Documentation examples corrections and update

diff --git a/doc/en/ref_algorithm_MeasurementsOptimalPositioningTask.rst b/doc/en/ref_algorithm_MeasurementsOptimalPositioningTask.rst
index a61ccd3358851a9b8e35baa037fde2e8db003d75..e2a66e374de113cef87f8f3dc80d2993d91cabba 100644 (file)
--- a/doc/en/ref_algorithm_MeasurementsOptimalPositioningTask.rst
+++ b/doc/en/ref_algorithm_MeasurementsOptimalPositioningTask.rst
@@ -232,20 +232,10 @@ StoreSupplementaryCalculations
 
 .. include:: snippets/Header2Algo11.rst
 
 
 .. include:: snippets/Header2Algo11.rst
 

-.. _simple_MeasurementsOptimalPositioningTask31:
-.. image:: scripts/simple_MeasurementsOptimalPositioningTask31.png
+.. _simple_MeasurementsOptimalPositioningTask3:
+.. image:: scripts/simple_MeasurementsOptimalPositioningTask3.png

   :align: center
   :align: center

-  :width: 60%
-
-.. _simple_MeasurementsOptimalPositioningTask32:
-.. image:: scripts/simple_MeasurementsOptimalPositioningTask32.png
-  :align: center
-  :width: 60%
-
-.. _simple_MeasurementsOptimalPositioningTask33:
-.. image:: scripts/simple_MeasurementsOptimalPositioningTask33.png
-  :align: center
-  :width: 60%
+  :width: 100%

 
 .. ------------------------------------ ..
 .. include:: snippets/Header2Algo06.rst
 
 .. ------------------------------------ ..
 .. include:: snippets/Header2Algo06.rst

diff --git a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.png b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.png
new file mode 100644 (file)
index 0000000..87e62a1
Binary files /dev/null and b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.png differ

diff --git a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.py b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.py
index 8878d1256f6d1b9d4ea653d5b89e5eda40aed265..a91213ffbea2dfc65dcabf3bc959329395f3a5c0 100644 (file)
--- a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.py
+++ b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.py
@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 np.random.seed(123456789)
 #
 dimension = 20
 np.random.seed(123456789)
 #
 dimension = 20

+nbmeasures = 15

 #
 print("Defining a set of artificial physical fields")
 from Models.TwoDimensionalInverseDistanceCS2010 \
 #
 print("Defining a set of artificial physical fields")
 from Models.TwoDimensionalInverseDistanceCS2010 \


@@ -20,7 +21,7 @@ case.setAlgorithmParameters(
     Parameters = {
         "Variant":"DEIM",
         "SampleAsnUplet":Eq.get_sample_of_mu(15, 15),
     Parameters = {
         "Variant":"DEIM",
         "SampleAsnUplet":Eq.get_sample_of_mu(15, 15),

-        "MaximumNumberOfLocations":50,
+        "MaximumNumberOfLocations":nbmeasures,

         "ErrorNorm":"Linf",
         "ErrorNormTolerance":0.,
         "StoreSupplementaryCalculations":[
         "ErrorNorm":"Linf",
         "ErrorNormTolerance":0.,
         "StoreSupplementaryCalculations":[


@@ -34,57 +35,61 @@ case.setBackground(Vector = [1,1] )
 case.setObservationOperator(OneFunction = Eq.OneRealisation)
 case.execute()
 #
 case.setObservationOperator(OneFunction = Eq.OneRealisation)
 case.execute()
 #

+#-------------------------------------------------------------------------------

 print()
 print("Graphical display of the results")
 #
 sp = case.get("EnsembleOfSimulations")[-1]
 print()
 print("Graphical display of the results")
 #
 sp = case.get("EnsembleOfSimulations")[-1]

+sv = case.get("SingularValues")[-1]
+op = case.get("OptimalPoints")[-1]
+#

 x1, x2 = Eq.get_x()
 x1, x2 = np.meshgrid(x1, x2)
 x1, x2 = Eq.get_x()
 x1, x2 = np.meshgrid(x1, x2)

-name = "Representation of a few snapshots of G out of a total of %i"%sp.shape[1]
-print("  -", name)
-fig, ax = plt.subplots(subplot_kw={"projection": "3d"}, figsize=(6, 6))
-fig.suptitle(name)
-ax.set_xlabel("Position x1", fontweight='bold', color='red')
-ax.set_ylabel("Position x2", fontweight='bold', color='red')
-ax.set_zlabel("Magnitude of G", fontweight='bold', color='red')
-for i in range(sp.shape[1]):
-    if i % 44 != 0: continue
-    ax.plot_surface(x1, x2, sp[:,i].reshape((dimension, dimension)), cmap='coolwarm')
-fig.savefig("simple_MeasurementsOptimalPositioningTask31.png")
-plt.close()
+posx1 = [x1.reshape((-1,))[ip] for ip in op]
+posx2 = [x2.reshape((-1,))[ip] for ip in op]
+Omega = Eq.get_bounds_on_space()

 #
 #

-sv = case.get("SingularValues")[-1]
-name = "Singular values of the set of G simulations"
+fig = plt.figure(figsize=(18, 6))
+name = "Measurement optimal points search by "
+name += '"ADAO/MeasurementsOptimalPositioningTask/DEIM"'
+fig.suptitle(name, fontsize=20, fontstyle='italic')
+#
+ax = fig.add_subplot(1, 3, 1)
+name = "Singular values of the set of %i G simulations"%sp.shape[1]

 print("  -", name)
 print("  -", name)

-fig, ax = plt.subplots(figsize=(6, 6))
-fig.suptitle(name)
+ax.set_title(name, fontstyle='italic', color='red')

 ax.set_xlabel("Index of singular values, numbered from 1")
 ax.set_ylabel("Magnitude of singular values")
 ax.set_xlim(1, len(sv))
 ax.set_yscale("log")
 ax.grid(True)
 ax.plot(range(1, 1 + len(sv)), sv)
 ax.set_xlabel("Index of singular values, numbered from 1")
 ax.set_ylabel("Magnitude of singular values")
 ax.set_xlim(1, len(sv))
 ax.set_yscale("log")
 ax.grid(True)
 ax.plot(range(1, 1 + len(sv)), sv)

-fig.savefig("simple_MeasurementsOptimalPositioningTask32.png")
-plt.tight_layout()
-plt.close()

 #
 #

-nbmax = 15
-op = case.get("OptimalPoints")[-1]
-posx1 = [x1.reshape((-1,))[ip] for ip in op[:nbmax]]
-posx2 = [x2.reshape((-1,))[ip] for ip in op[:nbmax]]
-name = "Set of %i first optimal points of measurement"%nbmax
-Omega = Eq.get_bounds_on_space()
+ax = fig.add_subplot(1, 3, 2, projection = "3d")
+name = "A few simulations of G out of a total of %i"%sp.shape[1]

 print("  -", name)
 print("  -", name)

-fig, ax = plt.subplots(figsize=(6, 6))
-fig.suptitle(name)
-ax.set_xlabel("Position x1", fontweight='bold', color='red')
-ax.set_ylabel("Position x2", fontweight='bold', color='red')
+ax.set_title(name, fontstyle='italic', color='red')
+ax.set_xlabel("Position x1")
+ax.set_ylabel("Position x2")
+ax.set_zlabel("Magnitude of G")
+for i in range(sp.shape[1]):
+    if i % 44 != 0: continue
+    Gfield = sp[:,i].reshape((dimension, dimension))
+    ax.plot_surface(x1, x2, Gfield, cmap='coolwarm')
+#
+ax = fig.add_subplot(1, 3, 3)
+name = "Set of %i first optimal points of measurement"%nbmeasures
+print("  -", name)
+ax.set_title(name, fontstyle='italic', color='red')
+ax.set_xlabel("Position x1")
+ax.set_ylabel("Position x2")

