def create_png(event_dir,
lsmodels=None,
lqmodels=None,
mercator=True,
lsmask=0.002,
lqmask=0.005,
legends=False,
eventsource='',
eventsourcecode=''):
"""
Creates transparent PNG file for website.
Args:
event_dir (srt): Directory containing ground failure results.
lsmodels (list): List of dictionaries of model summary info compiled
by the hazdev function. If not specified, code will search for
the hdf5 files for the preferred model and will create this
dictionary and will apply default colorbars and bins.
lqmodels (list): Same as above for liquefaction.
mercator (bool): Project raster to web mercator
lsmask (float): Mask all landslide cells with probabilities below this
threshold
lqmask (float): Mask all liquefaction cells with probabilities below
this threshold
legends (bool): if True, will produce png files of legends for each
preferred model
Returns:
.png map overlays and .json files specifying their mapped extents
"""
filenames = []
files = os.listdir(event_dir)
if lsmodels is None:
# Read in the "preferred" model for landslides
ls_mod_file = [f for f in files if 'jessee_2017.hdf5' in f]
if len(ls_mod_file) == 1:
ls_file = os.path.join(event_dir, ls_mod_file[0])
ls_mod = loadlayers(ls_file)
filesnippet = 'jessee_2017'
out = make_rgba(ls_mod['model']['grid'],
mask=lsmask,
mercator=mercator,
levels=DFBINS,
colorlist=DFCOLORS)
rgba_img, ls_extent, lmin, lmax, cmap = out
filen = os.path.join(event_dir, '%s_extent.json' % filesnippet)
filenames.append(filen)
with open(filen, 'w') as f:
json.dump(ls_extent, f)
filen = os.path.join(event_dir, '%s.png' % filesnippet)
plt.imsave(filen, rgba_img, vmin=lmin, vmax=lmax, cmap=cmap)
else:
raise OSError("Preferred landslide model result (%s) not found." %
ls_mod_file)
else:
for lsm in lsmodels:
# if lsm['preferred']:
filesnippet = lsm['id']
fsh = '%s.hdf5' % filesnippet
ls_mod_file = [f for f in files if fsh in f]
if len(ls_mod_file) == 1:
ls_file = os.path.join(event_dir, ls_mod_file[0])
ls_mod = loadlayers(ls_file)
else:
raise OSError(
"Specified landslide model result (%s) not found." % fsh)
out = make_rgba(ls_mod['model']['grid'],
mask=lsmask,
mercator=mercator,
levels=DFBINS,
colorlist=DFCOLORS)
rgba_img, ls_extent, lmin, lmax, cmap = out
filen = os.path.join(event_dir, '%s_extent.json' % filesnippet)
filenames.append(filen)
with open(filen, 'w') as f:
json.dump(ls_extent, f)
filen = os.path.join(event_dir, '%s.png' % filesnippet)
plt.imsave(filen, rgba_img, vmin=lmin, vmax=lmax, cmap=cmap)
if lqmodels is None:
# read in preferred model for liquefaction if none specified
lq_mod_file = [f2 for f2 in files if 'zhu_2017_general.hdf5' in f2]
if len(lq_mod_file) == 1:
lq_file = os.path.join(event_dir, lq_mod_file[0])
lq_mod = loadlayers(lq_file)
filesnippet = 'zhu_2017_general'
tempbins = DFBINS
tempbins[0] = 0.005
out = make_rgba(lq_mod['model']['grid'],
mask=lqmask,
mercator=mercator,
levels=tempbins,
colorlist=DFCOLORS)
rgba_img, lq_extent, lmin, lmax, cmap = out
filen = os.path.join(event_dir, '%s_extent.json' % filesnippet)
filenames.append(filen)
with open(filen, 'w') as f:
json.dump(lq_extent, f)
filen = os.path.join(event_dir, '%s.png' % filesnippet)
plt.imsave(filen, rgba_img, vmin=lmin, vmax=lmax, cmap=cmap)
filenames.append(filen)
else:
raise OSError(
"Preferred liquefaction model result (%s) not found." %
lq_mod_file)
else:
for lqm in lqmodels:
#if lqm['preferred']:
filesnippet = lqm['id']
tempbins = DFBINS
tempbins[0] = 0.005
fsh = '%s.hdf5' % filesnippet
lq_mod_file = [f2 for f2 in files if fsh in f2]
if len(lq_mod_file) == 1:
lq_file = os.path.join(event_dir, lq_mod_file[0])
lq_mod = loadlayers(lq_file)
else:
raise OSError(
"Specified liquefaction model result (%s) not found." %
fsh)
out = make_rgba(lq_mod['model']['grid'],
mask=lqmask,
mercator=mercator,
levels=tempbins,
colorlist=DFCOLORS)
rgba_img, lq_extent, lmin, lmax, cmap = out
filen = os.path.join(event_dir, '%s_extent.json' % filesnippet)
filenames.append(filen)
with open(filen, 'w') as f:
json.dump(lq_extent, f)
filen = os.path.join(event_dir, '%s.png' % filesnippet)
plt.imsave(filen, rgba_img, vmin=lmin, vmax=lmax, cmap=cmap)
filenames.append(filen)
if legends:
lsname, lqname = make_legends(lqmin=lqmask,
lsmin=lsmask,
outfolder=event_dir)
filenames.append(lsname)
filenames.append(lqname)
return filenames
def create_info(event_dir,
lsmodels=None,
lqmodels=None,
eventsource='',
eventsourcecode='',
point=True):
"""Create info.json for ground failure product.
Args:
event_dir (srt): Directory containing ground failure results.
lsmodels (list): List of dictionaries of model summary info compiled
by the hazdev function. If not specified, code will search for
the hdf5 files for the preferred model and will create this
dictionary and will apply default colorbars and bins.
lqmodels (list): Same as above for liquefaction.
point (bool): if True, event is a point source and warning should be
displayed
Returns:
creates info.json for this event
"""
filenames = []
# Find the shakemap grid.xml file
with open(os.path.join(event_dir, 'shakefile.txt'), 'r') as f:
shakefile = f.read()
files = os.listdir(event_dir)
if lsmodels is None and lqmodels is None:
# Read in the "preferred" model for landslides and liquefaction
ls_mod_file = [f2 for f2 in files if 'jessee_2017.hdf5' in f2]
if len(ls_mod_file) == 1:
ls_file = os.path.join(event_dir, ls_mod_file[0])
ls_mod = loadlayers(ls_file)
# get extents
lsext = get_zoomextent(ls_mod['model']['grid'])
else:
raise OSError("Preferred landslide model result not found.")
lq_mod_file = [f2 for f2 in files if 'zhu_2017_general.hdf5' in f2]
if len(lq_mod_file) == 1:
lq_file = os.path.join(event_dir, lq_mod_file[0])
lq_mod = loadlayers(lq_file)
# get extents
lqext = get_zoomextent(lq_mod['model']['grid'])
else:
raise OSError("Preferred liquefaction model result not found.")
# Read in extents
ls_extent_file = [
f2 for f2 in files if 'jessee_2017_extent.json' in f2
]
if len(ls_extent_file) == 1:
ls_file = os.path.join(event_dir, ls_extent_file[0])
with open(ls_file) as f:
jessee_extent = json.load(f)
else:
raise OSError("Landslide extent not found.")
lq_extent_file = [
f2 for f2 in files if 'zhu_2017_general_extent.json' in f2
]
if len(lq_extent_file) == 1:
lq_file = os.path.join(event_dir, lq_extent_file[0])
with open(lq_file) as f:
zhu_extent = json.load(f)
else:
raise OSError("Liquefaction extent not found.")
