bradlipovsky

#
# First, Convert the ground track to imagery coordinate system
#

from pyproj import Transformer
transformer = Transformer.from_crs("EPSG:4326", "EPSG:32617")

#
# Make a plot for the imagery
#

bradlipovsky

import subprocess
import s3fs
import h5py
import numpy as np

# Get Credentials

# See https://nsidc.org/support/how/how-do-i-programmatically-request-data-services#mac
inquiry = subprocess.run(['curl', "-b", "/home/ec2-user/.urs_cookies", \
                        "-c", "/home/ec2-user/.urs_cookies", \

bradlipovsky

# First, check to see if you have a .ssh directory.  
ls ~/.ssh
# If this doesn't return an error: continue to create a new SSH key. 
# If this does return an error, make a .ssh directory:
mkdir ~/.ssh

# Create a new SSH key
ssh-keygen -t ed25519 -C "your_email@example.com"
# Default file location and name are fine. I prefer to not use a passcode, but that's up to you.
# To do both of those things, hit enter three times!

bradlipovsky

import pickle
import pandas as pd
import numpy as np
import os

from matplotlib import cm
import matplotlib.pyplot as plt

shelf_name = 'ronne'
datapath = '/data/fast1/arc/'

bradlipovsky

dx_apparent = 14 # Known source-receiver distance
vtrue = 6e3      # Known p-wave speed
t0 = 380/48e3    # Measured travel time
dt = (5/48e3)    # Measured travel time change

dx_true = t0 * vtrue # Actual propagation path >>  source-receiver distance
print('True propagation path length: %f'%dx_true)

v1 = dx_true/t0
v2 = dx_true/(t0-dt)

bradlipovsky

'''
Code to calculate the dispersion relation of Flexural Gravity Waves
'''
import numpy as np
import matplotlib.pyplot as plt

g=9.81      # Gravity, m/s**2
hw=100      # Water depth, m
hi = 300    # Ice thickness, m
rhow=1000   # Water density, kg/m**3

bradlipovsky

import matplotlib.pyplot as plt
from firedrake import *
length = 1
width = 0.2
mesh = RectangleMesh(40, 20, length, width)

V = VectorFunctionSpace(mesh, "Lagrange", 1)
bc = DirichletBC(V, Constant([0, 0]), 1)

rho = Constant(0.01)

bradlipovsky

import numpy as np
import matplotlib.pyplot as plt

# Analytical solutions found in Zehnder
a = 10
x1 = np.linspace(0,2*a,401)
x2 = np.linspace(0,1.5*a,201)
X1,X2 = np.meshgrid(x1,x2)
Z = X1 + 1j*X2

bradlipovsky

import numpy as np
import matplotlib.pyplot as plt

cs=1500
g=9.8
h=100
kappa = np.linspace(-0.08,0.08,100)

fig,ax=plt.subplots(1,4,figsize=(15,4))
plt.subplot(141)

bradlipovsky

# Motivated by this example:
# https://jorgensd.github.io/dolfinx-tutorial/chapter1/fundamentals.html 
# ... but hopefully even simpler!

from mpi4py import MPI
from dolfinx import mesh
from dolfinx.fem import FunctionSpace
from dolfinx import fem
import numpy as np
import ufl