Saravanan666/pypi-org huggingface dataset:
Package: hlso
Haplotype-LSOHaplotype assignment ofCandidatus Liberibacter solanacearumfollowing IPPC (International Plant Protection Convention) standardDP 21: Candidatus Liberibacter solanacearum.Official Web AppTutorialQuick FactsLicense: MITProgramming Language PythonInput / Output - What it Does!This program takes as the input Sanger sequences from the 16S, 16S-23S, and 50S primers from the IPPC standard DP21.
It then aligns them to the GenBank reference sequencesEU812559andEU834131(as specified in DP21).
Based on the alignments and the document DP21, sequence identity is computed and haplotyping is performed, yielding:sequence identity toEU822559for identifying the speciesC. Liberibacter solanacearum, andhaplotyping of the read based on variation from the reference sequence.Sample names can be inferred from the read names or from a separate mapping TSV file.Quick StartThis is gonna be really quick!InstallationWe recommend usingBioconda.Firstinstall Bioconda.
Then (clsifyis the old package name of Haplotype-Lso and it will be renamed soon):# conda install -y clsifyAnd – tadaa – you’re ready to go!RunningYou can have one FASTA (or FASTQ) file with all of your reads or one file for each.
If you have a single sequence per FASTA (or FASTQ) file then you can use the file name instead of the sequence name.# hlso -o result.tsv INPUT.fasta
## OR
# hlso [--use-file-name] -o result.tsv INPUT1.fasta INPUT2.fasta [...]
## e.g.,
# hlso [--use-file-name] -o result.tsv INPUT*.fastaDeveloper GuideReleasing PackagesFor thePyPi package:$pythonsetup.pysdist$twineupload--repository-urlhttps://test.pypi.org/legacy/dist/hlso-*.tar.gz$twineuploaddist/hlso-*.tar.gzFor the Bioconda package, seethe great documentation.
The Docker image will automatically be created as a BioContainer when the Bioconda package is built.Historyv0.4.4Fix file conversion problem.v0.4.3Fixing issue with AB1/SCF support.v0.4.2Fix for multi-region sequences.Fixing collision, variant must be identified by sequence, position, and reference.v0.4.1Fixing bug with display of BLAST match.Adding link-out to NCBI WWWBLAST submission.Removing some cruft from repository.Fixing bug in phylogenetic coputation in case of BLAST all-to-all matches not a square number.v0.4.0Compute region-wise phylogenetic tree.v0.3.4Support for uploading to PyPi.v0.3.3Fixing regular expression.v0.3.2Changing file name pattern do dot-separated scheme.Greatly extending documentation.v0.3.1Starting out with tutorial and manual.v0.3.0Adding tutorial.Rebranding as “Haplotype-LSO” (hlso).Properly normalizing indels according to Tan et al. (2015).Adding support for haplotyping with indels.v0.2.0Removing dependency on bcftools.
Haplotyping is done from BLAST match now.Adding tests forblastmodule.Rewrite of the whole BLAST and haplotyping interface and architecture.v0.1.1Zapping gremlins in haplotype table.Change formatting of README.v0.1.0Initial release.Everything is new!
