Saravanan666/pypi-org huggingface dataset:
Package: dtscalibration
DocsTestsPackageCitableExample notebooksA Python package to load Distributed Temperature Sensing files, perform a calibration, and plot the result. A detailed description of the calibration procedure can be found athttps://doi.org/10.3390/s20082235.Free software: BSD 3-Clause LicenseInstallationpipinstalldtscalibrationOr the development version directly from GitHubpipinstallhttps://github.com/dtscalibration/python-dts-calibration/zipball/main--upgradePackage featuresAdvanced calibration routineBoth single- and double-ended setupsConfidence intervals of calibrated temperatureTime integration of calibration parametersWeighted least-squares calibrationFixing parameters to a previously determined value(Asymmetric) step loss correctionMatching temperature sectionsDynamic reference section definitionTools for merging and aligning double-ended setupsData formats of most manufacturers are supportedDevices currently supportedSilixa Ltd.:Ultima&XT-DTS.xml files(up to version 8.1)Sensornet Ltd.:Oryx,Halo&Sentinel.ddf filesAP Sensing:CP320.xml files(single ended only)SensorTran:SensorTran 5100.dat binary files(single ended only)DocumentationA full calibration procedure for single-ended setups is presented in notebook07Calibrate_single_ended.ipynband for double-ended setups in08Calibrate_double_ended.ipynb.Documentation athttps://python-dts-calibration.readthedocs.io/.Example notebooks (./docs/notebooks) that work within the browser can be viewedhere.How to citeThe following article explains and discusses the calibration procedure:des Tombe, B., Schilperoort, B., & Bakker, M. (2020). Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. Sensors, 20(8), 2235.https://doi.org/10.3390/s20082235Cite the specific implementation / repository via Zenodo:Check the version ofdtscalibrationthat is used in your Python console with:>>> # The following line introduces the .dts accessor for xarray datasets
>>> import dtscalibration  # noqa: E401
>>> dtscalibration.__version__
'3.0.0'Go toZenodoand follow the link to the version of interest.The citation is found on the bottom right of the page.
Package: dtsdk
DataTower.aiThis is the official Python SDK for DataTower.ai.Easy InstallationYou can get DataTower.ai SDK using pip.pipinstalldtsdkOnce the SDK is successfully installed, use the SDK likes:fromdtsdk.sdkimportDTAnalytics,DebugConsumerdt=DTAnalytics(DebugConsumer(app_id="app_id_xxxx",token="xxxxxxxxxxxxxxxxxxxxxxx",server_url="https://xxxx"))properties={"abc":123,"bcd":"xxx"}dt.track(dt_id="aaaa",acid='bbbb',event_name="ad_click",properties=properties)dt.flush()dt.close()More detailsSeehere
Package: dtsh
DTShis a Devicetree Source (DTS) files viewer with a shell-like command line interface:navigateandvisualizethe devicetreesearchfor devices, bindings, buses or interrupts with flexible criteriaredirect command output to files (text, HTML, SVG) todocumenthardware configurations
or illustrate notesrichTextual User Interface, command line auto-completion, command history, user themes$ dtsh build/zephyr/zephyr.dts
dtsh (0.2.0): A Devicetree Shell
How to exit: q, or quit, or exit, or press Ctrl-D

/
> cd &flash_controller

/soc/flash-controller@4001e000
> tree -l
                              Description
                              ─────────────────────────────────────────────────────────────────
flash-controller@4001e000     Nordic NVMC (Non-Volatile Memory Controller)
└── flash@0                   Flash node
    └── partitions            This binding is used to describe fixed partitions of a flash (or…
         ├── partition@0      Each child node of the fixed-partitions node represents…
         ├── partition@c000   Each child node of the fixed-partitions node represents…
         ├── partition@82000  Each child node of the fixed-partitions node represents…
         └── partition@f8000  Each child node of the fixed-partitions node represents…You can use it with:all DTS files generated byZephyrat build-time (akabuild/zephyr/zephyr.dts)arbitrary DTS files with bindings compatible with Zephyr’sDevicetre bindings syntaxStatusDTSh 0.2.x mirror and package the code base that serves as a proposal to upstream DTSh
as a new Zephyr extension to West:RFC - DTSh, shell-like interface with DevicetreeThis is the stable and maintained branch: if you have DTSh 0.1.x installed, please upgrade with
e.g.pip install-Udtsh.Please refer to theDTSh project documentation.
Package: dtshare
Target Usersfinancial market analyst of Chinalearners of financial data analysis with pandas/NumPypeople who are interested in China financial dataInstallationpip install dtshareUpgradepip install dtshare –upgradeQuick Startimport dtshare as dt

