Adding dataset example

Author: Arnau Miro

Diff for: Examples/example_dataset.py

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+#!/bin/env python
+import numpy as np
+import pyLOM
+
+
+## Parameters
+OUTFILE = "./example_dataset.h5" 
+
+
+## Synthetic data
+#
+# This is the data you want to perform ML on, i.e., the cooling function
+# or the Cp on the airfoil example
+K= np.array([[[1,2,3,4],[5, 6, 7, 8],[9,10,11,12]], [[13, 14, 15, 16],[17,18,19,20],[21,22,23,24]]],np.float32).T
+print('K:',K.shape) # (4, 3, 2)
+
+# So the first dimension, 4, are the xyz positions
+# then xyz must have length (4,3) or (4,2) in 2D
+xyz = np.array([[1,19,12],[1,20,12],[2,5,11],[3,6,11]],np.float32)
+print('xyz:',xyz.shape) # (4, 3)
+
+# For the point order array we simply numerate our
+# points consecutively
+pointO = np.arange(xyz.shape[0])
+print('pointO:',pointO.shape) # (4,)
+
+# Now we are missing two variables of size (3,) and (2,)
+# for this example we will call them `var1` and `var2`
+var1 = np.array([20,30,40],np.float32)
+print('var1:',var1.shape) # (3,)
+var2 = np.array([200,300],np.float32)
+print('var2:',var2.shape) # (2,)
+print()
+
+
+## Create a serial partition table
+# The number of points is simply the first dimension
+# of K and xyz
+npoints = xyz.shape[0]
+# Here we create a table with a single partition with the
+# same number of points and elements (i.e., a cloud of points)
+ptable  = pyLOM.PartitionTable.new(1,npoints,npoints)
+print(ptable)
+
+
+## Create a pyLOM dataset
+# Now we can arrange the dataset
+# In this example, it is indiferent if we treat the data as
+# points or cells. For simplicity we will assume the data to
+# be points.
+d = pyLOM.Dataset(xyz=xyz,ptable=ptable,order=pointO,point=True,
+    # Add the variables as a dictionary associated with
+    # their dimensions on the matrix K
+    vars  = {
+        # We associate var1 to the first dimension of K, i.e.,
+        # idim = 0 as dimension 0 of K is always the number of
+        # points
+        'var1' : {'idim':0,'value':var1},
+        # We associate var2 to the second dimension of K, i.e.,
+        # idim = 1        
+        'var2' : {'idim':1,'value':var2},
+    },
+    # Now we add the fields, i.e., the actual data to compute
+    # things. So K has ndim = 2 as there are two extra dimensions
+    # other than the number of points
+    K = {'ndim':2,'value':K}
+)
+print(d)
+
+# Now we store the dataset
+# we make sure to activate mode = 'w' to overwrite
+# any possible early results
+d.save(OUTFILE,mode='w')
+
+
+pyLOM.cr_info()