Package: hlsorigin
DescriptionThis package contains useful scripts to manipulate HLS manifests at
origin or edge server.ScriptDescriptionhls-captureCreate a VOD playlist between two timestamps in a LIVE playlisthls-live-from-vodCreate a LIVE playlist from a set of VOD playlistshls-startoverCreate a LIVE playlist from a specific starttime in another LIVE playlistPreconditionsThese scripts are based on the assumption that all HLS manifest files
and segments are archived at the origin server where these scripts are
executed. The archive for a specific live stream contains the history
for all HLS manifest updates. For example:Live HLS stream called ‘foo’ is archived in the directory /archive/foo
on the server. It contains all updated HLS manifest files with a unix
timestamp when it was last updated.master800.m3u8-1472977456
master800.m3u8-1472977465
master800.m3u8-1472977475All video segments are also stored in the same directory.InstallationFrom sourcegit clone https://github.com/Eyevinn/hls-origin-scripts.git
cd hls-origin-scripts
python setup.py installFrom Python Package Indexpip install hlsoriginUsageThese scripts are executed by the request handler at the origin
webserver. The scripts output the generated manifest file to stdouthls-captureGenerate a VOD playlist from a LIVE HLS where segments and manifest
files are archived at /hlsarchive/stream/. A 20 minutes VOD manifest
from 11:45 to 12:05 is created in this case.hls-capture /hlsarchive/stream/ --mediaplaylist master800.m3u8 --in "2016-09-04 11:45:00" --out "2016-09-04 12:05:00"By default any cueout periods (e.g. ads) are removed and a discontinuity
tag is added in the generated manifest. Available options:--help                  show help message and exit
--in TS             YYYY-mm-dd HH:MM:SS
--out TS                YYYY-mm-dd HH:MM:SS
--mediaplaylist PL  name of media playlist
--noremovecueout        do not remove segments in cue out periodshls-startoverGenerate a new LIVE HLS playlist with a new start position. This is
useful when needing a startover functionality but the user should not be
able to watch before the startover position.hls-startover /hlsarchive/stream/ --mediaplaylist master800.m3u8 --startover "2016-09-04 16:25:00"Available options:--help                  show help message and exit
--mediaplaylist PL  name of media playlist
--startover TS      YYYY-mm-dd HH:MM:SShls-live-from-vodTBDContributionWe welcome contributions to this project. Just follow the normal
procedures by forking this repository, create a topic branch for your
fix and then submit a pull request.LicenseSee LICENSE for details
Package: hlsscraper
HLS-scraperThis project is a webscraper for theHistorical Dictionary of Switzerland (HDS).InstallationpipinstallhlsscraperUsagePlease use the already scrapedhls_base.csvfrom 12.12.2022 as basis so only updates and new records need to be fetched.
This will help not to stress HLS servers to much.importhlsscraperhlsscraper.scrape(base_csv_path=f"{os.getcwd()}/data/hls_base.csv",update_csv_path=f"{os.getcwd()}/data/hls_updates.csv",new_csv_path=f"{os.getcwd()}/data/hls_new.csv",last_scraping="12.12.2022",crawl_delay=20,# as per https://hls-dhs-dss.ch/robots.txt)Development# download a fresh python 3.9condacreate-npy39python=3.9# create a .venv inside the project and link against the Python 3.9 version installed through condapoetryenvuse~/.conda/envs/py39/bin/python# install required packages defined in pyproject.toml into .venvpoetryinstall# set up git hooks for autoformatting and linting (black, isort8, flake8) --> .pre-commit-config.yamlpre-commitinstall
Package: hls_tools
hls-toolsPython package to help work with the Harmonized Landsat Sentinel 2 remote sensing dataset.Developmentgitclonehttps://github.com/hrodmn/hls-tools.gitcdhls-toolsCreate a virtual environmentpython3-mvenvenvsourceenv/bin/activateInstall test/dev dependenciespipinstall-e.["test","dev"]Linting and formatting are handled withpre-commit.
You will need to install pre-commit before committing any changes:pre-commitinstall
Package: hl-tables
hl_tablesA high level tables dispatcher for putting together multiple tables executorsExamplesMaking a histogramdataset=EventDataset(f'localds://mc16_13TeV:{ds["RucioDSName"].values[0]}')df=xaod_table(dataset)truth=df.TruthParticles('TruthParticles')llp_truth=truth[truth.pdgId==35]histogram(llp_truth.Count(),bins=3,range=(0,3))plt.yscale('log')plt.xlabel('Number of good LLPs in each event')plt.ylabel('a MC Sample')The histogram data will be calculated by the backend and returned to your local Jupyter instance.Plots will be rendered!Outstanding thingsDefinitely need to decide on an approach to this whole thing. Reducers - and where should they be applied, at the outer most or inner most level? So seq.count() - should that mean seq.Select(a: a.count()), or seq.count() (number of events, or a list of objects inside the event)?Count needs to be changed to num or dimensions, etc.
