def updateDataArea(cls, feature):
"""Update or compute moment rates for selected feature of area source
zone layer.
Input:
feature QGis polygon feature from area source layer
Output:
parameters dict of computed parameters
"""
provider = cls.area_source_layer.dataProvider()
parameters = {}
# get Shapely polygon from feature geometry
polylist, vertices = utils.polygonsQGS2Shapely((feature,))
poly = polylist[0]
# get polygon area in square kilometres
parameters["area_sqkm"] = utils.polygonAreaFromWGS84(poly) * 1.0e-6
# zone ID and title
(feature_id, feature_title, feature_name) = utils.getFeatureAttributes(
cls.area_source_layer, feature, features.AREA_SOURCE_ATTRIBUTES_ID
)
if feature_title.toString() == "" and feature_name.toString() == "":
zone_name_str = ""
elif feature_title.toString() == "" and feature_name.toString() != "":
zone_name_str = feature_name.toString()
elif feature_title.toString() != "" and feature_name.toString() == "":
zone_name_str = feature_title.toString()
else:
zone_name_str = "%s, %s" % (feature_title.toString(), feature_name.toString())
parameters["plot_title_fmd"] = "Zone %s, %s" % (feature_id.toInt()[0], zone_name_str)
## moment rate from EQs
# get quakes from catalog (cut with polygon)
poly_cat = QPCatalog.QPCatalog()
poly_cat.merge(cls.catalog)
poly_cat.cut(geometry=poly)
# cut catalog with min/max depth according to UI spinboxes
(mindepth, maxdepth) = eqcatalog.getMinMaxDepth(cls)
poly_cat.cut(mindepth=mindepth, maxdepth=maxdepth)
parameters["eq_count"] = poly_cat.size()
# sum up moment from quakes (converted from Mw with Kanamori eq.)
magnitudes = []
for ev in poly_cat.eventParameters.event:
mag = ev.getPreferredMagnitude()
magnitudes.append(mag.mag.value)
moment = numpy.array(momentrate.magnitude2moment(magnitudes))
# scale moment: per year and area (in km^2)
parameters["mr_eq"] = moment.sum() / (parameters["area_sqkm"] * cls.catalog_time_span[0])
## moment rate from activity (RM)
# get attribute index of AtticIvy result
attribute_map = utils.getAttributeIndex(
provider,
(features.AREA_SOURCE_ATTR_ACT_RM_A, features.AREA_SOURCE_ATTR_ACT_RM_B, features.AREA_SOURCE_ATTR_MMAX),
)
attribute_act_a_name = features.AREA_SOURCE_ATTR_ACT_RM_A["name"]
attribute_act_a_idx = attribute_map[attribute_act_a_name][0]
attribute_act_b_name = features.AREA_SOURCE_ATTR_ACT_RM_B["name"]
attribute_act_b_idx = attribute_map[attribute_act_b_name][0]
attribute_mmax_name = features.AREA_SOURCE_ATTR_MMAX["name"]
attribute_mmax_idx = attribute_map[attribute_mmax_name][0]
# get RM (a, b) values from feature attributes
try:
activity_a_str = str(feature[attribute_act_a_idx].toString())
activity_a_arr = activity_a_str.strip().split()
except KeyError:
activity_a_arr = 3 * [numpy.nan]
error_msg = "Moment balancing: no valid activity a parameter in %s" % (parameters["plot_title_fmd"])
QMessageBox.warning(None, "Moment balancing warning", error_msg)
try:
activity_b_str = str(feature[attribute_act_b_idx].toString())
activity_b_arr = activity_b_str.strip().split()
except KeyError:
activity_b_arr = 3 * [numpy.nan]
error_msg = "Moment balancing: no valid activity b parameter in %s" % (parameters["plot_title_fmd"])
