Saravanan666/pypi-org huggingface dataset:
Package: distance-metrics-mcda
distance-metrics-for-mcdaPython 3 library for Multi-Criteria Decision Analysis based on distance metrics. The documentation is providedhereInstallationpip install distance-metrics-mcdaMethodsThis is Python 3 library providing packagedistance_metrics_mcdathat includes metrics that can measure alternatives distance from
reference solutions in multi-criteria decision analysis. This library contains moduleweighting_methodswith the following distance metrics:Euclidean distanceeuclideanManhattan (Taxicab) distancemanhattanHausdorff distancehausdorffCorrelation distancecorrelationChebyshev distancechebyshevStandardized euclidean distancestd_euclideanCosine distancecosineCosine similarity measurecsmSquared Euclidean distancesquared_euclideanSorensen or Bray-Curtis distancebray_curtisCanberra distancecanberraLorentzian distancelorentzianJaccard distancejaccardDice distancediceBhattacharyya distancebhattacharyyaHellinger distancehellingerMatusita distancematusitaSquared-chord distancesquared_chordPearson chi-square distancepearson_chi_squareSquared chi-square distancesquared_chi_squareThe library also provides other methods necessary for multi-criteria decision analysis, which are as follows: The TOPSIS method for
multi-criteria decision analysis TOPSIS in modulemcda_methods. The TOPSIS method is based on measuring the distance of alternatives from
Positive Ideal Solution and Negative Ideal Solution usingdistance_metricsmentioned above.Normalization techniques:Linearlinear_normalizationMinimum-Maximumminmax_normalizationMaximummax_normalizationSumsum_normalizationVectorvector_normalizationCorrelation coefficients:Spearman rank correlation coefficient rsspearmanWeighted Spearman rank correlation coefficient rwweighted_spearmanPearson coefficentpearson_coeffObjective weighting methods:Entropy weighting methodentropy_weightingCRITIC weighting methodcritic_weightingExample of usage ofdistance-metrics-mcdaare provided onGitHubinexamples
Package: distances-calculator
Distances CalculatorThis is a distance calculator between 2 addressed, it is based onopenstreetmapapi.Getting startinstall,pip install distances-calculatoruninstallpip uninstall distances-calculatorget distance between 2 addresses2 parameters,address 1address 2distances-calc "125 Queen St, Auckland, 0620" "127 Queen St, Auckland, 0620"calculate the distance between a table's data2 parameters,address, the center, the original addresspath, the absolute path of your csv filedistances_writer "125 Queen St, Auckland, 0620" "/home/Username/test.csv"example of distance_writertable test.csvidaddress01 Fontenoy Street, Mount Albert, Auckland 102513A Fontenoy Street, Mount Albert, Auckland 1025after runningdistances_writer "125 Queen St, Auckland, 0620" "/home/Username/test.csv"new table test.csv will beidaddressdistance_to_139 Carrington Rd, Mount Albert, Auckland 102501 Fontenoy Street, Mount Albert, Auckland 10250.44778955178693513A Fontenoy Street, Mount Albert, Auckland 10250.471488332032505P.S.in your csv table, there should be one column namedaddress, and the writer can create a new column nameddistance_to_<address>How to calculate distanceBased on coordinateEarth is a sphere, so we can use simple triangle function to
calculate a not accurate distance, but most of time,
this direct distance is good enough.Based on open street map api (in future)this can be used to calculate route distance.APIxcodeextract_coordinateget_distancewrite_intoLinks Aboutgithub public repopypi distribution
Package: distans
distansDifferent distances for PythonInstallpipinstalldistansUsageLp distancefromdistansimportlpa=[1,-2,3]b=[3,4,-5]p=2norm=lp(a,p=p)dist=lp(a,b,p=p)Edit similarityfromdistansimportjaro_sim,jaro_winkler_simjaro_sim('hello','helo')jaro_winkler_sim('hi','hey')
Package: distanse-finner
Dette er et lite bibliotek for å hente ut hvor langt man har bevegd
seg basert på GPX-filer fra en hvilken som helst GPS-logger, eller
CSV-filene laget med open-source appen GPSLogger for Android.
Sistnevnte er en gratis app med åpen kildekode som lar deg logge GPS
spor uten at denne informasjonen deles med andre. Dessverre ble den
fjernet fra Play Store, men du kan finne installasjonsinstrukser påhttps://gpslogger.app/For å bruke dette biblioteket starter du scriptet ditt medfromdistanse_finnerimportlast_rådataellerfromdistanse_finnerimportlast_uniform_dataDeretter kan du bruke denne kommandoen:tidspunkt,avstander=last_rådata(datafil)for å hente ut avstandsdata og tidspunktene de dataene er hentet på.
Dessverre klarer ikke GPS-loggere å måle posisjonen med like lang
tid mellom hver måling, så for å få uniformt fordelte data
(altså data med like lang tid mellom hver måling), kan du bruke funksjonen:tidspunkt,avstander=last_uniform_data(datafil,sekund_mellom_målinger)hvorsekund_mellom_målingerer hvor mange sekund du ønsker mellom hver
måling.
Package: distarray
Think globally, act locally.DistArray provides general multidimensional NumPy-like distributed arrays to
Python.  It intends to bring the strengths of NumPy to data-parallel
high-performance computing.  DistArray has a similar API toNumPy.DistArray is ready for real-world testing and deployment; however, the project
is still evolving rapidly, and we appreciate continued input from the
scientific-Python community.DistArray is for users whoknow and love Python and NumPy,want to scale NumPy to larger distributed datasets,want to interactively play with distributed data but alsowant to run batch-oriented distributed programs;want an easier way to drive and coordinate existing MPI-based codes,have a lot of data that may already be distributed,want a global view (“think globally”) with local control (“act locally”),need to tap into existing parallel libraries like Trilinos, PETSc, or
Elemental,want the interactivity of IPython and the performance of MPI.DistArray is designed to work with other packages that implement theDistributed Array Protocol.Please see our documentation atreadthedocs(or in thedocsdirectory) for
more, or ask us questions on ourmailing list.  Pull requests gladly accepted.
Package: distask
a distribute task schedulerexampleimporttimefromdistaskimportcreate_scheduler,register_jobfromdistaskimportutilfromdistask.eventsimportEVENT_SCHEDULER_STARTenable_redis=Falseclient_data={"t":"mongo","client_args":{"host":"mongodb://admin:123456@192.168.99.27:27017"},"store_args":{"database":'testcase_distask',"schedules":'schedules',"jobs":'jobs',}}ifenable_redis:client_data={'t':'redis',"host":'127.0.0.1','port':6379,'db':15,}connection_details=[{'host':'127.0.0.1','port':6379,'db':15},]lock_data={"t":"rllock","reentrant":True,"connection_details":connection_details,"ttl":10_000}scheduler=create_scheduler(client_data,lock_data,serialize="pickle",limit=1,maxwait=5)@register_job(scheduler,"interval",(),group="11",subgroup="",seconds=3)deftest_exception(times,*args,**kwargs):print("test0 ======================")a=1/0@register_job(scheduler,'cron',(12),hour='0,6,12,18',minute=0,timezone='Asia/Shanghai')deftestcron(times,a,*arg,**kwargs):asserta==12,"arg is ok"print("cron====================== ",util.time_now())@register_job(scheduler,"interval",(),group="11",subgroup="",seconds=3)deftest00(times,*args,**kwargs):print("test0 ======================")time.sleep(0.1)defjob_execute(event):ifevent.code==EVENT_SCHEDULER_START:print("start success")scheduler.add_listener(job_execute,EVENT_SCHEDULER_START)scheduler.start()
Package: dist-builder
dist-builderBuild a wheel and source distribution and bundle with other files in a zippython -m dist-builderbuild.iniBy default, dist-builder expects setup.py to be in your current working directory, and it simply builds a wheel and source distribution. This is not particularly interesting, so add a build.ini file to customize your build. dist-builder will look in the current working directory for your build.ini, or you can specify it as an argument like this:python -m dist-builder /path/to/any-name-build.ini
Package: distcache
DistCache (Distributed Cache)Distcache is a open-source distributed in-memory cache and database.
Operations are mostly asynchronous to achieve high performance.
It is implemented purely in python without any external dependency
which should make it easier to install and get started with.
One of design goal of this project is ease of use and less cognitive load to users of
similar caching/database systems like Redis, Memcached.Seewikiorreadthedocsfor extensive documentation.Build and test statusFeaturesData types supported:All basic data types and their combination. For instance, int, str, dict, set, tuple, list, etc and
their objects that have only these types as their attributes are supported.You can even read any object (image, pdfs, etc) in binary format and save them as key, value pair.Key cache operations are logged so when the server fails, the cache can be reconstructed from the log files.The APIs are similar to Memcached and Redis to reduce cognitive when migrating between platforms.Since, distcache has pure python implementation the installation process should painless. It's makes it easier to get started up and running.Its' architecture assumes that the cache clients and servers can fail and plans for it. The impact is minimal on adding and removing cache servers.Snapshot the servers at regular intervals to avoid cold starts upon crash or planned shutdownsLog replays also available for slow but complete reconstruction of the cache upon server crash, error or shutdown.Thread safe increment and decrement operations on keys.Health of the cache servers is monitored by the client.Installpip install distcachePlatformLinuxWindowsPython 2.7 to Python 3.5Same Usage CodeRun server.py and client.py on server and client respectively. You can run client.py on as many PCs but remember
to ask the server to spawn it once you run client.py. You can run both server and one or more client.py on the same PC.
It will create both server and client locally.client.pyfrom distcache.cache_client import CacheClient

client = CacheClient()

# Cache operations
client.set("brazil", "football")
client.set("harry", "potter")
client.set(1, 2)
client.set(3, 6)
client.set("hey", "hola")
client.get("hey")
client.get(1)
client.set("hey", "there")
client.get("hey")
client.delete(3)
client.get(3)
client.get("brazil")server.pyfrom distcache.cache_server import CacheServer

server = CacheServer('localhost', 2050)There are more usage codes in the usage directory.UsageYou have multiple servers serving users.
And you need to increment user id across the server such that there is no duplicate.
Similarly you can store product_id, session_id, user_id, etc.80% of the database access is generated by 20% of the queries. You should absolutely not be doing duplicate computations.
Cache the results. And, the figures vary and you can still use caching service. It just makes things faster.ContributingPlease readcontributingto learn how to contribute to this project.
Package: dist-calculating
Distance CalculatorLocal time difference calculations.The meridian difference between the two determined points is revealed.If the meridians are located in the same hemisphere, they should be subtracted, if they are located in different hemispheres, they should be added.The resulting meridian difference is multiplied by 4 minutes.Additional information:Because the Earth rotates from west to east, local time is always ahead in the east and behind in the west.Local times of regions on the same meridian are always the same.# local_time_calculation(longitude1: int, longitude2: int)print(local_time_calculation(3,7))# 16Anti-meridian CalculationTo find the anti-meridian of a meridian, it is necessary to subtract it from 180.# find_anti_meridian(longitude: int)print(find_anti_meridian(34))# 146Calculating the distance between meridiansTo understand how many kilometers are between two meridians at our current point;First, we must calculate the length of the parallel at that point, using the angle of the parallel at that point.Then we have to divide by 360 since there are 360 meridians in the world.# distance_between_meridians(latitude: int)distance_between_meridians(10)Calculating the distance between two points on EarthCalculate the distance between two points on Earth withHaversineformula.# distance_calculation(latitude: float, longitude: float, target_latitude: float, target_longitude: float, miles=False)distance_calculation(13,24,42,12)
Package: distclassipy
DistClassiPyA python package for a distance-based classifier which can use several different distance metrics.InstallationTo install DistClassiPy, run the following command:pipinstalldistclassipyUsageHere's a quick example to get you started with DistClassiPy:importdistclassipyasdcpyclf=dcpy.DistanceMetricClassifier()# Add your data and labelsclf.fit(data,labels)# Predict new instancespredictions=clf.predict(new_data)FeaturesMultiple distance metrics supportEasy integration with existing data processing pipelinesEfficient and scalable for large datasetsDocumentationFor more detailed information about the package and its functionalities, please refer to theofficial documentation.ContributingContributions are welcome! If you have suggestions for improvements or bug fixes, please feel free to open anissueor submit apull request.LicenseDistClassiPy is released under theGNU General Public License v3.0. See the LICENSE file for more details.CitationIf you use DistClassiPy in your research or project, please consider citing the package. You can find citation information in theCITATION.cfffile.ContactFor any queries, please reach out to Siddharth Chaini atsidchaini@gmail.com.
Package: distconfig
distconfigLibrary to manage distributed configuration using eitherZooKeeperorEtcdorConsul.RationalWhen you have to manage configuration of a given services that are distributed across nodes, you may want
to consider using either one of the distributed configuration managers e.g. zookeeper, etcd, consul …, this
library goal is to give developers an easy access to configuration stored in the previous backends.Installation:To useZooKeeperas backend you should installdistconfigusing$ pip install distconfig[zookeeper]withetcd:$ pip install distconfig[etcd]withconsul:$ pip install distconfig[consul]Usage:Example using zookeeper as a backendfrom kazoo import client

from distconfig import Proxy

client = client.KazooClient()
# The user must call ``KazooClient.start()`` before using this particular
# backend
client.start()

proxy = Proxy.configure(
    'distconfig.backends.zookeeper.ZooKeeperBackend',
    client=client,
)

# config is a read only mapping-like object.
config = proxy.get_config('/distconfig/service_name/config')

print config['key']

# Getting nested values works by supplying key seperated by '/' char.
print config['key/inner']

# You can assert key value type by using typed get function e.g.
# get_int, get_float, get_unicode, get_bytes ... .
print config.get_int('key/inner/int_key')

# Getting a inner config.
print config.get_config('key/inner/dict_key')Development:Start by installing dependencies$ pip install -r requirements/dev.txt requirements/base.txtTo run unit test use tox$ toxTo run integration test, we recommend you to installdockerand then run$ ./run-tests.shThe above script will setup docker container for each of the backend
and run the integration tests on them.TODO:Add file as a backend (usehttps://pypi.python.org/pypi/watchdog)
Package: distconfig3
distconfig3This is a fork ofdistconfigremoving Python 2.7 support as well as six
and ujson dependencies.Library to manage distributed configuration using eitherZooKeeperorEtcdorConsul.RationalWhen you have to manage configuration of a given services that are distributed across nodes, you may want
to consider using either one of the distributed configuration managers e.g. zookeeper, etcd, consul …, this
library goal is to give developers an easy access to configuration stored in the previous backends.Installation:To useZooKeeperas backend you should installdistconfig3using$ pip install distconfig3[zookeeper]withetcd:$ pip install distconfig3[etcd]withconsul:$ pip install distconfig3[consul]Usage:Example using zookeeper as a backendfrom kazoo import client

from distconfig import Proxy

client = client.KazooClient()
# The user must call ``KazooClient.start()`` before using this particular
# backend
client.start()

proxy = Proxy.configure(
    'distconfig.backends.zookeeper.ZooKeeperBackend',
    client=client,
)

# config is a read only mapping-like object.
config = proxy.get_config('/distconfig/service_name/config')

print(config['key'])

# Getting nested values works by supplying key seperated by '/' char.
print(config['key/inner'])

# You can assert key value type by using typed get function e.g.
# get_int, get_float, get_unicode, get_bytes ... .
print(config.get_int('key/inner/int_key'))

