Saravanan666/pypi-org huggingface dataset:
Package: craigslist
Python wrapper for craigslist. Requires Python 3.6+.Installpip3 install --upgrade craigslistCLI$ craigslist
usage: craigslist [-h] {search,list} ...

examples:
craigslist search worcester apa
craigslist search newyork tlg
craigslist search sfbay mis
craigslist search washingtondc apa --postal 20071 --search_distance 1
craigslist search newyork aap --postal 10023 --search_distance 1 --hasPic --availabilityMode within_30_days --limit 100
craigslist search sfbay ccc --postal 94305 --search_distance 1 --limit 10
craigslist search vancouver sss "shoes" --condition new like_new --hasPic --max_price 20 --limit 10
craigslist search washingtondc jjj --is_telecommuting --is_internship
craigslist list areas
craigslist list categories

positional arguments:
  {search,list}
    search       search
    list         list

optional arguments:
  -h, --help     show this help message and exitFor more details, try:$ craigslist search
$ craigslist listAPISee theexamplesfolder.simpleimportcraigslistforpostincraigslist.search('washingtondc','apa',postal=20071,search_distance=1):print(post)# craiglist posts expire. replace the url below with something currentpost=craigslist.get('https://washingtondc.craigslist.org/nva/apa/5875729002.html')asyncimportasyncioimportcraigslistasyncdefmain():asyncforpostincraigslist.search_async('washingtondc','apa',postal=20071,search_distance=1):print(post)# craiglist posts expire. replace the url below with something currentpost=awaitcraigslist.get_async('https://washingtondc.craigslist.org/nva/apa/5875729002.html')loop=asyncio.get_event_loop()loop.run_until_complete(main())loop.close()DevelopmentSetupmake installTestmake testDisclaimerThis library is not associated with Craigslist.Please read the Craigslistterms of
use.
Package: craigslist-rental-market
Display Craigslist rental market statistics.InstallationInstall fromPyPIvia
pip:pip3 install --user craigslist-rental-marketCompatible with Python 3 only.UsagecraigslistAll HTTP requests are cached for 30 minutes (so you can re-run similar
queries without additional traffic). To disable this behavior, use the--no-cacheoption flag.Example[?] Which site?:
 > sf bayarea
   atlanta
   austin
   boston
   chicago
   dallas
   denver
   detroit
   houston
   las vegas
   los angeles
   miami
   minneapolis
   new york
   orange co
   philadelphia
   phoenix
   portland
   raleigh
   sacramento
   san diego
   seattle
   wash dc

[?] Which region?:
   all SF bay area
   east bay
   north bay
   peninsula
 > san francisco
   santa cruz
   south bay

[?] Which neighborhood?:
   None
   alamo square / nopa
   bayview
   bernal heights
   castro / upper market
   cole valley / ashbury hts
   downtown / civic / van ness
   excelsior / outer mission
   financial district
   glen park
   haight ashbury
   hayes valley
   ingleside / SFSU / CCSF
   inner richmond
   inner sunset / UCSF
   laurel hts / presidio
   lower haight
   lower nob hill
   lower pac hts
   marina / cow hollow
   mission district
   nob hill
   noe valley
   north beach / telegraph hill
   pacific heights
   portola district
   potrero hill
   richmond / seacliff
   russian hill
 > SOMA / south beach
   sunset / parkside
   tenderloin
   treasure island
   twin peaks / diamond hts
   USF / panhandle
   visitacion valley
   west portal / forest hill
   western addition

[?] How many bedrooms?:
   1br
 > 2br
   3br
   4br
   5br
   6br

Running query...

