1,720,974 research outputs found
AlrauneZ/MADE-model-comparison 0.1
No full text<p>alpha-release of project files for Manuscript "A Comparison of Six Transport Models of the MADE-1 Experiment Implemented with Different Types of Hydraulic Data".</p>
GeoStat-Framework/GSTools: Volatile Violet
No full textRelease Notes
This release comes with a totally reworked kriging sub-module, a new variogram estimator, Python3-only support and a set of minor bugfixes.
A shout out to @banesullivan for his work on the sphinx gallery and the pyvista interface!
Installation
You can install GSTools with conda:
conda install -c conda-forge gstools
or with pip:
pip install gstools
Documentation
The documentation can be found at: https://gstools.readthedocs.io/
What's new?
Enhancements
different variogram estimator functions can now be used #51
the TPLGaussian and TPLExponential now have analytical spectra #67
added property is_isotropic to CovModel #67
reworked the whole krige sub-module to provide multiple kriging methods #67
Simple
Ordinary
Universal
External Drift Kriging
Detrended Kriging
a new transformation function for discrete fields has been added #70
reworked tutorial section in the documentation #63
pyvista interface #29
Changes
Python versions 2.7 and 3.4 are no longer supported #40 #43
CovModel: in 3D the input of anisotropy is now treated slightly different: #67
single given anisotropy value [e] is converted to [1, e] (it was [e, e] before)
two given length-scales [l_1, l_2] are converted to [l_1, l_2, l_2] (it was [l_1, l_2, l_1] before)
Bugfixes
a race condition in the structured variogram estimation has been fixed #51
multiple minor bugfixe
GeoStat-Framework/GSTools: 2. Release candidate: Reverberating Red
No full textRelease Notes
Woah! GSTools went parallel. And at the same time the humongous memory consumption of the field generation became very modest.
The second big news is that GSTools can now finally generate conditioned random fields and provides kriging.
Installation
Since this is a pre-release you have to install it with:
pip install --pre -U gstools
For parallel compilation try:
pip install --pre --global-option="--openmp" -U gstools
Enhancements
by using Cython for all the heavy computations, we could achieve quite some speed ups and reduce the memory consumption significantly #16
parallel computation in Cython is now supported with the help of OpenMP and the performance increase is nearly linear with increasing cores #16
new submodule krige providing simple (known mean) and ordinary (estimated mean) kriging working analogous to the srf class
interface to pykrige to use the gstools CovModel with the pykrige routines (bsmurphy/PyKrige#124)
the srf class now provides a plot and a vtk_export routine
incompressible flow fields can now be generated #14
new submodule providing several field transformations like: Zinn&Harvey, log-normal, bimodal, ... #13
Python 3.4 and 3.7 wheel support #19
field can now be generated directly on meshes from meshio and ogs5py f4a3439
the srf and kriging classes now store the last pos, mesh_type and field values to keep them accessible 29f7f1b
tutorials on all important features of GSTools have been written for you guys #20
a new interface to pyvista is provided to export fields to python vtk representation, which can be used for plotting, exploring and exporting fields #29
Changes
the license was changed from GPL to LGPL in order to promote the use of this library #25
the rotation angles are now interpreted in positive direction (counter clock wise)
the force_moments keyword was removed from the SRF call method, it is now in provided as a field transformation #13
drop support of python implementations of the variogram estimators #18
the variogram_normed method was removed from the CovModel class due to redundance 25b1647
the position vector of 1D fields does not have to be provided in a list-like object with length 1 a6f5be8
we now require emcee version >= 3.0.0
Bugfixes
several minor bugfixe
GeoStat-Examples/welltestpy-field-site-analysis: v1.0.1
No full textPumping test analysis with welltestpy
Description
In this workflow, we analyse two pumping test campaigns on two field sites:
"Horkheimer Insel" (Heilbronn, Germany)
"Lauswiesen" (Tübingen, Germany)
The aim is to estimate parameters of heterogeneity from transient puming test data and to analyse their sensitivities.
Target parameters
mean of log-transmissivity
variance of log-transmissivity
length scale of log-transmissivity
storage
Applied methods
The applied methods utilizing effecitive head solutions for the groundwater flow equation under a pumping test condition are described in:
Zech, A., Müller, S., Mai, J., Heße, F., and Attinger, S.: Extending Theis' Solution: Using Transient Pumping Tests to Estimate Parameters of Aquifer Heterogeneity, Water Resour. Res., 52, 6156–6170, https://doi.org/10.1002/2015WR018509, 2016.
These methods were implemented in welltestpy to automatically interprete pumping test data.
The underlying type-curves are implemented in AnaFlow.
Data sources
The data for the "Horkheimer Insel" field site was manually taken from:
Schad H.: Variability of hydraulic parameters in non-uniform porous media: experiments and stochastic modeling at different scales. University Tübingen; 1997. Ph.D. thesis.
The pumping data from the "Lauswiesen" field site was kindly provided by Dr. Carsten Leven-Pfister and is made available on a repository of the University of Tübingen: Research Data Portal FDAT
Structure
The workflow is organized by the following structure:
data/
contains the campaign files for both sites in the welltestpy format
src/ - contains the scripts to produce the results
00_wtp_plot.py - plotting well-constellation and campaign overviews
01_est_run.sh - bash file running 02_para_estimation.py in parallel
02_para_estimation.py - estimate parameters of heterogeneity from the pumping tests
03_postpro_results.py - plotting the estimation results for both sites
04_postpro_sensitivity.py - plotting the sensitivity results for both sites
05_est_radial_sens.sh - bash file running 06_rad_sens.py in parallel
06_rad_sens.py - estimate parameter sensitivites depending on the radial distance to the pumping well. when run in serial, results will be plotted.
