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blueseis_utils.py
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blueseis_utils.py
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#! /usr/bin/env python
import sys, os
import numpy as np
import matplotlib
matplotlib.use("Qt4Agg")
import matplotlib.pyplot as plt
from matplotlib.pylab import *
from matplotlib import rc, font_manager
import ctypes
import numpy.ctypeslib as npct
from obspy.core import read, Stream, Trace, UTCDateTime
def rotate(u, v, w, M, inverse):
# rotates from UVW system to XYZ system by multiplying with the rotation matrix M
# compute the inverse of M
M_inv = np.linalg.inv(M)
# do the transformation
if inverse:
x, y, z = np.dot(M_inv, [u, v, w])
if not inverse:
x, y, z = np.dot(M, [u, v, w])
# Replace all negative zeros. These might confuse some further
# processing programs.
y = np.array(y).ravel()
y[y == -0.0] = 0
z = np.array(z).ravel()
z[z == -0.0] = 0
x = np.array(x).ravel()
x[x == -0.0] = 0
return x, y, z
def plot_deramp(fog, ramp, fog_corr, x, fog_sort, ramp_sort, _mean):
params = {'text.usetex': True,
'text.latex.preamble': [r'\usepackage{cmbright}', r'\usepackage{amsmath}']}
plt.rcParams.update(params)
sizeOfFont = 15
fontProperties = {'family':'sans-serif','sans-serif':['Helvetica'],
'weight' : 'bold', 'size' : sizeOfFont}
rc('font',**fontProperties)
x_axis = []
mean = []
for i in range(3):
x_axis.append(np.arange(len(fog[i])))
mean.append(np.ones(len(ramp_sort[i]))*_mean[i])
fig = plt.figure(figsize=(16, 16))
ax0 = []
ax1 = []
for i in range(3):
ax0.append(plt.subplot2grid((2, 3), (0, i)))
ax1.append(plt.subplot2grid((2, 3), (1, i)))
for i in range(3):
line_raw, = ax0[i].plot(x_axis[i], fog[i], color='k')
line_corr, = ax0[i].plot(x_axis[i], fog_corr[i], color='g')
line_fr, = ax1[i].plot(ramp[i], fog[i], 'o', markersize=1, color='k')
line_sort, = ax1[i].plot(ramp_sort[i], fog_sort[i], 'o', markersize=1.5, color='r')
line_fr_corr, = ax1[i].plot(ramp[i], fog_corr[i], 'o', markersize=1, color='y')
line_mod, = ax1[i].plot(ramp_sort[i], x[i], 'o', markersize=1, color='g')
line_m, = ax1[i].plot(ramp_sort[i], mean[i], color='b')
label_raw = 'raw'
label_corr = 'corrected'
label_fr = 'raw'
label_sort = 'sorted'
label_mod = 'model'
label_fr_corr = 'corrected'
label_m = 'average'
lines0 = (line_raw, line_corr)
labels0 = (label_raw, label_corr)
lines1 = (line_fr, line_sort, line_mod, line_fr_corr, line_m)
labels1 = (label_fr, label_sort, label_mod, label_fr_corr, label_m)
ax0[0].set_title('HJ1')
ax0[1].set_title('HJ2')
ax0[2].set_title('HJ3')
ax0[0].set_ylabel('rate')
ax1[0].set_ylabel('fog')
ax0[0].set_xlabel('samples')
ax0[1].set_xlabel('samples')
ax0[2].set_xlabel('samples')
ax1[0].set_xlabel('ramp')
ax1[1].set_xlabel('ramp')
ax1[2].set_xlabel('ramp')
bta0 = (0.5, 0.5, 0.5, 0.5)
bta1 = (0., 0.75, 1.0, 0.25)
ax0[2].legend(lines0, labels0, loc='best', bbox_to_anchor=bta0)
ax1[2].legend(lines1, labels1, loc='best', bbox_to_anchor=bta1, ncol=2)
plt.show()
def deramp(fog, ramp, navg, plot_flagg):
# bind in the deramp.so library
cwd = os.getcwd()
deramplib = ctypes.CDLL(cwd+'/deramp.so')
# define the argument variables
array_2d_double = npct.ndpointer(dtype=np.double, ndim=2, flags='CONTIGUOUS')
array_1d_double = npct.ndpointer(dtype=np.double, ndim=1, flags='CONTIGUOUS')
array_2d_int = npct.ndpointer(dtype=np.int64, ndim=2, flags='CONTIGUOUS')
deramplib.deramp.argtypes = [array_2d_double, array_2d_double, array_2d_double, array_1d_double, ctypes.c_int, ctypes.c_int, array_1d_double]
fog_corr = []
x = []
ramp_sort = []
fog_sort = []
mean = []
for i in range(len(fog)):
data = np.ascontiguousarray(np.transpose(np.asarray([ramp[i], fog[i]], dtype=np.double)))
# define length of moving average window
n_avg0 = navg
n_avg = navg
if len(data) < n_avg0:
n_avg = int(len(data) / 2)
data_orig = np.ascontiguousarray(data.copy(), dtype=np.double)
data_sort = np.ascontiguousarray(np.zeros((len(data), 2), dtype=np.double))
_mean = np.ascontiguousarray(0.0, dtype=np.double)
_x = np.ascontiguousarray(np.zeros(len(data)), dtype=np.double)
#######################################################
n = deramplib.deramp(data, data_orig, data_sort, _x, len(data), n_avg, _mean)
#######################################################
if plot_flagg:
x.append(_x[:n])
ramp_sort.append(np.transpose(data_sort)[1][:n])
fog_sort.append(np.transpose(data_sort)[0][:n])
mean.append(_mean)
fog_corr.append(np.transpose(data_orig)[1])
if not plot_flagg:
fog_corr.append(np.transpose(data_orig)[1])
return fog_corr, x, ramp_sort, fog_sort, mean
def deramp_median(fog, ramp, navg, plot_flagg):
# bind in the deramp.so library
cwd = os.getcwd()
deramplib = ctypes.CDLL(cwd+'/deramp_median.so')
# define the argument variables
array_2d_double = npct.ndpointer(dtype=np.double, ndim=2, flags='CONTIGUOUS')
array_1d_double = npct.ndpointer(dtype=np.double, ndim=1, flags='CONTIGUOUS')
array_2d_int = npct.ndpointer(dtype=np.int64, ndim=2, flags='CONTIGUOUS')
deramplib.deramp_median.argtypes = [array_2d_double, array_2d_double, array_2d_double, array_1d_double, ctypes.c_int, ctypes.c_int, array_1d_double]
fog_corr = []
x = []
ramp_sort = []
fog_sort = []
mean = []
for i in range(len(fog)):
data = np.ascontiguousarray(np.transpose(np.asarray([ramp[i], fog[i]], dtype=np.double)))
# define length of moving average window
n_avg0 = navg
n_avg = navg
if len(data) < n_avg0:
n_avg = int(len(data) / 2)
data_orig = np.ascontiguousarray(data.copy(), dtype=np.double)
data_sort = np.ascontiguousarray(np.empty((len(data), 2), dtype=np.double))
_mean = np.ascontiguousarray(0.0, dtype=np.double)
_x = np.ascontiguousarray(np.zeros(len(data)), dtype=np.double)
#######################################################
n = deramplib.deramp_median(data, data_orig, data_sort, _x, len(data), n_avg, _mean)
#######################################################
if plot_flagg:
x.append(_x[:n])
ramp_sort.append(np.transpose(data_sort)[1][:n])
fog_sort.append(np.transpose(data_sort)[0][:n])
mean.append(_mean)
fog_corr.append(np.transpose(data_orig)[1])
if not plot_flagg:
fog_corr.append(np.transpose(data_orig)[1])
return fog_corr, x, ramp_sort, fog_sort, mean
def deramp_mode(fog, ramp, navg, plot_flagg):
# bind in the deramp.so library
cwd = os.getcwd()
deramplib = ctypes.CDLL(cwd+'/deramp_mode.so')
# define the argument variables
array_2d_double = npct.ndpointer(dtype=np.double, ndim=2, flags='CONTIGUOUS')
array_1d_double = npct.ndpointer(dtype=np.double, ndim=1, flags='CONTIGUOUS')
array_2d_int = npct.ndpointer(dtype=np.int64, ndim=2, flags='CONTIGUOUS')
deramplib.deramp_mode.argtypes = [array_2d_double, array_2d_double, array_2d_double, array_1d_double, ctypes.c_int, ctypes.c_int, array_1d_double]
fog_corr = []
x = []
ramp_sort = []
fog_sort = []
mean = []
for i in range(len(fog)):
data = np.ascontiguousarray(np.transpose(np.asarray([ramp[i], fog[i]], dtype=np.double)))
# define length of moving average window
n_avg0 = navg
n_avg = navg
if len(data) < n_avg0:
n_avg = int(len(data) / 2)
data_orig = np.ascontiguousarray(data.copy(), dtype=np.double)
data_sort = np.ascontiguousarray(np.empty((len(data), 2), dtype=np.double))
_mean = np.ascontiguousarray(0.0, dtype=np.double)
_x = np.ascontiguousarray(np.zeros(len(data)), dtype=np.double)
#######################################################
n = deramplib.deramp_mode(data, data_orig, data_sort, _x, len(data), n_avg, _mean)
#######################################################
if plot_flagg:
x.append(_x[:n])
ramp_sort.append(np.transpose(data_sort)[1][:n])
fog_sort.append(np.transpose(data_sort)[0][:n])
mean.append(_mean)
fog_corr.append(np.transpose(data_orig)[1])
if not plot_flagg:
fog_corr.append(np.transpose(data_orig)[1])
return fog_corr, x, ramp_sort, fog_sort, mean