Saravanan666/pypi-org huggingface dataset:
Package: mmarch-pack
Mapped Memory Friendly Archive Format - MMArchGoalsProvide extensible platform-independent file archive format (what an invention!)Effective in-memory access, O(1) full-path file access, O(N) readdir (file lists are immediately available)Memory mapped - Rely on OS caching instead of caching in userspace.No dynamic memory allocationAll function compiled in native and foreign endianess mode, speeding up native accessStructurec/ C-librarypython/ python archiverunpack/ simple unpacker, test tool for c libraryFile FormatSome design considerations regarding format:Use only 32/64 bit types for effective aligned accessNo obscure arithmetics, all of offsets in header — absoluteGroup all headers at first 4G, saving on inner offsetsCould be either LE or BE1. File Header

u32     "MARC" magic, "CRAM"
u32     version, current == 1 (still experimental)
u32     page size, normally 4096, but you can use any arbitrary number, alignment for file data
u32     total header size. If you want to mmap header, use this number (aligned to page boundary)
u64     total file size. Aligned to page size. Reader shall not read anything past this point.

//all the following table offsets in header should be aligned (at least 4 bytes)
u32     object record table offset
u32     filename table offset
u32     readdir table offset

2. Object Record Table Header
u32     record fields counter, R (could be viewed as version, every record here has R*4 bytes size)
u32     record count, N. Total record counter. Size of this table is N * R + this header
u32     directory count, DN, all IDs below DN is directories

N *

3. Object Record, each of size R * u32
u64     file data offset (0 for directories)
u64     file size (0 for directories)
u32     full path offset (usually string pool below, must've been within header size)
u32     full path length (strings ARE NOT zero terminated, you shall read exact name size bytes from name offset)

[string pool]

4. Filename table
u32     hash function id. (0 - FNV1, 1 - FNV1A, 2 - PYTHON, 3 - R5A)
u32     bucket count, B

Filename table index:
(B + 1) * u32
u32     offset to bucket 0
u32     offset to bucket 1
…
u32     offset to the end of bucket B-1

Filename table entry:
u32     object id (see object record table, p.2)

5. Readdir table
(D + 1) * readdir table index follows
u32     offset to object list for directory 0
u32     offset to object list for directory 1
…
u32     offset to the end of for directory D - 1

Readdir table entry
u32     object id (see object record table, p.2)
u32     local name offset (no path)
u32     local name length

[gap to align to page size]
[file data referenced by offset/size in object table]
[gap to align to page size]
[file data referenced by offset/size in object table]
[gap to align to page size]
[file data referenced by offset/size in object table]
Package: mmath
Bienvenue sur le guide de Mmath !Le but de Mmath est de donner avec la plus grande précision possible la valeur certifiée du sinus de tout nombre réel (angle en radians modulo 2pi entre 0 et 2pi).Cette précision théorique est de l'ordre de 10-100et utilise les formules de Taylor-Lagrange pour les développements limités et les suites de Cauchy pour en certifier la convergence donc la stabilité et la précision.Ce module pourra dans l'avenir être complété avec d'autres champs comme le cosinus et la tangente.
Il est réalisé dans un but scolaire par Ibrahima BAH, Niloofar BAKHSHY, Nongma SORGHO et Kamran ASADOV.Exemple d'utilisation1. Installationpipinstallmmathou encorepython3-mpipinstallmmath2. Importimportmmathou encorefrommmathimport*3. Obtention du sinus de pi/4 radimportmathvaleur=mmath.Mmath(math.pi/4).sinouimportmathvaleur=Mmath(math.pi/4).sinselon les imports.DISCLAIMERCe module n'a pas vocation à remplacer le module de base math. Il est réalisé à titre scolaire et les auteurs ne sauraient en aucun cas être tenus responsables de sa mauvaise utilisation.
Package: mmaze
MMazeA python maze generator and solver.UsageGenerating a maze with specific width and height. Print on screen directly.importmmazem=mmaze.generate(width=3,height=3)print(m)"""||||||||||||||||      ||  ||||||||  ||  ||||  ||      ||||  ||||||  ||||          ||||||||||||||||"""Plot the maze to image.importmmazem=mmaze.generate(width=3,height=3)m.plot()Get solution and plot on screen:importmmazem=mmaze.generate(width=3,height=3)solutions=m.solve(start=(0,0),end=(2,2))print(m.tostring(solution=solutions[0],start=(0,0),end=(2,2)))"""||||||||||||||||S ********||||  ||||||**||||  ||    **||||  ||||||**||||  ||    E ||||||||||||||||"""Generate a solution and plot to an image.importmmazem=mmaze.generate(width=10,height=10)solutions=m.solve(start=(0,0),end=(9,9))m.plot(solution=solutions[0],start=(0,0),end=(9,9))To make a symmetric maze by passing a symmetry method. Note that width or height must be odd number when you want to
solve the generated maze.In this repo, only backtracking / growingtree / huntandkill / prims algorithms can generate symmetric maze.importmmazestart=(0,0)end=(10,10)m=mmaze.generate(width=11,height=11,symmetry="horizontal")solutions=m.solve(start=start,end=end)m.plot(solution=solutions[0],start=start,end=end)Installpip install mmazeMore demo use casesDemo can be found in test file:tests
Package: mmb
MMB is a command line tool for saving the state of an Elektron Monomachine:pip install mmbUsageConnect the monomachine to a midi device and run:mmb backup --port="Name of midi device"This will save all the kits, patterns, songs and global settings. To view a
list of all backups run:mmb infoAnd to restore the monomachine to a previous state, set the momomachine to
recieve Sysex messages and run:mmb send <BACKUP_ID>To configure mmb to always use a specific port run:mmb configureAnd save the port name in the opened settings file.
Package: mmbi
monitorThe monitor support stat the data from github lark and webot and save data to mongodbinstallaiongitclonehttps://github.com/vansin/monitor.gitcdmonitor
pipinstall-v-e.configsetting your envexportMONGO_URL=mongodb://localhost:27017exportfeishu_webhook_url=https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxx
Package: mmc
mmcmmcis aminimalMattermostclient for Python. It calls the v4 API endpoints and is meant for basic
uses.Installpip install mmcUsagefrommmcimportMattermostm=Mattermost("chat.example.com",access_token="...",team_id="...",team_slug="my-team",)print("Teams:")forteaminm.get_teams():print(f"*{team['display_name']}")print("Users:")foruserinm.get_users():print(f"*{user['username']}")forpostinm.get_channel_posts(channel_id="..."):print(post["message"])
Package: mmcif
mmCIF Core Access LibraryIntroductionThis module includes a native Python mmCIF API for data files and dictionaries along withpybind11wrappers for the PDB C++ Core mmCIF Library.InstallationDownload the library source software from the project repository:gitclone--recurse-submoduleshttps://github.com/rcsb/py-mmcif.gitOptionally, run test suite using the Tox test runner. The C++ library bindings have been tested
on Centos 7/Ubuntu 20.04 Linux with GCC/G++ > 4.8.5 and MacOS (10.15) with > clang-900.0.39.2 using
Python versions 2.7.18 and 3.9.4.toxInstallation is via the programpip.pipinstallmmcif

orfromthelocalrepository:

pipinstall.To generate API documentation usingSphinx:cdscripts# Check Sphinx dependencies in the introductory comments to the following script../initdocs.shA command-line script is provided as a preprocessor for modular dictionaries that
include definition and data content using categories pdbx_include_dictionary,
pdbx_include_category and pdbx_include_item.build_dict_cli--help
usage:build_dict_cli[-h]--opOP--input_dict_pathINPUT_DICT_PATH[--output_dict_pathOUTPUT_DICT_PATH][--cleanup]optionalarguments:-h,--helpshowthishelpmessageandexit--opOPOperation(build|get_version)--input_dict_pathINPUT_DICT_PATHPathtodictionarygeneratorfile--output_dict_pathOUTPUT_DICT_PATHPathtooutputdictionarytextfile--cleanupRemoveincludeinstructioncategoriesafterprocessing
________________________________________________________________________________
Package: mmcif-pdbx
PDBx/mmCIF Dictionary ResourcesThis is yet another PyPI package forhttp://mmcif.wwpdb.org/pdbx-mmcif-home-page.html.
It emphasizes a simple and pure Python interface to basic mmCIF functionality.The canonical mmCIF Python package can be found athttps://github.com/rcsb/py-mmcif.
It is full-featured and includes C/C++ code to accelerate I/O functions.Origin of this softwareAll of the code in this repository is based onhttp://mmcif.wwpdb.org/.
Specifically, this code is directly derived fromhttp://mmcif.wwpdb.org/docs/sw-examples/python/src/pdbx.tar.gzlinked fromhttp://mmcif.wwpdb.org/docs/sw-examples/python/html/.
Seehttp://mmcif.wwpdb.org/docs/sw-examples/python/html/for more information about the original package, including examples.VersionsVersions 0.* maintain API compatibility with the original code.
Subsequent versions break that compatibility, primarily by renaming methods in compliance with PEP8.Installing this softwareThis python package can be installed viasetuptools,pip install ., or viaPyPI.More information about this softwareMore information about this software can be found inthe documentation.
Guidelines for community behavior are provided in thecode of conductand information on contributing to the software is provided in ourcontribution guide.
Package: mmcif.sitegen
mmCIF Dictionary Site GeneratorIntroductionThis module contains a collection of utilities to generate HTML and image
content for themmCIF Resource SiteInstallationInstall from PyPi using:pipinstallmmcif.sitegenTo install from source, download the library source software from the project repository:gitclonehttps://github.com/rcsb/py-mmcif_sitegen.gitcdpy-mmcif_sitegen
pipinstall-e.Optionally, run the test suite, using:pythonsetup.pytestor

toxA command line entry point with the following options is provided to generate html and images:site_generator_cli--help

usage:SiteGeneratorExec.py[-h][--web_gen_pathWEB_GEN_PATH][--web_file_assets_pathWEB_FILE_ASSETS_PATH][--html][--images][--test_mode_flag]optionalarguments:-h,--helpshowthishelpmessageandexit--web_gen_pathWEB_GEN_PATHToppathtowebsitegeneratedcontent--web_file_assets_pathWEB_FILE_ASSETS_PATHToppathforwebsitesourcefileassests--htmlGenerateHTMLcontent--imagesGenerateimagecontent--test_mode_flagTestmodeflag(default=False)For example, the following script illustrates the steps required to
materialize the dynamic content and integrate this with static files
of the mmCIF resource site.#!/bin/bashcd/var/wwwecho"# Cloning static content for mmCIF website"gitclonehttps://github.com/rcsb/mmcif_website.gitecho"# Cloning file assets for mmCIF website"gitclonehttps://github.com/rcsb/mmcif_website_file_assets.git# ---echo"# Generating image content"/usr/local/bin/site_generator_cli--images\--web_gen_path/var/www/mmcif_website_generated\--web_file_assets_path/var/www/mmcif_website_file_assets#echo"# Generating site content"/usr/local/bin/site_generator_cli--html\--web_gen_path/var/www/mmcif_website_generated\--web_file_assets_path/var/www/mmcif_website_file_assets#echo"# Update search indices"/var/www/mmcif_website/mmcif_cgi/swish/MakeIndices.sh
Package: mmcq-numba
mmcq_numbaFaster MMCQ algorithm ( analyze dominant colors in image) with numba in pythonInstallationpip install mmcq-numbaUsagefrommmcq_numba.quantizeimportmmcqcolor_count=8# the number of dominant colorsquantize=5path=<pathtoimage>rgb=cv2.cvtColor(cv2.imread(path),cv2.COLOR_BGR2RGB)width,height,c=rgb.shapergb_resize=cv2.resize(rgb,(width//quantize,height//quantize))width,height,c=rgb_resize.shapecolors=rgb_resize.reshape(width*height,c).astype(np.int64)# input type must be 2d arrays((size, channels)), and dtype=np.int64c_map=mmcq(colors,color_count)ReferenceThis project is based onmmcq.py
Package: mmcq.py
mmcq.py – Python implementation of Modified Median Color Quantization (MMCQ)goal of this project is writeJS implemntation MMCQin python.MMCQ?See more atColor quantization using modified median cut by Dan S. BloombUsageTo get sample palette, you can usemmcq.get_palette.frommmcqimportget_palettefromPILimportImage,ImageDraw# create an imageout=Image.new("RGB",(1000,1000),(255,255,255))d=ImageDraw.Draw(out)withget_palette('something.jpg',8)aspalette:fori,colorinenumerate(palette):d.rectangle((((i-1)*100,0),(i*100,100)),fill=color)out.show()To get dominant color, you can usemmcq.get_dominant_colorwhich color is frist ofmmcq.get_palette.>>>frommmcqimportget_dominant_color>>>get_dominant_color(filename='/image/something.jpg')(255,234,0)See more atColor thief.Changelog0.1.0Only Python3 support.Default image library changed from Wand to Pillow.
Package: mmcrypto
mmcryptoPython package get and save crypto data from wazirx.Type these in our linux terminal to understand the usagepip3installmmcryptopython3>>>frommmcryptoimportCrypto,Price>>>directory_to_save_data='/home/user/data/'>>>Crypto.update(directory_to_save_data)>>>Crypto.all(directory_to_save_data)>>>crypto=Crypto('btc',directory_to_save_data)>>>list(crypto.get_prices())>>>forpincrypto.get_prices():...forattrinPrice.attrs:...print(attr,p[attr])...print()...>>>
Package: mmct
mmctProvides functionality for performing multinomial tests using monte carlo simulationBackgroundThis library contain python code that can be used to test whether a given set of observations are
likely to be drawn from a multinomial distribution with a given set of parameters (probabilities
for each case). The test is done using Monte Carlo methods. A bunch of random samples from the
hypothesised distribution is drawn and the probability of each sample is calculated.  These are all
compared to the probability of the sample under test. The p-value of the null-hypothesis (that the
sample comes from the distribution) is simply the fraction of random samples with probability
smaller than the sample under test.The library is inspired bymet, which achieve the same objective
as mmct, but does so by painstakingly enumerating every possible case for the given multinomial
distribution and calculating the p-value exactly. While this is certainly preferable, it becomes
very slow very quickly, so for performing many tests or tests with a large parameter space, a
monte carlo approximation may be good enough. mmct has also drawn inspiration
from theXNomialpackage,
which performs an identical task in the R programming language.UsageThe package is most easlily installed via pip:pip install mmctThe source code is also available on GitHub and is free for use and modification:mmct on GitHubWhen the package has been installed, a test can be performed following the example below, in which
we test whether a set of dice rolls could have been generated from rolling two fair dice 20 times
and adding the eyes:import mmct
import numpy as np
#     Eyes    2  3  4  5  6  7  8  9 10 11 12
x = np.array([0, 0, 2, 4, 5, 2, 3, 1, 0, 1, 2])
# Hypothsised probabilities:
p = np.array([1/36, 2/36, 3/36, 4/36, 5/36, 6/36, 5/36, 4/36, 3/36, 2/36, 1/36])
# Initialise tester:
t = mmct.mt_tester() # Use the multithreaded tester class
# Set number of Monte Carlo samples to generate
t.n_samples = 100000
pval = t.do_test(x,p)The result of the test will of course vary (unless the random simulator is seeded), but should in
general result in a p-value around 0.34, i.e. we cannot reject the hypothesis that the numbers
above are taken from a fair dice rolling (which they actually are).
Package: mmcterm
mmctermTerminal for the custom "serial over IPMB" protocol used by DMMC-STAMP.Installationpip3 install mmctermUsagemmcterm [-h] [-v] [-c CHANNEL] [-t INTERVAL] [-l] [-d] [-i] [-m MAX_PKT_SIZE] mch_addr mmc_addr

