[sextante] changed statisticByCategories, so now it does not depend on external libs

python/plugins/sextante/algs/FieldsCalculator.py

@@ -31,7 +31,6 @@ from PyQt4.QtCore import *
 from PyQt4.QtGui import *
 from sextante.parameters.ParameterString import ParameterString
 from sextante.core.QGisLayers import QGisLayers
-from sextante.core.GeoAlgorithmExecutionException import GeoAlgorithmExecutionException
 
 
 class FieldsCalculator(GeoAlgorithm):

python/plugins/sextante/algs/QGISAlgorithmProvider.py

@@ -16,9 +16,9 @@
 *                                                                         *
 ***************************************************************************
 """
-from sextante.algs.MeanAndStdDevPlot import MeanAndStdDevPlot
-from sextante.algs.BarPlot import BarPlot
-from sextante.algs.PolarPlot import PolarPlot
+#from sextante.algs.MeanAndStdDevPlot import MeanAndStdDevPlot
+#from sextante.algs.BarPlot import BarPlot
+#from sextante.algs.PolarPlot import PolarPlot
 from sextante.algs.RasterLayerStatistics import RasterLayerStatistics
 
 __author__ = 'Victor Olaya'
@@ -30,7 +30,7 @@ __revision__ = '$Format:%H$'
 import os
 from PyQt4 import QtGui
 #from sextante.algs.RasterLayerHistogram import RasterLayerHistogram
-#from sextante.algs.StatisticsByCategories import StatisticsByCategories
+from sextante.algs.StatisticsByCategories import StatisticsByCategories
 from sextante.algs.ftools.PointsInPolygon import PointsInPolygon
 from sextante.algs.ftools.PointsInPolygonUnique import PointsInPolygonUnique
 from sextante.algs.ftools.PointsInPolygonWeighted import PointsInPolygonWeighted
@@ -97,8 +97,7 @@ class QGISAlgorithmProvider(AlgorithmProvider):
                         SumLines(), PointsInPolygon(), PointsInPolygonWeighted(), PointsInPolygonUnique(),
                         BasicStatisticsStrings(), BasicStatisticsNumbers(), NearestNeighbourAnalysis(),
                         MeanCoords(), LinesIntersection(), UniqueValues(), PointDistance(), PointsLayerFromTable(),
-                        #StatisticsByCategories(),
-                        ReprojectLayer(),
+                        StatisticsByCategories(),ReprojectLayer(),
                         ExportGeometryInfo(), Centroids(), Delaunay(), VoronoiPolygons(),
                         SimplifyGeometries(), DensifyGeometries(), DensifyGeometriesInterval(),
                         MultipartToSingleparts(), SinglePartsToMultiparts(), PolygonsToLines(),

python/plugins/sextante/algs/StatisticsByCategories.py

@@ -16,7 +16,6 @@
 *                                                                         *
 ***************************************************************************
 """
-import math
 
 __author__ = 'Victor Olaya'
 __date__ = 'September 2012'
@@ -24,9 +23,9 @@ __copyright__ = '(C) 2012, Victor Olaya'
 # This will get replaced with a git SHA1 when you do a git archive
 __revision__ = '$Format:%H$'
 
+import math
 from PyQt4.QtCore import *
 from qgis.core import *
-from scipy import stats
 from sextante.outputs.OutputTable import OutputTable
 from sextante.core.GeoAlgorithm import GeoAlgorithm
 from sextante.core.QGisLayers import QGisLayers
@@ -50,7 +49,7 @@ class StatisticsByCategories(GeoAlgorithm):
         self.addParameter(ParameterTableField(self.CATEGORIES_FIELD_NAME, "Field with categories", 
                                               self.INPUT_LAYER, ParameterTableField.DATA_TYPE_ANY))        
 
-        self.addOutput(OutputTable(self.OUTPUT, "Statistics for numeric field"))
+        self.addOutput(OutputTable(self.OUTPUT, "Statistics"))
         
     def processAlgorithm(self, progress):
         layer = QGisLayers.getObjectFromUri(self.getParameterValue(self.INPUT_LAYER))
@@ -62,21 +61,55 @@ class StatisticsByCategories(GeoAlgorithm):
         categoriesField = layer.fieldNameIndex(categoriesFieldName)
         
         features = QGisLayers.features(layer)
-        nFeat = len(features)
+        nFeats = len(features)
         values = {}
+        nFeat = 0
         for feat in features:
+            nFeat += 1
+            progress.setPercentage(int((100 * nFeats) / nFeat))
             attrs = feat.attributes()
-            value = float(attrs[valuesField].toDouble()[0])
-            cat = unicode(attrs[categoriesField].toString())
-            if cat not in values:
-                values[cat] = []
-            values[cat].append(value)
+            try:
+                value = float(attrs[valuesField].toDouble()[0])
+                cat = unicode(attrs[categoriesField].toString())
+                if cat not in values:
+                    values[cat] = []
+                values[cat].append(value)
+            except:
+                pass
         
-        fields = [QgsField("category", QVariant.String), QgsField("mean", QVariant.Double), QgsField("variance", QVariant.Double)]
+        fields = [QgsField("category", QVariant.String), QgsField("min", QVariant.Double), 
+                  QgsField("max", QVariant.Double), QgsField("mean", QVariant.Double), 
+                  QgsField("stddev", QVariant.Double)]
         writer = output.getTableWriter(fields)
-        for cat, value in values.items():                       
-            n, min_max, mean, var, skew, kurt = stats.describe(value)
-            record = [cat, mean, math.sqrt(var)]            
+        for cat, v in values.items():                       
+            min, max, mean, stddev = calculateStats(v)
+            record = [cat, min, max, mean, stddev]            
             writer.addRecord(record)
             
-   
+def calculateStats(values):    
+    n = 0    
+    sum = 0
+    mean = 0
+    M2 = 0    
+    minvalue = None
+    maxvalue = None
+ 
+    for v in values:                
+        sum += v    
+        n = n + 1
+        delta = v - mean
+        mean = mean + delta/n
+        M2 = M2 + delta*(v - mean)
+        if minvalue is None:
+            minvalue = v
+            maxvalue = v
+        else:
+            minvalue = min(v, minvalue)
+            maxvalue = max(v, maxvalue)        
+            
+    if n > 1:
+         variance = M2/(n - 1)
+    else:
+        variance = 0;
+    stddev = math.sqrt(variance)
+    return minvalue,maxvalue, mean, stddev