Saravanan666/pypi-org huggingface dataset:
Package: mercari-scanner
Mercari ScannerInstallationpippip install mercari-scanner
python3 -m mercariscanner --slack-token xoxb-blah-blah -hDockerdocker pull chadbowman0/mercari-scanner:latest
docker run chadbowman0/mercari-scanner:latest --slack-token xoxb-blah-blah -hLocal (with config.ini)git clone https://github.com/ChadBowman/mercari-scanner.git ~/mercari-scanner
python3 -m venv ~/mercari-scanner/env && source ~/mercari-scanner/env/bin/activate
python3 -m pip install ~/mercari-scannerUsageusage: __main__.py [-h] [--min-price MIN_PRICE] [--max-price MAX_PRICE] [--delay DELAY] [--slack-token SLACK_TOKEN]
                   [--slack-channel SLACK_CHANNEL] [--tiers TIERS] [--start-stop-alert | --no-start-stop-alert]
                   keyword

positional arguments:
  keyword               Mercari search keyword

options:
  -h, --help            show this help message and exit
  --min-price MIN_PRICE
                        Amount in dollars to filter out items less than min-price
  --max-price MAX_PRICE
                        Amount in dollars to filter out items more than max-price
  --delay DELAY         Time in seconds to wait before the next scan (default: 60)
  --slack-token SLACK_TOKEN
                        Slack API token
  --slack-channel SLACK_CHANNEL
                        Slack channel to publish alerts to
  --tiers TIERS         Semi-colon-separated key-value pairs that define tier message templates. A template will be used if an item
                        is less than or equal to the amount.Example: "420=Wow! {name} is an amazing deal! Only ${price}; 1000=Hey
                        check out this less cool deal{newline}{url}". Available variables: price, name, url, newline
  --start-stop-alert, --no-start-stop-alert
                        Alert when scanner starts and stopsAlertersSlackPass the --slack-token argument:python3 -m mercariscanner --slack-token xoxb-blah-blah --slack-channel mercari "pokemon pillows"orModifyconfig.inito include your Slack API bot token and the channel you want to alert to:[slack]
token = xoxb-blah-blah
channel = mercari
Package: mercarius
Mercari US API WrapperQuick wrapper around US APIMercari US uses a graphql endpoint which seems locked down in the frontend in production environment. Instead of using POST requests like most graphql, they're usingAutomatic Persisted Queriesfor efficiency. What does that mean? Well, it means we can't custom craft properties to return which would let us make a query that makes it easier for their servers and requests less properties than we would use. But since I'm not gonna invest much time into reversing their graphql endpoint, I'll use their persisted queries in this project for now.Essentially, a long hash will correspond to a preset schema to search for. In our case, we use5b7b667eaf8a796406058428fa5df18e7cecd5229702ee0753a091d980884d38for asearchFacetQuerysince it's the first one that came up during my website usage. Looking into their source code, there's about ~20 other queries we can use but that will take some time to work out if they're needed and this seems to be good enough.One weird inconsistency I saw during my progress was that the count returned back varies wildly and is really annoying...FeaturesCurrently supported, you can pass in a query, and item status (on sale, sold out, or all) as well as a 'soft limit.' I call it soft because if you specify 100, you may receive up to 120, but you should never receive the limit + 30 ever. It's just easier to write this way and should be good enough for people. If you want to only look up 1 page, just pass a limit of 30 or lower and you'll get 30 results page if there's more than 30 listings available online.By default, if you iterate over the list, it'll continuously query and fetch all of them.To access properties of the items, they're all JSON objects and not coded to a class so you'll need to acces them all by in a similar fashion toitem["price"].For a sample script to see how to quickly run it and test if it's working:frommercariusimportsearch,SearchItemStatusimportjsonmerch=list(search('plush touhou',status=SearchItemStatus.ON_SALE))print("total merch count: ",len(merch))print('All IDs in query:')print('\n'.join([x["id"]forxinmerch]))print('All keys in a single item:')print(json.dumps(merch[0],indent=2))It'll print out a blob of JSON so you can see all the available properties on the item object.Note, you're not guaranteed to have all unique items and may see duplicates.I recommend to run the final list through a duplicate checker using the id property just to be safe. During my testing it was pretty random.DevelopmentClone this repo, install the dependencies inrequirement.txtand off you go.Deploying / Publishingpython setup.py sdisttwine upload dist/mercari-<version>.tar.gz
Package: mercati-energetici
