Minor documentation and code review corrections (37)

diff --git a/doc/en/ref_algorithm_KalmanFilter.rst b/doc/en/ref_algorithm_KalmanFilter.rst
index 57ce9e77611f566a9a7aad39c2abbd2f8aa70520..1bf41fe9a6b69eb8d3ceac175e7108409b515905 100644 (file)
--- a/doc/en/ref_algorithm_KalmanFilter.rst
+++ b/doc/en/ref_algorithm_KalmanFilter.rst
@@ -198,6 +198,7 @@ StoreSupplementaryCalculations
 .. _section_ref_algorithm_KalmanFilter_examples:
 .. include:: snippets/Header2Algo09.rst
 
+.. --------- ..
 .. include:: scripts/simple_KalmanFilter1.rst
 
 .. literalinclude:: scripts/simple_KalmanFilter1.py
@@ -205,6 +206,7 @@ StoreSupplementaryCalculations
 .. include:: snippets/Header2Algo10.rst
 
 .. literalinclude:: scripts/simple_KalmanFilter1.res
+    :language: none
 
 .. include:: snippets/Header2Algo11.rst
 
@@ -218,6 +220,7 @@ StoreSupplementaryCalculations
   :align: center
   :width: 90%
 
+.. --------- ..
 .. include:: scripts/simple_KalmanFilter2.rst
 
 .. literalinclude:: scripts/simple_KalmanFilter2.py
@@ -225,6 +228,7 @@ StoreSupplementaryCalculations
 .. include:: snippets/Header2Algo10.rst
 
 .. literalinclude:: scripts/simple_KalmanFilter2.res
+    :language: none
 
 .. include:: snippets/Header2Algo11.rst
 

diff --git a/doc/en/scripts/simple_Blue.py b/doc/en/scripts/simple_Blue.py
index c5ed14a61cc93d4787de0cb9dfb0149f65e6d335..1c9edebae4e96113c2326f6b2639d1258c6d254e 100644 (file)
--- a/doc/en/scripts/simple_Blue.py
+++ b/doc/en/scripts/simple_Blue.py
@@ -5,11 +5,11 @@ from adao import adaoBuilder
 case = adaoBuilder.New()
 case.setBackground( Vector = array([0., 1., 2.]), Stored=True )
 case.setBackgroundError( ScalarSparseMatrix = 1. )
-case.setObservation( Vector=array([10., 11., 12.]), Stored=True )
+case.setObservation( Vector = array([10., 11., 12.]), Stored=True )
 case.setObservationError( ScalarSparseMatrix = 1. )
-case.setObservationOperator( Matrix=array([[1., 0., 0.],
-                                           [0., 1., 0.],
-                                           [0., 0., 1.]]), )
+case.setObservationOperator( Matrix = array([[1., 0., 0.],
+                                             [0., 1., 0.],
+                                             [0., 0., 1.]]), )
 case.setAlgorithmParameters(
     Algorithm='Blue',
     Parameters={

diff --git a/doc/en/scripts/simple_ExtendedBlue.py b/doc/en/scripts/simple_ExtendedBlue.py
index 4a7ff5f789025542f280db930d66b37b148652b3..0ca43382479ec893dba832a20ac845e9b7ebbbc6 100644 (file)
--- a/doc/en/scripts/simple_ExtendedBlue.py
+++ b/doc/en/scripts/simple_ExtendedBlue.py
@@ -5,11 +5,11 @@ from adao import adaoBuilder
 case = adaoBuilder.New()
 case.setBackground( Vector = array([0., 1., 2.]), Stored=True )
 case.setBackgroundError( ScalarSparseMatrix = 1. )
-case.setObservation( Vector=array([10., 11., 12.]), Stored=True )
+case.setObservation( Vector = array([10., 11., 12.]), Stored=True )
 case.setObservationError( ScalarSparseMatrix = 1. )
-case.setObservationOperator( Matrix=array([[1., 0., 0.],
-                                           [0., 1., 0.],
-                                           [0., 0., 1.]]), )
+case.setObservationOperator( Matrix = array([[1., 0., 0.],
+                                             [0., 1., 0.],
+                                             [0., 0., 1.]]), )
 case.setAlgorithmParameters(
     Algorithm='ExtendedBlue',
     Parameters={

diff --git a/doc/en/scripts/simple_KalmanFilter1.rst b/doc/en/scripts/simple_KalmanFilter1.rst
index f6423e74f542dd621fccde38294b2a72e0d389a3..7a8c870772e925576dd58c854282e4f82cf41d6d 100644 (file)
--- a/doc/en/scripts/simple_KalmanFilter1.rst
+++ b/doc/en/scripts/simple_KalmanFilter1.rst
@@ -1,5 +1,8 @@
 .. index:: single: KalmanFilter (example)
 
