use the new load functions on dambreak colagrossi

2d/numericalTanks/randomWaves/README.rst

@@ -12,7 +12,7 @@ Random waves typically consist of non-repeatable wave sequences, so each individ
 
 Random wave sequences are generated by descritising the spectral distribution into frequency components and composing free-surface and velocity field by superimposing the free-surface elevation and velocity of each individual component. The amplitude of these components is calculated by the spectral distribution. The phasing of each component is either randomly allocated to generate a truly random series or pre-defined in order to generate focused waves. More information on random waves can be found in Goda (2009) and Dean and Dalrymple (1991).
 
-In this case, the numerical flume described in https://github.com/erdc-cm/air-water-vv/tree/adimako/merge/2d/numericalTanks/linearWaves is used to demonstrate the capability of Proteus for generating random waves.
+In this case, the numerical flume described in https://github.com/erdc/air-water-vv/tree/adimako/merge/2d/numericalTanks/linearWaves is used to demonstrate the capability of Proteus for generating random waves.
 
 Tests
 -----

2d/numericalTanks/randomWavesFast/README.rst

@@ -3,7 +3,7 @@ Fast Random wave  generation
 
 Description
 ----------
-The test case setup is described in https://github.com/erdc-cm/air-water-vv/tree/adimako/merge/2d/numericalTanks/randomWaves.
+The test case setup is described in https://github.com/erdc/air-water-vv/tree/adimako/merge/2d/numericalTanks/randomWaves.
 In this particular case, the generation methodology is optimised to allow fast generation of long non repeating random waves sequences using processing with spectral windows. More details on the methodology can be found in Dimakopoulos et al. (2017).
 
 Tests (to be added)

2d/numericalTanks/standingWaves/README.rst

@@ -6,7 +6,7 @@ Description
 
 Standing waves are formed when regular waves interact with a fully reflective wall. In this case, a distinctive patterns emerges and the wave amplitude becomes a sinusioidal function in space. The reflected waves are fully synconised with the incident ones at an even number of half wavelenghts from the wall, whilst cancelling each other at an odd number of wavelength, this creating the distinctive wave patterns of nodes and antinodes, where the amplitude is double and zero, respectively.
 
-In this case, the numerical flume described in https://github.com/erdc-cm/air-water-vv/tree/adimako/merge/2d/numericalTanks/linearWaves is used to demonstrate the capability of Proteus of modelling standing wave patterns and in particular of absorbing the reflected waves at the generation / absorption zone.
+In this case, the numerical flume described in https://github.com/erdc/air-water-vv/tree/adimako/merge/2d/numericalTanks/linearWaves is used to demonstrate the capability of Proteus of modelling standing wave patterns and in particular of absorbing the reflected waves at the generation / absorption zone.
 
 Tests
 -----

README.rst

@@ -2,7 +2,7 @@
 Verification and validation for air/water flow models
 =====================================================
 
-https://github.com/erdc-cm/air-water-vv
+https://github.com/erdc/air-water-vv
 
 Organization of the test set
 ----------------------------
@@ -16,7 +16,7 @@ test consists of a single directory including
 - All data files describing the geometry and physical parameters of
   the problem
 - A set of input files for a code
-  (e.g. https://github.com/erdc-cm/proteus)
+  (e.g. https://github.com/erdc/proteus)
 - A script for results postprocessing (Optional)
 - A script for running regression tests
 

Tests/test_dambreak_Colagrossi.py

@@ -1,16 +1,18 @@
 #!/usr/bin/env python
 import os
-os.chdir('/home/travis/build/erdc/proteus/air-water-vv/2d/benchmarks/dambreak_Colagrossi')
 import pytest
 from proteus.iproteus import *
 from proteus import Comm
 comm = Comm.get()
-import dambreak_Colagrossi_so
 import numpy as np
 import collections as cll
 import csv
 from proteus.test_utils import TestTools
-
+from proteus.defaults import (load_physics as load_p,
+                              load_numerics as load_n,
+                              load_system as load_so)
+modulepath = os.path.join(os.path.dirname(os.path.abspath(__file__)),'../2d/benchmarks/dambreak_Colagrossi')
+petsc_options = os.path.join(os.path.dirname(os.path.abspath(__file__)),"../inputTemplates/petsc.options.asm")
 class TestDambreakCollagrossiTetgen(TestTools.AirWaterVVTest):
 
     @classmethod
@@ -40,12 +42,12 @@ def test_run(self):
         from petsc4py import PETSc
         pList = []
         nList = []
-        for (p,n) in dambreak_Colagrossi_so.pnList:
-            pList.append(__import__(p))
-            nList.append(__import__(n))
+        so = load_so('dambreak_Colagrossi_so',modulepath)
+        for (p,n) in so.pnList:
+            pList.append(load_p(p,modulepath))
+            nList.append(load_n(n,modulepath))
             if pList[-1].name == None:
                 pList[-1].name = p
-        so = dambreak_Colagrossi_so
         so.name = "dambreak_Colagrossi"
         if so.sList == []:
             for i in range(len(so.pnList)):
@@ -55,7 +57,7 @@ def test_run(self):
         Profiling.verbose=True
         # PETSc solver configuration
         OptDB = PETSc.Options()
-        with open("../../../inputTemplates/petsc.options.asm") as f:
+        with open(petsc_options) as f:
             all = f.read().split()
             i=0
             while i < len(all):

private/proteus-mprans.yaml

@@ -4,7 +4,7 @@ dependencies:
   run: [proteus, numpy]
 
 sources:
-  - url: https://github.com/erdc-cm/proteus-mprans
+  - url: https://github.com/erdc/proteus-mprans
     key: git:fc5a90c2a50bb9716460876ad1ee91da598ff8f3
 
 profile_links: