bradlipovsky

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt

# Function that returns dz/dt = [dx/dt, dv/dt]
def model(z, t):
    x = z[0] # "total displacement" -- quotes because this quantity doesn't 
             # really make physical sense. These should be strains, but there 
             # is no sense of strain in a 0-D model.
    v = z[1] # velocity

bradlipovsky

import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt

# Function that returns dz/dt = [dx/dt, dv/dt]
def model(z, t):
    x = z[0]
    v = z[1]
    xp = z[2]
    

bradlipovsky

import h5py
import numpy as np
from scipy.fft import fft2, ifft2, fftfreq, fftshift, ifftshift

datafile=h5py.File('/1-fnp/petasaur/p-wd15/rainier-10-14-2023-drive1/rainier/decimator_2023-08-23_19.28.00_UTC.h5')
data=np.array(datafile['/Acquisition/Raw[0]/RawData'])
datafile.close()

import matplotlib.pyplot as plt
plt.imshow(data,aspect='auto',vmin=-1,vmax=1)

bradlipovsky

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from sklearn.model_selection import train_test_split

# Generate example data (you should replace this with your data)
# Here, we create three input time series and one target time series.
# You should load your own data accordingly.
n_samples = 1000

bradlipovsky

import numpy as np
import matplotlib.pyplot as plt
import datetime
import geopy.distance

T0 = datetime.datetime(2023,9,16,12,31,0).timestamp() # test origin time
v = 1 # surface wave speed

def G(lon,lat,m,vv):
    #m = (x0,y0,t0) is the source parameter vector

bradlipovsky

import numpy as np
import matplotlib.pyplot as plt
from scipy.fft import fftshift, fft2, fftfreq

g = 9.8
H=100
dt = 1

plt.subplots(2,2,figsize=(16,9))
dx_list = (10,20,40)

bradlipovsky

import numpy as np

pp = np.logspace(-5,-1,100)
zz = np.linspace(0,100,101)
q,z=np.meshgrid(pp,zz)

g = 9.8
H=1000
p = np.sqrt(g*q*np.tanh(g*H))

bradlipovsky

import numpy as np

n = 10
dt = 1
alpha = 1e-6 * 3.15e7
dx = 10
c = dt * alpha / (dx)**2
z = np.arange(0,n*dx,dx)
Q = 0 # change this
k = 1 # change this too

bradlipovsky

import numpy as np
import matplotlib.pyplot as plt

Lmax = 10.5e3

mustar = 3e9
tmax = 300

D_list = np.logspace(-3,1,100)
H_list = np.logspace(-1,np.log10(400),50)

bradlipovsky

import numpy as np

n_total = 20
mu = 100
sigma = 10
thickness_true = np.random.default_rng().normal(mu, sigma, n_total)
thickness_estimates = np.random.default_rng().normal(mu, sigma, n_total)
# thickness_uncertainty_estimates = np.random.default_rng().normal(2*sigma, 3*sigma, n_total)
thickness_uncertainty_estimates = np.abs(thickness_true - thickness_estimates)