Saravanan666/pypi-org huggingface dataset:
Package: autodailycheckin
免责声明本仓库发布的脚本及其中涉及的任何解密分析脚本，仅用于测试和学习研究，禁止用于商业用途，不能保证其合法性，准确性，完整性和有效性，请根据情况自行判断。本项目内所有资源文件，禁止任何公众号、自媒体进行任何形式的转载、发布。您必须在下载后的24小时内从计算机或手机中完全删除本仓库所有脚本。任何人使用本仓库脚本不得用于非法用途，任何人将本仓库脚本用于非法用途所造成的一切后果由使用者承担。本人无法100%保证使用本项目之后不会造成账号异常问题，若出现任何账号异常问题本人概不负责，请根据情况自行判断再下载执行！否则请勿下载运行！如果任何单位或个人认为该项目的脚本可能涉及侵犯其权利，则应及时通知并提供相关证明，将在收到认证文件后删除相关脚本。任何以任何方式查看此项目的人或直接或间接使用本项目的任何脚本的使用者都应仔细阅读此声明。本人保留随时更改或补充此免责声明的权利。一旦使用并复制了任何相关脚本或本项目的规则，则视为您已接受此免责声明。您使用或者复制了本仓库且本人制作的任何脚本，则视为已接受此声明，请仔细阅读您使用或者复制了本仓库且本人制作的任何脚本，则视为已接受此声明，请仔细阅读本平台不会获取用户的账号密码，不会上传至其他任何地方，如果有所怀疑，请直接删除本程序由于签到平台的限制，强烈建议签到时间在早上9：30以后Auto_CheckIn 多平台自动签到支持多用户签到支持推送消息到pushplus平台(server酱和pushplus选择一个就好，也可以同时推送，如果不配置则不进行推送)支持推送消息到server酱(server酱和pushplus选择一个就好，也可以同时推送，如果不配置则不进行推送)目前已全面支持阿里云盘新版本签到中所有已知的任务，并且自己部署到本地，安全无忧，全网目前唯一的可以部署到自己本地并且支持阿里云盘新版本签到的项目已打包文件地址，可直接下载解压使用，配置yaoys_checkin_config.json即可，进入AutoCheckin找到exe点击启动阿里云盘文件格式为exe格式，msi名称为安装包，green名称为免安装版选择免安装版和安装版下载后解压exe得到程序或者安装包，阿里云盘地址：https://www.aliyundrive.com/s/ZeotJdW4iuh提取码: 15oy版本说明Windows版本:最新版本为v0.1.14青龙面板版本：最新版本为v0.1.14目前支持签到平台GLaDOS平台天翼云盘bilibili直播科研通签到谷粉学术签到阿里云盘Wps会员时长(已失效)Wps云空间容量百度贴吧交易猫飘云阁吾爱破解当前需求或BUG(欢迎随时提出issues)Issues.md签到列表🟢: 正常运行 🔴: 脚本暂不可用 🔵: 可以执行(需更新至最新版) 🟤: 随缘状态类别终端任务名称名称Cookie 时长检查日期备注🟢️签到WEBGLADOShttps://glados.rocks/待测试2023-5-13每日签到获取时长,需要开通会员才可以获取时长🟢️签到WEBBILIBILIhttps://www.bilibili.com/待测试2023-02-21直播签到，获取硬币🟢️签到WEBCLOUD189https://cloud.189.cn/永久2023-02-21每日签到 +2次抽奖获得空间奖励🟢️签到WEBAbleScihttps://www.ablesci.com/待测试2023-5-13科研通平台每日签到获取积分🟢️签到WEBgufenxueshuhttp://bbs.99lb.net/待测试2023-5-13谷粉学术每日签到获取积分🟢️签到WEBAliyunpanhttps://www.aliyundrive.com/待测试2023-5-13阿里云盘每日签到🔴签到WEBWPS_Viphttps://vip.wps.cn/home待测试2023-5-28WPS签到的会员时长，有时会失败，失败请手动🟤签到WEBWPS_Cloudhttps://zt.wps.cn/spa/2019/vip_mobile_sign_v2/?csource=pc_cloud_membercenter&position=pc_cloud_sign待测试2023-5-28Wps签到得空间容量，有时会失败，失败请手动🟢️签到WEBtiebahttps://tieba.baidu.com/待测试2023-6-18百度贴吧签到🟢️签到WEBjiaoyimaohttps://www.jiaoyimao.com/2天/3天2023-7-5交易猫签到得积分🟢️签到WEBpiaoyungehttps://www.chinapyg.com待测试2023-7-10飘云阁签到得积分🟢️签到WEBwuaipojiehttps://www.52pojie.cn/portal.php待测试2023-7-10吾爱破解签到得积分支持的通知列表server 