Saravanan666/pypi-org huggingface dataset:
Package: satellite-parser
遥感卫星数据解析安装pipinstallsatellite-parser依赖xmltodict说明本程序为遥感卫星原始数据解析，作为内部使用类名GF1Satellite 高分一号数据处理类GF2Satellite 高分二号数据处理类GF3Satellite 高分三号数据处理类GF4Satellite 高分四号数据处理类GF5Satellite 高分五号数据处理类GF6Satellite 高分六号数据处理类GF7Satellite 高分七号数据处理类GF1BSatellite 高分一号B数据处理类GF1CSatellite 高分一号C数据处理类GF1DSatellite 高分一号D数据处理类SV101Satellite 高景一号01数据处理类SV102Satellite 高景一号02数据处理类SV103Satellite 高景一号03数据处理类SV104Satellite 高景一号04数据处理类类变量file_name 文件名file_path 文件路径image_name 图片文件名xml_name xml文件名ext 文件后缀名satellite_id 星源 IDsensor_id 传感器 IDresolution 分辨率image 图片对象meta 解析 xml 后转换成 字典对象satellite_idsensor_idreceive_timescene_idproduct_idproduct_levelproduct_formatproduce_timebandsresolutioncloud_percentgeometry Polygon Geometry 格式API初始化类fromsatellite_parserimportGF1Satellitefile_name='GF1_PMS1_E114.6_N22.7_20181006_L1A0003680028.tar.gz'file_path='/data/GF1_PMS1_E114.6_N22.7_20181006_L1A0003680028.tar.gz'try:satellite=GF1Satellite(file_name,file_path)print(satellite.meta)print(satellite.image_name)print(satellite.xml_name)exceptExceptionase:print(e)生成NIDgenerate_nidfromsatellite_parserimportGF1Satellitefile_name='GF1_PMS1_E114.6_N22.7_20181006_L1A0003680028.tar.gz'file_path='/data/GF1_PMS1_E114.6_N22.7_20181006_L1A0003680028.tar.gz'try:satellite=GF1Satellite(file_name,file_path)print(satellite.generate_nid())exceptExceptionase:print(e)更新日志v0.1 目前仅支持读取高分系列卫星v0.2 新增高景卫星处理类v0.2.3 卫星处理类新增两个变量 image_name 和 xml_namev0.2.4 修复bug，解析压缩包后执行 closev0.2.5 修复bug，在压缩包 close 前读取或解析所有需要的文件内容
Package: satellites
Satellitespipinstall-e.[test]jupyterlabextensiondevelop.--overwrite
jupyterlabextensionlist
jupyterserverextensionlist# open http://localhost:8686/api/jupyter/lab?token=60c1661cc408f978c309d04157af55c9588ff9557c9380e4fb50785750703da6yarnjupyterlab
Package: satellitetle
The python packagesatellitetleprovides functions to fetch TLEs from various
online sources (currently CelesTrak (SatNOGS), CalPoly and AMSAT) and allows
using custom ones or Space-Track.org.It was forked from the python packageorbit.UsageFetch TLEs for a single satellite from Celestrak:from satellite_tle import fetch_tle_from_celestrak

norad_id_iss = 25544 # ISS (ZARYA)
print(fetch_tle_from_celestrak(norad_id_iss))Fetch a large set of TLEs for a list of satllites from all available sources:from satellite_tle import fetch_all_tles, fetch_latest_tles

norad_ids = [25544, # ISS (ZARYA)
             42983, # QIKCOM-1
             40379] # GRIFEX

# Uses default sources and compares TLE set from each source and
# returns the latest one for each satellite
tles = fetch_latest_tles(norad_ids)

for norad_id, (source, tle) in tles.items():
    print('{:5d} {:23s}: {:23s}'.format(norad_id, tle[0], source))

# Uses default sources and returns the TLE sets from all source for
# each satellite
tles = fetch_all_tles(norad_ids)

for norad_id, tle_list in tles.items():
    for source, tle in tle_list:
        print('{:5d} {:23s}: {:23s}'.format(norad_id, tle[0], source))

# Defines custom sources
sources = [
    ('CalPoly','http://mstl.atl.calpoly.edu/~ops/keps/kepler.txt'),
    ('Celestrak (active)','https://celestrak.org/NORAD/elements/active.txt')
]

# Uses custom sources (fetch_all_tles can also be used with the same
# parameters)
tles = fetch_latest_tles(norad_ids, sources=sources)

for norad_id, (source, tle) in tles.items():
    print('{:5d} {:23s}: {:23s}'.format(norad_id, tle[0], source))

spacetrack_config= {
    'identity': 'my_username',
    'password': 'my_secret_password'
}

# Uses default sources and Space-Track.org (fetch_all_tles can also
# be used with the same parameters)
tles = fetch_latest_tles(norad_ids, spacetrack_config=spacetrack_config)

for norad_id, (source, tle) in tles.items():
    print('{:5d} {:23s}: {:23s}'.format(norad_id, tle[0], source))

# Uses only Space-Track.org (fetch_all_tles can also be used with the
# same parameters)
tles = fetch_latest_tles(norad_ids, sources=[], spacetrack_config=spacetrack_config)

for norad_id, (source, tle) in tles.items():
    print('{:5d} {:23s}: {:23s}'.format(norad_id, tle[0], source))

# Uses custom sources and Space-Track.org (fetch_all_tles can also be
# used with the same parameters)
tles = fetch_latest_tles(norad_ids, sources=sources, spacetrack_config=spacetrack_config)

for norad_id, (source, tle) in tles.items():
    print('{:5d} {:23s}: {:23s}'.format(norad_id, tle[0], source))NOTE:fetch_latest_tlesandfetch_all_tlesdownload the TLE sets
from all known sources, so they should only be used when fetching TLEs
for a large set of satellites.LicenseMIT
Package: satellitevu
SatelliteVu SDK for PythonLightweight API Client SDK for SatelliteVu's Platform APIs, providing authorization
handling and convenience methods to interact with the published APIs.InstallationThe package is published toPyPiand can be installed with pip:pip install satellitevuCurrently Python 3.8, 3.10 and 3.11 are supported.UsageA User API Client credential set consisting of anclient idandclient secretis
needed and should be set in your script's environment variables.Check out theexamplesprovided. They can for example be run locally withpoetry run python ./examples/archive.py --example=recentSimple Client UsageThe easiest way to get started is to use thesatellitevu.Clientclass, which needs
a client_id and client_secret only:import os

from satellitevu import Client


client = Client(os.getenv("CLIENT_ID"), os.getenv("CLIENT_SECRET"))
contract_id = os.getenv("CONTRACT_ID")
print(client.archive_v2.search(contract_id=contract_id).json())client.archive_v2.searchsupports all request body parameters documented
in theAPI docs, with special handling fordatetimewhich is
constructed from the optionaldate_fromanddate_toparameters and a default result
page size limit of 25.Authentication HandlingThesatellitevu.Authclass provides the main interface to retrieve an
authorization token required to interact with the API endpoints.import os

from satellitevu import Auth


auth = Auth(client_id=os.getenv("CLIENT_ID"), client_secret=os.getenv("CLIENT_SECRET"))
print(auth.token())Thus retrieved token can be used for bearer token authentication in HTTP request
Authorization headers.TheAuthclass by default uses a file based cache which will store the token in~/.cache/SatelliteVuon Linux~/Library/Caches/SatelliteVuon MacOSC:\Documents and Settings\<username>\Local Settings\Application Data\SatelliteVu\Cacheon WindowsOther cache implementations must implement thesatellitevu.auth.cache.AbstractCacheclass.HTTP Client WrappersConvenience wrapper classes for common HTTP client implementations are provided as
implementations ofsatellitevu.http.AbstractClient, which provides anrequestmethod
with an interface similar torequests.requestand returning ansatellitevu.http.ResponseWrapperinstance, where the response object of the underlying
implementation is available in therawproperty.Commonly used properties and methods are exposed on bothAbstractClientandResponseWrapper.satellitevu.http.UrllibClientfor Python standard lib'surllibsatellitevu.http.requests.RequestsSessionusingrequests.Sessionclasssatellitevu.http.httpx.HttpxClientusinghttpx.Client(Todo)Implementations based onrequestsandhttpxallow setting an instance of the
underlying implementation, but will provide a default instance if not.
Package: satellite-weather-downloader
Satellite Weather DownloaderXarrayCopernicusSWD is a system for downloading, transforming and analysing Copernicus weather data using Xarray. It consists in two major apps,satellite_downloaderandsatellite_weather.downloaderis responsible for extracting NetCDF4 files from Copernicus API, and theweatherimplements Xarray extensions for transforming and visualizing the files.InstallationThe app is available on PYPI, you can use the package without deploying the containers with the command in your shell:$pipinstallsatellite-weather-downloaderRequirementsFor downloading data fromCopernicus API, it is required an account. The credentials for your account can be found in Copernicus' User Page, in theAPI keysection. User UID and API Key will be needed in order to request data. Paste them when asked insatellite_downloaderconnection methods.NotesPython Versions = [3.10, 3.11]Version 1.X includes only methods for Brazil's data format and cities.Create requests via Interactive shellSince SWT version 1.5, it is possible to create dynamic requests using the interactive
python shell or via method call:fromsatellite.downloaderimportrequestfile=request.ERA5_reanalysis(filename='my_dataset_file'# Any ERA5 Reanalysis option can be passed in the method)NOTE: This feature is still in experimental versions, please submit an issue if you find any bug.Extract Brazil NetCDF4 file from a date rangefromsatelliteimportdownloaderfile=downloader.download_br_netcdf('2023-01-01','2023-01-07')Load the datasetfromsatelliteimportweatherassatbr_dataset=sat.load_dataset(file)Usage ofcopebrextensionrio_geocode=3304557# Rio de Janeiro's geocode (IBGE)rio_dataset=br_dataset.copebr.geocode_ds(rio_geocode)rio_dataset.to_dataframe(rio_geocode)It is also possible to create a dataframe directly from the National-wide dataset:br_dataset.copebr.to_dataframe(rio_geocode)All Xarray methods are extended when using thecopebrextension:rio_dataset.precip_med.to_array()rio_dataset.temp_med.plot()Usage ofDSEIextensionyanomami_ds=ds.DSEI['Yanomami']yanomami_polygon=ds.DSEI.get_polygon('Yanomami')List all DSEIsds.DSEI.DSEIs
Package: satellogic-quaternions
Quaternions libraryThis is a library for dealing with quaternions in python in a unified way.To install it:$pipinstallsatellogic_quaternionsExamples of code:In[1]:fromquaternionsimportQuaternionIn[2]:q1=Quaternion(1,2,3,4)In[3]:q2=Quaternion(2,3,5,8)In[4]:q1+q2Out[4]:Quaternion(3,5,8,12)In[5]:q1*q2Out[5]:Quaternion(-51,3,15,15)In[6]:q1/q2Out[6]:Quaternion(0.53921568627450989,0.049019607843137247,-0.029411764705882353,0.0098039215686274439)In[7]:print(q1)(1+2i+3j+4k)Most ofall the quaternions we use are unitary (not like the example above)In[8]:q1.is_unitary()Out[8]:FalseIn[9]:q1/q1.norm()Out[9]:Quaternion(0.18257418583505536,0.36514837167011072,0.54772255750516607,0.73029674334022143)In[10]:q1/=q1.norm()In[11]:q1.is_unitary()Out[11]:TrueUsually, quaternions are used for rotating vectors. This is done withnumpy:In[12]:q1.matrixOut[12]:array([[-0.66666667,0.66666667,0.33333333],[0.13333333,-0.33333333,0.93333333],[0.73333333,0.66666667,0.13333333]])In[14]:q1.matrix.dot([2,3,-4])Out[14]:array([-0.66666667,-4.46666667,2.93333333])In[16]:q1.matrix.dot([1,0,0])Out[16]:array([-0.66666667,0.13333333,0.73333333])A unitary quaternion matrix is unitary. The inverse is the transpose and it is also the
matrix of the inverse quaternion. And the inverse quaternion of a unitary quaternion is
the conjugate:In[17]:q1.conjugate()Out[17]:Quaternion(0.18257418583505536,-0.36514837167011072,-0.54772255750516607,-0.73029674334022143)In[18]:q1Out[18]:Quaternion(0.18257418583505536,0.36514837167011072,0.54772255750516607,0.73029674334022143)In[19]:q1*q1.conjugate()Out[19]:Quaternion(0.99999999999999978,0.0,0.0,0.0)In[20]:q1.conjugate().matrixOut[20]:array([[-0.66666667,0.13333333,0.73333333],[0.66666667,-0.33333333,0.66666667],[0.33333333,0.93333333,0.13333333]])Licensequaternions is Satellogic SA Copyright 2017-2022. All our code is GPLv3 licensed.
Package: satflow
# SatFlow*Sat***ellite Optical ***Flow*with machine learning models.The goal of this repo is to improve upon optical flow models for predicting
future satellite images from current and past ones, focused primarily on EUMETSAT data.## InstallationClone the repository, then run`shell conda env create-fenvironment.yml conda activate satflow pip install-e. ``Alternatively, you can also install a usually older version through`pip install satflow`## DataThe data used here is a combination of the UK Met Office’s rainfall radar data, EUMETSAT MSG
satellite data (12 channels), derived data from the MSG satellites (cloud masks, etc.), and
numerical weather prediction data. Currently, some example transformed EUMETSAT data can be downloaded
from the tagged release, as well as included under`datasets/`.
Package: satgpt
# SatGPTGPT for satellite mission planning. In development. Get your API keys ready!SatGPT heavily leverages Shell GPT, a command-line UI for OpenAI’s completion API,
and its features for custom role creation (as of 04/16/2023). Currently for SatGPT to work,
we need to overwrite the default shell roll using the instructions below.## InstallationClone this repo. Installing from source will be more straightforward than PyPi install at this stage.From inside repo root, install from source.` python-mpip install-e. `Overwriteshell-gptdefaultshellrole withsatgptalpha demo.` python src/satgpt/make_role.py `## UsageStart the REPL in shell mode.` sgpt--shel--repltemp `Request a STAC search using natural language` >>> find all landsat 8&9 imagery over washington, DC in April 2023 `Request edits or additional steps` >>> pipe the results to stacterm cal `4. Try other things!
Version 0.0.4:
- Moved role creation to manual script run
- Populated README with basic usageVersion 0.0.3:
- Fixed custom role creation for satgpt to grab target machine os/ shell
- Added pystac as install dependency for demos
- Added this CHANGELOGVersion 0.0.2:
- Integrated shell-gpt as a dependency using the custom role feature released in version 0.9.0
Package: sat-gt-fel-invoices-downloader
FEL Invoices Downloader for SAT of GuatemalaDownloads received and emited invoices in FEL (Factura En Linea) of Guatemala SATHow to use itInstallationpip install sat_gt_fel_invoices_downloaderExampleHow to download invoice in PDFfromsat_gt_fel_invoices_downloaderimportSATDownloaderimportdatetimeimportos.pathfromsat_gt_fel_invoices_downloader.modelsimportSatCredentialssat_credentials=SatCredentials("YOUR AGENCIA DIGITAL USER","YOUR AGENCIA DIGITAL PASSWORD")sat=SATDownloader()sat.setCredentials(credentials=sat_credentials)invoices=sat.get_invoices(datetime.date(2021,10,1),date_end=datetime.date.today(),received=True)dir=os.path.dirname('c:\\Users\\my-user\\Downloads\\"')forinvoiceininvoices:sat.get_pdf(invoice,save_in_dir=dir)How to download invoice in XMLfromsat_gt_fel_invoices_downloaderimportSATDownloaderimportdatetimeimportos.pathfromsat_gt_fel_invoices_downloader.modelsimportSatCredentialssat_credentials=SatCredentials("YOUR AGENCIA DIGITAL USER","YOUR AGENCIA DIGITAL