def test_stats_models():
conf_file = os.path.join(upone, 'defaultconfigfiles', 'models',
'zhu_2015.ini')
conf = ConfigObj(conf_file)
data_path = os.path.join(datadir, 'loma_prieta', 'model_inputs')
# Check slopefile trimming
conf['zhu_2015']['slopefile'] = 'global_gted_maxslope_30c.flt'
conf = correct_config_filepaths(data_path, conf)
# Run with divfactor of 1
conf['zhu_2015']['divfactor'] = '1.'
shakefile = os.path.join(datadir, 'loma_prieta', 'grid.xml')
lm = LM.LogisticModel(shakefile, conf, saveinputs=True)
#maplayers1 = lm.calculate()
conf_file = os.path.join(upone, 'defaultconfigfiles', 'models',
'zhu_2017_coastal.ini')
conf = ConfigObj(conf_file)
data_path = os.path.join(datadir, 'loma_prieta', 'model_inputs')
conf['zhu_2017_coastal']['slopefile'] = 'global_gted_maxslope_30c.flt'
conf = correct_config_filepaths(data_path, conf)
# Run with divfactor of 1
conf['zhu_2017_coastal']['divfactor'] = '1.'
shakefile = os.path.join(datadir, 'loma_prieta', 'grid.xml')
lm = LM.LogisticModel(shakefile, conf, saveinputs=True)
maplayers2 = lm.calculate()
# Change shakemap name so that it doesn't stomp on the other
maplayers2['model']['description']['shakemap'] = '19891018000415_ver2'
#model_list = [maplayers1, maplayers2]
#test_dict1 = assess_models.concatenateModels(model_list)
#test_dict2 = assess_models.concatenateModels(model_list, astitle='model')
# I think this test is freezing on travis
# tmp = assess_models.modelSummary(test_dict2, showplots=False,
# summary_figure=False,
# individual_plots=False)
# np.testing.assert_allclose(tmp[0][0], 0.025677016713957716)
# np.testing.assert_allclose(tmp[1][0], 0.00098462898029272805)
hagg = stats.computeHagg(maplayers2['model']['grid'])
np.testing.assert_allclose(hagg['hagg_0.00g'], 65.85571, atol=0.001)
parea = stats.computeParea(maplayers2['model']['grid'], probthresh=0.2)
np.testing.assert_allclose(parea, 77.03092, atol=0.001)
stats2 = stats.computeStats(maplayers2['model']['grid'],
probthresh=0.2,
shakefile=shakefile,
shakethreshtype='pga',
shakethresh=20.,
statprobthresh=0.0)
np.testing.assert_allclose(stats2['Max'], 0.41278, atol=0.001)
np.testing.assert_allclose(stats2['Median'], 0.00032317817, rtol=0.001)
np.testing.assert_allclose(stats2['Std'], 0.04855, atol=0.0001)
np.testing.assert_allclose(stats2['hagg_0.20g'], 55.47086, atol=0.001)
def test_stats_models():
conf_file = os.path.join(upone, 'defaultconfigfiles', 'models',
'zhu_2015.ini')
conf = ConfigObj(conf_file)
data_path = os.path.join(datadir, 'loma_prieta', 'model_inputs')
# Check slopefile trimming
conf['zhu_2015']['slopefile'] = 'global_gted_maxslope_30c.flt'
conf = correct_config_filepaths(data_path, conf)
# Run with divfactor of 1
conf['zhu_2015']['divfactor'] = '1.'
shakefile = os.path.join(datadir, 'loma_prieta', 'grid.xml')
lm = LM.LogisticModel(shakefile, conf, saveinputs=True)
#maplayers1 = lm.calculate()
conf_file = os.path.join(upone, 'defaultconfigfiles', 'models',
'zhu_2017_coastal.ini')
conf = ConfigObj(conf_file)
data_path = os.path.join(datadir, 'loma_prieta', 'model_inputs')
conf['zhu_2017_coastal']['slopefile'] = 'global_gted_maxslope_30c.flt'
conf = correct_config_filepaths(data_path, conf)
# Run with divfactor of 1
conf['zhu_2017_coastal']['divfactor'] = '1.'
