diff --git a/BayesValidRox/BayesInference/BayesInference.py b/BayesValidRox/BayesInference/BayesInference.py
index d4a7bd39cd9b22472c68ebf216a5ead7505db4b0..596ab81ac58311f19ec577090fc586956e3088e8 100644
--- a/BayesValidRox/BayesInference/BayesInference.py
+++ b/BayesValidRox/BayesInference/BayesInference.py
@@ -133,7 +133,7 @@ class BayesInference:
Samples = Samples
self.NrofSamples = len(Samples)
- ModelOutputs, _ = Model.Run_Model_Parallel(Samples,
+ ModelOutputs, _ = Model.run_model_parallel(Samples,
keyString=key+self.Name)
if index is not None:
@@ -150,8 +150,8 @@ class BayesInference:
# Clean up
# Zip the subdirectories
try:
- dir_name = Model.Name + 'MAP'+self.Name
- key = Model.Name + 'MAP'+self.Name + '_'
+ dir_name = f'{Model.name}MAP{self.Name}'
+ key = dir_name + '_'
Model.zip_subdirs(dir_name, key)
except:
pass
@@ -571,10 +571,7 @@ class BayesInference:
# Read observation data and perturb it if requested
if self.MeasuredData is None:
- if self.Name == 'Calib':
- self.MeasuredData = Model.Observations
- else:
- self.MeasuredData = Model.read_ObservationValid()
+ self.MeasuredData = Model.read_observation(case=self.Name)
if not isinstance(self.MeasuredData,pd.DataFrame):
self.MeasuredData = pd.DataFrame(self.MeasuredData)
@@ -757,11 +754,8 @@ class BayesInference:
# ---------- Preparation of observation data ----------
# Read observation data and perturb it if requested
if self.MeasuredData is None:
- if self.Name == 'Calib':
- self.MeasuredData = Model.read_Observation()
- else:
- self.MeasuredData = Model.read_ObservationValid()
-
+ self.MeasuredData = Model.read_observation(case=self.Name)
+
if not isinstance(self.MeasuredData, pd.DataFrame):
self.MeasuredData = pd.DataFrame(self.MeasuredData)
ObservationData = self.MeasuredData[OutputNames].to_numpy()
diff --git a/BayesValidRox/BayesInference/MCMC.py b/BayesValidRox/BayesInference/MCMC.py
index 2f40339261bf8c04b53c6ee492e5625a65ee9c21..ee276e084e6ed1a9c4bd99f1fa3742fc3e8078b8 100755
--- a/BayesValidRox/BayesInference/MCMC.py
+++ b/BayesValidRox/BayesInference/MCMC.py
@@ -494,7 +494,7 @@ class MCMC():
# Run function for theta
modelOuts = Function(theta.reshape(1, -1))
except:
- modelOuts, _ = Model.Run_Model_Parallel(theta[0],
+ modelOuts, _ = Model.run_model_parallel(theta[0],
prevRun_No=self.counter,
keyString='_MCMC',
mp=False)
diff --git a/BayesValidRox/PostProcessing/PostProcessing.py b/BayesValidRox/PostProcessing/PostProcessing.py
index 2da95ec033aa89370f5b01cebf164c0366876615..1a998760db9aa35a88028b0103945bc82cd4a009 100644
--- a/BayesValidRox/PostProcessing/PostProcessing.py
+++ b/BayesValidRox/PostProcessing/PostProcessing.py
@@ -86,7 +86,7 @@ class PostProcessing:
self.PCEMeans[Outkey] = PCEMean
self.PCEStd[Outkey] = np.sqrt(PCEVar)
try:
- self.Reference = Model.read_MCReference()
+ self.Reference = Model.read_mc_reference()
except:
pass
@@ -248,7 +248,7 @@ class PostProcessing:
PCEOutputs_std[:, idx] = y_std
# Model evaluation
- ModelOutputsDict, _ = Model.Run_Model_Parallel(SampleMesh, keyString='Valid3D')
+ ModelOutputsDict, _ = Model.run_model_parallel(SampleMesh, keyString='Valid3D')
ModelOutputs[:,idx]= ModelOutputsDict[Outkey].T
@@ -318,11 +318,11 @@ class PostProcessing:
Samples = Samples
self.NrofSamples = len(Samples)
- ModelOutputs, CollocationPoints = Model.Run_Model_Parallel(Samples, keyString=keyString)
+ ModelOutputs, CollocationPoints = Model.run_model_parallel(Samples, keyString=keyString)
if index is not None:
- self.ModelOutputs['x_values'] = {key: ModelOutputs['x_values'][key][:index[idx]] for idx, key in enumerate(Model.Output.Names)}#([list(ModelOutputs.keys())[0]])}
- Outputs = {key: ModelOutputs[key][:,:index[idx]] for idx, key in enumerate(Model.Output.Names)}
+ self.ModelOutputs['x_values'] = {key: ModelOutputs['x_values'][key][:index[idx]] for idx, key in enumerate(Model.Output.names)}#([list(ModelOutputs.keys())[0]])}
+ Outputs = {key: ModelOutputs[key][:,:index[idx]] for idx, key in enumerate(Model.Output.names)}
self.ModelOutputs.update(Outputs)
else:
self.ModelOutputs = ModelOutputs
@@ -669,8 +669,8 @@ class PostProcessing:
# Cleanup
#Zip the subdirectories
try:
- dir_name = Model.Name + 'Valid'
- key = Model.Name + 'Valid' + '_'
+ dir_name = Model.name + 'valid'
+ key = dir_name + '_'
Model.zip_subdirs(dir_name, key)
except:
pass
@@ -912,7 +912,7 @@ class PostProcessing:
NofPa = PCEModel.NofPa
max_order = PCEModel.MaxPceDegree
- for Output in PCEModel.ModelObj.Output.Names:
+ for Output in PCEModel.ModelObj.Output.names:
n_meas_points = len(CoeffsDict[Output])
@@ -1096,7 +1096,7 @@ class PostProcessing:
fig = plt.figure()
- for outIdx, Output in enumerate(PCEModel.ModelObj.Output.Names):
+ for outIdx, Output in enumerate(PCEModel.ModelObj.Output.names):
# Extract a list of x values
if type(x_values_orig) is dict:
diff --git a/BayesValidRox/PyLink/FuncForwardModel.py b/BayesValidRox/PyLink/FuncForwardModel.py
deleted file mode 100644
index 08b54b6e6b11863c21ef17734b46accc3055dd89..0000000000000000000000000000000000000000
--- a/BayesValidRox/PyLink/FuncForwardModel.py
+++ /dev/null
@@ -1,176 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-
-Author: Farid Mohammadi, M.Sc.
-E-Mail: farid.mohammadi@iws.uni-stuttgart.de
-Department of Hydromechanics and Modelling of Hydrosystems (LH2)
-Institute for Modelling Hydraulic and Environmental Systems (IWS), University
-of Stuttgart, www.iws.uni-stuttgart.de/lh2/
-Pfaffenwaldring 61
-70569 Stuttgart
-
-Created on Sat Aug 24 2019
-
-"""
-
-import os
-import numpy as np
-import pandas as pd
-import tqdm
-from functools import reduce
-import multiprocessing
-import h5py
-
-
-class FuncForwardModel:
-
- def __init__(self):
- #TODO: This must be set in the main file.
- self.Name = None
- self.nrCPUs = None
- self.pyFile = self.Name
- self.outMin = None
- self.outMax = None
- self.Observations = {}
- self.ObservationsValid = {}
- self.OutputMatrix = {}
- self.MeasurementFile = None
- self.MCReference = {}
-
- # Nested class
- class Output:
- def __init__(self):
- self.Names = None
-
- def run_forwardmodel(self, xx):
- Function, theta = xx
- return Function(np.array([theta]))
-
- def Run_Model_Parallel(self, CollocationPoints, prevRun_No=0, keyString=''):
-
- # Create hdf5 metadata
- hdf5file = 'ExpDesign'+'_'+self.Name+'.hdf5'
- hdf5_exist = os.path.exists(hdf5file)
- file = h5py.File(hdf5file, 'a')
-
- # Initilization
- P = len(CollocationPoints)
- OutputNames = self.Output.Names
- TotalOutputs = {}
-
- # Prepare the function
- Filename = self.pyFile
- Function = getattr(__import__(Filename), Filename)
-
- # Parallel runs with multiprocessing
- with multiprocessing.Pool() as p:
- group_results = list(tqdm.tqdm(
- p.imap(self.run_forwardmodel, zip([Function]*P, CollocationPoints)),
- total=P, desc='Running forward model'))
- print("")
- # Save time steps or x-values
- x_values = group_results[0][0]
- TotalOutputs["x_values"] = x_values
- if not hdf5_exist:
- file.create_dataset("x_values", data=x_values)
-
- # save each output in their corresponding array
- outRangeIdx = []
- for varIdx, var in enumerate(OutputNames):
-
- grpY = file.create_group("EDY/"+var) if not hdf5_exist else file.get("EDY/"+var)
-
- Outputs = np.asarray([item[varIdx+1] for item in group_results], dtype=np.float64)
-
- if prevRun_No == 0:
- grpY.create_dataset("init_"+keyString, data=Outputs)
- else:
- try:
- oldEDY = np.array(file['EDY/'+var+'/adaptive_'+keyString])
- del file['EDY/'+var+'/adaptive_'+keyString]
- data = np.vstack((oldEDY, Outputs))
- except:
- data = Outputs
- grpY.create_dataset('adaptive_'+keyString, data=data)
-
- # Check if all outputs lay between provided min and max
- if self.outMin is not None and P > 1:
- for outIdx, out in enumerate(Outputs):
- if not self.within_range(out, self.outMin, self.outMax):
- outRangeIdx.append(outIdx)
-
- TotalOutputs[var] = np.delete(Outputs, outRangeIdx, axis=0)
-
- if prevRun_No == 0:
- grpY.create_dataset("New_init_"+keyString, data=TotalOutputs[var])
- else:
- try:
- oldEDY = np.array(file['EDY/'+var+'/New_adaptive_'+keyString])
- del file['EDY/'+var+'/New_adaptive_'+keyString]
- data = np.vstack((oldEDY, TotalOutputs[var]))
- except:
- data = TotalOutputs[var]
- grpY.create_dataset('New_adaptive_'+keyString, data=data)
-
- # Print the collocation points whose simulations crashed
- if len(outRangeIdx) != 0:
- print('\n')
- print('*'*20)
- print("\nThe following parametersets have been removed:\n",
- CollocationPoints[outRangeIdx])
- print("\n")
- print('*'*20)
-
- # Pass it to the attribute
- NewCollocationPoint = np.delete(CollocationPoints, outRangeIdx, axis=0)
- self.OutputMatrix = TotalOutputs
-
- # Save CollocationPoints
- grpX = file.create_group("EDX") if not hdf5_exist else file.get("EDX")
- if prevRun_No == 0:
- grpX.create_dataset("init_"+keyString, data=CollocationPoints)
- if len(outRangeIdx) != 0:
- grpX.create_dataset("New_init_"+keyString, data=NewCollocationPoint)
-
- else:
- try:
- oldCollocationPoints = np.array(file['EDX/'+'adaptive_'+keyString])
- del file['EDX/'+'adaptive_'+keyString]
- data = np.vstack((oldCollocationPoints, NewCollocationPoint))
- except:
- data = NewCollocationPoint
- grpX.create_dataset('adaptive_'+keyString, data=data)
-
- if len(outRangeIdx) != 0:
- try:
- oldCollocationPoints = np.array(file['EDX/New_'+'adaptive_'+keyString])
- del file['EDX/New_'+'adaptive_'+keyString]
- data = np.vstack((oldCollocationPoints, NewCollocationPoint))
- except:
- data = NewCollocationPoint
- grpX.create_dataset('New_adaptive_'+keyString, data=data)
-
- # Close h5py file
- file.close()
-
- return self.OutputMatrix, NewCollocationPoint
-
- def within_range(self, out, minout, maxout):
- inside = False
- if (out > minout).all() and (out < maxout).all():
- inside = True
- return inside
-
- def read_Observation(self):
- obsDataFrame = pd.DataFrame.from_dict(self.Observations)
- self.nObs = reduce(lambda x, y: x*y, obsDataFrame[self.Output.Names].shape)
- return obsDataFrame
-
- def read_ObservationValid(self):
- obsDataFrame = pd.DataFrame.from_dict(self.ObservationsValid)
- self.nObsValid = reduce(lambda x, y: x*y, obsDataFrame[self.Output.Names].shape)
- return obsDataFrame
-
- def read_MCReference(self):
- return pd.DataFrame.from_dict(self.MCReference)
diff --git a/BayesValidRox/PyLink/PyLinkForwardModel.py b/BayesValidRox/PyLink/PyLinkForwardModel.py
index 7547f0340a7f1f96a16e2a1aae0ef8dc32ef83dd..1ca7fdcc9ae82f09307a54a148ed88a72a6b4cae 100644
--- a/BayesValidRox/PyLink/PyLinkForwardModel.py
+++ b/BayesValidRox/PyLink/PyLinkForwardModel.py
@@ -20,106 +20,184 @@ import os
import shutil
import h5py
import numpy as np
-import timeit
import time
import zipfile
import pandas as pd
from functools import reduce
import multiprocessing
import tqdm
-tic = timeit.default_timer()
-
-
-def float_formatter(x):
- return "%.5f" % x
-
-
-np.set_printoptions(formatter={'float_kind': float_formatter})
-
-
-class PyLinkForwardModel:
- def __init__(self):
- self.Type = 'PyLink'
- self.nrCPUs = None
- self.Name = None
- self.InputFile = None
- self.InputTemplate = None
- self.AuxFile = None
- self.Command = " "
- self.ExecutionPath = " "
- self.Output.Parser = ' '
- self.Output.Names = ''
- self.Output.FileNames = []
- self.MeasurementFile = None
- self.MeasurementFileValid = None
- self.MCReferenceFile = None
- self.Observations = []
- self.ObservationsValid = []
- self.MCReference = {}
- self.seperator = '/'
- self.OutputMatrix = None
+
+
+class PyLinkForwardModel(object):
+ """A forward model binder
+ This calss serves as a code wrapper. This wrapper allows the execution of
+ a third-party software/solver within the scope of BayesValidRox.
+ The wrapper provides two options:
+ 1) link_type='PyLink':
+ Runs the third-party software using a sell command with given input
+ files.
+ 2) link_type='function':
+ For this case, it is assumed that model can be run using a function
+ written separately in a Python script. This function recieves the
+ parameters in an array of shape (n_samples, n_params) and returns
+ a dictionary with the x_values and output arrays for given output
+ names.
+ """
+
+ def __init__(self, link_type='PyLink', name=None, shell_command='',
+ py_file=None, input_file=None, input_template=None,
+ aux_file=None, exe_path='', multi_process=True, n_cpus=None,
+ output_parser='', output_names=[], output_file_names=[],
+ meas_file=None, meas_file_valid=None, mc_ref_file=None,
+ obs_dict={}, obs_dict_valid={}, mc_ref_dict={}):
+ self.link_type = link_type
+ self.name = name
+ self.shell_command = shell_command
+ self.py_file = py_file
+ self.input_file = input_file
+ self.input_template = input_template
+ self.aux_file = aux_file
+ self.exe_path = exe_path
+ self.multi_process = multi_process
+ self.n_cpus = n_cpus
+ self.Output.parser = output_parser
+ self.Output.names = output_names
+ self.Output.file_names = output_file_names
+ self.meas_file = meas_file
+ self.meas_file_valid = meas_file_valid
+ self.mc_ref_file = mc_ref_file
+ self.observations = obs_dict
+ self.observations_valid = obs_dict_valid
+ self.mc_reference = mc_ref_dict
# Nested class
class Output:
def __init__(self):
- self.Parser = None
- self.Names = None
- self.FileNames = None
+ self.parser = None
+ self.names = None
+ self.file_names = None
# -------------------------------------------------------------------------
- def read_Observation(self):
- """Required for reading the measurement file."""
-
- FilePath = os.getcwd() + self.seperator + self.MeasurementFile
-
- self.Observations = pd.read_csv(FilePath, delimiter=',')
- self.nObs = reduce(lambda x, y: x*y,
- self.Observations[self.Output.Names].shape)
-
- return self.Observations
+ def within_range(self, out, minout, maxout):
+ inside = False
+ if (out > minout).all() and (out < maxout).all():
+ inside = True
+ return inside
# -------------------------------------------------------------------------
- def read_ObservationValid(self):
- """Required for reading the measurement file."""
-
- FilePath = os.getcwd() + self.seperator + self.MeasurementFileValid
-
- self.ObservationsValid = pd.read_csv(FilePath, delimiter=',')
+ def read_observation(self, case='calib'):
+ """
+ Reads/prepare the observation/measurement data for
+ calibration.
- self.nObsValid = reduce(lambda x, y: x*y,
- self.Observations[self.Output.Names].shape)
+ Returns
+ -------
+ DataFrame
+ A dataframe with the calibration data.
- return self.ObservationsValid
+ """
+ if case.lower() == 'calib':
+ if hasattr(self, 'observations') and \
+ isinstance(self.observations, dict):
+ obs = pd.DataFrame.from_dict(self.observations)
+ elif hasattr(self, 'observations'):
+ FilePath = os.path.join(os.getcwd(), self.meas_file)
+ obs = pd.read_csv(FilePath, delimiter=',')
+ else:
+ raise Exception("Please provide the observation data as a "
+ "dictionary via observations attribute or pass"
+ " the csv-file path to MeasurementFile "
+ "attribute")
+ elif case.lower() == 'valid':
+ if hasattr(self, 'observations_valid') and \
+ isinstance(self.observations_valid, dict):
+ obs = pd.DataFrame.from_dict(self.observations_valid)
+ elif hasattr(self, 'observations_valid'):
+ FilePath = os.path.join(os.getcwd(), self.meas_file_valid)
+ obs = pd.read_csv(FilePath, delimiter=',')
+ else:
+ raise Exception("Please provide the observation data as a "
+ "dictionary via Observations attribute or pass"
+ " the csv-file path to MeasurementFile "
+ "attribute")
+
+ # Compute the number of observation
+ nObs = reduce(lambda x, y: x*y, obs[self.Output.names].shape)
+
+ if case.lower() == 'calib':
+ self.observations = obs
+ self.nObs = nObs
+ return self.observations
+ elif case.lower() == 'valid':
+ self.observations_valid = obs
+ self.nObsValid = nObs
+ return self.observations_valid
# -------------------------------------------------------------------------
- def read_MCReference(self):
- FilePath = os.getcwd() + self.seperator + self.MCReferenceFile
+ def read_mc_reference(self):
+ """
+ Is used, if a Monte-Carlo reference is available for
+ further in-depth post-processing after meta-model training.
- self.MCReference = pd.read_csv(FilePath, delimiter=',').to_dict('list')
+ Returns
+ -------
+ None
- return self.MCReference
+ """
+ if hasattr(self, 'mc_reference') and \
+ isinstance(self.mc_reference, dict):
+ self.mc_reference = pd.DataFrame.from_dict(self.mc_reference)
+ elif hasattr(self, 'mc_reference'):
+ FilePath = os.path.join(os.getcwd(), self.mc_ref_file)
+ self.mc_reference = pd.read_csv(FilePath, delimiter=',')
+ else:
+ raise Exception("Please provide the MC reference data as a "
+ "dictionary via mc_reference attribute or pass the"
+ " csv-file path to mc_ref_file attribute")
# -------------------------------------------------------------------------
- def read_Output(self):
- """Required for reading the output files. """
-
- Filename = self.Output.Parser
+ def read_output(self):
+ """
+ Reads the the parser output file and returns it as an
+ executable function. It is required when the models returns the
+ simulation outputs in csv files.
- Function = getattr(__import__(Filename), Filename)
+ Returns
+ -------
+ Output : func
+ Output parser function.
- FileNames = []
- for File in self.Output.FileNames:
- FileNames.append(self.ExecutionPath + self.seperator + File)
+ """
+ output_func_name = self.Output.parser
- Output = Function(self.Name, FileNames)
+ output_func = getattr(__import__(output_func_name), output_func_name)
- return Output
+ file_names = []
+ for File in self.Output.file_names:
+ file_names.append(os.path.join(self.exe_path, File))
+ try:
+ output = output_func(self.name, file_names)
+ except TypeError:
+ output = output_func(file_names)
+ return output
# -------------------------------------------------------------------------
- def UpdateParamterNew(self, new_input_file, param_sets):
+ def update_input_params(self, new_input_file, param_sets):
"""
- Finds this pattern with <X1> and replace it with the new value from
- the array CollocationPoints.
+ Finds this pattern with <X1> in the new_input_file and replace it with
+ the new value from the array param_sets.
+
+ Parameters
+ ----------
+ new_input_file : TYPE
+ DESCRIPTION.
+ param_sets : TYPE
+ DESCRIPTION.
+
+ Returns
+ -------
+ None.
+
"""
NofPa = param_sets.shape[0]
text_to_search_list = [f'<X{i+1}>' for i in range(NofPa)]
@@ -138,22 +216,36 @@ class PyLinkForwardModel:
file.write(filedata)
# -------------------------------------------------------------------------
- def ModelExecutor(self, NewCommand, OutputFileNames):
+ def run_command(self, command, output_file_names):
"""
- ModelExecutor runs the execution command given by the user to run
- the given model. When the job is done it extracts and returns the
- requested output(s).
+ Runs the execution command given by the user to run the given model.
+ It checks if the output files have been generated. If yes, the jobe is
+ done and it extracts and returns the requested output(s). Otherwise,
+ it executes the command again.
+
+ Parameters
+ ----------
+ command : string
+ The command to be executed.
+ output_file_names : list
+ Name of the output file names.
+
+ Returns
+ -------
+ simulation_outputs : array of shape (n_obs, n_outputs)
+ Simulation outputs.
+
"""
- # check if simulation is finished
+ # Check if simulation is finished
while True:
time.sleep(3)
files = os.listdir(".")
- if all(elem in files for elem in OutputFileNames):
+ if all(elem in files for elem in output_file_names):
break
else:
# Run command
- Process = os.system(f'./../{NewCommand}')
+ Process = os.system(f'./../{command}')
if Process != 0:
print('\nMessage 1:')
print(f'\tIf value of \'{Process}\' is a non-zero value, '
@@ -162,9 +254,9 @@ class PyLinkForwardModel:
os.chdir("..")
