--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pca_raster.xml	Mon Jan 09 13:55:34 2023 +0000
@@ -0,0 +1,120 @@
+<tool id="srs_pca" name="Compute a PCA raster" version="@VERSION@" profile = "20.01">
+    <description>from remote sensing data</description>
+    <macros>
+        <import>macro.xml</import>
+    </macros>
+    <expand macro="SRS_requirements"/>
+    <command detect_errors="exit_code"><![CDATA[ 
+        #import re 
+        #if $method.origin == 'envi_bil': 
+          #set input_raster = $method.input_raster
+          #set input_raster_identifier = re.sub('[^\s\w\-]', '_', str($input_raster.element_identifier)) 
+          #set input_header = $method.input_header
+          #set input_header_identifier = re.sub('[^\s\w\-]+[^.hdr]', '_', str($input_header.element_identifier)) 
+          cp '${input_raster}' '${input_raster_identifier}' &&
+          cp '${input_header}' '${input_header_identifier}' &&
+        #end if
+        Rscript
+            '$__tool_directory__/pca_raster.r'
+             #if $method.origin == 'envi_bil':
+              '$input_raster_identifier' 
+              '$input_header_identifier'
+              ''
+            #else:
+              ''
+              ''
+              '$method.input'
+            #end if
+            '$type'
+            '$__tool_directory__/functions.r'
+            '$output_raster'
+            '$output_pca'
+            '$plots'
+        ]]>
+    </command>
+    <inputs>
+        <conditional name="method">
+            <param name="origin" type="select" label="In which format are your data ?">
+                <option value="zipper">The data you are using are in a zip folder Reflectance</option>
+                <option value="envi_bil">Your already have the files ENVI BIL</option>
+            </param>
+            <when value="zipper">
+                <param name="input" type="data" format="zip" multiple="true" label="Input data"/>
+            </when>
+            <when value="envi_bil">
+                <param name="input_raster" type="data" format="bil" label="Input raster"/>
+                <param name="input_header" type="data" format="hdr" label="Input raster header"/>
+            </when>
+        </conditional>
+        <param name="type" type="select" label="Do you want to do a PCA or a SPCA ?" display="radio" help="If you choose PCA there is no rescaling of the data as oppposed as if you choose SPCA">
+            <option value="SPCA">SPCA</option>
+            <option value="PCA">PCA</option>
+        </param>
+    </inputs>
+    <outputs>
+        <collection type="list" name="output_raster" label="PCA raster">
+            <discover_datasets pattern="(?P&lt;designation&gt;.+)" directory="RESULTS/" visible="false" recurse="true" format="bil,hdr"/>
+        </collection>
+        <data name="output_pca" from_work_dir="PCA.tabular" format="tabular" label="PCA tabular"/>
+        <collection type="list" name="plots" label="PCA plot">
+            <discover_datasets pattern="(?P&lt;designation&gt;.+)\.png" visible="false" format="png"/>
+        </collection>
+    </outputs>
+    <tests>
+        <test>
+            <param name="origin" value="envi_bil"/>
+            <param name="input_raster" value="S2A_Subset"/>
+            <param name="input_header" value="S2A_Subset.hdr"/>
+            <param name="type" value="PCA"/>
+            <output_collection name="output_raster" type="list" count="5"/>
+            <output name="output_pca">
+                <assert_contents>
+                    <has_n_lines n="960435"/>
+                </assert_contents>
+            </output>
+            <output_collection name="plots" type="list" count="1"/>
+        </test>
+    </tests>
+    <help><![CDATA[
+========================================================================
+Process satellite remote sensing data to produce biodiversity indicators
+========================================================================
+
+
+**What it does**
+
+Féret and Asner (2014) developed a method for **tropical forest** diversity mapping based on very high spatial resolution airborne imaging spectroscopy.
+
+The goal of this tool using the package biodivMapR is to produce a Principal Components Analysis (PCA) based on (optical) images. Principal component analysis prepares for feature extraction in order to reduce noise in the image. It is performed on a random subset of the image to ensure computational efficiency for large images.
+
+**Input description**
+
+It expects an image file as input, with a specific data format. ENVI HDR image with BIL interleave required.
+The image is an ENVI raster including :
+
+- A binary file (which has no extension here).
+
+- A header file (with .hdr extension).
+
+The header file is a text file including all necessary metadata which can be read with a text editor. It includes image dimensions, projection, and the name and central wavelength for each spectral band.
+
+In order to get such input we advise to use the tool preprocessing sentinel 2 data. 
+
++--------------+----------+
+|      BIL     | ENVI HDR |
++==============+==========+
+| raster stack | Metadata |
++--------------+----------+
+|      ...     |    ...   |
++--------------+----------+
+
+**Output**
+
+- One tabular with 3 columns : longitude, latitude and pca.
+
+- One png graph.
+
+- One  ENVI BIL files with the one file for the bil raster and one file for the hdr header.
+    ]]>    </help>
+        <expand macro="SRS_BDMRref"/>
+</tool>