shakefile = os.path.join(datadir, 'loma_prieta', 'grid.xml')
lm = LM.LogisticModel(shakefile, conf, saveinputs=True)
maplayers2 = lm.calculate()
# Change shakemap name so that it doesn't stomp on the other
maplayers2['model']['description']['shakemap'] = '19891018000415_ver2'
hagg = stats.computeHagg(maplayers2['model']['grid'])
np.testing.assert_allclose(hagg['hagg_0.00g'], 5.12723506006, atol=0.001)
stats2 = stats.computeStats(maplayers2['model']['grid'],
shakefile=shakefile,
shakethreshtype='pga',
shakethresh=20.,
probthresh=0.0)
np.testing.assert_allclose(stats2['Max'], 0.4105819792026343, atol=0.001)
np.testing.assert_allclose(stats2['Median'],
0.34855563636356035,
rtol=0.01)
np.testing.assert_allclose(stats2['Std'], 0.04494649549605273, atol=0.001)
np.testing.assert_allclose(stats2['hagg_0.20g'],
2.845917180389227,
atol=0.001)
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 hazdev(maplayerlist,
configs,
shakemap,
outfolder=None,
alpha=0.7,
shakethreshtype='pga',
probthresh=None,
shakethresh=10.,
prefLS='Nowicki Jessee and others (2017)',
prefLQ='Zhu and others (2017)',
pop_file=None,
defaultcolors=True,
point=True,
pager_alert='',
eventsource='',
eventsourcecode=''):
"""Create all files needed for product page creation
Assumes gfail has been run already with -w flag
Args:
maplayerlist (list): List of model outputs from gfail.
configs (list): List of dictionaries of config files corresponding to
each model in maplayerlist and in the same order.
shakemap (str): path to shakemap .xml file.
outfolder (str): Location in which to save outputs. If None, will use
current directory.
alpha (float): Transparency to use for overlay pngs, value from 0 to 1.
shakethreshtype (str): Type of ground motion to use for shakethresh,
'pga', 'pgv', or 'mmi'.
probthresh: Optional. Float or list of probability thresholds to apply
before computing stats.
shakethresh: Float or list of shaking thresholds in %g for pga, cm/s
for pgv, float for mmi. Used for Hagg and Exposure computation.
prefLS (str): shortref of "preferred" landslide model.
prefLQ (str): shortref of "preferred" liquefaction model.
pop_filt (str): file path to population file used to compute
population-based alert levels.
defaultcolors (bool): If True, will use DFCOLORS for all layers instead
of determining new ones. This will crash if any of the layers have
a different number of bins than the number of DFCOLORS
point (bool): if True, event is a point source and warning should be
displayed
pager_alert (str): PAGER alert level, e.g., 'green'. 'pending', ...
Returns:
Files that need to be sent to comcat for hazdev to create the product
webpage including:
- info.json
- transparent png overlays of all models
"""
event_id = maplayerlist[0]['model']['description']['event_id']
if pop_file is None:
# 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']
if outfolder is None:
outfolder = os.path.join(os.getcwd(), event_id)
filenames = []
# Separate the LS and LQ models
concLS = []
concLQ = []
lsmodels = []
lqmodels = []
logLS = []
limLS = []
colLS = []
logLQ = []
limLQ = []
colLQ = []
for conf, maplayer in zip(configs, maplayerlist):
mdict = maplayer['model']['description']
# config = ConfigObj(conf)
if 'landslide' in mdict['parameters']['modeltype'].lower():
title = maplayer['model']['description']['name']
plotorder, logscale, lims, colormaps, maskthreshes = \
parseConfigLayers(maplayer, conf, keys=['model'])
logLS.append(logscale[0])
limLS.append(lims[0])
colLS.append(colormaps[0])
concLS.append(title)
if 'godt' in maplayer['model']['description']['name'].lower():
statprobthresh = None
id1 = 'godt_2008'
maxP = 1.
else:
# Since logistic models can't equal one, need to eliminate
# placeholder zeros before computing stats
if 'jessee' in maplayer['model']['description']['name'].lower(
):
id1 = 'jessee_2017'
statprobthresh = 0.002
maxP = 0.26
else:
id1 = 'nowicki_2014_global'
statprobthresh = 0.0
maxP = 1.
if 'std' in list(maplayer.keys()):
stdgrid2D = maplayer['std']['grid']
else:
stdgrid2D = None
stats = computeStats(maplayer['model']['grid'],
stdgrid2D=stdgrid2D,
probthresh=probthresh,
shakefile=shakemap,
shakethresh=shakethresh,
statprobthresh=statprobthresh,
pop_file=pop_file,
shakethreshtype=shakethreshtype,
stdtype='mean',
maxP=maxP)
metadata = maplayer['model']['description']
if len(maplayer) > 1:
inputs = {}
inkeys = list(maplayer.keys())
for key in inkeys:
if key != 'model':
newkey = maplayer[key]['label']
inputs[newkey] = maplayer[key]['description']
metadata['inputs'] = inputs
if title == prefLS:
on = True
ls_haz_alert, ls_pop_alert, _, _, ls_alert, _ = get_alert(
stats['hagg_0.10g'], 0., stats['exp_pop_0.10g'], 0.)
