Initial commit

Author: MuellerSeb

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

LICENSE

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+MIT License
+
+Copyright (c) 2020 GeoStat-Examples
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+[![GS-Frame](https://img.shields.io/badge/github-GeoStat_Framework-468a88?logo=github&style=flat)](https://github.com/GeoStat-Framework)
+[![Gitter](https://badges.gitter.im/GeoStat-Examples/community.svg)](https://gitter.im/GeoStat-Examples/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+
+# Template
+
+This is a template for an example repository.
+
+You can create a new example by simply clicking on "Use this template".
+
+The included example is showing the generation of a conditioned random field ensemble
+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).
+
+
+## Structure
+
+Please try to organize your example in the given Structure
+- `data/` - here you should place your input data
+- `src/` - here you should place your python scripts
+- `results/` - here your computed results and plots should be stored
+- `README.md` - please describe your example in the readme, potentially showing results
+- `LICENSE` - the default license is MIT, you can use another one if wanted
+
+
+## Python environment
+
+To make the example reproducible, it would be a good practice to provide one of
+the following files:
+- `requirements.txt` - requirements for [pip](https://pip.pypa.io/en/stable/user_guide/#requirements-files) to install all needed packages
+- `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)
+
+
+## Workflow
+
+After finalizing your work, you should tag the repository with a version like `v1.0`.
+
+Then, a [Zenodo](https://zenodo.org/) release will be created, so you can cite the repository in you publication.
+
+Please keep your `master` branch in line with the latest release.
+For further development use the `develop` branch and update `master` with pull-requests.
+
+
+## Contact
+
+You can contact us via <info@geostat-framework.org>.
+
+
+## License
+
+MIT © 2020

data/conditions.txt

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+# 1.row: condition positions
+# 2.row: condition values
+2.999999999999999889e-01 1.899999999999999911e+00 1.100000000000000089e+00 3.299999999999999822e+00 4.700000000000000178e+00
+4.699999999999999734e-01 5.600000000000000533e-01 7.399999999999999911e-01 1.469999999999999973e+00 1.739999999999999991e+00

requirements.txt

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+gstools==1.2.1
+matplotlib

results/simulations.png

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+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: linux-64
+@EXPLICIT
+https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2020.4.5.1-hecc5488_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.34-h53a641e_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-7.3.0-hdf63c60_5.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-9.2.0-hdf63c60_2.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-10.0.0-hc9558a2_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/mpi-1.0-openmpi.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-1_llvm.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-9.2.0-h24d8f2e_2.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/expat-2.2.9-he1b5a44_2.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/icu-64.2-he1b5a44_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/jpeg-9c-h14c3975_1001.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1007.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.15-h516909a_1006.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.9-h5ec1e0e_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h14c3975_1000.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-0.10.0-he1b5a44_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.1-hf484d3e_1002.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/nspr-4.25-he1b5a44_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1g-h516909a_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/pcre-8.44-he1b5a44_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h14c3975_1001.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h14c3975_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h516909a_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.5-h516909a_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h516909a_1006.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-hc5be6a0_1002.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/hdf5-1.10.5-nompi_h3c11f04_1104.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libblas-3.8.0-16_openblas.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libllvm9-9.0.1-he513fc3_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-hed695b0_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h14c3975_1002.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.10-hee79883_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/openmpi-4.0.3-hdf1f1ad_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/readline-8.0-hf8c457e_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.10-hed695b0_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/freetype-2.10.1-he06d7ca_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.8.0-16_openblas.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/libclang-9.0.1-default_hde54327_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.8.0-16_openblas.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.30.1-hcee41ef_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.13.1-h86ecdb6_1001.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/nss-3.47-he751ad9_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/python-3.8.2-he5300dc_7_cpython.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/glib-2.64.2-h6f030ca_0.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/mpmath-1.1.0-py_0.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/pyparsing-2.4.7-pyh9f0ad1d_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.8-1_cp38.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/six-1.14.0-py_1.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/tqdm-4.45.0-pyh9f0ad1d_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/certifi-2020.4.5.1-py38h32f6830_0.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/cycler-0.10.0-py_2.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-he372182_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.14.5-h36ae1b5_2.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.2.0-py38hbf85e49_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/mpi4py-3.0.3-py38h246a051_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/numpy-1.18.1-py38h8854b6b_1.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.1-py_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/tornado-6.0.4-py38h1e0a361_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.14.5-h0935bb2_2.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/h5py-2.10.0-nompi_py38h513d04c_102.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/pyevtk-1.1.2-pyh24bf2e0_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/scipy-1.4.1-py38h18bccfc_3.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/setuptools-46.1.3-py38h32f6830_0.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/hankel-1.0.2-py_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.2.1-py38h2af1d28_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/qt-5.12.5-hd8c4c69_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/schwimmbad-0.3.1-py38h32f6830_0.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/wheel-0.34.2-py_1.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/emcee-3.0.2-pyh5ca1d4c_0.tar.bz2
+https://conda.anaconda.org/conda-forge/noarch/pip-20.1-pyh9f0ad1d_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.12.3-py38hcca6a23_1.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/gstools-1.2.1-py38h950e882_0.tar.bz2
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.2.1-0.tar.bz2

spec-file.txt

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+"""
+Example: Conditioning with Ordinary Kriging
+-------------------------------------------
+
+Here we use ordinary kriging in 1D (for plotting reasons) with 5 given observations/conditions,
+to generate an ensemble of conditioned random fields.
+The estimated mean can be accessed by ``srf.mean``.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+import gstools as gs
+
+# condtions
+cond = np.loadtxt("../data/conditions.txt")
+cond_pos = cond[0]
+cond_val = cond[1]
+gridx = np.linspace(0.0, 15.0, 151)
+
+###############################################################################
+
+# spatial random field class
+model = gs.Gaussian(dim=1, var=0.5, len_scale=2)
+srf = gs.SRF(model)
+srf.set_condition(cond_pos, cond_val, "ordinary")
+
+###############################################################################
+
+fields = []
+for i in range(100):
+    # print(i) if i % 10 == 0 else None
+    fields.append(srf(gridx, seed=i))
+    label = "Conditioned ensemble" if i == 0 else None
+    plt.plot(gridx, fields[i], color="k", alpha=0.1, label=label)
+plt.plot(gridx, np.full_like(gridx, srf.mean), label="estimated mean")
+plt.plot(gridx, np.mean(fields, axis=0), linestyle=":", label="Ensemble mean")
+plt.plot(gridx, srf.krige_field, linestyle="dashed", label="kriged field")
+plt.scatter(cond_pos, cond_val, color="k", zorder=10, label="Conditions")
+plt.legend()
+plt.show()
+plt.savefig("../results/simulations.png")
+
+###############################################################################
+# As you can see, the kriging field coincides with the ensemble mean of the
+# conditioned random fields and the estimated mean is the mean of the far-field.