Merge pull request #11 from luhipi/fix_links

Fix links

CONTRIBUTING.rst

@@ -15,7 +15,7 @@ Types of Contributions
 Report Bugs
 ~~~~~~~~~~~
 
-Report bugs at https://git.gfz-potsdam.de/fernlab/timeseries/issues.
+Report bugs at https://github.com/luhipi/sarvey/issues.
 
 If you are reporting a bug, please include:
 
@@ -26,13 +26,13 @@ If you are reporting a bug, please include:
 Fix Bugs
 ~~~~~~~~
 
-Look through the GitLab issues for bugs. Anything tagged with "bug" and "help
+Look through the Github issues for bugs. Anything tagged with "bug" and "help
 wanted" is open to whoever wants to implement it.
 
 Implement Features
 ~~~~~~~~~~~~~~~~~~
 
-Look through the GitLab issues for features. Anything tagged with "enhancement"
+Look through the Github issues for features. Anything tagged with "enhancement"
 and "help wanted" is open to whoever wants to implement it.
 
 Write Documentation
@@ -45,7 +45,7 @@ articles, and such.
 Submit Feedback
 ~~~~~~~~~~~~~~~
 
-The best way to send feedback is to file an issue at https://git.gfz-potsdam.de/fernlab/timeseries/issues.
+The best way to send feedback is to file an issue at https://github.com/luhipi/sarvey/issues.
 
 If you are proposing a feature:
 
@@ -60,10 +60,10 @@ Commit Changes
 How to
 ~~~~~~
 
-1. Fork the `sarvey` repo on GitLab.
+1. Fork the `sarvey` repo on Github.
 2. Clone your fork locally::
 
-    $ git clone [email protected]:fernlab/timeseries.git
+    $ git clone https://github.com/luhipi/sarvey.git
 
 3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development::
 
@@ -87,13 +87,13 @@ How to
 
    To get flake8 and tox, just pip install them into your virtualenv.
 
-6. Commit your changes and push your branch to GitLab::
+6. Commit your changes and push your branch to Github::
 
     $ git add .
     $ git commit -m "Your detailed description of your changes."
     $ git push origin name-of-your-bugfix-or-feature
 
-7. Submit a merge request through the GitLab website.
+7. Submit a merge request through the Github website.
 
 Sign your commits
 ~~~~~~~~~~~~~~~~~
@@ -150,7 +150,7 @@ Before you submit a pull request, check that it meets these guidelines:
    your new functionality into a function with a docstring, and add the
    feature to the list in README.rst.
 3. The pull request should work for Python 3.6, 3.7, 3.8 and 3.9. Check
-   https://gitlab.projekt.uni-hannover.de/ipi-sar4infra/sarvey/-/merge_requests
+   https://github.com/luhipi/sarvey/pulls
    and make sure that the tests pass for all supported Python versions.
 
 Tips

HISTORY.rst

@@ -13,3 +13,5 @@ History
 * Allow adding user comments in config.json file.
 * Improve documentation.
 * Adapt CI from gitlab to github.
+* Mask mean amplitude to avoid zero division warning in log10.
+* Set logging level to debug for log file.

Makefile

@@ -96,7 +96,7 @@ pytest: clean-test ## Runs pytest with coverage and creates coverage and test re
 		tests
 
 docs: ## generate Sphinx HTML documentation, including API docs
-	rm -f docs/timeseries.rst
+	rm -f docs/sarvey.rst
 	rm -f docs/modules.rst
 	sphinx-apidoc sarvey -o docs/ --private --doc-project 'Python API reference'
 	$(MAKE) -C docs clean

README.rst

@@ -50,15 +50,15 @@ If you use **SARvey** in your research, please cite the following.
 
 1. The paper describing the methodology:
 
-   Piter, A., Haghshenas Haghighi, M., Motagh, M.(2024). Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science. (paper in press).
+   Piter A, Haghshenas Haghighi M, Motagh M (2024). Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 92, 609-627.
 
