Saravanan666/pypi-org huggingface dataset:
Package: hsmltestfix
Hopsworks Model RegistryHSML is the library to interact with the Hopsworks Model Registry. The library makes it easy to export and manage models.The library automatically configures itself based on the environment it is run.
However, to connect from an external Python environment additional connection information, such as host and port, is required. For more information about the setup from external environments, see the setup section.Getting Started On HopsworksInstantiate a connection and get the project model registry handleimporthsml# Create a connectionconnection=hsmlfix.connection()# Get the model registry handle for the project's model registrymr=connection.get_model_registry()Create a new modelmnist_model_meta=mr.tensorflow.create_model(name="mnist",version=1,metrics={"accuracy":0.94},description="mnist model description")mnist_model_meta.save("/tmp/model_directory")Download a modelmnist_model_meta=mr.get_model("name",version=1)model_path=mnist_model_meta.download()Delete a modelmnist_model_meta.delete()Get best performing modelmnist_model_meta=mr.get_best_model('mnist','accuracy','max')You can find more examples on how to use the library inexamples.hopsworks.ai.DocumentationDocumentation is available atHopsworks Model Registry Documentation.IssuesFor general questions about the usage of Hopsworks Machine Learning please open a topic onHopsworks Community.Please report any issue usingGithub issue tracking.ContributingIf you would like to contribute to this library, please see theContribution Guidelines.
Package: hsmm
DocumentationTable of contentsMain GameEntitiesLevelsItemsEffectsUtilsMain Gamedazu gibts noch nichts zu sagen tbh, soll aber cool werden i guessEntitiesImport: Entities.pyUsed to create entities like the player and npcs.Methods:set namechange healthadd itemremove item by nameremove item by indexadd effectshow effectsremove effect by nameremove effect by index! all methods return False on failureset_nameStarts promt to set the player.name by the playerreads: selfwrites: self.nameusage:Hero=Entity()Hero.set_name()change_healthChanges the Player.health by adding "value"reads: self
writes: self.healthusage:Hero=Entity()Hero.change_health(-10)add_itemAdds item to inventory by using the item class.reads: self, iname, itypewrites: self.invusage:Hero=Entity()Hero.add_item("item name","item type")remove_item_by_nameRemoves item from entity inventory by given namereads: self, inamewrites: self.invusage:Hero=Entity()Hero.remove_item_by_name("Sword")remove_item_by_indexRemoves item from entity inventory by given indexreads: self, inamewrites: self.invusage:Hero=Entity()Hero.remove_item_by_index(0)#removes first item from inventory#if no index given → deletes last item from inventoryadd_effectAppends effect to corresponding list of effects.reads: self(obj), effect(obj)writes: entity(oby) → {geffects, beffects, eeffects} → obj.effectsusage:Hero=Entity()poisoning=Effect()Hero.add_effect(poisoning)show_effectsPrints element.name of entity.effects[]reads: self(obj), names(bool → default True)writes: self(obj)usage:Hero=Entity()poisoning=Effect()Hero.add_effect(poisoning)Hero.show_effects()#shows name for every effectHero.show_effects(False)#shows vars(effect) for every effectremove_effect_by_nameRemoves effect from entity by given name.reads: self(obj), ename(str)writes: self(obj)usage:Hero=Entity()poisoning=Effect()Hero.add_effect(poisoning)Hero.remove_effect_by_name(poisoning.name)remove_effect_by_indexusage:change_statusage:let_effects_take_effectusage:LevelsItemsEffectsUtils
Package: hsm-nibble
A library for generating HSM tool paths for CNC pocket milling.TLDRThis project generates HSM (High Speed Machining) style CNC tool paths which
minimise abrupt changes in tool engagement.It is intended to be used as a library in a CAM package.It is NOT intended to be used to directly generate gcode.See the "Projects using this library" section for CAM projects using this library
to generate gcode.The problemWhen CNC milling pockets, calculating a tool path that minimises endmill engagement
and abruptchangesin engagement is challenging. There are few choices available to
hobbyists and even less open source options.Contour milling is the standard way of milling pockets where successive cuts run
parallel to each other leading to high endmill engagement in tight corners and
abrupt changes in endmill engagement.This project generates a tool path which consists of a series of arcs. Each arc is
