Saravanan666/pypi-org huggingface dataset:
Package: mecons
MeConsMeCons is an open-source Python package for measuring consciousness. Built on the shoulders of giants like MNE-Python, SciPy, Numpy, etc., currently it includes a module for complexity metrics.DocumentationCurrently under development.Installing MeConsTo install the latest stable version of MeCons, you can usepipin a terminal:pipinstall-UmeconsGet the latest codeTo install the latest version of the code usingpipopen a terminal and type:pipinstall-Uhttps://github.com/chrisferreyra13/mecons-py/archive/main.zipTo get the latest code using git, open a terminal and type:gitclonehttps://github.com/chrisferreyra13/mecons-py.gitAlternatively, you can also download azip file of the latest development version.DependenciesPython >= 3.7MNE-Python >= 1.1.0NumPy >= 1.18.1SciPy >= 1.4.1Contributing to MeConsPlease see thecontributing guide.
Package: mecord-cli
Mecord Python ToolThe Mecord Python Tool is a official tool for mecodr, you can use it toRegisterdevice to mecord server, other person can useMecord Applicationassign tasks to you for implementationInstallationThe Mecord requiresPython3.10.6 or later.Installingpip install mecord-cliUninstallingpip uninstall mecord-cliUse1. RegisterDevice To Mecord Server$ mecord deviceiduse mecord mobile application scan qrcode to register your device to mecord server2. Running$ mecord service startstart a process to wait for the server to issue tasks.Please do not close itModule Developer$ mecord widget initin empty folder, use above command craete a mecord module, structure is like[widget folder]
    |-- h5
        |-- config.json     //*required, do not change*
        |-- icon.png
        |-- index.html      //*required* index web page
        |-- MekongJS.js      //*required, do not change*
    |-- script
        |-- main.py         //*required, do not change* 
        |-- run.pyif other person share widget code to you , you can add widget path in your computer to mecord environment$ mecord widget add [path_with_widget_code]you can modify script and h5 file yourself, then publish to mecord sever$ mecord widget publish#####other command
set/get multi process task number$ mecord set_multitask_num / get_multitask_num 1add this device joined to other group using token, or show current group token , you can share it to other person$ mecord add_token /  show_token [token_string]unregister this device from mecord server$ mecord unbindget mecord version
$ mecord version
Package: mecs
mecs is a Madurese python stemming library that allows you to transform word in the Madurese language (Bahasa Madura) to their basic form (lemma).
This application also presents the affixes of the word (prefix, suffix and nasal).InstalationpipinstallmecsExample Usage# import packagefrommecsimportStem# Create stemmerst=Stem.Stemmer()# stemterm="romana"st.stemming(term)print("lemma : ",st.lemma)# romaprint("prefix : ",st.prefix)# Noneprint("suffix : ",st.suffix)# naprint("nasal : ",st.nasal)# NoneDemoLive demo :Click here!ReferencesRachman, F. H., Ifada, N., Wahyuni, S., Ramadani, G. D., & Pawitra, A. (2022, November). ModifiedECS (mECS) Algorithm for Madurese-Indonesian Rule-Based Machine Translation. InProceedings of The 2022 International Conference of Science and Information Technology in Smart Administration (ICSINTESA)(pp. 51-56). IEEE. DOI:10.1109/ICSINTESA56431.2022.10041470Ifada, N., Rachman, F. H., Syauqy, M. W. M. A., Wahyuni, S., & Pawitra, A. (2023). MadureseSet: Madurese-Indonesian Dataset.Data in Brief, 48,109035. DOI:10.1016/j.dib.2023.109035
Package: mec-translator
A command line translator powered by [Youdao api](http://fanyi.youdao.com/openapi).## usagetranslator TEXTThe program can be used with a TEXT to be translated followed.`shell $translator good 好---------------------美音：[ɡʊd]英音：[gʊd]更多释义： n. 好处；善行；慷慨的行为 adj. 好的；优良的；愉快的；虔诚的 adv. 好 n.(Good)人名；(英)古德；(瑞典)戈德`translator without any argsIt can also be used without any args to enter the interactive mode.`shell $translator Enter your text to be translated.Type `q` to quit. >>hello 你好---------------------美音：[hɛˈlo,hə-]英音：[hə'ləʊ;he-]更多释义： n. 表示问候， 惊奇或唤起注意时的用语 int. 喂；哈罗 n.(Hello)人名；(法)埃洛>> `
Package: med2nlp
MED2NLPMED(Minimum Effective Dose) is our philosophy(Tao Da MED), with a simple goal of making NLP as easy and effectual as possible.BackgroundDeep learning for NLP has rapidly undergone some amazing advancements over the last few years and there are a wide array of amazing resources out there. This project is an attempt to extrapolate and simply the work of many of the smartest data scientist industry. As an active member of the fast.ai community most of this project is based on the works of member of the fast.ai community and their blog posts. Some of the most notable resources used were:Keita Kurita's articleA Tutorial to Fine-Tuning BERT with Fast AIDev Sharma's articleUsing RoBERTa with Fastai for NLPThilina Rajapakse articleSimple Transformers — Multi-Class Text Classification with BERT, RoBERTa, XLNet, XLM, and DistilBERTGetting