# Read in default paths to get location of the population grid
default_file = os.path.join(os.path.expanduser('~'), '.gfail_defaults')
defaults = ConfigObj(default_file)
pop_file = defaults['popfile']
# Landslide alert statistics
ls_stats = computeStats(ls_mod['model']['grid'],
probthresh=None,
shakefile=shakefile,
shakethresh=10.0,
shakethreshtype='pga',
statprobthresh=None,
pop_file=pop_file)
# Liquefaction alert statistics
lq_stats = computeStats(lq_mod['model']['grid'],
probthresh=None,
shakefile=shakefile,
shakethresh=10.0,
shakethreshtype='pga',
statprobthresh=None,
pop_file=pop_file)
# Get alert levels
ls_haz_level = ls_stats['hagg_0.10g']
lq_haz_level = lq_stats['hagg_0.10g']
ls_pop_level = ls_stats['exp_pop_0.10g']
lq_pop_level = lq_stats['exp_pop_0.10g']
# If hazard alert level is less than 0.1, zero it out
# (due to rounding to 2 sig digits later, this can give
# overly precise results, e.g., 0.000012 if we don't clip,
# but this doesn't happen with pop alerts because they are
# integers)
if ls_haz_level < 0.1:
ls_haz_level = 0.0
if lq_haz_level < 0.1:
lq_haz_level = 0.0
# Convert levels into categories
alert_info = get_alert(ls_haz_level, lq_haz_level, ls_pop_level,
lq_pop_level)
# Unpack info (I think we are now assuming that the statements will be
# constructed on the website and so we don't need them here)
ls_haz_alert, ls_pop_alert, lq_haz_alert, lq_pop_alert, \
ls_alert, lq_alert = alert_info
if lsmodels is None:
lsmodels = [{
'id': 'nowicki_jessee_2017',
'title': 'Nowicki Jessee and others (2017)',
'overlay': 'jessee_2017.png',
'extent': jessee_extent,
'units': "Proportion of area affected",
'preferred': True,
'alert': ls_alert,
'hazard_alert': {
'color': ls_haz_alert,
'value': set_num_precision(ls_haz_level, 2, 'float'),
'parameter': 'Aggregate Hazard',
'units': 'km^2'
},
'population_alert': {
'color': ls_pop_alert,
'value': set_num_precision(ls_pop_level, 2, 'int'),
'parameter': 'Population exposure',
'units': 'people'
},
'probability': {
'max': float("%.2f" % ls_stats['Max']),
'std': float("%.2f" % ls_stats['Std']),
'hagg0.1g': float("%.2f" % ls_stats['hagg_0.10g']),
'popexp0.1g': float("%.2f" % ls_stats['exp_pop_0.10g'])
}
}]
if lqmodels is None:
lqmodels = [{
'id': 'zhu_2017',
'title': 'Zhu and others (2017)',
'overlay': 'zhu_2017.png',
'extent': zhu_extent,
'units': "Proportion of area affected",
'preferred': True,
'alert': lq_alert,
'hazard_alert': {
'color': lq_haz_alert,
'value': set_num_precision(lq_haz_level, 2, 'float'),
'parameter': 'Aggregate Hazard',
'units': 'km^2'
},
'population_alert': {
'color': lq_pop_alert,
'value': set_num_precision(lq_pop_level, 2, 'int'),
'parameter': 'Population exposure',
'units': 'people'
},
'probability': {
'max': float("%.2f" % lq_stats['Max']),
'std': float("%.2f" % lq_stats['Std']),
'hagg0.1g': float("%.2f" % ls_stats['hagg_0.10g']),
'popexp0.1g': float("%.2f" % ls_stats['exp_pop_0.10g'])
}
}]
else:
# Get all info from dictionaries of preferred events, add in extent
# and filename
for lsm in lsmodels:
# Add extent and filename for preferred model
if lsm['preferred']:
filesnippet = lsm['id']
# Read in extents
flnm = '%s_extent.json' % filesnippet
ls_extent_file = [f for f in files if flnm in f]
if len(ls_extent_file) == 1:
ls_file = os.path.join(event_dir, ls_extent_file[0])
with open(ls_file) as f:
ls_extent = json.load(f)
else:
raise OSError("Landslide extent not found.")
lsm['extent'] = ls_extent
# lsm['filename'] = flnm
lsext = lsm['zoomext'] # Get zoom extent
ls_alert = lsm['alert']
rmkeys = ['bin_edges', 'bin_colors', 'zoomext']
else:
# Remove any alert keys
rmkeys = [
'bin_edges', 'bin_colors', 'zoomext', 'population_alert',
'alert', 'hazard_alert'
]
for key in rmkeys:
if key in lsm:
lsm.pop(key)
for lqm in lqmodels:
if lqm['preferred']:
filesnippet = lqm['id']
# Read in extents
flnm = '%s_extent.json' % filesnippet
lq_extent_file = [f2 for f2 in files if flnm in f2]
if len(lq_extent_file) == 1:
lq_file = os.path.join(event_dir, lq_extent_file[0])
with open(lq_file) as f:
lq_extent = json.load(f)
else:
raise OSError("Liquefaction extent not found.")
lqm['extent'] = lq_extent
# lqm['filename'] = flnm
lqext = lqm['zoomext'] # Get zoom extent
lq_alert = lqm['alert']
rmkeys = ['bin_edges', 'bin_colors', 'zoomext']
else:
# Remove any alert keys
rmkeys = [
'bin_edges', 'bin_colors', 'zoomext', 'population_alert',
'alert', 'hazard_alert'
]
for key in rmkeys:
if key in lqm:
lqm.pop(key)
# Try to get event info
shake_grid = ShakeGrid.load(shakefile, adjust='res')
event_dict = shake_grid.getEventDict()
sm_dict = shake_grid.getShakeDict()
base_url = 'https://earthquake.usgs.gov/earthquakes/eventpage/'
# Is this a point source?
# point = is_grid_point_source(shake_grid)
# Temporarily hard code this until we can get a better solution via
# new grid.xml attributes.
#point = True
net = eventsource
code = eventsourcecode
time = event_dict['event_timestamp'].strftime('%Y-%m-%dT%H:%M:%SZ')
event_url = '%s%s%s#executive' % (base_url, net, code)
# Get extents that work for both unless one is green and the other isn't
if lq_alert == 'green' and ls_alert != 'green' and ls_alert is not None:
xmin = lsext['xmin']
xmax = lsext['xmax']
ymin = lsext['ymin']
ymax = lsext['ymax']
elif lq_alert != 'green' and ls_alert == 'green' and lq_alert is not None:
xmin = lqext['xmin']
xmax = lqext['xmax']
ymin = lqext['ymin']
ymax = lqext['ymax']
else:
xmin = np.min((lqext['xmin'], lsext['xmin']))
xmax = np.max((lqext['xmax'], lsext['xmax']))
ymin = np.min((lqext['ymin'], lsext['ymin']))
ymax = np.max((lqext['ymax'], lsext['ymax']))
# Should we display the warning about point source?
rupture_warning = False
if point and event_dict['magnitude'] > 6.5:
rupture_warning = True
# Create info.json for website rendering and metadata purposes
info_dict = {
'Summary': {
'code': code,
'net': net,
'magnitude': event_dict['magnitude'],
'depth': event_dict['depth'],
'time': time,
'lat': event_dict['lat'],
'lon': event_dict['lon'],
'event_url': event_url,
'shakemap_version': sm_dict['shakemap_version'],
'rupture_warning': rupture_warning,
'point_source': point,
'zoom_extent': [xmin, xmax, ymin, ymax]
},
'Landslides': lsmodels,
'Liquefaction': lqmodels
}
info_file = os.path.join(event_dir, 'info.json')
with open(info_file, 'w') as f:
json.dump(info_dict, f) # allow_nan=False)
filenames.append(info_file)
return filenames
def create_png(event_dir, lsmodels=None, lqmodels=None, mercator=True,
lsmask=0.002, lqmask=0.005, legends=False):
"""
Creates transparent PNG file for website.
Args:
event_dir (srt): Directory containing ground failure results.
lsmodels (list): List of dictionaries of model summary info compiled
by the hazdev function. If not specified, code will search for
the hdf5 files for the preferred model and will create this
dictionary and will apply default colorbars and bins.
lqmodels (list): Same as above for liquefaction.
mercator (bool): Project raster to web mercator
lsmask (float): Mask all landslide cells with probabilities below this
threshold
lqmask (float): Mask all liquefaction cells with probabilities below this
threshold
legends (bool): if True, will produce png files of legends for each
preferred model
Returns:
.png map overlays and .json files specifying their mapped extents
"""
filenames = []
files = os.listdir(event_dir)
if lsmodels is None:
# Read in the "preferred" model for landslides
ls_mod_file = [f for f in files if 'jessee_2017.hdf5' in f]
if len(ls_mod_file) == 1:
ls_file = os.path.join(event_dir, ls_mod_file[0])
ls_mod = loadlayers(ls_file)
levels = DFBINS
colors1 = DFCOLORS
filesnippet = 'jessee_2017'
ls_grid = ls_mod['model']['grid']
ls_data = ls_grid.getData()
if lsmask:
ls_data[ls_data < lsmask] = float('nan')
ls_geodict = ls_grid.getGeoDict()
ls_extent = [
ls_geodict.xmin - 0.5*ls_geodict.dx,
ls_geodict.xmax + 0.5*ls_geodict.dx,
ls_geodict.ymin - 0.5*ls_geodict.dy,
ls_geodict.ymax + 0.5*ls_geodict.dy,
]
filen = os.path.join(event_dir, '%s_extent.json' % filesnippet)
filenames.append(filen)
with open(filen, 'w') as f:
json.dump(ls_extent, f)
lmin = levels[0]
lmax = levels[-1]
ls_data2 = np.clip(ls_data, lmin, lmax)
cmap = mpl.colors.ListedColormap(colors1)
norm = mpl.colors.BoundaryNorm(levels, cmap.N)
ls_data2 = np.ma.array(ls_data2, mask=np.isnan(ls_data))
#scalarMap = cm.ScalarMappable(norm=norm, cmap=cmap)
#rgba_img = scalarMap.to_rgba_array(ls_data2, alpha=alpha)
rgba_img = cmap(norm(ls_data2))
if mercator:
rgba_img = mercator_transform(
rgba_img, (ls_extent[2], ls_extent[3]), origin='upper')
filen = os.path.join(event_dir, '%s.png' % filesnippet)
plt.imsave(filen,
rgba_img,
vmin=lmin,
vmax=lmax,
cmap=cmap
)
else:
raise OSError(
"Preferred landslide model result (%s) not found." % ls_mod_file)
else:
for lsm in lsmodels:
#if lsm['preferred']:
levels = lsm['bin_edges']
colors1 = lsm['bin_colors']
filesnippet = lsm['id']
fsh = '%s.hdf5' % filesnippet
ls_mod_file = [f for f in files if fsh in f]
if len(ls_mod_file) == 1:
ls_file = os.path.join(event_dir, ls_mod_file[0])
ls_mod = loadlayers(ls_file)
else:
raise OSError(
"Specified landslide model result (%s) not found." % fsh)
ls_grid = ls_mod['model']['grid']
ls_data = ls_grid.getData()
if lsmask:
ls_data[ls_data < lsmask] = float('nan')
ls_geodict = ls_grid.getGeoDict()
ls_extent = [
ls_geodict.xmin - 0.5*ls_geodict.dx,
ls_geodict.xmax + 0.5*ls_geodict.dx,
ls_geodict.ymin - 0.5*ls_geodict.dy,
ls_geodict.ymax + 0.5*ls_geodict.dy,
]
filen = os.path.join(event_dir, '%s_extent.json' % filesnippet)
filenames.append(filen)
with open(filen, 'w') as f:
json.dump(ls_extent, f)
lmin = levels[0]
lmax = levels[-1]
ls_data2 = np.clip(ls_data, lmin, lmax)
cmap = mpl.colors.ListedColormap(colors1)
norm = mpl.colors.BoundaryNorm(levels, cmap.N)
ls_data2 = np.ma.array(ls_data2, mask=np.isnan(ls_data))
#scalarMap = cm.ScalarMappable(norm=norm, cmap=cmap)
#rgba_img = scalarMap.to_rgba_array(ls_data2, alpha=alpha)
rgba_img = cmap(norm(ls_data2))
if mercator:
rgba_img = mercator_transform(
rgba_img, (ls_extent[2], ls_extent[3]), origin='upper')
filen = os.path.join(event_dir, '%s.png' % filesnippet)
plt.imsave(filen,
rgba_img,
vmin=lmin,
vmax=lmax,
cmap=cmap
)
if lqmodels is None:
# read in preferred model for liquefaction if none specified
lq_mod_file = [f2 for f2 in files if 'zhu_2017_general.hdf5' in f2]
if len(lq_mod_file) == 1:
lq_file = os.path.join(event_dir, lq_mod_file[0])
lq_mod = loadlayers(lq_file)
levels = DFBINS
colors1 = DFCOLORS
filesnippet = 'zhu_2017_general'
lq_grid = lq_mod['model']['grid']
lq_data = lq_grid.getData()
if lqmask:
lq_data[lq_data < lqmask] = float('nan')
lq_geodict = lq_grid.getGeoDict()
lq_extent = [
lq_geodict.xmin - 0.5*lq_geodict.dx,
lq_geodict.xmax + 0.5*lq_geodict.dx,
lq_geodict.ymin - 0.5*lq_geodict.dy,
lq_geodict.ymax + 0.5*lq_geodict.dy,
]
filen = os.path.join(event_dir, '%s_extent.json' % filesnippet)
filenames.append(filen)
with open(filen, 'w') as f:
json.dump(lq_extent, f)
lmin = levels[0]
lmax = levels[-1]
lq_data2 = np.clip(lq_data, lmin, lmax)
cmap = mpl.colors.ListedColormap(colors1)
norm = mpl.colors.BoundaryNorm(levels, cmap.N)
lq_data2 = np.ma.array(lq_data2, mask=np.isnan(lq_data))
rgba_img = cmap(norm(lq_data2))
if mercator:
rgba_img = mercator_transform(
rgba_img, (lq_extent[2], lq_extent[3]), origin='upper')
filen = os.path.join(event_dir, '%s.png' % filesnippet)
plt.imsave(filen,
rgba_img,
vmin=lmin,
vmax=lmax,
cmap=cmap
)
filenames.append(filen)
else:
raise OSError(
"Preferred liquefaction model result (%s) not found." % lq_mod_file)
else:
for lqm in lqmodels:
if lqm['preferred']:
levels = lqm['bin_edges']
colors1 = lqm['bin_colors']
filesnippet = lqm['id']
fsh = '%s.hdf5' % filesnippet
lq_mod_file = [f2 for f2 in files if fsh in f2]
if len(lq_mod_file) == 1:
lq_file = os.path.join(event_dir, lq_mod_file[0])
lq_mod = loadlayers(lq_file)
else:
raise OSError(
"Specified liquefaction model result (%s) not found." % fsh)
lq_grid = lq_mod['model']['grid']
lq_data = lq_grid.getData()
if lqmask:
lq_data[lq_data < lqmask] = float('nan')
lq_geodict = lq_grid.getGeoDict()
lq_extent = [
lq_geodict.xmin - 0.5*lq_geodict.dx,
lq_geodict.xmax + 0.5*lq_geodict.dx,
lq_geodict.ymin - 0.5*lq_geodict.dy,
lq_geodict.ymax + 0.5*lq_geodict.dy,
]
filen = os.path.join(event_dir, '%s_extent.json' % filesnippet)
filenames.append(filen)
with open(filen, 'w') as f:
json.dump(lq_extent, f)
lmin = levels[0]
lmax = levels[-1]
lq_data2 = np.clip(lq_data, lmin, lmax)
cmap = mpl.colors.ListedColormap(colors1)
norm = mpl.colors.BoundaryNorm(levels, cmap.N)
lq_data2 = np.ma.array(lq_data2, mask=np.isnan(lq_data))
rgba_img = cmap(norm(lq_data2))
if mercator:
rgba_img = mercator_transform(
rgba_img, (lq_extent[2], lq_extent[3]), origin='upper')
filen = os.path.join(event_dir, '%s.png' % filesnippet)
plt.imsave(filen,
rgba_img,
vmin=lmin,
vmax=lmax,
cmap=cmap
)
filenames.append(filen)
if legends:
lsname, lqname = make_legend(lqmin=lqmask, lsmin=lsmask, outfolder=event_dir)
filenames.append(lsname)
filenames.append(lqname)
return filenames