- updated interface definition

Author: T-002

README.md

@@ -21,8 +21,7 @@ Typical usage often looks like this:
     ts.add_entry(4.0, 2.0)
 
     ts.normalize("second")
-    sma = SimpleMovingAverage(3)
-    ts.smooth(sma)
+    smootedTS = ts.apply(SimpleMovingAverage(3))
 
 
 Some usage examples of pycast in combination with other tools like gnuplot

pycast/common/timeseries.py

@@ -339,31 +339,18 @@ def is_normalized(self):
         """
         return self._normalized
 
-    def smooth(self, smoothingMethod):
-        """Applies the given SmoothingMethod from the pycast.smoothing module to the TimeSeries.
-
-        @param smoothingMethod SmoothingMethod that should be applied to the time series.
-                               For more information about the smoothing methods look into
-                               their corresponding documentation in the pycast.smoothing module.
-
-        @return Returns a new TimeSeries containing the smoothed values.
+    def apply(self, method):
+        """Applies the given ForecastingAlgorithm or SmoothingMethod from the pycast.forecasting and
+        pycast.smoothingmodule to the TimeSeries.
+        
+        @param method Method that should be used with the TimeSeries.
+                      For more information about the methods take a look into
+                      their corresponding documentation.
         """
-        print self
         ## sort and normalize, if necessary
-        if smoothingMethod.has_to_be_normalized():
+        if method.has_to_be_normalized():
             self.normalize()
-        elif smoothingMethod.has_to_be_sorted():
+        elif method.has_to_be_sorted():
             self.sort_timeseries()
-
-        print self
-        return smoothingMethod.execute(self)
-
-    def apply_forecasting(self, forecastingAlgorithm):
-        """Applies the given ForecastingAlgorithm from the pycast.forecasting module to the TimeSeries.
-
-        @param forecastingAlgorithm ForecastingAlgorithm that should be used with the TimeSeries.
-                                    Fore more information about the forecasting algorithms look into
-                                    their corresponding documentation in the pycasst.forecasting module.
-        """
-        ## @todo This is not implemetned yet.
-        ## @todo Check, if this interface definition works good with GridSearch or HookeJeaves opimization.
+        
+        return method.execute(self)

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name             = "pycast",
-    version          = "0.0.0-prealpha",
+    version          = "0.0.1-prealpha",
     author           = "Christian S",
     author_email     = "[email protected]",
     packages         = ["pycast"],

test.py

@@ -31,13 +31,26 @@
 #result =linear_interpolation(v1,v2,4)
 #assert(result == [1.0, 2.0, 3.0, 4.0])
 
+#from pycast.common.timeseries import TimeSeries
+#ts1 = TimeSeries()
+#ts1.add_entry(0.0, 0.0)
+#ts1.add_entry(1.0, 0.8)
+#ts1.add_entry(1.1, 0.9)
+#ts1.add_entry(1.2, 1.3)
+#ts1.add_entry(2.0, 2.0)
+#
+#ts1.normalize("second", "average", "linear")
+#print ts1
+
 from pycast.common.timeseries import TimeSeries
 ts1 = TimeSeries()
 ts1.add_entry(0.0, 0.0)
 ts1.add_entry(1.0, 0.8)
-ts1.add_entry(1.1, 0.9)
-ts1.add_entry(1.2, 1.3)
-ts1.add_entry(2.0, 2.0)
+ts1.add_entry(2.1, 0.9)
+ts1.add_entry(3.2, 1.3)
+ts1.add_entry(4.0, 2.0)
 
-ts1.normalize("second", "average", "linear")
-print ts1
+from pycast.smoothing.simplemovingaverage import SimpleMovingAverage
+sma = SimpleMovingAverage(3)
+ts1.normalize("second")
+print ts1.apply(sma)