Saravanan666/pypi-org huggingface dataset:
Package: mfire
MFireStatus (develop branch)Qu'est-ce-que Prométhée ?Prométhéeest un projet visant à automatiser la production de bulletins météorologiques. Ce projet se matérialise par une chaîne de traitement complexe allant de la configuration de bulletin sur Métronome, le traitement des données et la production des composants de bulletin sur super-calculateur, la mise à disposition de ces composants dans un service d'accès (CDP), et enfin l'assemblage de bulletins sur Métronome.Qu'est-ce-que MFire ?MFireest la librairie Python créée dans le cadre du projet Prométhée permettant de produire des composants de bulletin météo. Même si cete librairie est développée dans l'idée d'être déployée et utilisée sur super-calculateur, elle est parfaitement utilisable sur toute machine disposant des dépendances requises.InstallationL'installation se fait par le dépôt Python standard de Météo-France : leNexus(documentation du Nexus)pipinstall--index-urlhttp://nexm01-sidev.meteo.fr/repository/pypi-releases-mfire/simple--trusted-hostnexm01-sidev.meteo.frmfireDépendancesLes dépendances sont les suivantes :name: mfire-env
dependencies:
  - bandit=1.7.4
  - black=22.3.0
  - bottleneck=1.3.4
  - cfgrib=0.9.10.1
  - coverage=6.4.1
  - dask=2022.6.0
  - eccodes=2.26.0
  - flake8=4.0.1
  - geojson=2.5.0
  - geopandas=0.11.0
  - netcdf4=1.5.8
  - numpy=1.23.0
  - opencv=4.5.5
  - pandas=1.4.3
  - pillow=9.1.1
  - pint=0.19.2
  - pydantic=1.9.1
  - pytest=7.1.2
  - python=3.10.5
  - scikit-image=0.19.3
  - scikit-learn=1.1.1
  - scipy=1.8.1
  - shapely=1.8.2
  - tensorflow=2.8.1
  - xarray=2022.3.0
  - pip:
    - mflog==0.1.0
    - centerline==0.6.3
    - geojsoncontour==0.4.0
    - opencv-python==4.5.5
    - opencv-python-headless==4.6.0.66Note :Il est recommandé d'installer ces dépendances dans un environnementcondaen utilisant ce fichierenvironment.ymlet grâce à la commandeconda env create -f environment.yml.Installation en développementPour une utilisation en développement, il vous faut :une installation de conda (Minicondapar exemple) ;la commandemake;cloner ce dépôt git ;une fois dans le dossiermfire, exécuter :make installVoir la documentation complète d'installation en développementDocumentationVoir la documentation d'utilisationContribuer à MFireVoir leguide de contributionLicenceLicence
Package: mflag
mflag: powerful Python toolkit for mark/unmark files and directoriesWhat is it?mflagis a Python package that provides marking/unmarking files and directores. For years, dealing with processing files, we understood that we need to put flag for files/directories that are already processed. This package provides the ability to mark files/directories and avoid any unnecessary repeatitive processing of same files/directories.
It also can manage serveral processes that willing to write on/in same files/directories. For example, we want to write to a parquet file, the whole processing generate part of the parquet file managed by a separate parallel processes. Usingmflagavoid writing errors that happen at same time.Where to get itThe source code is currently hosted on GitHub at:https://github.com/MosesDastmard/mflagBinary installers for the latest released version are available at thePython