DESY MMC Serial over IPMB console

positional arguments:
  mch_addr              IP address or hostname of MCH
  mmc_addr              IPMB-L address of MMC or "AMCn" (n=1..12)

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit
  -c CHANNEL, --channel CHANNEL
                        console channel (default 0)
  -t INTERVAL, --interval INTERVAL
                        polling interval in ms (default 10)
  -l, --list            list available channels
  -d, --debug           pyipmi debug mode
  -i, --ipmitool        make pyipmi use ipmitool instead of native rmcp
  -m MAX_PKT_SIZE, --max-pkt-size MAX_PKT_SIZE
                        max IPMB packet size to use (Higher numbers give better performance, but can break depending on MCH model)ChannelsThe DMMC-STAMP always provides channel 0 for the MMC console. This is also the default channel, if the-cargument is not given tommcterm. Depending on the board implementation, there can be up to 2 additional channels for UARTs of payload FPGAs. The presence and names of those channels can be queried with the-largument.Note that only one channel can be opened to a MMC at the same time.ExampleOpen a console on AMC board at IPMB address 0x7a connected to the MCHmskmchhvf1.tech.lab:mmctermmskmchhvf1.tech.lab0x7aMax packet sizeWithout the-moption,mmctermuses the standard maximum IPMB packet size of 32 bytes. Due to the limitations of the IPMI protocol, this can quickly become a bottleneck, esp. when used with a SoC running Linux. To mitigate this bottleneck, bigger packet sizes can be set with-m. For NAT MCHs,-m 100was found to be working, even though it is more than 3 times the standard packet size.Protocol description"Serial over IPMB" is a lightweight "serial data forwarding" protocol using custom IPMI messages.Get channel infoNetFn 0x30, Command 0xf0Request:ByteContents0Channel NumberResponse:ByteContents0..nChannel NameStart/stop SOI sessionNetFn 0x30, Command 0xf1Request:ByteContents0Channel Number11 = start, 0 = stop2Max packet size (optional)Poll/exchange dataNetFn 0x30, Command 0xf2Request:ByteContents0..nSend data from terminal to MMCResponse:ByteContents0..nReceive data from MMC to terminal
Package: mmcv-win
IntroductionMMCV is a foundational python library for computer vision research and supports many
research projects in MMLAB, such asMMDetectionandMMAction.It provides the following functionalities.Universal IO APIsImage processingVideo processingImage and annotation visualizationUseful utilities (progress bar, timer, …)PyTorch runner with hooking mechanismVarious CNN architecturesSee thedocumentationfor more features and usage.InstallationTry and start withpip install mmcvor install from sourcegit clone https://github.com/open-mmlab/mmcv.git
cd mmcv
pip install .  # (add "-e" if you want to develop or modify the codes)
Package: mmd5sum
mmd5sum: calculate multiple MD5s using multiple processingIf you have a huge many files that want to calculate MD5,
you will needmmd5sum.mmd5sumcalculate a directory
of files (can have sub-directories) using multiple processes.installationpipinstallmmd5sumfeaturescan specify a directory (can have sub-directories)can read calculated results to avoid recalculatingcan specify how many processes to usedefault using local machine commandmd5sum, but can specify another oneusagemmd5sum-d/path/to/directory-omd5.txtIf you have a file that some file already calculated:mmd5sum-d/path/to/to/directory-iold_md5.txt-onew_md5.txtIf you want faster:mmd5sum-d/path/to/directory-omd5.txt-p10-p 10means you will have 10 processes in parallel, default is 4.mmd5sumdefault usemd5sumcommand to calculate MD5,
if the command calculate MD5 on you machine isn'tmd5sum:mmd5sum-d/path/to/directory-omd5.txt-mmd5-m md5means the command on your machine ismd5。
Package: mmdb
