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Fix python formatting #4

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131 changes: 66 additions & 65 deletions Metrics/Get_Alcubierre.py
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Original file line number Diff line number Diff line change
@@ -1,65 +1,66 @@
# Filename: Get_Alcubierre.py
import numpy as np
import tensorflow as tf

from setMinkowskiThreePlusOne import setMinkowskiThreePlusOne
from threePlusOneBuilder import threePlusOneBuilder

# Define shape function for Alcubierre metric
def shapeFunction_Alcubierre(r, R, sigma):
return tf.exp(-((r - R) / sigma) ** 2)

# Define function to build the Alcubierre metric
def metricGet_Alcubierre(gridSize, worldCenter, v, R, sigma, gridScale):
# Handle default input arguments
if len(gridScale) < 4:
gridScale += [1] * (4 - len(gridScale))

# Assign parameters to metric struct
metric = {}
metric['params'] = {
'gridSize': gridSize,
'worldCenter': worldCenter,
'velocity': v,
'R': R,
'sigma': sigma
}

# Assign quantities to metric struct
metric['type'] = "metric"
metric['name'] = 'Alcubierre'
metric['scaling'] = gridScale
metric['coords'] = "cartesian"
metric['index'] = "covariant"
metric['date'] = tf.strings.format("{}", tf.date)

# Declare a Minkowski space
alpha, beta, gamma = setMinkowskiThreePlusOne(gridSize)

# Add the Alcubierre modification
for i in range(gridSize[1]):
for j in range(gridSize[2]):
for k in range(gridSize[3]):
# Find grid center x, y, z
x = (i + 1) * gridScale[1] - worldCenter[1]
y = (j + 1) * gridScale[2] - worldCenter[2]
z = (k + 1) * gridScale[3] - worldCenter[3]

for t in range(gridSize[0]):
# Determine the x offset of the center of the bubble, centered in time
xs = (t + 1) * gridScale[0] - worldCenter[0] * v * tf.constant(299792458.0)

# Find the radius from the center of the bubble
r = tf.sqrt(tf.pow(x - xs, 2) + tf.pow(y, 2) + tf.pow(z, 2))

# Find shape function at this point in r
fs = shapeFunction_Alcubierre(r, R, sigma)

# Add alcubierre modification to shift vector along x
beta[0][t, i, j, k] = -v * fs

# Make tensor from the 3+1 functions
metric['tensor'] = threePlusOneBuilder(alpha, beta, gamma)

return metric

# Filename: Get_Alcubierre.py
import numpy as np
import tensorflow as tf

from setMinkowskiThreePlusOne import setMinkowskiThreePlusOne
from threePlusOneBuilder import threePlusOneBuilder

# Define shape function for Alcubierre metric
def shapeFunction_Alcubierre(r, R, sigma):
return tf.exp(-((r - R) / sigma) ** 2)

# Define function to build the Alcubierre metric
def metricGet_Alcubierre(gridSize, worldCenter, v, R, sigma, gridScale):
# Handle default input arguments
if len(gridScale) < 4:
gridScale += [1] * (4 - len(gridScale))

# Assign parameters to metric struct
metric = {}
metric['params'] = {
'gridSize': gridSize,
'worldCenter': worldCenter,
'velocity': v,
'R': R,
'sigma': sigma
}

# Assign quantities to metric struct
metric['type'] = "metric"
metric['name'] = 'Alcubierre'
metric['scaling'] = gridScale
metric['coords'] = "cartesian"
metric['index'] = "covariant"
metric['date'] = tf.strings.format("{}", tf.date)

# Declare a Minkowski space
alpha, beta, gamma = setMinkowskiThreePlusOne(gridSize)

# Add the Alcubierre modification
for i in range(gridSize[1]):
for j in range(gridSize[2]):
for k in range(gridSize[3]):
# Find grid center x, y, z
x = (i + 1) * gridScale[1] - worldCenter[1]
y = (j + 1) * gridScale[2] - worldCenter[2]
z = (k + 1) * gridScale[3] - worldCenter[3]

for t in range(gridSize[0]):
# Determine the x offset of the center of the bubble, centered in time
xs = (t + 1) * gridScale[0] - worldCenter[0] * v * tf.constant(299792458.0)

# Find the radius from the center of the bubble
r = tf.sqrt(tf.pow(x - xs, 2) + tf.pow(y, 2) + tf.pow(z, 2))

# Find shape function at this point in r
fs = shapeFunction_Alcubierre(r, R, sigma)

# Add alcubierre modification to shift vector along x
beta[0][t, i, j, k] = -v * fs

# Make tensor from the 3+1 functions
metric['tensor'] = threePlusOneBuilder(alpha, beta, gamma)

return metric


193 changes: 97 additions & 96 deletions Metrics/Get_AlcubierreComoving.py
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Original file line number Diff line number Diff line change
@@ -1,96 +1,97 @@
import numpy as np
import tensorflow as tf
import logging
import traceback
from Metrics.setMinkowskiThreePlusOne import setMinkowskiThreePlusOne
from Metrics.threePlusOneBuilder import threePlusOneBuilder

logging.basicConfig(level=logging.INFO, filename='singularities.log', filemode='w')

tf.compat.v1.disable_eager_execution()

# Define shape function for Alcubierre metric
def shapeFunction_Alcubierre(r, R, sigma):
return tf.exp(-((r - R) / sigma) ** 2)

# Define function to build the Alcubierre metric in a Galilean comoving frame
def metricGet_AlcubierreComoving(gridSize, worldCenter, v, R, sigma, gridScale):
try:
# Ensure worldCenter has enough elements
if len(worldCenter) < 4:
raise ValueError("worldCenter must have at least 4 elements.")

# Initialize TensorFlow session
with tf.compat.v1.Session() as sess:
# Define placeholders for r, R, and sigma
r_placeholder = tf.compat.v1.placeholder(tf.float32, shape=(None,))
R_placeholder = tf.compat.v1.placeholder(tf.float32)
sigma_placeholder = tf.compat.v1.placeholder(tf.float32)

# Compute shape function
shape_function = shapeFunction_Alcubierre(r_placeholder, R_placeholder, sigma_placeholder)

# Initialize variables
sess.run(tf.compat.v1.global_variables_initializer())

# Collect all r_value values
r_values = []
for i in range(gridSize[1]):
for j in range(gridSize[2]):
for k in range(gridSize[3]):
x = (i + 1) * gridScale[1] - worldCenter[1]
y = (j + 1) * gridScale[2] - worldCenter[2]
z = (k + 1) * gridScale[3] - worldCenter[3]
r_value = np.sqrt(x**2 + y**2 + z**2)
r_values.append(r_value)

# Evaluate shape function for all r_values
fs_values = sess.run(shape_function, feed_dict={r_placeholder: r_values, R_placeholder: R, sigma_placeholder: sigma})

# Check if gridSize in time is 1 and raise error if not
if gridSize[0] > 1:
raise ValueError('The time grid is greater than 1, only a size of 1 can be used in comoving')

# Assign parameters to metric struct
metric = {}
metric['params'] = {
'gridSize': gridSize,
'worldCenter': worldCenter,
'velocity': v,
'R': R,
'sigma': sigma
}

# Assign quantities to metric struct
metric['type'] = "metric"
metric['name'] = 'Alcubierre Comoving'
metric['scaling'] = gridScale
metric['coords'] = "cartesian"
metric['index'] = "covariant"

# Declare a Minkowski space
alpha, beta, gamma = setMinkowskiThreePlusOne(gridSize)

# Add the Alcubierre modification
t = 0 # only one timeslice is used
idx = 0 # index for fs_values
for i in range(gridSize[1]):
for j in range(gridSize[2]):
for k in range(gridSize[3]):
fs_value = fs_values[idx]
beta[0][t, i, j, k] = v * (1 - fs_value)
idx += 1

# Make tensor from the 3+1 functions
metric_tensor = threePlusOneBuilder(alpha, beta, gamma)

if metric_tensor is None:
raise ValueError("Unable to construct metric tensor.")

metric['tensor'] = metric_tensor

return metric
except Exception as e:
logging.error(f"Error occurred while computing the metric: {e}")
logging.error(traceback.format_exc())
return None
import numpy as np
import tensorflow as tf
import logging
import traceback
from Metrics.setMinkowskiThreePlusOne import setMinkowskiThreePlusOne
from Metrics.threePlusOneBuilder import threePlusOneBuilder

logging.basicConfig(level=logging.INFO, filename='singularities.log', filemode='w')

tf.compat.v1.disable_eager_execution()

# Define shape function for Alcubierre metric
def shapeFunction_Alcubierre(r, R, sigma):
return tf.exp(-((r - R) / sigma) ** 2)

# Define function to build the Alcubierre metric in a Galilean comoving frame
def metricGet_AlcubierreComoving(gridSize, worldCenter, v, R, sigma, gridScale):
try:
# Ensure worldCenter has enough elements
if len(worldCenter) < 4:
raise ValueError("worldCenter must have at least 4 elements.")

# Initialize TensorFlow session
with tf.compat.v1.Session() as sess:
# Define placeholders for r, R, and sigma
r_placeholder = tf.compat.v1.placeholder(tf.float32, shape=(None,))
R_placeholder = tf.compat.v1.placeholder(tf.float32)
sigma_placeholder = tf.compat.v1.placeholder(tf.float32)

# Compute shape function
shape_function = shapeFunction_Alcubierre(r_placeholder, R_placeholder, sigma_placeholder)

# Initialize variables
sess.run(tf.compat.v1.global_variables_initializer())

# Collect all r_value values
r_values = []
for i in range(gridSize[1]):
for j in range(gridSize[2]):
for k in range(gridSize[3]):
x = (i + 1) * gridScale[1] - worldCenter[1]
y = (j + 1) * gridScale[2] - worldCenter[2]
z = (k + 1) * gridScale[3] - worldCenter[3]
r_value = np.sqrt(x**2 + y**2 + z**2)
r_values.append(r_value)

# Evaluate shape function for all r_values
fs_values = sess.run(shape_function, feed_dict={r_placeholder: r_values, R_placeholder: R, sigma_placeholder: sigma})

# Check if gridSize in time is 1 and raise error if not
if gridSize[0] > 1:
raise ValueError('The time grid is greater than 1, only a size of 1 can be used in comoving')

# Assign parameters to metric struct
metric = {}
metric['params'] = {
'gridSize': gridSize,
'worldCenter': worldCenter,
'velocity': v,
'R': R,
'sigma': sigma
}

# Assign quantities to metric struct
metric['type'] = "metric"
metric['name'] = 'Alcubierre Comoving'
metric['scaling'] = gridScale
metric['coords'] = "cartesian"
metric['index'] = "covariant"

# Declare a Minkowski space
alpha, beta, gamma = setMinkowskiThreePlusOne(gridSize)

# Add the Alcubierre modification
t = 0 # only one timeslice is used
idx = 0 # index for fs_values
for i in range(gridSize[1]):
for j in range(gridSize[2]):
for k in range(gridSize[3]):
fs_value = fs_values[idx]
beta[0][t, i, j, k] = v * (1 - fs_value)
idx += 1

# Make tensor from the 3+1 functions
metric_tensor = threePlusOneBuilder(alpha, beta, gamma)

if metric_tensor is None:
raise ValueError("Unable to construct metric tensor.")

metric['tensor'] = metric_tensor

return metric
except Exception as e:
logging.error(f"Error occurred while computing the metric: {e}")
logging.error(traceback.format_exc())
return None

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