Saravanan666/pypi-org huggingface dataset:
Package: cars
CARS, a satellite multi view stereo frameworkOverview•Quick Start•Documentation•Contribution•ReferencesOverviewFrom stereo imagesCARS produces a Digital Surface Model (DSM)CARSis an open source 3D tool dedicated to produceDigital Surface Modelsfrom satellite imaging by photogrammetry.
This Multiview Stereo framework is intended for massive DSM production with a robust, performant and modular design.Be aware that the project is new and is evolving to maturity with CNES usage roadmaps and projects such as:CO3D projectAI4GEO projectQuick startCARS Docker ImageCARS is available on Docker Hub and can be downloaded by:dockerpullcnes/carsOne main pipeline to generate DSMYou only need to launch one command:docker run -w /data -v "$(pwd)"/data_gizeh:/data cnes/cars /data/configfile.jsonwith one configuration input file ("configfile.json") located in a "data" folder to be consistent with the previous command lines:{

        "inputs": {
            "sensors" : {
                "one": {
                    "image": "img1.tif",
                    "geomodel": "img1.geom"
                },
                "two": {
                    "image": "img2.tif",
                    "geomodel": "img2.geom"
                },
                "three": {
                    "image": "img3.tif",
                    "geomodel": "img3.geom"
                }
            },
            "pairing": [["one", "two"],["one", "three"]],
            "initial_elevation": "srtm_dir/N29E031_KHEOPS.tif"
        },

        "output": {
              "out_dir": "outresults"
        }

}On the way to the Pyramids...You want to build the pyramids by yourself? Download ouropen licencePleiadesdata sampleto give CARS a try!DocumentationGo toCARS Main Documentation.ContributionTo do a bug report or a contribution, see theContribution Guide.For project evolution, seeChangelog.CreditsSeeAuthors fileReferencesYoussefi D., Michel, J., Sarrazin, E., Buffe, F., Cournet, M., Delvit, J., L’Helguen, C., Melet, O., Emilien, A., Bosman, J., 2020. Cars: A photogrammetry pipeline using dask graphs to construct a global 3d model. IGARSS - IEEE International Geoscience and Remote Sensing Symposium.Michel, J., Sarrazin, E., Youssefi, D., Cournet, M., Buffe, F., Delvit, J., Emilien, A., Bosman, J., Melet, O., L’Helguen, C., 2020. A new satellite imagery stereo pipeline designed for scalability, robustness and performance. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.
Package: carsdata
CARS DataCARS Data is a package providing multivariate curve resolution algorithms and utilities functions for CARS cartographies.
The package provides:a Simplisma algorithm implementationMCR-ALS based onpymcrpackage:ALS and NNLS regressionsthe classical normalization (sum-to-one) and non-negative constraintsa new segmentation constraint based on Chan-Sandberg-Vese algorithmfit unknown spectra onto fixed signature spectra using ALS or NNLS and apply MCR constraints on the resultsAutoencoders based onPyTorchpackages.DependenciesNote: These are the developmental system specs. Others versions of certain packages may work.python 3Tested with 3.8.10numpyTested with 1.16.5pymcrTested with 0.3.2matplotlibTested with 3.4.2Optional depenciestorchTested with 1.9.0Installationpip install .
Package: cars-forge
AboutForge is a Python-based command-line tool that starts a single or cluster of EC2 instances, and runs the required scripts on these instances. Forge can be used by anyone to create their own environment where they can do anything like develop products, analyze data, host interactive dashboards, or run production workloads on terabytes of RAM. Forge gives you complete flexibility while making it easy to run everything on spot EC2 instances saving you up to 90%.InstallInstall Forge with Pip:pip install cars-forgeGetting StartedTo get started using Forge you can follow theDocumentation.VersioningThis project adheres toSemantic Versioning.
Please refer to theChangelogfor information regarding the differences between versions of the project.
Package: carskit-api
![PyPI](https://img.shields.io/pypi/v/carskit-api)# CARSKit-APICARSKit-API is a software based on a command line interface that serves as an abstraction layer for the [CARSKit](https://github.com/irecsys/CARSKit) recommendation system engine. Built entirely in python, it aims to provide both a more dynamic and secure way of handling the input and output of engine data.## RequirementsIt is necessary that both python 3.6 and java 1.7 or higher are installed on the computer to run the software.## InstallationUse the package manager [pip3](https://pip.pypa.io/en/stable/) to install carskit-api.`bash pip3 installcarskit-api`## UsageFirst, it is necessary to create a folder for the rating data that will be used.`shell $ mkdir data `Contextual datasets are referenced in the [CARSKIT repository](https://github.com/irecsys/CARSKit) to use as example. Remember that the data must be in the same format that the engine requires. If in doubt, look in the [User Guide](https://arxiv.org/abs/1511.03780).With the folder created, enter it and create another one for the results that the engine will generate and insert the data set that will be used.`shell $ cd data $ mkdir results $ paste ratings.csv `Now we need to specify both the data set path and the results folder name for the API.`shell $ capi settings--set"dataset_path"--value./ratings.csv $ capi settings--set"results_foldername"--value"results" `You can use the commandcapi -hto know more about the api’s commands.By now, it is assumed that we have everything to run the the engine. You do that using the following command:`shell $ capi run `After executing the command, two new json files will be created in the same directory where the data set is, one of them is a statistics file that the engine extracted from the data set and the other is a recommendations file where, for each user of the data set, the top N items recommended for the same, in each possible combination of context (if specified), will be listed.To change a specific configuration of the engine, the top N recommendations as example, we just need to use the following command:`shell $ capi settings-s"topN"--value10 `And a new settings file will be generated with the new configuration.## Saving on a MongoDB databaseThere are two things that must be done to store the results on a MongoDB server. First, a file called.secrets.keymust be created, whose path is at the user’s choice. Then it is necessary to specify the path of this file to the API using the command:`shell capi database--secrets-pathPATH `to then specify the URI address for the API using the command:`shell capi database--uriURI `Now with the correct settings, we can run the engine and save the results on a MongoDB server using the command:`shell capi run--save-mongo`## ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.## License[MIT](https://choosealicense.com/licenses/mit/)
Package: carsomenlp
Carsomenlp PackageWhy?There several tasks where natural language is used to extract information. These use-cases became frquent and therefore there should be a better way to execute them. The old way of using piles of python lines to extract a brand name or model are gone. This package is the new easy and effient way to deal with natural text in Carsome.RequirementsCheck the requirements.txt filePython>=3.7We recommend using virtualenvHow to install:Create virtualenv:virtualenvmyenvActivate virtual env:sourcemyenv/bin/activateInstall library:pipinstallcarsomenlpTo test locally, from root folder run:pipinstall-e.DocumentationTo learn how to use it checkout thedocumentationSupportFind Fares Hasan in workspace or email:fares.hasan@carsome.com
Package: carson-logging
1Installpip installcarson-logging2import packagesfromCarson.Class.LoggingimportCLogging3USAGE3.1Basiclog=CLogging("log_name","temp_dir/xxx.log")log.info('info msg')log.debug('info msg')log.warning('info msg')log.error('info msg')log.critical('info msg')3.2Advancedmy_define_format=logging.Formatter(u'%(asctime)s|%(levelname)s|%(name)s|%(message)s')log=CLogging("log_name","C:dir/xxx.log",logging.INFO,'w',my_define_format,stream_handler_on=True,stream_level=logging.ERROR)4IssuesIssues
Package: carson-ref.bezier
Source Code:https://github.com/CarsonSlovoka/carson-ref.bezier/blob/master/Carson/Ref/Bezier/bezier.pyCompatible:Python >3.6Platform:WindowsLicense:Apache 2.0Author Doc:https://carsonslovoka.github.io/CarsonDoc/Bezier curve simulation (including reductions the dimension from 3 to 2)1Installpip installcarson-ref.bezier2USAGEfromCarson.Ref.Bezierimportbezier_curve_monitorbezier_curve_monitor()EventKEYDesc.ESCexit appCredraw (clear canvas)LButtonset point
Package: carson-tool.create_template
Source Code:https://github.com/CarsonSlovoka/carson-tool.create_template/blob/master/Carson/Tool/CreateTemplate/create_template.pyCompatible:Python >3.6Platform:WindowsLicense:Apache 2.0Author Doc:https://carsonslovoka.github.io/CarsonDoc/File templates are specifications of the default contents to be generated when creating a new file.This Script helps you create the template file at the current work directory.Just like PyCharmFile and code templatesThe only difference is that it supports you using the command line to finished.1Installpip installcarson-tool.create_template2USAGEcreate_template.bat [REFERENCE TEMPLATE] [OUTPUT FILE NAME]to create template, for example:create_template.bat PEP PEP.0484.pycreate_template.bat-l*to get all available templatecreate_template.bat-oopenopen template directory so that you can put your template file there.positional arguments:-refreference template-outfileoutput file nameoptional arguments:-h,--helpshow this help message and exit--listLIST,-lLISTexample: -l *description: list current available template. (accept regex)--optionOPTION,-oOPTIONOptionExampleDescription-o openopen template directory so that you can put your template file there.
Package: carson-tool.HistoricalWeatherTW
1HistoricalWeatherTW 台灣歷史天氣爬蟲This script is to crawl the information of觀測資料查詢websiteData from觀測資料查詢系統2UsagePreparestation.csv.Notestation.csv (you can find it from thehttps://e-service.cwb.gov.tw/wdps/obs/state.htm)run the function ofcollect_weather_twyou will see the result!defcollect_weather_tw(station_csv_path:Path,output_path,end_date:datetime.date,begin_date:datetime.date,query_format,convert2num):You can refer to__init__.pyfor more helpPrepareconfig.yamland use this path as input parameterrun__init__.py2.1QUICKLY STARTfromCarson.Tool.HistoricalWeatherTWimportcollect_weather_tw,QueryFormatfrompathlibimportPathimportdatetimeimportosif__name__=='__main__':STATION_CSV='../config/CSV/station_test.csv'OUTPUT_PATH=Path(f'../temp/year_result.csv')BEGIN_DATE=datetime.date(2019,10,1)END_DATE=datetime.date(2019,10,2)QUERY_FORMAT=QueryFormat.DAYCONVERT2NUM=Truecollect_weather_tw(Path(STATION_CSV),OUTPUT_PATH,BEGIN_DATE,END_DATE,QUERY_FORMAT,CONVERT2NUM)os.startfile(OUTPUT_PATH)3Data資料會以觀測站ID+站名儲存NoteThere have some non-numeric forms of the original data.
Such as indefinite wind direction V, rain track T, etc. the variable ofCONVERT2NUMwill replace them with numbers.3.1資料欄位如下ObsTime觀測時間(LST)StnPres測站氣壓(hPa)SeaPres海平面氣壓(hPa)Temperature氣溫(℃)Tddewpoint露點溫度(℃)RH相對溼度(%)WS風速(m/s)WD風向(360degree)WSGust最大陣風(m/s)WDGust最大陣風風向(360degree)Precp降水量(mm)PrecpHour降水時數(hr)SunShine日照時數(hr)GloblRad全天空日射量(MJ/㎡)Visb能見度(km)4Release note4.1v4.0Encapsulated as API4.2v3.0Features:All outputs into a single file. (making it easier to use for SQL)The output header field will automatically grab the content on the web page (not use hard coding)You can choose the type of query (year, month, day) according to your needs.Other:Make the code easier to read.4.3V2.0加入全台觀測站4.4V1.0第一版
Package: car-sound-dataset
car_sound_datasetThis repo contains the source code of car sound dataset Python package.https://pypi.org/project/car-sound-dataset/How to install?pip install car-sound-datasetAbout the datasetThe dataset contains 17121 car sound events. Each event is recorded by at least 1 and maximum 6 devices.
The length of each recording is 3 sec. The sample frequency was 3906 Hz during the measurements.
You can download the dataset and select different recordings with this package.ClassA class used to represent the car sound dataset.