Package Index (PyPI)# !pip install flagifyfrommflagimportFlagimportpandasaspdimportos# lets make a simple csv filedata={'name':['mflag','pandas','numpy'],'toolkit':['marking','data analysis','data computing']}pd.DataFrame(data).to_csv('data.csv',index=False)classProcess(Flag):def__init__(self,process_name):self.process_name=process_nameFlag.__init__(self,process_name)defrun(self,csv_path,parquet_path):ifnotself.isFlagged(csv_path):pd.read_csv(csv_path).to_parquet(parquet_path)self.putFlag(csv_path)else:print(f"the file{csv_path}is already processed")Process(process_name='convert_csv_to_parquet').run('data.csv','data.parquet')Process(process_name='convert_csv_to_parquet').run('data.csv','data.parquet')the file data.csv is already processed
Package: mflike
An external likelihood usingcobaya.Installing the codeThe easiest way to install and to usemflikelikelihood isviapippipinstallmflikeIf you want to dig into the code, you’d better clone this repository to some locationgitclonehttps://github.com/simonsobs/LAT_MFLike.git/where/to/cloneThen you can install themflikelikelihood and its dependenciesviapipinstall-e/where/to/cloneThe-eoption allow the developer to make changes within themflikedirectory without having
to reinstall at every changes. If you plan to just use the likelihood and do not develop it, you can
remove the-eoption.Installing LAT likelihood dataPreliminary simulated data can be found atNERSC. You can download them by yourself but you
can also use thecobaya-installbinary and let it do the installation job. For instance, if you
do the next commandcobaya-install/where/to/clone/examples/mflike_example.yaml-p/where/to/put/packagesdata and code such asCAMBwill be downloaded and installed
within the/where/to/put/packagesdirectory. For more details, you can have a look tocobayadocumentation.Running/testing the codeYou can test themflikelikelihood by doingcobaya-run/where/to/clone/examples/mflike_example.yaml-p/where/to/put/packageswhich should run a MCMC sampler for the simulation n°44 (i.e.data_sacc_00044.fitsin themflike_example.yamlfile) using the combination of TT, TE and EE spectra (i.e.polarizations: ['TT', 'TE', 'ET', 'EE']). The results will be stored in thechains/mcmcdirectory.Simulation releasesv0.8release has spectra produced with high accuracy camb parameters up toell = 9000, the foreground components are integrated in frequency using top-hat bandpasses, stored in thesaccfilesv0.7.1is a bugfix release ofv0.7release where the mono-frequency and dirac bandpasses
have been correctly set with thesaccfilesv0.7release includes the ACT like foregrounds. Simulation parameters are also stored withinsaccmetadata and the associateddictfile can be viewedhere.v0.6release make use of CMB map based simulations (seehttps://github.com/simonsobs/map_based_simulations/blob/master/201911_lensed_cmb/README.md). Only temperature foregrounds were considered.Releases prior tov0.6version were development simulations and should not be used for simulation analysis.Contributors
Package: mflt-build-id
Memfault Build ID ToolThis package contains themflt_build_idCLI tool.The purpose of the tool is simplify reading or writingBuild IDsin a
firmware image irrespective of the compiler or build system being used in a
project.Example Usage$ mflt_build_id --help
usage: mflt_build_id [-h] [--dump [DUMP]] [--crc CRC] [--sha1 SHA1] elf

