def execute(self):
install_path, data_path = get_config_paths()
datadir = os.path.join(data_path, self._eventid, 'current', 'products')
datadir = os.path.join(data_path, self._eventid, 'current', 'products')
if not os.path.isdir(datadir):
raise NotADirectoryError('%s is not a valid directory.' % datadir)
datafile = os.path.join(datadir, 'shake_result.hdf')
if not os.path.isfile(datafile):
raise FileNotFoundError('%s does not exist.' % datafile)
# Open the ShakeMapOutputContainer and extract the data
container = ShakeMapOutputContainer.load(datafile)
if container.getDataType() != 'grid':
raise NotImplementedError('raster module can only operate on '
'gridded data, not sets of points')
# get the path to the products.conf file, load the config
config_file = os.path.join(install_path, 'config', 'products.conf')
config = ConfigObj(config_file)
# create GIS-readable .flt files of imt and uncertainty
self.logger.info('Creating GIS grids...')
layers = config['products']['raster']['layers']
for layer in layers:
fileimt = oq_to_file(layer)
imtdict = container.getIMTGrids(layer, 'Larger')
mean_grid = imtdict['mean']
std_grid = imtdict['std']
mean_gdal = GDALGrid.copyFromGrid(mean_grid)
std_gdal = GDALGrid.copyFromGrid(std_grid)
mean_fname = os.path.join(datadir, '%s_mean.flt' % fileimt)
std_fname = os.path.join(datadir, '%s_std.flt' % fileimt)
self.logger.info('Saving %s...' % mean_fname)
mean_gdal.save(mean_fname)
self.logger.info('Saving %s...' % std_fname)
std_gdal.save(std_fname)
def trim_ocean(grid2D, mask, all_touched=True, crop=False):
"""Use the mask (a shapefile) to trim offshore areas
Args:
grid2D: MapIO grid2D object of results that need trimming
mask: list of shapely polygon features already loaded in or string of
file extension of shapefile to use for clipping
all_touched (bool): if True, won't mask cells that touch any part of
polygon edge
crop (bool): crop boundaries of raster to new masked area
Returns:
grid2D file with ocean masked
"""
gdict = grid2D.getGeoDict()
tempdir = tempfile.mkdtemp()
# Get shapes ready
if type(mask) == str:
with fiona.open(mask, 'r') as shapefile:
bbox = (gdict.xmin, gdict.ymin, gdict.xmax, gdict.ymax)
hits = list(shapefile.items(bbox=bbox))
features = [feature[1]["geometry"] for feature in hits]
# hits = list(shapefile)
# features = [feature["geometry"] for feature in hits]
elif type(mask) == list:
features = mask
else:
raise Exception('mask is neither a link to a shapefile or a list of \
shapely shapes, cannot proceed')
if len(features) == 0:
print('No coastlines in ShakeMap area')
return grid2D
tempfilen = os.path.join(tempdir, 'temp.bil')
tempfile1 = os.path.join(tempdir, 'temp.tif')
tempfile2 = os.path.join(tempdir, 'temp2.tif')
GDALGrid.copyFromGrid(grid2D).save(tempfilen)
cmd = 'gdal_translate -a_srs EPSG:4326 -of GTiff %s %s' % \
(tempfilen, tempfile1)
rc, so, se = get_command_output(cmd)
if rc:
with rasterio.open(tempfile1, 'r') as src_raster:
out_image, out_transform = rasterio.mask.mask(
src_raster, features, all_touched=all_touched, crop=crop)
out_meta = src_raster.meta.copy()
out_meta.update({
"driver": "GTiff",
"height": out_image.shape[1],
"width": out_image.shape[2],
"transform": out_transform
})
with rasterio.open(tempfile2, "w", **out_meta) as dest:
dest.write(out_image)
newgrid = GDALGrid.load(tempfile2)
else:
print(se)
raise Exception('ocean trimming failed')
shutil.rmtree(tempdir)
return newgrid
def run_gfail(args):
"""Runs ground failure.
Args:
args: dictionary or argument parser Namespace output by bin/gfail
program.
Returns:
list: Names of created files.
"""
# TODO: ADD CONFIG VALIDATION STEP THAT MAKES SURE ALL THE FILES EXIST
filenames = []
# If args is a dictionary, convert to a Namespace
if isinstance(args, dict):
args = Namespace(**args)
if args.set_default_paths:
set_default_paths(args)
print('default paths set, continuing...\n')
if args.list_default_paths:
list_default_paths()
return
if args.reset_default_paths:
reset_default_paths()
return
if args.make_webpage:
# Turn on GIS and HDF5 flags
gis = True
hdf5 = True
kmz = True
else:
gis = args.gis
hdf5 = args.hdf5
kmz = args.kmz
# Figure out what models will be run
if args.shakefile is not None: # user intends to actually run some models
shakefile = args.shakefile
# make output location for things
if args.output_filepath is None:
outdir = os.getcwd()
else:
outdir = args.output_filepath
if hdf5 or gis or kmz:
if not os.path.exists(outdir):
os.makedirs(outdir)
# download if is url
# cleanup = False
if not os.path.isfile(shakefile):
if isURL(shakefile):
# getGridURL returns a named temporary file object
shakefile = getGridURL(shakefile)
# cleanup = True # Be sure to delete it after
else:
raise NameError('Could not find "%s" as a file or a valid url'
% shakefile)
eventid = getHeaderData(shakefile)[0]['event_id']
# Get entire path so won't break if running gfail with relative path
shakefile = os.path.abspath(shakefile)
if args.extract_contents:
outfolder = outdir
else: # Nest in a folder named by eventid
outfolder = os.path.join(outdir, eventid)
if not os.path.exists(outfolder):
os.makedirs(outfolder)
# Copy shake grid into output directory
# --- this is base on advice from Mike that when running in production
# the shake grids are not archived and so if we need/want to have
# the exact grid used for the calculation later if there's every a
# question about how the calculation was done, the safest thing is
# to store a copy of it here.
shake_copy = os.path.join(outfolder, "grid.xml")
shutil.copyfile(shakefile, shake_copy)
if args.uncertfile is not None:
uncertfile = os.path.abspath(args.uncertfile)
unc_copy = os.path.join(outfolder, "uncertainty.xml")
shutil.copyfile(uncertfile, unc_copy)
else:
uncertfile = None
# Write shakefile to a file for use later
shakename = os.path.join(outfolder, "shakefile.txt")
shake_file = open(shakename, "wt")
shake_file.write(shake_copy)
shake_file.close()
filenames.append(shakename)
# Check that shakemap bounds do not cross 180/-180 line
if args.set_bounds is None:
sd = ShakeGrid.getFileGeoDict(shakefile)
if sd.xmin > sd.xmax:
print('\nShakeMap crosses 180/-180 line, setting bounds so '
'only side with more land area is run')
if sd.xmax + 180. > 180 - sd.xmin:
set_bounds = '%s, %s, %s, %s' % (
sd.ymin, sd.ymax, -180., sd.xmax)
else:
set_bounds = '%s, %s, %s, %s' % (sd.ymin, sd.ymax, sd.xmin,
180.)
