diff --git a/component/message/en.json b/component/message/en.json
index 4c76643..4defbfc 100644
--- a/component/message/en.json
+++ b/component/message/en.json
@@ -41,7 +41,7 @@
"fad": {
"title": "FAD: Forest Area Density",
"description": "This module will conduct the fragmentation analysis at five fixed observation scales. Because forest fragmentation is scale-dependent, fragmentation is reported at five observation scales, which allows different observers to make their own choice about scales and threshold of concern. The change of fragmentation across different observation scales provides additional interesting information. Fragmentation is measured by determining the Forest Area Density (FAD) within a shifting, local neighborhood. It can be measured at pixel or patch level. The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the (mac: right+click or windows: cmd+click to open in new tab) [Fragmentation](https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Fragmentation-FADFOS.pdf) product sheet.",
- "prescision": "Computation prescision",
+ "prescision": "Computation precision",
"no_prescision": "Please set the prescision"
},
"frag": {
@@ -63,7 +63,7 @@
"description": "This module will conduct the Morphological Spatial Pattern Analysis. MSPA analyses shape and connectivity and conducts a segmentation of foreground patches in up to 25 feature classes. The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the (mac: right+click or windows: cmd+click to open in new tab) [Morphology](https://ies-ows.jrc.ec.europa.eu/gtb/GTB/psheets/GTB-Pattern-Morphology.pdf) product sheet.",
"edge_width": "edge width (pixels)",
"transition": "transition",
- "int_ext": "int ext",
+ "int_ext": "intext",
"no_edge_width": "Please provide a positive edge width"
},
@@ -91,16 +91,16 @@
"description": "This module will recode categorical land cover classes."
},
"requirement": [
- "select a classified tiff image. The image must contain at least 2 different classes. 0 will be considered as missing data.",
+ "Provide a byte-formatted input image. The image must contain at least 2 different classes. 0 will be considered as missing data.",
"",
- "Provide a byte image. Recode the classes into the following 2 classes : background (nonforest), foreground (forest). All remaining classes will be considered as missing data.",
- "Provide a byte images. Recode the classes into the 3 dominant land cover classes. All remaining classes will be considered as missing data.",
- "Provide a byte images. Recode the classes into the 4 following classes : background (nonforest), foreground (forest), special background 1 (e.g. water) and special background 2. All remaining classes will be considered as missing data.",
- "Provide a byte images. Recode the classes into the 3 following classes : background (nonforest), foreground (forest), special background 1 (for P23 only). All remaining classes will be considered as missing data."
+ "Provide a byte-formatted input image. Recode the classes into the following 2 classes : background (nonforest), foreground (forest). All remaining classes will be considered as missing data.",
+ "Provide a byte-formatted input image. Recode the classes into the 3 dominant land cover classes. All remaining classes will be considered as missing data.",
+ "Provide a byte-formatted input image. Recode the classes into the following 4 classes: background (nonforest), foreground (forest), special background 1 (optional, e.g. water) and special background 2 (optional). All remaining classes will be considered as missing data.",
+ "Provide a byte-formatted input image. Recode the classes into the 3 following classes : background (nonforest), foreground (forest), special background 1 (for P23 only). All remaining classes will be considered as missing data."
