From e7566f4738a4889a04888bca68ac3327af8c7098 Mon Sep 17 00:00:00 2001
From: Romain Boman <r.boman@uliege.be>
Date: Mon, 27 Sep 2021 16:44:04 +0200
Subject: [PATCH] remove "Metafor" folder (parametric calculations from Kim -
not tested)
---
Metafor/Mparams/Metafor_call.py | 78 -
Metafor/Mparams/TaskManager.py | 389 ---
Metafor/Mparams/__init__.py | 5 -
Metafor/Mparams/mparams.py | 98 -
Metafor/Mparams/sequential.py | 376 --
Metafor/Mparams/surrogate.py | 335 --
Metafor/__init__.py | 5 -
Metafor/config/bord01_config1.py | 133 -
Metafor/config/bord01_config10.py | 209 --
Metafor/config/bord01_config11.py | 218 --
Metafor/config/bord01_config12.py | 140 -
Metafor/config/bord01_config13.py | 297 --
Metafor/config/bord01_config2.py | 128 -
Metafor/config/bord01_config3.py | 128 -
Metafor/config/bord01_config4.py | 153 -
Metafor/config/bord01_config5.py | 128 -
Metafor/config/bord01_config6.py | 128 -
Metafor/config/bord01_config7.py | 152 -
Metafor/config/bord01_config8.py | 145 -
Metafor/config/bord01_config9.py | 294 --
Metafor/config/bord01_numericalSA.py | 299 --
Metafor/config/bord01_numericalSA2.py | 125 -
Metafor/config/mirror01_config1.py | 147 -
Metafor/config/mirror01_config2.py | 147 -
Metafor/config/mirror01_config3.py | 147 -
Metafor/config/mirror02_config2.py | 147 -
Metafor/config/omega01_config1.py | 129 -
Metafor/config/omega01_test.py | 129 -
Metafor/config/tube01_config1.py | 129 -
Metafor/config/tube01_config2.py | 129 -
Metafor/config/tube01_test.py | 129 -
Metafor/models/__init__.py | 5 -
Metafor/models/bord01/W_tp.json | 677 ----
Metafor/models/bord01/X_tp.json | 677 ----
Metafor/models/bord01/createNumericalSA.m | 85 -
Metafor/models/bord01/data.txt | 3087 -----------------
Metafor/models/bord01/frinctionSA.ascii | 21 -
.../models/bord01/hardening_yield.ascii.txt | 25 -
Metafor/models/bord01/model.py | 21 -
.../bord01/numericalSATrainingPoints.ascii | 3087 -----------------
.../bord01/numericalSATrainingPoints2.ascii | 500 ---
.../bord01/numericalSATrainingPoints3.ascii | 4 -
.../models/bord01/numericalSAWeights.ascii | 3087 -----------------
.../models/bord01/numericalSAWeights2.ascii | 500 ---
.../models/bord01/numericalSAWeights3.ascii | 4 -
Metafor/models/bord01/tombeBord.py | 268 --
Metafor/models/bord01/tombeBord_old.py | 248 --
Metafor/models/mirror01/__init__.py | 5 -
Metafor/models/mirror01/mirror.geo | 86 -
Metafor/models/mirror01/mirror.py | 197 --
Metafor/models/mirror01/model.py | 51 -
Metafor/models/mirror01/parameters_file.py | 38 -
Metafor/models/omega01/EmboutOmega.py | 583 ----
Metafor/models/omega01/model.py | 23 -
Metafor/models/tube01/model.py | 23 -
Metafor/models/tube01/tube.py | 19 -
56 files changed, 18517 deletions(-)
delete mode 100644 Metafor/Mparams/Metafor_call.py
delete mode 100644 Metafor/Mparams/TaskManager.py
delete mode 100644 Metafor/Mparams/__init__.py
delete mode 100644 Metafor/Mparams/mparams.py
delete mode 100644 Metafor/Mparams/sequential.py
delete mode 100644 Metafor/Mparams/surrogate.py
delete mode 100644 Metafor/__init__.py
delete mode 100644 Metafor/config/bord01_config1.py
delete mode 100644 Metafor/config/bord01_config10.py
delete mode 100644 Metafor/config/bord01_config11.py
delete mode 100644 Metafor/config/bord01_config12.py
delete mode 100644 Metafor/config/bord01_config13.py
delete mode 100644 Metafor/config/bord01_config2.py
delete mode 100644 Metafor/config/bord01_config3.py
delete mode 100644 Metafor/config/bord01_config4.py
delete mode 100644 Metafor/config/bord01_config5.py
delete mode 100644 Metafor/config/bord01_config6.py
delete mode 100644 Metafor/config/bord01_config7.py
delete mode 100644 Metafor/config/bord01_config8.py
delete mode 100644 Metafor/config/bord01_config9.py
delete mode 100644 Metafor/config/bord01_numericalSA.py
delete mode 100644 Metafor/config/bord01_numericalSA2.py
delete mode 100644 Metafor/config/mirror01_config1.py
delete mode 100644 Metafor/config/mirror01_config2.py
delete mode 100644 Metafor/config/mirror01_config3.py
delete mode 100644 Metafor/config/mirror02_config2.py
delete mode 100644 Metafor/config/omega01_config1.py
delete mode 100644 Metafor/config/omega01_test.py
delete mode 100644 Metafor/config/tube01_config1.py
delete mode 100644 Metafor/config/tube01_config2.py
delete mode 100644 Metafor/config/tube01_test.py
delete mode 100644 Metafor/models/__init__.py
delete mode 100644 Metafor/models/bord01/W_tp.json
delete mode 100644 Metafor/models/bord01/X_tp.json
delete mode 100644 Metafor/models/bord01/createNumericalSA.m
delete mode 100644 Metafor/models/bord01/data.txt
delete mode 100644 Metafor/models/bord01/frinctionSA.ascii
delete mode 100644 Metafor/models/bord01/hardening_yield.ascii.txt
delete mode 100644 Metafor/models/bord01/model.py
delete mode 100644 Metafor/models/bord01/numericalSATrainingPoints.ascii
delete mode 100644 Metafor/models/bord01/numericalSATrainingPoints2.ascii
delete mode 100644 Metafor/models/bord01/numericalSATrainingPoints3.ascii
delete mode 100644 Metafor/models/bord01/numericalSAWeights.ascii
delete mode 100644 Metafor/models/bord01/numericalSAWeights2.ascii
delete mode 100644 Metafor/models/bord01/numericalSAWeights3.ascii
delete mode 100644 Metafor/models/bord01/tombeBord.py
delete mode 100644 Metafor/models/bord01/tombeBord_old.py
delete mode 100644 Metafor/models/mirror01/__init__.py
delete mode 100644 Metafor/models/mirror01/mirror.geo
delete mode 100644 Metafor/models/mirror01/mirror.py
delete mode 100644 Metafor/models/mirror01/model.py
delete mode 100644 Metafor/models/mirror01/parameters_file.py
delete mode 100644 Metafor/models/omega01/EmboutOmega.py
delete mode 100644 Metafor/models/omega01/model.py
delete mode 100644 Metafor/models/tube01/model.py
delete mode 100644 Metafor/models/tube01/tube.py
diff --git a/Metafor/Mparams/Metafor_call.py b/Metafor/Mparams/Metafor_call.py
deleted file mode 100644
index 002bd84d..00000000
--- a/Metafor/Mparams/Metafor_call.py
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-import numpy as np
-import os
-import shutil
-
-import fwk
-
-isUnix = lambda: os.name == 'posix'
-
-def Metafor_call(d):
- import shlex, subprocess
- import platform
- command_line = d['Metafor_exe'] + ' -nogui'
- print(command_line)
- args = shlex.split(command_line)
-
- fileout = open('Metafor.log','w')
-
- workdir = os.getcwd()
-
- if isUnix(): # sans close_fds, ca freeze
- if platform.system() == 'Darwin':
- my_env = os.environ.copy()
- my_env["DYLD_LIBRARY_PATH"] = my_env.get("VTK_DIR", '')
- p = subprocess.Popen(args, stdin=subprocess.PIPE, stdout=fileout, stderr=fileout, env=my_env, shell=False,close_fds=True)
- else:
- p = subprocess.Popen(args, stdin=subprocess.PIPE, stdout=fileout, stderr=fileout, env=os.environ, shell=False,close_fds=True)
- else: # si nice+Windows => "shell=True" (pour "start")
- p = subprocess.Popen(args, stdin=subprocess.PIPE, stdout=fileout, stderr=fileout, env=os.environ, shell=True)
-
- pin = p.stdin
- pin.write("import sys\n")
- pin.write("sys.path.append(%s)\n" % repr(d['Metafor_model']))
- pin.write("load(%s)\n" % repr(d['Metafor_model_name']))
- #pin.write("setDir(%s)\n" % repr(workdir))
- pin.write("para = %s\n" % repr(d))
- pin.write("instance(para)\n" )
- pin.write("meta()\n")
- pin.close()
-
- retcode = p.wait()
-
- fileout.close()
-
- '''
- print os.path.isfile('pars.py')
- print os.path.isfile('Metafor.log')
- print 'workspace/' + d['Metafor_model_name'] + '/time.ascii'
- print os.path.isfile('workspace/' + d['Metafor_model_name'] + '/time.ascii')
- '''
-
- src = workdir + '/workspace/' + d['Metafor_model_name']
-
- src_files = os.listdir(src)
- for file_name in src_files:
- full_file_name = os.path.join(src, file_name)
- if os.path.isfile(full_file_name):
- shutil.copy(full_file_name, workdir)
-
- shutil.rmtree(workdir + '/workspace')
diff --git a/Metafor/Mparams/TaskManager.py b/Metafor/Mparams/TaskManager.py
deleted file mode 100644
index ac9764c0..00000000
--- a/Metafor/Mparams/TaskManager.py
+++ /dev/null
@@ -1,389 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import tbox
-import math
-
-import numpy as np
-import os
-import shutil
-
-import params.src.write as pwrite
-import params.src.indices as indices
-
-import fwk
-
-verb = False # set to True for (a lot of) debug info
-
-try:
- import mpi4py.MPI as mpi
- comm = mpi.COMM_WORLD
- rank = comm.rank
- siz = comm.size
- name = mpi.Get_processor_name()
- status = mpi.Status()
- print("info: MPI found")
-except:
- comm = None
- rank = 0
- siz = 1
- name = "noname"
- print("info: MPI not found => MPI disabled")
-
-def enum(*sequential, **named):
- enums = dict(list(zip(sequential, list(range(len(sequential))))), **named)
- return type('Enum', (), enums)
-
-tags = enum('READY', 'DONE', 'EXIT', 'START', 'WAKEUP', 'SUICIDE')
-
-
-
-class Job:
- """ Class containing data transmitted between MPI procs
- """
- def __init__(self, parameters, name, xi, m_i):
- # input
- self.m_i = m_i
- self.name = name
- self.parameters = parameters
- self.xi = xi
- # output
- self.QoI = 0.
-
- def execute(self):
- """
- [executed by worker processes]
- solve a given job and calculate "QoI"
- """
- import imp
- model = imp.load_source('Metafor_call',self.parameters['Metafor_model']+'/model.py')
-
- name = self.name
- if os.path.exists(name):
- shutil.rmtree(name)
- os.mkdir(name)
- os.chdir(name)
-
- self.QoI = model.model(self.parameters)
-
- os.chdir('..')
-
-
-class Master:
- """
- MPI Process with rank #0
- """
- def __init__(self):
- self.slaves = list(range(1,siz))
-
- def start(self,parameters, surrogate_parameters, images_parameters):
- """
- master loop
- """
- global rank
- if rank!=0:
- raise Exception("this routine should be called with MPI rank=0")
- if verb: print("[%d] starting master" % rank)
-
- ##-----------------------------------------
-
- for s in self.slaves:
- if verb: print("[%d] sending wake-up signal to worker %d" % (rank,s))
- comm.send(None, dest=s, tag=tags.WAKEUP)
-
- #################################################
- ## Read the parameters
- fname = surrogate_parameters['fname']
- d = surrogate_parameters['d']
- M = surrogate_parameters['M']
- N = surrogate_parameters['N']
-
- Dom_min = surrogate_parameters['Dom_min']
- Dom_max = surrogate_parameters['Dom_max']
- P_name = surrogate_parameters['P_name']
- solver = surrogate_parameters['solver']
-
- normalisation = surrogate_parameters['normalisation']
- ##
- if surrogate_parameters['function_type'] == 'monomial':
- from params.src.monomials import phi
- from params.src.monomials import compute_n_max
- n_max = compute_n_max(N,d)
- elif surrogate_parameters['function_type'] == 'monomial_1norm':
- from params.src.monomials_1norm import phi
- from params.src.monomials_1norm import compute_n_max
- n_max = compute_n_max(N,d)
- print(n_max)
- if surrogate_parameters['training_points'] == 'gauss':
- import params.src.gauss as tp
- elif surrogate_parameters['training_points'] == 'gamma':
- import params.arnst_ponthot.src.gamma as tp
- ##
-
- m_max = 1
- for j in range(0,d):
- m_max = m_max*M[j]
-
- if surrogate_parameters['training_points'] == 'precomputed':
-
-
- import codecs, json
-
- #obj_text = codecs.open(surrogate_parameters['precomputed_training_points'], 'r', encoding='utf-8').read()
- #b_new = json.loads(obj_text)
- #X = np.array(b_new)
-
- X = np.genfromtxt(surrogate_parameters['precomputed_training_points'], dtype=None)
-
- #X = np.loadtxt('/home/kliegeois/Codes/waves/params/CVT/python/cvt_circle7.txt')
- tmp = np.shape(X)
- m_max = tmp[0]
- w = np.ones((m_max,))
- else:
- X, w = tp.grid(M)
-
- print(X)
-
-
-
- i = np.zeros((d,), dtype =np.int)
- i_zero = np.zeros((d,), dtype =np.int)
- i_one = np.ones((d,), dtype =np.int)
- p = 0
-
- xi = np.zeros((d,))
-
- if surrogate_parameters['display']:
- import params.src.plot as plot
- fig, ax, current_title = plot.init2(d, images_parameters)
-
- if not os.path.exists('images'):
- os.mkdir('images')
- else:
- shutil.rmtree('images')
- os.mkdir('images')
-
- m_i = 0
- sol = np.zeros((m_max,1))
-
- Z = np.zeros((m_max,n_max))
- W_sr = np.zeros((m_max,m_max))
-
- #################################################
-
- num_workers = siz-1
- closed_workers = 0
- while closed_workers<num_workers:
- # get a msg from any source
- data = comm.recv(source=mpi.ANY_SOURCE,
- tag=mpi.ANY_TAG, status=status)
- source = status.Get_source()
- tag = status.Get_tag()
- if tag == tags.READY:
- if verb: print("[0] worker %d is ready" % source)
-
- #################################################
-
- if np.all(i != -i_one):
- if surrogate_parameters['training_points'] == 'precomputed':
- if normalisation == False:
- for j in range(0,d):
- print(X[0][0])
- xi[j] = X[i[j]][j]
- else:
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
- correlations = surrogate_parameters['correlations']
-
- for j in range(0,d):
- xi[j] = (X[m_i,j]-means[j]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[j]))
-
- elif surrogate_parameters['training_points'] == 'gamma':
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
- correlations = surrogate_parameters['correlations']
-
- xi_in = np.zeros((d,1))
- for j in range(0,d):
- xi_in[j] = X[i[j],j]
- xi = tp.f_mapping(xi_in,d,means,variances,correlations,surrogate_parameters['f_mapping_method'])
-
-
- print(xi)
- elif normalisation == True:
- for j in range(0,d):
- xi[j] = X[i[j],j]
- elif normalisation == False:
- for j in range(0,d):
- xi[j] = X[i[j],j]
- xi[j] = Dom_min[j] + (Dom_max[j] - Dom_min[j])*(xi[j]+1.)/2.
- parameters[P_name[j]] = xi[j]
-
- # -------------------
-
- if surrogate_parameters['training_points'] == 'precomputed':
- Z[m_i,:] = np.transpose(phi(xi,N,n_max,d))
- #print xi2
- elif surrogate_parameters['training_points'] == 'gamma':
- xi2 = np.zeros((d,))
- for j in range(0,d):
- xi2[j] = (xi[j][0]-means[j]) / np.sqrt(variances[j])
- Z[m_i,:] = np.transpose(phi(xi2,N,n_max,d))
- #print xi2
- else:
- Z[m_i,:] = np.transpose(phi(xi,N,n_max,d))
-
-
- tmp = 1
- if surrogate_parameters['training_points'] != 'precomputed':
- for j in range(0,d):
- tmp = tmp*w[i[j],j]
-
- W_sr[m_i,m_i] = np.sqrt(tmp)
-
- # -------------------
-
- if surrogate_parameters['training_points'] != 'precomputed':
- for j in range(0,d):
- tmp = xi[j]
- print(tmp)
- parameters[P_name[j]] = tmp
-
- if surrogate_parameters['training_points'] == 'precomputed':
- for j in range(0,d):
- parameters[P_name[j]] = X[m_i][j]
-
- elif surrogate_parameters['training_points'] == 'gamma':
- for j in range(0,d):
- tmp = xi[j]
- parameters[P_name[j]] = tmp[0]
-
- #print parameters
- elif normalisation == True:
- for j in range(0,d):
- xi[j] = Dom_min[j] + (Dom_max[j] - Dom_min[j])*(xi[j]+1.)/2.
- parameters[P_name[j]] = xi[j]
-
- # -------------------
-
- name = 'i_'
- for j in range(0,d-1):
- name = name + str(i[j]) + '_'
- name = name + str(i[d-1]) + '_'
- name = name + 'M_'
- for j in range(0,d-1):
- name = name + str(M[j]) + '_'
- name = name + str(M[d-1])
-
- # -------------------
-
- job = Job(parameters, name, xi, m_i)
-
- if verb: print("[0] sending job to %d" % source)
- comm.send(job, dest=source, tag=tags.START)
-
- # -------------------
-
- p,i = indices.my_next(p,i,M,d)
- m_i = m_i+1
- else:
-
- #################################################
-
- if verb: print("[%d] exit %d" % (rank,source))
- comm.send(None, dest=source, tag=tags.EXIT)
-
- elif tag==tags.DONE:
- if verb: print("[0] worker %d gives me its results" % source)
-
- #################################################
- m_ii = data.m_i
- xi = data.xi
- sol[m_ii] = data.QoI
-
- if surrogate_parameters['display']:
- plot.scatter(xi,sol[m_ii],d,fig,ax, True,m_ii)
-
- #################################################
-
- elif tag==tags.EXIT:
- closed_workers+=1
- if verb: print("[0] worker %d exited (%d worker(s) still running)" % (source, num_workers-closed_workers))
-
- ##-----------------------------------------
-
- self.killslaves()
-
-
- Image = {}
- if surrogate_parameters['display']:
- Image['fig'] = fig
- Image['ax'] = ax
- Image['current_title'] = current_title
- return sol, Z, W_sr, Image
-
- def killslaves(self):
- global rank
- for s in self.slaves:
- if verb: print("[%d] sending suicide job to %d" % (rank,s))
- comm.send(None, dest=s, tag=tags.SUICIDE)
-
-
-class Worker:
- """
- MPI Process with rank #1-#n
- """
- def __init__(self, rank):
- self.rank = rank
-
- def start(self):
- """
- worker loop
- """
- if self.rank==0:
- raise Exception("this routine should be called with MPI rank!=0")
-
- if verb: print("[%d] starting worker" % self.rank)
- timers = fwk.Timers()
- timers['total'].start()
- while True:
- comm.recv(source=0, tag=mpi.ANY_TAG, status=status)
- tag = status.Get_tag()
- if tag==tags.WAKEUP:
- if verb: print("[%d] waking up" % self.rank)
- while True:
- comm.send(None, dest=0, tag=tags.READY)
- job = comm.recv(source=0, tag=mpi.ANY_TAG, status=status)
- tag = status.Get_tag()
- if tag==tags.START:
- if verb: print("[%d] starting job" % self.rank)
- name = str(job.m_i)+'_'+job.name
- timers[name].start()
- job.execute()
- timers[name].stop()
- if verb: print("[%d] sending job results" % self.rank)
- comm.send(job, dest=0, tag=tags.DONE)
- elif tag==tags.EXIT:
- if verb: print("[%d] sending exit confirmation" % self.rank)
- comm.send(None, dest=0, tag=tags.EXIT)
- timers['total'].stop()
- print(timers)
- break
- elif tag==tags.SUICIDE:
- if verb: print("[%d] I'm dying..." % self.rank)
- break
diff --git a/Metafor/Mparams/__init__.py b/Metafor/Mparams/__init__.py
deleted file mode 100644
index 0b4a1527..00000000
--- a/Metafor/Mparams/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# -*- coding: utf-8; -*-
-# mirrors MODULE initialization file
-
-import tbox
-from mirrorsw import *
diff --git a/Metafor/Mparams/mparams.py b/Metafor/Mparams/mparams.py
deleted file mode 100644
index 10e6e3b6..00000000
--- a/Metafor/Mparams/mparams.py
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import sys
-import os
-
-import fwk
-
-import params.src.write as write
-
-from Metafor.Mparams.surrogate import *
-from Metafor.Mparams.sequential import evaluate
-import Metafor.Mparams.TaskManager as tm
-
-def master(model_parameters, surrogate_parameters, images_parameters):
- timers = fwk.Timers()
- timers['total'].start()
- timers['prepocessing'].start()
-
-
- dirname = 'master'
- if os.path.exists(dirname):
- shutil.rmtree(dirname)
- os.mkdir(dirname)
- os.chdir(dirname)
-
- f = write.init(surrogate_parameters['fname'])
- write.w_MPI_info(f, tm.name, tm.siz)
-
- write.w_list(f, model_parameters, 'Model parameters')
- write.w_list(f, surrogate_parameters, 'Surrogate parameters')
- ##
- timers['prepocessing'].stop()
- timers['training points pocessing'].start()
- if tm.siz == 1:
- sol, Z, W_sr, Image = evaluate(model_parameters, surrogate_parameters, images_parameters)
- else:
- master = tm.Master()
- sol, Z, W_sr, Image = master.start(model_parameters, surrogate_parameters, images_parameters)
-
- timers['training points pocessing'].stop()
- timers['surrogate pocessing'].start()
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- timers['surrogate pocessing'].stop()
- timers['total'].stop()
-
- write.w_vector(f, sol, 'Training points solutions')
- write.w_vector(f, s_hat, 'Computed coefficients')
- write.w_timers(f, timers)
- write.finish(f)
-
- return sol, Z, W_sr, s_hat
-
-def worker():
- worker = tm.Worker(tm.rank)
- worker.start()
-
-def AvailableComputedResults():
- out = True
- if not os.path.isfile('master/sol.json'):
- out = False
- if not os.path.isfile('master/Z.json'):
- out = False
- if not os.path.isfile('master/W_sr.json'):
- out = False
- return out
-
-def LoadComputedResults():
- import codecs, json
-
- obj_text = codecs.open('master/sol.json', 'r', encoding='utf-8').read()
- b_new = json.loads(obj_text)
- sol = np.array(b_new)
-
- obj_text = codecs.open('master/Z.json', 'r', encoding='utf-8').read()
- b_new = json.loads(obj_text)
- Z = np.array(b_new)
-
- obj_text = codecs.open('master/W_sr.json', 'r', encoding='utf-8').read()
- b_new = json.loads(obj_text)
- W_sr = np.array(b_new)
-
- return sol, Z, W_sr
diff --git a/Metafor/Mparams/sequential.py b/Metafor/Mparams/sequential.py
deleted file mode 100644
index f36bb847..00000000
--- a/Metafor/Mparams/sequential.py
+++ /dev/null
@@ -1,376 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import os
-import shutil
-
-import params.src.indices as indices
-
-def evaluate(parameters, surrogate_parameters, images_parameters):
- import imp
- model = imp.load_source('Metafor_call',parameters['Metafor_model']+'/model.py')
- ## Read the parameters
- fname = surrogate_parameters['fname']
- d = surrogate_parameters['d']
- M = surrogate_parameters['M']
- N = surrogate_parameters['N']
-
- Dom_min = surrogate_parameters['Dom_min']
- Dom_max = surrogate_parameters['Dom_max']
- P_name = surrogate_parameters['P_name']
- solver = surrogate_parameters['solver']
-
- normalisation = surrogate_parameters['normalisation']
- ##
- if surrogate_parameters['function_type'] == 'monomial':
- from params.src.monomials import phi
- from params.src.monomials import compute_n_max
- n_max = compute_n_max(N,d)
- elif surrogate_parameters['function_type'] == 'monomial_1norm':
- from params.src.monomials_1norm import phi
- from params.src.monomials_1norm import compute_n_max
- n_max = compute_n_max(N,d)
- if surrogate_parameters['training_points'] == 'gauss':
- import params.src.gauss as tp
- elif surrogate_parameters['training_points'] == 'gamma':
- import params.arnst_ponthot.src.gamma as tp
-
- ##
-
- X, w = tp.grid(M)
-
- m_max = 1
- for j in range(0,d):
- m_max = m_max*M[j]
-
- i = np.zeros((d,), dtype =np.int)
- i_zero = np.zeros((d,), dtype =np.int)
- i_one = np.ones((d,), dtype =np.int)
- p = 0
-
- xi = np.zeros((d,))
-
- if surrogate_parameters['display']:
- import params.src.plot as plot
- fig, ax, current_title = plot.init2(d, images_parameters)
-
- if not os.path.exists('images'):
- os.mkdir('images')
- else:
- shutil.rmtree('images')
- os.mkdir('images')
-
- m_i = 0
- sol = np.zeros((m_max,1))
-
- Z = np.zeros((m_max,n_max))
- W_sr = np.zeros((m_max,m_max))
-
- while np.all(i != -i_one):
-
-
- if normalisation == True:
- for j in range(0,d):
- xi[j] = X[i[j],j]
- elif normalisation == False:
- for j in range(0,d):
- xi[j] = X[i[j],j]
- xi[j] = Dom_min[j] + (Dom_max[j] - Dom_min[j])*(xi[j]+1.)/2.