print('Bounds applied: %s' % set_bounds)
else:
set_bounds = args.set_bounds
else:
set_bounds = args.set_bounds
config = args.config
if args.config_filepath is not None:
# only add config_filepath if full filepath not given and file
# ext is .ini
if (not os.path.isabs(config) and
os.path.splitext(config)[-1] == '.ini'):
config = os.path.join(args.config_filepath, config)
if os.path.splitext(config)[-1] == '.ini':
temp = ConfigObj(config)
if len(temp) == 0:
raise Exception(
'Could not find specified .ini file: %s' % config)
if args.data_path is not None:
temp = correct_config_filepaths(args.data_path, temp)
configs = [temp]
conffail = []
else:
# input is a list of config files
f = open(config, 'r')
configlist = f.readlines()
configs = []
conffail = []
for conf in configlist:
conf = conf.strip()
if not os.path.isabs(conf):
# only add config_filepath if full filepath not given
conf = os.path.join(args.config_filepath, conf)
try:
temp = ConfigObj(conf)
if temp:
if args.data_path is not None:
temp = correct_config_filepaths(
args.data_path, temp)
configs.append(temp)
else:
conffail.append(conf)
except BaseException:
conffail.append(conf)
print('\nRunning the following models:')
for conf in configs:
print('\t%s' % conf.keys()[0])
if len(conffail) > 0:
print('Could not find or read in the following config files:\n')
for conf in conffail:
print('\t%s' % conf)
print('\nContinuing...\n')
if set_bounds is not None:
if 'zoom' in set_bounds:
temp = set_bounds.split(',')
print('Using %s threshold of %1.1f to cut model bounds'
% (temp[1].strip(), float(temp[2].strip())))
bounds = get_bounds(shakefile, temp[1].strip(),
float(temp[2].strip()))
else:
temp = eval(set_bounds)
latmin = temp[0]
latmax = temp[1]
lonmin = temp[2]
lonmax = temp[3]
bounds = {'xmin': lonmin, 'xmax': lonmax,
'ymin': latmin, 'ymax': latmax}
print('Applying bounds of lonmin %1.2f, lonmax %1.2f, '
'latmin %1.2f, latmax %1.2f'
% (bounds['xmin'], bounds['xmax'],
bounds['ymin'], bounds['ymax']))
else:
bounds = None
if args.make_webpage:
results = []
# pre-read in ocean trimming file polygons so only do this step once
if args.trimfile is not None:
if not os.path.exists(args.trimfile):
print('trimfile defined does not exist: %s\n'
'Ocean will not be trimmed.' % args.trimfile)
trimfile = None
elif os.path.splitext(args.trimfile)[1] != '.shp':
print('trimfile must be a shapefile, '
'ocean will not be trimmed')
trimfile = None
else:
trimfile = args.trimfile
else:
trimfile = None
# Get finite fault ready, if exists
ffault = None
point = True
if args.finite_fault is not None:
point = False
try:
if os.path.splitext(args.finite_fault)[-1] == '.txt':
ffault = text_to_json(args.finite_fault)
elif os.path.splitext(args.finite_fault)[-1] == '.json':
ffault = args.finite_fault
else:
print('Could not read in finite fault, will '
'try to download from comcat')
ffault = None
except BaseException:
print('Could not read in finite fault, will try to '
'download from comcat')
ffault = None
if ffault is None:
# Try to get finite fault file, if it exists
try:
returned_ev = get_event_comcat(shakefile)
if returned_ev is not None:
testjd, detail, temp = returned_ev
evinfo = testjd['input']['event_information']
if 'faultfiles' in evinfo:
ffilename = evinfo['faultfiles']
if len(ffilename) > 0:
# Download the file
with tempfile.NamedTemporaryFile(
delete=False, mode='w') as f:
temp.getContent(ffilename, filename=f.name)
ffault = text_to_json(f.name)
os.remove(f.name)
point = False
else:
point = True
else:
print('Unable to determine source type, unknown if finite'
' fault or point source')
ffault = None
point = False
except Exception as e:
print(e)
print('Unable to determine source type, unknown if finite'
' fault or point source')
ffault = None
point = False
# Loop over config files
for conf in configs:
modelname = conf.keys()[0]
print('\nNow running %s:' % modelname)
notcov, newbnds = check_input_extents(
conf, shakefile=shakefile,
bounds=bounds
)
if len(notcov) > 0:
print('\nThe following input layers do not cover'
' the area of interest:\n\t%s' % '\n\t'.join(notcov))
if newbnds is None:
print('\nCannnot make bounds that work. '
'Skipping to next model\n')
continue
else:
pnt = '%s, %s, %s, %s' % (
newbnds['xmin'], newbnds['xmax'],
newbnds['ymin'], newbnds['ymax'])
print('Running model for new bounds that are fully covered'
' by input layer: %s' % pnt)
bounds2 = newbnds
else:
bounds2 = bounds
modelfunc = conf[modelname]['funcname']
if modelfunc == 'LogisticModel':
lm = LM.LogisticModel(shakefile, conf,
uncertfile=uncertfile,
saveinputs=args.save_inputs,
bounds=bounds2,
trimfile=trimfile)
maplayers = lm.calculate()
elif modelfunc == 'godt2008':
maplayers = godt2008(shakefile, conf,
uncertfile=uncertfile,
saveinputs=args.save_inputs,
bounds=bounds2,
trimfile=trimfile)
else:
print('Unknown model function specified in config for %s '
'model, skipping to next config' % modelfunc)
continue
# time1 = datetime.datetime.utcnow().strftime('%d%b%Y_%H%M')
# filename = ('%s_%s_%s' % (eventid, modelname, time1))
if args.appendname is not None:
filename = ('%s_%s_%s' % (eventid, modelname, args.appendname))
else:
filename = ('%s_%s' % (eventid, modelname))
if hdf5:
filenameh = filename + '.hdf5'
if os.path.exists(filenameh):
os.remove(filenameh)
savelayers(maplayers, os.path.join(outfolder, filenameh))
filenames.append(filenameh)
if gis or kmz:
for key in maplayers:
# Rename 'std' key to 'beta_sigma'
if key == 'std':
key_label = 'beta_sigma'
else:
key_label = key
if gis:
filen = os.path.join(outfolder, '%s_%s.bil'
% (filename, key_label))
fileh = os.path.join(outfolder, '%s_%s.hdr'
% (filename, key_label))
fileg = os.path.join(outfolder, '%s_%s.tif'
% (filename, key_label))
GDALGrid.copyFromGrid(
maplayers[key]['grid']).save(filen)
cflags = '-co COMPRESS=DEFLATE -co predictor=2'
srs = '-a_srs EPSG:4326'
cmd = 'gdal_translate %s %s -of GTiff %s %s' % (
srs, cflags, filen, fileg)
rc, so, se = get_command_output(cmd)
# Delete bil file and its header
os.remove(filen)
os.remove(fileh)
filenames.append(fileg)
if kmz and (not key.startswith('quantile') and not key.startswith('std')) :
plotorder, logscale, lims, colormaps, maskthresh = \
parseConfigLayers(maplayers, conf, keys=['model'])
maxprob = np.nanmax(maplayers[key]['grid'].getData())
if key == 'model':
qdict = {
k: maplayers[k] for k in maplayers.keys()
if k.startswith('quantile')
}
else:
qdict = None
if maskthresh is None:
maskthresh = [0.]
if maxprob >= maskthresh[0]:
filen = os.path.join(outfolder, '%s_%s.kmz'
% (filename, key_label))
filek = create_kmz(maplayers[key], filen,
mask=maskthresh[0],
levels=lims[0],
qdict=qdict)
filenames.append(filek)
else:
print('No unmasked pixels present, skipping kmz '
'file creation')
if args.make_webpage:
# Compile into list of results for later
results.append(maplayers)
# # Make binary output for ShakeCast
# filef = os.path.join(outfolder, '%s_model.flt'
# % filename)
# # And get name of header
# filefh = os.path.join(outfolder, '%s_model.hdr'
# % filename)
# # Make file
# write_floats(filef, maplayers['model']['grid'])
# filenames.append(filef)
# filenames.append(filefh)
eventid = getHeaderData(shakefile)[0]['event_id']
if not hasattr(args, 'eventsource'):
args.eventsource = 'us'
if not hasattr(args, 'eventsourcecode'):
args.eventsourcecode = eventid
if args.make_webpage:
if len(results) == 0:
raise Exception('No models were run. Cannot make webpages.')
outputs = hazdev(
results, configs,
shakefile, outfolder=outfolder,
pop_file=args.popfile,
pager_alert=args.property_alertlevel,
eventsource=args.eventsource,
eventsourcecode=args.eventsourcecode,
point=point, gf_version=args.gf_version,
pdlcall=args.pdlcall)
filenames = filenames + outputs
# # create transparent png file
# outputs = create_png(outdir)
# filenames = filenames + outputs
#
# # create info file
# infofile = create_info(outdir)
# filenames = filenames + infofile
print('\nFiles created:\n')
for filen in filenames:
print('%s' % filen)
return filenames
def godt2008(shakefile,
config,
uncertfile=None,
saveinputs=False,
displmodel=None,
bounds=None,
slopediv=100.,
codiv=10.,
numstd=None,
trimfile=None):
"""
This function runs the Godt and others (2008) global method for a given
ShakeMap. The Factor of Safety is calculated using infinite slope analysis
assumuing dry conditions. The method uses threshold newmark displacement
and estimates areal coverage by doing the calculations for each slope
quantile.
Args:
shakefile (str): Path to shakemap xml file.
config (ConfigObj): ConfigObj of config file containing inputs required
for running the model
uncertfile (str): Path to shakemap uncertainty xml file (optional).
saveinputs (bool): Whether or not to return the model input layers,
False (default) returns only the model output (one layer).
displmodel (str): Newmark displacement regression model to use
* ``'J_PGA'`` (default) -- PGA-based model, equation 6 from
Jibson (2007).
* ``'J_PGA_M'`` -- PGA and M-based model, equation 7 from
Jibson (2007).
* ``'RS_PGA_M'`` -- PGA and M-based model from from Rathje and
Saygili (2009).
* ``'RS_PGA_PGV'`` -- PGA and PGV-based model, equation 6
from Saygili and Rathje (2008).
bounds (dict): Optional dictionary with keys 'xmin', 'xmax', 'ymin',
'ymax' that defines a subset of the shakemap area to compute.
slopediv (float): Divide slope by this number to get slope in degrees
(Verdin datasets need to be divided by 100).
codiv (float): Divide cohesion input layer by this number
(For Godt method, need to divide by 10 because that is how it was
calibrated).
numstd (float): Number of (+/-) standard deviations to use if
uncertainty is computed (uncertfile must be supplied).
trimfile (str): shapefile of earth's land masses to trim offshore areas
of model
Returns:
dict: Dictionary containing output and input layers (if
saveinputs=True):
.. code-block:: python
{
'grid': mapio grid2D object,
'label': 'label for colorbar and top line of subtitle',
'type': 'output or input to model',
'description': {'name': 'short reference of model',
'longref': 'full model reference',
'units': 'units of output',
'shakemap': 'information about shakemap used',
'event_id': 'shakemap event id',
'parameters': 'dictionary of model parameters
used'
}
}
Raises:
NameError: when unable to parse the config correctly (probably a
formatting issue in the configfile) or when unable to find the
shakefile (Shakemap filepath) -- these cause program to end.
"""
# TODO:
# - Add 'all' -- averages Dn from all four equations, add term to
# convert PGA and PGV to Ia and use other equations, add Ambraseys and
# Menu (1988) option.
# Empty refs
slopesref = 'unknown'
slopelref = 'unknown'
cohesionlref = 'unknown'
cohesionsref = 'unknown'
frictionsref = 'unknown'
frictionlref = 'unknown'
modellref = 'unknown'
modelsref = 'unknown'
# See if trimfile exists
if trimfile is not None:
if not os.path.exists(trimfile):
print('trimfile defined does not exist: %s\n'
'Ocean will not be trimmed' % trimfile)
trimfile = None
if os.path.splitext(trimfile)[1] != '.shp':
print('trimfile must be a shapefile, ocean will not be trimmed')
trimfile = None
# Parse config
try: # May want to add error handling so if refs aren't given, just
# includes unknown
slopefilepath = config['godt_2008']['layers']['slope']['filepath']
slopeunits = config['godt_2008']['layers']['slope']['units']
cohesionfile = config['godt_2008']['layers']['cohesion']['file']
cohesionunits = config['godt_2008']['layers']['cohesion']['units']
frictionfile = config['godt_2008']['layers']['friction']['file']
frictionunits = config['godt_2008']['layers']['friction']['units']
thick = float(config['godt_2008']['parameters']['thick'])
uwt = float(config['godt_2008']['parameters']['uwt'])
nodata_cohesion = \
float(config['godt_2008']['parameters']['nodata_cohesion'])
nodata_friction = \
float(config['godt_2008']['parameters']['nodata_friction'])
dnthresh = float(config['godt_2008']['parameters']['dnthresh'])
fsthresh = float(config['godt_2008']['parameters']['fsthresh'])
acthresh = float(config['godt_2008']['parameters']['acthresh'])
try:
slopemin = float(config['godt_2008']['parameters']['slopemin'])
except:
slopemin = 0.01
print('No slopemin found in config file, using 0.01 deg '
'for slope minimum')
except Exception as e:
raise NameError('Could not parse configfile, %s' % e)
if displmodel is None:
try:
displmodel = config['godt_2008']['parameters']['displmodel']
except:
print('No regression model specified, using default of J_PGA_M')
displmodel = 'J_PGA_M'
# TO DO: ADD ERROR CATCHING ON UNITS, MAKE SURE THEY ARE WHAT THEY SHOULD
# BE FOR THIS MODEL
try: # Try to fetch source information from config
modelsref = config['godt_2008']['shortref']
modellref = config['godt_2008']['longref']
slopesref = config['godt_2008']['layers']['slope']['shortref']
slopelref = config['godt_2008']['layers']['slope']['longref']
cohesionsref = config['godt_2008']['layers']['cohesion']['shortref']
cohesionlref = config['godt_2008']['layers']['cohesion']['longref']
frictionsref = config['godt_2008']['layers']['friction']['shortref']
frictionlref = config['godt_2008']['layers']['friction']['longref']
except:
print('Was not able to retrieve all references from config file. '
'Continuing')
# 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.
geodict = geodict.getBoundsWithin(tempgdict)
basegeodict, firstcol = GDALGrid.getFileGeoDict(
os.path.join(slopefilepath, 'slope_min.bil'))
if basegeodict == geodict:
sampledict = geodict
else:
sampledict = basegeodict.getBoundsWithin(geodict)
# Do we need to subdivide baselayer?
if 'divfactor' in config['godt_2008'].keys():
divfactor = float(config['godt_2008']['divfactor'])
if divfactor != 1.:
# adjust sampledict so everything will be resampled (cut one cell
# of each edge so will be inside bounds)
newxmin = sampledict.xmin - sampledict.dx/2. + \
sampledict.dx/(2.*divfactor) + sampledict.dx
newymin = sampledict.ymin - sampledict.dy/2. + \
sampledict.dy/(2.*divfactor) + sampledict.dy
newxmax = sampledict.xmax + sampledict.dx/2. - \
sampledict.dx/(2.*divfactor) - sampledict.dx
newymax = sampledict.ymax + sampledict.dy/2. - \
sampledict.dy/(2.*divfactor) - sampledict.dy
newdx = sampledict.dx / divfactor
newdy = sampledict.dy / divfactor
sampledict = GeoDict.createDictFromBox(newxmin,
newxmax,
newymin,
newymax,
newdx,
newdy,
inside=True)
tmpdir = tempfile.mkdtemp()
# Load in ShakeMap and get new geodictionary
temp = ShakeGrid.load(shakefile) # , adjust='res')
junkfile = os.path.join(tmpdir, 'temp.bil')
GDALGrid.copyFromGrid(temp.getLayer('pga')).save(junkfile)
pga = quickcut(junkfile, sampledict, precise=True, method='bilinear')
os.remove(junkfile)
GDALGrid.copyFromGrid(temp.getLayer('pgv')).save(junkfile)
pgv = quickcut(junkfile, sampledict, precise=True, method='bilinear')
os.remove(junkfile)
# Update geodictionary
sampledict = pga.getGeoDict()
t2 = temp.getEventDict()
M = t2['magnitude']
event_id = t2['event_id']
shakedict = temp.getShakeDict()
del (temp)
# read in uncertainty if present
if uncertfile is not None:
try:
temp = ShakeGrid.load(uncertfile) # , adjust='res')
GDALGrid.copyFromGrid(temp.getLayer('stdpga')).save(junkfile)
uncertpga = quickcut(junkfile,
sampledict,
precise=True,
method='bilinear',
override=True)
os.remove(junkfile)
GDALGrid.copyFromGrid(temp.getLayer('stdpgv')).save(junkfile)
uncertpgv = quickcut(junkfile,
sampledict,
precise=True,
method='bilinear',
override=True)
os.remove(junkfile)
except:
print('Could not read uncertainty file, ignoring uncertainties')
uncertfile = None
if numstd is None:
numstd = 1.
# Read in all the slope files, divide all by 100 to get to slope in
# degrees (because input files are multiplied by 100.)
slopes = []
quantiles = [
'slope_min.bil', 'slope10.bil', 'slope30.bil', 'slope50.bil',
'slope70.bil', 'slope90.bil', 'slope_max.bil'
]
for quant in quantiles:
tmpslp = quickcut(os.path.join(slopefilepath, quant), sampledict)
tgd = tmpslp.getGeoDict()
if tgd != sampledict:
raise Exception('Input layers are not aligned to same geodict')
else:
slopes.append(tmpslp.getData() / slopediv)
slopestack = np.dstack(slopes)
# Change any zero slopes to a very small number to avoid dividing by
# zero later
slopestack[slopestack == 0] = 1e-8
# Read in the cohesion and friction files and duplicate layers so they
# are same shape as slope structure
tempco = quickcut(cohesionfile, sampledict, method='near')
tempco = tempco.getData()[:, :, np.newaxis] / codiv
cohesion = np.repeat(tempco, 7, axis=2)
cohesion[cohesion == -999.9] = nodata_cohesion
cohesion = np.nan_to_num(cohesion)
cohesion[cohesion == 0] = nodata_cohesion
tempfric = quickcut(frictionfile, sampledict, method='near')
tempfric = tempfric.getData().astype(float)[:, :, np.newaxis]
friction = np.repeat(tempfric, 7, axis=2)
friction[friction == -9999] = nodata_friction
friction = np.nan_to_num(friction)
friction[friction == 0] = nodata_friction
# Do the calculations using Jibson (2007) PGA only model for Dn
FS = (cohesion / (uwt * thick * np.sin(slopestack * (np.pi / 180.))) +
np.tan(friction * (np.pi / 180.)) / np.tan(slopestack *
(np.pi / 180.)))
FS[FS < fsthresh] = fsthresh
# Compute critical acceleration, in g
# This gives ac in g, equations that multiply by g give ac in m/s2
Ac = (FS - 1) * np.sin(slopestack * (np.pi / 180.)).astype(float)
Ac[Ac < acthresh] = acthresh
# Get PGA in g (PGA is %g in ShakeMap, convert to g)
PGA = np.repeat(pga.getData()[:, :, np.newaxis] / 100., 7,
axis=2).astype(float)
if 'PGV' in displmodel: # Load in PGV also, in cm/sec
PGV = np.repeat(pgv.getData()[:, :, np.newaxis], 7,
axis=2).astype(float)
else:
PGV = None
if uncertfile is not None:
stdpga = np.repeat(uncertpga.getData()[:, :, np.newaxis], 7,
axis=2).astype(float)
stdpgv = np.repeat(uncertpgv.getData()[:, :, np.newaxis], 7,
axis=2).astype(float)
# estimate PGA +- 1std
PGAmin = np.exp(np.log(PGA * 100) - numstd * stdpga) / 100
PGAmax = np.exp(np.log(PGA * 100) + numstd * stdpga) / 100
if 'PGV' in displmodel:
PGVmin = np.exp(np.log(PGV) - numstd * stdpgv)
PGVmax = np.exp(np.log(PGV) + numstd * stdpgv)
else:
PGVmin = None
PGVmax = None
# Ignore errors so still runs when Ac > PGA, just leaves nan instead
# of crashing.
np.seterr(invalid='ignore')
Dn, logDnstd, logtype = NMdisp(Ac, PGA, model=displmodel, M=M, PGV=PGV)
if uncertfile is not None:
Dnmin, logDnstdmin, logtype = NMdisp(Ac,
PGAmin,
model=displmodel,
M=M,
PGV=PGVmin)
Dnmax, logDnstdmax, logtype = NMdisp(Ac,
PGAmax,
model=displmodel,
M=M,
PGV=PGVmax)
PROB = Dn.copy()
PROB[PROB < dnthresh] = 0.
PROB[PROB >= dnthresh] = 1.
PROB = np.sum(PROB, axis=2)
if uncertfile is not None:
PROBmin = Dnmin.copy()
PROBmin[PROBmin <= dnthresh] = 0.
PROBmin[PROBmin > dnthresh] = 1.
