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
fig = plt.figure(figsize=(width, height))
ax = plt.subplot(111)
# Get data
#######################################################################
# 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"])
lena = len(a.mb_H)
a.sync(b)
assert len(a.reset().mb_H) == lena
assert np.equal(a.sync(b).mb_H, b.sync(a).mb_H).all()
c.sync(d.sync(e.sync(f)))
for ii in range(len(f.stns)):
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"])
lena = len(a.mb_H)
a.sync(b)
assert len(a.reset().mb_H) == lena
assert np.equal(a.sync(b).mb_H, b.sync(a).mb_H).all()
c.sync(d.sync(e.sync(f)))
M = (A - np.mean(A.T,axis=1)).T # subtract the mean (along columns)
[latent,coeff] = np.linalg.eig( np.cov(M))
p = np.size(coeff,axis=1)
idx = np.argsort(latent) # sorting the eigenvalues
idx = idx[::-1] # in ascending order
# sorting eigenvectors according to the sorted eigenvalues
coeff = coeff[:,idx]
latent = latent[idx] # sorting eigenvalues
if numpc < p or numpc >= 0:
coeff = coeff[:,range(numpc)] # cutting some PCs
score = np.dot(coeff.T, M) # projection of the data in the new space
return coeff, score, latent
if __name__ == "__main__":
m = Params(stnfile, ["mb::H","mb::Vp", "mb::stdVp"])
# m = Params(stnfile, ["hk::H","hk::R", "hk::stdR"])
# m.mb_H = m.hk_H
# m.mb_Vp = m.hk_R
## Figure Properties #######
width = 12
height = 9
legsize = width + 3
###########################
#############################################################################
# Vp estimates against H
##############################################################################
A = np.vstack( (m.mb_H, m.mb_Vp) )
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
R1std = p0.stdR
###########################################################################
# 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.",
# python-mode comment/uncomment region M-;
###########################################################################
# 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.",
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
fig = plt.figure( figsize = (width, height) )
ax = plt.subplot(111)
# Get data
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
R1std = p0.stdR
ms = 12 / ratio# marker size
caplen = 7 / ratio
capwid = 2 / ratio
elw = 2 / ratio
ticks = 16 / ratio
label = 16 / ratio
title = 18 / ratio
leg = 14 / ratio
#############################################################################
# FIG 1: Vp estimates against H
##############################################################################
if 0:
f = Params(stnfile, ["fg::H","fg::Vp", "fg::stdVp", "hk::stdR"])
corrfg = pearsonr(f.fg_Vp, f.fg_H)
print "FG: Vp vs H: {} stations, correlation = {}".format(len(f.stns), corrfg[0])
A = np.vstack( (f.fg_H, f.fg_Vp) )
M = (A.T - np.mean(A,1)).T
M[0] = M[0] / np.std(M,1)[0]
M[1] = M[1] / np.std(M,1)[1]
eigVect, pcomp, eigValue, C = princomp(M, 2)
var = np.sum(pcomp * pcomp, axis = 1)
var /= np.sum(var)
print "variance of components is {}".format(var)