QMessageBox.warning(None, "Moment balancing warning", error_msg)
# ignore weights
parameters["activity_mmin"] = atticivy.ATTICIVY_MMIN
activity_a = [float(x) for x in activity_a_arr]
activity_b = [float(x) for x in activity_b_arr]
mmax = float(feature[attribute_mmax_idx].toDouble()[0])
parameters["activity_a"] = activity_a
parameters["activity_b"] = activity_b
parameters["mmax"] = mmax
## Maximum likelihood a/b values
if poly_cat.size() == 0:
cls.feature_data_area_source["fmd"] = None
(parameters["ml_a"], parameters["ml_b"], parameters["ml_mc"], parameters["ml_magctr"]) = 4 * [numpy.nan]
error_msg = "Moment balancing: no EQs in %s" % (parameters["plot_title_fmd"])
QMessageBox.warning(None, "Moment balancing warning", error_msg)
else:
cls.feature_data_area_source["fmd"] = fmd.computeZoneFMD(cls, feature, poly_cat)
(parameters["ml_a"], parameters["ml_b"], parameters["ml_mc"], parameters["ml_magctr"]) = fmd.getFMDValues(
cls.feature_data_area_source["fmd"]
)
## moment rate from activity
a_values = activity_a
momentrates_arr = (
numpy.array(momentrate.momentrateFromActivity(a_values, activity_b, mmax)) / cls.catalog_time_span[0]
)
parameters["mr_activity"] = momentrates_arr.tolist()
## moment rate from geodesy (strain)
momentrate_strain_barba = momentrate.momentrateFromStrainRateBarba(
poly, cls.data.strain_rate_barba, cls.data.deformation_regimes_bird
)
parameters["mr_strain_barba"] = momentrate_strain_barba / (cls.catalog_time_span[0])
momentrate_strain_bird = momentrate.momentrateFromStrainRateBird(
poly, cls.data.strain_rate_bird, cls.data.deformation_regimes_bird
)
parameters["mr_strain_bird"] = momentrate_strain_bird / (cls.catalog_time_span[0])
return parameters
def updateDataFaultBackgr(cls,
feature,
m_threshold=recurrence.FAULT_BACKGROUND_MAG_THRESHOLD
):
"""Update or compute moment rates for selected feature of fault background
zone layer.
Input:
feature QGis polygon feature from fault background zone layer
Output:
parameters dict of computed parameters
"""
provider = cls.fault_background_layer.dataProvider()
provider_fault = cls.fault_source_layer.dataProvider()
provider_area = cls.area_source_layer.dataProvider()
provider_back = cls.background_zone_layer.dataProvider()
parameters = {}
# get Shapely polygon from feature geometry
polylist, vertices = utils.polygonsQGS2Shapely((feature, ))
poly = polylist[0]
# get polygon area in square kilometres
parameters['area_background_sqkm'] = \
utils.polygonAreaFromWGS84(poly) * 1.0e-6
# get mmax and mcdist for FBZ from background zone
(mcdist_qv, mmax_qv) = areasource.getAttributesFromBackgroundZones(
poly.centroid, provider_back, areasource.MCDIST_MMAX_ATTRIBUTES)
mmax = float(mmax_qv.toDouble()[0])
mcdist = str(mcdist_qv.toString())
parameters['mmax'] = mmax
## moment rate from EQs
# get quakes from catalog (cut with fault background zone polygon)
poly_cat = QPCatalog.QPCatalog()
poly_cat.merge(cls.catalog)
poly_cat.cut(geometry=poly)
# cut catalog with min/max depth according to UI spinboxes
(mindepth, maxdepth) = eqcatalog.getMinMaxDepth(cls)
poly_cat.cut(mindepth=mindepth, maxdepth=maxdepth)
parameters['eq_count'] = poly_cat.size()