],
"bin": {
- "btn": "Convert the image classes",
- "title": "Select map classes",
+ "btn": "Convert the input image classes",
+ "title": "Select input map classes",
"file_exist": "the file {} already exists, no new bin maps have been created",
"running": "The bin map is being processed",
"finished": "the bin map is ready to be used",
@@ -110,7 +110,8 @@
"no_bin": "Please provide a bin map using the first process tile",
"default": {
"btn": "Download test dataset",
- "msg": "the test files {} and {} have been added to the download folder"
+ "msg": "the test files \"{}\" and \"{}\" have been added to the \"downloads\" folder",
+ "tooltip": "Click the button above to download the test files \"clc3class.tif\" and \"example.tif\" into the folder \"downloads\""
}
},
"gwb": {
diff --git a/component/parameter/convert.py b/component/parameter/convert.py
index 00f2bab..b8a7639 100644
--- a/component/parameter/convert.py
+++ b/component/parameter/convert.py
@@ -7,8 +7,8 @@
'label': [
'background',
'foreground',
- 'special background 1',
- 'special background 2'
+ 'special background 1 (optional)',
+ 'special background 2 (optional)'
],
'io': [
'background',
@@ -31,7 +31,7 @@
'label': [
'Dominant land cover 1 (Agriculture)',
'Dominant land cover 2 (Natural)',
- 'Dominant land cover 3 (developped)'
+ 'Dominant land cover 3 (Developed)'
],
'io': [
'lc_1',
diff --git a/component/parameter/process.py b/component/parameter/process.py
index 90f876c..300f7f3 100644
--- a/component/parameter/process.py
+++ b/component/parameter/process.py
@@ -7,7 +7,7 @@
]
acc_options = [
- {'value':'default', 'text': "stats + image of viewport" },
+ {'value':'default', 'text': "stats + image of viewport (default)" },
{'value':'detailed', 'text': 'stats + images of ID, area, viewport'}
]
@@ -15,7 +15,7 @@
## dist ##
####################
dist_options = [
- {'text': 'Euclidian distance only', 'value': 1},
+ {'text': 'Euclidian distance only (default)', 'value': 1},
{'text': 'Euclidian distance + Hysometric Curve', 'value': 2}
]
@@ -29,7 +29,7 @@
]
prescision = [
- {'text': "float precision (require more disk space)", 'value': 1},
+ {'text': "float precision (default, require more disk space)", 'value': 1},
{'text': 'rounded byte', 'value': 0}
]
@@ -51,8 +51,8 @@
## spa ##
###################
spa_options = [
- {'text': 'Small & linear features (SLF), Coherent', 'value': 2},
- {'text': 'Core, Core-Openings, Margin', 'value': 3},
- {'text': 'Core, Core-Openings, Edge, Perforation, Margin', 'value': 5},
- {'text': 'Core, Core-Openings, Edge, Perforation, Islet, Margin', 'value': 6}
+ {'text': '2: Small & linear features (SLF), Coherent', 'value': 2},
+ {'text': '3: Core, Core-Openings, Margin', 'value': 3},
+ {'text': '5: Core, Core-Openings, Edge, Perforation, Margin', 'value': 5},
+ {'text': '6: Core, Core-Openings, Edge, Perforation, Islet, Margin', 'value': 6}
]
\ No newline at end of file
diff --git a/component/tile/convert_tile.py b/component/tile/convert_tile.py
index a9cb275..7ab86ef 100644
--- a/component/tile/convert_tile.py
+++ b/component/tile/convert_tile.py
@@ -13,8 +13,12 @@ def __init__(self, io, nb_class):
# gather the io
self.io = io
- # create the widgets
+ # create the download layout
self.down_test = sw.Btn(cm.bin.default.btn, icon="mdi-cloud-download-outline", small=True, outlined=True, class_="ma-5")
+ tooltip = sw.Tooltip(widget=self.down_test, tooltip=cm.bin.default.tooltip)
+
+ # create the widgets
+
self.file = sw.FileInput(['.tif', '.tiff'])
self.classes = [v.Select(
label = cp.convert[nb_class]['label'][i],
@@ -39,7 +43,7 @@ def __init__(self, io, nb_class):
super().__init__(
self.io.tile_id,
cm.bin.title,
- inputs = [self.down_test, v.Divider(), requirements, self.file] + self.classes,
+ inputs = [tooltip, v.Divider(), requirements, self.file] + self.classes,
output = self.output,
btn = btn
)
diff --git a/doc/en.rst b/doc/en.rst
index 5b1c974..4594d90 100644
--- a/doc/en.rst
+++ b/doc/en.rst
@@ -1,10 +1,1091 @@
Guidos Workbench
================
+This document provides usage instructions for the image analysis module **GWB** (GuidosToolbox Workbench). **GWB** is a subset of the desktop software package GuidosToolbox (`GTB `_) designed as Jupyter dashboard. More information is available at the **GWB** `homepage `_.
+
+Introduction
+------------
+
+the GuidosToolbox (`GTB `_) was developed as a graphical user interface to morphological spatial pattern analysis of raster data (`Soille and Vogt 2009 `_). The GTB has since been enhanced with numerous modules for analysis of landscape objects, patterns, and networks, and specialized modules for assessing fragmentation and restoration (`Vogt and Riitters 2017 `_). The GTB has gained global acceptance as a free, intuitive, interactive, and generic stand-alone image analysis platform on several popular operating systems. Here we implemented the most popular GTB modules inside the SEPAL platform as a Jupyter dashboard using the `GWB CLI tool `_.
+
+Presentation
+^^^^^^^^^^^^
+
+To launch the app please follow the SEPAL registration steps and then move to the SEPAL apps' dashboard.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/dashboard.png
+ :alt: SEPAL dashboard
+
+The application should launch itself in the About section, allowing to select the tool you want to use.
+
+.. note::
+
+ If this is the first time you use the app, you will actually face the following popup:
+
+ .. image:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/licence.png
+ :alt: licence
+
+ This licence needs to be accepted to use the **GWB** tools. It is reminded in the section :code:`Licence` of the app.
+ If you don't want to accept this Licence, just close the app tab.
+
+General structure
+^^^^^^^^^^^^^^^^^
+
+The application is strucured as followed:
+
+On the left side you will find a navigation drawer that you can open and close using the `hamburger button `_.
+
+.. tip::
+
+ On small devices such as tablet or phones, the navigation drawer will be hidden by default. click on the button and it will overlay the rest of the app
+
+ .. image:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/small_device_without_menu.png
+ :alt: small screen without drawer
+ :width: 40%
+
+ .. image:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/small_device_with_menu.png
+ :alt: small screen with drawer
+ :width: 40%
+
+Each name in the list correspond to a tool of the **GWB** module, they will be prensented individually in the next sections. By clicking on it you will display the panels relative to the function you want to use.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/landing.png
+ :alt: presentation of the structure
+
+.. danger::
+
+ All the tools in this module use a categorical raster as input. This raster need to use discrete integer value to be manipulated. Any raster with continuous values will raise an error.
+
+Modules
+-------
+
+Every module is presented independantely, you can directly jump to the tool you are intersted and this documentation will guide you trough the full process.
+
+ACC
+^^^
+
+This module will conduct the **Accounting** analysis. Accounting will label and calculate the area of all foreground objects (coded with 2 byte). The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Accounting `_ product sheet.
+
+Set up image
+""""""""""""
+
+.. tip::
+
+ You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, 2 files will be added to your :code:`downloads` folder (:code:`example.tif` and :code:`clc3classes.tif`).
+
+ .. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
+ :alt: download tes dataset
+
+
+ Once the file is downloaded follow the normal process using the :code:`downloads/example.tif` file (2 classes)
+
+The first step requires you to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+
+.. warning::
+
+ If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL `_ page of this documentation.
+
+The dropdowns menu will hydrate themselves with the discrete values of your raster. Select each class in your image and place them in one of the following categories:
+
+- background
+- foreground
+- special background 1 (optional)
+- special background 2 (optional)
+
+Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte) and removed from the analysis.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/4_classes.png
+ :alt: upload 4 classes
+
+.. tip::
+
+ for forest analysis you will want to set forest as foreground and all the other classes in background. If you sepcify sepcial background they will be treated separately in the analysis (e.g. water, buildings).