centered on a point equidistant to two or more pockets edges and with an arc radius
set so the pocket edge is a tangent to the arc.
This leads to gradual increases in tool engagement, greatly decreasing the change
of endmill damage.How to use this libraryBasic calculation of a internal pocket:$ ./examples/example_basic.py ./test_cases/stegasorus.dxfCalculation of a internal pocket starting from pre-drilled hole:./examples/example_with_predrill.py ./test_cases/triceratops.dxfCalculation of an outside clearing operation where the edge of the stock to be cut is encoded as the outside polygon in the .dxf file:examples/example_outer.py ./test_cases/hextest.dxfCalculation of an outside clearing operation where the edge of the stock to be cut is calculated as a simple offset from the outer edge of the .dxf part file.examples/example_refine.py ./test_cases/thrash.dxfOther useful libraries in this repoThe VoronoiCenters class in hsm_nibble/voronoi_centers.py is a wrapper around the pyvoronoi package.
pyvoronoi only deals with straight lines and points. Not arcs.
The VoronoiCenters class prunes the voroni diagram and tries to do same things when simplifying a voronoi diagram created from geometry containing arc and circles.Projects using this libraryDerpCAMA 2.5D CAM aimed at hobby CNC machine users.
Package: hsms
HSMS: hardware security module software/simulatorThis project is intended to run on an air-gapped computer to sign chia spends using bls12_381 keys.Install$ pip install -e .If on windows, you need one extra package:$ pip install pyreadlineToolsCommand-line tools installed include:hsms- HSM sim that acceptsUnsignedSpendobjects and produces signatures, full or partialhsmgen- generate secret keyshsmpk- show public keys for secret keyshsmmerge- merge signatures for a multisig spendqrint- convert binary to/from qrint asciiFor testing & debugging:hsm_test_spend- create a simple testUnsignedSpendmultisig spendhsm_dump_sb- debug utility to dump information about aSpendBundlehsm_dump_us- debug utility to dump information about anUnsignedSpend
Package: hsnf
hsnfComputing Hermite normal form and Smith normal form with transformation matrices.Document:https://hsnf.readthedocs.io/en/latest/Document(develop):https://lan496.github.io/hsnf/developGithub:https://github.com/lan496/hsnfPyPI:https://pypi.org/project/hsnfUsageimportnumpyasnpfromhsnfimportcolumn_style_hermite_normal_form,row_style_hermite_normal_form,smith_normal_form# Integer matrix to be decomposedM=np.array([[-6,111,-36,6],[5,-672,210,74],[0,-255,81,24],])# Smith normal formD,L,R=smith_normal_form(M)"""D = array([[   1    0    0    0][   0    3    0    0][   0    0 2079    0]])"""assertnp.allclose(L@M@R,D)assertnp.around(np.abs(np.linalg.det(L)))==1# unimodularassertnp.around(np.abs(np.linalg.det(R)))==1# unimodular# Row-style hermite normal formH,L=row_style_hermite_normal_form(M)"""H = array([[     1      0    420  -2522][     0      3   1809 -10860][     0      0   2079 -12474]])"""assertnp.allclose(L@M,H)assertnp.around(np.abs(np.linalg.det(L)))==1# unimodular# Column-style hermite normal formH,R=column_style_hermite_normal_form(M)"""H = array([[   3    0    0    0][   0    1    0    0][1185  474 2079    0]])"""assertnp.allclose(np.dot(M,R),H)assertnp.around(np.abs(np.linalg.det(R)))==1# unimodularInstallationhsnf works with Python3.8+ and can be installed via PyPI:pipinstallhsnfor in local:gitclonegit@github.com:lan496/hsnf.gitcdhsnf
pipinstall-e.[dev,docs]Referenceshttp://www.dlfer.xyz/post/2016-10-27-smith-normal-form/I appreciate Dr. D. L. Ferrario's instructive blog post and his approval for referring his scripts.CSE206A: Lattices Algorithms and Applications (Spring 2014)Henri Cohen, A Course in Computational Algebraic Number Theory (Springer-Verlag, Berlin, 1993).
Package: hsoft
test packaginghttps://www.youtube.com/watch?v=wCGsLqHOT2I&list=PLlM3i4cwc8zD-sac6lBQvC_yre7d9tbkjhttps://packaging.python.org/en/latest/tutorials/packaging-projects/make a distribution file so that it can be installed via pippip install --upgrade setuptoolshttps://setuptools.pypa.io/en/latest/userguide/quickstart.htmlpip install --upgrade buildbuild is a tool which makes a distribution file (e.g., tar.gz or .whl) that can be uploaded to pyPI.create setup.py# https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html
from setuptools import setup
setup()create pyproject.tolm[build-system]
requires = ["setuptools", "setuptools-scm"]
build-backend = "setuptools.build_meta"