- parameters[P_name[j]] = xi[j]
-
- # -------------------
-
- Z[m_i,:] = np.transpose(phi(xi,N,n_max,d))
-
- tmp = 1
- for j in range(0,d):
- tmp = tmp*w[i[j],j]
-
- W_sr[m_i,m_i] = np.sqrt(tmp)
-
-
- # -------------------
-
- if normalisation == True:
- for j in range(0,d):
- xi[j] = Dom_min[j] + (Dom_max[j] - Dom_min[j])*(xi[j]+1.)/2.
- parameters[P_name[j]] = xi[j]
-
- # -------------------
-
- name = 'i_'
- for j in range(0,d-1):
- name = name + str(i[j]) + '_'
- name = name + str(i[d-1]) + '_'
- name = name + 'M_'
- for j in range(0,d-1):
- name = name + str(M[j]) + '_'
- name = name + str(M[d-1])
-
- # -------------------
-
- if os.path.exists(name):
- shutil.rmtree(name)
- os.mkdir(name)
- os.chdir(name)
-
- sol[m_i] = model.model(parameters)
-
- os.chdir('..')
-
- if surrogate_parameters['display']:
- plot.scatter(xi,sol[m_i],d,fig,ax, True,m_i)
-
-
- m_i = m_i + 1
-
- p,i = indices.my_next(p,i,M,d)
-
-
- #------------------
- Image = {}
- if surrogate_parameters['display']:
- Image['fig'] = fig
- Image['ax'] = ax
- Image['current_title'] = current_title
- return sol, Z, W_sr, Image
-
-
-def Z_evaluate(parameters, surrogate_parameters):
- import imp
- model = imp.load_source('Metafor_call',parameters['Metafor_model']+'/model.py')
- ## Read the parameters
- fname = surrogate_parameters['fname']
- d = surrogate_parameters['d']
- M = surrogate_parameters['M']
- N = surrogate_parameters['N']
-
- Dom_min = surrogate_parameters['Dom_min']
- Dom_max = surrogate_parameters['Dom_max']
- P_name = surrogate_parameters['P_name']
- solver = surrogate_parameters['solver']
-
- normalisation = surrogate_parameters['normalisation']
- ##
- if surrogate_parameters['function_type'] == 'monomial':
- from params.src.monomials import phi
- from params.src.monomials import compute_n_max
- n_max = compute_n_max(N,d)
- elif surrogate_parameters['function_type'] == 'monomial_1norm':
- from params.src.monomials_1norm import phi
- from params.src.monomials_1norm import compute_n_max
- n_max = compute_n_max(N,d)
- if surrogate_parameters['training_points'] == 'gauss':
- import params.src.gauss as tp
- elif surrogate_parameters['training_points'] == 'gamma':
- import params.arnst_ponthot.src.gamma as tp
-
- ##
-
- if surrogate_parameters['training_points'] != 'precomputed':
-
- X, w = tp.grid(M)
-
- m_max = 1
- for j in range(0,d):
- m_max = m_max*M[j]
- if surrogate_parameters['training_points'] == 'precomputed':
-
-
- import codecs, json
-
- obj_text = codecs.open(surrogate_parameters['precomputed_training_points'], 'r', encoding='utf-8').read()
- b_new = json.loads(obj_text)
- X = np.array(b_new)
-
-
- #X = np.loadtxt('/home/kliegeois/Codes/waves/params/CVT/python/cvt_circle7.txt')
- tmp = np.shape(X)
- m_max = tmp[0]
-
- obj_text = codecs.open(surrogate_parameters['precomputed_weights'], 'r', encoding='utf-8').read()
- b_new = json.loads(obj_text)
- w = np.array(b_new)
-
- i = np.zeros((d,), dtype =np.int)
- i_zero = np.zeros((d,), dtype =np.int)
- i_one = np.ones((d,), dtype =np.int)
- p = 0
-
- xi = np.zeros((d,))
-
- m_i = 0
- sol = np.zeros((m_max,1))
-
- Z = np.zeros((m_max,n_max))
- W_sr = np.zeros((m_max,m_max))
-
- Xi = np.zeros((m_max,d))
- Xi2 = np.zeros((m_max,d))
- while np.all(i != -i_one):
-
-
- if surrogate_parameters['training_points'] == 'precomputed':
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
- correlations = surrogate_parameters['correlations']
-
- for j in range(0,d):
- xi[j] = ((X[m_i,j]-means[j])/(surrogate_parameters['nstddeviation']*np.sqrt(variances[j])))
-
- elif surrogate_parameters['training_points'] == 'gamma':
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
- correlations = surrogate_parameters['correlations']
-
- xi_in = np.zeros((d,1))
- for j in range(0,d):
- xi_in[j] = X[i[j],j]
- xi = tp.f_mapping(xi_in,d,means,variances,correlations,surrogate_parameters['f_mapping_method'])
-
- #print xi
-
- elif normalisation == True:
- for j in range(0,d):
- xi[j] = X[i[j],j]
- elif normalisation == False:
- for j in range(0,d):
- xi[j] = X[i[j],j]
- xi[j] = Dom_min[j] + (Dom_max[j] - Dom_min[j])*(xi[j]+1.)/2.
- parameters[P_name[j]] = xi[j]
-
- # -------------------
- xi2 = np.zeros((d,))
- if surrogate_parameters['training_points'] == 'gamma':
-
- xi2[0] = (xi[0][0]-means[0]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[0]))
- xi2[1] = (xi[1][0]-means[1]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[1]))
- Z[m_i,:] = np.transpose(phi(xi2,N,n_max,d))
- #print xi2
- else:
- Z[m_i,:] = np.transpose(phi(xi,N,n_max,d))
-
- tmp = 1
- if surrogate_parameters['training_points'] != 'precomputed':
- for j in range(0,d):
- tmp = tmp*w[i[j],j]
- else:
- tmp = w[m_i]
-
- W_sr[m_i,m_i] = np.sqrt(tmp)
-
-
-
- Xi[m_i,:] = xi.T
- Xi2[m_i,:] = xi2.T
- W_sr[m_i,m_i] = np.sqrt(tmp)
-
- m_i = m_i + 1
-
- p,i = indices.my_next(p,i,M,d)
-
- return Z, W_sr, Xi, Xi2
-
-
-def scatterplot(sol, surrogate_parameters, images_parameters):
- Image = {}
- if surrogate_parameters['display']:
-
- d = surrogate_parameters['d']
- M = surrogate_parameters['M']
-
- Dom_min = surrogate_parameters['Dom_min']
- Dom_max = surrogate_parameters['Dom_max']
-
- if surrogate_parameters['training_points'] == 'gauss':
- import params.src.gauss as tp
- elif surrogate_parameters['training_points'] == 'gamma':
- import params.arnst_ponthot.src.gamma as tp
-
- import params.src.plot as plot
- fig, ax, current_title = plot.init2(d, images_parameters)
-
- if not os.path.exists('images'):
- os.mkdir('images')
- else:
- shutil.rmtree('images')
- os.mkdir('images')
-
- if surrogate_parameters['training_points'] != 'precomputed':
-
- X, w = tp.grid(M)
-
- m_max = 1
- for j in range(0,d):
- m_max = m_max*M[j]
- if surrogate_parameters['training_points'] == 'precomputed':
-
-
- import codecs, json
-
- obj_text = codecs.open(surrogate_parameters['precomputed_training_points'], 'r', encoding='utf-8').read()
- b_new = json.loads(obj_text)
- X = np.array(b_new)
-
- tmp = np.shape(X)
- print(tmp)
- m_max = tmp[0]
- print(max(X[:,0]))
- print(min(X[:,0]))
- print(max(X[:,1]))
- print(min(X[:,1]))
-
-
- xi = np.zeros((d,))
- xi_tp = np.zeros((m_max,d))
- m_i = 0
-
- i = np.zeros((d,), dtype =np.int)
- i_zero = np.zeros((d,), dtype =np.int)
- i_one = np.ones((d,), dtype =np.int)
- p = 0
-
- while np.all(i != -i_one):
- if surrogate_parameters['training_points'] == 'gamma':
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
- correlations = surrogate_parameters['correlations']
-
- xi_in = np.zeros((d,1))
- for j in range(0,d):
- xi_in[j] = X[i[j],j]
- xi = tp.f_mapping(xi_in,d,means,variances,correlations,surrogate_parameters['f_mapping_method'])
-
- elif surrogate_parameters['training_points'] == 'precomputed':
- for j in range(0,d):
- xi[j] = X[m_i,j]
-
- else:
- for j in range(0,d):
- xi[j] = X[i[j],j]
- xi[j] = Dom_min[j] + (Dom_max[j] - Dom_min[j])*(xi[j]+1.)/2.
- for j in range(0,d):
- xi_tp[m_i,j] = xi[j]
- m_i = m_i + 1
- p,i = indices.my_next(p,i,M,d)
-
- plot.scatters(xi_tp,sol,d,fig,ax, True,m_i)
- Image['fig'] = fig
- Image['ax'] = ax
- Image['current_title'] = current_title
- return Image
diff --git a/Metafor/Mparams/surrogate.py b/Metafor/Mparams/surrogate.py
deleted file mode 100644
index 8b13e8c5..00000000
--- a/Metafor/Mparams/surrogate.py
+++ /dev/null
@@ -1,335 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import numpy as np
-import os
-import shutil
-
-import params.src.indices as indices
-
-
-def surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters):
- if surrogate_parameters['display']:
- import params.src.plot as plot
- fig = Image['fig']
- ax = Image['ax']
- current_title = Image['current_title']
- d = surrogate_parameters['d']
- M = surrogate_parameters['M']
- N = surrogate_parameters['N']
-
- m1 = surrogate_parameters['m1']
- m2 = surrogate_parameters['m2']
-
- Dom_min = surrogate_parameters['Dom_min']
- Dom_max = surrogate_parameters['Dom_max']
- P_name = surrogate_parameters['P_name']
- solver = surrogate_parameters['solver']
-
- normalisation = surrogate_parameters['normalisation']
- ##
- if surrogate_parameters['function_type'] == 'monomial':
- from params.src.monomials import phi
- elif surrogate_parameters['function_type'] == 'monomial_1norm':
- from params.src.monomials_1norm import phi
- ##
-
- shape = Z.shape
- m_max = shape[0]
- n_max = shape[1]
-
- x = np.linspace(-1.,1.,m1)
- y = np.linspace(-1.,1.,m2)
- X = np.zeros((m1,m2))
- Y = np.zeros((m1,m2))
- surrogate = np.zeros((m1,m2))
- X, Y = np.meshgrid(x, y)
- phi_tmp = np.zeros((m1,m2,n_max))
-
- #------------------
-
- fixed_value = np.zeros((d-2,))
- if d > 2:
- fixed_value = surrogate_parameters['fixed_value']
- for i in range(0,m1):
- for j in range(0,m2):
- xi = np.zeros((d,))
-
- if surrogate_parameters['training_points'] == 'gamma':
- X[i,j] = Dom_min[0] + (Dom_max[0] - Dom_min[0])*(X[i,j]+1.)/2.
- Y[i,j] = Dom_min[1] + (Dom_max[1] - Dom_min[1])*(Y[i,j]+1.)/2.
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
-
- xi[0] = (X[i,j]-means[0])/(surrogate_parameters['nstddeviation']*np.sqrt(variances[0]))
- xi[1] = (Y[i,j]-means[1])/(surrogate_parameters['nstddeviation']*np.sqrt(variances[1]))
- for k in range(2,d):
- xi[k] = (fixed_value[k-2]-means[k]) / np.sqrt(variances[k])
-
-
- elif surrogate_parameters['training_points'] == 'precomputed':
- X[i,j] = Dom_min[0] + (Dom_max[0] - Dom_min[0])*(X[i,j]+1.)/2.
- Y[i,j] = Dom_min[1] + (Dom_max[1] - Dom_min[1])*(Y[i,j]+1.)/2.
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
-
- xi[0] = (X[i,j]-means[0]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[0]))
- xi[1] = (Y[i,j]-means[1]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[1]))
- for k in range(2,d):
- xi[k] = (fixed_value[k-2]-means[k]) / np.sqrt(variances[k])
-
- elif normalisation == False:
- X[i,j] = Dom_min[0] + (Dom_max[0] - Dom_min[0])*(X[i,j]+1.)/2.
- Y[i,j] = Dom_min[1] + (Dom_max[1] - Dom_min[1])*(Y[i,j]+1.)/2.
-
- xi[0] = X[i,j]
- xi[1] = Y[i,j]
- else:
- xi[0] = X[i,j]
- xi[1] = Y[i,j]
-
-
- phi_tmp[i,j,:] = phi(xi,N,n_max,d)
-
-
- if normalisation == True:
- for i in range(0,m1):
- for j in range(0,m2):
- X[i,j] = Dom_min[0] + (Dom_max[0] - Dom_min[0])*(X[i,j]+1.)/2.
- Y[i,j] = Dom_min[1] + (Dom_max[1] - Dom_min[1])*(Y[i,j]+1.)/2.
-
-
- if solver == 'QR':
- W_srZ = np.dot(W_sr,Z)
- print(np.linalg.cond(W_srZ))
- Q, R = np.linalg.qr(W_srZ)
- rhs = np.dot(np.transpose(Q),np.dot(W_sr,sol))
- s_hat = np.linalg.solve(R, rhs)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
-
- if solver == 'sp':
- W_srZ = np.dot(W_sr,Z)
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
- print(np.linalg.cond(ZWZ))
- rhs = np.dot(np.transpose(W_srZ),np.dot(W_sr,sol))
- import scipy.sparse.linalg
- s_hat = scipy.sparse.linalg.spsolve(ZWZ, rhs, 'NATURAL', False)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if solver == 'numpy_linalg':
- W_srZ = np.dot(W_sr,Z)
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
- print(np.linalg.cond(ZWZ))
- rhs = np.dot(np.transpose(W_srZ),np.dot(W_sr,sol))
- s_hat = np.linalg.solve(ZWZ, rhs)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if solver == 'scipy_linalg':
- W_srZ = np.dot(W_sr,Z)
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
- print(np.linalg.cond(ZWZ))
- rhs = np.dot(np.transpose(W_srZ),np.dot(W_sr,sol))
- import scipy.sparse.linalg
- s_hat = scipy.linalg.solve(ZWZ, rhs)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if solver == 'scipy_lu':
- W_srZ = np.dot(W_sr,Z)
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
- print(np.linalg.cond(ZWZ))
- rhs = np.dot(np.transpose(W_srZ),np.dot(W_sr,sol))
- import scipy.sparse.linalg
- PL, U = scipy.linalg.lu(ZWZ,True)
- print(np.linalg.cond(PL))
- print(np.linalg.cond(U))
- tmp = scipy.linalg.solve(PL,rhs)
- s_hat = scipy.linalg.solve(U,tmp)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if solver == 'minres':
- W_srZ = np.dot(W_sr,Z)
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
- print(np.linalg.cond(ZWZ))
- rhs = np.dot(np.transpose(W_srZ),np.dot(W_sr,sol))
- import scipy.sparse.linalg
- s_hat, info = scipy.sparse.linalg.minres(ZWZ, rhs)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if solver == 'gmres':
- W_srZ = np.dot(W_sr,Z)
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
- print(np.linalg.cond(ZWZ))
- rhs = np.dot(np.transpose(W_srZ),np.dot(W_sr,sol))
- import scipy.sparse.linalg
- s_hat, info = scipy.sparse.linalg.gmres(ZWZ, rhs)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if solver == 'lgmres':
- W_srZ = np.dot(W_sr,Z)
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
- print(np.linalg.cond(ZWZ))
- rhs = np.dot(np.transpose(W_srZ),np.dot(W_sr,sol))
- import scipy.sparse.linalg
- s_hat, info = scipy.sparse.linalg.lgmres(ZWZ, rhs)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if solver == 'lsqr':
- W_srZ = np.dot(W_sr,Z)
- print(np.linalg.cond(W_srZ))
- rhs = np.dot(W_sr,sol)
- import scipy.sparse.linalg
- s_hat, istop, itn, r1norm, r2norm, anorm, acond, arnorm, xnorm, var = scipy.sparse.linalg.lsqr(W_srZ, rhs)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if solver == 'lsmr':
- W_srZ = np.dot(W_sr,Z)
- print(np.linalg.cond(W_srZ))
- rhs = np.dot(W_sr,sol)
- import scipy.sparse.linalg
- s_hat, lstop, itn, normr, normar, norma, conda, normx = scipy.sparse.linalg.lsmr(W_srZ, rhs,1e045)
- print(lstop)
- print(itn)
- print(normr)
- print(normar)
- print(norma)
- print(conda)
- print(normx)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if solver == 'lstsq':
- W_srZ = np.dot(W_sr,Z)
- print(np.linalg.cond(W_srZ))
- rhs = np.dot(W_sr,sol)
- import scipy.linalg
- s_hat, residues, rank, s = scipy.linalg.lstsq(W_srZ, rhs)
- print(rank)
- for i in range(0,m1):
- for j in range(0,m2):
- surrogate[i,j] = np.dot(np.transpose(phi_tmp[i,j,:]),s_hat)
-
- if surrogate_parameters['display']:
- plot.surf(X, Y, surrogate, sol, fixed_value, fig, ax, current_title, True, True, 'Surrogate model','surrogate.png',d)
- #from matplotlib2tikz import save as tikz_save
- #tikz_save('surrogate.tex', figureheight = '\\figureheight', figurewidth = '\\figurewidth')
- plot.finish()
-
- return s_hat
-
-def surrogate_4d(sol, Z, W_sr, s_hat, surrogate_parameters, Image, images_parameters):
- if surrogate_parameters['display']:
- import params.src.plot as plot
- fig = Image['fig']
- ax = Image['ax']
- current_title = Image['current_title']
- d = surrogate_parameters['d']
- M = surrogate_parameters['M']
- N = surrogate_parameters['N']
-
- m1 = surrogate_parameters['m1']
- m2 = surrogate_parameters['m2']
-
- Dom_min = surrogate_parameters['Dom_min']
- Dom_max = surrogate_parameters['Dom_max']
- P_name = surrogate_parameters['P_name']
- solver = surrogate_parameters['solver']
-
- normalisation = surrogate_parameters['normalisation']
- ##
- if surrogate_parameters['function_type'] == 'monomial':
- from params.src.monomials import phi
- elif surrogate_parameters['function_type'] == 'monomial_1norm':
- from params.src.monomials_1norm import phi
- ##
-
- shape = Z.shape
- m_max = shape[0]
- n_max = shape[1]
-
- x = np.linspace(-1.,1.,m1)
- y = np.linspace(-1.,1.,m2)
-
- surrogate = np.zeros((m1,m2))
- X0, Y0 = np.meshgrid(x, y)
- phi_tmp = np.zeros((m1,m2,n_max))
-
- n_c = images_parameters['number_of_cuts']
-
- index_c = images_parameters['index_c']
- point_c = images_parameters['point_c']
-
- c_min = sol.min()
- c_max = sol.max()
-
- print(index_c)
- print(point_c)
-
- for c in range(0,n_c):
- X = np.zeros((m1,m2))
- Y = np.zeros((m1,m2))
- Z = np.zeros((m1,m2))
-
- xi0 = point_c[c]
- print(index_c[c])
- for i in range(0,m1):
- for j in range(0,m2):
- xi = np.zeros((d,))
-
- if surrogate_parameters['training_points'] == 'gamma':
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
-
- X[i,j] = Dom_min[index_c[c,0]] + (Dom_max[index_c[c,0]] - Dom_min[index_c[c,0]])*(X0[i,j]+1.)/2.
