Saravanan666/pypi-org huggingface dataset:
Package: inventree-cups-plugin
inventree-cups-pluginA label printer driver plugin forInvenTree, which provides support forCups label printing servers. If your printer is not cups compatible, you can setup a cups printing server. Thisarticledescribes how to setup a cups printing server for the DYMO LabelWriter 450 Duo.Installation[!IMPORTANT]
This plugin needscups-develinstalled to install its dependencies. You can read more about the requirements atpycups. If you're usingaptas a package manager runapt install libcups2-devbefore. For docker seebelow.[!IMPORTANT]
For InvenTree<0.14 use this package with version0.1.0Goto "Settings > Plugins > Install Plugin" and enterinventree-cups-pluginas package name.DockerFor docker installs you need to build your own docker image based on the inventree image to install the required system dependencies. The followingDockerfileis using a multistage build to only install what is needed onto the inventree image and do the building in a separate stage.[!NOTE]
This only works for the inventree alpine based docker image which is shipped with inventree>=0.13.To use it you have to do some slight modifications of thedocker-compose.ymlfile and create theDockerfileas follows:docker-compose.yml changesdiff --git a/docker-compose.yml b/docker-compose.ymlindex 8adee63..dc3993c 100644--- a/docker-compose.yml+++ b/docker-compose.yml@@ -69,7 +69,14 @@ services:# Uses gunicorn as the web serverinventree-server:# If you wish to specify a particular InvenTree version, do so here-        image: inventree/inventree:${INVENTREE_TAG:-stable}+        image: inventree/inventree:${INVENTREE_TAG:-stable}-printing+        pull_policy: never+        build:+          context: .+          dockerfile: Dockerfile+          target: production+          args:+            INVENTREE_TAG: ${INVENTREE_TAG:-stable}# Only change this port if you understand the stack.# If you change this you have to change:# - the proxy settings (on two lines)@@ -88,7 +95,8 @@ services:# Background worker process handles long-running or periodic tasksinventree-worker:# If you wish to specify a particular InvenTree version, do so here-        image: inventree/inventree:${INVENTREE_TAG:-stable}+        image: inventree/inventree:${INVENTREE_TAG:-stable}-printing+        pull_policy: nevercommand: invoke workerdepends_on:- inventree-serverDockerfileARGINVENTREE_TAG# prebuild stage - needs a lot of build dependenciesFROMpython:3.10-alpine3.18asprebuildRUNapkadd--no-cachecups-devgccgitmusl-dev&&\pipinstall--user--no-cache-dirgit+https://github.com/wolflu05/inventree-cups-plugin# production image - only install the cups shared libraryFROMinventree/inventree:${INVENTREE_TAG}asproductionRUNapkadd--no-cachecups-libsCOPY--from=prebuild/root/.local/root/.localConfiguration OptionsNameDescriptionExampleServerIP/Hostname to connect to the cups server192.168.1.5PortPort to connect to the cups server631UserUser to connect to the cups servercan also be emptyPasswordPassword to connect to the cups servercan also be emptyPrinterPrinter from cups server, can be selected if valid connection options are setmyprinter
Package: inventree_digikey_integration
Digikey-Inventree-IntegrationA CLI to ease importingDigikeyparts into anInventreeinstance.SetupInstallationInstall from Pypipip install inventree_digikey_integrationInstall from sourceInstallpoetryClone the repogit clone git@github.com:EUdds/digikey-inventree-integration.gitcd digikey-inventree-integrationpoetry installConfigurationCreate a config.ini file according to the template and specify it's location with the-c <path>flag[DIGIKEY_API]
CLIENT_ID=
CLIENT_SECRET=

[INVENTREE_API]
URL=<URL to the inventree instance>
USER=
PASSWORD=UsageInvoke the cli by runningimport_digikey_partsusage:import_digikey_parts[-h][-y][-cCONFIG]query_numbers[query_numbers...]ImportDigikeypartnumbersintoInvenTree

positionalarguments:query_numbersPartnumber(s)toimport

optionalarguments:-h,--helpshowthishelpmessageandexit-y,--yesBypassuserpromptsandassume"yes"-cCONFIG,--configCONFIGPathtoconfigfileTestingRun the test suite by runningpoetry run pytest
Package: inventree-dymo-plugin
inventree-dymo-pluginA label printer driver plugin forInvenTree, which provides support for Dymo Label Writer® printers.CompatibilityThe following printers are already supported by the driver:DYMO Label Writer 450DYMO Label Writer 450 Duo (Tape is not supported currently)DYMO Label Writer 450 TurboDYMO Label Writer 450 Twin TurboRequirementsCurrently only printing over network is supported, so an RAW network socket server needs to be connected to the printer. A raspberry pi zero w is just enough for that job.The easiest way to set this up, is using cups and configure a RAW printer device in combination withxinetdlike described in thisblog post.Installation[!IMPORTANT]
This plugin is only compatible with InvenTree>=0.14 because this uses the new label printer driver interface introduced withinventree/InvenTree#4824.Goto "Admin Center > Plugins > Install Plugin" and enterinventree-dymo-pluginas package name.Then goto "Admin Center > Machines" and create a new machine using this driver.Technical workingThis driver implements the RAW Dymo LabelWriter® 450 Series commands like described in thetechnical reference manualto send the label data to the printer.
