Saravanan666/pypi-org huggingface dataset:
Package: orthogonal-cli
Orthogonal CLIHere is the Orthogonal CLI for the DataCrunch competition byCrunchDAO.InstallationUsepipto install theorthogonal-cli.pipinstallorthogonal-cli--upgradeUsageThis library require aCrunchDAO's API Key.importorthogonalclient=orthogonal.Client(api_key="..."# can also be read from CRUNCHDAO_API_KEY environment variable.)y=pandas.read_csv("y.csv",index_col=0)print(y.head())#          Moons       id      abcd# 1030371      1  AAAAAAA -2.122527# 1030372      1  BBBBBBB -0.386952# 1030373      1  CCCCCCC  0.028175# 1030374      1  DDDDDDD -1.276682# 1030375      1  EEEEEEE  0.939289dataframe,jacobians=client.orthogonalize(y)print(dataframe.head())#    Moons       id      abcd# 0      1  AAAAAAA -2.149988# 1      1  BBBBBBB -0.395232# 2      1  CCCCCCC  0.030502# 3      1  DDDDDDD -1.311531# 4      1  EEEEEEE  0.917134Please seeherefor the backend implementation.ContributingPull requests are always welcome! If you find any issues or have suggestions for improvements, please feel free to submit a pull request or open an issue in the GitHub repository.LicenseMIT
Package: orthogonalizer
Polygon OrthogonalizerPip implementation for polygons oforthogonalize-polygonwhich is an improved implementation of JOSM Orthogonalize function (JOSM Q Function). Credits to all the respective involved authorsInstallationYou can install OSM Conflator using pip:pipinstallorthogonalizerUsageHere's an example of how to use Polygon orthogonalizer in your Python code:fromorthogonalizerimportothogonalize_polygeojson_str='{"type": "FeatureCollection", "features": ... }'orthogonalized_poly=othogonalize_poly(geojson_str)print(orthogonalized_poly)Options :maxAngleChange (int, optional):
angle (0,45> degrees. Sets the maximum angle deviation
from the cardinal direction for the segment to be still
considered to continue in the same direction as the
previous segment. Defaults to 15.skewTolerance (int, optional):
angle <0,45> degrees.
Sets skew tolerance for segments that
are at 45˚±Tolerance angle from the overal rectangular shape
of the polygon. Usefull when preserving e.g. bay windows on a
house. Defaults to 15.ExampleInput ---> OutputContributingContributions are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request. When contributing to this project, please follow the Contributing Guidelines.LicenseThis project is licensed under the GNU General Public License v3.0 License. See the LICENSE file for more information.
Package: orthogram
Orthogram is a command line program and Python library that lets you
draw block diagrams.  It reads the definition of a diagram from a YAML
file and produces a PNG file like this one:Target audienceThis project might be of interest to you if:You do not want to use a GUI.  You prefer your diagrams defined in
plain text files.You know where your blocks should be, but you would rather have the
computer maintain the connections for you.You tried to forceGraphvizorPlantUMLto output the layout
you want, but to no avail.Installation and usageInstall from PyPI:pip install orthogramAssuming there is a diagram definition file nameddiagram.yamlin
your current directory, run the following command to produce a PNG
file:python -m orthogram diagram.yamlPlease read the full onlinedocumentationfor more.
Package: orthojax
Orthojax - Orthogonal bases in JaxThis is a package for orthogonal bases in Jax. Currently, it supports:Orthogonal polynomials (polynomial chaos) with respect to an arbitrary measure. This is achieved by reimplementing in Python some of the functions in the `orthpol`` package by Walter Gautschi. The original Fortran code can be foundhere.Tensor products of orthogonal bases (product basis).ExamplesYou can learn more about how the package workshere.You can find an example to uncertainty propagationhere.An example on how to use a tensor product basis can be found inexamples/tensor_product.py.TodoAdd Fourier basis.Add documentation of API.
Package: orthoproj
# OrthogonalProjection
Easily create Orthogonal Projections graphs with matplotlib.[Orthogonal Projections](https://en.wikipedia.org/wiki/Multiview_orthographic_projection) (or  Orthographic Projections) are 2D visualisations of 3D objects from different directions all parallel to one of the coordinate axes of the object.
This class implements the first-angle scheme that is basically used worldwide but the United States (that uses the third-angle scheme)The following picture (taken from larapedia.com) show an example:
![proiezioni_ortogonali](https://cloud.githubusercontent.com/assets/12557177/15484325/ca1bf938-2139-11e6-9617-af6c9d92d9e5.jpg)### FeaturesAll axes are synchronisedDifferent kind of plot supported:
* Plot
* Scatter
* Wireframe
* Surface (in 2D subplots a wireframe is still shown)
* CollectionAll arguments supported by underlying matplotlib plot are supportedBlocking/Non Blocking show### Documentation/Example
See [orthoproj_demo.py](https://github.com/apbard/orthoproj/blob/master/example/orthoproj_demo.py) to see an example that will produce the following plot:![example](https://cloud.githubusercontent.com/assets/12557177/15484323/c9fd5adc-2139-11e6-81ae-0f400952de62.png)
Package: orthoseg
Orthophoto segmentationThis project provides a procedure and (python) scripts to make it (relatively) easy tocreate a training dataset for a topic of your choicetrain a neural network to segment orthophotosrun the segmentation on a larger area + vectorize the resultapply some basic postprocessing like dissolve,...Only open source software is needed, eg. QGIS and tensorflow.Installation and usage instructions can be found in theorthoseg wiki
Package: orthosnap
Docs·Report Bug·Request FeatureOrthoSNAP: a tree splitting and pruning algorithm for retrieving single-copy orthologs from gene family trees.If you found orthosnap useful, please citeOrthoSNAP: a tree splitting and pruning algorithm for retrieving single-copy orthologs from gene family trees. Steenwyk et al. 2022, PLOS Biology. doi:10.1371/journal.pbio.3001827.This documentation covers downloading and installing orthosnap. Details about orthosnap usage including a tutorial are available on ouronline documentation.InstallationIf you are having trouble installing orthosnap, please contact the lead developer, Jacob L. Steenwyk, viaemailortwitterto get help.To install usingpip, we strongly recommend building a virtual environment to avoid software dependency issues. To do so, execute the following commands:# create virtual environmentpython-mvenv.venv# activate virtual environmentsource.venv/bin/activate# install orthosnappipinstallorthosnapNote, the virtual environment must be activated to useorthosnap.After using orthosnap, you may wish to deactivate your virtual environment and can do so using the following command:# deactivate virtual environmentdeactivateSimilarly, to install from source, we strongly recommend using a virtual environment. To do so, use the following commands:# downloadgitclonehttps://github.com/JLSteenwyk/orthosnap.gitcdorthosnap/# create virtual environmentpython-mvenv.venv# activate virtual environmentsource.venv/bin/activate# installmakeinstallTo deactivate your virtual environment, use the following command:# deactivate virtual environmentdeactivateNote, the virtual environment must be activated to useorthosnap.To install via anaconda, execute the follwoing command:condainstall-cjlsteenwykorthosnapVisit here for more information:https://anaconda.org/jlsteenwyk/orthosnap
Package: ortho-view-napari
ortho-view-napariIt displays the lateral view of the current 3D stack. This could be a starting point for a orthorviewer.Thisnapariplugin was generated withCookiecutterusing with@napari'scookiecutter-napari-plugintemplate.InstallationYou can installortho-view-napariviapip:pip install ortho-view-napariContributingContributions are very welcome. Tests can be run withtox, please ensure
the coverage at least stays the same before you submit a pull request.LicenseDistributed under the terms of theBSD-3license,
"ortho-view-napari" is free and open source softwareIssuesIf you encounter any problems, pleasefile an issuealong with a detailed description.
Package: orthpol
This is theorthpolpackage. It is a frontend to the Fortran package orthpol written by W Gautschi [1].[1] Gautschi, W. (1994). Algorithm 726: ORTHPOL–a package of routines for generating orthogonal polynomials and Gauss-type quadrature rules. ACM Transactions on Mathematical Software (TOMS), 20(1), 21–62. doi:10.1145/174603.174605StatusPyPi:LaunchPad:TestPyPi:RequirementsThe package depends on a C++ porting of the Fortran library orthpol. Thus both C++ and Fortran compilers must be installed on the machine. The code is tested using theGCCcompiler suite orclangfor the compilation and linking of C++ code, and thegfortrancompiler for the Fortran part of the code.InstallationMake sure to have an up-to-date version of pip:$ pip install –upgrade pipAutomatically install the software using the command:$ pip install orthpolIf this doesn’t work, the package can be manually downloaded and installed through the following procedure:$ cd <download_dir>$ pip download orthpol$ tar xzf orthpol-X.X.X.tar.gz$ cd orthpol-X.X.X$ python setup.py installChange Log0.1.0:Initial python porting (working only for python 2.x)0.2.0:Modified interface to account for changes in theSpectralToolboxComplete integration of orthpol intoSpectralToolbox0.2.1:Porting to python 3.x and back compatibility with python 2.xAutomated installation through pip0.2.2:Bug fix0.2.13:Improved install script.0.2.14:Fixed bug in Py_polyeval. Access to data is now done only through PyArray_GETPTR macros.0.2.18:Added function to compute monomial coefficients
Package: orthpol-light
