bradlipovsky

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import ticker, cm
from matplotlib.colors import LogNorm
import matplotlib 
matplotlib.rc('xtick', labelsize=20) 
matplotlib.rc('ytick', labelsize=20) 

m,h = np.meshgrid(np.logspace(-3,1,100),np.logspace(1,3.5,100))
T = h/m

bradlipovsky

import numpy as np
import matplotlib.pyplot as plt

rho = 910
g = 9.8
h = 100
alpha = 5 * np.pi/180
y = np.linspace(0,100)
dy = np.mean(np.diff(y))

bradlipovsky

import numpy as np
import matplotlib.pyplot as plt

wavelength = 100 #m
c = 4000 # m/s
z = np.linspace(-0.5*wavelength,0,100) #m

k = 2*np.pi/wavelength

up   = np.cos(k*z) # phasor; assume exp(i omega t) dependence

bradlipovsky

'''
Demo SVD for "PCA" analysis -- using DAS data.
See https://stats.stackexchange.com/a/134283 for PCA v SVD distinction
'''
    
import h5py
import matplotlib.pyplot as plt
from scipy.sparse.linalg import svds
import numpy as np

bradlipovsky

'''
 Pull the data for Figure 2 of Olsen and Nettles (2019),

 Constraints on Terminus Dynamics at Greenland Glaciers From Small Glacial Earthquakes

 https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JF005054
 
'''

from obspy import UTCDateTime

bradlipovsky

# The following are all shell commands that you can run from a terminal on sermeq

# 1. Create a new conda environment with cartopy and ipykernel:
conda create -n comcat --channel conda-forge python=3.9

# 2. Activate that environment
conda activate comcat

# 3. Install ipykernel FOR THIS ENVIRONMENT
python3 -m ipykernel install --user

bradlipovsky

import matplotlib.pyplot as plt
import numpy as np
import cutde.fullspace as FS
from scipy.spatial.transform import Rotation as R

# Update the following four lines with well coordinates
obsx = np.linspace(-2,2,100)
obsy = np.linspace(-2,2,100)
obsz = np.linspace(-2,2,100)
pts = np.array([obsx, obsy, 0 * obsz]).reshape((3, -1)).T.copy()

bradlipovsky

import numpy as np
import matplotlib.pyplot as plt
data = [[ 66386, 174296,  75131, 577908,  32015],
        [ 58230, 381139,  78045,  99308, 160454],
        [ 89135,  80552, 152558, 497981, 603535],
        [ 78415,  81858, 150656, 193263,  69638],
        [139361, 331509, 343164, 781380,  52269]]

columns = ('Region 1', 'Region 2', 'Region 3', 'Region 4', 'Region 5')
rows = ('M','F','E','X','X')

bradlipovsky

import h5py
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import butter, filtfilt

path = "/data/data2/south-data-ejm/hdd/South-C1-LR-95km-P1kHz-GL50m-SP2m-FS200Hz_2021-11-01T16_09_15-0700/"
file = "South-C1-LR-95km-P1kHz-GL50m-SP2m-FS200Hz_2021-11-01T231114Z.h5"
f = h5py.File(path + file, 'r')
data = f['Acquisition']['Raw[0]']['RawData'][:, :].astype('int64')
f.close()

bradlipovsky

import matplotlib.pyplot as plt

plt.subplots()

y = (158,141,90)
x = ("Farinotti","Milan","Edasi")
yr = (41,40,38)
plt.errorbar(x,y,yerr=yr,linestyle='None',marker='o')

plt.grid()