Saravanan666/pypi-org huggingface dataset:
Package: emote-utils
emote-utilsEmote utilities for text-based games.This package is most useful for formatting social strings for use with text-based games like MUDs and MOOs.A social string might look like:%1N smile%1s at %2n.QuickstartFirst get a factory ready. You could use the baseSocialsFactoryclass or the helpfully-populatedPopulatedSocialsFactory.This second class contains useful english grammar suffixes for properly converting words like are to is and proper use of e and s for use at the end of words.from emote_utils import PopulatedSocialsFactory
f = PopulatedSocialsFactory()
@f.suffix('n', 'name')
    def get_name(obj, suffix):
        return ('you', obj.name)Next let's create a test class.class Person:
    def __init__(self, name):
        self.name = name

p1 = Person('Jack')
p2 = Person('Jill')Now we'll get strings which can be sent to Jack, Jill and anyone else who should see the message.jack_string, jill_string, others_string = f.get_strings('%1N smile%1s at %2n.', [p1, p2])
print(jack_string)You smile at Jill.print(jill_string)Jack smiles at you.print(others_string)Jack smiles at Jill.More advanced usageSocial FormattersEach social formatter is made up of three parts:a per cent (%) sign representing the start of a social formatter.an optional number indicating the index of the object you wish to reference in the list of objects.An optional filter name preceded by a vertical bar (|).DefaultsIf no index is provided,SocialsFactory.default_indexis used.If no suffix is provided,SocialsFactory.default_suffixis used.If no filter is provided then one of 3 things will happen.In the below list suffix refers either to the name of a suffix provided as part of the social string or thedefault_suffixattribute on the instance ofSocialsFactorythatget_stringsis being called on.Attribute names refer to the attributes ofSocialsFactorythatget_stringsis being called on.If the suffix is title case then the title_case_filter attribute will be used.If the suffix is all upper case then the upper_case_filter attribute will be used.If neither of these things are true then suffix is assumed to be lower case and the lower_case_filter is used.Of course any of these names could beNonein which case no filtering is applied.FiltersHere is an example of a custom filter:f.filters['strong'] = lambda string: f'<strong>{string}</strong>'If you were printing your socil strings to HTML you could use this filter to make certain parts of the text stand out.With this filter created, you could do:%1N punch%1e %2n|strong.A string might then look like:John punchesJack.ConclusionTo see all the other configuration options see the docstrings in the package.
Package: emotext
emotextemotext is a python library to analize sentiment it can be negative (0), positive (4) or neutral (2).Installationpip3installemotextInstructions for usefromemotextimportSentimentProbabilityprob=SentimentProbability()prob.fit("positive_words.txt","negative_words.txt","stop_words.txt")pred=prob.predict("hari ini sangat indah sekali")print(pred)# outout# 4 or positivenamedescriptionpositive_words.txtput all the positive words on this file it can be in any language. in this documentation i use indonesian language,so the positive words like example : optimis, kuat, menyenangkan and etcnegative_words.txtall the negative words, example : mengganggu, menindas, kejam and etcstop_words.txtall the stop words, example : yang, di, dan itu, etc*noteall of those file wrote in indonesian language, it possible to change with any language. just put all of the word into .txt file and make sure 1 line for 1 word, look at example below :# dooptimiskuatmenyenangkan# don'toptimiskuatmenyenangkanIf you are indonesian i have created all of those file just look at folderdatawarningYou don't have to worry about noise on your text likeomoji : 🥸 😘 🤬punctuation : #?!^&*%@, etcall of that will be removed automatically and if you grab the data fromtwitterit automatically remove username and hastag
Package: emoticon
EmoticonEmoticon is a Python package to transform or translate emoticon into text or its meaning. Based on list of emotion in wikipedia, i created a library for text mining. This package contain western and eastern emoticon with over 300 emoticon. This package can also translate emoticon into desired language by adding optional argument.How to installpip install emoticonUsageFollowing code will show you example how to use this packageFrom text to emoticon :from emoticon import emoticon

text = ":) :("
emoticon(text)Output : "Smile Sad"You can also specify the desired language by adding optional argument, for example if you want to return indonesian language, use 'id' as optional parameter.from emoticon import emoticon

text = ":) :("
emoticon(text, 'id')Output : "Senyum Sedih"if you don't specify, it will return english by default.From emoticon to text :from emoticon import demoticon

text = "Smile Sad"
demoticon(text)Output : ":) :("
Package: emoticon-fix
emoticon_fixA Python package to transform emoticon in a string to text, e.g., :) => Smile.What are emoticons?An emoticon short for "emotion icon", also known simply as an emote, is a pictorial representation of a facial expression using characters—usually punctuation marks, numbers, and letters—to express a person's feelings or mood, or as a time-saving method. The first ASCII emoticons, :-) and :-(, were written by Scott Fahlman in 1982, but emoticons actually originated on the PLATO IV computer system in 1972.Why do we need to transform emoticon to text?When we preprocess the text for the NLP model, if we just remove all the punctuations in the text, emoticons will leave some meaningless letters. For example, ":D" (laugh) will become just "D". Obviously, these meaningless letters will affect the performance of the NLP model.Installationpip install emoticon-fixExamplefromemoticon_fiximportemoticon_fixtext='test :) test :D test'emoticon_fix.emoticon_fix(text)The output will be:'test Smile test Laugh test'PyPI Linkhttps://pypi.org/project/emoticon-fix/
Package: emotional
emotionalACommitizentemplate forconventional commitwith emojis and integrations.InstallationpipinstallemotionalThen setemotionalas the Commitizen template:[tool.commitizen]name="emotional"ConfigurationAs a starter, remember that allCommitizen configurationis available.ChangelogBy default, changes by types are kept in order of commit and ignore the scope for ordering.
You can however force scope to be sorted first by settingorder_by_scope:[tool.commitizen]name="emotional"order_by_scope=trueYou can also group changes into subsections by scope by settinggroup_by_scope:[tool.commitizen]name="emotional"group_by_scope=trueGithub integrationTo enablegithubintegration, just provide your github repository asgithubsetting:[tool.commitizen]name="emotional"github="author/repository"For github enterprise, you can use the full repository URL:[tool.commitizen]name="emotional"github="https://git.company.com/author/repository"Gitlab integrationTo enablegitlabintegration, just provide your gitlab repository asgitlabsetting:[tool.commitizen]name="emotional"gitlab="author/repository"Use the full URL for hosted gitlab instances:[tool.commitizen]name="emotional"gitlab="https://git.company.com/author/repository"Jira integrationTo enableJiraintegration,
provide your JIRA instance URL asjira_urlsetting
and the list of project prefix you want ho be processed injira_prefixes:[tool.commitizen]name="emotional"jira_url="https://emotional.atlassian.net"jira_prefixes=["EMO-","PRJ-",]Multiple integrationsWhile it is totally possible to mix integrations,
keep in mind thanjirais compatible with bothgithubandgitlabwhilegithubandgitlabare conflicting because they use the same format.
Package: emotional-detection
OverviewThis packages is a very basic script that captures video from the webcam, detects all faces in the video and then runs the DeepFace emotional classifier on each face. The output of the emotional classfications will be written to a Unix pipe to be consumed by any other process.InstructionsEverything is contained inemotional_detection/main.py. You simply install the dependencies and run. This project works out of the box withpoetry, but you could install the dependencies using any dependency manager + virtual environment combination you choose.
