repository_name
stringlengths
5
67
func_path_in_repository
stringlengths
4
234
func_name
stringlengths
0
314
whole_func_string
stringlengths
52
3.87M
language
stringclasses
6 values
func_code_string
stringlengths
39
1.84M
func_code_tokens
sequencelengths
15
672k
func_documentation_string
stringlengths
1
47.2k
func_documentation_tokens
sequencelengths
1
3.92k
split_name
stringclasses
1 value
func_code_url
stringlengths
85
339
gem/oq-engine
openquake/calculators/views.py
ebr_data_transfer
def ebr_data_transfer(token, dstore): """ Display the data transferred in an event based risk calculation """ attrs = dstore['losses_by_event'].attrs sent = humansize(attrs['sent']) received = humansize(attrs['tot_received']) return 'Event Based Risk: sent %s, received %s' % (sent, received)
python
def ebr_data_transfer(token, dstore): attrs = dstore['losses_by_event'].attrs sent = humansize(attrs['sent']) received = humansize(attrs['tot_received']) return 'Event Based Risk: sent %s, received %s' % (sent, received)
[ "def", "ebr_data_transfer", "(", "token", ",", "dstore", ")", ":", "attrs", "=", "dstore", "[", "'losses_by_event'", "]", ".", "attrs", "sent", "=", "humansize", "(", "attrs", "[", "'sent'", "]", ")", "received", "=", "humansize", "(", "attrs", "[", "'tot_received'", "]", ")", "return", "'Event Based Risk: sent %s, received %s'", "%", "(", "sent", ",", "received", ")" ]
Display the data transferred in an event based risk calculation
[ "Display", "the", "data", "transferred", "in", "an", "event", "based", "risk", "calculation" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L345-L352
gem/oq-engine
openquake/calculators/views.py
view_totlosses
def view_totlosses(token, dstore): """ This is a debugging view. You can use it to check that the total losses, i.e. the losses obtained by summing the average losses on all assets are indeed equal to the aggregate losses. This is a sanity check for the correctness of the implementation. """ oq = dstore['oqparam'] tot_losses = dstore['losses_by_asset']['mean'].sum(axis=0) return rst_table(tot_losses.view(oq.loss_dt()), fmt='%.6E')
python
def view_totlosses(token, dstore): oq = dstore['oqparam'] tot_losses = dstore['losses_by_asset']['mean'].sum(axis=0) return rst_table(tot_losses.view(oq.loss_dt()), fmt='%.6E')
[ "def", "view_totlosses", "(", "token", ",", "dstore", ")", ":", "oq", "=", "dstore", "[", "'oqparam'", "]", "tot_losses", "=", "dstore", "[", "'losses_by_asset'", "]", "[", "'mean'", "]", ".", "sum", "(", "axis", "=", "0", ")", "return", "rst_table", "(", "tot_losses", ".", "view", "(", "oq", ".", "loss_dt", "(", ")", ")", ",", "fmt", "=", "'%.6E'", ")" ]
This is a debugging view. You can use it to check that the total losses, i.e. the losses obtained by summing the average losses on all assets are indeed equal to the aggregate losses. This is a sanity check for the correctness of the implementation.
[ "This", "is", "a", "debugging", "view", ".", "You", "can", "use", "it", "to", "check", "that", "the", "total", "losses", "i", ".", "e", ".", "the", "losses", "obtained", "by", "summing", "the", "average", "losses", "on", "all", "assets", "are", "indeed", "equal", "to", "the", "aggregate", "losses", ".", "This", "is", "a", "sanity", "check", "for", "the", "correctness", "of", "the", "implementation", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L357-L366
gem/oq-engine
openquake/calculators/views.py
view_portfolio_losses
def view_portfolio_losses(token, dstore): """ The losses for the full portfolio, for each realization and loss type, extracted from the event loss table. """ oq = dstore['oqparam'] loss_dt = oq.loss_dt() data = portfolio_loss(dstore).view(loss_dt)[:, 0] rlzids = [str(r) for r in range(len(data))] array = util.compose_arrays(numpy.array(rlzids), data, 'rlz') # this is very sensitive to rounding errors, so I am using a low precision return rst_table(array, fmt='%.5E')
python
def view_portfolio_losses(token, dstore): oq = dstore['oqparam'] loss_dt = oq.loss_dt() data = portfolio_loss(dstore).view(loss_dt)[:, 0] rlzids = [str(r) for r in range(len(data))] array = util.compose_arrays(numpy.array(rlzids), data, 'rlz') return rst_table(array, fmt='%.5E')
[ "def", "view_portfolio_losses", "(", "token", ",", "dstore", ")", ":", "oq", "=", "dstore", "[", "'oqparam'", "]", "loss_dt", "=", "oq", ".", "loss_dt", "(", ")", "data", "=", "portfolio_loss", "(", "dstore", ")", ".", "view", "(", "loss_dt", ")", "[", ":", ",", "0", "]", "rlzids", "=", "[", "str", "(", "r", ")", "for", "r", "in", "range", "(", "len", "(", "data", ")", ")", "]", "array", "=", "util", ".", "compose_arrays", "(", "numpy", ".", "array", "(", "rlzids", ")", ",", "data", ",", "'rlz'", ")", "# this is very sensitive to rounding errors, so I am using a low precision", "return", "rst_table", "(", "array", ",", "fmt", "=", "'%.5E'", ")" ]
The losses for the full portfolio, for each realization and loss type, extracted from the event loss table.
[ "The", "losses", "for", "the", "full", "portfolio", "for", "each", "realization", "and", "loss", "type", "extracted", "from", "the", "event", "loss", "table", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L382-L393
gem/oq-engine
openquake/calculators/views.py
view_portfolio_loss
def view_portfolio_loss(token, dstore): """ The mean and stddev loss for the full portfolio for each loss type, extracted from the event loss table, averaged over the realizations """ data = portfolio_loss(dstore) # shape (R, L) loss_types = list(dstore['oqparam'].loss_dt().names) header = ['portfolio_loss'] + loss_types mean = ['mean'] + [row.mean() for row in data.T] stddev = ['stddev'] + [row.std(ddof=1) for row in data.T] return rst_table([mean, stddev], header)
python
def view_portfolio_loss(token, dstore): data = portfolio_loss(dstore) loss_types = list(dstore['oqparam'].loss_dt().names) header = ['portfolio_loss'] + loss_types mean = ['mean'] + [row.mean() for row in data.T] stddev = ['stddev'] + [row.std(ddof=1) for row in data.T] return rst_table([mean, stddev], header)
[ "def", "view_portfolio_loss", "(", "token", ",", "dstore", ")", ":", "data", "=", "portfolio_loss", "(", "dstore", ")", "# shape (R, L)", "loss_types", "=", "list", "(", "dstore", "[", "'oqparam'", "]", ".", "loss_dt", "(", ")", ".", "names", ")", "header", "=", "[", "'portfolio_loss'", "]", "+", "loss_types", "mean", "=", "[", "'mean'", "]", "+", "[", "row", ".", "mean", "(", ")", "for", "row", "in", "data", ".", "T", "]", "stddev", "=", "[", "'stddev'", "]", "+", "[", "row", ".", "std", "(", "ddof", "=", "1", ")", "for", "row", "in", "data", ".", "T", "]", "return", "rst_table", "(", "[", "mean", ",", "stddev", "]", ",", "header", ")" ]
The mean and stddev loss for the full portfolio for each loss type, extracted from the event loss table, averaged over the realizations
[ "The", "mean", "and", "stddev", "loss", "for", "the", "full", "portfolio", "for", "each", "loss", "type", "extracted", "from", "the", "event", "loss", "table", "averaged", "over", "the", "realizations" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L397-L407
gem/oq-engine
openquake/calculators/views.py
sum_table
def sum_table(records): """ Used to compute summaries. The records are assumed to have numeric fields, except the first field which is ignored, since it typically contains a label. Here is an example: >>> sum_table([('a', 1), ('b', 2)]) ['total', 3] """ size = len(records[0]) result = [None] * size firstrec = records[0] for i in range(size): if isinstance(firstrec[i], (numbers.Number, numpy.ndarray)): result[i] = sum(rec[i] for rec in records) else: result[i] = 'total' return result
python
def sum_table(records): size = len(records[0]) result = [None] * size firstrec = records[0] for i in range(size): if isinstance(firstrec[i], (numbers.Number, numpy.ndarray)): result[i] = sum(rec[i] for rec in records) else: result[i] = 'total' return result
[ "def", "sum_table", "(", "records", ")", ":", "size", "=", "len", "(", "records", "[", "0", "]", ")", "result", "=", "[", "None", "]", "*", "size", "firstrec", "=", "records", "[", "0", "]", "for", "i", "in", "range", "(", "size", ")", ":", "if", "isinstance", "(", "firstrec", "[", "i", "]", ",", "(", "numbers", ".", "Number", ",", "numpy", ".", "ndarray", ")", ")", ":", "result", "[", "i", "]", "=", "sum", "(", "rec", "[", "i", "]", "for", "rec", "in", "records", ")", "else", ":", "result", "[", "i", "]", "=", "'total'", "return", "result" ]
Used to compute summaries. The records are assumed to have numeric fields, except the first field which is ignored, since it typically contains a label. Here is an example: >>> sum_table([('a', 1), ('b', 2)]) ['total', 3]
[ "Used", "to", "compute", "summaries", ".", "The", "records", "are", "assumed", "to", "have", "numeric", "fields", "except", "the", "first", "field", "which", "is", "ignored", "since", "it", "typically", "contains", "a", "label", ".", "Here", "is", "an", "example", ":" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L410-L427
gem/oq-engine
openquake/calculators/views.py
view_exposure_info
def view_exposure_info(token, dstore): """ Display info about the exposure model """ assetcol = dstore['assetcol/array'][:] taxonomies = sorted(set(dstore['assetcol'].taxonomies)) cc = dstore['assetcol/cost_calculator'] ra_flag = ['relative', 'absolute'] data = [('#assets', len(assetcol)), ('#taxonomies', len(taxonomies)), ('deductibile', ra_flag[int(cc.deduct_abs)]), ('insurance_limit', ra_flag[int(cc.limit_abs)]), ] return rst_table(data) + '\n\n' + view_assets_by_site(token, dstore)
python