Inspects provided ELF for a Build ID and when missing adds one if possible.

If a pre-existing Build ID is found (either a GNU Build ID or a Memfault Build ID),
no action is taken.

If no Build ID is found, this script will generate a unique ID by computing a SHA1 over the
contents that will be in the final binary. Once computed, the build ID will be "patched" into a
read-only struct defined in memfault-firmware-sdk/components/core/src/memfault_build_id.c to hold
the info.

If the --crc <symbol_holding_crc32> argument is used, instead of populating the Memfault Build ID
structure, the symbol specified will be updated with a CRC32 computed over the contents that will
be in the final binary.

If the --sha1 <symbol_holding_sha> argument is used, instead of populating the Memfault Build ID
structure, the symbol specified will be updated directly with Memfault SHA1 using the same strategy
discussed above. The only expectation in this mode is that a global symbol has been defined as follow:

const uint8_t g_your_symbol_build_id[20] = { 0x1, };

For further reading about Build Ids in general see:
  https://mflt.io//symbol-file-build-ids

positional arguments:
  elf

options:
  -h, --help     show this help message and exit
  --dump [DUMP]
  --crc CRC
  --sha1 SHA1
Package: mflt-compact-log
Memfault Compact Log LibraryThis library enables decoding Memfault-flavored compact logs. For background
information on compact logs, see here:https://mflt.io/compact-logsUsageSome brief usage information below. See the source for detailed usage.Extracting compact log format strings from .elfTo extract the format strings from the symbol file:frommflt_compact_log.log_fmtimportLogFormatElfSectionParserelf="path to elf file"# parse the elf filemappings=LogFormatElfSectionParser.get_mapping_from_elf_file(elf)# 'mappings' is a dictionary mapping log id to LogFormatInfo dataprint(mappings)>>>{8:LogFormatInfo(filename='./main/console_example_main.c',line=245,n_args=0,fmt='This is a compact log example')}Decoding compact logsTo apply the format string to raw compact log data:frommflt_compact_log.compactimportCompactLogDecoder# example format string; this could instead be retrieved from the elf filefmt="An Integer Format String:%d"# compact log hex encoded raw datacompact_log="820A0B"# decode the log!compact_log_as_bytes=bytes.fromhex(compact_log)log=CompactLogDecoder.from_cbor_array(fmt,compact_log_as_bytes)log.decode()>>>'An Integer Format String: 11'Changes0.0.2support Python 3.9 and 3.10updateprettytabledependency from0.7.2to3.4.1updatepyelftoolsdependency from^0.28.0to^0.29.0
Package: mflux-ai
mflux-aiThis is the officialmflux-aipython library forMFlux.aiFeaturesFetch connection strings and tell MLflow how to connect with MFlux.aiDownload and upload objects/datasets from/to the MFlux.ai cloud serviceQuickstartInstallationpip install mflux-aiBasic usageimportmflux_aimflux_ai.init("INSERT_YOUR_PROJECT_TOKEN_HERE")# MLflow now knows how to connect with your project server, hosted on MFlux.aiStore and retrieve datasetsmy_dataset=np.zeros(shape=(10000,100),dtype=np.float32)dataset_filename="my-dataset.pkl"mflux_ai.put_dataset(my_dataset,dataset_filename)my_loaded_dataset=mflux_ai.get_dataset(dataset_filename)assert_array_equal(my_dataset,my_loaded_dataset)Historyv0.7.0 (2020-01-14)Implementmflux_ai.get_best_run(), which returns the best run in a model group defined in MFlux.ai.v0.6.0 (2019-12-13)Move functions from mflux_ai.mflux_ai to mflux_ai.core. The API stays backwards-compatible for now, but warnings are shown if the old API is used. This change was applied to get cleaner and more consistent import statements.v0.5.3 (2019-09-23)Improve the performance and the support for special characters in object names inmflux_ai.get_datasetby unpickling in memory instead of using a temporary file on disk.Don’t expose non-public variables and imports on the top-level packagev0.5.2 (2019-09-20)Improve the performance ofmflux_ai.put_datasetby pickling in memory instead of using a temporary file on disk.v0.5.1 (2019-09-12)Add support for MLflow authenticationImprove the performance ofmflux_ai.put_datasetCorrectly reset the MinIO client wheninitcompletes successfullySpecify the desired API version and let the user know if an upgrade is neededv0.4.0 (2019-09-01)Markmflux_ai.set_env_vars()as deprecated. Usemflux_ai.init()instead.Remove support for Python 3.4Add support for secure MinIO connectionsv0.3.0 (2019-08-16)Add a functioninitthat will eventually replaceset_env_varsCheck if the provided project token is valid.v0.2.1 (2019-08-16)Set licence to Apache License 2.0Transition from pre-alpha to alpha.v0.2.0 (2019-08-14)Add convenience functions for storing and retrieving datasetsv0.1.1 (2019-08-14)First release on PyPI. Has support for setting environment variables for MLflow based on an MFlux.ai project token.