Package: emotion-nrc-affect-lex
emotionThis module is a emotion classifier that classifies emotion of a list of words and gives aggregated scores for each emotion. The classifier is based on NRC Affect Intensity Lexicon. The lexicon has close to 10,000 entries for eight emotions that Robert Plutchik argued to be basic or universal:JoySadnessAngerFearDisgustAnticipationTrustSurpriseLexicon source is (C) 2020 National Research Council Canada (NRC) and this package isfor research purposes only. Source: [lexicons for research] (http://www.saifmohammad.com/WebPages/AffectIntensity.htm)
Some words has multiple emotions to varying degree. Here is how the classifier works:The classifier first matches the words with the lexicon and fetches the
emotions and intensity score for each matching from the lexicon.Then it sums the scores along the emotions and gives a mood score of
the song. The emotion intensity are given from 0 to 1 in floats so the
aggregated score is a float as well.It multiplies the number of appearances of these mood words in the song
with the intensity score to give a corrected weighting of each emotion.For now the input has to be a list of words rather than a text body. I will change it in the next update.  Please convert the text body into a list of words for using this package.InstallationRun the following to install:pipinstallemotion_nrc_affect_lexUsagefromemotionimport*text=['I','hate','everything','about','you','Why','do','I','love','you']# The input should be a list of words. Please convert your text body to a list before using this package.emo_object=Emo(text)# Gives aggregated emotion scores for the whole list.emo_object.weighted_emotion_scores# Dictionary of each of the affect wordsemo_object.affect_dict# Gives number of words for each emotionemo_object.raw_emotion_scores# Gives a list of affect words and their frequenciesemo_object.word_frequencies
Package: emotionpyy
What is emotionpyy?Emotions are biological states associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?emotionpyy is the python package which will help you to extract the emotions from the content.Processes any textual message and recognize the emotions embedded in it.Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.Features1. Text Pre-processingAt first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.Remove the unwanted textual part from the message.Perform the natural language processing techniques.Bring out the well pre-processed text from the text pre-processing.2. Emotion InvestigationDetect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.Find the appropriate words that express emotions or feelings.Check the emotion category of each word.Store the count of emotions relevant to the words found.3. Emotion AnalysisAfter emotion investigation, there is the time of getting the significant output for the textual message we input earlier.The output will be in the form of dictionary.There will be keys as emotion categories and values as emotion score.Higher the score of a particular emotion category, we can conclude that the message belongs to that category.Check DemoLet's experience the library, test your multiple use cases on web app and check whether the library performs as per your expectations.
Package: emotlib
# emotlib: Python emoji + emoticon Library[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/emotlib.svg)](https://pypi.org/project/emotlib/)[![PyPI](https://img.shields.io/pypi/v/emotlib.svg)](https://pypi.org/project/emotlib/)[![Travis branch](https://img.shields.io/travis/steven5538/emotlib/master.svg)](https://travis-ci.org/steven5538/emotlib)[![Coveralls github](https://img.shields.io/coveralls/github/steven5538/emotlib.svg)](https://coveralls.io/github/steven5538/emotlib)[![Read the Docs](https://img.shields.io/readthedocs/emotlib.svg)](http://emotlib.readthedocs.io/en/latest/)![PyPI - License](https://img.shields.io/pypi/l/emotlib.svg)Installation------------To install emotlib, simply use [pipenv](http://pipenv.org/) (or pip, of course):```$ pipenv install emotlib🍩🎉```Satisfaction guaranteed.Example-------------Note: In python2.x, your string need to be unicode. Ex: u'I\'m :elf:'``` python>> import emotlib>> print(emotlib.emoji())🧙‍>> print(emotlib.emoji(category='food-fruit'))🍉>> print(emotlib.emoji(num=3))👨‍🚀👨‍🚀👨‍🚀>> print(emotlib.emoji(num=3, sep= ' ~ '))🤸 ~ 🤸 ~ 🤸 ~>> print(emotlib.emojify('I\'m :man_technologist:'))I'm 👨‍💻>> print(emotlib.emojify('I\'m :man~technologist:', alias='~'))I'm 👨‍💻>> print(emotlib.demojify('I\'m 👨‍💻'))I'm :man_technologist:>> print(emotlib.demojify('I\'m 👨‍💻', alias='~'))I'm :man~technologist:>> print(emotlib.emoticon())( ´ ▽ ` )ﾉ>> print(emotlib.emoticon(feel='confused'))(´−｀) ﾝｰ>> print(emotlib.emoticon(num=3))┌（★ｏ☆）┘┌（★ｏ☆）┘┌（★ｏ☆）┘>> print(emotlib.emoticon(num=3, sep=' ~ '))(´∇ﾉ｀*)ノ ~ (´∇ﾉ｀*)ノ ~ (´∇ﾉ｀*)ノ ~>> print(emotlib.emoticonify('I\'m a happy developer.'))I'm a happy developer. *(*´∀｀*)☆```Check categories & feels```python>> import emotlib>> emotlib.emoji_categories>> emotlib.emoticon_feels```
Package: emout
emoutEMSESの出力ファイルを取り扱うパッケージDocumentation:https://nkzono99.github.io/emout/Installationpip install emoutUsage以下のようなフォルダ構成の場合の使い方..
└── output_dir
    ├── plasma.inp
    ├── phisp00_0000.h5
    ├── nd1p00_0000.h5
    ├── nd2p00_0000.h5
    ├── j1x00_0000.h5
    ├── j1y00_0000.h5
    ...