PROBmin = np.sum(PROBmin, axis=2)
PROBmax = Dnmax.copy()
PROBmax[PROBmax <= dnthresh] = 0.
PROBmax[PROBmax > dnthresh] = 1.
PROBmax = np.sum(PROBmax, axis=2)
PROB[PROB == 1.] = 0.01
PROB[PROB == 2.] = 0.10
PROB[PROB == 3.] = 0.30
PROB[PROB == 4.] = 0.50
PROB[PROB == 5.] = 0.70
PROB[PROB == 6.] = 0.90
PROB[PROB == 7.] = 0.99
if uncertfile is not None:
PROBmin[PROBmin == 1.] = 0.01
PROBmin[PROBmin == 2.] = 0.10
PROBmin[PROBmin == 3.] = 0.30
PROBmin[PROBmin == 4.] = 0.50
PROBmin[PROBmin == 5.] = 0.70
PROBmin[PROBmin == 6.] = 0.90
PROBmin[PROBmin == 7.] = 0.99
PROBmax[PROBmax == 1.] = 0.01
PROBmax[PROBmax == 2.] = 0.10
PROBmax[PROBmax == 3.] = 0.30
PROBmax[PROBmax == 4.] = 0.50
PROBmax[PROBmax == 5.] = 0.70
PROBmax[PROBmax == 6.] = 0.90
PROBmax[PROBmax == 7.] = 0.99
if slopemin is not None:
PROB[slopestack[:, :, 6] <= slopemin] = 0.
# uncert too
if uncertfile is not None:
PROBmin[slopestack[:, :, 6] <= slopemin] = 0.
PROBmax[slopestack[:, :, 6] <= slopemin] = 0.
# Turn output and inputs into into grids and put in mapLayers dictionary
maplayers = collections.OrderedDict()
shakedetail = '%s_ver%s' % (shakedict['shakemap_id'],
shakedict['shakemap_version'])
description = {
'name': modelsref,
'longref': modellref,
'units': 'Proportion of Area Affected',
'shakemap': shakedetail,
'event_id': event_id,
'parameters': {
'displmodel': displmodel,
'thickness_m': thick,
'unitwt_kNm3': uwt,
'dnthresh_cm': dnthresh,
'acthresh_g': acthresh,
'fsthresh': fsthresh,
'modeltype': 'Landslide'
}
}
PROBgrid = GDALGrid(PROB, sampledict)
if trimfile is not None:
PROBgrid = trim_ocean(PROBgrid, trimfile)
maplayers['model'] = {
'grid': PROBgrid,
'label': 'Landslide - Proportion of Area Affected',
'type': 'output',
'description': description
}
if uncertfile is not None:
PROBmingrid = GDALGrid(PROBmin, sampledict)
PROBmaxgrid = GDALGrid(PROBmax, sampledict)
if trimfile is not None:
PROBmingrid = trim_ocean(PROBmingrid, trimfile)
PROBmaxgrid = trim_ocean(PROBmaxgrid, trimfile)
maplayers['modelmin'] = {
'grid': PROBmingrid,
'label': 'Landslide Probability-%1.2fstd' % numstd,
'type': 'output',
'description': description
}
maplayers['modelmax'] = {
'grid': PROBmaxgrid,
'label': 'Landslide Probability+%1.2fstd' % numstd,
'type': 'output',
'description': description
}
if saveinputs is True:
maplayers['pga'] = {
'grid': GDALGrid(PGA[:, :, 0], sampledict),
'label': 'PGA (g)',
'type': 'input',
'description': {
'units': 'g',
'shakemap': shakedetail
}
}
if 'PGV' in displmodel:
maplayers['pgv'] = {
'grid': GDALGrid(PGV[:, :, 0], sampledict),
'label': 'PGV (cm/s)',
'type': 'input',
'description': {
'units': 'cm/s',
'shakemap': shakedetail
}
}
maplayers['minFS'] = {
'grid': GDALGrid(np.min(FS, axis=2), sampledict),
'label': 'Min Factor of Safety',
'type': 'input',
'description': {
'units': 'unitless'
}
}
maplayers['max slope'] = {
'grid': GDALGrid(slopestack[:, :, -1], sampledict),
'label': r'Maximum slope ($^\circ$)',
'type': 'input',
'description': {
'units': 'degrees',
'name': slopesref,
'longref': slopelref
}
}
maplayers['cohesion'] = {
'grid': GDALGrid(cohesion[:, :, 0], sampledict),
'label': 'Cohesion (kPa)',
'type': 'input',
'description': {
'units': 'kPa (adjusted)',
'name': cohesionsref,
'longref': cohesionlref
}
}
maplayers['friction angle'] = {
'grid': GDALGrid(friction[:, :, 0], sampledict),
'label': r'Friction angle ($^\circ$)',
'type': 'input',
'description': {
'units': 'degrees',
'name': frictionsref,
'longref': frictionlref
}
}
if uncertfile is not None:
maplayers['pgamin'] = {
'grid': GDALGrid(PGAmin[:, :, 0], sampledict),
'label': 'PGA - %1.2fstd (g)' % numstd,
'type': 'input',
'description': {
'units': 'g',
'shakemap': shakedetail
}
}
maplayers['pgamax'] = {
'grid': GDALGrid(PGAmax[:, :, 0], sampledict),
'label': 'PGA + %1.2fstd (g)' % numstd,
'type': 'input',
'description': {
'units': 'g',
'shakemap': shakedetail
}
}
if 'PGV' in displmodel:
if uncertfile is not None:
maplayers['pgvmin'] = {
'grid': GDALGrid(PGVmin[:, :, 0], sampledict),
'label': 'PGV - %1.2fstd (cm/s)' % numstd,
'type': 'input',
'description': {
'units': 'cm/s',
'shakemap': shakedetail
}
}
maplayers['pgvmax'] = {
'grid': GDALGrid(PGVmax[:, :, 0], sampledict),
'label': 'PGV + %1.2fstd (cm/s)' % numstd,
'type': 'input',
'description': {
'units': 'cm/s',
'shakemap': shakedetail
}
}
shutil.rmtree(tmpdir)
return maplayers
def computeParea(grid2D,
proj='moll',
probthresh=0.0,
shakefile=None,
shakethreshtype='pga',
shakethresh=0.0):
"""
Alternative to Aggregate Hazard (Hagg), which is equal to the
the sum of the area of grid cells that exceeds a given probability.
Args:
grid2D: grid2D object of model output.
proj: projection to use to obtain equal area, 'moll' mollweide, or
'laea' lambert equal area.
probthresh: Optional, Float or list of probability thresholds.
shakefile: Optional, path to shakemap file to use for ground motion
threshold.
shakethreshtype: Optional, Type of ground motion to use for
shakethresh, 'pga', 'pgv', or 'mmi'.
shakethresh: Optional, Float of shaking thresholds in %g for
pga, cm/s for pgv, float for mmi.
Returns:
Parea (float) if no or only one probthresh defined,
otherwise, a list of floats of Parea corresponding to all
specified probthresh values.
"""
if type(probthresh) != list and type(probthresh) != np.ndarray:
probthresh = [probthresh]
Parea = []
bounds = grid2D.getBounds()
lat0 = np.mean((bounds[2], bounds[3]))
lon0 = np.mean((bounds[0], bounds[1]))
projs = ('+proj=%s +lat_0=%f +lon_0=%f +x_0=0 +y_0=0 +ellps=WGS84 '
'+units=km +no_defs' % (proj, lat0, lon0))
geodict = grid2D.getGeoDict()
if shakefile is not None:
if shakethresh < 0.:
raise Exception('shaking threshold must be equal or greater '
'than zero')
tmpdir = tempfile.mkdtemp()
# resample shakemap to grid2D
temp = ShakeGrid.load(shakefile)
junkfile = os.path.join(tmpdir, 'temp.bil')
GDALGrid.copyFromGrid(temp.getLayer(shakethreshtype)).save(junkfile)
shk = quickcut(junkfile, geodict, precise=True, method='bilinear')
shutil.rmtree(tmpdir)
if shk.getGeoDict() != geodict:
raise Exception('shakemap was not resampled to exactly the same '
'geodict as the model')
grid = grid2D.project(projection=projs)
geodictRS = grid.getGeoDict()
cell_area_km2 = geodictRS.dx * geodictRS.dy
model = grid.getData()
model[np.isnan(model)] = -1.
for probt in probthresh:
if probt < 0.:
raise Exception('probability threshold must be equal or greater '
'than zero')
modcop = model.copy()
if shakefile is not None:
shkgrid = shk.project(projection=projs)
shkdat = shkgrid.getData()
# use -1 to avoid nan errors and warnings, will always be thrown
# out because default probthresh is 0 and must be positive.
shkdat[np.isnan(shkdat)] = -1.
modcop[shkdat < shakethresh] = -1.
one_mat = np.ones_like(modcop)
Parea.append(np.sum(one_mat[modcop >= probt] * cell_area_km2))
if len(Parea) == 1:
Parea = Parea[0]
return Parea
def execute(self):
"""
Write raster.zip file containing ESRI Raster files of all the IMTs
in shake_result.hdf.
Raises:
NotADirectoryError: When the event data directory does not exist.
FileNotFoundError: When the the shake_result HDF file does not
exist.