Package: mfn
mfnImplementation in Python of the MFN method to measure complexity & entropy of time series.
This is useful if you need features for a model such as Porfolio Optimization, clustering of time series, etc.
This method is the implementation of the paperScientific progress in information theory quantifiers.(Chaos, Solitons & Fractals, 170, 113260.,
Martins, A. M. F., Fernandes, L. H. S., & Nascimento, A. D. C. (2023).)Installationpipinstallmfnor using PoetrypoetryaddmfnUsageimportnumpyasnpimportpandasaspdimportseabornassnsimportmatplotlib.pyplotaspltfrommfn.entropyimportMFN## Generating a time series with trend and noise.time_series=np.arange(0,100,1)time_series=time_series+np.random.normal(0,10,size=len(time_series))value_dict=MFN(time_series,b=10,B=.1,size=100,dx=3)f,ax=plt.subplots(figsize=(6,6))value_df=pd.DataFrame(value_dict).reset_index()value_df=value_df.melt(id_vars='index',value_vars=value_df.columns[1:])sns.barplot(value_df,x='variable',y='value',errorbar="sd")plt.title("MFN method results")f.tight_layout()plt.show()
Package: mfnbc
LinksPyPi -https://pypi.python.org/pypi/mfnbc/Github -https://github.com/wharton/mfnbcAbout The MathThis package computes a set of simple arithmetic to calculate the final posterior probabilities for a set of features over a set of texts within a corpus.So for each text in the corpus, the package looks to see if the word in contained in the provided likelihood table. If it is found, the posterior probabilities for each feature is updated as usingBayesian statistics.whereis:RequirementsPython >= 3.3Installpip install mfnbcSetup (Likelihood Input File)It is assumed you have a word based likelihood table (csv file) where
the headers consists of the literal wordWordand the remaining
columns are the features you would like to classify.For example:WordAnimalHumanPlantcat0.330.030.05dog0.330.020.05leaves0.050.030.4tree0.050.020.4man0.120.450.05women0.120.450.05Setup (Unlabeled Data File)IDText1The cat is my pet and he is lovely. A dog will not do.2The man and women had a cat and lived under a tree3The tree had lots of leaves4A man lives under a tree with many leaves. A women has a cat as a pet5The dog and cat chase the man under the tree6The man and women live in a house.The key is having the header titledTextany other fields will be
included unmodified in the output file.ImportfrommfnbcimportMFNBCInstantiatem=MFNBC(<likelihoods_input_file-locationofLikelihoodtable(str)>,<unlabeled_data_file-Locationofunlabeleddatafile(str)>,<verboseoutput-Turnonofoffverboseoutput,default:off><outputfilename-defaultstoout.csv,(str)>Examplem=MFNBC('likeli_sample.csv','input_sample.csv',False,'my_output.csv')m.read_likelihoods()m.calc_posteriors()m.write_csv()or you can do it all in a single commandm=MFNBC('likeli_sample.csv','input_sample.csv',False).write_csv()Example ResultsIDTextAnimalHumanPlant1The cat is my pet and he is lovely. A dog will not do.0.9723214290.0053571430.0223214292The man and women had a cat and lived under a tree0.5807870940.29699340.1222195063The tree had lots of leaves0.015328020.0036787250.9809932564A man lives under a tree with many leaves. A women has a cat as a pet0.3344123860.10260380.5629838145The dog and cat chase the man under the tree0.9218397290.007618510.0705417616The man and women live in a house.0.0656335460.9229717410.011394713
Package: mfn-cli
KNIX MicroFunctions Command Line Interface (CLI)The package provides themfncommand based on theKNIX MicroFunctions SDK.The latest version of the CLI can be obtained fromKNIX releases.
It's also hosted at PyPI and can be installed with the PyPA recommended tool for installing Python packages:pip3installmfn_cliUsing the climfn loginThe login action is used to obtain a valid user token for all subsequent operations.
The token will be updated/stored in ~/.mfn/config.yamlmfnlogin[--url<url>][--user<username>][--password<password>]$ mfn login --url https://knix.io --user test@example.com
Logging into https://knix.io as user test@example.com
Please enter the password: ************
$Commands$ mfn -h