# Read the output
- SimulationsDataMatrix = self.read_Output()
+ simulation_outputs = self.read_output()
- return SimulationsDataMatrix
+ return simulation_outputs
# -------------------------------------------------------------------------
def run_forwardmodel(self, xx):
@@ -176,14 +268,14 @@ class PyLinkForwardModel:
CollocationPoints, Run_No, keyString = xx
# Handle if only one imput file is provided
- if not isinstance(self.InputTemplate, list):
- self.InputTemplate = [self.InputTemplate]
- if not isinstance(self.InputFile, list):
- self.InputFile = [self.InputFile]
+ if not isinstance(self.input_template, list):
+ self.input_template = [self.input_template]
+ if not isinstance(self.input_file, list):
+ self.input_file = [self.input_file]
NewInputfile = []
# Loop over the InputTemplates:
- for InputTemplate in self.InputTemplate:
+ for InputTemplate in self.input_template:
if '/' in InputTemplate:
InputTemplate = InputTemplate.split('/')[-1]
NewInputfile.append(InputTemplate.split('.tpl')[0] + keyString +
@@ -191,94 +283,121 @@ class PyLinkForwardModel:
InputTemplate.split('.tpl')[1])
# Create directories
- newpath = self.Name + keyString + f'_{Run_No+1}'
+ newpath = self.name + keyString + f'_{Run_No+1}'
if not os.path.exists(newpath):
os.makedirs(newpath)
# Copy the necessary files to the directories
- for InputTemplate in self.InputTemplate:
+ for InputTemplate in self.input_template:
# Input file(s) of the model
shutil.copy2(InputTemplate, newpath)
# Auxiliary file
- if self.AuxFile is not None:
- shutil.copy2(self.AuxFile, newpath) # Auxiliary file
+ if self.aux_file is not None:
+ shutil.copy2(self.aux_file, newpath) # Auxiliary file
# Rename the Inputfile and/or auxiliary file
os.chdir(newpath)
- for input_tem, input_file in zip(self.InputTemplate, NewInputfile):
+ for input_tem, input_file in zip(self.input_template, NewInputfile):
if '/' in InputTemplate:
input_tem = input_tem.split('/')[-1]
os.rename(input_tem, input_file)
# Update the parametrs in Input file
- self.UpdateParamterNew(NewInputfile, CollocationPoints)
+ self.update_input_params(NewInputfile, CollocationPoints)
# Update the user defined command and the execution path
try:
- NewCommand = self.Command.replace(self.InputFile[0],
- NewInputfile[0]).replace(
- self.InputFile[1],
- NewInputfile[1])
+ NewCommand = self.shell_command .replace(self.input_file[0],
+ NewInputfile[0]).replace(
+ self.input_file[1],
+ NewInputfile[1])
except:
- NewCommand = self.Command.replace(self.InputFile[0],
- NewInputfile[0])
+ NewCommand = self.shell_command.replace(self.input_file[0],
+ NewInputfile[0])
- OutputFileNames = self.Output.FileNames
- self.ExecutionPath = os.getcwd()
+ OutputFileNames = self.Output.file_names
+ self.exe_path = os.getcwd()
# Run the model
- Output = self.ModelExecutor(NewCommand, OutputFileNames)
+ Output = self.run_command(NewCommand, OutputFileNames)
return Output
# -------------------------------------------------------------------------
- def Run_Model_Parallel(self, CollocationPoints, prevRun_No=0, keyString='',
+ def run_model_parallel(self, c_points, prevRun_No=0, keyString='',
mp=True):
"""
- ################ Parallel Simulation #####################
- OutputFile :
+ Runs model simulations. If mp is true (default), then the simulations
+ are started in parallel.
+
+ Parameters
+ ----------
+ c_points : array like of shape (n_samples, n_params)
+ Collocation points (training set).
+ prevRun_No : int, optional
+ Previous run number, in case the sequential design is selected.
+ The default is 0.
+ keyString : string, optional
+ A descriptive string for validation runs. The default is ''.
+ mp : bool, optional
+ Multiprocessing. The default is True.
+
+ Returns
+ -------
+ all_outputs : dict
+ A dictionary with x values (time step or point id) and all outputs.
+ Each key contains an array of the shape (n_samples, n_obs).
+ new_c_points : array
+ Updated collocation points (training set). If a simulation does not
+ executed successfully, the parameter set is removed.
- This gives a dictionary with time step and all outputs.
- Each key contains an array of the shape P X TotalTimeStep.
"""
# Create hdf5 metadata
- hdf5file = 'ExpDesign'+'_'+self.Name+'.hdf5'
+ hdf5file = 'ExpDesign'+'_'+self.name+'.hdf5'
hdf5_exist = os.path.exists(hdf5file)
file = h5py.File(hdf5file, 'a')
# Initilization
- P = len(CollocationPoints)
- OutputNames = self.Output.Names
+ P = len(c_points)
+ OutputNames = self.Output.names
self.NofVar = len(OutputNames)
- TotalOutputs = {}
-
- # Check if the operating system is not unix
- if os.name == 'nt':
- self.seperator = '\\'
+ all_outputs = {}
+ # Extract the function
+ if self.link_type.lower() == 'function':
+ # Prepare the function
+ Function = getattr(__import__(self.py_file), self.py_file)
# ---------------------------------------------------------------
# -------------- Multiprocessing with Pool Class ----------------
# ---------------------------------------------------------------
# Start a pool with the number of CPUs
- if self.nrCPUs is None:
+ if self.n_cpus is None:
nrCPUs = multiprocessing.cpu_count()
else:
- nrCPUs = self.nrCPUs
+ nrCPUs = self.n_cpus
# Run forward model either normal or with multiprocessing
- if not mp:
- group_results = list([self.run_forwardmodel((CollocationPoints,
+ if not self.multi_process:
+ group_results = list([self.run_forwardmodel((c_points,
prevRun_No,
keyString))])
else:
with multiprocessing.Pool(nrCPUs) as p:
- group_results = list(tqdm.tqdm(p.imap(self.run_forwardmodel, zip(CollocationPoints,[prevRun_No+i for i in range(P)], [keyString]*P)),
- total=P, desc='Running forward model '+keyString))
+ desc = f'Running forward model {keyString}'
+ if self.link_type.lower() == 'function':
+ imap_var = p.imap(Function, c_points[:, np.newaxis])
+ else:
+ imap_var = p.imap(self.run_forwardmodel,
+ zip(c_points,
+ [prevRun_No+i for i in range(P)],
+ [keyString]*P))
+
+ group_results = list(tqdm.tqdm(imap_var, total=P, desc=desc))
# Save time steps or x-values
x_values = group_results[0][0]
- TotalOutputs["x_values"] = x_values
+ all_outputs["x_values"] = x_values
if not hdf5_exist:
if type(x_values) is dict:
grp_x_values = file.create_group("x_values/")
@@ -291,81 +410,103 @@ class PyLinkForwardModel:
NaN_idx = []
for varIdx, var in enumerate(OutputNames):
- grpY = file.create_group("EDY/"+var) if not hdf5_exist else file.get("EDY/"+var)
+ if not hdf5_exist:
+ grpY = file.create_group("EDY/"+var)
+ else:
+ grpY = file.get("EDY/"+var)
- Outputs = np.asarray([item[varIdx+1] for item in group_results], dtype=np.float64)
+ Outputs = np.asarray([item[varIdx+1] for item in group_results],
+ dtype=np.float64)
if prevRun_No == 0 and keyString == '':
- grpY.create_dataset("init_"+keyString, data=Outputs)
+ grpY.create_dataset(f'init_{keyString}', data=Outputs)
else:
try:
- oldEDY = np.array(file['EDY/'+var+'/adaptive_'+keyString])
- del file['EDY/'+var+'/adaptive_'+keyString]
+ oldEDY = np.array(file[f'EDY/{var}/adaptive_{keyString}'])
+ del file[f'EDY/{var}/adaptive_{keyString}']
data = np.vstack((oldEDY, Outputs))
except:
data = Outputs
grpY.create_dataset('adaptive_'+keyString, data=data)
NaN_idx = np.unique(np.argwhere(np.isnan(Outputs))[:, 0])
- TotalOutputs[var] = np.delete(Outputs, NaN_idx, axis=0)
+ all_outputs[var] = np.delete(Outputs, NaN_idx, axis=0)
if prevRun_No == 0 and keyString == '':
- grpY.create_dataset(f"New_init_{keyString}", data=TotalOutputs[var])
+ grpY.create_dataset(f"New_init_{keyString}",
+ data=all_outputs[var])
else:
try:
- oldEDY = np.array(file[f'EDY/{var}/New_adaptive_'+keyString])
+ name = f'EDY/{var}/New_adaptive_{keyString}'
+ oldEDY = np.array(file[name])
del file[f'EDY/{var}/New_adaptive_{keyString}']
- data = np.vstack((oldEDY, TotalOutputs[var]))
+ data = np.vstack((oldEDY, all_outputs[var]))
except:
- data = TotalOutputs[var]
- grpY.create_dataset('New_adaptive_'+keyString, data=data)
+ data = all_outputs[var]
+ grpY.create_dataset(f'New_adaptive_{keyString}', data=data)
# Print the collocation points whose simulations crashed
if len(NaN_idx) != 0:
print('\n')
print('*'*20)
- print("\nThe following parametersets have been removed:\n",
- CollocationPoints[NaN_idx])
+ print("\nThe following parametersets have been removed:\n",
+ c_points[NaN_idx])
print("\n")
print('*'*20)
# Pass it to the attribute
- NewCollocationPoint = np.delete(CollocationPoints, NaN_idx, axis=0)
- self.OutputMatrix = TotalOutputs
+ new_c_points = np.delete(c_points, NaN_idx, axis=0)
+ self.OutputMatrix = all_outputs
# Save CollocationPoints
grpX = file.create_group("EDX") if not hdf5_exist else file.get("EDX")
if prevRun_No == 0 and keyString == '':
- grpX.create_dataset("init_"+keyString, data=CollocationPoints)
+ grpX.create_dataset("init_"+keyString, data=c_points)
if len(NaN_idx) != 0:
- grpX.create_dataset("New_init_"+keyString, data=NewCollocationPoint)
+ grpX.create_dataset("New_init_"+keyString, data=new_c_points)
else:
try:
- oldCollocationPoints = np.array(file[f'EDX/adaptive_{keyString}'])
+ name = f'EDX/adaptive_{keyString}'
+ oldCollocationPoints = np.array(file[name])
del file[f'EDX/adaptive_{keyString}']
- data = np.vstack((oldCollocationPoints, NewCollocationPoint))
+ data = np.vstack((oldCollocationPoints, new_c_points))
except:
- data = NewCollocationPoint
+ data = new_c_points
grpX.create_dataset('adaptive_'+keyString, data=data)
if len(NaN_idx) != 0:
try:
- oldCollocationPoints = np.array(file[f'EDX/New_adaptive_{keyString}'])
+ name = f'EDX/New_adaptive_{keyString}'
+ oldCollocationPoints = np.array(file[name])
del file[f'EDX/New_adaptive_{keyString}']
- data = np.vstack((oldCollocationPoints, NewCollocationPoint))
+ data = np.vstack((oldCollocationPoints, new_c_points))
except:
- data = NewCollocationPoint
+ data = new_c_points
grpX.create_dataset('New_adaptive_'+keyString, data=data)
# Close h5py file
file.close()
- return self.OutputMatrix, NewCollocationPoint
+ return all_outputs, new_c_points
# -------------------------------------------------------------------------
def zip_subdirs(self, dir_name, key):
+ """
+ Zips all the files containing the key(word).
+ Parameters
+ ----------
+ dir_name : string
+ Directory name.
+ key : string
+ Keyword to search for.
+
+ Returns
+ -------
+ None.
