Saravanan666/pypi-org huggingface dataset:
Package: larch6
Larch v6This repository contains the under-development next generation of the Larch
package, for estimating and applying discrete choice models. Version 6
is a substantial rewrite of the package, changing to a platform that allows
swapping out the underlying computational engine, so that the same model
can run in numba or JAX.:warning:This is a work in progress.A lot of things are working, but not
everything.  The interface is quite similar to Larch v5 and existing users will
likely find it familiar. If you want to try it out, please do, and feel free to
open issues in the issue tracker.  But, please don't expect it to work perfectly
yet, especially for more advanced models.Quick Start GuideYou can install Larch v6 with pip:python-mpipinstalllarch6This will install the package and all of its required dependencies.  Note that
while the installation name is "larch6", the package import name is "larch", and
you cannot install both Larch v5 and Larch v6 in the same environment.Or you can install it usingmambato create a new environment:mambaenvcreate-pARBORETUM-fhttps://raw.githubusercontent.com/driftlesslabs/larch/main/envs/arboretum.yml
condaactivate./ARBORETUMDeveloper's InstallationBefore you start with the installation, you need have the following tools already:miniforge for Mac or Linuxorminiforge for Windowsgh, the github command line tool (mamba install ghshould work)For now, you also need to have a github account and have authenticated
withghusinggh auth login.  Once this repository is public, this
will no longer be necessary.#!/usr/bin/env zsh# exit this script immediately upon any failureset-e# Change this directory to where you want to install this whole messexportTARGET_DIR="${HOME}/driftless"# make the target working directory if it doesn't already existmkdir-p"${TARGET_DIR}"# change to the target working directorycd--"${TARGET_DIR}"# clone the various repositoriesghrepoclonedriftlesslabs/larch
ghrepoclonedriftlesslabs/sharrow# install the mamba development environmentmkdir-p.env/LARIX
mambaenvupdate-p.env/LARIX-flarch/envs/development.yaml
condaactivate.env/LARIX# make this environment available to jupyteripythonkernelinstall--user--name=LARIX# rip examples to loadable modulespythonlarch/tools/rip_examples.py# compile and installpython-mpipinstall-e./sharrow
python-mpipinstall-e./larch# run unit tests (optional)python-mpytest-v./sharrow/sharrow/tests
python-mpytest-v./larch/testsWindows InstallationThe above script shouldmostlywork on Windows as well, but one minor modification
is required, as the JAX library is not yet available for Windows via conda.  You need to
install it with pip instead.  So, replace themamba env updateline in the script
above with the following:mamba env update -p .env/LARIX -f larch/envs/windows.yaml
Package: larch-pickle
This module can be used as transparent replacement for pickle.Difference to original python pickle:no memo attribute.noclear_memo().nopersistent_idinterface.byte strings are always assumed to beutf-8encoded.Picklerhas an additionalwith_refsparameter. Settingwith_refstofalse, the pickler will ignore object references. This can result
in an extra speed boost.Installationlarch-pickle needs the boost library for compilation. If boost is not in
the standard include path install it with:python build_ext -I /path/to/boost installSpeed compared to some other pickler packagesdump Dictionaries (10 loops)PackageSecondsSizemarshal0.4614115975952larch-pickle0.6643339714411ujson1.9421413141354msgpack2.047049843459json2.1411714365311cPickle4.0959912781866load Dictionaries (10 loops)PackageSecondsmsgpack1.38378marshal1.49512larch-pickle1.51489ujson2.42431cPickle2.42524json6.04131dump Objects (10 loops)PackageSecondsSizelarch-pickle1.0744310094415ujson2.5550213331354cPickle4.7080512961909load Objects (10 loops)PackageSecondslarch-pickle1.6615ujson2.45786cPickle2.58697dump Strings (10 loops)PackageSecondsSizemarshal0.5674732826787msgpack0.97522629464951larch-pickle1.1636812103729json1.1900531966498ujson1.5164830987525cPickle4.1295219871780load Strings (10 loops)PackageSecondslarch-pickle0.353255marshal0.435936msgpack0.469068cPickle1.72308ujson1.82656json3.01798dump Lists (10 loops)PackageSecondsSizemarshal0.86188642703907larch-pickle2.3357416054577json3.1105435917346ujson4.428434938373msgpack5.5532131440375cPickle15.553133700258load Lists (10 loops)PackageSecondslarch-pickle2.07305marshal2.14713msgpack2.46054ujson4.14067json5.20221cPickle8.1974
Package: larcv
LArCV (Version 3)Software framework for image(2D)/volumetric(3D) data processing with APIs to interface deep neural network open-source softwares, written in C++ with extensive Python supports.  Originally developed for analyzing data fromtime-projection-chamber (TPC). It is now converted to be a generic tool to handle 2D-projected images and 3D-voxelized data. LArCV is particularly suitable for sparse data processing.InstallationYou can install larcv through pypi:pip install larcvand it should work.  You can also build from source:gitclonehttps://github.com/DeepLearnPhysics/larcv3.gitcdlarcv3
gitsubmoduleupdate--init# Pulls pybind11 subpackagepythonsetup.pybuild[-j12]# Optional parallel build for faster compilationpythonsetup.pyinstall[--user|-prefix${INSTALLATION_DIR}]To verify your larcv installation, after install has completed:cd larcv3/tests
py.test .DependenciesPythonNumpyHDF5 (for IO)cmake (for building)scikit-build (for installation)pytest (for continuous integration)HDF5 and cmake can all be installed by package managers.  Conda will also work.For compilation, a gcc > 4.8 is required.  GCC versions 5 to 8 are all known to work, as is clang on MacOS.To install requirements on ubuntu, you can do:
sudo apt-get install cmake libhdf5-serial-dev python-dev
pip install numpy scikit-build pytestTo install requirements on mac, you can do:
sudo port install cmake hdf5
pip install numpy scikit-build pytestTo install in a generic system, you can try conda or a virtual environment.  It has been shown to work on many linux distributions.Compatibilitylarcv3 works on mac and many flavors of linux.  It has never been tested on windows as far as I know.  If you try to install and need help, please open an Issue.Use CasesLarcv is predominantly used as an IO framework and data preprocessing tool for machine learning and deep learning.  It has run on many systems and in many scenarios.  Larcv has a suite of test cases available that test the serialization, read back, threaded IO tools, and distributed IO tools.Larcv has run on some of the biggest systems in the world, including Summit (ORNL) and Theta (ANL).  It has been used for distributed io of sparse, non-uniform data up to hundreds of CPUs/GPUs, and had good performance.If you would like to use larcv for your application and want to benchmark the performance, you are welcome to use the larcv3 open dataset (more info on deeplearnphysics.org) and if you would like help, open an issue or contact the authors directly.
Package: largeblue.order
largeblue.order allows you to adapt ‘’selected’’ containers and objects
to make the container’s contents orderable.  It build on and patches
bebop.ordering, the source code and dependencies of which are included
here (because it’s not available as a standalone egg on the pypi or
via iwm-kmrc.de).(Note that the bebop code is all (c) iwm-kmrc.de and is released under
GPL).The original bebop implementation makes all containers ordering and
all content objects orderable.  This package re-configures the
underlying ordering machinery, so you can adapt specific containers
and specific objects to make the ordering and orderable.This way you can choose, say, which objects that are contained within
a container should be orderable.  Plus you can choose which containers
to hang this functionality off explicitly. I should note also that the
view that this package hangs off the container also includes the
default container view functionality of add, delete, rename, etc.To use it, include largeblue.order in your project dependencies, adapt
container(s) to say they implement
largeblue.order.interfaces.IMarkedAsOrdering and adapt the objects you
want to order to say they implement
largeblue.order.interfaces.IMarkedAsOrderable.Then go tohttp://…path/to/my_container/@@orderable_contents.htmlFor an example usage, seelargeblue.pages(configuring the container and object is done here).
