increased version

davidfrantz · Aug 3, 2020 · 11c3845 · 11c3845
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diff --git a/README.md b/README.md
@@ -2,7 +2,7 @@
 
 **Framework for Operational Radiometric Correction for Environmental monitoring**
 
-**Version 3.3.0**
+**Version 3.4.0**
 
 ![FORCE Logo](/images/force.png)
 

diff --git a/docs/source/history/v3.rst b/docs/source/history/v3.rst
@@ -3,6 +3,142 @@
 Version 3
 =========
 
+FORCE v. 3.4.0
+--------------
+
+Release: 19.03.2020
+
+
+* **General changes**
+
+  * FORCE no longer uses the terms white-list, master, and slave.
+    These were replaced (in code and docs) with allow-list, base, and target.
+
+  * Bandnames were added to all output products.
+
+* **Changes for Docker**
+
+  * In Docker, retrieving the user credentials was problematic, i.e. the user/password for ``force-level2-sentinel2`` and ``force-lut-modis``.
+    We have now solved it by adding an environment variable.
+    These two programs will look for an environment variable ``FORCE_CREDENTIALS``, which the Docker user can specify with s.th. like this: 
+
+    .. code-block:: bash
+
+       docker run --env FORCE_CREDENTIALS=/app/credentials fegyi001/force env
+    
+    In this directory, you should place the ``.scihub`` and ``.laads`` files.
+
+    If the environment variable is not set, FORCE will look in the user's home directory (as before).
+    Thus, for non-Docker users, nothing changes (although you can choose the environment variable, too).
+
+    Thanks to Haili Hu and Gergely Padányi-Gulyás for developing this solution.
+
+* **FORCE L2PS**
+
+  * Due to the ban of the term "master", the ``DIR_MASTER`` and ``MASTER_NODATA`` tags have changed to ``DIR_COREG_BASE`` and ``COREG_BASE_NODATA``.
+
+* **FORCE HIGHER LEVEL**
+
+  * added new sub-module to force-higher-level:
+
+    library-completeness LIB. 
+    This submodule takes a feature table (e.g. spectral library used for training a machine learning classifier), and tests each feature vector against the image features.
+    The output is a minimum MAE map, which indicates if your library is complete - or if there are e.g. landcovers that you do not have any samples for (likely your classification/regression will be worse there).
+    It is suggested to not use this sub-module on the native spatial resolution, but on 100m or similar.
+    force-parameter has a new option to generate a LIB parameter file.
+    Thanks to Franz Schug for prototyping this method.
+
+  * in force-higher-level, most sub-modules: 
+
+    Added a new parameter ``OUTPUT_EXPLODE``.
+    If FALSE, multi-band images are written (as before).
+    If TRUE, the output is exploded into single-band images.
+    Note that this can result in an extremely large number of files.
+
+  * in force-higher-level, various sub-modules: 
+
+    Explicitly added the nodata value for output products, which formerly caused strange behaviour when there only was nodata within the processing mask of one block.
+    Thanks to Stefan Ernst for reporting this issue.
+
+  * in force-higher-level, sampling sub-module: 
+
+    The limitation of only having one response variable was lifted.
+    Accordingly, the input table can have more than 3 columns, i.e. 1) X-, 2) Y-coordinates, and 3+) response variables.
+    The output response file will hold all response variables.
+    Some improvements were made w.r.t. performance, i.e. the input table is only read once, and a "we-already-have-sampled-this-coordinate" is used to skip finished samples.
+
+  * in force-higher-level, CSO sub-module: 
+
+    Fixed a critical memory error related to the CSO nodata value.
+
+  * in force-higher-level, machine learning sub-module, random forest classification:
+
+    Random Forest class probabilities can now be output. 
+    The Random Forest classification margin can now be output.
+    Two new parameters were added: ``OUTPUT_RFP`` & ``OUTPUT_RFM``.
+    Thanks to Benjamin Jakimow for suggesting this improvement.
+
+  * in force-higher-level, machine learning sub-module:
+
+    Added a check if all provided models do exist.
+    Thanks to Stefan Ernst for reporting this bug.
+
+
+  * in force-higher-level, TSA sub-module: 
+
+    Added additional spectral indices: Normalized Difference Tillage Index, and Normalized Difference Moisture Index
+    Thanks to Benjamin Jakimow for suggesting this improvement.
+
+  * in force-higher-level, TSA sub-module: 
+
+    Fixed a bug in the outlier detection, which caused some unexpected behaviour when multiple snowy observations were ommitted in the L2 QAI screening
+
+* **FORCE AUX**
+
+  * new program force-synthmix:
+
+    Andreas Rabe has provided a SynthMix program!
+    SynthMix can be used to generate training data for machine learning regression to map sub-pixel fractions of land cover, tree cover etc.
+    SynthMix is a very elegant method to create a proper training dataset, makes it much easier to generate training data for fractional cover, and needs very few input data (as opposed to traditional methods).
+    For details, see. `Okujeni et al. "Support vector regression and synthetically mixed training data for quantifying urban land cover." Remote Sensing of Environment 137 (2013): 184-197. <https://www.sciencedirect.com/science/article/pii/S0034425713002009>`_. 
+    For a ecent example, see `Schug et al. "Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data." Remote Sensing of Environment 246 (2020): 111810 <https://www.sciencedirect.com/science/article/pii/S0034425720301802>`_
+    force-parameter has a new option to generate a SynthMix parameter file.
+
+  * new program force-procmask:
+
+    This program can generate processing masks from cubed, continuous input images, e.g. to generate a mask with all pixels that have NDVI > 0.8
+
+  * new program force-tile-extent:
+
+    This program takes a polygon vector file (e.g. shapefile of a country), and suggests a processing extent for higher-level processing (``X_TILE_RANGE`` & ``Y_TILE_RANGE``)
+    It further gives a recommendation whether you should use a tile allow-list.
+    This list is also generated.
+
+  * new program force-magic-parameters:
+
+    This program lets you define multiple replacement vectors in any FORCE parameterfile.
+    When executing this program, the vectors are combined with each other, and multiple new parameterfiles are generated.
+
+  * in force-train:
+
+    The response file can now have multiple columns, i.e. different variables.
+    A new tag ``RESPONSE_VARIABLE`` is used to select the variable, which should be used for training the model.
+    See :ref:`train-param`.
+
+  * in force-train:
+
+    introduced new parameter ``FEATURE_WEIGHTS``, which allows to use à priori class weights for Random Forest and Support Vector Machine classification.
+    See :ref:`train-param`.
+
+  * in force-cube:
+
+    If a resulting image is completely nodata, it will automatically be removed.
+
+  * in force-mosaic:
+
+    Mosaicking is now performed in parallel.
+
+
 FORCE v. 3.3.0
 --------------
 

diff --git a/docs/source/history/vdev.rst b/docs/source/history/vdev.rst
@@ -8,119 +8,5 @@ FORCE-dev
 
 Master release: TBA
 
-* **General changes**
-
-  * FORCE no longer uses the terms white-list, master, and slave.
-    These were replaced (in code and docs) with allow-list, base, and target.
-
-  * Bandnames were added to all output products.
-
-* **Changes for Docker**
-
-  * In Docker, retrieving the user credentials was problematic, i.e. the user/password for ``force-level2-sentinel2`` and ``force-lut-modis``.
-    We have now solved it by adding an environment variable.
-    These two programs will look for an environment variable ``FORCE_CREDENTIALS``, which the Docker user can specify with s.th. like this: 
-
-    .. code-block:: bash
-
-       docker run --env FORCE_CREDENTIALS=/app/credentials fegyi001/force env
-    
-    In this directory, you should place the ``.scihub`` and ``.laads`` files.
-
-    If the environment variable is not set, FORCE will look in the user's home directory (as before).
-    Thus, for non-Docker users, nothing changes (although you can choose the environment variable, too).
-
-    Thanks to Haili Hu and Gergely Padányi-Gulyás for developing this solution.
-
-* **FORCE L2PS**
-
-  * Due to the ban of the term "master", the ``DIR_MASTER`` and ``MASTER_NODATA`` tags have changed to ``DIR_COREG_BASE`` and ``COREG_BASE_NODATA``.
-
-* **FORCE HIGHER LEVEL**
-
-  * added new sub-module to force-higher-level:
-
-    library-completeness LIB. 
-    This submodule takes a feature table (e.g. spectral library used for training a machine learning classifier), and tests each feature vector against the image features.
-    The output is a minimum MAE map, which indicates if your library is complete - or if there are e.g. landcovers that you do not have any samples for (likely your classification/regression will be worse there).
-    It is suggested to not use this sub-module on the native spatial resolution, but on 100m or similar.
-    force-parameter has a new option to generate a LIB parameter file.
-    Thanks to Franz Schug for prototyping this method.
-
-  * in force-higher-level, most sub-modules: 
-
-    Added a new parameter ``OUTPUT_EXPLODE``.
-    If FALSE, multi-band images are written (as before).
-    If TRUE, the output is exploded into single-band images.
-    Note that this can result in an extremely large number of files.
-
-  * in force-higher-level, various sub-modules: 
-
-    Explicitly added the nodata value for output products, which formerly caused strange behaviour when there only was nodata within the processing mask of one block.
-    Thanks to Stefan Ernst for reporting this issue.
-
-  * in force-higher-level, sampling sub-module: 
-
-    The limitation of only having one response variable was lifted.
-    Accordingly, the input table can have more than 3 columns, i.e. 1) X-, 2) Y-coordinates, and 3+) response variables.
-    The output response file will hold all response variables.
-    Some improvements were made w.r.t. performance, i.e. the input table is only read once, and a "we-already-have-sampled-this-coordinate" is used to skip finished samples.
-
-  * in force-higher-level, CSO sub-module: 
-
-    Fixed a critical memory error related to the CSO nodata value.
-
-  * in force-higher-level, machine learning sub-module, random forest classification:
-
-    Random Forest class probabilities can now be output. 
-    The Random Forest classification margin can now be output.
-    Two new parameters were added: ``OUTPUT_RFP`` & ``OUTPUT_RFM``.
-    Thanks to Benjamin Jakimow for suggesting this improvement.
-
-  * in force-higher-level, TSA sub-module: 
-
-    Added additional spectral indices: Normalized Difference Tillage Index, and Normalized Difference Moisture Index
-    Thanks to Benjamin Jakimow for suggesting this improvement.
-
-* **FORCE AUX**
-
-  * new program force-synthmix:
-
-    Andreas Rabe has provided a SynthMix program!
-    SynthMix can be used to generate training data for machine learning regression to map sub-pixel fractions of land cover, tree cover etc.
-    SynthMix is a very elegant method to create a proper training dataset, makes it much easier to generate training data for fractional cover, and needs very few input data (as opposed to traditional methods).
-    For details, see. `Okujeni et al. "Support vector regression and synthetically mixed training data for quantifying urban land cover." Remote Sensing of Environment 137 (2013): 184-197. <https://www.sciencedirect.com/science/article/pii/S0034425713002009>`_. 
-    For a ecent example, see `Schug et al. "Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data." Remote Sensing of Environment 246 (2020): 111810 <https://www.sciencedirect.com/science/article/pii/S0034425720301802>`_
-    force-parameter has a new option to generate a SynthMix parameter file.
-
-  * new program force-procmask:
-
-    This program can generate processing masks from cubed, continuous input images, e.g. to generate a mask with all pixels that have NDVI > 0.8
-
-  * new program force-tile-extent:
-
-    This program takes a polygon vector file (e.g. shapefile of a country), and suggests a processing extent for higher-level processing (``X_TILE_RANGE`` & ``Y_TILE_RANGE``)
-    It further gives a recommendation whether you should use a tile allow-list.
-    This list is also generated.
-
-  * new program force-magic-parameters:
-
-    This program lets you define multiple replacement vectors in any FORCE parameterfile.
-    When executing this program, the vectors are combined with each other, and multiple new parameterfiles are generated.
-
-  * in force-train:
-
-    The response file can now have multiple columns, i.e. different variables.
-    A new tag ``RESPONSE_VARIABLE`` is used to select the variable, which should be used for training the model.
-    See :ref:`train-param`.
-
-  * in force-train:
-
-    introduced new parameter ``FEATURE_WEIGHTS``, which allows to use à priori class weights for Random Forest and Support Vector Machine classification.
-    See :ref:`train-param`.
-
-  * in force-cube:
-
-    If a resulting image is completely nodata, it will automatically be removed.
-
-
+No dev changes yet.
+Develop and Master are synchronized.
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -5,7 +5,7 @@ FORCE documentation
 
 **FORCE: Framework for Operational Radiometric Correction for Environmental monitoring**
 
-**Version 3.3.0**
+**Version 3.4.0**
 
 `Download from Github <https://github.com/davidfrantz/force>`_.
 

diff --git a/src/cross-level/_version-cl.h b/src/cross-level/_version-cl.h
@@ -32,7 +32,7 @@ Version number
 extern "C" {
 #endif
 
-#define _VERSION_ "3.3.0"
+#define _VERSION_ "3.4.0"
 
 #ifdef __cplusplus
 }