PASSWORD")sat=SATDownloader()sat.setCredentials(credentials=sat_credentials)invoices=sat.get_invoices(datetime.date(2021,10,1),date_end=datetime.date.today(),received=True)dir=os.path.dirname('c:\\Users\\my-user\\Downloads\\"')forinvoiceininvoices:sat.get_xml(invoice,save_in_dir=dir)How to get invoices in model structurefromsat_gt_fel_invoices_downloaderimportSATDownloaderimportdatetimeimportos.pathfromsat_gt_fel_invoices_downloader.modelsimportSatCredentialssat_credentials=SatCredentials("YOUR AGENCIA DIGITAL USER","YOUR AGENCIA DIGITAL PASSWORD")sat=SATDownloader()sat.setCredentials(credentials=sat_credentials)invoices=sat.get_invoices(datetime.date(2021,10,1),date_end=datetime.date.today(),received=True)dir=os.path.dirname('c:\\Users\\my-user\\Downloads\\"')forinvoiceininvoices:print(sat.get_model(invoice))
Package: sati
Statistical analysis of Topographic ImageThis package providessubtracting background (a polynomial surface, logarithmic decays, exponential decays)labeling terracesestimating terrace heightsestimating a unit height of stepsThe subtraction is possible even in the presence of steps.InstallYou can install the package from the git repogitory usingpip$ pip install satiRequirementsSati requires the following dependencies:python 3.8matplotlibnumexprnumpyscipyDocumentsThe usage examples and the API documentation are available athttps://yuksk.github.io/sati/index.html
Package: sati-ac
sati-ac - python client for sati.acusage exampleimportasynciofromsatiimportSatiasyncdefmain():sati=Sati(token)print(awaitsati.get_balance())task=awaitsati.solve('Turnstile',siteKey='0x4AAAAAAAHMEd1rGJs9qy-0',pageUrl='https://polygon.sati.ac/Turnstile')print(task.result.token)asyncio.run(main())
Package: satin
satin-python============satin-python is simple UI matcher and driver library, built on top of hamcrest and PyQtUsage=====Finding specific widget-----------------------To find a specific widget, use function widget:sub_widget = widget(dialog,matcher)Where dialog is Qt object to examine and matcher is hamcrest matcher capableof detecting specific widget.Labels------To check that a widget has label with text 'Title':assert_that(item, has_label('Title'))Event loop----------To start event loop, use @satin_suite class decorator. This will modify yourtest class to start QApplication behind the scenes in order to have the eventloop running. When the test method completes, QApplication is automaticallyshutdown.@satin_suitedef TestWidget():def test_clicking_around():...License=======satin-python is copyrighted by Tuukka Turto and is placed underGNU General Public License. For more information, please refer to license.txtfound in the main folder.
Package: satindex
This package calculates several indexes based on multiband satellite images.How to usefromsatindeximportSatelliteImagefromsatindeximportexample_data_path# load a rgbi geotifimg=SatelliteImage(example_data_path)# save ndwi image to example_data/ndwi.tifimg.save_ndwi(example_data_path.with_name("ndwi.tif"))# save ndvi image to example_data/ndvi.tifimg.save_ndvi(example_data_path.with_name("ndvi.tif"))# get ndwi as numpy arrayndwi=img.ndwiInstallationTo install satindex, do:git clone https://gitlab.com/rwsdatalab/public/codebase/image/satindex.git
cd satindex
pip install .Run tests (including coverage) with:pip install -r requirements-dev.txt
python setup.py testDocumentationFind the full documentation athttps://rwsdatalab.gitlab.io/public/codebase/image/satindexLicenseCopyright (c) 2022, RijkswaterstaatLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: satisfactory-planner
Satisfactory Data AnalysisThis repository is a Jupyter Notebook managed repository. It contains information for
planning within the game Satisfactory. It has a related project, which has been detailed below.Runninggitclonehttps://github.com/supratikchatterjee16/satsifactory-notebook.gitsatisfactory-notebookcdsatisfactory-notebook
jupyter-labProjectStatementUsing the information about the game available to us, create a program that is able to create symmetrical architectures,
packing the machines within itself.By doing so, we can design efficient manufacturing plants, with low regards for the manufacturing requirements, and pay more attention to architecture within the game, to achieve beautiful designs such as the ones we see below :https://www.youtube.com/watch?v=QQeO1RyHrsAhttps://www.youtube.com/watch?v=djcqGe02tdc&t=40shttps://www.youtube.com/watch?v=98ujAFCWGoQDesignWe do not need to work on feature extraction, as we intend to solve a problem statement, rather than make a 'product' or 'service'.The functional design is as such :DataThe data is sourced and stored as raw non-normalized data first.