+First example
+.............
+
 The Kalman Filter can be used for a **reanalysis of observations of a given
 dynamical model**. It is because the whole set of the observation  full history
 is already known at the beginning of the time windows that it is called

diff --git a/doc/en/scripts/simple_KalmanFilter2.rst b/doc/en/scripts/simple_KalmanFilter2.rst
index f516425be0e60789588257291064dd86d1731ee7..7c43714ab51c59ecc9a51f9659f07f18dbe70530 100644 (file)
--- a/doc/en/scripts/simple_KalmanFilter2.rst
+++ b/doc/en/scripts/simple_KalmanFilter2.rst
@@ -1,3 +1,6 @@
+Second example
+..............
+
 The Kalman filter can also be used for a **running analysis of the observations
 of a given dynamic model**. In this case, the analysis is conducted
 iteratively, at the arrival of each observation.

diff --git a/doc/fr/ref_algorithm_KalmanFilter.rst b/doc/fr/ref_algorithm_KalmanFilter.rst
index 345069da4c46decd6b61413a4e48310c77a41b3a..71a098966c7489e2c1b4ed8e3295fb8067457af3 100644 (file)
--- a/doc/fr/ref_algorithm_KalmanFilter.rst
+++ b/doc/fr/ref_algorithm_KalmanFilter.rst
@@ -199,6 +199,7 @@ StoreSupplementaryCalculations
 .. _section_ref_algorithm_KalmanFilter_examples:
 .. include:: snippets/Header2Algo09.rst
 
+.. --------- ..
 .. include:: scripts/simple_KalmanFilter1.rst
 
 .. literalinclude:: scripts/simple_KalmanFilter1.py
@@ -220,6 +221,7 @@ StoreSupplementaryCalculations
   :align: center
   :width: 90%
 
+.. --------- ..
 .. include:: scripts/simple_KalmanFilter2.rst
 
 .. literalinclude:: scripts/simple_KalmanFilter2.py

diff --git a/doc/fr/scripts/simple_KalmanFilter1.rst b/doc/fr/scripts/simple_KalmanFilter1.rst
index 95a52c5cc2da28fbd6ffc6090da552a633d0f6a6..59b454654630963f53de084d6fbbd82bbcd70d2c 100644 (file)
--- a/doc/fr/scripts/simple_KalmanFilter1.rst
+++ b/doc/fr/scripts/simple_KalmanFilter1.rst
@@ -1,5 +1,8 @@
 .. index:: single: KalmanFilter (exemple)
 
+Premier exemple
+...............
+
 Le filtre de Kalman peut être utilisé pour une **réanalyse des observations
 d'un modèle dynamique donné**. C'est parce que l'ensemble de l'historique
 complet de l'observation est déjà connu au début des fenêtres temporelles qu'on

diff --git a/doc/fr/scripts/simple_KalmanFilter2.rst b/doc/fr/scripts/simple_KalmanFilter2.rst
index 3c62ec937bc7e707a67ab9db8b904bdb577cd9e3..a0b85ca431e25c52c0f46dda9715b2ac1fd7f1a8 100644 (file)
--- a/doc/fr/scripts/simple_KalmanFilter2.rst
+++ b/doc/fr/scripts/simple_KalmanFilter2.rst
@@ -1,3 +1,6 @@
+Deuxième exemple
+................
+
 Le filtre de Kalman peut aussi être utilisé pour une **analyse courante des
 observations d'un modèle dynamique donné**. Dans ce cas, l'analyse est conduite
 de manière itérative, lors de l'arrivée de chaque observation.

diff --git a/src/daComposant/daCore/PlatformInfo.py b/src/daComposant/daCore/PlatformInfo.py
index 8a4105e4fab7298d33da90c59795f671b563a163..f37c23e6e0c5c630e6a2aa732e5c64a26f7a1000 100644 (file)
--- a/src/daComposant/daCore/PlatformInfo.py
+++ b/src/daComposant/daCore/PlatformInfo.py
@@ -292,6 +292,8 @@ def isIterable( __sequence, __check = False, __header = "" ):
         __isOk = True
     elif "_iterator" in type(__sequence).__name__:
         __isOk = True
+    elif "itertools" in str(type(__sequence)):
+        __isOk = True
     else:
         __isOk = False
     if __check and not __isOk: