Package: wasar
Wasar: Analysis of A-DInSAR time series.This package allows to analyze the ground deformation of a region and to compare it with other climatic variables, such as groundwater levels or rainfall. In addition, the inclusion ofwavelettools allows to analyze the main periodicities of the model variables and estimate cause-effect processes.IntroductionMany regions worldwide are affected byground subsidence phenomena. Abusive water withdrawal from aquifers is one the main factors than can lead to this kind of processes. Although ground motion monitoring can be performed with in-situ instruments, one of the most widely used techniques in the last decades is theAdvanced Differential Interferometry Synthetic Aperture Radar(A-DInSAR). The A-DInSAR technique consists on the superposition of numerous SAR images of the same region of the Earth, thus obtaining an image of the ground motion occurring between the SAR images acquisitions. Analyzing multiple time series of ground movement in a given region, and comparing them with groundwater level variatons (or other variables), are the manin purposes of the present program.InstallReleased source packages are available onPyPi. You cansimply installit as:pip install wasarSince geopandas dependencies could cause conflicts with other spatial packages, it's highly recommended to create first anew environment, as well as taking a look at thegeopandasinstallation guidelines.An optional package must be installed for the use ofwaveletanalysis. It'srpy2package, and it can be simply installed viapip:pip install rpy2In case you find problems installing geopandas, here we leave the procedure we have used to install geopandas from the Anaconda Powershell Prompt:Create a new environment (in this example with the name enviname):conda create --name envinameActivate the new environment:conda activate envinameInstall geopandas:conda install -c conda-forge python=3.9 fiona shapely rasterio pyproj pandas jupyterlab jupyter geopandasOpen a new jupyter notebook from your working directory:jupyter notebook --notebook-dir=c:\working_directoryDependenciesgeopandaspandasmatplotlibfoliumContactWe are Miguel González Jiménez and Carolina Guardiola Albert. You can contact us just via GitHub or through our e-mails:miguigonn@gmail.comandc.guardiola@igme.es.Get startedIn theexamplefolder you can find severaltutorialsthat will help you to get started with the program. Also, the functions, classes and modules are fully explained in Spanish, so if you have doubts about their behavior, just use the built-inhelp, the?mark or the tab button in Jupyter Notebook.Example:help(wasar.Dataset.find_element)orwasar.Dataset.find_element?orwasar.Dataset.find_element+.+press tabLicenceThis project is licensed under the terms of the GNU General Public License v3.0How to cite wasarIf you use the program, please cite it as follows:González-Jiménez, M. & Guardiola-Albert, C. (XXXX). Wasar: Analysis of A-DInSAR time series. (Version X.X.X). Zenodo.http://doi.org/10.5281/zenodo.6334996Example>>> import wasar
>>> Model = wasar.example.get_model()

>>> mymap = Model.mapa(LayerControl=False)
>>> Model.get('Asc').mapa(m=mymap)A very useful tool ofwasararewavelet tools, which allow to perform frequency analysis of the time series.The following example shows thecommon periodicitiesbetween a rainfall station and a piezometer, being theannualfrequency the main common one.>>> from wasar import Wavelet
>>> Doñana = wasar.example.get_model()

>>> piezometer = Doñana.get('Piezo_bbdd').take('104080065')
>>> piezometer = piezometer.pivot(index='Fechas',columns='Nombre', values='Valores')

>>> rainfall = Doñana.get('P').take('Almonte')

>>> Wavelet('M', piezometer, rainfall, dt=2, dj=1/20, lowerPeriod=2, upperPeriod=30)