Create ICs on Demand¶

In [1]:

import numpy as np
import matplotlib.pylab as plt

from models.multiresolution_flow_3d import *
from models.trainer import *

%matplotlib inline

Setup the model¶

In [2]:

nlevel=5

shape = (2**nlevel,2**nlevel,2**nlevel)

model = multi_scale_model(nlevel=nlevel)

tm = trainer(model)

Trainer model: tm
load initial state

In [6]:

for i in np.arange(1000):
    x_train = np.random.normal(0,0.01,shape)+3.1415
    tm.train_single(x_train, silent=True)
tm.transfer(silent=False)

Train model...
Training done

Out[6]:

In [7]:

x = model.generate()
print(np.mean(x[nlevel].flatten()),np.std(x[nlevel].flatten()))


plt.hist(x[nlevel].flatten(),bins=100)
plt.show()

0.006170461447273722 1.006313544079366

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