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# For neural networks
import keras
# For train-test splits
import sklearn.model_selection
# For random calculations
import numpy
# For help with saving and opening things
import os
# Disable eager execution because its bad
from tensorflow.python.framework.ops import disable_eager_execution
disable_eager_execution()
# Start a session for checking calculations and stuff
import tensorflow as tf
sess = tf.compat.v1.Session()
from keras import backend as K
K.set_session(sess)
# Do you want it loud?
VERBOSE = 1
# This function loads a fuckton of data
def load_data():
# Open all the files we downloaded at the beginning and take out hte good bits
curves = numpy.load('/content/data_curves.npz')['curves']
geometry = numpy.load('/content/data_geometry.npz')['geometry']
constants = numpy.load('/content/constants.npz')
S = constants['S']
N = constants['N']
D = constants['D']
F = constants['F']
G = constants['G']
# Some of the good bits need additional processining
new_curves = numpy.zeros((S*N, D * F))
for i, curveset in enumerate(curves):
new_curves[i, :] = curveset.T.flatten() / 1000000
new_geometry = numpy.zeros((S*N, G * G * G))
for i, geometryset in enumerate(geometry):
new_geometry[i, :] = geometryset.T.flatten()
# Return good bits to user
return curves, geometry, S, N, D, F, G, new_curves, new_geometry
import gradio
import pandas
class Network(object):
def __init__(self, structure, weights):
# Instantiate variables
self.curves = 0
self.new_curves = 0
self.geometry = 0
self.new_geometry = 0
self.S = 0
self.N = 0
self.D = 0
self.F = 0
self.G = 0
# Load network
with open(structure, 'r') as file:
self.network = keras.models.model_from_json(file.read())
self.network.load_weights(weights)
# Load data
self._load_data()
def _load_data(self):
self.curves, self.geometry, self.S, self.N, self.D, self.F, self.G, self.new_curves, self.new_geometry = load_data()
def analysis(self, idx=None):
print(idx)
if idx is None:
idx = numpy.random.randint(1, self.S * self.N)
else:
idx = int(idx)
# Get the input
data_input = self.new_geometry[idx:(idx+1), :]
other_data_input = data_input.reshape((self.G, self.G, self.G), order='F')
# Get the outputs
predicted_output = self.network.predict(data_input)
true_output = self.new_curves[idx].reshape((3, self.F))
predicted_output = predicted_output.reshape((3, self.F))
f = numpy.linspace(0.05, 2.0, 64)
fd = pandas.DataFrame(f).rename(columns={0: "Frequency"})
df_pred = pandas.DataFrame(predicted_output.transpose()).rename(columns={0: "Surge", 1: "Heave", 2: "Pitch"})
df_true = pandas.DataFrame(true_output.transpose()).rename(columns={0: "Surge", 1: "Heave", 2: "Pitch"})
# return idx, other_data_input, true_output, predicted_output
return pandas.concat([fd, df_pred], axis=1), pandas.concat([fd, df_true], axis=1)
def synthesis(self, idx=None):
print(idx)
if idx is None:
idx = numpy.random.randint(1, self.S * self.N)
else:
idx = int(idx)
# Get the input
data_input = self.new_curves[idx:(idx+1), :]
other_data_input = data_input.reshape((3, self.F))
# Get the outputs
predicted_output = self.network.predict(data_input)
true_output = self.new_geometry[idx].reshape((self.G, self.G, self.G), order='F')
predicted_output = predicted_output.reshape((self.G, self.G, self.G), order='F')
# return idx, other_data_input, true_output, predicted_output
return predicted_output, true_output
def get_geometry(self, idx=None):
if idx is None:
idx = numpy.random.randint(1, self.S * self.N)
else:
idx = int(idx)
idx = int(idx)
# Get the input
data_input = self.new_geometry[idx:(idx+1), :]
other_data_input = data_input.reshape((self.G, self.G, self.G), order='F')
# return idx, other_data_input, true_output, predicted_output
return other_data_input
def get_performance(self, idx=None):
if idx is None:
idx = numpy.random.randint(1, self.S * self.N)
else:
idx = int(idx)
idx = int(idx)
# Get the input
data_input = self.new_curves[idx:(idx+1), :]
other_data_input = data_input.reshape((3, self.F))
f = numpy.linspace(0.05, 2.0, 64)
fd = pandas.DataFrame(f).rename(columns={0: "Frequency"})
df_pred = pandas.DataFrame(other_data_input.transpose()).rename(columns={0: "Surge", 1: "Heave", 2: "Pitch"})
table = pandas.concat([fd, df_pred], axis=1)
# return idx, other_data_input, true_output, predicted_output
return table
def simple_analysis(index):
net = Network("/content/16forward_structure.json", "/content/16forward_weights.h5")
return net.analysis(index)
def simple_synthesis(index):
net = Network("/content/16inverse_structure.json", "/content/16inverse_weights.h5")
pred, true = net.synthesis(index)
return plotly_fig(pred), plotly_fig(true)
import plotly.graph_objects as go
import numpy as np
def performance(index):
net = Network("/content/16forward_structure.json", "/content/16forward_weights.h5")
return net.get_performance(index)
def geometry(index):
net = Network("/content/16forward_structure.json", "/content/16forward_weights.h5")
values = net.get_geometry(index)
return plotly_fig(values)
def plotly_fig(values):
X, Y, Z = np.mgrid[0:1:32j, 0:1:32j, 0:1:32j]
fig = go.Figure(data=go.Volume(
x=X.flatten(),
y=Y.flatten(),
z=Z.flatten(),
value=values.flatten(),
isomin=-0.1,
isomax=0.8,
opacity=0.1, # needs to be small to see through all surfaces
surface_count=21, # needs to be a large number for good volume rendering
))
return fig
with gradio.Blocks() as analysis_demo:
with gradio.Row():
with gradio.Column():
num = gradio.Number(42, label="data index")
btn1 = gradio.Button("Select")
with gradio.Column():
geo = gradio.Plot(label="Geometry")
with gradio.Row():
btn2 = gradio.Button("Estimate Spectrum")
with gradio.Row():
with gradio.Column():
pred = gradio.Timeseries(x="Frequency", y=['Surge', 'Heave', 'Pitch'], label="Predicted")
with gradio.Column():
true = gradio.Timeseries(x="Frequency", y=['Surge', 'Heave', 'Pitch'], label="True")
btn1.click(fn=geometry, inputs=[num], outputs=[geo])
btn2.click(fn=simple_analysis, inputs=[num], outputs=[pred, true])
with gradio.Blocks() as synthesis_demo:
with gradio.Row():
with gradio.Column():
num = gradio.Number(42, label="data index")
btn1 = gradio.Button("Select")
with gradio.Column():
perf = gradio.Timeseries(x="Frequency", y=['Surge', 'Heave', 'Pitch'], label="Performance")
with gradio.Row():
btn2 = gradio.Button("Synthesize Geometry")
with gradio.Row():
with gradio.Column():
pred = gradio.Plot(label="Predicted")
with gradio.Column():
true = gradio.Plot(label="True")
btn1.click(fn=performance, inputs=[num], outputs=[perf])
btn2.click(fn=simple_synthesis, inputs=[num], outputs=[pred, true])
all_synthesis_demos = gradio.TabbedInterface([synthesis_demo], ["Random Spectrum from Data"])
all_analysis_demos = gradio.TabbedInterface([analysis_demo], ["Random Geometry from Data"])
demo = gradio.TabbedInterface([all_analysis_demos, all_synthesis_demos], ["Analysis", "Synthesis"])
demo.launch() |