 2. The software itself. Please specify the version you use:
 
-   Piter, A., Haghshenas Haghighi, M., FERN.Lab, & Motagh, M. (2024). SARvey - survey with SAR [version]. Zenodo. https://doi.org/10.5281/zenodo.12544131
+   Piter A, Haghshenas Haghighi M, FERN.Lab, Motagh M (2024). SARvey - survey with SAR [version]. Zenodo. https://doi.org/10.5281/zenodo.12544131
 
 3. If you use the PUMA method for unwrapping in your research, please cite the following publication as indicated in the license:
 
-   An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. Yuri Boykov and Vladimir Kolmogorov. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), September 2004. `Link to paper <https://ieeexplore.ieee.org/document/1316848>`_.
+   Boykov Y, Kolmogorov V (2004). An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9):1124–1137, DOI 10.1109/TPAMI.2004.60. `Link to paper <https://ieeexplore.ieee.org/document/1316848>`_.
 
 
 Processing overview

docs/index.rst

@@ -7,12 +7,12 @@ SARvey documentation
    :caption: Contents:
 
    readme
-   Source code repository <https://gitlab.projekt.uni-hannover.de/ipi-sar4infra/timeseries>
+   Source code repository <https://github.com/luhipi/sarvey>
    installation
    usage
    preparation
    processing
-   demo_datasets   
+   demo_datasets
    modules
    contributing
    authors

docs/installation.rst

@@ -25,8 +25,8 @@ Using mamba_ (latest version recommended), **SARvey** is installed as follows:
 
    .. code-block:: bash
 
-    git clone [email protected]:ipi-sar4infra/timeseries.git
-    cd timeseries
+    git clone https://github.com/luhipi/sarvey.git
+    cd sarvey
 
 
 2. Create virtual environment for **SARvey** (optional but recommended):
@@ -57,8 +57,8 @@ Using conda_ (latest version recommended), **SARvey** is installed as follows:
 
    .. code-block:: bash
 
-    git clone [email protected]:ipi-sar4infra/timeseries.git
-    cd timeseries
+    git clone https://github.com/luhipi/sarvey.git
+    cd sarvey
 
 
 1. Create virtual environment for **SARvey** (optional but recommended):
@@ -117,8 +117,8 @@ Using conda_ (latest version recommended), SARvey is installed as follows:
     .. code-block:: bash
 
         cd ~/software/sarvey
-        git clone [email protected]:ipi-sar4infra/timeseries.git
-        cd timeseries
+        git clone https://github.com/luhipi/sarvey.git
+        cd sarvey
 
    2.2 Open `tests/CI_docker/context/environment_sarvey.yml` in an editor of your choice and comment out the lines `isce2` and `gcc_linux-64`. Alternatively, you can run the following commands.
 
@@ -129,7 +129,7 @@ Using conda_ (latest version recommended), SARvey is installed as follows:
 
     Note: As of the time of creation of this document, `isce2` for MacOS ARM64 is not available in Conda repositories. Therefore, it is skipped, but it should not cause any problems for running SARvey. Also, `gcc_linux-64` is not required on ARM64.
 
-   2.3 Install Timeseries using the same environment that you used to install MiaplPy.
+   2.3 Install SARvey using the same environment that you used to install MiaplPy.
 
     .. code-block:: bash
 
@@ -145,9 +145,9 @@ Using conda_ (latest version recommended), SARvey is installed as follows:
 
         echo 'export miaplpy_path=~/software/sarvey/MiaplPy/src/' > ~/source_sarvey.sh
         echo 'export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}$miaplpy_path' >> ~/source_sarvey.sh
-        echo 'export timeseries_path=~/software/sarvey/timeseries' >> ~/source_sarvey.sh
-        echo 'export PATH=${PATH}:$timeseries_path:$timeseries_path/sarvey' >> ~/source_sarvey.sh
-        echo 'export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}:$timeseries_path' >> ~/source_sarvey.sh
+        echo 'export sarvey_path=~/software/sarvey/sarvey' >> ~/source_sarvey.sh
+        echo 'export PATH=${PATH}:sarvey_path:$sarvey_path/sarvey' >> ~/source_sarvey.sh
+        echo 'export PYTHONPATH=${PYTHONPATH:+$PYTHONPATH:}:$sarvey_path' >> ~/source_sarvey.sh
 
 4. **Test the installation**
 
@@ -174,7 +174,7 @@ On Windows, SARvey is tested on Windows Subsystem for Linux (WSL_) version 2. Pl
 
 .. note::
 
-    Timeseries has been tested with Python 3.6+., i.e., should be fully compatible to all Python versions from 3.6 onwards.
+    SARvey has been tested with Python 3.6+., i.e., should be fully compatible to all Python versions from 3.6 onwards.
 
