Saravanan666/pypi-org huggingface dataset:
Package: lcmap-pyccd
PyCCD - Python Continuous Change Detectionpyccd creates change segments from Landsat timeseries dataUsing PyCCD>>>importccd>>>results=ccd.detect(dates,blues,greens,reds,nirs,swir1s,swir2s,thermals,qas,prev_results)>>>>>>type(results)<class'dict'>>>>>>>results{algorithm:'pyccd:x.x.x',processing_mask:[bool,bool,...],snow_prob:float,water_prob:float,cloud_prob:float,change_models:[{start_day:int,end_day:int,break_day:int,observation_count:int,change_probability:float,curve_qa:int,blue:{magnitude:float,rmse:float,coefficients:(float,float,...),intercept:float},green:{magnitude:float,rmse:float,coefficients:(float,float,...),intercept:float},red:{magnitude:float,rmse:float,coefficients:(float,float,...),intercept:float},nir:{magnitude:float,rmse:float,coefficients:(float,float,...),intercept:float},swir1:{magnitude:float,rmse:float,coefficients:(float,float,...),intercept:float},swir2:{magnitude:float,rmse:float,coefficients:(float,float,...),intercept:float},thermal:{magnitude:float,rmse:float,coefficients:(float,float,...),intercept:float}}]}Default processing parameters can be overridden using a dictionary (see parameters.yaml for valid keys):>>>importccd>>>params={'QA_BITPACKED':False,'QA_FILL':255,'QA_CLEAR':0,'QA_WATER':1,'QA_SHADOW':2,'QA_SNOW':3,'QA_CLOUD':4}>>>results=ccd.detect(dates,blues,greens,reds,nirs,swir1s,swir2s,thermals,qas,params=params)InstallingSystem requirements (Ubuntu)python3-devgfortranlibopenblas-devliblapack-devgraphvizpython-virtualenvSystem requirements (Centos)python3-develgfortranblas-devlapack-devgraphvizpython-virtualenvIt's highly recommended to do all your development & testing in a virtual environment.user@dev:/home/user/$mkdirpyccd
user@dev:/home/user/$cdpyccd
user@dev:/home/user/pyccd$virtualenv-ppython3.venv
user@dev:/home/user/pyccd$..venv/bin/activate(.venv)user@dev:/home/user/pyccd$Install$pipinstall-e.[test,dev,docs,deploy,profile]Testing$pytest
$pytest--profile
$pytest--profile-svg# pytest-watch$ptwMake targets$makebuild
$maketests
$makedocs
$makedeploy
$makeprofileProfilingDecorate the function to be profiled with@profileand
runmake profile.  Remove decorations before committing code.ContributingBefore committing to this repository, run the following command.gitconfig--localcommit.template.gitmessageThis will add the LCMAP commit template togit commit.jira/lcmap-xxxx: Title
DescriptionContributions are most welcome.Open an issue and discuss the change.Branch from major version and name it after the issuejira/lcmap-xxxxWrite automated tests for your changes and make sure all tests pass.Update documentation in project.Submit pull request to the appropriate branch (e.g. 1,x)VersionsPyCCD previously followed MAJOR.MINOR.PATCH.LABEL semantic versioning but has
changed to date based semantic versioning, thus: YYYY.MM.DD[.HH.MM.SS][-label].PyCCD's version is defined by theccd/version.py/__version__attribute.ReferencesPyCCD Digital Object Identifier 10.5066/P90V8IIXTest DataLandsat Band SpecificationsLandsat 8 Surface Reflectance SpecsLandsat 4-7 Surface Reflectance SpecsLandsat Analysis Ready DataLCMAP CCDC Collection 1.0 Algorithm Description Document
Package: lcm-export
LCM Log Export Toollcm-exportis a tool for converting log files generated bylcm-loggerto .mat or
.pkl files importable to Matlab and Python scripts.lcm-exportis an updated version of thelcm-log2smatmoduleby G. Troni.
This module reuses some code and is heavily based on the approach taken in that module.UsageThe installation process below will install a script namedlcm-exportto your binaries folder.
To use, runlcm-export [options] file.fileis the relative path to your lcm log file, and accepts glob references (e.g. use lcmlog_* to generate .mat files for all files beginning lcmlog_)LCM-TypesThis utility requires the python versions of your lcm types to read the log (those generated bylcm-gen).Direct it to the directory containing those files with the--lcmtypes PATHoption (it will also search all subdirectories).Other options:-pto generate Python .pkl files-mto generate Matlab .mat files (default)InstallationRequirementsRequires Python 3.6+ andlcmto run, and pip for installation.InstallationClone this repocd into the root directoryRunpip3 install .UninstallationUninstall withpip3 uninstall lcm-exportLicenseLCM Log Export Tool is based on code written by G. Troni in theirlcm-log2smatmodule,
which itself is based on a script inlibbot2.(libbot2)libbot2 is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.libbot2 is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.(lcm-log2smat)lcm-log2smat is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.lcm-log2smat is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.(LCM Log Export Tool)LCM Log Export Tool is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.LCM Log Export Tool is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public License along with LCM Log Export Tool. If not, seehttp://www.gnu.org/licenses/.
Package: lcmlog-py
lcmlog-pyLCM log utilitiesInstallationlcmlog-pyis available on PyPI:pipinstalllcmlog-pyUsageEventslcmlog-pyprovides classes to abstract the LCM log file format. TheEventclass represents a single event in the log file. AnEventis composed of a header, channel string, and data byte array.TheHeaderclass represents a fixed-length header at the beginning of each event which describes the event number, timestamp, and channel/data lengths. This is parsed first, and then the channel and data are read in.If the header is read incorrectly or is corrupt, aBadSyncErroris raised.To read a header, call theHeader.from_filemethod:fromlcmlogimportHeaderwithopen('lcmlog','rb')asf:header=Header.from_file(f)print(header.timestamp)# e.g.A full event can be read via theEvent.from_filemethod:fromlcmlog.eventimportEventwithopen('lcmlog','rb')asf:event=Event.from_file(f)print(event.header.timestamp)print(event.channel)# e.g.Log Reader & Writerlcmlog-pyalso provides two utility classes for reading and writing LCM log files in order.TheLogReaderreads events from a file (starting at the beginning) and theLogWriterwrites events to a file. TheLogReaderclass is iterable. Both classes close their respective files when they are garbage collected.fromlcmlogimportLogReader,LogWriterreader=LogReader('lcmlog_source')writer=LogWriter('lcmlog_dest')# Read and write each eventforeventinreader:writer.write(event)
