def get_mesh_csvdata(csvfile, imts, num_values, validvalues):
"""
Read CSV data in the format `IMT lon lat value1 ... valueN`.
:param csvfile:
a file or file-like object with the CSV data
:param imts:
a list of intensity measure types
:param num_values:
dictionary with the number of expected values per IMT
:param validvalues:
validation function for the values
:returns:
the mesh of points and the data as a dictionary
imt -> array of curves for each site
"""
number_of_values = dict(zip(imts, num_values))
lon_lats = {imt: set() for imt in imts}
data = AccumDict() # imt -> list of arrays
check_imt = valid.Choice(*imts)
for line, row in enumerate(csv.reader(csvfile, delimiter=' '), 1):
try:
imt = check_imt(row[0])
lon_lat = valid.longitude(row[1]), valid.latitude(row[2])
if lon_lat in lon_lats[imt]:
raise DuplicatedPoint(lon_lat)
lon_lats[imt].add(lon_lat)
values = validvalues(' '.join(row[3:]))
if len(values) != number_of_values[imt]:
raise ValueError('Found %d values, expected %d' %
(len(values), number_of_values[imt]))
except (ValueError, DuplicatedPoint) as err:
raise err.__class__('%s: file %s, line %d' % (err, csvfile, line))
data += {imt: [numpy.array(values)]}
points = lon_lats.pop(imts[0])
for other_imt, other_points in lon_lats.iteritems():
if points != other_points:
raise ValueError('Inconsistent locations between %s and %s' %
(imts[0], other_imt))
lons, lats = zip(*sorted(points))
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
return mesh, {imt: numpy.array(lst) for imt, lst in data.iteritems()}
def calc_gmfs(oqparam, sitecol):
"""
Build all the ground motion fields for the whole site collection
"""
correl_model = get_correl_model(oqparam)
rnd = random.Random()
rnd.seed(getattr(oqparam, 'random_seed', 42))
imts = get_imts(oqparam)
gsim = get_gsim(oqparam)
trunc_level = getattr(oqparam, 'truncation_level', None)
n_gmfs = getattr(oqparam, 'number_of_ground_motion_fields', 1)
rupture = get_rupture(oqparam)
computer = gmf.GmfComputer(rupture, sitecol, imts, gsim, trunc_level,
correl_model)
seeds = [rnd.randint(0, MAX_INT) for _ in xrange(n_gmfs)]
res = AccumDict() # imt -> gmf
for seed in seeds:
for imt, gmfield in computer.compute(seed):
res += {imt: [gmfield]}
# res[imt] is a matrix R x N
return {imt: numpy.array(matrix).T for imt, matrix in res.iteritems()}
def get_mesh_hcurves(oqparam):
"""
Read CSV data in the format `lon lat, v1-vN, w1-wN, ...`.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
the mesh of points and the data as a dictionary
imt -> array of curves for each site
"""
imtls = oqparam.imtls
lon_lats = set()
data = AccumDict() # imt -> list of arrays
ncols = len(imtls) + 1 # lon_lat + curve_per_imt ...
csvfile = oqparam.inputs['hazard_curves']
for line, row in enumerate(csv.reader(csvfile), 1):
try:
if len(row) != ncols:
raise ValueError('Expected %d columns, found %d' %
ncols, len(row))
x, y = row[0].split()
lon_lat = valid.longitude(x), valid.latitude(y)
if lon_lat in lon_lats:
raise DuplicatedPoint(lon_lat)
lon_lats.add(lon_lat)
for i, imt in enumerate(imtls, 1):
values = valid.decreasing_probabilities(row[i])
if len(values) != len(imtls[imt]):
raise ValueError('Found %d values, expected %d' %
(len(values), len(imtls([imt]))))
data += {imt: [numpy.array(values)]}
except (ValueError, DuplicatedPoint) as err:
raise err.__class__('%s: file %s, line %d' % (err, csvfile, line))
lons, lats = zip(*sorted(lon_lats))
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
return mesh, {imt: numpy.array(lst) for imt, lst in data.iteritems()}