def view_exposure_info(token, dstore): assetcol = dstore['assetcol/array'][:] taxonomies = sorted(set(dstore['assetcol'].taxonomies)) cc = dstore['assetcol/cost_calculator'] ra_flag = ['relative', 'absolute'] data = [(' (' ('deductibile', ra_flag[int(cc.deduct_abs)]), ('insurance_limit', ra_flag[int(cc.limit_abs)]), ] return rst_table(data) + '\n\n' + view_assets_by_site(token, dstore)
[ "def", "view_exposure_info", "(", "token", ",", "dstore", ")", ":", "assetcol", "=", "dstore", "[", "'assetcol/array'", "]", "[", ":", "]", "taxonomies", "=", "sorted", "(", "set", "(", "dstore", "[", "'assetcol'", "]", ".", "taxonomies", ")", ")", "cc", "=", "dstore", "[", "'assetcol/cost_calculator'", "]", "ra_flag", "=", "[", "'relative'", ",", "'absolute'", "]", "data", "=", "[", "(", "'#assets'", ",", "len", "(", "assetcol", ")", ")", ",", "(", "'#taxonomies'", ",", "len", "(", "taxonomies", ")", ")", ",", "(", "'deductibile'", ",", "ra_flag", "[", "int", "(", "cc", ".", "deduct_abs", ")", "]", ")", ",", "(", "'insurance_limit'", ",", "ra_flag", "[", "int", "(", "cc", ".", "limit_abs", ")", "]", ")", ",", "]", "return", "rst_table", "(", "data", ")", "+", "'\\n\\n'", "+", "view_assets_by_site", "(", "token", ",", "dstore", ")" ]
Display info about the exposure model
[ "Display", "info", "about", "the", "exposure", "model" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L431-L444
gem/oq-engine
openquake/calculators/views.py
view_fullreport
def view_fullreport(token, dstore): """ Display an .rst report about the computation """ # avoid circular imports from openquake.calculators.reportwriter import ReportWriter return ReportWriter(dstore).make_report()
python
def view_fullreport(token, dstore): from openquake.calculators.reportwriter import ReportWriter return ReportWriter(dstore).make_report()
[ "def", "view_fullreport", "(", "token", ",", "dstore", ")", ":", "# avoid circular imports", "from", "openquake", ".", "calculators", ".", "reportwriter", "import", "ReportWriter", "return", "ReportWriter", "(", "dstore", ")", ".", "make_report", "(", ")" ]
Display an .rst report about the computation
[ "Display", "an", ".", "rst", "report", "about", "the", "computation" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L461-L467
gem/oq-engine
openquake/calculators/views.py
performance_view
def performance_view(dstore): """ Returns the performance view as a numpy array. """ data = sorted(dstore['performance_data'], key=operator.itemgetter(0)) out = [] for operation, group in itertools.groupby(data, operator.itemgetter(0)): counts = 0 time = 0 mem = 0 for _operation, time_sec, memory_mb, counts_ in group: counts += counts_ time += time_sec mem = max(mem, memory_mb) out.append((operation, time, mem, counts)) out.sort(key=operator.itemgetter(1), reverse=True) # sort by time return numpy.array(out, perf_dt)
python
def performance_view(dstore): data = sorted(dstore['performance_data'], key=operator.itemgetter(0)) out = [] for operation, group in itertools.groupby(data, operator.itemgetter(0)): counts = 0 time = 0 mem = 0 for _operation, time_sec, memory_mb, counts_ in group: counts += counts_ time += time_sec mem = max(mem, memory_mb) out.append((operation, time, mem, counts)) out.sort(key=operator.itemgetter(1), reverse=True) return numpy.array(out, perf_dt)
[ "def", "performance_view", "(", "dstore", ")", ":", "data", "=", "sorted", "(", "dstore", "[", "'performance_data'", "]", ",", "key", "=", "operator", ".", "itemgetter", "(", "0", ")", ")", "out", "=", "[", "]", "for", "operation", ",", "group", "in", "itertools", ".", "groupby", "(", "data", ",", "operator", ".", "itemgetter", "(", "0", ")", ")", ":", "counts", "=", "0", "time", "=", "0", "mem", "=", "0", "for", "_operation", ",", "time_sec", ",", "memory_mb", ",", "counts_", "in", "group", ":", "counts", "+=", "counts_", "time", "+=", "time_sec", "mem", "=", "max", "(", "mem", ",", "memory_mb", ")", "out", ".", "append", "(", "(", "operation", ",", "time", ",", "mem", ",", "counts", ")", ")", "out", ".", "sort", "(", "key", "=", "operator", ".", "itemgetter", "(", "1", ")", ",", "reverse", "=", "True", ")", "# sort by time", "return", "numpy", ".", "array", "(", "out", ",", "perf_dt", ")" ]
Returns the performance view as a numpy array.
[ "Returns", "the", "performance", "view", "as", "a", "numpy", "array", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L470-L486
gem/oq-engine
openquake/calculators/views.py
stats
def stats(name, array, *extras): """ Returns statistics from an array of numbers. :param name: a descriptive string :returns: (name, mean, std, min, max, len) """ std = numpy.nan if len(array) == 1 else numpy.std(array, ddof=1) return (name, numpy.mean(array), std, numpy.min(array), numpy.max(array), len(array)) + extras
python
def stats(name, array, *extras): std = numpy.nan if len(array) == 1 else numpy.std(array, ddof=1) return (name, numpy.mean(array), std, numpy.min(array), numpy.max(array), len(array)) + extras
[ "def", "stats", "(", "name", ",", "array", ",", "*", "extras", ")", ":", "std", "=", "numpy", ".", "nan", "if", "len", "(", "array", ")", "==", "1", "else", "numpy", ".", "std", "(", "array", ",", "ddof", "=", "1", ")", "return", "(", "name", ",", "numpy", ".", "mean", "(", "array", ")", ",", "std", ",", "numpy", ".", "min", "(", "array", ")", ",", "numpy", ".", "max", "(", "array", ")", ",", "len", "(", "array", ")", ")", "+", "extras" ]
Returns statistics from an array of numbers. :param name: a descriptive string :returns: (name, mean, std, min, max, len)
[ "Returns", "statistics", "from", "an", "array", "of", "numbers", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L497-L506
gem/oq-engine
openquake/calculators/views.py
view_num_units
def view_num_units(token, dstore): """ Display the number of units by taxonomy """ taxo = dstore['assetcol/tagcol/taxonomy'].value counts = collections.Counter() for asset in dstore['assetcol']: counts[taxo[asset['taxonomy']]] += asset['number'] data = sorted(counts.items()) data.append(('*ALL*', sum(d[1] for d in data))) return rst_table(data, header=['taxonomy', 'num_units'])
python
def view_num_units(token, dstore): taxo = dstore['assetcol/tagcol/taxonomy'].value counts = collections.Counter() for asset in dstore['assetcol']: counts[taxo[asset['taxonomy']]] += asset['number'] data = sorted(counts.items()) data.append(('*ALL*', sum(d[1] for d in data))) return rst_table(data, header=['taxonomy', 'num_units'])
[ "def", "view_num_units", "(", "token", ",", "dstore", ")", ":", "taxo", "=", "dstore", "[", "'assetcol/tagcol/taxonomy'", "]", ".", "value", "counts", "=", "collections", ".", "Counter", "(", ")", "for", "asset", "in", "dstore", "[", "'assetcol'", "]", ":", "counts", "[", "taxo", "[", "asset", "[", "'taxonomy'", "]", "]", "]", "+=", "asset", "[", "'number'", "]", "data", "=", "sorted", "(", "counts", ".", "items", "(", ")", ")", "data", ".", "append", "(", "(", "'*ALL*'", ",", "sum", "(", "d", "[", "1", "]", "for", "d", "in", "data", ")", ")", ")", "return", "rst_table", "(", "data", ",", "header", "=", "[", "'taxonomy'", ",", "'num_units'", "]", ")" ]
Display the number of units by taxonomy
[ "Display", "the", "number", "of", "units", "by", "taxonomy" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L510-L520
gem/oq-engine
openquake/calculators/views.py
view_assets_by_site
def view_assets_by_site(token, dstore): """ Display statistical information about the distribution of the assets """ taxonomies = dstore['assetcol/tagcol/taxonomy'].value assets_by_site = dstore['assetcol'].assets_by_site() data = ['taxonomy mean stddev min max num_sites num_assets'.split()] num_assets = AccumDict() for assets in assets_by_site: num_assets += {k: [len(v)] for k, v in group_array( assets, 'taxonomy').items()} for taxo in sorted(num_assets): val = numpy.array(num_assets[taxo]) data.append(stats(taxonomies[taxo], val, val.sum())) if len(num_assets) > 1: # more than one taxonomy, add a summary n_assets = numpy.array([len(assets) for assets in assets_by_site]) data.append(stats('*ALL*', n_assets, n_assets.sum())) return rst_table(data)
python
def view_assets_by_site(token, dstore): taxonomies = dstore['assetcol/tagcol/taxonomy'].value assets_by_site = dstore['assetcol'].assets_by_site() data = ['taxonomy mean stddev min max num_sites num_assets'.split()] num_assets = AccumDict() for assets in assets_by_site: num_assets += {k: [len(v)] for k, v in group_array( assets, 'taxonomy').items()} for taxo in sorted(num_assets): val = numpy.array(num_assets[taxo]) data.append(stats(taxonomies[taxo], val, val.sum())) if len(num_assets) > 1: n_assets = numpy.array([len(assets) for assets in assets_by_site]) data.append(stats('*ALL*', n_assets, n_assets.sum())) return rst_table(data)
[ "def", "view_assets_by_site", "(", "token", ",", "dstore", ")", ":", "taxonomies", "=", "dstore", "[", "'assetcol/tagcol/taxonomy'", "]", ".", "value", "assets_by_site", "=", "dstore", "[", "'assetcol'", "]", ".", "assets_by_site", "(", ")", "data", "=", "[", "'taxonomy mean stddev min max num_sites num_assets'", ".", "split", "(", ")", "]", "num_assets", "=", "AccumDict", "(", ")", "for", "assets", "in", "assets_by_site", ":", "num_assets", "+=", "{", "k", ":", "[", "len", "(", "v", ")", "]", "for", "k", ",", "v", "in", "group_array", "(", "assets", ",", "'taxonomy'", ")", ".", "items", "(", ")", "}", "for", "taxo", "in", "sorted", "(", "num_assets", ")", ":", "val", "=", "numpy", ".", "array", "(", "num_assets", "[", "taxo", "]", ")", "data", ".", "append", "(", "stats", "(", "taxonomies", "[", "taxo", "]", ",", "val", ",", "val", ".", "sum", "(", ")", ")", ")", "if", "len", "(", "num_assets", ")", ">", "1", ":", "# more than one taxonomy, add a summary", "n_assets", "=", "numpy", ".", "array", "(", "[", "len", "(", "assets", ")", "for", "assets", "in", "assets_by_site", "]", ")", "data", ".", "append", "(", "stats", "(", "'*ALL*'", ",", "n_assets", ",", "n_assets", ".", "sum", "(", ")", ")", ")", "return", "rst_table", "(", "data", ")" ]
Display statistical information about the distribution of the assets
[ "Display", "statistical", "information", "about", "the", "distribution", "of", "the", "assets" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L524-L541
gem/oq-engine
openquake/calculators/views.py
view_required_params_per_trt
def view_required_params_per_trt(token, dstore): """ Display the parameters needed by each tectonic region type """ csm_info = dstore['csm_info'] tbl = [] for grp_id, trt in sorted(csm_info.grp_by("trt").items()): gsims = csm_info.gsim_lt.get_gsims(trt) maker = ContextMaker(trt, gsims) distances = sorted(maker.REQUIRES_DISTANCES) siteparams = sorted(maker.REQUIRES_SITES_PARAMETERS) ruptparams = sorted(maker.REQUIRES_RUPTURE_PARAMETERS) tbl.append((grp_id, ' '.join(map(repr, map(repr, gsims))), distances, siteparams, ruptparams)) return rst_table( tbl, header='grp_id gsims distances siteparams ruptparams'.split(), fmt=scientificformat)
python
def view_required_params_per_trt(token, dstore): csm_info = dstore['csm_info'] tbl = [] for grp_id, trt in sorted(csm_info.grp_by("trt").items()): gsims = csm_info.gsim_lt.get_gsims(trt) maker = ContextMaker(trt, gsims) distances = sorted(maker.REQUIRES_DISTANCES) siteparams = sorted(maker.REQUIRES_SITES_PARAMETERS) ruptparams = sorted(maker.REQUIRES_RUPTURE_PARAMETERS) tbl.append((grp_id, ' '.join(map(repr, map(repr, gsims))), distances, siteparams, ruptparams)) return rst_table( tbl, header='grp_id gsims distances siteparams ruptparams'.split(), fmt=scientificformat)
[ "def", "view_required_params_per_trt", "(", "token", ",", "dstore", ")", ":", "csm_info", "=", "dstore", "[", "'csm_info'", "]", "tbl", "=", "[", "]", "for", "grp_id", ",", "trt", "in", "sorted", "(", "csm_info", ".", "grp_by", "(", "\"trt\"", ")", ".", "items", "(", ")", ")", ":", "gsims", "=", "csm_info", ".", "gsim_lt", ".", "get_gsims", "(", "trt", ")", "maker", "=", "ContextMaker", "(", "trt", ",", "gsims", ")", "distances", "=", "sorted", "(", "maker", ".", "REQUIRES_DISTANCES", ")", "siteparams", "=", "sorted", "(", "maker", ".", "REQUIRES_SITES_PARAMETERS", ")", "ruptparams", "=", "sorted", "(", "maker", ".", "REQUIRES_RUPTURE_PARAMETERS", ")", "tbl", ".", "append", "(", "(", "grp_id", ",", "' '", ".", "join", "(", "map", "(", "repr", ",", "map", "(", "repr", ",", "gsims", ")", ")", ")", ",", "distances", ",", "siteparams", ",", "ruptparams", ")", ")", "return", "rst_table", "(", "tbl", ",", "header", "=", "'grp_id gsims distances siteparams ruptparams'", ".", "split", "(", ")", ",", "fmt", "=", "scientificformat", ")" ]
Display the parameters needed by each tectonic region type
[ "Display", "the", "parameters", "needed", "by", "each", "tectonic", "region", "type" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L545-L561
gem/oq-engine
openquake/calculators/views.py
view_task_info
def view_task_info(token, dstore): """ Display statistical information about the tasks performance. It is possible to get full information about a specific task with a command like this one, for a classical calculation:: $ oq show task_info:classical """ args = token.split(':')[1:] # called as task_info:task_name if args: [task] = args array = dstore['task_info/' + task].value rduration = array['duration'] / array['weight'] data = util.compose_arrays(rduration, array, 'rduration') data.sort(order='duration') return rst_table(data) data = ['operation-duration mean stddev min max outputs'.split()] for task in dstore['task_info']: val = dstore['task_info/' + task]['duration'] if len(val): data.append(stats(task, val)) if len(data) == 1: return 'Not available' return rst_table(data)
python
def view_task_info(token, dstore): args = token.split(':')[1:] if args: [task] = args array = dstore['task_info/' + task].value rduration = array['duration'] / array['weight'] data = util.compose_arrays(rduration, array, 'rduration') data.sort(order='duration') return rst_table(data) data = ['operation-duration mean stddev min max outputs'.split()] for task in dstore['task_info']: val = dstore['task_info/' + task]['duration'] if len(val): data.append(stats(task, val)) if len(data) == 1: return 'Not available' return rst_table(data)
[ "def", "view_task_info", "(", "token", ",", "dstore", ")", ":", "args", "=", "token", ".", "split", "(", "':'", ")", "[", "1", ":", "]", "# called as task_info:task_name", "if", "args", ":", "[", "task", "]", "=", "args", "array", "=", "dstore", "[", "'task_info/'", "+", "task", "]", ".", "value", "rduration", "=", "array", "[", "'duration'", "]", "/", "array", "[", "'weight'", "]", "data", "=", "util", ".", "compose_arrays", "(", "rduration", ",", "array", ",", "'rduration'", ")", "data", ".", "sort", "(", "order", "=", "'duration'", ")", "return", "rst_table", "(", "data", ")", "data", "=", "[", "'operation-duration mean stddev min max outputs'", ".", "split", "(", ")", "]", "for", "task", "in", "dstore", "[", "'task_info'", "]", ":", "val", "=", "dstore", "[", "'task_info/'", "+", "task", "]", "[", "'duration'", "]", "if", "len", "(", "val", ")", ":", "data", ".", "append", "(", "stats", "(", "task", ",", "val", ")", ")", "if", "len", "(", "data", ")", "==", "1", ":", "return", "'Not available'", "return", "rst_table", "(", "data", ")" ]
Display statistical information about the tasks performance. It is possible to get full information about a specific task with a command like this one, for a classical calculation:: $ oq show task_info:classical
[ "Display", "statistical", "information", "about", "the", "tasks", "performance", ".", "It", "is", "possible", "to", "get", "full", "information", "about", "a", "specific", "task", "with", "a", "command", "like", "this", "one", "for", "a", "classical", "calculation", "::" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L565-L589
gem/oq-engine
openquake/calculators/views.py
view_task_durations
def view_task_durations(token, dstore): """ Display the raw task durations. Here is an example of usage:: $ oq show task_durations:classical """ task = token.split(':')[1] # called as task_duration:task_name array = dstore['task_info/' + task]['duration'] return '\n'.join(map(str, array))
python
def view_task_durations(token, dstore): task = token.split(':')[1] array = dstore['task_info/' + task]['duration'] return '\n'.join(map(str, array))
[ "def", "view_task_durations", "(", "token", ",", "dstore", ")", ":", "task", "=", "token", ".", "split", "(", "':'", ")", "[", "1", "]", "# called as task_duration:task_name", "array", "=", "dstore", "[", "'task_info/'", "+", "task", "]", "[", "'duration'", "]", "return", "'\\n'", ".", "join", "(", "map", "(", "str", ",", "array", ")", ")" ]
Display the raw task durations. Here is an example of usage:: $ oq show task_durations:classical
[ "Display", "the", "raw", "task", "durations", ".", "Here", "is", "an", "example", "of", "usage", "::" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L593-L601
gem/oq-engine
openquake/calculators/views.py
view_task_hazard
def view_task_hazard(token, dstore): """ Display info about a given task. Here are a few examples of usage:: $ oq show task_hazard:0 # the fastest task $ oq show task_hazard:-1 # the slowest task """ tasks = set(dstore['task_info']) if 'source_data' not in dstore: return 'Missing source_data' if 'classical_split_filter' in tasks: data = dstore['task_info/classical_split_filter'].value else: data = dstore['task_info/compute_gmfs'].value data.sort(order='duration') rec = data[int(token.split(':')[1])] taskno = rec['taskno'] arr = get_array(dstore['source_data'].value, taskno=taskno) st = [stats('nsites', arr['nsites']), stats('weight', arr['weight'])] sources = dstore['task_sources'][taskno - 1].split() srcs = set(decode(s).split(':', 1)[0] for s in sources) res = 'taskno=%d, weight=%d, duration=%d s, sources="%s"\n\n' % ( taskno, rec['weight'], rec['duration'], ' '.join(sorted(srcs))) return res + rst_table(st, header='variable mean stddev min max n'.split())
python
def view_task_hazard(token, dstore): tasks = set(dstore['task_info']) if 'source_data' not in dstore: return 'Missing source_data' if 'classical_split_filter' in tasks: data = dstore['task_info/classical_split_filter'].value else: data = dstore['task_info/compute_gmfs'].value data.sort(order='duration') rec = data[int(token.split(':')[1])] taskno = rec['taskno'] arr = get_array(dstore['source_data'].value, taskno=taskno) st = [stats('nsites', arr['nsites']), stats('weight', arr['weight'])] sources = dstore['task_sources'][taskno - 1].split() srcs = set(decode(s).split(':', 1)[0] for s in sources) res = 'taskno=%d, weight=%d, duration=%d s, sources="%s"\n\n' % ( taskno, rec['weight'], rec['duration'], ' '.join(sorted(srcs))) return res + rst_table(st, header='variable mean stddev min max n'.split())
[ "def", "view_task_hazard", "(", "token", ",", "dstore", ")", ":", "tasks", "=", "set", "(", "dstore", "[", "'task_info'", "]", ")", "if", "'source_data'", "not", "in", "dstore", ":", "return", "'Missing source_data'", "if", "'classical_split_filter'", "in", "tasks", ":", "data", "=", "dstore", "[", "'task_info/classical_split_filter'", "]", ".", "value", "else", ":", "data", "=", "dstore", "[", "'task_info/compute_gmfs'", "]", ".", "value", "data", ".", "sort", "(", "order", "=", "'duration'", ")", "rec", "=", "data", "[", "int", "(", "token", ".", "split", "(", "':'", ")", "[", "1", "]", ")", "]", "taskno", "=", "rec", "[", "'taskno'", "]", "arr", "=", "get_array", "(", "dstore", "[", "'source_data'", "]", ".", "value", ",", "taskno", "=", "taskno", ")", "st", "=", "[", "stats", "(", "'nsites'", ",", "arr", "[", "'nsites'", "]", ")", ",", "stats", "(", "'weight'", ",", "arr", "[", "'weight'", "]", ")", "]", "sources", "=", "dstore", "[", "'task_sources'", "]", "[", "taskno", "-", "1", "]", ".", "split", "(", ")", "srcs", "=", "set", "(", "decode", "(", "s", ")", ".", "split", "(", "':'", ",", "1", ")", "[", "0", "]", "for", "s", "in", "sources", ")", "res", "=", "'taskno=%d, weight=%d, duration=%d s, sources=\"%s\"\\n\\n'", "%", "(", "taskno", ",", "rec", "[", "'weight'", "]", ",", "rec", "[", "'duration'", "]", ",", "' '", ".", "join", "(", "sorted", "(", "srcs", ")", ")", ")", "return", "res", "+", "rst_table", "(", "st", ",", "header", "=", "'variable mean stddev min max n'", ".", "split", "(", ")", ")" ]
Display info about a given task. Here are a few examples of usage:: $ oq show task_hazard:0 # the fastest task $ oq show task_hazard:-1 # the slowest task
[ "Display", "info", "about", "a", "given", "task", ".", "Here", "are", "a", "few", "examples", "of", "usage", "::" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L605-L628
gem/oq-engine
openquake/calculators/views.py
view_task_risk
def view_task_risk(token, dstore): """ Display info about a given risk task. Here are a few examples of usage:: $ oq show task_risk:0 # the fastest task $ oq show task_risk:-1 # the slowest task """ [key] = dstore['task_info'] data = dstore['task_info/' + key].value data.sort(order='duration') rec = data[int(token.split(':')[1])] taskno = rec['taskno'] res = 'taskno=%d, weight=%d, duration=%d s' % ( taskno, rec['weight'], rec['duration']) return res
python
def view_task_risk(token, dstore): [key] = dstore['task_info'] data = dstore['task_info/' + key].value data.sort(order='duration') rec = data[int(token.split(':')[1])] taskno = rec['taskno'] res = 'taskno=%d, weight=%d, duration=%d s' % ( taskno, rec['weight'], rec['duration']) return res
[ "def", "view_task_risk", "(", "token", ",", "dstore", ")", ":", "[", "key", "]", "=", "dstore", "[", "'task_info'", "]", "data", "=", "dstore", "[", "'task_info/'", "+", "key", "]", ".", "value", "data", ".", "sort", "(", "order", "=", "'duration'", ")", "rec", "=", "data", "[", "int", "(", "token", ".", "split", "(", "':'", ")", "[", "1", "]", ")", "]", "taskno", "=", "rec", "[", "'taskno'", "]", "res", "=", "'taskno=%d, weight=%d, duration=%d s'", "%", "(", "taskno", ",", "rec", "[", "'weight'", "]", ",", "rec", "[", "'duration'", "]", ")", "return", "res" ]
Display info about a given risk task. Here are a few examples of usage:: $ oq show task_risk:0 # the fastest task $ oq show task_risk:-1 # the slowest task
[ "Display", "info", "about", "a", "given", "risk", "task", ".", "Here", "are", "a", "few", "examples", "of", "usage", "::" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L632-L646
gem/oq-engine
openquake/calculators/views.py
view_hmap
def view_hmap(token, dstore): """ Display the highest 20 points of the mean hazard map. Called as $ oq show hmap:0.1 # 10% PoE """ try: poe = valid.probability(token.split(':')[1]) except IndexError: poe = 0.1 mean = dict(extract(dstore, 'hcurves?kind=mean'))['mean'] oq = dstore['oqparam'] hmap = calc.make_hmap_array(mean, oq.imtls, [poe], len(mean)) dt = numpy.dtype([('sid', U32)] + [(imt, F32) for imt in oq.imtls]) array = numpy.zeros(len(hmap), dt) for i, vals in enumerate(hmap): array[i] = (i, ) + tuple(vals) array.sort(order=list(oq.imtls)[0]) return rst_table(array[:20])
python
def view_hmap(token, dstore): try: poe = valid.probability(token.split(':')[1]) except IndexError: poe = 0.1 mean = dict(extract(dstore, 'hcurves?kind=mean'))['mean'] oq = dstore['oqparam'] hmap = calc.make_hmap_array(mean, oq.imtls, [poe], len(mean)) dt = numpy.dtype([('sid', U32)] + [(imt, F32) for imt in oq.imtls]) array = numpy.zeros(len(hmap), dt) for i, vals in enumerate(hmap): array[i] = (i, ) + tuple(vals) array.sort(order=list(oq.imtls)[0]) return rst_table(array[:20])
[ "def", "view_hmap", "(", "token", ",", "dstore", ")", ":", "try", ":", "poe", "=", "valid", ".", "probability", "(", "token", ".", "split", "(", "':'", ")", "[", "1", "]", ")", "except", "IndexError", ":", "poe", "=", "0.1", "mean", "=", "dict", "(", "extract", "(", "dstore", ",", "'hcurves?kind=mean'", ")", ")", "[", "'mean'", "]", "oq", "=", "dstore", "[", "'oqparam'", "]", "hmap", "=", "calc", ".", "make_hmap_array", "(", "mean", ",", "oq", ".", "imtls", ",", "[", "poe", "]", ",", "len", "(", "mean", ")", ")", "dt", "=", "numpy", ".", "dtype", "(", "[", "(", "'sid'", ",", "U32", ")", "]", "+", "[", "(", "imt", ",", "F32", ")", "for", "imt", "in", "oq", ".", "imtls", "]", ")", "array", "=", "numpy", ".", "zeros", "(", "len", "(", "hmap", ")", ",", "dt", ")", "for", "i", ",", "vals", "in", "enumerate", "(", "hmap", ")", ":", "array", "[", "i", "]", "=", "(", "i", ",", ")", "+", "tuple", "(", "vals", ")", "array", ".", "sort", "(", "order", "=", "list", "(", "oq", ".", "imtls", ")", "[", "0", "]", ")", "return", "rst_table", "(", "array", "[", ":", "20", "]", ")" ]
Display the highest 20 points of the mean hazard map. Called as $ oq show hmap:0.1 # 10% PoE
[ "Display", "the", "highest", "20", "points", "of", "the", "mean", "hazard", "map", ".", "Called", "as", "$", "oq", "show", "hmap", ":", "0", ".", "1", "#", "10%", "PoE" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L650-L667
gem/oq-engine
openquake/calculators/views.py
view_global_hcurves
def view_global_hcurves(token, dstore): """ Display the global hazard curves for the calculation. They are used for debugging purposes when comparing the results of two calculations. They are the mean over the sites of the mean hazard curves. """ oq = dstore['oqparam'] nsites = len(dstore['sitecol']) rlzs_assoc = dstore['csm_info'].get_rlzs_assoc() mean = getters.PmapGetter(dstore, rlzs_assoc).get_mean() array = calc.convert_to_array(mean, nsites, oq.imtls) res = numpy.zeros(1, array.dtype) for name in array.dtype.names: res[name] = array[name].mean() return rst_table(res)
python
def view_global_hcurves(token, dstore): oq = dstore['oqparam'] nsites = len(dstore['sitecol']) rlzs_assoc = dstore['csm_info'].get_rlzs_assoc() mean = getters.PmapGetter(dstore, rlzs_assoc).get_mean() array = calc.convert_to_array(mean, nsites, oq.imtls) res = numpy.zeros(1, array.dtype) for name in array.dtype.names: res[name] = array[name].mean() return rst_table(res)
[ "def", "view_global_hcurves", "(", "token", ",", "dstore", ")", ":", "oq", "=", "dstore", "[", "'oqparam'", "]", "nsites", "=", "len", "(", "dstore", "[", "'sitecol'", "]", ")", "rlzs_assoc", "=", "dstore", "[", "'csm_info'", "]", ".", "get_rlzs_assoc", "(", ")", "mean", "=", "getters", ".", "PmapGetter", "(", "dstore", ",", "rlzs_assoc", ")", ".", "get_mean", "(", ")", "array", "=", "calc", ".", "convert_to_array", "(", "mean", ",", "nsites", ",", "oq", ".", "imtls", ")", "res", "=", "numpy", ".", "zeros", "(", "1", ",", "array", ".", "dtype", ")", "for", "name", "in", "array", ".", "dtype", ".", "names", ":", "res", "[", "name", "]", "=", "array", "[", "name", "]", ".", "mean", "(", ")", "return", "rst_table", "(", "res", ")" ]
Display the global hazard curves for the calculation. They are used for debugging purposes when comparing the results of two calculations. They are the mean over the sites of the mean hazard curves.
[ "Display", "the", "global", "hazard", "curves", "for", "the", "calculation", ".", "They", "are", "used", "for", "debugging", "purposes", "when", "comparing", "the", "results", "of", "two", "calculations", ".", "They", "are", "the", "mean", "over", "the", "sites", "of", "the", "mean", "hazard", "curves", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L671-L686
gem/oq-engine
openquake/calculators/views.py
view_dupl_sources_time
def view_dupl_sources_time(token, dstore): """ Display the time spent computing duplicated sources """ info = dstore['source_info'] items = sorted(group_array(info.value, 'source_id').items()) tbl = [] tot_time = 0 for source_id, records in items: if len(records) > 1: # dupl calc_time = records['calc_time'].sum() tot_time += calc_time + records['split_time'].sum() tbl.append((source_id, calc_time, len(records))) if tbl and info.attrs.get('has_dupl_sources'): tot = info['calc_time'].sum() + info['split_time'].sum() percent = tot_time / tot * 100 m = '\nTotal time in duplicated sources: %d/%d (%d%%)' % ( tot_time, tot, percent) return rst_table(tbl, ['source_id', 'calc_time', 'num_dupl']) + m else: return 'There are no duplicated sources'
python
def view_dupl_sources_time(token, dstore): info = dstore['source_info'] items = sorted(group_array(info.value, 'source_id').items()) tbl = [] tot_time = 0 for source_id, records in items: if len(records) > 1: calc_time = records['calc_time'].sum() tot_time += calc_time + records['split_time'].sum() tbl.append((source_id, calc_time, len(records))) if tbl and info.attrs.get('has_dupl_sources'): tot = info['calc_time'].sum() + info['split_time'].sum() percent = tot_time / tot * 100 m = '\nTotal time in duplicated sources: %d/%d (%d%%)' % ( tot_time, tot, percent) return rst_table(tbl, ['source_id', 'calc_time', 'num_dupl']) + m else: return 'There are no duplicated sources'
[ "def", "view_dupl_sources_time", "(", "token", ",", "dstore", ")", ":", "info", "=", "dstore", "[", "'source_info'", "]", "items", "=", "sorted", "(", "group_array", "(", "info", ".", "value", ",", "'source_id'", ")", ".", "items", "(", ")", ")", "tbl", "=", "[", "]", "tot_time", "=", "0", "for", "source_id", ",", "records", "in", "items", ":", "if", "len", "(", "records", ")", ">", "1", ":", "# dupl", "calc_time", "=", "records", "[", "'calc_time'", "]", ".", "sum", "(", ")", "tot_time", "+=", "calc_time", "+", "records", "[", "'split_time'", "]", ".", "sum", "(", ")", "tbl", ".", "append", "(", "(", "source_id", ",", "calc_time", ",", "len", "(", "records", ")", ")", ")", "if", "tbl", "and", "info", ".", "attrs", ".", "get", "(", "'has_dupl_sources'", ")", ":", "tot", "=", "info", "[", "'calc_time'", "]", ".", "sum", "(", ")", "+", "info", "[", "'split_time'", "]", ".", "sum", "(", ")", "percent", "=", "tot_time", "/", "tot", "*", "100", "m", "=", "'\\nTotal time in duplicated sources: %d/%d (%d%%)'", "%", "(", "tot_time", ",", "tot", ",", "percent", ")", "return", "rst_table", "(", "tbl", ",", "[", "'source_id'", ",", "'calc_time'", ",", "'num_dupl'", "]", ")", "+", "m", "else", ":", "return", "'There are no duplicated sources'" ]
Display the time spent computing duplicated sources
[ "Display", "the", "time", "spent", "computing", "duplicated", "sources" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L690-L710
gem/oq-engine
openquake/calculators/views.py
view_global_poes
def view_global_poes(token, dstore): """ Display global probabilities averaged on all sites and all GMPEs """ tbl = [] imtls = dstore['oqparam'].imtls header = ['grp_id'] + [str(poe) for poe in imtls.array] for grp in sorted(dstore['poes']): poes = dstore['poes/' + grp] nsites = len(poes) site_avg = sum(poes[sid].array for sid in poes) / nsites gsim_avg = site_avg.sum(axis=1) / poes.shape_z tbl.append([grp] + list(gsim_avg)) return rst_table(tbl, header=header)
python
def view_global_poes(token, dstore): tbl = [] imtls = dstore['oqparam'].imtls header = ['grp_id'] + [str(poe) for poe in imtls.array] for grp in sorted(dstore['poes']): poes = dstore['poes/' + grp] nsites = len(poes) site_avg = sum(poes[sid].array for sid in poes) / nsites gsim_avg = site_avg.sum(axis=1) / poes.shape_z tbl.append([grp] + list(gsim_avg)) return rst_table(tbl, header=header)
[ "def", "view_global_poes", "(", "token", ",", "dstore", ")", ":", "tbl", "=", "[", "]", "imtls", "=", "dstore", "[", "'oqparam'", "]", ".", "imtls", "header", "=", "[", "'grp_id'", "]", "+", "[", "str", "(", "poe", ")", "for", "poe", "in", "imtls", ".", "array", "]", "for", "grp", "in", "sorted", "(", "dstore", "[", "'poes'", "]", ")", ":", "poes", "=", "dstore", "[", "'poes/'", "+", "grp", "]", "nsites", "=", "len", "(", "poes", ")", "site_avg", "=", "sum", "(", "poes", "[", "sid", "]", ".", "array", "for", "sid", "in", "poes", ")", "/", "nsites", "gsim_avg", "=", "site_avg", ".", "sum", "(", "axis", "=", "1", ")", "/", "poes", ".", "shape_z", "tbl", ".", "append", "(", "[", "grp", "]", "+", "list", "(", "gsim_avg", ")", ")", "return", "rst_table", "(", "tbl", ",", "header", "=", "header", ")" ]
Display global probabilities averaged on all sites and all GMPEs
[ "Display", "global", "probabilities", "averaged", "on", "all", "sites", "and", "all", "GMPEs" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L714-L727
gem/oq-engine
openquake/calculators/views.py
view_global_hmaps
def view_global_hmaps(token, dstore): """ Display the global hazard maps for the calculation. They are used for debugging purposes when comparing the results of two calculations. They are the mean over the sites of the mean hazard maps. """ oq = dstore['oqparam'] dt = numpy.dtype([('%s-%s' % (imt, poe), F32) for imt in oq.imtls for poe in oq.poes]) array = dstore['hmaps/mean'].value.view(dt)[:, 0] res = numpy.zeros(1, array.dtype) for name in array.dtype.names: res[name] = array[name].mean() return rst_table(res)
python
def view_global_hmaps(token, dstore): oq = dstore['oqparam'] dt = numpy.dtype([('%s-%s' % (imt, poe), F32) for imt in oq.imtls for poe in oq.poes]) array = dstore['hmaps/mean'].value.view(dt)[:, 0] res = numpy.zeros(1, array.dtype) for name in array.dtype.names: res[name] = array[name].mean() return rst_table(res)
[ "def", "view_global_hmaps", "(", "token", ",", "dstore", ")", ":", "oq", "=", "dstore", "[", "'oqparam'", "]", "dt", "=", "numpy", ".", "dtype", "(", "[", "(", "'%s-%s'", "%", "(", "imt", ",", "poe", ")", ",", "F32", ")", "for", "imt", "in", "oq", ".", "imtls", "for", "poe", "in", "oq", ".", "poes", "]", ")", "array", "=", "dstore", "[", "'hmaps/mean'", "]", ".", "value", ".", "view", "(", "dt", ")", "[", ":", ",", "0", "]", "res", "=", "numpy", ".", "zeros", "(", "1", ",", "array", ".", "dtype", ")", "for", "name", "in", "array", ".", "dtype", ".", "names", ":", "res", "[", "name", "]", "=", "array", "[", "name", "]", ".", "mean", "(", ")", "return", "rst_table", "(", "res", ")" ]
Display the global hazard maps for the calculation. They are used for debugging purposes when comparing the results of two calculations. They are the mean over the sites of the mean hazard maps.
[ "Display", "the", "global", "hazard", "maps", "for", "the", "calculation", ".", "They", "are", "used", "for", "debugging", "purposes", "when", "comparing", "the", "results", "of", "two", "calculations", ".", "They", "are", "the", "mean", "over", "the", "sites", "of", "the", "mean", "hazard", "maps", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L731-L745
gem/oq-engine
openquake/calculators/views.py
view_global_gmfs
def view_global_gmfs(token, dstore): """ Display GMFs averaged on everything for debugging purposes """ imtls = dstore['oqparam'].imtls row = dstore['gmf_data/data']['gmv'].mean(axis=0) return rst_table([row], header=imtls)
python
def view_global_gmfs(token, dstore): imtls = dstore['oqparam'].imtls row = dstore['gmf_data/data']['gmv'].mean(axis=0) return rst_table([row], header=imtls)
[ "def", "view_global_gmfs", "(", "token", ",", "dstore", ")", ":", "imtls", "=", "dstore", "[", "'oqparam'", "]", ".", "imtls", "row", "=", "dstore", "[", "'gmf_data/data'", "]", "[", "'gmv'", "]", ".", "mean", "(", "axis", "=", "0", ")", "return", "rst_table", "(", "[", "row", "]", ",", "header", "=", "imtls", ")" ]
Display GMFs averaged on everything for debugging purposes
[ "Display", "GMFs", "averaged", "on", "everything", "for", "debugging", "purposes" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L749-L755
gem/oq-engine
openquake/calculators/views.py
view_mean_disagg
def view_mean_disagg(token, dstore): """ Display mean quantities for the disaggregation. Useful for checking differences between two calculations. """ tbl = [] for key, dset in sorted(dstore['disagg'].items()): vals = [ds.value.mean() for k, ds in sorted(dset.items())] tbl.append([key] + vals) header = ['key'] + sorted(dset) return rst_table(sorted(tbl), header=header)
python
def view_mean_disagg(token, dstore): tbl = [] for key, dset in sorted(dstore['disagg'].items()): vals = [ds.value.mean() for k, ds in sorted(dset.items())] tbl.append([key] + vals) header = ['key'] + sorted(dset) return rst_table(sorted(tbl), header=header)
[ "def", "view_mean_disagg", "(", "token", ",", "dstore", ")", ":", "tbl", "=", "[", "]", "for", "key", ",", "dset", "in", "sorted", "(", "dstore", "[", "'disagg'", "]", ".", "items", "(", ")", ")", ":", "vals", "=", "[", "ds", ".", "value", ".", "mean", "(", ")", "for", "k", ",", "ds", "in", "sorted", "(", "dset", ".", "items", "(", ")", ")", "]", "tbl", ".", "append", "(", "[", "key", "]", "+", "vals", ")", "header", "=", "[", "'key'", "]", "+", "sorted", "(", "dset", ")", "return", "rst_table", "(", "sorted", "(", "tbl", ")", ",", "header", "=", "header", ")" ]
Display mean quantities for the disaggregation. Useful for checking differences between two calculations.
[ "Display", "mean", "quantities", "for", "the", "disaggregation", ".", "Useful", "for", "checking", "differences", "between", "two", "calculations", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L759-L769
gem/oq-engine
openquake/calculators/views.py
view_elt
def view_elt(token, dstore): """ Display the event loss table averaged by event """ oq = dstore['oqparam'] R = len(dstore['csm_info'].rlzs) dic = group_array(dstore['losses_by_event'].value, 'rlzi') header = oq.loss_dt().names tbl = [] for rlzi in range(R): if rlzi in dic: tbl.append(dic[rlzi]['loss'].mean(axis=0)) else: tbl.append([0.] * len(header)) return rst_table(tbl, header)
python
def view_elt(token, dstore): oq = dstore['oqparam'] R = len(dstore['csm_info'].rlzs) dic = group_array(dstore['losses_by_event'].value, 'rlzi') header = oq.loss_dt().names tbl = [] for rlzi in range(R): if rlzi in dic: tbl.append(dic[rlzi]['loss'].mean(axis=0)) else: tbl.append([0.] * len(header)) return rst_table(tbl, header)
[ "def", "view_elt", "(", "token", ",", "dstore", ")", ":", "oq", "=", "dstore", "[", "'oqparam'", "]", "R", "=", "len", "(", "dstore", "[", "'csm_info'", "]", ".", "rlzs", ")", "dic", "=", "group_array", "(", "dstore", "[", "'losses_by_event'", "]", ".", "value", ",", "'rlzi'", ")", "header", "=", "oq", ".", "loss_dt", "(", ")", ".", "names", "tbl", "=", "[", "]", "for", "rlzi", "in", "range", "(", "R", ")", ":", "if", "rlzi", "in", "dic", ":", "tbl", ".", "append", "(", "dic", "[", "rlzi", "]", "[", "'loss'", "]", ".", "mean", "(", "axis", "=", "0", ")", ")", "else", ":", "tbl", ".", "append", "(", "[", "0.", "]", "*", "len", "(", "header", ")", ")", "return", "rst_table", "(", "tbl", ",", "header", ")" ]
Display the event loss table averaged by event
[ "Display", "the", "event", "loss", "table", "averaged", "by", "event" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L773-L787
