Saravanan666/pypi-org huggingface dataset:
Package: hydromt_fiat
hydroMTis a python package, developed by Deltares, to build and analysis hydro models.
It provides a generic model api with attributes to access the model schematization,
(dynamic) forcing data, results and states. This plugin provides an implementation
for theFIATmodel.InstallationhydroMT-fiat will soon be availble from pypi and conda-forge (in progress).To install hydromt_fiat using pip do:pip install hydromt_fiatWe recommend installing a hydromt-fiat environment including the hydromt_fiat package
based on the environment.yml file. This environment will install all package dependencies
including the core ofhydroMT.conda env create -f binder/environment.yml
conda activate hydromt-fiat
pip install hydromt_fiatDocumentationLearn more about the hydroMT_fiat plugin in itsonline documentationContributingYou can find information about contributing to hydroMT at ourContributing page.LicenseCopyright (c) 2021, DeltaresThis 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 <https://www.gnu.org/licenses/>.
Package: hydromt-sfincs
What is the HydroMT-SFINCS plugin?HydroMT(Hydro Model Tools) is an open-source Python package that facilitates the process of
building and analyzing spatial geoscientific models with a focus on water system models.
It does so by automating the workflow to go from raw data to a complete model instance which
is ready to run and to analyse model results once the simulation has finished.
This plugin provides an implementation of the model API for theSFINCSmodel.What is SFINCS?SFINCS is Deltares’ new open-source reduced-complexity model designed for super-fast modelling of compound flooding events in a dynamic way!
What HydroMT-SFINCS does provide is a powerful Python based set of tools to help you build and analyse the best possible SFINCS models!
This HydroMT-SFINCS plugin does not include the SFINCS model or executable itself, for that see theSFINCS download portalor the source code repository onGithub.
For general documentation about the model, how to run it and what the input files are see theSFINCS documentation.Why HydroMT-SFINCS?Setting up hydrodynamic models typically requires many (manual) steps
to process input data and might therefore be time consuming and hard to reproduce.
Especially improving models based on global geospatial datasets, which are
rapidly becoming available at increasingly high resolutions, might be challenging.
HydroMT-SFINCS aims to make the model building processfast,modularandreproducibleand to facilitate the analysis of SFINCS model resultsHow to use HydroMT-SFINCS?The HydroMT-SFINCS plugin can be used as acommand line + configuration fileapplication, which provides commands tobuild,updatethe SFINCS model with a single line, orfrom pythonto exploit its rich interface.
You can learn more about how to use HydroMT-SFINCS in itsonline documentation.For a smooth installing experience we recommend installing HydroMT-SFINCS and its dependencies
from conda-forge in a clean environment, seeinstallation guide.How to cite?To reference the software please use the the DOI provided in the Zenodo badge that points to the latest release.The following paper presents a real-world application of HydroMT-SFINCS:Eilander, D., Couasnon, A., Leijnse, T., Ikeuchi, H., Yamazaki, D., Muis, S., Dullaart, J., Haag, A., Winsemius, H. C., and Ward, P. J.:
A globally applicable framework for compound flood hazard modeling, Nat. Hazards Earth Syst. Sci., 23, 823–846,https://doi.org/10.5194/nhess-23-823-2023, 2023.How to contribute?If you find any issues in the code or documentation feel free to leave an issue on thegithub issue tracker.You can find information about how to contribute to the HydroMT project at ourcontributing page.HydroMT seeks active contribution from the (hydro) geoscientific community.
So far, it has been developed and tested with a range ofDeltaresmodels, but
we believe it is applicable to a much wider set of geoscientific models and are
happy to discuss how it can be implemented for your model.
Package: hydromt-wflow
What is the HydroMT-Wflow pluginHydroMT(Hydro Model Tools) is an open-source Python package that facilitates the process of
building and analyzing spatial geoscientific models with a focus on water system models.
It does so by automating the workflow to go from raw data to a complete model instance which
is ready to run and to analyze model results once the simulation has finished.
This plugin provides an implementation of the model API for theWflowmodel.Why HydroMT-Wflow?Setting up distributed hydrological models typically requires many (manual) steps
to process input data and might therefore be time consuming and hard to reproduce.
Especially improving models based on global-local geospatial datasets, which are
rapidly becoming available at increasingly high resolutions, might be challenging.