Package: lcms
Python Little CMSA minimalistic module to utilizeLittle CMSto apply color profiles to 16 bit TIFF
images.MotivationCurrently there seems to be no python packages available to apply color profiles
to 16 bit TIFF images.Pillowhas
built in support forLittle CMS, but does currently
not have16 bit supportfor RGB images.Installationgit submodule update --init
python setup.py bdist_wheel --build-type ReleaseThen install the wheel present in thedistfolder.ExampleTo run the example you need a 16 bit TIFF image and a color profile, for example
a*.icmfile.
Then runpython example.py your_image.tif your_profile.icmand have a look at the generated imageyour_image-transformed.tif.
Package: lcmtools
_______________||/___|\/|__|_____||___||||||\/||||/_\/_\|/__|||__||___||||||(_)|(_)|\__\|_____\____|_||_||_|\___/\___/|_|___/----byLuoDebinLCMTools: A script that calls LCModel through Python:一个在Python里面方便调用LCModel的Python模块。怎么使用(兼容linux/MacOS)1.安装方法一（推荐）>>> pip install lcmtools方法二>>> git clone https://github.com/luodeb/LCMTools.git
>>> python3 setup.py installLICENSEMIT
Package: lcm-websocket-server
lcm-websocket-serverWebSocket server for republishing LCM messages.InstallationYou must have the LCM Python package installed before usinglcm-websocket-server.See theLCM build instructionsfor more information.From PyPIpipinstalllcm-websocket-serverFrom sourcepoetrybuild
pipinstalldist/lcm_websocket_server-*-py3-none-any.whlUsageFor a list of available options, run:lcm-websocket-server--helpTo run the server locally on port 8765 and republish messages on all channels:lcm-websocket-server--hostlocalhost--port8765--channel".*"your_lcm_types_packageThelcm_packagesargument is the name of the package (or comma-separated list of packages) that contains the LCM Python message definitions. Submodules are scanned recursively and registered so they can be automatically identified, decoded, and republished.Example:compas_lcmtypesFor example, thecompas_lcmtypespackage contains LCM types for the CoMPAS lab. These can be installed with:pipinstallcompas-lcmtypes==0.1.0Then, the server can be run with:lcm-websocket-servercompas_lcmtypesDockerBuildA Docker image to run thelcm-websocket-servercan be built with:./scripts/docker_build.shThis will create thembari/lcm-websocket-serverimage.RunThe container can be run with:dockerrun\--namelcm-websocket-server\--rm\-eHOST=0.0.0.0\-ePORT=8765\-eCHANNEL=".*"\-v/path/to/your_lcm_types_package:/app/your_lcm_types_package\-eLCM_PACKAGES=your_lcm_types_package\--network=host\-d\mbari/lcm-websocket-serverNote that theHOST,PORT, andCHANNELenvironment variables specified above are the defaults for thembari/lcm-websocket-serverimage. These can be omitted if the defaults are acceptable.TheLCM_PACKAGESenvironment variable should be set to the name of the package (or comma-separated list of packages) that contains the LCM Python message definitions. The/appdirectory is included in thePYTHONPATHso that any packages mounted there (as shown with-vabove) can be imported.It's recommended to run with--network=hostto avoid issues with LCM over UDP. This will allow the container to use the host's network stack.
Package: lcn-frontend
LCN FrontendThis repository contains the frontend files for the Home Assistant LCN configuration panel.FeaturesDevice ConfigurationSet up modules and groups for all your LCN integrations.Search for modules and groups and have them added automatically.Select a module or group to access the respective Entity Configuration panel.Entity ConfigurationSet up Home Assistant entities for each module or group.Create new entities that map the functions of your LCN modules and configure their parameters.New entities are automatically added to your Home Assistant configuration and you can add them to your dashboards just like for any other integration.
Package: lco
LightConfig (lco)Load a config file once and use it as you would use enviroment variables from anywhere in your code.Somewhere:importlcolco.init("some_config.yml")You can also set the enviroment variableLCO_CONFIGinstead.Somewhere else entirely, 10 layers deep in some object.importlcolco["some_value"]lco["some_group"]["some_value"]This works across threads and processes.But why?This is mostly useful for research code, MVPs or Prototypes, where on doesn't want to pass configuration variables from object to object or across scopes. I wouldn't recommend this for production code as you're essentially using global variables.What filetypes are supported?So far, only yaml. Feel free to open a pull request with the filetype of your choice.
Package: lco-bpm-maker
pixel-mask-genPython utility to generate a bad pixel mask from a set of calibration images.Authors: Matt Daily, Raleigh Littles, Curtis McCullyInstallationFrom PyPiThis package is available via PyPi, and can be installed via pip:pip3 install lco-bpm-makerFrom GithubTo install the tool, clone this repository and run:cd pixel-mask-gen
python3 setup.py installTestsTo run the unit tests, simply run:python3 setup.py testUsageOnce you've installed the tool, it can be run simply by:lco_bpm_makerusage: lco_bpm_maker [-h] [--log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}]
                     [--dark-current-threshold DARK_CURRENT_THRESHOLD]
                     [--flat-sigma-threshold FLAT_SIGMA_THRESHOLD]
                     [--bias-sigma-threshold BIAS_SIGMA_THRESHOLD]
                     input_directory output_directory