+
+ """
# setup file paths variable
DirectoryList = []
filePaths = []
@@ -377,7 +518,7 @@ class PyLinkForwardModel:
for directory in directories:
# Create the full filepath by using os module.
if key in directory:
- folderPath = dirName + self.seperator + directory
+ folderPath = os.path.join(dirName, directory)
DirectoryList.append(folderPath)
# Loop over the identified directories to store the file paths
@@ -402,6 +543,6 @@ class PyLinkForwardModel:
shutil.rmtree(path)
print("\n")
- print(dir_name+'.zip file has been created successfully!\n')
+ print(f'{dir_name}.zip file has been created successfully!\n')
return
diff --git a/BayesValidRox/surrogate_models/sequential_design.py b/BayesValidRox/surrogate_models/sequential_design.py
index 33580f73c30d9272b241dd328d82297993f74dfc..f77b080b73620c4d61297ae999547b67da2a7f74 100644
--- a/BayesValidRox/surrogate_models/sequential_design.py
+++ b/BayesValidRox/surrogate_models/sequential_design.py
@@ -258,9 +258,9 @@ class SeqDesign():
# Create the SparseBayes-based PCE metamodel:
PCEModel.Inputs.polycoeffsFlag = False
univ_p_val = self.MetaModel.univ_basis_vals(X_can)
- G_n_m_all = np.zeros((len(Model.Output.Names), Model.nObs))
+ G_n_m_all = np.zeros((len(Model.Output.names), Model.nObs))
- for idx, key in enumerate(Model.Output.Names):
+ for idx, key in enumerate(Model.Output.names):
for i in range(Model.nObs):
BasisIndices = PCEModel.BasisDict[key]["y_"+str(i+1)]
clf_poly = PCEModel.clf_poly[key]["y_"+str(i+1)]
@@ -911,7 +911,7 @@ class SeqDesign():
NCandidate = allCandidates.shape[0]
# TODO: Loop over outputs
- OutputName = Model.Output.Names[0]
+ OutputName = Model.Output.names[0]
# To avoid changes ub original aPCE object
PCEModel = deepcopy(PCEModelOrig)
@@ -988,7 +988,7 @@ class SeqDesign():
# -------------------------------------------------------------------------
def normpdf(self, PCEOutputs, std_PC_MC, obs_data, Sigma2s):
- output_names = self.Model.Output.Names
+ output_names = self.Model.Output.names
SampleSize, index = PCEOutputs[output_names[0]].shape
if type(self.MetaModel.index) is list:
@@ -1251,7 +1251,7 @@ class SeqDesign():
PCEModel = self.MetaModel
Model = self.Model
- OutputName = Model.Output.Names
+ OutputName = Model.Output.names
# Generate random samples
Samples = PCEModel.validSamples
@@ -1334,7 +1334,7 @@ class SeqDesign():
pce = True if PCEModel.metaModel.lower() != 'gpe' else False
mc_ref = True if hasattr(Model, 'MCReference') else False
if mc_ref:
- Model.read_MCReference()
+ Model.read_mc_reference()
# Get the parameters
max_n_samples = PCEModel.ExpDesign.MaxNSamples
@@ -1352,7 +1352,7 @@ class SeqDesign():
initPCEModel = deepcopy(PCEModel)
# TODO: Loop over outputs
- OutputName = Model.Output.Names
+ OutputName = Model.Output.names
# Estimation of the integral via Monte Varlo integration
obs_data = PCEModel.Observations
@@ -1361,7 +1361,7 @@ class SeqDesign():
TotalSigma2 = np.empty((0, 1))
if len(obs_data) != 0:
# ------ Prepare diagonal enteries for co-variance matrix ---------
- for keyIdx, key in enumerate(Model.Output.Names):
+ for keyIdx, key in enumerate(Model.Output.names):
# optSigma = 'B'
sigma2 = np.array(PCEModel.Discrepancy.Parameters[key])
TotalSigma2 = np.append(TotalSigma2, sigma2)
@@ -1478,7 +1478,7 @@ class SeqDesign():
print("\n")
# Evaluate the full model response at the new sample:
- Ynew, _ = Model.Run_Model_Parallel(
+ Ynew, _ = Model.run_model_parallel(
Xnew, prevRun_No=total_n_samples
)
total_n_samples += Xnew.shape[0]
diff --git a/BayesValidRox/surrogate_models/surrogate_models.py b/BayesValidRox/surrogate_models/surrogate_models.py
index 5511d0afe39680702502dcaa4f0d1f32f4a75c5f..1451f505a7857ef7ce1ca44f3284d5a3136c85d8 100644
--- a/BayesValidRox/surrogate_models/surrogate_models.py
+++ b/BayesValidRox/surrogate_models/surrogate_models.py
@@ -78,8 +78,8 @@ class Metamodel:
# Cleanup
# Zip the subdirectories
try:
- dir_name = Model.Name
- key = Model.Name + '_'
+ dir_name = Model.name
+ key = f'{Model.name}_'
Model.zip_subdirs(dir_name, key)
except:
pass
@@ -163,7 +163,7 @@ class Metamodel:
# ---- Run simulations at X ----
if self.ExpDesign.Y is None:
print('\n Now the forward model needs to be run!\n')
- ED_Y, up_ED_X = Model.Run_Model_Parallel(ED_X)
+ ED_Y, up_ED_X = Model.run_model_parallel(ED_X)
self.ExpDesign.X = up_ED_X
self.ModelOutputDict = ED_Y
self.ExpDesign.Y = ED_Y
@@ -836,8 +836,8 @@ class Metamodel:
# Read observations or MCReference
if self.ExpDesignFlag != 'sequential':
- if len(Model.Observations) != 0:
- self.Observations = Model.read_Observation()
+ if len(Model.observations) != 0:
+ self.Observations = Model.read_observation()
# Define the DegreeArray
nSamples, ndim = CollocationPoints.shape
@@ -975,10 +975,7 @@ class Metamodel:
self.errorScale = self.AutoVivification()
# Read data
- if Name.lower() == 'calib':
- MeasuredData = Model.read_Observation()
- else:
- MeasuredData = Model.read_ObservationValid()
+ MeasuredData = Model.read_observation(case=Name)
# Fitting GPR based bias model
for out in outputNames:
diff --git a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
index a2ff59d7721b5e110122548e81c164ab7e6f0641..18bdb95592a3025234a60d11a46bae978680f067 100755
--- a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
+++ b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
@@ -25,13 +25,7 @@ matplotlib.use('agg')
sys.path.insert(0, './../../../BayesValidRox')
-try:
- import cPickle as pickle
-except ModuleNotFoundError:
- import _pickle as pickle
-
-# from PyLink.FuncForwardModel import FuncForwardModel
-from PyLink.PyLinkForwardModel_v2 import PyLinkForwardModel
+from PyLink.PyLinkForwardModel import PyLinkForwardModel
from surrogate_models.Input import Input
from surrogate_models.surrogate_models import Metamodel
from PostProcessing.PostProcessing import PostProcessing
@@ -54,15 +48,15 @@ if __name__ == "__main__":
Model.Output.names = ['Z']
# For Bayesian inversion synthetic data with X=[0,0]
- Model.Observations = {}
- Model.Observations['Time [s]'] = np.arange(0, 10, 1.) / 9
- Model.Observations['Z'] = np.repeat([2.], 10)
+ Model.observations = {}
+ Model.observations['Time [s]'] = np.arange(0, 10, 1.) / 9
+ Model.observations['Z'] = np.repeat([2.], 10)
# For Checking with the MonteCarlo refrence
- Model.MCReference = {}
- Model.MCReference['Time [s]'] = np.arange(0, 10, 1.) / 9
- Model.MCReference['mean'] = np.load(f"data/mean_{ndim}.npy")
- Model.MCReference['std'] = np.load(f"data/std_{ndim}.npy")
+ Model.mc_reference = {}
+ Model.mc_reference['Time [s]'] = np.arange(0, 10, 1.) / 9
+ Model.mc_reference['mean'] = np.load(f"data/mean_{ndim}.npy")
+ Model.mc_reference['std'] = np.load(f"data/std_{ndim}.npy")
# =====================================================
# ========= PROBABILISTIC INPUT MODEL ==============
@@ -149,7 +143,7 @@ if __name__ == "__main__":
MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-16
# Defining the measurement error, if it's known a priori
- obsData = pd.DataFrame(Model.Observations, columns=Model.Output.Names)
+ obsData = pd.DataFrame(Model.observations, columns=Model.Output.names)
DiscrepancyOpts = Discrepancy('')
DiscrepancyOpts.Type = 'Gaussian'
DiscrepancyOpts.Parameters = obsData**2
diff --git a/BayesValidRox/tests/BeamTest/InputParameters.csv b/BayesValidRox/tests/BeamTest/InputParameters.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a0ff5dcf2f2576aa57f06a6c696b42db6856d4e2
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/InputParameters.csv
@@ -0,0 +1,501 @@
+Beam width,Beam height,Youngs modulus,Uniform load
+0.157273329338393,0.315533780303244,31680127940.9143,10643.4004588287
+0.138141895643606,0.308218660642714,28321606595.7333,7647.68743220827
+0.149535905747769,0.306605844437465,30552276402.2584,9236.23189018043
+0.153383076910337,0.295497711468594,31761399663.944,7309.16022380918
+0.148200930653566,0.293817818770393,28018177950.2595,11966.8615497731
+0.155163413920491,0.298580429033683,26833968221.9714,12509.103705398
+0.15127259014584,0.316329725288591,35178534974.5191,9214.47221317446
+0.14875343824136,0.311973945216133,24850753238.6524,9387.67488771496
+0.151756833722548,0.296311569518993,25961923101.4477,10889.4840523989
+0.150728984042148,0.354436738364159,22957269053.1972,9642.72342687284
+0.150163255227266,0.306203460414847,22160128744.8397,14239.2076304184
+0.146098657456629,0.312141500135723,31358307777.8954,10684.3198694204
+0.159272350629841,0.307411055030341,39307062162.4182,9977.19695224498
+0.154839475948027,0.294281413758414,30242289491.0974,9702.12972655218
+0.15587358045112,0.280152827690146,23121684518.2006,9064.17404524275
+0.152972209050633,0.324842476073234,31014319169.6111,13895.0556840787
+0.142687801246096,0.299336640713574,29221071579.1713,8431.81667864198
+0.155313584912084,0.306312280867227,30769034883.6714,9073.18034281152
+0.150834807159884,0.30176628986798,23455841842.6986,12853.1092616226
+0.15705376912135,0.318983539423807,29806311279.8473,9118.90317351403
+0.142772783709399,0.304533185614055,34222907263.8048,11382.2856802332
+0.152741198796834,0.271688989917762,27609276746.9817,7626.82876146585
+0.149480548119323,0.29362624908125,32190969319.3093,10076.8351340425
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+0.151515250705557,0.314919881171,30912221439.4448,12777.1495258022
+0.143045613699742,0.325217637957378,32274204094.4085,6732.64470989785
+0.145960779680579,0.292136380290302,38375526659.6108,11138.6558787486
+0.149904777869286,0.28223995061897,35737057874.7975,10171.4499182627
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+0.140565282328202,0.285119556474362,25814768425.2241,9527.04173113151
+0.158405425840665,0.308015378461452,29457140340.3715,13026.4591511358
+0.147062712940694,0.287337832761341,28492800025.1372,8182.37591617192
+0.152818491723875,0.280636382305268,27249509229.5066,9276.57511433137
+0.143616207885392,0.303759929152041,29419129987.4364,10425.3339540397
+0.155847218453832,0.28618257416615,31993300636.2147,8426.08722073316
+0.15954342687246,0.316243931263279,24378400639.9206,10764.3544204923
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+0.144982470930532,0.285625531209732,32086277045.9272,12940.1079293741
+0.147494497056934,0.321298053881594,24273332319.4764,8248.31760372167
+0.160195032193099,0.321915676919562,32975333654.6655,10417.2063092623
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+0.141255546713097,0.27711640150862,29441333905.6025,10486.9772189557
+0.145868162156837,0.274919344045281,26237098467.9988,9102.33162097466
+0.156799222936308,0.303191200616944,37812275680.1961,8683.27599768718
+0.166017619489282,0.314437909511048,28896826447.6357,9471.51814186481
+0.161566550394723,0.292739048587377,25986821393.9433,12287.6531594793
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+0.154540094641902,0.311213582003469,27575409589.6803,6659.86098640352
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new file mode 100644
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diff --git a/BayesValidRox/tests/BeamTest/PosteriorGeneration/PosteriorGeneration.py b/BayesValidRox/tests/BeamTest/PosteriorGeneration/PosteriorGeneration.py
new file mode 100644
index 0000000000000000000000000000000000000000..c07594b7493d70b5e1fb2f1d0ca33b3a50916144
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/PosteriorGeneration/PosteriorGeneration.py
@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov 11 2019
+
+This script generates the posterior distribution of the CO2Benchmark model
+for a synthetic data for three given uncertain parameters using 10000 MC-Simulations
+of the original model.