- Y[i,j] = Dom_min[index_c[c,1]] + (Dom_max[index_c[c,1]] - Dom_min[index_c[c,1]])*(Y0[i,j]+1.)/2.
- Z[i,j] = xi0[index_c[c,2]]
-
-
- xi[index_c[c,0]] = (X[i,j]-means[index_c[c,0]]) / np.sqrt(variances[index_c[c,0]])
- xi[index_c[c,1]] = (Y[i,j]-means[index_c[c,1]]) / np.sqrt(variances[index_c[c,1]])
- xi[index_c[c,2]] = (Z[i,j]-means[index_c[c,2]]) / np.sqrt(variances[index_c[c,2]])
-
- tmp = phi(xi,N,n_max,d)
- surrogate[i,j] = np.dot(np.transpose(tmp),s_hat)
-
- if index_c[c,0] == 1:
- plot.surf4d(Z, X, Y, surrogate, c_min, c_max, fig, ax, current_title, True, True, 'Surrogate model','surrogate.png',d)
- elif index_c[c,1] == 2:
- plot.surf4d(X, Z, Y, surrogate, c_min, c_max, fig, ax, current_title, True, True, 'Surrogate model','surrogate.png',d)
- else:
- plot.surf4d(X, Y, Z, surrogate, c_min, c_max, fig, ax, current_title, True, True, 'Surrogate model','surrogate.png',d)
- plot.finish()
diff --git a/Metafor/__init__.py b/Metafor/__init__.py
deleted file mode 100644
index 0b4a1527..00000000
--- a/Metafor/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# -*- coding: utf-8; -*-
-# mirrors MODULE initialization file
-
-import tbox
-from mirrorsw import *
diff --git a/Metafor/config/bord01_config1.py b/Metafor/config/bord01_config1.py
deleted file mode 100644
index 9ed4452a..00000000
--- a/Metafor/config/bord01_config1.py
+++ /dev/null
@@ -1,133 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- import platform
-
- if isUnix():
- if platform.system() == 'Darwin':
- model_parameters['Metafor_exe'] = '/Users/kliegeois/dev/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/Users/kliegeois/dev/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'sigEl0', 'Thickness'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 3
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 350., 1])
- surrogate_parameters['Dom_max'] = np.array([ 450., 2.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'Yield stress $[$MPa$]$'
- images_parameters['ylabel'] = 'Thickness $[$mm$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config10.py b/Metafor/config/bord01_config10.py
deleted file mode 100644
index 0e5e2f17..00000000
--- a/Metafor/config/bord01_config10.py
+++ /dev/null
@@ -1,209 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = False
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'ih_H', 'sigEl0'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 15
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1200., 200.])
- surrogate_parameters['Dom_max'] = np.array([ 1800., 650.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'gamma'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.])
- surrogate_parameters['variances'] = np.array([1390.,660.])
- rho = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho],[rho,1.]])
- surrogate_parameters['f_mapping_method'] = 'Cholesky'
- surrogate_parameters['rho'] = rho
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$[$MPa$]$'
- images_parameters['ylabel'] = '$[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
- images_parameters['z_min'] = 0.04
- images_parameters['z_max'] = 0.07
-
-
-
- from Metafor.Mparams.sequential import Z_evaluate
-
- m_max = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
- p_max = m_max
-
- Zcond = np.zeros((len(m_max),len(p_max)))
- Zsigma_max = np.zeros((len(m_max),len(p_max)))
- Zsigma_min = np.zeros((len(m_max),len(p_max)))
- ZZrank = np.zeros((len(m_max),len(p_max)))
- ZZdet = np.zeros((len(m_max),len(p_max)))
-
- W_srZcond = np.zeros((len(m_max),len(p_max)))
- ZWZrank = np.zeros((len(m_max),len(p_max)))
- ZWZdet = np.zeros((len(m_max),len(p_max)))
- W_srZsigma_max = np.zeros((len(m_max),len(p_max)))
- W_srZsigma_min = np.zeros((len(m_max),len(p_max)))
-
- np.set_printoptions(linewidth = 210)
- surrogate_parameters['nstddeviation'] = 1.
- for m_i in range(0,len(m_max)):
-
- for j in range(0,d):
- M[j] = m_max[m_i]
-
- surrogate_parameters['M'] = M
- for p_i in range(0,m_i+1):
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = p_max[p_i]
-
- surrogate_parameters['N'] = N
-
- Z, W_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
-
- ZZ = np.dot(Z.T,Z)
- W_srZ = np.dot(W_sr,Z)
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
-
- U1, s1, V1_t = np.linalg.svd(Z, full_matrices=True)
- U2, s2, V2_t = np.linalg.svd(W_srZ, full_matrices=True)
-
-
- Zcond[m_i,p_i] = np.linalg.cond(Z)
-
- ZZrank[m_i,p_i] = np.linalg.matrix_rank(ZZ)
- ZZdet[m_i,p_i] = np.linalg.det(ZZ)
-
- W_srZcond[m_i,p_i] = np.linalg.cond(W_srZ)
-
- ZWZrank[m_i,p_i] = np.linalg.matrix_rank(ZWZ)
- ZWZdet[m_i,p_i] = np.linalg.det(ZWZ)
-
- Zsigma_max[m_i,p_i] = s1.max()
- Zsigma_min[m_i,p_i] = s1.min()
-
- W_srZsigma_max[m_i,p_i] = s2.max()
- W_srZsigma_min[m_i,p_i] = s2.min()
- print('Condition number of Z')
- print(Zcond)
- print('Rank of ZZ')
- print(ZZrank)
- print('Determinant of ZZ')
- print(ZZdet)
-
- print('Maximal singular value of Z')
- print(Zsigma_max)
- print('Minimal singular value of Z')
- print(Zsigma_min)
-
- print('Condition number of W_srZ')
- print(W_srZcond)
- print('Rank of ZWZ')
- print(ZWZrank)
- print('Determinant of ZWZ')
- print(ZWZdet)
-
- print('Maximal singular value of W_srZ')
- print(W_srZsigma_max)
- print('Minimal singular value of W_srZ')
- print(W_srZsigma_min)
- '''
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
- '''
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config11.py b/Metafor/config/bord01_config11.py
deleted file mode 100644
index 12e789b2..00000000
--- a/Metafor/config/bord01_config11.py
+++ /dev/null
@@ -1,218 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = False
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'ih_H', 'sigEl0'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 15
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1200., 200.])
- surrogate_parameters['Dom_max'] = np.array([ 1800., 650.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'gamma'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.])
- surrogate_parameters['variances'] = np.array([1390.,660.])
- rho = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho],[rho,1.]])
- surrogate_parameters['f_mapping_method'] = 'Cholesky'
- surrogate_parameters['rho'] = rho
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$[$MPa$]$'
- images_parameters['ylabel'] = '$[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
- images_parameters['z_min'] = 0.04
- images_parameters['z_max'] = 0.07
-
-
-
- from Metafor.Mparams.sequential import Z_evaluate
-
- m_max = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
- nstddeviation = np.linspace(0.5,10.,100) #np.array([0.5, 0.75, 1., 1.25, 1.5, 1.75, 2., 2.25, 2.5, 2.75, 3., 3.25, 3.5, 3.75, 4.,4.25,4.5,4.75,5.])
-
- Xi2max = np.zeros((len(nstddeviation),len(m_max)))
- Xi2min = np.zeros((len(nstddeviation),len(m_max)))
-
- Zcond = np.zeros((len(nstddeviation),len(m_max)))
- Zsigma_max = np.zeros((len(nstddeviation),len(m_max)))
- Zsigma_min = np.zeros((len(nstddeviation),len(m_max)))
- ZZrank = np.zeros((len(nstddeviation),len(m_max)))
- ZZdet = np.zeros((len(nstddeviation),len(m_max)))
-
- W_srZcond = np.zeros((len(nstddeviation),len(m_max)))
- ZWZrank = np.zeros((len(nstddeviation),len(m_max)))
- ZWZdet = np.zeros((len(nstddeviation),len(m_max)))
- W_srZsigma_max = np.zeros((len(nstddeviation),len(m_max)))
- W_srZsigma_min = np.zeros((len(nstddeviation),len(m_max)))
-
- np.set_printoptions(linewidth = 210)
- for m_i in range(0,len(m_max)):
-
- for j in range(0,d):
- M[j] = m_max[m_i]
-
- for j in range(0,d):
- N[j] = m_max[m_i]
-
- surrogate_parameters['M'] = M
- surrogate_parameters['N'] = N
-
- for nst_i in range(0,len(nstddeviation)):
-
- surrogate_parameters['nstddeviation'] = nstddeviation[nst_i]
- Z, W_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
- ZZ = np.dot(Z.T,Z)
- W_srZ = np.dot(W_sr,Z)
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
-
- U1, s1, V1_t = np.linalg.svd(Z, full_matrices=True)
- U2, s2, V2_t = np.linalg.svd(W_srZ, full_matrices=True)
-
- Xi2max[nst_i,m_i] = np.abs(Xi2).max()
- Xi2min[nst_i,m_i] = np.abs(Xi2).min()
-
- Zcond[nst_i,m_i] = np.linalg.cond(Z)
-
- ZZrank[nst_i,m_i] = np.linalg.matrix_rank(ZZ)
- ZZdet[nst_i,m_i] = np.linalg.det(ZZ)
-
- W_srZcond[nst_i,m_i] = np.linalg.cond(W_srZ)
-
- ZWZrank[nst_i,m_i] = np.linalg.matrix_rank(ZWZ)
- ZWZdet[nst_i,m_i] = np.linalg.det(ZWZ)
-
- Zsigma_max[nst_i,m_i] = s1.max()
- Zsigma_min[nst_i,m_i] = s1.min()
-
- W_srZsigma_max[nst_i,m_i] = s2.max()
- W_srZsigma_min[nst_i,m_i] = s2.min()
-
- print('Max of abs(Xi2)')
- print(Xi2max)
- print('Min of abs(Xi2)')
- print(Xi2min)
- print('Condition number of Z')
- print(Zcond)
- print('Rank of ZZ')
- print(ZZrank)
- print('Determinant of ZZ')
- print(ZZdet)
-
- print('Maximal singular value of Z')
- print(Zsigma_max)
- print('Minimal singular value of Z')
- print(Zsigma_min)
-
- print('Condition number of W_srZ')
- print(W_srZcond)
- print('Rank of ZWZ')
- print(ZWZrank)
- print('Determinant of ZWZ')
- print(ZWZdet)
-
- print('Maximal singular value of W_srZ')
- print(W_srZsigma_max)
- print('Minimal singular value of W_srZ')
- print(W_srZsigma_min)
- '''
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
- '''
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config12.py b/Metafor/config/bord01_config12.py
deleted file mode 100644
index 58e98ed7..00000000
--- a/Metafor/config/bord01_config12.py
+++ /dev/null
@@ -1,140 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'ih_H', 'sigEl0'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 5
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1300., 300.])
- surrogate_parameters['Dom_max'] = np.array([ 1700., 550.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 5
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'precomputed'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.])
- surrogate_parameters['variances'] = np.array([1390.,660.])
- rho = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho],[rho,1.]])
- surrogate_parameters['f_mapping_method'] = 'Cholesky'
- surrogate_parameters['nstddeviation'] = 1.
- surrogate_parameters['rho'] = rho
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$[$MPa$]$'
- images_parameters['ylabel'] = '$[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
- images_parameters['z_min'] = 0.04
- images_parameters['z_max'] = 0.07
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_4d
- surrogate_4d(sol, Z, W_sr, s_hat, surrogate_parameters, Image, images_parameters)
-
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config13.py b/Metafor/config/bord01_config13.py
deleted file mode 100644
index 6e880060..00000000
--- a/Metafor/config/bord01_config13.py
+++ /dev/null
@@ -1,297 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = False
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'ih_H', 'sigEl0'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 15
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1400., 350.])
- surrogate_parameters['Dom_max'] = np.array([ 1600., 450.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'precomputed'
- surrogate_parameters['precomputed_training_points'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01/X_tp.json'
- surrogate_parameters['precomputed_weights'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01/W_tp.json'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.])
- surrogate_parameters['variances'] = np.array([1390.,660.])
- rho = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho],[rho,1.]])
- surrogate_parameters['f_mapping_method'] = 'Cholesky'
- surrogate_parameters['rho'] = rho
- surrogate_parameters['nstddeviation'] = 2.6281407
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$H\;[$MPa$]$'
- images_parameters['ylabel'] = '$S\;[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
- images_parameters['z_min'] = 0.04
- images_parameters['z_max'] = 0.07
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
-
- from Metafor.Mparams.sequential import Z_evaluate
- from Metafor.Mparams.sequential import scatterplot
- from Metafor.Mparams.surrogate import surrogate_creation
-
- surrogate_parameters['nstddeviation'] = 1
- Z1, W1_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
- i_max = 1
- cond = np.zeros((i_max,1))
- ZWZrank = np.zeros((i_max,1))
- ZWZdet = np.zeros((i_max,1))
- residual = np.zeros((i_max,1))
- max_residual = np.zeros((i_max,1))
- min_residual = np.zeros((i_max,1))
- w_residual = np.zeros((i_max,1))
- max_w_residual = np.zeros((i_max,1))
- min_w_residual = np.zeros((i_max,1))
-
- np.set_printoptions(linewidth = 210)
-
- for j in range(0,d):
- N[j] = 9
-
- surrogate_parameters['N'] = N
-
-
- nstddeviation = np.linspace(0.1,10.,i_max)
- for i in range(0,i_max):
- surrogate_parameters['nstddeviation'] = nstddeviation[i]
- Z, W_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
-
- #W_sr = np.eye(W_sr.shape[0])
-
-
-
- W_srZ = np.dot(W_sr,Z)
- cond[i] = np.linalg.cond(W_srZ)
-
- '''
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
-
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
-
- ZWZrank[i] = np.linalg.matrix_rank(ZWZ)
- ZWZdet[i] = np.linalg.det(ZWZ)
- res = np.dot(Z,s_hat)-sol
- residual[i] = np.linalg.norm(res)
- max_residual[i] = np.abs(res).max()
- min_residual[i] = np.abs(res).min()
-
- w_res = np.dot(W,res)
- w_residual[i] = np.linalg.norm(w_res)
- max_w_residual[i] = np.abs(w_res).max()
- min_w_residual[i] = np.abs(w_res).min()
- '''
- print(i)
-
- #import matplotlib.pyplot as plt
- #plt.semilogy(nstddeviation, cond)
- #plt.show()
- print('Condition number of W_srZ')
- print(cond.T)
- print('nstddeviation')
- print(nstddeviation.T)
-
- #'''
- surrogate_parameters['nstddeviation'] = 2.6281407
- for j in range(0,d):
- N[j] = 9
-
- surrogate_parameters['N'] = N
- surrogate_parameters['display'] = True
- Z, W_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- s_hat_w = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- print(s_hat_w.T)
-
- W_sr = np.eye(W_sr.shape[0])
-
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- s_hat_wout = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- print(s_hat_wout.T)
-
- ## ---------------------------
- from params.src.monomials_1norm import phi
- from params.src.monomials_1norm import compute_n_max
- n_max = compute_n_max(N,d)
- import params.arnst_ponthot.src.gamma as tp
-
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
- correlations = surrogate_parameters['correlations']
-
- n_MC = 200
-
-
- values_w = np.zeros((n_MC,1))
- values_wout = np.zeros((n_MC,1))
- xi_s = np.zeros((2,n_MC))
- for i in range(0,n_MC):
- if np.mod(i,1000) == 0:
- print(i)
- xi_in = np.random.normal(0., 1., 2)
-
-
-
- xi_in[0] = 5.273790423018157
-
-
- xi = tp.f_mapping(xi_in,2,means,variances,correlations,surrogate_parameters['f_mapping_method'])
- xi_s[0,i] = (xi[0]-means[0]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[0]))
- xi_s[1,i] = (xi[1]-means[1]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[1]))
- phi_xi = phi(xi_s[:,i],N,n_max,d)
- values_w[i] = np.dot(np.transpose(phi_xi),s_hat_w)
- values_wout[i] = np.dot(np.transpose(phi_xi),s_hat_wout)
- '''
- mean = 0.
- variance = 0.
- exceedence = 0.
- for i in range(0,n_MC):
- mean = mean + values[i]
-
- if values[i] >= 0.065:
- exceedence = exceedence + 1.
-
- mean = mean/n_MC
-
- xi_s = np.zeros((2,1))
- for i in range(0,n_MC):
- variance = variance + (mean - values[i])**2
-
- variance = variance/(n_MC-1.)
-
- print 'Number of MC points'
- print n_MC
- print 'Mean'
- print mean
- print 'Variance'
- print variance
- print 'exceedence'
- print exceedence
- '''
- '''
- import codecs, json
- tmp = values_w.tolist()
- file_path = "values_w.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = values_wout.tolist()
- file_path = "values_wout.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = xi_s.tolist()
- file_path = "xi_s.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
- '''
- #import matplotlib.pyplot as plt
- #count, bins, ignored = plt.hist(values, 100, normed=True)
- #plt.show()
- #'''
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config2.py b/Metafor/config/bord01_config2.py
deleted file mode 100644
index 897d250c..00000000
--- a/Metafor/config/bord01_config2.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'sigEl0', 'ih_H'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 10
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 350., 1300.])
- surrogate_parameters['Dom_max'] = np.array([ 450., 1700.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 5
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'Yield stress $[$MPa$]$'
- images_parameters['ylabel'] = 'Hardening modulus $[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config3.py b/Metafor/config/bord01_config3.py
deleted file mode 100644
index 0d222b8b..00000000
--- a/Metafor/config/bord01_config3.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = False
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 4
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'sigEl0', 'ih_H', 'Thickness', 'Gap'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 4
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 350., 1300., 0.5, 1.])
- surrogate_parameters['Dom_max'] = np.array([ 450., 1700., 1.0, 2.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'Yield stress $[$MPa$]$'
- images_parameters['ylabel'] = 'Thickness $[$mm$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config4.py b/Metafor/config/bord01_config4.py
deleted file mode 100644
index efc986ff..00000000
--- a/Metafor/config/bord01_config4.py
+++ /dev/null
@@ -1,153 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'ih_H', 'sigEl0'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 15
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1200., 200.])
- surrogate_parameters['Dom_max'] = np.array([ 1800., 650.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'gamma'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.])
- surrogate_parameters['variances'] = np.array([1390.,660.])
- rho = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho],[rho,1.]])
- surrogate_parameters['f_mapping_method'] = 'Cholesky'
- surrogate_parameters['rho'] = rho
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$[$MPa$]$'
- images_parameters['ylabel'] = '$[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
- images_parameters['z_min'] = 0.04
- images_parameters['z_max'] = 0.07
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- if 1:
- from Metafor.Mparams.sequential import Z_evaluate
- Z1, W1_sr = Z_evaluate(model_parameters, surrogate_parameters)
- cond = np.zeros((14,1))
- for i in range(1,15):
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = i
-
- surrogate_parameters['N'] = N
- Z, W_sr = Z_evaluate(model_parameters, surrogate_parameters)
-
- W_srZ = np.dot(W_sr,Z)
- cond[i-1] = np.linalg.cond(W_srZ)
- print(cond)
-
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config5.py b/Metafor/config/bord01_config5.py
deleted file mode 100644
index f63b8675..00000000
--- a/Metafor/config/bord01_config5.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'sigEl0', 'ih_H'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 2
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 350., 1300.])