    └── bz00_0000.h5データをロードする>>> import emout
>>> data = emout.Emout('output_dir')
>>>
>>> data.phisp  # data of "phisp00_0000.h5"
>>> len(data.phisp)
11
>>> data.phisp[0].shape
(513, 65, 65)
>>> data.j1x  # data of "j1x00_0000.h5"
>>> data.bz  # data of "bz00_0000.h5"データをプロットする>>> x, y, z = 32, 32, 100
>>> data.phisp[1][z, :, :].plot()  # plot xy-plane at z=100
>>> data.phisp[1][:, y, x].plot()  # plot center line along z-axis

>>> data.phisp[1][z, :, :].plot(use_si=True)  # can plot with SI-unit (such as x[m], y[m], phisp[V])

>>> data.phisp[1][z, :, :].plot(show=True)  # to view the plot on the fly (same as matplotlib.pyplot.show())
>>> data.phisp[1][z, :, :].plot(savefilename='phisp.png')  # to save to the fileパラメータファイル(plasma.inp)を取得する>>> data.inp  # namelist of 'plasma.inp'
>>> data.inp['tmgrid']['nx']  # inp[group_name][parameter_name]
64
>>> data.inp['nx']  # can omit group name
64
>>> data.inp.tmgrid.nx  # can access like attribute
>>> data.inp.nx  # can also omit group name単位変換を行う[!NOTE]
パラメータファイル (plasma.inp) の一行目に以下を記述している場合のみ、EMSES単位からSI単位系への変換がサポートされます。!!key dx=[0.5],to_c=[10000.0]dx: グリッド幅 [m]to_c: EMSES内部での光速の規格化された値>>> data.unit.v.trans(1)  # velocity: Physical unit to EMSES unit
3.3356409519815205e-05
>>> data.unit.v.reverse(1)  # velocity: EMSES unit to Physical unit
29979.2458SI単位系への変換[!NOTE]
パラメータファイル (plasma.inp) の一行目に以下を記述している場合のみ、EMSES単位からSI単位系への変換がサポートされます。!!key dx=[0.5],to_c=[10000.0]dx: グリッド幅 [m]to_c: EMSES内部での光速の規格化された値>>> # SI単位系に変換した値を取得する
>>> phisp_volt = data.phisp[-1, :, :, :].val_si
>>> j1z_A_per_m2 = data.j1z[-1, :, :, :].val_si
>>> nd1p_per_cc = data.nd1p[-1, :, :, :].val_si継続したシミュレーション結果を扱う>>> import emout
>>> data = emout.Emout('output_dir', append_directories=['output_dir2', 'output_dir3'])データマスクを適用する>>> # mask below average values
>>> data.phisp[1].masked(lambda phi: phi < phi.mean())
>>>
>>> # above code does the same as this code
>>> phi = data.phisp[1].copy()
>>> phi[phi < phi.mean()] = np.nan
Package: empack
empackPack a mamba environment with emsdkInstallationmambainstallempack-cconda-forgeor with pip:pipinstallempackUsageYou can pack the  environment (located at/path/to/env) with the following command:empackpackenv--env-prefix/path/to/envThis will generate a few.tag.gzfiles that correspond to the packages in the environment located at/path/to/env.You can also provide a custom config with the--configflag. A sample config is located intests/empack_test_config.yamlRun the following command for more information about the other CLI parameters:empackpackenv--help
Package: empanada-dl
empanadaOverviewempanada (EMPanopticAnyDimensionAnnotation) is a tool for panoptic segmentation of organelles in 2D and 3D electron microscopy (EM) images.
Panoptic segmentation combines both instance and semantic segmentation enabling a holistic approach to image annotation
with a single deep learning model. To make model training and inference lightweight and broadly applicable to many EM
imaging modalities, empanada runs all expensive operations on 2D images or run length encoded versions of 3D volumes.Note: Development is active and breaking changes should be expected. Not all features are implemented.HighlightsTrain 2D panoptic segmentation models by customizing easy to read .yaml config files.Get better models faster by using state-of-the-art CEM pretrained model weights to initialize training.Run models on 2D images, videos, or isotropic and anisotropic volumes with orthoplane and stack inference, respectively.Export GPU and quantized CPU trained models for use in napari withempanada-napari.InstallationInstall empanada with pip:pipinstallempanada-dlTo install the latest development version of empanada directly from GitHub:pipinstallgit+https://github.com/volume-em/empanada.git
Package: empanada-napari
empanada-napariThe preprint describing this work is now availableon bioRxiv.Documentation for the plugin, including more detailed installation instructions, can be foundhere.empanadais a tool for deep learning-based panoptic segmentation of 2D and 3D electron microscopy images of cells.