# sum up moment from quakes (converted from Mw with Kanamori eq.)
magnitudes = []
for ev in poly_cat.eventParameters.event:
mag = ev.getPreferredMagnitude()
magnitudes.append(mag.mag.value)
moment = numpy.array(momentrate.magnitude2moment(magnitudes))
# scale moment: per year and area (in km^2)
parameters['mr_eq'] = moment.sum() / (parameters['area_background_sqkm'] *
cls.catalog_time_span[0])
## moment rate from activity (RM)
parameters['activity_mmin'] = atticivy.ATTICIVY_MMIN
activity = atticivy.computeActivityAtticIvy(
(poly, ), (mmax, ), (mcdist, ),
cls.catalog,
mmin=parameters['activity_mmin'])
# get RM (a, b) values from feature attribute
activity_str_a = activity[0][3]
activity_arr_a = activity_str_a.strip().split()
activity_str_b = activity[0][4]
activity_arr_b = activity_str_b.strip().split()
# ignore weights
activity_a = [float(x) for x in activity_arr_a]
activity_b = [float(x) for x in activity_arr_b]
parameters['activity_a'] = activity_a
parameters['activity_b'] = activity_b
a_values = activity_a
momentrates_arr = numpy.array(
momentrate.momentrateFromActivity(a_values, activity_b,
mmax)) / cls.catalog_time_span[0]
parameters['mr_activity'] = momentrates_arr.tolist()
# get separate catalogs below and above magnitude threshold
cat_below_threshold = QPCatalog.QPCatalog()
cat_below_threshold.merge(poly_cat)
cat_below_threshold.cut(maxmag=m_threshold, maxmag_excl=True)
parameters['eq_count_below'] = cat_below_threshold.size()
cat_above_threshold = QPCatalog.QPCatalog()
cat_above_threshold.merge(poly_cat)
cat_above_threshold.cut(minmag=m_threshold, maxmag_excl=False)
parameters['eq_count_above'] = cat_above_threshold.size()
activity_below_threshold = atticivy.computeActivityAtticIvy(
(poly, ), (mmax, ), (mcdist, ),
cat_below_threshold,
mmin=parameters['activity_mmin'])
activity_above_threshold = atticivy.computeActivityAtticIvy(
(poly, ), (mmax, ), (mcdist, ),
cat_above_threshold,
mmin=parameters['activity_mmin'])
# get RM (weight, a, b) values from feature attribute
activity_below_str_a = activity_below_threshold[0][3]
activity_below_arr_a = activity_below_str_a.strip().split()
activity_below_str_b = activity_below_threshold[0][4]
activity_below_arr_b = activity_below_str_b.strip().split()
activity_above_str_a = activity_above_threshold[0][3]
activity_above_arr_a = activity_above_str_a.strip().split()
activity_above_str_b = activity_above_threshold[0][4]
activity_above_arr_b = activity_above_str_b.strip().split()
# ignore weights
activity_below_a = [float(x) for x in activity_below_arr_a]
activity_below_b = [float(x) for x in activity_below_arr_b]
activity_above_a = [float(x) for x in activity_above_arr_a]
activity_above_b = [float(x) for x in activity_above_arr_b]
a_values_below = activity_below_a
momentrates_below_arr = numpy.array(
momentrate.momentrateFromActivity(a_values_below, activity_below_b,
mmax)) / cls.catalog_time_span[0]
a_values_above = activity_above_a
momentrates_above_arr = numpy.array(
momentrate.momentrateFromActivity(a_values_above, activity_above_b,
mmax)) / cls.catalog_time_span[0]
parameters['activity_below_a'] = activity_below_a
parameters['activity_below_b'] = activity_below_b
parameters['activity_above_a'] = activity_above_a
parameters['activity_above_b'] = activity_above_b
parameters['mr_activity_below'] = momentrates_below_arr.tolist()
parameters['mr_activity_above'] = momentrates_above_arr.tolist()
parameters['activity_m_threshold'] = m_threshold