 
 .. _pip: https://pip.pypa.io

docs/processing.rst

@@ -7,7 +7,7 @@ Multitemporal InSAR processing workflow
 The `sarvey` command line interface executes the multitemporal InSAR processing workflow.
 The workflow is described in the paper
 
-    Piter, A., Haghshenas Haghighi, M., Motagh, M.(2024). An in-depth study on Sentinel-1 InSAR for transport infrastructure monitoring. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science. (paper currently under review).
+    Piter A, Haghshenas Haghighi M, Motagh M (2024). Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 92, 609-627.
 
 All processing steps are described in detail in the following sections.
 Two processing strategies are provided with either one- or two-step unwrapping.
@@ -237,7 +237,7 @@ However, the step 3 has to be executed as the second-order points are selected d
     The estimation of the APS takes place in time-domain and not interferogram-domain to reduce the computational time.
     The phase contributions are removed from the first-order points which were selected for atmospheric filtering.
     Their residual time series contains atmospheric phase contributions and noise.
-    As the APS is assumed to be spatially correlated, the residuals of all points are spatially filtered (**filtering:interpolation_method**) independently for each time step.
+    As the APS is assumed to be spatially correlated, the residuals of all points are spatially filtered e.g. with Kriging (Müller et al. 2022) or simple polynomial interpolation(**filtering:interpolation_method**) independently for each time step.
     After filtering, the estimated APS is interpolated to the location of the second-order points.
 
 - Output of this step
@@ -325,7 +325,7 @@ Since the densification step is not performed, you should reduce the coherence t
 Literature
 ----------
 
-* Piter, A., Haghshenas Haghighi, M., Motagh, M.(2024). Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science. (paper in press).
+* Piter A, Haghshenas Haghighi M, Motagh M (2024). Challenges and Opportunities of Sentinel-1 InSAR for Transport Infrastructure Monitoring. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 92, 609-627.
 
 * Zhao F, Mallorqui JJ (2019). A Temporal Phase Coherence Estimation Algorithm and Its Application on DInSAR Pixel Selection. IEEE Transactions on Geoscience and Remote Sensing 57(11):8350–8361, DOI 10.1109/TGRS.2019.2920536
 
@@ -341,4 +341,6 @@ Literature
 
 * Van Leijen FJ (2014). Persistent scatterer interferometry based on geodetic estimation theory. PhD thesis
 
-* Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9):1124–1137, DOI 10.1109/TPAMI.2004.60
+* Boykov Y, Kolmogorov V (2004). An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9):1124–1137, DOI 10.1109/TPAMI.2004.60
+
+* Müller S, Schüler L, Zech A, Heße F (2022). GSTools v1.3: a toolbox for geostatistical modelling in Python. Geoscientific Model Development, 15, 3161-3182.

setup.py

@@ -67,8 +67,8 @@
     author_email='[email protected]',
     python_requires='>=3.7',
     classifiers=[
-        'Development Status :: 2 - Pre-Alpha',
-        'Intended Audience :: Developers',
+        'Development Status :: Release Candidate',
+        'Intended Audience :: Researchers',
         'None',
         'Natural Language :: English',
         'Programming Language :: Python :: 3',
@@ -103,7 +103,7 @@
     setup_requires=req_setup,
     test_suite='tests',
     tests_require=req_test,
-    url='https://gitlab.projekt.uni-hannover.de/ipi-sar4infra/sarvey/',
+    url='https://github.com/luhipi/sarvey',
     version=version['__version__'],
     zip_safe=False,
 )