07_comparison_len_scale.py - generate comparison plot for different length scales
results/ - all produced results (except *.csv files generated by spotpy)
Python environment
Main Python dependencies are stored in requirements.txt:
welltestpy==1.0.2
anaflow==1.0.1
spotpy==1.5.9
mpi4py==3.0.2
matplotlib
You can install them with pip (potentially in a virtual environment):
pip install -r requirements.txt
Contact
You can contact us via [email protected].
License
MIT © 202
GeoStat-Framework/GSTools: Volatile Violet v1.2.1
No full textRelease Notes
This is a bug-fix release.
Installation
You can install GSTools with conda:
conda install -c conda-forge gstools
or with pip:
pip install gstools
Documentation
The documentation can be found at: https://gstools.readthedocs.io/
What's new?
Bugfixes
ModuleNotFoundError is not present in py35
Fixing Cressie-Bug #76
Adding analytical formula for integral scales of rational and stable model
remove prange from IncomprRandMeth summators to prevent errors on Win and macO
GeoStat-Examples/extended-GRF-model: v1.0
No full text<p><strong>The eGRF model and its application to effective conductivity for TPL variograms </strong></p>
<p><strong>Description</strong></p>
<p>The <em>extended</em> generalized radial flow (eGRF) model is an extension to the GRF model by allowing radial variable transmissivity and storativity values. The GRF model was derived by:</p>
<blockquote>
<p>Barker, J.A., 1988. A generalized radial flow model for hydraulic tests infractured rock. Water Resources Research 24, 1796–1804. <a href="https://doi.org/10.1029/WR024i010p01796">https://doi.org/10.1029/WR024i010p01796</a></p>
</blockquote>
<p>In this workflow, we demonstrate the abilities of the eGRF model and numerically prove, that the effective transmissivity for truncated power law (TPL) variograms reproduces the ensemble mean drawdown of pumping tests on synthetic aquifers.</p>
<p><strong>Structure</strong></p>
<p>The workflow is organized by the following structure:</p>
<ul>
<li><code>src/</code> - here you should place your python scripts
<ul>
<li><code>00_ext_theis_tpl.py</code> - plotting the effective head for TPL variograms</li>
<li><code>01_est_run.sh</code> - bash file running <code>02_para_estimation.py</code> in parallel</li>
<li><code>01_convergence.py</code> - demonstating the convergence of the effective TPL solution</li>
<li><code>02_step_function.py</code> - plot different step function approximations</li>
<li><code>03_literature_transmissivities.py</code> - comparision of drawdowns for different transimissivites from literature</li>
<li><code>04_trans_plot.py</code> - plot a realization of a TPL transmissivity field</li>
<li><code>comparison/</code> - scripts for the comparison of ensemble mean to effective TPL heads
<ul>
<li><code>00_run_sim_mpi.sh</code> - bash file running <code>01_run_sim.py</code> in parallel</li>
<li><code>01_run_sim.py</code> - run all ensemble simulations for pumping tests on TPL aquifers</li>
<li><code>02_compare_mean.py</code> - generate comparision plots for the ensemble means</li>
</ul>
</li>
</ul>
</li>
<li><code>results/</code> - all produced results</li>
</ul>
<p><strong>Python environment</strong></p>
<p>Main Python dependencies are stored in <code>requirements.txt</code>:</p>
<pre><code>gstools==1.2.1
anaflow==1.0.1
ogs5py==1.1.1
matplotlib
</code></pre>
<p>You can install them with <code>pip</code> (potentially in a virtual environment):</p>
<pre><code>pip install -r requirements.txt
</code></pre>
<p><strong>Contact</strong></p>
<p>You can contact us via <a href="mailto:[email protected]">[email protected]</a>.</p>
<p><strong>License</strong></p>
<p>MIT © 2020</p>
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
GeoStat-Framework/GSTools: Volatile Violet RC2
No full textRelease Notes
This release comes with a totally reworked kriging sub-module, a new variogram estimator, Python3-only support and a set of minor bugfixes.
A shout out to @banesullivan for his work on the sphinx gallery and the pivista interface!
Installation
You can install GSTools with conda:
conda install -c conda-forge gstools
or with pip:
pip install gstools
Documentation
The documentation can be found at: https://gstools.readthedocs.io/
What's new?
Enhancements
different variogram estimator functions can now be used #51
the TPLGaussian and TPLExponential now have analytical spectra #67
added property is_isotropic to CovModel #67
reworked the whole krige sub-module to provide multiple kriging methods #67
Simple
Ordinary
Universal
External Drift Kriging
Detrended Kriging
a new transformation function for discrete fields has been added #70
reworked tutorial section in the documentation #63
pyvista interface #29
Changes
Python versions 2.7 and 3.4 are no longer supported #40 #43
CovModel: in 3D the input of anisotropy is now treated slightly different: #67
single given anisotropy value [e] is converted to [1, e] (it was [e, e] before)
two given length-scales [l_1, l_2] are converted to [l_1, l_2, l_2] (it was [l_1, l_2, l_1] before)
Bugfixes
a race condition in the structured variogram estimation has been fixed #51
multiple minor bugfixe
GeoStat-Framework/GSTools: v1.3.5 'Pure Pink'
No full textRelease Notes
Bugfix release.
Installation
You can install GSTools with conda:
conda install -c conda-forge gstools
or with pip:
pip install gstools
Documentation
The documentation can be found at: https://gstools.readthedocs.io/
What's new?
Changes
remove caps for dependencies #229
build linux wheels with manylinux2014 for all versions (CIBW v2.3.1) #227
Bugfixes
Field.mesh was not compatible with meshio v5.1+ #22
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