 ax.set_xlim(Omega[0][0] - 0.05, Omega[0][1] + 0.05)
 ax.set_ylim(Omega[1][0] - 0.05, Omega[1][1] + 0.05)
 ax.grid(True, which='both', linestyle=(0, (1, 5)), linewidth=0.5)
 ax.plot(posx1, posx2, markersize=6, marker="o", linestyle='')
 for i in range(len(posx1)):
     ax.text(posx1[i] + 0.005, posx2[i] + 0.01, str(i + 1), fontweight='bold')
 ax.set_xlim(Omega[0][0] - 0.05, Omega[0][1] + 0.05)
 ax.set_ylim(Omega[1][0] - 0.05, Omega[1][1] + 0.05)
 ax.grid(True, which='both', linestyle=(0, (1, 5)), linewidth=0.5)
 ax.plot(posx1, posx2, markersize=6, marker="o", linestyle='')
 for i in range(len(posx1)):
     ax.text(posx1[i] + 0.005, posx2[i] + 0.01, str(i + 1), fontweight='bold')

-fig.savefig("simple_MeasurementsOptimalPositioningTask33.png")
+#

 plt.tight_layout()
 plt.tight_layout()

+fig.savefig("simple_MeasurementsOptimalPositioningTask3.png")

 plt.close()
 plt.close()

diff --git a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.res b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.res
index 455a3d306879f66c1e9570bc32887786dfc3b790..7945bf1c4de1367fc2078ca54521bca70e753130 100644 (file)
--- a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.res
+++ b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.res
@@ -3,6 +3,6 @@ Defining a set of artificial physical fields
 Search for optimal measurement positions
 
 Graphical display of the results
 Search for optimal measurement positions
 
 Graphical display of the results

-  - Representation of a few snapshots of G out of a total of 225
-  - Singular values of the set of G simulations
+  - Singular values of the set of 225 G simulations
+  - A few simulations of G out of a total of 225

   - Set of 15 first optimal points of measurement
   - Set of 15 first optimal points of measurement

diff --git a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.rst b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.rst
index 94452855e9dd9bebb8981ad517ab95b950cd4209..3d57f870e534b0420aaefa61bc6127eb604b9d46 100644 (file)
--- a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.rst
+++ b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask3.rst
@@ -18,9 +18,13 @@ in the 2D plane, and on the parameter
 
 The function is represented on a regular :math:`\Omega_G` spatial grid of size
 20x20 points. It is available in ADAO built-in test models under the name
 
 The function is represented on a regular :math:`\Omega_G` spatial grid of size
 20x20 points. It is available in ADAO built-in test models under the name

-`TwoDimensionalInverseDistanceCS2010`. So here we first build a set of
-simulations of :math:`G`, then apply the DEIM-type decomposition algorithm to
-it, and derive some simple illustrations.
+``TwoDimensionalInverseDistanceCS2010``, together with the spatial and
+parametric domain default definition. So here we first build a set of
+simulations of :math:`G`, then we look for the best locations for measurements
+to obtain an DEIM interpolation representation of the fields, by applying the
+DEIM-type decomposition algorithm to it, and then derive some simple
+illustrations. We choose to look for an arbitrary number ``nbmeasures`` of 15
+measurement locations.

 
 It can be seen that the singular values decrease steadily down to numerical
 noise, indicating that around a hundred basis elements are needed to fully
 
 It can be seen that the singular values decrease steadily down to numerical
 noise, indicating that around a hundred basis elements are needed to fully

diff --git a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask31.png b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask31.png
deleted file mode 100644 (file)
index 128980d..0000000
Binary files a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask31.png and /dev/null differ

diff --git a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask32.png b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask32.png
deleted file mode 100644 (file)
index 8c78ab5..0000000
Binary files a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask32.png and /dev/null differ

diff --git a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask33.png b/doc/en/scripts/simple_MeasurementsOptimalPositioningTask33.png
deleted file mode 100644 (file)
index e1aa57a..0000000
Binary files a/doc/en/scripts/simple_MeasurementsOptimalPositioningTask33.png and /dev/null differ

diff --git a/doc/en/snippets/ModuleCompatibility.rst b/doc/en/snippets/ModuleCompatibility.rst
index ce47ccfcd91c86a22e7c6f04364975d41453693a..60cf2a5685954f62c9dcec071a58a5046bc2905a 100644 (file)
--- a/doc/en/snippets/ModuleCompatibility.rst
+++ b/doc/en/snippets/ModuleCompatibility.rst
@@ -16,7 +16,7 @@ versions within the range described below.
 
    Python,     3.6.5,    3.12.3
    Numpy,      1.14.3,    1.26.4
 
    Python,     3.6.5,    3.12.3
    Numpy,      1.14.3,    1.26.4

-   Scipy,      0.19.1,    1.13.0
+   Scipy,      0.19.1,    1.13.1

    MatplotLib, 2.2.2,    3.8.4
    GnuplotPy,  1.8,    1.8
    NLopt,      2.4.2,    2.7.1
    MatplotLib, 2.2.2,    3.8.4
    GnuplotPy,  1.8,    1.8
    NLopt,      2.4.2,    2.7.1

diff --git a/doc/fr/ref_algorithm_MeasurementsOptimalPositioningTask.rst b/doc/fr/ref_algorithm_MeasurementsOptimalPositioningTask.rst
index a4adb23bd9c30da43f57d06c74e46823ed9400ad..54e2cb12c74011b4bd35266441595b1153bd6c7e 100644 (file)
--- a/doc/fr/ref_algorithm_MeasurementsOptimalPositioningTask.rst
+++ b/doc/fr/ref_algorithm_MeasurementsOptimalPositioningTask.rst
@@ -235,20 +235,10 @@ StoreSupplementaryCalculations
 
 .. include:: snippets/Header2Algo11.rst
 
 
 .. include:: snippets/Header2Algo11.rst
 

-.. _simple_MeasurementsOptimalPositioningTask31:
-.. image:: scripts/simple_MeasurementsOptimalPositioningTask31.png
+.. _simple_MeasurementsOptimalPositioningTask3:
+.. image:: scripts/simple_MeasurementsOptimalPositioningTask3.png

   :align: center
   :align: center

-  :width: 60%
-
-.. _simple_MeasurementsOptimalPositioningTask32:
-.. image:: scripts/simple_MeasurementsOptimalPositioningTask32.png
-  :align: center
-  :width: 60%
-
-.. _simple_MeasurementsOptimalPositioningTask33:
-.. image:: scripts/simple_MeasurementsOptimalPositioningTask33.png
-  :align: center
-  :width: 60%
+  :width: 100%

 
 .. ------------------------------------ ..
 .. include:: snippets/Header2Algo06.rst
 
 .. ------------------------------------ ..
 .. include:: snippets/Header2Algo06.rst

diff --git a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.png b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.png
new file mode 100644 (file)
index 0000000..bec4390
Binary files /dev/null and b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.png differ

diff --git a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.py b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.py
index d9fca5a000beb70ae0b494d988e305ac1f074639..2ced8252e025f7783b048c95d50745fcc3bcfea8 100644 (file)
--- a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.py
+++ b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.py
@@ -5,6 +5,7 @@ import matplotlib.pyplot as plt
 np.random.seed(123456789)
 #
 dimension = 20
 np.random.seed(123456789)
 #
 dimension = 20

+nbmeasures = 15

 #
 print("Définition d'un ensemble artificiel de champs physiques")
 from Models.TwoDimensionalInverseDistanceCS2010 \
 #
 print("Définition d'un ensemble artificiel de champs physiques")
 from Models.TwoDimensionalInverseDistanceCS2010 \


@@ -20,7 +21,7 @@ case.setAlgorithmParameters(
     Parameters = {
         "Variant":"DEIM",
         "SampleAsnUplet":Eq.get_sample_of_mu(15, 15),
     Parameters = {
         "Variant":"DEIM",
         "SampleAsnUplet":Eq.get_sample_of_mu(15, 15),

-        "MaximumNumberOfLocations":50,
+        "MaximumNumberOfLocations":nbmeasures,

         "ErrorNorm":"Linf",
         "ErrorNormTolerance":0.,
         "StoreSupplementaryCalculations":[
         "ErrorNorm":"Linf",
         "ErrorNormTolerance":0.,
         "StoreSupplementaryCalculations":[


@@ -34,57 +35,61 @@ case.setBackground(Vector = [1,1] )
 case.setObservationOperator(OneFunction = Eq.OneRealisation)
 case.execute()
 #
 case.setObservationOperator(OneFunction = Eq.OneRealisation)
 case.execute()
 #

+#-------------------------------------------------------------------------------

 print()
 print("Affichage graphique des résultats")
 #
 sp = case.get("EnsembleOfSimulations")[-1]
 print()
 print("Affichage graphique des résultats")
 #
 sp = case.get("EnsembleOfSimulations")[-1]

+sv = case.get("SingularValues")[-1]
+op = case.get("OptimalPoints")[-1]
+#

 x1, x2 = Eq.get_x()
 x1, x2 = np.meshgrid(x1, x2)
 x1, x2 = Eq.get_x()
 x1, x2 = np.meshgrid(x1, x2)

-name = "Représentation de quelques snapshots de G sur un total de %i"%sp.shape[1]
-print("  -", name)
-fig, ax = plt.subplots(subplot_kw={"projection": "3d"}, figsize=(6, 6))
-fig.suptitle(name)
-ax.set_xlabel("Position x1", fontweight='bold', color='red')
-ax.set_ylabel("Position x2", fontweight='bold', color='red')
-ax.set_zlabel("Amplitude de G", fontweight='bold', color='red')
-for i in range(sp.shape[1]):
-    if i % 44 != 0: continue
-    ax.plot_surface(x1, x2, sp[:,i].reshape((dimension, dimension)), cmap='coolwarm')
-fig.savefig("simple_MeasurementsOptimalPositioningTask31.png")
-plt.close()
+posx1 = [x1.reshape((-1,))[ip] for ip in op]
+posx2 = [x2.reshape((-1,))[ip] for ip in op]
+Omega = Eq.get_bounds_on_space()

 #
 #

-sv = case.get("SingularValues")[-1]
-name = "Valeurs singulières de l'ensemble des simulations de G"
+fig = plt.figure(figsize=(18, 6))
+name = "Recherche de points optimaux de mesure par "
+name += '"ADAO/MeasurementsOptimalPositioningTask/DEIM"'
+fig.suptitle(name, fontsize=20, fontstyle='italic')
+#
+ax = fig.add_subplot(1, 3, 1)
+name = "Valeurs singulières de l'ensemble des %i simulations de G"%sp.shape[1]

 print("  -", name)
 print("  -", name)

-fig, ax = plt.subplots(figsize=(6, 6))
-fig.suptitle(name)
+ax.set_title(name, fontstyle='italic', color='red')

 ax.set_xlabel("Index des valeurs singulières, numérotées à partir de 1")
 ax.set_ylabel("Amplitude des valeurs singulières")
 ax.set_xlim(1, len(sv))
 ax.set_yscale("log")
 ax.grid(True)
 ax.plot(range(1, 1 + len(sv)), sv)
 ax.set_xlabel("Index des valeurs singulières, numérotées à partir de 1")
 ax.set_ylabel("Amplitude des valeurs singulières")
 ax.set_xlim(1, len(sv))
 ax.set_yscale("log")
 ax.grid(True)
 ax.plot(range(1, 1 + len(sv)), sv)

-fig.savefig("simple_MeasurementsOptimalPositioningTask32.png")
-plt.tight_layout()
-plt.close()

 #
 #

-nbmax = 15
-op = case.get("OptimalPoints")[-1]
-posx1 = [x1.reshape((-1,))[ip] for ip in op[:nbmax]]
-posx2 = [x2.reshape((-1,))[ip] for ip in op[:nbmax]]
-name = "Ensemble des %i premiers points optimaux de mesure"%nbmax
-Omega = Eq.get_bounds_on_space()
+ax = fig.add_subplot(1, 3, 2, projection = "3d")
+name = "Quelques simulations de G sur un total de %i"%sp.shape[1]

 print("  -", name)
 print("  -", name)

-fig, ax = plt.subplots(figsize=(6, 6))
-fig.suptitle(name)
-ax.set_xlabel("Position x1", fontweight='bold', color='red')
-ax.set_ylabel("Position x2", fontweight='bold', color='red')
+ax.set_title(name, fontstyle='italic', color='red')
+ax.set_xlabel("Position x1")
+ax.set_ylabel("Position x2")
+ax.set_zlabel("Amplitude de G")
+for i in range(sp.shape[1]):
+    if i % 44 != 0: continue
+    Gfield = sp[:,i].reshape((dimension, dimension))
+    ax.plot_surface(x1, x2, Gfield, cmap='coolwarm')
+#
+ax = fig.add_subplot(1, 3, 3)
+name = "Ensemble des %i premiers points optimaux de mesure"%nbmeasures
+print("  -", name)
+ax.set_title(name, fontstyle='italic', color='red')
+ax.set_xlabel("Position x1")
+ax.set_ylabel("Position x2")

 ax.set_xlim(Omega[0][0] - 0.05, Omega[0][1] + 0.05)
 ax.set_ylim(Omega[1][0] - 0.05, Omega[1][1] + 0.05)
 ax.grid(True, which='both', linestyle=(0, (1, 5)), linewidth=0.5)
 ax.plot(posx1, posx2, markersize=6, marker="o", linestyle='')
 for i in range(len(posx1)):
     ax.text(posx1[i] + 0.005, posx2[i] + 0.01, str(i + 1), fontweight='bold')
 ax.set_xlim(Omega[0][0] - 0.05, Omega[0][1] + 0.05)
 ax.set_ylim(Omega[1][0] - 0.05, Omega[1][1] + 0.05)
 ax.grid(True, which='both', linestyle=(0, (1, 5)), linewidth=0.5)
 ax.plot(posx1, posx2, markersize=6, marker="o", linestyle='')
 for i in range(len(posx1)):
     ax.text(posx1[i] + 0.005, posx2[i] + 0.01, str(i + 1), fontweight='bold')

-fig.savefig("simple_MeasurementsOptimalPositioningTask33.png")
+#

 plt.tight_layout()
 plt.tight_layout()

+fig.savefig("simple_MeasurementsOptimalPositioningTask3.png")

 plt.close()
 plt.close()

diff --git a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.res b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.res
index 756b5bf41d74932ae1cddaf080e44fb07f58a0f0..c43b05b3c5ba8a9558327a190b9013ad348a4beb 100644 (file)
--- a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.res
+++ b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.res
@@ -3,6 +3,6 @@ Définition d'un ensemble artificiel de champs physiques
 Recherche des positions optimales de mesure
 
 Affichage graphique des résultats
 Recherche des positions optimales de mesure
 
 Affichage graphique des résultats

-  - Représentation de quelques snapshots de G sur un total de 225
-  - Valeurs singulières de l'ensemble des simulations de G
+  - Valeurs singulières de l'ensemble des 225 simulations de G
+  - Quelques simulations de G sur un total de 225

   - Ensemble des 15 premiers points optimaux de mesure
   - Ensemble des 15 premiers points optimaux de mesure

diff --git a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.rst b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.rst
index 8225906bd14cb33be762c26f5526fd91cbfd45ae..6a2d2e0798acf00d4e7a68f1ff03ed2c8f1ec095 100644 (file)
--- a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.rst
+++ b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask3.rst
@@ -19,10 +19,12 @@ dans le plan 2D, et du paramètre
 
 La fonction est représenté sur une grille spatiale régulière :math:`\Omega_G`
 de taille 20x20 points. Elle est disponible dans les modèles de tests intégrés
 
 La fonction est représenté sur une grille spatiale régulière :math:`\Omega_G`
 de taille 20x20 points. Elle est disponible dans les modèles de tests intégrés

-pour ADAO sous le nom `TwoDimensionalInverseDistanceCS2010`. On construit donc
-ici tout d'abord un ensemble de simulations de :math:`G`, pour lui appliquer
-ensuite l'algorithme de décomposition de type DEIM, et en tirer des
-illustrations simples.
+pour ADAO sous le nom ``TwoDimensionalInverseDistanceCS2010``. On construit
+donc ici tout d'abord un ensemble de simulations de :math:`G`, puis on cherche
+les meilleurs positions de mesures pour obtenir une représentation par
+interpolation DEIM des champs, en appliquant l'algorithme de décomposition de
+type DEIM, et on en tire ensuite des illustrations simples. On choisit de
+rechercher un nombre arbitraire ``nbmeasures`` de 15 positions de mesures.

 
 On observe ainsi que les valeurs singulières décroissent régulièrement jusqu'au
 bruit numérique, indiquant qu'il faut environ une centaine d'éléments de base
 
 On observe ainsi que les valeurs singulières décroissent régulièrement jusqu'au
 bruit numérique, indiquant qu'il faut environ une centaine d'éléments de base

diff --git a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask31.png b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask31.png
deleted file mode 100644 (file)
index 4e21179..0000000
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diff --git a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask32.png b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask32.png
deleted file mode 100644 (file)
index a84b5bd..0000000
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diff --git a/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask33.png b/doc/fr/scripts/simple_MeasurementsOptimalPositioningTask33.png
deleted file mode 100644 (file)
index 296b6ea..0000000
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diff --git a/doc/fr/snippets/ModuleCompatibility.rst b/doc/fr/snippets/ModuleCompatibility.rst
index fe333b5d53c4643eee45e6b65c725bf9d3c4f88e..0eecbfddf2a62a9a8000efb86e13fd293cb9bb91 100644 (file)
--- a/doc/fr/snippets/ModuleCompatibility.rst
+++ b/doc/fr/snippets/ModuleCompatibility.rst
@@ -17,7 +17,7 @@ l'étendue décrite ci-dessous.
 
    Python,     3.6.5,    3.12.3
    Numpy,      1.14.3,    1.26.4
 
    Python,     3.6.5,    3.12.3
    Numpy,      1.14.3,    1.26.4

-   Scipy,      0.19.1,    1.13.0
+   Scipy,      0.19.1,    1.13.1

    MatplotLib, 2.2.2,    3.8.4
    GnuplotPy,  1.8,    1.8
    NLopt,      2.4.2,    2.7.1
    MatplotLib, 2.2.2,    3.8.4
    GnuplotPy,  1.8,    1.8
    NLopt,      2.4.2,    2.7.1