mercati-energeticiAsync python library for Italian energy marketsUnofficial wrapper of the GME (Gestore dei Mercati Energetici S.p.A.) APP API. It allows to retrieve prices and volumes traded on the Italian energy markets (electricity, gas and environmental) in a simple and asynchronous way.DisclaimerThis library is not affiliated with GME (Gestore dei Mercati Energetici S.p.A.) in any way. It is provided as is, without any warranty. By using this library, you agree to the terms of use of GME API, which can be obtained withget_general_conditions()or can be foundhere. Please, be aware that all the data belongs to GME and can't be used for profit. Also, be aware of the disclaimer from GME retrivable withget_disclaimer().Example"""How to get the average price of electricity in Italy (PUN) for a specific date."""importasynciofrommercati_energeticiimportMGPfromdatetimeimportdateasyncdefmain():asyncwithMGP()asmgp:# By using this library, you agree to the terms of use of GME API# which can be obtained with:print(mgp.get_general_conditions())# Also, be aware of the following disclaimer from GMEprint(mgp.get_disclaimer())# Get PUN priceprint("PUN avg: ",awaitmgp.daily_pun(date(2023,3,28)))if__name__=="__main__":asyncio.run(main())DocumentationThe library exposes three low level classes to access the GME API:MercatiElettrici,MercatiGasandMercatiAmbientali. A further class,MGP, offers a higher level interface over the day-ahead electricity market.Installationpipinstallmercati-energeticiUsageSee thedocumentationfor more details.
Package: mercator
version 0.1.11Python DSL to leverage translation of dictionaries and SQLAlchemy into Protobuf objectsWhat is Mercator ?Mercator is a Python library that simplifies the following of
serializing dictionary data into Protobuf binary data.Mercator actually supports extracting data from:dictionariesSQLAlchemy model instancesAny opaque python objects (e.g.:collections.namedtuple)When should I use Mercator ?When migrating custom implementations of RPC to gGRPC.When migrating in-memory data to Protobuf.When should Inotuse Mercator ?When writing gRPC services from scratch.When writing anything that uses Protobuf gets called.InstallpipinstallmercatorDocumentationhttps://mercator.readthedocs.orgBasic Usage1. Given a protobuf declarationsyntax="proto3";packageservices.social_platform;import"google/protobuf/timestamp.proto";messageUser{messageAuthToken{stringvalue=1;google.protobuf.Timestampcreated_at=2;google.protobuf.Timestampexpires_at=3;}}2. Declare Mappingsfrommercatorimport(ProtoMapping,ProtoKey,ProtoList,SinglePropertyMapping,)fromgoogle.protobuf.timestamp_pb2importTimestampfromgoogle.protobuf.struct_pb2importStructfromgoogle.protobufimportstruct_pb2ProtobufTimestamp=SinglePropertyMapping(int,Timestamp,'seconds')classUserAuthTokenMapping(ProtoMapping):__proto__=domain_pb2.User.AuthTokenvalue=ProtoKey('data',str)created_at=ProtoKey('created_at',ProtobufTimestamp)expires_at=ProtoKey('expires_at',ProtobufTimestamp)classUserMapping(ProtoMapping):__proto__=domain_pb2.Usertokens=ProtoList('tokens',UserAuthTokenMapping)3. Generate python filespython-mgrpc_tools.protoc-I./--python_out=./--grpc_python_out=././*.proto4. Process data!info={'login':'Hulk','email':'bruce@avengers.world','tokens':[{'data':'this is the token','created_at':1552240433,'expires_at':1552240733,}],}user=UserMapping(info).to_protobuf()assertisinstance(user,domain_pb2.User)ContributingCheck thecode structure documentationWrite testsWrite codeSend a pull-request
Package: merc-common
Merc Common PackageMerc Common is a Django app common package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "merc_common" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'merc_common',
]Include the merc_common URLconf in your project urls.py like this::path('', include('merc_common.urls')),
Package: mercedestrenz
mercedestrenzThis python package is for inspecting and analyzing used Mercedes Benz car prices. The package helps users to get simple answers on how to choose the used Mercedes Benz car in the market. The package also includes useful visualization tool and trained model to serve buyers and sellers.The full documentation of mercedestrenz package can be viewedhere.CollaboratorsKelly Wu, Morris Zhao, Spencer Gerlach, Ty AndrewsPython ecosystemOur package is unique, it provides an easy way to investigate used Mercedes Benz car prices. It provide people a big picture about the market. The package relies on a real market data set to plot, filter and predict. It also gives advice to buyers and sellers when they try to make a decision.FunctionsThe package contains the following functions:load_sample_mercedes_listings: Retrieves a data frame that contains sample data of used Mercedez Benz vehicles.plot_mercedes_price: Plot a density plot of a Mercedes-Benz model to see where the current vehicle's price falls for that same model in the market.listing_search: Retrieves the top listings that are within the budget range specified by the user.predict_mercedes_price: Predicts the price in USD of a Mercedes-Benz given the year, model, odometer reading, condition and paint color.Package datasetThe package contains a static dataset for Craiglist used-car listings that were previously web scraped. Several key attributes about the used-car are available in the dataset, such as vehicle prices, models, car conditions, odometer readings, VINs, regions and transmission. The package's dataset was adapted from verison 10 of the raw dataset created byAustinReese.Installation$pipinstallmercedestrenzUsageBelow is a basic example of how to use each of the four functions included in this package.# Load all required package functions
from mercedestrenz.data import load_sample_mercedes_listings, listing_search
from mercedestrenz.modelling import train_mercedes_price_prediction_model
from mercedestrenz.modelling import predict_mercedes_price