"""
install_path, data_path = get_config_paths()
datadir = os.path.join(data_path, self._eventid, 'current', 'products')
if not os.path.isdir(datadir):
raise NotADirectoryError('%s is not a valid directory.' % datadir)
datafile = os.path.join(datadir, 'shake_result.hdf')
if not os.path.isfile(datafile):
raise FileNotFoundError('%s does not exist.' % datafile)
# Open the ShakeMapOutputContainer and extract the data
container = ShakeMapOutputContainer.load(datafile)
if container.getDataType() != 'grid':
raise NotImplementedError('raster module can only operate on '
'gridded data, not sets of points')
# create GIS-readable .flt files of imt and uncertainty
self.logger.debug('Creating GIS grids...')
layers = container.getIMTs()
# Package up all of these files into one zip file.
zfilename = os.path.join(datadir, 'rasters.zip')
zfile = zipfile.ZipFile(zfilename,
mode='w',
compression=zipfile.ZIP_DEFLATED)
files_written = []
for layer in layers:
fileimt = oq_to_file(layer)
# This is a bit hacky -- we only produce the raster for the
# first IMC returned. It should work as long as we only have
# one IMC produced per ShakeMap run.
imclist = container.getComponents(layer)
imtdict = container.getIMTGrids(layer, imclist[0])
mean_grid = imtdict['mean']
std_grid = imtdict['std']
mean_gdal = GDALGrid.copyFromGrid(mean_grid)
std_gdal = GDALGrid.copyFromGrid(std_grid)
mean_fname = os.path.join(datadir, '%s_mean.flt' % fileimt)
mean_hdr = os.path.join(datadir, '%s_mean.hdr' % fileimt)
std_fname = os.path.join(datadir, '%s_std.flt' % fileimt)
std_hdr = os.path.join(datadir, '%s_std.hdr' % fileimt)
self.logger.debug('Saving %s...' % mean_fname)
mean_gdal.save(mean_fname)
files_written.append(mean_fname)
files_written.append(mean_hdr)
self.logger.debug('Saving %s...' % std_fname)
std_gdal.save(std_fname)
files_written.append(std_fname)
files_written.append(std_hdr)
zfile.write(mean_fname, '%s_mean.flt' % fileimt)
zfile.write(mean_hdr, '%s_mean.hdr' % fileimt)
zfile.write(std_fname, '%s_std.flt' % fileimt)
zfile.write(std_hdr, '%s_std.hdr' % fileimt)
zfile.close()
container.close()
# nuke all of the copies of the files we just put in the zipfile
for file_written in files_written:
os.remove(file_written)
def computeHagg(grid2D, proj='moll', probthresh=0., shakefile=None,
shakethreshtype='pga', shakethresh=0., stdgrid2D=None,
stdtype='full', maxP=1., sill1=None, range1=None):
"""
Computes the Aggregate Hazard (Hagg) which is equal to the
probability * area of grid cell For models that compute areal coverage,
this is equivalant to the total predicted area affected in km2.
Args:
grid2D: grid2D object of model output.
proj: projection to use to obtain equal area, 'moll' mollweide, or
'laea' lambert equal area.
probthresh: Probability threshold, any values less than this will not
be included in aggregate hazard estimation.
shakefile: Optional, path to shakemap file to use for ground motion
threshold.
shakethreshtype: Optional, Type of ground motion to use for
shakethresh, 'pga', 'pgv', or 'mmi'.
shakethresh: Optional, Float or list of shaking thresholds in %g for
pga, cm/s for pgv, float for mmi.
stdgrid2D: grid2D object of model standard deviations (optional)
stdtype (str): assumption of spatial correlation used to compute
the stdev of the statistics, 'max', 'min', 'mean' of max and min,
or 'full' (default) which estimates the range of correlation and
accounts for covariance. Will return 'mean' if
ridge and sill cannot be estimated.
maxP (float): the maximum possible probability of the model
sill1 (float): If known, the sill of the variogram of grid2D, will be
estimated if None and stdtype='full'
range1 (float): If known, the range of the variogram of grid2D, will
be estimated if None and stdtype='full'
Returns:
dict: Dictionary with keys:
hagg_#g where # is the shakethresh
std_# if stdgrid2D is supplied (stdev of exp_pop)
hlim_#, the maximum exposure value possible with the
applied thresholds and given maxP value
cell_area_km2 grid cell area
p_hagg_# beta distribution shape factor p (sometimes called alpha)
q_hagg_# beta distribution shape factor q (sometimes called beta)
"""
bounds = grid2D.getBounds()
lat0 = np.mean((bounds[2], bounds[3]))
lon0 = np.mean((bounds[0], bounds[1]))
projs = ('+proj=%s +lat_0=%f +lon_0=%f +x_0=0 +y_0=0 +ellps=WGS84 '
'+units=km +no_defs' % (proj, lat0, lon0))
geodict = grid2D.getGeoDict()
if shakefile is not None:
if shakethresh < 0.:
raise Exception('shaking threshold must be equal or greater '
'than zero')
# resample shakemap to grid2D
temp = ShakeGrid.load(shakefile)
shk = temp.getLayer(shakethreshtype)
shk = shk.interpolate2(geodict)
if shk.getGeoDict() != geodict:
raise Exception('shakemap was not resampled to exactly the same '
'geodict as the model')
if probthresh < 0.:
raise Exception('probability threshold must be equal or greater '
'than zero')
grid = grid2D.project(projection=projs, method='bilinear')
geodictRS = grid.getGeoDict()
cell_area_km2 = geodictRS.dx * geodictRS.dy
model = grid.getData().copy()
Hagg = {}
if shakefile is not None:
shkgrid = shk.project(projection=projs)
shkdat = shkgrid.getData()
model[shkdat < shakethresh] = float('nan')
else:
shakethresh = 0.
shkdat = None
mu = np.nansum(model[model >= probthresh] * cell_area_km2)
Hagg['hagg_%1.2fg' % (shakethresh/100.,)] = mu
Hagg['cell_area_km2'] = cell_area_km2
N = np.nansum([model >= probthresh])
#Hagg['N_%1.2fg' % (shakethresh/100.,)] = N
hlim = cell_area_km2*N*maxP
Hagg['hlim_%1.2fg' % (shakethresh/100.,)] = hlim
if stdgrid2D is not None:
stdgrid = GDALGrid.copyFromGrid(stdgrid2D) # Make a copy
stdgrid = stdgrid.project(projection=projs, method='bilinear')
std = stdgrid.getData().copy()
if np.nanmax(std) > 0. and np.nanmax(model) >= probthresh:
totalmin = cell_area_km2 * np.sqrt(np.nansum((std[model >= probthresh])**2.))
totalmax = np.nansum(std[model >= probthresh] * cell_area_km2)
if stdtype == 'full':
if sill1 is None or range1 is None:
range1, sill1 = semivario(grid.getData().copy(), probthresh,
shakethresh=shakethresh,
shakegrid=shkdat)
if range1 is None:
# Use mean
Hagg['hagg_std_%1.2fg' % (shakethresh/100.,)] = (totalmax+totalmin)/2.
else:
# Zero out std at cells where the model probability was below
# the threshold because we aren't including those cells in Hagg
stdz = std.copy()
stdz[model < probthresh] = 0.
svar1 = svar(stdz, range1, sill1, scale=cell_area_km2)
Hagg['hagg_std_%1.2fg' % (shakethresh/100.,)] = np.sqrt(svar1)
#Hagg['hagg_range_%1.2fg' % (shakethresh/100.,)] = range1
#Hagg['hagg_sill_%1.2fg' % (shakethresh/100.,)] = sill1
elif stdtype == 'max':
Hagg['hagg_std_%1.2fg' % (shakethresh/100.,)] = totalmax
elif stdtype == 'min':
Hagg['hagg_std_%1.2fg' % (shakethresh/100.,)] = totalmin
else:
Hagg['hagg_std_%1.2fg' % (shakethresh/100.,)] = (totalmax+totalmin)/2.
var = Hagg['hagg_std_%1.2fg' % (shakethresh/100.,)]**2.
# Beta distribution shape factors
Hagg['p_hagg_%1.2fg' % (shakethresh/100.,)] = (mu/hlim)*((hlim*mu-mu**2)/var-1)
Hagg['q_hagg_%1.2fg' % (shakethresh/100.,)] = (1-mu/hlim)*((hlim*mu-mu**2)/var-1)
else:
print('No model values above threshold, skipping uncertainty '
'and filling with zeros')
Hagg['hagg_std_%1.2fg' % (shakethresh/100.,)] = 0.
Hagg['p_hagg_%1.2fg' % (shakethresh/100.,)] = 0.
Hagg['q_hagg_%1.2fg' % (shakethresh/100.,)] = 0.
else:
print('No uncertainty provided, filling with zeros')
Hagg['hagg_std_%1.2fg' % (shakethresh/100.,)] = 0.
Hagg['p_hagg_%1.2fg' % (shakethresh/100.,)] = 0.
Hagg['q_hagg_%1.2fg' % (shakethresh/100.,)] = 0.
return Hagg
def computeHagg(grid2D,
proj='moll',
probthresh=0.0,
shakefile=None,
shakethreshtype='pga',
shakethresh=0.0):
"""
Computes the Aggregate Hazard (Hagg) which is equal to the
probability * area of grid cell For models that compute areal coverage,
this is equivalant to the total predicted area affected in km2.
Args:
grid2D: grid2D object of model output.
proj: projection to use to obtain equal area, 'moll' mollweide, or
'laea' lambert equal area.
probthresh: Probability threshold, any values less than this will not
be included in aggregate hazard estimation.
shakefile: Optional, path to shakemap file to use for ground motion
threshold.
shakethreshtype: Optional, Type of ground motion to use for
shakethresh, 'pga', 'pgv', or 'mmi'.
shakethresh: Optional, Float or list of shaking thresholds in %g for
pga, cm/s for pgv, float for mmi.
