Doc: Fix label

Closes #122

.ci_support/run_docker_linux.sh

@@ -7,7 +7,7 @@ aurman -S python-sphinx-renku-theme python-sphinx_rtd_theme --noconfirm --noedit
 
 cp -r /io/* /tmp
 cd /tmp
-pip install . --user --break-system-packages
+pip install . --user --break-system-packages --no-deps
 ~/.local/bin/mo2fmu -h
 ~/.local/bin/mo2fmu ./otfmi/example/file/deviation.mo ./otfmi/example/file/fmu/linux-x86_64/deviation.fmu
 pythonfmu build --file ./otfmi/example/file/DeviationSlave.py --dest ./otfmi/example/file/fmu/linux-x86_64

doc/application/plot_cantilever_beam.py

@@ -29,7 +29,7 @@
 import openturns as ot
 import otfmi
 import otfmi.example.utility
-from openturns.viewer import View
+import openturns.viewer as otv
 
 path_fmu = otfmi.example.utility.get_path_fmu("deviation")
 
@@ -41,7 +41,7 @@
 # We test the function wrapping the deviation model on a point:
 point = ot.Point([3e7, 2e4, 255, 350])
 model_evaluation = model_fmu(point)
-print("Running the FMU: deviation = {}".format(model_evaluation))
+print(f"Running the FMU: deviation = {model_evaluation}")
 
 
 # %%
@@ -112,15 +112,18 @@
 # Draw the distribution of threshold excedance probability:
 monte_carlo_result = algo.getResult()
 probabilityDistribution = monte_carlo_result.getProbabilityDistribution()
-graph = View(probabilityDistribution.drawPDF())
+graph = otv.View(probabilityDistribution.drawPDF())
 
 # %%
 # Get the probability with which the beam deviation exceeds 30 cm:
 
 probability = monte_carlo_result.getProbabilityEstimate()
-print("Threshold excedance probability: {}".format(probability))
+print(f"Threshold excedance probability: {probability}")
 
 # %%
 # Given the uncertainties on the load applied and the beam mechanical
 # parameters, the beam bending has a probability of 0.01 to exceed 30 cm.
 # Is this probability low or not ? It depends on your context 🙂
+
+# %%
+otv.View.ShowAll()

doc/application/plot_metamodel.py

@@ -47,7 +47,7 @@
 
 import otfmi.example.utility
 import openturns as ot
-import openturns.viewer as viewer
+import openturns.viewer as otv
 
 path_fmu = otfmi.example.utility.get_path_fmu("epid")
 mesh = ot.RegularGrid(0.0, 0.05, 20)
@@ -75,12 +75,11 @@
 graph.setTitle("FMU simulations")
 graph.setXTitle("Time")
 graph.setYTitle("Number of infected")
-graph.setLegends(["{:.3f}".format(line[0]) for line in inputSample])
-view = viewer.View(graph, legend_kw={"title": "infection rate", "loc": "upper left"})
-view.ShowAll()
+graph.setLegends([f"{line[0]:.3f}" for line in inputSample[:15]] + ["_"] * 15)
+view = otv.View(graph, legend_kw={"title": "infection rate", "loc": "upper left"})
 
 # %%
-# We define a function to visualize the upcoming Karhunen-Loevem modes.
+# We define a function to visualize the upcoming Karhunen-Loeve modes.
 
 
 def drawKL(scaledKL, KLev, mesh, title="Scaled KL modes"):
@@ -121,15 +120,14 @@ def drawKL(scaledKL, KLev, mesh, title="Scaled KL modes"):
 phi_Y = resultKL.getScaledModesAsProcessSample()
 lambda_Y = resultKL.getEigenvalues()
 graph_modes_Y, graph_ev_Y = drawKL(phi_Y, lambda_Y, mesh, "Y")
-view = viewer.View(graph_modes_Y)
-view.ShowAll()
+view = otv.View(graph_modes_Y)
 
 # %%
 # Now that Karhunen-Loeve algorithm is trained, we can project them
 # in the smaller-dimension space:
 projectionSample = resultKL.project(outputFMUSample)
 n_mode = projectionSample.getDimension()
-print("Karhunen-Loeve projection is dimension {}".format(n_mode))
+print(f"Karhunen-Loeve projection in dimension {n_mode}")
 
 # %%
 # We keep on following our road map, by metamodeling the projection
@@ -186,20 +184,20 @@ def globalMetamodel(sample):
 for graph in [graph1, graph2]:
     graph.setXTitle("Time")
     graph.setYTitle("Number of infected")
-    graph.setLegends(["{:.3f}".format(line[0]) for line in inputSample])
+    graph.setLegends([f"{line[0]:.3f}" for line in inputSample[:10]])
 
 gridLayout.setGraph(0, 0, graph1)
 gridLayout.setGraph(0, 1, graph2)
-view = viewer.View(
+view = otv.View(
     gridLayout, legend_kw={"title": "infection rate", "loc": "upper left"}
 )
-view.ShowAll()
 
 # %%
 # We validate the pertinence of Karhunen-Loeve decomposition:
 
 validationKL = ot.KarhunenLoeveValidation(outputFMUTestSample, resultKL)
 graph = validationKL.computeResidualMean().draw()
+graph.setYTitle("infected residual mean")
 ot.Show(graph)
 
 # %%
@@ -237,3 +235,6 @@ def globalMetamodel(sample):
 # - `estimate a failure probability <openturns.github.io/openturns/latest/auto_reliability_sensitivity/index.html#reliability>`_,
 #
 # etc.
+
+# %%
+otv.View.ShowAll()

doc/example/dynamic/plot_dyn_init.py

@@ -91,6 +91,6 @@
 graph.setTitle("")
 graph.setXTitle("FMU simulation time (s)")
 graph.setYTitle("Number of infected")
-graph.setLegends(["{:.4f}".format(line[0]) for line in inputSample])
+graph.setLegends([f"{line[0]:.4f}" for line in inputSample])
 view = viewer.View(graph, legend_kw={"title": "infection rate", "loc": "upper left"})
 view.ShowAll()

doc/example/low_level/plot_explore.py

@@ -30,15 +30,15 @@
 
 for name in list_name:
     causality = otfmi.fmi.get_causality_str(model, name)
-    print("{}: {}".format(name, causality))
+    print(f"{name}: {causality}")
 
 # %%
 # | Yet the variables type is not known: real, integer, boolean, string?
 # | Let check using `PyFMI's method <http://shorturl.at/dJ157>`_:
 
 for name in list_name:
     typ = model.get_variable_data_type(name)
-    print("{}: {}".format(name, typ))
+    print(f"{name}: {typ}")
 
 # %%
 # | The type `0` corresponds to `Real` (aka "float") variables.

doc/example/static/plot_basics.py

@@ -28,7 +28,7 @@
 # Simulate the FMU on a point:
 inputPoint = ot.Point([3.0e7, 30000, 200, 400])
 outputPoint = function(inputPoint)
-print("y = {}".format(outputPoint))
+print(f"y = {outputPoint}")
 
 # %%
 # Simulate the FMU on a sample: