'''
if len(sys.argv) < 2:
print help
else:
plotnum[int(sys.argv[1])] = True
def poisson(R):
''' Function to go from Vp/Vs -> Poisson's ratio '''
return ((R**2 - 2) / (2 * (R**2 - 1)))
#######################################################################
# F0
if plotnum[0]:
arg1 = Args()
arg1.addQuery("hk::H", "lt", "45")
arg2 = Args()
arg2.addQuery("hk::H", "lt", "40")
arg3 = Args()
arg3.addQuery("hk::H", "lt", "35")
arg4 = Args()
fstns = os.environ['HOME'] + '/thesis/stations.json'
a = Params(fstns, arg1, ["mb::H"])
b = Params(fstns, arg2, ["mb::H"])
c = Params(fstns, arg2, ["hk::H"])
d = Params(fstns, arg3, ["hk::H"])
e = Params(fstns, arg3, ["wm::H"])
f = Params(fstns, arg4, ["wm::H"])
def rmsDifference(x, y):
assert (len(x) == len(y))
n = len(x)
md = 0
for i in range(n):
md += (x[i] - y[i])**2
return np.sqrt(md / (n))
for i in range(1, 2):
pro = os.environ['HOME'] + data[2 * i + 1]
pub = os.environ['HOME'] + data[2 * i]
arg = Args().addQuery("stdR", "lt", 0.06)
p0 = Params(pro, ["H", "R", "stdR", "stdH"], arg)
p2 = Params(pub, ["H", "R", "stdR", "stdH"], arg)
p1 = Params(stnfile, ["hk::H", "hk::R", "hk::stdR", "hk::stdH"])
p0.sync(p2)
stns = p0.stns
R1 = p0.R
R2 = p2.R
H1 = p0.H
H2 = p2.H
plotnum = [False for i in range(15)]
if len(sys.argv) < 2:
print help
exit()
else:
plotnum[int(sys.argv[1])] = True
####################################################
## Figure Properties #######
width = 12
height = 5
legsize = height + 3
###########################
## Prep Data
arg = Args()
arg.addQuery("status", "eq", "processed-ok")
# Load station params
d = Params(stnfile, ["hk::H","hk::R"], arg)
arg = Args()
m = Params(moonfile, ["H","Vp"])
m.filter(arg.addQuery("geoprov", "not in", "oceanic"))
m.filter(arg.addQuery("geoprov", "not in", "Shelf"))
#######################################################################
# F0
# CANADA
if plotnum[0]:
## Kanamori Poisson Histogram
# Set figure
###########################################################################
# IMPORTS
###########################################################################
import os, json
import numpy as np
import matplotlib.pyplot as plt
from plotTools import Args, Params
stnfile = os.environ['HOME'] + '/thesis/data/stations.json'
#stdict = json.loads( dbfile.read() )
lim = 0.055
# Load station params
d = Params(stnfile, ["hk::H", "hk::R", "hk::stdR", "hk::c0R", "hk::c1R"])
d.filter(Args().addQuery("status", "in", "processed"))
ix1 = np.abs(d.hk_c0R - d.hk_R) < 2 * d.hk_stdR
ix2 = np.abs(d.hk_c1R - d.hk_R) < 2 * d.hk_stdR
ix3 = np.abs(d.hk_c1R - d.hk_c0R) < lim
ixfail3 = (np.logical_not(ix1) | np.logical_not(ix2)) & np.logical_not(ix3)
ixfail2 = (np.logical_not(ix1) | np.logical_not(ix2)) & ix3
ixpass3 = (ix1 & ix2) & ix3
ixpass2 = (ix1 & ix2) & np.logical_not(ix3)
msg = [
"For stations failing (1) or (2) and (3) there is a likely significant lateral heterogeneity or some processing flaw and the station may be marked as unused.",
"For stations failing (1) or (2) but passing (3) the data may be reasonable but my bootstrap error estimate is much too kind. Readjustment of error required.",
"For station passing (1) and (2) and (3), Great.",
"For stations passing (1) and (2) but failing (3) the cluster Vp/Vs difference may be above the arbitrary cut-off of +\- 0.05 but could be included provided the std Vp/Vs error is not above some other arbitrarily chosen cap."
plt.plot(M[0], M[1], 'ob', lw = lw, ms = ms, label = "normalized Vp vs H")
plt.plot(x2, y2,'--r', lw = lw, label="Primary PCA: variance = {:2.1f}%".format(var[0]*100))
plt.plot(x1, y1,'--g', lw = lw, label="Secondary PCA: variance = {:2.1f}%".format(var[1]*100))
plt.legend(loc= 2, prop={'size': leg})
plt.ylabel("normalized Vp", size = label)
plt.xlabel("normalized H", size = label)
plt.grid(True)
plt.axis("equal")
#############################################################################
# FIG 2: Vp estimates and controlled source
##############################################################################
if 0:
maxerr = 0.25#0.24#0.21
arg = Args().addQuery("fg::stdVp", "lt", str(maxerr))
f = Params(stnfile, ["fg::H","fg::Vp", "fg::stdH", "fg::stdVp", "hk::stdR"], arg)
c = Params(csfile, ["H","Vp"])
c.sync(f)
stdVp = 2 * f.fg_stdVp # 2 stdError
stdH = 2 * f.fg_stdH
t = np.arange(len(f.fg_Vp))
corr = pearsonr(f.fg_Vp, c.Vp)
print "Active Source vs FG Vp with {} stations: correlation = {}".format(len(f.stns), corr[0])
fig = plt.figure()
ax1 = fig.add_subplot(111)