Saravanan666/pypi-org huggingface dataset:
Package: imoc
This library can be used for any simple image classification problems, You only have to give the data path, #of epochs, and
if you have any model that you created you can give that also (if none given the model if use it's own model).After importing the library create a object of it by providing the path of the data and other parameters.Then call the create_model function with the object.Then the library will starts to work starting with cleaning the data by checking for any unsupported or duplicated files
and deleting them.After cleanig it will preprocessing the images and splits it to train/test/validation, and it also plots the images
according to the classesThe if no model is provided it will create it's own model for traning.And Starts to train for the #of epochs given (default=70).You can use the funtion plot_performance() to plot the model loss and accuracy growth.model_evaluation() function evaluate the model based on the test data.And you can use the function predict() to predict any new values.And to save the model use model_save() function
Package: imod
TheimodPython package is an open source project to make working with
MODFLOW groundwater models in Python easier. It builds on top of popular
packages such asxarray,pandas,geopandas,dask,  andrasterioto provide a versatile toolset for working with (large) groundwater (modeling)
data:Preparing and modifying data from a variety of GIS, scientific, and MODFLOW
file formats;Writing data to MODFLOW-based models;Selecting and evaluating for e.g. time series comparison or water budgets;Visualizing cross sections, time series, or 3D animations.We currently support the following MODFLOW-based models:USGS MODFLOW 6, structured (DIS) and discretization by
vertices (DISV) grids only, and not all advanced stress packages yet (LAK,
MAW, SFR, UZF)iMOD-WQ, which integrates SEAWAT (density-dependent
groundwater flow) and MT3DMS (multi-species reactive transport calculations)iMODFLOWThis Python package is developed primarily by the Groundwater Management Group
atDeltares. It is used together with a broader set of open source tools and
standards for reproducible modeling and data analysis:Git: version control of (Python) scripts;DVC: version control of data, on top of Git;netCDF: open standard of a flexible, self describing data format;Snakemake: workflow manager to turn a collection of scripts into a
workflow.Documentation:https://deltares.github.io/imod-pythonSource code:https://github.com/Deltares/imod-pythonIssues:https://github.com/Deltares/imod-python/issues
Package: imod-coupler
iMOD-couplerTheimod_coupleris used to couple hydrological kernels.
It currently focuses on groundwater and supports coupling between MetaSWAP and Modflow6.It can be installed by runningpip install imod_couplerThen you can run it as command line app viaimodc /path/to/imod_coupler.tomlIn order to receive help for its usage, runimodc --helpIssuesDeltares colleagues can find the issue tracker atJiraContributingIn order to develop onimod_couplerlocally, please follow the following steps:Download and installminiconda.Initializecondaby running the following in theMiniconda prompt:conda initDepending on your company settings, you might also have to run the following in a Powershell terminal run as administrator:Set-ExecutionPolicy -ExecutionPolicy RemoteSignedCreate the environment by executing the following in your terminal:conda env create --file=environment.ymlActivate the environmentconda activate imod_couplerInstallimod_couplerby executing (this will also put the executableimodcin yourPATH):pip install -e .With your Deltares credentials downloadthelatest imod_collector, andtheregression imod_collector.Unpack the two zip files in a path of your choice and name the latestimod_collector_develand the regressionimod_collector_regression.Check out the MetaSWAP lookup table with your Deltares credentials which resides athttps://repos.deltares.nl/repos/DSCTestbench/trunk/cases/e150_metaswap/f00_common/c00_common/LHM2016_v01vrzTo run the tests it is advisable to have a.envfile at the root of the project directory instead of modifying global environment variables.