MMDBCreate a MaxMind Databases for your own needs.pip install mmdb[cli]FeaturesQuery any maxmind database:mmdb get <IP> -d <DATABASE>Download and buildDBIPdatabaseASN Lite,Country Lite, andCity Lite:mmdb dbip-buildCreate an IP database from a CSV file:mmdb build <CSV>LogstashGeoIP Filter Plugincompatibility:mmdb build <CSV> --lscAdditional country data such asis_eu,is_nato, oris_g7:mmdb build <CSV> -f countryExamplesLogstash CompatibilityLogstash ships with theGeoIP Filter Pluginwhich enriches a document with IP GeoData. However, the plugin supports specific MaxMind database types only.
As a result, any other database type disables the plugin.Regarding this, the plag--lscenables logstash support. Long story short:
You get a MaxMind ASN Database, but the IP info as an embedded json string within theasn_organization_namefield. The logstash pipeline must load that json data and adds it to
the document, exemplified belowfilter {
  geoip {
    source => "ip"
    database => "/path/to/my/database.mmdb"
    ecs_compatibility => disabled
    target => "wrapped_ip_data"
  }
  json {
    source => "[wrapped_ip_data][organization_name]"
    target => "myip"
  }
  mutate {
    remove_field => ["wrapped_ip_data"]
  }
}
Package: mmdb-writer
MaxMind-DB-Writer-pythonMakemmdbformat ip library file which can be read bymaxmindofficial language readerThe official perl writerwas written in perl, which was difficult to customize. So I implemented theMaxmindDB formatip library in python languageInstallpipinstall-Ugit+https://github.com/VimT/MaxMind-DB-Writer-pythonUsagefromnetaddrimportIPSetfrommmdb_writerimportMMDBWriterwriter=MMDBWriter()writer.insert_network(IPSet(['1.1.0.0/24','1.1.1.0/24']),{'country':'COUNTRY','isp':'ISP'})writer.to_db_file('test.mmdb')importmaxminddbm=maxminddb.open_database('test.mmdb')r=m.get('1.1.1.1')assertr=={'country':'COUNTRY','isp':'ISP'}Examplesseecsv_to_mmdb.pyReference:MaxmindDB formatgeoip-mmdb
Package: mmdesigner
Author:Roie R. BlackEmail:roie.black@gmail.comDocs:https://rblack42.github.io/math-magiktestsThis project usesOpenSCADand a few supporting libraries to generate 3D models
of free-flight model airplanes. The accompanying Python software assists
in creating a design and performs analysis on the design to produce an estimate
of the weight and center of gravity of the design. This project is the subject
of an article in the 2021 edition of the National Free Flight Society
Symposium.The project documentation is available at the link above. There you will find
material detailing the Python code developed to support this project, and
information on howOpenSCADcan be used to build 3D models of proposed designs.
The primary focus of this documentation is the design of a Limited Pennyplane
indoor model, but other designs will be included here as well.If you are interested in assisting with the development of this project, or
need help with anything found here, please contact me by email. I welcome
questions and ideas for extending this project.
Package: mmdf
MacroMolecular DataFrames (mmdf)MacroMolecularDataFrames (mmdf) is a small package for reading macromolecular structure files
(.pdb/.mmCIF) into pandas dataframes.The heavy lifting of reading structure files is performed bygemmi.Usageimport mmdf

df = mmdf.read('4v6x.cif')
df.head()
Out[3]: 
  model chain residue  residue_id  ...       z charge occupancy  b_isotropic
0     1    Az     ASN           3  ... -54.829      0       1.0         10.0
1     1    Az     ASN           3  ... -54.691      0       1.0         10.0
2     1    Az     ASN           3  ... -53.642      0       1.0         10.0
3     1    Az     ASN           3  ... -53.007      0       1.0         10.0
4     1    Az     ASN           3  ... -54.239      0       1.0         10.0
[5 rows x 13 columns]