def sampleGridFile(gridfile, xypoints, method='nearest'):
"""Sample grid file (ESRI or GMT format) at each of a set of XY (decimal degrees) points.
:param gridfile:
Name of ESRI or GMT grid format file from which to sample values.
:param xypoints:
2D numpy array of XY points, decimal degrees.
:param method:
Interpolation method, either 'nearest' or 'linear'.
:returns:
1D numpy array of grid values at each of input XY points.
"""
xmin = np.min(xypoints[:, 0])
xmax = np.max(xypoints[:, 0])
ymin = np.min(xypoints[:, 1])
ymax = np.max(xypoints[:, 1])
gridtype = None
try:
fdict = GMTGrid.getFileGeoDict(gridfile)
gridtype = 'gmt'
except Exception as error:
try:
fdict = GDALGrid.getFileGeoDict(gridfile)
gridtype = 'esri'
except:
pass
if gridtype is None:
raise Exception(
'File "%s" does not appear to be either a GMT grid or an ESRI grid.'
% gridfile)
xmin = xmin - fdict.dx * 3
xmax = xmax + fdict.dx * 3
ymin = ymin - fdict.dy * 3
ymax = ymax + fdict.dy * 3
#bounds = (xmin, xmax, ymin, ymax)
if gridtype == 'gmt':
fgeodict = GMTGrid.getFileGeoDict(gridfile)
else:
fgeodict = GDALGrid.getFileGeoDict(gridfile)
dx, dy = (fgeodict.dx, fgeodict.dy)
sdict = GeoDict.createDictFromBox(xmin, xmax, ymin, ymax, dx, dy)
if gridtype == 'gmt':
grid = GMTGrid.load(gridfile,
samplegeodict=sdict,
resample=False,
method=method,
doPadding=True)
else:
grid = GDALGrid.load(gridfile,
samplegeodict=sdict,
resample=False,
method=method,
doPadding=True)
return sampleFromGrid(grid, xypoints)
def getFileType(filename):
"""
Determine whether input file is a shapefile or a grid (ESRI or GMT).
:param filename:
String path to candidate filename.
:returns:
String, one of 'shapefile','grid','unknown'.
"""
fname, fext = os.path.splitext(filename)
dbf = fname + '.dbf'
ftype = 'unknown'
if os.path.isfile(dbf):
ftype = 'shapefile'
else:
try:
fdict = GMTGrid.getFileGeoDict(filename)
ftype = 'grid'
except Exception as error:
try:
fdict = GDALGrid.getFileGeoDict(filename)
ftype = 'grid'
except:
pass
return ftype
def sampleGridFile(gridfile,xypoints,method='nearest'):
"""
Sample grid file (ESRI or GMT format) at each of a set of XY (decimal degrees) points.
:param gridfile:
Name of ESRI or GMT grid format file from which to sample values.
:param xypoints:
2D numpy array of XY points, decimal degrees.
:param method:
Interpolation method, either 'nearest' or 'linear'.
:returns:
1D numpy array of grid values at each of input XY points.
"""
xmin = np.min(xypoints[:,0])
xmax = np.max(xypoints[:,0])
ymin = np.min(xypoints[:,1])
ymax = np.max(xypoints[:,1])
gridtype = None
try:
fdict = GMTGrid.getFileGeoDict(gridfile)
gridtype = 'gmt'
except Exception,error:
try:
fdict = GDALGrid.getFileGeoDict(gridfile)
gridtype = 'esri'
except:
pass
def getFileType(filename):
"""
Determine whether input file is a shapefile or a grid (ESRI or GMT).
:param filename:
String path to candidate filename.
:returns:
String, one of 'shapefile','grid','unknown'.
"""
fname, fext = os.path.splitext(filename)
dbf = fname + '.dbf'
ftype = 'unknown'
if os.path.isfile(dbf):
ftype = 'shapefile'
else:
try:
fdict = GMTGrid.getFileGeoDict(filename)
ftype = 'grid'
except Exception as error:
try:
fdict = GDALGrid.getFileGeoDict(filename)
ftype = 'grid'
except:
pass
return ftype
def sampleGridFile(gridfile,xypoints,method='nearest'):
"""
Sample grid file (ESRI or GMT format) at each of a set of XY (decimal degrees) points.
:param gridfile:
Name of ESRI or GMT grid format file from which to sample values.
:param xypoints:
2D numpy array of XY points, decimal degrees.
:param method:
Interpolation method, either 'nearest' or 'linear'.
:returns:
1D numpy array of grid values at each of input XY points.
"""
if not len(xypoints):
return np.array([])
xmin = np.min(xypoints[:,0])
xmax = np.max(xypoints[:,0])
ymin = np.min(xypoints[:,1])
ymax = np.max(xypoints[:,1])
gridtype = None
try:
fdict,tmp = GMTGrid.getFileGeoDict(gridfile)
gridtype = 'gmt'
except Exception as error:
try:
fdict,tmp = GDALGrid.getFileGeoDict(gridfile)
gridtype = 'esri'
except:
pass
if gridtype is None:
raise Exception('File "%s" does not appear to be either a GMT grid or an ESRI grid.' % gridfile)
xmin = xmin - fdict.dx*3
xmax = xmax + fdict.dx*3
ymin = ymin - fdict.dy*3
ymax = ymax + fdict.dy*3
bounds = (xmin,xmax,ymin,ymax)
if gridtype == 'gmt':
fgeodict,tmp = GMTGrid.getFileGeoDict(gridfile)
else:
fgeodict,tmp = GDALGrid.getFileGeoDict(gridfile)
dx,dy = (fgeodict.dx,fgeodict.dy)
sdict = GeoDict.createDictFromBox(xmin,xmax,ymin,ymax,dx,dy)
if gridtype == 'gmt':
grid = GMTGrid.load(gridfile,samplegeodict=sdict,resample=True,method=method,doPadding=True)
else:
grid = GDALGrid.load(gridfile,samplegeodict=sdict,resample=True,method=method,doPadding=True)
return sampleFromGrid(grid,xypoints)
def _getFileGeoDict(fname):
geodict = None
try:
geodict = GMTGrid.getFileGeoDict(fname)
except Exception as msg1:
try:
geodict = GDALGrid.getFileGeoDict(fname)
except Exception as msg2:
msg = 'File geodict failure with %s - error messages: "%s"\n "%s"' % (fname,str(msg1),str(msg2))
raise ShakeMapException(msg)
return geodict
def getGridType(gridfile):
gridtype = None
try:
fdict = GMTGrid.getFileGeoDict(gridfile)
gridtype = 'gmt'
except Exception as error:
try:
fdict = GDALGrid.getFileGeoDict(gridfile)
gridtype = 'esri'
except:
pass
return gridtype
def _getFileGeoDict(fname):
geodict = None
try:
geodict = GMTGrid.getFileGeoDict(fname)
except Exception as msg1:
try:
geodict = GDALGrid.getFileGeoDict(fname)
except Exception as msg2:
msg = 'File geodict failure with %s - error messages: "%s"\n "%s"' % (
fname, str(msg1), str(msg2))
raise ShakeMapException(msg)
return geodict
def contains(self, lat, lon):
"""Check to see if input coordinates are contained inside Slab model.
Args:
lat (float): Hypocentral latitude in decimal degrees.
lon (float): Hypocentral longitude in decimal degrees.
Returns:
bool: True if point falls inside minimum bounding box of slab model.
"""
gdict, tmp = GMTGrid.getFileGeoDict(self._depth_file)
gxmin = gdict.xmin
gxmax = gdict.xmax
if lon < 0:
if gxmin > gxmax:
gxmin -= 360
else:
if gxmin > gxmax:
gxmax += 360
if lat >= gdict.ymin and lat <= gdict.ymax and lon >= gxmin and lon <= gxmax:
return True
return False
def getFileGeoDict(filename,gridtype):
if gridtype == 'gmt':
fgeodict,tmp = GMTGrid.getFileGeoDict(filename)
else:
fgeodict,tmp = GDALGrid.getFileGeoDict(filename)
return fgeodict
def run_one_old_shakemap(eventid, topo=True, genex=True):
"""
Convenience method for running old (v 3.5) shakemap with new estimates. This
allows for us to generate all the products with the old code since the new
code cannot do this yet, but use the new code for computing the ground
motions.
Args:
eventid (srt): Specifies the id of the event to process.
topo (bool): Include topography shading?
genex (bool): Should genex be run?
Returns:
dictionary: Each entry is the log file for the different ShakeMap3.5
calls.
"""
config = ConfigObj(os.path.join(os.path.expanduser('~'), 'scenarios.conf'))
shakehome = config['system']['shakehome']
log = {}
shakebin = os.path.join(shakehome, 'bin')
datadir = os.path.join(shakehome, 'data')
# Read event.xml
eventdir = os.path.join(datadir, eventid)
inputdir = os.path.join(eventdir, 'input')
xml_file = os.path.join(inputdir, 'event.xml')
# Read in event.xml
event = read_event_file(xml_file)
# Read in gmpe set name
gmpefile = open(os.path.join(inputdir, "gmpe_set_name.txt"), "r")
set_name = gmpefile.read()
gmpefile.close()
# Add scenario-specific fields:
eventtree = ET.parse(xml_file)
eventroot = eventtree.getroot()
for eq in eventroot.iter('earthquake'):
description = eq.attrib['description']
directivity = eq.attrib['directivity']
if 'reference' in eq.attrib.keys():
reference = eq.attrib['reference']
else:
reference = ''
event['description'] = description
event['directivity'] = directivity
event['reference'] = reference
grd = os.path.join(inputdir, 'pgv_estimates.grd')
gdict = GMTGrid.getFileGeoDict(grd)[0]
# Tolerance is a bit hacky but necessary to prevent GMT
# from barfing becasue it thinks that the estimates files
# do not cover the desired area sampled by grind's call
# with grdsample.
tol = gdict.dx
W = gdict.xmin + tol
E = gdict.xmax - tol
S = gdict.ymin + tol
N = gdict.ymax - tol
# Put into grind.conf (W S E N)
confdir = os.path.join(eventdir, 'config')
if os.path.isdir(confdir) == False:
os.mkdir(confdir)
# need to copy default grind.conf
default_grind_conf = os.path.join(shakehome, 'config', 'grind.conf')
grind_conf = os.path.join(confdir, 'grind.conf')
shutil.copyfile(default_grind_conf, grind_conf)
# Set strictbound and resolution to match estiamtes.grd files
with open(grind_conf, 'a') as f:
f.write('x_grid_interval : %.16f\n' % gdict.dx)
f.write('y_grid_interval : %.16f\n' % gdict.dy)
f.write('strictbound : %.9f %.9f %.9f %.9f\n' % (W, S, E, N))
# Grind
callgrind = os.path.join(shakebin, 'grind') + \
' -event ' + eventid + ' -psa'
rc, so, se = get_command_output(callgrind)
log['grind'] = {'rc': rc, 'so': so, 'se': se}
# Add GMPE set name to info.json
cmd = os.path.join(shakebin, 'edit_info') + ' -event ' + eventid + \
' -tag gmpe_reference' + ' -value ' + set_name
rc, so, se = get_command_output(cmd)
log['edit_info'] = {'rc': rc, 'so': so, 'se': se}
# Tag
calltag = os.path.join(shakebin, 'tag') + \
' -event ' + eventid + ' -name \"' + event['locstring'] + ' - ' + \
event['description'] + '\"'
rc, so, se = get_command_output(calltag)
log['tag'] = {'rc': rc, 'so': so, 'se': se}
# Copy rock_grid.xml from input to output directory
rg_scr = os.path.join(inputdir, 'rock_grid.xml')
rg_dst = os.path.join(eventdir, 'output', 'rock_grid.xml')
cmd = shutil.copy(rg_scr, rg_dst)
# Mapping
if topo is True:
topostr = '-itopo'
else:
topostr = ''
callmapping = os.path.join(shakebin, 'mapping') + ' -event ' + \
eventid + ' -timestamp -nohinges ' + topostr
rc, so, se = get_command_output(callmapping)
log['mapping'] = {'rc': rc, 'so': so, 'se': se}
# Genex
if genex is True:
callgenex = os.path.join(shakebin, 'genex') + ' -event ' + \
eventid + ' -metadata -zip -verbose -shape shape -shape hazus'
rc, so, se = get_command_output(callgenex)
log['genex'] = {'rc': rc, 'so': so, 'se': se}
return log
def drawContourMap(self,outfolder,cmin=None,cmax=None):
if self.contour_colormap is None:
raise ShakeMapException('MapMaker.setGMTColormap() has not been called.')
