Merge remote-tracking branch 'upstream/master' into Issue_8725-OGR

qgis · Nov 14, 2013 · dc078b8 · dc078b8
2 parents c26fecf + af73f30
commit dc078b8
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diff --git a/cmake_templates/qgsconfig.h.in b/cmake_templates/qgsconfig.h.in
@@ -32,6 +32,9 @@
 //used by Mac to find system Qt plugins when bundle is run from build directory
 #define QTPLUGINSDIR "${QT_PLUGINS_DIR}"
 
+//used by Mac to find system Open Scene Graph plugins when bundle is run from build directory
+#define OSG_PLUGINS_PATH "${OSG_PLUGINS_PATH}"
+
 #cmakedefine USING_NMAKE
 
 #cmakedefine HAVE_POSTGRESQL

diff --git a/doc/TRANSLATORS b/doc/TRANSLATORS
diff --git a/i18n/qgis_hi.ts b/i18n/qgis_hi.ts
diff --git a/python/gui/qgisinterface.sip b/python/gui/qgisinterface.sip
@@ -408,6 +408,7 @@ class QgisInterface : QObject
     virtual QAction *actionSelectFreehand() = 0;
     virtual QAction *actionSelectRadius() = 0;
     virtual QAction *actionIdentify() = 0;
+    virtual QAction *actionFeatureAction() = 0;
     virtual QAction *actionMeasure() = 0;
     virtual QAction *actionMeasureArea() = 0;
     virtual QAction *actionZoomFullExtent() = 0;

diff --git a/python/plugins/processing/gui/CrsSelectionPanel.py b/python/plugins/processing/gui/CrsSelectionPanel.py
@@ -27,13 +27,13 @@
 
 from PyQt4 import QtGui, QtCore
 from processing.gui.CrsSelectionDialog import CrsSelectionDialog
-
+from qgis.core import QgsCoordinateReferenceSystem
 
 class CrsSelectionPanel(QtGui.QWidget):
 
     def __init__(self, default):
         super(CrsSelectionPanel, self).__init__(None)
-        self.authid = default
+        self.authid = QgsCoordinateReferenceSystem(default).authid()
         self.horizontalLayout = QtGui.QHBoxLayout(self)
         self.horizontalLayout.setSpacing(2)
         self.horizontalLayout.setMargin(0)

diff --git a/python/plugins/processing/r/scripts/Advanced_Raster_histogram.rsx b/python/plugins/processing/r/scripts/Advanced_Raster_histogram.rsx
diff --git a/python/plugins/processing/r/scripts/Advanced_raster_histogram.rsx.help b/python/plugins/processing/r/scripts/Advanced_raster_histogram.rsx.help
@@ -0,0 +1,22 @@
+(dp0
+S'ALG_CREATOR'
+p1
+V
+p2
+sS'ALG_DESC'
+p3
+VThis algorithm generates a histogram or a density plot for the given raster. NOTE that you should not use this algorithm to process large rasters.\u000a\u000aR dependencies: rpanel, rasterVis. If you are using Linux you need to install "tcktk" and "BWidget" from your package master.
+p4
+sS'Dens_or_Hist'
+p5
+VUse 'hist' to produce histogram of the raster values (separate plots for each band) and 'dens' if you want to create a density plot (single plot for all bands).
+p6
+sS'RPLOTS'
+p7
+VRaster histogram.
+p8
+sS'Layer'
+p9
+VA single- or multi-band raster.
+p10
+s.
diff --git a/python/plugins/processing/r/scripts/Characteristic_hull_method.rsx b/python/plugins/processing/r/scripts/Characteristic_hull_method.rsx
@@ -0,0 +1,8 @@
+##Home Range Analysis=group
+##Layer=vector
+##Field=Field Layer
+##Home_ranges=Output vector
+library(adehabitatHR)
+library(deldir)
+res <- CharHull(Layer[,Field])
+Home_ranges<-getverticeshr(res)
diff --git a/python/plugins/processing/r/scripts/Characteristic_hull_method.rsx.help b/python/plugins/processing/r/scripts/Characteristic_hull_method.rsx.help
@@ -0,0 +1,26 @@
+(dp0
+S'ALG_DESC'
+p1
+VThis  script computes the Characteristic Hull method that relies on the calculation of the Delaunay triangulation of the set of relocations. Then, the triangles are ordered according to their area (and not their perimeter). The smallest triangles correspond to the areas the most intensively used by the animals. It is then possible to derive the home range estimated for a given percentage level.\u000a\u000aR depencies: library "adehabitatHR" and "deldir".\u000a
+p2
+sS'Home_ranges'
+p3
+VThe home-range contours.
+p4
+sS'ALG_CREATOR'
+p5
+VFilipe S. Dias, filipesdias(at)gmail.com
+p6
+sS'Layer'
+p7
+VA layer containing the relocations of one or more animals
+p8
+sS'Field'
+p9
+VThe field containing the unique indentifer for each animal (type "string").
+p10
+sS'ALG_HELP_CREATOR'
+p11
+VFilipe S. Dias, filipesdias(at)gmail.com
+p12
+s.
diff --git a/python/plugins/processing/r/scripts/Compute_Ripley-Rasson_spatial_domain.rsx b/python/plugins/processing/r/scripts/Compute_Ripley-Rasson_spatial_domain.rsx
diff --git a/python/plugins/processing/r/scripts/Create_random_sampling_grid.rsx b/python/plugins/processing/r/scripts/Create_random_sampling_grid.rsx
diff --git a/python/plugins/processing/r/scripts/Create_regular_sampling_grid.rsx b/python/plugins/processing/r/scripts/Create_regular_sampling_grid.rsx
diff --git a/python/plugins/processing/r/scripts/Dotplot.rsx b/python/plugins/processing/r/scripts/Dotplot.rsx
@@ -0,0 +1,4 @@
+##Vector processing=group
+##Layer = raster
+##showplots
+hist(as.matrix(Layer),main="Histogram",xlab="Layer")
diff --git a/python/plugins/processing/r/scripts/Dotplot.rsx.help b/python/plugins/processing/r/scripts/Dotplot.rsx.help
@@ -0,0 +1,22 @@
+(dp0
+S'ALG_CREATOR'
+p1
+VFilipe S. Dias
+p2
+sS'Field'
+p3
+VA numeric field.
+p4
+sS'ALG_DESC'
+p5
+VThis tool creates a dotplot of the input numeric field using the function dotchart().\u000a
+p6
+sS'Layer'
+p7
+VA vector layer with a numeric field.
+p8
+sS'ALG_HELP_CREATOR'
+p9
+VFilipe S. Dias
+p10
+s.
diff --git a/python/plugins/processing/r/scripts/F_function.rsx b/python/plugins/processing/r/scripts/F_function.rsx
@@ -0,0 +1,8 @@
+##Point pattern analysis=group
+##Layer=vector
+##Nsim=number 10
+##showplots
+library("maptools")
+library("spatstat")
+ppp=as(as(Layer, "SpatialPoints"),"ppp")
+plot(envelope(ppp, Fest, nsim=Nsim))
diff --git a/python/plugins/processing/r/scripts/F_function.rsx.help b/python/plugins/processing/r/scripts/F_function.rsx.help