# Getting a inner config.
print(config.get_config('key/inner/dict_key'))Development:Start by installing dependencies$ pip install -r requirements/dev.txtTo run unit test use tox$ toxTo run integration test, we recommend you to installdockerand then run$ ./run-tests.shThe above script will setup docker container for each of the backend
and run the integration tests on them.TODO:Add file as a backend (usehttps://pypi.python.org/pypi/watchdog)
Package: distdrone
This is much of the backbone for my, Isaac Robinson’s, high school science project.
The idea is that a drone swarm where the drones work together to process difficult tasts,
such as image processing, will be much more efficient, especially if that task has
a certain threshold beyond which it becomes relevant. Here, that task is facial detection
and soon-to-be facial recognition.
I have also designed several search algorithms with which a drone swarm may search an area.
This package uses OpenCV and IPython to do its image processing and parallel computing,
respectively.
As this package is still in its basic development phases, I am not going to go into much
detail here. If you have questions, email me atisaacrob@me.com.
This does not currently support other people, ie it is tailored to the way I’ve set things
up on my various computers.
After installation, running installdronedeps will install opencv, scipy, and numpy, along
with all their various dependencies, on a Raspberry Pi running Raspbian. Modifying the
directory changes should make it install on any Ubuntu-based platform.
Running trigger will start a simple motion detector and face finder using opencv and any
attached camera in parallel across an ippython cluster with some specified name passed
as a command line argument and interpreted with click.
Running testpredictor will try to generate an opencv face recognizer, trying to best center
and align any faces in the images taken on cue by the camera.
Various other command line accessable scripts will be installed.
The distdrone module has several parts, notably the image processing portion, run with opencv,
the different motion algorithms, including a search algorithm on a simulated map run with
ipython which will perfectly search certain spaces with multiple simulated drones exploring
it at once without ever exploring the same portion of the simulated space twice. All of the
litte algorithms that make this, along with other things, possible are defined in gears.
There are other things provided by this package. Email me if you would care to know what
they are, and I will provide a write up for all of them.
Package: distdrone3
This is the backbone of my (Isaac Robinson) high school research project. The idea is that drones have limited processing power and so cannot actually do very intensive tasks on their own. My solution to this problem was to make each drone borrow CPU time from other drones in a swarm when necessary. The method essentially changes the architecture of the system from a bunch of individual drones in a swarm to a computational cluster just long enough to get an intensive task done. This saves money, power, and makes it possible to use low-power drones for high-power tasks.I chose to demonstrate this idea by creating a trigger which would alert a drone of the precense of something requiring more intensive processing, and then distributing this processing. For me, the intensive processing so far is facial detection. I have shown this method to be highly effective, and am currently in the process of improving it.I am also building real drones to demonstrate this idea. My project is documented at sites.google.com/site/paralleldrone.If you have any questions or comments, please feel free to contact me atisaacrob@me.com.
Package: distend
Distendgenerate targeted word lists for password crackingPerhaps you have a stubborn hash that refuses to crack under a generic word list. Maybe you have specific intel about the person/password in question such as aDate of birthMother's maiden namePet namesAgeAddressOther important datesBusiness specific termsFavorite foodsCatch phrasesEtcWhy not combine the generic word list with the extra details for a more targeted list?If you think that's a good idea, then Distend is for you. It can even implement common leet speak character replacements.If you think Distend is useful, pleasestarthe repository.InstallationYou can install the package from PyPi using pippipinstalldistendor you can directly clone the repository then install locallygitclone[url_from_code_button_above]cd[your_clone_location]pipinstall.Distend is supported on Python 3.6 and above. Also, there are no dependencies, but Distend does use a few standard library packages (included by default with your Python install).UsageBasic Usagedistendinfile.txtoutfile.txtFor more details and options use the help flagdistend-hDevelopment setupTo run the unit tests, navigate to the test directory and run the followingpython3-munittesttest_modifier.py
python3-munittesttest_io_utils.py
python3-munittesttest_serializer.py
python3-munittesttest_cli.pyYou may have to visit the repository to get the test files because they are not included with the pip install.MetaHanwen Zuo –HanwenZuo1@gmail.comDistend is distributed under the Apache License, Version 2.0. SeeLICENSEfor more information.https://github.com/not-sponsoredSpecial thanks to@dbader_orgfor the readme template.ContributingFork it (https://github.com/not-sponsored/distend/fork)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull RequestDisclaimerPlease refrain from malicious use of the software.
For full details view theLICENSE.Don't forget tostarthe repository if you like it.
Package: disterminal
# Disterminal[![image](https://img.shields.io/pypi/v/disterminal.svg)](https://pypi.python.org/pypi/disterminal)[![image](https://img.shields.io/travis/jlmcgehee21/disterminal.svg)](https://travis-ci.org/jlmcgehee21/disterminal)Sometimes I want to plot a distribution really quick to get a feel for whether
or not it will be an appropriate prior.  This tool is basically a thin wrapper
around [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html) that
quickly plots the distribution you specify.If you want to fit distributions to data or do fancier things in your terminal
check out [visidata](https://github.com/saulpw/visidata).![image](./img/palette.png)# Installation$ pip install disterminal– also ensuregnuplotis in your path.# Usage Instructions$ disterminal -h# License/Attributions
Free software: MIT licenseThis package was created with
[Cookiecutter](https://github.com/audreyr/cookiecutter) and the
[audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage)
project template.