startedSince we are going to be taking advantage of some the SOTA deep learning libraries and project you are going to need to installFastaiand🤗Transformersand I would also highly recommend that you useanacondato set up a virtual environment.Basic system setup( Anaconda / Text_editor / git )add steps/requirements1.xx setup - virtual environmentTo set up your virtual environment you will open your terminal and enter the following commands:1.1 -Create a conda environmentwith python version 3.7, and the namemed2condaconda create -n med2conda python=3.71.2 -Activate your conda environment:(Now you can add to the conda environment, it should show (med2conda) in the command lineconda activate med2conda1.3 - Addcuda toolkitto your conda env (in this case it is cudatoolkit=10.0)check herefor alternativeconda install pytorch cudatoolkit=10.0 -c pytorch1.4 - Addpytorchandfastaito your conda envconda install -c pytorch -c fastai fastai1.5 - Addtransformersto your conda envconda install -c conda-forge transformers1.6 - Addjupyter notebooksto your conda envconda install jupyter notebook1.6b Some people need it to tell conda which notebook to useconda install nb_conda1.7 - When you are done using the med2nlp library you will want to exit your conda env with:conda deactivatepip install med2nlpAssumptionsYour data is in a dataframeThe best way to simplify your life is format your data into a standard format so that can use your tools in a consistent manner. This is a fairly common practice is is used a lot in pipelines, some people refer to it as tidy data. As a starting point I am going to assume that your data is in a dataframe(pandas,rapid,etc.).TODO - add more assumptionsHow to useWhen you want to use the med2nlp library you are going to start your med2conda env from the terminal with:conda activate med2condaMake sure you are in the med2nlp folder then start a jupyter notebook with: >jupyter notebookget your data into a dataframe
Package: medal-api
medal-apiUnofficial API Client formedal.tvUsage# Don't know if this can be done any betterfrommedal_apiimportMedalAPI# Create the API objectapi=MedalAPI()# Query user infoprint(api.get_user("Test"))# Get a categoryprint(api.get_category(0))# Get recent gamesprint(api.get_recent_games("Test"))
Package: medali
medalistands forMEtaDAtaLIbrary and is responsible for parsing, tagging and interpreting product metadata.Descriptionmedalishould be a place to establish version-controlled metadata definitions to guarantee homogeneous reading and writing
of metadata within the scope of one product. It provides one module, thecoremodule containing the classMetaData.
This class offers a simple interface to actual metadata items, which are defined with respect to reference metadata.
Such reference metadata can either be prepared manually as a dictionary or can be set in a configuration file.
For mature products, the configuration files are stored in the folder in “src/medali/lib” under a product ID and a metadata version.TheMetadataclass offers the following functionality:initialisation via a metadata dictionary (+ optionally a reference metadata dictionary), via a product ID and
metadata version combination, or via a configuration file pathsetting and encoding (from a given data type to a string) the given metadata itemsreturning and decoding (from a string to a given data type) the given metadata itemschecking if given metadata meets the criteria defined in the reference metadata (e.g., if the value is within a list of allowed values or if the value contains a specific string, …)Installationmedaliis a very light-weight package and has no dependencies. It can be installed viapip:pipinstallmedaliAdding productsA template configuration file for creating new reference metadata for a product can be found in the “templates” folder.
The file should be named according to the metadata version and should be placed in a sub-folder in “src/medali/lib” named after the product ID.
The reference metadata should have two sections:“Metadata”: all needed tags and their data type (currently supported: string, boolean, datetime, integer, number)“Expected_value”: should list metadata items that need to meet some specific criteria
Package: medaprep
medaprep
#########medaprep is used to preparexarrayDatasets for downstream tasks.Usagemedaprep.skim.features.. code-block:: python>>> import numpy as np
>>> import pandas as pd
>>> import xarray as xr
>>> from medaprep import skim
>>> temp = 15 + 8 * np.random.randn(2, 2, 3)
>>> precip = 10 * np.random.rand(2, 2, 3)
>>> lon = [[-99.83, -99.32], [-99.79, -99.23]]
>>> lat = [[42.25, 42.21], [42.63, 42.59]]
>>> ds = xr.Dataset(
  {
      "temperature": (["x", "y", "time"], temp),
      "precipitation": (["x", "y", "time"], precip),
      },
  coords={
      "lon": (["x", "y"], lon),
      "lat": (["x", "y"], lat),
      "time": pd.date_range("2014-09-06", periods=3),
      "reference_time": pd.Timestamp("2014-09-05"),
      },
                 )
 >>> df = skim.features(ds)
 >>> df
     variables       data_types  NaNs    mean    std     maximums    minimums
