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hazmap-bkp20120713.py
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import math
import scipy
#
import pylab as plt
import matplotlib
import matplotlib.mpl as mpl
import numpy
from PIL import Image as ipp
#import numpy.fft as nft
import scipy.optimize as spo
#from matplotlib import pyplot as plt
#from matplotlib import rc
from matplotlib.patches import Ellipse
#
import string
import sys
#from matplotlib import *
#from pylab import *
import os
import random
import time
#
# gamma function lives here:
#import scipy.special
from scipy.special import gamma
#from scipy.optimize import leastsq
from matplotlib import axis as aa
from threading import Thread
#
#
import datetime as dtm
import matplotlib.dates as mpd
import calendar
import operator
import urllib
import MySQLdb
import yodapy as yp
import rbIntervals as rbi
class hazCrack(object):
# record-breaking hazard map on crack.
hazmaps=[]
plotstyle=0 #{0: little squares, 1: little squares mean-val, 2: mean-val contours, 3: overlapping big squares, 4: mean-val big squares}
gridsize=1.0 # sometimes called L; it's the length of the grid size.
ndithers=3 # so the total number of elements will be ndithers^2 (aka, 3-> dither into thirds on each side).
dL=None # will be L/ndithers
hmdate=None
#
#
def __init__(self, catalog=None, catnum=0, mc=2.5, gridsize=.5, ndithers=3, winlen=128, avlen=1, bigmag=5.0, fignum=0, logZ=1.0):
self.dL=gridsize/float(ndithers)
self.hazmaps=[]
self.mc=mc
self.gridsize=gridsize
self.ndithers=ndithers
self.winlen=winlen
self.avlen=avlen
self.bigmag=bigmag
self.fignum=fignum
if logZ==None: logZ=math.log10(winlen)
self.logZ=logZ # for log-normalizing rb-ratios
#self.logZ=1.0
#self.forecastDate=dtm.datetime.now() # or we might change this.
self.forecastDate=dtm.datetime(2011, 3,5)
self.hmdate=dtm.datetime.now()
self.rb=rbi.intervalRecordBreaker(None)
#
for i in xrange(ndithers*ndithers):
xphase=(i%ndithers)*self.dL # lon phase
yphase=(int(i/ndithers))*self.dL # lat pahse
# in the end, it is going to be easier to rewrite the hazmap object. make a lean class excluding the graphical parts - just
# an array of subcats. we'll load a single own rb object to this class or rewrite the RB counting script.
# catalog=None, catnum=0, mc=2.5, gridsize=.5, phi=[0,0], winlen=128, avlen=1, bigmag=5.0
thishm=rbHazMapLean(catalog=catalog, catnum=catnum, mc=mc, gridsize=gridsize, phi=[yphase, xphase], winlen=winlen, avlen=avlen, bigmag=bigmag, logZ=self.logZ) # but we'll need to suppress fig-plotting and i don't know if the phases are switched.
a=thishm.hazMapTo(self.forecastDate)
self.hazmaps+=[thishm]
def getNlon(self):
#nlon=0
#for hm in self.hazmaps:
# nlon+=hm.Nlon
return self.hazmaps[0].Nlon*self.ndithers
#return nlon
def getNlat(self):
#nlat=0
#for hm in self.hazmaps:
# nlat+=hm.Nlat
return self.hazmaps[0].Nlat*self.ndithers
#return nlat
def boxesTo(self, hmdate=dtm.datetime.now(), fignum=0):
self.hazMapTo(hmdate)
return self.simpleBoxes(fignum)
def hazMapTo(self, hmdate=dtm.datetime.now()):
self.hmdate=hmdate
for i in xrange(len(self.hazmaps)):
self.hazmaps[i].hazMapTo(hmdate)
def contourData(self, dobinary=0):
# for our first trick, just combine all the hazard maps into one big map and contour it.
# later, we'll do some averaging schemes.
#
# first, get one big array of X,Y,Z:
#cX=[]
#cY=[]
#cZ=[]
cdata=[]
#for i in xrange(len(self.hazmaps[0].rbRatios)):
# for hm in self.hazmaps:
for hm in self.hazmaps:
#print len(hm.rbRatios)
for i in xrange(len(hm.rbRatios)):
#cX+=[hm.getLon(i) + (self.gridsize-self.dL)/2.0]
#cY+=[hm.getLat(i) + (self.gridsize-self.dL)/2.0]
#cZ+=[hm.rbRatios[i]]
#print "ahhh!!"
thisval=hm.rbRatios[i]
#if thisval==None: thisval=0
if thisval!=None:
thisval=-thisval # because we've defined r<1 to be high risk. the easiest way to color these "hot" colors is to reverse them.
if dobinary:
# return binary (or arguably tri-mary, {-1, None, 1} (noting at this point that the parity has been reversed)
if thisval>=0: thisval=1
if thisval<0: thisval=-1
cdata+=[[hm.getLon(i) + (self.gridsize-self.dL)/2.0, hm.getLat(i) + (self.gridsize-self.dL)/2.0, thisval]]
#
# so now we have a [[x, y, rb]] array from all the hazard maps.
# sort them by x,y (row vectors if you will). [x0, y0], [x1, y0], [x2, y0]..., [x0, y1], [x1, y1], [x2, y1], ...]
# sort first by the secondary key (x), then on primary (y):
cdata.sort(key=operator.itemgetter(0))
cdata.sort(key=operator.itemgetter(1))
#
# give all the clusters a 0-value border (for prettiness).
#width=self.hazmaps[0].Nlon*self.ndithers
width=self.getNlon()
maxi=len(cdata) # actually, max i + 1, so tread carefully.
#
# pad the clusters to make better contours:
# spin through cdata. if for every element, if an adjacent cell is None and not over an edge, make it zero. note this is made easy because
# the lat/lon are provided...
for i in xrange(maxi):
if cdata[i][2]==0. or cdata[i][2]==None: continue #(0, None) because, if we start placing 0 values, we fill the whole space with 0's.
#thislat=cdata[1]
#thislon=cdata[0]
# on the left border?
if i%width>0:
if cdata[i-1][2]==None: cdata[i-1][2]=0.
# right:
if i%width<(width-1):
if cdata[i+1][2]==None: cdata[i+1][2]=0.
# top:
#if i/width!=0:
if (i-width)>=0:
if cdata[i-width][2]==None: cdata[i-width][2]=0.
if (i+width)<maxi:
if cdata[i+width][2]==None: cdata[i+width][2]=0.
#
#
# though we might eventually return a later form of this data (as per see contourPlot()...
return cdata
def contourArrays(self):
#
cdata=self.contourData()
# now, borrowing from ffgridplot.py contouring methods, we "shape" the array:
# can we just contour() from here? no.
#
Xs=map(operator.itemgetter(0), cdata)
Ys=map(operator.itemgetter(1), cdata)
Xvals=list(set(Xs))
Yvals=list(set(Ys))
Xvals.sort()
Yvals.sort()
#
#Xax=list(set(map(operator.itemgetter(0), cdata))).sort()
#Yax=list(set(map(operator.itemgetter(1), cdata))).sort()
#
X,Y=numpy.meshgrid(Xvals, Yvals)
#z=map(operator.itemgetter(2), cdata)
Z=map(operator.itemgetter(2), cdata)
Z2d=scipy.array(Z)
Z2d.shape=(len(Yvals), len(Xvals))
#
# for now, this is a fully class-contained function, so we can set some class-scope
# variables.
self.X_i = X
self.Y_i = Y
self.Z2d = Z2d
#
return [X,Y,Z2d]
def contourPlot(self, fignum=1):
cary=self.contourArrays()
X = cary[0]
Y = cary[1]
Z2d = cary[2]
plt.ion()
plt.figure(fignum+1)
plt.clf()
plt.figure(fignum)
plt.clf()
#plt.contourf(map(operator.itemgetter(0), cdata), map(operator.itemgetter(1), cdata), map(operator.itemgetter(2), cdata))
#return X, Y, Z2d
self.conts = plt.contourf(X,Y,Z2d, alpha=.3)
plt.colorbar()
plt.spectral()
#return [cX, cY, cZ]
return cdata
def plotEvent(self, coords=None, symbolstr='r*', msize=18, fignum=0, zorder=5):
cm=self.hazmaps[0]
if coords==None: coords=cm.getMainEvent()
x,y=cm.catalog.catmap(coords[0], coords[1])
plt.figure(fignum)
plt.plot([x], [y], symbolstr, ms=msize, zorder=zorder)
return None
def plotEvents(self, events=None, symbolstr=',', msize=1, fignum=0, alpha=1.0, zorder=5):
cm=self.hazmaps[0]
print "doing events. %d, %d" % (len(events), fignum)
#
#fignum=1
# assume events come in 'catalog' form [dtm, lat, lon, mag, ...]
lats=map(operator.itemgetter(1), events)
lons=map(operator.itemgetter(2), events)
X,Y=cm.catalog.catmap(lons, lats)
plt.figure(fignum)
plt.plot(X,Y, symbolstr, ms=msize, alpha=alpha, zorder=zorder)
return None
def contourMap(self, fignum=1, nconts=12, mapres='i', maptransform=True):
# maptransform: transform to matplotlig.basemap coords. for KML export, set this to false (coords in LL).
# (though we might not use this feature; we might do it another way...)
cdata=self.contourData()
#
# set up the map. we'll need it to process the contour data.
#
catdate=self.hazmaps[0].catalog.cat[-1][0] # most recent date in catalog.
#
plt.ion()
fig1=plt.figure(fignum+1)
plt.clf()
#plt.title("Record-breaking contour hazard map\ndtm=%s, $d\\lambda=%.4f$, nDith=%d, mc=%.2f, rblen=%d, avlen=%d\n\n" % (str(self.hmdate), self.gridsize, self.ndithers, self.mc, self.winlen, self.avlen))
plt.title("Record-breaking contour hazard map\ndtm=%s, $d\\lambda=%.4f$, nDith=%d, mc=%.2f, rblen=%d, avlen=%d\n\n" % (str(catdate), self.gridsize, self.ndithers, self.mc, self.winlen, self.avlen))
ax1 = fig1.add_subplot(111)
#
fig=plt.figure(fignum)
fig.clf()
ax = fig.add_subplot(111)
#plt.title('(diagnostic) Record-breaking intervals Hazard Map')
plt.title("Record-breaking contour haz-map\ndtm=%s, $d\\lambda=%.4f$, nDith=%d, mc=%.2f, rblen=%d\n\n" % (str(catdate), self.gridsize, self.ndithers, self.mc, self.winlen))
#
llrange=self.hazmaps[0].catalog.getLatLonRange(self.hazmaps[0].thiscat)
deltaLat=abs(float(self.llrange[1][0])-self.llrange[0][0])
deltaLon=abs(float(self.llrange[1][1])-self.llrange[0][1])