Package: inventree-gridlabel-plugin
inventree-gridlabel-pluginA label printing plugin forInvenTree, which provides support for printing out labels in a grid layout.InstallationInstall this plugin manually as follows:pip install inventree-gridlabel-pluginOr, add to yourplugins.txtfile to install automatically using theinvoke installcommand:inventree-label-pluginConfiguration OptionsTODO
Package: inventree-kom2
inventree-kom2Use InvenTree with KiCad - usingKom2byclj.SetupInstall
Install this plugin in the webinterface with the packagenameinventree-kom2Enable
Enable the plugin in the plugin settings. You need to be signed in as a superuser for this.The server will restart if you enable the pluginInstall and configure Kom2
A new panel KiCad will appear in the main naviagtion. You can download and setup the latest release of Kom2 from there.LicenseThis project is licensed as MIT license.Current state: betaThis plugin is in a early state. It is not recommended to use it in production. You can use it to test the functionality (both of the plugin but also kom2) and report bugs, it assumes the data structure of thedemo dataset.There are no instructions for Windows or Linux, feel free to suggest them.
Package: inventree-rapidoc
inventree-rapidocUse RapiDoc for Inventree API docs.SetupInstall
Install this plugin in the webinterface with the packagenameinventree-rapidocEnable
Enable the plugin in the plugin settings. You need to be signed in as a superuser for this.The server will restart if you enable the pluginConfigure
There are no configuration options for this plugin. Your server needs to haveURLandAppmixins enabled.LicenseThis project is licensed under the MIT license.
Package: inventree-shopify
inventree-shopifyIntegrate yourInvenTreeinstance stock withShopify.FeaturesSync changes between Shopify inventory levels (nventory per location) and InvenTree stock items. Only supports non-serialized InvenTree stock items at the moment.InstallationInstall in your instance viapip install method.Add a private app to your Shopify store.Go to the inventree-shopify settings in InvenTree and fill in the settings for the plugin from your new private app.Click the webhooks link in the settings - make sure your instance is reachable for shopify.Open the Shopify plane in InvenTree. You can now link your Shopify inventroy levels to your InvenTree stock items.CaveatYour instance must be reachable for webhooks from Shopify so use ngrok or something like that to expose your instance with HTTPS.
Open your instance on that URL for the first setup.State of the codeThis code is only running on a few instances with rarely used Shopify stores (maybe 40-50 inventory shifts a month). It is not tested with a lot of inventory shifts and it is not tested with a lot of different Shopify stores. So use it at your own risk.If you use this more extensively please let me know so I can remove this warning.ContributeFeel free to submit issues or feature requests here or just send me a mail to code AT mjmair DOT comLicenseThis code is licensed under MIT. I persume no liability for any damages and do not provide any warranty.
Package: inventree-wled-locator
inventree-wled-locatorUseWLEDto locate InvenTree StockLocations.SetupInstall
Install this plugin in the webinterface with the packagenameinventree-wled-locatorEnable
Enable the plugin in the plugin settings. You need to be signed in as a superuser for this.The server will restart if you enable the pluginConfigure
Set the WLED IP and the LED count in the plugin settings.
Map the LEDs to the locations in the StockLocation settings.LicenseThis project is licensed as MIT license.
Package: inventree-zapier
inventree-zapierIntegrate Zapier into InvenTreeSetupInstall
Install this plugin in the webinterface with the packagenameinventree-zapierEnable
Enable the plugin in the plugin settings. You need to be signed in as a superuser for this.The server will restart if you enable the pluginMigrate
Access your instances python environment and runinvmigrateConfigure
Create an API-token in the admin interface and add set up the zapier integration in Zapier.com itself.LicenseThis project is licensed as MIT. Copy and do what you want - maybe tag your new plugin so others can find it. The more the merrier.State of the CodeThis is currently a PoC / 'beta' - at least till the Zapier App is published. Please feel free to file FRs, issues or just ideas.
Package: inverselap
Numerical inversion of Laplace transform1.Use the following statement to importfrom inverselap import inverseOne-dimensional Laplace inversionTake a one-dimensional function as an exampledef fs_1d(s):
    return 1/(s+1)2.1 method Gaver-StehfestUse the following statement for inversion: inverse.GS_1d(f,t,M), f means functon, t meanstime parameter， M means the number of nodes, M is a positive even number while 14, 16 and 18 are recommendedinverse.GS_1d(f,1,18)2.2 method EulerUse the following statement for inversion: inverse.Euler_1d(f,t,M), f means functon, t meanstime parameter， M means the number of nodes, M is a positive number while 17 and 19 are recommendedinverse.Euler_1d(fs_1d,1,19)2.3 method TalbotUse the following statement for inversion: inverse.Talbot_1d(f,t,M), f means functon, t meanstime parameter, M means the number of nodes, M is a positive number while 21 is recommendedinverse.Talbot_1d(fs_1d,1,21)Two-dimensional Laplace inversionTake a rwo-dimensional function as an exampledef fs_2d(s1,s2):
    return 1/(s1+1)/(s2+2)3.1 method seriesUse the following statement for inversion: inverse.series_2d(f,t1,t2,N,c1,c2), f means functon, t1 and t2 mean time parameter, N means the number of nodes, N is a positive number while 64 and 128 is recommended, c1 and c2 should be larger than the s1 and s2 where the value fs_2d(s1,s2) is infinite if possible.inverse.series_2d(fs_2d,1,1,128,0,-1)3.2 method partialUse the following statement for inversion: inverse.Partial_2d(f,t1,t2,N,par1,par2), f means functon, t1 and t2 mean time parameter, N means the number of nodes, N is a positive number while 64 and 128 is recommended, alpha1 and alpha1 should be the s1 and s2 where the value fs_2d(s1,s2) is infinite if possible.inverse.Partial_2d(fs_2d,1,1,128,-1,-2)3.3 mtehod TalbotUse the following statement for inversion: inverse.Talbot_2d(f,t1,t2,M), f means functon, t1 and t2 mean time parameter, M means the number of nodes, M is a positive number while 21 is recommendedinverse.Talbot_2d(fs_2d,1,1,18)3.4 mtehod EulerUse the following statement for inversion: inverse.Euler_2d(f,t1,t2,M), f means functon, t1 and t2 mean time parameter, M means the number of nodes, M is a positive number while 17 and 19 are recommendedinverse.Euler_2d(fs_2d,1,1,21)3.5 method epsilonUse the following statement for inversion: inverse.epsilon(f,t1,t2,alpha1,alpha2), f means functon, t1 and t2 mean time parameter, alpha1 and alpha1 should be the s1 and s2 where the value fs_2d(s1,s2) is infinite if possible.inverse.inverse.epsilon(fs_2d,1,1,-1,-1)
Package: inversion-test-problems
Inversion Test ProblemsInversion Test Problems (ITP) is a collection of clearly defined forward codes that simulate a wide range of geophysical processes. The goal of ITP is to bring together people developing physical simulations with those who need them. ITP's simple and consistent code structure enables users to acces a wide range of different forward code and contributers to share their solutions with a wider audience. For more information, please visit our documentation (coming soon).InstallationIt is recommended to use a clean virtual environment for the install:conda create -n ITP_env scipy jupyterlab numpy matplotlibconda activate ITP_envInversion Test Problemsis available on PyIP and can be installed using this command:Linux/MacOSpython3 -m pip install --index-url https://test.pypi.org/simple/ inversiontestproblems-h-hollmannWindows:py -m pip install --index-url https://test.pypi.org/simple/ inversiontestproblems-h-hollmannBasic usageOnce installed, each test problem can be imported using the following command:from inversiontestproblems import testproblemReplacetestproblemwith one of the following currently available problems:gravityforward:Once a problem is imported, it's main functions can be called using the same structure for each problem. For more information for each function please use help():from inversiontestproblems import testproblemtp = testproblem()model=tp.get_model()data=tp.get_data()synthetic_data = tp.forward(model)jacobian=tp.gradient(model)tp.plot_model()Other problem-specific values can be accessed through the 'testproblem' object, such as:tp.rec_coords # coordinates of recording locationstp.x_nodes # x-coordinates of all nodes of the modelWhich additional values are set is highly example-specific and we suggest to use 'help(tp)' or 'dir(tp)' to quickly see what is available, or consult the Inversion Test Suite Documentation (coming soon).