Usage: mfn [OPTIONS] COMMAND [ARGS]...

Options:
  -q          Quiet, (Log level -q: ERROR, -qq: no output).
  -v          Verbose, reduces log levels (-v: INFO, -vv: DEBUG).
  --version   Show the version and exit.
  -h, --help  Show this message and exit.

Commands:
  version    Show software versions used
  login      Log in to your MicroFunctions platform
  invoke     Invoke a workflow.
  deploy     Deploy a workflow
  undeploy   Undeploy a workflow.
  logs       Fetch logs of a workflow.
  create     Create a resource [workflow|function].
  get        Get a resource [workflow|function].
  edit       Edit a resource [workflow|function].
  delete     Delete a resource [workflow|function].
  workflow   Show workflow specification.
  workflows  List workflows.
  function   Show code of a function.
  functions  List functions.
Package: mfNRcatpy
mfNRcatpymfNRcatpy is a Python package designed for analyzing Gravitational-wave data, created and maintained by Sebastian Gomez Lopez. The package includes a variety of tools for working with binary neutron star numerical relativity waveform catalogs and the matched filtering algorithm.InstallationClone the repository from thesourceand run the commandpip install ./mfNRcatpy.Alternatively, you can install it from Pypi by running the commandpip install mfNRcatpy.For those looking to learn more about the package's basic functionalities, we recommend checking out theexample notebooks, which provide detailed explanations.
Package: m-formatter-logging
m-formatter-logging là thư viện python để write log cho các module BackEnd-MobioVersion:Phiên bản hiện tại1.0.4Cài đặt:pip3 install m-formatter-loggingSử dụng:Gọi func log tương ứng với level mong muốn.Sử dụng tham sốsensitive=True(default=False) trong trường hợp dữ liệu log có data nhạy cảm (Ví dụ: thông tin PII của profile, mật khẩu của user, mã voucher,...).Sử dụng key extra để log thêm thông tin (chỉ show trên hệ thống ELK).if __name__ == '__main__':
    def test():
        MobioLogging().info('app_test_lib_logging::test():info', sensitive=True, extra={"key1": "value1"})
        try:
            a = 1 / 0
            MobioLogging().info('__init__::test():a: %s' % a)
        except Exception as ex1:
            MobioLogging().error('app_test_lib_logging::test():error: %s' % ex1, sensitive=True)

        MobioLogging().debug('app_test_lib_logging::test():debug')
        MobioLogging().warning('app_test_lib_logging::test():warning', sensitive=False)

    test()Sử dụng file custom config:config_file_path = 'path/to/config/file'
MobioLogging().file_config(self, config_file_path)Sử dụng custom filter để xử lý cụ thể 1 trường hợp log:class CustomizeFilterRecords():
    def filter(self, record):
        if record.key1 == 'value1':
            return False # no log
        return True

