From e5e464aed25302fc34cbafb5dd396999845cf0fa Mon Sep 17 00:00:00 2001
From: farid <farid.mohammadi@iws.uni-stuttgart.de>
Date: Tue, 1 Feb 2022 09:23:26 +0100
Subject: [PATCH] [PyLink] fixed some styling issues.
---
BayesValidRox/PyLink/FuncForwardModel.py | 69 ++++++++++++------------
1 file changed, 34 insertions(+), 35 deletions(-)
diff --git a/BayesValidRox/PyLink/FuncForwardModel.py b/BayesValidRox/PyLink/FuncForwardModel.py
index 2a0aa16bf..08b54b6e6 100644
--- a/BayesValidRox/PyLink/FuncForwardModel.py
+++ b/BayesValidRox/PyLink/FuncForwardModel.py
@@ -21,10 +21,7 @@ import tqdm
from functools import reduce
import multiprocessing
import h5py
-try:
- import cPickle as pickle
-except ModuleNotFoundError:
- import pickle
+
class FuncForwardModel:
@@ -35,54 +32,57 @@ class FuncForwardModel:
self.pyFile = self.Name
self.outMin = None
self.outMax = None
- self.Observations = {}
+ 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'))
+ 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)
-
+ 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:
@@ -93,15 +93,15 @@ class FuncForwardModel:
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:
@@ -112,27 +112,27 @@ class FuncForwardModel:
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",
+ print("\nThe following parametersets have been removed:\n",
CollocationPoints[outRangeIdx])
print("\n")
print('*'*20)
-
- # Pass it to the attribute
+
+ # 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])
@@ -141,7 +141,7 @@ class FuncForwardModel:
except:
data = NewCollocationPoint
grpX.create_dataset('adaptive_'+keyString, data=data)
-
+
if len(outRangeIdx) != 0:
try:
oldCollocationPoints = np.array(file['EDX/New_'+'adaptive_'+keyString])
@@ -150,28 +150,27 @@ class FuncForwardModel:
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)
--
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