酱（微信）pushplus（微信）更新日志更新日志.md使用教程参考使用说明.md项目打包成exe使用 Auto PY to EXE将项目打包为exe,然后建立config文件夹，并将想要签到的平台cookie写入即可签到，打包命令：在conda环境下命令行模式输入:'auto-py-to-exe'使用 cx_Freeze 打包为 exe & msi  'python setup.py build' python setup.py bdist_msi如何设置为Winodws服务使用NSSM工具，具体下载和使用步骤如下:1. 下载安装启动工具，具体可以百度如何使用
 2. 启动工具后选择下载的release中exe文件，设置相应的任务名称，描述等信息，安装服务即可
 3. 安装服务之前必须确保已经下载了已经打包好的exe文件以及相关目录，并且按照要求在相应的目录放置了并配置了config文件，**最好在服务启动之前自己验证下启动exe有没有错误**
 4. 设置服务为自动启动，可以在log日志文件中看到已成功启动，如果是定时任务，则可以按照设置的时间执行签到，如果是立即执行，则每次服务启动时执行签到。
 5. 建议：如果是服务器，则设置为定时任务，每日定时执行，如果是自己的电脑，不能保证每天都开着，那么就设置服务启动时立即执行签到，即："is_scheduler": false
 6. 如果服务启动后修改了配置文件中的‘common_config’ 和 ‘scheduler’中的相关配置，必须要重新启动服务才可以生效青龙面板使用请参考青龙面板配置使用.md
Package: autodata
OverviewThis is auto allocate dict and list like Perl.importautodatad=autodata.autodict()d["key1"]["key2"]="val"# => d["key1"] == {"key2": "val"}d["key2"]+=1# => d["key2"] == 1d["key3"][2]=1# => d["key3"] == [None, None, 1]RestrictionThe type of dict key is only strings.Dict item is not allocated by dict.get method.None value is not returned by autolist[number]. Instead, an emptyitem object is returned.Known issuesd=autodata.autodict()a=d["key"]a+=1# => d == {"key" : 1}a+=1# => d == {"key" : 1}
Package: auto_dataclass
Auto dataclassAutoDataclass is a simple package that helps easy to map data into DataTransferObject for transporting that data between system layers.
The package uses specified Dataclass structure for retrieving data and creating DTO.
Currently, supported Django model object to Dataclass object conversion.Installationpipinstallauto_dataclassQuickstart# Django models.pyfromdjango.dbimportmodels