- surrogate_parameters['Dom_max'] = np.array([ 450., 1700.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'Yield stress $[$MPa$]$'
- images_parameters['ylabel'] = 'Hardening modulus $[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config6.py b/Metafor/config/bord01_config6.py
deleted file mode 100644
index 638e28ce..00000000
--- a/Metafor/config/bord01_config6.py
+++ /dev/null
@@ -1,128 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'sigEl0', 'ih_H'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 10
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 350., 1300.])
- surrogate_parameters['Dom_max'] = np.array([ 450., 1700.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 10
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'scipy_lu'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'Yield stress $[$MPa$]$'
- images_parameters['ylabel'] = 'Hardening modulus $[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config7.py b/Metafor/config/bord01_config7.py
deleted file mode 100644
index 02ea9d55..00000000
--- a/Metafor/config/bord01_config7.py
+++ /dev/null
@@ -1,152 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'ih_H', 'sigEl0'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 5
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1300., 300.])
- surrogate_parameters['Dom_max'] = np.array([ 1700., 550.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 5
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'gamma'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.])
- surrogate_parameters['variances'] = np.array([1390.,660.])
- rho = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho],[rho,1.]])
- surrogate_parameters['f_mapping_method'] = 'PCA'
- surrogate_parameters['rho'] = rho
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$[$MPa$]$'
- images_parameters['ylabel'] = '$[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
- images_parameters['z_min'] = 0.04
- images_parameters['z_max'] = 0.07
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- if 1:
- from Metafor.Mparams.sequential import Z_evaluate
- Z1, W1_sr = Z_evaluate(model_parameters, surrogate_parameters)
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 5
-
- surrogate_parameters['N'] = N
- Z, W_sr = Z_evaluate(model_parameters, surrogate_parameters)
-
- TMP = Z1.shape
- #print Z[5,0:TMP[1]] - Z1[5,0:TMP[1]]
- print(Z[5,:])
- print(Z1[5,:])
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config8.py b/Metafor/config/bord01_config8.py
deleted file mode 100644
index f2a1858a..00000000
--- a/Metafor/config/bord01_config8.py
+++ /dev/null
@@ -1,145 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 3
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'ih_H', 'sigEl0','Young'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 5
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1300., 300.,209600.0])
- surrogate_parameters['Dom_max'] = np.array([ 1700., 600.,210300.0])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 3
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'gamma'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.,210000.0])
- surrogate_parameters['variances'] = np.array([1390.,660.,10000.0])
- rho12 = -0.233
- rho13 = -0.233
- rho23 = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho12,rho13],[rho12,1.,rho23],[rho13,rho23,1.]])
- surrogate_parameters['f_mapping_method'] = 'PCA'
- surrogate_parameters['fixed_value'] = np.array([209800.0])
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 20
- surrogate_parameters['m2'] = 20
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$[$MPa$]$'
- images_parameters['ylabel'] = '$[$MPa$]$'
- images_parameters['zlabel'] = '$[$MPa$]$'
- images_parameters['z_min'] = 180000.0
- images_parameters['z_max'] = 240000.0
-
- images_parameters['number_of_cuts'] = 3
- images_parameters['index_c'] = np.array([[0,1,2],[0,2,1],[1,2,0]])
- images_parameters['point_c'] = np.array([[1300., 300.,209600.0],[1300., 300.,209600.0],[1300., 300.,209600.0]])
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_4d
- surrogate_4d(sol, Z, W_sr, s_hat, surrogate_parameters, Image, images_parameters)
-
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_config9.py b/Metafor/config/bord01_config9.py
deleted file mode 100644
index a3b7ed54..00000000
--- a/Metafor/config/bord01_config9.py
+++ /dev/null
@@ -1,294 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = False
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'ih_H', 'sigEl0'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 15
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1200., 200.])
- surrogate_parameters['Dom_max'] = np.array([ 1800., 650.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'gamma'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.])
- surrogate_parameters['variances'] = np.array([1390.,660.])
- rho = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho],[rho,1.]])
- surrogate_parameters['f_mapping_method'] = 'Cholesky'
- surrogate_parameters['rho'] = rho
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$H\;[$MPa$]$'
- images_parameters['ylabel'] = '$S\;[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
- images_parameters['z_min'] = 0.04
- images_parameters['z_max'] = 0.07
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
-
- from Metafor.Mparams.sequential import Z_evaluate
- from Metafor.Mparams.sequential import scatterplot
- from Metafor.Mparams.surrogate import surrogate_creation
-
- surrogate_parameters['nstddeviation'] = 1
- Z1, W1_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
- i_max = 2
- cond = np.zeros((i_max,1))
- ZWZrank = np.zeros((i_max,1))
- ZWZdet = np.zeros((i_max,1))
- residual = np.zeros((i_max,1))
- max_residual = np.zeros((i_max,1))
- min_residual = np.zeros((i_max,1))
- w_residual = np.zeros((i_max,1))
- max_w_residual = np.zeros((i_max,1))
- min_w_residual = np.zeros((i_max,1))
-
- np.set_printoptions(linewidth = 210)
-
- for j in range(0,d):
- N[j] = 9
-
- surrogate_parameters['N'] = N
-
-
- nstddeviation = np.linspace(1.,10.,i_max)
- for i in range(0,i_max):
- surrogate_parameters['nstddeviation'] = nstddeviation[i]
- Z, W_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
-
- #W_sr = np.eye(W_sr.shape[0])
-
-
-
- W_srZ = np.dot(W_sr,Z)
- cond[i] = np.linalg.cond(W_srZ)
-
- '''
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
-
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
-
- ZWZrank[i] = np.linalg.matrix_rank(ZWZ)
- ZWZdet[i] = np.linalg.det(ZWZ)
- res = np.dot(Z,s_hat)-sol
- residual[i] = np.linalg.norm(res)
- max_residual[i] = np.abs(res).max()
- min_residual[i] = np.abs(res).min()
-
- w_res = np.dot(W,res)
- w_residual[i] = np.linalg.norm(w_res)
- max_w_residual[i] = np.abs(w_res).max()
- min_w_residual[i] = np.abs(w_res).min()
- '''
- print(i)
-
- #import matplotlib.pyplot as plt
- #plt.semilogy(nstddeviation, cond)
- #plt.show()
- print('Condition number of W_srZ')
- print(cond.T)
- print('nstddeviation')
- print(nstddeviation.T)
-
- #'''
- surrogate_parameters['nstddeviation'] = 2.6281407 # 6.32653061
- for j in range(0,d):
- N[j] = 9
-
- surrogate_parameters['N'] = N
- surrogate_parameters['display'] = True
- Z, W_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- s_hat_w = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- print(s_hat_w.T)
-
- W_sr = np.eye(W_sr.shape[0])
-
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- s_hat_wout = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- print(s_hat_wout.T)
-
- ## ---------------------------
- from params.src.monomials_1norm import phi
- from params.src.monomials_1norm import compute_n_max
- n_max = compute_n_max(N,d)
- import params.arnst_ponthot.src.gamma as tp
-
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
- correlations = surrogate_parameters['correlations']
-
- n_MC = 200
-
-
- values_w = np.zeros((n_MC,1))
- values_wout = np.zeros((n_MC,1))
- xi_s = np.zeros((2,n_MC))
- for i in range(0,n_MC):
- if np.mod(i,1000) == 0:
- print(i)
- xi_in = np.random.normal(0., 1., 2)
-
-
-
- xi_in[0] = 5.273790423018157
-
-
- xi = tp.f_mapping(xi_in,2,means,variances,correlations,surrogate_parameters['f_mapping_method'])
- xi_s[0,i] = (xi[0]-means[0]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[0]))
- xi_s[1,i] = (xi[1]-means[1]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[1]))
- phi_xi = phi(xi_s[:,i],N,n_max,d)
- values_w[i] = np.dot(np.transpose(phi_xi),s_hat_w)
- values_wout[i] = np.dot(np.transpose(phi_xi),s_hat_wout)
- '''
- mean = 0.
- variance = 0.
- exceedence = 0.
- for i in range(0,n_MC):
- mean = mean + values[i]
-
- if values[i] >= 0.065:
- exceedence = exceedence + 1.
-
- mean = mean/n_MC
-
- xi_s = np.zeros((2,1))
- for i in range(0,n_MC):
- variance = variance + (mean - values[i])**2
-
- variance = variance/(n_MC-1.)
-
- print 'Number of MC points'
- print n_MC
- print 'Mean'
- print mean
- print 'Variance'
- print variance
- print 'exceedence'
- print exceedence
- '''
- '''
- import codecs, json
- tmp = values_w.tolist()
- file_path = "values_w.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = values_wout.tolist()
- file_path = "values_wout.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = xi_s.tolist()
- file_path = "xi_s.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
- '''
- #import matplotlib.pyplot as plt
- #count, bins, ignored = plt.hist(values, 100, normed=True)
- #plt.show()
- #'''
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_numericalSA.py b/Metafor/config/bord01_numericalSA.py
deleted file mode 100644
index 06b66c07..00000000
--- a/Metafor/config/bord01_numericalSA.py
+++ /dev/null
@@ -1,299 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = False
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 4
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'NRTol', 'nx', 'ny', 'peno'])
-
- M = np.zeros((d,), dtype =np.int)
- M[0] = 7
- M[1] = 9
- M[2] = 7
- M[3] = 7
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1400., 350.])
- surrogate_parameters['Dom_max'] = np.array([ 1600., 450.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'precomputed'
- surrogate_parameters['precomputed_training_points'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01/numericalSATrainingPoints.ascii'
- surrogate_parameters['precomputed_weights'] = '/home/kliegeois/Codes/waves/Metafor/models/bord01/numericalSAWeights.ascii'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.])
- surrogate_parameters['variances'] = np.array([1390.,660.])
- rho = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho],[rho,1.]])
- surrogate_parameters['f_mapping_method'] = 'Cholesky'
- surrogate_parameters['rho'] = rho
- surrogate_parameters['nstddeviation'] = 2.6281407
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$H\;[$MPa$]$'
- images_parameters['ylabel'] = '$S\;[$MPa$]$'
- images_parameters['zlabel'] = 'Angle $[$rad$]$'
- images_parameters['z_min'] = 0.04
- images_parameters['z_max'] = 0.07
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
-
- from Metafor.Mparams.sequential import Z_evaluate
- from Metafor.Mparams.sequential import scatterplot
- from Metafor.Mparams.surrogate import surrogate_creation
-
- surrogate_parameters['nstddeviation'] = 1
- Z1, W1_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
- i_max = 1
- cond = np.zeros((i_max,1))
- ZWZrank = np.zeros((i_max,1))
- ZWZdet = np.zeros((i_max,1))
- residual = np.zeros((i_max,1))
- max_residual = np.zeros((i_max,1))
- min_residual = np.zeros((i_max,1))
- w_residual = np.zeros((i_max,1))
- max_w_residual = np.zeros((i_max,1))
- min_w_residual = np.zeros((i_max,1))
-
- np.set_printoptions(linewidth = 210)
-
- for j in range(0,d):
- N[j] = 9
-
- surrogate_parameters['N'] = N
-
-
- nstddeviation = np.linspace(0.1,10.,i_max)
- for i in range(0,i_max):
- surrogate_parameters['nstddeviation'] = nstddeviation[i]
- Z, W_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
-
- #W_sr = np.eye(W_sr.shape[0])
-
-
-
- W_srZ = np.dot(W_sr,Z)
- cond[i] = np.linalg.cond(W_srZ)
-
- '''
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
-
- ZWZ = np.dot(np.transpose(W_srZ),W_srZ)
-
- ZWZrank[i] = np.linalg.matrix_rank(ZWZ)
- ZWZdet[i] = np.linalg.det(ZWZ)
- res = np.dot(Z,s_hat)-sol
- residual[i] = np.linalg.norm(res)
- max_residual[i] = np.abs(res).max()
- min_residual[i] = np.abs(res).min()
-
- w_res = np.dot(W,res)
- w_residual[i] = np.linalg.norm(w_res)
- max_w_residual[i] = np.abs(w_res).max()
- min_w_residual[i] = np.abs(w_res).min()
- '''
- print(i)
-
- #import matplotlib.pyplot as plt
- #plt.semilogy(nstddeviation, cond)
- #plt.show()
- print('Condition number of W_srZ')
- print(cond.T)
- print('nstddeviation')
- print(nstddeviation.T)
-
- #'''
- surrogate_parameters['nstddeviation'] = 2.6281407
- for j in range(0,d):
- N[j] = 9
-
- surrogate_parameters['N'] = N
- surrogate_parameters['display'] = True
- Z, W_sr, Xi, Xi2 = Z_evaluate(model_parameters, surrogate_parameters)
-
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- s_hat_w = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- print(s_hat_w.T)
-
- W_sr = np.eye(W_sr.shape[0])
-
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- s_hat_wout = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- print(s_hat_wout.T)
-
- ## ---------------------------
- from params.src.monomials_1norm import phi
- from params.src.monomials_1norm import compute_n_max
- n_max = compute_n_max(N,d)
- import params.arnst_ponthot.src.gamma as tp
-
- means = surrogate_parameters['means']
- variances = surrogate_parameters['variances']
- correlations = surrogate_parameters['correlations']
-
- n_MC = 200
-
-
- values_w = np.zeros((n_MC,1))
- values_wout = np.zeros((n_MC,1))
- xi_s = np.zeros((2,n_MC))
- for i in range(0,n_MC):
- if np.mod(i,1000) == 0:
- print(i)
- xi_in = np.random.normal(0., 1., 2)
-
-
-
- xi_in[0] = 5.273790423018157
-
-
- xi = tp.f_mapping(xi_in,2,means,variances,correlations,surrogate_parameters['f_mapping_method'])
- xi_s[0,i] = (xi[0]-means[0]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[0]))
- xi_s[1,i] = (xi[1]-means[1]) / (surrogate_parameters['nstddeviation']*np.sqrt(variances[1]))
- phi_xi = phi(xi_s[:,i],N,n_max,d)
- values_w[i] = np.dot(np.transpose(phi_xi),s_hat_w)
- values_wout[i] = np.dot(np.transpose(phi_xi),s_hat_wout)
- '''
- mean = 0.
- variance = 0.
- exceedence = 0.
- for i in range(0,n_MC):
- mean = mean + values[i]
-
- if values[i] >= 0.065:
- exceedence = exceedence + 1.
-
- mean = mean/n_MC
-
- xi_s = np.zeros((2,1))
- for i in range(0,n_MC):
- variance = variance + (mean - values[i])**2
-
- variance = variance/(n_MC-1.)
-
- print 'Number of MC points'
- print n_MC
- print 'Mean'
- print mean
- print 'Variance'
- print variance
- print 'exceedence'
- print exceedence
- '''
- '''
- import codecs, json
- tmp = values_w.tolist()
- file_path = "values_w.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = values_wout.tolist()
- file_path = "values_wout.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = xi_s.tolist()
- file_path = "xi_s.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
- '''
- #import matplotlib.pyplot as plt
- #count, bins, ignored = plt.hist(values, 100, normed=True)
- #plt.show()
- #'''
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/bord01_numericalSA2.py b/Metafor/config/bord01_numericalSA2.py
deleted file mode 100644
index a8831c69..00000000
--- a/Metafor/config/bord01_numericalSA2.py
+++ /dev/null
@@ -1,125 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/Users/kliegeois/dev/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/Users/kliegeois/dev/waves/Metafor/models/bord01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/bord01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = False
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = False
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 6
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'NRTol', 'peno', 'peta', 'PEAS', 'nx', 'ny'])
-
- M = np.zeros((d,), dtype =np.int)
- M[0] = 1
- M[1] = 1
- M[2] = 1
- M[3] = 1
- M[4] = 1
- M[5] = 4
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 1400., 350.])
- surrogate_parameters['Dom_max'] = np.array([ 1600., 450.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial_1norm'
- surrogate_parameters['training_points'] = 'precomputed'
- surrogate_parameters['precomputed_training_points'] = '/Users/kliegeois/dev/waves/Metafor/models/bord01/numericalSATrainingPoints3.ascii'
- surrogate_parameters['precomputed_weights'] = '/Users/kliegeois/dev/waves/Metafor/models/bord01/numericalSAWeights3.ascii'
- surrogate_parameters['solver'] = 'QR'
- surrogate_parameters['means'] = np.array([1495.,396.])
- surrogate_parameters['variances'] = np.array([1390.,660.])
- rho = -0.233
- surrogate_parameters['correlations'] = np.array([[1.,rho],[rho,1.]])
- surrogate_parameters['f_mapping_method'] = 'Cholesky'
- surrogate_parameters['rho'] = rho
- surrogate_parameters['nstddeviation'] = 2.6281407
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
-
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/mirror01_config1.py b/Metafor/config/mirror01_config1.py
deleted file mode 100644
index 830ae520..00000000
--- a/Metafor/config/mirror01_config1.py
+++ /dev/null
@@ -1,147 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/mirror01'
- model_parameters['geo'] = '/home/kliegeois/Codes/waves/Metafor/models/mirror01/mirror.geo'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/mirror01'
- model_parameters['geo'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/mirror01/mirror.geo'
- #----------------------Geometry--------------------------
- model_parameters['L'] = 250.
- model_parameters['L_1'] = 250.
- model_parameters['H'] = 50.
- model_parameters['W'] = 150.
- #------------------------Mesh----------------------------
- model_parameters['nL'] = 6
- model_parameters['nL_1'] = 6
- model_parameters['nH'] = 6
- model_parameters['nH_1'] = 6
- model_parameters['nW'] = 5
- #------------------------Load----------------------------
- model_parameters['tmax'] = 1. #sec
- model_parameters['dtinit'] = 0.01 #sec
- model_parameters['dtmax'] = 0.1 #sec
-
- model_parameters['TempInit'] = 70.0 #°
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'TempFinal', 'H_1'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 10
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 100., 2.])
- surrogate_parameters['Dom_max'] = np.array([ 500., 10.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 5
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$T [$C$]$'
- images_parameters['ylabel'] = '$H_1 [$mm$]$'
- images_parameters['zlabel'] = 'max $\sigma_{VM} [$MPa$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/mirror01_config2.py b/Metafor/config/mirror01_config2.py
deleted file mode 100644
index 55a3e275..00000000
--- a/Metafor/config/mirror01_config2.py
+++ /dev/null
@@ -1,147 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/mirror01'
- model_parameters['geo'] = '/home/kliegeois/Codes/waves/Metafor/models/mirror01/mirror.geo'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/mirror01'
- model_parameters['geo'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/mirror01/mirror.geo'
- #----------------------Geometry--------------------------
- model_parameters['L'] = 250.
- model_parameters['L_1'] = 250.
- model_parameters['H'] = 50.
- model_parameters['W'] = 150.
- #------------------------Mesh----------------------------
- model_parameters['nL'] = 6
- model_parameters['nL_1'] = 6
- model_parameters['nH'] = 6
- model_parameters['nH_1'] = 6
- model_parameters['nW'] = 5
- #------------------------Load----------------------------
- model_parameters['tmax'] = 1. #sec
- model_parameters['dtinit'] = 0.01 #sec
- model_parameters['dtmax'] = 0.1 #sec
-
- model_parameters['TempInit'] = 70.0 #°
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'TempFinal', 'H_1'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 40
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 100., 2.])
- surrogate_parameters['Dom_max'] = np.array([ 500., 10.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 20
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$T [$C$]$'
- images_parameters['ylabel'] = '$H_1 [$mm$]$'
- images_parameters['zlabel'] = 'max $\sigma_{VM} [$MPa$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/mirror01_config3.py b/Metafor/config/mirror01_config3.py
deleted file mode 100644
index aa591d01..00000000
--- a/Metafor/config/mirror01_config3.py
+++ /dev/null
@@ -1,147 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/mirror01'
- model_parameters['geo'] = '/home/kliegeois/Codes/waves/Metafor/models/mirror01/mirror.geo'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/mirror01'
- model_parameters['geo'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/mirror01/mirror.geo'
- #----------------------Geometry--------------------------
- model_parameters['L'] = 250.
- model_parameters['L_1'] = 250.
- model_parameters['H'] = 50.
- model_parameters['W'] = 150.
- #------------------------Mesh----------------------------
- model_parameters['nL'] = 6
- model_parameters['nL_1'] = 6
- model_parameters['nH'] = 6
- model_parameters['nH_1'] = 6
- model_parameters['nW'] = 5
- #------------------------Load----------------------------
- model_parameters['tmax'] = 1. #sec
- model_parameters['dtinit'] = 0.01 #sec
- model_parameters['dtmax'] = 0.1 #sec
-
- model_parameters['TempInit'] = 70.0 #°
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'TempFinal', 'H_1'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 20
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 100., 2.])
- surrogate_parameters['Dom_max'] = np.array([ 500., 10.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 10
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$T [$C$]$'
- images_parameters['ylabel'] = '$H_1 [$mm$]$'
- images_parameters['zlabel'] = 'max $\sigma_{VM} [$MPa$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/mirror02_config2.py b/Metafor/config/mirror02_config2.py
deleted file mode 100644
index a16cefb4..00000000
--- a/Metafor/config/mirror02_config2.py
+++ /dev/null
@@ -1,147 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/mirror01'
- model_parameters['geo'] = '/home/kliegeois/Codes/waves/Metafor/models/mirror01/mirror.geo'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/mirror01'
- model_parameters['geo'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/mirror01/mirror.geo'
- #----------------------Geometry--------------------------
- model_parameters['L'] = 250.
- model_parameters['L_1'] = 250.
- model_parameters['H'] = 50.
- model_parameters['W'] = 150.
- #------------------------Mesh----------------------------
- model_parameters['nL'] = 6
- model_parameters['nL_1'] = 6
- model_parameters['nH'] = 6
- model_parameters['nH_1'] = 6
- model_parameters['nW'] = 5
- #------------------------Load----------------------------
- model_parameters['tmax'] = 1. #sec
- model_parameters['dtinit'] = 0.01 #sec
- model_parameters['dtmax'] = 0.1 #sec
-
- model_parameters['TempInit'] = 70.0 #°
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = False
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'TempFinal', 'H_1'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 2
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 100., 2.])
- surrogate_parameters['Dom_max'] = np.array([ 500., 10.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = '$T [$C$]$'
- images_parameters['ylabel'] = '$H_1 [$mm$]$'
- images_parameters['zlabel'] = 'max $\sigma_{VM} [$MPa$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/omega01_config1.py b/Metafor/config/omega01_config1.py
deleted file mode 100644
index 7c41ab7a..00000000
--- a/Metafor/config/omega01_config1.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/omega01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/omega01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = False
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'LxP1', 'Lx'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 8
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 30., 150.])
- surrogate_parameters['Dom_max'] = np.array([ 50., 250.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 4
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'LxP1 $[$mm$]$'
- images_parameters['ylabel'] = 'Lx $[$mm$]$'
- images_parameters['zlabel'] = 'Springback $[$mm$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/omega01_test.py b/Metafor/config/omega01_test.py
deleted file mode 100644
index 18bbec79..00000000
--- a/Metafor/config/omega01_test.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/omega01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/omega01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'LxP1', 'Lx'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 2
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 30., 150.])
- surrogate_parameters['Dom_max'] = np.array([ 50., 250.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'LxP1 $[$mm$]$'
- images_parameters['ylabel'] = 'Lx $[$mm$]$'
- images_parameters['zlabel'] = 'Springback $[$mm$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/tube01_config1.py b/Metafor/config/tube01_config1.py
deleted file mode 100644
index f5298794..00000000
--- a/Metafor/config/tube01_config1.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/tube01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/tube01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'R1', 'R2'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 8
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 3., 4.5])
- surrogate_parameters['Dom_max'] = np.array([ 4., 5.5])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 4
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'R_1 $[$mm$]$'
- images_parameters['ylabel'] = 'R_2 $[$mm$]$'
- images_parameters['zlabel'] = 'F_x $[$N$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/tube01_config2.py b/Metafor/config/tube01_config2.py
deleted file mode 100644
index cba67f7f..00000000
--- a/Metafor/config/tube01_config2.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/tube01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/tube01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'R1', 'pressure'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 8
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 3., 45.])
- surrogate_parameters['Dom_max'] = np.array([ 4.5, 55.])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 4
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'R1 $[$mm$]$'
- images_parameters['ylabel'] = 'Pressure $[$MPa$]$'
- images_parameters['zlabel'] = 'F_x $[$N$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/config/tube01_test.py b/Metafor/config/tube01_test.py
deleted file mode 100644
index d62373f1..00000000
--- a/Metafor/config/tube01_test.py
+++ /dev/null
@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-# Copyright 2020 University of Liège
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-
-
-import numpy as np
-import os
-import shutil
-
-import Metafor.Mparams.TaskManager as tm
-import Metafor.Mparams.mparams as mp
-
-
-def main():
- if tm.rank == 0:
- #--------------------------------------------------------
- # Model parameters
- #--------------------------------------------------------
- model_parameters = {}
- isUnix = lambda: os.name == 'posix'
-
- if isUnix():
- model_parameters['Metafor_exe'] = '/home/kliegeois/Codes/Metafor/oo_metaB/bin/Metafor'
- model_parameters['Metafor_model'] = '/home/kliegeois/Codes/waves/Metafor/models/tube01'
- else:
- model_parameters['Metafor_exe'] = 'C:/Users/kimli_000/dev/oo_metaB/bin/Release/Metafor'
- model_parameters['Metafor_model'] = 'C:/Users/kimli_000/dev/waves/Metafor/models/tube01'
- #--------------------------------------------------------
- # Surrogate parameters
- #--------------------------------------------------------
- surrogate_parameters = {}
- surrogate_parameters['display'] = True
- if not isUnix():
- surrogate_parameters['display'] = False
- surrogate_parameters['normalisation'] = True
- #--------------------------------------------------------
- surrogate_parameters['fname'] = 'mparams.log'
- d = 2
- surrogate_parameters['d'] = d
- surrogate_parameters['P_name'] = np.array([ 'R1', 'R2'])
-
- M = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- M[j] = 2
-
- surrogate_parameters['M'] = M
-
- surrogate_parameters['Dom_min'] = np.array([ 3., 4.5])
- surrogate_parameters['Dom_max'] = np.array([ 4., 5.5])
-
- N = np.zeros((d,), dtype =np.int)
- for j in range(0,d):
- N[j] = 2
-
- surrogate_parameters['N'] = N
- #--------------------------------------------------------
- #--------------------------------------------------------
- surrogate_parameters['function_type'] = 'monomial'
- surrogate_parameters['training_points'] = 'gauss'
- surrogate_parameters['solver'] = 'QR'
- #--------------------------------------------------------
- surrogate_parameters['m1'] = 40
- surrogate_parameters['m2'] = 40
- #--------------------------------------------------------
- # Images parameters
- #--------------------------------------------------------
- images_parameters = {}
- images_parameters['xlabel'] = 'R_1 $[$mm$]$'
- images_parameters['ylabel'] = 'R_2 $[$mm$]$'
- images_parameters['zlabel'] = 'F_x $[$N$]$'
-
- if mp.AvailableComputedResults():
- if tm.rank == 0:
- print("the master/sol.json file will be loaded")
- print("the master/Z.json file will be loaded")
- print("the master/W_sr.json file will be loaded")
- sol, Z, W_sr = mp.LoadComputedResults()
-
- from Metafor.Mparams.sequential import scatterplot
- Image = scatterplot(sol, surrogate_parameters, images_parameters)
- from Metafor.Mparams.surrogate import surrogate_creation
- s_hat = surrogate_creation(sol, Z, W_sr, surrogate_parameters, Image, images_parameters)
- else:
- if tm.rank == 0:
- ##
-
- sol, Z, W_sr, s_hat = mp.master(model_parameters,surrogate_parameters,images_parameters)
-
- ##
-
- import codecs, json
-
- tmp = sol.tolist()
- file_path = "sol.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = Z.tolist()
- file_path = "Z.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = W_sr.tolist()
- file_path = "W_sr.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
- tmp = s_hat.tolist()
- file_path = "s_hat.json"
- json.dump(tmp, codecs.open(file_path, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
-
-
- else:
- mp.worker()
-
-if __name__ == "__main__":
- main()
diff --git a/Metafor/models/__init__.py b/Metafor/models/__init__.py
deleted file mode 100644
index 0b4a1527..00000000
--- a/Metafor/models/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# -*- coding: utf-8; -*-
-# mirrors MODULE initialization file
-
-import tbox
-from mirrorsw import *
diff --git a/Metafor/models/bord01/W_tp.json b/Metafor/models/bord01/W_tp.json
deleted file mode 100644
index 1443c74f..00000000
--- a/Metafor/models/bord01/W_tp.json
+++ /dev/null
@@ -1,677 +0,0 @@
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\ No newline at end of file
diff --git a/Metafor/models/bord01/X_tp.json b/Metafor/models/bord01/X_tp.json
deleted file mode 100644
index 66a4cc86..00000000
--- a/Metafor/models/bord01/X_tp.json
+++ /dev/null
@@ -1,677 +0,0 @@
-[
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\ No newline at end of file
diff --git a/Metafor/models/bord01/createNumericalSA.m b/Metafor/models/bord01/createNumericalSA.m
deleted file mode 100644
index 5ce49892..00000000
--- a/Metafor/models/bord01/createNumericalSA.m
+++ /dev/null
@@ -1,85 +0,0 @@
-clear all
-close all
-clc
-
-format long
-
-%% old
-NRTolExpo = -5:-0.5:-8;
-NRTol = 10.^(NRTolExpo);
-penoExpo = 5: 0.5: 8;
-peno = 10.^(penoExpo);
-nx = 20:5:60;
-ny = 4:2:16;
-
-length(NRTol)
-length(nx)
-length(ny)
-length(peno)
-i = 1;
-trainingPoints = zeros(length(NRTol)*length(nx)*length(ny)*length(peno),4);
-weights = ones(length(NRTol)*length(nx)*length(ny)*length(peno),1);
-for i_NR = 1:length(NRTol)
-for i_nx = 1:length(nx)
-for i_ny = 1:length(ny)
-for i_peno= 1:length(peno)
- trainingPoints(i,:) = [NRTol(i_NR), nx(i_nx), ny(i_ny), peno(i_peno)];
- i = i+1;
-end
-end
-end
-end
-fid = fopen('numericalSATrainingPoints.ascii', 'wt');
-for i = 1:length(NRTol)*length(nx)*length(ny)*length(peno)
- fprintf(fid,'%e ',trainingPoints(i,1));
- fprintf(fid,'%d ',trainingPoints(i,2));
- fprintf(fid,'%d ',trainingPoints(i,3));
- fprintf(fid,'%e \n',trainingPoints(i,4));
-end
-fclose(fid)
-%save numericalSATrainingPoints.ascii trainingPoints -ASCII -DOUBLE;
-save numericalSAWeights.ascii weights -ASCII -DOUBLE;
-
-clear all
-close all
-
-
-%% new
-NRTolExpo = linspace(-4,-5,5);
-NRTol = 10.^(NRTolExpo);
-
-penoExpo = linspace(5,6,5);
-peno = 10.^(penoExpo);
-peta = peno/10;
-
-PEASExpo = linspace(-6,-7,5);
-PEAS = 10.^(PEASExpo);
-
-ny = 6:2:12;
-nx = ny*10;
-
-i = 1;
-trainingPoints = zeros(length(NRTol)*length(peno)*length(PEAS)*length(ny),6);
-weights = ones(length(NRTol)*length(peno)*length(PEAS)*length(ny),1);
-for i_NR = 1:length(NRTol)
-for i_peno= 1:length(peno)
-for i_PEAS = 1:length(PEAS)
-for i_ny = 1:length(ny)
- trainingPoints(i,:) = [NRTol(i_NR), peno(i_peno), peta(i_peno), PEAS(i_PEAS), nx(i_ny), ny(i_ny)];
- i = i+1;
-end
-end
-end
-end
- fid = fopen('numericalSATrainingPoints2.ascii', 'wt');
- for i = 1:length(NRTol)*length(peno)*length(PEAS)*length(ny)
- fprintf(fid,'%e ',trainingPoints(i,1));
- fprintf(fid,'%e ',trainingPoints(i,2));
- fprintf(fid,'%e ',trainingPoints(i,3));
- fprintf(fid,'%e ',trainingPoints(i,4));
- fprintf(fid,'%d ',trainingPoints(i,5));
- fprintf(fid,'%d \n',trainingPoints(i,6));
- end
- fclose(fid)
-%save numericalSATrainingPoints2.ascii trainingPoints -ASCII -DOUBLE;
-save numericalSAWeights2.ascii weights -ASCII -DOUBLE;
\ No newline at end of file
diff --git a/Metafor/models/bord01/data.txt b/Metafor/models/bord01/data.txt
deleted file mode 100644
index 4a844daf..00000000
--- a/Metafor/models/bord01/data.txt
+++ /dev/null
@@ -1,3087 +0,0 @@
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-1.000000e-05 20 14 3.162278e+05
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-1.000000e-05 20 16 1.000000e+05
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-1.000000e-05 25 4 1.000000e+05
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diff --git a/Metafor/models/bord01/frinctionSA.ascii b/Metafor/models/bord01/frinctionSA.ascii
deleted file mode 100644
index fd19aedd..00000000
--- a/Metafor/models/bord01/frinctionSA.ascii
+++ /dev/null
@@ -1,21 +0,0 @@
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diff --git a/Metafor/models/bord01/hardening_yield.ascii.txt b/Metafor/models/bord01/hardening_yield.ascii.txt
deleted file mode 100644
index 24054cbb..00000000
--- a/Metafor/models/bord01/hardening_yield.ascii.txt
+++ /dev/null
@@ -1,25 +0,0 @@
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diff --git a/Metafor/models/bord01/model.py b/Metafor/models/bord01/model.py
deleted file mode 100644
index f590fc9b..00000000
--- a/Metafor/models/bord01/model.py
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-import numpy as np
-import os
-import shutil
-
-from Metafor.Mparams.Metafor_call import Metafor_call
-
-isUnix = lambda: os.name == 'posix'
-
-def model(d):
- d['Metafor_model_name'] = 'tombeBord'
- Metafor_call(d)
-
- Angle = np.genfromtxt("Angle1.ascii", dtype=None)
-
- sol = Angle[len(Angle)-1]
-
- return sol
-
diff --git a/Metafor/models/bord01/numericalSATrainingPoints.ascii b/Metafor/models/bord01/numericalSATrainingPoints.ascii
deleted file mode 100644
index 4a844daf..00000000
--- a/Metafor/models/bord01/numericalSATrainingPoints.ascii
+++ /dev/null
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diff --git a/Metafor/models/bord01/numericalSATrainingPoints2.ascii b/Metafor/models/bord01/numericalSATrainingPoints2.ascii
deleted file mode 100644
index 1e0037ba..00000000
--- a/Metafor/models/bord01/numericalSATrainingPoints2.ascii
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diff --git a/Metafor/models/bord01/numericalSAWeights.ascii b/Metafor/models/bord01/numericalSAWeights.ascii
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diff --git a/Metafor/models/bord01/numericalSAWeights2.ascii b/Metafor/models/bord01/numericalSAWeights2.ascii
deleted file mode 100644
index 5e6de452..00000000
--- a/Metafor/models/bord01/numericalSAWeights2.ascii
+++ /dev/null
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diff --git a/Metafor/models/bord01/numericalSAWeights3.ascii b/Metafor/models/bord01/numericalSAWeights3.ascii
deleted file mode 100644
index 4bb72405..00000000
--- a/Metafor/models/bord01/numericalSAWeights3.ascii
+++ /dev/null
@@ -1,4 +0,0 @@
- 1.0000000000000000e+00
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diff --git a/Metafor/models/bord01/tombeBord.py b/Metafor/models/bord01/tombeBord.py
deleted file mode 100644
index 6ac138a0..00000000
--- a/Metafor/models/bord01/tombeBord.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# -*- coding: utf-8; -*-
-# $Id: tombeBordEas2D.py 1422 2011-03-24 08:38:18Z papeleux $
-#################################################
-# Tombe de bord #
-#===============================================#
-#################################################
-
-from wrap import *
-import math
-
-metafor = None
-
-def getMetafor(_parameters={}):
- global metafor
- if not metafor :
- '''
- params = {'npgdy':50}
- params.update(_parameters)
- metafor = buildMetafor(params)
- '''
- metafor = buildMetafor(_parameters)
- return metafor
-
-def buildMetafor(p={}):
- #-- parametres par defaut :
- parameters = {
- 'NIPDETA' : 2, # nbre de pts de Gauss selon l'epaisseur
- 'Young' : 210000.0,
- 'Poisson' : 0.3,
- 'sigEl0' : 420.0,
- 'ih_H' : 1500.0,
- 'nx' : 60,
- 'ny' : 6,
- 'Thickness' : 1.0,
- 'Rm' : 3.0, # rayon matrice
- 'Rt' : 1.0, # rayon outil
- 'Gap' : 1.0,
- 'nbArch' : 1,
- 'peno' : 5e+05,
- 'peta' : 5e+04,
- 'mu_sta' : 0.1,
- 'mu_dyn' : 0.1,
- 'NRTol' : 1e-04,
- 'PEAS' : 1e-06
- }
-
- print(p)
- parameters.update(p)
- metafor = Metafor()
- domain = metafor.getDomain()
-
- geometry = domain.getGeometry()
- geometry.setDimPlaneStrain(1.0)
- #-- dimension du maillage
- X0 = -3.0
- Y0 = 0.0
- Z0 = 0.0
- Lx = 20.0
- Th = parameters['Thickness']
- nx1 = parameters['nx']
- nx2 = parameters['nx']
- ny = parameters['ny']
- # Tool
- Rm = parameters['Rm'] # Rm : rayon Matrice
- Rt = parameters['Rt'] # Rt : rayon Tool
- Gap = parameters['Gap'] # Jeu matrice - poincon
- Lp = 3.0 # longueur du poincon
-
- #-- maillage --
- Lx1 = Lx / 2.0 #-2*X0 + (math.pi/2.0) * Rm
- Lx2 = Lx - Lx1
- from toolbox.meshedGeometry2D import createRectangleSameId
- createRectangleSameId(domain, nx1, ny, Lx1, Th, X0, Y0, 0)
- from toolbox.meshedGeometry2D import addQuadrangle2X
- addQuadrangle2X(domain, 100, nx2, ny, Lx2, Th, X0+Lx1, Y0,
- 2, 3, 2)
- #-- Fonctions d'evolution --
- FCT_CHA = PieceWiseLinearFunction()
- FCT_CHA.setData(0.0, 0.0)
- FCT_CHA.setData(10.0, 1.0)
- FCT_CHA.setData(20.0 , 0.0)
- #Outils de mise a forme
- from apps.toolbox.formingTools import createFlangingTools2D
- createFlangingTools2D(domain, 1000, (X0-1), Y0, Rm, Rt, Gap, Th, Lp, Lx,-Lx, FCT_CHA)
-
- prp_c1 = ElementProperties(Contact2DElement)
- prp_c1.put(MATERIAL, 1001)
- prp_c1.put(AREAINCONTACT,AIC_ONCE)
-
- interactionset = domain.getInteractionSet()
- curveset = geometry.getCurveSet()
- wireset = geometry.getWireSet()
- sideset = geometry.getSideSet()
-
- wireset.add( Wire(201, [curveset(1), curveset(101)]) )
- wireset.add( Wire(202, [curveset(3), curveset(103)]) )
-
- #Outil 1 : Matrice
- ci1 = RdContactInteraction(1)
- ci1.setTool(wireset(1001))
- ci1.push(wireset(201))
- ci1.addProperty(prp_c1)
- interactionset.add(ci1)
-
- #Outil 2 : Poincon
- ci2 = RdContactInteraction(2)
- ci2.setTool(wireset(1101))
- ci2.push(wireset(202))
- ci2.addProperty(prp_c1)
- interactionset.add(ci2)
-
- #Outil 3 : Serre-Flanc
- ci3 = RdContactInteraction(3)
- ci3.setTool(wireset(1201))
- ci3.push(curveset(3))
- ci3.addProperty(prp_c1)
- interactionset.add(ci3)
-
- #-- FieldApplicator --
- app001 = FieldApplicator(99)
- app001.push(sideset( 1))
- interactionset.add(app001)
-
- app101 = FieldApplicator(101)
- app101.push(sideset(101))
- interactionset.add(app101)
-
- #-- Fnn - Loadings --
- loadingset = domain.getLoadingSet()
- loadingset.define(curveset(4), Field1D(TX,RE))
- loadingset.define(curveset(4), Field1D(TY,RE))
-
- #-- Materiaux --
- materset = domain.getMaterialSet()
- materlawset = domain.getMaterialLawSet()
- materset.define(1, EvpIsoHHypoMaterial)
- materset(1).put(MASS_DENSITY, 8900.0E-12)
- materset(1).put(ELASTIC_MODULUS, parameters['Young'])
- materset(1).put(POISSON_RATIO, parameters['Poisson'])
- materset(1).put(YIELD_NUM, 2)
- '''
- materlawset.define(1, RambergOsgoodIsotropicHardening)
- materlawset(1).put(IH_SIGEL, 330.3)
- materlawset(1).put(IH_A, 591.72)
- materlawset(1).put(IH_N, 5.35)
- '''
- materlawset.define(2, LinearIsotropicHardening)
- materlawset(2).put(IH_SIGEL, parameters['sigEl0'])
- materlawset(2).put(IH_H, parameters['ih_H'])
-
-
- materset.define(1001, CoulombContactMaterial)
- mat1001 = materset(1001)
- mat1001.put(PEN_NORMALE, parameters['peno'])
- mat1001.put(PEN_TANGENT, parameters['peta'])
- mat1001.put(PROF_CONT, 0.9)
- mat1001.put(COEF_FROT_STA, parameters['mu_sta']) # usually between 0.3 and 0.6 for dry materials
- mat1001.put(COEF_FROT_DYN, parameters['mu_dyn'])
-
- #-- Propelem --
- prp001 = ElementProperties(Volume2DElement)
- prp001.put(CAUCHYMECHVOLINTMETH, VES_CMVIM_EAS)
- prp001.put(MATERIAL, 1)
- prp001.put(NIPDETA, parameters['NIPDETA'])
- prp001.put(EASS, 2)
- prp001.put(EASV, 2)
- prp001.put(PEAS, parameters['PEAS'])
- prp001.put(VERBOSE, True)
- interactionset(99).addProperty(prp001)
-
- prp101 = ElementProperties(Volume2DElement)
- prp101.put(CAUCHYMECHVOLINTMETH, VES_CMVIM_EAS)
- prp101.put(MATERIAL, 1)
- prp101.put(NIPDETA, parameters['NIPDETA'])
- prp101.put(EASS, 2)
- prp101.put(EASV, 2)
- prp101.put(PEAS, parameters['PEAS'])
- prp101.put(VERBOSE, True)
- interactionset(101).addProperty(prp101)
-
- #
-
- mim = metafor.getMechanicalIterationManager()
- mim.setResidualComputationMethod(Method4ResidualComputation())
- mim.setResidualTolerance(parameters['NRTol'])
-
- #-- tsm --
- tsm = metafor.getTimeStepManager()
- tsm.setInitialTime(0.0, 1e-4)
- tsm.setNextTime(10.0, parameters['nbArch'], 1.0)
- tsm.setNextTime(20.0, parameters['nbArch'], 1.0)
-
- # Time Integration
- ti = AlphaGeneralizedTimeIntegration(metafor)
- metafor.setTimeIntegration(ti)
-
- # Courbes
- valuesmanager = metafor.getValuesManager()
- pointset = geometry.getPointSet()
- curveset = geometry.getCurveSet()
- valuesmanager.add(1, MiscValueExtractor(metafor,EXT_T),'time')
- ave1 = AngleValueExtractor(Axe(curveset(1003)),Axe(pointset(2),pointset(102)))
- valuesmanager.add(11,ave1,'Angle1')
-
- # Objective Function
- # ------------------
- objectivefunctionset = metafor.getObjectiveFunctionSet()
-
- Index = 2000
- X0 = -10.0
- dX0 = 3.0
- Y0 = -10.0
- Z0 = 0.0
- Rm = 4.0
- Ll = 50.0
- alphaDeg = 80.0
-
- alphaRad = alphaDeg * math.pi / 180.0
- cosAlphaDemi = math.cos(alphaRad / 2.0)
- sinAlphaDemi = math.sin(alphaRad / 2.0)
- cosAlpha = math.cos(alphaRad)
- sinAlpha = math.sin(alphaRad)
- cos90moinsAlpha = math.cos(math.pi / 2.0 - alphaRad)
- sinAlpha = math.sin(alphaRad)
-
- from toolbox.domainTools import getGeoReferences
- [pointset, curveset, wireset, surfaceset, sideset, skinset, volumeset] = getGeoReferences(domain)
- piDemi = math.asin(1)
- C45 = math.cos(piDemi / 2.0)
- Z0 = 0.0
-
- # Outil 1 : matrice
- pointset.define(Index + 1, X0 - dX0, Y0, Z0)
- pointset.define(Index + 2, X0, Y0, Z0)
- pointset.define(Index + 3, X0 + (Rm * sinAlphaDemi), (Y0 - Rm * (1.0-cosAlphaDemi)), Z0)
- pointset.define(Index + 4, X0 + (Rm * sinAlpha), (Y0 - Rm * (1.0-cosAlpha)), Z0)
- pointset.define(Index + 5, X0 + (Rm * sinAlpha + Ll * cosAlpha), (Y0 - Rm * (1.0 - cosAlpha) - Ll * sinAlpha), Z0)
- pointset.define(Index + 6, X0 - dX0 , (Y0 + 2.0), Z0)
-
- curveset.add( Line(Index + 1, pointset(Index + 1), pointset(Index + 2) ))
- curveset.add( Arc(Index + 2, pointset(Index + 2), pointset(Index + 3), pointset(Index + 4) ))
- curveset.add( Line(Index + 3, pointset(Index + 4), pointset(Index + 5) ))
- curveset.add( Line(Index + 4, pointset(Index + 6), pointset(Index + 1) ))
-
- wireset.add(MultiProjWire(Index + 1, [curveset(Index+i) for i in (1, 2, 3)]))
- wireset.add(Wire(Index+2, [curveset(1), curveset(101)]))
-
- sve1 = ShapeValueExtractor(wireset(Index+1), wireset(Index+2))
- sve1.setTriedreRef(Triedre(pointset(Index+1),pointset(Index+2),pointset(Index+6)))
- sve1.setTriedreMesh(Triedre(pointset(1),pointset(1002),pointset(4)))
-
- valuesmanager.add(12,sve1,'shape1')
- valuesmanager.add(13,sve1,NormOperator(),'shape2')
-
- sof1 = ValueExtractorObjectiveFunction(1,ave1)
- objectivefunctionset.add(sof1)
- sof2 = ValueExtractorObjectiveFunction(2,sve1)
- objectivefunctionset.add(sof2)
-
- # Verification des Objective Function in .res File
- testSuite = metafor.getTestSuiteChecker()
- testSuite.checkObjectiveFunction(2)
- testSuite.checkExtractor(11)
- testSuite.checkExtractor(12,0)
- testSuite.checkExtractor(12,5)
- testSuite.checkExtractor(13)
-
- return metafor
diff --git a/Metafor/models/bord01/tombeBord_old.py b/Metafor/models/bord01/tombeBord_old.py
deleted file mode 100644
index af42c8e4..00000000
--- a/Metafor/models/bord01/tombeBord_old.py
+++ /dev/null
@@ -1,248 +0,0 @@
-# -*- coding: utf-8; -*-
-# $Id: tombeBordEas2D.py 1422 2011-03-24 08:38:18Z papeleux $
-#################################################
-# Tombe de bord #
-#===============================================#
-#################################################
-
-from wrap import *
-import math
-
-metafor = None
-
-def getMetafor(_parameters={}):
- global metafor
- if not metafor :
- '''
- params = {'npgdy':50}
- params.update(_parameters)
- metafor = buildMetafor(params)
- '''
- metafor = buildMetafor(_parameters)
- return metafor
-
-def buildMetafor(p={}):
- #-- parametres par defaut :
- parameters = {'NIPDETA': 6, 'Young':210000.0,'Poisson':0.3,
- 'sigEl0':420.0,'ih_H':1500.0,
- 'npgdy': 16,'nx':35,'ny':8,
- 'Thickness':1.0,'Rm':3.0,'Rt':1.0,
- 'Gap':1.0,'nbArch':1,
- 'peno':1e+08, 'mu_sta':0.6, 'mu_dyn':0.6,
- 'NRTol':1e-07}
-
- print(p)
- parameters.update(p)
- metafor = Metafor()
- domain = metafor.getDomain()
-
- geometry = domain.getGeometry()
- geometry.setDimPlaneStrain(1.0)
- #-- dimension du maillage
- X0 = -3.0
- Y0 = 0.0
- Z0 = 0.0
- Lx = 20.0
- Th = parameters['Thickness']
- nx1 = parameters['nx']
- nx2 = parameters['nx']
- ny = parameters['ny']
- # Tool
- Rm = parameters['Rm'] #Rm : rayon Matrice
- Rt = parameters['Rt'] #Rt : rayon Tool
- Gap = parameters['Gap'] #Jeu matrice - poincon
- Lp = 3.0 #longueur du poincon
-
- #-- maillage --
- Lx1 = -2*X0 + (math.pi / 2.0) * Rm
- Lx2 = Lx - Lx1
- from toolbox.meshedGeometry2D import createRectangleSameId
- createRectangleSameId(domain, nx1, ny, Lx1, Th, X0, Y0, 0)
- from toolbox.meshedGeometry2D import addQuadrangle2X
- addQuadrangle2X(domain, 100, nx2, ny, Lx2, Th, X0+Lx1, Y0,
- 2, 3, 2)
- #-- Fonctions d'evolution --
- FCT_CHA = PieceWiseLinearFunction()
- FCT_CHA.setData(0.0, 0.0)
- FCT_CHA.setData(10.0, 1.0)
- FCT_CHA.setData(20.0 , 0.0)
- #Outils de mise a forme
- from apps.toolbox.formingTools import createFlangingTools2D
- createFlangingTools2D(domain, 1000, (X0-1), Y0, Rm, Rt, Gap, Th, Lp, Lx,-Lx, FCT_CHA)
-
- prp_c1 = ElementProperties(Contact2DElement)
- prp_c1.put(MATERIAL, 1001)
- prp_c1.put(AREAINCONTACT,AIC_ONCE)
-
- interactionset = domain.getInteractionSet()
- curveset = geometry.getCurveSet()
- wireset = geometry.getWireSet()
- sideset = geometry.getSideSet()
-
- wireset.add( Wire(201, [curveset(1), curveset(101)]) )
- wireset.add( Wire(202, [curveset(3), curveset(103)]) )
- #Outil 1 : Matrice
- ci1 = RdContactInteraction(1)
- ci1.setTool(wireset(1001))
- ci1.push(wireset(201))
- ci1.addProperty(prp_c1)
- interactionset.add(ci1)
- #Outil 2 : Poincon
- ci2 = RdContactInteraction(2)
- ci2.setTool(wireset(1101))
- ci2.push(wireset(202))
- ci2.addProperty(prp_c1)
- interactionset.add(ci2)
- #Outil 3 : Serre-Flanc
- ci3 = RdContactInteraction(3)
- ci3.setTool(wireset(1201))
- ci3.push(curveset(3))
- ci3.addProperty(prp_c1)
- interactionset.add(ci3)
- #-- FieldApplicator --
- app001 = FieldApplicator(99)
- app001.push(sideset( 1))
- interactionset.add(app001)
-
- app101 = FieldApplicator(101)
- app101.push(sideset(101))
- interactionset.add(app101)
- #-- Fnn - Loadings --
- loadingset = domain.getLoadingSet()
- loadingset.define(curveset(4), Field1D(TX,RE))
- loadingset.define(curveset(4), Field1D(TY,RE))
-
- #-- Materiaux --
- materset = domain.getMaterialSet()
- materlawset = domain.getMaterialLawSet()
- materset.define(1, EvpIsoHHypoMaterial)
- materset(1).put(MASS_DENSITY, 8900.0E-12)
- materset(1).put(ELASTIC_MODULUS, parameters['Young'])
- materset(1).put(POISSON_RATIO, parameters['Poisson'])
- materset(1).put(YIELD_NUM, 2)
- '''
- materlawset.define(1, RambergOsgoodIsotropicHardening)
- materlawset(1).put(IH_SIGEL, 330.3)
- materlawset(1).put(IH_A, 591.72)
- materlawset(1).put(IH_N, 5.35)
- '''
- materlawset.define(2, LinearIsotropicHardening)
- materlawset(2).put(IH_SIGEL, parameters['sigEl0'])
- materlawset(2).put(IH_H, parameters['ih_H'])
-
-
- materset.define(1001, CoulombContactMaterial)
- mat1001 = materset(1001)
- mat1001.put(PEN_NORMALE, parameters['peno'])
- mat1001.put(PEN_TANGENT, 1e+06)
- mat1001.put(PROF_CONT, 0.9)
- mat1001.put(COEF_FROT_STA, parameters['mu_sta']) # usually between 0.3 and 0.6 for dry materials
- mat1001.put(COEF_FROT_DYN, parameters['mu_dyn'])
-
- #-- Propelem --
- prp001 = ElementProperties(Volume2DElement)
- prp001.put(CAUCHYMECHVOLINTMETH, VES_CMVIM_EAS)
- prp001.put(MATERIAL, 1)
- prp001.put(NIPDETA, parameters['NIPDETA'])
- prp001.put(EASS, 2)
- prp001.put(EASV, 4)
- prp001.put(VERBOSE, True)
- interactionset(99).addProperty(prp001)
-
- prp101 = ElementProperties(Volume2DElement)
- prp101.put(CAUCHYMECHVOLINTMETH, VES_CMVIM_EAS)
- prp101.put(MATERIAL, 1)
- prp101.put(NIPDETA, parameters['NIPDETA'])
- prp101.put(EASS, 2)
- prp101.put(EASV, 4)
- prp101.put(VERBOSE, True)
- interactionset(101).addProperty(prp101)
-
- #
-
- mim = metafor.getMechanicalIterationManager()
- mim.setResidualTolerance(parameters['NRTol'])
-
- #-- tsm --
- tsm = metafor.getTimeStepManager()
- tsm.setInitialTime(0.0, 0.1)
- tsm.setNextTime(10.0, parameters['nbArch'], 1.0)
- tsm.setNextTime(20.0, parameters['nbArch'], 1.0)
-
- #Time Integration
- ti = AlphaGeneralizedTimeIntegration(metafor)
- ti.setAlphaM(-0.97)
- ti.setAlphaF(0.01)
- metafor.setTimeIntegration(ti)
-
- #Courbes
- valuesmanager = metafor.getValuesManager()
- pointset = geometry.getPointSet()
- curveset = geometry.getCurveSet()
- valuesmanager.add(1, MiscValueExtractor(metafor,EXT_T),'time')
- ave1 = AngleValueExtractor(Axe(curveset(1003)),Axe(pointset(2),pointset(102)))
- valuesmanager.add(11,ave1,'Angle1')
-
- # Objective Function :
- #---------------------
- objectivefunctionset = metafor.getObjectiveFunctionSet()
-
- Index = 2000
- X0 = -10.0
- dX0 = 3.0
- Y0 = -10.0
- Z0 = 0.0
- Rm = 4.0
- Ll = 50.0
- alphaDeg = 80.0
-
- alphaRad = alphaDeg * math.pi / 180.0
- cosAlphaDemi = math.cos(alphaRad / 2.0)
- sinAlphaDemi = math.sin(alphaRad / 2.0)
- cosAlpha = math.cos(alphaRad)
- sinAlpha = math.sin(alphaRad)
- cos90moinsAlpha = math.cos(math.pi / 2.0 - alphaRad)
- sinAlpha = math.sin(alphaRad)
-
- from toolbox.domainTools import getGeoReferences
- [pointset, curveset, wireset, surfaceset, sideset, skinset, volumeset] = getGeoReferences(domain)
- piDemi = math.asin(1)
- C45 = math.cos(piDemi / 2.0)
- Z0 = 0.0
- # Outil 1 : matrice
- pointset.define(Index + 1, X0 - dX0, Y0, Z0)
- pointset.define(Index + 2, X0, Y0, Z0)
- pointset.define(Index + 3, X0 + (Rm * sinAlphaDemi), (Y0 - Rm * (1.0-cosAlphaDemi)), Z0)
- pointset.define(Index + 4, X0 + (Rm * sinAlpha), (Y0 - Rm * (1.0-cosAlpha)), Z0)
- pointset.define(Index + 5, X0 + (Rm * sinAlpha + Ll * cosAlpha), (Y0 - Rm * (1.0 - cosAlpha) - Ll * sinAlpha), Z0)
- pointset.define(Index + 6, X0 - dX0 , (Y0 + 2.0), Z0)
-
- curveset.add( Line(Index + 1, pointset(Index + 1), pointset(Index + 2) ))
- curveset.add( Arc(Index + 2, pointset(Index + 2), pointset(Index + 3), pointset(Index + 4) ))
- curveset.add( Line(Index + 3, pointset(Index + 4), pointset(Index + 5) ))
- curveset.add( Line(Index + 4, pointset(Index + 6), pointset(Index + 1) ))
-
- wireset.add(MultiProjWire(Index + 1, [curveset(Index+i) for i in (1, 2, 3)]))
- wireset.add(Wire(Index+2, [curveset(1), curveset(101)]))
-
- sve1 = ShapeValueExtractor(wireset(Index+1), wireset(Index+2))
- sve1.setTriedreRef(Triedre(pointset(Index+1),pointset(Index+2),pointset(Index+6)))
- sve1.setTriedreMesh(Triedre(pointset(1),pointset(1002),pointset(4)))
-
- valuesmanager.add(12,sve1,'shape1')
- valuesmanager.add(13,sve1,NormOperator(),'shape2')
-
- sof1 = ValueExtractorObjectiveFunction(1,ave1)
- objectivefunctionset.add(sof1)
- sof2 = ValueExtractorObjectiveFunction(2,sve1)
- objectivefunctionset.add(sof2)
-
- # Verification des Objective Function in .res File
- testSuite = metafor.getTestSuiteChecker()
- testSuite.checkObjectiveFunction(2)
- testSuite.checkExtractor(11)
- testSuite.checkExtractor(12,0)
- testSuite.checkExtractor(12,5)
- testSuite.checkExtractor(13)
-
- return metafor
diff --git a/Metafor/models/mirror01/__init__.py b/Metafor/models/mirror01/__init__.py
deleted file mode 100644
index 0b4a1527..00000000
--- a/Metafor/models/mirror01/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# -*- coding: utf-8; -*-
-# mirrors MODULE initialization file
-
-import tbox
-from mirrorsw import *
diff --git a/Metafor/models/mirror01/mirror.geo b/Metafor/models/mirror01/mirror.geo
deleted file mode 100644
index 5c4c4e77..00000000
--- a/Metafor/models/mirror01/mirror.geo
+++ /dev/null
@@ -1,86 +0,0 @@
-// a mirror+holder meshed with hex
-
-
-DefineConstant[ L = { 1, Min 50, Max 500, Step 1, Name "Lengths/L" } ];
-DefineConstant[ L_1 = { 1, Min 50, Max 250, Step 1, Name "Lengths/L_1" } ];
-DefineConstant[ H = { 1, Min 10, Max 100, Step 1, Name "Lengths/H" } ];
-DefineConstant[ H_1 = { 1, Min 1, Max 20, Step 1, Name "Lengths/H_1" } ];
-DefineConstant[ W = { 1, Min 25, Max 250, Step 1, Name "Lengths/W" } ];
-
-DefineConstant[ nL = { 1, Min 1, Max 51, Step 1, Name "Mesh/nL" } ];
-DefineConstant[ nL_1 = { 1, Min 1, Max 51, Step 1, Name "Mesh/nL_1" } ];
-DefineConstant[ nH = { 1, Min 1, Max 51, Step 1, Name "Mesh/nH" } ];
-DefineConstant[ nH_1 = { 1, Min 1, Max 51, Step 1, Name "Mesh/nH_1" } ];
-DefineConstant[ nW = { 1, Min 1, Max 51, Step 1, Name "Mesh/nW" } ];
-
-lc = 1; // useless
-
-Point(1) = { 0, 0, 0, lc};
-Point(2) = { 0, 0, H, lc};
-Point(3) = { L_1, 0, 0, lc};
-Point(4) = { L_1, 0, H, lc};
-Point(5) = { L_1, 0, H+H_1, lc};
-Point(6) = { L+L_1, 0, 0, lc};
-Point(7) = { L+L_1, 0, H, lc};
-Point(8) = { L+L_1, 0, H+H_1, lc};
-
-Line(1) = {2, 1};
-Line(2) = {1, 3};
-Line(3) = {4, 2};
-Line(4) = {3, 4};
-Line(5) = {4, 5};
-Line(6) = {3, 6};
-Line(7) = {7, 4};
-Line(8) = {5, 8};
-Line(9) = {6, 7};
-Line(10) = {8, 7};
-
-Transfinite Line {6,7,8} = nL Using Progression 1;
-Transfinite Line {2,3} = nL_1 Using Progression 1;
-Transfinite Line {1,4,9} = nH Using Progression 1;
-Transfinite Line {5,10} = nH_1 Using Progression 1;
-
-Line Loop(11) = {2, 4, 3, 1};
-Plane Surface(12) = {11};
-Transfinite Surface {12};
-Recombine Surface {12};
-
-Line Loop(13) = {6,9,7,-4};
-Plane Surface(14) = {13};
-Transfinite Surface {14};
-Recombine Surface {14};
-
-Line Loop(15) = {7, 10, 8, 5};
-Plane Surface(16) = {15};
-Transfinite Surface {16};
-Recombine Surface {16};
-
-
-Extrude {0, W, 0}
-{
- Surface{12}; Layers{nW}; Recombine;
-}
-
-Extrude {0, W, 0}
-{
- Surface{14}; Layers{nW}; Recombine;
-}
-
-Extrude {0, W, 0}
-{
- Surface{16}; Layers{nW}; Recombine;
-}
-
-// physical entities
-
-Physical Line("Vertical clamped line") = {1};
-Physical Line("Horizontal clamped line") = {23};
-Physical Surface("Clamped Side") = {37};
-Physical Surface("Mirror Side") = {77};
-Physical Surface("Contact") = {55};
-Physical Volume("Holder") = {1, 2};
-Physical Volume("Mirror") = {3};
-
-
-
-
diff --git a/Metafor/models/mirror01/mirror.py b/Metafor/models/mirror01/mirror.py
deleted file mode 100644
index 4e7ca016..00000000
--- a/Metafor/models/mirror01/mirror.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# -*- coding: utf-8; -*-
-#
-# $Id: TestThermExpan.py 2424 2015-10-11 21:09:13Z wautelet $
-#
-#
-
-
-
-from wrap import *
-metafor = None
-
-
-#======================================================================#
-# Definition des parametres #
-#======================================================================#
-
-
-def getMetafor(_parameters):
- global metafor
- if metafor: return metafor
- metafor = Metafor()
- from .parameters_file import *
- parameters = getParameters(_parameters)
-
- domain = metafor.getDomain()
- geometry = domain.getGeometry()
- geometry.setDim3D()
-
-
- #==================================================================#
- # import du fichier .msh #
- #==================================================================#
- from toolbox.gmsh import GmshImport
- #f = os.path.join(os.path.dirname(__file__), 'mirrors03.msh')
- f = parameters['mesh']
- importer = GmshImport(f, domain)
- importer.setOrder(order=1)
- importer.setOpti(opti=True)
- importer.setAlgo(algo='default')
- importer.execute()
-
- groupset = domain.getGeometry().getGroupSet()
-
- #==================================================================#
- # Visualisation du maillage #
