Separate input generation from cre stacking #37

The 'SConscript' it is now responsible for model and data cube generation.
It limits the 'SConstruct' purpose to cre stacking of the generated data cubes.

experiments/multiLayerModel/cre/SConscript

@@ -0,0 +1,71 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# SConscript  (Madagascar Script)
+#
+# Purpose: Generate model and data cubes to CRE stacking process.
+# This script generates model, original and PEF interpolated data cubes.
+# It depends of the 'creStack.py' script that defines functions.
+#
+# Site: http://www.dirackslounge.online
+# 
+# Version 1.0
+#
+# Programer: Rodolfo A. C. Neves (Dirack) 04/03/2020
+#
+# Email: rodolfo_profissional@hotmail.com
+#
+# License: GPL-3.0 <https://www.gnu.org/licenses/gpl-3.0.txt>.
+
+# Madagascar package
+from rsf.proj import *
+
+# CRE recipe
+from creStack import pefInterpolation as pefin
+from creStack import multiLayerModel as mlmod
+from creStack import kirchoffNewtonModeling as kinewmod
+
+xmax = 6.0
+zmax = 2.0
+
+layers = ((0.30,0.50,0.20,0.30),
+	  (1.65,1.85,1.55,1.65))
+
+velocities = (1.508,
+	      1.690,
+	      2.0)
+
+# Generate multi layer model and data cube
+mlmod(interfaces='interfaces',
+	dipsfile='interfacesDip',
+	modelfile='mod1',
+	xmax=xmax,
+	zmax=zmax,
+	layers=layers,
+	velocities=velocities)
+
+kinewmod(reflectors='interfaces',
+	reflectorsDip='interfacesDip',
+	filename='multiLayerDataCube',
+	velocities=velocities)
+
+# PEF interpolation of the data cube
+pefin('multiLayerDataCube',
+    'interpolatedDataCube',
+    nm=201,
+    dm=0.025,
+    nt=1001,
+    dt=0.004,
+    nhi=161)
+
+# Do the PEF interpolation one more time
+# to increase the CMP sampling
+pefin('interpolatedDataCube',
+    'interpolatedDataCube2',
+    nm=402,
+    dm=0.0125,
+    nt=1001,
+    dt=0.004,
+    nhi=161)
+
+End()

experiments/multiLayerModel/cre/SConstruct

@@ -19,65 +19,20 @@
 # Madagascar package
 from rsf.proj import *
 
-# CRE recipe
-from creStack import pefInterpolation as pefin
-from creStack import multiLayerModel as mlmod
-from creStack import kirchoffNewtonModeling as kinewmod
-
 # Import glob python library
 import glob
 
-xmax = 6.0
-zmax = 2.0
-
-layers = ((0.30,0.50,0.20,0.30),
-	  (1.65,1.85,1.55,1.65))
-
-velocities = (1.508,
-	      1.690,
-	      2.0)
-
-# Generate multi layer model and data cube
-mlmod(interfaces='interfaces',
-	dipsfile='interfacesDip',
-	modelfile='mod1',
-	xmax=xmax,
-	zmax=zmax,
-	layers=layers,
-	velocities=velocities)
-
-kinewmod(reflectors='interfaces',
-	reflectorsDip='interfacesDip',
-	filename='multiLayerDataCube',
-	velocities=velocities)
-
-# PEF interpolation of the data cube
-pefin('multiLayerDataCube',
-    'interpolatedDataCube',
-    nm=201,
-    dm=0.025,
-    nt=1001,
-    dt=0.004,
-    nhi=161)
-
-# Do the PEF interpolation one more time
-# to increase the CMP sampling
-pefin('interpolatedDataCube',
-    'interpolatedDataCube2',
-    nm=402,
-    dm=0.0125,
-    nt=1001,
-    dt=0.004,
-    nhi=161)
-
+# Call SConscript to generate input: original and interpolated data cubes
+# SConscript('SConscript')
+
 # CRE stacking
 # It uses Very Fast Simulated Aneeling and non hyperbolic CRS
 # to get zero offset CRS parameters (RN, RNIP and BETA) from data cube
 v0 = 1.5
 ot0 = 1.0
 dt0 = 0.004
 nt0 = 500
-om0 = 2
+om0 = 2+1.5
 dm0 = 0.5
 nm0 = 6
 dataCube='multiLayerDataCube'