicates the cost of edge (i, j), where i is the row and j the column.Supports Python up to 3.11. Uses mip as the underlying LP solving library. By default it uses included CBC binaries.PerformanceThe main peformance bottleneck is the computation of the min cut to find the constraint to be added to the LP, although for larger instances solving the LP's also takes significant time.On my 5-year old laptop,gr48takes under 10 seconds. Computing it directly as an LP with a solver can take 30-50 seconds. For larger instances, this solver performs even better.
Package: tispoon
Tispoon티스토리 오픈 API를 활용한 파이썬 라이브러리 입니다.InstallationpipinstalltispoonUsageusage: tispoon [-h] [--token TOKEN] [--client-id CLIENT_ID]
               [--client-secret CLIENT_SECRET] [--blog BLOG] [--verbose]
               [--version]
               {info,post,category,comment,import} ...

positional arguments:
  {info,post,category,comment,import}
    info                자신의 블로그 정보를 가져오는 API
                        입니다.
    post                블로그 글을 관리하는 API 입니다.
    category            블로그 카테고리를 정보를 가져오는 API
                        입니다.
    comment             블로그 댓글을 관리하는 API 입니다.
    import              게시된 게시글을 불러오는
                        명령어입니다.

optional arguments:
  -h, --help            show this help message and exit
  --token TOKEN, -t TOKEN
                        인증 토큰을 설정합니다.
  --client-id CLIENT_ID, -u CLIENT_ID
                        Open API의 client id값을 설정합니다.
  --client-secret CLIENT_SECRET, -p CLIENT_SECRET
                        Open API의 client secret값을 설정합니다.
  --blog BLOG, -b BLOG  블로그 이름을 설정합니다. 예)
                        `xvezda.tistory.com` 의 경우 `xvezda`
  --verbose, -v         로그의 정보량을 설정합니다. `v`의
                        갯수에 따라 정보량이 달라집니다.
  --version, -V         버전 정보를 출력하고 종료합니다.
                        `xvezda.tistory.com` 의 경우 `xvezda`블로그 정보tispooninfo게시글 작성# 마크다운 파일로 블로그 포스팅하기cat<<EOD > test.md---title: 테스트visibility: 3slogan: /안녕하세요---테스트 게시글 입니다. :)EODtispoonposttest.md게시글 마크다운 파일로 불러오기tispoonimportCopyrightMIT LicenseReferenceTispoon은티스토리와 관련이 없는 서드파티 라이브러리 입니다.티스토리와 티스토리 Open API는Kakao Corp.의 상표 또는 등록상표 입니다.티스토리 오픈 API 가이드
Package: tispost
TispostKey FeaturesSupports asyncio.Getting startedtispostallows you to quickly use postgres as a nosql database.Example# importfromtispostimportServer,Collection,Session# create connectionserver=Server(dbname=database,user=postgres,password=postgres,host=dlnxiot001)# connectserver.connect()# get sessionsession=awaitserver.session()# create new collectionsession.create("collection")# delete a collectionsession.delete("collection")# get a collectioncollection=session.collection("collection")# insert document into a collection:collection.save({'item':'value'...})# get document from collection with idcollection.get(id="930f43ed-7bb5-46b9-a6d2-45c345ec959e")# query itemscollection.query(filter={'key':'value'},offset=0,limit=50)InstallationIt's very simple to install tispost:pipinstalltispostNotesThe db user must have the create/drop table permissionRequirementsPython >= 3.6aiopgLicensetispostis offered under the Apache 2 license.Source codeThe latest developer version is available in a GitHub repository:https://github.com/aiselis/tispost
Package: tissu
About=====Functions to handle settings with FabricUsage=====Usage in your python code:::>>> from tissu.api import *>>> load_settings(mysettings)>>> from tissu.conf import settings>>> my_setting = gettattr(settings, "my_settings")Usage in your fabfile:::>>> from tissu.tasks import *$ fab e:mysettingsINSTALLATION============Tissu is on PyPI so you can either use ``easy_install -U tissu``or ``pip install tissu`` to install it. Otherwise, you can downloadthe source from `GitHub <http://github.com/thierrystiegler/tissu>`_ andrun ``python setup.py install``.LICENCE=======BSD