MobioLogging().logger.addFilter(CustomizeFilterRecords())Changelog:v1.0.4:Cập nhật lại mô tả trong README.v1.0.3:Phiên bản đầu tiên.
Package: mfoutparser
mfoutparser
by Arthur G. Palmer, III and Michelle L. GillA series of python subroutines to read and extract information from the
STAR format mfout file generated by the program ModelFree.Unlike the BMRB STAR format, the format used by ModelFree
uses the data_name field, rather than the save_name field
and also allows nested loops (the mfout file has loops nested to
a depth of two only).mfoutparserhas been tested on python 2.7, 3.4, and 3.5. It requires the
Numpy (tested on version 1.10.1) and Pandas (tested on version 0.17.1)
libraries. IPython notebook (version >= 3.2.1) is required to run the
demonstration notebook located in theexamplesdirectory. Matplotlib
is required if plotting of the data is desired.See theexamplesdirectory in the installation for a demonstration or
run the following from the command line:python -c ‘import mfoutparser as mf; mf.copy_examples()’
Package: mfperfquery
Mutual Funds Performance QueryPython library for extracting certain performance parameters of Mutual Funds in India fromhttps://moneycontrol.comVersion 1.0.2IntroductionWere you given a set of Mutual Funds as suggestion to invest? Are you already invested in few mutual funds?Run this script to keep track of their performances and key metrics to help you decide on buying or holding or redeeming your investments.FeaturesGet performance and key metrics for any Mutual Fund in India on-demand.All data from Mutual Funds onhttps://www.moneycontrol.com/mutualfundindia/.Input can be through a direct URL or an Excel file with list of Mutual Funds URLs.Obtains the following performance metrics and parameters:Category of the fundCRISIL Star RankRisk RatingTotal Expense Ratio % vs Category Average3 Year Return %3 Year Rank among Funds in Category5 Year Return %5 Year Rank among Funds in CategorySince Inception Return %Since Inception Rank among Funds in CategoryInstructionsDownload the package.Launch terminal session from the root folder of the package.Install requirementspip3 install -r requirements.txtFor single mutual fund, obtain the URL of the requisite mutual fund.For multiple mutual funds, build an Excel (.xlsx) workbook with the following. See Sample.xlsx for sample excel workbook.Sheetname should be Mutual FundsColumn 1 with the URL(s) of requisite mutual funds.Row 1 with headers from column 2 onwards for writing the data in respective cells. Order of headers is not significant.CategoryCRISIL Star RankRisk RatingTER % vs Avg3 YR Return3 YR Rank / Cat Funds5 YR Return5 YR Rank / Cat FundsSince Inception ReturnSince Inception Rank / Cat FundsUpdated OnRun the scriptpython3 MFPerfQuery.pyChoose 1 if providing a direct mutual fund URL. Output will be presented on the terminal session itself.Choose 2 if providing a Excel workbook with mutual fund URL(s). Output will be written to the same Excel workbook.Author Contact:aditya14920251@gmail.com
Package: mfpml
Documentation|Installation|GitHub|TutorialsSummaryThis repository aims to provide a package for multi-fidelity probabilistic machine learning. The package is developed by Jiaxiang Yi and Ji Cheng based on their learning curve on multi-fidelity probabilistic machine learning, and multi-fidelity Bayesian optimization, and multi-fidelity reliability analysis.Overall, thismfpmlpackage has two main goals, the first one is to provide basic code on implement typical methods in modeling, optimization, and reliability analysis field. Then, based on the basic code, we also provide some advanced methods based on our publications.(1) Basic methodsModelsKriging modelMulti-fidelity Kriging modelOptimizationsEvolutionary algorithmsSingle fidelity Bayesian optimizationMulti-fidelity Bayesian optimizationReliability analysisActive learning reliability analysisMulti-fidelity reliability analysis(2) Advanced methodsFor the advanced methods, we will provide code based on our publications.
please check out those papers:Jiang, Ping, et al. "Variable-fidelity lower confidence bounding approach for engineering optimization problems with expensive simulations." AIAA Journal 57.12 (2019): 5416-5430.Cheng, Ji, Qiao Lin, and Jiaxiang Yi. "An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization." Structural and Multidisciplinary Optimization 65.7 (2022): 188.Yi, Jiaxiang, et al. "Efficient adaptive Kriging-based reliability analysis combining new learning function and error-based stopping criterion." Structural and Multidisciplinary Optimization 62 (2020): 2517-2536.Yi, Jiaxiang, et al. "An active-learning method based on multi-fidelity Kriging model for structural reliability analysis." Structural and Multidisciplinary Optimization 63 (2021): 173-195.Yi, Jiaxiang, Yuansheng Cheng, and Jun Liu. "A novel fidelity selection strategy-guided multifidelity kriging algorithm for structural reliability analysis." Reliability Engineering & System Safety 219 (2022): 108247.AuthorshipAuthors:Jiaxiang Yi(yagafighting@gmail.com)[1]Ji Cheng (jicheng9617@gmail.com)[2]Authors affiliation:[1] Delft University of Technology[2] City University of Hong KongCommunity SupportIf you find any issues, bugs or problems with this package, you can raise an issue
on the github page, or contact the authors directly.LicenseCopyright 2023, Jiaxiang Yi and Ji ChengAll rights reserved.