####################################################################### # 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)):
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
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
# 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.",
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)