Returns: Aggregate hazard (float) if no shakethresh or only one shakethresh was defined,
otherwise, a list of floats of aggregate hazard for all shakethresh values.
"""
Hagg = []
bounds = grid2D.getBounds()
lat0 = np.mean((bounds[2], bounds[3]))
lon0 = np.mean((bounds[0], bounds[1]))
projs = ('+proj=%s +lat_0=%f +lon_0=%f +x_0=0 +y_0=0 +ellps=WGS84 '
'+units=km +no_defs' % (proj, lat0, lon0))
geodict = grid2D.getGeoDict()
if shakefile is not None:
if type(shakethresh) != list and type(shakethresh) != np.ndarray:
shakethresh = [shakethresh]
for shaket in shakethresh:
if shaket < 0.:
raise Exception('shaking threshold must be equal or greater '
'than zero')
tmpdir = tempfile.mkdtemp()
# resample shakemap to grid2D
temp = ShakeGrid.load(shakefile)
junkfile = os.path.join(tmpdir, 'temp.bil')
GDALGrid.copyFromGrid(temp.getLayer(shakethreshtype)).save(junkfile)
shk = quickcut(junkfile, geodict, precise=True, method='bilinear')
shutil.rmtree(tmpdir)
if shk.getGeoDict() != geodict:
raise Exception('shakemap was not resampled to exactly the same '
'geodict as the model')
if probthresh < 0.:
raise Exception('probability threshold must be equal or greater '
'than zero')
grid = grid2D.project(projection=projs, method='bilinear')
geodictRS = grid.getGeoDict()
cell_area_km2 = geodictRS.dx * geodictRS.dy
model = grid.getData()
model[np.isnan(model)] = -1.
if shakefile is not None:
for shaket in shakethresh:
modcop = model.copy()
shkgrid = shk.project(projection=projs)
shkdat = shkgrid.getData()
# use -1 to avoid nan errors and warnings, will always be thrown
# out because default is 0.
shkdat[np.isnan(shkdat)] = -1.
modcop[shkdat < shaket] = -1.
Hagg.append(np.sum(modcop[modcop >= probthresh] * cell_area_km2))
else:
Hagg.append(np.sum(model[model >= probthresh] * cell_area_km2))
if len(Hagg) == 1:
Hagg = Hagg[0]
return Hagg
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 trim_ocean(grid2D,
mask,
all_touched=True,
crop=False,
invert=False,
nodata=0.):
"""Use the mask (a shapefile) to trim offshore areas
Args:
grid2D: MapIO grid2D object of results that need trimming
mask: list of shapely polygon features already loaded in or string of
file extension of shapefile to use for clipping
all_touched (bool): if True, won't mask cells that touch any part of
polygon edge
crop (bool): crop boundaries of raster to new masked area
invert (bool): if True, will mask areas that do not overlap with the
polygon
nodata (flt): value to use as mask
Returns:
grid2D file with ocean masked
"""
gdict = grid2D.getGeoDict()
tempdir = tempfile.mkdtemp()
tempfile1 = os.path.join(tempdir, 'temp.tif')
tempfile2 = os.path.join(tempdir, 'temp2.tif')
# Get shapes ready
if type(mask) == str:
with fiona.open(mask, 'r') as shapefile:
bbox = (gdict.xmin, gdict.ymin, gdict.xmax, gdict.ymax)
hits = list(shapefile.items(bbox=bbox))
features = [feature[1]["geometry"] for feature in hits]
# hits = list(shapefile)
# features = [feature["geometry"] for feature in hits]
elif type(mask) == list:
features = mask
else:
raise Exception('mask is neither a link to a shapefile or a list of \
shapely shapes, cannot proceed')
tempfilen = os.path.join(tempdir, 'temp.bil')
tempfile1 = os.path.join(tempdir, 'temp.tif')
tempfile2 = os.path.join(tempdir, 'temp2.tif')
GDALGrid.copyFromGrid(grid2D).save(tempfilen)
cmd = 'gdal_translate -a_srs EPSG:4326 -of GTiff %s %s' % \
(tempfilen, tempfile1)
rc, so, se = get_command_output(cmd)
# #Convert grid2D to rasterio format
#
# source_crs = rasterio.crs.CRS.from_string(gdict.projection)
# src_transform = rasterio.Affine.from_gdal(gdict.xmin - gdict.dx/2.0,
# gdict.dx, 0.0, gdict.ymax + gdict.dy/2.0,
# 0.0, -1*gdict.dy) # from mapio.grid2D
# with rasterio.open(tempfile1, 'w', driver='GTIff',
# height=gdict.ny, # numpy of rows
# width=gdict.nx, # number of columns
# count=1, # number of bands
# dtype=rasterio.dtypes.float64, # this must match the dtype of our array
# crs=source_crs,
# transform=src_transform) as src_raster:
# src_raster.write(grid2D.getData().astype(float), 1) # optional second parameter is the band number to write to
# #ndvi_raster.nodata = -1 # set the raster's nodata value
if rc:
with rasterio.open(tempfile1, 'r') as src_raster:
out_image, out_transform = rasterio.mask.mask(
src_raster, features, all_touched=all_touched, crop=crop)
out_meta = src_raster.meta.copy()
out_meta.update({
"driver": "GTiff",
"height": out_image.shape[1],
"width": out_image.shape[2],
"transform": out_transform
})
with rasterio.open(tempfile2, "w", **out_meta) as dest:
dest.write(out_image)
newgrid = GDALGrid.load(tempfile2)
else:
raise Exception('ocean trimming failed')
print(se)
shutil.rmtree(tempdir)
return newgrid
def run_gfail(args):
"""Runs ground failure.
Args:
args: dictionary or argument parser Namespace output by bin/gfail
program.
Returns:
list: Names of created files.
"""
# TODO: ADD CONFIG VALIDATION STEP THAT MAKES SURE ALL THE FILES EXIST
filenames = []
# If args is a dictionary, convert to a Namespace
if isinstance(args, dict):
args = Namespace(**args)
if args.set_default_paths:
set_default_paths(args)
print('default paths set, continuing...\n')
if args.list_default_paths:
list_default_paths()
return
if args.reset_default_paths:
reset_default_paths()
return
if args.make_webpage:
# Turn on GIS and HDF5 flags
gis = True
hdf5 = True
else:
gis = args.gis
hdf5 = args.hdf5
# Figure out what models will be run
if args.shakefile is not None: # user intends to actually run some models
shakefile = args.shakefile
# make output location for things
if args.output_filepath is None:
outdir = os.getcwd()
else:
outdir = args.output_filepath
if (hdf5 or args.make_static_pngs or
args.make_static_pdfs or
args.make_interactive_plots or
gis):
if not os.path.exists(outdir):
os.makedirs(outdir)
# download if is url
# cleanup = False
if not os.path.isfile(shakefile):
if isURL(shakefile):
# getGridURL returns a named temporary file object
shakefile = getGridURL(shakefile)
# cleanup = True # Be sure to delete it after
else:
raise NameError('Could not find "%s" as a file or a valid url'
% (shakefile))
eventid = getHeaderData(shakefile)[0]['event_id']
# Get entire path so won't break if running gfail with relative path
shakefile = os.path.abspath(shakefile)
if args.extract_contents:
outfolder = outdir
else: # Nest in a folder named by eventid
outfolder = os.path.join(outdir, eventid)
if not os.path.exists(outfolder):
os.makedirs(outfolder)