classProduct(models.Model):name=models.CharField(max_length=128)description=models.TextField(max_length=1000)classPhoto(models.Model):product=models.ForeignKey(Product,related_name="photos",on_delete=models.CASCADE)image=models.ImageField(blank=True)Define your Dataclasses that describe retrieved data structure from DB.# dto.py@dataclass(frozen=True)classPhotoDataclass:id:intimage:str

@dataclass(frozen=True)classProductDataclass:id:intname:strdescription:strphotos:List[ProductDataclass]=field(default_factory=list)CreateConverterinstance and callto_dtomethod with passed data from the query and previously defined Dataclass.# repository.pyfromauto_dataclass.dj_model_to_dataclassimportFromOrmToDataclass

fromdtoimportProductDataclass
frommodelsimportProduct# Creating Converter instanceconverter=FromOrmToDataclass()defget_product(product_id:int)->ProductDataclass:product_model_instance=Product.objects\.prefetch_related('photos')\.get(pk=product_id)# Converting Django model object from the query to DTO.retrunconverter.to_dto(product_model_instance,ProductDataclass)Recursive Django model relationIf your data has a recursive relation you can also map them with the same way.# Django models.pyfromdjango.dbimportmodels

classCategory(models.Model):name=models.CharField(max_length=128)parent=models.ForeignKey("Category",related_name="sub_categories",null=True,blank=True,on_delete=models.CASCADE)# dto.py@dataclass
classCategoriesDTO:id:intname:strsub_categories:List['CategoriesDTO']=field(default_factory=list)# repository.pyfromitertoolsimportrepeat
fromauto_dataclass.dj_model_to_dataclassimportFromOrmToDataclass

frommodelsimportCategory
fromdtoimportCategoriesDTOconverter=FromOrmToDataclass()defget_categories(self)->Iterable[CategoriesDTO]:category_model_instances=Category.objects.filter(parent__isnull=True)returnmap(converter.to_dto,category_model_instances,repeat(CategoriesDTO))Future Dataclass data typesExample of mapping future relation in Dataclasses structure.# dto.py@dataclass
classDataclass:id:intdc:Optional['FutureDataclass']@dataclass
classFutureDataclass:id:intname:strTo handle this you just need to pass a future Dataclasses as a next arguments.fromauto_dataclass.dj_model_to_dataclassimportFromOrmToDataclass
fromdtoimportDataclass,FutureDataclassconverter=FromOrmToDataclass()dataclass_with_future_relation=converter.to_dto(django_data_model,Dataclass,FutureDataclass)
Package: autodatadictionary
DataDictionaryAutoDataDictionary generates data dictionary from source files and database tables including:Column NameSample ValueSource File NameNon Null Values CountUnique Values CountData TypeUnique Values ListHow to useGenerate data dictionary from dbimport autodatadictionary as ad

ad.to_dictionary_from_db(
     sql_alchemy_connection_string='postgresql://username:password@domain:5432/db',
     schema='schema')Generate data dictionary from csv filesimport autodatadictionary as ad

ad.to_dictionary_from_file(['/path/data1.csv', '/path/data2.csv', '/path/dataN.csv'], sep=',')Generate data dictionary from dataframeimport autodatadictionary as ad

titanic = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')
ad.to_dictionary_from_dataframe([titanic])
ad.to_dictionary_from_dataframe(dataframes=[titanic], source_names=['titanic_dataframe'], source=True)
Package: autodatap
Automating Data PreprocessingShortlyADPis now a Python Library and you can use it by just installing using the following commandspip install autodatapAnd will install the package into you systemPurpose of autodatap:to help you in data preprocessingto know how can you use it:import the packageimport autodatap as adpThe main function in autodatap package is mainMethod so,adp.mainMethod("link to data set")and that's it, everything is done, you are good to go.Now everything you will be doing will be in console (run)Currently supported funcitonsCategorical Values (One-Hot-Encoding)NormalizationCheck for Imbalanced DataNull values finder and filling with 0 (in future with mean)dropping duplicateCategorical Values (One-Hot-Encoding):So, Categorical values are those values which may have to are more values of same class, if we look at the example belowLet's say we have gender class which is a categorical variable because it has 2 or more values (male, female etc)
example1gendermalefemalemalefemalenow as machine learning only except numerical values it does not support string values, we have to convert it from string to numerical valuesso achieve that we have to (or more) option either we have to give custom values by replace functiondata.replace("male",1,inplace=True)or we can use builtin function likelabel encodingandOne-Hot-Encoding.in this library we are achieving this functionality usingOne-Hot-Encoding.so, the above example could be likegender_male1010-------and---------gender_female0101how to use:To use this function you have to write the exact column name the step of preprocessing like[u'name', u'age', u'class', u'code']in the above code the coulmn name should beu'name'LicenceMIT LicenseCopyright (c) 2023 Syed Syab AhmadPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.ContributionTo contribute to the package follow the following linkhttps://github.com/SyabAhmad/Automating-Data-Preprocessing
Package: autodc
AutoDCAutomated data-centric processingThis repository is the official Python implementation of position paper "AutoDC: Automated data-centric processing". The implementation will continue being updated in the coming months.AutoDC is a framework to enable domain experts to automatically and systematically improve datasets without much coding requirement and manual process, the idea similar with AutoML (automated machine learning).By using the AutoML system, such as Google Cloud AutoML, domain experts only need to bring in the input data, and AutoML takes care of the manual ML processes, then produces output predictions, along with user-defined evaluation metrics. With a similar idea, AutoDC is designed for domain experts to bring in a labeled dataset, such as annotated images, to the system; AutoDC takes care of the manual data improvement processes, and produces the improved dataset, by automatically correcting the incorrect labels (with user feedbacks), detecting edge cases, and augmenting edge cases.CitationZac Yung-Chun Liu, Shoumik Roychowdhury, Scott Tarlow, Akash Nair, Shweta Badhe, and Tejas Shah. AutoDC: Automated data-centric processing, NeurIPS 2021: DCAI workshop,arXiv: 2111.12548.
Package: autodcf
The autodcf package aims to make it extremely simple to create DCF models, simple and complex alike.
If you are tired of running sensitivity tests to see how your model will behave with different sales growth patterns, this package is for you.Getting StartedYou can clone the repo from GitHub usinggit clone https://github.com/jackmoody11/autodcfSetup the project by navigating to the project root directory and runningpython setup.pyBasic UsageThere are a few important classes which will help you build your DCF.
TheCompanyclass encapsulates three classes:BalanceSheet,CashFlows,
andIncomeStatement. Once aCompanyobject is created using the 3
financial statements, this can be passed to one of the DCF models (currently eitherSimpleDCForDCF).fromautodcf.companyimportBalanceSheet,CashFlows,Company,IncomeStatementfromautodcf.modelsimportSimpleDCFbalance_sheet=BalanceSheet(assets=100,liabilities=50)cash_flows=CashFlows(capex=3)income_statement=IncomeStatement(sales=100,cogs=50,sga=25,rd=0,depreciation=4,amortization=2,interest=0,nonrecurring_cost=3,tax=4)company=Company(balance_sheet=balance_sheet,cash_flows=cash_flows,income_statement=income_statement,price_per_share=2.00,fully_diluted_shares=100)simple_dcf=SimpleDCF(change_in_nwc_to_change_in_sales=0.1,company=company,discount_rate=0.14,sales_growth=0.03,tax_rate=0.21,terminal_growth_rate=0.03,window=5)forecast=simple_dcf.forecast()The code above will provide a filled out pandas DataFrame with estimated growth of different
line items from the income statement.TODOAdd docs and deploy using doctr to GitHub Pages site (automate deployment with Travis CI)Publish package to PyPiFuture Enhancementsio: Allow for DCF model to be exported to Exceldata: Allow users to specify what company and data source
to use and fetch necessary data for B/S, CF, and I/S behind the scenescompany: Allow for integration with financial data APIsmodels: Add different types of models (with/without synergies)
Package: auto-ddns
Auto-ddns一个可扩展的 DNS 管理工具。安装使用 pip/pipx 安装在此之前请确保安装Python3.10及以上版本，并安装了pip。pipinstallauto-ddns[all]如果想要尝试 Nightly 版本，可尝试（需确保使用Python3.12）pipinstallgit+https://github.com/zrr1999/auto-ddns@main在此之前请确保安装了pipx。pipxinstallauto-ddns[all]源码安装（需确保使用Python3.12）gitclonehttps://github.com/zrr1999/auto-ddnscdauto-ddns
pipinstall.使用说明首先创建一个配置文件，例如sdns.json，内容如下：{"domain":"mydomain.com","records":[["test",// 也就是 test.mydomain.com 指向的路径"baidu.com"// 解析值，目前只支持 A 记录和 CNAME 记录，会根据此处的值自动判断],]}然后执行以下命令：auto-ddnsupdate--path~/.config/auto-ddns/sdns.json此时，你的解析记录就会增加一条test.mydomain.com的 CNAME 记录指向baidu.com。维护者@zrr1999如何贡献非常欢迎你的加入！提一个 Issue或者提交一个Pull Request。贡献者感谢以下参与项目的人：使用许可GNU© Rongrui Zhan
Package: autodebug
OverviewThis is a simple module which enables you to have automatic post-mortem debugging as simply as possibleUsageInstall it usingpip install autodebug.Then in your Python script, before the possible exception writeimport autodebugThen if anyuncaughtexception happens, you’ll be immediately transferred to debugger.
Exactly the momentbeforethe exception occured. So you’ll have access to all variables and everything.DebuggerYou can specify which Python debugger you want to use.To do that simply set the environmental variableAUTO_DEBUG.
Possible debuggers currently are:pdbfor built-in Python debuggeripdbforipdbPython debuggerpudbfor full-screen console Python debugger.If you do not set the variable,pdbwill be used, as it is built-in.Post notesPlease note that this module uses customsys.excepthook, so if you are using your own, this module will not work, or will overwrite yourexcepthook(This depends on order ofimportand setting your ownexcepthook)In order to make it work withuwsgiyou have to pass--honour-stdinto uwsgi configuration. This will make it possible to debuguwsgiapplications!.
But keep in mind, that if you’ve got more than 1 worker, and multiple people are using your application, the terminal output will be ugly.The debugger won’t start if it is imported inside interactive console, so it may not work in some IDEs. Experiment yourself, however it is adviced to start your
programs using command line.
Package: autodesk-forge-sdk
autodesk-forge-sdkUnofficialAutodesk ForgeSDK for Python (3.*).UsageInstall the package from PyPI:pip3installautodesk-forge-sdkAuthenticationimportosfromautodesk_forge_sdkimportAuthenticationClient,Scopeclient=AuthenticationClient()auth=client.authenticate(os.environ["FORGE_CLIENT_ID"],os.environ["FORGE_CLIENT_SECRET"],[Scope.VIEWABLES_READ])print(auth["access_token"])Data Managementimportosfromautodesk_forge_sdkimportOSSClient,OAuthTokenProviderclient=OSSClient(OAuthTokenProvider(os.environ["FORGE_CLIENT_ID"],os.environ["FORGE_CLIENT_SECRET"]))buckets=client.get_all_buckets()print(buckets)Or, if you already have an access token:importosfromautodesk_forge_sdkimportOSSClient,SimpleTokenProviderclient=OSSClient(SimpleTokenProvider(os.environ["FORGE_ACCESS_TOKEN"]))buckets=client.get_all_buckets()print(buckets)
Package: autodevice
autodeviceAutomatically assign devices in-line with pytorch codeUsagefromautodeviceimportAutoDevicex=torch.randn([200,50]).to(AutoDevice())CUDA/GPU:tensor([[ 2.6905, -0.3037, -0.3607],
        [ 0.2258, -0.1755,  0.6599],
        [ 1.3046, -0.9389,  0.7358]], device='cuda:0')CPU:tensor([[ 2.6905, -0.3037, -0.3607],
        [ 0.2258, -0.1755,  0.6599],
        [ 1.3046, -0.9389,  0.7358]])On Apple Silicon (M1, M2):tensor([[ 0.5382,  1.1173,  1.1175],
        [-0.0125, -0.2406,  0.2343],
        [-0.6067, -0.7728,  0.1697]], device='mps:0')Installationpip install autodevice
Package: autodice
AutoDiCE Project.The source for this AutoDiCE project is available here.How to use the Partition### Prepare AlexNet Model and Computing nodes
### mapping.json Template (AlexNet):
{"lenovo_cpu0": ["conv1_1", "conv1_2", "norm1_1", "pool1_1", "conv2_1", "conv2_2", "norm2_1", "pool2_1", "conv3_1", "conv3_2", "conv4_1", "conv4_2", "conv5_1", "conv5_2", "pool5_1", "OC2_DUMMY_0", "fc6_1", "fc6_2"], "lenovo_cpu1": ["fc6_3", "fc7_1", "fc7_2", "fc7_3", "fc8_1", "prob_1"]}

