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You can create a new example by simply clicking on "Use this template".
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## Description
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The included example is showing the generation of a conditioned random field ensemble
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in 1D taken from [GSTools](https://geostat-framework.readthedocs.io/projects/gstools/en/stable/examples/06_conditioned_fields/00_condition_ensemble.html#sphx-glr-examples-06-conditioned-fields-00-condition-ensemble-py).
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The extended Theis solution presented by *Zech et al. (2016)*
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reproduces the ensemble mean drawdown
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of pumping tests in heterogenous media with a log-normal transmissivity
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distribution following a Gaussian variogram.
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In this workflow, we demonstrate that the extended Theis solution reproduces
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the ensemble mean drawdown of pumping tests on synthetic aquifers for multiple
> Zech, A., Müller, S., Mai, J., Heße, F., & Attinger, S., 2016.
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> Extending theis’ solution: Using transient pumping tests to estimate parameters of aquifer heterogeneity.
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> Water Resources Research 52, 6156–617. https://dx.doi.org/10.1002/2015WR018509
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## Structure
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Please try to organize your example in the given Structure
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-`data/` - here you should place your input data
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The workflow is organized by the following structure:
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-`src/` - here you should place your python scripts
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-`results/` - here your computed results and plots should be stored
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-`README.md` - please describe your example in the readme, potentially showing results
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-`LICENSE` - the default license is MIT, you can use another one if wanted
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-`00_run_sim_mpi.sh` - bash file running `01_run_sim.py` in parallel
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-`01_run_sim.py` - run all ensemble simulations for pumping tests
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-`02_compare_mean.py` - generate comparision plots for the ensemble means
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-`03_trans_plot.py` - plot a realization of a gaussian transmissivity field
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-`04_ext_theis_compare.py` - plotting the effective Theis solution against classical Theis
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-`results/` - all produced results
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## Python environment
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To make the example reproducible, it would be a good practice to provide one of
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the following files:
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-`requirements.txt` - requirements for [pip](https://pip.pypa.io/en/stable/user_guide/#requirements-files) to install all needed packages
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-`spec-file.txt` - specification file to create the original [conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#building-identical-conda-environments)
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## Workflow
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Main Python dependencies are stored in `requirements.txt`:
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After finalizing your work, you should tag the repository with a version like `v1.0`.
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```
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gstools==1.3.0
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anaflow==1.0.1
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ogs5py==1.1.1
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matplotlib
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```
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Then, a [Zenodo](https://zenodo.org/) release will be created, so you can cite the repository in you publication.
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You can install them with `pip` (potentially in a virtual environment):
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Please keep your `master` branch in line with the latest release.
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For further development use the `develop` branch and update `master` with pull-requests.
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```bash
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pip install -r requirements.txt
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```
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## Contact
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