# Copy shake grid into output directory
# --- this is base on advice from Mike that when running in production
# the shake grids are not archived and so if we need/want to have
# the exact grid used for the calculation later if there's every a
# question about how the calculation was done, the safest thing is
# to store a copy of it here.
shake_copy = os.path.join(outfolder, "grid.xml")
shutil.copyfile(shakefile, shake_copy)
# Write shakefile to a file for use later
shakename = os.path.join(outfolder, "shakefile.txt")
shake_file = open(shakename, "wt")
shake_file.write(shake_copy)
shake_file.close()
filenames.append(shakename)
config = args.config
if args.config_filepath is not None:
# only add config_filepath if full filepath not given and file
# ext is .ini
if (not os.path.isabs(config) and
os.path.splitext(config)[-1] == '.ini'):
config = os.path.join(args.config_filepath, config)
if os.path.splitext(config)[-1] == '.ini':
temp = ConfigObj(config)
if len(temp) == 0:
raise Exception(
'Could not find specified .ini file: %s' % config)
if args.data_path is not None:
temp = correct_config_filepaths(args.data_path, temp)
configs = [temp]
conffail = []
else:
# input is a list of config files
f = open(config, 'r')
configlist = f.readlines()
configs = []
conffail = []
for conf in configlist:
conf = conf.strip()
if not os.path.isabs(conf):
# only add config_filepath if full filepath not given
conf = os.path.join(args.config_filepath, conf)
try:
temp = ConfigObj(conf)
if temp:
if args.data_path is not None:
temp = correct_config_filepaths(
args.data_path, temp)
configs.append(temp)
else:
conffail.append(conf)
except:
conffail.append(conf)
print('\nRunning the following models:')
for conf in configs:
print('\t%s' % conf.keys()[0])
if len(conffail) > 0:
print('Could not find or read in the following config files:\n')
for conf in conffail:
print('\t%s' % conf)
print('\nContinuing...\n')
if args.set_bounds is not None:
if 'zoom' in args.set_bounds:
temp = args.set_bounds.split(',')
print('Using %s threshold of %1.1f to cut model bounds'
% (temp[1].strip(), float(temp[2].strip())))
bounds = get_bounds(shakefile, temp[1].strip(),
float(temp[2].strip()))
else:
temp = eval(args.set_bounds)
latmin = temp[0]
latmax = temp[1]
lonmin = temp[2]
lonmax = temp[3]
bounds = {'xmin': lonmin, 'xmax': lonmax,
'ymin': latmin, 'ymax': latmax}
print('Applying bounds of lonmin %1.2f, lonmax %1.2f, '
'latmin %1.2f, latmax %1.2f'
% (bounds['xmin'], bounds['xmax'],
bounds['ymin'], bounds['ymax']))
else:
bounds = None
if args.make_webpage or args.make_summary:
results = []
# pre-read in ocean trimming file polygons so only do this step once
if args.trimfile is not None:
if not os.path.exists(args.trimfile):
print('trimfile defined does not exist: %s\n'
'Ocean will not be trimmed.' % args.trimfile)
trimfile = None
elif os.path.splitext(args.trimfile)[1] != '.shp':
print('trimfile must be a shapefile, '
'ocean will not be trimmed')
trimfile = None
else:
trimfile = args.trimfile
else:
trimfile = None
# Get finite fault ready, if exists
ffault = None
point = True
if args.finite_fault is not None:
point = False
try:
if os.path.splitext(args.finite_fault)[-1] == '.txt':
ffault = text_to_json(args.finite_fault)
elif os.path.splitext(args.finite_fault)[-1] == '.json':
ffault = args.finite_fault
else:
print('Could not read in finite fault, will '
'try to download from comcat')
ffault = None
except:
print('Could not read in finite fault, will try to '
'download from comcat')
ffault = None
if ffault is None:
# Try to get finite fault file, if it exists
try:
returned_ev = get_event_comcat(shakefile)
if returned_ev is not None:
testjd, detail, temp = returned_ev
if 'faultfiles' in testjd['input']['event_information']:
ffilename = testjd['input']['event_information']['faultfiles']
if len(ffilename) > 0:
# Download the file
with tempfile.NamedTemporaryFile(delete=False, mode='w') as f:
temp.getContent(ffilename, filename=f.name)
ffault = text_to_json(f.name)
os.remove(f.name)
point = False
else:
point = True
else:
print('Unable to determine source type, unknown if finite'
' fault or point source')
ffault = None
point = False
except Exception as e:
print(e)
print('Unable to determine source type, unknown if finite'
' fault or point source')
ffault = None
point = False
# Loop over config files
for conf in configs:
modelname = conf.keys()[0]
print('\nNow running %s:' % modelname)
modelfunc = conf[modelname]['funcname']
if modelfunc == 'LogisticModel':
lm = LM.LogisticModel(shakefile, conf,
uncertfile=args.uncertfile,
saveinputs=args.save_inputs,
bounds=bounds,
numstd=float(args.std),
trimfile=trimfile)
maplayers = lm.calculate()
elif modelfunc == 'godt2008':
maplayers = godt2008(shakefile, conf,
uncertfile=args.uncertfile,
saveinputs=args.save_inputs,
bounds=bounds,
numstd=float(args.std),
trimfile=trimfile)
else:
print('Unknown model function specified in config for %s '
'model, skipping to next config' % modelfunc)
continue
# time1 = datetime.datetime.utcnow().strftime('%d%b%Y_%H%M')
# filename = ('%s_%s_%s' % (eventid, modelname, time1))
if args.appendname is not None:
filename = ('%s_%s_%s' % (eventid, modelname, args.appendname))
else:
filename = ('%s_%s' % (eventid, modelname))
if hdf5:
filenameh = filename + '.hdf5'
if os.path.exists(filenameh):
os.remove(filenameh)
savelayers(maplayers, os.path.join(outfolder, filenameh))
filenames.append(filenameh)
if args.make_static_pdfs or args.make_static_pngs:
plotorder, logscale, lims, colormaps, maskthreshes = \
parseConfigLayers(maplayers, conf)
mapconfig = ConfigObj(args.mapconfig)
kwargs = parseMapConfig(
mapconfig, fileext=args.mapdata_filepath)
junk, filenames1 = modelMap(
maplayers, shakefile,
suptitle=conf[modelname]['shortref'],
boundaries=None,
zthresh=0.,
lims=lims,
plotorder=plotorder,
maskthreshes=maskthreshes,
maproads=False,
mapcities=True,
colormaps=colormaps,
savepdf=args.make_static_pdfs,
savepng=args.make_static_pngs,
printparam=True,
inventory_shapefile=None,
outputdir=outfolder,
outfilename=filename,
scaletype='continuous',
logscale=logscale, **kwargs)
for filen in filenames1:
filenames.append(filen)
# make model only plots too
if len(maplayers) > 1:
plotorder, logscale, lims, colormaps, maskthreshes = \
parseConfigLayers(maplayers, conf, keys=['model'])
junk, filenames1 = modelMap(
maplayers, shakefile,
suptitle=conf[modelname]['shortref'], boundaries=None,
zthresh=0., lims=lims, plotorder=plotorder,
maskthreshes=maskthreshes, maproads=False,
mapcities=True, savepdf=args.make_static_pdfs,
savepng=args.make_static_pngs, printparam=True,
inventory_shapefile=None, outputdir=outfolder,
outfilename=filename + '-just_model',
colormaps=colormaps, scaletype='continuous',
logscale=logscale, **kwargs)
for filen in filenames1:
filenames.append(filen)
if args.make_interactive_plots:
plotorder, logscale, lims, colormaps, maskthreshes = \
parseConfigLayers(maplayers, conf)
junk, filenames1 = interactiveMap(
maplayers, plotorder=plotorder, shakefile=shakefile,
inventory_shapefile=None, maskthreshes=maskthreshes,
colormaps=colormaps, isScenario=False,
scaletype='continuous', lims=lims, logscale=logscale,
ALPHA=0.7, outputdir=outfolder, outfilename=filename,
tiletype='Stamen Terrain', separate=True,
faultfile=ffault)
for filen in filenames1:
filenames.append(filen)
if gis:
for key in maplayers:
# Get simplified name of key for file naming
RIDOF = '[+-]?(?=\d*[.eE])(?=\.?\d)'\
'\d*\.?\d*(?:[eE][+-]?\d+)?'
OPERATORPAT = '[\+\-\*\/]*'
keyS = re.sub(OPERATORPAT, '', key)
# remove floating point numbers
keyS = re.sub(RIDOF, '', keyS)
# remove parentheses
keyS = re.sub('[()]*', '', keyS)
# remove any blank spaces
keyS = keyS.replace(' ', '')
filen = os.path.join(outfolder, '%s_%s.bil'
% (filename, keyS))
fileh = os.path.join(outfolder, '%s_%s.hdr'
% (filename, keyS))
fileg = os.path.join(outfolder, '%s_%s.tif'
% (filename, keyS))
GDALGrid.copyFromGrid(maplayers[key]['grid']).save(filen)
cmd = 'gdal_translate -a_srs EPSG:4326 -of GTiff %s %s' % (
filen, fileg)
rc, so, se = get_command_output(cmd)
# Delete bil file and its header
os.remove(filen)
os.remove(fileh)
filenames.append(fileg)
if args.make_webpage:
# Compile into list of results for later
results.append(maplayers)
# Make binary output for ShakeCast
filef = os.path.join(outfolder, '%s_model.flt'
% filename)
# And get name of header
filefh = os.path.join(outfolder, '%s_model.hdr'
% filename)
# Make file
write_floats(filef, maplayers['model']['grid'])
filenames.append(filef)
filenames.append(filefh)
if args.make_summary and not args.make_webpage:
# Compile into list of results for later
results.append(maplayers)
eventid = getHeaderData(shakefile)[0]['event_id']
if not hasattr(args, 'eventsource'):
args.eventsource = 'us'
if not hasattr(args, 'eventsourcecode'):
args.eventsourcecode = eventid
if args.make_webpage:
outputs = hazdev(
results, configs,
shakefile, outfolder=outfolder,
pop_file=args.popfile,
pager_alert=args.property_alertlevel,
eventsource=args.eventsource,
eventsourcecode=args.eventsourcecode)
filenames = filenames + outputs
if args.make_summary:
outputs = GFSummary(
results, configs, args.web_template,
shakefile, outfolder=outfolder, cleanup=True,
faultfile=ffault, point=point, pop_file=args.popfile)
filenames = filenames + outputs
# # create transparent png file
# outputs = create_png(outdir)
# filenames = filenames + outputs
#
# # create info file
# infofile = create_info(outdir)
# filenames = filenames + infofile
print('\nFiles created:\n')
for filen in filenames:
print('%s' % filen)
return filenames
def computeHagg(grid2D,
proj='moll',
probthresh=0.0,
shakefile=None,
shakethreshtype='pga',
shakethresh=0.0,
stdgrid2D=None,
stdtype='mean',
maxP=1.):
"""
Computes the Aggregate Hazard (Hagg) which is equal to the
probability * area of grid cell For models that compute areal coverage,
this is equivalant to the total predicted area affected in km2.
Args:
grid2D: grid2D object of model output.
proj: projection to use to obtain equal area, 'moll' mollweide, or
'laea' lambert equal area.
probthresh: Probability threshold, any values less than this will not
be included in aggregate hazard estimation.
shakefile: Optional, path to shakemap file to use for ground motion
threshold.
shakethreshtype: Optional, Type of ground motion to use for
shakethresh, 'pga', 'pgv', or 'mmi'.
shakethresh: Optional, Float or list of shaking thresholds in %g for
pga, cm/s for pgv, float for mmi.
stdgrid2D: grid2D object of model standard deviations (optional)
stdtype (str): assumption of spatial correlation used to compute
the stdev of the statistics, 'max', 'min' or 'mean' of max and min
maxP (float): the maximum possible probability of the model
Returns:
dict: Dictionary with keys:
hagg_#g where # is the shakethresh
std_# if stdgrid2D is supplied (stdev of exp_pop)
hlim_#, the maximum exposure value possible with the
applied thresholds and given maxP value
N_# the number of cells exceeding that value (in projected coords)
cell_area_km2 grid cell area
p_hagg_# beta distribution shape factor p (sometimes called alpha)
q_hagg_# beta distribution shape factor q (sometimes called beta)
"""
bounds = grid2D.getBounds()
lat0 = np.mean((bounds[2], bounds[3]))
lon0 = np.mean((bounds[0], bounds[1]))
projs = ('+proj=%s +lat_0=%f +lon_0=%f +x_0=0 +y_0=0 +ellps=WGS84 '
'+units=km +no_defs' % (proj, lat0, lon0))
geodict = grid2D.getGeoDict()
if shakefile is not None:
if type(shakethresh) != list and type(shakethresh) != np.ndarray:
shakethresh = [shakethresh]
for shaket in shakethresh:
if shaket < 0.:
raise Exception('shaking threshold must be equal or greater '
'than zero')
tmpdir = tempfile.mkdtemp()
# resample shakemap to grid2D
temp = ShakeGrid.load(shakefile)
junkfile = os.path.join(tmpdir, 'temp.bil')
GDALGrid.copyFromGrid(temp.getLayer(shakethreshtype)).save(junkfile)
shk = quickcut(junkfile, geodict, precise=True, method='bilinear')
shutil.rmtree(tmpdir)
if shk.getGeoDict() != geodict:
raise Exception('shakemap was not resampled to exactly the same '
'geodict as the model')
if probthresh < 0.:
raise Exception('probability threshold must be equal or greater '
'than zero')
grid = grid2D.project(projection=projs, method='bilinear')
geodictRS = grid.getGeoDict()
cell_area_km2 = geodictRS.dx * geodictRS.dy
model = grid.getData().copy()
if stdgrid2D is not None:
stdgrid = stdgrid2D.project(projection=projs, method='bilinear')
std = stdgrid.getData().copy()
std[np.isnan(model)] = -1.
Hagg = {}
model[np.isnan(model)] = -1.
if shakefile is not None:
shkgrid = shk.project(projection=projs)
shkdat = shkgrid.getData()
for shaket in shakethresh:
# use -1 to avoid nan errors and warnings, will always be thrown
# out because default probthresh is 0.
model[np.isnan(shkdat)] = -1.
model[shkdat < shaket] = -1.
mu = np.sum(model[model >= probthresh] * cell_area_km2)
Hagg['hagg_%1.2fg' % (shaket / 100., )] = mu
Hagg['cell_area_km2'] = cell_area_km2
N = np.sum([model >= probthresh])
Hagg['N_%1.2fg' % (shaket / 100., )] = N
hlim = cell_area_km2 * N * maxP
Hagg['hlim_%1.2fg' % (shaket / 100., )] = hlim
if stdgrid2D is not None:
totalmin = cell_area_km2 * np.sqrt(
np.nansum((std[model >= probthresh])**2.))
totalmax = np.nansum(std[model >= probthresh] * cell_area_km2)
if stdtype == 'max':
Hagg['hagg_std_%1.2fg' % (shaket / 100., )] = totalmax
elif stdtype == 'min':
Hagg['hagg_std_%1.2fg' % (shaket / 100., )] = totalmin
else:
Hagg['hagg_std_%1.2fg' %
(shaket / 100., )] = (totalmax + totalmin) / 2.
var = Hagg['hagg_std_%1.2fg' % (shaket / 100., )]**2.
# Beta distribution shape factors
Hagg['p_hagg_%1.2fg' % (shaket / 100., )] = (mu / hlim) * (
(hlim * mu - mu**2) / var - 1)
Hagg['q_hagg_%1.2fg' % (shaket / 100., )] = (1 - mu / hlim) * (
(hlim * mu - mu**2) / var - 1)
else:
mu = np.sum(model[model >= probthresh] * cell_area_km2)
Hagg['hagg_0.00g'] = mu
Hagg['cell_area_km2'] = cell_area_km2
N = np.sum([model >= probthresh])
Hagg['N_0.00g'] = N
hlim = cell_area_km2 * N * maxP
Hagg['hlim_0.00g'] = hlim
if stdgrid2D is not None:
totalmax = np.nansum(std[model >= probthresh] * cell_area_km2)
totalmin = cell_area_km2 * np.sqrt(
np.nansum((std[model >= probthresh])**2.))
if stdtype == 'max':
Hagg['hagg_std_0.00g'] = totalmax
elif stdtype == 'min':
Hagg['hagg_std_0.00g'] = totalmin
else:
Hagg['std_0.00g'] = (totalmax + totalmin) / 2.
var = Hagg['hagg_std_0.00g']
# Beta distribution shape factors
Hagg['p_hagg_0.00g'] = (mu / hlim) * (
(hlim * mu - mu**2) / var - 1)
Hagg['q_hagg_0.00g'] = (1 - mu / hlim) * (
(hlim * mu - mu**2) / var - 1)
return Hagg
def execute(self):
"""
Write raster.zip file containing ESRI Raster files of all the IMTs
in shake_result.hdf.
Raises:
NotADirectoryError: When the event data directory does not exist.
FileNotFoundError: When the the shake_result HDF file does not
exist.
"""
install_path, data_path = get_config_paths()
datadir = os.path.join(data_path, self._eventid, 'current', 'products')
if not os.path.isdir(datadir):
raise NotADirectoryError('%s is not a valid directory.' % datadir)
datafile = os.path.join(datadir, 'shake_result.hdf')
if not os.path.isfile(datafile):
raise FileNotFoundError('%s does not exist.' % datafile)
# Open the ShakeMapOutputContainer and extract the data
container = ShakeMapOutputContainer.load(datafile)
if container.getDataType() != 'grid':
raise NotImplementedError('raster module can only operate on '
'gridded data, not sets of points')
# create GIS-readable .flt files of imt and uncertainty
self.logger.debug('Creating GIS grids...')
layers = container.getIMTs()
# Package up all of these files into one zip file.
zfilename = os.path.join(datadir, 'raster.zip')
zfile = zipfile.ZipFile(zfilename,
mode='w',
compression=zipfile.ZIP_DEFLATED)
files_written = []
for layer in layers:
_, layer = layer.split('/')
fileimt = oq_to_file(layer)
# This is a bit hacky -- we only produce the raster for the
# first IMC returned. It should work as long as we only have
# one IMC produced per ShakeMap run.
imclist = container.getComponents(layer)
imtdict = container.getIMTGrids(layer, imclist[0])
mean_grid = Grid2D(imtdict['mean'],
GeoDict(imtdict['mean_metadata']))
std_grid = Grid2D(imtdict['std'], GeoDict(imtdict['std_metadata']))
mean_gdal = GDALGrid.copyFromGrid(mean_grid)
std_gdal = GDALGrid.copyFromGrid(std_grid)
mean_fname = os.path.join(datadir, '%s_mean.flt' % fileimt)
mean_hdr = os.path.join(datadir, '%s_mean.hdr' % fileimt)
std_fname = os.path.join(datadir, '%s_std.flt' % fileimt)
std_hdr = os.path.join(datadir, '%s_std.hdr' % fileimt)
self.logger.debug('Saving %s...' % mean_fname)
mean_gdal.save(mean_fname)
files_written.append(mean_fname)
files_written.append(mean_hdr)
self.logger.debug('Saving %s...' % std_fname)
std_gdal.save(std_fname)
files_written.append(std_fname)
files_written.append(std_hdr)
zfile.write(mean_fname, '%s_mean.flt' % fileimt)
zfile.write(mean_hdr, '%s_mean.hdr' % fileimt)
zfile.write(std_fname, '%s_std.flt' % fileimt)
zfile.write(std_hdr, '%s_std.hdr' % fileimt)
zfile.close()
# nuke all of the copies of the files we just put in the zipfile
for file_written in files_written:
os.remove(file_written)
# make a transparent PNG of intensity and a world file
imclist = container.getComponents('MMI')
mmidict = container.getIMTGrids('MMI', imclist[0])
mmi_array = mmidict['mean']
geodict = GeoDict(mmidict['mean_metadata'])
palette = ColorPalette.fromPreset('mmi')
mmi_rgb = palette.getDataColor(mmi_array, color_format='array')
img = Image.fromarray(mmi_rgb)
pngfile = os.path.join(datadir, 'intensity_overlay.png')
img.save(pngfile, "PNG")
# write out a world file
# https://en.wikipedia.org/wiki/World_file
worldfile = os.path.join(datadir, 'intensity_overlay.pngw')
with open(worldfile, 'wt') as f:
f.write('%.4f\n' % geodict.dx)
f.write('0.0\n')
f.write('0.0\n')
f.write('-%.4f\n' % geodict.dy)
f.write('%.4f\n' % geodict.xmin)
f.write('%.4f\n' % geodict.ymax)
container.close()