[project]
name = "hsoft"
version = "0.0.2"
description = "My package description"
readme = "README.md"
requires-python = ">=3.6"python -m buildinside of my project folder
this makes distribution file (e.g., tar.gz file and a .whl file) that can be uploaded to PyPI!pip install twinetwine upload dist/*check at the PyPI
Package: hspatial
hspatialUtilities for spatial integration of time series.Free software: GNU General Public License v3Documentation:https://hspatial.readthedocs.io.History2.1.4 (2021-08-31)Added compatibility with gdal 3.2.1.3 (2021-04-13)Updated dependencies (essentially htimeseries).2.1.2 (2021-04-08)Updated requirements to be compatible with Django 3.2.1.1 (2021-01-05)Fixed an error in extract_point_from_raster(). It was sometimes
picking up a neigbouring pixel instead of the correct one.2.1.0 (2020-10-02)h_integrate() now adds a “UNIT” metadata item to the produced raster
with the unit of measurement as specified in the input files.
Conversely, PointTimeseries reads the “UNIT” metadata item from the
rasters and sets it in the returned HTimeseries object.2.0.0 (2020-01-05)Now uses version 5 of hts file (i.e. different time step notation).1.3.0 (2019-12-12)Added option default_time to PointTimeseries.1.2.2 (2019-11-24)Fixed a bug where extract_point_from_raster() was modifying the point
passed to it.1.2.1 (2019-10-23)Fixed a bug where hts files were opened in the wrong mode, with
inconsistent results (this is another occurence of the bug that had
been fixed in 0.1.2).1.2.0 (2019-09-11)Added test utility setup_test_raster() to make unit testing easier.1.1.0 (2019-08-23)Added option “version” to PointTimeseries.get_cached() so that it can
save in different file format versions.1.0.1 (2019-08-22)Fixed a bug in extract_point_from_timeseries() where it was sometimes
raising the wrong exception type (depending on GDAL version).1.0.0 (2019-08-16)The API for extract_point_timeseries_from_rasters() has changed.
Instead of a function, there’s now a PointTimeseries class.When extracting a point timeseries from rasters, start_date and
end_date can now be specified.0.2.0 (2019-08-13)When extracting point time series from a set of rasters, it is now
possible to provide a prefix as well as a list of files, and it is
also possible to save the extracted time series to a file, optionally
only if the file is outdated.extract_point_from_raster (and extract_point_timeseries_from_rasters)
now supports GeoDjango (GEOS) Point objects besides GDAL Point
objects.Function coordinates2point has been added.0.1.2 (2019-07-30)Fixed a bug where hts files were opened in the wrong mode, with
inconsistent results.0.1.1 (2019-07-05)Fixed an ugly timezone bug that caused the data to refer to a
different time than what the timestamp actually said.When the timezone was missing from the input files, there was an
incomprehensible AttributeError. This was fixed and now it provides an
understandable error message.0.1.0 (2019-06-21)Initial release
Package: hspcore
Hansen Solubility Parameters (HSP) core functions.InstallationInstallhspcoreeasily with pip:pipinstallhspcoreUsage Examplefromhspcoreimportget_hsp#hsp grid is a list of solvents as [[Solvent,D,P,H,Score],...]hsp_grid=[['Acetonitrile',15.3,18.0,6.1,1],['1-Bromonaphthalene',20.6,3.1,4.1,6],['n-Butyl Acetate',15.8,3.7,6.3,3],...]hsp,radius,error=get_hsp(hsp_grid)LinksSee on GitHub:https://github.com/Gnpd/hspcoreSee on PyPI:https://pypi.org/project/hspcore
Package: hspfbintoolbox
Documentation for hspfbintoolboxThehspfbintoolboxis a Python script and library of functions to read
Hydrological Simulation Program Fortran (HSPF) binary files and print to
screen.  The time series can then be redirected to file, or piped to other
command line programs liketstoolbox.Requirementspython 3.7 or latertstoolbox - utilities to process time-seriesInstallationpippipinstallhspfbintoolboxcondacondainstall-cconda-forgehspfbintoolboxUsage - Command LineJust run ‘hspfbintoolbox –help’ to get a list of subcommands:catalogPrints out a catalog of data sets in the binary file.extractPrints out data to the screen from a HSPF binary output file.For the subcommands that output data it is printed to the screen and you can
then redirect to a file.Usage - APIYou can use all of the command line subcommands as functions.  The function
signature is identical to the command line subcommands.  The return is always
a PANDAS DataFrame.  Input can be a CSV or TAB separated file, or a PANDAS
DataFrame and is supplied to the function via the ‘input_ts’ keyword.Simply import hspfbintoolbox:import hspfbintoolbox

# Then you could call the functions
ntsd = hspfbintoolbox.extract('tests/test.hbn', 'yearly', ',905,,AGWS')

# Once you have a PANDAS DataFrame you can use that as input.
ntsd = tstoolbox.aggregate(statistic='mean', agg_interval='daily', input_ts=ntsd)
Package: hspf-reader
hspf_reader - Quick GuideThe hspf_reader is a pure Python module and command line script to read HSPF
time-series from WDM files, HSPF binary files, and HSPF plotgen files.Use “wdmtoolbox” to create, examine, read, and write to WDM files.Use “hspfbintoolbox” to catalog and read HSPF binary output files.Installationpippipinstallhspf_readercondacondainstall-cconda-forgehspf_readerUsage - Command LineJust run ‘hspf_reader –help’ to get a list of subcommands:usage: hspf_reader [-h]
                 {wdm, hbn, plotgen, about} ...

positional arguments:
  {wdm, hbn, plotgen, about}

wdm
    Read HSPF WDM files.
hbn
    Read HSPF binary files.
plotgen
    Read HSPF plotgen files.
about
    Display version number and system information.

optional arguments:
  -h, --help            show this help message and exitThe output time-series data is printed to the screen and you can then redirect
to a file.Usage - APIYou can use all of the command line subcommands as functions.  The function
signature is identical to the command line subcommands.  The return is always
a PANDAS DataFrame.Simply import hspf_reader:from hspf_reader import hspf_reader

# Then you could call the functions
ntsd = hspf_reader.wdm('wdm_file.wdm', 202)

# Once you have a PANDAS DataFrame you can use that as input to other
# hspf_reader functions.
ntsd = hspf_reader.hbn('hbn_file.hbn', "yearly", [,,,"TAET"])
Package: hspf-utils
hspf_utils - Quick GuideThe hspf_utils is a command line script and Python library of utilities to work
with the Hydrological Simulation Program - FORTRAN (HSPF).  Uses pandas
(http://pandas.pydata.org/) or numpy (http://numpy.scipy.org) for any heavy
lifting.Requirementshspfbintoolbox - Utility to extract time-series from HSFP binary output
files; collected and installed by ‘pip’ or ‘easy_install’ command.InstallationShould be as easy as runningpip install hspf_utilsorconda install-cconda-forgehspf_utilsat any command line.Usage - Command LineJust run ‘hspf_utils –help’ to get a list of subcommands:usage: hspf_utils [-h] {about,detailed,summary,mapping,parameters} ...

positional arguments:
  {about,detailed,summary,mapping,parameters}
    about               Display version number and system information.
    detailed            Develops a detailed water balance.
    summary             Develops a summary water balance.
    mapping             Develops a csv file appropriate for joining to a GIS
                        layer.
    parameters          Develops a table of parameter values.

optional arguments:
  -h, --help            show this help message and exitFor the subcommands that output data it is printed to the screen and you can
then redirect to a file.Usage - APIYou can use all of the command line subcommands as functions.  The function
signature is identical to the command line subcommands.  The return is always
a PANDAS DataFrame.  Input can be a CSV or TAB separated file, or a PANDAS
DataFrame and is supplied to the function via the ‘input_ts’ keyword.Simply import hspf_utils:import hspf_utils