gem/oq-engine
openquake/calculators/views.py
view_pmap
def view_pmap(token, dstore): """ Display the mean ProbabilityMap associated to a given source group name """ grp = token.split(':')[1] # called as pmap:grp pmap = {} rlzs_assoc = dstore['csm_info'].get_rlzs_assoc() pgetter = getters.PmapGetter(dstore, rlzs_assoc) pmap = pgetter.get_mean(grp) return str(pmap)
python
def view_pmap(token, dstore): grp = token.split(':')[1] pmap = {} rlzs_assoc = dstore['csm_info'].get_rlzs_assoc() pgetter = getters.PmapGetter(dstore, rlzs_assoc) pmap = pgetter.get_mean(grp) return str(pmap)
[ "def", "view_pmap", "(", "token", ",", "dstore", ")", ":", "grp", "=", "token", ".", "split", "(", "':'", ")", "[", "1", "]", "# called as pmap:grp", "pmap", "=", "{", "}", "rlzs_assoc", "=", "dstore", "[", "'csm_info'", "]", ".", "get_rlzs_assoc", "(", ")", "pgetter", "=", "getters", ".", "PmapGetter", "(", "dstore", ",", "rlzs_assoc", ")", "pmap", "=", "pgetter", ".", "get_mean", "(", "grp", ")", "return", "str", "(", "pmap", ")" ]
Display the mean ProbabilityMap associated to a given source group name
[ "Display", "the", "mean", "ProbabilityMap", "associated", "to", "a", "given", "source", "group", "name" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L791-L800
gem/oq-engine
openquake/calculators/views.py
view_act_ruptures_by_src
def view_act_ruptures_by_src(token, dstore): """ Display the actual number of ruptures by source in event based calculations """ data = dstore['ruptures'].value[['srcidx', 'serial']] counts = sorted(countby(data, 'srcidx').items(), key=operator.itemgetter(1), reverse=True) src_info = dstore['source_info'].value[['grp_id', 'source_id']] table = [['src_id', 'grp_id', 'act_ruptures']] for srcidx, act_ruptures in counts: src = src_info[srcidx] table.append([src['source_id'], src['grp_id'], act_ruptures]) return rst_table(table)
python
def view_act_ruptures_by_src(token, dstore): data = dstore['ruptures'].value[['srcidx', 'serial']] counts = sorted(countby(data, 'srcidx').items(), key=operator.itemgetter(1), reverse=True) src_info = dstore['source_info'].value[['grp_id', 'source_id']] table = [['src_id', 'grp_id', 'act_ruptures']] for srcidx, act_ruptures in counts: src = src_info[srcidx] table.append([src['source_id'], src['grp_id'], act_ruptures]) return rst_table(table)
[ "def", "view_act_ruptures_by_src", "(", "token", ",", "dstore", ")", ":", "data", "=", "dstore", "[", "'ruptures'", "]", ".", "value", "[", "[", "'srcidx'", ",", "'serial'", "]", "]", "counts", "=", "sorted", "(", "countby", "(", "data", ",", "'srcidx'", ")", ".", "items", "(", ")", ",", "key", "=", "operator", ".", "itemgetter", "(", "1", ")", ",", "reverse", "=", "True", ")", "src_info", "=", "dstore", "[", "'source_info'", "]", ".", "value", "[", "[", "'grp_id'", ",", "'source_id'", "]", "]", "table", "=", "[", "[", "'src_id'", ",", "'grp_id'", ",", "'act_ruptures'", "]", "]", "for", "srcidx", ",", "act_ruptures", "in", "counts", ":", "src", "=", "src_info", "[", "srcidx", "]", "table", ".", "append", "(", "[", "src", "[", "'source_id'", "]", ",", "src", "[", "'grp_id'", "]", ",", "act_ruptures", "]", ")", "return", "rst_table", "(", "table", ")" ]
Display the actual number of ruptures by source in event based calculations
[ "Display", "the", "actual", "number", "of", "ruptures", "by", "source", "in", "event", "based", "calculations" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L804-L816
gem/oq-engine
openquake/calculators/views.py
view_dupl_sources
def view_dupl_sources(token, dstore): """ Show the sources with the same ID and the truly duplicated sources """ fields = ['source_id', 'code', 'gidx1', 'gidx2', 'num_ruptures'] dic = group_array(dstore['source_info'].value[fields], 'source_id') sameid = [] dupl = [] for source_id, group in dic.items(): if len(group) > 1: # same ID sources sources = [] for rec in group: geom = dstore['source_geom'][rec['gidx1']:rec['gidx2']] src = Source(source_id, rec['code'], geom, rec['num_ruptures']) sources.append(src) if all_equal(sources): dupl.append(source_id) sameid.append(source_id) if not dupl: return '' msg = str(dupl) + '\n' msg += ('Found %d source(s) with the same ID and %d true duplicate(s)' % (len(sameid), len(dupl))) fakedupl = set(sameid) - set(dupl) if fakedupl: msg += '\nHere is a fake duplicate: %s' % fakedupl.pop() return msg
python
def view_dupl_sources(token, dstore): fields = ['source_id', 'code', 'gidx1', 'gidx2', 'num_ruptures'] dic = group_array(dstore['source_info'].value[fields], 'source_id') sameid = [] dupl = [] for source_id, group in dic.items(): if len(group) > 1: sources = [] for rec in group: geom = dstore['source_geom'][rec['gidx1']:rec['gidx2']] src = Source(source_id, rec['code'], geom, rec['num_ruptures']) sources.append(src) if all_equal(sources): dupl.append(source_id) sameid.append(source_id) if not dupl: return '' msg = str(dupl) + '\n' msg += ('Found %d source(s) with the same ID and %d true duplicate(s)' % (len(sameid), len(dupl))) fakedupl = set(sameid) - set(dupl) if fakedupl: msg += '\nHere is a fake duplicate: %s' % fakedupl.pop() return msg
[ "def", "view_dupl_sources", "(", "token", ",", "dstore", ")", ":", "fields", "=", "[", "'source_id'", ",", "'code'", ",", "'gidx1'", ",", "'gidx2'", ",", "'num_ruptures'", "]", "dic", "=", "group_array", "(", "dstore", "[", "'source_info'", "]", ".", "value", "[", "fields", "]", ",", "'source_id'", ")", "sameid", "=", "[", "]", "dupl", "=", "[", "]", "for", "source_id", ",", "group", "in", "dic", ".", "items", "(", ")", ":", "if", "len", "(", "group", ")", ">", "1", ":", "# same ID sources", "sources", "=", "[", "]", "for", "rec", "in", "group", ":", "geom", "=", "dstore", "[", "'source_geom'", "]", "[", "rec", "[", "'gidx1'", "]", ":", "rec", "[", "'gidx2'", "]", "]", "src", "=", "Source", "(", "source_id", ",", "rec", "[", "'code'", "]", ",", "geom", ",", "rec", "[", "'num_ruptures'", "]", ")", "sources", ".", "append", "(", "src", ")", "if", "all_equal", "(", "sources", ")", ":", "dupl", ".", "append", "(", "source_id", ")", "sameid", ".", "append", "(", "source_id", ")", "if", "not", "dupl", ":", "return", "''", "msg", "=", "str", "(", "dupl", ")", "+", "'\\n'", "msg", "+=", "(", "'Found %d source(s) with the same ID and %d true duplicate(s)'", "%", "(", "len", "(", "sameid", ")", ",", "len", "(", "dupl", ")", ")", ")", "fakedupl", "=", "set", "(", "sameid", ")", "-", "set", "(", "dupl", ")", "if", "fakedupl", ":", "msg", "+=", "'\\nHere is a fake duplicate: %s'", "%", "fakedupl", ".", "pop", "(", ")", "return", "msg" ]
Show the sources with the same ID and the truly duplicated sources
[ "Show", "the", "sources", "with", "the", "same", "ID", "and", "the", "truly", "duplicated", "sources" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L838-L864
gem/oq-engine
openquake/calculators/views.py
view_extreme_groups
def view_extreme_groups(token, dstore): """ Show the source groups contributing the most to the highest IML """ data = dstore['disagg_by_grp'].value data.sort(order='extreme_poe') return rst_table(data[::-1])
python
def view_extreme_groups(token, dstore): data = dstore['disagg_by_grp'].value data.sort(order='extreme_poe') return rst_table(data[::-1])
[ "def", "view_extreme_groups", "(", "token", ",", "dstore", ")", ":", "data", "=", "dstore", "[", "'disagg_by_grp'", "]", ".", "value", "data", ".", "sort", "(", "order", "=", "'extreme_poe'", ")", "return", "rst_table", "(", "data", "[", ":", ":", "-", "1", "]", ")" ]
Show the source groups contributing the most to the highest IML
[ "Show", "the", "source", "groups", "contributing", "the", "most", "to", "the", "highest", "IML" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/views.py#L868-L874
gem/oq-engine
openquake/hazardlib/gsim/silva_2002.py
SilvaEtAl2002MblgAB1987NSHMP2008.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES for stddev_type in stddev_types) C = self.COEFFS[imt] mag = self._convert_magnitude(rup.mag) mean = ( C['c1'] + C['c2'] * mag + C['c10'] * (mag - 6) ** 2 + (C['c6'] + C['c7'] * mag) * np.log(dists.rjb + np.exp(C['c4'])) ) mean = clip_mean(imt, mean) stddevs = self._compute_stddevs(C, dists.rjb.size, stddev_types) return mean, stddevs
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES for stddev_type in stddev_types) C = self.COEFFS[imt] mag = self._convert_magnitude(rup.mag) mean = ( C['c1'] + C['c2'] * mag + C['c10'] * (mag - 6) ** 2 + (C['c6'] + C['c7'] * mag) * np.log(dists.rjb + np.exp(C['c4'])) ) mean = clip_mean(imt, mean) stddevs = self._compute_stddevs(C, dists.rjb.size, stddev_types) return mean, stddevs
[ "def", "get_mean_and_stddevs", "(", "self", ",", "sites", ",", "rup", ",", "dists", ",", "imt", ",", "stddev_types", ")", ":", "assert", "all", "(", "stddev_type", "in", "self", ".", "DEFINED_FOR_STANDARD_DEVIATION_TYPES", "for", "stddev_type", "in", "stddev_types", ")", "C", "=", "self", ".", "COEFFS", "[", "imt", "]", "mag", "=", "self", ".", "_convert_magnitude", "(", "rup", ".", "mag", ")", "mean", "=", "(", "C", "[", "'c1'", "]", "+", "C", "[", "'c2'", "]", "*", "mag", "+", "C", "[", "'c10'", "]", "*", "(", "mag", "-", "6", ")", "**", "2", "+", "(", "C", "[", "'c6'", "]", "+", "C", "[", "'c7'", "]", "*", "mag", ")", "*", "np", ".", "log", "(", "dists", ".", "rjb", "+", "np", ".", "exp", "(", "C", "[", "'c4'", "]", ")", ")", ")", "mean", "=", "clip_mean", "(", "imt", ",", "mean", ")", "stddevs", "=", "self", ".", "_compute_stddevs", "(", "C", ",", "dists", ".", "rjb", ".", "size", ",", "stddev_types", ")", "return", "mean", ",", "stddevs" ]
See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.