# FMD from quakes in FBZ
cls.feature_data_fault_background['fmd'] = fmd.computeZoneFMD(
cls, feature, poly_cat)
(parameters['ml_a'], parameters['ml_b'], parameters['ml_mc'],
parameters['ml_magctr']) = fmd.getFMDValues(
cls.feature_data_fault_background['fmd'])
## moment rate from slip rate
sliprate_min_name = features.FAULT_SOURCE_ATTR_SLIPRATE_MIN['name']
sliprate_max_name = features.FAULT_SOURCE_ATTR_SLIPRATE_MAX['name']
attribute_map = utils.getAttributeIndex(
provider_fault, (features.FAULT_SOURCE_ATTR_SLIPRATE_MIN,
features.FAULT_SOURCE_ATTR_SLIPRATE_MAX),
create=False)
moment_rate_min = 0.0
moment_rate_max = 0.0
parameters['area_fault_sqkm'] = 0.0
parameters['fault_count'] = 0
provider_fault.rewind()
for fault in provider_fault:
fault_poly, vertices = utils.polygonsQGS2Shapely((fault, ))
if fault_poly[0].intersects(poly):
parameters['fault_count'] += 1
sliprate_min = \
fault[attribute_map[sliprate_min_name][0]].toDouble()[0]
sliprate_max = \
fault[attribute_map[sliprate_max_name][0]].toDouble()[0]
area_fault = utils.polygonAreaFromWGS84(fault_poly[0])
# TODO(fab): correct scaling of moment rate from slip rate
(rate_min, rate_max) = momentrate.momentrateFromSlipRate(
sliprate_min, sliprate_max, area_fault)
moment_rate_min += rate_min
moment_rate_max += rate_max
parameters['area_fault_sqkm'] += area_fault
moment_rate_min /= cls.catalog_time_span[0]
moment_rate_max /= cls.catalog_time_span[0]
parameters['mr_slip'] = [moment_rate_min, moment_rate_max]
parameters['area_fault_sqkm'] *= 1.0e-6
## moment rate from geodesy (strain)
momentrate_strain_barba = momentrate.momentrateFromStrainRateBarba(
poly, cls.data.strain_rate_barba, cls.data.deformation_regimes_bird)
parameters['mr_strain_barba'] = momentrate_strain_barba / (
cls.catalog_time_span[0])
momentrate_strain_bird = momentrate.momentrateFromStrainRateBird(
poly, cls.data.strain_rate_bird, cls.data.deformation_regimes_bird)
parameters['mr_strain_bird'] = momentrate_strain_bird / (
cls.catalog_time_span[0])
return parameters
def updateDataFaultBackgr(cls, feature,
m_threshold=recurrence.FAULT_BACKGROUND_MAG_THRESHOLD):
"""Update or compute moment rates for selected feature of fault background
zone layer.
Input:
feature QGis polygon feature from fault background zone layer
Output:
parameters dict of computed parameters
"""
provider = cls.fault_background_layer.dataProvider()
provider_fault = cls.fault_source_layer.dataProvider()
provider_area = cls.area_source_layer.dataProvider()
provider_back = cls.background_zone_layer.dataProvider()
parameters = {}
# get Shapely polygon from feature geometry
polylist, vertices = utils.polygonsQGS2Shapely((feature,))
poly = polylist[0]
# get polygon area in square kilometres
parameters['area_background_sqkm'] = \
utils.polygonAreaFromWGS84(poly) * 1.0e-6
# get mmax and mcdist for FBZ from background zone
(mcdist_qv, mmax_qv) = areasource.getAttributesFromBackgroundZones(
poly.centroid, provider_back, areasource.MCDIST_MMAX_ATTRIBUTES)
mmax = float(mmax_qv.toDouble()[0])
mcdist = str(mcdist_qv.toString())
parameters['mmax'] = mmax
## moment rate from EQs
# get quakes from catalog (cut with fault background zone polygon)
poly_cat = QPCatalog.QPCatalog()
poly_cat.merge(cls.catalog)
poly_cat.cut(geometry=poly)
# cut catalog with min/max depth according to UI spinboxes
(mindepth, maxdepth) = eqcatalog.getMinMaxDepth(cls)
poly_cat.cut(mindepth=mindepth, maxdepth=maxdepth)
parameters['eq_count'] = poly_cat.size()