### Quick Start.
import os,sys
 
from partition.interface import *

if __name__ == '__main__':

    output_dirs= './models'
    if not os.path.exists(output_dirs):
    # Create a new directory because it does not exist
        os.makedirs(output_dirs)
        print("The output directory %s is created!" % (output_dirs))

    origin_model = "bvlcalexnet-9.onnx"
    ### Can be downloaded from:
    # https://github.com/onnx/models/blob/main/vision/classification/alexnet/model/bvlcalexnet-9.onnx
    input_model = format_onnx(origin_model)
    model =  onnx.load(input_model)
    # resourceid = { 1:'lenovo_cpu0', 2:'lenovo_cpu1'}
    platforms = ['lenovo']

    ### How the layers in alexnet model distribute over two computing nodes???
    random_map = {
            "lenovo_cpu0":
                ["conv1_1", "conv1_2", "norm1_1", "pool1_1", "conv2_1",
                "conv2_2", "norm2_1", "pool2_1", "conv3_1", "conv3_2",
                "conv4_1", "conv4_2", "conv5_1", "conv5_2", "pool5_1",
                "OC2_DUMMY_0", "fc6_1", "fc6_2"],
            "lenovo_cpu1":
                ["fc6_3", "fc7_1", "fc7_2", "fc7_3", "fc8_1", "prob_1"]
             }
    

    InputSpecs = Interface(model=input_model, mappings=random_map, platforms=platforms)
    #cppname, NodesList, ComputingNodes
    ### Generate Cpp Files for Multinode-Inference.
    GenerateCode = EngineCode(
        CppName = "./models/multinode",
        Platforms = InputSpecs.platforms,
        NodesList = InputSpecs.nodes,
        ComputingNodes = InputSpecs.computingnodes,
        ValueInfos = InputSpecs.value_map,
        Inputs = InputSpecs.inputs,
        Outputs = InputSpecs.outputs,
        Benchmark = False)

# ...
Package: autodidaqt
autodidaqt := DAQ + UI generation + Reactivity + InstrumentsYou should be spending your time designing and running experiments,
not your DAQ software.autodidaqt is a nuts and bolts included framework for scientific data acquisition (DAQ),
designed for rapid prototyping and the challenging DAQ environment of angle resolved
photoemission spectroscopy. If you specify how to sequence motions and data collection,
autodidaqt can manage the user interface, talking to and managing instruments,
plotting interim data, data collation, and IO for you.autodidaqt also has logging and notification support built in and can let you know
over email or Slack when your experiment finishes (successfully or not!).If autodidaqt doesn’t do exactly what you need, get in contact with us or
check out the examples. There’s a good chance that if it isn’t built in,
autodidaqt is flexible enough to support your use case.RequirementsPython 3.7 overNoArchFeaturesAutomated DAQautodidaqt wraps instruments and data sources in a uniform interface, if you specify how
to sequence motion and acquisition, autodidaqt handles async collection, IO, and visualizing
your data as it is acquired.UI Generationautodidaqt using PyQt and Qt5 to generate UIs for your experiments. It also
provides simple bindings (autodidaqt.ui) that make making managing the day to day
of working on PyQt simpler, if you need to do UI scripting of your own.It also ships with a window manager that you can register your windows against,
making it seamless to add extra functionality to your experiments.The autodidaqt UI bindings are wrapped to publish as RxPY observables, making it easier
to integrate your PyQT UI into a coherent asynchronous application.Installation$ pip install autodidaqtInstallation from SourceClone this repositoryInstallmakeif you are on a Windows systemInstallpoetry(the alternative Python package manager)Runmake installfrom the directory containing this READMEUsageFor usage examples, explore the scripts in the examples folder. You can run them with$ python -m autodidaqt.examples.[example_name]replacing [example_name] with one of:minimal_appplot_datasimple_actorsui_panelswrapping_instrumentsscanning_experimentscanning_experiment_revisitedscanning_interlocksscanning_custom_plotsscanning_setup_and_teardownscanning_properties_and_profilesmanuscript_fig4You can also get a list of all the available examples by running$ python -m autodidaqt.examplesExamples for “remote control”, including a “virtual nanoXPS lab”
are available in integration_tests folder of AutodiDAQt receiver in
its companion repository.
Package: autodidaqt-common
autodidaqt-commonAnalyis-side bridge for autodiDAQt.Installationpipinstall-Uautodidaqt-commonor install withPoetrypoetryaddautodidaqt-commonBuilding and releasingBuilding a new version of the application contains steps:Bump the version of your packagepoetry version <version>. You can pass the new version explicitly, or a rule such asmajor,minor, orpatch. For more details, refer to theSemantic Versionsstandard.Make a commit toGitHub.Create aGitHub release.Publishpoetry publish --build
Package: autodidaqt-receiver
autodidaqt-receiverAnalysis-side bridge for AutodiDAQt.Installationpipinstall-Uautodidaqt-receiveror install withPoetrypoetryaddautodidaqt-receiverInstallation from SourceClone this repositoryInstallmakeif you are on a Windows systemInstallpoetry(the alternative Python package manager)Runmake installfrom the directory containing this READMEBuilding and releasingBuilding a new version of the application contains steps:Bump the version of your packagepoetry version <version>. You can pass the new version explicitly, or a rule such asmajor,minor, orpatch. For more details, refer to theSemantic Versionsstandard.Make a commit toGitHub.Create aGitHub release.Publishpoetry publish --build
Package: autodiff-ADdictedtoCS
cs207-FinalProjectGroup number: 28Team Members:Josh BodnerTheo GuenaisDaiki InaJunzhi GongDownload and use autodiffActivate virtual environmentInstall the packagepipinstall"pip install autodiff-ADdictedtoCS"(Optional) Run tests with pytest$pytestEnjoy the features and natural approach ofautodifffor automatic differentiation. Refer to the documentation for further information,
or the demo.ipynb notebook for basic use case.
Package: auto-diff-pkg
cs107-FinalProjectGroup #21John AllingMoriya DechtiarAl-Muataz KhalilTianen LiuInstalling the PackageThe package can be installed by making a pip install from PyPI:pip install auto_diff_pkgAlternatively, the package can be installed by navigating to the top level of the repository and running a pip install:pip install .UsageThe package can then be imported and utilized.import auto_diff_pkg.AutoDiff as ADPlease seedocs/demo.ipynbfor demonstrations of the AutoDiff class.
Package: autodiffpy
# autodiffpy[![Build Status](https://travis-ci.org/rajayuco/cs207-FinalProject.svg?branch=master)](https://travis-ci.org/rajayuco/cs207-FinalProject.svg?branch=master))[![Coverage Status](https://coveralls.io/repos/github/rajayuco/cs207-FinalProject/badge.svg?branch=master)](https://coveralls.io/github/rajayuco/cs207-FinalProject?branch=master)Group name: rajayucoGroup number: 6Team members: Rachel Moon, Jamila Pegues, Yudi Wang, Cory WilliamsWelcome! Thank you for downloading our autodiffpy package.Details of our package can be found at:https://github.com/rajayuco/cs207-FinalProject/blob/master/docs/documentation.ipynbEnjoy autodiffing! :D