lsext = get_zoomextent(maplayer['model']['grid'])
ls_haz_value = set_num_precision(stats['hagg_0.10g'], 2,
'float')
ls_pop_value = set_num_precision(stats['exp_pop_0.10g'], 2,
'int')
else:
on = False
ls_haz_alert = None
ls_pop_alert = None
ls_haz_value = None
ls_pop_value = None
ls_alert = None
lsext = None
ls_haz_std = None
ls_pop_std = None
ls_hlim = None
ls_elim = None
ls_hp = None
ls_hq = None
ls_ep = None
ls_eq = None
if stdgrid2D is not None and title == prefLS:
ls_haz_std = float("%.4f" % stats['hagg_std_0.10g'])
ls_pop_std = float("%.4f" % stats['exp_std_0.10g'])
ls_hlim = float("%.4f" % stats['hlim_0.10g'])
ls_elim = float("%.4f" % stats['elim_0.10g'])
ls_hp = float("%.4f" % stats['p_hagg_0.10g'])
ls_hq = float("%.4f" % stats['q_hagg_0.10g'])
ls_ep = float("%.4f" % stats['p_exp_0.10g'])
ls_eq = float("%.4f" % stats['q_exp_0.10g'])
edict = {
'id': id1,
'title': metadata['name'],
'overlay': '%s.png' % id1,
'extent': '%s_extent.json' % id1,
'units': metadata['units'],
'preferred': on,
'alert': ls_alert,
'hazard_alert': {
'color': ls_haz_alert,
'value': ls_haz_value,
'std': ls_haz_std,
'parameter': 'Aggregate Hazard',
'units': 'km^2'
},
'population_alert': {
'color': ls_pop_alert,
'value': ls_pop_value,
'std': ls_pop_std,
'parameter': 'Population exposure',
'units': 'people'
},
'bin_edges': list(lims[0]),
'probability': {
'max': float("%.2f" % stats['Max']),
'std': float("%.2f" % stats['Std']),
'hagg0.1g': float("%.2f" % stats['hagg_0.10g']),
'popexp0.1g': float("%.2f" % stats['exp_pop_0.10g'], ),
'hagg0.1g_std': ls_haz_std,
'popexp0.1g_std': ls_pop_std,
'hlim0.1g': ls_hlim,
'elim0.1g': ls_elim,
'p_hagg': ls_hp,
'q_hagg': ls_hq,
'p_exp': ls_ep,
'q_exp': ls_eq
},
'longref': metadata['longref'],
'parameters': metadata['parameters'],
'zoomext': lsext
}
lsmodels.append(edict)
elif 'liquefaction' in mdict['parameters']['modeltype'].lower():
title = maplayer['model']['description']['name']
plotorder, logscale, lims, colormaps, maskthreshes = \
parseConfigLayers(maplayer, conf, keys=['model'])
logLQ.append(logscale[0])
limLQ.append(lims[0])
colLQ.append(colormaps[0])
concLQ.append(title)
if '2015' in maplayer['model']['description']['name'].lower():
id1 = 'zhu_2015'
statprobthresh = 0.0
maxP = 1.
elif '2017' in maplayer['model']['description']['name'].lower():
id1 = 'zhu_2017_general'
statprobthresh = 0.005
maxP = 0.487
if 'std' in list(maplayer.keys()):
stdgrid2D = maplayer['std']['grid']
else:
stdgrid2D = None
stats = computeStats(maplayer['model']['grid'],
stdgrid2D=stdgrid2D,
probthresh=probthresh,
shakefile=shakemap,
shakethresh=shakethresh,
pop_file=pop_file,
shakethreshtype=shakethreshtype,
statprobthresh=statprobthresh,
stdtype='mean',
maxP=maxP)
metadata = maplayer['model']['description']
if len(maplayer) > 1:
inputs = {}
inkeys = list(maplayer.keys())
for key in inkeys:
if key != 'model':
newkey = maplayer[key]['label']
inputs[newkey] = maplayer[key]['description']
metadata['inputs'] = inputs
if title == prefLQ:
on = True
_, _, lq_haz_alert, lq_pop_alert, _, lq_alert = get_alert(
0., stats['hagg_0.10g'], 0., stats['exp_pop_0.10g'])
lqext = get_zoomextent(maplayer['model']['grid'])
lq_haz_value = set_num_precision(stats['hagg_0.10g'], 2,
'float')
lq_pop_value = set_num_precision(stats['exp_pop_0.10g'], 2,
'int')
else:
on = False
lq_haz_alert = None
lq_pop_alert = None
lq_haz_value = None
lq_pop_value = None
lq_alert = None
lqext = None
lq_haz_std = None
lq_pop_std = None
lq_hlim = None
lq_elim = None
lq_hp = None
lq_hq = None
lq_ep = None
lq_eq = None
if stdgrid2D is not None and title == prefLQ:
lq_haz_std = float("%.2f" % stats['hagg_std_0.10g'])
lq_pop_std = float("%.2f" % stats['exp_std_0.10g'])
lq_hlim = float("%.4f" % stats['hlim_0.10g'])
lq_elim = float("%.4f" % stats['elim_0.10g'])
lq_hp = float("%.4f" % stats['p_hagg_0.10g'])
lq_hq = float("%.4f" % stats['q_hagg_0.10g'])
lq_ep = float("%.4f" % stats['p_exp_0.10g'])
lq_eq = float("%.4f" % stats['q_exp_0.10g'])
edict = {
'id': id1,
'title': metadata['name'],
'overlay': '%s.png' % id1,
'extent': '%s_extent.json' % id1,
'units': metadata['units'],
'preferred': on,
'alert': lq_alert,
'hazard_alert': {
'color': lq_haz_alert,
'value': lq_haz_value,
'std': lq_haz_std,
'parameter': 'Aggregate Hazard',
'units': 'km^2'
},
'population_alert': {
'color': lq_pop_alert,
'value': lq_pop_value,
'std': lq_pop_std,
'parameter': 'Population exposure',
'units': 'people'
},
'bin_edges': list(lims[0]),
'probability': {
'max': float("%.2f" % stats['Max']),
'std': float("%.2f" % stats['Std']),
'hagg0.1g': float("%.2f" % stats['hagg_0.10g']),
'popexp0.1g': float("%.2f" % stats['exp_pop_0.10g']),
'hagg0.1g_std': lq_haz_std,
'popexp0.1g_std': lq_pop_std,
'hlim0.1g': lq_hlim,
'elim0.1g': lq_elim,
'p_hagg': lq_hp,
'q_hagg': lq_hq,
'p_exp': lq_ep,
'q_exp': lq_eq
},
'longref': metadata['longref'],
'parameters': metadata['parameters'],
'zoomext': lqext
}
lqmodels.append(edict)
else:
raise Exception("model type is undefined, check "
"maplayer['model']['parameters']"
"['modeltype'] to ensure it is defined")
if defaultcolors:
for ls in lsmodels:
ls['bin_colors'] = DFCOLORS
for lq in lqmodels:
lq['bin_colors'] = DFCOLORS
else:
defaultcolormap = cm.CMRmap_r
# Get colors and stuff into dictionaries
sync, colorlistLS, reflims = setupsync(prefLS,
concLS,
limLS,
colLS,
defaultcolormap,
logscale=logLS,
alpha=alpha)
if reflims is None:
raise Exception('Check input config files, they must all have the '
'same number of bin edges')
else:
# Stuff colors into dictionary
for ls in lsmodels:
ls['bin_colors'] = list(colorlistLS)
sync, colorlistLQ, reflims = setupsync(prefLQ,
concLQ,
limLQ,
colLQ,
defaultcolormap,
logscale=logLQ,
alpha=alpha)
if reflims is None:
raise Exception('Check input config files, they must all have the '
'same number of bin edges')
else:
# Stuff colors into dictionary
for lq in lqmodels:
lq['bin_colors'] = list(colorlistLQ)
# Create pngs
pngfiles = create_png(outfolder, lsmodels, lqmodels)
filenames.append(pngfiles)
# If PAGER alert is pending, overwrite our alerts
if pager_alert == 'pending':
for ls in lsmodels:
ls['alert'] = 'pending'
ls['hazard_alert']['color'] = 'pending'
ls['population_alert']['color'] = 'pending'
for lq in lqmodels:
lq['alert'] = 'pending'
lq['hazard_alert']['color'] = 'pending'
lq['population_alert']['color'] = 'pending'
# Create info.json
infojson = create_info(outfolder, lsmodels, lqmodels, eventsource,
eventsourcecode, point)
filenames.append(infojson)
return filenames