Sourced 288 prices in 0.233 seconds

Mean:   $5359.29
Median: $5167.50
Hi/Lo:  $6701.58/$4017.00
StDev:  1342.29
Package: craigslist-reposter
craigslist-reposterSimple automation of craigslist listing renewal and reposting:Selenium to access craigslist.com, IMAPClient package to authenticate loginUser information is imported from environment variablesPings an optional Discord webhook when reposts are completedInstallationgitclonehttps://github.com/PorkyC/craigslist-reposter.gitcdcraigslist-reposter
pipinstall-rrequirements.txtUsageSet environment variables (e.g. for Windows)SETXCL_USERcraigslist_login@gmail.com
SETXCL_PASScraigslistpassword123
SETXCL_EMAIL_PASSgmailpassword123# Optional: if using a webhook:SETXDISCORD_WEBHOOKdiscord.com/webhook-addressChange CLICK_TYPE in craigslist.py depending on whether script is running on Windows or Mac# CLICK_TYPE configures which key is used to open the post in a new tabCLICK_TYPE=Keys.COMMAND# Use this if you are on a MacCLICK_TYPE=Keys.CONTROL# Use this if you are on a PCSet maximum days a post can be active before reposting, and imap address in reposter.py# MAX_POST_DAYS configures the maximum number of days after posting before repostingMAX_POST_DAYS=11# IMAP_SERVER for email provider, default for gmailIMAP_SERVER='imap.gmail.com'Run reposter.py (can also be scheduled, e.g. Launchd on MacOS) with arg[1]: max reposts allowed and arg[2]: max renewals allowed.python3reposter.py38TODO:Improve repost process so that no additional configuration needed between MacOS and WindowsSave and reload Selenium browser session to reduce new logins and verificationsAdded 1/14/2023Filter verification emails by subject
Package: craigslistscraper
CraigslistScraperNote:CraigslistScraper is for personal use and data science only.Updated (12/26/2023):Craigslist made a few updates a couple of months ago that broke
the previous version of this library. The recent are a full refactor on this project, that
also fix the changes Craigslist made. That said, the new version 1.1.1 is not backwards
compatible with the previous version 1.0.1.CraigslistScraper is a lightweight tool for scraping Craigslist. Users can define
what they would like to search for, then CraigslistScraper can fetch and parse data
from both searches and individual ads.There are no official docs, but the code-base is ~200 lines of code and is documented.Table of ContentsInstallationUsageAnalyzingLicenseInstallationTo install the package just run:pip install craigslistscraperThe only requirements are Python 3.7+, and therequestsandbeautifulsoup4libraries.UsageCraigslistScraper is built around 6 functions/classes for flexibility. These
functions/classes are listed below.For general searches:SearchSearchParserfetch_searchFor single ads/posts:AdAdParserfetch_adSearchParser and AdParser are BeautifulSoup-like abstractions for extracting certain
fields from the html data received from Craigslist. Developers may find this useful.Search and Ad are classes that lazily fetch data from user-defined searches and
ads. To define a search you need at least a query and city, and to define an ad you
need at least a url. Examples are provied below and in theexamples/folder.fetch_search() and fetch_ad() are eager and functional implementations that return a
Search and Ad.Below is a simple example, more examples can be found in theexamples/folder.importcraigslistscraperascsimportjson# Define the search. Everything is done lazily, and so the html is not# fetched at this step.search=cs.Search(query="bmw e46",city="minneapolis",category="cto")# Fetch the html from the server. Don't forget to check the status.status=search.fetch()ifstatus!=200:raiseException(f"Unable to fetch search with status <{status}>.")foradinsearch.ads:# Fetch additional information about each ad. Check the status again.status=ad.fetch()ifstatus!=200:print(f"Unable to fetch ad '{ad.title}' with status <{status}>.")continue# There is a to_dict() method for convenience.data=ad.to_dict()# json.dumps is merely for pretty printing.print(json.dumps(data,indent=4))AnalyzingData can easily be converted to your json, csv, etc. and used in various
downstream data analysis tasks.This data can then be analyzed, some examples include:LicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: craigslist-scraper
Provide information for craigslist related metadataUsageThis is a developer focused library to provide information from craigslist:>>> from craigslist_scraper import scrape_url
>>> data = scrape_url('http://craigslist.com/mcy/1234.html')
>>> print(data.title)
'2015 Victory Vegas 8-Ball'LicenseCopyright (c) 2015-2017 Jon RobisonSee included LICENSE for licensing information
Package: craigslist-watcher
Craigslist WatcherDescriptionEmails a list of urls to new posts found on craigslist page.Installationpip install craigslist-watcherorpipenv install craigslist-watcherArgumentsurl - The craigslist page to watchemail-to-addr/email_to_addr - The email to send the new posts to.email-from-addr/email_from_addr - The email to send the new posts from.email-password/email_password - The password for the email-from account.email-host/email_host - The ip/hostname of the email provider.email-port/email_port - The port to send the smtp message over.email-subject/email_subject - The subject of the emails that are sent.watch-interval/watch_interval - The number of seconds between each watch.watch-duration/watch_duration - The number of minutes to do all watching for.Usage>> craigslist_watcher --url "https://southcoast.craigslist.org/search/jjj?query=&excats=&cat_id=11&userid=&postedToday=1&search_distance=&postal=" --email-to-addr "recipient_email_here@gmail.com" --email-from-addr "sending_email_here@gmail.com" --email-password "123456789" --email-host "smtp.gmail.com" --email-port 587 --email-subject "New craigslist posts" --watch-duration 1 --watch-interval 30from craigslist_watcher import Watcher

watcher = Watcher(
    url="https://southcoast.craigslist.org/search/jjj?query=&excats=&cat_id=11&userid=&postedToday=1&search_distance=&postal=",
    email_to_addr="recipient_email_here@gmail.com",
    email_from_addr="sending_email_here@gmail.com",
    email_password="123456789",
    email_host="smtp.gmail.com",
    email_port=587,
    email_subject="New craigslist posts",
    watch_duration=1,
    watch_interval=30
)

watcher.start()
Package: crak
crakA password file dictionary based password crackerrequirementslinux shadow password file containing passwords to attackfile containing word dictionary.usageinstallationpip install crakhelpcrak -h
usage: crak [-h] [--version] [-d WORDDICT] [-u USERNAME] shadowfile

Perform a dictionary crack on a list of encrypted passwords using a wordlist

positional arguments:
  shadowfile            Shadow password file containing crypted passwords

optional arguments:
  -h, --help            show this help message and exit
  --version             prints version information
  -d WORDDICT, --worddict WORDDICT
                        Word dictionary files to use in attack
  -u USERNAME, --username USERNAME
                        specific username - if not defined then all users in
                        shadow file will be attemptedlist userswill list all valid users in the shadow file and is useful to see what username to target specificallycrak shadow 

-=( crak v 0.0.1 )=-

list of usernames in shadow file
                                                        password
username                                                        
root           $6$QlJt0cnr$hmgN/fzUrHFFI1SaGXVNzE060TPuwsZdzP...
backup         $6$Tye3KuC5$rVIT3u5M9IhZZI.jRanteGT3o7DbkLFWb/...
allison        $6$sPsSvR2J$wk59pi4or6QR5IobArTZpn4k7i2jZQ07pY...
paul           $6$YGG4oFLp$avrVGY6.S59aApmCY/60A7AWfGDBh/zI7L...
dr_balustrade  $6$3kgge6ym$OcIOZS8bJy41YsLYXToOW2Ag3imG1KEXkP...dictionary attack all userswill run a dictionary attack against all the valid users in the shadow filecrak shadow -d dicttest.txt

-=( crak.py v 0.0.1 )=-

starting to crack passwords for all users
No password found: root
No password found: backup
No password found: allison
No password found: paul
*** FOUND PASSWORD ***
Username: dr_balustrade
Password: pinky
**********************Specify user to perform brute force attack onWill target a specific usercrak shadow -d dicttest.txt -u dr_balustrade

-=( crak v 0.0.1 )=-

starting to crack password for user: dr_balustrade
*** FOUND PASSWORD ***
Username: dr_balustrade
Password: pinky
**********************Display versiondisplays the current versioncrak --version
crak v0.0.1
Package: cral
CNN Research Abstraction LibraryThe CNN Research Abstraction Library or CRAL in short is a deep learning computer vision library for data scientists, researchers, and developers. With a primary focus on applied deep learning, the CRAL library encourages rapid development and comes with ready-to-use state-of-the-art networks and other pragmatic tools for a variety of applications in the computer vision space.Our aim is also to make it easier to reproduce and extend the results of various Deep Learning-powered Computer Vision (DLCV) algorithms developed in academia and industrial labs.List of AlgorithmsObject detectionRetinaNetyolov3SSDFasterRCNNInstance SegmentationMaskRCNNSemantic SegmentationUNetUNet ++Deeplabv3+FpnNetPspNetSegNetLinkNetGuiding PrinciplesSimple:To make it easy for deep learning engineers & students alike to use neural networks to build computer vision applications of their choice, using low code approach.Fast:To accelerate going from experimentation to a working model.Reproducible:To offer implementations that can easily be trained and reproduced on your own data.ComponentsCRAL has a modular design to enable you to use each of its components independently, Alternatively, you can use the pipeline to get started quickly with multiple networks out-of-the-box.ComponentsDescriptionCNN modelsReady to use implementations of State-of-the-art (SOTA) algorithms.Pipeline toolsLoad and validate your data before you start training.Optimization and debuggingIntegration with Experiment Tracking, HP Optimization and other toolsets to help faster and build transparent modelsDetailed documentation:Link
Package: cram2fastq
cram2fastqA python script to retrieve and convert crams from irods to fastq files. For internal use within the sanger HPC environment.Installationpipinstallcram2fastq# orpipinstallgit+https://github.com/clatworthylab/cram2fastq.gitBefore using the tool, check that:samtools is available on your$PATH. If not:exportPATH=/nfs/team297/kt16/Softwares/samtools-1.11/bin:$PATHREF_PATHis set as well. If not:exportREF_PATH=/lustre/scratch117/core/sciops_repository/cram_cache/%2s/%2s/%s:/lustre/scratch118/core/sciops_repository/cram_cache/%2s/%2s/%s:URL=http:://refcache.dnapipelines.sanger.ac.uk::8000/%sIf setting it up for the first time, just do this once:echo'export PATH=/nfs/team297/kt16/Softwares/samtools-1.11/bin:$PATH'>>~/.bashrcecho'export REF_PATH=/lustre/scratch117/core/sciops_repository/cram_cache/%2s/%2s/%s:/lustre/scratch118/core/sciops_repository/cram_cache/%2s/%2s/%s:URL=http:://refcache.dnapipelines.sanger.ac.uk::8000/%s'>>~/.bashrcsource~/.bashrcInstructionsusage:cram2fastq.py[-h][--metaMETA][--studySTUDY][--outpathOUTPATH][--bulk][--bsub][--DNAP][--queueQUEUE][--ncpuNCPU][--memMEM][--dryrun]optionalarguments:-h,--helpshowthishelpmessageandexit--metaMETAtxt/csvfilecontainingtheSANGERSAMPLEIDSaspermanifestasaseparatelineforeachsample.--studySTUDYStudyID.Thiswillbethenameoftheoutputfolder.--outpathOUTPATHPathtothedirectoryholdingtheconvertedfiles.--bulkIfpassed,assumefileisbulkdataratherthan10xdata.--bsubIfpassed,submitsasjobtobsub.--DNAPIfpassed,treatssamplesascreatedusingsemiautomatedpipelinefromDNAP(i.e.sameIDforGEX/TCR/BCR).Outputwillbeseparatedasfolders.--queueQUEUEbsubqueue.Onlyworksif--bsubispassed.--ncpuNCPUbsubncpu.Onlyworksif--bsubispassed.--memMEMbsubmemory.Onlyworksif--bsubispassed.--dryrunIfpassed,printscommandratherthanactuallyrun.After installation, it is as easy as doing:cram2fastq.py--metasampleids.txt--studytest--outpath/path/to/folder--bulkAdding the--bsuboption will submit this as a job if you have many samples to process.cram2fastq.py--metasampleids.txt--studytest--outpath/path/to/folder--bulk--bsubsampleids.txtis simply a single column.txtor.csvfile with the sanger sample ids (no header).
The IDs should correspond toSANGER SAMPLE IDcolumn in the manifest.For example:SangerSampleID00000001
SangerSampleID00000002
SangerSampleID00000003OutputOnce it is all finished, a folder (with the name as whatever you provide for--study) will be created under--outpathwith the appropriate.cramfiles converted to.fastq.gzfiles.
Package: cramer
CramerA library designed to solve systems of linear equations using the cramer solution method.In order to function correctly, it is necessary to have a system with the same number of unknowns as equations.Functions:matr:generates an n-dimensional matrix with the matrices from which the different coefficients will be extracted. The first matrix inside the array corresponds to the "original" deltadelta:receives a matrix as input and returns a coefficient, calculated by subtracting the products obtained by the cross multiplication.Example Code:#import numpy, cramer and creates a matrix with the factors to which each unknown number is multiplied and at the end of each row it adds the result of the linear equation

import numpy as np
from cramer import cramer

b = [[2 , 4 , 9 , 2],
     [4 , 7 , 2 , 3],
     [9 , 5 , 3 , 1]]

b = np.asarray(b)

# create an instance of the cramer class
cr = cramer(b)

#gets an array with the matrices corresponding to each coefficient
mds = cr.matr()

# generate a list with the coefficients obtained in each matrix after using the matr() method
deltas = []
for i in mds:
    deltas.append(cr.delta(i))

# to obtain the values ​​of each unknown, divide the different coefficients obtained by the first one, corresponding to that of the matrix of unknowns