+
+@author: Farid Mohammadi
+"""
+import pandas as pd
+import numpy as np
+import os
+from tqdm import tqdm
+import corner
+from scipy.stats import multivariate_normal
+import scipy.stats as st
+
+
+
+
+def BeamModel(theta):
+ #### write an input file
+ global beam_span, i
+ Beam_width, Beam_height, Youngs_modulus,Uniform_load = theta
+ Youngs_modulus = 1e9 * Youngs_modulus
+ Uniform_load = 1000 * Uniform_load
+ # Get the number of function runs each 100 NOT necessary, just checking
+ i +=1
+ if i%100 ==0:
+ print("number of evaluations {}".format(i))
+
+ #### Run your surrogate model here.
+ I = Beam_width * Beam_height**3 / 12;
+ output = np.zeros((11))
+ for i in range(1,10):
+ X = (i/10.)*beam_span;
+ output[i] = -1*Uniform_load*X*(beam_span**3 -2* X**2 * beam_span + X**3)/(24*Youngs_modulus*I);
+
+ return output
+
+
+def RejectionSampling(mcParametersets, origModelOutput, Data, InputNames):
+
+
+ MCSamples = mcParametersets
+ NofMeasurements = len(Data)
+ NrofSamples = len(MCSamples)
+ Sigma2 = (0.01*synthData)**2
+ Sigma2[0], Sigma2[-1]= 1e-10, 1e-10
+
+ # Covariance Matrix
+ covMatrix = np.zeros((NofMeasurements, NofMeasurements), float)
+ np.fill_diagonal(covMatrix, Sigma2)
+
+ # Likelihood
+ Likelihoods = multivariate_normal.pdf(origModelOutput, mean=Data, cov=covMatrix)
+
+ # Rejection
+ # Take the first column of Likelihoods (Observation data without noise)
+ NormLikehoods = Likelihoods / np.max(Likelihoods)
+
+
+ # Random numbers between 0 and 1
+ unif = np.random.rand(1, NrofSamples)[0]
+
+ # Reject the poorly performed prior
+ acceptedSamples = MCSamples[NormLikehoods >= unif]
+
+ # Output the Posterior
+
+ Posterior_df = pd.DataFrame(acceptedSamples, columns=InputNames)
+
+ # Save the posterior data frame
+ Posterior_df.to_csv('./Posterior.csv', columns=InputNames, index=False)
+
+ return Posterior_df
+
+def posteriorPlot(Posterior, MAP, InputNames, nParams, figsize=(10,10)):
+
+ figPosterior = corner.corner(Posterior, labels=InputNames,
+ show_titles=True, title_kwargs={"fontsize": 12})
+
+ # This is the true mean of the second mode that we used above:
+ value1 = MAP
+
+ # This is the empirical mean of the sample:
+ value2 = np.mean(Posterior, axis=0)
+
+ # Extract the axes
+ axes = np.array(figPosterior.axes).reshape((nParams, nParams))
+
+ # Loop over the diagonal
+ for i in range(nParams):
+ ax = axes[i, i]
+ ax.axvline(value1[i], color="g")
+ ax.axvline(value2[i], ls='--', color="r")
+
+ # Loop over the histograms
+ for yi in range(nParams):
+ for xi in range(yi):
+ ax = axes[yi, xi]
+ ax.axvline(value1[xi], color="g")
+ ax.axvline(value2[xi], ls='--', color="r")
+ ax.axhline(value1[yi], color="g")
+ ax.axhline(value2[yi], ls='--', color="r")
+ ax.plot(value1[xi], value1[yi], "sg")
+ ax.plot(value2[xi], value2[yi], "sr")
+
+ figPosterior.set_size_inches(figsize)
+ figPosterior.savefig('Posterior.svg',bbox_inches='tight')
+
+ return
+
+
+
+if __name__ == '__main__':
+
+ # --------------------------------------
+ # ---------- Initialization -----------
+ # --------------------------------------
+ np.random.seed(42)
+ beam_span = 5
+ i = 0
+
+ InputNames = ['Beam width', 'Beam height', 'Youngs modulus', 'Uniform load']
+
+ # --------------------------------------
+ # ---------- Data generation -----------
+ # --------------------------------------
+
+ xdata = np.linspace(0, 5, num=11, endpoint=True)
+ theta_true = (0.150064, 0.299698, 30.763206, 10.164872)
+ model_true = BeamModel(theta_true)
+ synthData = model_true
+
+ #plt.plot(xdata, synthData, 'ok')
+ #plt.xlabel('x')
+ #plt.ylabel('y');
+
+ # -------------------------------------------------------------------------
+ # ------------------ Naive Monte-Carlo with rejection sampling ------------
+ # -------------------------------------------------------------------------
+ ndim = 4
+ mcSize = 500000
+ nofmeasurement = 11
+ Likelihood = np.zeros((mcSize))
+ mcParametersets = np.zeros((mcSize,ndim))
+
+ # Beam_width
+ mcParametersets[:,0] = st.lognorm(s=0.0075, scale=0.15).rvs(size=mcSize)
+ # Beam_height
+ mcParametersets[:,1] = st.lognorm(s=0.015, scale=0.30).rvs(size=mcSize)
+ #Youngs_modulus
+ mcParametersets[:,2] = st.norm(loc=30, scale=4.5).rvs(size=mcSize)
+ #Uniform_load
+ mcParametersets[:,3] = st.norm(loc=10, scale=2).rvs(size=mcSize)
+ #sigma
+ #mcParametersets[:,4] = st.halfcauchy(loc=0, scale = 10).rvs(size=mcSize)
+
+ # origModelOutput
+ origModelOutput = np.zeros((mcSize, nofmeasurement))
+ for idx, theta in enumerate(mcParametersets):
+ origModelOutput[idx] = BeamModel(theta)
+
+
+ # ------- Generate & plot the posterior -------
+ # Rejection sampling
+ Posterior = RejectionSampling(mcParametersets, origModelOutput, synthData, InputNames)
+
+ # Plot the posterior distributions
+ posteriorPlot(Posterior, theta_true, InputNames, nParams=ndim)
+
diff --git a/BayesValidRox/tests/BeamTest/SA_Beam.py b/BayesValidRox/tests/BeamTest/SA_Beam.py
new file mode 100644
index 0000000000000000000000000000000000000000..9766a7a2653a4603e4b8160690a13890bf682b7c
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/SA_Beam.py
@@ -0,0 +1,183 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul 10 14:27:35 2019
+
+@author: farid
+"""
+import sys
+import os
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+try:
+ import cPickle as pickle
+except ModuleNotFoundError:
+ import pickle
+
+
+from PyLink.PyLinkForwardModel import PyLinkForwardModel
+from surrogate_models.Input import Input
+from surrogate_models.surrogate_models import aPCE
+from PostProcessing.PostProcessing import PostProcessing
+from BayesInference.BayesInference import BayesInference, Discrepancy
+
+
+
+
+
+if __name__ == "__main__":
+
+ #=====================================================
+ #============= COMPUTATIONAL MODEL ================
+ #=====================================================
+ Model = PyLinkForwardModel()
+
+ Model.Type = 'PyLink'
+ Model.Name = 'Beam9points'
+ Model.InputFile = "SSBeam_Deflection.inp"
+ Model.InputTemplate = "SSBeam_Deflection.tpl.inp"
+
+
+ Model.Command = "myBeam9points SSBeam_Deflection.inp"
+ Model.ExecutionPath = os.getcwd()
+ Model.Output.Parser = 'read_Beam_Deflection'
+ Model.Output.Names = ['Deflection [m]']
+ Model.Output.FileNames = ["SSBeam_Deflection_.out"]
+
+ # For Bayesian inversion
+ Model.MeasurementFile = 'MeasuredData.csv'
+
+ # For Checking with the MonteCarlo refrence
+ Model.MCReferenceFile = 'MCrefs_MeanStd.csv'
+
+ #=====================================================
+ #========= PROBABILISTIC INPUT MODEL ==============
+ #=====================================================
+ # Define the uncertain parameters with their mean and
+ # standard deviation
+ Inputs = Input()
+
+ Inputs.addMarginals()
+ Inputs.Marginals[0].Name = 'Beam width'
+ Inputs.Marginals[0].DistType = 'lognorm'
+ Inputs.Marginals[0].Moments = [0.15, 0.0075]
+
+ Inputs.addMarginals()
+ Inputs.Marginals[1].Name = 'Beam height'
+ Inputs.Marginals[1].DistType = 'lognorm'
+ Inputs.Marginals[1].Moments = [0.3, 0.015]
+
+ Inputs.addMarginals()
+ Inputs.Marginals[2].Name = 'Youngs modulus'
+ Inputs.Marginals[2].DistType = 'lognorm'
+ Inputs.Marginals[2].Moments = [30000e+6, 4500e+6]
+
+ Inputs.addMarginals()
+ Inputs.Marginals[3].Name = 'Uniform load'
+ Inputs.Marginals[3].DistType = 'lognorm'
+ Inputs.Marginals[3].Moments = [10000, 2000]
+
+ #=====================================================
+ #====== POLYNOMIAL CHAOS EXPANSION METAMODELS ======
+ #=====================================================
+ MetaModelOpts = aPCE(Inputs)
+
+ # Specify the max degree to be compared by the adaptive algorithm:
+ # The degree with the lowest Leave-One-Out cross-validation (LOO)
+ # error (or the highest score=1-LOO)estimator is chosen as the final
+ # metamodel.
+ MetaModelOpts.MaxPceDegree = 10
+
+ # Select the sparse least-square minimization method for
+ # the PCE coefficients calculation:
+ # 1)AaPCE: Adaptive aPCE 2)BRR: Bayesian Ridge Regression
+ # 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression (Not Working)
+
+ MetaModelOpts.RegMethod = 'BRR'
+
+ # Print summary of the regression results
+ #MetaModelOpts.DisplayFlag = True
+
+ # ------ Experimental Design --------
+ # Generate an experimental design of size NrExpDesign based on a latin
+ # hypercube sampling of the input model or user-defined values of X and/or Y:
+ MetaModelOpts.addExpDesign()
+ MetaModelOpts.ExpDesign.MCSize = 100000
+
+ MetaModelOpts.ExpDesign.NrSamples = 75
+ MetaModelOpts.ExpDesign.SamplingMethod = 'PCM' # 1)MC 2)LHS 3)PCM 4)LSCM 5)user
+ MetaModelOpts.ExpDesign.Method = 'normal' # 1) normal 2) sequential
+ #MetaModelOpts.ExpDesign.X = np.load('CollocationPoints.npy')
+
+ # Sequential experimental design (needed only for sequential ExpDesign)
+ MetaModelOpts.ExpDesign.MaxNSamples = 50 #150
+ MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-3
+
+ # 1)'dual annealing' 2) 'minimization' 3) MC (Alph. OptDesign:MC search)
+ MetaModelOpts.ExpDesign.SeqOptimMethod = 'MC'
+ MetaModelOpts.ExpDesign.MaxFunItr = 100
+
+ MetaModelOpts.ExpDesign.NCandidate = 500
+
+ # 'dual annealing'
+ # 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
+ # 3)APP (A-Posterior-percision)
+ MetaModelOpts.ExpDesign.UtilityFunction = 'DPP'
+
+ # Optimality criteria (MC search)
+ # 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
+ # 3)K-Opt (K-Optimality)
+ MetaModelOpts.ExpDesign.UtilityFunction = 'D-Opt'
+
+ # >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
+ # Adaptive sparse arbitrary polynomial chaos expansion
+ PCEModel = MetaModelOpts.create_PCE(Model)
+
+
+ #=====================================================
+ #========= POST PROCESSING OF METAMODELS ===========
+ #=====================================================
+ PostPCE = PostProcessing(PCEModel)
+
+ # Compute the moments
+ PostPCE.PCEMoments(PCEModel)
+
+ # Comparison with Monte-Carlo reference
+ # Plot Mean & Std for all Outputs
+ PostPCE.MomentLineplot(PCE_Means = PostPCE.PCEMeans,
+ PCE_Stds = PostPCE.PCEStd,
+ x_label = 'x [m]', SaveFig = True)
+
+ # Sensitivity analysis with Sobol indices
+ PostPCE.SobolIndicesPCE(Output='Deflection [m]', SaveFig = True, PlotType='box')
+
+ #=====================================================
+ #============== Save class objects =================
+ #=====================================================
+ with open('Beam_Results.pkl', 'wb') as output:
+ pickle.dump(PCEModel, output, pickle.HIGHEST_PROTOCOL)
+
+ pickle.dump(PostPCE, output, pickle.HIGHEST_PROTOCOL)
+
+
+# del PCEModel
+# del PostPCE
+
+ # Load the objects
+# with open('CO2Benchmark_Results.pkl', 'rb') as input:
+# PCEModel = pickle.load(input)
+# PostPCE = pickle.load(input)
+
+
+
+# PostPCE.plotFlag = True
+#
+# PostPCE.ValidMetamodel(MetaModel)
+
+
+# # Sensitivity analysis with Sobol indices
+# PostPCE.SobolIndicesPCE(MetaModel)
+#
+
\ No newline at end of file
diff --git a/BayesValidRox/tests/BeamTest/SSBeam_Deflection.inp b/BayesValidRox/tests/BeamTest/SSBeam_Deflection.inp
new file mode 100644
index 0000000000000000000000000000000000000000..00b170341d3c547ee6c7e4f2ed881ab6c2b215da
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/SSBeam_Deflection.inp
@@ -0,0 +1,6 @@
+% Input file for the simply supported beam model
+0.15 % b in m
+0.3 % h in m
+5 % L in m
+30000e+6 % E in Pa
+10000 % p in N/m
diff --git a/BayesValidRox/tests/BeamTest/SSBeam_Deflection.tpl.inp b/BayesValidRox/tests/BeamTest/SSBeam_Deflection.tpl.inp
new file mode 100644
index 0000000000000000000000000000000000000000..3ef1713b66a8f2ca8efddd1992faf6973105c601
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/SSBeam_Deflection.tpl.inp
@@ -0,0 +1,6 @@
+% Input file for the simply supported beam model
+<X1> % b in m
+<X2> % h in m
+5 % L in m
+<X3> % E in Pa
+<X4> % p in N/m
diff --git a/BayesValidRox/tests/BeamTest/Test_Beam.py b/BayesValidRox/tests/BeamTest/Test_Beam.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d7d1a141d6ea07f1f5d61f6a5c8405974532020
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/Test_Beam.py
@@ -0,0 +1,345 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This test shows a surrogate-assisted Bayesian calibration of a beam deflection
+ model.
+
+Author: Farid Mohammadi, M.Sc.
+E-Mail: farid.mohammadi@iws.uni-stuttgart.de
+Department of Hydromechanics and Modelling of Hydrosystems (LH2)
+Institute for Modelling Hydraulic and Environmental Systems (IWS), University
+of Stuttgart www.iws.uni-stuttgart.de/lh2/
+Pfaffenwaldring 61
+70569 Stuttgart
+
+Created on Wed Jul 10 2019
+
+"""
+import os
+import numpy as np
+import pandas as pd
+import joblib
+from PyLink.PyLinkForwardModel import PyLinkForwardModel
+from surrogate_models.Input import Input
+from surrogate_models.surrogate_models import Metamodel
+from PostProcessing.PostProcessing import PostProcessing
+from BayesInference.BayesInference import BayesInference, Discrepancy
+
+
+if __name__ == "__main__":
+
+ # =====================================================
+ # ============= COMPUTATIONAL MODEL ================
+ # =====================================================
+ Model = PyLinkForwardModel()
+
+ Model.link_type = 'PyLink'
+ Model.name = 'Beam9points'
+ Model.input_file = "SSBeam_Deflection.inp"
+ Model.input_template = "SSBeam_Deflection.tpl.inp"
+
+ Model.shell_command = "myBeam9points SSBeam_Deflection.inp"
+ Model.exe_path = os.getcwd()
+ Model.Output.parser = 'read_Beam_Deflection'
+ Model.Output.names = ['Deflection [m]']
+ Model.Output.file_names = ["SSBeam_Deflection.out"]
+
+ # For Bayesian inversion
+ Model.meas_file = 'MeasuredData.csv'
+ Model.meas_file_valid = 'MeasuredData_Valid.csv'
+
+ # For Checking with the MonteCarlo refrence
+ Model.mc_ref_file = 'MCrefs_MeanStd.csv'
+
+ # =====================================================
+ # ========= PROBABILISTIC INPUT MODEL ==============
+ # =====================================================
+ # Define the uncertain parameters with their mean and
+ # standard deviation
+ Inputs = Input()
+
+ Inputs.addMarginals()
+ Inputs.Marginals[0].Name = 'Beam width'
+ Inputs.Marginals[0].DistType = 'lognormal'
+ Inputs.Marginals[0].Parameters = [0.15, 0.0075]
+
+ Inputs.addMarginals()
+ Inputs.Marginals[1].Name = 'Beam height'
+ Inputs.Marginals[1].DistType = 'lognormal'
+ Inputs.Marginals[1].Parameters = [0.3, 0.015]
+
+ Inputs.addMarginals()
+ Inputs.Marginals[2].Name = 'Youngs modulus'
+ Inputs.Marginals[2].DistType = 'lognormal'
+ Inputs.Marginals[2].Parameters = [30000e+6, 4500e+6]
+
+ Inputs.addMarginals()
+ Inputs.Marginals[3].Name = 'Uniform load'
+ Inputs.Marginals[3].DistType = 'lognormal'
+ Inputs.Marginals[3].Parameters = [1e4, 2e3]
+
+ # =====================================================
+ # ========== DEFINITION OF THE METAMODEL ============
+ # =====================================================
+ MetaModelOpts = Metamodel(Inputs)
+
+ # Select if you want to preserve the spatial/temporal depencencies
+ # MetaModelOpts.DimRedMethod = 'PCA'
+
+ # Select your metamodel method
+ # 1) PCE (Polynomial Chaos Expansion) 2) GPE (Gaussian Process Emulator)
+ MetaModelOpts.metaModel = 'PCE'
+
+ # ------------------------------------------------
+ # ------------- PCE Specification ----------------
+ # ------------------------------------------------
+ # Select the sparse least-square minimization method for
+ # the PCE coefficients calculation:
+ # 1)OLS: Ordinary Least Square 2)BRR: Bayesian Ridge Regression
+ # 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression
+ # 5)FastARD: Fast Bayesian ARD Regression
+ MetaModelOpts.RegMethod = 'FastARD'
+
+ # Specify the max degree to be compared by the adaptive algorithm:
+ # The degree with the lowest Leave-One-Out cross-validation (LOO)
+ # error (or the highest score=1-LOO)estimator is chosen as the final
+ # metamodel.
+ MetaModelOpts.MinPceDegree = 6
+ MetaModelOpts.MaxPceDegree = 6
+
+ # Print summary of the regression results
+ # MetaModelOpts.DisplayFlag = True
+
+ # ------------------------------------------------
+ # ------ Experimental Design Configuration -------
+ # ------------------------------------------------
+ MetaModelOpts.addExpDesign()
+
+ # One-shot (normal) or Sequential Adaptive (sequential) Design
+ MetaModelOpts.ExpDesign.Method = 'normal'
+ MetaModelOpts.ExpDesign.NrSamples = 20
+
+ # Sampling methods
+ # 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley
+ # 6) chebyshev(FT) 7) korobov 8) grid(FT) 9) nested_grid(FT) 10)user
+ MetaModelOpts.ExpDesign.SamplingMethod = 'latin_hypercube'
+
+ # Sequential experimental design (needed only for sequential ExpDesign)
+ MetaModelOpts.ExpDesign.NrofNewSample = 2
+ MetaModelOpts.ExpDesign.MaxNSamples = 50
+ MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-6
+
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.Type = 'Gaussian'
+ sigma2s = np.square([1e-8, 1e-2, 1e-2, 1e-3, 4e-3, 5e-3,
+ 1e-3, 1e-2, 1e-2, 1e-2, 1e-2, 1e-9])
+ DiscrepancyOpts.Parameters = sigma2s
+ MetaModelOpts.Discrepancy = DiscrepancyOpts
+
+ # Plot the posterior snapshots for SeqDesign
+ MetaModelOpts.ExpDesign.PostSnapshot = True
+ MetaModelOpts.ExpDesign.stepSnapshot = 1
+ MetaModelOpts.ExpDesign.MAP = (0.150064, 0.299698, 30.763206e9, 10.164872e2)
+ MetaModelOpts.ExpDesign.parNames = ['Beam width', 'Beam height',
+ 'Youngs modulus', 'Uniform load']
+
+ # ------------------------------------------------
+ # ------- Sequential Design configuration --------
+ # ------------------------------------------------
+ # 1) 'None' 2) 'epsilon-decreasing'
+ MetaModelOpts.ExpDesign.TradeOffScheme = 'None'
+ # MetaModelOpts.ExpDesign.nReprications = 2
+ # -------- Exploration ------
+ # 1)'Voronoi' 2)'MC' 3)'LHS' 4)'dual annealing'
+ MetaModelOpts.ExpDesign.ExploreMethod = 'Voronoi'
+
+ # Use when 'dual annealing' chosen
+ MetaModelOpts.ExpDesign.MaxFunItr = 200
+
+ # Use when 'Voronoi' or 'MC' or 'LHS' chosen
+ MetaModelOpts.ExpDesign.NCandidate = 1000
+ MetaModelOpts.ExpDesign.NrofCandGroups = 4
+
+ # -------- Exploitation ------
+ # 1)'BayesOptDesign' 2)'VarOptDesign' 3)'alphabetic' 4)'Space-filling'
+ MetaModelOpts.ExpDesign.ExploitMethod = 'VarOptDesign'
+
+ # BayesOptDesign -> when data is available
+ # 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
+ # 3)APP (A-Posterior-percision)
+ # MetaModelOpts.ExpDesign.UtilityFunction = 'DKL' #['DKL', 'DPP']
+
+ # VarBasedOptDesign -> when data is not available
+ # Only with Vornoi >>> 1)Entropy 2)EIGF, 3)ALM, 4)LOOCV
+ MetaModelOpts.ExpDesign.UtilityFunction = 'Entropy'
+
+ # alphabetic
+ # 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
+ # 3)K-Opt (K-Optimality)
+ # MetaModelOpts.ExpDesign.UtilityFunction = 'D-Opt'
+
+ # >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
+ PCEModel = MetaModelOpts.create_metamodel(Model)
+
+ # =====================================================
+ # ========= POST PROCESSING OF METAMODELS ===========
+ # =====================================================
+ PostPCE = PostProcessing(PCEModel)
+
+ # Compute the moments and compare with the Monte-Carlo reference
+ PostPCE.plotMoments()
+
+ # Plot the sobol indices
+ if MetaModelOpts.metaModel != 'GPE':
+ sobol_cell, total_sobol = PostPCE.sobolIndicesPCE()
+
+ # Plot to check validation visually.
+ PostPCE.validMetamodel(nValidSamples=3)
+ import sys
+ sys.exit()
+ # =====================================================
+ # ============ Bayesian calibration =================
+ # =====================================================
+ BayesOpts = BayesInference(PCEModel)
+ BayesOpts.Name = 'Calib'
+ BayesOpts.emulator = True
+
+ # Evaluation of surrogate model predictions
+ BayesOpts.NrofSamples = 50000
+
+ # Bootstrap for BME calulations
+ BayesOpts.Bootstrap = True
+ BayesOpts.BootstrapItrNr = 1
+
+ BayesOpts.PlotPostDist = True
+ BayesOpts.PlotPostPred = True
+
+ # ----- Define the discrepancy model -------
+ # (Option A)
+ # Do nothing
+
+ # (Option B)
+ obsData = pd.read_csv(Model.MeasurementFile)
+ DiscrepancyOpts = Discrepancy('')
+ DiscrepancyOpts.Type = 'Gaussian'
+ DiscrepancyOpts.Parameters = (0.01*obsData)**2
+ BayesOpts.Discrepancy = DiscrepancyOpts
+
+ # (Option C)
+ # DiscInputOpts = Input()
+ # DiscInputOpts.addMarginals()
+ # DiscInputOpts.Marginals[0].Name = 'Sigma2'
+ # DiscInputOpts.Marginals[0].DistType = 'unif'
+ # DiscInputOpts.Marginals[0].Parameters = [0, 5e-1]
+
+ # DiscrepancyOpts = Discrepancy(DiscInputOpts)
+
+ # BayesOpts.Discrepancy = DiscrepancyOpts
+
+ Bayes_Calib = BayesOpts.create_Inference()
+
+ # =====================================================
+ # ======= Compare Posterior distributions =============
+ # =====================================================
+ def posteriorPlot(Posterior, MAP, parNames, key, figsize=(10, 10)):
+ import corner
+ newpath = (r'Outputs_PosteriorComparison')
+ if not os.path.exists(newpath):
+ os.makedirs(newpath)
+
+ nParams = len(parNames)
+ figPosterior = corner.corner(Posterior, labels=parNames,
+ show_titles=True,
+ title_kwargs={"fontsize": 12})
+
+ # This is the true mean of the second mode that we used above:
+ value1 = MAP
+
+ # This is the empirical mean of the sample:
+ value2 = np.mean(Posterior, axis=0)
+
+ # Extract the axes
+ axes = np.array(figPosterior.axes).reshape((nParams, nParams))
+
+ # Loop over the diagonal
+ for i in range(nParams):
+ ax = axes[i, i]
+ ax.axvline(value1[i], color="g")
+ ax.axvline(value2[i], ls='--', color="r")
+
+ # Loop over the histograms
+ for yi in range(nParams):
+ for xi in range(yi):
+ ax = axes[yi, xi]
+ ax.axvline(value1[xi], color="g")
+ ax.axvline(value2[xi], ls='--', color="r")
+ ax.axhline(value1[yi], color="g")
+ ax.axhline(value2[yi], ls='--', color="r")
+ ax.plot(value1[xi], value1[yi], "sg")
+ ax.plot(value2[xi], value2[yi], "sr")
+
+ figPosterior.set_size_inches(figsize)
+ figPosterior.savefig(f'./{newpath}/{key}.svg', bbox_inches='tight')
+
+ theta_true = (0.150064, 0.299698, 30.763206, 10.164872)
+ parNames = ['Beam width', 'Beam height', 'Youngs modulus', 'Uniform load']
+ # Extract the posterior with emulator
+ surr_posterior = Bayes_Calib.Posterior_df
+
+ # Plot surrogate posteriors
+ posteriorPlot(surr_posterior, theta_true, parNames, 'surrogatePosterior')
+
+ # Read the original posterior
+ orig_posterior = pd.read_csv("origPosterior.csv")
+
+ # Plot original posteriors
+ posteriorPlot(orig_posterior, theta_true, parNames, 'origPosterior')
+
+ # =====================================================
+ # ============= Bayesian validation =================
+ # =====================================================
+ BayesOpts_Valid = BayesInference(PCEModel)
+ BayesOpts_Valid.Name = 'Valid'
+ BayesOpts_Valid.Samples = Bayes_Calib.Posterior_df
+
+ BayesOpts_Valid.emulator = True
+
+ # Bootstrap for BME calulations
+ BayesOpts_Valid.Bootstrap = True
+ BayesOpts_Valid.BootstrapItrNr = 20
+
+ BayesOpts_Valid.PlotPostDist = True
+ BayesOpts_Valid.PlotPostPred = True
+
+ # ----- Define the discrepancy model -------
+ # (Option A)
+ # Do nothing
+
+ # (Option B)
+ # DiscInputOpts_Valid = Discrepancy('')
+ # DiscInputOpts_Valid.Type = 'Gaussian'
+ # DiscInputOpts_Valid.Parameters = [0, 1e-3, 1e-3, 1e-2, 1e-2, 1e-2, 1e-2,
+ # 1e-3, 1e-3, 1e-3, 1e-3, 0] #1e-5
+
+ # (Option C)
+ # DiscInputOpts_Valid = Input()
+
+ # DiscInputOpts_Valid.addMarginals()
+ # DiscInputOpts_Valid.Marginals[0].Name = 'Sigma2'
+ # DiscInputOpts_Valid.Marginals[0].DistType = 'unif'
+ # DiscInputOpts_Valid.Marginals[0].Parameters = [0, 5e-3]
+
+ # DiscrepancyOptsValid = Discrepancy(DiscInputOpts_Valid)
+
+ # BayesOpts_Valid.Discrepancy = DiscrepancyOptsValid
+
+ Bayes_Valid = BayesOpts_Valid.create_Inference()
+
+ # =====================================================
+ # ============== Save class objects =================
+ # =====================================================
+ with open('Beam_Results.pkl', 'wb') as output:
+ joblib.dump(PCEModel, output, 2)
+ joblib.dump(PostPCE, output, 2)
+ joblib.dump(Bayes_Calib, output, 2)
+ joblib.dump(Bayes_Valid, output, 2)
diff --git a/BayesValidRox/tests/BeamTest/__init__.py b/BayesValidRox/tests/BeamTest/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4287ca8617970fa8fc025b75cb319c7032706910
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/__init__.py
@@ -0,0 +1 @@
+#
\ No newline at end of file
diff --git a/BayesValidRox/tests/BeamTest/myBeam9points b/BayesValidRox/tests/BeamTest/myBeam9points
new file mode 100755
index 0000000000000000000000000000000000000000..a3665cde7ade283f93e530021b82207535cda6f9
Binary files /dev/null and b/BayesValidRox/tests/BeamTest/myBeam9points differ
diff --git a/BayesValidRox/tests/BeamTest/myBeam9points.cpp b/BayesValidRox/tests/BeamTest/myBeam9points.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..b78b641e05f5fdfd8caf6d9eff4bf8d1e9c790da
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/myBeam9points.cpp
@@ -0,0 +1,131 @@
+#include <iostream> // for terminal output
+#include <string>
+#include <fstream> // for file operations
+#include <cstdlib> // for exit()
+#include <sstream> // for stringstream
+#include <cstring> // for strlen()
+
+using namespace std;
+
+// C++ implementation of the simply supported beam example from UQLAB: uq_SimplySupportedBeam()
+//
+// UQ_SIMPLYSUPPORTEDBEAM computes the midspan deflection of a simply supported beam under uniform loading
+//% Model with five input parameters X= [b h L E p]
+//% b: beam width
+//% h: beam height
+//% L: beam span
+//% E: Young's modulus
+//% p: uniform load
+//%
+//% Output: V = (5/32)*pL^4/(E*b*h^3)
+//%
+//% See also: UQ_EXAMPLE_PCE_03_SIMPLYSUPPORTEDBEAM/
+
+
+int main(int argc, char* argv[]) {
+
+ // If input filename not given on command line
+ if(argc < 2){
+ cout << "Usage: " << argv[0] << " <input filename>" << endl;
+ cout << "Exiting..." << endl;
+ exit(EXIT_FAILURE);
+ }
+
+ // Input filename given as a command line argument
+ char* inputfilename = argv[1];
+ cout << "Input filename supplied on terminal: " << inputfilename << endl;
+
+ int array_length = strlen(inputfilename);
+ if(array_length > 256){ // This is necessary because C++ doesn't allow variable-length arrays
+ cout << "Error: input filename too long! Choose a name with less than 256 characters." << endl;
+ exit(EXIT_FAILURE);
+ }
+
+ // Variables to be read
+ double params[5];
+ double beam_width;
+ double beam_height;
+ double beam_span;
+ double youngs_modulus;
+ double load;
+
+ // Reading from input file
+ ifstream inputfile(inputfilename);
+ if(inputfile.is_open()){
+ string line;
+ getline(inputfile, line); // skip first line
+ for(int p = 0; p < 5; ++p){
+ getline(inputfile, line);
+ istringstream iss(line);
+ iss >> params[p];
+ if(iss.fail()){
+ cout << "Error while reading line " << p+1 << " from file " << inputfilename << "! Exiting..." << endl;
+ exit(EXIT_FAILURE);
+ }
+ }
+ beam_width = params[0];
+ beam_height = params[1];
+ beam_span = params[2];
+ youngs_modulus = params[3];
+ load = params[4];
+
+ inputfile.close();
+ } else {
+ cout << "Error: Input file " << inputfilename << " not found! Exiting..." << endl;
+ exit(EXIT_FAILURE);
+ }
+
+ cout << "Beam width = " << beam_width << endl;
+ cout << "Beam height = " << beam_height << endl;
+ cout << "Beam span = " << beam_span << endl;
+ cout << "Young's modulus = " << youngs_modulus << endl;
+ cout << "Uniform load = " << load << endl;
+
+ // Compute midspan deflection
+// double V = 5./32. * load * beam_span * beam_span * beam_span * beam_span / (youngs_modulus * beam_width * beam_height * beam_height * beam_height);
+// cout << endl << "Midspan deflection = " << V << endl;
+ double I = beam_width * beam_height * beam_height * beam_height / 12;
+ cout << endl << "Moment of intertia = " << I << endl;
+
+ double V [9] = {0};
+ double X;
+
+ for(int i=1; i<=9; i++){
+ X = (i/10.)*beam_span;
+ V[i-1] = -load*X*(beam_span*beam_span*beam_span-2*X*X*beam_span + X*X*X)/(24*youngs_modulus*I);
+
+ }
+
+ // Extract the name of the input file by reading until the first dot
+ char outputfilename[260]; // input filename has maximal 256 characters, plus we need to append ".out"
+ istringstream s(inputfilename);
+ /*s.get(outputfilename, array_length+1, '.'); */// extract characters until the first '.' and store in outputfilename
+ for(int i = 0; i < array_length; ++i)
+ {
+ if (!((inputfilename[i] >= 'a' && inputfilename[i]<='z') || (inputfilename[i] >= 'A' && inputfilename[i]<='Z') || (inputfilename[i]=='_')))
+ {
+ inputfilename[i] = '\0';
+ }
+ }
+
+
+ // Converting to a string to make appending the file extension easier
+ std::string outputfilename_s("SSBeam_Deflection.out");
+ cout << "Output filename: " << outputfilename_s << endl;
+
+ // Writing to the output file
+ ofstream outputfile(outputfilename_s.c_str()); //re-converting to char-array necessary
+ if (outputfile.is_open()){
+ for(int count = 0; count < 9; count ++){
+ outputfile << V[count] << endl;
+ }
+ outputfile.close();
+ } else {
+ cout << "Error while opening output file " << outputfilename << "! Exiting..." << endl;
+ exit(EXIT_FAILURE);
+ }
+
+
+
+ return 0;
+}
diff --git a/BayesValidRox/tests/BeamTest/origPosterior.csv b/BayesValidRox/tests/BeamTest/origPosterior.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c5ac7661b1293e88b1d8f257c7bf183962be336f
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/origPosterior.csv
@@ -0,0 +1,6458 @@
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diff --git a/BayesValidRox/tests/BeamTest/read_Beam_Deflection.py b/BayesValidRox/tests/BeamTest/read_Beam_Deflection.py
new file mode 100644
index 0000000000000000000000000000000000000000..92ae490334e47c07e63c117ef70a47c2d11dd10f
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/read_Beam_Deflection.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jul 19 16:48:59 2019
+
+@author: farid
+"""
+import pandas as pd
+import numpy as np
+
+
+def read_Beam_Deflection(FileNames):
+
+ df_Mean = pd.read_csv(FileNames[0], header=None, delimiter=' ')
+ x_values = np.arange(0, 5.6, 5./9) # [1:]
+ Deflection = df_Mean[df_Mean.columns[0]]
+
+ Deflection_zero = np.zeros((1))
+ Deflection_mid = np.append(Deflection_zero, Deflection)
+ Deflection_Final = np.append(Deflection_mid, Deflection_zero)
+
+ return np.vstack((x_values, Deflection_Final))
diff --git a/BayesValidRox/tests/BeamTest/surrogateBeam/MCrefs_MeanStd.csv b/BayesValidRox/tests/BeamTest/surrogateBeam/MCrefs_MeanStd.csv
new file mode 100644
index 0000000000000000000000000000000000000000..435da6a3fa0d17ead4712f67b90b1707f704a495
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/surrogateBeam/MCrefs_MeanStd.csv
@@ -0,0 +1,12 @@
+x [m],mean,std
+0,0,0
+0.555555555555556,-0.002623091088022,6.21E-07
+1.11111111111111,-0.004962749295994,2.22E-06
+1.66666666666667,-0.006794367435948,4.16E-06
+2.22222222222222,-0.007957511802197,5.71E-06
+2.77777777777778,-0.008355922171325,6.3E-06
+3.33333333333333,-0.007957511802197,5.71E-06
+3.88888888888889,-0.006794367435948,4.16E-06
+4.44444444444444,-0.004962749295994,2.22E-06
+5,-0.002623091088022,6.21E-07
+5.55555555555556,0,0
diff --git a/BayesValidRox/tests/BeamTest/surrogateBeam/MeasuredData.csv b/BayesValidRox/tests/BeamTest/surrogateBeam/MeasuredData.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5d6002bf17e5e93700e291a7e6f2e9cdc5700a4a
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/surrogateBeam/MeasuredData.csv
@@ -0,0 +1,12 @@
+x [m],Deflection [m]
+0,0
+0.555555555555556,-0.002623091088022
+1.11111111111111,-0.004962749295994
+1.66666666666667,-0.006794367435948
+2.22222222222222,-0.007957511802197
+2.77777777777778,-0.008355922171325
+3.33333333333333,-0.007957511802197
+3.88888888888889,-0.006794367435948
+4.44444444444444,-0.004962749295994
+5,-0.002623091088022
+5.55555555555556,0
diff --git a/BayesValidRox/tests/BeamTest/surrogateBeam/PCE_Beam.py b/BayesValidRox/tests/BeamTest/surrogateBeam/PCE_Beam.py
new file mode 100644
index 0000000000000000000000000000000000000000..27d404a26e68caf65b5c39727b5ea69d90a73b56
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/surrogateBeam/PCE_Beam.py
@@ -0,0 +1,180 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul 10 14:27:35 2019
+
+@author: farid
+"""
+import sys
+import os
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+try:
+ import cPickle as pickle
+except ModuleNotFoundError:
+ import pickle
+
+
+from PyLink.PyLinkForwardModel import PyLinkForwardModel
+from surrogate_models.Input import Input
+from surrogate_models.surrogate_models import aPCE
+from PostProcessing.PostProcessing import PostProcessing
+
+
+
+
+
+if __name__ == "__main__":
+
+ #=====================================================
+ #============= COMPUTATIONAL MODEL ================
+ #=====================================================
+ Model = PyLinkForwardModel()
+
+ Model.Type = 'PyLink'
+ Model.Name = 'Beam9points'
+ Model.InputFile = "SSBeam_Deflection.inp"
+ Model.InputTemplate = "SSBeam_Deflection.tpl.inp"
+
+
+ Model.Command = "myBeam9points SSBeam_Deflection.inp"
+ Model.ExecutionPath = os.getcwd()
+ Model.Output.Parser = 'read_Beam_Deflection'
+ Model.Output.Names = ['Deflection [m]']
+ Model.Output.FileNames = ["SSBeam_Deflection_.out"]
+
+ # For Bayesian inversion
+ Model.MeasurementFile = 'MeasuredData.csv'
+
+ # For Checking with the MonteCarlo refrence
+ Model.MCReferenceFile = 'MCrefs_MeanStd.csv'
+
+ #=====================================================
+ #========= PROBABILISTIC INPUT MODEL ==============
+ #=====================================================
+ # Define the uncertain parameters with their mean and
+ # standard deviation
+ Inputs = Input()
+
+ Inputs.addMarginals()
+ Inputs.Marginals[0].Name = 'Beam width'
+ Inputs.Marginals[0].DistType = 'lognorm'
+ Inputs.Marginals[0].Moments = [0.15, 0.0075]
+
+ Inputs.addMarginals()
+ Inputs.Marginals[1].Name = 'Beam height'
+ Inputs.Marginals[1].DistType = 'lognorm'
+ Inputs.Marginals[1].Moments = [0.3, 0.015]
+
+ Inputs.addMarginals()
+ Inputs.Marginals[2].Name = 'Youngs modulus'
+ Inputs.Marginals[2].DistType = 'lognorm'
+ Inputs.Marginals[2].Moments = [30000e+6, 4500e+6]
+
+ Inputs.addMarginals()
+ Inputs.Marginals[3].Name = 'Uniform load'
+ Inputs.Marginals[3].DistType = 'lognorm'
+ Inputs.Marginals[3].Moments = [10000, 2000]
+
+ #=====================================================
+ #====== POLYNOMIAL CHAOS EXPANSION METAMODELS ======
+ #=====================================================
+ MetaModelOpts = aPCE(Inputs)
+
+ # Specify the max degree to be compared by the adaptive algorithm:
+ # The degree with the lowest Leave-One-Out cross-validation (LOO)
+ # error (or the highest score=1-LOO)estimator is chosen as the final
+ # metamodel.
+ MetaModelOpts.MaxPceDegree = 5
+
+ # Select the sparse least-square minimization method for
+ # the PCE coefficients calculation:
+ # 1)AaPCE: Adaptive aPCE 2)BRR: Bayesian Ridge Regression
+ # 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression (Not Working)
+
+ MetaModelOpts.RegMethod = 'BRR'
+
+ # Print summary of the regression results
+ #MetaModelOpts.DisplayFlag = True
+
+ # ------ Experimental Design --------
+ # Generate an experimental design of size NrExpDesign based on a latin
+ # hypercube sampling of the input model or user-defined values of X and/or Y:
+ MetaModelOpts.addExpDesign()
+ MetaModelOpts.ExpDesign.MCSize = 100000
+
+ MetaModelOpts.ExpDesign.NrSamples = 10
+ MetaModelOpts.ExpDesign.SamplingMethod = 'LHS' # 1)MC 2)LHS 3)PCM 4)LSCM 5)user
+ MetaModelOpts.ExpDesign.Method = 'sequential' # 1) normal 2) sequential
+ #MetaModelOpts.ExpDesign.X = np.load('CollocationPoints.npy')
+
+ # Sequential experimental design (needed only for sequential ExpDesign)
+ MetaModelOpts.ExpDesign.MaxNSamples = 60 #150
+ MetaModelOpts.ExpDesign.NrofNewSample = 1
+ MetaModelOpts.ExpDesign.NrofCandGroups = 2
+ MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-3
+
+ # 1)'dual annealing' 2)'minimization' 3)'alphabetic' 4)'BayesOptDesign'
+ MetaModelOpts.ExpDesign.SeqOptimMethod = 'alphabetic'
+ MetaModelOpts.ExpDesign.ExplorationMethod = 'Voronoi' #1)'Voronoi' 2)'MC'
+
+ MetaModelOpts.ExpDesign.MaxFunItr = 100
+
+ MetaModelOpts.ExpDesign.NCandidate = 2000
+
+ # 'dual annealing' 'minimization' 'BayesOptDesign'
+ # 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
+ # 3)APP (A-Posterior-percision)
+ #MetaModelOpts.ExpDesign.UtilityFunction = 'DKL'
+
+ # Optimality criteria (alphabetic search)
+ # 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
+ # 3)K-Opt (K-Optimality)
+ MetaModelOpts.ExpDesign.UtilityFunction = 'D-Opt'
+
+ # >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
+ # Adaptive sparse arbitrary polynomial chaos expansion
+ PCEModel = MetaModelOpts.create_PCE(Model)
+
+
+ #=====================================================
+ #========= POST PROCESSING OF METAMODELS ===========
+ #=====================================================
+ PostPCE = PostProcessing(PCEModel)
+
+ # Compute the moments
+ PostPCE.PCEMoments(PCEModel)
+
+ # Comparison with Monte-Carlo reference
+ # Plot Mean & Std for all Outputs
+ PostPCE.MomentLineplot(PCE_Means = PostPCE.PCEMeans,
+ PCE_Stds = PostPCE.PCEStd,
+ x_label = 'x [m]', SaveFig = True)
+
+
+ PostPCE.NrofSamples = 3
+ PostPCE.plotFlag = True
+
+ PostPCE.ValidMetamodel()
+
+
+ #=====================================================
+ #============== Save class objects =================
+ #=====================================================
+ with open('Beam_Results.pkl', 'wb') as output:
+ pickle.dump(PCEModel, output, pickle.HIGHEST_PROTOCOL)
+
+ pickle.dump(PostPCE, output, pickle.HIGHEST_PROTOCOL)
+
+
+# del PCEModel
+# del PostPCE
+
+ # Load the objects
+# with open('CO2Benchmark_Results.pkl', 'rb') as input:
+# PCEModel = pickle.load(input)
+# PostPCE = pickle.load(input)
+
diff --git a/BayesValidRox/tests/BeamTest/surrogateBeam/SSBeam_Deflection.inp b/BayesValidRox/tests/BeamTest/surrogateBeam/SSBeam_Deflection.inp
new file mode 100644
index 0000000000000000000000000000000000000000..f63b22168fe0df7d62c928cfa8dd95d56c699e4f
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/surrogateBeam/SSBeam_Deflection.inp
@@ -0,0 +1,6 @@
+% Input file for the simply supported beam model
+0.15 % b in m
+0.3 % h in m
+5 % L in m
+30000e6 % E in Pa
+10000 % p in N/m
\ No newline at end of file
diff --git a/BayesValidRox/tests/BeamTest/surrogateBeam/SSBeam_Deflection.tpl.inp b/BayesValidRox/tests/BeamTest/surrogateBeam/SSBeam_Deflection.tpl.inp
new file mode 100644
index 0000000000000000000000000000000000000000..3ef1713b66a8f2ca8efddd1992faf6973105c601
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/surrogateBeam/SSBeam_Deflection.tpl.inp
@@ -0,0 +1,6 @@
+% Input file for the simply supported beam model
+<X1> % b in m
+<X2> % h in m
+5 % L in m
+<X3> % E in Pa
+<X4> % p in N/m
diff --git a/BayesValidRox/tests/BeamTest/surrogateBeam/myBeam9points b/BayesValidRox/tests/BeamTest/surrogateBeam/myBeam9points
new file mode 100755
index 0000000000000000000000000000000000000000..aebc0776e5b0f5ecfe838685197a3ccc9590eb3f
Binary files /dev/null and b/BayesValidRox/tests/BeamTest/surrogateBeam/myBeam9points differ
diff --git a/BayesValidRox/tests/BeamTest/surrogateBeam/read_Beam_Deflection.py b/BayesValidRox/tests/BeamTest/surrogateBeam/read_Beam_Deflection.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb6e09e0c6e74a9e3b0423a19dce328d1f40242f
--- /dev/null
+++ b/BayesValidRox/tests/BeamTest/surrogateBeam/read_Beam_Deflection.py
@@ -0,0 +1,22 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jul 19 16:48:59 2019
+
+@author: farid
+"""
+import pandas as pd
+import numpy as np
+
+def read_Beam_Deflection(FileNames):
+
+ df_Mean = pd.read_csv(FileNames[0], header = None, delimiter = ' ')
+
+ x_values = np.arange(0, 5.6, 5./9)#[1:]
+ Deflection= df_Mean[df_Mean.columns[0]]
+
+ Deflection_zero= np.zeros((1))
+ Deflection_mid = np.append(Deflection_zero, Deflection)
+ Deflection_Final = np.append(Deflection_mid, Deflection_zero)
+
+ return np.vstack((x_values, Deflection_Final))
\ No newline at end of file