The content of.envwould then look similar to this with the variablesIMOD_COLLECTOR_DEVEL,IMOD_COLLECTOR_REGRESSIONandMETASWAP_LOOKUP_TABLEadjusted to your local machine:IMOD_COLLECTOR_DEVEL='D:\checkouts\imod_collector_devel'IMOD_COLLECTOR_REGRESSION='D:\checkouts\imod_collector_regression'METASWAP_LOOKUP_TABLE='D:\checkouts\DSCtestbench\cases\e150_metaswap\f00_common\c00_common\LHM2016_v01vrz'IMOD_COUPLER_EXEC_DEVEL='imodc'# Specify an absolute path here to use a packaged version of iMOD CouplerIMOD_COUPLER_EXEC_REGRESSION='${IMOD_COLLECTOR_REGRESSION}/imodc.exe'METASWAP_DLL_DEP_DIR_DEVEL='${IMOD_COLLECTOR_DEVEL}'METASWAP_DLL_DEP_DIR_REGRESSION='${IMOD_COLLECTOR_REGRESSION}'METASWAP_DLL_DEVEL='${IMOD_COLLECTOR_DEVEL}/MetaSWAP.dll'METASWAP_DLL_REGRESSION='${IMOD_COLLECTOR_REGRESSION}/MetaSWAP.dll'MODFLOW_DLL_DEVEL='${IMOD_COLLECTOR_REGRESSION}/libmf6.dll'MODFLOW_DLL_REGRESSION='${IMOD_COLLECTOR_REGRESSION}/libmf6.dll'The tests can then be run with:pytesttests-n=auto--basetemp=tests/temp
Package: imodmodel
imodmodelReadIMOD model filesaspandas dataframesin Python.UsageAs pandas DataFrameimportimodmodeldf=imodmodel.read('my_model_file.mod')In [3]: df.head()
Out[3]: 
   object_id  contour_id          x          y     z
0          0           0  64.333336  64.666664  80.0
1          0           0  47.000000  77.333336  80.0
2          0           0  51.333332  45.666668  80.0
3          0           0  87.333336  49.666668  80.0
4          0           0  76.000000  82.000000  80.0As ImodModel objectfromimodmodelimportImodModelmodel=ImodModel.from_file("my_model_file.mod")In [3]: model.objects[0].contours[0].points
Out[3]: 
array([[  6.875,  62.875, 124.   ], ...])

In [4]: model.objects[0].meshes[0].vertices
Out[4]: 
array([[ 6.87500000e+00,  6.28750000e+01,  1.24000000e+02], ...])

In [5]: model.objects[0].meshes[0].indices
Out[5]: 
array([[156,  18, 152], ...])

In [6]: model.objects[0].meshes[0].face_values
Out[6]: 
array([0., 0., 35.22094345, ...])That's it!Installationimodmodelcan be installed from thePython Package Index(PyPI)pipinstallimodmodelWe recommend installing into a clean virtual environment.
Package: imojify
IMOJIFYa python library that maps almost every emoji to its color imageit's used to get visual representation of emoji unicodeimojify can be used to solve the problem of plotting emojis as labels in python plotting librariesInstallationpip install imojifyQuickstartGet emoji ImagefromPILimportImagefromimojifyimportimojifyImage.open(imojify.get_img_path('😂'))multiple emoji is also supportedfromPILimportImagefromimojifyimportimojifyImage.open(imojify.get_img_path('😂😂😂😂'))Plotting emojis in matplotlibfromimojifyimportimojifyfrommatplotlibimportpyplotaspltfrommatplotlib.offsetboximportOffsetImage,AnnotationBboxdefoffset_image(cords,emoji,ax):img=plt.imread(imojify.get_img_path(emoji))im=OffsetImage(img,zoom=0.08)im.image.axes=axab=AnnotationBbox(im,(cords[0],cords[1]),frameon=False,pad=0)ax.add_artist(ab)emjis=['😂','🤣','😔','😏','😍','🥰','😘']values=[30,50,15,29,15,50,12]fig,ax=plt.subplots(figsize=(12,8))ax.bar(range(len(emjis)),values,width=0.5,align="center")ax.set_xticks(range(len(emjis)))ax.set_xticklabels([])ax.tick_params(axis='x',which='major',pad=26)ax.set_ylim((0,ax.get_ylim()[1]+10))fori,einenumerate(emjis):offset_image([i,values[i]+5],e,ax)
Package: i-mongodb
i-MongoDBThis is a standardized and customized connector to MongoDB.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.PrerequisitesThis package supports the following version of Python. It probably supports older versions, but they have not been tested.Python 3.10 or laterInstallingInstall the latest package using pip.$pipinstalli-mongodbEnd with an example of getting some data out of the system or using it for a little demoRunning the testsExplain how to run the automated tests for this system$python-mpytestUsageTODOAuthorsJason Romano-AracnidSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: imongo-kernel
A MongoDB kernel for Jupyter. Mainly for educational purposes.This kernel wraps the Mongo shell usingpexpectand was inspired bybash_kernelandipython_mysql_kernel.