Package: inversion-test-test
Inversion Test ProblemsInversion Test Problems (ITP) is a collection of clearly defined forward codes that simulate a wide range of geophysical processes. The goal of ITP is to bring together people developing physical simulations with those who need them. ITP's simple and consistent code structure enables users to acces a wide range of different forward code and contributers to share their solutions with a wider audience. For more information, please visit our documentation (coming soon).InstallationIt is recommended to use a clean virtual environment for the install:conda create -n ITP_env scipy jupyterlab numpy matplotlibconda activate ITP_envInversion Test Problemsis available on PyIP and can be installed using this command:Linux/MacOSpython3 -m pip install --index-url https://test.pypi.org/simple/ inversiontestproblems-h-hollmannWindows:py -m pip install --index-url https://test.pypi.org/simple/ inversiontestproblems-h-hollmannBasic usageOnce installed, each test problem can be imported using the following command:from inversiontestproblems import testproblemReplacetestproblemwith one of the following currently available problems:gravityforward:Once a problem is imported, it's main functions can be called using the same structure for each problem. For more information for each function please use help():from inversiontestproblems import testproblemtp = testproblem()model=tp.get_model()data=tp.get_data()synthetic_data = tp.forward(model)jacobian=tp.gradient(model)tp.plot_model()Other problem-specific values can be accessed through the 'testproblem' object, such as:tp.rec_coords # coordinates of recording locationstp.x_nodes # x-coordinates of all nodes of the modelWhich additional values are set is highly example-specific and we suggest to use 'help(tp)' or 'dir(tp)' to quickly see what is available, or consult the Inversion Test Suite Documentation (coming soon).
Package: invert4geom
Invert4geom3D geometric gravity inversionsDocumentation LinkInvert4geomis a Python library for performing 3D geometric gravity
inversions, where the aim is to recover the geometry of a density contrast.Typical use cases include modeling the topography of the Moho, the
sediment-basement contact, or bathymetry. These density contrasts are
represented by a layer of vertical right-rectangular prisms. Since we use
vertical prisms, they don't take the curvature of the Earth into account. For
large-scale applications, such as continental studies, it would be better to use
tesseroids instead of prisms.See theoverviewfor further description of this package and what
it can be used for.
Package: invertedai-simulate
Product Interface ClientSafety ValidationSynthetic Data GenerationTraining and TestingReal-World Results From SimulationInstall invertedai_simulate in your project environment.pipinstallinvertedai_simulateAlternatively, build the environment with the packages in the 'requirements.txt'.poetryinstall--no-devRun your simulation! 🎉For driving run:pythonclient_app_driving.py--zmq_server_address'x.x.x.x:5555'For data generation run:pythonclient_app_trafficlight_data.py--zmq_server_address'x.x.x.x:5555'Sample codes are also provided as Jupyter-notebooks.For driving:For data generation:For data generation as a pytorch dataloader:
Package: inverted-encoding
Inverted EncodingPython package for easy implementation of inverted encoding modeling as described in Scotti, Chen, & Golomb (in-prep).Contact:scottibrain@gmail.com(Paul Scotti)InstallationRun the following to install:pipinstallinverted-encodingUsagefrominverted_encodingimportIEM,permutation,circ_diffimportnumpyasnppredictions,confidences,aligned_at_prediction_recons,aligned_at_zero_recons=IEM(trialbyvoxel,features,stim_max=180,nfolds=num_runs,is_circular=True)# use "help(IEM)" for more information, below is a summary:# trialbyvoxel: your matrix of brain activations, does not necessarily have to be voxels# features: array of your stimulus features (must be integers within range defined by stim_max)# stim_max=180 means that your stimulus space ranges 0-179° degrees# nfolds refers to the K to use for KFold cross-validation. We recommend setting this to the number of runs you have for 1-run-left-out CV.# is_circular=True for a circular stimulus space, False for non-circular stimulus space# predictions: array of predicted stimulus for each trial# confidences: array of goodness of fit values for each trial# aligned_at_prediction_recons: trial-by-trial reconstructions (matrix of num_trials x stim_max) such that# when plotted ideally each reconstruction is centered at the original trial stimulus# aligned_at_zero_recons: trial-by-trial reconstructions aligned at zero, such that when# plotted ideally each reconstruction is centered at zero on the x axis (e.g., plt.plot(aligned_at_zero_recons[trial,:]))## Compute mean absolute error (MAE) by doing the following, then compare to null distribution:ifis_circular:# if your stimulus space is circular, need to compute circular differencesmae=np.mean(np.abs(circ_diff(predictions,features,stim_max)))else:mae=np.mean(np.abs(predictions-features))null_mae_distribution=permutation(features,stim_max=180,num_perm=1000,is_circular=True)
Package: inverted-index
Inverted IndexA small extensiable python inverted index. It supports retevial by booleanANDs and ranking of the returned results. The point is a skeleton to add
quick and dirty search to a script.It also includes aprompt_toolkitcompleter that can each the inverted index as you type. When you select a auto-suggest option it will replace your prompt.
