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advertorch | No description available on PyPI. |
advex-uar | No description available on PyPI. |
advhash | AdvHash: Adversarial collision attacks on perceptual hashing functionsSummaryAdvHash is a Python package that provides a simple to use interface for performing adversarial collision attacks on perceptual hashing functions.PyTorch is used to re-create the target hashing functions and generating adversarial examples. AdvHash supports both CPU and GPU computations. Install the CUDA enabled version of PyTorch to use a GPU with AdvHash and specifydevice='cuda'when instantiating anattackorhash.Adversarial collision attacks on image hashing functionsComponentsGetting StartedInstallationExample UsageAttacksFuture DevelopmentHashing FunctionsAttack MethodsDefense MethodsContributingAdversarial collision attacks on image hashing functionsCurrently AdvHash supports collision attacks on hashing functions from the popularimagehashpackage using methods described inAdversarial collision attacks on image hashing functions.ComponentsAdvHash is divided into multiple granular components:ComponentDescriptionadvhasha PyTorch based library for performing adversarial attacksadvhash.attackadversarial attack methodsadvhash.hashperceptual hashing functionsadvhash.utilsutility functions for performing common resizing, conversion, and comparison operationsGetting StartedInstallationpip install advhash*Install a CUDA enabled version of PyTorch to use a GPU with AdvHash.Example UsageThis example shows how theL2Attackcan be used to perform an adversarial collision attack ondHashusing theresizemethod as the target split point.importtorchimportnumpyasnpfromPILimportImagefromadvhash.attack.l2importL2Attacktarget_img=Image.open('forest.jpg')source_img=Image.open('cat.jpg')target=torch.tensor((np.array(target_img).astype('float32')))source=torch.tensor((np.array(source_img).astype('float32')))l2=L2Attack(hash_fn='dhash',split_point='resize')im_adv=l2.attack(target,source)AttacksCollision Attacks for Image Hashingadvhash.attack.l2.L2Attackadvhash.attack.hinge.HingeAttackThe above attacks accept a source image, target image, and hashing function as an input. The source image will be perturbed to create an adversarial image that has the same hash as the target image when hashed by the selected hashing function. Some attacks require additional configuration.Hashing FunctionsdHashFuture DevelopmentHashing FunctionspHashaHashpqdAttack MethodsTBDDefense MethodsTBDContributingContributions are welcome! If you plan to contribute new features, methods, or enhancements, please open an issue to discuss the addition further, or comment on an existing issue. |
advice | AdviceAspect-oriented programmingUsageimportadvicedefmultiply(context):print(context.args)print(context.kwargs)yieldcontext.result*=100advice.register(handler=multiply,modules=advice.match(equals='math'),targets=advice.match(regexp='(sin|cos)'))Ok, let's check:In[2]:importmathIn[3]:math.cos(0)(0,){}Out[3]:100.0 |
adviceslip | No description available on PyPI. |
adviewer | AreaDetector configurator (and -one day- viewer)adviewer - despite theviewerin its name - is primarily a configuration
tool for already-running areaDetector IOCs. It can help you determine how
any given plugin pipeline has been configured, displaying an interactively-
reconfigurable node graph and tree-based view. It may also help you discover
what plugins are installed for an unfamiliar installation.Eventually, there will be a full-featured viewer based on PyDM integrated
as well. For now, a simple PyDM user interface is spawned in a separate
process when using the image plugin right-click context menu in the graph.RequirementsPython 3.6+qtpy+ PyQt5qtpynodeeditor, a pure Python port ofnodeeditornetworkxophydOptionally:PyDM, to spawn an image viewerRunning adviewerTo install:$ pip install git+https://github.com/pcdshub/adviewerTo run:$ adviewer --help
$ adviewer 13SIM1:
$ adviewer --pvlist filename.pvlistRunning the Tests$ python run_tests.py |
advi-jax | ADVI in JAXDesign considerationsADVI class is an object butADVI.objective_funis a pure function that can be optimized withoptaxorjaxoptor any other jax supported optimizers.variational distribution parameters can be initialized withADVI.initusing adistraxortfpdistribution as an initializer (or any jax distribution that implements.sample()method in a similar way).Users can pass the suitable bijectors of classdistrax.Bijectorto the variational distribution.Transformation is directly applied to posterior and thus prior and likelihood stay untouched during the entire process. This way, after the training, the variational distribution is ready for sampling without any additional transformations. Also, this gives freedom to variational distribution to be constructed in more complex way as it is separated from the other parts of the model (see the example below).If we do not change thekeyduring the training, the method is called the deterministic ADVI.Users can implement their ownlikelihood_log_prob_funbecause likelihood does not necessarily have to be a
distribution.A Coin Toss Exampleimportjaximportjax.numpyasjnpfromadvi_jaximportADVIfromadvi_jax.variational_distributionsimportMeanFieldfromadvi_jax.initimportinitializeimporttensorflow_probability.substrates.jaxastfpdist=tfp.distributions# Datatosses=jnp.array([0,1,0,0,1,0])# Prior and likelihoodprior_dist=dist.Beta(2.0,3.0)likelihood_log_prob_fun=lambdatheta:dist.Bernoulli(probs=theta).log_prob(tosses).sum()# ADVI modelmodel=ADVI(prior_dist,likelihood_log_prob_fun,tosses)# Variational distribution and bijectorbijector=distrax.Sigmoid()variational_dist=MeanField(u_mean=jnp.array(0.0),u_scale=jnp.array(0.0),bijector=bijector)# Initialize the parameters of variational distributionkey=jax.random.PRNGKey(0)variational_dist=initialize(key,variational_dist,initializer=dist.Normal(0.0,1.0))# Define the value and grad functionvalue_and_grad_fun=jax.jit(jax.value_and_grad(model.objective_fun,argnums=1),static_argnums=2)# Do gradient descent!learning_rate=0.01foriinrange(100):key=jax.random.PRNGKey(i)# If this is constant, this becomes deterministic ADVIloss_value,grads=value_and_grad_fun(key,variational_dist,n_samples=10)variational_dist=variational_dist-learning_rate*grad# Get the posterior sampleskey=jax.random.PRNGKey(2)posterior_samples=variational_dist.sample(seed=key,sample_shape=(100,)) |
advik | Advik's Terminal Resumepipinstalladvik-U
advik |
advimport | No description available on PyPI. |
adviserserver | Failed to fetch description. HTTP Status Code: 404 |
ad-vision | Failed to fetch description. HTTP Status Code: 404 |
ad-vision-01 | Failed to fetch description. HTTP Status Code: 404 |
ad-vision-02 | Failed to fetch description. HTTP Status Code: 404 |
advisor | No description available on PyPI. |
advisor-areeh-fork | No description available on PyPI. |
advisor-build-tools | This is a security placeholder package.
If you want to claim this name for legitimate purposes,
please contact us [email protected]@yandex-team.ru |
advisor-client | Failed to fetch description. HTTP Status Code: 404 |
advisor-clients | No description available on PyPI. |
advisorhelper | Failed to fetch description. HTTP Status Code: 404 |
advisorserver | Failed to fetch description. HTTP Status Code: 404 |
advisortool | Failed to fetch description. HTTP Status Code: 404 |
advisortools | Failed to fetch description. HTTP Status Code: 404 |
advisory-parser | This library allows you to parse data from security advisories of certain
projects to extract information about security issues. The parsed
information includes metadata such as impact, CVSS score, summary,
description, and others; for a full list, see theadvisory_parser/flaw.pyfile.DISCLAIMER: Much of the advisory parsing is fairly fragile. Because web
pages change all the time, it is not uncommon for parsers to break when a
page is changed in some way. Also, the advisory parsers only work with the
latest version of the advisory pages.The need for parsing raw security advisories in this way could be avoided
if vendors provided their security pages in a machine readable (and
preferably standardized) format. An example of this would be Red Hat’s
security advisories that can be pulled in from a separate Security Data API
(RHSA-2016:1883.json)
or downloaded as an XML file
(cvrf-rhsa-2016-1883.xml),
or OpenSSL’s list of issues available in XML
(vulnerabilities.xml).If you are a vendor or an upstream project owner interested in providing
your security advisories in a machine readable format and don’t know where
to start, feel free to reach out [email protected] available parsers include:ProjectExample URLGoogle Chromehttps://chromereleases.googleblog.com/2017/06/stable-channel-update-for-desktop_15.htmlAdobe Flashhttps://helpx.adobe.com/security/products/flash-player/apsb17-17.htmlJenkinsMySQLhttp://www.oracle.com/technetwork/security-advisory/cpujul2017verbose-3236625.htmlphpMyAdminWiresharkInstallationpip install advisory-parserUsagefrompprintimportpprintfromadvisory_parserimportParserurl='https://helpx.adobe.com/security/products/flash-player/apsb17-17.html'flaws,warnings=Parser.parse_from_url(url)forflawinflaws:print()pprint(vars(flaw)) |
adviz | advizGet startd quickly and see exmaples belowInstallpython3-mpipinstalladvizExplore the available visualizations |
advlink | Simple package used for url management |
advlog | PyAdvancedLoggerAdvanced logger inspired by other languages' standard logging classes (e.g. Java and Kotlin).InstallationSimply install throughpip install advlog.ExamplefromadvlogimportLoggerlogger=Logger(logfile='./log.txt')# Passes varargs on to `print`# These methods exist: d (debug), v (verbose), i (info), w (warning), e (error), f (fatal)# Can specify `file` kwarg to write to - next to logfile. d, v, i, w methods default to `sys.stdout`; e, f default to `sys.stderr`.# Can also specify `None` to only write to `logfile`. One of log file or file keyword must be present.logger.d('debug',42)LicenseMIT LicenseCopyright (c) 2020 KirusePermission 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. |
adv-math | Introduction:Advance Math
This is a library for introducing mathematical concepts that are not in-built on python, like ratios, fractions, factors of numbers, etc.UsageThis module currently has one out of the many mathematical operational modes (algebra, arithmetic, geometry, trigonometry, etc.)So to use any function you need to:from adv_math *(mode).(function)For example:// Finding HCF of three numbers (5, 40, 75)
from adv_math import arithmetichcf = Factor(5, 40, 75).HCF()print(hcf)Other classes in this libraryartithmetic:Ratio (methods: get, simplify, add, subtract, multiply, divide)Fraction (methods: get, getType, simplify, add, subtract, multiply, divide)Factor (methods: HCF, LCM)Just Functions (functions: Factors, PrimeNumbers, CompositeNumbers) |
adv-math-captcha-ostrichb | ADV-Math-captchaAn advanced mathematical captcha for simple human verification.How to useThis is an example to make an verification:fromadv_math_captchaimportCaptchaif__name__=='__main__':formula1=Captcha(5,4,15,10,50,5,5)inp=input("Solve this simple question:{0}= ".format(formula1.formula_str))ifformula1.verify(float(inp)):print("Verify success!")else:print("Verify failed! The correct answer is{0:.0f}!".format(float(formula1.evaluation)))UsageThis module only provides one class with only one method. To make a captcha, just useCaptcha(difficulty, term, number_range), which is the shortest form. If you wanna use more parameters, useCaptcha(difficulty, term, number_range, factor_range, exponentiation_range, exp_base_range, exp_index_range)Details: (All the parameters are integers)difficulty: Decides the difficulty of the generated expression. Valid value is 1-5 in integer.1: Only additions2: Additions and subtractions3: Operations in2, with Mutliplications and divisions (For divisions, the result will always be an integer)4: Operations in3, with exponentiation calculations5: Operations in4, with logarithm calculations (The result will always in simple form to insure the result is reducable, for example, 'ln(e^3)', 'log_2 (8)')term: Determines how many items will appear in an expression. Althogh there is no limits, don't be too much, or it will be hard to get the answer. :) (P.S. an division will be counted as one item. E.g. (6 / 3) )number_range: Determines the range of the coefficient.factor_range: Determines the range of division results and dominators. Default value is 10.exponentiation_range: Determines the range of exponential calculation. Default value is 50.exp_base_range: Determines the range of base number. Default value is 6.exp_index_range: Determines the range of index number. Default value is 5.Once you initialized an captcha object, you can get the expression string fromCaptcha.formula_strvariable in class, or make an verification using theCaptcha.verify(user_input). ATruewill be returned if the verification is successful, orFalseif failed.You can also get the actual value of the expression fromCaptcha.evaluationvariable.AboutIf you want to contact me, mail tomailto |
adv-ml | adv-mlDocsSeehttps://irad-zehavi.github.io/adv-ml/Installpipinstalladv_mlHow to useHow to UseAs an nbdev library,adv-mlsupportsimport *(without importing
unwanted symbols):fromadv_ml.allimport*Adversarial Examplesmnist=MNIST()classifier=MLP(10)learn=Learner(mnist.dls(),classifier,metrics=accuracy)learn.fit(1)epochtrain_lossvalid_lossaccuracytime00.1544100.1774100.95390000:32sub_dsets=mnist.valid.random_sub_dsets(64)learn.show_results(shuffle=False,dl=sub_dsets.dl())attack=InputOptimizer(classifier,LinfPGD(epsilon=.15),n_epochs=10,epoch_size=20)perturbed_dsets=attack.perturb(sub_dsets)epochtrain_losstime0-4.30257300:001-7.58570700:002-9.01496800:003-9.70054800:004-10.07511000:005-10.29663600:006-10.43383400:007-10.52114100:008-10.57767300:009-10.61474000:00learn.show_results(shuffle=False,dl=TfmdDL(perturbed_dsets))Data Poisoningpatch=torch.tensor([[1,0,1],[0,1,0],[1,0,1]]).int()*255trigger=F.pad(patch,(25,0,25,0)).numpy()learn=Learner(mnist.dls(),MLP(10),metrics=accuracy,cbs=BadNetsAttack(trigger,'0'))learn.fit_one_cycle(1)epochtrain_lossvalid_lossaccuracytime00.1036520.0970750.97140000:23Benign performance:learn.show_results()Attack success:learn.show_results(2) |
advocate | AdvocateAdvocate is a set of tools based around therequests libraryfor safely making
HTTP requests on behalf of a third party. Specifically, it aims to prevent
common techniques that enableSSRF attacks.Advocate was inspired byfin1te’s SafeCurl project.InstallationpipinstalladvocateAdvocate is officially supported on CPython 2.7+, CPython 3.4+ and PyPy 2. PyPy 3 may work as well, but
you’ll need a copy of the ipaddress module from elsewhere.See it in actionIf you want to try out Advocate to see what kind of things it catches, there’s atest site up on advocate.saynotolinux.com.ExamplesAdvocate is more-or-less a drop-in replacement for requests. In most cases you can just replace “requests” with
“advocate” where necessary and be good to go:>>>importadvocate>>>printadvocate.get("http://google.com/")<Response[200]>Advocate also provides a subclassedrequests.Sessionwith sane defaults for
validation already set up:>>>importadvocate>>>sess=advocate.Session()>>>printsess.get("http://google.com/")<Response[200]>>>>printsess.get("http://localhost/")advocate.exceptions.UnacceptableAddressException:('localhost',80)All of the wrapped request functions accept avalidatorkwarg where you
can set additional rules:>>>importadvocate>>>validator=advocate.AddrValidator(hostname_blacklist={"*.museum",})>>>printadvocate.get("http://educational.MUSEUM/",validator=validator)advocate.exceptions.UnacceptableAddressException:educational.MUSEUMIf you require more advanced rules than the defaults, but don’t want to have to pass
the validator kwarg everywhere, there’sRequestsAPIWrapper. You can
define a wrapper in a common file and import it instead of advocate:>>>fromadvocateimportAddrValidator,RequestsAPIWrapper>>>fromadvocate.packagesimportipaddress>>>dougs_advocate=RequestsAPIWrapper(AddrValidator(ip_blacklist={...# Contains data incomprehensible to mere mortals...ipaddress.ip_network("42.42.42.42/32")...}))>>>printdougs_advocate.get("http://42.42.42.42/")advocate.exceptions.UnacceptableAddressException:('42.42.42.42',80)Other than that, you can do just about everything with Advocate that you can
with an unwrapped requests. Advocate passes requests’ test suite with the
exception of tests that requireSession.mount().Conditionally bypassing protectionIf you want to allow certain users to bypass Advocate’s restrictions, just
use plain ‘ol requests by doing something like:ifuser=="mr_skeltal":requests_module=requestselse:requests_module=advocateresp=requests_module.get("http://example.com/doot_doot")requests-futures supportA thin wrapper aroundrequests-futuresis provided to ease writing async-friendly code:>>>fromadvocate.futuresimportFuturesSession>>>sess=FuturesSession()>>>fut=sess.get("http://example.com/")>>>fut<Futureat0x10c717f28state=finishedreturnedResponse>>>>fut.result()<Response[200]>You can do basically everything you can do with regularFuturesSessions andadvocate.Sessions:>>>fromadvocateimportAddrValidator>>>fromadvocate.futuresimportFuturesSession>>>sess=FuturesSession(max_workers=20,validator=AddrValidator(hostname_blacklist={"*.museum"}))>>>fut=sess.get("http://anice.museum/")>>>fut<Futureat0x10c696668state=running>>>>fut.result()Traceback(mostrecentcalllast):# [...]advocate.exceptions.UnacceptableAddressException:anice.museumWhen should I use Advocate?Any time you’re fetching resources over HTTP for / from someone you don’t trust!When should I not use Advocate?That’s a tough one. There are a few cases I can think of where I wouldn’t:When good, safe support for IPv6 is importantWhen internal hosts use globally routable addresses and you can’t guess their prefix to blacklist it ahead of timeYou already have a good handle on network security within your networkActually, if you’re comfortable enough with Squid and network security, you should set up a secured Squid instance on a segregated subnet
and proxy through that instead. Advocate attempts to guess whether an address references an internal host
and block access, but it’s definitely preferable to proxy through a host can’t access anything internal in the first place!Of course, if you’re writing an app / library that’s meant to be usable OOTB on other people’s networks, Advocate + a user-configurable
blacklist is probably the safer bet.This seems like it’s been done beforeThere’ve been a few similar projects, but in my opinion Advocate’s approach is the best because:It sees URLs the same as the underlying HTTP libraryParsing URLs is hard, and no two URL parsers seem to behave exactly the same. The tiniest
differences in parsing between your validator and the underlying HTTP library can lead
to vulnerabilities. For example, differences between PHP’sparse_urland cURL’s
URL parserallowed a blacklist bypass in SafeCurl.Advocate doesn’t do URL parsing at all, and lets requests handle it. Advocate only looks at the
address requests actually tries to open a socket to.It deals with DNS rebindingTwo consecutive calls tosocket.getaddrinfoaren’t guaranteed to return the same
info, depending on the system configuration. If the “safe” looking record TTLs between
the verification lookup and the lookup for actually opening the socket, we may end
up connecting to a very different server than the one we OK’d!Advocate gets around this by only using onegetaddrinfocall for both verification
and connecting the socket. In pseudocode:defconnect_socket(host,port):forresinsocket.getaddrinfo(host,port):# where `res` will be a tuple containing the IP for the hostifnotis_blacklisted(res):# ... connect the socket using `res`SeeWikipedia’s article on DNS rebinding attacksfor more info.It handles redirects sanelyMost of the other SSRF-prevention libs cover this, but I’ve seen a lot
of sample code online that doesn’t. Advocate will catch it since it inspectseveryconnection attempt the underlying HTTP lib makes.TODOProper IPv6 Support?Advocate’s IPv6 support is still a work-in-progress, since I’m not
that familiar with the spec, and there are so many ways to tunnel IPv4 over IPv6,
as well as other non-obvious gotchas. IPv6 records are ignored by default
for now, but you can enable by using anAddrValidatorwithallow_ipv6=True.It should mostly work as expected, but Advocate’s approach might not even make sense with
most IPv6 deployments, seeIssue #3for
more info.If you can think of any improvements to the IPv6 handling, please submit an issue or PR!CaveatsThis is beta-quality software, the API might change without warning!mount()ing other adapters is disallowed to prevent Advocate’s validating adapters from being clobbered.Advocate does not, and might never support the use of HTTP proxies.Proper IPv6 support is still a WIP as noted above.Acknowledgementshttps://github.com/fin1te/safecurlfor inspirationhttps://github.com/kennethreitz/requestsfor the lovely requests modulehttps://bitbucket.org/kwi/py2-ipaddressfor the backport of ipaddresshttps://github.com/hakobe/paranoidhttpa similar project targeting golanghttps://github.com/uber-common/paranoid-requesta similar project targeting Nodehttp://search.cpan.org/~tsibley/LWP-UserAgent-Paranoid/a similar project targeting Perl 5 |
advocate-sdk | Advocate Python SDKThis SDK is for developing tools on/with the Advocate live-streaming platform. Currently, it is primarily used for creating Dynamic Calls to Action (i.e. interactive widgets that are displayed on broadcaster's stream), although more functionality may be added in the future.UsageTo start working with theAdvocate SDK, load the client using:>>> from adv.client import AdvClient
>>> client = AdvClient('my-super-secret-api-key')Note: If you don't have an API key, please reach out to use [email protected] tell us about your needs.Create a New Dynamic Call to ActionTo create a new DCTA (Dynamic Call to Action) -- our interactive, on-screen applications -- you first need to fetch your currently active campaigns:>>> my_campaigns = client.get_campaigns()
>>> my_campaigns
[<Campaign: My Hearthstone Campaign>, <Campaign: Going to E3>]you can create a new DCTA on a campaign using thecreate_dctamethod:>>> my_campaigns = client.get_campaigns()
>>> campaign = my_campaigns[0]
>>> campaign.create_dcta(name='Lower Thirds DCTA')
<DCTA: Lower Thirds DCTA>Possible kwargs are:name(required): A short, human-readable name describing your DCTAglobal_styes: A python dictionary of of dictionaries, describing CSS styles that will be added to the browersource's<head>tag. For example:{'.my-class':{'position':'absolute','top':'10px'...},'#my-id: {...}...}These can be updated later, and do not have to be defined when you initial create the DCTA.Fetch Existing DCTAsTo get all of your current DCTAS, you can use theget_dctasmethod:>>> client.get_dctas()
[<DCTA: Triva Night App>, <DCTA: Lower Thirds DCTA>]Render a DCTACalling therenderfunction on a dcta will re-render the DCTA for all currently streaming broadcasters that are displaying this DCTA on their stream. Call this after updating your widgets to make sure the newly updated widgets are rendered properly:>>> dctas = client.get_dctas()
>>> my_dcta = dctas[0]
>>> my_dcta.render()Add a widget to a DCTADCTAs are built out of a combination ofWidgetobjects that correspond to certain HTML elements. They currently include:Text Widget: For inserting easily-updatable text into your DCTA. Creates a<p>tag.Image Widget: Adds images to your DCTA. Creates a<img>tag.Group Widget: For grouping multiple elements together (e.g. for applying CSS animations or position to a group of elmenets). Creates a<div>tag.Video Widget: Coming soon.to create a new text widget, use theadd_text_widgetmethod on any DCTA object:>>> my_dcta.add_text_widget(name='Lower Thirds Headline Text', text='Breaking News!')
<Widget (text): Lower Thirds Headline Text>Once a widget has been added to a DCTA, you'll be able to see it using thewidgetsfield on the DCTA:>>> my_dcta.widgets
[<Widget (text): Lower Thirds Headline Text>]The following kwargs are shared on all widget types:name(required): A short, human-readable name describing your Widgetstyles: A dictionary of CSS styles that will be applied, inline, to your widgetattributes: A dictionary of addition HTML attributs (e.g.class) that will be added to your Widgetbroadcasters: A list of broadcaster usernames to add to this widget. If a widget hasnobroadcasters, it will be visible toallbroadcasters. If the widget has broadcasters, it will only be shown to those broadcasters. This allows specific parts of a widget to be targeted to specific broadcasters (e.g. unique, broadcasters specific text for each broadcaster).parent: ID of a Group widget that is the parent of the current widget. Can be None if the widget has no parent (i.e. is a root element)The following kwargs are on particular widgets:Text Widget:text(required): The actual text content to be displayedImage Widget:src(required): URL to the image to show.Video Widget:Coming SoonUpdate a WidgetUse theupdatemethod on any widget to update any of the above properties on the widget:>>> my_widget = my_dcta.widgets[0]
>>> my_widget
<Widget (text): Lower Thirds Headline Text>
>>> my_widget.text
'Breaking News!'