Package: hltv-data
Welcome to hltv-data 👋 🎮Data from popular CS:GO website hltv.orgInstallpipinstallhltv-dataUsageThe public methods can be reached usingHLTVClientfromhltv_dataimportHLTVClienthltv_client=HLTVClient()Current rankinghltv_client.get_ranking()Response:[{"position":1,"name":"Natus Vincere","points":1000},{"position":2,"name":"Gambit","points":822},{"position":3,"name":"G2","points":629},{"position":4,"name":"FaZe","points":409},{"position":5,"name":"Virtus.pro","points":362},{"position":6,"name":"Astralis","points":346},{"position":7,"name":"Heroic","points":333},{"position":8,"name":"NIP","points":287},{"position":9,"name":"Vitality","points":282},{"position":10,"name":"OG","points":241},{"position":11,"name":"BIG","points":239},{"position":12,"name":"Spirit","points":231},{"position":13,"name":"Liquid","points":196},{"position":14,"name":"mousesports","points":183},{"position":15,"name":"forZe","points":179},{"position":16,"name":"FURIA","points":176},{"position":17,"name":"Complexity","points":164},{"position":18,"name":"Entropiq","points":151},{"position":19,"name":"Sinners","points":102},{"position":20,"name":"ENCE","points":100},{"position":21,"name":"Renegades","points":94},{"position":22,"name":"SKADE","points":94},{"position":23,"name":"AGO","points":89},{"position":24,"name":"K23","points":82},{"position":25,"name":"Extra Salt","points":79},{"position":26,"name":"Endpoint","points":77},{"position":27,"name":"Copenhagen Flames","points":76},{"position":28,"name":"Evil Geniuses","points":71},{"position":29,"name":"FunPlus Phoenix","points":60},{"position":30,"name":"DBL PONEY","points":58},]Matcheshltv_client.get_matches()Response:[{"event":"ESL Pro League Season 16","date":"2022-09-10T16:00:00","team_1":"Outsiders","team_2":"MIBR","star_rating":1},{"event":"ESL Pro League Season 16","date":"2022-09-10T19:30:00","team_1":"FaZe","team_2":"G2","star_rating":3},...]Resultshltv_client.get_results()Response:[{"event":"ESL Pro League Season 14","team_1":{"name":"Vitality","result":2},"team_2":{"name":"Natus Vincere","result":3}},{"event":"Fragadelphia 15","team_1":{"name":"Third Impact","result":1},"team_2":{"name":"Coldest Riders","result":2}},...]Author👤Dariusz ChoruzyWebsite:http://choruzy.com/Github:@dchoruzyLinkedIn:@dchoruzy🤝 ContributingContributions, issues and feature requests are welcome!Feel free to checkissues page.Show your supportGive a ⭐️ if this project helped you!
Package: hltv-stats
Hi, I analyze hltv.org as a part of my pet project.
This parser can help you build a prematch analytics dataset with data fromTeam statsandAnalyticspages.Installpipinstallhltv-statsUsageHLTVMatchprovides public methods forAnalytics, usefilenameparameter to save data to a file.fromhltv_statsimportHLTVMatchmatch_url="/matches/2361342/natus-vincere-vs-outsiders-iem-katowice-2023"match=HLTVMatch(match_url)match.parse_analytics_summary(filename=None)Response:[{'team':'natus-vincere','indicator':'plus','insight':'natus vincere has better form ranking','match_id':'2361342'},...]match.parse_head_to_head()Response:[{'player_team':'natus-vincere','player_nickname':'s1mple','table_3_months':'1.13','table_event':'1.17','match_id':'2361342'},...]match.parse_pick_ban_stats()Response:[{'analytics_map_name':'mirage','team':'natus-vincere','analytics_map_stats_pick_percentage':'39%','analytics_map_stats_ban_percentage':'0%','analytics_map_stats_win_percentage':'29%','analytics_map_stats_played':'7','analytics_map_stats_comment':'First pick','match_id':'2361342'},...]match.parse_analytics_center()method combines all above methods and returns a tuple of lists.HLTVTeamprovides public methods for parsingTeam stats page, with filtering by time usingtime_filterparameter, usefilenameparameter to save data to a file.fromhltv_statsimportHLTVTeamteam=HLTVTeam("/4608/natus-vincere")#You can use match_id to assign team's current state(statistic) to specific match#this will add match_id field to all json filesteam.match_id=match.match_id#time_filter: 3 - last 3 months, 6 - last 6 months, 0 - all time, ...team.parse_matches(time_filter=1)#returns list of played matches in json formatteam.parse_players(time_filter=1)#returns list of team players' statistics in json formatteam.parse_maps(time_filter=1)#returns maps statistics in json formatteam.parse_events(time_filter=1)#returns events statistics in json formatteam.parse_all_stats(time_filter=1).parse_all_stats(time_filter=1)method combines all above methods and returns a tuple of lists.parse_upcoming_matches()method parses all upcoming matches fromMatch page.#Parse upcoming matches and save to json files#:param months: list of months to parse, i.e. [1,3] <=> [last month, last 3 months]#:param with_teams: bool, if True, parse teams' statistic as wellfromhltv_statsimportparse_upcoming_matches
parse_upcoming_matches(months=[1],with_teams=True)Result folder tree contains json files with all match data, and team data if with_teams=True.
.
├── configs - contains 2 config files: which holds data about all parsed matches and teams and are used by is_parsed() method to check if match is already parsed and skips it.
├── output
│   ├── matches 
│   └── teamsAlso check out example.ipynb or contact me.🤝 ContributingContributions, issues and feature requests are welcome!Feel free to checkissues page.Show your supportGive a ⭐️ if this project helped you!
Package: hlucb
Hamming-LUCBThehlucbpackage is a Python implementation of the paperApproximate ranking from pairwise comparisons.Heckel, R., Simchowitz, M., Ramchandran, K., & Wainwright, M. (2018, March). Approximate ranking from pairwise comparisons. In International Conference on Artificial Intelligence and Statistics (pp. 1057-1066). PMLR.Installationhlucbis installable with pip:$pipinstallhlucbUsageRanking with a comparatorfromhlucbimportHammingLUCBitems=[4,1,2,6,5,8,9,3]k=5h=2delta=0.9defcompare(item_a:int,item_b:int)->bool:returnitem_a>item_bscores,bounds=HammingLUCB.from_comparator(items,k,h,delta,compare,seed=42)print("Scores: ",scores)print("Bounds: ",bounds)Ranking with a generatorfromhlucbimportHammingLUCBitems=[4,1,2,6,5,8,9,3]n=len(items)k=5h=2delta=0.9generator=HammingLUCB.get_generator(n,k,h,delta,seed=42)scores=Nonebounds=Nonefor(i,j),(scores,bounds)ingenerator:comparison=items[i]>items[j]generator.send(comparison)print("Scores: ",scores)print("Bounds: ",bounds)IntuitionThe Hamming-LUCB algorithm approximately ranks $n$ items, estimates the score of each item, and provides confidence bounds for each score. The intuition behind the approximate ranking is that it's easier to compare items with very different scores, so it should be possible to separate high-scoring items from low-scoring items with few comparisons and high confidence even if the exact ranking is not discovered.The sets of high- and low-scoring items are designated $S_1$ and $S_2$ respectively. Hamming-LUCB extracts $S_1$ and $S_2$ such that all items in $S_1$ are expected to have higher scores than all items in $S_2$.Parameters:$n$ - the total number of items$k$ - the number of items to extract as high-scoring items$h$ - half the margin between $S_1$ and $S_2$$\delta$ - confidence parameter for the probability of achieving $h$-Hamming accuracyDefinitions:The Hamming distance between two sets: $D_H(S_1, S_2) = \lvert (S_1 \cup S_2) \setminus (S_1 \cap S_2) \rvert$A ranking $\hat{S_1}$, $\hat{S_2}$ is $h$-Hamming accurate if: $D_H(\hat{S_l}, S_l) \leq 2h$ for$\lvert \hat{S_l} \rvert = \lvert S_l \rvert$$l \in {1, 2}$A ranking algorithm is $(h, \delta)$-accurate if the ranking returned is $h$-Hamming accurate with probability at least $1 - \delta$.
Package: hlvox
HLVoxOriginally intended to create sentences from the word snippets for the
Half Life 1 Vox, this project can take a folder of word audio files and piece
them into sentences.
This repo when used standalone (ie by runningvoice.pydirectly) will output
the sentences as audio files.Getting StartedTo use this project, you will first need a folder full of voice files.
