avgv = []
provs = ["Churchill Province", "Superior Province", "Slave Province", "Grenville Province"]
color = ["b", "g", "m", "y", "k", "c"]
handles = ()
labels = ()
plt.subplots_adjust(left=None, bottom=None, right=0.75, top=None, wspace=None, hspace=0.05)
for (ii, province) in enumerate(provs):
d.reset()
arg = Args()
arg.addQuery("geoprov", "in", province)
d2 = Params(stnfile, ["hk::H", "hk::R"], arg)
d.sync(d2)
prov = province.replace(" ", "")
dk, avgdk, bins, ptype = getdata("kan", d, m, vdict, prov)
avgp.append(avgdk)
ax = plt.subplot(4, 1, ii + 1)
n, bins, patches = plt.hist(
dk, bins=bins, histtype="bar", color=color[ii], rwidth=1, alpha=0.6, label=province
)
handle, label = ax.get_legend_handles_labels()
handles += (handle[0],)
labels += (label[0],)
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)):
assert c.stns[ii] == f.stns[ii]
assert c.hk_H[ii] == d.hk_H[ii]
assert f.sync(d) == f.reset().sync(e)
c.reset()
d.reset()
e.reset()
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)):
assert c.stns[ii] == f.stns[ii]
assert c.hk_H[ii] == d.hk_H[ii]
assert f.sync(d) == f.reset().sync(e)
c.reset()
d.reset()
e.reset()
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
R2std = p2.stdR
H1std = p0.stdH
H2std = p2.stdH
plt.legend(loc= 2)
plt.ylabel("Station Vp [km/s]")
plt.xlabel("Station Thickness H [km]")
plt.grid(True)
#############################################################################
# Vp estimates and controlled source
##############################################################################
arg = Args().stations(["ALE","ALGO","ARVN","BANO","CBRQ","DAWY","DELO","FCC","FFC","HAL","KGNO","KSVO","LMN","MBC","MNT","MOBC","ORIO","PEMO","PGC","PLVO","PMB","PTCO","SJNN","SUNO","ULM","ULM2","WAPA ","WHY","WSLR ","YKW1","YOSQ"])
m.filter(arg)
m.filter(Args().addQuery("mb::Vp", "gt", "5.5"))
m.filter(Args().addQuery("mb::stdVp", "lt", "0.8"))
c = Params(csfile, ["H","Vp"])
c.sync(m)
stdVp = 2 * m.mb_stdVp # 2 stdError
t = np.arange(len(m.mb_Vp[0:11]))
corr = spearmanr(m.mb_Vp[0:11], c.Vp[0:11])
fig = plt.figure( figsize = (width, height) )
plt.plot(t, m.mb_Vp[0:11], '-ob', lw = 4, ms = 12, label = "Bostock (2010) Vp estimate")
plt.errorbar(t, m.mb_Vp[0:11], yerr=stdVp[0:11], xerr=None, fmt=None, ecolor = 'blue',
elinewidth = 2, capsize = 7, mew = 2, label = "2 std dev Bootstrap")
plt.plot(t, c.Vp[0:11], '-og', lw = 4, ms = 12, label = "Proximal active source estimate")
plt.title("Active Source P-wave velocity comparison\n Correlation = {0:2.3f}".format(corr[0]), size = 18)
plt.legend()
plt.xlabel("Stations")
plt.ylabel("Vp [km/s]")
plt.xticks(t, c.stns[0:11], size = 12)
"Grenville Province"
]
color = ['b', 'g', 'm', 'y', 'k', 'c']
handles = ()
labels = ()
plt.subplots_adjust(left= None, bottom=None, right = 0.75, top=None,
wspace=None, hspace=0.05)
for (ii, province) in enumerate(provs):
d.reset()
arg = Args()
arg.addQuery("geoprov", "in", province)
d2 = Params(stnfile, ["hk::H","hk::R"], arg)
d.sync(d2)
prov = province.replace(" ", "")
dk, avgdk, bins, ptype = getdata("kan", d, m, vdict, prov)
avgp.append( avgdk )
ax = ( plt.subplot(4,1,ii + 1) )
n, bins, patches = plt.hist(dk, bins = bins, histtype = "bar",
color = color[ii], rwidth = 1, alpha = 0.6,
label = province)
handle, label = ax.get_legend_handles_labels()
handles += (handle[0],)
labels += (label[0],)
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
R2std = p2.stdR
H1std = p0.stdH
H2std = p2.stdH
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)
l1=ax1.plot(t, f.fg_Vp, '-ob', lw = lw, ms = ms, label = "Full Gridsearch Vp estimate")
l2=ax1.errorbar(t, f.fg_Vp, yerr=stdVp, xerr=None, fmt=None, ecolor = 'blue',