diff --git a/src/daComposant/daNumerics/Models/TwoDimensionalInverseDistanceCS2010.py b/src/daComposant/daNumerics/Models/TwoDimensionalInverseDistanceCS2010.py
index 6df69eb2dc4b3df41798c9fb8e2542ca4ee83ab2..c86e9e072c37acfd24af1a12102236e214ec712e 100644 (file)
--- a/src/daComposant/daNumerics/Models/TwoDimensionalInverseDistanceCS2010.py
+++ b/src/daComposant/daNumerics/Models/TwoDimensionalInverseDistanceCS2010.py
@@ -37,12 +37,14 @@ class TwoDimensionalInverseDistanceCS2010:
         SIAM Journal on Scientific Computing, 32(5), pp. 2737-2764 (2010).
     """
     def __init__(self, nx: int = 20, ny: int = 20):
         SIAM Journal on Scientific Computing, 32(5), pp. 2737-2764 (2010).
     """
     def __init__(self, nx: int = 20, ny: int = 20):

+        "Définition du maillage spatial"

         self.nx  = max(1, nx)
         self.ny  = max(1, ny)
         self.x   = numpy.linspace(0.1, 0.9, self.nx, dtype=float)
         self.y   = numpy.linspace(0.1, 0.9, self.ny, dtype=float)
 
     def FunctionH(self, XX ):
         self.nx  = max(1, nx)
         self.ny  = max(1, ny)
         self.x   = numpy.linspace(0.1, 0.9, self.nx, dtype=float)
         self.y   = numpy.linspace(0.1, 0.9, self.ny, dtype=float)
 
     def FunctionH(self, XX ):

+        "Fonction simulation pour un paramètre donné"

         __mu1, __mu2 = numpy.ravel(XX)
         #
         __x, __y = numpy.meshgrid( self.x, self.y )
         __mu1, __mu2 = numpy.ravel(XX)
         #
         __x, __y = numpy.meshgrid( self.x, self.y )


@@ -51,15 +53,18 @@ class TwoDimensionalInverseDistanceCS2010:
         return __sxymu
 
     def get_x(self):
         return __sxymu
 
     def get_x(self):

+        "Renvoie le maillage spatial"

         return self.x, self.y
 
     def get_sample_of_mu(self, ns1: int = 20, ns2: int = 20):
         return self.x, self.y
 
     def get_sample_of_mu(self, ns1: int = 20, ns2: int = 20):

+        "Renvoie l'échantillonnage paramétrique régulier"

         smu1 = numpy.linspace(-1, -0.01, ns1, dtype=float)
         smu2 = numpy.linspace(-1, -0.01, ns2, dtype=float)
         smu = numpy.array([(mu1, mu2) for mu1 in smu1 for mu2 in smu2])
         return smu
 
     def get_random_sample_of_mu(self, ns1: int = 1, ns2: int = 1):
         smu1 = numpy.linspace(-1, -0.01, ns1, dtype=float)
         smu2 = numpy.linspace(-1, -0.01, ns2, dtype=float)
         smu = numpy.array([(mu1, mu2) for mu1 in smu1 for mu2 in smu2])
         return smu
 
     def get_random_sample_of_mu(self, ns1: int = 1, ns2: int = 1):

+        "Renvoie l'échantillonnage paramétrique aléatoire"

         smu = []
         for i in range(ns1 * ns2):
             smu1 = numpy.random.uniform(-1, -0.01)
         smu = []
         for i in range(ns1 * ns2):
             smu1 = numpy.random.uniform(-1, -0.01)


@@ -69,9 +74,11 @@ class TwoDimensionalInverseDistanceCS2010:
         return smu
 
     def get_bounds_on_space(self):
         return smu
 
     def get_bounds_on_space(self):

+        "Renvoie les bornes sur le maillage spatial"

         return [[min(self.x), max(self.x)], [min(self.y), max(self.y)]]
 
     def get_bounds_on_parameter(self):
         return [[min(self.x), max(self.x)], [min(self.y), max(self.y)]]
 
     def get_bounds_on_parameter(self):

+        "Renvoie les bornes sur le maillage paramétrique"

         return [[-1, -0.01]] * 2
 
     OneRealisation = FunctionH
         return [[-1, -0.01]] * 2
 
     OneRealisation = FunctionH