If you have Half Life 1 installed, the voices can be extracted from the VPK
files using something like GCFScape.PrerequisitesCheck pipenv file for Python package requirements.An installation of FFMpeg is required. On Ubuntu, just run the following:apt update
apt install ffmpegFor persistence, a localsqlitedatabase can be used, or apostgresqldatabase. These are not exposed through the basic CLI.InstallingHow to set up a basic dev environment:Pull this repogit pull https://gitlab.com/bhagen/hlvox.gitChange into the new repo directorycd hlvoxInstall requirements using pipenvpipenv installEnter pipenvpipenv shellQuick StartIf you want to jump right into generating a sentence, you can pointvoice.pyat a folder of voice audio files!python hlvox/voice.py --voice-dir ~/my_folder_of_voice_files "the sentence I want to generate"Your sentence will be exported in the same directory
that you ran the file from.Installing package locally for developmentAfter entering pipenv shell:pip install --editable .Running the testsJust runpytestfrom the base directory of the repoDocumentation GenerationI usedSphinxto create autodoc documentation
from docstrings in the modules.Change into doc directorycd doc(If you've modified the source code, grab the new apidocssphinx-apidoc -f -o ./source ../hlvoxGenerate documentationmake htmlAuthorsBlair Hagen-Initial work-bhagenLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for detailsAcknowledgmentsThanks to Valve for making such an amazing voice
Package: hlwy-lsh
hlwy-lshLSH is a Python implementation of locality sensitive hashing with minhash. It is very useful for detecting
near duplicate documents.The implementation uses the MurmurHash v3 library to create document finger prints.Cython is needed if you want to regenerate the .cpp files for the hashing and shingling code. By default
the setup script uses the pregenerated .cpp sources, you can change this with the USE_CYTHON flag in
setup.pyNumPy is needed to run the code.The MurmurHash3 library is distributed under the MIT license. More informationhttps://github.com/aappleby/smhasherInstallation$pipinstallhlwy-lsh
…✨🍰✨
Package: hm
hmMnemonic toolsTo install:pip install hmMnemonic Major SystemThemajor systemis a mnemonic technique used to aid in memorizing numbers.It works as follows:>>> from hm import MajorSystem
>>> m = MajorSystem()The "Mnemonic Major System" (https://en.wikipedia.org/wiki/Mnemonic_major_system)
assigns a set of similar phonemes to each digit:>>> assert m.phones_for_num == {
...     0: {'S', 'Z'},
...     1: {'D', 'DH', 'T', 'TH'},
...     2: {'N'},
...     3: {'M'},
...     4: {'R'},
...     5: {'L'},
...     6: {'CH', 'JH', 'SH'},
...     7: {'G', 'K'},
...     8: {'F', 'V'},
...     9: {'B', 'P'}
... }As a consequence these phonemes are mapped to numbers:>>> assert m.num_of_phone == {
...     'B': 9,
...     'CH': 6,
...     'D': 1,
...     'DH': 1,
...     'F': 8,
...     'G': 7,
...     'JH': 6,
...     'K': 7,
...     'L': 5,
...     'M': 3,
...     'N': 2,
...     'P': 9,
...     'R': 4,
...     'S': 0,
...     'SH': 6,
...     'T': 1,
...     'TH': 1,
...     'V': 8,
...     'Z': 0
... }Any sentence has a corresponding phoneme sequence:>>> m.term_to_phones('wild cat')
['W', 'AY1', 'L', 'D', 'K', 'AE1', 'T']The system doesn't contain all phonemes; only some of the consonant phonemes.