>>> my_widget.update(text='Old News!')
<Widget (text): Lower Thirds Headline Text>
>>> my_widget.text
'Old News!'Note: This will update the widget data on the server, but it willnotcause the DCTA to re-render and display the new information. This is because rendering can be computationally expensive, and you may want to update multiple widgets before you render. To force a re-render of the DCTA after an update, you can add theforce_renderkwarg:>>> my_widget.update(text='Look Ma, Immediate Update!', force_render=True)This is the equivalent of calling:>>> my_widget.update(text='Look Ma, Manual Render!')
>>> my_widget.dcta.render() |
advoco | AdvocoAdvent of Code helper library - why not??
Dear future contributor traipsing through history,
this was when this was a private repo that me and a buddy were collaborating on.
If this README does not look more polished in the current version, hunt me down
and slap me.InstallpoetryinstallI'm still working actively so do this often.tl;drDo this once a year:advocogenerate-yearThen grab your session cookie from AoC using browser tools and add it to the
environment asAOC_TOKEN. Put it in your.bashrcand never think about it
again (until next year). The session lasts like a month so you shouldn't have to
do this that often.Then, every day,from the directory created with the generate-year command:advocogenerate-d<dayyouwanttogenerate>or if it's after midnight, AoC time (Eastern):advocogenerateIn the generated script file, you'll find a function for part1 and part2 of the
daily AoC puzzle. You'll also find atransformfunction. This function will act
on eachlineof the input. For instance, input always comes in as a string, so
if you want your puzzle input to be a list of ints, do this:deftransform(line):returnint(line)The last thing you'll find in the script is the call toadvoco.do(). It starts
out withsample=Trueand will therefore attempt to parse the sample input Eric
uses on the webpage of the daily puzzle. It'll throw an error and give you advice
if it can't find sample input. When you're ready to try your solutions on your
real input, remove the sample argument.ExplanationsSome context...WTF isdodoing?When you calldo(), advoco inspects the call stack for the file that called it.
It looks through that file (as a module, usinggetattr) for a few things:Atransformfunc. If it doesn't find one, the default one is basicallylambda x: return xSome part functions, i.e.part1,part2It gets the raw input for the puzzle you're working on (more on that below), runs
that through whatever transform it's using, then passes it as the arg to thepartfunctions it found in your script file.Finally, it gets the result of yourpartfunction and prints it. It does not yet
attempt to submit the answer for you, and it may not do that ever (not sure yet).WHATYEAR(AND DAY) IS IT???Advoco needs to figure out which puzzle input to go get, both the puzzle year and
the puzzle day. To determine this - really in all instances where advoco needs to
figure out a year and/or day - the following order is honored:Anything explicitly passed. If you pass adayoryeararg todo, that is
what advoco is going to work with. End of discussion. Explicit declaration trumps
all.The calling context. Where isdobeing called from? Where is a cli command
being invoked? Ifdois being called from a python file, and that file name contains
digits, those digits are assumed to represent the day you want to work on. Further,
if that python file lives in a directory whose name contains the regexr"20\d{2}",
then that regex match is assumed to be the year. For cli invocations, the folder
== year convention holds, but the day is never inferred from calling context.The current day and year, in the AoC timezone (America/New_York).NotesThis still assumes that we're gonna be working with a list of lines as our input.
I haven't yet figured out the best way to handle the case where you want the whole
damn input string as one blob. Good ideas are welcome here.Any input fetched from the AoC site is cached in~/.cache/advocoto not pester
the site. The inputs, as far as I can tell, have never and will never change, so
there's no cache expiry mechanism.Thegenerate-yearcommand creates a top level folder called_localonly_2022.
This is just so you can AoC from within the advoco repo without git stressing out.
When it's ready for prime time, I'll change it to make a2022directory.ContributingBug reports are awesome but if you see something and you know how to fix it, by
all means! Just make a new branch and open a PR. Hell, just push directly to main
if you're super confident. |
advoservice-integration | External API for AdvoService # noqa: E501 |
adv-prodcon | Advanced Producer-ConsumerAdvanced Producer-Consumer is a python package implementing a full featured producer-consumer pattern for concurrent workers. This is useful for developing data acquisition programs or programs that involve real-time data processing while maintatining a responsive UI. It is compatible with PyQt5, which allows it to be used to develop graphical data acquisition and visualisation applications.Free software: MIT licenseDocumentation:https://adv-prodcon.readthedocs.io.FeaturesProducer and Consumer background workers are defined as metaclasses that can be extended by the user. They implement work functions that run in separate processes.Consumers are buffered and can be configured to run based on a timeout, a max buffer size, or both.The queues that connect Producers to Consumers can be either non-lossy or lossy.Producers and Consumers are connected by a subscription model. Producers can have multiple consumers subscribed to them. Consumers can be subscribed to multiple Producers.Producers and Consumers have on_start and on_stop functions that can be defined to run code for setup and teardown.Results from Consumers (and Producers) can be accessed in the main process through a user-defined callback.User defined functions can be defined to communicate between the main process and the work functions.Compatible with PyQt5, which allows development of graphical data acquisition and visualisation applications.InstallationTo install Advanced Producer Consumer, run this command in your terminal:$pipinstalladv_prodconQuick StartThe following is a quick example of how to use the adv_prodcon packageimportadv_prodconimporttimefromitertoolsimportcountImports.classExampleProducer(adv_prodcon.Producer):@staticmethoddefon_start(state,message_pipe,*args,**kwargs):return{"count":count()}@staticmethoddefwork(on_start_result,state,message_pipe,*args):returnnext(on_start_result["count"])Define a Producer class. Here we are using the on_start method to establish a itertools.count iterator. This is made available in the work function through the on_start_result argument. The work function will return the next count each time it is run.classExampleConsumer(adv_prodcon.Consumer):@staticmethoddefwork(items,on_start_result,state,message_pipe,*args):returnf"Got :{items}from producer"defon_result_ready(self,result):print(result)Define a Consumer Class. This Consumer will just be used as a buffer, returning a string with the items received from the Producer.
The on_result_ready function is called when the main process receives the result of the work function. Here we are just printing out the result.if__name__=="__main__":example_producer=ExampleProducer(work_timeout=1)example_consumer=ExampleConsumer(work_timeout=2,max_buffer_size=1000)example_producer.set_subscribers([example_consumer.get_work_queue()])example_producer.start_new()example_consumer.start_new()time.sleep(10)In the main code block, we create an instance of both our ExampleProducer and our ExampleConsumer. We set the work_timeout of the ExampleProducer to 1 so that it runs once per second. We set the work_timeout of the ExampleConsumer to 2 so that every 2 seconds it performs work on all items in its queue. The max_buffer_size is set high so that the ExampleConsumer is controlled by its work_timeout.The output of this code is shown below:Got :[0, 1] from producer
Got :[2, 3] from producer
Got :[4, 5] from producer
Got :[6, 7] from producer
Process finished with exit code 0Note that the output may be slightly different depending on the time taken to start the worker processes.CreditsDevelopment Lead: Andrew Creegan <[email protected]>This package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-05-05)First release on PyPI. |
advpy | UNKNOWN |
advpyneng-cli-course | apynengУстановка и запускУстановить модульpip install advpyneng-cli-courseПосле этого проверка заданий вызывается через утилиту apyneng в CLI.Этапы работы с заданиямиВыполнение заданийПроверка, что задание отрабатывает как нужноpython task_4_2.pyили запуск скрипта в редакторе/IDEПроверка заданий тестамиapyneng 1-5Сдача заданий на проверкуapyneng 1-5 -cВторой шаг очень важен, потому что на этом этапе намного проще найти ошибки в синтаксисе
и подобные проблемы с работой скрипта, чем при запуске кода через тест на 3 этапе.Проверка заданий тестамиПосле выполнения задания, его надо проверить с помощью тестов.
Для запуска тестов, надо вызвать apyneng в каталоге заданий.
Например, если вы делаете 4 раздел заданий, надо находиться в каталоге exercises/04_data_structures/
и запустить apyneng одним из способов, в зависимости от того какие задания на проверять.Примеры вывода тестов с пояснениямиЗапуск проверки всех заданий текущего раздела:pynengЗапуск тестов для задания 4.1:apyneng 1Запуск тестов для заданий 4.1, 4.2, 4.3:apyneng 1-3Если есть задания с буквами, например, в 7 разделе, можно запускать так,
чтобы запустить проверку для заданий 7.2a, 7.2b (надо находиться в каталоге 07_files):apyneng 2a-bили так, чтобы запустить все задания 7.2x с буквами и без:apyneng 2*Сдача заданий на проверкуДля сдачи задания на проверку, надо сгенерировать токен на Github.
Как это сделать написано в инструкцииПодготовка к работе с заданиямиПосле того как задания прошли тесты и вы посмотрели варианты решения заданий,
можно сдавать задания на проверку.Для этого надо добавить-cк вызову pyneng:
Такой вызов значит запустить тесты для заданий 1 и 2 и сдать их на проверку, если тесты прошли:apyneng 1-2 -cЗапустить тесты для всех заданий и сдать их на проверку, если тесты прошли:apyneng -cПри добавлении-capyneng делает git add файлам заданий, которые прошли тесты, делает commit,
и git push. После этого пишет комментарий на github, что задания такие-то сданы на проверку.Запустить тесты и сдать на проверку задания,
которые прошли тесты, но при этом загрузить на github все изменения
в текущем каталоге:apyneng 1-5 -c --allЗагрузить все изменения в текущем каталоге на github, без привязки к тому проходят ли тестыapyneng --save-allВыполняет командыgit add .
git commit -m "Все изменения сохранены"
git push origin mainОбновление разделовВ apyneng есть два варианта обновления: обновлять разделами или конкретные
задания/тесты. При обновлении раздела, каталог раздела удаляется и копируется
новая версия. Это подходит только для тех разделов, которые вы еще не начинали
выполнять. Если надо обновить конкретное задание, лучше использовать обновление
конкретных заданий (рассматривается дальше).Перед любым вариантом обновления желательно сохранить все локальные изменения
на github!Для обновления разделов, надо перейти в каталог online-x-имя-фамилия/exercises/
и дать команду:apyneng --update-chapters 12-25В этом случае обновятся разделы 12-25. Также можно указывать один раздел:apyneng --update-chapters 11Или несколько через запятуюapyneng --update-chapters 12,15,17Обновление заданий и тестовВ заданиях и тестах встречаются неточности и чтобы их можно было исправить,
apyneng добавлена опция--update.Общая логика:задания и тесты копируются из репозиторияhttps://github.com/pyneng/pyneng-course-tasksкопируется весь файл задания, не только описание, поэтому файл перепишетсяперед выполнением --update, лучше созранить все изменения на githubКак работает --updateесли в репозитории есть несохраненные измененияутилита предлагает их сохранить (сделаетgit add .,git commit,git push)если несохраненных изменений нет, копируются указанные задания и тестыутилита предлагает сохранить изменения и показывает какие файлы изменены, но не какие именно сделаны измененияможно отказаться сохранять изменения и посмотреть изменения git diffВарианты вызоваОбновить все задания и тесты раздела:apyneng --updateОбновить все тесты раздела (только тесты, не задания):apyneng --update --test-onlyОбновить задания 1,2 и соответствующие тесты разделаapyneng 1,2 --updateЕсли никаких обновлений нет, будет такой вывод$ apyneng --update
#################### git pull
Already up-to-date.
Обновленные задания и тесты скопированы
Задания и тесты уже последней версии
Aborted!В любой момент можно прервать обновление Ctrl-C.Пример вывода с несохраненными изменениями и наличием обновленийapyneng --update
ОБНОВЛЕНИЕ ТЕСТОВ И ЗАДАНИЕ ПЕРЕЗАПИШЕТ СОДЕРЖИМОЕ НЕСОХРАНЕННЫХ ФАЙЛОВ!
В текущем каталоге есть несохраненные изменения! Хотите их сохранить? [y/n]: y
#################### git add .
#################### git commit -m "Сохранение изменений перед обновлением заданий"
[main 0e8c1cb] Сохранение изменений перед обновлением заданий
1 file changed, 1 insertion(+)
#################### git push origin main
To [email protected]:pyneng/online-14-natasha-samoylenko.git
fa338c3..0e8c1cb main -> main
Все изменения в текущем каталоге сохранены. Начинаем обновление...
#################### git pull
Already up-to-date.
Обновленные задания и тесты скопированы
Были обновлены такие файлы:
#################### git diff --stat
exercises/04_data_structures/task_4_0.py | 0
exercises/04_data_structures/task_4_1.py | 1 -
exercises/04_data_structures/task_4_3.py | 3 ---
3 files changed, 0 insertions(+), 4 deletions(-)
Это короткий diff, если вы хотите посмотреть все отличия подробно, нажмите n и дайте команду git diff.
Также при желании можно отменить внесенные изменения git checkout -- file (или git restore file).
Сохранить изменения и добавить на github? [y/n]: n
Задания и тесты успешно обновлены
Aborted!
`-`` |
advq7_offline_phits | This is a security placeholder package.
If you want to claim this name for legitimate purposes,
please contact us [email protected]@yandex-team.ru |
advsecurenet | AdvSecureNet - Adversarial Secure NetworksAdvSecureNet is a Python library to for Machine Learning Security. It has been developed byMelih CatalatUniversity of Zurichas a part of his Master's Thesis under the supervision ofProf. Dr. Manuel Günther. Currently, the main focus of the library is on adversarial attacks and defenses on vision tasks. However, it's planned to extend the library to support other tasks such as natural language processing.The library provides a set of tools to generate adversarial examples and to evaluate the robustness of machine learning models against adversarial attacks. It also provides a set of tools to train robust machine learning models. The library is built on top ofPyTorch. It is designed to be modular and extensible. So, anyone can easily run experiments with different configurations.The library currently supports the following attacks:FGSMFGSM TargetedPGDDeepFoolCWLOTSDecision BoundaryThe library currently supports the following defenses:Adversarial TrainingEnsemble Adversarial TrainingThe library supports any model that is implemented in PyTorch. It also provides a set of pre-trained models that can be used for experiments. It's also possible to create and use custom models.The library supports multi-GPU training and adversarial training with DDP (Distributed Data Parallel) from PyTorch. This allows the library to be used for large-scale experiments.InstallationYou can install the library usingpip:pipinstalladvsecurenetYou can also install the library from source:gitclonecdadvsecurenet
pipinstall-e.UsageThe library can be used as a command line tool or as an importable Python package.Command Line Tooladvsecurenetcommand can be used to interact with the library. You can useadvsecurenet --helpto see the available commands and options. Available commands are:attackCommand to execute attacks.config-defaultGenerate a default configuration file based on the name...configsReturn the list of available configuration files.defenseCommand to execute defenses.model-layersCommand to list the layers of a model.modelsCommand to list available models.testCommand to evaluate a model.trainCommand to train a model.weightsCommand to model weights.You can useadvsecurenet <command> --helpto see the available options for a command. For example, you can useadvsecurenet attack --helpto see the available options for theattackcommand. The CLI supports both config yml files and arguments.Python PackageYou can import the library as a Python package. You can use theadvsecurenetmodule to access the library. You can find the available modules and classes in thedocumentation.ExamplesYou can find various examples in theexamplesdirectory. The examples show different use cases of the library and how to use the library as a Python package/CLI tool.ArchitectureThe high-level architecture of the library is shown in the figure below.LicenseThis project is licensed under the terms of the MIT license. SeeLICENSEfor more details.Further InformationFurther information about the library can be found in thedocumentation. |
advstats | Library for mathematical analysis of various sampling methodsAboutAdvStats is a python library for mathematical analysis of various sampling methods. So far the package supports analysis on cluster samples and stratifed samples. And the functions support various data formats. If using data from a table, the data can stored in two ways.1DataCluster/ StratumData Point 1Cluster/ Stratum 1Data Point 2Cluster/ Stratum 22Cluster/ Stratum 1Cluster/ Stratum 2Data Point 1Data Point 3Data Point 2Data Point 4The required supporting can also just be manually entered. For more information look at the documentation or the tests.InstallionpipinstalladvstatsDocumentationDocumetation for the package can be found at pavanadapa.github.io(pavanadapa.github.io).FutureImplementation of a two-stage cluster sampling and more sampling methods are coming soon. |
advt | Attack & Defence on Video TasksOverviewADVT(Attack Defence on Video Tasks) is an adversarial attack/defence toolbox open source library based on Pytorch.
This repository mainly implements some adversarial attack & defence algorithms and provides some video processing apis.FeaturesThe ADVT library has five functional features, which cover the whole process:PreprocessAttackDefenceRecordVisualizationAttackThis module implements attack methods. All attack methods are from top computer conferences in recent 5 years:FGSM [Explaining and harnessing adversarial examples]BIM [Adversarial examples in the physical world]MIM [Boosting adversarial attacks with momentum(CVPR-18)]DeepFool [DeepFool: A simple and accurate method to fool deep neural networks(CVPR-16)]DIM [Improving Transferability of Adversarial Examples with Input Diversity(CVPR-18)]C&W [Towards evaluating the robustness of neural networks(IEEE SP-17)]Universal [Universal adversarial perturbations(CVPR-17)]ZOO [ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models(ACMAIS)]Sparse ADV [Sparse Adversarial Perturbations for Videos(AAAI-19)]DefenceThis module implements defence methods.Bit-depth ReductionTotal Variance MinimizationImage QuiltingComDefend [ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples(CVPR-18)]Randomization [Mitigating Adversarial Effects Through Randomization(ICLR-18)]Evaluation & VisualizationADVT provides some useful evaluation & visualization tools.Evaluation Metric:PSNRSSMImAPVisualization:Video-to-FramesFrames-to-VideoChanged-PixelInstallationBefore installation, make sure you install fellow requirements.numpy==1.18.5
opencv-python==4.4.0.42torch==1.7.0urllib==1.26.4You can install advt through pypi or build from source code.1. from pypipipinstalladvt2. from source codegitclonehttps://github.com/WindFantasy98/ADVT.git
pipinstall-e.Usageattack example# python3.7 torch1.7importtorchimporttorchvision.transformsastransformsimporttorchvision.datasetsasdatasetsfromtorch.utils.dataimportDataLoaderfromadvt.model.cnnimportCNNfromadvt.attackimportFGSMPATH_PARAMETERS='tests/cnn_model.pth'defmain():transform=transforms.Compose([transforms.ToTensor()])t=transforms.Compose([transforms.ToPILImage()])test_dataset=datasets.CIFAR10(root='/data',train=False,transform=transform,download=True)test_loader=DataLoader(dataset=test_dataset,batch_size=1,shuffle=False)device=torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")net=CNN()net.load_state_dict(torch.load(PATH_PARAMETERS))net=net.to(device)fgsm=FGSM(net,device)attack_succ=0total_num=0fori,(img,lbl)inenumerate(test_loader):img,lbl=img.to(device),lbl.to(device)adv_img=fgsm.attack(img,lbl)output=net(adv_img)_,pred_indice=output.max(1)total_num+=len(lbl)attack_succ+=(pred_indice==lbl).sum().item()if(i+1)%20==0:print('batch{}:'.format((i+1)//20),'total tested number:{}, correct number:{}'.format(total_num,attack_succ))if__name__=='__main__':main()defence example# python3.7 torch1.7importtorchimporttorchvision.transformsastransformsimporttorchvision.datasetsasdatasetsfromtorch.utils.dataimportDataLoaderfromadvt.model.cnnimportCNNfromadvt.attackimportDeepFoolfromadvt.defenceimportRandomizationPATH_PARAMETERS='tests/cnn_model.pth'defmain():# initialize datasettransform=transforms.Compose([transforms.ToTensor()])t=transforms.Compose([transforms.ToPILImage()])test_dataset=datasets.CIFAR10(root='/data',train=False,transform=transform,download=True)test_loader=DataLoader(dataset=test_dataset,batch_size=1,shuffle=False)device=torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")# load victim modelnet=CNN()net.load_state_dict(torch.load(PATH_PARAMETERS))net=net.to(device)# initialize attack methoddf=DeepFool(net,device)# initialize defend methodrand_defend=Randomization(net,device)# initialize indicatorattack_succ=0total_num=0# start attackfori,(img,lbl)inenumerate(test_loader):img,lbl=img.to(device),lbl.to(device)adv_img=df.attack(img,lbl)# get adv sampleoutput=rand_defend.defend(adv_img)# get processed sample_,pred_indice=output.max(1)total_num+=len(lbl)attack_succ+=(pred_indice==lbl).sum().item()if(i+1)%20==0:print('batch{}:'.format((i+1)//20),'total tested number:{}, correct number:{}'.format(total_num,attack_succ))if__name__=='__main__':main()This repo is still under maintenance. For more information, please contact with me. |
advtrain | ADV-TRAIN : ADV-TRAIN is Deep Vision TRaining And INference frameworkThis is a framework built on top of pytorch to make machine learning training and inference tasks easier. Along with that it also enables easy dataset and network instantiations, visualize boundaries and more.Read the latest documentation athttps://adv-train.readthedocs.io/en/latest/Why use this framework?It is very easy to use and well documented and testedThe framework supports resume (Yes you can restart training from where ever you left off when your server crashed!).The framework also implements support for train/validation splits of your choice with early stopping baked in.Single argument change for using different datasets and models i.e. convenience at you fingertipsDataloader parameters optimized for highest possbile performance when traning.Supports multi-gpu training (single parameter update required)InstallingTo install the pip package use the commandpip install advtrainContributingTo clone the repo, it is recommended to use a shallow clone, this is recommended as previous versions have hosted large pretrained modelsgit clone --depth <specify depth> https://github.com/DeepakTatachar/ADV-TRAINRequirements are listed in requirements.txt. Use the commandpip install -r requirements.txtto install all required dependenciesDocumentationRead the latest documentation athttps://adv-train.readthedocs.io/en/latest/To locally make the documentation, navigate to /docs and typemake htmlThis will generate a build directory and will house a html folder within which you shall find index.html (i.e. path is /docs/build/html/index.html)Open this in any web browser. This project uses Sphnix to autogenerate this documentation.Running ExamplesThis repo also has examples on how to train and visualize boundaries in /examples folder. A readme file is provided in the ./examples folder to help out with using and running the examples.Pretrained ModelsWe provide pretrained models in a previous version of the repo. It is "hosted"here. These models have various weight quantized VGG and ResNet models, named according to the naming conventiondatasetname_inputQuant_architecture_activationQuant_weightQuant.ckptWhen running you program using advtrain, place the models in the (current working directory) cwd/pretrained/dataset_name and when load is set to true in instantiate_model it will automatically load the correct model. |
advurl-shortner | advurl_shortner - Advanced URL Shortener for PythonEasy-to-use, powerful python library to help you to shorten urls with advanced options. Supports multiple domains, URL TTL, split tests, visit statistics etc.
An extended native Python wrapper forAdvUrlShortner APIwith minimal requirements. Supports all methods and types of responses.FeaturesEasy to useMultiple domainsURL TTL (Time-To-Live) in seconds. Auto expiry URL after some time.Redirect to second URL after primary URL expires (TTL)Randomized and weighted randomnized redirect to different URLs (split tests etc.)Password-protected statistics of visits to a shortened URLPassword protected shortened URL parameters (TTL, second URL, etc.)InstallationIn order install this package, simply run:pipinstalladvurl_shortnerUsageTo use shorten_url, you first need to import the package:importadvurl_shortnerShorten URL:advurl_shortner.short("https://google.com/")# Returns the shortened URLs in JSON# Example: {"urls": ["https://liii.pw/N", "https://illi.ink/N", "https://illi.cfd/N"]}advurl_shortner.short("https://google.com/",description="Google search engine",password="1234x")# Returns shortened URLs, sets a password to access visiting statistics and shortened URL parameters.advurl_shortner.short("https://google.com/",ttl=86400)# Returns the shortened URLs., after 24 hours (86400 seconds) will return "The Link You Followed Has Expired"advurl_shortner.short("https://google.com/",ttl=86400,second_url="https://bing.com/")# Returns the shortened URLs., after 24 hours (86400 seconds) shortened link will redirect to second_urladvurl_shortner.short("https://google.com/",second_url="https://bing.com/")# Returns shortened URLs. The shortened link is randomly redirected to one of the provided URLs.advurl_shortner.short("https://google.com/",second_url="https://bing.com/",weights=[0.3,0.7])# Returns shortened URLs.# The shortened link is randomly (weighted by weight parameter) redirected to one of the provided URLs.# approximately 30% to "https://google.com/ and 70% to "https://bing.com/"Getting short URL parameters and visit statistics:advurl_shortner.stat("https://illi.cfd/H","aaa")# Returns the parameters and visit statistics of the shortened URL stored with password "1234x".# Example:#{# "url": "http://google.com/?z=1",# "second_url": "http://yahoo.com/",# "ttl": null,# "date_created": "1703411470",# "weights": null,# "description":"Yahoo search engine",# "visits": 4,# "primary_url_expired": false#}TODO:More than 2 URLsAdvanced visit statistics |
advutils | OverviewThis module encapsulates advanced algorithms for the manipulation data,
counters, events, queues, multiprocessing and more…Stable:Documentation:http://pythonhosted.org/advutilsDownload Page:https://pypi.python.org/pypi/advutilsLatest:Documentation:http://advutils.readthedocs.io/Project Homepage:https://github.com/davtoh/advutilsBSD license, (C) 2015-2017 David Toro <[email protected]>DocumentationFor API documentation, usage and examples see files in the “documentation”
directory. The “.rst” files can be read in any text editor or being converted to
HTML or PDF usingSphinx. Read the HTML version is online athttp://advutils.readthedocs.io/en/latest/.Examples are found in the directoryexamplesand unit tests intests.Installationpip install advutilsshould work for most users.Once advutils is successfully installed you can import the it in python as:>>>> import advutilsReleasesAll releases follow semantic rules proposed inhttps://www.python.org/dev/peps/pep-0440/andhttp://semver.org/Contributions and bug reports are appreciated.author: David Toroe-mail:[email protected]:https://github.com/davtoh/advutils |
adw | No description available on PyPI. |
adwise-campaignstat-util | No description available on PyPI. |
adwise-venuemanage-util | No description available on PyPI. |
adwite | Adwite使用tkinter与web组件进行开发基础示例python-madwite库信息版本0.3作者向秦希许可证麻省理工许可证文档https://adwite.netlify.app/zh/ |
adwords-client | AdWords Client============== |
adwordspy | Adwords API wrapperFree software: BSD licenseInstallationpip install adwordspyDocumentationhttps://adwordspy.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (2016-05-16)First release on PyPI. |
adwords-reports | No description available on PyPI. |
adworld-render-worker | Coming soon... |
adwrapper | UNKNOWN |
adx2wav | ADX2WAVAn adx2wav c-extension for Python based onadx2wavmod3found onhcs64.com/vgm_ripping.InstallationPIPpip install adx2wavManualpython setup.py installExamplefromadx2wavimportadx2wavwithopen("test.adx","rb")asf:data=f.read()wav=adx2wav(data)withopen("test.wav","wb")asf:f.write(wav) |
adx-arrow | ADX-ArrowHigh performance Kusto (Azure Data Explorer) client based on Apache Arrow and Rust.Usagefromarrow_adximportKustoClientimportpyarrowaspaclient=KustoClient("<service-url>")batches=client.execute("<database>","<query>")table=pa.Table.from_batches(batches) |
adxbw | Group #36 cs107-FinalProjectAuthors:Lanting LiJenny DongJiaye ChenIntroductionAutomatic Differentiation (AD) is a powerful tool in optimization problem, such as root finding with Newton's method. It
has been applied in science and engineering. We implement a python package calledadxbw.In an optimization problem, the core is to find out in which condition we can reach the local and global maxima or
minima and the zero points. Compared to linear functions, it's computationally harder to find the roots of non-linear
functions. Numeric and symbolic methods (and of course manually working and coding) failed under such a high complexity
independently. Thus, AD, which integrates the advantages of numeric and symbolic differentiation, can be used to solve
that problem.How to useTo install:pipinstalladxbwImportimport adxbwUnivariate example:# forward-mode imbedded in node.pyx=AD(math.e,1)f=x.log()# Default base is eOutput:The value and derivative of current function are 1.0 and 0.36787944117144233As an additional feature, we have used the forward mode AD to compute the Jacobian in Newton's root finding method. We
wrote a wrapper for Newton's method that works forsingle/multiple scalar inputs and single vector input.For Newton's optimization with single scalar or vector input, we require the user to input an AD object with value (
first parameter to intialize AD object) to be numeric or a numpy array and partial derivative (second parameter to
intialize AD object) to be also scalar numeric (CANNOTbe array or list). Then they should input a string represents
the function to optimize with the variable named byx_k(see third line code below). User should be careful with
initial guess (value of the input AD object) because it plays an important role in convergence of Newton's method.
Thenewtonfunction also allow other optional parameter such as learning rate and number of max iteration. Please
check code documention for details.Newton's optimization with single scalar:fromadxbw.optimizationimportnewtonx=AD(100,1)# 100 is the initial guessfunct="- x_k**2 - 2*x_k"temp_root=newton(funct,x)Newton's optimization with single vector:x=AD(np.array([-2,-5,-8]),1)funct="- x_k**2 - 2*x_k"temp_root=newton(funct,x)See belowExtensionsection for further Newton's method details.Multivariate example:Suppose you have a function $f(x, y) = x + {y}^2$, needed to evaluate at (1,2).# numpy is imported as np in package, no need to re-import# build input node: multivariate case x = 1, y = 2;x=AD(1,np.array([1,0]))y=AD(2,np.array([0,1]))f=x+y**2print(f.val)print(f.partial_ders)print(f)Output:5
[1 4]
The value and derivative of current function are 5 and [1 4]Newton's optimization for multiple scalar variables:x=AD(-1,np.array([1,0]))y=AD(2,np.array([0,1]))# All values of the initial nodes should be scalar (single float/int)# Length of partial derivatives numpy array should correspond to number of variablestemp_dict={"x1":x,"x2":y}# Initialize a dictionary with keys to be customized names of variables# Use keys of the dictionary to construct the representative function stringfunct="x1.sin() - x2.cos()"# First two parameters should be representative function string# and dictionary of the initial nodestemp_root=newton_multi(funct,temp_dict,lr=0.05)Similar to single input newton's, users can set different learning rate, maximum iteration. See documentation in package
for details. (help(adxbw.optimization.newton_multi))Broader Impact and Inclusivity StatementAutomatic differentiation and its potential application in root finding are the corner stones in optimization problem.
This means that our package could be used beyond the mathematical world and more broadly in science and engineering.For example, as computational biologists, from cell signaling to gene networks, we could model cellular and molecular
processes using nonlinear equations, and root finding using AD will enable machine-level accuracy. This implementation
of automatic differentiation provides a convenient way for users to calculate and evaluate values and derivatives of the
functions. People who would like to use the partial derivatives for higher level computation do not have to calculate
the partial derivatives by hand and thus reduce a lot of unnecessary workload of the scientific researchers.However, the potential implementation errors in our package may cause negative impact when users don't realize it.
Notably, there's no peer review process for developers to upload their packages onto any platform, such as conda and
PyPI. The underlying bugs within the package won't be easily found by users. The misuse of those packages will
potentially lead to error-borne results in those research project. Furthermore, the conclusion drawn or implications
made based on those erroneous results will presumably cause significant social impacts.For example, the high school or college students who just get in touch with Calculus may misuse it that they rely
heavily on the automatic differentiation tools to calculate derivatives. As a result, they may never learn about the
methmatical mechanism of differentiation calculation and thus a misuse of our tool may lead to an educational failure. |
adxl345 | ADXL345=======Python module to use ADXL345. Compatible with Python 2 and Python 3.Based on [pimoroni/adxl345-python](https://github.com/pimoroni/adxl345-python).## InstallInstall it as a dependency using pip:``` bashpip install adxl345```## Usage- Import module and instantiate an ADXL345``` pythonfrom adxl345 import ADXL345adxl345 = ADXL345()```- Get 3-axis accelerations in m.s-²``` pythonaxes = adxl345.get_axes()# Returns something like:# axes['x'] => -0.1614# axes['y'] => 0.0691# axes['z'] => 9.8064```- Get 3-axis accelerations in g (Earth gravity)``` pythonaxes = adxl345.get_axes(True)# Returns something like:# axes['x'] => -0.0014# axes['y'] => 0.0001# axes['z'] => 1.0012```- Use another IC2 addressBy default, this library uses the `0x53` I2C address.To use another address, set it when creating an instance of ADXL345:``` pythonadxl345 = ADXL345(0x1D)```## Full example``` pythonfrom adxl345 import ADXL345adxl345 = ADXL345()axes = adxl345.get_axes(True)# Returns something like:# axes['x'] => -0.0014# axes['y'] => 0.0001# axes['z'] => 1.0012```## LicenseThis project is under [MIT](LICENSE) license. |
adx-logging-handler | No description available on PyPI. |
ady | No description available on PyPI. |
adydezai | UNKNOWN |
adyengo | No description available on PyPI. |
adyeno | No description available on PyPI. |
adys | For teaching purpose |
adz | ADZCommand line interface for HTTP requests defined in yaml configuration file.Installpip install adzQuick startHaving a yaml configuration fileendpoints:
endpoint:
request: GET https://httpbin.org/get
headers:
Content-Type: application/jsonand running on command lineadz endpointwill executeendpointrequest defined in configuration file and printGET https://httpbin.org/get
HTTP/1.1 200 OK
• access-control-allow-credentials: true
• access-control-allow-origin: *
• content-encoding: gzip
• content-type: application/json
• date: Thu, 06 Jun 2019 06:06:06 GMT
• referrer-policy: no-referrer-when-downgrade
• server: nginx
• x-content-type-options: nosniff
• x-frame-options: DENY
• x-xss-protection: 1; mode=block
• content-length: 204
• connection: keep-alive
{
"args": {},
"headers": {
"Accept": "*/*",
"Accept-Encoding": "gzip, deflate",
"Content-Type": "application/json",
"Host": "httpbin.org",
"User-Agent": "python-httpx/0.7.1"
},
"url": "https://httpbin.org/get"