for i in range(1 , len(deltas)):
    print("Variable" , i , ":")
    print(deltas[i] / deltas[0])
Package: cramjam
cramjam-pythonAPI DocumentationInstallpip install --upgrade cramjam  # Requires no Python or system dependencies!CLIA CLI interface is available ascramjam-cliExtremely thin Python bindings to de/compression algorithms in Rust.
Allows for using algorithms such as Snappy, without any system dependencies.This is handy when being used in environments like AWS Lambda, where installing
packages likepython-snappybecomes difficult because of system level dependencies.BenchmarksSome basic benchmarks are availablein the benchmarks directoryAvailable algorithms:SnappyBrotliBzip2Lz4GzipDeflateZSTDLZMA / XZ (cramjam.experimental.lzma)  # experimental support!All available for use as:>>>importcramjam>>>importnumpyasnp>>>compressed=cramjam.snappy.compress(b"bytes here")>>>decompressed=cramjam.snappy.decompress(compressed)>>>decompressedcramjam.Buffer(len=10)# an object which implements the buffer protocol>>>bytes(decompressed)b"bytes here">>>np.frombuffer(decompressed,dtype=np.uint8)array([98,121,116,101,115,32,104,101,114,101],dtype=uint8)Where the API iscramjam.<compression-variant>.compress/decompressand acceptsbytes/bytearray/numpy.array/cramjam.File/cramjam.Bufferobjects.de/compress_intoAdditionally, all variants supportdecompress_intoandcompress_into.
Ex.>>>importnumpyasnp>>>fromcramjamimportsnappy,Buffer>>>>>>data=np.frombuffer(b'some bytes here',dtype=np.uint8)>>>dataarray([115,111,109,101,32,98,121,116,101,115,32,104,101,114,101],dtype=uint8)>>>>>>compressed=Buffer()>>>snappy.compress_into(data,compressed)33# 33 bytes written to compressed buffer>>>>>>compressed.tell()# Where is the buffer position?33# goodie!>>>>>>compressed.seek(0)# Go back to the start of the buffer so we can prepare to decompress>>>decompressed=b'0'*len(data)# let's write to `bytes` as output>>>decompressedb'000000000000000'>>>>>>snappy.decompress_into(compressed,decompressed)15# 15 bytes written to decompressed>>>decompressedb'some bytes here'Special note!If you know the length of the de/compress output, you
can provideoutput_len=<<some int>>to anyde/compressto get ~1.5-3x performance increase as this allows single
buffer allocation; doesn't really apply if you're usingcramjam.Bufferorcramjam.Fileobjects.
Package: cramjam-cli
cramjam-cliInstall  (only via pip or conda for now)pip install --upgrade cramjam-cli  # Requires no Python or system dependencies!Simple CLI to a variety of compression algorithmsAvailable algorithms:snappybrotlibzip2lz4gzipdeflatezstdAll available for use as:cramjam-clisnappycompress--inputmyfile.txt--outputmyfile.txt.snappy
cramjam-clilz4compress--inputmyfile.txt# omitting --output will write to stdoutcatmyfile.txt|cramjam-clizstdcompress--outputmyfile.txt.zstd# omitting --input will read from stdin
Package: crams-allocation
Crams AllocationCrams Allocation is a Django app package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "crams_allocation" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'crams_allocation',
]Include the crams_allocation URLconf in your project urls.py like this::path('', include('crams_allocation.urls')),
Package: crams-collection
Crams CollectionCrams Collection is a Django app package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "crams_collection" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'crams_collection',
]Include the crams_collection URLconf in your project urls.py like this::path('', include('crams_collection.urls')),
Package: crams-compute
Crams Compute PackageCrams Compute is a Django app package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "crams_compute" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'crams_compute',
]Include the crams_compute URLconf in your project urls.py like this::path('', include('crams_compute.urls')),
Package: crams-contact
Crams ContactCrams Contact is a Django app package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "crams_contact" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'crams_contact',
]Include the crams_contact URLconf in your project urls.py like this::path('', include('crams_contact.urls')),
Package: crams-log
Crams Log PackageCrams Log is a Django app package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "crams_log" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'crams_log',
]Include the crams_log URLconf in your project urls.py like this::path('', include('crams_log.urls')),
Package: crams-member
Crams Member PackageCrams Member is a Django app package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "crams_member" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'crams_member',
]Include the crams_member URLconf in your project urls.py like this::path('', include('crams_member.urls')),
Package: crams-notification
Crams Notification PackageCrams Notification is a Django app package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "crams_notification" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'crams_notification',
]Include the crams_notification URLconf in your project urls.py like this::path('', include('crams_notification.urls')),
Package: crams-provision
Crams Provision PackageCrams Provision is a Django app package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "crams_provision" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'crams_provision',
]Include the crams_provision URLconf in your project urls.py like this::path('', include('crams_provision.urls')),
Package: crams-reports
Crams Reports PackageCrams Reports is a Django app package for CRAMS api open source. Detailed documentation is in the "docs" directory.
Souce code is available athttps://github.com/CRAMS-Dashboard/crams-apiQuick startAdd "crams_reports" to your INSTALLED_APPS setting like this::INSTALLED_APPS = [
...
'crams_reports',
]Include the crams_reports URLconf in your project urls.py like this::path('', include('crams_reports.urls')),