def _parse_csv_line(headers, values, req_site_params):
"""
Parse a single line from data file.
:param headers:
A list of header names, the strings from the first line of csv file.
:param values:
A list of values of a single row to parse.
:returns:
A tuple of the following values (in specified order):
sctx
An instance of :class:`openquake.hazardlib.gsim.base.SitesContext`
with attributes populated by the information from in row in a form
of single-element numpy arrays.
rctx
An instance of
:class:`openquake.hazardlib.gsim.base.RuptureContext`.
dctx
An instance of
:class:`openquake.hazardlib.gsim.base.DistancesContext`.
stddev_types
An empty list, if the ``result_type`` column says "MEAN"
for that row, otherwise it is a list with one item --
a requested standard deviation type.
expected_results
A dictionary mapping IMT-objects to one-element arrays of expected
result values. Those results represent either standard deviation
or mean value of corresponding IMT depending on ``result_type``.
result_type
A string literal, one of ``'STDDEV'`` or ``'MEAN'``. Value
is taken from column ``result_type``.
"""
rctx = RuptureContext()
sctx = SitesContext(slots=req_site_params)
dctx = DistancesContext()
expected_results = {}
stddev_types = result_type = damping = None
for param, value in zip(headers, values):
if param == 'result_type':
value = value.upper()
if value.endswith('_STDDEV'):
# the row defines expected stddev results
result_type = 'STDDEV'
stddev_types = [getattr(const.StdDev, value[:-len('_STDDEV')])]
else:
# the row defines expected exponents of mean values
assert value == 'MEAN'
stddev_types = []
result_type = 'MEAN'
elif param == 'damping':
damping = float(value)
elif param.startswith('site_'):
# value is sites context object attribute
if param == 'site_vs30measured' or param == 'site_backarc':
value = float(value) != 0
elif param in ('site_siteclass', 'site_ec8', 'site_ec8_p18',
'site_geology'):
value = numpy.string_(value)
else:
value = float(value)
# site_lons, site_lats, site_depths -> lon, lat, depth
if param.endswith(('lons', 'lats', 'depths')):
attr = param[len('site_'):-1]
else: # vs30s etc
attr = param[len('site_'):]
setattr(sctx, attr, numpy.array([value]))
elif param.startswith('dist_'):
# value is a distance measure
value = float(value)
setattr(dctx, param[len('dist_'):], numpy.array([value]))
elif param.startswith('rup_'):
# value is a rupture context attribute
try:
value = float(value)
except ValueError:
if value != 'undefined':
raise
setattr(rctx, param[len('rup_'):], value)
elif param == 'component_type':
pass
else:
# value is the expected result (of result_type type)
value = float(value)
if param == 'arias': # ugly legacy corner case
param = 'ia'
if param == 'avgsa':
imt = from_string('AvgSA')
else:
try: # The title of the column should be IMT(args)
imt = from_string(param.upper())
except KeyError: # Then it is just a period for SA
imt = registry['SA'](float(param), damping)
expected_results[imt] = numpy.array([value])
assert result_type is not None
return sctx, rctx, dctx, stddev_types, expected_results, result_type
def _parse_csv_line(headers, values):
"""
Parse a single line from data file.
:param headers:
A list of header names, the strings from the first line of csv file.
:param values:
A list of values of a single row to parse.
:returns:
A tuple of the following values (in specified order):
sctx
An instance of :class:`openquake.hazardlib.gsim.base.SitesContext`
with attributes populated by the information from in row in a form
of single-element numpy arrays.
rctx
An instance of
:class:`openquake.hazardlib.gsim.base.RuptureContext`.
dctx
An instance of
:class:`openquake.hazardlib.gsim.base.DistancesContext`.
stddev_types
An empty list, if the ``result_type`` column says "MEAN"
for that row, otherwise it is a list with one item --
a requested standard deviation type.
expected_results
A dictionary mapping IMT-objects to one-element arrays of expected
result values. Those results represent either standard deviation
or mean value of corresponding IMT depending on ``result_type``.
result_type
A string literal, one of ``'STDDEV'`` or ``'MEAN'``. Value
is taken from column ``result_type``.
"""
rctx = RuptureContext()
sctx = SitesContext()
dctx = DistancesContext()
expected_results = {}
stddev_types = result_type = damping = None
for param, value in zip(headers, values):
if param == 'result_type':
value = value.upper()
if value.endswith('_STDDEV'):
# the row defines expected stddev results
result_type = 'STDDEV'
stddev_types = [getattr(const.StdDev, value[:-len('_STDDEV')])]
else:
# the row defines expected exponents of mean values
assert value == 'MEAN'
stddev_types = []
result_type = 'MEAN'
elif param == 'damping':
damping = float(value)
elif param.startswith('site_'):
# value is sites context object attribute
if (param == 'site_vs30measured') or (param == 'site_backarc'):
value = float(value) != 0
else:
value = float(value)
setattr(sctx, param[len('site_'):], numpy.array([value]))
elif param.startswith('dist_'):
# value is a distance measure
value = float(value)
setattr(dctx, param[len('dist_'):], numpy.array([value]))
elif param.startswith('rup_'):
# value is a rupture context attribute
value = float(value)
setattr(rctx, param[len('rup_'):], value)
elif param == 'component_type':
pass
else:
# value is the expected result (of result_type type)
value = float(value)
if param == 'pga':
imt = PGA()
elif param == 'pgv':
imt = PGV()
elif param == 'pgd':
imt = PGD()
elif param == 'cav':
imt = CAV()
elif param == 'mmi':
imt = MMI()
elif param == "arias":
imt = IA()
elif param == "rsd595":
imt = RSD595()
elif param == "rsd575":
imt = RSD575()
elif param == "rsd2080":
imt = RSD2080()
else:
period = float(param)
assert damping is not None
imt = SA(period, damping)
expected_results[imt] = numpy.array([value])
assert result_type is not None
return sctx, rctx, dctx, stddev_types, expected_results, result_type