+
+Select parameters
+"""""""""""""""""
+You will need to select parameters for your computation:
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/acc_params.png
+ :alt: acc params
+
+.. note::
+
+ These parameters can be used to perform the default computation:
+
+ - Foreground connectivity: 8
+ - spatial pixel resolution: 25
+ - area thresholds: 200 2000 20000 100000 200000
+ - options: default
+
+Foreground connectivity
+#######################
+
+This set the foreground connectivity of your analysis:
+
+- 8 neigbors (default) will use every pixel in the vincinity (including diagonals)
+- 4 neigbors only use the vertical and horizontal one
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
+ :alt: connectivity image
+ :width: 50%
+
+spatial pixel resolution
+########################
+
+Set the spatial pixel resolution of your image in meters. It is only use for the summary.
+
+area thresholds
+###############
+
+Set up to 5 area thresholds in pixels.
+
+options
+#######
+
+Two computation options are available:
+
+- stats + image of viewport (default)
+- stats + images of ID, area, viewport (detailed)
+
+run analysis
+""""""""""""
+
+Once your parameters are all set you can launch the analysis. The blue rectangle will display you informations about the computation. It will turn to green at the end and display some computation logs.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/acc_results.png
+ :alt: information logs
+
+The final files can be retreived in :code:`module_results/gwb/acc/` folder. it should include:
+
+- :code:`_bin_map.tif`
+- :code:`_bin_map_acc.tif`
+- :code:`_bin_map_acc.csv`
+- :code:`_bin_map_acc.txt`
+
+.. danger::
+
+ If the rectangle become red, read attentively the instruction of the logs. Usually the instance your using is too small to handle the file you want to analyse. If it's the case, close the app, open a bigger instance and run your analysis again.
+
+Here is the result of the computation using the default parameters on the :code:`example.tif` file.
+
+.. figure:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_acc.tif
+ :width: 50%
+ :align: center
+
+
+DIST
+^^^^
+
+This module will conduct the **Euclidean Distance** analysis. Each pixel will show the shortest distance to the foreground (coded with 2 byte) boundary. Pixels inside a foreground object have a positive distance value while background pixels have a negative distance value. The result are spatially explicit maps and tabular summary statistics.
+Details on the methodology and input/output options can be found in the `Distance `_ product sheet.
+
+Set up image
+""""""""""""
+
+.. tip::
+
+ You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, 2 files will be added to your :code:`downloads` folder (:code:`example.tif` and :code:`clc3classes.tif`).
+
+ .. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
+ :alt: download tes dataset
+
+
+ Once the file is downloaded follow the normal process using the :code:`downloads/example.tif` file (2 classes)
+
+The first step requires you to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+
+.. warning::
+
+ If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL `_ page of this documentation.
+
+The dropdowns menu will hydrate themselves with the discrete values of your raster. Select each class in your image and place them in one of the following categories:
+
+- background
+- foreground
+
+Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte) and removed from the analysis.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/2_classes.png
+ :alt: upload 2 classes
+
+.. tip::
+
+ for forest analysis you will want to set forest as foreground and all the other classes in background.
+
+Select parameters
+"""""""""""""""""
+You will need to select parameters for your computation:
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/dist_params.png
+ :alt: dist params
+
+.. note::
+
+ These parameters can be used to perform the default computation:
+
+ - Foreground connectivity: 8
+ - Options: Euclidian Distance only
+
+Foreground connectivity
+#######################
+
+This set the foreground connectivity of your analysis:
+
+- 8 neigbors (default) will use every pixel in the vincinity (including diagonals)
+- 4 neigbors only use the vertical and horizontal one
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
+ :alt: connectivity image
+ :width: 50%
+
+Options
+#######
+
+Two computation options are available:
+
+- compute the Euclidian Distance only
+- compute the Euclidian Distance and the Hysometric Curve
+
+
+run analysis
+""""""""""""
+
+Once your parameters are all set you can launch the analysis. The blue rectangle will display you informations about the computation. It will turn to green at the end and display some computation logs.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/dist_results.png
+ :alt: information logs
+
+The final files can be retreived in :code:`module_results/gwb/dist/` folder. it should include:
+
+- :code:`_bin_map.tif`
+- :code:`_bin_map_dist.tif`
+- :code:`_bin_map_dist.txt`
+- :code:`_bin_map_dist_hist.png`
+- :code:`_bin_map_dist_viewport.tif`
+
+.. danger::
+
+ If the rectangle become red, read attentively the instruction of the logs. Usually the instance you're using is too small to handle the file you want to analyse. If it's the case, close the app, open a bigger instance and run your analysis again.
