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Cleaned up Generate
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JoeBell committed Mar 5, 2025
1 parent 22876e9 commit 13a589f
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200 changes: 102 additions & 98 deletions ShipGen/Generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,59 +5,126 @@
import torch
from .tools import Guided_Cond_DDPM_Tools as GC_DDPM
from .tools.HullParameterization import Hull_Parameterization as HP
from .__init__ import data_path, trained_models_path
from stl import mesh
from stl import Mesh

data_path = os.path.normpath(os.path.join(__file__,'../data'))
trained_models_path = os.path.normpath(os.path.join(__file__,'../TrainedModels'))

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

np.set_printoptions(suppress=True) # don't use scientific notation
class ShipCharacteristics:
LOA:float
Beam:float
Draft:float
Depth:float
Volume:float
U:float
def __init__(self,
LOA:float,
Beam:float,
Draft:float,
Depth:float,
Volume:float,
U:float):
self.LOA = LOA
self.Beam = Beam
self.Draft = Draft
self.Depth = Depth
self.Volume = Volume
self.U = U
def as_array(self) -> np.ndarray:
return np.array([self.LOA, self.Beam, self.Draft, self.Depth, self.Volume, self.U])

def generate_hulls(find_best:int,
out_of:int|None = None,
characteristics:ShipCharacteristics|None = None,
out_of_ratio:int = 10) -> list[Mesh]:
if characteristics is None:
Ship = np.array([333, 42.624, 11.28, 29.064, 97561,16])
else: Ship = characteristics.as_array()
if out_of is None: num_samples = find_best * out_of_ratio
else: num_samples = out_of

LOA:float = Ship[0] #in meters
BoL:float = Ship[1]/LOA #beam to length ratio
ToD:float = Ship[2]/Ship[3] #Draft to depth ratio
DoL:float = Ship[3]/LOA #Depth to length ratio
Vol:float = np.log10(Ship[4]/LOA**3) # to normalize Volume by LOA**3

# Load in the Data:
U:float = Ship[5] # 12.86 #m/s = 25 knots

#Step 1: Load in the data
DesVec = np.load(f'{data_path}/DesVec_82k.npy', allow_pickle=True)
dim_d = np.array([[ToD, U, LOA]]) #Drag_conditioning is [ToD, U(m/s), LOA (m)]

DesVec_neg = np.load(f'{data_path}/Negative_DesVec_82k.npy', allow_pickle=True)
drag_cond = np.repeat(dim_d, num_samples, axis=0) #reapeat
dim_g = np.array([[ToD, BoL, DoL, Vol]])

# Now lets clean up X
geom_cond = np.repeat(dim_g, num_samples, axis=0) #reapeat

idx_BBFactors = [33,34,35,36,37]
idx_BB = 31
x_samples, unnorm = T.gen_vol_drag_guided_samples(geom_cond, drag_cond)

idx_SBFactors = [38,39,40,41,42,43,44]
idx_SB = 32
Rt_guidance = T.Predict_Drag(unnorm, drag_cond)

for i in range(0,len(DesVec)):
DesVec[i,idx_BBFactors] = DesVec[i,idx_BB] * DesVec[i,idx_BBFactors]
DesVec[i,idx_SBFactors] = DesVec[i,idx_SB] * DesVec[i,idx_SBFactors]
valid_idx = check_feasibility(x_samples)

idxs = np.argsort(Rt_guidance[valid_idx].flatten())
meshs:list[Mesh] = []
for i in tqdm(range(0,find_best)):
Hull = HP(x_samples[valid_idx[idxs[i]]])
#make the .stl file of the hull:
try:
meshs.append(Hull.gen_stl(NUM_WL=47, PointsPerWL=151, bit_AddTransom = 1, bit_AddDeckLid = 1, bit_RefineBowAndStern = 1,namepath = None))
except:
print('Error at hull {}'.format(valid_idx[idxs[i]]))
return meshs

Y = np.load(f'{data_path}/GeometricMeasures.npy', allow_pickle=True)

LenRatios = np.load(f'{data_path}/Length_Ratios.npy', allow_pickle=True)
def load_data():
# Load in the Data:

X_LIMITS = np.load(f'{data_path}/X_LIMITS.npy')
#Step 1: Load in the data
DesVec = np.load(f'{data_path}/DesVec_82k.npy', allow_pickle=True)

X_lower_lim = [X_LIMITS[:,0].tolist()]
X_upper_lim = [X_LIMITS[:,1].tolist()]
DesVec_neg = np.load(f'{data_path}/Negative_DesVec_82k.npy', allow_pickle=True)

#Set up Conditioning Vectors:
num_WL_Steps = 101
# Now lets clean up X

old_err = np.seterr(all='ignore')
VolVec = np.log10(Y[:,1*num_WL_Steps:2*num_WL_Steps])
idx = np.where(np.isnan(VolVec))
VolVec[idx] = -6.0 #fix nan to dummy value
np.seterr(**old_err)
idx_BBFactors = [33,34,35,36,37]
idx_BB = 31

idx_SBFactors = [38,39,40,41,42,43,44]
idx_SB = 32

DdVec = DesVec[:,4]
BOAVec = np.amax(LenRatios[:,1:3], axis=1)
for i in range(0,len(DesVec)):
DesVec[i,idx_BBFactors] = DesVec[i,idx_BB] * DesVec[i,idx_BBFactors]
DesVec[i,idx_SBFactors] = DesVec[i,idx_SB] * DesVec[i,idx_SBFactors]

def create_network() -> GC_DDPM.GuidedDiffusionEnv:
# Set up the file for architecting the network, diffusion parameters, and training

Y = np.load(f'{data_path}/GeometricMeasures.npy', allow_pickle=True)

LenRatios = np.load(f'{data_path}/Length_Ratios.npy', allow_pickle=True)

X_LIMITS = np.load(f'{data_path}/X_LIMITS.npy')

#Set up Conditioning Vectors:
num_WL_Steps = 101

old_err = np.seterr(all='ignore')
VolVec = np.log10(Y[:,1*num_WL_Steps:2*num_WL_Steps])
idx = np.where(np.isnan(VolVec))
VolVec[idx] = -6.0 #fix nan to dummy value
np.seterr(**old_err)


BOAVec = np.amax(LenRatios[:,1:3], axis=1)
return (
(idx_BB, idx_SB, idx_BBFactors, idx_SBFactors),
(DesVec, X_LIMITS, DesVec_neg, VolVec, BOAVec)
)

def create_network(DesVec, X_LIMITS, DesVec_neg, VolVec, BOAVec) -> GC_DDPM.GuidedDiffusionEnv:
# Set up the file for architecting the network, diffusion parameters, and training
X_lower_lim = [X_LIMITS[:,0].tolist()]
X_upper_lim = [X_LIMITS[:,1].tolist()]
DDPM_Dict = {
'xdim' : len(DesVec[0])-1, # Dimension of parametric design vector
'datalength': len(DesVec), # number of samples
Expand Down Expand Up @@ -147,7 +214,7 @@ def create_network() -> GC_DDPM.GuidedDiffusionEnv:
X_neg= DesVec_neg,
VolVec = VolVec,
BOAVec = BOAVec,
DdVec = DdVec)
DdVec = DesVec[:,4])

diffusion_path = f'{trained_models_path}/CShipGen_diffusion.pth'
T.load_trained_diffusion_model(diffusion_path)
Expand All @@ -161,34 +228,12 @@ def create_network() -> GC_DDPM.GuidedDiffusionEnv:

T.load_trained_Drag_regression_models(PATHS)
return T
T = create_network()

class ShipCharacteristics:
LOA:float
Beam:float
Draft:float
Depth:float
Volume:float
U:float
def __init__(self,
LOA:float,
Beam:float,
Draft:float,
Depth:float,
Volume:float,
U:float):
self.LOA = LOA
self.Beam = Beam
self.Draft = Draft
self.Depth = Depth
self.Volume = Volume
self.U = U
def as_array(self) -> np.ndarray:
return np.array([self.LOA, self.Beam, self.Draft, self.Depth, self.Volume, self.U])
feasibility_data, network_data = load_data()
T = create_network(*network_data)

def check_feasibility(x_samples:np.ndarray):
idx_BB, idx_SB, idx_BBFactors, idx_SBFactors = feasibility_data
for i in range(0,len(x_samples)):

x_samples[i,idx_BB] = (x_samples[i,idx_BB] + 0.5) // 1 #int rounds to 1 or 0
x_samples[i,idx_SB] = (x_samples[i,idx_SB] + 0.5) // 1 #int rounds to 1 or 0

Expand All @@ -205,44 +250,3 @@ def check_feasibility(x_samples:np.ndarray):
valid_idx.append(i)
return valid_idx

def generate_hulls(find_best:int,
out_of:int|None = None,
characteristics:ShipCharacteristics|None = None,
out_of_ratio:int = 10) -> list[mesh.Mesh]:
if characteristics is None:
Ship = np.array([333, 42.624, 11.28, 29.064, 97561,16])
else: Ship = characteristics.as_array()
if out_of is None: num_samples = find_best * out_of_ratio
else: num_samples = out_of

LOA:float = Ship[0] #in meters
BoL:float = Ship[1]/LOA #beam to length ratio
ToD:float = Ship[2]/Ship[3] #Draft to depth ratio
DoL:float = Ship[3]/LOA #Depth to length ratio
Vol:float = np.log10(Ship[4]/LOA**3) # to normalize Volume by LOA**3

U:float = Ship[5] # 12.86 #m/s = 25 knots

dim_d = np.array([[ToD, U, LOA]]) #Drag_conditioning is [ToD, U(m/s), LOA (m)]

drag_cond = np.repeat(dim_d, num_samples, axis=0) #reapeat
dim_g = np.array([[ToD, BoL, DoL, Vol]])

geom_cond = np.repeat(dim_g, num_samples, axis=0) #reapeat

x_samples, unnorm = T.gen_vol_drag_guided_samples(geom_cond, drag_cond)

Rt_guidance = T.Predict_Drag(unnorm, drag_cond)

valid_idx = check_feasibility(x_samples)

idxs = np.argsort(Rt_guidance[valid_idx].flatten())
meshs:list[mesh.Mesh] = []
for i in tqdm(range(0,find_best)):
Hull = HP(x_samples[valid_idx[idxs[i]]])
#make the .stl file of the hull:
try:
meshs.append(Hull.gen_stl(NUM_WL=47, PointsPerWL=151, bit_AddTransom = 1, bit_AddDeckLid = 1, bit_RefineBowAndStern = 1,namepath = None))
except:
print('Error at hull {}'.format(valid_idx[idxs[i]]))
return meshs
4 changes: 1 addition & 3 deletions ShipGen/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1 @@
import os
data_path = os.path.normpath(os.path.join(__file__,'../data'))
trained_models_path = os.path.normpath(os.path.join(__file__,'../TrainedModels'))
from .Generate import generate_hulls, ShipCharacteristics

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