# Load the sample mercedes listings data into a dataframe
data = load_sample_mercedes_listings()

# Return the top listings that are within a budget range specified by the user. Returns a pandas dataframe of results.
listing_search(data, budget=[2000, 20000], model = "any", sort_feature = "odometer_mi", ascending = True)

# Plot a price distribution of specific mercedes models, and see where an input price falls in the distribution.
plot_mercedes_price(model='s-class', price=80000, market_df=data))

# Predict the price (in USD) of a Mercedes-Benz given the year, model, condition, paint color, and odometer reading.
predict_mercedes_price("e-class", 2015, 55_000, "fair", "silver")ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensemercedestrenzwas created by Kelly Wu, Morris Zhao, Spencer Gerlach, Ty Andrews. It is licensed under the terms of the MIT license.Creditsmercedestrenzwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: merchantapi
Miva Merchant API SDK for Python (3.x)This python library wraps the Miva Merchant JSON API introduced in
Miva Merchant 9.12. It allows you to quickly integrate your python
applications with a Miva Merchant store to fetch, create, and update
store data.For api documentation visithttps://docs.miva.com/json-api.RequirementsMiva Merchant 10.00+Python 3.xFor Miva Merchant 9.x, use the 1.x releaseInstallation via pipTo install the SDK simply add it to your project with pip:pip install merchantapiNote: On some operating systems thepipprogram may be namedpip3Adding as a dependency in your packagesYou can also add it to your project'srequirements.txtfile:merchantapi>=2.5.0Then installpip install -r requirements.txtNote: On some operating systems thepipprogram may be namedpip3Getting StartedFor usage see the examples provided in theexamples/directory.SSH Private Key AuthenticationCompatible Private Key FormatsOpenSSH PEMPKCS#1 PEMPKCS#8 PEMWhen specifying the key to use within theSSHClientorSSHPrivateKeyAuthenticator, specify the full path to your private key file.Create PKCS#1 from OpenSSH private key formatIf your private key is in OpenSSH format (starts with-----BEGIN OPENSSH PRIVATE KEY-----) then you will need to convert it.Create a copy of your key preserving permissions:cp -p /path/to/private/key/id_rsa /path/to/private/key/id_rsa.pemConvert in place to the proper format:ssh-keygen -p -m PEM -f /path/to/private/key/id_rsa.pemCreate PKCS#8 PEM from PKCS#1 PEM private key formatConverting the key with encryption:openssl pkcs8 -in /path/to/private_key.pem -topk8 -out /path/to/private_key.pkcs8.pemConverting the key without encryption:openssl pkcs8 -in /path/to/private_key.pem -topk8 -nocrypt -out /path/to/private_key.pkcs8.pemSSH Agent AuthenticationCompatible Public Key FormatsYour public key must be in the OpenSSH Public Key format. The default public key format is usually the correct type if you generated your key usingssh-keygen.Seehttps://tools.ietf.org/html/rfc4253#section-6.6for format.A quick way to get the correct format if you have the key associated with your local SSH agent is to run the commandssh-add -Land copying the corresponding key.LicenseThis library is licensed under theMiva SDK License Agreement.See theLICENSEfile for more information.
Package: merchant-rest-service
When you need to make use of Paga Merchant Service API, using our libraries to interact with the APIs makes your
integration fast as most of the API logic has been handled when using the Paga Merchant LibrariesFree software: MIT licenseInstallationpip install merchant_rest_serviceYou can also install the in-development version with:pip install git+ssh://git@https://github.com/pagadevcomm/merchant_rest_service.git/pagadevcomm/merchant_rest_service.git@masterDocumentationTo use the project:importmerchant_rest_servicemerchant_rest_service.longest()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 toxChangelog0.0.1 (3)First release on PyPI.
Package: merchant-wallet
What is HD Merchant Wallet ?It is a simple lightweight tool for receiving cryptocurrency payment, generating hd cryptocurrency wallet addresses and verifying payment made to an address. You can
receive cryptocurrency payment easily in any python script or web app in just 3 simple steps. It perfectly works with electrum wallet so you can receive payment with this tool and spend your coins with electrum wallet.Installationpip install merchant-walletRunningGet your master public key from a local or personal wallet e.g Electrum.Generate addresses with this script, addresses will correspond with addresses on your electrum wallet, so you can easily spend your coins.Give address for payment and verify transaction using this tool.Accept Bitcoin on a website In 3 Stepsfrom merchant_wallet.backends.btc import BitcoinBackend
      