+
+Here is the result of the computation using the default parameters on the :code:`example.tif` file.
+
+.. image:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_dist_hmc.png
+ :width: 49%
+
+.. image:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_dist.tif
+ :width: 49%
+
+FAD
+^^^
+
+This module will conduct the **fragmentation** analysis at **five fixed observation scales**. Because forest fragmentation is scale-dependent, fragmentation is reported at five observation scales, which allows different observers to make their own choice about scales and threshold of concern. The change of fragmentation across different observation scales provides additional interesting information. Fragmentation is measured by determining the Forest Area Density (**FAD**) within a shifting, local neighborhood. It can be measured at pixel or patch level. The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Fragmentation `_ product sheet.
+
+Set up image
+""""""""""""
+
+.. tip::
+
+ You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, 2 files will be added to your :code:`downloads` folder (:code:`example.tif` and :code:`clc3classes.tif`).
+
+ .. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
+ :alt: download tes dataset
+
+
+ Once the file is downloaded follow the normal process using the :code:`downloads/example.tif` file (2 classes)
+
+The first step requires you to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+
+.. warning::
+
+ If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL `_ page of this documentation.
+
+The dropdowns menu will hydrate themselves with the discrete values of your raster. Select each class in your image and place them in one of the following categories:
+
+- background
+- foreground
+- special background 1 (optional)
+- special background 2 (optional)
+
+Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte) and removed from the analysis.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/4_classes.png
+ :alt: upload 4 classes
+
+.. tip::
+
+ for forest analysis you will want to set forest as foreground and all the other classes in background. If you sepcify sepcial background they will be treated separately in the analysis (e.g. water, buildings)
+
+Select parameters
+"""""""""""""""""
+You will need to select parameters for your computation:
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/fad_params.png
+ :alt: acc params
+
+.. note::
+
+ These parameters can be used to perform the default computation:
+
+ - Foreground connectivity: 8
+ - Computation prescision: float-prescision
+ - Options: per-pixel density, color-coded into 6 fragmentation classes (FAD)
+
+Foreground connectivity
+#######################
+
+This set the foreground connectivity of your analysis:
+
+- 8 neigbors (default) will use every pixel in the vincinity (including diagonals)
+- 4 neigbors only use the vertical and horizontal one
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
+ :alt: connectivity image
+ :width: 50%
+
+Computation prescision
+######################
+
+Set the prescision used to compute you image. Float prescision (default) will give more accurate results that bytes but will also take more Ressource to compute.
+
+Options
+#######
+
+Three computation options are available:
+
+- FAD: per-pixel density, color-coded into 6 fragmentation classes
+- FAD-APP2: average per-patch density, color-coded into 2 classes
+- FAD-APP5: average per-patch density, color-coded into 5 classes
+
+run analysis
+""""""""""""
+
+Once your parameters are all set you can launch the analysis. The blue rectangle will display you information about the computation. It will turn to green at the end and display some computation logs.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/fad_results.png
+ :alt: information logs
+
+The final files can be retreived in :code:`module_results/gwb/fad/` folder. it should include:
+
+- :code:`_bin_map.tif`
+- :code:`_bin_map_fad_.tif`
+- :code:`_bin_map_fad_barplot.png`
+- :code:`_bin_map_fad_mscale.csv`
+- :code:`_bin_map_fad_mscale.tif`
+- :code:`_bin_map_fad_mscale.txt`
+- :code:`_bin_map_fad_mscale.sav`
+
+.. danger::
+
+ If the rectangle become red, read attentively the instruction of the logs. Usually the instance you're using is too small to handle the file you want to analyse. If it's the case, close the app, open a bigger instance and run your analysis again.
+
+Here is the result of the computation using the default parameters on the :code:`example.tif` file.
+
+.. image:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_fad_barplot.png
+ :width: 49%
+
+.. image:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_fad_mscale.tif
+ :width: 49%
+
+FRAG
+^^^^
+
+This module will conduct the **fragmentation** analysis at a **user-selected observation scale**. This module and its option are similar to :ref:`gwb_fad` but allow the user to specify a single (or multiple) specific observation scale. The result are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Fragmentation `_ product sheet.
+
+Set up image
+""""""""""""
+
+.. tip::
+
+ You can use the default dataset to test the module. Click on the :code:`Download test dataset` button on the top of the second panel. By clicking on this button, 2 files will be added to your :code:`downloads` folder (:code:`example.tif` and :code:`clc3classes.tif`).
+
+ .. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/test_dataset.png
+ :alt: download tes dataset
+
+
+ Once the file is downloaded follow the normal process using the :code:`downloads/example.tif` file (2 classes).
+
+The first step requires you to reclassify your image. Using the reclassifying panel, select your image in your SEPAL folder.
+
+.. warning::
+
+ If the image is not in your SEPAL folders but in your local computer consider reading the `exchange file with SEPAL `_ page of this documentation.
+
+The dropdowns menu will hydrate themselves with the discrete values of your raster. Select each class in your image and place them in one of the following categories:
+
+- background
+- foreground
+- special background 1 (optional)
+- special background 2 (optional)
+
+Every class that is not set to a reclassifying category will be considered as "missing data" (0 byte) and removed from the analysis.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/4_classes.png
+ :alt: upload 4 classes
+
+.. tip::
+
+ for forest analysis you will want to set forest as foreground and all the other classes in background. If you specify special background they will be treated separately in the analysis (e.g. water, buildings).
+
.. warning::
- The english documentation of the module have not been set.
+ the second special background is the non-fragmenting background (optional)
+
+Select parameters
+"""""""""""""""""
+
+You will need to select parameters for your computation:
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/frag_params.png
+ :alt: acc params
+
+.. note::
+
+ These parameters can be used to perform the default computation:
+
+ - Foreground connectivity: 8
+ - Spatial pixel resolution: 25
+ - Computation prescision: float-prescision
+ - Windows size: 23
+ - Pptions: average per-patch density, color-coded into 2 classes (FAD-APP2)
+
+Foreground connectivity
+#######################
+
+This set the foreground connectivity of your analysis:
+
+- 8 neigbors (default) will use every pixel in the vincinity (including diagonals)
+- 4 neigbors only use the vertical and horizontal one
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/connectivity.png
+ :alt: connectivity image
+ :width: 50%
+
+spatial pixel resolution
+########################
+
+Set the spatial pixel resolution of your image in meters. Only use for the summary.
+
+window size
+###########
+
+Set up to 10 observation windows size (in pixels).
+
+options
+#######
+
+Three computation options are available:
+
+- FAD: per-pixel density, color-coded into 6 fragmentation classes
+- FAD-APP2: average per-patch density, color-coded into 2 classes
+- FAD-APP5: average per-patch density, color-coded into 5 classes
+
+run analysis
+""""""""""""
+
+Once your parameters are all set you can launch the analysis. The blue rectangle will display you information about the computation. It will turn to green at the end and display some computation logs.
+
+.. figure:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/frag_results.png
+ :alt: information logs
+
+The final files can be retreived in :code:`module_results/gwb/frag/` folder. it should include:
+
+- :code:`_bin_map.tif`
+- :code:`_bin_map_frag_fad-