    ...

    Attributes
    ----------
    path : str
        A path, where you want to download and use the dataset.
        The default path is the current working directory.Methodsdownload_and_get_dataset()
        Downloads the dataset and the json file, which identifies the events.

    list_events_of_dict( car_dataset_dict)
        This method prints to console the event IDs of a car_dataset_dict.
        
    plot_event(car_dataset_dict, event_id)
        You can plot an event with this method.

    play_event(car_dataset_dict, event_id, nomralize=True)
        You can play the recordings of an event with this method.

    filter_by_pattern(car_dataset_dict, pattern)
        This method helps you to select events by a pattern.
        The pattern should contain 'X', '0' or '1'.
        'X' - You don't care, if the device recorded the event or not.
        '0' - You skip the recordings of this device.
        '1' - This device recorded the event.
        You have to give the pattern as a list. Each list element represents each device.
        An example pattern: ['0', '1', 'X', '0', 'X', '1']
        This means we skip the recordings of DEVICE1 and DEVICE4. We select the events,
        where DEVICE2 and DEVICE6 recorded the event. Each event will contain 2-4 recordings.
        It depends on DEVICE3 and DEVICE5. If one of DEVICE3 and DEVICE5 recorded the event,
        the event will contain 3 recordings and if both is recorded the event, it will contain
        4 recordings.

    filter_by_exact_device_count(car_dataset_dict, device_count)
        This method selects the events, which is recorded by the given count of devices.

    filter_by_min_device_count(car_dataset_dict, device_count)
        This method selects the events, which is recorded by at least the given count of devices.

    load_dict_as_pool(car_dataset_dict)
        This method loads the recordings into a 3D numpy array. In this case each element
        contains only one recording. The recordings of an event are split to single recordings.

    load_dict(car_dataset_dict, is_random = False, device_indexes = [])
        This method loads the recordings into a 3D numpy array. In this case each element
        contains events. One event usually contains more than one recordings.
Package: car-speed-detection
Camera-based Car Speed Detection for Autonomous DrivingCar-Speed-Detection provides a python library to detect the speed of the driving
car itself by the video stream from the dashboard camera installed on the car.Car-Speed-Detection separates the speed detection process into three steps,
preprocessing, training, and speed detection. By using Gunnar-Farneback optical flow
algorithm along with the pipeline we developed, we are able to extract each frame into
a small size matrix depends on developers preference. We use the Artifitial Neural
Network (ANN) to train our model with the preprocessed matrix acquired from preprocessing
function. Developers could use the trained model to detect the speed of the car
at each frame using our speed detection function.Getting StartedInstallationCar-Speed-Detection is available onPyPIand can be
installed viapip. Seecar-speed-detection.readthedocs.ioto learn about the API and Q&A of our library.pipinstallcar-speed-detectionRead, Preprocess, Train, and Detect the Car SpeedTheCar-Speed-Detectionlibrary consists of the
following parts:Read (Read the mp4 video and output each frame into a designated directory)Preprocess (Preprocess each frame and output a feature set for training)Train (Train the model using the feature set and Artifitial Neural Network)Speed Detection (Detect the speed using the model and video)API and Example CodeTake a look at theAPIto know more about
the Application Programming Interface andSamplefor further information on how to use our library.ResultIn ourexample code, we are able to train the model with MSE error less than 2 using the training video provided by comma.ai. We separate the video into 75% for training and 25% for testing so the result woud
be fair. The ANN model has also substaintially small amount of parameters (< 45,000), which yeild a lower latency
compare toother solutions.Bugs ReportIssues and bugs can be reported by emailinglienshaochieh@gmail.comAt a minimum, the report must contain the following:Description of the program.Expected Result.Actual Result.Steps to reproduce the issue.Please do not use the GitHub issue tracker to submit bugs reports. The
issue tracker is intended to make feature requests.AcknowledgeThis project is managed by Shao-Chieh Lien, the software architect and student at Purdue University.This was a senior design project at Purdue University. Christopher Crocker was in charge of generating the training data using Carla simulator that could be seen fromthis Github repositoryand Data Section. Meenakshi was in charge or paper writing and documentation.Special thanks to Professor Santiago Torres-Arias at Purdue ECE department for his guidance throughout our whole software development cycle!
Package: cars-rasterize
cars-rasterizeOverview•Installation•Quick Start•How It Works•ContributionOverviewcars-rasterizeaims to convert a point cloud into a digital surface (or terrain) model with colors.It is a part of the  photogrammetry toolcarsextracting Digital Surface Models from satellite images.Installationcars-rasterizeis available on Pypi and can be installed by:pip install cars-rasterizeQuick startDownloadsubsampled_nimes.laz*:wget https://raw.githubusercontent.com/CNES/cars-rasterize/master/data/subsampled_nimes.lazsubsampled_nimes.lazsubsampled_nimes.laz*is fromhttps://geoservices.ign.fr/lidarhd. and has been downsampled (1 point every 50cm) to make the file smaller.Runlas2tifexecutable:las2tif subsampled_nimes.laz dsm.tif --clr_out clr.tif✅ Done! The executable generates two files:dsm.tif: the elevation of the points (Z dimension) are projected into a regular grid to generate a raster file named Digital Surface Model.clr.tif: the red, the green and the blue dimensions can be also projected producing a color interpretation map superimposable on DSMdsm.tifclr.tifHow it worksA LAS file contains a set of points $P = {(x, y, z, r, g, b)_k}$ each having several dimensions:$x$ and $y$ correspond to planimetric information$z$ corresponds to the altitude$r$, $g$ and $b$ correspond to colorimetric information (respectively red, green, blue )To create a raster digital surface model, we define a regular grid on a region of interestroiof origin $(x_{start}, y_{start})$, size $(x_{size}, y_{size})$ with a constantresolution.For each cell of center $(c_x, c_y)$, we consider the subset of points contained in the disk $D$ (parameterradius) centered on this cell (see figure below):Contributing pointsThen, the altitude assigned $z(c_x, c_y)$ to the cell is a Gaussian  weighted average (standard deviationsigma$\sigma$) of the distance $d$ to its center :$$z(c_x, c_y) = \frac{\sum_{p_k \in D} z_k e^{-d_k^2/2\sigma^2}}{\sum_{p_k \in D} e^{-d_k^2/2\sigma^2}}$$Finally, to have a superimposable color to this dsm, the colors are averaged in the same way.Contributioncars-rasterizeis a free software: Apache Software License 2.0. SeeContributionmanual.
Package: cars-resample
cars-resampleOverview•Installation•ContributionOverviewcars-resampleaims to resample an image according to a resampling grid.It is a part of the  photogrammetry toolcarsextracting Digital Surface Models from satellite images.Installationcars-resampleis available on Pypi and can be installed by:pip install cars-resampleContributioncars-resampleis a free software: Apache Software License 2.0. SeeContributionmanual.
Package: carst
Cryosphere And Remote Sensing Toolkit (CARST)This package analyzes satellite images, particularly SAR and optical images, and monitor changes of glaciers and ice caps through time. CARST toolkit has two main scripts: elevation changes (also known as dh/dt) and feature tracking. You can also import carst with python for using its various modules.DependenciesRequired packages:python >= 3.0scipygdalshapelyrasteriogeopandasmatplotlibOptional package:ISCE >= 2.0.0 (built with your python, required for feature tracking)Installationpip install carstCLI$ dhdt.py --help$ featuretrack.py --helpUsing CARST in Pythonimport carstfrom carst import SingleRasterDocumentationdoc/dhdt.rst: dh/dt documentationdoc/featuretrack.rst: feature tracking documentationVersions & How to cite CARSTCiting CARST software:Zheng, W., Durkin, W. J., Melkonian, A. K., & Pritchard, M. E. (2021, March 9). Cryosphere And Remote
Sensing Toolkit (CARST) v2.0.0a1 (Version v2.0.0a1). Zenodo.http://doi.org/10.5281/zenodo.3475693In addtional to citing this package, we also recommend that you cite one or more of these papers based on what park of CARST you use.CARST dh/dt[v1.0 and after]:Zheng, W., Pritchard, M. E., Willis, M. J., Tepes, P., Gourmelen, N., Benham, T. J., &
Dowdeswell, J. A. (2018). Remote Sensing of Environment Accelerating glacier mass loss
on Franz Josef Land, Russian Arctic. Remote Sensing of Environment 211, 357–375.http://doi.org/10.1016/j.rse.2018.04.004CARST feature tracking[v1.0 and after]:Zheng, W., Pritchard, M. E., Willis, M. J., & Stearns, L. A. (2019). The possible transition
from glacial surge to ice stream on Vavilov Ice Cap. Geophysical Research Letters, 46,
13892– 13902.https://doi.org/10.1029/2019GL084948CARST[v0.2]:Willis, M. J., Zheng, W., Durkin, W. J., Pritchard, M. E., Ramage, J. M.,
Dowdeswell, J. A., … Porter, C. C. (2018). Massive destabilization of an Arctic ice cap.
Earth and Planetary Science Letters, 502, 146–155.http://doi.org/10.1016/j.epsl.2018.08.049CARST[v0.1]:Melkonian, A. K., Willis, M. J., & Pritchard, M. E. (2014).
Satellite-derived volume loss rates and glacier speeds for
the Juneau Icefield , Alaska. Journal of Glaciology,
60(222), 743–760.https://doi.org/10.3189/2014JoG13J181How to contributeWe welcome any suggestions/ideas; a pull request would be always appreciated.