 0   temperature     float64     False   14.3177 9.08339 30.3361     -7.76803
 1   precipitation   float64     False   4.62568 3.03081 9.89768     0.147005For more details seeDocumentation_ andExample Notebooks_.Installation
############Using pip.. code-block:: bashpip install medaprepUsing Conda.. code-block:: bashconda install -c conda-forge medaprepDeveloping
##########pre-commit setupThis project usespre-commit,isort,black, andflake8to help enforce best practices. These libraries are all included inrequirements-dev.txtand can be installed withpipby running:.. code-block:: bashpip install -r requirements-dev.txtOnce pre-commit is installed, install the hooks specified by the config file into.git:.. code-block:: bashpre-commit installYou can then test pre-commit by running:.. code-block:: bashpre-commit.. _Documentation:https://medaprep.readthedocs.io/.. _Example Notebooks:https://medaprep.readthedocs.io/en/latest/examples.html
Package: medblocks
Medblocks.pyInstallationpip install -r requirements.txtUsagefrommedblocksimportClientapi=Client("165.22.211.124")api.register("email@example.com","yoursecretpassword")api.login("email@example.com","yoursecretpassword")api.list()# Returns all medblocks on the blockchainapi.addBlock("file.txt","anotheremail@example.com")
Package: medblocks.py
Medblocks.pyInstallationpip install -r requirements.txtUsagefrommedblocksimportClientapi=Client("165.22.211.124")api.register("email@example.com","yoursecretpassword")api.login("email@example.com","yoursecretpassword")api.list()# Returns all medblocks on the blockchainapi.addBlock("file.txt","anotheremail@example.com")
Package: medbooklib
MedBookLib provides a python interface to MedBook (R version should come next).PYTHONMedBookConnection(<name optional>, <password optional>)You begin the conversation by creating a MedBook Connection object which will be usedMedBookConnection("ted@soe.ucsc.edu", "test1234");or using the MedBook environment  variables: MEDBOOKUSER, MEDBOOKPASSWORDmedbook = MedBookConnection();This will throw an exception object if it does not make authenticated connection to the MedBook server.`MedBookConnection.find(<collection required>, <mongo query optional>, <keyword querys optional>)The simplest find command will return all of the data objects in a collection. For exampledata = medbook.find("Clinical_Info");Some tables such as Expression2 are very large and if just need one gene from one study, you can do this by simply doing:data = medbook.find("Expression2", { "Study_ID": "prad_wcdt", "gene": "BRCA1" });This can be more easily expressed using the python keyword idiom:data = medbook.find("Expression2", Study_ID="prad_wcdt", gene="BRCA1");But use the mongo syntax to express complicated queriess,data = medbook.find("Expression2", { "Study_ID": {"$in": [ "prad_tcga", "prad_wcdt"] },  "gene": { "$in": ["EGFR","BRCA1"]}});You cannot combine keyword queries and mongo queries.OPTIONAL ENVIRONEMNT VARIABLE CREDENTIALSYou must provide credentials to MedBook in the form of a name and password. There are two waysto do this:Preferred way: Set the MEDBOOKUSER and MEDBOOKPASSWORD environment variables. For exmaple:export MEDBOOKUSER=ted@soe.ucsc.eduexport MEDBOOKPASSWORD=test1234There is also a MEDBOOKSERVER environment variable.  If no server is specificedThe default server is https://medbook.ucsc.edu. But you can also set the server to be your laptop.Common ones are:export MEDBOOKSERVER=https://su2c-dev.ucsc.edu/export MEDBOOKSERVER=http://su2c-dev.ucsc.edu:20012/export MEDBOOKSERVER=http://localhost:20012/Alternatively, you can provide the name and password in the parameters of the MedBookConnection object (see above).
Package: medcam3d
A library that generates the 3D CAM attention maps for 3D networks for 3D medical imagesHow to install it?pip install medcam3dThis package is a 3D extension ofpytorch-grad-camhttps://github.com/jacobgil/pytorch-grad-cam. But at present, onlyGrad-CAMis supported. Other CAM implementations like Grad-CAM++ will be supported later once I have enough time. Also very welcome your pull request.All the usage and configuration are the same as the originalpytorch-grad-campackage.MethodWhat it doesGradCAMWeight the 3D activations by the average gradientChosing the Target LayerYou need to choose the target layer to compute CAM for.
Some common choices are:FasterRCNN: model.backboneResnet18 and 50: model.layer4[-1]VGG and densenet161: model.features[-1]mnasnet1_0: model.layers[-1]ViT: model.blocks[-1].norm1SwinT: model.layers[-1].blocks[-1].norm1If you passa list with several layers, the CAM will beaveragedaccross them.
This can be useful if you're not sure what layer will perform best.
However, it may be not wise to select multiple layers at once because the CAMs's values of shallow layers may be way smaller than the deeper layers (e.g. 1/10). So the shallower layers' CAM would be ignored.from medcam3d import GradCAM
from medcam3d.utils.model_targets import ClassifierOutputTarget
from medcam3d.utils.image import show_cam_on_image