      
      btc = BitcoinBackend('master_public_key_gotten_from_an_offline_wallet')
      
      btc.generate_new_address(index=0) #index=0 will give the first address displayed on your electrum wallet, increase index to get more addresses as displayed on your wallet
      
      btc.confirm_address_payment(
        address="1Ge6rDuyCdYVGhXZjcK4251q67GXMKx6xK", total_crypto_amount=0.01, confirmation_number=3
      )#Confirm payment on the address with the specified confirmation and amount it will return a tuple of transaction status and value of transaction
      
      #Returned values
      #Unconfirmed payment -> (UNCONFIRMED_ADDRESS_BALANCE, transaction_hash)
      #Confirmed payment -> (CONFIRMED_ADDRESS_BALANCE, sent_value)
      #Underpaid payment -> (UNDERPAID_ADDRESS_BALANCE, remaining value)
      #No transaction -> (NO_HASH_ADDRESS_BALANCE, None )
      btc.confirm_address_payment(
        address="1Ge6rDuyCdYVGhXZjcK4251q67GXMKx6xK", total_crypto_amount=0.01, confirmation_number=5000, tx_hash='hash_returned_when_transaction_was_unconfirmed'
      )NoteThis tool depends on blockcypher for verifying blockchain transactions and uses forex-python tool for calculating crypto pricesHelper methodsbitcoin.convert_to_fiat( amount, currency)bitcoin.convert_from_fiat(amount, currency)Check tests for moreTodoAdd more crypto currency supportAdd support for local private blockchain service e.g bitcoindLICENSEMIT
Package: mercoa
Mercoa Python LibraryDocumentationAPI reference documentation is availablehere.InstallationAdd this dependency to your project's build file:pipinstallmercoa# orpoetryaddmercoaUsagefrommercoa.clientimportMercoamercoa_client=Mercoa(token="YOUR_API_KEY")entity=mercoa_client.entity.get(entity_id='YOUR_ENTITY_ID')print(entity)Async clientThis SDK also includes an async client, which supports theawaitsyntax:importasynciofrommercoa.clientimportAsyncMercoamercoa_client=AsyncMercoa(token="YOUR_API_KEY")asyncdefget_entity()->None:entity=awaitmercoa_client.entity.get(entity_id='YOUR_ENTITY_ID')print(entity)asyncio.run(get_entity())Beta statusThis SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning the package version to a specific version in your lock file. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.ContributingWhile we value open-source contributions to this SDK, this library is generated programmatically. Additions made directly to this library would have to be moved over to our generation code, otherwise they would be overwritten upon the next generated release. Feel free to open a PR as a proof of concept, but know that we will not be able to merge it as-is. We suggest opening an issue first to discuss with us!On the other hand, contributions to the README are always very welcome!
Package: mercor-sdk
Mercor SDKIntroductionThe Mercor software development kit is meant for users of the Mercor trading
API that wish to access it using the Python programming language. Using this
SDK will have the benefit of removing some of the boilerplate for calling the
different endpoints.Installationpython -m pip install mercor-sdkUsageImporting the client and token:frommercor_sdk.clientimportMercorClientfrommercor_sdk.tokensimportCryptoTokenInstantiating the client:client=MercorClient("<my_algorithm_address>","<my_password>")Viewing the current balance of the algorithm:balance=client.balance()print(balance.supply)Placing a buy order:trade=client.buy(slippage=0.05,relative_amount=0.5)print(trade.transaction_hash)Placing a sell order:trade=client.sell(slippage=0.05,relative_amount=0.5)print(trade.transaction_hash)Retrieving the status of a buy or sell order:status=client.status(transaction_hash=trade.transaction_hash.value)print(status.message)Accessing data from the ticker:ticker=client.ticker_list()print(ticker[CryptoToken.ETH])Accessing data for a specific token from the ticker:token=client.ticker(CryptoToken.ETH)print(token.price)
Package: mercs
MERCSMERCS stands formulti-directional ensembles of classification and regression trees. It is a novel ML-paradigm under active development at theDTAI-lab at KU Leuven.InstallationEasy via pip;pip install mercsWebsiteOur (very small) website can be foundhere.TutorialsCf. thequickstart sectionof the website.CodeMERCS is fully open-source cf. ourgithub-repositoryPublicationsMERCS is an active research project, hence we periodically publish our findings;MERCS: Multi-Directional Ensembles of Regression and Classification TreesAbstractLearning a function f(X) that predicts Y from X is the archetypal Machine Learning (ML) problem. Typically, both sets of attributes (i.e., X,Y) have to be known before a model can be trained. When this is not the case, or when functions f(X) that predict Y from X are needed for varying X and Y, this may introduce significant overhead (separate learning runs for each function). In this paper, we explore the possibility of omitting the specification of X and Y at training time altogether, by learning a multi-directional, or versatile model, which will allow prediction of any Y from any X. Specifically, we introduce a decision tree-based paradigm that generalizes the well-known Random Forests approach to allow for multi-directionality. The result of these efforts is a novel method called MERCS: Multi-directional Ensembles of Regression and Classification treeS. Experiments show the viability of the approach.AuthorsElia Van Wolputte, Evgeniya Korneva, Hendrik BlockeelOpen AccessA pdf version can be found atAAAI-publicationsPeoplePeople involved in this project:Elia Van WolputteEvgeniya KornevaProf. Hendrik Blockeel
Package: mercs-mixed
MERCSMERCS stands formulti-directional ensembles of classification and regression trees. It is a novel ML-paradigm under active development at theDTAI-lab at KU Leuven.InstallationEasy via pip:pip install mercs-mixedWebsiteCf.https://systemallica.github.io/mercs/TutorialsCf. the quickstart section of the website,https://systemallica.github.io/mercs/quickstart.CodeMERCS is fully open-source cf. ourgithub-repositoryPublicationsMERCS is an active research project, hence we periodically publish our findings;MERCS: Multi-Directional Ensembles of Regression and Classification TreesAbstractLearning a function f(X) that predicts Y from X is the archetypal Machine Learning (ML) problem. Typically, both sets of attributes (i.e., X,Y) have to be known before a model can be trained. When this is not the case, or when functions f(X) that predict Y from X are needed for varying X and Y, this may introduce significant overhead (separate learning runs for each function). In this paper, we explore the possibility of omitting the specification of X and Y at training time altogether, by learning a multi-directional, or versatile model, which will allow prediction of any Y from any X. Specifically, we introduce a decision tree-based paradigm that generalizes the well-known Random Forests approach to allow for multi-directionality. The result of these efforts is a novel method called MERCS: Multi-directional Ensembles of Regression and Classification treeS. Experiments show the viability of the approach.AuthorsElia Van Wolputte, Evgeniya Korneva, Hendrik BlockeelOpen AccessA pdf version can be found atAAAI-publicationsPeoplePeople involved in this project:Elia Van WolputteEvgeniya KornevaProf. Hendrik BlockeelAndrés Reverón Molina
Package: mercupy
MercupyPython interface definition for Mercury.Maps Mercury objects (manifests, filters, etc.) to Python native objects.Manifest elements maps tomanifest_elements.ManifestElement.Filter elements maps tofilter_elements.FilterElement.Condensed tags map totags.TagTokenTree.
However,tags.CondensedTagand overriden operators (*, /, +)
should be used to construct condensed tags;tags._TagTokenTreeshould not be used directly.Data passed to and received from deep learning models are mapped todata_elements.DataElement.LinksMercury Project HomepageMercury GitHub RepositoryMercury Documentation