}ConfigurationFor an example configuration look atdocs/example.yml.settingscolors:bool, default:truecontrol output print in colorsresponse:bool, default:truecontrol response body outputtheme:str, defaultnativeany theme name fromhereshould workvariablesused to interpolate values in headers and urlsvariable: abcapplied onurl: http://example.org/$variableresults inhttp://example.org/abcvariable value starting withfile://is opened as file and loaded as string into variableendpointsdescriptionmethodhttp methodsurlrequestmethod urle.g.get http://example.orgparamsquery string parametersheadersjsonjson stringstring starting withfile://is loaded as json filedatajson stringstring starting withfile://is loaded as json filecookiesfilespath to a file:path/to/file.txtfile name and path:filename: path/to/file.txtConfiguration fileExpected configuration file namesadz.yamloradz.ymlapi.yamlorapi.ymlrest.yamlorrest.ymlExpected locationscurrent location:.user's home:~/.adzdirectory in user's home e.g.~/.adz/Configuration file path can also be set using environmental variableADZ.CLIRunadz -hcommandsadz --config,adz -cpath to yaml configuration fileadz --details <endpoint>,adz -d <endpoint>output details about endpoint from configuration fileadz --list,adz -llist available endpoints in configuration fileadz --output,adz -ooutput processed configuration file as jsonadz --settings,adz -soutput settings in configuration fileadz --var name=value,adz -v name=valueset or override variables in configurationadz --colors,adz --no-colorscontrol output print in colorsadz --response,adz --no-responsecontrol response body outputLicenseADZ is licensed under a three clause BSD License. Full license text can be foundhere. |
adze-modeler | No description available on PyPI. |
adzerk-decision-sdk | Adzerk Decision SDK
Python Software Development Kit for Adzerk Decision & UserDB APIshttps://github.com/adzerk/adzerk-decision-sdk-python |
adzuki | AdzukiORM for GCP datastore |
ae | No description available on PyPI. |
aea | AEA FrameworkCreate Autonomous Economic Agents (AEAs)The AEA framework allows you to createAutonomous Economic Agents:An AEA is anAgent, representing an individual, family, organisation or object (a.k.a. its "owner") in the digital world. It looks after its owner's interests and has their preferences in mind when acting on their behalf.AEAs areAutonomous; acting with no, or minimal, interference from their owners.AEAs have a narrow and specific focus: creatingEconomicvalue for their owners.To installEnsure you have Python (version3.8,3.9or3.10).(optional) Use a virtual environment (e.g.pipenvorpoetry).Install:pip install aea[all]Please see theinstallation pagefor more details.DocumentationThe full documentation, including how to get started, can be foundhere.ContributingAll contributions are very welcome! Remember, contribution is not only PRs and code, but any help with docs or helping other developers solve their issues are very appreciated!Read below to learn how you can take part in the AEA project.Code of ConductPlease be sure to read and follow ourCode of Conduct. By participating, you are expected to uphold this code.Contribution GuidelinesRead ourcontribution guidelinesto learn about our issue and PR submission processes, coding rules, and more.Development GuidelinesRead ourdevelopment guidelinesto learn about the development processes and workflows when contributing to different parts of the AEA project.Issues, Questions and DiscussionsWe useGitHub Issuesfor tracking requests and bugs, andGitHub Discussionsfor general questions and discussion.LicenseThe AEA project is licensed underApache License 2.0. |
aea-cli-ipfs | No description available on PyPI. |
aead | UNKNOWN |
ae-ae | aenamespace-root projectae namespace-root: bundling and maintaining templates and documentation of the portions of this namespace.ae namespace root package use-casesthis package is the root project of the ae namespace and their portions (the modules
and sub-packages of the namespace ae). it provides helpers and templates in order to
bundle and ease the maintenance, for example to:update and deploy common outsourced files, optionally generated from templates.merge docstrings of all portions into a single combined and cross-linked documentation.compile and publish documentation via Sphinx ontoReadTheDocs.bulk refactor multiple portions of this namespace simultaneously using thegit repository manager tool (grm).to enable the update and deployment of outsourced files generated from the templates provided by
this root package, add this root package to the development requirements file (dev_requirements.txt)
of each portion project of this namespace. in this entry you can optionally specify the version of
this project.and because this namespace-root package is only needed for development tasks, it will never need to
be added to the installation requirements file (requirements.txt) of a project.please check thegit repository manager manualfor more detailed information on the provided actions of thegrmtool.installationno installation is needed to use this project for your portion projects, because thegrmtool is
automatically fetching this and the other template projects fromhttps://gitlab.com/ae-group(and
in the specified version).an installation is only needed if you want to adapt this namespace-root project for your needs or if you want
to contribute to this root package. in this case please follow the instructions given in the
:ref:contributingdocument.namespace portionsthe following 40 portions are currently included in this namespace:ae_baseae_deepae_django_utilsae_droidae_notifyae_validae_filesae_pathsae_coreae_locknameae_dynamicodae_i18nae_parse_dateae_literalae_progressae_updaterae_consoleae_sys_coreae_sys_dataae_sys_core_shae_sys_data_shae_db_coreae_db_oraae_db_pgae_transfer_serviceae_sideloading_serverae_gui_appae_gui_helpae_kivy_glslae_kivy_dyn_chiae_kivy_relief_canvasae_kivyae_kivy_auto_widthae_kivy_file_chooserae_kivy_iterable_displayerae_kivy_qr_displayerae_kivy_sideloadingae_kivy_user_prefsae_lisz_app_dataae_enaml_app |
aea-ledger-cosmos | No description available on PyPI. |
aea-ledger-ethereum | No description available on PyPI. |
aea-ledger-fetchai | No description available on PyPI. |
aeat-web-services | MasterSpanish Tax Agency Electronic Office (AEAT) Integration.Make requestsAEAT Web Servicesand sign your connection and xml using your certificate.Integración con la Agencia Estatal de Administración Tributaria EspañolaRealiza peticiones a losServicios Web de AEATy firma tu conexión y mensajes XML utilizando tu certificado.Usage (English)Example for requesting a list of ENS’s.Initialize a Config object with the desired preconfigured service and if you want to request AEAT test or production endpoints (test_mode).
Finally initialize controller with the config and the desired certificate and make the request with your payload.If you need more control just build the controller by hand, see build_from_config method for inspiration.Preconfigured aduanet services.Official AEAT Web Servicesimportaeatconfig=aeat.Config('ens_presentation',test_mode=True)ctrl=aeat.Controller.build_from_config(config,'cert.pem','key.pem')result=ctrl.request(payload)# See factories for examplesassertresult.valid,f'Error requesting aeat:{result.error}'assertresult.dataisnotNoneUsage (Spanish)Ejemplo de consulta de ENSs.Inicializa el objecto Config con el servicio preconfigurado y si quieres usar los endpoints de AEAT de test o de producción (test_mode).Por último inicializa el controlador con la config y el certificado que gustes y realiza la petición pasándole los datos que necesites.Si necesitas un mayor control simplemente construye el controlador a mano, puedes inspirarte en el método build_from_config.Lista de Servicios Preconfigurados.Servicios Web oficial de AEATimportaeatconfig=aeat.Config('ens_presentation',test_mode=True)ctrl=aeat.Controller.build_from_config(config,'cert.pem','key.pem')result=ctrl.request(payload)# Ver factories para ejemplosassertresult.valid,f'Error requesting aeat:{result.error}'assertresult.dataisnotNoneDjango Rest FrameworkSeveral AEAT Validators and Serializers are provided.Validators: Validate input data to send to AEATSerializers: Serialize AEAT requestfromaeat.rest_frameworkimportvalidatorsvalidator=validators.ENSPresentationValidator(data=payload)assertvalidator.is_valid(raise_exception=True)# Send the request to AEATimportaeatconfig=aeat.Config(service_name,test_mode=settings.AEAT_TEST_MODE)ctrl=aeat.Controller.build_from_config(config,cert_path,key_path)result=ctrl.request(validator.data)assertresult.valid# Parse the responsefromaeat.rest_frameworkimportserializersserializer=serializers.get_class_for_aeat_response(data=result.data)assertserializer.is_valid(raise_exception=False)assert{'mrn':'XXXX'}==serializer.dataassertnotserializer.is_errorPrerequisitesInstall xmlsec prerequisites.
Checkhttps://github.com/mehcode/python-xmlsecInstall$pipinstallaeat-web-servicesDevelop$pythonsetup.pydevelop$pipinstall-rrequirements_test.txtTest$pipinstalltox$toxReleaseshttps://github.com/initios/aeat-web-services/releasesUsefull LinksAEAT Web ServicesAvailable preconfigured servicesStructure, rules and conditions |
aeb43 | aeb43 is a parser for AEB43 files.NutshellImport:>>> import os
>>> from aeb43 import AEB43Instantiate:>>> aeb43 = AEB43('aeb43/AEB43.txt')The accounts:>>> len(aeb43.accounts)
1
>>> account = aeb43.accounts[0]
>>> account.number
'0001414452'
>>> account.start_date
datetime.date(2018, 3, 18)
>>> account.end_date
datetime.date(2018, 3, 20)
>>> account.initial_balance
Decimal('3005')
>>> account.final_balance
Decimal('2994.02')
>>> account.currency
'978'The transactions:>>> len(account.transactions)
1
>>> transaction = account.transactions[0]
>>> transaction.transaction_date
datetime.date(2018, 3, 19)
>>> transaction.value_date
datetime.date(2018, 3, 19)
>>> transaction.amount
Decimal('-10.98')
>>> transaction.shared_item
'12'
>>> transaction.own_item
'408'
>>> transaction.document
'0000000000'
>>> transaction.reference1
'000000000000'
>>> transaction.reference2
'5540014387733014'
>>> transaction.items
['COMPRA TARG 5540XXXXXXXX3014 DNH*MICHA', 'EL SCOTT']To report issues please visit theaeb43 bugtracker. |
ae-base | base 0.3.35ae namespace module portion base: basic constants, helper functions and context manager.installationexecute the following command to install the
ae.base module
in the currently active virtual environment:pipinstallae-baseif you want to contribute to this portion then first forkthe ae_base repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_base):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aebo | No description available on PyPI. |
aebo-pi | No description available on PyPI. |
aec | ActiveMQ Easy ConfigThis project aims a easy way to setup a network of ActiveMQ brokers. You can change easily the topology and this
script will create the configurations based on your setup. ActiveMQ Easy Config will produce for you:Anactivemq.xmlfile for each broker with the specific configuration for the brokerA Docker image for each broker to easy adapt in your container orchestrator.A script for run the docker image locally in order to test your network locally.How it works?Let's take an example of a duplex connection between two ActiveMQ brokers:+----------+ +----------+
| | Duplex | |
| Broker 1 | ---<-->----<-->--- | Broker 2 |
| | | |
+----------+ +----------+First you have to define theyamlfile with the configuration of your network:brokers:node1:"static:(tcp://node1:61616)"node2:"static:(tcp://node2:61616)"networks:common_configuration:set_broker_name:"false"queue:conduit_subscriptions:"false"consumer_ttl:"1"duplex:"true"message_ttl:"-1"physical_name:">"user_name:"admin"topic:conduit_subscriptions:"true"consumer_ttl:"1"duplex:"true"message_ttl:"-1"physical_name:">"user_name:"admin"network_connector:node1:to:-node2config:queue:duplex:"false"topic:_ignore:"true"node2:to:-node1The first topic in the yaml will define the broker and your respective connection.
If you intend to use docker, the address can be the broker name.You can see a lot of examples of network of brokers in the directorytemplates. In the same directory
you have anactivemq.xmlfile used as base also.After create your template you can just run:pythonaec.py\--configtemplates/simple-duplex-config.yaml\--activemqtemplates/activemq.xml\--save-to/my/path/projectAnd you'll get the follow files as result:node1.xmlnode2.xmlOptionally if you pass a path to aDockerfileand a registry name, the system will create:run.shbuild.shDockerfileYou can runbuild.shto build the docker images andrun.shto run the containers in your local machine.The docker image created can easily adapted to run on Kubernetes, Docker Swarm or ECS.In the foldercheckyou can run a simple producer/consumer to check the communication.InstallingRecommended:pipinstallaecget from sourceJust clone the [email protected]:byjg/activemq-easy-configTo DoHelp here is appreciate :)This configuration can be expanded to other features on ActiveMQ.K8s implementationDocker swarm implementationECS implementation |
aecc | AECCAECC is the air quality database maintained by SNU APCC.InstallationTo installaecc,pip install https://github.com/danielmsk/snuair/dist/aecc-0.0.1-py3-none-any.whl
pip install aeccPrerequisitepandas (https://pandas.pydata.org/)Getting Started>>> import aecc
>>> aecc.download_key(file="~/user.key")
email: [email protected]
password: userpassword
>>> conn = aecc.connector()
>>> conn.all_regions
['','', '', '', '', ...