# sum up moment from quakes (converted from Mw with Kanamori eq.)
magnitudes = []
for ev in poly_cat.eventParameters.event:
mag = ev.getPreferredMagnitude()
magnitudes.append(mag.mag.value)
moment = numpy.array(momentrate.magnitude2moment(magnitudes))
# scale moment: per year and area (in km^2)
parameters['mr_eq'] = moment.sum() / (
parameters['area_background_sqkm'] * cls.catalog_time_span[0])
## moment rate from activity (RM)
parameters['activity_mmin'] = atticivy.ATTICIVY_MMIN
activity = atticivy.computeActivityAtticIvy(
(poly, ), (mmax, ), (mcdist, ), cls.catalog,
mmin=parameters['activity_mmin'])
# get RM (a, b) values from feature attribute
activity_str_a = activity[0][3]
activity_arr_a = activity_str_a.strip().split()
activity_str_b = activity[0][4]
activity_arr_b = activity_str_b.strip().split()
# ignore weights
activity_a = [float(x) for x in activity_arr_a]
activity_b = [float(x) for x in activity_arr_b]
parameters['activity_a'] = activity_a
parameters['activity_b'] = activity_b
a_values = activity_a
momentrates_arr = numpy.array(momentrate.momentrateFromActivity(
a_values, activity_b, mmax)) / cls.catalog_time_span[0]
parameters['mr_activity'] = momentrates_arr.tolist()
# get separate catalogs below and above magnitude threshold
cat_below_threshold = QPCatalog.QPCatalog()
cat_below_threshold.merge(poly_cat)
cat_below_threshold.cut(maxmag=m_threshold, maxmag_excl=True)
parameters['eq_count_below'] = cat_below_threshold.size()
cat_above_threshold = QPCatalog.QPCatalog()
cat_above_threshold.merge(poly_cat)
cat_above_threshold.cut(minmag=m_threshold, maxmag_excl=False)
parameters['eq_count_above'] = cat_above_threshold.size()
activity_below_threshold = atticivy.computeActivityAtticIvy(
(poly,), (mmax,), (mcdist,), cat_below_threshold,
mmin=parameters['activity_mmin'])
activity_above_threshold = atticivy.computeActivityAtticIvy(
(poly,), (mmax,), (mcdist,), cat_above_threshold,
mmin=parameters['activity_mmin'])
# get RM (weight, a, b) values from feature attribute
activity_below_str_a = activity_below_threshold[0][3]
activity_below_arr_a = activity_below_str_a.strip().split()
activity_below_str_b = activity_below_threshold[0][4]
activity_below_arr_b = activity_below_str_b.strip().split()
activity_above_str_a = activity_above_threshold[0][3]
activity_above_arr_a = activity_above_str_a.strip().split()
activity_above_str_b = activity_above_threshold[0][4]
activity_above_arr_b = activity_above_str_b.strip().split()
# ignore weights
activity_below_a = [float(x) for x in activity_below_arr_a]
activity_below_b = [float(x) for x in activity_below_arr_b]
activity_above_a = [float(x) for x in activity_above_arr_a]
activity_above_b = [float(x) for x in activity_above_arr_b]
a_values_below = activity_below_a
momentrates_below_arr = numpy.array(momentrate.momentrateFromActivity(
a_values_below, activity_below_b, mmax)) / cls.catalog_time_span[0]
a_values_above = activity_above_a
momentrates_above_arr = numpy.array(momentrate.momentrateFromActivity(
a_values_above, activity_above_b, mmax)) / cls.catalog_time_span[0]
parameters['activity_below_a'] = activity_below_a
parameters['activity_below_b'] = activity_below_b
parameters['activity_above_a'] = activity_above_a
parameters['activity_above_b'] = activity_above_b
parameters['mr_activity_below'] = momentrates_below_arr.tolist()
parameters['mr_activity_above'] = momentrates_above_arr.tolist()
parameters['activity_m_threshold'] = m_threshold