dt.get_hist_data('600848')return:open    high   close     low     volume    p_change  ma5
date
2012-01-11   6.880   7.380   7.060   6.880   14129.96     2.62   7.060
2012-01-12   7.050   7.100   6.980   6.900    7895.19    -1.13   7.020
2012-01-13   6.950   7.000   6.700   6.690    6611.87    -4.01   6.913
2012-01-16   6.680   6.750   6.510   6.480    2941.63    -2.84   6.813
2012-01-17   6.660   6.880   6.860   6.460    8642.57     5.38   6.822
2012-01-18   7.000   7.300   6.890   6.880   13075.40     0.44   6.788
2012-01-19   6.690   6.950   6.890   6.680    6117.32     0.00   6.770
2012-01-20   6.870   7.080   7.010   6.870    6813.09     1.74   6.832
Package: dt-suntime
SunTimeSimple sunset and sunrise time calculation python library forked from pypisuntimelibrary.UsageYou can use the library to get UTC and local time sunrise and sunset times typing:fromsuntimeimportSun,DegreesOffset,SunTimeExceptionMADRID_LAT=40.416775MADRID_LON=-3.703790madrid_sun=Sun(MADRID_LAT,MADRID_LON)# Get today's sunrise and sunset in UTCsunrise_madrid=madrid_sun.get_sunrise_time()today_ss=sun.get_sunset_time()today_sr=sun.get_local_sunset_time()print('Today at Madrid the sun raised at{}and get down at{}UTC'.format(today_sr.strftime('%H:%M'),today_ss.strftime('%H:%M')))# On a special date in your machine's local time zoneabd=datetime.date(2014,10,3)abd_sr=sun.get_local_sunrise_time(abd)abd_ss=sun.get_local_sunset_time(abd)print('On{}the sun at Madrid raised at{}and get down at{}.'.format(abd,abd_sr.strftime('%H:%M'),abd_ss.strftime('%H:%M')))# offsetshorizon=madrid_sun.get_local_sunset_time(degrees_offset=DegreesOffset.horizon.value)# 0civil=madrid_sun.get_local_sunset_time(degrees_offset=DegreesOffset.civil.value)# 6nautical=madrid_sun.get_local_sunset_time(degrees_offset=DegreesOffset.nautilcal.value)# 12astronomical=madrid_sun.get_local_sunset_time(degrees_offset=DegreesOffset.astronomical.value)# 18Referenceslibrary improvement: Dusk dawn calculationForked fromsuntime
Package: dtt
dtt is the text-mode interface for docker and kubectl command.
the ui of dtt is inspired by vim, ranger and tig.A main usecase for dtt is to login container or pod with shell.Exec dtt command and move to container with j or k key (like vim) and press Enter-key when you want to login the container or pod.You can exec another shell or command withXkey when you select the container or pod.
Package: dttimeframe
dtTimeFramedtTimeFrame is a module that packtime track functionsandsubprocess call functionsinto one single class.Homepage:https://github.com/SotongDJ/dtTimeFrameInstall dtTimeFramepipinstalldttimeframeExample codestag()fromdtTimeFrame.timeFrameimporttag# type: ignore# InitiationTool=tag()Tool.log.name="log.txt"# filename of the log file, store stdout infoTool.error.name="err.txt"# filename of the err file, store stderr infoTool.extra.name="log.json"# filename of the extra command record, store time stamp, commands and others info# start logging and write header into log/err filesTool.start()# > your code insert here <# print time stamp with personalized msgTool.timeStamp("PREVIEW: show something you want to info")# command runner, base on subprocess.call()phrase_str=F"echo this command line"Tool.runCommand(phrase_str)# command runner with specific output filephrase_str=F"echo this command line into specific output file: test.txt"Tool.runCommand(phrase_str,export_file="test.txt")# > your code insert here <# end logging and write footer into log/err filesTool.stop()detector()fromdtTimeFrame.timeFrameimportdetector# type: ignorefile_be_process_handle=detector(print_func=print,call_func=print)# "file_be_process_handle = detector(print_func=Tool.timeStamp,call_func=Tool.runCommand)"# combine use with tag()file_be_process_handle.do(target_str="target.txt")iffile_be_process_handle.missing():# if target.txt missing?withopen(file_be_process_handle.doing_str,"w")astarget_handle:# doing-target.txttarget_handle.write("Hello world\n")file_be_process_handle.done()# rename doing-target.txt as target.txtprint(open(file_be_process_handle.target_str).read())
Package: dtu-config
DTU ConfigurationThis package provides a configuration class to get, process, edit and store user input.It is a thin wrapper around ConfigParser that provides utilities for adding validated configuration options and prompting the user for them, or allowing them to be filled by a CLI.Usagefromdtu_confimportDtuConfigapp_name="example_app"app_author="author_name"conf=DtuConfig(app_name=app_name,app_author=app_author)# Add options.conf.add_str("User metadata","institution",input_text="Please enter your institution")conf.add_email("User metadata","email",input_text="Please enter your email (optional)",required=False)conf.create_config()
Package: dtumathtools
dtumathtoolsDescriptionThis package is a collection of easy-to-use commands for plotting using SymPy. It is designed to fit the requirements of01001 - Mathematics 1course at the Technical University of Denmark, foundhere. This includes, but is not limited to:Plotting of vector fields and vectors2D plotting of SymPy functions3D plotting of SymPy functionsScatterplots in 2D and 3DThe plotting functions are an extension ofsympy-plot-backends, and so documentation and examples of plots (for all functions butdtuplot.scatter()anddtuplot.quiver()) can be found atsympy-plot-backends.readthedocs.io/. Additional functionality and usage is given through course material.InstallationTo install dtumathtools using PyPi, run the following command$ pip install dtumathtoolsThen one can import all the utility by writingfrom dtumathtools import *All plotting functionality can be found usingdtuplot.xxx(including all spb plotting functions).UsageUse is designed for the01001 - Mathematics 1course at the Technical University of Denmark. Any use-case outside this scope is thus not considered, but very welcome!BackendsMultiple different backends (plotting libraries) can be used (especially helpful for some 3D plots). Currently,Matplotlib(MB),Plotly(PB),Bokeh(BB,2D only), andK3D-Jupyter(KB,3D only) are supported (Mayavi(MAB,3D only) might be supported in the future). The user can change the backend for a plot using thebackendkeyword argument (eg.backend=dtuplot.PB), or change the default (here for 3D, but similar for 2D) using:dtuplot.cfg["backend_3D"] = "plotly"