This plugin allows the running of panoptic segmentation models trained in empanada withinnapari.
For help with this plugin please open anissue, for issues with napari specifically
raise anissue here instead.Implemented ModelsMitoNet: A generalist mitochondrial instance segmentation model.Example DatasetsVolume EM datasets for benchmarking mitochondrial instance segmentation are available fromEMPIAR-10982.InstallationIt's recommended to have installed napari throughconda.
Then to install this plugin:pipinstallempanada-napariLaunch napari:napariLook for empanada-napari under the "Plugins" menu.GPU SupportNote: Mac doesn't support NVIDIA GPUS. This section only applies to Windows and Linux systems.As for any deep learning models, having a GPU installed on your system will significantly
increase model throughput (although we ship CPU optimized versions of all models with the plugin).This plugin relies on torch for running models. If a GPU was found on your system, then you will see that the
"Use GPU" checkbox is checked by default in the "2D Inference" and "3D Inference" plugin widgets. Or if when running
inference you see a message that says "Using CPU" in the terminal that means a GPU is not being used.Make sure that GPU drivers are correctly installed. In terminal or command prompt:nvidia-smiIf this returns "command not found" then you need toinstall the driver from NVIDIA. Instead, if
if the driver is installed correctly, you may need to switch to the GPU enabled version of torch.First, uninstall the current version of torch:pipuninstalltorchTheninstall torch >= 1.10 using conda for your system.
This command should work:condainstallpytorchcudatoolkit=11.3-cpytorchCiting this workIf you use results generated by this plugin in a publication, please cite:@article{Conrad2022.03.17.484806,author={Conrad, Ryan and Narayan, Kedar},title={Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model},elocation-id={2022.03.17.484806},year={2022},doi={10.1101/2022.03.17.484806},publisher={Cold Spring Harbor Laboratory},URL={https://www.biorxiv.org/content/early/2022/03/18/2022.03.17.484806},eprint={https://www.biorxiv.org/content/early/2022/03/18/2022.03.17.484806.full.pdf},journal={bioRxiv}}
Package: empathy-machines
# empathyMachines
> A standalone NLP sentiment classifier you can import as a module## Purposes1. Offer a batteries-included NLP classifier you can use either on it’s own, or to make sentiment predictions as part of a broder NLP project (for example, when classifying customer messages, whether the customer is angry or not might help you determine if this is a compensation request, or a request to adjust their address.)
1. Have the entire sentiment prediction process scaffolded so you can feed in your own training corpus, and easily train an NLP sentiment classifier.## How to use1. Download the repo from GitHub (pip install coming later)
1.cdinto repo, andpip install -r requirements.txt1. In your Python code,from EmpathyMachines import EmpathyMachines1.nlp_classifier = EmpathyMachines()1.nlp_classifier.train(corpus=’Twitter’)1.nlp_classifier.predict(text_string)### Corpora included### Include your own corpus (UNDER CONSTRUCTION)Feel free to train a classifier on your own corpus!Two ways to do this:
1. Read in a .csv file with header row containing “sentiment”, “text”, and optionally, “confidence”
1. Pass in an array of Python dictionaries, with attributes for “sentiment”, “text”, and optionally, “confidence”1. Create a .csv file with the following fields
1.nlp_classifier.train(corpus=’custom’, corpus_path=’path/to/custom/corpus.csv’, analytics_output=False)
Package: emperor
EmperorEmperor is a next-generation tool for the analysis and visualization of large microbial ecology datasets. Amongst its many features, Emperor provides a modern user interface that can be rapidly adjusted to your data analysis workflow.To start using Emperor, please refer to theinstallation notes.Before contributing code to Emperor, please familiarize yourself with thecontributing guidelines.UsageYou can start using Emperor throughQIIME 2'sinterfaces(the command line or the graphical user interface), or alternatively using the Python interface (compatible with the Jupyter notebook, seethis example). For more details, refer to ouronline documentation.PublicationsEMPeror: a tool for visualizing high-throughput microbial community data. GigaScience, 2013.Bringing the Dynamic Microbiome to Life with Animations. Cell Host & Microbe, 2016.