@@ -0,0 +1,26 @@
+(dp0
+S'ALG_DESC'
+p1
+VThis R script computes simulation envelopes of the F(r) - empty space function.\u000a\u000aThe empty space function (also called the \u201cspherical contact distribution\u201d or the \u201cpoint-to-nearest-event\u201d distribution) of a stationary point process X is the cumulative distribution function F of the distance from a fixed point in space to the nearest point of X. An estimate of F derived from a spatial point pattern dataset can be used in exploratory data analysis and formal inference about the pattern . In exploratory analyses, the estimate of F is a useful statistic summarising the sizes of gaps in the pattern. For inferential purposes, the estimate of F is usually compared to the true value of F for a completely random (Poisson) point process.\u000a\u000aR dependencies: library "maptools" and "spatstat"
+p2
+sS'ALG_CREATOR'
+p3
+VVictor Olaya - volaya(at)gmail.com
+p4
+sS'Layer'
+p5
+VA vector containg a point pattern.
+p6
+sS'Nsim'
+p7
+VNumber of simulated point patterns to be generated when computing the envelopes.\u000a\u000a
+p8
+sS'RPLOTS'
+p9
+VPlot with the simulation envelopes.
+p10
+sS'ALG_HELP_CREATOR'
+p11
+VFilipe S. Dias - filipesdias(at)gmail.com
+p12
+s.
diff --git a/python/plugins/processing/r/scripts/F_function_-_distance_from_a_point_to_nearest_event.rsx b/python/plugins/processing/r/scripts/F_function_-_distance_from_a_point_to_nearest_event.rsx
diff --git a/python/plugins/processing/r/scripts/Field_dotplot.rsx b/python/plugins/processing/r/scripts/Field_dotplot.rsx
diff --git a/python/plugins/processing/r/scripts/Field_histogram.rsx b/python/plugins/processing/r/scripts/Field_histogram.rsx
diff --git a/python/plugins/processing/r/scripts/Field_summary_statistics.rsx b/python/plugins/processing/r/scripts/Field_summary_statistics.rsx
diff --git a/python/plugins/processing/r/scripts/Field_table_of_counts.rsx b/python/plugins/processing/r/scripts/Field_table_of_counts.rsx
diff --git a/python/plugins/processing/r/scripts/Frequency_table.rsx b/python/plugins/processing/r/scripts/Frequency_table.rsx
@@ -0,0 +1,4 @@
+##Basic statistics=group
+##Layer=vector
+##Field=Field Layer
+>table(Layer[[Field]])
diff --git a/python/plugins/processing/r/scripts/Frequency_table.rsx.help b/python/plugins/processing/r/scripts/Frequency_table.rsx.help
@@ -0,0 +1,26 @@
+(dp0
+S'ALG_DESC'
+p1
+VThis tool builds a frequency table using the table() function.
+p2
+sS'R_CONSOLE_OUTPUT'
+p3
+VFrequency table.
+p4
+sS'ALG_CREATOR'
+p5
+VFilipe S. Dias, filipesdias(at)gmail.com 
+p6
+sS'Layer'
+p7
+VA vector layer with a numeric or string field.
+p8
+sS'Field'
+p9
+VA string or numeric field.
+p10
+sS'ALG_HELP_CREATOR'
+p11
+VFilipe S. Dias, filipesdias(at)gmail.com  
+p12
+s.
diff --git a/python/plugins/processing/r/scripts/G_function.rsx b/python/plugins/processing/r/scripts/G_function.rsx
@@ -0,0 +1,8 @@
+##Point pattern analysis=group
+##Layer=vector
+##Nsim=number 10
+##showplots
+library("maptools")
+library("spatstat")
+ppp=as(as(Layer, "SpatialPoints"),"ppp")
+plot(envelope(ppp, Gest, nsim=Nsim))
diff --git a/python/plugins/processing/r/scripts/G_function.rsx.help b/python/plugins/processing/r/scripts/G_function.rsx.help
@@ -0,0 +1,26 @@
+(dp0
+S'ALG_DESC'
+p1
+VThis R script computes simulation envelopes of the G(r) - nearest neighbour distance distribution function.\u000a\u000aThe nearest neighbour distance distribution function (also called the \u201cevent-to-event\u201d or \u201cinter-event\u201d distribution) of a point process X is the cumulative distribution function G of the distance from a typical random point of X to the nearest other point of X. An estimate of G derived from a spatial point pattern dataset can be used in exploratory data analysis and formal inference about the pattern. In exploratory analyses, the estimate of G is a useful statistic summarising one aspect of the \u201cclustering\u201d of points. For inferential purposes, the estimate of G is usually compared to the true value of G for a completely random (Poisson) point process, which is where lambda is the intensity (expected number of points per unit area). Deviations between the empirical and theoretical G curves may suggest spatial clustering or spatial regularity.\u000a\u000aR dependencies: library "maptools" and "spatstat"
+p2
+sS'ALG_CREATOR'
+p3
+VVictor Olaya, volayaf(at)gmail.com
+p4
+sS'Layer'
+p5
+VA point pattern process.
+p6
+sS'Nsim'
+p7
+VNumber of simulated point patterns to be generated when computing the envelopes.
+p8
+sS'RPLOTS'
+p9
+VPlot with the simulation envelopes.
+p10
+sS'ALG_HELP_CREATOR'
+p11
+VFilipe S. Dias, filipesdias(at)gmail.com
+p12
+s.
diff --git a/python/plugins/processing/r/scripts/G_function_-_distance_to_nearest_event.rsx b/python/plugins/processing/r/scripts/G_function_-_distance_to_nearest_event.rsx
diff --git a/python/plugins/processing/r/scripts/Histogram.rsx b/python/plugins/processing/r/scripts/Histogram.rsx
@@ -0,0 +1,5 @@
+##Vector processing=group
+##showplots
+##Layer=vector
+##Field=Field Layer
+hist(Layer[[Field]],main=paste("Histogram of",Field),xlab=paste(Field))
diff --git a/python/plugins/processing/r/scripts/Histogram.rsx.help b/python/plugins/processing/r/scripts/Histogram.rsx.help
@@ -0,0 +1,14 @@
+(dp0
+S'Field'
+p1
+VA nuneric field.
+p2
+sS'ALG_DESC'
+p3
+VThis tool creates a histogram of the input numeric field using the hist() function.
+p4
+sS'Layer'
+p5
+VA vector layer with a numeric field.
+p6
+s.
diff --git a/python/plugins/processing/r/scripts/K_function_-_Ripley_K.rsx b/python/plugins/processing/r/scripts/K_function_-_Ripley_K.rsx
diff --git a/python/plugins/processing/r/scripts/Kernel_h_ref.rsx b/python/plugins/processing/r/scripts/Kernel_h_ref.rsx
@@ -0,0 +1,15 @@
+##Home Range Analysis=group
+##Layer=vector
+##Field=Field Layer
+##Grid=number 10
+##Percentage=number 10
+##Home_ranges=Output vector
+##Folder=folder
+library(adehabitatHR)
+Layer[,Field]->relocs
+kud <- kernelUD(relocs, grid=,Grid, h="href")
+names(kud)->Names
+for(i in 1:length(Names)){
+writeGDAL(kud[[i]],paste(paste(Folder,"/",sep=""),paste(Names[i],".tiff",sep=""), sep=""),drivername="GTiff")
+}
+Home_ranges<- getverticeshr(kud,percent=Percentage)
diff --git a/python/plugins/processing/r/scripts/Kernel_h_ref.rsx.help b/python/plugins/processing/r/scripts/Kernel_h_ref.rsx.help
@@ -0,0 +1,38 @@
+(dp0
+S'ALG_DESC'
+p1
+VThis algorithm computes the home range of one or more animals using a kernel density estimator and it uses the ad-hoc method to estimate the "h" parameter (href).\u000a\u000aR depencies: library "adehabitatHR" 
+p2
+sS'Home_ranges'
+p3
+VA vector containing the home ranges corresponding to the smallest area on which the probability of relocating an animal is equal to value chosen for the "Percentage" parameter.\u000a
+p4
+sS'ALG_CREATOR'
+p5
+VFilipe S. Dias, filipesdias(at)gmail.com \u000a \u000a
+p6
+sS'Layer'
+p7
+VA vector containing the relocations of one or more animails.
+p8
+sS'Field'
+p9
+VThe field that contains the unique identifier (type "string") for each animal.
+p10
+sS'Grid'
+p11
+VThe size of the grid (number of cells) on which the utilization distribution is calculated by the kernel function.
+p12
+sS'ALG_HELP_CREATOR'
+p13
+VFilipe S. Dias, filipesdias(at)gmail.com 
+p14
+sS'Folder'
+p15
+VThe ouput folder where the rasters containing the utilization distributions generated for each animal by the kernel funciton will be sent.
+p16
+sS'Percentage'
+p17
+VA single value giving the percentage level for home-range estimation. \u000a\u000aFor example, Percentage= 95  corresponds to the smallest area on which the probability to relocate the animal is equal to 0,95.
+p18
+s.
diff --git a/python/plugins/processing/r/scripts/Kolmogorov-Smirnov_normality_test.rsx b/python/plugins/processing/r/scripts/Kolmogorov-Smirnov_normality_test.rsx