Nlat=1+int(self.deltaLat/self.gridsize)
Nlon=1+int(self.deltaLon/self.gridsize) #number of cells in row/col
#
lat0=self.llrange[0][0]
lon0=self.llrange[0][1]
#
#thismev=self.hazmaps[0].catalog.getMainEvent(self.hazmaps[0].catalog.getcat(0))
#epicen=[thismev[2], thismev[1]]
try:
epicen=self.epicen
except:
c=self.hazmaps[0].catalog.getcat(0)
i=0
m0=c[i][3]
i0=0
while i<len(c):
if c[i][3]>m0 and c[i][0]>self.hmdate:
m0=c[i][3]
i0=i
i+=1
self.epicen=[c[i0][2], c[i0][1]]
plt.figure(fignum)
plottingCat=yp.eqcatalog(self.hazmaps[0].catalog.getcat(0))
# now, make two sub-cats, one before, one after the haz-date:
# addTimeRangeCat(self, subcatname='dtSubcat', fullcat=None, dtFrom=None, dtTo=None)
plottingCat.addTimeRangeCat(subcatname='before', fullcat=None, dtFrom=self.hmdate-dtm.timedelta(days=1000), dtTo=self.hmdate)
plottingCat.addTimeRangeCat(subcatname='after', fullcat=None, dtFrom=self.hmdate, dtTo=self.hmdate+dtm.timedelta(days=1000))
plotcat=[[plottingCat.subcats[0][0], plottingCat.subcats[0][1]], [plottingCat.subcats[1][0], plottingCat.subcats[1][1][-1000:]]]
#cm=self.hazmaps[0].catalog.plotCatMap(self.hazmaps[0].catalog.getcat(0), True, False, None, epicen, 'best', True, 'g,', ax, fignum)
self.hazmaps[0].catalog.mapres=mapres
cm=self.hazmaps[0].catalog.plotCatMap(self.hazmaps[0].catalog.getcat(0), True, False, None, None, 'best', True, 'g,', ax, fignum, plotevents=1)
#cm1=plottingCat.plotCatsMap(catalogses=[plottingCat.subcats[0]], legendLoc='best', doCLF=True, fignum=fignum+1, bigmag=6.0)
#cm1=self.hazmaps[0].catalog.plotCatsMap(catalogses=plottingCat.subcats, legendLoc='best', doCLF=False, fignum=fignum+1, bigmag=self.bigmag)
#cm1=self.hazmaps[0].catalog.plotCatsMap(catalogses=[plottingCat.subcats[0], plottingCat.subcats[1]], legendLoc='best', doCLF=False, fignum=fignum+1, bigmag=self.bigmag)
#cm1=self.hazmaps[0].catalog.plotCatsMap(catalogses=plotcat, legendLoc='best', doCLF=False, fignum=fignum+1, bigmag=self.bigmag)
#### (this (above) produces a skewed catalog-map. instead, plot a map with no events and add the events separately.
cm1=self.hazmaps[0].catalog.plotCatMap(self.hazmaps[0].catalog.getcat(0), True, False, None, None, 'best', True, 'g,', ax1, fignum+1, plotevents=0)
#cm=self.hazmaps[0].catalog.plotCatMap(self.hazmaps[0].catalog.getcat(0), True, False, None, epicen, 'best', True, 'g,', ax, fignum)
####
plt.figure(fignum)
#
Xs=map(operator.itemgetter(0), cdata)
Ys=map(operator.itemgetter(1), cdata)
Xvals=list(set(Xs))
Yvals=list(set(Ys))
Xvals.sort()
Yvals.sort()
#
X,Y=numpy.meshgrid(Xvals, Yvals)
X1, Y1=numpy.meshgrid(Xvals, Yvals)
# and now, we have to mash these into map coordinates:
for i in xrange(len(Y)):
for j in xrange(len(Y[0])):
myx, myy = X[i][j], Y[i][j]
myxm, myym = cm(myx, myy) # map coords...
#
if maptransform==True:
myxm1, myym1 = cm1(myx, myy)
else:
myxm1, myym1 = myx, myy
#
X[i][j]=myxm
Y[i][j]=myym
#
X1[i][j]=myxm1
Y1[i][j]=myym1
#
#X[i][j],Y[i][j] =cm(myx, myy)
#
Z=map(operator.itemgetter(2), cdata)
Z2d=scipy.array(Z)
Z2d.shape=(len(Yvals), len(Xvals))
#
plt.figure(fignum)
#plt.clf()
self.conts = plt.contourf(X,Y,Z2d, nconts, alpha=.85, zorder=10)
plt.colorbar()
plt.spectral()
plt.figure(fignum+1)
plt.contourf(X1,Y1,Z2d, nconts, alpha=.85, zorder=10)
plt.colorbar()
plt.spectral()
return None
def makeColorbar(self, cset=None, colorbarname=None, reffMag=5.0, contfact=5.0):
#
# reffMag:
if reffMag==None:
#reffMag=self.reffMag
reffMag=5.0
#
if colorbarname==None: colorbarname='scale.png'
if cset==None: cset=self.conts
cs=cset.collections
#resolution = 5
#LevelsNumber = 5 * resolution
warnings = ['No','Low','Guarded','Elevated','High','Severe']
#
#resolution = int(len(cs)/self.contfact)
resolution = int(len(cs)/contfact)
#
startindex=0
#
fig = plt.figure(figsize=(1,3))
axe = fig.add_axes([0.05, 0.05, 0.2, 0.9])
#
fig.figurePatch.set_alpha(0)
#
matplotlib.rc('font', family='monospace', weight='black')
#
cmap = mpl.cm.spectral
#norm = mpl.colors.Normalize(vmin=Z_i.min(), vmax=Z_i.max())
#ratefactor=10**(self.mc-self.reffMag)
ratefactorExp=self.mc-reffMag # so the raw rate probably should be with mc_catalog = mc_etas.
# but, we'll want to report some rate of m>reffMag
#
#print self.Z2d.min(), self.Z2d.max(), norm
#tics = [0,10,20,40,80]
#tics = [round(self.Z2d.min(),3), round(self.Z2d.max(),3)]
#timefactExp = math.log10(year2secs)
# min, max functions puking on None types, so do it manually:
zmin=None
zmax=None
for i in xrange(len(self.Z2d)):
for ii in xrange(len(self.Z2d[0])):
z0=self.Z2d[i][ii]
if z0==None: continue
if zmin==None: zmin=z0
if zmax==None: zmax=z0
#
if z0>zmax: zmax=z0
if z0<zmin: zmin=z0
#
norm = mpl.colors.Normalize(vmin=zmin, vmax=zmax)
#
t1='%.2f' % (zmin)
t2='%.2f' % (zmax)
#print "max set: ", self.Z2d.max(), timefactExp, ratefactorExp
tics = [zmin, zmax]
#print t1, t2
#
#cb1 = mpl.colorbar.ColorbarBase(axe, norm=norm, ticks=tics, format="%g%%", orientation='vertical')
cb1 = mpl.colorbar.ColorbarBase(axe, norm=norm, ticks=tics, format="%.2f", orientation='vertical')
cb1.set_ticklabels([t1, t2], update_ticks=True)
cb1.set_label('Local RB ratio, $r=\\log (n_{rb-short}/n_{rb-long})$')
#cb1.set_label('ETAS rate')
plt.savefig(colorbarname) # vertical
#
#suffix=''
i=len(colorbarname)-1
#while colorbarname[i]!='.':
# #suffix.insert(0, colorbarcopy[-i])
# # this will get us the last '.'
# i-=1
i=colorbarname.rfind('.')
hname=colorbarname[:i] + '-h' + colorbarname[i:]
#
im = ipp.open(colorbarname, 'r')
im.load() # just to be sure. open() only creates a pointer; data are not loaded until analyzed.
im=im.rotate(-90)
#im.show()
im.save(hname)
#
# depricated method:
#os.system('convert -rotate 90 %s %s' % (colorbarname, hname))
#os.system('cp %s %s' % (colorbarname, hname))
#
return [colorbarname, hname]
def simplePlot(self, fignum=0, thresh=0.):
# do a simple matplotlib plot of hazmap. this is probably diagnostic, so it may just be points of some sort.
# note: this plots the ll-corner of each box. for a science-interesting plot, we should center these points (aka, [x+L/2, y+L/2]
#
if thresh==None or type(thresh).__name__ not in ('float', 'int'): thresh=0.0
plt.figure(fignum)
plt.clf()
plt.ion()
#
plt.title("diagnostic plot of dithered rb-hazmaps")
hmindex=0
for hm in self.hazmaps:
llrange=hm.llrange
lonsRed=[]
latsRed=[]
#rbvals=[]
lonsBlue=[]
latsBlue=[]
#
lonsCyan=[]
latsCyan=[] # for None , at least temporarily
#
for i in xrange(len(hm.rbRatios)):
#
#x=llrange[0][1]+self.gridsize*(i%hm.Nlon) #lon
#y=llrange[0][0]+self.gridsize*int(i/hm.Nlon) # lat
x=hm.getLon(i)
y=hm.getLat(i)
#if x<0: x+=180. # this seems to be more trouble than it's worth...
if hm.rbRatios[i]==None:
continue
#lonsCyan+=[x]
#latsCyan+=[y]
#
if hm.rbRatios[i]<=thresh:
lonsRed+=[x]
latsRed+=[y]
elif hm.rbRatios[i]>thresh:
lonsBlue+=[x]
latsBlue+=[y]
#plt.plot(lonsRed, latsRed, 'r%s' % yp.pyicon(hmindex))
#plt.plot(lonsBlue, latsBlue, 'b%s' % yp.pyicon(hmindex))
plt.plot(lonsRed, latsRed, 'r.')
plt.plot(lonsBlue, latsBlue, 'b.')
#plt.plot(lonsCyan, latsCyan, '%s' % yp.pyicon(hmindex), color='y')
#print hmindex
hmindex+=1
def simpleBoxes(self, fignum=0, thresh=0.0, plotevents=1, mapres='l'):
# do a simple matplotlib plot of hazmap. this is probably diagnostic, so it may just be points of some sort.
# note: this plots the ll-corner of each box. for a science-interesting plot, we should center these points (aka, [x+L/2, y+L/2]
# eventually, turn this into a map plot.
#
#if thresh==None or type(thresh).__name__ not in ('float', 'int'): thresh=0.0
noisethresh=thresh
if thresh==None:
thresh=((self.winlen/2.)-.577 - math.log(self.winlen))/((self.winlen/2.)+.577 + math.log(self.winlen))
thresh=abs(math.log10(thresh))
# print "noisethresh: %f" % (noisethresh)
#
catdate=self.hazmaps[0].catalog.cat[-1][0]
#
plt.ion()
self.fig=plt.figure(fignum)
self.fig.clf()
self.ax = self.fig.add_subplot(111)
#plt.title('(diagnostic) Record-breaking intervals Hazard Map')
#plt.title("Record-breaking hazardmap\ndtm=%s, $d\\lambda=%.4f$, nDith=%d, mc=%.2f, rblen=%d, avlen=%d\n\n" % (str(self.hmdate), self.gridsize, self.ndithers, self.mc, self.winlen, self.avlen))
plt.title("Record-breaking hazardmap\ndtm=%s, $d\\lambda=%.4f$, nDith=%d, mc=%.2f, rblen=%d, avlen=%d\n\n" % (str(catdate), self.gridsize, self.ndithers, self.mc, self.winlen, self.avlen))
#
self.llrange=self.hazmaps[0].catalog.getLatLonRange(self.hazmaps[0].thiscat)
self.deltaLat=abs(float(self.llrange[1][0])-self.llrange[0][0])
self.deltaLon=abs(float(self.llrange[1][1])-self.llrange[0][1])
self.Nlat=1+int(self.deltaLat/self.gridsize)
self.Nlon=1+int(self.deltaLon/self.gridsize) #number of cells in row/col
#
self.lat0=self.llrange[0][0]
self.lon0=self.llrange[0][1]
#
#ilat=0
#ilon=0 # index counters
#self.cm=self.catalog.plotCatMap(self.thiscat, True, False, None, None, 'best', True, 'g,', None, self.fignum)
#self.cm=self.catalog.plotCatMap(self.thiscat, True, False, None, None, 'best', True, 'g,', self.ax)
#minicat=self.catalog.getcat(0)[0:10]
#
#thismev=self.hazmaps[0].catalog.getMainEvent(self.hazmaps[0].catalog.getcat(0))
#epicen=[thismev[2], thismev[1]]
#
#self.cm=self.hazmaps[0].catalog.plotCatMap(self.hazmaps[0].catalog.getcat(0), True, False, None, self.epicen, 'best', True, 'g,', self.ax, fignum, plotevents)
self.hazmaps[0].catalog.mapres=mapres
self.cm=self.hazmaps[0].catalog.plotCatMap(self.hazmaps[0].catalog.getcat(0), True, False, None, self.epicen, 'best', True, 'g,', self.ax, fignum, plotevents)
#
plt.figure(fignum)
hmindex=0
for hm in self.hazmaps:
llrange=hm.llrange
lonsRed=[]
latsRed=[]
#rbvals=[]
lonsBlue=[]
latsBlue=[]
#
lonsCyan=[]
latsCyan=[] # for None , at least temporarily
#
for i in xrange(len(hm.rbRatios)):
#
#x=llrange[0][1]+self.gridsize*(i%hm.Nlon) #lon
#y=llrange[0][0]+self.gridsize*int(i/hm.Nlon) # lat
x=hm.getLon(i) + self.gridsize/2.0 - self.dL/2.0
y=hm.getLat(i) + self.gridsize/2.0 - self.dL/2.0 # to center the little boxes on their area of influence. note, this visually
# excludes the left side of the cluster.
# let's try always casting all lon values as positive:
# if x<0: x+=180. # basemap appears to not understand the int. dt. line.
if hm.rbRatios[i]==None:
continue
#lonsCyan+=[x]
#latsCyan+=[y]
#
#mapX, mapY=self.cm(map(operator.itemgetter(0), thissquare), map(operator.itemgetter(1), thissquare))
#self.gridSquares+=self.ax.fill(mapX, mapY, alpha=.1, color='b', lw=2, zorder=2)
thissq=hm.getSquare([x,y], float(self.gridsize)/self.ndithers)
#thissq=hm.getSquare([x,y], float(self.gridsize))
X=map(operator.itemgetter(0), thissq)
Y=map(operator.itemgetter(1), thissq)
X,Y=self.cm(X,Y)
#thisalpha=abs(hm.rbRatios[i])
if hm.rbRatios[i]<(-thresh):
# (note: ratios are "loggoe": r[i]=log(nrb1/nrb2)
# getSquare(self, xy=[], dx=.1):
#plt.fill(X,Y, alpha=thisalpha, color='r', lw=2, zorder=9)
plt.fill(X,Y, alpha=.55, color='r', lw=2, zorder=9)
#lonsRed+=[x]
#latsRed+=[y]
elif hm.rbRatios[i]>=thresh:
#elif hm.rbRatios[i]>thresh:
#lonsBlue+=[x]
#latsBlue+=[y]
#plt.fill(X,Y, alpha=thisalpha/2., color='b', lw=2, zorder=8)
plt.fill(X,Y, alpha=.2, color='b', lw=2, zorder=8)
elif hm.rbRatios[i]>=-thresh and hm.rbRatios[i]<0.:
plt.fill(X,Y, alpha=.4, color='y', lw=2, zorder=8)
elif hm.rbRatios[i]>=0. and hm.rbRatios[i]<=thresh:
#lonsBlue+=[x]
#latsBlue+=[y]
plt.fill(X,Y, alpha=.2, color='c', lw=2, zorder=8)
#plt.plot(lonsRed, latsRed, 'r%s' % yp.pyicon(hmindex))
#plt.plot(lonsBlue, latsBlue, 'b%s' % yp.pyicon(hmindex))
#plt.plot(lonsRed, latsRed, 'r.')
#plt.plot(lonsBlue, latsBlue, 'b.')
#plt.plot(lonsCyan, latsCyan, '%s' % yp.pyicon(hmindex), color='y')
#print hmindex
hmindex+=1
def getClusters(self, beta=2):
#cdata=self.contourData()
clusts=self.findClusters(map(operator.itemgetter(2), self.contourData()), self.getNlon())
'''
clusts=[]
for rw in clusts0:
if len(rw[1])==0: continue
clusts+=rw[1]
'''
return clusts
#
def getClustStats(self, clusts=None, beta=2):
if clusts==None: clusts=self.getClusters()
clustStats=[]
for i in xrange(len(clusts)):
#for rw in clusts:
rw=clusts[i]
#clustStats+=[[len(rw), self.guessMag(len(rw), beta=beta)]]
clustStats+=[[i, len(rw), self.guessMag(len(rw), beta=beta)]]
#clustStats.sort() # i think this will, by default, sort by first element. if not, use a key, itemgetter(), etc.
clustStats.sort(key=lambda rw: rw[1])
return clustStats
def plotClustRBratios(self, clustlist=None, rblen=None, minmag=1.5, fignum=1, avlen=1, justTop=False):
# notes: the idea here is to see if a cluster is coherent, in other words, if two adjacen sites show r<1, is r<1 for both sites together?
# a problem arises for small clusters in dithered maps. for example, if ndithers=3, each square represents only 1/9 of a full sequence.
# we estimate the area and sequence length basically from n(rblen/9). in the simple example of 1 square, we end up trying to compute rb-statistics
# as n=rblen/9, when in fact the statistics for that cell were computed over rblen intervals, so the result it toally meaningless. not until
# N_clusts is large does this approximation work. we could try to re/de-construct the clusters to figure out the actual area covered and
# actual number of events in the calculation, but as per shape-statistics, this will be nasty.
#
# given this problem of estimating cluster areas, maybe the thing to do is to look at the source haz-maps themselves. basically filter non-coherent
# clusters in hazmaps[i], then plot only the coherent clusters. of course, the next problem is that our coherent cluster might be connected to other
# clusters by noisy pixels. maybe this problem will be mitigated in the dithering, but we might end up filtering out our target. finding an efficient
# way to look for coherent clusters within larger clusters will be a big job, but it might be tractable. of course, we'll be back to the question of,
# how do we define "coherent". all red? mostly red?
#
if clustlist==None: clustlist=self.findClusters() # so do all of them?
if type(clustlist[0]).__name__ == 'int':
# we've got a list of indeces.
tmpclist=self.findClusters()
newlist=[]
for i in clustlist:
newlist+=[tmpclist[i]]
clustlist=newlist
tmpclist=None
newlist=None
#
c1=yp.eqcatalog(self.catFromClusters(clustlist, justTop))
if rblen==None:
# optimal (??) rblen:
nsquares=0
for clst in clustlist:
nsquares+=len(clst)
rblen=2*int(self.winlen*nsquares/(self.ndithers**2))
print "rblen = %d" % rblen
self.rb.fullCat=c1.getcat(0)
self.rb.shockCat=c1.getcat(0)
#
self.rb.plotIntervalRatios(windowLen=rblen, minmag=minmag, fignum=fignum, avlen=avlen)
def catFromClusters(self, clustlist=[0], justTop=False):
# form a sub-catalog from one or more clusters.
# justTop -> take only top self.winlen events from each cluster. this weights each cell equally, rather than focusing on the most active sites.
#
# clustlist is a collection (list) of clusters from findClusters(). each entry is a sub-cat in a hazmap.catalog object.
#
# since we've dithered, we have to choose a way to choose which events. aka, do we choose all events inside the dithered boundary as we've drawn it,
# or all elements in the catalogs used to draw the cluster... or, a set of sub-cats for each dithered set.
# for now, combine all the catalogs (they will overlap a lot) and then remove duplicates.
#
# de-index the cluster-map (nLon, nLat are for for dithered set; ndithers is dither-length (ndithers=3 -> 3x3), / implies integer division).
# see getMapIndex() for index to mapID mapping. as per dithering, we're arranging our maps like (where the mapID's below represent the indeces
# of the maps as they were created. it would probably be a good idea to actually name the maps to make this process easier).
# 0 1 2| 9 10 11 | 18 19 20
# 3 4 5|12 13 14 | 21 22 23
# 6 7 8|15 16 17 | 23 25 26
# 27 o o * o o * o o
# o o o o o o o o o
# o o o o o o o o o
# * o o * o o * o o
#
# ndithers=3
# nLon=9
# then, the mapID is mapIndex%(ndithers**2),
# the gridIndex (for that map) is mapID/(ndithers**2)
#
# so combine all the indeces from the entries in clustlist, make one big catalog from all the events; strip duplicates.
newcat=[]
i0=0
if justTop==True: i0=-self.winlen
for clust in clustlist:
for elem in clust:
#print "clustLen: %d" % len(clust[i0:])
(hazmapIndex, subcatIndex) = self.getMapIndex(elem)
#newcat+=self.hazmaps[hazmapIndex].catalog.subcats[subcatIndex][1] # because eacy point on the haz-map represents a stastic from a set of events in a subcat.
newcat+=self.hazmaps[hazmapIndex].catalog.subcats[subcatIndex][1][i0:] # because eacy point on the haz-map represents a stastic from a set of events in a subcat.
#print hazmapIndex, subcatIndex
#
# now, strip duplicates:
#rcat=list(set(newcat))
#rcat=newcat[[0]]
rcat=[]
i=0
while i<len(newcat):
#if newcat[i]==rcat[-1]: continue
if newcat[i] not in rcat: rcat+=[newcat[i]] # this is the slow way, but it's straight forward. otherwise, sort and look at rcat[-1]
i+=1
#
rcat.sort()
return rcat
def getMapIndexMod(self, clustIndex=0, ndithers=None, nLon=None):
# given an index from a dithered map, returns [mapID (aka, dithered hazmap object index), gridIndex (grid index from that hazmap)]
# (a funky way of doing it) (and both of these methods seem to work... at lest they are identically flawed).
if ndithers==None: ndithers=self.ndithers
if nLon==None: nLon=self.getNlon()
#
i=clustIndex
nd=ndithers
mapIndex = (i%nd) + nd*int(int(i/nLon)%nd) + nd*nd*int((i%nLon)/nd)
# but i think this is giving me only the x-position, not the proper y-pos.
fullypos=i/self.getNlon() # y position for dithered map.
# add this separately to return value or lump into mapIndex? dunno just yet...
ydithered=fullypos/self.ndithers # y position for each map.
mapGrid=mapIndex/(ndithers*ndithers) + (self.getNlon()/self.ndithers)*ydithered
return [mapIndex%(ndithers*ndithers), mapGrid]
def getMapIndex(self, clustIndex=0, ndithers=None, nLon=None):
# given an index from a dithered map, returns [mapID (aka, dithered hazmap object index), gridIndex (grid index from that hazmap)]
# (less elegant but more straight-forward) (and both of these methods seem to work... at lest they are identically flawed).
if ndithers==None: ndithers=self.ndithers
if nLon==None: nLon=self.getNlon()
#
x=clustIndex%nLon
y=clustIndex/nLon #x,y on dithered haz-map.
#
mapindex=x%ndithers + ndithers*(y%ndithers) # hazardmap index (haxmaps[mapindex])
#
# and the index on the haz-map...
mapx=x/ndithers
mapy=y/ndithers
gridindex=mapx + nLon*mapy/ndithers
#
return [mapindex, gridindex] # so this is [the hazard map being used, grid index on that hazard map]
def findClusters(self, gridlist=[], L=None):
if gridlist==[] or gridlist==None:gridlist=map(operator.itemgetter(2), self.contourData())
if L==None: L=self.getNlon()
# find clusters and their size.
# two passes total (i think) through the data:
# - walk gridk "left" to "right" (aka, i -> i+1). L/R adjacent elements are elements of a cluster; record clusters in clustList=[[j0, [indeces]], [j1, [ind.]] ]
# - if there is an element "below" (aka z[i-L]):
# - z[i] -> that cluster
# - members of z[i]'s cluster are tagged for renaming (add an entry to clustMap -> [j, j']. use new clust number going forward.
# - after walking the grid, update clustList indeces from top to bottom according to clustMap, also top to bottom.
#
mygridlist=scipy.array(gridlist)
clusterIDs=[] # will be same dim. as gridlist, but will be populated with clusterID indices.
clustList=[[0, []]] #[[j0, [indices for j0]], [j1, [indeces for j1]...] # and we might not need the index name j0, j1, ... we can probably just use order as index.
clustMap=[] # when we join two clusters: [[j0, j0'], [j1, j1'], ...]
#
# 1D or 2D array?
#if type(gridlist[0]).__name__ in ('float', 'int')
# more robustly?
'''
if type(gridlist[0])==type(5.0) or type(gridlist[0])==(5):
# in case type names change in later pythons? aka, float64, intlong, or whatever. of course, this dos not guarantee that the flavor
# of float/int we provide is the same.
#
# anyway, this is a 1D array. infer 2D from L.
# let's just cast this into a 2D array (it's got to be one or the other).
mygridlist.shape=(len(gridlist)/L, L) # assuming config. [x0,y0; x1, y0; x2,y0... we should also trap for not integer division, etc.
#
# otherwise, it's a 2D array (or something that will make a big mess and break or produce nonsense).
'''
# but i seem to be happier with 1D array management, so let's do it this way:
if type(gridlist[0]).__name__ in ('tuple', 'list', 'ndarray'):
# elemtens are list-like, aka a 2+D array. assume 2D.
L=len(gridlist[0])
mygridlist.shape=(len(mygridlsit))
#
gridlen=len(gridlist)
'''
if L==None:
# before giving up, guess a square:
squareL=((float(gridlen))**.5)
if squareL%1==0:
# it could be a square...
L=squareL
else:
# we don't know how to parse it.
print "array width not defined."
return None
'''
#
clustIndex=0
#
i=0
while i<gridlen:
if mygridlist[i]==None or mygridlist[i]<=0:
clusterIDs+=[None]
i+=1
continue # though we might just use NONE and to allow for 0-value perimeter padding.
#
# at this point, we've found an occupied site.
#
# is it a (potentially) new cluster on the left edge?
#if i%L==0:
# for starters, assume it is:
myclustID=clustList[-1][0]+1
if i%L>0:
# not on left edge
if clusterIDs[i-1]!=None:
# join cluster to left
myclustID=clusterIDs[i-1]
if i>=L: # a more efficient (aka, not always checking i>=L) approach might be to process the first row separately.
# we've passed the first row. is it part of a prev-row cluster?
if clusterIDs[i-L]!=None:
myclustID=clusterIDs[i-L] # join the cluster below.
# was it part of a cluster to the left?
if clusterIDs[i-1]!=None and clusterIDs[i-1]!=myclustID:
# i suppose we could change this guy while we're here, but it is not necessary. write the label change:
#clustMap+=[[myclustID, clusterIDs[i-1]]] #[[i_from, i_to]] as executed from the top of the list.
clustMap+=[[clusterIDs[i-1], myclustID]] #[[i_from, i_to]] as executed from the top of the list.
#
#
#
# write to cluterIDS:
clusterIDs+=[myclustID]
#
# add this element to its cluter list.
if len(clustList)<(myclustID+1):
# we've added a new cluster.
clustList+=[[myclustID, []]]
clustList[myclustID][1]+=[i]
#
i+=1
# now, stitch together the clusters:
newclusts={}
for i in xrange(len(clustList)):
newclusts[i]=i
clustMap.reverse()
for rw in clustMap:
srcindex=newclusts[rw[0]]
targetindex=newclusts[rw[1]]
#
#clustList[rw[1]][1]+=clustList[rw[0]][1]
#clustList[rw[0]][1]=[]
if targetindex==srcindex:
#print "target/source problem"
continue
clustList[targetindex][1]+=clustList[srcindex][1]
clustList[srcindex][1]=[]
newclusts[srcindex] = targetindex
#
rlist=[]
for rw in clustList:
if len(rw[1])==0: continue
rlist+=[rw[1]]
return rlist
def guessMagL(self, nx=None, ny=None, dithers=1.0, lat=None, gridsize=None, llkm=111.0, gamma=1.0, degs=1):
# gridsize can be calculated from haz-map info.. eventually.
rlat=lat
if degs==1: rlat=2*math.pi*lat/360.
#
xsq=(nx*math.cos(rlat)*gridsize*llkm/dithers)**2. + (ny*gridsize*llkm/dithers)**2
L=xsq**.5
print "length: %f" % L
#
m=2.*math.log10(L*gamma)+3.25
return m
def guessMag(self, nsquares=None, mc=None, deltas=2.0, beta=1.0, rblen=None, dithers=None):
# we must have mc, rblen, dithers, nsquares. other prams we can guess.
#if mc==None or nsquares==None or rblen==None or dithers==None: return None
if mc==None: mc=self.mc
if rblen==None: rblen=self.winlen
if dithers==None: dithers=self.ndithers*self.ndithers
if nsquares==None: nsquares=1 # minimun estimate, though maybe we should break it.
#
return mc + deltas + math.log10(beta*rblen*nsquares/float(dithers))
class rbHazMapLean(object):
#
# a very lean hazard map - aka, just the data. primarily, this class is to be used in a dithered haz-map. all the graphical/mapping
# bits will be in the parent class.
#plt.ion()
def __init__(self, catalog=None, catnum=0, mc=2.5, gridsize=.5, phi=[0,0], winlen=128, avlen=1, bigmag=5.0, logZ=1.0):
# catalog: yp.eqcatalog object, phi: spatial phase shift(s) [lat,lon]
self.catnum=catnum
self.mc=mc
self.gridsize=gridsize
self.phi=phi
self.winlen=winlen
self.avlen=avlen
self.bigmag=bigmag
self.logZ=logZ
#self.fignum=fignum
#self.catalog=catalog
#self.catalog=yp.eqcatalog(catalog.getcat(catnum)) # move to after "if self.catalog==None"
#self.avlen=10
self.precatindex=None
#
self.rbRatios=[] #rb ratios for grid. this will either be a single array [r_0, r1, r2...] or an array of lists; we'll take the last val or average over the
# last rblen vals [[r00, r01, r02...], [r10, r11, r12...], ... ]
#
if catalog==None:
#catalog=getMexicaliCat(mc)
self.catalog=getSocalCat(mc)
# clean up subcats:
else:
self.catalog=yp.eqcatalog(catalog.getcat(catnum))
#
while len(self.catalog.subcats)>0: self.catalog.subcats.pop()
#
try:
self.catalog.rb
#except NameError:
except:
self.catalog.rb=rbi.intervalRecordBreaker(None)
#
#self.thiscat0=self.catalog.getcat(catnum)
self.thiscat0=self.catalog.getcat(catnum)
self.thiscat=self.thiscat0 # simplify this? do we need these two subcats?
#self.thiscat=self.thiscat0
#print "mainEvent: %s" % mev
#
self.llrange=self.catalog.getLatLonRange(self.thiscat)
# maps will stack better if we add phi to both sides of llrange:
self.llrange[0]=[self.llrange[0][0]+phi[0], self.llrange[0][1]+phi[1]]
self.llrange[1]=[self.llrange[1][0]+phi[0], self.llrange[1][1]+phi[1]]
#
self.deltaLat=abs(float(self.llrange[1][0])-self.llrange[0][0])
self.deltaLon=abs(float(self.llrange[1][1])-self.llrange[0][1])
self.Nlat=1+int(self.deltaLat/self.gridsize)
self.Nlon=1+int(self.deltaLon/self.gridsize) #number of cells in row/col
#
self.lat0=self.llrange[0][0]
self.lon0=self.llrange[0][1]
#
ilat=0
ilon=0 # index counters
#
# add empty sub-cats:
#Z0=100 # z-order for squares
for i in xrange(self.Nlat*self.Nlon):
self.catalog.subcats+=[[i, []]]
self.rbRatios+=[[None]]
# thissquare=getSquare([self.getLon(i), self.getLat(i)], self.gridsize) # where [lon, lat] are the ll corner.
#
return None
#
def deleteSubcats(self):
while len(self.catalog.subcats)>0: self.catalog.subcats.pop()
def emptySubcats(self):
for i in xrange(len(self.catalog.subcats)):
self.catalog.subcats[i][1]=[]
def getLat(self, indx):
# clat=int(rw[0]/Nlon)*gridsize+lat0
# clon=int(rw[0]%Nlon)*gridsize+lon0
return float(int(indx/self.Nlon))*self.gridsize+self.lat0
def getLon(self, indx):
return float(int(indx%self.Nlon))*self.gridsize+self.lon0
#
def hazMapTo(self, forecastDate=None, mev=None):
# mev: provide a main-event if the default is not working for us.
if forecastDate==None: return hazMap()
#
# otherwise, make a precat and a post cat.
precat=[]
postcat=[]
#
for rw in self.catalog.getcat(0):
if rw[3]<self.mc: continue
#print rw[0], forecastDate
if mpd.date2num(rw[0])<mpd.date2num(forecastDate): precat+=[rw]
if mpd.date2num(rw[0])>=mpd.date2num(forecastDate): postcat+=[rw]
#
#self.precatindex=len(precat)-1
self.hazMap(precat, postcat, mev)
return None
#
def hazMap(self, preCat=None, postCat=None, mev=None):
# set up the hazard map. divide the catalog into lat/lon bins; assign values to subcats (each bin is a subcat).
if preCat==None: preCat=self.thiscat
if postCat==None:
if mev==None: mev=self.catalog.getMainEvent(self.thiscat0)
postCat=self.thiscat0[mev[4]:]
#if mev==None: mev=self.mev