This is theorthpolpackage. It is a frontend to the Fortran package orthpol written by W Gautschi [1].[1] Gautschi, W. (1994). Algorithm 726: ORTHPOL–a package of routines for generating orthogonal polynomials and Gauss-type quadrature rules. ACM Transactions on Mathematical Software (TOMS), 20(1), 21–62. doi:10.1145/174603.174605StatusPyPi:LaunchPad:TestPyPi:RequirementsThe package depends on a C++ porting of the Fortran library orthpol. Thus both C++ and Fortran compilers must be installed on the machine. The code is tested using theGCCcompiler suite orclangfor the compilation and linking of C++ code, and thegfortrancompiler for the Fortran part of the code.InstallationMake sure to have an up-to-date version of pip:$ pip install –upgrade pipAutomatically install the software using the command:$ pip install orthpolIf this doesn’t work, the package can be manually downloaded and installed through the following procedure:$ cd <download_dir>$ pip download orthpol$ tar xzf orthpol-X.X.X.tar.gz$ cd orthpol-X.X.X$ python setup.py installChange Log1.0:Light porting of ORTHPOL. No additional functions are present (moved to SpectralToolbox).
Package: ort-nightly-directml
ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
For more information on ONNX Runtime, please seeaka.ms/onnxruntimeor theGithub project.Changes1.11.0Release Notes : TBD1.10.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.10.01.9.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.9.01.8.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.21.8.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.11.8.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.8.01.7.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.7.01.6.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.6.01.5.3Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.31.5.2Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.21.5.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.5.11.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.4.01.3.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.11.3.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.01.2.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.2.01.1.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.01.0.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.0.00.5.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.00.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
Package: ort-nightly-featurizer
ONNX Runtime is a performance-focused scoring engine for Open Neural Network Exchange (ONNX) models.
For more information on ONNX Runtime, please seeaka.ms/onnxruntimeor theGithub project.Changes1.3.1Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.11.3.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.3.01.2.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.2.01.1.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.1.01.0.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v1.0.00.5.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.00.4.0Release Notes :https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
Package: ortografix
ortografixWelcome to ortografix, a seq2seq model for automatic ortografic simplification, coded with pytorch 1.4.Installvia pip:pip3installortografixor, after a git clone:python3setup.pyinstallTrainTo train a model, run:ortografixtrain\--data/abs/path/to/training/data\--model-typegru\--shuffle\--hidden-size256\--num-layers1\--bias\--dropout0\--learning-rate0.01\--epochs10\--print-every100\--use-teacher-forcing\--teacher-forcing-ratio0.5\--output-dirpath/abs/path/to/output/directory/whereto/save/model\--with-attention\--character-basedTestQualitative evaluationTo qualitatively evaluate the output of the model on a set of 10 randomly selected sentences from a given dev/test set, run:ortografixevaluate\--data/abs/path/to/test/data.txt\--model/abs/path/to/model/directory/\--random10Quantitative evaluationTo quantitatively evaluate the output of the model on a given dev/test set, run:ortografixevaluate\--data/abs/path/to/test/data.txt\--model/abs/path/to/model/directoryQuantitative evaluation will return:The sum of all edit (Levenshtein) distance computed across all test pairsThe average edit distance computed across all test pairsThe average normalized edit distanceThe average normalized edit similarityAll measure are computed viatextdistance.
Package: ortoolpy
ortoolpyis a package for Operations Research.
It is user’s responsibility for the use ofortoolpy.from ortoolpy import knapsack
size = [21, 11, 15, 9, 34, 25, 41, 52]
weight = [22, 12, 16, 10, 35, 26, 42, 53]
capacity = 100
knapsack(size, weight, capacity)Show Tableimport ortoolpy.optimization
%typical_optimizationRequirementsPython 3, pandas, pulp, more-itertoolsFeaturesThis is a sample. So it may not be efficient.ortools_vrpusing Google OR-Tools (https://developers.google.com/optimization/).Setup$ pip install ortoolpyHistory0.0.1 (2015-6-26)first release
Package: ortools
This project hosts operations research tools developed at Google and
made available as open source under the Apache 2.0 License.OR-Tools includes solvers for:Constraint Programming
- CP-SAT solver: A constraint programming solver that uses SAT (satisfiability)
methods.
- Original CP solver: A constraint programming solver.Linear and Mixed-Integer Programming
- Glop: A linear optimizer to find the optimal value of a linear objective
function, given a set of linear inequalities as constraints.
- MPSolver, ModelBuilder: Wrappers around commercial and other open source
solvers, including mixed integer solvers: CBC, CLP, GLPK, Gurobi or SCIP.Vehicle Routing
A specialized library for identifying best vehicle routes given constraints.Graph Algorithms
Code for finding shortest paths in graphs, min-cost flows, max flows, and
linear sum assignments.
Package: ortools-stubs
ortools-stubs: unofficial typing stubs for Google OR-ToolsPEP 484typing annotations for theprotofiles inortoolsusingmypy-protobuf.InstallationFollowingPEP 561, it is released as a separate package.pip install ortools-stubsNoteThis is a temporary solution, it would be better to generate these files in the main package.https://github.com/google/or-tools/issues/1547https://github.com/google/or-tools/pull/1544
Package: ortu
DEPRECATION WARNINGOCI has upgraded the DRG (Dynamic Router Gateway) service which simplifies the network architecture with multiple VCNs:https://docs.oracle.com/en-us/iaas/Content/Network/Tasks/scenario_d.htm#scenariodSimple utility to insert route rule to OCI Route tableThe reason behind this utility is described here:1251This simple utility is intended to update the routing table during the terraform run.
It inserts the routing rule in an idempotent way (so, it checks first if exact rule is present in routing table).ExamplesThe example usage is insidenull_resourceterraform resource after VCN peering establishment:resource"null_resource""right_route_table_update"{triggers={rt_id=data.oci_core_vcn.right_vcn.default_route_table_idcidr=data.oci_core_vcn.left_vcn.cidr_blocklpg_id=oci_core_local_peering_gateway.right_lpg.id}provisioner"local-exec"{command="ortu --rt-ocid ${self.triggers.rt_id} --cidr ${self.triggers.cidr} --ne-ocid ${self.triggers.lpg_id}"}provisioner"local-exec"{when=destroycommand="ortu delete --rt-ocid ${self.triggers.rt_id} --cidr ${self.triggers.cidr} --ne-ocid ${self.triggers.lpg_id}"}}resource"null_resource""left_route_table_update"{triggers={rt_id=data.oci_core_vcn.left_vcn.default_route_table_idcidr=data.oci_core_vcn.right_vcn.cidr_blocklpg_id=oci_core_local_peering_gateway.left_lpg.id}provisioner"local-exec"{command="ortu --rt-ocid ${self.triggers.rt_id} --cidr ${self.triggers.cidr} --ne-ocid ${self.triggers.lpg_id}"}provisioner"local-exec"{when=destroycommand="ortu delete --rt-ocid ${self.triggers.rt_id} --cidr ${self.triggers.cidr} --ne-ocid ${self.triggers.lpg_id}"}}There is a default route table updated. Don't forget to ignore theroute_ruleschanges for this resource:resource"oci_core_default_route_table""default_route_table"{manage_default_resource_id=oci_core_vcn.vcn.default_route_table_iddisplay_name=var.vcn_namefreeform_tags=merge(local.default_tags,var.tags)lifecycle{ignore_changes=[route_rules,]}}resource"null_resource""default_route_table_update"{triggers={rt_id=oci_core_default_route_table.default_route_table.idig_id=oci_core_internet_gateway.internet_gateway.idcidr=local.anywhere}provisioner"local-exec"{command="ortu --rt-ocid ${self.triggers.rt_id} --cidr ${self.triggers.cidr} --ne-ocid ${self.triggers.ig_id} --debug"}provisioner"local-exec"{when=destroycommand="ortu delete --rt-ocid ${self.triggers.rt_id} --cidr ${self.triggers.cidr} --ne-ocid ${self.triggers.ig_id} --debug"}}AuthenticationI'm using Ansible in my projects, so, this tool relies on~/.oci/configandOCI_CONFIG_PROFILEenvironment variable.In the case of OCI instance usage set theOCI_CLI_AUTHenvironment variable toinstance_principalvalue.
Package: oru
This is a utility package to interact with an Orange and Rockland Utility smart energy meterOru calls the APIs of the Orange and Rockland Utility smart energy meter to return the latest meter read value and unit of measurement.It requires the oru.com credentials (email, password, MFA type, MFA secret) and the account id and meter number.
MFA type can be either Security Question or TOTP (e.g. Google Authenticator).
For MFA Security Question, to set up your MFA secret (answer), log ingto oru.com, go to your profile and reset your 2FA method. When setting up 2FA again, there will be option to say you do not have texting on your phone. Select this and you should be able to use a security question instead.
For MFA TOTP, choose Google Authenticator, choose a device type and when presented with the QR code, click on “Can’t scan?”. It should provide you with the MFA secret.
The account id and meter number can be found on your Orange and Rockland Utility bill.Example usage:from oru import Meter

meter = Meter(
    email="myemail@email.com",
    password="myorupassword",
    mfa_type="TOTP",
    mfa_secret="myorumfasecret",
    account_id="cd754d65-5380-11e8-2307-2656615779bf",
    meter_id="703437804")

value, unit_of_measurement = event_loop.run_until_complete(meter.last_read())
Package: orun
Orun (Object RUNtime) is a small/lightweight library that provides a fast
to build Python RIA, the client communicates with the server through ajax.
Typical usage often looks like this:#!/usr/bin/env python
# Cherrypy + ExtJS example

    from orun.extjs import *
    from orun.extjs import cp

    def ok_click(id, *args, **kwargs):
        cli << Ext.getCmp(id).setText('Clicked')
        cli << js.client.alert('Server side message')

    def button_click(id, *args, **kwargs):
        js.write("""
        Ext.getCmp("%s").setText('Clicked');
        alert('Server side callback message');
        """ % id)

    class MyApplication(cp.ExtApplication):
        def main(self, *args, **kwargs):
            wnd = Ext.create('widget.window', {'title': 'My Window', 'width': 300, 'height': 250,
                'items': [{'xtype': 'button', 'text': 'Click Here', 'handler': button_click}],
                'buttons': [
                    {'text': 'OK', 'handler': ok_click},
                    {'text': 'Close', 'handler': js.function('this.up("window").close();')}]})
            wnd.show()
            wnd.setHeight(200)

    app = MyApplication('Orun (ExtJS Application)')
    app.run()The example above, runs cherrypy application on 8080 http port, and exposes
extjs method.Installation
Package: orwell-translators
Orwell Package For Building TranslatorsAboutOrwell is an open-source platform that allows you to integrate different metrics collectors into one large super-system, storing the data from different sources in Prometheus' format.With that goal in mind, this package tries to facilitate as much as possible the task of creating translator services compatible with Orwell's architecture.DevelopmentIf a given service's output follows the scheme "host,metric,value", its translator could be built with the following example:from orwell import Metric, Runner


# The translator function should be able to receive multiple lines
# of the desired service output and return a list of Metric objects

def translate (lines: str) -> list[Metric]:
  metrics = [ line.split(',') for line in lines.split('\n') ]
  return [ Metric(metric, value, { 'host': host }) for host, metric, value in metrics ]


translator = Runner(translate)
translator.run()UsageWith the example given above we now have three options for running the service:Command-Line Modepython main.py cmd <output>The output will be processed and printed.Server Modepython main.py serverA Flask server will start accepting POST requests for /metrics.
The body of the request must be a dictionary with the only keymetrics.
The response will be the processed output whenever the status code is 200.Environment VariablesFLASK_HOSTDefault"localhost"FLASK_PORTDefault"5000""FLASK_DEBUGIf set the server will start in debug mode.Production Modepython main.py prodThis is the most important mode as it allows to read messages from Kafka and write them to a Redis database, according to the Orwell's architecture.Environment VariablesREDIS_HOSTDefault"localhost"REDIS_PASSWORDDefault"root"KAFKA_HOSTDefault"localhost""KAFKA_PORTDefault9093KAFKA_TOPICDefault"general"Pull Modepython main.py pullThis mode allows gathering data from a custom endpoint.Environment VariablesPULL_ENDPOINTPULL_INTERVALDefault"localhost"REDIS_PASSWORDDefault"root"