model = resnet50(pretrained=True)  # your 3D network
target_layers = [model.layer4[-1]]  # normally the layer before the first fully connected layer
input_tensor = # Create an input tensor of 3D image for your model. Shape: (n_samples, n_channels, length, width, height)
# Note: input_tensor can be a batch tensor with several images! But Jingnan only tested the batch size of only 1.

# Construct the CAM object once, and then re-use it on many images:
cam = GradCAM(model=model, target_layers=target_layers, use_cuda=args.use_cuda)

# You can also use it within a with statement, to make sure it is freed,
# In case you need to re-create it inside an outer loop:
# with GradCAM(model=model, target_layers=target_layers, use_cuda=args.use_cuda) as cam:
#   ...

# We have to specify the target we want to generate
# the Class Activation Maps for.
# If targets is None, the highest scoring category
# will be used for every image in the batch.
# Here we use ClassifierOutputTarget, but you can define your own custom targets
# That are, for example, combinations of categories, or specific outputs in a non standard model.

targets = [ClassifierOutputTarget(281)]  # 281 could be replace by 0 or 1 for two-class classification.

# You can also pass aug_smooth=True and eigen_smooth=True, to apply smoothing.
cam_img = cam(input_tensor=input_tensor, targets=targets)  # output shape: (n_samples, length, width, height)

# In this example grayscale_cam has only one image in the batch:
cam_img = cam_img[0]  # output numpy array shape: (length, width, height)

# Then you can save the array as .mha file and visualize it in MeVisLab like the following figure.If this repository helps you in anyway, show your love ❤️ by putting a ⭐ on this project.
Package: medchem
Medchem - Molecular filtering for drug discoveryMedchem is a Python library that proposes multiple molecular medchem filters to a wide range of use cases relevant in a drug discovery context.Installationmicromambainstall-cconda-forgemedchem# or using pippipinstallmedchemDocumentationVisithttps://medchem-docs.datamol.io/.Development lifecycleSetup dev environmentmicromambacreate-nmedchem-fenv.yml
micromambaactivatemedchem

pipinstall--no-deps-e.TestsYou can run tests locally with:pytestLicenseUnder the Apache-2.0 license. SeeLICENSE.md.
Package: medcom-ble
Medcom BLE communication libraryThis Python package implements communications over BLE, and is meant to be used by the Home Assistant Medcom BLE integration.LicenseLicense: MITThis library was written usinghttps://github.com/vincegio/airthings-bleas the reference, which is also on the MIT license, (c) 2018 by Daniel Høyer Iversen.
Package: med-dataloader
Medical Images DataloaderA general-purpose Dataloader for Tensorflow 2.x. It supports many medical image formats.Free software: MIT licenseDocumentation:https://med-dataloader.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.16 (2022-06-15)Patching is now performed withSAMEpadding0.1.15 (2022-05-17)img_size support also None value.0.1.14 (2022-05-16)Improved handling of img_size parameters for both 2D and 3D images.User can now declare img_size as a 2- or 3-elements list.Automatic zero-padding or center-based cropping is performed to adapt the size of the image to the declare img_size parameter.New features: 3D volumes can be patched into smaller cubic patches. Overlapping between patches is also supported.0.1.13 (2022-03-10)New function: now dataset can be generated also by reading a json file containing list of file paths.0.1.12 (2021-10-29)Fixed minor bug in function norm_with_bounds0.1.11 (2021-09-10)Fixed support for 3D ImagesFixed minor bugs0.1.10 (2021-05-11)Added support one-hot encoding in case of multi-class label0.1.9 (2021-05-11)Added support for RGB ImagesFixed some bugs related to norm_bounds types0.1.8 (2021-05-09)Main Changes in the package structure. Now there are two main functions:
generate_dataset and get_dataset, both leveraging on DataLoader class.The generation of the dataset can be handled also by CLI, to simplify usage.Processed data can live by themself. No more need to transfer also original
file (e.g. to Drive to make use of them on Colab)0.1.6 (2021-05-06)Improved flexibility for image data types. Now cache dimension reflects the
actual dataset dimension.0.1.5 (2021-04-30)Added support for 3D files: now Dataloader automatically detects whether a
file is 2D or 3D and returns the properly sized dataset. Please remember that
med_dataloader returns tf.data.Dataset object for 2D tasks, 3D is not yet
supported.Added new notebook in examples folder.0.1.4 (2021-04-29)Improved code flexibility:It is possibile to choose which type of data augmentation is performedBoundaries for data normalization can be set by the userImages can be resized automatically by the userAdded basic_usage example also as a notebook0.1.1 (2021-04-20)Added code for packageBasic example of usage inside folder “examples”Partial documentation0.1.0 (2021-04-16)First release on PyPI.
Package: med-data-science-helper-utility
med-data-science-helper1. Organización del proyecto├── README.md                   <- README principal para los desarrolladores usando este proyecto.
├── config                      <- Archivos de configuración.
├── data
│   ├── external                <- Datos de terceras fuentes.
│   ├── interim                 <- Datos intermedios que ya han sido transformados.
│   ├── processed               <- Datos en forma final, listo para modelamiento.
│   └── raw                     <- Datos originales, datos inmutable sin ninguna transformación.
│
├── docs                        <- Documentación del proyecto, ver detalles.
│
├── models                      <- Modelos entrenados y serializados, predicción de los modelos y resúmenes.
│
├── references                  <- Diccionarios de datos, manuales y todos material que explique los datos.
│
├── reports                     <- Análisis generado puede ser como HTML, PDF, LaTex, etc.
│   └── figures                 <- Gráficos y figuras generados usados en los reports.
│
├── med_data_science_helper     <- Carpeta principal para el uso del proyecto donde se encuentran los códigos.
│   ├── __init__.py                 <- Convierte al folder en un modulo de Python.
│   │
│   ├── helper_acces_db.py          <- Exploración inicial para entender el negocio.
│   │
│   ├── helper_common.py            <- Exploración para entender los datos y sus disponibilidad.
│   │
│   ├── helper_constantes.py        <- Seleccionar, ordenar, agrupar, remover, etc. los datos para alcanzar los 
│   │
│   ├── helper_integracion.py       <- Scripts para generación del modelo y afinamiento de parámetros.
│   │
│   ├── helper_siagie_kpi_old.py    <- Scripts para evaluación de resultados establecidos al inicio del proyecto.
│   │
│   ├── helper_siagie_kpi.py        <- Scripts para evaluación de resultados establecidos al inicio del proyecto.
│   │
│   └── helper_terceros_kpi.py      <- Scripts para el despliegue y pase a producción.
│   
├── enviroment.yml             <- Listado de los paquetes necesarios para reproducir el entorno de análisis.
│
├── 00.create_env.bat          <- Ejecutable para crear el entorno virtual con los parámetros del archivo "enviroment.yml".
│
└── 01.update_enviroment.bat   <- Ejecutable para actualizar el archivo "enviroment.yml" antes de ser compartido.2. med-data-science-helper documentacionVer Documentacion