Package: largeblue.tag
largeblue.tag provides an uber simple tagging adapter.  If included:<include package="largeblue.tag" />It will adapt any object configured to implement largeblue.tag.interfaces.ITaggable,
e.g:<class class=".objects.MyContentObject">
  <implements interface="largeblue.tag.interfaces.ITaggable" />
  ...
</class>This will hang a @@tags.html view of each instance of the adapted content object,
which provides a single textline input to enter tags (seperated by a space).
Thus if you enter ‘foo bar dolores’ into the text input and submit, you’ll find:>>> context = my_obj
>>> from largeblue.tag.interfaces import ITag
>>> t = ITag(context)
>>> t.tags
['foo', 'bar', 'dolores']
>>> t.tagstring
u'foo bar dolores'You can add, remove and update:>>> t.add_tag('elephants')
>>> t.tags
['foo', 'bar', 'dolores', 'elephants']
>>> t.remove_tag('dolores')
>>> t.tags
['foo', 'bar', 'elephants']
>>> t.update_tags(['coffee', 'milk'])
>>> t.tags
['coffee', 'milk']That’s it.
Package: largeblue.util
Atm largeblue.util just has an emailer utitity for sending emails, which wraps the
zope.sendmail functionality, plus a batch utility which can be used to batch search results
/ listings etc.:>>> from largeblue.util.interfaces import IEmail, IBatch
>>> from zope.component import getUtilityThe package configures the two utilities so they’re lookable-upable (after all zcml
registrations have been parsed) under:>>> getUtility(IEmail)
<class 'largeblue.util.emailer.Email'>

>>> getUtility(IBatch)
<class 'largeblue.util.batch.Batch'>You’ll need to configure your own smtpMailer & queuedDelivery utilities (these should be
done manually for each site / instance - see the comment in configure.zcml).
Package: large-file-finder
Large file finderSometimes it's very hard to find if some giant files are eating up your system storage. We might need to hunt those down. This simple CLI application helps you to find those giant files that are eating up your system storage.How to install and use?pipinstalllarge-file-finderNow run the following command to your desired path.large-file-finder./Requirements:Python 3.4 or later versions need to be installedAvailble cli arguments:large-file-finder-hpositional arguments:
  path                  relative path where you want to start search. For example ./

optional arguments:
  -h, --help            show this help message and exit
  --limit LIMIT, -limit LIMIT, -l LIMIT
                        Number of files you want to see in the table. Default value is 10. Maximum accepted value is 200
  --min-size MIN_FILE_SIZE, -min-size MIN_FILE_SIZE
                        Minimum file size to search. Accepted values are b, KB, MB, TB. For example --min_file_size 10KB
  --max-size MAX_FILE_SIZE, -max-size MAX_FILE_SIZE
                        Maximum file size to search. Accepted values are b, KB, MB, TB. For example --max_file_size 10KB
  --search SEARCH, -search SEARCH, -s SEARCH
                        Search by name. --search 'my_file.png'
  --hide-limited-access HIDE_LIMITED_ACCESS, -hide-limited-access HIDE_LIMITED_ACCESS
                        Hide the table that contains directories with no access right. Only true or false are accepted
  --extension [EXTENSION ...], -ext [EXTENSION ...]
                        Extension of file such as .txt, .jpeg, .png, .mp3. Multiple extensions are accepted. Such as --extension .txt .pngFile Search result uiLisenceMIT
Package: large-file-splitter
large_file_splitter下の方に日本語の説明がありますOverviewThe tool considers a file so large that it does not fit in memory as a single string and performs a split process of the string. The tool stores the result as separate files.under constructionUsageimportlarge_file_splitter# Split a large file [large_file_splitter].large_file_splitter.split("dummy_large_file.txt",# File to be splitsplit_str="SPLIT_MARK\r\n",# Split string (For convenience of splitting, it is processed as binary internally, so setting this to a single character is not recommended because it may lead to erroneous splitting of multi-byte characters, etc.)div_mode="start",# mode for handling split strings (delete: split string is not included in output; start: split string is concatenated at the beginning of the next chunk; end: split string is concatenated at the end of the previous chunk)output_filename_frame="./output/div_%d.txt",# Template for output filename (an integer value is automatically inserted for %d)cache_size=10*1024*1024# Specify the size of the chunk of data to work with in memory (in bytes; memory capacity must be at least several times this size.))Example of usage (in the context of a for loop)importlarge_file_splitter# Split a large file (for loop version) [large_file_splitter]forone_strinlarge_file_splitter.for_split("dummy_large_file.txt",# Target file for splittingsplit_str="SPLIT_MARK\r\n",# Split string (for internal processing, it is treated as binary, so it is not recommended to make this a single character, etc., as it may lead to incorrect splitting of multi-byte characters)div_mode="start",# Mode of handling the split string (delete: split string is not included in the output; start: split string is joined to the beginning of the next block; end: split string is joined to the end of the previous block)cache_size=1024# Specifies the size of the data block to work with in memory (in bytes; at least this multiple of memory capacity is required)):# Some processing using the string `one_str`print(one_str)概要メモリに乗らないほど巨大なファイルを一つの文字列とみなし、文字列のsplit処理を実施。その結果を別々のファイルとして格納するツール。説明は執筆中です使用例importlarge_file_splitter# 巨大ファイルの分割 [large_file_splitter]large_file_splitter.split("dummy_large_file.txt",# 分割対象ファイルsplit_str="SPLIT_MARK\r\n",# 分割文字列 (分割の都合上内部ではbinaryとして処理するので、ここを一文字等にするのは、マルチバイト文字等の誤分割に繋がる可能性があるため非推奨)div_mode="start",# 分割文字列の扱いのモード (delete: 分割文字列は出力に含まない; start: 分割文字列は次の塊の先頭に結合される; end: 分割文字列は前の塊の末尾に結合される)output_filename_frame="./output/div_%d.txt",# 出力先ファイル名のテンプレート (%dのところは自動で整数値が挿入される)cache_size=10*1024*1024# メモリで作業するデータ塊の大きさの指定 (バイト単位; メモリ容量は少なくともこの数倍は必要))使用例 (for文脈での利用)importlarge_file_splitter# 巨大ファイルの分割 (for文脈バージョン) [large_file_splitter]forone_strinlarge_file_splitter.for_split("dummy_large_file.txt",# 分割対象ファイルsplit_str="SPLIT_MARK\r\n",# 分割文字列 (分割の都合上内部ではbinaryとして処理するので、ここを一文字等にするのは、マルチバイト文字等の誤分割に繋がる可能性があるため非推奨)div_mode="start",# 分割文字列の扱いのモード (delete: 分割文字列は出力に含まない; start: 分割文字列は次の塊の先頭に結合される; end: 分割文字列は前の塊の末尾に結合される)cache_size=1024# メモリで作業するデータ塊の大きさの指定 (バイト単位; メモリ容量は少なくともこの数倍は必要)):# 文字列 `one_str` を用いた何らかの処理print(one_str)
Package: large-image-converter
Convert a variety of images into the most efficient format for Large Image.Geospatial files are converted into cloud-optimizedgeotiffvia gdal_translate.
Single-image non-geospatial files are converted into pyramidal tiff files via pyvips.
Multi-image tiff files are converted into tiff files with multiple pyramidal tiff images and have a custom image description to store frame details.Some files can be read via the various tile sources in large_image without conversion but are inefficient (for example, uncompressed data in nd2 files).  Converting these files will result in more efficient data access.InstallationTo install via pip with custom-built wheels:pip installlarge-image-converter[sources]--find-linkshttps://girder.github.io/large_image_wheelsThe[sources]extra requirement is optional.  When specified, all of the default large-image tile sources are installed for additional metadata extraction and format support.RequirementsIf the custom-built wheels do not cover your platform, or you want to use different versions of tools, you can install the prerequisites manually.  For full functionality, the following packages and libraries are needed:GDAL 3.1.0 or greater, including the command-line tools and the python librarylibtiff, including the command-line toolslibvipsAdditionally, the various tile sources for large_image can be used to read input files to extract and preserve metadata and to read files that can’t be read via libvips or GDAL.  The requirements of those sources need to be installed.UsageCommand LineIn the simplest use, an image can be converted via:large_image_converter <source path>An output image will be generated in the same directory as the source image.The full list of options can be obtained via:large_image_converter--helpFrom PythonThe convert function contains all of the main functionality:from large_image_converter import convert

convert(<source path>)

# See the options
print(convert.__doc__)From GirderThe converter is installed by default whengirder-large-imageis installed.  It relies on Girder Worker to actually run the conversion.The conversion task can be reached via the user interface on the item details pages, via thecreateImageItemmethod on theImageItemmodel, or via thePOSTitem/{itemId}/tilesendpoint.Limitations and Future DevelopmentThere are some limitations that may be improved with additional development.For some multi-image files, such as OME Tiff files that cannot be read by an existing large_image tile source, the specific channel, z-value, or time step is not converted to readily usable metadata.Whether the original file is stored in a lossy or lossless format is not always determined.  If unknown, the output defaults to lossless, which may be needlessly large.
Package: large-image-tasks
These are Girder Worker tasks used by Large Image.The conversion task is typically accessed by selecting the Make Large Image
icon on the items detail page in Girder, or by accessing thePOSTitem/{itemId}/tilesendpoint.  Using the endpoint allows specifying more
options and converting an inefficient file into a preferred format.  See thelarge_image_converterfor more details on the various options.See the large-image package for more details.
Package: large-language-model-cli
llmcliA command line interface for using some large language models.Support models:OpenAI'sChatGPTOther models when I want to use them. I might end up hooking this up to huggingface and gptInstall:pip install large-language-model-clichatgptllmcli chatgpt "prompt"Prompts for password if necessarily. Saves a session tokkenUse--loginto force a new login.Prior workThere are libraries inPythonandnodeto talk to chatgptFor ChatGPTrevChatGPTwhich has aText User Interfacebut notcommand line interface
Package: largentemp-openapi-client
LargenTemp Python-bibliotekDetta Python-bibliotek gör det möjligt att hämta temperaturdata frånLargenTemps API.InstallationBiblioteket finns tillgängligt via PyPi:pip install largentemp-openapi-clientAnvändningHämta senaste temperaturdatanfromlargentemp_openapi_client.clientimportClientclient=Client()# Skapa en ny instans av en API-klientdata=client.get_data()Data-variabeln kan sedan användas. Här är ett exempel för aktuellt värde:>>>data.temperaturedata.reading20.4>>>data.temperaturedata.reading_rounded20.4>>>data.temperaturedata.timestamp.local_timestampdatetime.datetime(2022,6,4,18,21,56,93239,tzinfo=<DstTzInfo'Europe/Stockholm'CEST+2:00:00DST>)>>>data.temperaturedata.timestamp.string_timestamp'Senast uppdaterad för 1 minuter sedan'Även daglig statistik är tillgänglig. Här är ett exempel:>>>data.statistics.today_high.reading22.875>>>data.statistics.today_high.reading_rounded22.9>>>data.statistics.today_high.timestamp.local_timestampdatetime.datetime(2022,6,4,17,32)>>>data.statistics.today_mean21.03125>>>data.statistics.value_count_today10.0FörfrågningslimiteratLargenTemps API är förfrågningslimitierat - sehär.
Om dessa förfrågningslimitationer skulle drabba dig så kommer du att få ett felmeddelande:largentemp_openapi_client.exceptions.RateLimitedException:Ettfelinträffade:duharblivitförfrågningslimiterad(rate-limited)frånAPI:etAlla felmeddelanden finns ilargentemp_openapi_client.exceptions.
Package: largest-frontal-face-detector
Largest Face DetectorThe Largest Face Detector is a basic face detector that detects largest frontal face from image using external python dlib library (http://dlib.net/python/index.html).InstallationYou can install the Largest Face Detector fromPyPI:pip install largest-frontal-face-detectorThe reader is supported on Python 3.6 and above.Pre-requisites1. numpy (pip install numpy)
2. dlib (pip install dlib)
3. PIL (pip install pillow)NOTE :: These dependency packages will be installed automatically upon installing largest-face-detector package.How to useThe Largest Face Detector is a pip package, namedlargest_face_detector.  To detect face in a particular image, call the functiondetect_largest_facewith image path as input (relative to current directory you are in)>>> import largest_face_detector
>>> image = largest_face_detector.detect_largest_face('image.jpeg')
$ Cropped face saved successfully at : /Users/kulshd/2019-10-05T14:08:20.086934_cropped_image.jpegThe function will return the cropped image as a 2D numpy array and also save it.Following are some requirements and assumptions for using this package -1. Image size must be within [1024,1024]. If it exceeds, NotImplementedError is raised.   
2. If no face is detected by the package, a warning message is displayed
3. Currently following image formats are supported - [.jpeg, .jpg, .png]You can also run test-cases to validate package functionality by downloading source .tar.gz file (https://pypi.org/project/largest-frontal-face-detector/1.0.0/#files) ,extracting and cd into folder largest_frontal_face_detector-1.0.0, and runningpython3 -m unittest discover -v
Package: lariat-python-sdk
Lariat Python SDKA Python module to interact with Lariat API and perform various operations like querying indicators, fetching datasets, etc.InstallationTo install the package, run the following command:pipinstalllariat_python_sdkUsageFirst, import the necessary components:fromlariat_clientimportconfigure,get_raw_datasets,get_datasets,get_indicators,get_indicator,query,Filter,FilterClauseimportdatetimeSet up your API key and application key:API_KEY="your-api-key"APP_KEY="your-app-key"configure(api_key=API_KEY,application_key=APP_KEY)Get raw datasets:raw_datasets=get_raw_datasets(dataset_ids=[1,2,3])Get computed datasets:datasets=get_datasets()Get indicators:indicators=get_indicators(datasets=datasets)Get a specific indicator:indicator=get_indicator(id=1)Query an indicator:from_ts=datetime.datetime(2022,1,1)to_ts=datetime.datetime(2022,2,1)group_by=["country"]filter_clause=FilterClause(field="country",operator="in",values="US,UK")query_filter=Filter(clauses=[filter_clause],operator="and")results=query(indicator.id,from_ts,to_ts,group_by,query_filter=query_filter)# Convert results to a DataFrameresults_df=results.to_df()# Save results to a CSV fileresults.to_csv("results.csv")Use theRawQueryinterface to add additional query argumentsNote: Query arguments attached via the RawQuery interface are subject to changes in their backend interpretation. Use with cautionimportlariat_clientimportdatetimelariat_client.configure(api_key="some_key",application_key="some_other_key")indicator=lariat_client.get_indicator(id=1234)from_ts=datetime.datetime(2023,5,1)to_ts=datetime.datetime(2023,5,10)filter_clause=lariat_client.FilterClause(field="country",operator="in",values="USA")query_filter=lariat_client.Filter(clauses=[filter_clause],operator="and")raw_query=lariat_client.RawQuery(indicator_id=indicator.id,from_ts=from_ts,to_ts=to_ts,aggregate="distinct",query_filter=query_filter)raw_query.add_query_argument("x_axis","custom_x_axis")records=raw_query.send()