Create a bad pixel mask from a set of calibration frames.

positional arguments:
  input_directory       Input directory of calibration images

  output_directory      Output directory for bad pixel mask

optional arguments:
  -h, --help            show this help message and exit

  --log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}
                        Logging level to be displayed

  --dark-current-threshold DARK_CURRENT_THRESHOLD
                        Threshold for pixel dark current when flagging bad
                        pixels in dark frames. Pixels above this will be
                        flagged. Default = 20 [electrons/second]

  --flat-sigma-threshold FLAT_SIGMA_THRESHOLD
                        Number of standard deviations from the median of the
                        combined flat image for a pixel to be flagged. Default = 10

  --bias-sigma-threshold BIAS_SIGMA_THRESHOLD
                        Number of standard deviations from the median of the
                        combined bias image for a pixel to be flagged. Default = 10
                        
  --fpack               Flag to fpack output BPM
Package: lcode
lcodePyOverviewLCODE is a free software for numerical simulation of
particle beam-driven plasma wakefield acceleration.
LCODE is based on the quasistatic approximation, capable
of simulation in 2D and 3D geometry, and can use GPUs and CPUs.For now, this is new and experimental software. This is
a complete overhaul of the old C version in Python.You can also find a more mature 2D version of LCODE athttp://lcode.info/.InstalationWe useAnacondaand we recommend
installing lcode in a separate environment.
Any other python installation should work fine, but has not been tested.Create a new environment and install the dependencies:conda create -n lcode-env -c conda-forge numba numpy scipy matplotliborconda env create -f conda-env.ymlwhereconda-env.ymlis avalible in sources.Acivate the new environment:conda activate lcode-envOptional: in order to run simulations on GPU, add cupy and other necessary packages to the line of dependencies when creating a new eviroment. Check in advance if you have the drivers for your GPU installed. It is not necessary to install CUDA Toolkit in advance. For any other questions about cupy, please checkhttps://docs.cupy.dev/en/stable/install.htmlconda create -n lcode-env -c conda-forge numba numpy scipy matplotlib cupyInstall lcode:pip install lcodeor download sources from GitHub and run the forlowing command
in downloaded directory:pip install .Fill free to contactteam@lcode.infofor assistance with it.
Package: lcoe
A command line tool and Python package to calculate levelised cost of electricityDescriptionCalculate levelised cost of electricity on the command line with thelcoecommand:$ lcoe 500000000 2000000000 25000000 0.07 20
LCOE is 0.03455442190262982Use the lcoe library in your Python code:from lcoe.lcoe import lcoe

operating_cost = 25000000  #$million/year
capital_cost = 500000000  # $million
discount_rate = 0.07  # %
lifetime = 20
annual_output = 2000000000  # kWh

lcoe(annual_output, capital_cost, operating_cost, discount_rate, lifetime)
Package: lco-ingester
Ingester LibraryA library for adding new science data products to an observatory control system's science archive. The library
handles uploading FITS files into AWS S3, as well as adding records to the science archive's database containing
the searchable metadata of all available FITS files.PrerequisitesOptional prerequisites may be skipped for reduced functionality.Python >= 3.6A runningscience archiveWrite access to the same S3 bucket that the running science archive is using(Optional) A runningOpenTSDBfor metrics collectionInstallationIt is highly recommended that you install and run your python code inside a dedicated pythonvirtual environment.Add thelco_ingesterpackage to your python environment:(venv)$pipinstalllco_ingesterConfigurationAWS and science archive credentials must be set in order to upload data. Science archive configuration as well as the
AWS Bucket can be either passed explicitly or set as environment variables. The rest of the configuration must be
set as environment variables.Environment VariablesVariableDescriptionDefaultScience ArchiveAPI_ROOTScience Archive URL"http://localhost:8000/"AUTH_TOKENScience Archive Authentication Token. This token must be associated with an admin user.empty stringAWSBUCKETAWS S3 Bucket NameingestertestAWS_ACCESS_KEY_IDAWS Access Key with write access to the S3 bucketempty stringAWS_SECRET_ACCESS_KEYAWS Secret Access Keyempty stringAWS_DEFAULT_REGIONAWS S3 Default Regionempty stringMetricsOPENTSDB_HOSTNAMEOpenTSDB Host to send metrics toempty stringOPENTSDB_PYTHON_METRICS_TEST_MODESet to any value to turn off metrics collectionFalseINGESTER_PROCESS_NAMEA tag set with the collected metrics to identify where the metrics are coming fromingesterSUBMIT_METRICS_ASYNCHRONOUSLYOptionally submit metrics asynchronously. This option does not apply when the command line entrypoint is used, in which case metrics are always submitted synchronously. Note that some metrics may be lost when submitted asynchronously.FalsePostprocessingFITS_BROKERFITS exchange brokermemory://localhostPROCESSED_EXCHANGE_NAMEProcessed files RabbitMQ Exchange Namearchived_fitsPOSTPROCESS_FILESOptionally submit files to fits queueTrueFor DevelopersRunning the TestsAfter cloning this project, from the project root and inside your virtual environment:(venv)$pipinstall-rrequirements.txt(venv)$pytest
Package: lcom
Cohesion metrics measure how well methods of a class are related to each other.
A cohesive class has one responsibility. A non-cohesive class has more
unrelated functions, thus more than one responsibility.LCOM4considers method as related with other method, when both of them use common attribute or method call.
Methods are not compared with constructors / initializers eg.__init__, since they often involve all attributes.
Also all inherited methods are ignored - as they come from different class.Score:==1- indicates a cohesive class, which is the “good” class;>=2- indicates a problem. The class should be split into so many smaller classes;==0-  happens when there are no methods in a class. This is also a “bad” class.UsageJust point it to a module or a package that needs to be measured:bin/lcom srcThis will result in such output:Calculating LCOM using LCOM4
+---------------------------------+------+
| Method                          | LCOM |
+---------------------------------+------+
| src.command.FileSystem          | 1    |
| src.command.LCOMFactory         | 0    |
| src.command.Printer             | 0    |
| src.command.PrinterFactory      | 0    |
| src.command.Runner              | 1    |
| src.command.STDOut              | 0    |
| src.lcom.LCOM4                  | 1    |
| src.lcom.LCOMAlgorithm          | 0    |
| src.reflection.ClassReflection  | 1    |
| src.reflection.MethodReflection | 1    |
| src.reflection.ModuleReflection | 1    |
| src.reflection.Reflection       | 0    |
| src.reflection.ReflectionError  | 0    |