It takes the form of :After normalization, it takes the form of :Parts of the projectCompletedGathering data regarding mines and resources on the mapNormalization of the data map dataVisualization of nodes on mapK-Means clustering for logistical evaluation on single nodeExtension of K-Means clustering for multiple resourcesRecipe data gatheringData normalized from satisfactory-calculator(outdated)Normalized data downloaded from wiki(updated and current)Recipe extraction for any itemGetting building count and energy for the recipe treeRemainingBy-product handlingEnergy cost for generating the energy itself.Packing for industryLogistical planningSuper structure planningCreditsThe Satisfactory team and their community manager
Package: satispy
Satispy is a Python library that aims to be an interface to various
SAT (boolean satisfiability) solver applications.Supported solvers:[MiniSAT](http://minisat.se/) (Linux)[Lingeling](http://fmv.jku.at/lingeling/) (Linux, Cygwin)Support for other solvers should be fairly easy as long as they accept the
[DIMACS CNF SAT format](http://www.satcompetition.org/2009/format-benchmarks2009.html).InstallingYou can grab the current version from pypi:$ sudo pip install satispyOr you can download a copy fromhttps://github.com/netom/satispy, and
runs$ sudo ./setup.py installin the directory of the project.If you want to develop on the library, use:$ ./setup.py developYou can run the tests found in the test folder by running run_tests.py.How it worksYou need a SAT solver and numpy to be installed on your machine for this to work.Let’s see an example:from satispy import Variable, Cnf
from satispy.solver import Minisatv1 = Variable(‘v1’)
v2 = Variable(‘v2’)
v3 = Variable(‘v3’)exp = v1 & v2 | v3solver = Minisat()solution = solver.solve(exp)if solution.success:print “Found a solution:”
print v1, solution[v1]
print v2, solution[v2]
print v3, solution[v3]else:print “The expression cannot be satisfied”This program tries to satisfy the boolean expressionv1 & v2 | v3You can make this true by assigning true to all variables for example,
but there are other solutions too. This program finds a single arbitrary
solution.First, the program imports the various classes so we can build an expression
and try to solve it.Every expression is in CNF form, but we don’t have to enter the expression
in it. The Cnf class takes care of the proper arranging of the boolean
terms.Expressions can be built by creating variables and gluing them together
arbitrarily with boolean operators:NOT: - (unary)AND: &OR:  |XOR: ^IMPLICATION >>The solver class Minisat is used to solve the formula.Note: the Minisat class creates two temporary files, so it needs write
access to the system’s temporary directoryThe returned solution can be checked by reading the “success” boolean
flag.Then, the solution can be queried for variable assignments by using it
like a dictionary. Note that Variable objects are used, not strings.(This very example can be found in the examples directory)
Package: satlas2
SATLAS2 -- Statistical Analysis Toolbox for Laser Spectroscopy Version 2PurposeContributors:Ruben de Groote (ruben.degroote@kuleuven.be)Wouter Gins (wouter.gins@kuleuven.be)Bram van den Borne (bram.vandenborne@kuleuven.be)An updated version of thesatlaspackage. A different architecture of the code is used, resulting in a speedup of roughly 2 orders of magnitude in fitting, with increased support for simultaneous fitting and summing models. Documentation can be foundhere.DependenciesThis package makes use of the following packages:NumPyMatplotlibSciPyh5pyemceesympyLMFITnumdifftoolsuncertaintiestqdmpandasOnly Python 3.x is supported! Parts of the code have been based on other resources; this is signaled in the documentation when this is the case. Inspiration has been drawn from thetriangle.pyscript, written by Dan Foreman-Mackey et al., for the correlation plot code.InstallationA package is available on PyPi, so 'pip install satlas2' should provide a working environment.