def MagTimePlot(Db,
Mag0=[],
Mag1=[],
Year0=[],
Year1=[],
CompTable=[],
OutFile=[]):
if not Mag0:
Mag0 = min(Db.Extract('MagSize'))
if not Mag1:
Mag1 = max(Db.Extract('MagSize'))
if not Year0:
Year0 = min(Db.Extract('Year'))
if not Year1:
Year1 = max(Db.Extract('Year'))
DbS = Sel.MagRangeSelect(Db, Mag0, Mag1, Owrite=0, TopEdge=True)
DbS = Sel.TimeSelect(DbS, Year0, Year1, Owrite=0)
X = DbS.Extract('Year')
Y = DbS.Extract('MagSize')
plt.figure(figsize=(7, 4))
plt.plot(X,
Y,
'o',
markersize=3,
color=[0, 0, 0],
markeredgecolor=[0, 0, 0],
markeredgewidth=1.5)
# Plot completeness
if CompTable:
PlotCompTable(CompTable)
plt.gca().yaxis.grid(color='0.', linestyle='-')
plt.gca().xaxis.grid(color='0.65', linestyle='--')
plt.gca().xaxis.set_ticks_position('none')
plt.gca().yaxis.set_ticks_position('none')
plt.title('Time-Magnitude Distribution', fontsize=14, fontweight='bold')
plt.xlabel('Years', fontsize=14, fontweight='bold')
plt.ylabel('Magnitude', fontsize=14, fontweight='bold')
plt.axis([Year0, Year1, Mag0, Mag1])
# plt.tight_layout()
plt.show(block=False)
if OutFile:
plt.savefig(OutFile, bbox_inches='tight', dpi=150)
def GetEventRates(Db, CompTable, Area=1.):
"""
Method to compute observed annual rates (incremental and cumulative) from a given
completeness table. In this implementation, completeness is one window per
magnitude bin in M. Example:
CompTable = [[4.50, 0.25, 2000., 2013.],
[4.75, 0.25, 1980., 2013.],
[5.00, 0.25, 1970., 2013.],
[5.25, 0.25, 1960., 2013.],
[5.50, 0.50, 1950., 2013.],
[6.00, 1.50, 1901., 2013.]]
"""
Enum = []
Data = [[],[]]
for CT in CompTable:
MinM = CT[0]
MaxM = CT[0]+CT[1]
MinY = CT[2]
MaxY = CT[3]
# Catalogue selection (Magnitude-Year)
DbM = Sel.MagRangeSelect(Db, MinM, MaxM)
DbY = Sel.TimeSelect(DbM, MinY, MaxY)
# Computing incremental rates
RY = float(DbY.Size())/float(MaxY-MinY)
Enum.append(RY/float(Area))
# Data per magnitude bin
Data[0].append(DbY.Extract(Key='Year'))
Data[1].append(DbY.Extract(Key='MagSize'))
# Cumulative distribution
Ecum = np.cumsum(Enum[::-1])[::-1]
return Enum, Ecum, Data
def SmoothMFD(Db,
a,
XY,
Window=GaussWin,
Par=50.,
Delta=0.1,
SphereGrid=False,
Box=[],
Buffer=[],
Grid=[],
Threshold=-100,
Unwrap=False,
ZeroRates=False):
if isinstance(XY, CU.Polygon):
P = cp.deepcopy(XY)
else:
P = CU.Polygon()
P.Load(XY)
if Par <= 0:
Par = np.inf
# Catalogue selection
DbS = Sel.AreaSelect(Db, P, Owrite=0, Buffer=Buffer, Unwrap=Unwrap)
x, y, z = Exp.GetHypocenter(DbS)
# Unwrapping coordinates
if Unwrap:
x = [i if i > 0. else i + 360. for i in x]
P.Unwrap()
# Creating the mesh grid
if Grid:
XY = [G for G in Grid if P.IsInside(G[0], G[1])]
else:
if SphereGrid:
XY = P.SphereGrid(Delta=Delta, Unwrap=Unwrap)
else:
XY = P.CartGrid(Dx=Delta, Dy=Delta, Bounds=Box)
Win = []
for xyP in XY:
Win.append(0)
for xyE in zip(x, y):
Dis = CU.WgsDistance(xyP[1], xyP[0], xyE[1], xyE[0])
Win[-1] += Window(Dis, Par)