The best way I have found to use this is to set it up to trigger only when you ask by hitting tab like so:fromprompt_toolkitimportpromptfrominverted_index.promptimportSearchCompleterfrominverted_index.inverted_indeximportInvertedIndex# Create your indexii=InvertedIndex(...)ii.index(my_documents)user_input=prompt("> ",completer=SearchCompleter(ii),complete_while_typing=False)# Do things with user_inputprint(user_input)
Package: inverted-index-search
inverted-index-searchinverted-index-search is python library for searching up keywords or sub words in a corpus of data using inverted index lookupInstallationUse the package manager pip to install.pipinstallinverted-index-searchUsageThe library's usage is straightforward, and it can be easily imported into your Python script. The make_doc_ngrams function breaks down the document into n-grams, and the search_doc function finds matching substrings in the document. You can specify the n-gram size and the type of n-gram level (word or character) for the search.Here is an example of how to use the library:frominverted_index_searchimportsearch_doc#This breaks down the document in ngrams to be used for searchingdocument_ngrams=make_doc_ngrams("this is big document with multiple words and sentences","word",[1,2],verbose=True)>>DOCUMENTNGRAMS=>[1,2]>>Removingthesengrams:>>DOCUMENTNGRAMLOOKUPTABLE=>{'this':[(0,4)],'is':[(2,4),(5,7)],'big':[(8,11)],'document':[(12,20)],'with':[(21,25)],'multiple':[(26,34)],'words':[(35,40)],'and':[(41,44)],'sentences':[(45,54)],'this is':[(0,7)],'is big':[(5,11)],'big document':[(8,20)],'document with':[(12,25)],'with multiple':[(21,34)],'multiple words':[(26,40)],'words and':[(35,44)],'and sentences':[(41,54)]}#This breaks down the phrases and actually does the matchingsearch_doc(document_ngrams,['document','multiple words'],[1],'word',verbose=True))>>PhraseNGRAMS=>[1]Checkingforphrasengram:documentCheckingforphrasengram:multipleCheckingforphrasengram:words>>{'document':{'document':{'count':1,'occured':[(12,20)]}},'multiple words':{'multiple':{'count':1,'occured':[(26,34)]},'words':{'count':1,'occured':[(35,40)]}}}print(search_doc.__doc__)>>""" This function creates ngrams out of the phrases you haveentered and finds the matching substrings in the document. You can specify what ngram for using phrase_ngrams paramter and. Simply pass phrase_ngrams=[1,7,2] to create ngrams of size 1,7 and 2. There are two level ngram either words or chaarcter whichyou can change by changing the n_gram_level to either 'char' or 'word'. To turn on logging setting verbose to True"""FeaturesEfficient inverted index search for large text data sets
Customizable n-gram size and level (word or character)
Simple and easy-to-use API
Built-in logging for debugging and testing purposesContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.GithubAffan
Package: inverter
0.1.2 (2021-01-31)set error message correctly in relevant fields on form
validation failure0.1.1 (2020-12-24)addeddc2esmapping- converts dataclass to elasticsearch mappingdefault string length to 256index integer as longfix null format handlingfix multi-session issue with formvalidatorsupdate timezone when reading data in ES Storage0.1.0 (2020-11-13)Initial release
Package: inverterd
inverterd-clientThis is a Pythoninverterdclient library.InstallationIt's available on Pypi:pip install inverterdUsage examplefrominverterdimportClient,Formatc=Client(8305,'127.0.0.1')c.connect()c.format(Format.JSON)print(c.exec('get-status'))print(c.exec('get-year-generated',(2021,)))LicenseMIT