It usesRenderjsonfor JSON pretty-printing.IMongo in actionHow to installMajor requirementsIMongo requiresJupyterandMongoDB.Install MongoDBOn macOS, useHomebrew:brew install mongodbFor other platforms, please refer to the MongoDBdocumentationInstall Jupyter and IMongo Kernel usingpipTo install Jupyter, IMongo and all other dependencies, usepip install:$pipinstallimongo-kernelInstall Jupyter and IMongo Kernel usingcondaAlthough the installation withpipworks fine , we recommend using thecondapackage manager to create a virtual environment. You need to have
eitherAnacondaorMinicondainstalled.$condacreate-nimongojupyter"python>3.6"$sourceactivateimongo$pipinstallimongo-kernelBoth methods install theimongopackage and configure Jupyter to be
used with the new kernel by installing akernel spec.Configuration (optional)MongoDB configuration such as host/port can be passed as a YAML file,
located at the Jupyterconfiguration directory.
The default path for UNIX systems is~/.jupyter/imongo_config.yml.
The options available are the same as the ones available for themongoCLI tool.
This configuration is necessary in case your MongoDB instance has authentication enabled,
runs at a non-standard port, or runs on a remote (non-localhost) location.Sampleimongo_config.yml:host:some.host.ioport:27017username:usernamepassword:passwordauthenticationDatabase:adminquiet:nullIfimongo_config.ymldoesn’t exist or is empty, IMongo will attempt
to connect to the MongoDB instance atlocalhost:27017, without any
user authentication.Uninstall:# Remove library:$pipuninstallimongo# Remove kernel spec$jupyterkernelspecremoveimongoTODO:Implement code completion functionalityFix long command issueSend Mongo shell Javascript errors/exceptions to stderr
Package: imongo-orm
Schema-first mongoDB interface (inspired byMongoose). Library serves as a thin
wrapper aroundPyMongo.Why use imongo?Schema first API 😎Built-In on-write validation for insertions and updates ✅Last updated timestamp on every update forSlowly changing
dimensionsSupport for nested schemas 🪢Getting Started0. Installpipinstallimongo-orm1. Writing your first ModelfromimongoimportKeyfromimongoimportModelfromimongoimportSchemafromimongoimportTypesEMAIL_REGEX="""(?:[a-z..."""# trimmed because of spaceuser_schema=Schema({"name":Key(type=Types.String,required=True,min_length=5,max_length=50,lowercase=True),"email":Key(type=Types.String,required=True,regex=EMAIL_REGEX),"role":Key(type=Types.String,required=True,default=None,enum=["A","B",None]),"details":{"last_visit":Key(type=Types.Date,required=False),"age":Key(type=Types.String,required=False),}},timestamps=True,validate=True,)User=Model("users",user_schema)2. Connect with MongoDB and link imongofrompymongoimportMongoClientdefconnect_db(db_name:str="helloworld")->MongoClient:client=MongoClient("mongodb://root:example@localhost:27017")# register schemasUser.register_client(client,db_name)returnclient3. Use imongo through pyMongo operationsconnect_db("helloworld")# Insert new usernew_user=User({"name":"Roberto","email":"asd@google.com","role":"A"})res=User.insert_one(new_user)new_user_id=res.inserted_id# Fail to Update new user due to validationres=User.update_one({"_id":new_user_id},{"$set":{"name":"Rob"}})# Find usersforuserinUser.find({"name":"roberto"}):print(user,"\n")Validation Config for Keyrequired:bool=True#   Specify whether key is requireddefault:Union[Any,None]=None#   Specify default value for key if value is not providedenum:Union[List[Any],None]=None#   Specify allowed values for keyimmutable:bool=False#   Specify if value is immutable (i.e. it can't be altered after creation)lowercase:bool=False#   Apply inline lowercase transformation (Types.String)uppercase:bool=False#   Apply inline uppercase transformation (Types.String)regex:str=None#   Regex match check against value (Types.String)min:int=None#   Specify key's minimum value (Types.Integer, Types.Long, Types.Double)max:int=None#   Specify key's maximum value (Types.Integer, Types.Long, Types.Double)min_length:int=None#   Specify key's minimum length (Types.String)max_length:int=None#   Specify key's minimum length (Types.String)