dtuplot.set_defaults(dtuplot.cfg)For further descriptions of the strengths and benefits of each, seethis page.ContributingYou are very welcome to contribute in the way that makes sense to you! The development team will consider all pull requests at the repohere. For changes to plotting functionality outsidescatterandquiver, direct queries tospb.Authors and acknowledgmentThe project would have never gotten off the ground without Jakob Lemvig and his engagement with SymPy in Mathematics 1. Thank you to Ulrik Engelund Pedersen for trusting us with this task. Finally, a huge thanks to the professors of the course for making great and engaging education: Micheal Pedersen and Karsten Schmidt.LicenseOpen-source under the BSD license. This means that you are free to use it whatever you like, be it academic, commercial, creating forks or derivatives, as long as you copy the BSD statement if you redistribute it (see the LICENSE file for details).
Package: dtuprosys
NOTEThis repository has been archived and no further development/maintanance will done. I will contiune and extend the work in this project in the new project namedfermentoolsdtu.prosysAbout this project 🚀This project intends to be a didactic tool to teach spectroscopy and chemometrics in the context of fermentation technology. During my studies, I often felt that many courses were theory-based only due to the limited access to real-world data. For this reason, I have decided to distribute the data I generated during my studies hoping to improve the learning experience of future students.This module contains:Training data (spectra of different samples and the glucose concentration).Fermentation spectra (spectra measured inreal-timeevery minute).Fermentation HPLC data (measured off-line every hour).Common preprocessing operations used in chemometrics.Workflow to train partial least squares (PLS) models.Plotting functions for time-series and spectral data.These functions can be used as a starting point for the, but more advanced users are encouradged to explore other packages to play with this data (e.g.,scikit-learn, orscipy).About the data 📈This project provides two datasets (a training and a validation set). Both data sets were recorded at the Technical University of Denmark, at the PROSYS research center (department of Chemical and Biochemical engineering) during 2019. More information about the dataset can be found in the following articleTransforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentationThe training setThe training set contains the spectra of 20 semi-synthetic samples and their reference glucose concentration measured with high performance liquid chromatography (HPLC). The spectra were measured using attenuated total refractance mid infrared (ATR-MIR) spectroscopy.Validation setThe validation contains spectra measured every minute during a lignocellulose to ethanol fermentation. These spectra were collected inreal-timeusing the same ATR-MIR instrument, connected to a flow-cell. Moreover, the extracellular concentrations of glucose, xylose, ethanol, furfural and acetic acid were also measured every hour using HPLC.Installation 💻DependenciesThis project is build targetting Python >= 3.7 to ensure compatibility with Google Colab.User installationpippip install -U dtuprosysQuick start 🏁A complete example of can be found in theexample.ipynb. The raw data can be conviniently accessed using as the following commands:from dtuprosys.datasets import load_train_data, load_fermentation_datato access the training datatrain_spectra, train_hplc = load_train_data()to access the validation data:fermentation_spectra, fermentation_hplc = load_fermentation_data()
Package: dtviz
Generategraphvizdot file based on dependecies from:pyproject.tomlpackage.jsonRequirementspython >=3.10Install$ pip install dtvizRun$ dtviz --helpLicensedtviz - dependency tree visualizerCopyright (C) 2023 Bozo KopicThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program 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 General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
Package: dtwalign
Comprehensive dynamic time warping module for python.FeaturesFast computation by [Numba](https://numba.pydata.org)Local constraint (step pattern)Global constraint (windowing)Alignment path visualizationReferenceSakoe, H.; Chiba, S., Dynamic programming algorithm optimization for
spoken word recognition, Acoustics, Speech, and Signal ProcessingPaolo Tormene, Toni Giorgino, Silvana Quaglini, Mario Stefanelli
(2008). Matching Incomplete Time Series with Dynamic Time Warping:
An Algorithm and an Application to Post-Stroke Rehabilitation.
Artificial Intelligence in Medicine, 45(1), 11-34.Toni Giorgino (2009). Computing and Visualizing Dynamic Time Warping
Alignments in R: The dtw Package. Journal of Statistical Software,