>>> conn.all_pollutants
['CO', '']
>>> conn.count_pollutants
{'CO': 323, 'NO': 25}
>>> r = conn.request(region="", from="2007-08-12", to="2020-09-15")
>>> r.download_to_file(path="data/raw.tsv", type="tsv")
>>> r.all_pollutants
['CO', '']
>>> r.count_pollutants
{'CO': 323, 'NO': 25}
>>> list1 = r.to_list()
>>> list1
[
{datetime:"2007-08-12", region="CN", CO2=25.3, },
{datetime:"2007-08-13", region="CN", CO2=25.3, },
]
>>> s1 = r.to_series()
>>> df1 = r.to_dataframe()
### upload data
>>> conn.dataupload(file="data/update.tsv", type="tsv")
>>> conn.close()Sing-up and sign-inSign-up>>> import aecc
>>> aecc.signup()
user email: [email protected]
user name: Tom
affiliation: snu
password: userpasswordDownload token file>>> aecc.download_token(file="~/user.aecctoken")
email: [email protected]
password: userpasswordRequest DataList Datalisting regions>>> conn.all_regions
['','', '', '', '', ...listing pollutants>>> conn.all_regions
['','', '', '', '', ...listing pmf results>>> conn.list_pmf
['','', '', '', '', ...Print statistics>>> conn.stat
>>> conn.stat_regions
>>> r.statDownload Data>>> r.download_to_file(path="data/raw.csv", type="csv")
>>> r.download_to_file(path="data/raw.xlsx", type="xlsx")Upload DataThis uploading data function is for the users that have permission to upload data. If you don't have permission to upload data, please contact the admin.from fileWhen the excel file has two sheets, the sheet name should include 'conc_' or 'unc_'.
'conc_' means concentration and 'unc_' means uncertainty.>>> conn.upload_from_file(file="data/raw_unc.tsv", filetype="raw", datatype="unc", region="GJ")
>>> conn.upload_from_file(file="data/raw_unc.tsv", filetype="raw", datatype="unc", region="GJ", update_force=True)
>>> conn.upload_from_file(file="data/raw_conc.tsv", filetype="raw", datatype="conc", region="GJ")
>>> conn.upload_from_file(file="data/raw.xlsx", filetype="raw")
>>> conn.upload_from_file(file="data/test_Constrained.xls", filetype="pmf", title="2019_Seoul")AdminitstrationAdministration ManualVersion HistoryVersion History |
aec-cli | AWS EC2 CLI"Instead of using the console, use the cli!".Command-line tools for managing pet EC2 instances by name.Defaults only need to be supplied once via aconfig file, which supports multiple profiles for different regions or AWS accounts.For examples see:EC2- manipulate EC2 instances by name, and launch them with tags and EBS volumes of any size, as per the settings in the configuration file (subnet, security group etc).AMI- describe, delete and share imagesCompute Optimizer- show over-provisioned instancesSSM- run commands on instances, andssm patch managementPrerequisitespython 3.8+InstallRun the following to install the latest master version using pip:pip install aec-cliIf you have previously installed aec, run the following to upgrade to the latest version:pip install --upgrade aec-cliNB: Consider usingpipxto install aec-cli into its own isolated virtualenv.ConfigureBefore you can use aec, you will need to create the config files in~/.aec/. The config files contain settings for your AWS account including VPC details and additional tagging requirements.To get started, runaec configure exampleto install theexample config filesand then update them as needed.Handy aliasesFor even faster access to aec subcommands, you may like to add the following aliases to your .bashrc:alias ec2='COLUMNS=$COLUMNS aec ec2'
alias ami='COLUMNS=$COLUMNS aec ami'
alias ssm='COLUMNS=$COLUMNS aec ssm'COLUMNS=$COLUMNSwill ensure output is formatted to the width of your terminal when piped.FAQHow do I use aec with other AWS profiles?To use aec with the named profile 'production':export AWS_DEFAULT_PROFILE=production
aec ec2 describeSimilar projectswallix/awlessis written in Go, and is an excellent substitute for awscli with
support for many AWS services. It has human friendly commands for use on the command line or in templates. Unlikeaecits ec2 create instance command doesn't allow you to specify the EBS volume size, or add tags.achiku/jungleis written in Python, and incorporates a smaller subset of ec2 commands and doesn't launch new instances. It does however have an ec2 ssh command. It also supports other AWS services like ELB, EMR, RDS.ContributingSeeCONTRIBUTING.mdto get started and develop in this repo. |
ae-console | console 0.3.68ae namespace module portion console: console application environment.installationexecute the following command to install the
ae.console module
in the currently active virtual environment:pipinstallae-consoleif you want to contribute to this portion then first forkthe ae_console repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_console):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
ae-core | core 0.3.60ae namespace module portion core: application core constants, helper functions and base classes.installationexecute the following command to install the
ae.core module
in the currently active virtual environment:pipinstallae-coreif you want to contribute to this portion then first forkthe ae_core repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_core):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aecSpace | README.MD |
aeda | Aeda APIAn Aeda service for getting detailed info for cars using registration number.Getting StartedInstallation process1. Install from PyPi using:python -m pip install aeda2. Add global variablesAEDA_API_CAR_USER
AEDA_API_CAR_PASSWORD2.1 UbuntuAdd~/.secrets/aeda.yaml, a YAML file containing:aeda:
api:
car:
user: XXXXXXXX
password: XXXXXXXXXXXXXXRun in console or add to~/.bashrcor~/.zshrcor other system command config files:export AEDA_API_CAR_USER=$(yq -r '.aeda.api.car.user' ~/.secrets/aeda.yaml)export AEDA_API_CAR_PASSWORD=$(yq -r '.aeda.api.car.password' ~/.secrets/aeda.yaml)Install yq with:apt install yqReload env with:. .~/.bashrc2.2 WindowsUsageRun either using command line or as a python library.1. Command lineRun from command line using:python -m aeda.car.main --regnumber XXXXXXXXXGet help and show all command options:python -m aeda.car.main --help2. APIRun in a python script using:importaeda.caraeda.car.get_car_from_reg_number("XXXXXXXXX")API referencesEndpoint:https://aedacar.azurewebsites.net/api/carParams:"code": "BzaCA1bdqU21cfZVn8r3KJwaoivzaOla7o6sLa-qD0elAzFulXlzyA=="
"username": USERNAME
"regNumber": REGNUMBERHeaders:"api-key": APIKEYCURL example:curl --header "api-key: APIKEY" -X GET "https://aedacar.azurewebsites.net/api/car?code=BzaCA1bdqU21cfZVn8r3KJwaoivzaOla7o6sLa-qD0elAzFulXlzyA==&username=USERNAME®Number=REGNUMBER"VersionsUsing Calender Versioning:YYYY.MM.PATCHversion "2022.12.20"December 2022Update README.mdChange header key nameversion "2022.12.19"December 2022Update README.mdversion "2022.12.18"December 2022Key moved to headerversion "2022.12.17"December 2022Update error catchingversion "2022.12.17"December 2022Change request structureversion "2022.12.16"December 2022add help descriptionsclean up codeversion "2022.12.15"December 2022extend debug loggingversion "2022.12.14"December 2022add debug loggingversion "2022.12.13"December 2022add csv write and readversion "2022.12.12"December 2022update README.mdversion "2022.12.11"December 2022update README.mdfix small bugsversion "2022.12.10"December 2022update README.mdfix samll bugsimporve structureversion "2022.12.1"December 2022first release |
aeda-data | IntroductionTODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project.pep440_version "2022.12.1"
version "2022.12.1"Getting StartedTODO: Guide users through getting your code up and running on their own system. In this section you can talk about:Installation processSoftware dependenciesLatest releasesAPI referencesBuild and TestTODO: Describe and show how to build your code and run the tests.ContributeTODO: Explain how other users and developers can contribute to make your code better.If you want to learn more about creating good readme files then refer the followingguidelines. You can also seek inspiration from the below readme files:ASP.NET CoreVisual Studio CodeChakra Core |
aedat | AEDAT is a fast AEDAT 4 python reader, with a Rust underlying implementation.Runpip install aedatto install it.ExampleProcess framesPillow (PIL)OpenCVDetailed exampleInstall from sourceLinuxmacOSWindowsContributePublishExampleTheaedatlibrary provides a single class:Decoder. A decoder object is created by passing a file name toDecoder. The file name must be apath-like object.importaedatdecoder=aedat.Decoder("/path/to/file.aedat")print(decoder.id_to_stream())forpacketindecoder:print(packet["stream_id"],end=": ")if"events"inpacket:print("{}polarity events".format(len(packet["events"])))elif"frame"inpacket:print("{}x{}frame".format(packet["frame"]["width"],packet["frame"]["height"]))elif"imus"inpacket:print("{}IMU samples".format(len(packet["imus"])))elif"triggers"inpacket:print("{}trigger events".format(len(packet["triggers"])))Process framesPillow (PIL)importaedatimportPIL.Image# https://pypi.org/project/Pillow/index=0forpacketindecoder:if"frame"inpacket:image=PIL.Image.fromarray(packet["frame"]["pixels"],mode=packet["frame"]["format"],)image.save(f"{index}.png")index+=1OpenCVimportaedatimportcv2# https://pypi.org/project/opencv-python/index=0forpacketindecoder:if"frame"inpacket:image=packet["frame"]["pixels"]ifpacket["frame"]["format"]=="RGB":image=cv2.cvtColor(image,cv2.COLOR_RGB2BGR)elifpacket["frame"]["format"]=="RGBA":image=cv2.cvtColor(image,cv2.COLOR_RGBA2BGRA)cv2.imwrite(f"{index}.png",image)index+=1Detailed exampleThis is the same as the first example, with detailed comments:importaedatdecoder=aedat.Decoder("/path/to/file.aedat")"""decoder is a packet iterator with an additional method id_to_streamid_to_stream returns a dictionary with the following structure:{<int>: {"type": <str>,}}type is one of "events", "frame", "imus", "triggers"if type is "events" or "frame", its parent dictionary has the following structure:{"type": <str>,"width": <int>,"height": <int>,}"""print(decoder.id_to_stream())forpacketindecoder:"""packet is a dictionary with the following structure:{"stream_id": <int>,}packet also has exactly one of the following fields:"events", "frame", "imus", "triggers""""print(packet["stream_id"],end=": ")if"events"inpacket:"""packet["events"] is a structured numpy array with the following dtype:[("t", "<u8"),("x", "<u2"),("y", "<u2"),("on", "?"),]"""print("{}polarity events".format(len(packet["events"])))elif"frame"inpacket:"""packet["frame"] is a dictionary with the following structure:{"t": <int>,"begin_t": <int>,"end_t": <int>,"exposure_begin_t": <int>,"exposure_end_t": <int>,"format": <str>,"width": <int>,"height": <int>,"offset_x": <int>,"offset_y": <int>,"pixels": <numpy.array(shape=(height, width), dtype=uint8)>,}format is one of "L", "RGB", "RGBA""""print("{}x{}frame".format(packet["frame"]["width"],packet["frame"]["height"]))elif"imus"inpacket:"""packet["imus"] is a structured numpy array with the following dtype:[("t", "<u8"),("temperature", "<f4"),("accelerometer_x", "<f4"),("accelerometer_y", "<f4"),("accelerometer_z", "<f4"),("gyroscope_x", "<f4"),("gyroscope_y", "<f4"),("gyroscope_z", "<f4"),("magnetometer_x", "<f4"),("magnetometer_y", "<f4"),("magnetometer_z", "<f4"),]"""print("{}IMU samples".format(len(packet["imus"])))elif"triggers"inpacket:"""packet["triggers"] is a structured numpy array with the following dtype:[("t", "<u8"),("source", "u1"),]the source value has the following meaning:0: timestamp reset1: external signal rising edge2: external signal falling edge3: external signal pulse4: external generator rising edge5: external generator falling edge6: frame begin7: frame end8: exposure begin9: exposure end"""print("{}trigger events".format(len(packet["triggers"])))Because the lifetime of the file handle is managed by Rust, decoder objects are not compatible with thewithstatement. To ensure garbage collection, point the decoder variable to something else, for exampleNone, when you are done using it:importaedatdecoder=aedat.Decoder("/path/to/file.aedat")# do something with decoderdecoder=NoneInstall from sourceLocal build (first run).python3-mvenv.venvsource.venv/bin/activate
pipinstall--upgradepip
pipinstallmaturinnumpy
maturindevelop# or maturin develop --release to build with optimizationsLocal build (subsequent runs).source.venv/bin/activate
maturindevelop# or maturin develop --release to build with optimizationsAfter changing any of the files inframebuffers, one must run:flatc--rust-osrc/flatbuffers/*.fbsTo format the code, run:cargofmtPublishBump the version number inCargo.tomlandpyproject.toml.Create a new release on GitHub. |
ae-db-core | db_core 0.3.15ae namespace module portion db_core: database connection and data manipulation base classes.installationexecute the following command to install the
ae.db_core module
in the currently active virtual environment:pipinstallae-db-coreif you want to contribute to this portion then first forkthe ae_db_core repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_db_core):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
ae-db-ora | db_ora 0.3.7ae namespace module portion db_ora: database system core layer to access Oracle databases.installationexecute the following command to install the
ae.db_ora module
in the currently active virtual environment:pipinstallae-db-oraif you want to contribute to this portion then first forkthe ae_db_ora repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_db_ora):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
ae-db-pg | db_pg 0.3.7ae namespace module portion db_pg: postgres database layer.installationexecute the following command to install the
ae.db_pg module
in the currently active virtual environment:pipinstallae-db-pgif you want to contribute to this portion then first forkthe ae_db_pg repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_db_pg):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aede | UNKNOWN |
ae-deep | deep 0.3.10ae namespace module portion deep: easy handling of deeply nested data structures.installationexecute the following command to install the
ae.deep module
in the currently active virtual environment:pipinstallae-deepif you want to contribute to this portion then first forkthe ae_deep repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_deep):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aedes | AEDESThis repository contains codes that demonstrate the use of Project AEDES for data collection on remote sensing using LANDSAT, MODIS and SENTINEL. Full repository is linkedhere.Author: Xavier PuspusAffiliation:Cirrolytix Research ServicesInstallationInstall using:foo@bar:~$pipinstallaedesSatellite DataImport the modules of the package using:import aedes
from aedes.remote_sensing_utils import generate_random_ee_points, df_to_ee_points, get_satellite_measures_from_points
from aedes.remote_sensing_utils import visualize_on_map
from aedes.osm_utils import reverse_geocode_points
from aedes.automl_utils import perform_clustering, perform_classificationAuthentication and InitializationThis packages uses Google Earth Engine (sign-up for accesshere) to query remote sensing data. To authenticate, simply use:aedes.remote_sensing_utils.authenticate()This script will open a google authenticator that uses your email (provided you've signed up earlier) to authenticate your script to query remote sensing data. After authentication, initialize access using:aedes.remote_sensing_utils.initialize()Area of InterestFirst, find the bounding box geojson of an Area of Interest (AOI) of your choice using thislink.Get Normalized Difference Indices and Weather DataUse the one-liner codeget_satellite_measures_from_pointsto extract NDVI, NDWI, NDBI, Aerosol Index (Air Quality), Surface Temperature, Precipitation Rate and Relative Humidity for your preset number of points of interestsample_pointswithin a specified date durationdate_fromtodate_to.%%time