# FMD from quakes in FBZ
cls.feature_data_fault_background['fmd'] = fmd.computeZoneFMD(cls,
feature, poly_cat)
(parameters['ml_a'], parameters['ml_b'], parameters['ml_mc'],
parameters['ml_magctr']) = fmd.getFMDValues(
cls.feature_data_fault_background['fmd'])
## moment rate from slip rate
sliprate_min_name = features.FAULT_SOURCE_ATTR_SLIPRATE_MIN['name']
sliprate_max_name = features.FAULT_SOURCE_ATTR_SLIPRATE_MAX['name']
attribute_map = utils.getAttributeIndex(provider_fault,
(features.FAULT_SOURCE_ATTR_SLIPRATE_MIN,
features.FAULT_SOURCE_ATTR_SLIPRATE_MAX), create=False)
moment_rate_min = 0.0
moment_rate_max = 0.0
parameters['area_fault_sqkm'] = 0.0
parameters['fault_count'] = 0
provider_fault.rewind()
for fault in provider_fault:
fault_poly, vertices = utils.polygonsQGS2Shapely((fault,))
if fault_poly[0].intersects(poly):
parameters['fault_count'] += 1
sliprate_min = \
fault[attribute_map[sliprate_min_name][0]].toDouble()[0]
sliprate_max = \
fault[attribute_map[sliprate_max_name][0]].toDouble()[0]
area_fault = utils.polygonAreaFromWGS84(fault_poly[0])
# TODO(fab): correct scaling of moment rate from slip rate
(rate_min, rate_max) = momentrate.momentrateFromSlipRate(
sliprate_min, sliprate_max, area_fault)
moment_rate_min += rate_min
moment_rate_max += rate_max
parameters['area_fault_sqkm'] += area_fault
moment_rate_min /= cls.catalog_time_span[0]
moment_rate_max /= cls.catalog_time_span[0]
parameters['mr_slip'] = [moment_rate_min, moment_rate_max]
parameters['area_fault_sqkm'] *= 1.0e-6
## moment rate from geodesy (strain)
momentrate_strain_barba = momentrate.momentrateFromStrainRateBarba(
poly, cls.data.strain_rate_barba,
cls.data.deformation_regimes_bird)
parameters['mr_strain_barba'] = momentrate_strain_barba / (
cls.catalog_time_span[0])
momentrate_strain_bird = momentrate.momentrateFromStrainRateBird(poly,
cls.data.strain_rate_bird, cls.data.deformation_regimes_bird)
parameters['mr_strain_bird'] = momentrate_strain_bird / (
cls.catalog_time_span[0])
return parameters
def updateDataArea(cls, feature):
"""Update or compute moment rates for selected feature of area source
zone layer.
Input:
feature QGis polygon feature from area source layer
Output:
parameters dict of computed parameters
"""
provider = cls.area_source_layer.dataProvider()
parameters = {}
# get Shapely polygon from feature geometry
polylist, vertices = utils.polygonsQGS2Shapely((feature, ))
poly = polylist[0]
# get polygon area in square kilometres
parameters['area_sqkm'] = utils.polygonAreaFromWGS84(poly) * 1.0e-6
# zone ID and title
(feature_id, feature_title, feature_name) = utils.getFeatureAttributes(
cls.area_source_layer, feature, features.AREA_SOURCE_ATTRIBUTES_ID)
if feature_title.toString() == '' and feature_name.toString() == '':
zone_name_str = ""
elif feature_title.toString() == '' and feature_name.toString() != '':
zone_name_str = feature_name.toString()
elif feature_title.toString() != '' and feature_name.toString() == '':
zone_name_str = feature_title.toString()
else:
zone_name_str = "%s, %s" % (feature_title.toString(),
feature_name.toString())
parameters['plot_title_fmd'] = 'Zone %s, %s' % (feature_id.toInt()[0],
zone_name_str)
## moment rate from EQs
# get quakes from catalog (cut with polygon)
poly_cat = QPCatalog.QPCatalog()
poly_cat.merge(cls.catalog)
poly_cat.cut(geometry=poly)
# cut catalog with min/max depth according to UI spinboxes
(mindepth, maxdepth) = eqcatalog.getMinMaxDepth(cls)
poly_cat.cut(mindepth=mindepth, maxdepth=maxdepth)
parameters['eq_count'] = poly_cat.size()
# sum up moment from quakes (converted from Mw with Kanamori eq.)
magnitudes = []
for ev in poly_cat.eventParameters.event:
mag = ev.getPreferredMagnitude()
magnitudes.append(mag.mag.value)
moment = numpy.array(momentrate.magnitude2moment(magnitudes))
# scale moment: per year and area (in km^2)
parameters['mr_eq'] = moment.sum() / (parameters['area_sqkm'] *
cls.catalog_time_span[0])
## moment rate from activity (RM)
# get attribute index of AtticIvy result
attribute_map = utils.getAttributeIndex(
provider,
(features.AREA_SOURCE_ATTR_ACT_RM_A,
features.AREA_SOURCE_ATTR_ACT_RM_B, features.AREA_SOURCE_ATTR_MMAX))
attribute_act_a_name = features.AREA_SOURCE_ATTR_ACT_RM_A['name']
attribute_act_a_idx = attribute_map[attribute_act_a_name][0]
attribute_act_b_name = features.AREA_SOURCE_ATTR_ACT_RM_B['name']
attribute_act_b_idx = attribute_map[attribute_act_b_name][0]
attribute_mmax_name = features.AREA_SOURCE_ATTR_MMAX['name']
attribute_mmax_idx = attribute_map[attribute_mmax_name][0]
# get RM (a, b) values from feature attributes
try:
activity_a_str = str(feature[attribute_act_a_idx].toString())
activity_a_arr = activity_a_str.strip().split()
except KeyError:
activity_a_arr = 3 * [numpy.nan]
error_msg = "Moment balancing: no valid activity a parameter in %s" % (
parameters['plot_title_fmd'])
QMessageBox.warning(None, "Moment balancing warning", error_msg)
try:
activity_b_str = str(feature[attribute_act_b_idx].toString())
activity_b_arr = activity_b_str.strip().split()
except KeyError:
activity_b_arr = 3 * [numpy.nan]
error_msg = "Moment balancing: no valid activity b parameter in %s" % (
parameters['plot_title_fmd'])
QMessageBox.warning(None, "Moment balancing warning", error_msg)
# ignore weights
parameters['activity_mmin'] = atticivy.ATTICIVY_MMIN
activity_a = [float(x) for x in activity_a_arr]
activity_b = [float(x) for x in activity_b_arr]
mmax = float(feature[attribute_mmax_idx].toDouble()[0])
parameters['activity_a'] = activity_a
parameters['activity_b'] = activity_b
parameters['mmax'] = mmax
## Maximum likelihood a/b values
if poly_cat.size() == 0:
cls.feature_data_area_source['fmd'] = None
(parameters['ml_a'], parameters['ml_b'], parameters['ml_mc'],
parameters['ml_magctr']) = 4 * [numpy.nan]
error_msg = "Moment balancing: no EQs in %s" % (
parameters['plot_title_fmd'])
QMessageBox.warning(None, "Moment balancing warning", error_msg)
else:
cls.feature_data_area_source['fmd'] = fmd.computeZoneFMD(
cls, feature, poly_cat)
(parameters['ml_a'], parameters['ml_b'], parameters['ml_mc'],
parameters['ml_magctr']) = fmd.getFMDValues(
cls.feature_data_area_source['fmd'])
## moment rate from activity
a_values = activity_a
momentrates_arr = numpy.array(
momentrate.momentrateFromActivity(a_values, activity_b,
mmax)) / cls.catalog_time_span[0]
parameters['mr_activity'] = momentrates_arr.tolist()
## moment rate from geodesy (strain)
momentrate_strain_barba = momentrate.momentrateFromStrainRateBarba(
poly, cls.data.strain_rate_barba, cls.data.deformation_regimes_bird)
parameters['mr_strain_barba'] = momentrate_strain_barba / (
cls.catalog_time_span[0])
momentrate_strain_bird = momentrate.momentrateFromStrainRateBird(
poly, cls.data.strain_rate_bird, cls.data.deformation_regimes_bird)
parameters['mr_strain_bird'] = momentrate_strain_bird / (
cls.catalog_time_span[0])
return parameters