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

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ahuarte47 committed Nov 14, 2013
2 parents c26fecf + af73f30 commit dc078b8122c21775a2bbfeabe2c1fb5db8b98b0c
Showing with 4,777 additions and 2,775 deletions.
  1. +3 −0 cmake_templates/qgsconfig.h.in
  2. +56 −56 doc/TRANSLATORS
  3. +3,774 −2,588 i18n/qgis_hi.ts
  4. +1 −0 python/gui/qgisinterface.sip
  5. +2 −2 python/plugins/processing/gui/CrsSelectionPanel.py
  6. +0 −8 python/plugins/processing/r/scripts/Advanced_Raster_histogram.rsx
  7. +22 −0 python/plugins/processing/r/scripts/Advanced_raster_histogram.rsx.help
  8. +8 −0 python/plugins/processing/r/scripts/Characteristic_hull_method.rsx
  9. +26 −0 python/plugins/processing/r/scripts/Characteristic_hull_method.rsx.help
  10. +0 −9 python/plugins/processing/r/scripts/Compute_Ripley-Rasson_spatial_domain.rsx
  11. +0 −6 python/plugins/processing/r/scripts/Create_random_sampling_grid.rsx
  12. +0 −6 python/plugins/processing/r/scripts/Create_regular_sampling_grid.rsx
  13. +4 −0 python/plugins/processing/r/scripts/Dotplot.rsx
  14. +22 −0 python/plugins/processing/r/scripts/Dotplot.rsx.help
  15. +8 −0 python/plugins/processing/r/scripts/F_function.rsx
  16. +26 −0 python/plugins/processing/r/scripts/F_function.rsx.help
  17. +0 −7 python/plugins/processing/r/scripts/F_function_-_distance_from_a_point_to_nearest_event.rsx
  18. +0 −5 python/plugins/processing/r/scripts/Field_dotplot.rsx
  19. +0 −5 python/plugins/processing/r/scripts/Field_histogram.rsx
  20. +0 −14 python/plugins/processing/r/scripts/Field_summary_statistics.rsx
  21. +0 −4 python/plugins/processing/r/scripts/Field_table_of_counts.rsx
  22. +4 −0 python/plugins/processing/r/scripts/Frequency_table.rsx
  23. +26 −0 python/plugins/processing/r/scripts/Frequency_table.rsx.help
  24. +8 −0 python/plugins/processing/r/scripts/G_function.rsx
  25. +26 −0 python/plugins/processing/r/scripts/G_function.rsx.help
  26. +0 −7 python/plugins/processing/r/scripts/G_function_-_distance_to_nearest_event.rsx
  27. +5 −0 python/plugins/processing/r/scripts/Histogram.rsx
  28. +14 −0 python/plugins/processing/r/scripts/Histogram.rsx.help
  29. +0 −7 python/plugins/processing/r/scripts/K_function_-_Ripley_K.rsx
  30. +15 −0 python/plugins/processing/r/scripts/Kernel_h_ref.rsx
  31. +38 −0 python/plugins/processing/r/scripts/Kernel_h_ref.rsx.help
  32. +0 −5 python/plugins/processing/r/scripts/Kolmogorov-Smirnov_normality_test.rsx
  33. +5 −0 python/plugins/processing/r/scripts/Kolmogrov-Smirnov_test.rsx
  34. +20 −0 python/plugins/processing/r/scripts/Kolmogrov-Smirnov_test.rsx.help
  35. +7 −0 python/plugins/processing/r/scripts/Minimum_convex_polygon.rsx
  36. +36 −0 python/plugins/processing/r/scripts/Minimum_convex_polygon.rsx.help
  37. +12 −0 python/plugins/processing/r/scripts/Monte-Carlo_spatial_randomness.rsx
  38. +34 −0 python/plugins/processing/r/scripts/Monte-Carlo_spatial_randomness.rsx.help
  39. +4 −4 python/plugins/processing/r/scripts/Quadrat_analysis.rsx
  40. +26 −0 python/plugins/processing/r/scripts/Quadrat_analysis.rsx.help
  41. +6 −0 python/plugins/processing/r/scripts/Random_sampling_grid.rsx
  42. +26 −0 python/plugins/processing/r/scripts/Random_sampling_grid.rsx.help
  43. +3 −3 python/plugins/processing/r/scripts/Raster_histogram.rsx
  44. +22 −0 python/plugins/processing/r/scripts/Raster_histogram.rsx.help
  45. +6 −0 python/plugins/processing/r/scripts/Regular_sampling_grid.rsx
  46. +18 −0 python/plugins/processing/r/scripts/Regular_sampling_grid.rsx.help
  47. +42 −0 python/plugins/processing/r/scripts/Relative_distribution_(distance_covariate).rsx