[ "See", ":", "meth", ":", "superclass", "method", "<", ".", "base", ".", "GroundShakingIntensityModel", ".", "get_mean_and_stddevs", ">", "for", "spec", "of", "input", "and", "result", "values", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/silva_2002.py#L89-L109
gem/oq-engine
openquake/commonlib/oqzip.py
zip_all
def zip_all(directory): """ Zip source models and exposures recursively """ zips = [] for cwd, dirs, files in os.walk(directory): if 'ssmLT.xml' in files: zips.append(zip_source_model(os.path.join(cwd, 'ssmLT.xml'))) for f in files: if f.endswith('.xml') and 'exposure' in f.lower(): zips.append(zip_exposure(os.path.join(cwd, f))) total = sum(os.path.getsize(z) for z in zips) logging.info('Generated %s of zipped data', general.humansize(total))
python
def zip_all(directory): zips = [] for cwd, dirs, files in os.walk(directory): if 'ssmLT.xml' in files: zips.append(zip_source_model(os.path.join(cwd, 'ssmLT.xml'))) for f in files: if f.endswith('.xml') and 'exposure' in f.lower(): zips.append(zip_exposure(os.path.join(cwd, f))) total = sum(os.path.getsize(z) for z in zips) logging.info('Generated %s of zipped data', general.humansize(total))
[ "def", "zip_all", "(", "directory", ")", ":", "zips", "=", "[", "]", "for", "cwd", ",", "dirs", ",", "files", "in", "os", ".", "walk", "(", "directory", ")", ":", "if", "'ssmLT.xml'", "in", "files", ":", "zips", ".", "append", "(", "zip_source_model", "(", "os", ".", "path", ".", "join", "(", "cwd", ",", "'ssmLT.xml'", ")", ")", ")", "for", "f", "in", "files", ":", "if", "f", ".", "endswith", "(", "'.xml'", ")", "and", "'exposure'", "in", "f", ".", "lower", "(", ")", ":", "zips", ".", "append", "(", "zip_exposure", "(", "os", ".", "path", ".", "join", "(", "cwd", ",", "f", ")", ")", ")", "total", "=", "sum", "(", "os", ".", "path", ".", "getsize", "(", "z", ")", "for", "z", "in", "zips", ")", "logging", ".", "info", "(", "'Generated %s of zipped data'", ",", "general", ".", "humansize", "(", "total", ")", ")" ]
Zip source models and exposures recursively
[ "Zip", "source", "models", "and", "exposures", "recursively" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqzip.py#L27-L39
gem/oq-engine
openquake/commonlib/oqzip.py
zip_source_model
def zip_source_model(ssmLT, archive_zip='', log=logging.info): """ Zip the source model files starting from the smmLT.xml file """ basedir = os.path.dirname(ssmLT) if os.path.basename(ssmLT) != 'ssmLT.xml': orig = ssmLT ssmLT = os.path.join(basedir, 'ssmLT.xml') with open(ssmLT, 'wb') as f: f.write(open(orig, 'rb').read()) archive_zip = archive_zip or os.path.join(basedir, 'ssmLT.zip') if os.path.exists(archive_zip): sys.exit('%s exists already' % archive_zip) oq = mock.Mock(inputs={'source_model_logic_tree': ssmLT}) checksum = readinput.get_checksum32(oq) checkfile = os.path.join(os.path.dirname(ssmLT), 'CHECKSUM.txt') with open(checkfile, 'w') as f: f.write(str(checksum)) files = logictree.collect_info(ssmLT).smpaths + [ os.path.abspath(ssmLT), os.path.abspath(checkfile)] general.zipfiles(files, archive_zip, log=log, cleanup=True) return archive_zip
python
def zip_source_model(ssmLT, archive_zip='', log=logging.info): basedir = os.path.dirname(ssmLT) if os.path.basename(ssmLT) != 'ssmLT.xml': orig = ssmLT ssmLT = os.path.join(basedir, 'ssmLT.xml') with open(ssmLT, 'wb') as f: f.write(open(orig, 'rb').read()) archive_zip = archive_zip or os.path.join(basedir, 'ssmLT.zip') if os.path.exists(archive_zip): sys.exit('%s exists already' % archive_zip) oq = mock.Mock(inputs={'source_model_logic_tree': ssmLT}) checksum = readinput.get_checksum32(oq) checkfile = os.path.join(os.path.dirname(ssmLT), 'CHECKSUM.txt') with open(checkfile, 'w') as f: f.write(str(checksum)) files = logictree.collect_info(ssmLT).smpaths + [ os.path.abspath(ssmLT), os.path.abspath(checkfile)] general.zipfiles(files, archive_zip, log=log, cleanup=True) return archive_zip
[ "def", "zip_source_model", "(", "ssmLT", ",", "archive_zip", "=", "''", ",", "log", "=", "logging", ".", "info", ")", ":", "basedir", "=", "os", ".", "path", ".", "dirname", "(", "ssmLT", ")", "if", "os", ".", "path", ".", "basename", "(", "ssmLT", ")", "!=", "'ssmLT.xml'", ":", "orig", "=", "ssmLT", "ssmLT", "=", "os", ".", "path", ".", "join", "(", "basedir", ",", "'ssmLT.xml'", ")", "with", "open", "(", "ssmLT", ",", "'wb'", ")", "as", "f", ":", "f", ".", "write", "(", "open", "(", "orig", ",", "'rb'", ")", ".", "read", "(", ")", ")", "archive_zip", "=", "archive_zip", "or", "os", ".", "path", ".", "join", "(", "basedir", ",", "'ssmLT.zip'", ")", "if", "os", ".", "path", ".", "exists", "(", "archive_zip", ")", ":", "sys", ".", "exit", "(", "'%s exists already'", "%", "archive_zip", ")", "oq", "=", "mock", ".", "Mock", "(", "inputs", "=", "{", "'source_model_logic_tree'", ":", "ssmLT", "}", ")", "checksum", "=", "readinput", ".", "get_checksum32", "(", "oq", ")", "checkfile", "=", "os", ".", "path", ".", "join", "(", "os", ".", "path", ".", "dirname", "(", "ssmLT", ")", ",", "'CHECKSUM.txt'", ")", "with", "open", "(", "checkfile", ",", "'w'", ")", "as", "f", ":", "f", ".", "write", "(", "str", "(", "checksum", ")", ")", "files", "=", "logictree", ".", "collect_info", "(", "ssmLT", ")", ".", "smpaths", "+", "[", "os", ".", "path", ".", "abspath", "(", "ssmLT", ")", ",", "os", ".", "path", ".", "abspath", "(", "checkfile", ")", "]", "general", ".", "zipfiles", "(", "files", ",", "archive_zip", ",", "log", "=", "log", ",", "cleanup", "=", "True", ")", "return", "archive_zip" ]
Zip the source model files starting from the smmLT.xml file
[ "Zip", "the", "source", "model", "files", "starting", "from", "the", "smmLT", ".", "xml", "file" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqzip.py#L42-L64
gem/oq-engine
openquake/commonlib/oqzip.py
zip_exposure
def zip_exposure(exposure_xml, archive_zip='', log=logging.info): """ Zip an exposure.xml file with all its .csv subfiles (if any) """ archive_zip = archive_zip or exposure_xml[:-4] + '.zip' if os.path.exists(archive_zip): sys.exit('%s exists already' % archive_zip) [exp] = Exposure.read_headers([exposure_xml]) files = [exposure_xml] + exp.datafiles general.zipfiles(files, archive_zip, log=log, cleanup=True) return archive_zip
python
def zip_exposure(exposure_xml, archive_zip='', log=logging.info): archive_zip = archive_zip or exposure_xml[:-4] + '.zip' if os.path.exists(archive_zip): sys.exit('%s exists already' % archive_zip) [exp] = Exposure.read_headers([exposure_xml]) files = [exposure_xml] + exp.datafiles general.zipfiles(files, archive_zip, log=log, cleanup=True) return archive_zip
[ "def", "zip_exposure", "(", "exposure_xml", ",", "archive_zip", "=", "''", ",", "log", "=", "logging", ".", "info", ")", ":", "archive_zip", "=", "archive_zip", "or", "exposure_xml", "[", ":", "-", "4", "]", "+", "'.zip'", "if", "os", ".", "path", ".", "exists", "(", "archive_zip", ")", ":", "sys", ".", "exit", "(", "'%s exists already'", "%", "archive_zip", ")", "[", "exp", "]", "=", "Exposure", ".", "read_headers", "(", "[", "exposure_xml", "]", ")", "files", "=", "[", "exposure_xml", "]", "+", "exp", ".", "datafiles", "general", ".", "zipfiles", "(", "files", ",", "archive_zip", ",", "log", "=", "log", ",", "cleanup", "=", "True", ")", "return", "archive_zip" ]
Zip an exposure.xml file with all its .csv subfiles (if any)
[ "Zip", "an", "exposure", ".", "xml", "file", "with", "all", "its", ".", "csv", "subfiles", "(", "if", "any", ")" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqzip.py#L67-L77
gem/oq-engine
openquake/commonlib/oqzip.py
zip_job
def zip_job(job_ini, archive_zip='', risk_ini='', oq=None, log=logging.info): """ Zip the given job.ini file into the given archive, together with all related files. """ if not os.path.exists(job_ini): sys.exit('%s does not exist' % job_ini) archive_zip = archive_zip or 'job.zip' if isinstance(archive_zip, str): # actually it should be path-like if not archive_zip.endswith('.zip'): sys.exit('%s does not end with .zip' % archive_zip) if os.path.exists(archive_zip): sys.exit('%s exists already' % archive_zip) # do not validate to avoid permissions error on the export_dir oq = oq or readinput.get_oqparam(job_ini, validate=False) if risk_ini: risk_ini = os.path.normpath(os.path.abspath(risk_ini)) risk_inputs = readinput.get_params([risk_ini])['inputs'] del risk_inputs['job_ini'] oq.inputs.update(risk_inputs) files = readinput.get_input_files(oq) if risk_ini: files = [risk_ini] + files return general.zipfiles(files, archive_zip, log=log)
python
def zip_job(job_ini, archive_zip='', risk_ini='', oq=None, log=logging.info): if not os.path.exists(job_ini): sys.exit('%s does not exist' % job_ini) archive_zip = archive_zip or 'job.zip' if isinstance(archive_zip, str): if not archive_zip.endswith('.zip'): sys.exit('%s does not end with .zip' % archive_zip) if os.path.exists(archive_zip): sys.exit('%s exists already' % archive_zip) oq = oq or readinput.get_oqparam(job_ini, validate=False) if risk_ini: risk_ini = os.path.normpath(os.path.abspath(risk_ini)) risk_inputs = readinput.get_params([risk_ini])['inputs'] del risk_inputs['job_ini'] oq.inputs.update(risk_inputs) files = readinput.get_input_files(oq) if risk_ini: files = [risk_ini] + files return general.zipfiles(files, archive_zip, log=log)
[ "def", "zip_job", "(", "job_ini", ",", "archive_zip", "=", "''", ",", "risk_ini", "=", "''", ",", "oq", "=", "None", ",", "log", "=", "logging", ".", "info", ")", ":", "if", "not", "os", ".", "path", ".", "exists", "(", "job_ini", ")", ":", "sys", ".", "exit", "(", "'%s does not exist'", "%", "job_ini", ")", "archive_zip", "=", "archive_zip", "or", "'job.zip'", "if", "isinstance", "(", "archive_zip", ",", "str", ")", ":", "# actually it should be path-like", "if", "not", "archive_zip", ".", "endswith", "(", "'.zip'", ")", ":", "sys", ".", "exit", "(", "'%s does not end with .zip'", "%", "archive_zip", ")", "if", "os", ".", "path", ".", "exists", "(", "archive_zip", ")", ":", "sys", ".", "exit", "(", "'%s exists already'", "%", "archive_zip", ")", "# do not validate to avoid permissions error on the export_dir", "oq", "=", "oq", "or", "readinput", ".", "get_oqparam", "(", "job_ini", ",", "validate", "=", "False", ")", "if", "risk_ini", ":", "risk_ini", "=", "os", ".", "path", ".", "normpath", "(", "os", ".", "path", ".", "abspath", "(", "risk_ini", ")", ")", "risk_inputs", "=", "readinput", ".", "get_params", "(", "[", "risk_ini", "]", ")", "[", "'inputs'", "]", "del", "risk_inputs", "[", "'job_ini'", "]", "oq", ".", "inputs", ".", "update", "(", "risk_inputs", ")", "files", "=", "readinput", ".", "get_input_files", "(", "oq", ")", "if", "risk_ini", ":", "files", "=", "[", "risk_ini", "]", "+", "files", "return", "general", ".", "zipfiles", "(", "files", ",", "archive_zip", ",", "log", "=", "log", ")" ]
Zip the given job.ini file into the given archive, together with all related files.