# Then you could call the functions

ntsd = hspf_utils.summary("test.hbn", uci="test.uci")
Package: hs-process
Welcome to hs_processAn open-source Python package for geospatial processing of aerial hyperspectral imageryhs_processemphasizes the ability to batch process datacubes, with the overall goal of keeping the processing pipeline as "hands-off" as possible. There is also a focus on maintaining the ability to have control over the subjective aspects of image processing (e.g., segmentation thresholds) and achieving repeatability in image post-processing.Abouths_processis a Python package for geospatial post-processing of aerial hyperspectral imagery. The principle motivation for developinghs_processwas to streamline the post-processing steps required prior to hyperspectral data analysis. Although commercial software perhaps exists for such needs, open source software that is both flexible and streamlined did not previously exist. The specific motivations were:Minimize the time and effort required to perform post-processing tasks.Minimize (and potentially eliminate) the number of steps where user intervention is required.Provide a framework for post-processing an entire aerial hyperspectral dataset that, when repeated, does not require any user intervention.Enable users to more easily document the complex processes and inherently subjective parameters used for post-processing aerial hyperspectral imagery (e.g., segmentation thresholds, spatial plot buffers, etc.).In addressing these motivations, it was important to keep each post-processing steps as flexible as possible so the specific requirements of individual research objectives can be met.For more information and tutorials, check out thehs_process documentation.Data requirementsThe minimum data requirement to utilize this package is a pre-processed hyperspectral datacube (i.e., radiometric calibration, georectification, and reflectance conversion should have already been completed). Sample imagery captured from aResononPika II VIS-NIR line scanning imager can be downloaded from thislink.To perform spatial cropping, a polygon boundary file is required with "plot" column indicating the plot number of each boundary feature (an example geojson can be downloaded from thislink).Sample dataDownload from this linkhs_processwas developed using images from the Resonon Pika II hyperspectral imager, so it tends to be tailored towards modifying metadata specific to Resonon, with the Pika II model imager in particular.hs_processleverages theSpectral Python libraryfor reading and writing hyperspectral datacubes.Intended audienceThis package was built for those looking for a clean, streamlined solution for post-processing aerial hyperspectral imagery. This package is especially well-suited for R&D departments looking for a straightforward way to achieve repeatable post-processing results so data analysis can proceed more quickly and systematically.TroubleshootingPlease report any issues you encounter through theGithub issue tracker.Code of ConductEveryone interacting in the packaging project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow thePyPA Code of Conduct.
Package: hspsms
HSP SMS APIAPI Integration ofHSPSMSContributors:WE'RE LOOKING FOR SOMEONE WHO CAN CONTRIBUTE IN DOCSCivil Machines Technologies Private Limited: For providing me platform and
funds for research work. This project is hosted currently withCMTonly.Himanshu Shankar: Himanshu Shankar has initiated this project and worked on this
project to collect useful functions and classes that are being used in various projects.InstallationDownload and Install viapippip install hspsmsorDownload and Install viaeasy_installeasy_install hspsmsSend SMSFollowing is a simple examplefrom hspsms import HSPConnector

hspconn = HSPConnector(username="", apikey="", sender="", smstype="")
hspconn.send_sms(recipient=[], message="", sendername="", smstype="", scheduled="")Using with DjangoUsingdjango-sendsmsInstalldjango-sendsms:pip install django-sendsmsSpecify configuration forHSP APIinsettings.pyHSPSMS = {
    "USER": "",
    "APIKEY": "",
    "SENDER": "",
    "DEFAULT_SMSTYPE": ""
}Specify defaultdjango-sendsmsbackend insettings.pySENDSMS_BACKEND = 'hspsms.backends.DjangoSendSMSBackend'Using withDRF AddonsInstalldrfaddons:pip install drfaddonsDRF Addonsusedjango-sendsmsto send sms. Follow steps fordjango-sendsms.Usesend_messagefunction ofdrfaddonsfrom drfaddons.utils import send_message

send_message(message="message", subject="Fallback Email Subject", "recip": [...mobile_numbers...],
             "recip_email": [...respective_fallback_email_addresses...],
             "html_message": "fallback_html_message")
Package: hs-pyrender
PyRenderThis is a Rendering engine made in Python by ArnavInstallingYou Can Install it either by running the commandgit clone https://github.com/HotShot0901/PyRenderIf you have git installedORYou can Download the latest ZIP or any release.Runpython test.py(in CMD) to check your Installation.ORYou can use pip.pip install hs-pyrenderIf downloaded from GITYou could paste thepyrendersub-folder inPython_Dir/Lib/site-packagesto use it in your project.Or, you could also make a sub-project in the mainPyRenderand usefrom pyrender import *.UsageTo import:from pyrender import *Making a Renderer:renderer=PyRender(dimension)dimension could be(640, 360)Vector2's: They are very essencial to rendering. They tell the position and dimension of the shape.Drawing:renderer.drawRect(posX, posY, dimX, dimY, Color(r, g, b), DrawMode.Top)Frame loop:for _ in range(180):
    renderer.rotate(math.randins(_))
    renderer.drawRect(posX, posY, dimX, dimY, Color(r, g, b), DrawMode.Top)

    renderer.nextFrame(delCurr=True)Rendering:renderer.render(renderPath, videoTitle)You will get your video inrenderPath/frames/videoTitle.mp4Finally:from pyrender import *
renderer=PyRender((640, 360))

for _ in range(180):
    renderer.rotate(math.randins(_))
    renderer.drawRect(posX, posY, dimX, dimY, Color(r, g, b), DrawMode.Top)

    renderer.nextFrame(delCurr=True)

renderer.render("E:", "video")