31(7), 1-24.
Package: dtwc
Desktop Wallpaper ChangerThis python script fetches images from popular sources and set those as your desktop wallpaper.Desktop Wallpaper Changer fetches images from popular sources and set those as your desktop wallpaper. Currently supported sources are:NASA Astronomical Picture of the DayBing Picture of the DayRandom Pictures from Unsplash.comNational Geographic Picture of the DayRandom Images from Desktoppr.InstallationFirst install the package from pypi by running:pip install dtwcIf you want to get latest code from master branch of the repository, you can
do so by running:pip install git+https://github.com/ayan-b/Desktop-Wallpaper-ChangerUsageIn order to change the background you can use the CLI:dwc -iwill open all the wallpaper sources and you can select the one
you like.dwc -op <number>will update the wallpaper from the number-th source
as specified above.Platforms Supported:WindowsLinux
If you want your favorite platform to be supported please open an issue in theissue tracker.ContributingWant to contribute? Awesome! :tada:
Please head over toCONTRIBUTING.mdto check out how the
code works and some guidelines regarding contributions!
Package: dtweb
DTWeb-pythonPython library for accessing the Digital Twin Web:exclamation: Alpha release :exclamation:0.0.x denotes an alpha phase release. Anything may change in future releases without notice.Install# Create and activate virtual environment (recommended)python3-mvenvenvsourceenv/bin/activate# Install with pippipinstalldtwebTo deactivate virtual environment:deactivateUsageimportdtwebdtweb.client.fetch_host_url('<DTID>')dtweb.client.fetch_dt_doc('<DTID>')Usage example:importdtweb# Fetch and pring hosting URL from DTID of Ilmatar cranecrane_hosturl=dtweb.client.fetch_host_url('http://d-t.fi/konecranes-K16052')print(crane_hosturl)# Fetch and pring DT document from DTID of Ilmatar cranecrane_doc=dtweb.client.fetch_dt_doc('http://d-t.fi/konecranes-K16052')print(crane_doc)Development instructionsClick to showOS: Ubuntu 20.04 WSL (Ubuntu 20.04.1 LTS (GNU/Linux 4.4.0-18362-Microsoft x86_64))Setup new computerHint: You can just copy and paste this whole code block to terminal. Dirty but works.# Clone repositorygitclonehttps://github.com/juusoautiosalo/dtweb-python.git# Change directorycddtweb-python# Create virtual environment (recommended)python3-mvenvenv# Activate virtual environment (recommended)sourceenv/bin/activate# Install python package builderpipinstallbuild# Build the dtweb packagepython3-mbuild# Uninstall earlier installation (required if earlier install with same/higher version number exists)pipuninstalldtweb# Install package (edit version number if needed)pipinstalldist/dtweb-0.0.1-py3-none-any.whl# Show library info (optional)pipshowdtwebUploading to TestPyPI# Install twinepipinstalltwine# Upload to test repository (You will need TestPyPI credentials for this)twineupload--repositorytestpypidist/*Install from TestPyPIpipinstall-ihttps://test.pypi.org/simple/dtweb
Package: dtw-flex
dtw_flexDescriptionThis package allows to match flexibel user defined data patterns in a data array. The algorithm is based on dynamic time warping (dtw) but contains considerable changes. In contrast to classical dtw, an offset and amplitude are dynamically calculated. Moreover, the user can specify weights and allowable repetitions for each data point. Note that this algorithm is greedy and does not guarantee an optimal solution.The algorithm is also implemented in cython for better performance. The package also implements a running window in order to analyse longer data arrays.
Pattern initialisation and point tracking can be set through a visual interfaceGetting StartedInstallingpipinstalldtw_flexUsagePlease check the following examples:basic exampleECG exampleexample with running windowLicenseThis project is licensed under the GPLv3 License - see the LICENSE file for detailsThis program 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 General Public License for more details.
Package: dtwhaclustering
Dynamic Time Warping based Hierarchical Agglomerative ClusteringCodes to perform Dynamic Time Warping Based Hierarchical Agglomerative Clustering of GPS dataDocumentationInstallation and usage information can be obtained from the documentation:dtwhaclustering.pdfComplete documentation at:dtwhaclustering-docsDetailsThis package include codes for processing the GPS displacement data including least-square modelling for trend, co-seismic jumps,
seasonal and tidal signals. Finally, it can be used to cluster the GPS displacements based on the similarity of the waveforms. The
similarity among the waveforms will be obtained using the DTW distance.UsageLeast-squares modelingfrom dtwhaclustering.leastSquareModeling import lsqmodeling
final_dU, final_dN, final_dE = lsqmodeling(dUU, dNN, dEE,stnlocfile="helper_files/stn_loc.txt",  plot_results=True, remove_trend=False, remove_seasonality=True, remove_jumps=False)Plot station mapfrom dtwhaclustering import plot_stations
plot_stations.plot_station_map(station_data = 'helper_files/selected_stations_info.txt', outfig=f'{outloc}/station_map.pdf')Plot linear trendslopeFile=f'stn_slope_res_U.txt'
df = pd.read_csv(slopeFile, names=['stn','lon','lat','slope'], delimiter='\s+')
plot_linear_trend_on_map(df, outfig=f"Maps/slope-plot_U.pdf")Note:slopeFileis obtained fromlsqmodeling.Dynamic Time Warping Analysisfrom dtwhaclustering.dtw_analysis import dtw_signal_pairs, dtw_clustering
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt

np.random.seed(0)
# sampling parameters
fs = 100   # sampling rate, in Hz
T = 1      # duration, in seconds
N = T * fs  # duration, in samples

# time variable
t = np.linspace(0, T, N)

SNR = 0.2 #noise

XX0 = np.sin(2 * np.pi * t * 7+np.pi/2) #+ np.random.randn(1, N) * SNR
XX1 = signal.sawtooth(2 * np.pi * t * 5+np.pi/2) #+ np.random.randn(1, N) * SNR
# XX1 = np.abs(np.cos(2 * np.pi * t * 3)) - 0.5
s1, s2 = XX0, XX1

dtwsig = dtw_signal_pairs(s1, s2, labels=['S1', 'S2'])

dtwsig.plot_signals()
plt.show()dtwsig.plot_matrix(windowfrac=0.6, psi=None) #Only allow for shifts up to 60% of the minimum signal length away from the two diagonals.
plt.show()ReferencesKumar, U., Chao, B.F., Chang, E.T.-Y.Y., 2020. What Causes the Common‐Mode Error in Array GPS Displacement Fields: Case Study for Taiwan in Relation to Atmospheric Mass Loading. Earth Sp. Sci. 0–2.https://doi.org/10.1029/2020ea001159License© 2021 Utpal KumarLicensed under the Apache License, Version 2.0