+---------------------------------+------+
| Average                         | 0.46 |
+---------------------------------+------+It is also possible to measure single module:bin/lcom src/command.pyOr mix them:bin/lcom src/command.py tests
Package: lcommit.hook
Commit hook (github)Reproduce Commit webhook from github.MotivationGithub webhook are super awesome! But are not available to everyone since only the owner of the repo can create webhook. Sometimes it's very frustrating because you would like to do action in response to an other action concerning a repo that you don't own.
That's why I created this little package.It checks if a new commit has been pushed, then hit a webhook.InstallPython 3.6 or higher is requiredpip install ...UsageThe ideal is to create a crontab of your choice that execute this script.For example, a crontab that check everyday if a new commit has been pushed to a repo, then react in consequence.Cli usagelcommit [usernameGithub] [repoName] [urlToHit] [locationConfig]LicenseMIT Paul Rosset
Package: lcoptview
Lcoptview---------Share your Lcopt results with a wider audienceWatch the video and get full installation instructions [here](http://lcoptview.rtfd.io)Installation------------------------The easiest way to install lcoptview is via conda. This ensures the version of Python (3.6) is correct, all of the dependencies are correct and there are no conflicts.If you have conda installed, you can do it in one command.If you don't, first install miniconda (available [here](https://conda.io/miniconda.html))Then, on the command line/console, create a new environment called lcoptview and install lcoptview with this command```conda create -n lcoptview -c pjamesjoyce python=3.6 lcoptview```Launch lcoptview----------------1) Activate the lcoptview environment using one of these```# Mac/Linuxsource activate lcoptview# Windowsactivate lcoptview```2) Launch lcoptview by typing```lcoptview```You'll see this dialog![lcoptview launcher](/img/launcher.jpg)Click on the button and choose an .lcoptview file.Your default browser will open up, and you can explore the model and results.
Package: l-coscmd
介绍腾讯云COS命令行工具, 目前可以支持Python2.6与Python2.7以及Python3.x。安装指南使用pip安装pip install -U l-coscmd手动安装:python setup.py install使用方法使用coscmd，参照https://cloud.tencent.com/document/product/436/10976配置文件中添加：
l5 = l5名称
Scheme需要设置为http
endpoint不需要设置
Package: lcov
lcovGraphical front-end for GCC’s coverage testing tool gcov.Overviewlcovis a strict Python implementation of theLCOVpackage.LCOV is an extension of GCOV, a GNU tool which provides information about
what parts of a program are actually executed (i.e. “covered”) while running
a particular test case. The extension consists of a set of scripts which build
on the textual GCOV output to implement the following enhanced functionality:HTML based output: coverage rates are additionally indicated using bar
graphs and specific colors.Support for large projects: overview pages allow quick browsing of
coverage data by providing three levels of detail: directory view,
file view and source code view.LCOV was initially designed to support Linux kernel coverage measurements,
but works as well for coverage measurements on standard user space applications.UsageTBD…InstallationPrerequisites:Python 3.7 or higherhttps://www.python.org/3.7 is a primary test environment.pip and setuptoolshttps://pypi.org/project/pip/https://pypi.org/project/setuptools/To install run:python -m pip install --upgrade lcovDevelopmentPrerequisites:Development is strictly based ontox. To install it run:python -m pip install --upgrade toxVisitdevelopment page.Installation from sources:clone the sources:git clonehttps://github.com/karpierz/lcov.gitlcovand run:python -m pip install ./lcovor on development mode:python -m pip install --editable ./lcovLicenseCopyright (c) 2020-2022, Adam KarpierzLicensed under the BSD licensehttps://opensource.org/licenses/BSD-3-ClausePlease refer to the accompanying LICENSE file.AuthorsAdam Karpierz <adam@karpierz.net>Changelog0.0.1 (2021-10-14)Initial commit.