- #==================================================================#
- if 0:
- winMesh = VizWin()
- winMesh.add(geometry.getMesh().getPointSet())
- winMesh.add(geometry.getMesh().getCurveSet())
- winMesh.open()
- input("pause : Visualisation du maillage")
-
-
- #==================================================================#
- # Lois de comportement #
- #==================================================================#
- # ---------------------------- #
- # Definition des materiaux
- materialset = domain.getMaterialSet()
- noMat = 1
- noMatLaw = 1
-
- materialset.define (noMat, TmEvpIsoHHypoMaterial)
- material = materialset(noMat)
- material.put(MASS_DENSITY, 8.93e-9)
- material.put(ELASTIC_MODULUS, 200000.0)
- material.put(POISSON_RATIO, 0.3)
- material.put(YIELD_NUM, noMatLaw)
- material.put(CONDUCTIVITY, 30.0)
- material.put(DISSIP_TE, 1.0)
- material.put(DISSIP_TQ, 0.0)
- material.put(THERM_EXPANSION, 1.0); material.depend(THERM_EXPANSION, parameters['fct_TExp'], Field1D(TO,RE))
- material.put(HEAT_CAPACITY, 4.6E+08)
-
- materialLawset = domain.getMaterialLawSet()
- materialLawset.define (noMatLaw, LinearIsotropicHardening)
- materialLawset(noMatLaw).put(IH_SIGEL, 400.0)
- materialLawset(noMatLaw).put(IH_H, 0.0)
-
- noMat = 2
- noMatLaw = 2
- materialset.define (noMat, TmEvpIsoHHypoMaterial)
- material = materialset(noMat)
- material.put(MASS_DENSITY, 8.93e-9)
- material.put(ELASTIC_MODULUS, 200000.0)
- material.put(POISSON_RATIO, 0.3)
- material.put(YIELD_NUM, noMatLaw)
- material.put(CONDUCTIVITY, 30.0)
- material.put(DISSIP_TE, 1.0)
- material.put(DISSIP_TQ, 0.0)
- material.put(THERM_EXPANSION, 0.5); material.depend(THERM_EXPANSION, parameters['fct_TExp'], Field1D(TO,RE))
- material.put(HEAT_CAPACITY, 4.6E+08)
-
- materialLawset = domain.getMaterialLawSet()
- materialLawset.define (noMatLaw, LinearIsotropicHardening)
- materialLawset(noMatLaw).put(IH_SIGEL, 400.0)
- materialLawset(noMatLaw).put(IH_H, 0.0)
-
-
- #==================================================================#
- # Schema d integration #
- #==================================================================#
- # ---------------------------- #
- # Gestion des pas de temps
- tsm = metafor.getTimeStepManager()
- tsm.setInitialTime(0.0, parameters['dtinit']) #t = 0 <-> stage 0
- tsm.setNextTime(parameters['tmax'], 1, parameters['dtmax'])
-
- # ---------------------------- #
- # Gestion des iterations mechaniques
- mim = metafor.getMechanicalIterationManager()
- mim.setResidualTolerance(1.0e-5)
-
- # ---------------------------- #
- # Gestion des iterations thermiques
- tim = metafor.getThermalIterationManager()
- tim.setResidualTolerance(1.0e-5)
-
- # ---------------------------- #
- # Time Integration
- tiMech = QuasiStaticTimeIntegration(metafor)
- tiTher = TrapezoidalThermalTimeIntegration(metafor)
- ti = StaggeredTmTimeIntegration(metafor)
- ti.setIsAdiabatic(False)
- ti.setWithStressReevaluation(False)
- ti.setMechanicalTimeIntegration(tiMech)
- ti.setThermalTimeIntegration(tiTher)
- metafor.setTimeIntegration(ti)
-
-
- #==================================================================#
- # Definition des elements finis volumiques #
- #==================================================================#
- # ---------------------------- #
- # Definition des proprietes des elements finis volumiques
- prp1 = ElementProperties( TmVolume3DElement )
- prp1.put( MATERIAL, 1 )
-
- prp2 = ElementProperties( TmVolume3DElement )
- prp2.put( MATERIAL, 2 )
-
- # ---------------------------- #
- # Generation des elements finis volumiques sur le maillage
-
- interactionset = domain.getInteractionSet()
- app1 = FieldApplicator(1)
- app1.push( groupset(6) )
- interactionset.add( app1 )
-
- app2 = FieldApplicator(2)
- app2.push( groupset(7) )
- interactionset.add( app2 )
-
- interactionset(1).addProperty( prp1 )
- interactionset(2).addProperty( prp2 )
- #==================================================================#
- # Conditions initiales #
- #==================================================================#
- initialconditionset = metafor.getInitialConditionSet()
- initialconditionset.define(groupset(6),Field1D(TO,AB), parameters['TempInit'])
- initialconditionset.define(groupset(7),Field1D(TO,AB), parameters['TempInit'])
-
- #==================================================================#
- # Fixations #
- #==================================================================#
- loadingset = domain.getLoadingSet()
- loadingset.define(groupset(1),Field1D(TY,RE),0.0)
- loadingset.define(groupset(2),Field1D(TZ,RE),0.0)
- loadingset.define(groupset(3),Field1D(TX,RE),0.0)
-
- fct = PieceWiseLinearFunction()
- fct.setData(0.0, parameters['TempInit'])
- fct.setData(parameters['tmax'], parameters['TempFinal'])
- # Chargements
- loadingset.define(groupset(6), Field1D(TO,RE), 1.0, fct)
- loadingset.define(groupset(7), Field1D(TO,RE), 1.0, fct)
-
- #==================================================================#
- # Archivages #
- #==================================================================#
- valuesmanager = metafor.getValuesManager()
-
- valuesmanager.add(1, MiscValueExtractor(metafor,EXT_T),'time')
- valuesmanager.add(2, DbNodalValueExtractor(groupset(4), Field1D(TO,RE)), 'mirror_Temperature')
- valuesmanager.add(3, DbNodalValueExtractor(groupset(4), Field1D(TX,RE)), 'mirror_displacementX')
- valuesmanager.add(4, DbNodalValueExtractor(groupset(4), Field1D(TY,RE)), 'mirror_displacementY')
- valuesmanager.add(5, DbNodalValueExtractor(groupset(4), Field1D(TZ,RE)), 'mirror_displacementZ')
- valuesmanager.add(6, IFNodalValueExtractor(groupset(4), IF_EVMS), 'mirror_EVMS')
- valuesmanager.add(7, DbNodalValueExtractor(groupset(4), Field1D(TX,AB)), 'mirror_X')
- valuesmanager.add(8, DbNodalValueExtractor(groupset(4), Field1D(TY,AB)), 'mirror_Y')
- valuesmanager.add(9, DbNodalValueExtractor(groupset(4), Field1D(TZ,AB)), 'mirror_Z')
- valuesmanager.add(10, IFNodalValueExtractor(groupset(4), IF_EVMS), 'contact_EVMS')
- valuesmanager.add(11, DbNodalValueExtractor(groupset(4), Field1D(TX,AB)), 'contact_X')
- valuesmanager.add(12, DbNodalValueExtractor(groupset(4), Field1D(TY,AB)), 'contact_Y')
- valuesmanager.add(13, DbNodalValueExtractor(groupset(4), Field1D(TZ,AB)), 'contact_Z')
- return metafor
\ No newline at end of file
diff --git a/Metafor/models/mirror01/model.py b/Metafor/models/mirror01/model.py
deleted file mode 100644
index 8c85d1ea..00000000
--- a/Metafor/models/mirror01/model.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-import numpy as np
-import os
-import shutil
-
-import fwk
-
-from Metafor.Mparams.Metafor_call import Metafor_call
-
-isUnix = lambda: os.name == 'posix'
-
-def model(d):
- # modify the mesh
- import tbox.gmsh as gmsh
-
- pars={ 'L' : d['L'] , 'L_1' : d['L_1'], 'H' : d['H'], 'H_1' : d['H_1'], 'W' : d['W'], 'nL' : d['nL'], 'nL_1' : d['nL_1'], 'nH' : d['nH'], 'nH_1' : d['nH_1'], 'nW' : d['nW'] }
-
- gmsh.MeshLoader(d['geo'],__file__).execute(**pars)
-
- # clean the mesh
-
- print_line = True
-
- with open("mirror.msh","r") as input:
- with open("mirror2.msh","w") as output:
- for line in input:
- if line=="$PhysicalNames"+"\n":
- print_line = False
- if print_line:
- output.write(line)
- if line=="$EndPhysicalNames"+"\n":
- print_line = True
-
- d['mesh'] = os.getcwd() + '/mirror2.msh'
- # end of mesh part
-
- d['Metafor_model_name'] = 'mirror'
- Metafor_call(d)
-
- mirror_X = np.genfromtxt("mirror_X.ascii", dtype=None)
- mirror_Y = np.genfromtxt("mirror_X.ascii", dtype=None)
- mirror_Z = np.genfromtxt("mirror_X.ascii", dtype=None)
- mirror_displacementZ = np.genfromtxt("mirror_displacementZ.ascii", dtype=None)
- contact_EVMS = np.genfromtxt("contact_EVMS.ascii", dtype=None)
-
- sol = np.amax(contact_EVMS)
-
- return sol
-
diff --git a/Metafor/models/mirror01/parameters_file.py b/Metafor/models/mirror01/parameters_file.py
deleted file mode 100644
index 3a591667..00000000
--- a/Metafor/models/mirror01/parameters_file.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# -*- coding: utf-8; -*-
-# parameters_file.py:
-
-from wrap import *
-
-def getParameters(_parameters):
- parameters = {}
- parameters['tmax'] = 1. #sec
- parameters['dtinit'] = 0.01 #sec
- parameters['dtmax'] = 0.1 #sec
-
- parameters['TempInit'] = 70.0 #°
- parameters['TempFinal'] = 500.0 #°
-
- parameters['fct_TExp'] = PieceWiseLinearFunction()
-
- eps=0.0001
- parameters['fct_TExp'].setData( 0., 1.61572E-005)
- parameters['fct_TExp'].setData( 902.-eps, 1.61572E-005)
-
- parameters['fct_TExp'].setData( 902.+eps, 8.333333E-06)
- parameters['fct_TExp'].setData( 998.-eps, 8.333333E-06)
-
- parameters['fct_TExp'].setData( 998.+eps, -2.727273E-05)
- parameters['fct_TExp'].setData( 1053.-eps, -2.727273E-05)
-
- parameters['fct_TExp'].setData( 1053.+eps, 4.347826E-06)
- parameters['fct_TExp'].setData( 1076.-eps, 4.347826E-06)
-
- parameters['fct_TExp'].setData( 1076.+eps, 2.371134E-05)
- parameters['fct_TExp'].setData( 1173.-eps, 2.371134E-05)
-
- parameters['fct_TExp'].setData( 1173.+eps, 2.371134E-05)
- parameters['fct_TExp'].setData( 1e5, 2.371134E-05)
-
-
- parameters.update(_parameters)
- return parameters
\ No newline at end of file
diff --git a/Metafor/models/omega01/EmboutOmega.py b/Metafor/models/omega01/EmboutOmega.py
deleted file mode 100644
index c477515d..00000000
--- a/Metafor/models/omega01/EmboutOmega.py
+++ /dev/null
@@ -1,583 +0,0 @@
-# -*- coding: utf-8; -*-
-# $Id: $
-#################################################
-#
-#################################################
-#-- Initialisation python
-
-from wrap import *
-import math
-metafor = None
-
-#=========================================================================
-#
-def getParameters(_parameters):
- parameters = {}
- # Param�tres serre-Flanc
- parameters['forceSF'] = 1500.0 # Force de serrage du serre-Flanc
- parameters['amortissement'] = 5.0e-7 # Param�tre d'amortissement
- #param�tres contact
- parameters['peno'] = 1.0e4
- parameters['mu'] = 0.12
- # Donn�es mat�riau t�le
- parameters['rho'] = 7.8e-9 # densit�
- parameters['Young'] = 210000.0 # Module de Young
- parameters['poisson'] = 0.3 # Coef Poisson
-
- parameters['trac_Rm'] = 625 # Donn�es mat�riau
- parameters['trac_R02'] = 369 # Donn�es mat�riau
- parameters['trac_n'] = 0.194 # Donn�es mat�riau
-
- # G�om�trie - maillage du Flanc
- parameters['nx'] = 135
- parameters['ny'] = 2
- parameters['Lx'] = 135.
- parameters['Ly'] = 1.
- parameters['nipX'] = 2
- parameters['nipY'] = 2
-
- # G�om�trie de la Matrice
- parameters['Y0M'] = 0.0
- parameters['LxM1'] = 50.0
- #parameters['LxM2'] = post trait�
- parameters['LxM3'] = 140.0
- parameters['LyM'] = 61.1
- parameters['rM1'] = 10.0
- parameters['rM2'] = 10.0
- parameters['thetaM'] = 80.0
- parameters['ddyM'] = 1.0
-
- # G�om�trie du Poincon
- parameters['Y0P'] = parameters['Ly']
- parameters['LxP1'] = 49.9
- parameters['rP1'] = 8.9
- parameters['rP2'] = 11.1
- parameters['rP3'] = 5.0
- parameters['LyP'] = 61.1 #0.110000002E+01+0.590441971E+02
- parameters['thetaP'] = 80.0
- parameters['ddyP'] = parameters['LyP']
-
- # G�om�trie du Serre Flanc
- parameters['X0SF'] = 85.8
- parameters['rSF'] = 2.0
- parameters['Y0SF'] = parameters['Ly'] + parameters['rSF']
- parameters['LxSF'] = 50.0
- parameters['ddySF'] = 0.01 # d�placement initial du serre-Flanc
-
- # Param�tres du calcul
- #parameters['ndyn'] = 0
- parameters['ndyn'] = 2
- parameters['prec'] = 1.0e-4
-
- parameters['cmvim'] = VES_CMVIM_SRI
- parameters['PEAS'] = 1.0e-8
-
-
-
- # Mise � jour des parametres par defaut avec les parametres pass�s
- parameters.update(_parameters)
- #
- #Ne pas remonter ce qui est ci dessous au dessus parameters.update(_parameters)
- parameters['ih_n'] = parameters['trac_n'] # Krupkowsky
- parameters['ih_k'] = parameters['trac_Rm']*math.exp(parameters['trac_n'])/ math.pow(parameters['trac_n'], parameters['trac_n']) # Krupkowsky
- parameters['ih_evpl0'] = math.pow(((parameters['trac_R02'] / parameters['ih_k'])), ((1.0 / parameters['trac_n'])) )-0.002 # Krupkowsky : evpl0
-
- print("ih_n = ", parameters['ih_n'])
- print("ih_k = ", parameters['ih_k'])
- print("ih_evpl0 = ", parameters['ih_evpl0'])
-
- parameters['thetaMRad'] = parameters['thetaM'] * math.pi / 180.0
- parameters['thetaPRad'] = parameters['thetaP'] * math.pi / 180.0
-
- lxm2 = parameters['LxM1'] + parameters['rM1'] * math.sin(parameters['thetaMRad'])
- lxm2 += ((parameters['LyM'] - (parameters['rM1'] + parameters['rM2']) * (1.0-math.cos(parameters['thetaMRad']))) / math.tan(parameters['thetaMRad']))
- lxm2 += parameters['rM2'] * math.sin(parameters['thetaMRad'])
- parameters['LxM2'] = lxm2
-
- lxp2 = parameters['LxP1'] + parameters['rP1'] * math.sin(parameters['thetaPRad'])
- lxp2 += ((parameters['LyP'] - (parameters['rP1'] + parameters['rP2']) * (1.0-math.cos(parameters['thetaPRad']))) / math.tan(parameters['thetaPRad']))
- lxp2 += parameters['rP2'] * math.sin(parameters['thetaPRad'])
- parameters['LxP2'] = lxp2
- #parameters['LxM2'] = parameters['LxM1'] + (parameters['LyM'] - parameters['rM2'] * (1.0-math.cos(parameters['thetaMRad']))) / math.tan(parameters['thetaMRad']) + parameters['rM1']*math.sin(parameters['thetaMRad'])
- # Calcul des param�tres d�pendant des param�tres de base
- return parameters
-#=========================================================================
-
-
-def getMetafor(_parameters={}):
- global metafor
- if metafor: return metafor
- p = getParameters(_parameters)
- metafor = Metafor()
- domain = metafor.getDomain()
- geometry = domain.getGeometry()
- geometry.setDimPlaneStrain(1.0)
-
- #-- maillage --
- from toolbox.meshedGeometry2D import createRectangleSameId
- createRectangleSameId(domain, p['nx'], p['ny'], p['Lx'], p['Ly'], 0.0, 0.0, 0)
-
- # G�om�trie des outils : matrice
- pset = geometry.getPointSet()
- p1001 = pset.define(1001, -1.0, p['Y0M']-p['LyM'])
- p1002 = pset.define(1002, p['LxM1'], p['Y0M']-p['LyM'])
- p1003 = pset.define(1003, p['LxM1']+p['rM1']*math.sin((p['thetaMRad']/2.0)), p['Y0M']-p['LyM']+p['rM1']*(1.-math.cos((p['thetaMRad']/2.0))))
- p1004 = pset.define(1004, p['LxM1']+p['rM1']*math.sin(p['thetaMRad']), p['Y0M']-p['LyM']+p['rM1']*(1.-math.cos(p['thetaMRad'])))
- p1005 = pset.define(1005, p['LxM2']-p['rM2']*math.sin(p['thetaMRad']), p['Y0M']-p['rM2']*(1.-math.cos(p['thetaMRad'])))
- p1006 = pset.define(1006, p['LxM2']-p['rM2']*math.sin((p['thetaMRad']/2.0)), p['Y0M']-p['rM2']*(1.-math.cos((p['thetaMRad']/2.))))
- p1007 = pset.define(1007, p['LxM2'], p['Y0M'])
- p1008 = pset.define(1008, p['LxM3'], p['Y0M'])
-
- # curves
- cset = geometry.getCurveSet()
- c1001 = cset.add(Line(1001, p1001, p1002))
- c1002 = cset.add(Arc( 1002, p1002, p1003, p1004))
- c1003 = cset.add(Line(1003, p1004, p1005))
- c1004 = cset.add(Arc( 1004, p1005, p1006, p1007))
- c1005 = cset.add(Line(1005, p1007, p1008))
-
- wset = geometry.getWireSet()
- #w1001 = wset.add(Wire(1001,[c1001, c1002, c1003, c1004]))
- w1001 = wset.add(Wire(1001,[c1005, c1004, c1003, c1002, c1001]))
- sidset = geometry.getSideSet()
- sid1001 = sidset.add(Side(1001,[w1001, ]))
-
- # G�om�trie des outils : poincon
- p2001 = pset.define(2001, -1.0, p['Y0P'])
- p2002 = pset.define(2002, p['LxP1'], p['Y0P'])
- p2003 = pset.define(2003, p['LxP1']+p['rP1']*math.sin((p['thetaPRad']/2.0)), p['Y0P']+p['rP1']*(1.-math.cos((p['thetaPRad']/2.0))))
- p2004 = pset.define(2004, p['LxP1']+p['rP1']*math.sin(p['thetaPRad']), p['Y0P']+p['rP1']*(1.-math.cos(p['thetaPRad'])) )
- #p2005 = pset.define(2005, p['LxP1']+p['rP1']*math.sin(p['thetaPRad'])+p['LyP']*math.cos(p['thetaPRad']), p['Y0P']+p['rP1']*(1.-math.cos(p['thetaPRad']))+p['LyP']*math.sin(p['thetaPRad']))
- p2005 = pset.define(2005, p['LxP2']-p['rP2']*math.sin(p['thetaPRad']), p['Y0P']+p['LyP']-p['rP2']*(1.-math.cos(p['thetaPRad'])))
- p2006 = pset.define(2006, p['LxP2']-p['rP2']*math.sin((p['thetaPRad']/2.0)), p['Y0P']+p['LyP']-p['rP2']*(1.-math.cos((p['thetaPRad']/2.))))
- p2007 = pset.define(2007, p['LxP2'], p['Y0P']+p['LyP'])
- p2008 = pset.define(2008, p['LxP2']+p['rP3']*math.sin((math.pi/4.0)), p['Y0P']+p['LyP']+p['rP3']*(1.-math.cos((math.pi/4.0))))
- p2009 = pset.define(2009, p['LxP2']+p['rP3']*math.sin((math.pi/2.0)), p['Y0P']+p['LyP']+p['rP3']*(1.-math.cos((math.pi/2.0))))
-
-
- # curves
- cset = geometry.getCurveSet()
- c2001 = cset.add(Line(2001, p2001, p2002))
- c2002 = cset.add(Arc( 2002, p2002, p2003, p2004))
- c2003 = cset.add(Line(2003, p2004, p2005))
- c2004 = cset.add(Arc( 2004, p2005, p2006, p2007))
- c2005 = cset.add(Arc( 2005, p2007, p2008, p2009))
- wset = geometry.getWireSet()
- w2001 = wset.add(Wire(2001,[c2001, c2002, c2003, c2004, c2005]))
- sidset = geometry.getSideSet()
- sid2001 = sidset.add(Side(2001,[w2001, ]))
-
- # G�om�trie des outils : Serre-Flanc
-
- p3001 = pset.define(3001, p['X0SF'], p['Y0SF'])
- p3002 = pset.define(3002, p['X0SF']+p['rSF']*(1.0-math.cos((math.pi/3.))), p['Y0SF']-p['rSF']*math.sin((math.pi/3.)))
- p3003 = pset.define(3003, p['X0SF']+p['rSF'], p['Y0SF']-p['rSF'])
- p3004 = pset.define(3004, p['X0SF']+p['LxSF']-p['rSF'], p['Y0SF']-p['rSF'])
- p3005 = pset.define(3005, p['X0SF']+p['LxSF']-p['rSF']*(1.0-math.cos((math.pi/3.))), p['Y0SF']-p['rSF']*math.sin((math.pi/3.)))
- p3006 = pset.define(3006, p['X0SF']+p['LxSF'], p['Y0SF'])
-
- # curves
- cset = geometry.getCurveSet()
- c3001 = cset.add(Arc(3001, p3001, p3002, p3003))
- c3002 = cset.add(Line(3002, p3003, p3004))
- c3003 = cset.add(Arc( 3003, p3004, p3005, p3006))
- wset = geometry.getWireSet()
- w3001 = wset.add(Wire(3001,[c3001, c3002, c3003]))
- sidset = geometry.getSideSet()
- sid3001 = sidset.add(Side(3001,[w3001, ]))
-
- # == SCHEMA D'INTEGRATION ======================================================================
- # Sch�ma d'int�gration 0 : QS / 1 : Expl / 2 : Implicite dynamique
- # Time Integration
- if p['ndyn'] == 0 :
- ti=QuasiStaticTimeIntegration(metafor)
- metafor.setTimeIntegration(ti)
- elif p['ndyn'] == 2 :
- ti = AlphaGeneralizedTimeIntegration(metafor)
- metafor.setTimeIntegration(ti)
- else:
- raise Exception("Unknown time integration")
- mim = metafor.getMechanicalIterationManager()
- mim.setResidualComputationMethod(Method4ResidualComputation(100.0))
- mim.setResidualTolerance(p['prec'] )
- mim.setMaxNbOfIterations(10)
- #mim.setVerbose()
-
-
- tsm = metafor.getTimeStepManager()
- stages = metafor.getStageManager()
- tsm.setInitialTime(0.0, 0.01)
- tsm.setNextTime(0.1, 1, 0.01)
- tsm.setNextTime(1., 1, 0.01)
- tsm.setNextTime(2., 1, 0.1)
- tsm.setNextTime(3., 1, 0.01)
- tsm.setNextTime(10., 1, 0.1)
-
- # == Mat�riaux ==
- materials = domain.getMaterialSet()
- mat1 = materials.define (1, EvpIsoHHypoMaterial)
- mat1.put(MASS_DENSITY, p['rho']) # tonne/mm^3
- mat1.put(ELASTIC_MODULUS, p['Young']) # MPa
- mat1.put(POISSON_RATIO, p['poisson'])
- mat1.put(YIELD_NUM, 1)
- matLawSet = domain.getMaterialLawSet()
- matlaw = matLawSet.define(1, KrupkowskyIsotropicHardening)
- matlaw.put(IH_EVPL0, p['ih_evpl0'])
- matlaw.put(IH_K, p['ih_k'])
- matlaw.put(IH_N, p['ih_n'])
-
- # Elements
- prp1 = ElementProperties(Volume2DElement)
- prp1.put(MATERIAL, 1)
- prp1.put(CAUCHYMECHVOLINTMETH , p['cmvim'])
- if p['cmvim'] == VES_CMVIM_EAS:
- prp1.put(EASS, 2)
- prp1.put(EASV, 2)
- app = FieldApplicator(1)
- app.push(sidset(1))
- app.addProperty(prp1)
- domain.getInteractionSet().add(app)
- '''
- # Elements
- mat2 = materials.define (2, DampingMaterial)
- mat2.put(SPRING_FC, 0.0) #.0001)
-
- prp2 = ElementProperties(Damping2DElement)
- prp2.put(MATERIAL, 2)
- prp2.put(STIFFMETHOD, STIFF_NUMERIC)
- app2 = FieldApplicator(2)
- app2.push(sidset(1))
- app2.addProperty(prp2)
- domain.getInteractionSet().add(app2)
-
- app2.deactivate(stages[0])
- app2.activate(stages[4])
- '''
-
- # == FIXATION ==================================================================================
- fctM = PieceWiseLinearFunction()
- fctM.setData(0.0, 0.0)
- fctM.setData(1.0, 0.0)
- fctM.setData(2.0, 0.0)
- fctM.setData(3.0, 0.0)
-
- fctP = PieceWiseLinearFunction()
- fctP.setData(0.0, -0.01)
- fctP.setData(0.1, -0.01)
- fctP.setData(1.0, 1.0)
- fctP.setData(2.0, 0.0)
- fctP.setData(3.0, 0.0)
-
-
- inicond = metafor.getInitialConditionSet()
- loads = domain.getLoadingSet()
- loads.define(cset(4),Field1D(TX,RE))
- fP1 = loads.define(pset(1),Field1D(TY,RE))
- fP1.deactivate(stages[0])
- fP1.activate(stages[3])
-
-
- loads.define(w1001,Field1D(TY,RE), -p['ddyM'], fctM , TOTAL_LOAD)
- loads.define(w2001,Field1D(TY,RE), -p['ddyP'], fctP , TOTAL_LOAD)
-
- p['pilotF'] = True
- if p['pilotF'] :
-
- mesher = RigidToolMesher(w3001)
- mesher.execute()
- loads.define(w3001,Field1D(TX,RE))
-
- fctSF = PieceWiseLinearFunction()
- fctSF.setData(0.0, 0.0)
- fctSF.setData(0.1, 1.0)
- fctSF.setData(1.0, 1.0)
- fctSF.setData(1.5, 1.0)
- fctSF.setData(2.0, 1.0)
- fctSF.setData(3.0, 1.0)
-
- TYSFLoad = loads.define(w3001,Field1D(TY,RE), -p['ddySF'],fctSF, INCREMENTAL_LOAD)
- TYSFLoad.deactivate(stages[0])
- TYSFLoad.activate(stages[2])
-
- fctForceSF = PieceWiseLinearFunction()
- fctForceSF.setData(0.0, 0.0)
- fctForceSF.setData(0.1, 1.0)
- fctForceSF.setData(1.0, 1.0)
- fctForceSF.setData(2.0, 1.0)
- fctForceSF.setData(3.0, 1.0)
-
- GYFSLoad = loads.define(w3001,Field1D(TY,GF2), p['forceSF'], fctForceSF, TOTAL_LOAD)# force driven
-
- #GYFSLoad.deactivate(stages[0])
- #GYFSLoad.activate(stages[1])
- GYFSLoad.deactivate(stages[2])
-
- else :
- fctSF = PieceWiseLinearFunction()
- fctSF.setData(0.0, 0.0)
- fctSF.setData(0.1, 1.0)
- fctSF.setData(1.0, 1.0)
- fctSF.setData(2.0, 1.0)
- fctSF.setData(3.0, 1.0)
- TYSFLoad = loads.define(w3001,Field1D(TY,RE), -p['ddySF'],fctSF, TOTAL_LOAD)
-
- # == Contact ==
-
- fctPeno = PieceWiseLinearFunction()
- fctPeno.setData(0.0, 0.5)
- fctPeno.setData(0.1, 1.0)
- fctPeno.setData(1.0, 1.0)
- fctPeno.setData(2.0, 1.0)
- fctPeno.setData(3.0, 0.0)
- fctPeno.setData(4.0, 0.0)
-
- mat1001 = materials.define(1001, CoulombContactMaterial)
- mat1001.put(PEN_NORMALE, p['peno'])
- mat1001.depend(PEN_NORMALE, fctPeno, Field(TM))
- mat1001.put(PEN_TANGENT, p['peno']*p['mu'])
- mat1001.depend(PEN_TANGENT, fctPeno, Field(TM))
- mat1001.put(COEF_FROT_STA, p['mu'])
- mat1001.put(COEF_FROT_DYN, p['mu'])
- mat1001.put(PROF_CONT, 0.95*p['rM2'] )
-
- prp_c1 = ElementProperties(Contact2DElement)
- prp_c1.put(MATERIAL, 1001)
- prp_c1.put(AREAINCONTACT,AIC_ONCE)
-
-
- fctPeno2 = PieceWiseLinearFunction()
- fctPeno2.setData(0.0, 0.5)
- fctPeno2.setData(0.1, 1.0)
- fctPeno2.setData(2.0, 1.0)
- fctPeno2.setData(2.5, 0.01)
- fctPeno2.setData(3.0, 0.0)
- fctPeno2.setData(4.0, 0.0)
-
- mat1002 = materials.define(1002, CoulombContactMaterial)
- mat1002.put(PEN_NORMALE, p['peno'])
- mat1002.depend(PEN_NORMALE, fctPeno2, Field(TM))
- mat1002.put(PEN_TANGENT, p['peno']*p['mu'])
- mat1002.depend(PEN_TANGENT, fctPeno, Field(TM))
- mat1002.put(COEF_FROT_STA, p['mu'])
- mat1002.put(COEF_FROT_DYN, p['mu'])
- mat1002.put(PROF_CONT, 0.95*p['rSF'] )
- prp_c2 = ElementProperties(Contact2DElement)
- prp_c2.put(MATERIAL, 1002)
- prp_c2.put(AREAINCONTACT,AIC_ONCE)
-
- fctPeno3 = PieceWiseLinearFunction()
- fctPeno3.setData(0.0, 0.5)
- fctPeno3.setData(0.1, 1.0)
- fctPeno3.setData(2.0, 1.0)
- fctPeno3.setData(2.5, 0.0)
- fctPeno3.setData(3.0, 0.0)
- fctPeno3.setData(4.0, 0.0)
-
- mat1003 = materials.define(1003, CoulombContactMaterial)
- mat1003.put(PEN_NORMALE, p['peno'])
- mat1003.depend(PEN_NORMALE, fctPeno3, Field(TM))
- mat1003.put(PEN_TANGENT, p['peno']*p['mu'])
- mat1003.depend(PEN_TANGENT, fctPeno, Field(TM))
- mat1003.put(COEF_FROT_STA, p['mu'])
- mat1003.put(COEF_FROT_DYN, p['mu'])
- mat1003.put(PROF_CONT, 0.95*p['rP3'] )
- prp_c3 = ElementProperties(Contact2DElement)
- prp_c3.put(MATERIAL, 1003)
- prp_c3.put(AREAINCONTACT,AIC_ONCE)
-
-
- #Outil 1 : Matrice
-
- ci1 = RdContactInteraction(1001)
- ci1.setTool(w1001)
- ci1.push(cset(1))
- ci1.addProperty(prp_c1)
- domain.getInteractionSet().add(ci1)
- #Outil 2 : Poincon
- ci2 = RdContactInteraction(2001)
- ci2.setTool(w2001)
- ci2.push(cset(3))
- ci2.addProperty(prp_c2)
- domain.getInteractionSet().add(ci2)
- #Outil 3 : Serre-Flanc
- if p['pilotF'] :
- ci3 = FdRdContactInteraction(3001)
- else :
- ci3 = RdContactInteraction(3001)
- ci3.setTool(w3001)
- ci3.push(cset(3))
- ci3.addProperty(prp_c3)
- domain.getInteractionSet().add(ci3)
-
-
- ci1.deactivate(stages[4])
- ci2.deactivate(stages[4])
- ci3.deactivate(stages[4])
-
- valuesmanager = metafor.getValuesManager()
- valuesmanager.add(1, MiscValueExtractor(metafor,EXT_T),'time')
- valuesmanager.add(2, IFNodalValueExtractor(pset(1), IF_EVMS), 'evms_p1')
- valuesmanager.add(11, DbNodalValueExtractor(pset(2), Field1D(TX,RE)), 'TX_p2')
- valuesmanager.add(12, DbNodalValueExtractor(pset(2), Field1D(TY,RE)), 'TY_p2')
- valuesmanager.add(13, DbNodalValueExtractor(pset(2), Field1D(TX,GV)), 'VX_p2')
- valuesmanager.add(14, DbNodalValueExtractor(pset(2), Field1D(TY,GV)), 'VY_p2')
-
- valuesmanager.add(16, DbNodalValueExtractor(pset(2001), Field1D(TY,RE)), 'TY_Poincon')
- valuesmanager.add(17, DbNodalValueExtractor(pset(3001), Field1D(TY,RE)), 'TY_SerreFlanc')
-
-
- objFSet = metafor.getObjectiveFunctionSet()
- objFSet.add(SpringBackObjF(1, metafor, 11, 12, 1, 2.0, 10.0) )
- objFSet.add(LastVelocity(2, metafor, 13, 14, 1, 10.0) )
-
- testSuite = metafor.getTestSuiteChecker()
- testSuite.checkObjectiveFunction(1)
- testSuite.checkObjectiveFunction(2)
-
-# valuesmanager.add(21, InteractionValueExtractor(ci1, TX, GEN_EXT_FORC), 'FX_Matrice')
-# valuesmanager.add(22, InteractionValueExtractor(ci1, TY, GEN_EXT_FORC), 'FY_Matrice')
-# valuesmanager.add(23, InteractionValueExtractor(ci2, TX, GEN_EXT_FORC), 'FX_Poincon')
-# valuesmanager.add(24, InteractionValueExtractor(ci2, TY, GEN_EXT_FORC), 'FY_Poincon')
-# valuesmanager.add(25, InteractionValueExtractor(ci3, TX, GEN_EXT_FORC), 'FX_SerreFlan')
-# valuesmanager.add(26, InteractionValueExtractor(ci3, TY, GEN_EXT_FORC), 'FY_SerreFlan')
- valuesmanager.add(21, ContactForceValueExtractor(ci1, TX, TOTAL_FORCE), SumOperator(), 'FX_Matrice')
- valuesmanager.add(22, ContactForceValueExtractor(ci1, TY, TOTAL_FORCE), SumOperator(), 'FY_Matrice')
- valuesmanager.add(23, ContactForceValueExtractor(ci2, TX, TOTAL_FORCE), SumOperator(), 'FX_Poincon')
- valuesmanager.add(24, ContactForceValueExtractor(ci2, TY, TOTAL_FORCE), SumOperator(), 'FY_Poincon')
- valuesmanager.add(25, ContactForceValueExtractor(ci3, TX, TOTAL_FORCE), SumOperator(), 'FX_SerreFlan')
- valuesmanager.add(26, ContactForceValueExtractor(ci3, TY, TOTAL_FORCE), SumOperator(), 'FY_SerreFlan')
-
- valuesmanager.add(31, NormalGapValueExtractor(ci1), MaxOperator(), 'GapMaxMatrice')
- valuesmanager.add(32, NormalGapValueExtractor(ci2), MaxOperator(), 'GapMaxPoincon')
- valuesmanager.add(33, NormalGapValueExtractor(ci3), MaxOperator(), 'GapMaxSerreFlanc')
-
- cur1 = VectorDataCurve(1, valuesmanager.getDataVector(1), valuesmanager.getDataVector(11),valuesmanager.getDataVector(11).getName())
- cur2 = VectorDataCurve(2, valuesmanager.getDataVector(1), valuesmanager.getDataVector(12),valuesmanager.getDataVector(12).getName())
- cur3 = VectorDataCurve(3, valuesmanager.getDataVector(1), valuesmanager.getDataVector(16),valuesmanager.getDataVector(16).getName())
- cur4 = VectorDataCurve(4, valuesmanager.getDataVector(1), valuesmanager.getDataVector(17),valuesmanager.getDataVector(17).getName())
-
- cur11 = VectorDataCurve(1, valuesmanager.getDataVector(1), valuesmanager.getDataVector(21), valuesmanager.getDataVector(21).getName())
- cur12 = VectorDataCurve(2, valuesmanager.getDataVector(1), valuesmanager.getDataVector(22), valuesmanager.getDataVector(22).getName())
- cur13 = VectorDataCurve(3, valuesmanager.getDataVector(1), valuesmanager.getDataVector(23), valuesmanager.getDataVector(23).getName())
- cur14 = VectorDataCurve(4, valuesmanager.getDataVector(1), valuesmanager.getDataVector(24), valuesmanager.getDataVector(24).getName())
- cur15 = VectorDataCurve(5, valuesmanager.getDataVector(1), valuesmanager.getDataVector(25), valuesmanager.getDataVector(25).getName())
- cur16 = VectorDataCurve(6, valuesmanager.getDataVector(1), valuesmanager.getDataVector(26), valuesmanager.getDataVector(26).getName())
-
- cur31 = VectorDataCurve(31, valuesmanager.getDataVector(1), valuesmanager.getDataVector(31),valuesmanager.getDataVector(31).getName())
- cur32 = VectorDataCurve(32, valuesmanager.getDataVector(1), valuesmanager.getDataVector(32),valuesmanager.getDataVector(32).getName())
- cur33 = VectorDataCurve(33, valuesmanager.getDataVector(1), valuesmanager.getDataVector(33),valuesmanager.getDataVector(33).getName())
-
-
- dataCurveSet1 = DataCurveSet()
- dataCurveSet1.add(cur1)
- dataCurveSet1.add(cur2)
- dataCurveSet1.add(cur3)
- dataCurveSet1.add(cur4)
-
- dataCurveSet2 = DataCurveSet()
- dataCurveSet2.add(cur11)
- dataCurveSet2.add(cur12)
- dataCurveSet2.add(cur13)
- dataCurveSet2.add(cur14)
- dataCurveSet2.add(cur15)
- dataCurveSet2.add(cur16)
-
- dataCurveSet3 = DataCurveSet()
- dataCurveSet3.add(cur31)
- dataCurveSet3.add(cur32)
- dataCurveSet3.add(cur33)
-
- try:
- winc1 = VizWin()
- winc1.add(dataCurveSet1)
- metafor.addObserver(winc1)
-
- winc2 = VizWin()
- winc2.add(dataCurveSet2)
- metafor.addObserver(winc2)
-
- winc3 = VizWin()
- winc3.add(dataCurveSet3)
- metafor.addObserver(winc3)
- except NameError:
- pass
-
-
- solman = metafor.getSolverManager();
- try:
- solman.setSolver(DSSolver());
- except NameError:
- pass
-
- return metafor
-
-
-class SpringBackObjF(PythonObjectiveFunction):
- def __init__(self,_no, _meta, _noPtTXRe, _noPtTYRe, _noTime , _time1, _time2) :
- print("SpringBackObjF : __init__")
- PythonObjectiveFunction.__init__(self,_no,_meta)
- self.noPtTXRe = _noPtTXRe
- self.noPtTYRe = _noPtTYRe
- self.noTime = _noTime
- self.time1 = _time1
- self.time2 = _time2
-
- print("SpringBackObjF : __init__ finished")
-
- def __del__(self):
- print("SpringBackObjectiveFunction : __del__")
- print("callToDestructor of SpringBackObjectiveFunction not allowed. Add SpringBackObjectiveFunction.__disown__()")
- input('')
- exit(1)
-
- def compute(self, meta):
- print("entering SpringBackObjF compute")
- valuesmanager = meta.getValuesManager()
- curTX = VectorDataCurve(1, valuesmanager.getDataVector(self.noTime), valuesmanager.getDataVector(self.noPtTXRe))
- curTY = VectorDataCurve(2, valuesmanager.getDataVector(self.noTime), valuesmanager.getDataVector(self.noPtTYRe))
-
- DX = curTX.eval(self.time2) - curTX.eval(self.time1)
- DY = curTY.eval(self.time2) - curTY.eval(self.time1)
- print("TX(%f) = %f \t TX(%f) = %f \t DX = %f "% (self.time1, curTX.eval(self.time1), self.time2, curTX.eval(self.time2), DX))
- print("TY(%f) = %f \t TY(%f) = %f \t DY = %f "% (self.time1, curTY.eval(self.time1), self.time2, curTY.eval(self.time2), DY))
- springBack = math.sqrt(DX*DX+DY*DY)
- print("SpringBack = ", springBack)
-
-
- SpringBackfile = open('SpringBack.ascii','w')
- SpringBackfile.write(str(springBack))
- SpringBackfile.close()
-
- return springBack
-
-class LastVelocity(PythonObjectiveFunction):
- def __init__(self,_no, _meta, _noPtTXGV, _noPtTYGV, _noTime, _time) :
- print("LastVelocity : __init__")
- PythonObjectiveFunction.__init__(self,_no,_meta)
- self.noPtTXGV = _noPtTXGV
- self.noPtTYGV = _noPtTYGV
- self.noTime = _noTime
- self.time = _time
-
- print("LastVelocity : __init__ finished")
-
- def __del__(self):
- print("LastVelocity : __del__")
- print("callToDestructor of LastVelocity not allowed. Add LastVelocity.__disown__()")
- input('')
- exit(1)
-
- def compute(self, meta):
- print("entering LastVelocity compute")
- valuesmanager = meta.getValuesManager()
- curVX = VectorDataCurve(1, valuesmanager.getDataVector(self.noTime), valuesmanager.getDataVector(self.noPtTXGV))
- curVY = VectorDataCurve(2, valuesmanager.getDataVector(self.noTime), valuesmanager.getDataVector(self.noPtTYGV))
-
- VX = curVX.eval(self.time)
- VY = curVY.eval(self.time)
- print("VX(%f) = %f "% (self.time, VX))
- print("VY(%f) = %f "% (self.time, VY))
- Veloc = math.sqrt(VX*VX+VY*VY)
- print("Veloc = ", Veloc)
- return Veloc
-
diff --git a/Metafor/models/omega01/model.py b/Metafor/models/omega01/model.py
deleted file mode 100644
index d1bcb320..00000000
--- a/Metafor/models/omega01/model.py
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-import numpy as np
-import os
-import shutil
-
-import fwk
-
-from Metafor.Mparams.Metafor_call import Metafor_call
-
-isUnix = lambda: os.name == 'posix'
-
-def model(d):
-
- d['Metafor_model_name'] = 'EmboutOmega'
- Metafor_call(d)
- try:
- sol = np.genfromtxt("SpringBack.ascii", dtype=None)
- except:
- sol = -1
- return sol
-
diff --git a/Metafor/models/tube01/model.py b/Metafor/models/tube01/model.py
deleted file mode 100644
index 3b4f6e6c..00000000
--- a/Metafor/models/tube01/model.py
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8; -*-
-
-import numpy as np
-import os
-import shutil
-
-import fwk
-
-from Metafor.Mparams.Metafor_call import Metafor_call
-
-isUnix = lambda: os.name == 'posix'
-
-def model(d):
- d['Metafor_model_name'] = 'tube'
- Metafor_call(d)
-
- FcZ = np.genfromtxt("FcZ.ascii", dtype=None)
-
- sol = np.amax(FcZ)
-
- return sol
-
diff --git a/Metafor/models/tube01/tube.py b/Metafor/models/tube01/tube.py
deleted file mode 100644
index 4a1fe334..00000000
--- a/Metafor/models/tube01/tube.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# -*- coding: utf-8; -*-
-# $Id: tube.py 2356 2015-08-25 07:43:43Z papeleux $
-
-import math
-
-def getMetafor(p={}):
- d={}
- import wrap
- wrap.StrVectorBase.useTBB()
- wrap.StrMatrixBase.useTBB()
- #wrap.IntelTBB.setNumThreads(2)
- #wrap.Blas.setPardisoNumThreads(2)
- d['therm'] = False
- d['alp1'] = 0.01 # facilite la convergence
- d['alp2'] = math.pi-0.01
- d['nr'] = 2
- d.update(p)
- import apps.iso.tubeT as m
- return m.getMetafor(d)
--
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