# Using homogenous zone if no events are found
#if all(i == 0 for i in Win):
# Win = [1. for i in Win]
# Scaling and normalising the rates
Norm = np.sum(Win)
A = []
X = []
Y = []
for I, W in enumerate(Win):
aT = -np.inf
if Norm > 0. and W > 0.:
aT = a + np.log10(W / Norm)
if aT < Threshold:
# Filter below threshold
aT = -np.inf
if ZeroRates:
A.append(aT)
X.append(XY[I][0])
Y.append(XY[I][1])
else:
if aT > -np.inf:
A.append(aT)
X.append(XY[I][0])
Y.append(XY[I][1])
if Unwrap:
# Wrap back longitudes
X = [x if x < 180. else x - 360. for x in X]
return X, Y, A
#-----------------------------------------------------------------------------------------
# Import Catalogues
Db1 = Cat.Database()
Db1.Load('data/isc-rev-africa-select.bin')
Db2 = Cat.Database()
Db2.Load('data/isc-gem-v3.bin')
#-----------------------------------------------------------------------------------------
# Duplicate findings
# Between Catalogues
Db3, Log = Sel.MergeDuplicate(Db1,
Db2,
Twin=60.,
Swin=50.,
Log=1,
Owrite=False)
# Within a catalogue
Log = Sel.MergeDuplicate(Db1, Twin=60., Swin=50., Log=1)
#-----------------------------------------------------------------------------------------
# Magnitude conversion
# Apply to all agency
Sel.MagConvert(Db1, '*', ['Ms', 'MS'], 'Mw', MR.MsMw_Scordillis2006)
# Apply to single agency
Sel.MagConvert(Db1, 'ISC', 'Ms', 'Mw', MR.MsMw_Scordillis2006)
def RateDensityPlot(Db,
Mag0=[],
Mag1=[],
MBin=0.25,
Year0=[],
Year1=[],
Delta=2,
CompTable=[],
Normalise=True,
OutFile=[]):
if not Mag0:
Mag0 = min(Db.Extract('MagSize'))
if not Mag1:
Mag1 = max(Db.Extract('MagSize'))
MBins = np.arange(Mag0, Mag1 + MBin, MBin)
if not Year0:
Year0 = min(Db.Extract('Year'))
if not Year1:
Year1 = max(Db.Extract('Year'))
YBins = np.arange(Year0, Year1 + Delta, Delta)
Histo = np.zeros((np.size(MBins), np.size(YBins)))
# Catalogue selection (Magnitude-Year)
DbM = Sel.MagRangeSelect(Db, Mag0, Mag1, TopEdge=True)
DbY = Sel.TimeSelect(DbM, Year0, Year1)
M = DbY.Extract('MagSize')
Y = DbY.Extract('Year')
Hist = np.histogram2d(Y, M, bins=(YBins, MBins))[0]
Hist = np.transpose(Hist)
if Normalise:
for I in range(0, len(Hist)):
Max = np.max(Hist[I])
if Max > 0:
Hist[I] = Hist[I] / Max
# Plot
plt.figure(figsize=(7, 4))
plt.pcolormesh(YBins, MBins, Hist, cmap='Greys', vmin=0)
# Plot completeness
if CompTable:
PlotCompTable(CompTable)
plt.gca().xaxis.grid(color='0.65', linestyle='--')
plt.gca().yaxis.grid(color='0.', linestyle='-')
plt.gca().xaxis.set_ticks_position('none')
plt.gca().yaxis.set_ticks_position('none')
plt.title('Occurrence Rate Density', fontsize=14, fontweight='bold')
plt.xlabel('Years', fontsize=12, fontweight='bold')
plt.ylabel('Magnitude', fontsize=12, fontweight='bold')
plt.gca().xaxis.grid(color='0.65', linestyle='-')
plt.gca().yaxis.grid(color='0.65', linestyle='-')
plt.axis([Year0, Year1, Mag0, Mag1])
# plt.tight_layout()
plt.show(block=False)
if OutFile:
plt.savefig(OutFile, bbox_inches='tight', dpi=150)