t0 = time.time()
#resample shakemap to topogrid
#get the geodict for the topo file
topodict = GMTGrid.getFileGeoDict(self.topofile)
#get the geodict for the ShakeMap
smdict = self.shakemap.getGeoDict()
#get a geodict that is aligned with topo, but inside shakemap
sampledict = topodict.getBoundsWithin(smdict)
self.shakemap = self.shakemap.interpolateToGrid(sampledict)
gd = self.shakemap.getGeoDict()
#establish the basemap object
m = self._setMap(gd)
#get topo layer and project it
topogrid = GMTGrid.load(self.topofile,samplegeodict=sampledict,resample=False)
topodata = topogrid.getData().copy()
ptopo = self._projectGrid(topodata,m,gd)
#get contour layer and project it1
imtdata = self.shakemap.getLayer(self.contour_layer).getData().copy()
pimt = self._projectGrid(imtdata,m,gd)
#get the draped intensity data
hillshade = self._getShaded(ptopo)
#draw the draped intensity data
m.imshow(hillshade, interpolation='none',zorder=IMG_ZORDER);
#draw the contours of imt data
xmin = gd.xmin
if gd.xmax < gd.xmin:
xmin -= 360
lons = np.linspace(xmin, gd.xmax, gd.nx)
lats = np.linspace(gd.ymax, gd.ymin, gd.ny) # backwards so it plots right side up
x, y = m(*np.meshgrid(lons,lats))
pimt = gaussian_filter(pimt,5.0)
dmin = pimt.min()
dmax = pimt.max()
levels = self.getContourLevels(dmin,dmax,self.contour_layer)
cs = m.contour(x,y,np.flipud(pimt),colors='w',cmap=None,levels=levels,zorder=CONTOUR_ZORDER)
clabels = plt.clabel(cs,colors='k',fmt='%.1f',fontsize=8.0,zorder=CONTOUR_ZORDER)
for cl in clabels:
bbox = dict(boxstyle="round",facecolor='white',edgecolor='w')
cl.set_bbox(bbox)
cl.set_zorder(CONTOUR_ZORDER)
#draw country/state boundaries
self._drawBoundaries(m)
#draw lakes
self._drawLakes(m,gd)
#draw oceans (pre-processed with islands taken out)
t1 = time.time()
self._drawOceans(m,gd)
t2 = time.time()
print('%.1f seconds to render oceans.' % (t2-t1))
#draw coastlines
self._drawCoastlines(m,gd)
#draw meridians, parallels, labels, ticks
self._drawGraticules(m,gd)
#draw filled symbols for MMI and instrumented measures
self._drawStations(m,fill=True,imt=self.contour_layer)
#draw map scale
scalex = gd.xmin + (gd.xmax-gd.xmin)/5.0
scaley = gd.ymin + (gd.ymax-gd.ymin)/10.0
yoff = (0.007*(m.ymax-m.ymin))
clon = (gd.xmin + gd.xmax)/2.0
clat = (gd.ymin + gd.ymax)/2.0
m.drawmapscale(scalex,scaley,clon,clat,length=100,barstyle='fancy',yoffset=yoff,zorder=SCALE_ZORDER)
#draw fault polygon, if present
self._drawFault(m) #get the fault loaded
#draw epicenter
hlon = self.shakemap.getEventDict()['lon']
hlat = self.shakemap.getEventDict()['lat']
m.plot(hlon,hlat,'k*',latlon=True,fillstyle='none',markersize=22,mew=1.2,zorder=EPICENTER_ZORDER);
#draw cities
#reduce the number of cities to those whose labels don't collide
#set up cities
if self.city_cols is not None:
self.cities = self.cities.limitByBounds((gd.xmin,gd.xmax,gd.ymin,gd.ymax))
self.cities = self.cities.limitByGrid(nx=self.city_cols,ny=self.city_rows,
cities_per_grid=self.cities_per_grid)
self.cities = self.cities.limitByMapCollision(m)
self.cities.renderToMap(m.ax,zorder=CITIES_ZORDER)
#draw title and supertitle
eventid = self._drawTitle(isContour=True)
#draw whatever road data is available
#self._drawRoads(m)
#save plot to file
plt.draw()
outfile = os.path.join(outfolder,'contour_%s_%s.pdf' % (self.contour_layer,eventid))
plt.savefig(outfile)
tn = time.time()
print('%.1f seconds to render entire map.' % (tn-t0))
return outfile
def calculate(self):
"""Calculate the model
:returns:
a dictionary containing the model results and model inputs if saveinputs was set to
True when class was set up, see <https://github.com/usgs/groundfailure#api-for-model-output> for a
description of the structure of this output
"""
X = eval(self.equation)
P = 1 / (1 + np.exp(-X))
if 'vs30max' in self.config[self.model].keys():
vs30 = self.layerdict['vs30'].getData()
P[vs30 > float(self.config[self.model]['vs30max'])] = 0.0
if 'minpgv' in self.config[self.model].keys():
pgv = self.shakemap.getLayer('pgv').getData()
P[pgv < float(self.config[self.model]['minpgv'])] = 0.0
if 'coverage' in self.config[self.model].keys():
eqn = self.config[self.model]['coverage']['eqn']
ind = copy.copy(P)
P = eval(eqn)
if self.uncert is not None:
print(self.numstd)
print(type(self.numstd))
Xmin = eval(self.equationmin)
Xmax = eval(self.equationmax)
Pmin = 1 / (1 + np.exp(-Xmin))
Pmax = 1 / (1 + np.exp(-Xmax))
if 'vs30max' in self.config[self.model].keys():
vs30 = self.layerdict['vs30'].getData()
Pmin[vs30 > float(self.config[self.model]['vs30max'])] = 0.0
Pmax[vs30 > float(self.config[self.model]['vs30max'])] = 0.0
if 'minpgv' in self.config[self.model].keys():
pgv = self.shakemap.getLayer('pgv').getData()
Pmin[pgv < float(self.config[self.model]['minpgv'])] = 0.0
Pmax[pgv < float(self.config[self.model]['minpgv'])] = 0.0
if 'coverage' in self.config[self.model].keys():
eqnmin = eqn.replace('P', 'Pmin')
eqnmax = eqn.replace('P', 'Pmax')
Pmin = eval(eqnmin)
Pmax = eval(eqnmax)
if self.slopefile is not None:
ftype = getFileType(self.slopefile)
sampledict = self.shakemap.getGeoDict()
if ftype == 'gmt':
if GMTGrid.getFileGeoDict(self.slopefile)[0] == sampledict:
slope = GMTGrid.load(
self.slopefile).getData() / self.slopediv
else:
slope = GMTGrid.load(
self.slopefile,
sampledict,
resample=True,
method='linear',
doPadding=True).getData() / self.slopediv
# Apply slope min/max limits
print('applying slope thresholds')
P[slope > self.slopemax] = 0.
P[slope < self.slopemin] = 0.
if self.uncert is not None:
Pmin[slope > self.slopemax] = 0.
Pmin[slope < self.slopemin] = 0.
Pmax[slope > self.slopemax] = 0.
Pmax[slope < self.slopemin] = 0.
elif ftype == 'esri':
if GDALGrid.getFileGeoDict(self.slopefile)[0] == sampledict:
slope = GDALGrid.load(
self.slopefile).getData() / self.slopediv
else:
slope = GDALGrid.load(
self.slopefile,
sampledict,
resample=True,
method='linear',
doPadding=True).getData() / self.slopediv
# Apply slope min/max limits
print('applying slope thresholds')
P[slope > self.slopemax] = 0.
P[slope < self.slopemin] = 0.
if self.uncert is not None:
Pmin[slope > self.slopemax] = 0.
Pmin[slope < self.slopemin] = 0.
Pmax[slope > self.slopemax] = 0.
Pmax[slope < self.slopemin] = 0.
else:
print(
'Slope file does not appear to be a valid GMT or ESRI file, not applying any slope thresholds.'
% (self.slopefile))
else:
print('No slope file provided, slope thresholds not applied')
# Stuff into Grid2D object
temp = self.shakemap.getShakeDict()
shakedetail = '%s_ver%s' % (temp['shakemap_id'],
temp['shakemap_version'])
description = {
'name': self.modelrefs['shortref'],
'longref': self.modelrefs['longref'],
'units': 'probability',
'shakemap': shakedetail,
'parameters': {
'slopemin': self.slopemin,
'slopemax': self.slopemax
}
}
Pgrid = Grid2D(P, self.geodict)
rdict = collections.OrderedDict()
rdict['model'] = {
'grid': Pgrid,
'label': ('%s Probability') % (self.modeltype.capitalize()),
'type': 'output',
'description': description
}
if self.uncert is not None:
rdict['modelmin'] = {
'grid':
Grid2D(Pmin, self.geodict),
'label': ('%s Probability (-%0.1f std ground motion)') %
(self.modeltype.capitalize(), self.numstd),
'type':
'output',
'description':
description
}
rdict['modelmax'] = {
'grid':
Grid2D(Pmax, self.geodict),
'label': ('%s Probability (+%0.1f std ground motion)') %
(self.modeltype.capitalize(), self.numstd),
'type':
'output',
'description':
description
}
if self.saveinputs is True:
for layername, layergrid in list(self.layerdict.items()):
units = self.units[layername]
if units is None:
units = ''
rdict[layername] = {
'grid': layergrid,
'label': '%s (%s)' % (layername, units),
'type': 'input',
'description': {
'units': units,
'shakemap': shakedetail
}
}
for gmused in self.gmused:
if 'pga' in gmused:
units = '%g'
getkey = 'pga'
elif 'pgv' in gmused:
units = 'cm/s'
getkey = 'pgv'
elif 'mmi' in gmused:
units = 'intensity'
getkey = 'mmi'
else:
continue
# Layer is derived from several input layers, skip outputting this layer
if getkey in rdict:
continue
layer = self.shakemap.getLayer(getkey)
rdict[getkey] = {
'grid': layer,
'label': '%s (%s)' % (getkey.upper(), units),
'type': 'input',
'description': {
'units': units,
'shakemap': shakedetail
}
}
if self.uncert is not None:
layer1 = np.exp(
np.log(layer.getData()) -
self.uncert.getLayer('std' + getkey).getData())
rdict[getkey + 'modelmin'] = {
'grid':
Grid2D(layer1, self.geodict),
'label':
'%s - %0.1f std (%s)' %
(getkey.upper(), self.numstd, units),
'type':
'input',
'description': {
'units': units,
'shakemap': shakedetail
}
}
layer2 = np.exp(
np.log(layer.getData()) +
self.uncert.getLayer('std' + getkey).getData())
rdict[getkey + 'modelmax'] = {
'grid':
Grid2D(layer2, self.geodict),
'label':
'%s + %0.1f std (%s)' %
(getkey.upper(), self.numstd, units),
'type':
'input',
'description': {
'units': units,
'shakemap': shakedetail
}
}
return rdict
def __init__(self,
shakefile,
config,
uncertfile=None,
saveinputs=False,
slopefile=None,
slopediv=1.,
bounds=None,
numstd=1):
"""Set up the logistic model
# ADD BOUNDS TO THIS MODEL
:param config: configobj (config .ini file read in using configobj) defining the model and its inputs. Only one
model should be described in each config file.
:type config: dictionary
:param shakefile: Full file path to shakemap.xml file for the event of interest
:type shakefile: string
:param uncertfile: Full file path to xml file of shakemap uncertainties
:type uncertfile: string
:param saveinputs: if True, saves all the input layers as Grid2D objects in addition to the model
if false, it will just output the model
:type saveinputs: boolean
:param slopefile: optional file path to slopefile that will be resampled to the other input files for applying
thresholds OVERWRITES VALUE IN CONFIG
:type slopefile: string
:param slopediv: number to divide slope by to get to degrees (usually will be default
of 1.)
:type slopediv: float
:param numstd: number of +/- standard deviations to use if uncertainty is computed (uncertfile is not None)
"""
mnames = getLogisticModelNames(config)
if len(mnames) == 0:
raise Exception(
'No config file found or problem with config file format')
if len(mnames) > 1:
raise Exception(
'Config file contains more than one model which is no longer allowed,\
update your config file to the newer format')
self.model = mnames[0]
self.config = config
cmodel = config[self.model]
self.modeltype = cmodel['gfetype']
self.coeffs = validateCoefficients(cmodel)
self.layers = validateLayers(
cmodel) # key = layer name, value = file name
self.terms, timeField = validateTerms(cmodel, self.coeffs, self.layers)
self.interpolations = validateInterpolations(cmodel, self.layers)
self.units = validateUnits(cmodel, self.layers)
self.gmused = [
value for term, value in cmodel['terms'].items()
if 'pga' in value.lower() or 'pgv' in value.lower()
or 'mmi' in value.lower()
]
self.modelrefs, self.longrefs, self.shortrefs = validateRefs(cmodel)
self.numstd = numstd
if cmodel['baselayer'] not in list(self.layers.keys()):
raise Exception(
'You must specify a base layer corresponding to one of the files in the layer section.'
)
self.saveinputs = saveinputs
if slopefile is None:
try:
self.slopefile = cmodel['slopefile']
except:
print(
'Could not find slopefile term in config, no slope thresholds will be applied\n'
)
self.slopefile = None
else:
self.slopefile = slopefile
self.slopediv = slopediv
#get the geodict for the shakemap
geodict = ShakeGrid.getFileGeoDict(shakefile, adjust='res')
griddict, eventdict, specdict, fields, uncertainties = getHeaderData(
shakefile)
#YEAR = eventdict['event_timestamp'].year
MONTH = MONTHS[(eventdict['event_timestamp'].month) - 1]
#DAY = eventdict['event_timestamp'].day
#HOUR = eventdict['event_timestamp'].hour
#now find the layer that is our base layer and get the largest bounds we can guarantee not to exceed shakemap bounds
basefile = self.layers[cmodel['baselayer']]
ftype = getFileType(basefile)
if ftype == 'esri':
basegeodict, firstcol = GDALGrid.getFileGeoDict(basefile)
sampledict = basegeodict.getBoundsWithin(geodict)
elif ftype == 'gmt':
basegeodict, firstcol = GMTGrid.getFileGeoDict(basefile)
sampledict = basegeodict.getBoundsWithin(geodict)
else:
raise Exception(
'All predictor variable grids must be a valid GMT or ESRI file type'
)
#now load the shakemap, resampling and padding if necessary
if ShakeGrid.getFileGeoDict(shakefile, adjust='res') == sampledict:
self.shakemap = ShakeGrid.load(shakefile, adjust='res')
flag = 1
else:
self.shakemap = ShakeGrid.load(shakefile,
samplegeodict=sampledict,
resample=True,
doPadding=True,
adjust='res')
flag = 0
# take uncertainties into account
if uncertfile is not None:
try:
if flag == 1:
self.uncert = ShakeGrid.load(uncertfile, adjust='res')
else:
self.uncert = ShakeGrid.load(uncertfile,
samplegeodict=sampledict,
resample=True,
doPadding=True,
adjust='res')
except:
print(
'Could not read uncertainty file, ignoring uncertainties')
self.uncert = None
else:
self.uncert = None
#load the predictor layers into a dictionary
self.layerdict = {} # key = layer name, value = grid object
for layername, layerfile in self.layers.items():
if isinstance(layerfile, list):
for lfile in layerfile:
if timeField == 'MONTH':
if lfile.find(MONTH) > -1:
layerfile = lfile
ftype = getFileType(layerfile)
interp = self.interpolations[layername]
if ftype == 'gmt':
if GMTGrid.getFileGeoDict(
layerfile)[0] == sampledict:
lyr = GMTGrid.load(layerfile)
else:
lyr = GMTGrid.load(layerfile,
sampledict,
resample=True,
method=interp,
doPadding=True)
elif ftype == 'esri':
if GDALGrid.getFileGeoDict(
layerfile)[0] == sampledict:
lyr = GDALGrid.load(layerfile)
else:
lyr = GDALGrid.load(layerfile,
sampledict,
resample=True,
method=interp,
doPadding=True)
else:
msg = 'Layer %s (file %s) does not appear to be a valid GMT or ESRI file.' % (
layername, layerfile)
raise Exception(msg)
self.layerdict[layername] = lyr
else:
#first, figure out what kind of file we have (or is it a directory?)
ftype = getFileType(layerfile)
interp = self.interpolations[layername]
if ftype == 'gmt':
if GMTGrid.getFileGeoDict(layerfile)[0] == sampledict:
lyr = GMTGrid.load(layerfile)
else:
lyr = GMTGrid.load(layerfile,
sampledict,
resample=True,
method=interp,
doPadding=True)
elif ftype == 'esri':
if GDALGrid.getFileGeoDict(layerfile)[0] == sampledict:
lyr = GDALGrid.load(layerfile)
else:
lyr = GDALGrid.load(layerfile,
sampledict,
resample=True,
method=interp,
doPadding=True)
else:
msg = 'Layer %s (file %s) does not appear to be a valid GMT or ESRI file.' % (
layername, layerfile)
raise Exception(msg)
self.layerdict[layername] = lyr
shapes = {}
for layername, layer in self.layerdict.items():
shapes[layername] = layer.getData().shape
self.nuggets = [str(self.coeffs['b0'])]
ckeys = list(self.terms.keys())
ckeys.sort()
for key in ckeys:
term = self.terms[key]
coeff = self.coeffs[key]
self.nuggets.append('(%g * %s)' % (coeff, term))
self.equation = ' + '.join(self.nuggets)
if self.uncert is not None:
self.nugmin = copy.copy(self.nuggets)
self.nugmax = copy.copy(self.nuggets)
# Find the term with the shakemap input and replace for these nuggets
for k, nug in enumerate(self.nuggets):
if "self.shakemap.getLayer('pga').getData()" in nug:
self.nugmin[k] = self.nugmin[k].replace(
"self.shakemap.getLayer('pga').getData()",
"(np.exp(np.log(self.shakemap.getLayer('pga').getData())\
- self.numstd * self.uncert.getLayer('stdpga').getData()))"
)
self.nugmax[k] = self.nugmax[k].replace(
"self.shakemap.getLayer('pga').getData()",
"(np.exp(np.log(self.shakemap.getLayer('pga').getData())\
+ self.numstd * self.uncert.getLayer('stdpga').getData()))"
)
elif "self.shakemap.getLayer('pgv').getData()" in nug:
self.nugmin[k] = self.nugmin[k].replace(
"self.shakemap.getLayer('pgv').getData()",
"(np.exp(np.log(self.shakemap.getLayer('pgv').getData())\
- self.numstd * self.uncert.getLayer('stdpgv').getData()))"
)
self.nugmax[k] = self.nugmax[k].replace(
"self.shakemap.getLayer('pgv').getData()",
"(np.exp(np.log(self.shakemap.getLayer('pgv').getData())\
+ self.numstd * self.uncert.getLayer('stdpgv').getData()))"
)
elif "self.shakemap.getLayer('mmi').getData()" in nug:
self.nugmin[k] = self.nugmin[k].replace(
"self.shakemap.getLayer('mmi').getData()",
"(np.exp(np.log(self.shakemap.getLayer('mmi').getData())\
- self.numstd * self.uncert.getLayer('stdmmi').getData()))"
)
self.nugmax[k] = self.nugmax[k].replace(
"self.shakemap.getLayer('mmi').getData()",
"(np.exp(np.log(self.shakemap.getLayer('mmi').getData())\
+ self.numstd * self.uncert.getLayer('stdmmi').getData()))"
)
self.equationmin = ' + '.join(self.nugmin)
self.equationmax = ' + '.join(self.nugmax)
else:
self.equationmin = None
self.equationmax = None
self.geodict = self.shakemap.getGeoDict()
try:
self.slopemin = float(config[self.model]['slopemin'])
self.slopemax = float(config[self.model]['slopemax'])
except:
print(
'could not find slopemin and/or slopemax in config, no limits will be applied'
)
self.slopemin = 0.
self.slopemax = 90.
def __init__(self, config, shakefile, model, uncertfile=None):
"""Set up the logistic model
:param config: configobj (config .ini file read in using configobj) defining the model and its inputs
:type config: dictionary
:param shakefile: Full file path to shakemap.xml file for the event of interest
:type shakefile: string
:param model: Name of model defined in config that should be run for the event of interest
:type model: string
:param uncertfile:
:type uncertfile:
"""
if model not in getLogisticModelNames(config):
raise Exception('Could not find a model called "%s" in config %s.' % (model, config))
#do everything here short of calculations - parse config, assemble eqn strings, load data.
self.model = model
cmodel = config['logistic_models'][model]
self.modeltype = cmodel['gfetype']
self.coeffs = validateCoefficients(cmodel)
self.layers = validateLayers(cmodel) # key = layer name, value = file name
self.terms, timeField = validateTerms(cmodel, self.coeffs, self.layers)
self.interpolations = validateInterpolations(cmodel, self.layers)
self.units = validateUnits(cmodel, self.layers)
self.gmused = [value for term, value in cmodel['terms'].items() if 'pga' in value.lower() or 'pgv' in
value.lower() or 'mmi' in value.lower()]
self.modelrefs, self.longrefs, self.shortrefs = validateRefs(cmodel)
if 'baselayer' not in cmodel:
raise Exception('You must specify a base layer file in config.')
if cmodel['baselayer'] not in list(self.layers.keys()):
raise Exception('You must specify a base layer corresponding to one of the files in the layer section.')