So if we only keep those phonemes that the system covers, we get:>>> m.term_to_mst_sequence('wild cat')
['L', 'D', 'K', 'T']Which corresponds to a number.>>> m.term_to_nums('wild cat')
[5, 1, 7, 1]But really, the system is used to be able to create words (therefore images)
that correspond to a sequence of numbers, so that one can remember them:>>> m.terms_of_numstr['3214']  # doctest: +NORMALIZE_WHITESPACE
['hammontree', 'mahindra', 'manteer', 'mantra', 'mentor', 'minteer', 'mondry',
'monetary', 'monteiro', 'monterey', 'montero', 'monterrey', 'montrouis',
'montroy', 'montuori', 'omohundro']
Package: hmac
HMACThis is a stand alone packaging of the hashlib compatible hmac library
included with Python 2.5 so that it can be used together with the hashlib
backport on older versions of Python (tested on 2.4). Not needed with Python
2.5 or later.Changelog20101005Initial standalone releaseImport hmac.py from Python 2.7 (hg revision a7637168ee41)
Package: hmac_authentication
# hmac_authentication Python packageSigns and authenticates HTTP requests based on a shared-secret HMAC signature.Developed in parallel with the following packages for other languages:
- Go: [github.com/18F/hmacauth](https://github.com/18F/hmacauth/)
- Ruby: [hmac_authentication](https://rubygems.org/gems/hmac_authentication)
- Node.js: [hmac-authentication](https://www.npmjs.com/package/hmac-authentication)## Installation`sh $ pip installhmac-authentication`## Validating incoming requestsInject something resembling the following code fragment into your request
handling logic as the first thing that happens before the request body is
parsed, whereheadersis a list of headers factored into the signature andsecret_keyis the shared secret between your application and the service
making the request:_…coming soon…_## Signing outgoing requests_…coming soon…_## Running tests`sh $ pip install nose $ nosetests `## Public domainThis project is in the worldwide [public domain](LICENSE.md). As stated in [CONTRIBUTING](CONTRIBUTING.md):> This project is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the [CC0 1.0 Universal public domain dedication](https://creativecommons.org/publicdomain/zero/1.0/).
>
> All contributions to this project will be released under the CC0
>dedication. By submitting a pull request, you are agreeing to comply
>with this waiver of copyright interest.
Package: hmacchanger
MAC Address ChangerThe aim of this module is to build a MAC changer which is used to change theMAC addressof the systems runningLINUXoperating systems with simple and easy commands. This is helpful to maintain privacy in thisOnline World!Requirements:net-toolsInstallation:sudo pip install hmacchanger
sudo apt install net-tools          //as mentioned in requirementsUsage:For help just typesudo hmacchanger --helpin linux terminal.Specify'-i'or'--interface'for selecting the interface of which you want to change the MAC address.
Specify'-m'or'--mac'to give your desired MAC address as input.Syntax1: sudo hmacchanger -i  -m <MAC_address>// here  = eth0,wlan0,etc. and <MAC_address> = Any MACeg. sudo hmacchanger -i wlan0 -m 00:11:22:33:44:55Syntax2: sudo hmacchanger -i// here  = eth0,wlan0,etc.eg. sudo hmacchanger -i wlan0//random mac address will be auto generated.Syntax3: sudo hmacchanger -m <MAC_address>// here <MAC_address> = Any MACeg. sudo hmacchanger -m 00:11:22:33:44:55//Interface will be asked as input.Syntax4: sudo hmacchangereg. sudo hmacchanger//Interface will be asked as input and random mac address will be auto generated.Author:Name:Hariprasath.S.SContact:hariprasath6112001@gmail.comPyPi:https://pypi.org/user/hariprasath6112001/Github:https://github.com/hairprasath6112001Report Bugs and Improvements@hariprasath6112001@gmail.com
Package: hmac_cli
Simple CLI for encrypting data with a private key, using HMAC$ hmac --help
Usage: hmac [OPTIONS] INPUT_FILE

  Encrypt data in INPUT_FILE using HMAC

  with provided secret cryptographic key

Options:
  --key TEXT                      encryption key. Will prompt for if not
                                  provided.
  --algorithm [md5|sha1|sha224|sha256|sha384|sha512]
                                  algorithm to use
  --help                          Show this message and exit.

$ hmac --key foo README.rst
515902266c1a02e60452d152855aa3b46ed5b006

$ echo "Hello" | hmac --key foo -
aabbe57c6a0ba953cd907071004a1db16fe78891

$ hmac -
Key: ******
Repeat for confirmation: ******
Here is some text
^D
c7ab7eed09fff2d0fcf258dfde8222d264013c4f
Package: hmac-http
hmac-httpPython 3 library for thedraft-cavage-http-signatures-10specification.UsageCurrently, only verifying requests is supported.To verify requests, runhmac_http.verify(request, secret)function, whererequestis a request from an ASGI-based application, andsecretis the secret shared between the client and reciever:fromhmac_httpimportverifysecret="asdf"verify(request,secret)