Package: im-openapi-validator
Informatics Matters OpenAPI Validator (oval)A utility designed as apre-commitOpenAPI file validator:oval --helpUse it by adding it to your repository’s.pre-commit-config.yamlfile:# Basic OpenAPI Validator
- repo: https://github.com/informaticsmatters/openapi-validator
  rev: '1.0.0'
  hooks:
  - id: im-oval
    files: app/openapi/openapi.yamlInstallationThe OpenAPI validator is published onPyPIand can be installed from there:pip install im-openapi-validatorThis is a Python 3 utility, so try to run it from a recent (ideally 3.10)
Python environment.Get in touchReport bugs, suggest features or view the source codeon GitHub.
Package: imouapi
imouapiThis python library helps in interacting withImou Life Open APIfor remote controlling programmatically yourImou devices, especially those settings such as motion detection, human detection, privacy, etc that can be changed by the Imou Life App only.FeaturesProvide classes for both low level API interaction as well as device and sensors abastractionsExceptions and error handlingBased on asyncio moduleQuickstartInstall the library withpip install imouapiRegister a developer account onImou Life Open APIand get yourappIdandappSecretInstantiate the Imou API client (from imouapi.api import ImouAPIClient) and initialize it (e.g.api_client = ImouAPIClient(app_id, app_secret, session))Discover registered devices by importing the Discover service (from imouapi.device import ImouDiscoverService), inializing it (e.g.discover_service = ImouDiscoverService(api_client)) and running a discovery (e.g.discovered_devices = await discover_service.async_discover_devices())Either use the high level API by importing the Imou Device class (from imouapi.device import ImouDevice) and initializing it (e.g.device = ImouDevice(api_client, device_id)) or using directly the low level API provided byImouAPIClientto interact with the deviceFull details on the installation process, requirements, usage and classes and methods made available by the library are available athttps://user2684.github.io/imouapi
Package: imovel-scraper
FeaturesTODORequirementsTODOInstallationYou can installImovel ScraperviapipfromPyPI:$pipinstallimovel-scraperUsagePlease see theCommand-line Referencefor details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,Imovel Scraperis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz’sHypermodern Python Cookiecuttertemplate.
Package: imo-vmdb
imo-vmdbThis project provides a command line tool to maintain a SQL interface to theVisual Meteor Database (VMDB)of theInternational Meteor Organization (IMO).The IMO provides the data in the form of pure text files(CSV files).
This format is not suitable for the evaluation of this data.
Withimo-vmdbthis data is imported into a relational SQL database.
A large number of programming languages have interfaces to these databases.
This makes it possible to load data from the VMDB according to different criteria in different applications.No evaluations can be performed withimo-vmdbitself.
However, during the import of the data, various error and plausibility checks are performed.
This ensures that the data are suitable for analysis.
In addition, the observations are supplemented with properties in order to be able to filter according to these properties.
Examples are the positions of the radiants, the sun and the moon.For more information seehttps://imo-vmdb.readthedocs.io/en/latest/.
Package: imp3
imp3 (Image pre-preocessing pipeline) :Interactive tool for image pre-processing and automated pipeline creationInstallationrun following command in terminalpipinstallimp3Usageimp3.runAbove command will lauch the app on default port 8501.Open the browser and go tohttp://localhost:8501Select the image and then select the appropriate set of operations you want to perform on that perticular image.play with the parameters interatively untill you reach at optimal configuration.imp3.run--port8080Above command can be used to specify the port on which you want to run the app.UIFollowing is the list of currently supported operations:0. Resize input image1. map the image to different color spacesFolowwing methods are available:
a. 'rgb'
b. 'Gray scale'
c. 'hsv', 
d. 'lab', 
e. 'brg', 
f. 'ch_one',
g. 'ch_two',
h. 'ch_three',
i. 'merge_first_two_ch',
j. 'merge_last_two_ch', 
k. 'merge_last_first_ch'2. change the brightness and contrast3. SmoothingthFolowwing methods are available:
a. avg
b. gaussian
c. median
d. bilateral4. intensity histogram and histogram equalization5. thresholdingFolowwing methods are available:
a. thresh
b. adaptive_thresh
c. otsu6. edge detectionFolowwing methods are available:
a. sobel
b. laplasian
c. canny7. dialate / erode8. find countours9. shape matching with Hu moment on contour10. feature extraction11. Feature Matching12. Template matching and removalDevelopment tools:setuptools (https://pypi.org/project/setuptools/): Used to create a python packagepipreqs (https://pypi.org/project/pipreqs/): Used to create requirements.txt filetwine (https://pypi.org/project/twine/): Used to upload the package to pypi.orgGithub Actions (): Used to automate the process of uploading the package to pypi.orgpytest (https://pypi.org/project/pytest/): Used to write unit testswheel (https://pypi.org/project/wheel/): Used to create a wheel file
Package: impacket-py35
What is Impacket?Impacket is a collection of Python classes for working with network
protocols. Impacket is focused on providing low-level
programmatic access to the packets and for some protocols (e.g.
SMB1-3 and MSRPC) the protocol implementation itself.
Packets can be constructed from scratch, as well as parsed from
raw data, and the object oriented API makes it simple to work with
deep hierarchies of protocols. The library provides a set of tools
as examples of what can be done within the context of this library.A description of some of the tools can be found at:https://www.secureauth.com/labs/open-source-tools/impacketWhat protocols are featured?Ethernet, Linux "Cooked" capture.IP, TCP, UDP, ICMP, IGMP, ARP.IPv4 and IPv6 Support.NMB and SMB1, SMB2 and SMB3 (high-level implementations).MSRPC version 5, over different transports: TCP, SMB/TCP, SMB/NetBIOS and HTTP.Plain, NTLM and Kerberos authentications, using password/hashes/tickets/keys.Portions/full implementation of the following MSRPC interfaces: EPM, DTYPES, LSAD, LSAT, NRPC, RRP, SAMR, SRVS, WKST, SCMR, BKRP, DHCPM, EVEN6, MGMT, SASEC, TSCH, DCOM, WMI.Portions of TDS (MSSQL) and LDAP protocol implementations.Getting ImpacketCurrent and past releasesTrunkSetupQuick startGrab the latest stable release, unpack it and runpip install .from the directory where you placed it. Isn't that easy?RequirementsA Python interpreter. Python 2.6/2.7 and Python 3.6 are known to work.If you want to run the examples and you have Python < 2.7, you
will need to install theargparsepackage for them to work.For Kerberos support you will needpyasn1packageFor cryptographic operations you will needpycryptodomexpackageFor some examples you will needpyOpenSSL(rdp_check.py) and ldap3 (ntlmrelayx.py)For ntlmrelayx.py you will also needldapdomaindump,flaskandldap3If you're under Windows, you will needpyReadlineA recent release of Impacket.InstallingIn order to install the source execute the following command from the
directory where the Impacket's distribution has been unpacked:pip install .This will install the classes into the default
Python modules path; note that you might need special permissions to
write there. For more information on what commands and options are
available from setup.py, runpython setup.py --help-commands.TestingIf you want to run the library test cases you need to do mainly three things:Install and configure a Windows 2012 R2 Domain Controller.Be sure the RemoteRegistry service is enabled and running.Configure thedcetest.cfgfile with the necessary informationInstall tox (pip install tox)Once that's done, you can runtoxand wait for the results. If all goes well, all test cases should pass.
You will also have a coverage HTML report located atimpacket/tests/htlmcov/index.htmlLicensingThis software is provided under under a slightly modified version of
the Apache Software License. See the accompanying LICENSE file for
more information.SMBv1 and NetBIOS support based on Pysmb by Michael Teo.Contact UsWhether you want to report a bug, send a patch or give some
suggestions on this package, drop us a few lines atoss@secureauth.com.
Package: impact
ImpactImpactis a Modelica package manager.![ImpactLogo](https://rawgithub.com/xogeny/impact/master/images/logo_glossy.svg)The concept was first presented in [impact - A Modelica Package Manager](docs/modelica2014/paper/impact.md)> Michael Tiller, Dietmar Winkler (2014). impact - A Modelica Package Manager,
> Proceedings of the 10th International Modelica Conference, March 10-12, 2014,
> Lund, Swedenhttp://dx.doi.org/10.3384/ecp14096543Installation and UsageInstall by using [pip](http://www.pip-installer.org):Linux/Mac:$ pip install impactWindows:c:> pip.exe install impactand you should be able to use the commandimpact -hto get the usage information displayed.ConventionsImpactfollows a “convention over configuration” philosophy.  That
means that if you follow some reasonable conventions (that generally
reflect best practices), the system should work without the need for
any manual configuration.  Here are the conventions thatImpactexpects:The name of the repository should match (case included) the name
of your library.Semantic Versioning - To identify a library release, simply
attach a tag to the release that is a [semantic
version](http://semver.org) (an optional “v” at the start of the
tag name is allowed).Place thepackage.mofile for your library in one of the
following locations within the repository:./package.mo(i.e., at the root of the repository)./<LibraryName>/package.mo(i.e., within a directory sharing
the name of the library)./<LibraryName> <Version>/package.mo(i.e., within a directory sharing
the name of the library followed by a space followed by the tag name,
without any leadingvpresent)DevelopmentThe development takes place onhttps://github.com/xogeny/impact
Package: impact-factor
最新SCI期刊影响因子查询系统已更新2022年数据包含JCR分区表数据Installationpython3-mpip-Uinstallimpact_factorUse in CMDimpact_factor-hbuildbuild/update the database# optional, only required when you need build or update the databaseimpact_factorbuild# with api_keyimpact_factorbuild-kYOUR_NCBI_API_KEYsearchsearch withjournal,journal_abbr,issn,eissnornlm_idimpact_factorsearchnature# search journalimpact_factorsearch'nature c%'# like search journalimpact_factorsearch0028-0836# search ISSNimpact_factorsearch1476-4687# search eISSNimpact_factorsearch0410462# search nlm_idimpact_factorsearchnature--color# colorful outputfilterfilterfactorwithmin_valueandmax_valueimpact_factorfilter-m100-M200--color# output with pubmed filter formatimpact_factorfilter-m100-M200--pubmed-filterUse in Pythonfromimpact_factor.coreimportFactorfa=Factor()print(fa.dbfile)fa.search('nature')fa.search('nature c%')fa.filter(min_value=100,max_value=200)fa.filter(min_value=100,max_value=200,pubmed_filter=True)Documentshttps://impact-factor.readthedocs.io