#get the geodict for the shakemap
geodict = ShakeGrid.getFileGeoDict(shakefile, adjust='res')
griddict, eventdict, specdict, fields, uncertainties = getHeaderData(shakefile)
#YEAR = eventdict['event_timestamp'].year
MONTH = MONTHS[(eventdict['event_timestamp'].month)-1]
#DAY = eventdict['event_timestamp'].day
#HOUR = eventdict['event_timestamp'].hour
#now find the layer that is our base layer and get the largest bounds we can guarantee not to exceed shakemap bounds
basefile = self.layers[cmodel['baselayer']]
ftype = getFileType(basefile)
if ftype == 'esri':
basegeodict, firstcol = GDALGrid.getFileGeoDict(basefile)
sampledict = basegeodict.getBoundsWithin(geodict)
elif ftype == 'gmt':
basegeodict, firstcol = GMTGrid.getFileGeoDict(basefile)
sampledict = basegeodict.getBoundsWithin(geodict)
else:
raise Exception('All predictor variable grids must be a valid GMT or ESRI file type')
#now load the shakemap, resampling and padding if necessary
self.shakemap = ShakeGrid.load(shakefile, samplegeodict=sampledict, resample=True, doPadding=True, adjust='res')
# take uncertainties into account
if uncertfile is not None:
try:
self.uncert = ShakeGrid.load(uncertfile, samplegeodict=sampledict, resample=True, doPadding=True,
adjust='res')
except:
print('Could not read uncertainty file, ignoring uncertainties')
self.uncert = None
else:
self.uncert = None
#load the predictor layers into a dictionary
self.layerdict = {} # key = layer name, value = grid object
for layername, layerfile in self.layers.items():
if isinstance(layerfile, list):
for lfile in layerfile:
if timeField == 'MONTH':
if lfile.find(MONTH) > -1:
layerfile = lfile
ftype = getFileType(layerfile)
interp = self.interpolations[layername]
if ftype == 'gmt':
lyr = GMTGrid.load(layerfile, sampledict, resample=True, method=interp, doPadding=True)
elif ftype == 'esri':
lyr = GDALGrid.load(layerfile, sampledict, resample=True, method=interp, doPadding=True)
else:
msg = 'Layer %s (file %s) does not appear to be a valid GMT or ESRI file.' % (layername, layerfile)
raise Exception(msg)
self.layerdict[layername] = lyr
else:
#first, figure out what kind of file we have (or is it a directory?)
ftype = getFileType(layerfile)
interp = self.interpolations[layername]
if ftype == 'gmt':
lyr = GMTGrid.load(layerfile, sampledict, resample=True, method=interp, doPadding=True)
elif ftype == 'esri':
lyr = GDALGrid.load(layerfile, sampledict, resample=True, method=interp, doPadding=True)
else:
msg = 'Layer %s (file %s) does not appear to be a valid GMT or ESRI file.' % (layername, layerfile)
raise Exception(msg)
self.layerdict[layername] = lyr
shapes = {}
for layername, layer in self.layerdict.items():
shapes[layername] = layer.getData().shape
self.nuggets = [str(self.coeffs['b0'])]
ckeys = list(self.terms.keys())
ckeys.sort()
for key in ckeys:
term = self.terms[key]
coeff = self.coeffs[key]
self.nuggets.append('(%g * %s)' % (coeff, term))
self.equation = ' + '.join(self.nuggets)
if self.uncert is not None:
self.nugmin = copy.copy(self.nuggets)
self.nugmax = copy.copy(self.nuggets)
# Find the term with the shakemap input and replace for these nuggets
for k, nug in enumerate(self.nuggets):
if "self.shakemap.getLayer('pga').getData()" in nug:
self.nugmin[k] = self.nugmin[k].replace("self.shakemap.getLayer('pga').getData()", "(np.exp(np.log(self.shakemap.getLayer('pga').getData()) - self.uncert.getLayer('stdpga').getData()))")
self.nugmax[k] = self.nugmax[k].replace("self.shakemap.getLayer('pga').getData()", "(np.exp(np.log(self.shakemap.getLayer('pga').getData()) + self.uncert.getLayer('stdpga').getData()))")
elif "self.layerdict['pgv'].getData()" in nug:
self.nugmin[k] = self.nugmin[k].replace("self.shakemap.getLayer('pgv').getData()", "(np.exp(np.log(self.shakemap.getLayer('pgv').getData()) - self.uncert.getLayer('stdpgv').getData()))")
self.nugmax[k] = self.nugmax[k].replace("self.shakemap.getLayer('pgv').getData()", "(np.exp(np.log(self.shakemap.getLayer('pgv').getData()) + self.uncert.getLayer('stdpgv').getData()))")
elif "self.layerdict['mmi'].getData()" in nug:
self.nugmin[k] = self.nugmin[k].replace("self.shakemap.getLayer('mmi').getData()", "(np.exp(np.log(self.shakemap.getLayer('mmi').getData()) - self.uncert.getLayer('stdmmi').getData()))")
self.nugmax[k] = self.nugmax[k].replace("self.shakemap.getLayer('mmi').getData()", "(np.exp(np.log(self.shakemap.getLayer('mmi').getData()) + self.uncert.getLayer('stdmmi').getData()))")
self.equationmin = ' + '.join(self.nugmin)
self.equationmax = ' + '.join(self.nugmax)
else:
self.equationmin = None
self.equationmax = None
self.geodict = self.shakemap.getGeoDict()
try:
self.slopemin = float(config['logistic_models'][model]['slopemin'])
self.slopemax = float(config['logistic_models'][model]['slopemax'])
except:
print('could not find slopemin and/or slopemax in config, no limits will be applied')
self.slopemin = 0.
self.slopemax = 90.
def drawContourMap(self, imt, outfolder, cmin=None, cmax=None):
"""
Render IMT data as contours over topography, with oceans, coastlines,
etc.
Args:
outfolder (str): Path to directory where output map should be
saved.
Returns:
str: Path to output IMT map.
"""
if self.contour_colormap is None:
raise Exception('MapMaker.setGMTColormap() has not been called.')
t0 = time.time()
# resample shakemap to topogrid
# get the geodict for the topo file
topodict = GMTGrid.getFileGeoDict(self.topofile)[0]
# get the geodict for the ShakeMap
comp = self.container.getComponents(imt)[0]
imtdict = self.container.getIMTGrids(imt, comp)
imtgrid = imtdict['mean']
smdict = imtgrid.getGeoDict()
# get a geodict that is aligned with topo, but inside shakemap
sampledict = topodict.getBoundsWithin(smdict)
imtgrid = imtgrid.interpolateToGrid(sampledict)
gd = imtgrid.getGeoDict()
# establish the basemap object
m = self._setMap(gd)
# get topo layer and project it
topogrid = GMTGrid.load(
self.topofile, samplegeodict=sampledict, resample=False)
topodata = topogrid.getData().copy()
ptopo = self._projectGrid(topodata, m, gd)
# get contour layer and project it1
imtdata = imtgrid.getData().copy()
# convert units if necessary
if imt == 'MMI':
pass
elif imt == 'PGV':
imtdata = np.exp(imtdata)
else:
imtdata = np.exp(imtdata) * 100
pimt = self._projectGrid(imtdata, m, gd)
# get the draped intensity data
hillshade = self._getShaded(ptopo)
# draw the draped intensity data
m.imshow(hillshade, interpolation='none', zorder=IMG_ZORDER)
# draw the contours of imt data
xmin = gd.xmin
if gd.xmax < gd.xmin:
xmin -= 360
lons = np.linspace(xmin, gd.xmax, gd.nx)
# backwards so it plots right side up
lats = np.linspace(gd.ymax, gd.ymin, gd.ny)
x, y = m(*np.meshgrid(lons, lats))
pimt = gaussian_filter(pimt, 5.0)
dmin = pimt.min()
dmax = pimt.max()
levels = self.getContourLevels(dmin, dmax, imt)
cs = m.contour(x, y, np.flipud(pimt), colors='w',
cmap=None, levels=levels, zorder=CONTOUR_ZORDER)
clabels = plt.clabel(cs, colors='k', fmt='%.1f',
fontsize=8.0, zorder=CONTOUR_ZORDER)
for cl in clabels:
bbox = dict(boxstyle="round", facecolor='white', edgecolor='w')
cl.set_bbox(bbox)
cl.set_zorder(CONTOUR_ZORDER)
# draw country/state boundaries
self._drawBoundaries(m)
# draw lakes
self._drawLakes(m, gd)
# draw oceans (pre-processed with islands taken out)
t1 = time.time()
self._drawOceans(m, gd)
t2 = time.time()
self.logger.debug('%.1f seconds to render oceans.' % (t2 - t1))
# draw coastlines
self._drawCoastlines(m, gd)
# draw meridians, parallels, labels, ticks
self._drawGraticules(m, gd)
# draw filled symbols for MMI and instrumented measures
self._drawStations(m, fill=True, imt=imt)
# draw map scale
self._drawMapScale(m, gd)
# draw fault polygon, if present
self._drawFault(m) # get the fault loaded
# draw epicenter
origin = self.fault.getOrigin()
hlon = origin.lon
hlat = origin.lat
m.plot(hlon, hlat, 'k*', latlon=True, fillstyle='none',
markersize=22, mew=1.2, zorder=EPICENTER_ZORDER)
# draw cities
# reduce the number of cities to those whose labels don't collide
# set up cities
if self.city_cols is not None:
self.cities = self.cities.limitByBounds(
(gd.xmin, gd.xmax, gd.ymin, gd.ymax))
self.cities = self.cities.limitByGrid(
nx=self.city_cols, ny=self.city_rows,
cities_per_grid=self.cities_per_grid)
if 'Times New Roman' in self.cities._fontlist:
font = 'Times New Roman'
else:
font = 'DejaVu Sans'
self.cities = self.cities.limitByMapCollision(m, fontname=font)
self.cities.renderToMap(m.ax, zorder=CITIES_ZORDER)
# draw title and supertitle
self._drawTitle(imt)
# save plot to file
fileimt = oq_to_file(imt)
plt.draw()
outfile = os.path.join(outfolder, 'contour_%s.pdf' %
(fileimt))
plt.savefig(outfile)
tn = time.time()
self.logger.debug('%.1f seconds to render entire map.' % (tn - t0))
return outfile
def drawIntensityMap(self, outfolder):
"""
Render the MMI data as intensity draped over topography, with oceans,
coastlines, etc.
Args:
outfolder (str): Path to directory where output map should be
saved.
Returns:
str: Path to output intensity map.
"""
t0 = time.time()
# resample shakemap to topogrid
# get the geodict for the topo file
topodict = GMTGrid.getFileGeoDict(self.topofile)[0]
# get the geodict for the ShakeMap
comp = self.container.getComponents('MMI')[0]
imtdict = self.container.getIMTGrids('MMI', comp)
mmigrid = imtdict['mean']
smdict = mmigrid.getGeoDict()
# get a geodict that is aligned with topo, but inside shakemap
sampledict = topodict.getBoundsWithin(smdict)
mmigrid = mmigrid.interpolateToGrid(sampledict)
gd = mmigrid.getGeoDict()
# establish the basemap object
m = self._setMap(gd)
# get topo layer and project it
topogrid = GMTGrid.load(
self.topofile, samplegeodict=sampledict, resample=False)
topodata = topogrid.getData().copy()
ptopo = self._projectGrid(topodata, m, gd)
# get intensity layer and project it
imtdata = mmigrid.getData().copy()
pimt = self._projectGrid(imtdata, m, gd)
# get the draped intensity data
draped_hsv = self._getDraped(pimt, ptopo) # where will 10.0 come from
# draw the draped intensity data
m.imshow(draped_hsv, interpolation='none', zorder=IMG_ZORDER)
# draw country/state boundaries
self._drawBoundaries(m)
# draw whatever road data is available
# self.logger.debug('Drawing roads...')
# self._drawRoads(m)
# self.logger.debug('Done drawing roads...')
# draw lakes
self._drawLakes(m, gd)
# draw oceans (pre-processed with islands taken out)
t1 = time.time()
self._drawOceans(m, gd)
t2 = time.time()
self.logger.debug('%.1f seconds to render oceans.' % (t2 - t1))
# draw coastlines
self._drawCoastlines(m, gd)
# draw meridians, parallels, labels, ticks
self._drawGraticules(m, gd)
# draw map scale
self._drawMapScale(m, gd)
# draw fault polygon, if present
self._drawFault(m) # get the fault loaded
# draw epicenter
origin = self.fault.getOrigin()
hlon = origin.lon
hlat = origin.lat
m.plot(hlon, hlat, 'k*', latlon=True, fillstyle='none',
markersize=22, mew=1.2, zorder=EPICENTER_ZORDER)
# draw cities
# reduce the number of cities to those whose labels don't collide
# set up cities
if self.city_cols is not None:
self.cities = self.cities.limitByBounds(
(gd.xmin, gd.xmax, gd.ymin, gd.ymax))
self.cities = self.cities.limitByGrid(
nx=self.city_cols, ny=self.city_rows,
cities_per_grid=self.cities_per_grid)
# self.logger.debug("Available fonts: ", self.cities._fontlist)
if 'Times New Roman' in self.cities._fontlist:
font = 'Times New Roman'
else:
font = 'DejaVu Sans'
self.cities = self.cities.limitByMapCollision(m, fontname=font)
self.cities.renderToMap(m.ax, zorder=CITIES_ZORDER)
# draw title and supertitle
self._drawTitle('MMI')
# draw station and macroseismic locations
self._drawStations(m) # need stationlist object
# save plot to file
plt.draw()
outfile = os.path.join(outfolder, 'intensity.pdf')
plt.savefig(outfile)
tn = time.time()
self.logger.debug('%.1f seconds to render entire map.' % (tn - t0))
return outfile
def __init__(self,config,shakefile,model):
if model not in getLogisticModelNames(config):
raise Exception('Could not find a model called "%s" in config %s.' % (model,config))
#do everything here short of calculations - parse config, assemble eqn strings, load data.
self.model = model
cmodel = config['logistic_models'][model]
self.coeffs = validateCoefficients(cmodel)
self.layers = validateLayers(cmodel)#key = layer name, value = file name
self.terms,timeField = validateTerms(cmodel,self.coeffs,self.layers)
self.interpolations = validateInterpolations(cmodel,self.layers)
self.units = validateUnits(cmodel,self.layers)
if 'baselayer' not in cmodel:
raise Exception('You must specify a base layer file in config.')
if cmodel['baselayer'] not in list(self.layers.keys()):
raise Exception('You must specify a base layer corresponding to one of the files in the layer section.')
#get the geodict for the shakemap
geodict = ShakeGrid.getFileGeoDict(shakefile,adjust='res')
griddict,eventdict,specdict,fields,uncertainties = getHeaderData(shakefile)
YEAR = eventdict['event_timestamp'].year
MONTH = MONTHS[(eventdict['event_timestamp'].month)-1]
DAY = eventdict['event_timestamp'].day
HOUR = eventdict['event_timestamp'].hour
#now find the layer that is our base layer and get the largest bounds we can guaranteed not to exceed shakemap bounds
basefile = self.layers[cmodel['baselayer']]
ftype = getFileType(basefile)
if ftype == 'esri':
basegeodict = GDALGrid.getFileGeoDict(basefile)
sampledict = basegeodict.getBoundsWithin(geodict)
elif ftype == 'gmt':
basegeodict = GMTGrid.getFileGeoDict(basefile)
sampledict = basegeodict.getBoundsWithin(geodict)
else:
raise Exception('All predictor variable grids must be a valid GMT or ESRI file type')
#now load the shakemap, resampling and padding if necessary
self.shakemap = ShakeGrid.load(shakefile,samplegeodict=sampledict,resample=True,doPadding=True,adjust='res')
#load the predictor layers into a dictionary
self.layerdict = {} #key = layer name, value = grid object
for layername,layerfile in self.layers.items():
if isinstance(layerfile,list):
for lfile in layerfile:
if timeField == 'MONTH':
if lfile.find(MONTH) > -1:
layerfile = lfile
ftype = getFileType(layerfile)
interp = self.interpolations[layername]
if ftype == 'gmt':
lyr = GMTGrid.load(layerfile,sampledict,resample=True,method=interp,doPadding=True)
elif ftype == 'esri':
lyr = GDALGrid.load(layerfile,sampledict,resample=True,method=interp,doPadding=True)
else:
msg = 'Layer %s (file %s) does not appear to be a valid GMT or ESRI file.' % (layername,layerfile)
raise Exception(msg)
self.layerdict[layername] = lyr
else:
#first, figure out what kind of file we have (or is it a directory?)
ftype = getFileType(layerfile)
interp = self.interpolations[layername]
if ftype == 'gmt':
lyr = GMTGrid.load(layerfile,sampledict,resample=True,method=interp,doPadding=True)
elif ftype == 'esri':
lyr = GDALGrid.load(layerfile,sampledict,resample=True,method=interp,doPadding=True)
else:
msg = 'Layer %s (file %s) does not appear to be a valid GMT or ESRI file.' % (layername,layerfile)
raise Exception(msg)
self.layerdict[layername] = lyr
shapes = {}
for layername,layer in self.layerdict.items():
shapes[layername] = layer.getData().shape
x = 1
self.nuggets = [str(self.coeffs['b0'])]
ckeys = list(self.terms.keys())
ckeys.sort()
for key in ckeys:
term = self.terms[key]
coeff = self.coeffs[key]
self.nuggets.append('(%g * %s)' % (coeff, term))
self.equation = ' + '.join(self.nuggets)
self.geodict = self.shakemap.getGeoDict()
def drawIntensityMap(self,outfolder):
if self.intensity_colormap is None:
raise ShakeMapException('MapMaker.setGMTColormap() has not been called.')
t0 = time.time()
#resample shakemap to topogrid
#get the geodict for the topo file
topodict = GMTGrid.getFileGeoDict(self.topofile)
#get the geodict for the ShakeMap
smdict = self.shakemap.getGeoDict()
#get a geodict that is aligned with topo, but inside shakemap
sampledict = topodict.getBoundsWithin(smdict)
self.shakemap = self.shakemap.interpolateToGrid(sampledict)
gd = self.shakemap.getGeoDict()
#establish the basemap object
m = self._setMap(gd)
#get topo layer and project it
topogrid = GMTGrid.load(self.topofile,samplegeodict=sampledict,resample=False)
topodata = topogrid.getData().copy()
ptopo = self._projectGrid(topodata,m,gd)
#get intensity layer and project it
imtdata = self.shakemap.getLayer(self.imt_layer).getData().copy()
pimt = self._projectGrid(imtdata,m,gd)
#get the draped intensity data
draped_hsv = self._getDraped(pimt,ptopo) #where will 10.0 come from
#draw the draped intensity data
m.imshow(draped_hsv, interpolation='none',zorder=IMG_ZORDER);
#draw country/state boundaries
self._drawBoundaries(m)
#draw whatever road data is available
self._drawRoads(m)
#draw lakes
self._drawLakes(m,gd)
#draw oceans (pre-processed with islands taken out)
t1 = time.time()
self._drawOceans(m,gd)
t2 = time.time()
print('%.1f seconds to render oceans.' % (t2-t1))
#draw coastlines
self._drawCoastlines(m,gd)
#draw meridians, parallels, labels, ticks
self._drawGraticules(m,gd)
#draw map scale
scalex = gd.xmin + (gd.xmax-gd.xmin)/5.0
scaley = gd.ymin + (gd.ymax-gd.ymin)/10.0
yoff = (0.007*(m.ymax-m.ymin))
clon = (gd.xmin + gd.xmax)/2.0
clat = (gd.ymin + gd.ymax)/2.0
m.drawmapscale(scalex,scaley,clon,clat,length=100,barstyle='fancy',yoffset=yoff,zorder=SCALE_ZORDER)
#draw fault polygon, if present
self._drawFault(m) #get the fault loaded
#draw epicenter
hlon = self.shakemap.getEventDict()['lon']
hlat = self.shakemap.getEventDict()['lat']
m.plot(hlon,hlat,'k*',latlon=True,fillstyle='none',markersize=22,mew=1.2,zorder=EPICENTER_ZORDER);
#draw cities
#reduce the number of cities to those whose labels don't collide
#set up cities
if self.city_cols is not None:
self.cities = self.cities.limitByBounds((gd.xmin,gd.xmax,gd.ymin,gd.ymax))
self.cities = self.cities.limitByGrid(nx=self.city_cols,ny=self.city_rows,
cities_per_grid=self.cities_per_grid)
self.cities = self.cities.limitByMapCollision(m)
self.cities.renderToMap(m.ax,zorder=CITIES_ZORDER)
#draw title and supertitle
eventid = self._drawTitle()
#draw station and macroseismic locations
self._drawStations(m) #need stationlist object
#save plot to file
plt.draw()
outfile = os.path.join(outfolder,'intensity_%s.pdf' % eventid)
plt.savefig(outfile)
tn = time.time()
print('%.1f seconds to render entire map.' % (tn-t0))
return outfile
def __init__(self,
shakefile,
config,
uncertfile=None,
saveinputs=False,
slopefile=None,
bounds=None,
slopemod=None,
trimfile=None):
"""
Sets up the logistic model
Args:
shakefile (str): Path to shakemap grid.xml file for the event.
config: configobj object defining the model and its inputs. Only
one model should be described in each config file.
uncertfile (str): Path to uncertainty.xml file.
saveinputs (bool): Save input layers as Grid2D objects in addition
to the model? If false (the default), it will just output the
model.
slopefile (str): Optional path to slopefile that will be resampled
to the other input files for applying thresholds. OVERWRITES
VALUE IN CONFIG.
bounds (dict): Default of None uses ShakeMap boundaries, otherwise
a dictionary of boundaries to cut to like
.. code-block:: python
bounds = {
'xmin': lonmin, 'xmax': lonmax,
'ymin': latmin, 'ymax': latmax
}
slopemod (str): How slope input should be modified to be in
degrees: e.g., ``np.arctan(slope) * 180. / np.pi`` or
``slope/100.`` (note that this may be in the config file
already).
trimfile (str): shapefile of earth's landmasses to use to cut
offshore areas.
"""
mnames = getLogisticModelNames(config)
if len(mnames) == 0:
raise Exception('No config file found or problem with config '
'file format')
if len(mnames) > 1:
raise Exception('Config file contains more than one model which '
'is no longer allowed, update your config file '
'to the newer format')
self.model = mnames[0]
self.config = config
cmodel = config[self.model]
self.modeltype = cmodel['gfetype']
self.coeffs = validateCoefficients(cmodel)
# key = layer name, value = file name
self.layers = validateLayers(cmodel)
self.terms, timeField = validateTerms(cmodel, self.coeffs, self.layers)
self.interpolations = validateInterpolations(cmodel, self.layers)
self.units = validateUnits(cmodel, self.layers)
self.gmused = [
value for term, value in cmodel['terms'].items()
if 'pga' in value.lower() or 'pgv' in value.lower()
or 'mmi' in value.lower()
]
self.modelrefs, self.longrefs, self.shortrefs = validateRefs(cmodel)
#self.numstd = numstd
self.clips = validateClips(cmodel, self.layers, self.gmused)
self.notes = ''
if cmodel['baselayer'] not in list(self.layers.keys()):
raise Exception('You must specify a base layer corresponding to '
'one of the files in the layer section.')
self.saveinputs = saveinputs
if slopefile is None:
try:
self.slopefile = cmodel['slopefile']
except:
# print('Slopefile not specified in config, no slope '
# 'thresholds will be applied\n')
self.slopefile = None
else:
self.slopefile = slopefile
if slopemod is None:
try:
self.slopemod = cmodel['slopemod']
except:
self.slopemod = None
# See if trimfile exists
if trimfile is not None:
if not os.path.exists(trimfile):
print('trimfile defined does not exist: %s\nOcean will not be '
'trimmed' % trimfile)
self.trimfile = None
elif os.path.splitext(trimfile)[1] != '.shp':
print('trimfile must be a shapefile, ocean will not be '
'trimmed')
self.trimfile = None
else:
self.trimfile = trimfile
else:
self.trimfile = None
# Get month of event
griddict, eventdict, specdict, fields, uncertainties = \
getHeaderData(shakefile)
MONTH = MONTHS[(eventdict['event_timestamp'].month) - 1]
# Figure out how/if need to cut anything
geodict = ShakeGrid.getFileGeoDict(shakefile, adjust='res')
if bounds is not None: # Make sure bounds are within ShakeMap Grid
if geodict.xmin < geodict.xmax: # only if signs are not opposite
if (geodict.xmin > bounds['xmin']
or geodict.xmax < bounds['xmax']
or geodict.ymin > bounds['ymin']
or geodict.ymax < bounds['ymax']):
print('Specified bounds are outside shakemap area, using '
'ShakeMap bounds instead.')
bounds = None
if bounds is not None:
tempgdict = GeoDict.createDictFromBox(bounds['xmin'],
bounds['xmax'],
bounds['ymin'],
bounds['ymax'],
geodict.dx,
geodict.dy,
inside=False)
# If Shakemap geodict crosses 180/-180 line, fix geodict so things don't break
if geodict.xmin > geodict.xmax:
if tempgdict.xmin < 0:
geodict._xmin -= 360.
else:
geodict._xmax += 360.
gdict = geodict.getBoundsWithin(tempgdict)
else:
gdict = geodict
# Now find the layer that is our base layer and get the largest bounds
# we can guarantee not to exceed shakemap bounds
basefile = self.layers[cmodel['baselayer']]
ftype = getFileType(basefile)
if ftype == 'esri':
basegeodict, firstcol = GDALGrid.getFileGeoDict(basefile)
if basegeodict == gdict:
sampledict = gdict
else:
sampledict = basegeodict.getBoundsWithin(gdict)
elif ftype == 'gmt':
basegeodict, firstcol = GMTGrid.getFileGeoDict(basefile)
if basegeodict == gdict:
sampledict = gdict
else:
sampledict = basegeodict.getBoundsWithin(gdict)
else:
raise Exception('All predictor variable grids must be a valid '
'GMT or ESRI file type.')
# Do we need to subdivide baselayer?
if 'divfactor' in self.config[self.model].keys():
divfactor = float(self.config[self.model]['divfactor'])
if divfactor != 1.:
# adjust sampledict so everything will be resampled
newxmin = sampledict.xmin - sampledict.dx / \
2. + sampledict.dx/(2.*divfactor)
newymin = sampledict.ymin - sampledict.dy / \
2. + sampledict.dy/(2.*divfactor)
newxmax = sampledict.xmax + sampledict.dx / \
2. - sampledict.dx/(2.*divfactor)
newymax = sampledict.ymax + sampledict.dy / \
2. - sampledict.dy/(2.*divfactor)
newdx = sampledict.dx / divfactor
newdy = sampledict.dy / divfactor
sampledict = GeoDict.createDictFromBox(newxmin,
newxmax,
newymin,
newymax,
newdx,
newdy,
inside=True)
# Find slope thresholds, if applicable
self.slopemin = 'none'
self.slopemax = 'none'
if self.slopefile is not None:
try:
self.slopemin = float(config[self.model]['slopemin'])
self.slopemax = float(config[self.model]['slopemax'])
except:
print('Could not find slopemin and/or slopemax in config, '
'limits. No slope thresholds will be applied.')
self.slopemin = 'none'
self.slopemax = 'none'
# Make temporary directory for hdf5 pytables file storage
self.tempdir = tempfile.mkdtemp()
# now load the shakemap, resampling and padding if necessary
temp = ShakeGrid.load(shakefile) # , adjust='res')
self.shakedict = temp.getShakeDict()
self.eventdict = temp.getEventDict()
self.shakemap = {}
# Read both PGA and PGV in, may need them for thresholds
for gm in ['pga', 'pgv']:
junkfile = os.path.join(self.tempdir, 'temp.bil')
GDALGrid.copyFromGrid(temp.getLayer(gm)).save(junkfile)
if gm in self.interpolations.keys():
intermeth = self.interpolations[gm]
else:
intermeth = 'bilinear'
junkgrid = quickcut(junkfile,
sampledict,
precise=True,
method=intermeth)
if gm in self.clips:
junkgrid.setData(
np.clip(junkgrid.getData(), self.clips[gm][0],
self.clips[gm][1]))
self.shakemap[gm] = TempHdf(
junkgrid, os.path.join(self.tempdir, '%s.hdf5' % gm))
os.remove(junkfile)
del (temp)
# get updated geodict
sampledict = junkgrid.getGeoDict()
# take uncertainties into account, if available
if uncertfile is not None:
self.uncert = {}
try:
# Only read in the ones that will be needed
temp = ShakeGrid.load(uncertfile)
already = []
for gm in self.gmused:
if 'pgv' in gm:
gmsimp = 'pgv'
elif 'pga' in gm:
gmsimp = 'pga'
elif 'mmi' in gm:
gmsimp = 'mmi'
if gmsimp in already:
continue
junkfile = os.path.join(self.tempdir, 'temp.bil')
GDALGrid.copyFromGrid(temp.getLayer('std%s' %
gmsimp)).save(junkfile)
if gmsimp in self.interpolations.keys():
intermeth = self.interpolations[gmsimp]
else:
intermeth = 'bilinear'
junkgrid = quickcut(junkfile,
sampledict,
precise=True,
method=intermeth)
if gmsimp in self.clips:
junkgrid.setData(
np.clip(junkgrid.getData(), self.clips[gmsimp][0],
self.clips[gmsimp][1]))
self.uncert['std' + gmsimp] = TempHdf(
junkgrid,
os.path.join(self.tempdir, 'std%s.hdf5' % gmsimp))
already.append(gmsimp)
os.remove(junkfile)
del (temp)
except:
print('Could not read uncertainty file, ignoring '
'uncertainties')
self.uncert = None
else:
self.uncert = None
# Load the predictor layers, save as hdf5 temporary files, put file
# locations into a dictionary.
# Will be replaced in the next section if a slopefile was defined
self.nonzero = None
# key = layer name, value = grid object
self.layerdict = {}
didslope = False
for layername, layerfile in self.layers.items():
start = timer()
if isinstance(layerfile, list):
for lfile in layerfile:
if timeField == 'MONTH':
if lfile.find(MONTH) > -1:
layerfile = lfile
ftype = getFileType(layerfile)
interp = self.interpolations[layername]
temp = quickcut(layerfile,
sampledict,
precise=True,
method=interp)
if layername in self.clips:
temp.setData(
np.clip(temp.getData(),
self.clips[layername][0],
self.clips[layername][1]))
self.layerdict[layername] = TempHdf(
temp,
os.path.join(self.tempdir,
'%s.hdf5' % layername))
del (temp)
else:
interp = self.interpolations[layername]
temp = quickcut(layerfile,
sampledict,
precise=True,
method=interp)
if layername in self.clips:
temp.setData(
np.clip(temp.getData(), self.clips[layername][0],
self.clips[layername][1]))
if layername == 'rock': # Convert unconsolidated sediments to a more reasonable coefficient
sub1 = temp.getData()
# Change to mixed sed rock coeff
sub1[sub1 <= -3.21] = -1.36
temp.setData(sub1)
self.notes += 'unconsolidated sediment coefficient changed\
to -1.36 (weaker) from -3.22 to better reflect that this \
unit is not actually strong\n'
self.layerdict[layername] = TempHdf(
temp, os.path.join(self.tempdir, '%s.hdf5' % layername))
td = temp.getGeoDict()
if td != sampledict:
raise Exception(
'Geodictionaries of resampled files do not match')
if layerfile == self.slopefile:
flag = 0
if self.slopemin == 'none' and self.slopemax == 'none':
flag = 1
if self.slopemod is None:
slope1 = temp.getData().astype(float)
slope = 0
else:
try:
slope = temp.getData().astype(float)
slope1 = eval(self.slopemod)
except:
print('slopemod provided not valid, continuing '
'without slope thresholds.')