HydroMT-Wflow aims to make the Wflow model building and updating processesfast,modularandreproducibleand to facilitate the analysis of the model results.How to use HydroMT-Wflow?The HydroMT-Wflow plugin can be used as acommand lineapplication, which provides commands tobuild,updateandclipa Wflow model with a single line, orfrom pythonto exploit its rich interface.
You can learn more about how to use HydroMT-Wflow in itsonline documentation.For a smooth installing experience we recommend installing HydroMT-Wflow and its dependencies
from conda-forge in a clean environment, seeinstallation guide.How to cite?For publications, please cite our work using the DOI provided in the Zenodo badgethat points to the latest release.How to contribute?If you find any issues in the code or documentation feel free to leave an issue on thegithub issue tracker.You can find information about how to contribute to the HydroMT project at ourcontributing page.HydroMT seeks active contribution from the (hydro) geoscientific community.
So far, it has been developed and tested with a range ofDeltaresmodels, but
we believe it is applicable to a much wider set of geoscientific models and are
happy to discuss how it can be implemented for your model.
Package: hydronetapi
Hydronet APISummary:
This API is for use with a Hydronet WMM API implementing the most recent version of the HydroMesh library.To import the hydronet library:fromhydronetimportwmmTo establish connection to a modem with an Ethernet interface IP of  (xx.xx.xx.xx):modem=wmm.wmm(<ipaddr>,<BufferSize>,<Port>)In order to send data from an underwater node to a internet endpoint, the network must be made aware of the endpoint either by an edge node or an underwater node.
To introduce a new internet UDP endpoint (at :) to the mesh network from an underwater application (where  is an alias for the internet endpoint understood by HydroMesh):<dest>=modem.establish_connection(<ipaddr>,port>)The destination field is a one byte value representing an endpoint.
The range of valid destinations are as follows:0x00      : Reserved0x01-0x3F : UW endpoints0x40-0xFE : Alias destinations0xFF      : ReservedSend data to a destination:modem.send(<data>,<destination>)Receive data from the LAN modem:<data>,<addr>,<src>=modem.recv(self,<cut_header>=True)The  (source) field is of the same type as the destination, and can therefore be used as a destination for further communication.
Package: hydropt
HydroptHydropt is a dynamic prgrogramming tool for the optimization of hydro power plants. It includes classes to model power plants, their basins and turbines as well as to define scenarios with constraints.Different scenarios can easily be compared with each other to compute and evalutate opportunity costs. A typical use case would be to price constraints due to ancillary services commitments or machine outages.InstallationRun the following to install:pipinstallhydroptUsageThe following example shows howhydroptcan be used for the optimization of
a simple hydro power plant with only one basin and a single turbine.importnumpyasnpimporthydroptasho# Define basinbasin=ho.Basin('basin_1',volume=75e6,#m3num_states=101,levels=(1700,1792),#m ü. Mstart_volume=60e6,#m3)# Define outflowoutflow=ho.Outflow(outflow_level=1090)# Connect basin and outlow via a turbineturbine=ho.Turbine('turbine_1',max_power=45e6,#Wattbase_load=1e6,#Wattefficiency=0.8,upper_basin=basin,lower_basin=outflow,actions=[ho.Standing(),ho.MinPower(),ho.MaxPower(),],)# Define power plant modelpower_plant=ho.PowerPlant([basin,],[turbine,])# Load spot pricesspot_2019=ho.load_spot_data()# Set optimization time frame and assign price curvetime=spot_2019.index.to_numpy().astype('datetime64[h]')spot=spot_2019['Switzerland[EUR/MWh]'].to_numpy()# Compute inflow rateinflow_rate=5*np.ones((len(spot),1))# Define a scenariounderlyings=ho.Underlyings(time,spot,inflow_rate)scenario=ho.Scenario(power_plant,underlyings,name='base case')# Run optimizationscenario.run()# ... wait about 30 seconds ...# Plot resultsscenario.results_.plot()
Package: hydropt-oc
HYDROPT is an open-source framework for forward and inverse modelling of multi- and hyperspectral observations from oceans, coastal and inland waters. The remote sensing reflectance, Rrs, is calculated by specifying the inherent optical properties (IOP) of the water column, the sensor viewing geometry and solar zenith angle. Our framework is based on radiative transfer principles and is sensor agnostic allowing Rrsto be calculated for any wavelength in the 400 - 710nmrange.Inversion of Rrsspectra is achieved by minimizing the difference between the HYDROPT forward calculations and the reflectance measured by the sensor. Different optimization routines can be selected to minimize the cost function. An extensive description of the theoretical basis of the framework as well as applications are provided in the following scientific papers:Holtrop, T., & Van Der Woerd, H. J. (2021). HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters.Remote Sensing, 13(15), 3006.doi:10.3390/rs13153006Van Der Woerd, H.J. & Pasterkamp, R. (2008). HYDROPT: A fast and flexible method to retrieve chlorophyll-a from multispectral satellite observations of optically complex coastal waters.Remote Sensing of Environment, 112, 1795–1807.doi:10.1016/j.rse.2007.09.001
Package: hydropump
HydroPump-pythonConfiguration Management LibraryHydroPump is an open-source Python library that provides a simple and efficient way to manage configuration files. It allows you to store and retrieve configuration data using different backends such as local file system and various cloud storage solutions.InstallationYou can install HydroPump using pip:pipinstallhydropumpUsageTo use HydroPump, you need to import thehydropumpmodule:importhydropumpExampleHere's a complete example that demonstrates the usage of HydroPump:fromhydropumpimportServiceservice_config={"backend_type":"FileSystem","file_extension":"json",}service=Service(config=service_config)template_id1="example_template1"template1={"sqlEngine":"mysql"}service.create_template(metadata={},source=template1,template_id=template_id1)template_id2="example_template2"template2={"sqlEngine":"postgres","cloudProvider":"AWS",}service.create_template(metadata={},source=template2,template_id=template_id2)instruction_id="client-12345"metadata={"createdBy":"mvecchione145",# templates are compiled from left to right# (common keys in example_template1 will be# overridden by values in example_template2)"templates":["example_template1","example_template2"],}source={"system":"darwin"}service.create_instruction(instruction_id=instruction_id,metadata=metadata,source=source)instruction=service.get_instruction(instruction_id=instruction_id)print(instruction)# {#   "instruction": {#     "cloudProvider": "AWS",#     "sqlEngine": "postgres",#     "system": "darwin"#   },#   "metadata": {#     "compiled": True,#     "createdBy": "mvecchione145",#     "createdAt": "YYYY-MM-DD HH:MM:SS.NNNNNN"#     "templates": [#       "example_template",#       "example_template2"#     ]#   }ContributingContributions to HydroPump are welcome! If you find any issues or have suggestions for improvement, please open an issue on theGitHub repository. Pull requests are also appreciated.Please make sure to follow thecontribution guidelineswhen submitting your contributions.LicenseHydroPump is released under theMIT License.Thank you for using HydroPump! We hope it helps simplify your configuration management process. If you have any questions or need further assistance, feel free to reach out to us atsupport@hydropump.com.
Package: hydropy
# Hydropy[![Pypi](https://img.shields.io/pypi/v/hydropy.svg)](https://pypi.python.org/pypi/hydropy) [![Build Status](https://img.shields.io/travis/stijnvanhoey/hydropy.svg)](https://travis-ci.org/stijnvanhoey/hydropy) [![License](https://img.shields.io/badge/License-BSD%202–Clause-blue.svg)](https://opensource.org/licenses/BSD-2-Clause)Analysis of hydrological oriented time series.This package is designed to simplify the collection and analysis of
hydrology data.  Use HydroPy in a Jupyter
notebook and save your analysis so that you can recreate your procedures and
share them with others.Hydropy uses the power of Numpy and Pandas to quickly process large datasets.
Matplotlib and Seaborn are built-in to Hydropy, allowing you to create
publication-ready diagrams quickly and easily.Try Hydropy in a notebook: [hydropy_tutorial.ipynb](https://github.com/stijnvanhoey/hydropy/blob/master/hydropy_tutorial.ipynb)## Example:# Recession periods in June 2011:
myflowserie.get_year(‘2011’).get_month(“Jun”).get_recess()![Recession periods](./data/recession.png)# Peak values above 90th percentile for station LS06_347 in july 2010:
myflowserie[‘LS06_347’].get_year(‘2010’).get_month(“Jul”).get_highpeaks(150, above_percentile=0.9)![Selected peaks](./data/peaks.png)# Select 3 storms out of the series
storms = myflowserie.derive_storms(raindata[‘P06_014’], ‘LS06_347’, number_of_storms=3, drywindow=96, makeplot=True)![Selected storms](./data/storms.png)A more extended tutorial/introduction is provided in a ipython notebook: [hydropy_tutorial.ipynb](https://github.com/stijnvanhoey/hydropy/blob/master/hydropy_tutorial.ipynb)We acknowledge the Flemish Environmental Agency (VMM) for the data used in the tutorial. It can be downloaded fromhttp://www.waterinfo.be/.To install this, git clone the repo and then install it by:python setup.py installTo test the functionalities yourself without installing it, use following test environment provided by Binder:
[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/repo/stijnvanhoey/hydropy)Inspiration or possible useful extensions:
* Basically this is a restart of hydropyhttps://code.google.com/p/hydropy/* Hydroclimpyhttp://hydroclimpy.sourceforge.net/* Georgakakos2004, ROC
*http://cran.r-project.org/web/packages/hydroTSM/vignettes/hydroTSM_Vignette.pdfThe slides version of the notebook was made with nbconvert (using reveal.js), by following command:ipython nbconvert hydropy_tutorial.ipynb –to=slides –post=serve –reveal-prefix=reveal.js –config slides_config.pyhttp://nbviewer.ipython.org/format/slides/github/stijnvanhoey/hydropy/blob/master/hydropy_tutorial.ipynb#/Copyright (c) 2015-2017 Stijn Van Hoey, Martin Roberge, and ContributorsCreditsDevelopment LeadStijn Van Hoey <stvhoey.vanhoey@ugent.be>ContributorsMartin Roberge <mroberge@towson.edu>
Package: hydroqc2mqtt
hydroqc2mqttThe full updated project documentation can be found athttps://hydroqc.caWe have a discord server where you can come to discuss and find help with the projecthttps://discord.gg/2NrWKC7sfFThis module extracts data from your Hydro-Quebec account using theHydro Quebec API Wrapperand will post it to your MQTT server. Home-Assistant MQTT Discovery topics are also provided, automating the creation of your sensors in Home-Assistant.We have implemented a feature that will send historical hourly consumption from Hydro-Quebec to your Home-Assistant statistics to be used in the Energy Dashboard. This feature does not work over MQTT but send the information directly to Home-Assistant via Websocket.DonationsWe put a lot of heart and effort in this project, any contribution is greatly appreciated!DisclaimerNot an official Hydro-Quebec APIThis is a non official way to extract your data from Hydro-Quebec, while it works now it may break at anytime if or when Hydro-Quebec change their systems.Special message to Hydro-Quebec's employeesWe would very much like to improve this module and it'sAPI. We tried to reach out to HQ but never were able to get in contact with anyone there interested in discussing this project. If you have some feedback, complaints or are interested to discuss this project, please reach out to us on ourdevelopment discord channel.
Package: hydro-raster
hydro_rasterPython code to process raster data for hydrological or hydrodynamic modelling,
e.g.,SynxFloworHiPIMS-CUDA. The style of this package follows theGoogle Python Style Guide.Python version: >=3.6. To use the full function of this package for processing
raster and feature files,rasterioandpyshpare required.The CRS of both DEM and Shapfiles must be projected crs whose map unit is meter.Functions included in this package:merge raster filesedit raster cell values based on shapefileconvert cross-section lines to river bathymetry rasterremove overhead buildings/bridges on rasterread, write, and visualise raster fileTo install hydro_raster from command window/terminal:pip install hydro_rasterTo install using github repo:git clone https://github.com/mingxiaodong/hydro-raster
cd hydro-raster
pip install .TutorialAjupyter-notebook fileis available to show a more detailed tutorial with outputs/plots of its codes.Read a raster filefrom hydro_raster.Raster import Raster
from hydro_raster import get_sample_data
tif_file_name = get_sample_data('tif')
ras_obj = Raster(tif_file_name)Visualize a raster fileras_obj.mapshow()
ras_obj.rankshow(breaks=[0, 10, 20, 30, 40, 50])Clip raster fileclip_extent = (340761, 341528, 554668, 555682) # left, right, bottom, top
ras_obj_cut = ras_obj.rect_clip(clip_extent) # raster can aslo be cut by a shapfile using 'clip' function
ras_obj_cut.mapshow()Rasterize polygons on a raster and return an index array with the same dimension of the raster arrayshp_file_name = get_sample_data('shp')
index_array = ras_obj_cut.rasterize(shp_file_name)
index_array = index_array>=0Change raster cell values within the polygons by adding a fixed valueras_obj_new = ras_obj_cut.duplicate()
ras_obj_new.array[index_array] = ras_obj_cut.array[index_array]+20Show the edited raster with the shapefile polygonsimport matplotlib.pyplot as plt
from hydro_raster.grid_show import plot_shape_file
fig, ax = plt.subplots(1, 2)
ras_obj_cut.mapshow(ax=ax[0])
plot_shape_file(shp_file_name, ax=ax[0], linewidth=1)
ras_obj_new.mapshow(ax=ax[1])
plot_shape_file(shp_file_name, ax=ax[1], linewidth=1)
# values can also be changed based on the attributes of each shapefile features
Package: hydro-relic374
HydroThe Hydro CLI is a way to quickly and easily package electron apps without the need for large commands.
Currently it has the bare bones features needed to package an app, but please let me know if you have any suggestions of electron packager flags to use! My discord:Relic374#4880InstallationNon-Python DependenciesElectronElectron PackagerInstallInstall withpipx hydro-relic374. Run usinghydro.
Package: hydrorolldemo
word-countDemonstrates searching for a file in plain python, with rust singlethreaded and with rust multithreaded.Buildpipinstall.Usagefromword_countimportsearch_py,search,search_sequentialsearch_py("foo bar","foo")search("foo bar","foo")search_sequential("foo bar","foo")TestingTo test install nox globally and runnoxBenchmarkTo test install nox globally and runnox-sbenchCopying this exampleUsecargo-generate:$cargoinstallcargo-generate
$cargogenerate--githttps://github.com/PyO3/pyo3examples/word-count(cargo generatewill take a little while to clone the PyO3 repo first; be patient when waiting for the command to run.)
Package: hydroshare-on-jupyter
HydroShare on JupyterHydroShare on Jupyter brings HydroShare resource management to JupyterLab's development environment.
Download, edit, upload, and synchronize your HydroShare resources without leaving Jupyter!
Collaborate, iterate, and stay up to date with HydroShare on Jupyter.InstallationIn accordance with the python community, we support and advise the usage of virtual environments in
any workflow using python. In the following installation guide, we use python's built-in venv module
to create a virtual environment in which the tools will be installed. Note this is just personal
preference, any python virtual environment manager should work just fine (conda, pipenv, etc. ).# Create and activate python environment, requires python >= 3.7python3-mvenvvenvsourcevenv/bin/activatepython3-mpipinstall--upgradepip# Installpython3-mpipinstallhydroshare_on_jupyter# Link extension to JupyterLabpython3-mhydroshare_on_jupyterconfigure# Launch JupyterLab and start collaborating!python3-mjupyterlabConfigurationHydroShare on Jupyter looks for configuration information in environment variables first, then at
the following path locations:~/.config/hydroshare_on_jupyter/config~/.hydroshare_on_jupyter_configThe first configuration file found is used if it exists. However, environment variables take
precedence, meaning they override configuration file values if they are set.HydroShare on Jupyter configuration files useKEY=VALUEsemantics (example below). Only one
configuration variable should be specified per line. Line comments can be created by starting a line
with#.Configuration VariablesDATA: directory where HydroShare resources are saved, default~/hydroshare.OAUTH: canonical HydroShare OAuth2 pickle file, default None. Allows bypassing login by using OAuth2 via HydroShare.Example configuration file# file: ~/.config/hydroshare_on_jupyter/configDATA=~/DownloadsNoteBy default, HydroShare on Jupyter saves HydroShare resources to~/hydroshare. This means, if
JupyterLab is started from a directory where~/hydroshareis not a descendent (e.g.~/Downloads), you will not be able to open the HydroShare resource files you download using
HydroShare on Jupyter. To resolve this, either open JupyterLab from~or change the directory
HydroShare on Jupyter saves resources to using the dataDATAconfiguration variable.