up manual ci

.github/workflows/manual.yml

@@ -21,7 +21,7 @@ jobs:
       run: |
         python -m venv --system-site-packages .venv
         source .venv/bin/activate
-        pip install -r requirements.txt  
+        pip install -I -r requirements.txt  
     - name: Npm Install 
       run: |
         npm i 

docs/mor/modules/ROOT/pages/taylor-green-vortex/index.adoc

@@ -146,19 +146,92 @@ where stem:[\eta_1, \eta_2, \eta_3] are three Wendland functions stem:[\psi_{2,1
 == Setup for the notebook simulation
 
 .Setup the variables for the notebook simulation
-//[%dynamic,python]
-[source,python]
+[%dynamic,python]
 ----
 import numpy as np
 girder_path = "https://girder.math.unistra.fr/api/v1/item/64c165a5b0e9570499e1cc3c/download"
 fpp_name = 'ad.fpp'
 time = np.linspace(start=0, stop=2.5, num=251)
 ----
 
+== Downloading the reduced order model from Girder
+
+The offline creation of the reduced basis has already been performed, and an archive is downloaded from Girder. It contains the basis, the model and the configuration files that are necessary for the online simulation. The following code snippet performs the download.
+
+.Download the archive from Girder
+//[%dynamic,python]
+[source,python]
+----
+import requests
+r=requests.get(girder_path)
+with open(fpp_name,'wb') as f:
+    f.write(r.content)
+----
+
+//include::partial$online_notebook.adoc[]
+// include do not work in asciidoctor-jupyter
+== Running the case using a Jupyter notebook
+
+It is possible to download this page as a Jupyter notebook and run it in an environment that contains a local installation of {feelpp} and its Python wrappers.
+
+
+
+.Create the online model, choose randomly 4 parameter vectors and simulate them using a reduced basis of 10 elements
+//[%dynamic,python]
+[source,python]
+----
+import feelpp as fppc
+from feelpp.mor import *
+ms=feelpp.mor.MORModels(fpp_name)
+muspace = ms.parameterSpace()
+sampling = muspace.sampling()
+sampling.sample(4, "random")
+r=ms.run(sampling,{"N":10})
+----
+
+.Print the outputs and the associated parameter vector
+//[%dynamic,python]
+[source,python]
+----
+from pandas import DataFrame as df
+from pandas import options as op
+from pandas import set_option
+outputs={}
+errors={}
+output_dataframes = list()
+errors_dataframes = list()
+for i in range(len(r)):
+    outputs={}
+    errors={}
+    for o in range(len(r[i])):
+        str_time = "Time"
+        str_output = "Output "+str(o)
+        str_error = "Error "+str(o)
+
+        outputs[str_time] = time
+        outputs[str_output] = np.array(r[i][o].outputs())
+        outputs[str_error] = [np.array(r[i][o].errors())]
+
+
+    output_frame = df(data=outputs)
+
+
+    set_option('display.float_format', '{:.2E}'.format)
+
+    output_dataframes.append(output_frame)
+
+op.display.max_colwidth = 100
+
+i=0
+for frame in output_dataframes:
+    print("Parameters :",sampling[i])
+    print(frame)
+    print("\n")
+    i=i+1
+
+----
 
-include::partial$girder_fpp.adoc[]
 
-include::partial$online_notebook.adoc[]
 
 .Show the values of the outputs for different parameters
 //[%dynamic,python]