flag = 1
if flag == 0:
nonzero = np.array([(slope1 > self.slopemin) &
(slope1 <= self.slopemax)])
self.nonzero = nonzero[0, :, :]
del (slope1)
del (slope)
else:
# Still remove areas where the slope equals exactly
# 0.0 to remove offshore liq areas.
nonzero = np.array([slope1 != 0.0])
self.nonzero = nonzero[0, :, :]
del (slope1)
didslope = True
del (temp)
print('Loading %s layer: %1.1f sec' % (layername, timer() - start))
if didslope is False and self.slopefile is not None:
# Slope didn't get read in yet
temp = quickcut(self.slopefile,
sampledict,
precise=True,
method='bilinear')
flag = 0
if self.slopemin == 'none' and self.slopemax == 'none':
flag = 1
if self.slopemod is None:
slope1 = temp.getData().astype(float)
slope = 0
else:
try:
slope = temp.getData().astype(float)
slope1 = eval(self.slopemod)
except:
print('slopemod provided not valid, continuing without '
'slope thresholds')
flag = 1
if flag == 0:
nonzero = np.array([
(slope1 > self.slopemin) & (slope1 <= self.slopemax)
])
self.nonzero = nonzero[0, :, :]
del (slope1)
del (slope)
else:
# Still remove areas where the slope equals exactly
# 0.0 to remove offshore liq areas.
nonzero = np.array([slope1 != 0.0])
self.nonzero = nonzero[0, :, :]
del (slope1)
self.nuggets = [str(self.coeffs['b0'])]
ckeys = list(self.terms.keys())
ckeys.sort()
for key in ckeys:
term = self.terms[key]
coeff = self.coeffs[key]
self.nuggets.append('(%g * %s)' % (coeff, term))
self.equation = ' + '.join(self.nuggets)
self.geodict = sampledict
def check_input_extents(config, shakefile=None, bounds=None):
"""Make sure all input files exist and cover the extent desired
Args:
config: configObj of a single model
shakefile: path to ShakeMap grid.xml file (used for bounds). If not
provided, bounds must be provided
bounds: dictionary of bounds with keys: 'xmin', 'xmax', 'ymin', 'ymax'
Returns:
tuple containing:
notcovered: list of files that do not cover the entire area
defined by bounds or shakefile
newbounds: new dictionary of bounds of subarea of original
bounds or shakefile extent that is covered by all input files
"""
if shakefile is None and bounds is None:
raise Exception('Must define either a shakemap file or bounds')
modelname = config.keys()[0]
# Make dummy geodict to use
if bounds is None:
evdict = ShakeGrid.getFileGeoDict(shakefile)
else:
evdict = GeoDict.createDictFromBox(
bounds['xmin'], bounds['xmax'],
bounds['ymin'], bounds['ymax'],
0.00001, 0.00001, inside=False)
# Check extents of all input layers
notcovered = []
notcovgdicts = []
newbounds = None
for item, value in config[modelname]['layers'].items():
if 'file' in value.keys():
filelook = value['file']
if getFileType(filelook) == 'gmt':
tmpgd, _ = GMTGrid.getFileGeoDict(filelook)
else:
tmpgd, _ = GDALGrid.getFileGeoDict(filelook)
# See if tempgd contains evdict
contains = tmpgd.contains(evdict)
if not contains:
notcovered.append(filelook)
notcovgdicts.append(tmpgd)
# print(filelook)
if len(notcovered) > 0:
# Figure out what bounds COULD be run
xmins = [gd.xmin for gd in notcovgdicts]
xmaxs = [gd.xmax for gd in notcovgdicts]
ymins = [gd.ymin for gd in notcovgdicts]
ymaxs = [gd.ymax for gd in notcovgdicts]
# Set in by a buffer of 0.05 degrees because mapio doesn't like
# when bounds are exactly the same for getboundswithin
newbounds = dict(xmin=evdict.xmin + 0.05,
xmax=evdict.xmax - 0.05,
ymin=evdict.ymin + 0.05,
ymax=evdict.ymax - 0.05)
# Which one is the problem?
if evdict.xmin < np.max(xmins):
newbounds['xmin'] = np.max(xmins) + 0.05
if evdict.xmax > np.min(xmaxs):
newbounds['xmax'] = np.min(xmaxs) - 0.05
if evdict.ymin < np.max(ymins):
newbounds['ymin'] = np.max(ymins) + 0.05
if evdict.ymax > np.min(ymaxs):
newbounds['ymax'] = np.min(ymaxs) - 0.05
# See if this is a possible extent
try:
test = GeoDict.createDictFromBox(
newbounds['xmin'], newbounds['xmax'],
newbounds['ymin'], newbounds['ymax'],
0.00001, 0.00001, inside=False)
except BaseException:
print('Cannot make new bounds that will work')
newbounds = None
return notcovered, newbounds
def holzer_liq(shakefile,
config,
uncertfile=None,
saveinputs=False,
modeltype=None,
displmodel=None,
probtype=None,
bounds=None):
"""
Method for computing the probability of liquefaction using the Holzer method
using the Wills et al. (2015) Vs30 map of California to define the
susceptibility classes and the Fan et al. global water table model.
"""
layers = config['holzer_liq_cal']['layers']
vs30_file = layers['vs30']['file']
wtd_file = layers['watertable']['file']
shkgdict = ShakeGrid.getFileGeoDict(shakefile)
fgeodict = GMTGrid.getFileGeoDict(vs30_file)[0]
#---------------------------------------------------------------------------
# Loading info
#---------------------------------------------------------------------------
shakemap = ShakeGrid.load(shakefile,
fgeodict,
resample=True,
method='linear',
doPadding=True)
PGA = shakemap.getLayer('pga').getData() / 100 # convert to g
griddict, eventdict, specdict, fields, uncertainties = getHeaderData(
shakefile)
mag = eventdict['magnitude']
#---------------------------------------------------------------------------
# Logistic funciton parameters from Vs30
#---------------------------------------------------------------------------
vs30_grid = GMTGrid.load(vs30_file)
vs30 = vs30_grid.getData()
a0 = np.zeros_like(vs30)
b0 = np.zeros_like(vs30)
c0 = np.zeros_like(vs30)
a1 = np.zeros_like(vs30)
b1 = np.zeros_like(vs30)
c1 = np.zeros_like(vs30)
for k, v in config['holzer_liq_cal']['parameters'].items():
ind = np.where(vs30 == float(v[0]))
a0[ind] = v[1]
b0[ind] = v[2]
c0[ind] = v[3]
a1[ind] = v[4]
b1[ind] = v[5]
c1[ind] = v[6]
#---------------------------------------------------------------------------
# Water table
#---------------------------------------------------------------------------
wtd_grid = GMTGrid.load(wtd_file,
fgeodict,
resample=True,
method=layers['watertable']['interpolation'],
doPadding=True)
tmp = wtd_grid._data
tmp = np.nan_to_num(tmp)
# Compute water weights
w0, w1 = get_water_weights(tmp)
#---------------------------------------------------------------------------
# Compute probability of liquefaction
#---------------------------------------------------------------------------
prob0 = get_prob(PGA, a0, b0, c0, mag)
prob1 = get_prob(PGA, a1, b1, c1, mag)
prob = prob0 * w0 + prob1 * w1
#---------------------------------------------------------------------------
# Turn output and inputs into into grids and put in maplayers dictionary
#---------------------------------------------------------------------------
maplayers = collections.OrderedDict()
temp = shakemap.getShakeDict()
shakedetail = '%s_ver%s' % (temp['shakemap_id'], temp['shakemap_version'])
modelsref = config['holzer_liq_cal']['shortref']
modellref = config['holzer_liq_cal']['longref']
modeltype = 'Holzer/Wills'
maplayers['model'] = {
'grid': GDALGrid(prob, fgeodict),
'label': 'Probability',
'type': 'output',
'description': {
'name': modelsref,
'longref': modellref,
'units': 'coverage',
'shakemap': shakedetail,
'parameters': {
'modeltype': modeltype
}
}
}
if saveinputs is True:
maplayers['pga'] = {
'grid': GDALGrid(PGA, fgeodict),
'label': 'PGA (g)',
'type': 'input',
'description': {
'units': 'g',
'shakemap': shakedetail
}
}
maplayers['vs30'] = {
'grid': GDALGrid(vs30, fgeodict),
'label': 'Vs30 (m/s)',
'type': 'input',
'description': {
'units': 'm/s'
}
}
maplayers['wtd'] = {
'grid': GDALGrid(wtd_grid._data, fgeodict),
'label': 'wtd (m)',
'type': 'input',
'description': {
'units': 'm'
}
}
return maplayers
def quickcut(filename,
gdict,
tempname=None,
extrasamp=5.,
method='bilinear',
precise=True,
cleanup=True,
verbose=False,
override=False):
"""
Use gdal to trim a large global file down quickly so mapio can read it
efficiently. (Cannot read Shakemap.xml files, must save as .bil filrst)
Args:
filename (str): File path to original input file (raster).
gdict (geodict): Geodictionary to cut around and align with.
tempname (str): File path to desired location of clipped part of
filename.
extrasamp (int): Number of extra cells to cut around each edge of
geodict to have resampling buffer for future steps.
method (str): If resampling is necessary, method to use.
precise (bool): If true, will resample to the gdict as closely as
possible, if False it will just roughly cut around the area of
interest without changing resolution
cleanup (bool): if True, delete tempname after reading it back in
verbose (bool): if True, prints more details
override (bool): if True, if filename extent is not fully contained by
gdict, read in the entire file (only used for ShakeMaps)
Returns: New grid2D layer
Note: This function uses the subprocess approach because ``gdal.Translate``
doesn't hang on the command until the file is created which causes
problems in the next steps.
"""
if gdict.xmax < gdict.xmin:
raise Exception('quickcut: your geodict xmax is smaller than xmin')
try:
filegdict = GDALGrid.getFileGeoDict(filename)
except:
try:
filegdict = GMTGrid.getFileGeoDict(filename)
except:
raise Exception('Cannot get geodict for %s' % filename)
if tempname is None:
tempdir = tempfile.mkdtemp()
tempname = os.path.join(tempdir, 'junk.tif')
deltemp = True
else:
tempdir = None
deltemp = False
# if os.path.exists(tempname):
# os.remove(tempname)
# print('Temporary file already there, removing file')
filegdict = filegdict[0]
# Get the right methods for mapio (method) and gdal (method2)
if method == 'linear':
method2 = 'bilinear'
if method == 'nearest':
method2 = 'near'
if method == 'bilinear':
method = 'linear'
method2 = 'bilinear'
if method == 'near':
method = 'nearest'
method2 = 'near'
else:
method2 = method
if filegdict != gdict:
# First cut without resampling
tempgdict = GeoDict.createDictFromBox(gdict.xmin,
gdict.xmax,
gdict.ymin,
gdict.ymax,
filegdict.dx,
filegdict.dy,
inside=True)
try:
egdict = filegdict.getBoundsWithin(tempgdict)
ulx = egdict.xmin - extrasamp * egdict.dx
uly = egdict.ymax + extrasamp * egdict.dy
lrx = egdict.xmax + (extrasamp + 1) * egdict.dx
lry = egdict.ymin - (extrasamp + 1) * egdict.dy
cmd = 'gdal_translate -a_srs EPSG:4326 -of GTiff -projwin %1.8f \
%1.8f %1.8f %1.8f -r %s %s %s' % (ulx, uly, lrx, lry, method2,
filename, tempname)
except Exception as e:
if override:
# When ShakeMap is being loaded, sometimes they won't align
# right because it's already cut to the area, so just load
# the whole file
cmd = 'gdal_translate -a_srs EPSG:4326 -of GTiff -r %s %s %s' \
% (method2, filename, tempname)
else:
raise Exception('Failed to cut layer: %s' % e)
rc, so, se = get_command_output(cmd)
if not rc:
raise Exception(se.decode())
else:
if verbose:
print(so.decode())
newgrid2d = GDALGrid.load(tempname)
if precise:
# Resample to exact geodictionary
newgrid2d = newgrid2d.interpolate2(gdict, method=method)
if cleanup:
os.remove(tempname)
if deltemp:
shutil.rmtree(tempdir)
else:
ftype = GMTGrid.getFileType(filename)
if ftype != 'unknown':
newgrid2d = GMTGrid.load(filename)
elif filename.endswith('.xml'):
newgrid2d = ShakeGrid.load(filename)
else:
newgrid2d = GDALGrid.load(filename)
return newgrid2d
gridtype = 'gmt'
except Exception,error:
try:
fdict = GDALGrid.getFileGeoDict(gridfile)
gridtype = 'esri'
except:
pass
if gridtype is None:
raise Exception('File "%s" does not appear to be either a GMT grid or an ESRI grid.' % gridfile)
xmin = xmin - fdict.dx*3
xmax = xmax + fdict.dx*3
ymin = ymin - fdict.dy*3
ymax = ymax + fdict.dy*3
bounds = (xmin,xmax,ymin,ymax)
if gridtype == 'gmt':
fgeodict = GMTGrid.getFileGeoDict(gridfile)
else:
fgeodict = GDALGrid.getFileGeoDict(gridfile)
dx,dy = (fgeodict.dx,fgeodict.dy)
sdict = GeoDict.createDictFromBox(xmin,xmax,ymin,ymax,dx,dy)
if gridtype == 'gmt':
grid = GMTGrid.load(gridfile,samplegeodict=sdict,resample=False,method=method,doPadding=True)
else:
grid = GDALGrid.load(gridfile,samplegeodict=sdict,resample=False,method=method,doPadding=True)
return sampleFromGrid(grid,xypoints)
def sampleFromGrid(grid,xypoints,method='nearest'):
"""
Sample 2D grid object at each of a set of XY (decimal degrees) points.
:param grid:
def hazus_liq(shakefile,
config,
uncertfile=None,
saveinputs=False,
modeltype=None,
displmodel=None,
probtype=None,
bounds=None):
"""
Method for computing the probability of liquefaction using the Hazus method
using the Wills et al. (2015) Vs30 map of California to define the
susceptibility classes and the Fan et al. global water table model.
"""
layers = config['hazus_liq_cal']['layers']
vs30_file = layers['vs30']['file']
wtd_file = layers['watertable']['file']
shkgdict = ShakeGrid.getFileGeoDict(shakefile)
fgeodict = GMTGrid.getFileGeoDict(vs30_file)[0]
#---------------------------------------------------------------------------
# Loading
#---------------------------------------------------------------------------
shakemap = ShakeGrid.load(shakefile,
fgeodict,
resample=True,
method='linear',
doPadding=True)
PGA = shakemap.getLayer('pga').getData() / 100 # convert to g
griddict, eventdict, specdict, fields, uncertainties = getHeaderData(
shakefile)
mag = eventdict['magnitude']
# Correction factor for moment magnitudes other than M=7.5
k_m = 0.0027 * mag**3 - 0.0267 * mag**2 - 0.2055 * mag + 2.9188
#---------------------------------------------------------------------------
# Susceptibility from Vs30
#---------------------------------------------------------------------------
vs30_grid = GMTGrid.load(vs30_file)
vs30 = vs30_grid.getData()
p_ml = np.zeros_like(vs30)
a = np.zeros_like(vs30)
b = np.zeros_like(vs30)
for k, v in config['hazus_liq_cal']['parameters'].items():
ind = np.where(vs30 == float(v[0]))
if v[1] == "VH":
p_ml[ind] = 0.25
a[ind] = 9.09
b[ind] = -0.82
if v[1] == "H":
p_ml[ind] = 0.2
a[ind] = 7.67
b[ind] = -0.92
if v[1] == "M":
p_ml[ind] = 0.1
a[ind] = 6.67
b[ind] = -1.0
if v[1] == "L":
p_ml[ind] = 0.05
a[ind] = 5.57
b[ind] = -1.18
if v[1] == "VL":
p_ml[ind] = 0.02
a[ind] = 4.16
b[ind] = -1.08
# Conditional liquefaction probability for a given susceptibility category
# at a specified PGA
p_liq_pga = a * PGA + b
p_liq_pga = p_liq_pga.clip(min=0, max=1)
#---------------------------------------------------------------------------
# Water table
#---------------------------------------------------------------------------
wtd_grid = GMTGrid.load(wtd_file,
fgeodict,
resample=True,
method=layers['watertable']['interpolation'],
doPadding=True)
tmp = wtd_grid._data
tmp = np.nan_to_num(tmp)
# Convert to ft
wt_ft = tmp * 3.28084
# Correction factor for groundwater depths other than five feet
k_w = 0.022 * wt_ft + 0.93
#---------------------------------------------------------------------------
# Combine to get conditional liquefaction probability
#---------------------------------------------------------------------------
p_liq_sc = p_liq_pga * p_ml / k_m / k_w
#---------------------------------------------------------------------------
# Turn output and inputs into into grids and put in maplayers dictionary
#---------------------------------------------------------------------------
maplayers = collections.OrderedDict()
temp = shakemap.getShakeDict()
shakedetail = '%s_ver%s' % (temp['shakemap_id'], temp['shakemap_version'])
modelsref = config['hazus_liq_cal']['shortref']
modellref = config['hazus_liq_cal']['longref']
modeltype = 'Hazus/Wills'
maplayers['model'] = {
'grid': GDALGrid(p_liq_sc, fgeodict),
'label': 'Probability',
'type': 'output',
'description': {
'name': modelsref,
'longref': modellref,
'units': 'coverage',
'shakemap': shakedetail,
'parameters': {
'modeltype': modeltype
}
}
}
if saveinputs is True:
maplayers['pga'] = {
'grid': GDALGrid(PGA, fgeodict),
'label': 'PGA (g)',
'type': 'input',
'description': {
'units': 'g',
'shakemap': shakedetail
}
}
maplayers['vs30'] = {
'grid': GDALGrid(vs30, fgeodict),
'label': 'Vs30 (m/s)',
'type': 'input',
'description': {
'units': 'm/s'
}
}
maplayers['wtd'] = {
'grid': GDALGrid(wtd_grid._data, fgeodict),
'label': 'wtd (m)',
'type': 'input',
'description': {
'units': 'm'
}
}
return maplayers
def slhrf_liq(shakefile, config, uncertfile=None, saveinputs=False,
modeltype=None, displmodel=None,
probtype=None, bounds=None):
"""
Method for computing the probability of liquefaction using the SLHRF,
primarily relying on the Wills et al. (2015) Vs30 map of California and
Hydrosheds distance to rivers.
"""
layers = config['slhrf_liq_cal']['layers']
vs30_file = layers['vs30']['file']
elev_file = layers['elev']['file']
dc_file = layers['dc']['file']
dr_file = layers['dr']['file']
fgeodict = GMTGrid.getFileGeoDict(vs30_file)[0]
#---------------------------------------------------------------------------
# Read in data layers
#---------------------------------------------------------------------------
shakemap = ShakeGrid.load(shakefile, fgeodict, resample=True,
method='linear', doPadding=True)
PGA = shakemap.getLayer('pga').getData()/100 # convert to g
griddict,eventdict,specdict,fields,uncertainties = getHeaderData(shakefile)
mag = eventdict['magnitude']
vs30_grid = GMTGrid.load(vs30_file)
vs30 = vs30_grid.getData()
elev = GDALGrid.load(elev_file, fgeodict, resample=True,
method=layers['elev']['interpolation'],
doPadding = True).getData()
dc = GDALGrid.load(dc_file, fgeodict, resample=True,
method=layers['dc']['interpolation'],
doPadding = True).getData()
dr = GDALGrid.load(dr_file, fgeodict, resample=True,
method=layers['dr']['interpolation'],
doPadding = True).getData()
dw = np.minimum(dr, dc)
#---------------------------------------------------------------------------
# Evaluate the different factors
#---------------------------------------------------------------------------
Fgeo = np.zeros_like(vs30)
for k,v in config['slhrf_liq_cal']['parameters'].items():
ind = np.where(vs30 == float(v[0]))
Fgeo[ind] = float(v[1])
Fz = z_factor(elev)
Fmag = mag_factor(mag)
Fpga = pga_factor(PGA)
Fdw = dw_factor(dw)
Fnehrp = nehrp_factor(vs30)
#---------------------------------------------------------------------------
# Combine factors
#---------------------------------------------------------------------------
SLHRF = Fz * Fmag * Fpga * Fdw * Fgeo * Fnehrp
# Transform into a 'probability'
prob = 0.4 * (1 - np.exp(-0.2 * SLHRF**2) )
#---------------------------------------------------------------------------
# Turn output and inputs into into grids and put in maplayers dictionary
#---------------------------------------------------------------------------
maplayers = collections.OrderedDict()
temp = shakemap.getShakeDict()
shakedetail = '%s_ver%s' % (temp['shakemap_id'], temp['shakemap_version'])
modelsref = config['slhrf_liq_cal']['shortref']
modellref = config['slhrf_liq_cal']['longref']
modeltype = 'SLHRF/Wills'
maplayers['model'] = {'grid': GDALGrid(prob, fgeodict),
'label': 'Probability',
'type': 'output',
'description': {'name': modelsref,
'longref': modellref,
'units': 'coverage',
'shakemap': shakedetail,
'parameters': {'modeltype': modeltype}
}
}
if saveinputs is True:
maplayers['slhrf'] = {'grid': GDALGrid(SLHRF, fgeodict),
'label': 'SLHRF',
'type': 'input',
'description': {'units': 'none'}}
maplayers['pga'] = {'grid': GDALGrid(PGA, fgeodict),
'label': 'PGA (g)',
'type': 'input',
'description': {'units': 'g', 'shakemap': shakedetail}}
maplayers['vs30'] = {'grid': GDALGrid(vs30, fgeodict),
'label': 'Vs30 (m/s)',
'type': 'input',
'description': {'units': 'm/s'}}
maplayers['dw'] = {'grid': GDALGrid(dw, fgeodict),
'label': 'dw (km)',
'type': 'input',
'description': {'units': 'km'}}
maplayers['elev'] = {'grid': GDALGrid(elev, fgeodict),
'label': 'elev (m)',
'type': 'input',
'description': {'units': 'm'}}
maplayers['FPGA'] = {'grid': GDALGrid(Fpga, fgeodict),
'label': 'Fpga',
'type': 'input',
'description': {'units': 'none'}}
maplayers['FDW'] = {'grid': GDALGrid(Fdw, fgeodict),
'label': 'Fdw',
'type': 'input',
'description': {'units': 'none'}}
maplayers['FGEO'] = {'grid': GDALGrid(Fgeo, fgeodict),
'label': 'Fgeo',
'type': 'input',
'description': {'units': 'none'}}
maplayers['FZ'] = {'grid': GDALGrid(Fz, fgeodict),
'label': 'Fz',
'type': 'input',
'description': {'units': 'none'}}
maplayers['FNEHRP'] = {'grid': GDALGrid(Fnehrp, fgeodict),
'label': 'Fnehrp',
'type': 'input',
'description': {'units': 'none'}}
return maplayers
