more spelling fixes

qgis · May 3, 2013 · e0907b0 · e0907b0
1 parent 13e7b33
commit e0907b0
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Showing 57 changed files with 73 additions and 67 deletions.
diff --git a/python/core/qgsdataitem.sip b/python/core/qgsdataitem.sip
@@ -37,7 +37,7 @@ class QgsDataItem : QObject
     // remove and delete child item, signals to browser are emitted
     virtual void deleteChildItem( QgsDataItem * child );
 
-    // remove child item but don't delete it, signals to browser are emited
+    // remove child item but don't delete it, signals to browser are emitted
     // returns pointer to the removed item or null if no such item was found
     virtual QgsDataItem *removeChildItem( QgsDataItem * child ) /TransferBack/;
 

diff --git a/python/core/qgsvectorlayer.sip b/python/core/qgsvectorlayer.sip
@@ -859,7 +859,7 @@ class QgsVectorLayer : QgsMapLayer
 
   signals:
 
-    /** This signal is emited when selection was changed */
+    /** This signal is emitted when selection was changed */
     void selectionChanged();
 
     /** This signal is emitted when modifications has been done on layer */

diff --git a/python/core/raster/qgsrasterdataprovider.sip b/python/core/raster/qgsrasterdataprovider.sip
@@ -199,7 +199,7 @@ class QgsRasterDataProvider : QgsDataProvider, QgsRasterInterface
 
    signals:
     /** Emit a signal to notify of the progress event.
-      * Emited theProgress is in percents (0.0-100.0) */
+      * Emitted theProgress is in percents (0.0-100.0) */
     void progress( int theType, double theProgress, QString theMessage );
     void progressUpdate( int theProgress );
 

diff --git a/python/core/symbology-ng/qgscptcityarchive.sip b/python/core/symbology-ng/qgscptcityarchive.sip
@@ -76,10 +76,10 @@ class QgsCptCityDataItem : QObject
     // refresh - refresh populated item, emit signals to model
     virtual void addChildItem( QgsCptCityDataItem * child /Transfer/, bool refresh = false );
 
-    // remove and delete child item, signals to browser are emited
+    // remove and delete child item, signals to browser are emitted
     virtual void deleteChildItem( QgsCptCityDataItem * child );
 
-    // remove child item but don't delete it, signals to browser are emited
+    // remove child item but don't delete it, signals to browser are emitted
     // returns pointer to the removed item or null if no such item was found
     virtual QgsCptCityDataItem * removeChildItem( QgsCptCityDataItem * child ) /TransferBack/;
 

diff --git a/python/gui/qgisinterface.sip b/python/gui/qgisinterface.sip
@@ -491,7 +491,7 @@ class QgisInterface : QObject
     virtual int messageTimeout() = 0;
 
   signals:
-    /** Emited whenever current (selected) layer changes.
+    /** Emitted whenever current (selected) layer changes.
      *  The pointer to layer can be null if no layer is selected
      */
     void currentLayerChanged( QgsMapLayer * layer );

diff --git a/python/gui/qgsexpressionbuilderwidget.sip b/python/gui/qgsexpressionbuilderwidget.sip
@@ -122,7 +122,7 @@ class QgsExpressionBuilderWidget : QWidget
     void loadAllValues();
 
   signals:
-    /** Emited when the user changes the expression in the widget.
+    /** Emitted when the user changes the expression in the widget.
       * Users of this widget should connect to this signal to decide if to let the user
       * continue.
       * @param isValid Is true if the expression the user has typed is valid.

diff --git a/python/plugins/mapserver_export/mapserverexport.py b/python/plugins/mapserver_export/mapserverexport.py
@@ -185,7 +185,7 @@ def saveMapFile(self):
     settings = QSettings()
     # mapfile name
     settings.setValue("/MapserverExport/mapfileName", QVariant(self.dlg.ui.txtMapFilePath.text()))
-    # map width and heigth
+    # map width and height
     settings.setValue("/MapserverExport/mapWidth", QVariant(self.dlg.ui.txtMapWidth.text()))
     settings.setValue("/MapserverExport/mapHeight", QVariant(self.dlg.ui.txtMapHeight.text()))
     # mapserver url

diff --git a/python/plugins/sextante/otb/description/doc/DimensionalityReduction.html b/python/plugins/sextante/otb/description/doc/DimensionalityReduction.html
@@ -251,7 +251,7 @@ <h5 class="subsubsectionHead"><a
 
      </li>
      <li class="itemize"><span 
-class="ptmb7t-">ICA</span>: Independant Component Analysis.
+class="ptmb7t-">ICA</span>: Independent Component Analysis.
          <ul class="itemize2">
          <li class="itemize"><span 
 class="ptmb7t-">number of iterations :</span>

diff --git a/python/plugins/sextante/otb/description/doc/EdisonMeanShiftSegmentation.html b/python/plugins/sextante/otb/description/doc/EdisonMeanShiftSegmentation.html
@@ -28,7 +28,7 @@ <h5 class="subsubsectionHead"><a
 
 
 regions. The labeled output can be passed to the ColorMapping application to render regions with
-contrasted colours. Please note that this mode loads the whole input image into memory, and as such can
+contrasted colors. Please note that this mode loads the whole input image into memory, and as such can
 not handle large images. <br 
 class="newline" /><br 
 class="newline" />To segment large data, one can use the vector mode. In this case, the output of the application is a

diff --git a/python/plugins/sextante/otb/description/doc/EdisonMeanShiftSegmentationVector.html b/python/plugins/sextante/otb/description/doc/EdisonMeanShiftSegmentationVector.html
@@ -28,7 +28,7 @@ <h5 class="subsubsectionHead"><a
 
 
 regions. The labeled output can be passed to the ColorMapping application to render regions with
-contrasted colours. Please note that this mode loads the whole input image into memory, and as such can
+contrasted colors. Please note that this mode loads the whole input image into memory, and as such can
 not handle large images. <br 
 class="newline" /><br 
 class="newline" />To segment large data, one can use the vector mode. In this case, the output of the application is a

diff --git a/python/plugins/sextante/otb/description/doc/HyperspectralUnmixing.html b/python/plugins/sextante/otb/description/doc/HyperspectralUnmixing.html
@@ -1,4 +1,4 @@
 <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0//ENhttp://www.w3.org/TR/REC-html40/strict.dtd"><html><head><meta name="qrichtext" content="1" /><style type="text/css">p, li { white-space: pre-wrap; }</style></head><body style=" font-family:'Sans Serif'; font-size:9pt; font-weight:400; font-style:normal;"></style></head><body style=" font-family:'Sans Serif'; font-size:9pt; font-weight:400; font-style:normal;"><p align="center" style=" margin-top:16px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:x-large; font-weight:600;"><span style=" font-size:x-large;">Hyperspectral data unmixing</span></p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Brief Description</span></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Estimate abundance maps from an hyperspectral image and a set of endmembers.</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Tags</span></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Hyperspectral</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Long Description</span></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">The application applies a linear unmixing algorithm to an hyperspectral data cube. This method supposes that the mixture between materials in the scene is macroscopic and simulate a linear mixing model of spectra.
 The Linear Mixing Model (LMM) acknowledges that reflectance spectrum associated with each pixel is a linear combination of pure materials in the recovery area, commonly known as endmembers.Endmembers can be estimated using the VertexComponentAnalysis application.
-The application allows to estimate the abundance maps with several algorithms : Unconstrained Least Square (ucls), Fully Constrained Least Square (fcls),Image Space Reconstruction Algorithm (isra) and Non-negative constrained Least Square (ncls) and Minimum Dispertion Constrained Non Negative Matrix Factorization (MDMDNMF).
-</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Parameters</span></p><ul><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[param] Input Image Filename (-in): </span>The hyperspectral data cube to unmix</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[param] Output Image (-out): </span>The output abundance map</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[param] Input endmembers (-ie): </span>The endmembers (estimated pure pixels) to use for unmixing. Must be stored as a multispectral image, where each pixel is interpreted as an endmember</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[choice] Unmixing algorithm (-ua): </span>The algorithm to use for unmixing</p><ul><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] UCLS: </span>Unconstrained Least Square</p><ul></ul></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] FCLS: </span>Fully constrained Least Square</p><ul></ul></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] NCLS: </span>Non-negative constrained Least Square</p><ul></ul></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] ISRA: </span>Image Space Reconstruction Algorithm</p><ul></ul></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] MDMDNMF: </span>Minimum Dispertion Constrained Non Negative Matrix Factorization</p><ul></ul></li></ul></li><br /></ul><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Limitations</span></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">None</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Authors</span></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">OTB-Team</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">See also</span></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">VertexComponentAnalysis</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Example of use</span></p><ul><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><li>Parameters to set value:</li></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><ul><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Input Image Filename: hsi_cube.tif</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Output Image: HyperspectralUnmixing.tif double</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Input endmembers: endmembers.tif</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Unmixing algorithm: ucls</p></li></ul></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><li>Command line to execute:</li></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-family:'Courier New, courier';">otbcli_HyperspectralUnmixing -in hsi_cube.tif -out HyperspectralUnmixing.tif double -ie endmembers.tif -ua ucls</p></ul></body></html>
+The application allows to estimate the abundance maps with several algorithms : Unconstrained Least Square (ucls), Fully Constrained Least Square (fcls),Image Space Reconstruction Algorithm (isra) and Non-negative constrained Least Square (ncls) and Minimum Dispersion Constrained Non Negative Matrix Factorization (MDMDNMF).
+</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Parameters</span></p><ul><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[param] Input Image Filename (-in): </span>The hyperspectral data cube to unmix</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[param] Output Image (-out): </span>The output abundance map</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[param] Input endmembers (-ie): </span>The endmembers (estimated pure pixels) to use for unmixing. Must be stored as a multispectral image, where each pixel is interpreted as an endmember</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[choice] Unmixing algorithm (-ua): </span>The algorithm to use for unmixing</p><ul><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] UCLS: </span>Unconstrained Least Square</p><ul></ul></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] FCLS: </span>Fully constrained Least Square</p><ul></ul></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] NCLS: </span>Non-negative constrained Least Square</p><ul></ul></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] ISRA: </span>Image Space Reconstruction Algorithm</p><ul></ul></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><span style=" font-family:'Courier New, courier'; font-weight:600;"; >[group] MDMDNMF: </span>Minimum Dispersion Constrained Non Negative Matrix Factorization</p><ul></ul></li></ul></li><br /></ul><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Limitations</span></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">None</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Authors</span></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">OTB-Team</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">See also</span></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">VertexComponentAnalysis</p><p style=" margin-top:14px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:large; font-weight:600;"><span style=" font-size:large;">Example of use</span></p><ul><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><li>Parameters to set value:</li></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><ul><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Input Image Filename: hsi_cube.tif</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Output Image: HyperspectralUnmixing.tif double</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Input endmembers: endmembers.tif</p></li><li><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;">Unmixing algorithm: ucls</p></li></ul></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;"><li>Command line to execute:</li></p><p style=" margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-family:'Courier New, courier';">otbcli_HyperspectralUnmixing -in hsi_cube.tif -out HyperspectralUnmixing.tif double -ie endmembers.tif -ua ucls</p></ul></body></html>
diff --git a/python/plugins/sextante/otb/description/doc/MeanShiftSegmentation.html b/python/plugins/sextante/otb/description/doc/MeanShiftSegmentation.html
@@ -28,7 +28,7 @@ <h5 class="subsubsectionHead"><a
 
 
 regions. The labeled output can be passed to the ColorMapping application to render regions with
-contrasted colours. Please note that this mode loads the whole input image into memory, and as such can
+contrasted colors. Please note that this mode loads the whole input image into memory, and as such can
 not handle large images. <br 
 class="newline" /><br 
 class="newline" />To segment large data, one can use the vector mode. In this case, the output of the application is a

diff --git a/python/plugins/sextante/otb/description/doc/MeanShiftSegmentationVector.html b/python/plugins/sextante/otb/description/doc/MeanShiftSegmentationVector.html
@@ -28,7 +28,7 @@ <h5 class="subsubsectionHead"><a
 
 
 regions. The labeled output can be passed to the ColorMapping application to render regions with
-contrasted colours. Please note that this mode loads the whole input image into memory, and as such can
+contrasted colors. Please note that this mode loads the whole input image into memory, and as such can
 not handle large images. <br 
 class="newline" /><br 
 class="newline" />To segment large data, one can use the vector mode. In this case, the output of the application is a