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--- tutorials:grolink-on-kubernetes [2024/12/02 15:36] – tim
+++ tutorials:grolink-on-kubernetes [2024/12/02 15:38] (current) – [Generate input data with SALib] tim
@@ Line 270: / Line 270: @@
 </code>
-''2**4'' describes the number of input sets, we will define this very low by now (16) because we work on a simulated cluster and don't want to cause trouble.
+''%%2**4%%'' describes the number of input sets, we will define this very low by now (16) because we work on a simulated cluster and don't want to cause trouble.
@@ Line 349: / Line 349: @@
 </code>
+For simplicity you can find the last python code here in one file:
+<code python>
+import numpy as np
+from SALib.sample import saltelli
+from GroPy import GroPy
+import multiprocessing
+import kr8s
+from kr8s.objects import Pod
+WORKERCOUNT =9
+# defining the problem
+problem = {
+    'num_vars': 2,
+    'names': ['lenV', 'angle'],
+    'bounds': [[0.1, 1],[30, 70]]
+}
+param_values = saltelli.sample(problem, 2**2) # create parameter set
+#creating a link for each pod
+links=[]
+selector = {'app': 'grolink'}
+for podS in kr8s.get("pods", namespace="grolinktutorial", label_selector=selector):
+    print("x"+podS.status.podIP)
+    links.append(GroPy.GroLink("http://"+podS.status.podIP+":58081/api/"))
+# create an queue to assign pods to workers
+pods = multiprocessing.Queue()
+n = len(links)
+for i in range(0,WORKERCOUNT):
+    pods.put(links[i%n])
+#initialize each worker
+def init_worker(function,pods ):
+    function.cursor = pods.get().openWB(content=open("model.gsz",'rb').read()).run().read()
+# the actual execution
+def grow(val):
+    lenV, angle = val
+    results = []
+    #overwrite the parameters in the file
+    grow.cursor.updateFile("param/parameters.rgg",bytes("""
+            static float lenV="""+str(lenV)+""";
+            static float angle="""+str(angle)+""";
+            """,'utf-8')).run()
+    grow.cursor.compile().run()
+    for x in range(0,10): #execute 10 times
+        data=grow.cursor.runRGGFunction("run").run().read()
+        results.append(float(data['console'][0]))
+    return results
+# Multi processing
+pool = multiprocessing.Pool(processes=WORKERCOUNT,initializer=init_worker, initargs=(grow,pods,))
+results = pool.map(grow,param_values)
+pool.close()
+y = np.array(results)
+# save result
+np.savetxt("result.csv", y, delimiter=",")
+</code>