  48. +42 −0 python/plugins/processing/r/scripts/Relative_distribution_(distance_covariate).rsx.help
  49. +29 −0 python/plugins/processing/r/scripts/Relative_distribution_(raster_covariate).rsx
  50. +42 −0 python/plugins/processing/r/scripts/Relative_distribution_(raster_covariate).rsx.help
  51. +12 −0 python/plugins/processing/r/scripts/Ripley_-_Rasson_spatial_domain.rsx
  52. +22 −0 python/plugins/processing/r/scripts/Ripley_-_Rasson_spatial_domain.rsx.help
  53. +12 −0 python/plugins/processing/r/scripts/Single-linkage_cluster_analysis.rsx
  54. +34 −0 python/plugins/processing/r/scripts/Single-linkage_cluster_analysis.rsx.help
  55. +14 −0 python/plugins/processing/r/scripts/Summary_statistics.rsx
  56. +26 −0 python/plugins/processing/r/scripts/Summary_statistics.rsx.help
  57. +7 −1 python/plugins/processing/script/ScriptAlgorithm.py
  58. +1 −1 scripts/tsstat.pl
  59. +1 −0 src/core/qgscoordinatereferencesystem.cpp
  60. +1 −0 src/core/qgsexpression.cpp
  61. +6 −3 src/core/qgsexpression.h
  62. +2 −2 src/core/qgsfeaturerequest.cpp
  63. +9 −0 src/core/qgsproject.cpp
  64. +2 −2 src/core/symbology-ng/qgsellipsesymbollayerv2.cpp
  65. +1 −1 src/core/symbology-ng/qgsrulebasedrendererv2.cpp
  66. +1 −1 src/core/symbology-ng/qgssymbollayerv2utils.cpp
  67. +2 −0 src/gui/qgisinterface.h
  68. +80 −0 src/gui/qgsdatumtransformdialog.cpp
  69. +20 −0 src/gui/qgsdatumtransformdialog.h
  70. +2 −2 src/plugins/georeferencer/qgsgeorefplugingui.cpp
  71. +4 −1 src/plugins/georeferencer/qgstransformsettingsdialog.cpp
  72. +32 −11 src/plugins/globe/globe_plugin.cpp
  73. BIN src/plugins/globe/images/gui/sync-extent.png
  74. +1 −1 src/providers/ogr/qgsogrfeatureiterator.cpp
  75. +6 −6 src/providers/ogr/qgsogrprovider.cpp
  76. +1 −1 src/providers/ogr/qgsogrprovider.h
  77. +6 −2 src/providers/spatialite/qgsspatialitefeatureiterator.cpp
  78. +1 −1 tests/src/core/testqgsexpression.cpp
  79. +3 −3 tests/src/core/testqgsogcutils.cpp
@@ -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

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@@ -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;
@@ -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)

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@@ -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.
@@ -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)
@@ -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.

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@@ -0,0 +1,4 @@
##Vector processing=group
##Layer = raster
##showplots
hist(as.matrix(Layer),main="Histogram",xlab="Layer")
@@ -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.
@@ -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))
@@ -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.

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@@ -0,0 +1,4 @@
##Basic statistics=group
##Layer=vector
##Field=Field Layer
>table(Layer[[Field]])
@@ -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.
@@ -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))
@@ -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.

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@@ -0,0 +1,5 @@
##Vector processing=group
##showplots
##Layer=vector
##Field=Field Layer
hist(Layer[[Field]],main=paste("Histogram of",Field),xlab=paste(Field))
@@ -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.

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@@ -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)
@@ -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.

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