QC_AOI = [[[120.98976275,14.58936896],
[121.13383232,14.58936896],
[121.13383232,14.77641364],
[120.98976275,14.77641364],
[120.98976275,14.58936896]]] # Quezon city
# Get random points sampled from the AOI if you don't have ground truth data points yet.
# You can also generate your own Earth Engine Points from your own long-lat pairs using generate_random_ee_points()
points = generate_random_ee_points(QC_AOI, sample_points=50)
# Get satellite features on each point
qc_df = get_satellite_measures_from_points(points, QC_AOI,
date_from='2017-07-01',
date_to='2017-09-30')Reverse GeocodingThis package also provides an easy-to-use one-liner reverse geocoder that usesNominatim%%time
rev_geocode_qc_df = reverse_geocode_points(qc_df)
rev_geocode_qc_df.head()Geospatial ClusteringThis packages uses KMeans as the unsupervised learning technique of choice to perform clustering on the geospatial data enriched with normalized indices, air quality and surface temperatures with your chosen number of clusters.clustering_model = perform_clustering(rev_geocode_qc_df, n_clusters=3)
rev_geocode_qc_df['labels'] = pd.Series(clustering_model.labels_)Visualize Hotspots on a MapThis packages also provides the capability of visualizing all the points of interest with their proper labels using one line of code.vizo = visualize_on_map(rev_geocode_qc_df)
vizoOpenStreetMap DataThe package needed is imported as follows:from aedes.osm_utils import initialize_OSM_network, get_OSM_network_dataSpatial Data from Map NetworksIn order to initialize and create an OpenStreetMap (OSM) network from a geojson of an AOI, use:%%time
network = initialize_OSM_network(aoi_geojson)Query Amenities DataIn order to pull data for, say, healthcare facilities (more documentation on amenitieshere), use this one-liner:final_df, amenities_df, count_distance_df = get_OSM_network_data(network,
satellite_df,
aoi_geojson,
['clinic', 'hospital', 'doctors'],
5,
5000,
show_viz=True)This function pulls the count and distance of each node from a possible healthcare facility (for this example). It also outputs the original dataframe concatenated with the count and distances. The actual amenities data is also returned. We can then pass the resultingfinal_dfdataframe into another clustering algorithm to produce dengue risk clusters with the added health capacity features.Social Listening DataTo query for Google search trends, import:from aedes.social_listening_utils import get_search_trendsthen use:iso_geotag = "PH-00"
search_df = get_search_trends(iso_geotag)This pulls data for the top 5 dengue-related searches within a geolocation dictated by an ISO tag listed and describedhere. Below is a sample:datedenguedengue symptomsdengue feversymptoms of denguedengue sintomasisPartial2021-09-12 00:00:00172301False2021-09-19 00:00:00123111False2021-09-26 00:00:0061001False2021-10-03 00:00:0051000False2021-10-10 00:00:0091000False2021-10-17 00:00:0091000False2021-10-24 00:00:0091000False2021-10-31 00:00:0051100False2021-11-07 00:00:0081100False2021-11-14 00:00:0081000False2021-11-21 00:00:00122100False2021-11-28 00:00:00142210False2021-12-05 00:00:00103100False2021-12-12 00:00:0060000False2021-12-19 00:00:0072110False2021-12-26 00:00:0071211False2022-01-02 00:00:00115111False2022-01-09 00:00:00104210False2022-01-16 00:00:0073110False2022-01-23 00:00:0071100TrueAEDES Demo Web ApplicationIn order to demonstrate the functionalities of using the AEDES python package, we can use Streamlit to display a web application that takes in a geojson and outputs the hotspots and the recommended cities at risk. Clone this repository,cdinto it and follow the instructions below.Streamlit SetupInstall streamlit using:foo@bar:~$pipinstallstreamlitRunstreamlit helloto see if the installation was successful.Running the sample web applicationSimply run the code below to run a local version of your web application that outputs the at-risk areas as hotspots on a map as well as a subsequent list of places to prioritize disease-related proactive measures.The one below is for a dengue hotspot map for Quezon City, Philippines.This other screenshot shows the web application demonstrating the use of the geospatial modelling in outputting locations of high-risk areas.Another example for Cotabato City, Philippines is shown below.AEDES Automated Machine LearningWe have also added functionality to this package that performs tree-based pipeline optimization (TPOT) that optimizes machine learning pipelines using genetic algorithm as describedhere.Data preparation is still required as we as train-test splitting as needed. Using the one-linerperform_classificationorperform_regression, we can train a machine learning model hundreds to thousands of times in different configurations, feature engineering methodologies and various models in order to output:the best ml model (in-memory and saved as a pickle file, default isbest_model.pkl)the best ml pipeline which is a python script that describes the ml methodologyand the feature importances (both as a dataframe, and as a plot)model, feature_imps_df = perform_classification(X_train, y_train)The output should look like this:which also generates a python script of the best machine learning model pipeline similar to thisscript. |
aedev | aedevnamespace-root projectaedev namespace-root: aedev namespace root, providing setup, development and documentation tools/templates for Python projects.aedev namespace root package use-casesthis project is maintaining all the portions (modules and sub-packages) of the aedev namespace to:update and deploy common outsourced files, optionally generated from templatesmerge docstrings of all portions into a single combined and cross-linked documentationpublish documentation via Sphinx ontoReadTheDocsrefactor multiple or all portions of this namespace simultaneously using the grm portions actionsthis namespace-root package is only needed for development tasks, so never add it to the installation requirements
file (requirements.txt) of a project.to ensure the update and deployment of outsourced files generated from the templates provided by this root package via
thegit repository manager tool, add this root package to the
development requirements file (dev_requirements.txt) of a portion project of this namespace.the following portions are currently included in this namespace:aedev_tpl_namespace_rootaedev_tpl_projectaedev_git_repo_manageraedev_setup_projectaedev_tpl_appaedev_setup_hook |
aedev-aedev | aedevnamespace-root projectaedev namespace-root: aedev namespace root, providing setup, development and documentation tools/templates for Python projects.aedev namespace root package use-casesthis package is the root project of the aedev namespace and their portions (the modules
and sub-packages of the namespace aedev). it provides helpers and templates in order to
bundle and ease the maintenance, for example to:update and deploy common outsourced files, optionally generated from templates.merge docstrings of all portions into a single combined and cross-linked documentation.compile and publish documentation via Sphinx ontoReadTheDocs.bulk refactor multiple portions of this namespace simultaneously using thegit repository manager tool (grm).to enable the update and deployment of outsourced files generated from the templates provided by
this root package, add this root package to the development requirements file (dev_requirements.txt)
of each portion project of this namespace. in this entry you can optionally specify the version of
this project.and because this namespace-root package is only needed for development tasks, it will never need to
be added to the installation requirements file (requirements.txt) of a project.please check thegit repository manager manualfor more detailed information on the provided actions of thegrmtool.installationno installation is needed to use this project for your portion projects, because thegrmtool is
automatically fetching this and the other template projects fromhttps://gitlab.com/aedev-group(and
in the specified version).an installation is only needed if you want to adapt this namespace-root project for your needs or if you want
to contribute to this root package. in this case please follow the instructions given in the
:ref:contributingdocument.namespace portionsthe following 6 portions are currently included in this namespace:aedev_setup_projectaedev_tpl_projectaedev_tpl_namespace_rootaedev_tpl_appaedev_git_repo_manageraedev_setup_hook |
aedev-git-repo-manager | git_repo_manager 0.3.71aedev namespace package portion git_repo_manager: create and maintain local/remote git repositories of Python projects.installationexecute the following command to install the
aedev.git_repo_manager package
in the currently active virtual environment:pipinstallaedev-git-repo-managerif you want to contribute to this portion then first forkthe aedev_git_repo_manager repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(aedev_git_repo_manager):pipinstall-e.[dev]the last command will install this package portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aedev-pythonanywhere | pythonanywhere 0.3.3aedev namespace module portion pythonanywhere: web api forwww.pyanywhere.comand eu.pyanywhere.com.installationexecute the following command to install the
aedev.pythonanywhere module
in the currently active virtual environment:pipinstallaedev-pythonanywhereif you want to contribute to this portion then first forkthe aedev_pythonanywhere repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(aedev_pythonanywhere):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aedev-setup-hook | setup_hook 0.3.6aedev namespace module portion setup_hook: individually configurable setup hook.installationexecute the following command to install the
aedev.setup_hook module
in the currently active virtual environment:pipinstallaedev-setup-hookif you want to contribute to this portion then first forkthe aedev_setup_hook repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(aedev_setup_hook):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
aedev-setup-project | setup_project 0.3.19aedev namespace module portion setup_project: project setup helper functions.installationexecute the following command to install the
aedev.setup_project module
in the currently active virtual environment:pipinstallaedev-setup-projectif you want to contribute to this portion then first forkthe aedev_setup_project repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(aedev_setup_project):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs. |
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