[ "Zip", "the", "given", "job", ".", "ini", "file", "into", "the", "given", "archive", "together", "with", "all", "related", "files", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqzip.py#L80-L103
gem/oq-engine
openquake/calculators/reportwriter.py
build_report
def build_report(job_ini, output_dir=None): """ Write a `report.csv` file with information about the calculation without running it :param job_ini: full pathname of the job.ini file :param output_dir: the directory where the report is written (default the input directory) """ calc_id = logs.init() oq = readinput.get_oqparam(job_ini) if oq.calculation_mode == 'classical': oq.calculation_mode = 'preclassical' oq.ground_motion_fields = False output_dir = output_dir or os.path.dirname(job_ini) from openquake.calculators import base # ugly calc = base.calculators(oq, calc_id) calc.save_params() # needed to save oqparam # some taken is care so that the real calculation is not run: # the goal is to extract information about the source management only calc.pre_execute() if oq.calculation_mode == 'preclassical': calc.execute() rw = ReportWriter(calc.datastore) rw.make_report() report = (os.path.join(output_dir, 'report.rst') if output_dir else calc.datastore.export_path('report.rst')) try: rw.save(report) except IOError as exc: # permission error sys.stderr.write(str(exc) + '\n') readinput.exposure = None # ugly hack return report
python
def build_report(job_ini, output_dir=None): calc_id = logs.init() oq = readinput.get_oqparam(job_ini) if oq.calculation_mode == 'classical': oq.calculation_mode = 'preclassical' oq.ground_motion_fields = False output_dir = output_dir or os.path.dirname(job_ini) from openquake.calculators import base calc = base.calculators(oq, calc_id) calc.save_params() calc.pre_execute() if oq.calculation_mode == 'preclassical': calc.execute() rw = ReportWriter(calc.datastore) rw.make_report() report = (os.path.join(output_dir, 'report.rst') if output_dir else calc.datastore.export_path('report.rst')) try: rw.save(report) except IOError as exc: sys.stderr.write(str(exc) + '\n') readinput.exposure = None return report
[ "def", "build_report", "(", "job_ini", ",", "output_dir", "=", "None", ")", ":", "calc_id", "=", "logs", ".", "init", "(", ")", "oq", "=", "readinput", ".", "get_oqparam", "(", "job_ini", ")", "if", "oq", ".", "calculation_mode", "==", "'classical'", ":", "oq", ".", "calculation_mode", "=", "'preclassical'", "oq", ".", "ground_motion_fields", "=", "False", "output_dir", "=", "output_dir", "or", "os", ".", "path", ".", "dirname", "(", "job_ini", ")", "from", "openquake", ".", "calculators", "import", "base", "# ugly", "calc", "=", "base", ".", "calculators", "(", "oq", ",", "calc_id", ")", "calc", ".", "save_params", "(", ")", "# needed to save oqparam", "# some taken is care so that the real calculation is not run:", "# the goal is to extract information about the source management only", "calc", ".", "pre_execute", "(", ")", "if", "oq", ".", "calculation_mode", "==", "'preclassical'", ":", "calc", ".", "execute", "(", ")", "rw", "=", "ReportWriter", "(", "calc", ".", "datastore", ")", "rw", ".", "make_report", "(", ")", "report", "=", "(", "os", ".", "path", ".", "join", "(", "output_dir", ",", "'report.rst'", ")", "if", "output_dir", "else", "calc", ".", "datastore", ".", "export_path", "(", "'report.rst'", ")", ")", "try", ":", "rw", ".", "save", "(", "report", ")", "except", "IOError", "as", "exc", ":", "# permission error", "sys", ".", "stderr", ".", "write", "(", "str", "(", "exc", ")", "+", "'\\n'", ")", "readinput", ".", "exposure", "=", "None", "# ugly hack", "return", "report" ]
Write a `report.csv` file with information about the calculation without running it :param job_ini: full pathname of the job.ini file :param output_dir: the directory where the report is written (default the input directory)
[ "Write", "a", "report", ".", "csv", "file", "with", "information", "about", "the", "calculation", "without", "running", "it" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/reportwriter.py#L125-L159
gem/oq-engine
openquake/calculators/reportwriter.py
ReportWriter.add
def add(self, name, obj=None): """Add the view named `name` to the report text""" if obj: text = '\n::\n\n' + indent(str(obj)) else: text = views.view(name, self.dstore) if text: title = self.title[name] line = '-' * len(title) self.text += '\n'.join(['\n\n' + title, line, text])
python
def add(self, name, obj=None): if obj: text = '\n::\n\n' + indent(str(obj)) else: text = views.view(name, self.dstore) if text: title = self.title[name] line = '-' * len(title) self.text += '\n'.join(['\n\n' + title, line, text])
[ "def", "add", "(", "self", ",", "name", ",", "obj", "=", "None", ")", ":", "if", "obj", ":", "text", "=", "'\\n::\\n\\n'", "+", "indent", "(", "str", "(", "obj", ")", ")", "else", ":", "text", "=", "views", ".", "view", "(", "name", ",", "self", ".", "dstore", ")", "if", "text", ":", "title", "=", "self", ".", "title", "[", "name", "]", "line", "=", "'-'", "*", "len", "(", "title", ")", "self", ".", "text", "+=", "'\\n'", ".", "join", "(", "[", "'\\n\\n'", "+", "title", ",", "line", ",", "text", "]", ")" ]
Add the view named `name` to the report text
[ "Add", "the", "view", "named", "name", "to", "the", "report", "text" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/reportwriter.py#L74-L83
gem/oq-engine
openquake/calculators/reportwriter.py
ReportWriter.make_report
def make_report(self): """Build the report and return a restructed text string""" oq, ds = self.oq, self.dstore for name in ('params', 'inputs'): self.add(name) if 'csm_info' in ds: self.add('csm_info') if ds['csm_info'].source_models[0].name != 'scenario': # required_params_per_trt makes no sense for GMFs from file self.add('required_params_per_trt') self.add('rlzs_assoc', ds['csm_info'].get_rlzs_assoc()) if 'csm_info' in ds: self.add('ruptures_per_trt') if 'rup_data' in ds: self.add('ruptures_events') if oq.calculation_mode in ('event_based_risk',): self.add('avglosses_data_transfer') if 'exposure' in oq.inputs: self.add('exposure_info') if 'source_info' in ds: self.add('slow_sources') self.add('times_by_source_class') self.add('dupl_sources') if 'task_info' in ds: self.add('task_info') tasks = set(ds['task_info']) if 'classical' in tasks: self.add('task_hazard:0') self.add('task_hazard:-1') self.add('job_info') if 'performance_data' in ds: self.add('performance') return self.text
python
def make_report(self): oq, ds = self.oq, self.dstore for name in ('params', 'inputs'): self.add(name) if 'csm_info' in ds: self.add('csm_info') if ds['csm_info'].source_models[0].name != 'scenario': self.add('required_params_per_trt') self.add('rlzs_assoc', ds['csm_info'].get_rlzs_assoc()) if 'csm_info' in ds: self.add('ruptures_per_trt') if 'rup_data' in ds: self.add('ruptures_events') if oq.calculation_mode in ('event_based_risk',): self.add('avglosses_data_transfer') if 'exposure' in oq.inputs: self.add('exposure_info') if 'source_info' in ds: self.add('slow_sources') self.add('times_by_source_class') self.add('dupl_sources') if 'task_info' in ds: self.add('task_info') tasks = set(ds['task_info']) if 'classical' in tasks: self.add('task_hazard:0') self.add('task_hazard:-1') self.add('job_info') if 'performance_data' in ds: self.add('performance') return self.text
[ "def", "make_report", "(", "self", ")", ":", "oq", ",", "ds", "=", "self", ".", "oq", ",", "self", ".", "dstore", "for", "name", "in", "(", "'params'", ",", "'inputs'", ")", ":", "self", ".", "add", "(", "name", ")", "if", "'csm_info'", "in", "ds", ":", "self", ".", "add", "(", "'csm_info'", ")", "if", "ds", "[", "'csm_info'", "]", ".", "source_models", "[", "0", "]", ".", "name", "!=", "'scenario'", ":", "# required_params_per_trt makes no sense for GMFs from file", "self", ".", "add", "(", "'required_params_per_trt'", ")", "self", ".", "add", "(", "'rlzs_assoc'", ",", "ds", "[", "'csm_info'", "]", ".", "get_rlzs_assoc", "(", ")", ")", "if", "'csm_info'", "in", "ds", ":", "self", ".", "add", "(", "'ruptures_per_trt'", ")", "if", "'rup_data'", "in", "ds", ":", "self", ".", "add", "(", "'ruptures_events'", ")", "if", "oq", ".", "calculation_mode", "in", "(", "'event_based_risk'", ",", ")", ":", "self", ".", "add", "(", "'avglosses_data_transfer'", ")", "if", "'exposure'", "in", "oq", ".", "inputs", ":", "self", ".", "add", "(", "'exposure_info'", ")", "if", "'source_info'", "in", "ds", ":", "self", ".", "add", "(", "'slow_sources'", ")", "self", ".", "add", "(", "'times_by_source_class'", ")", "self", ".", "add", "(", "'dupl_sources'", ")", "if", "'task_info'", "in", "ds", ":", "self", ".", "add", "(", "'task_info'", ")", "tasks", "=", "set", "(", "ds", "[", "'task_info'", "]", ")", "if", "'classical'", "in", "tasks", ":", "self", ".", "add", "(", "'task_hazard:0'", ")", "self", ".", "add", "(", "'task_hazard:-1'", ")", "self", ".", "add", "(", "'job_info'", ")", "if", "'performance_data'", "in", "ds", ":", "self", ".", "add", "(", "'performance'", ")", "return", "self", ".", "text" ]
Build the report and return a restructed text string
[ "Build", "the", "report", "and", "return", "a", "restructed", "text", "string" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/reportwriter.py#L85-L117
gem/oq-engine
openquake/calculators/reportwriter.py
ReportWriter.save
def save(self, fname): """Save the report""" with open(fname, 'wb') as f: f.write(encode(self.text))
python
def save(self, fname): with open(fname, 'wb') as f: f.write(encode(self.text))
[ "def", "save", "(", "self", ",", "fname", ")", ":", "with", "open", "(", "fname", ",", "'wb'", ")", "as", "f", ":", "f", ".", "write", "(", "encode", "(", "self", ".", "text", ")", ")" ]
Save the report
[ "Save", "the", "report" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/calculators/reportwriter.py#L119-L122
gem/oq-engine
openquake/hazardlib/gsim/bradley_2013.py
convert_to_LHC
def convert_to_LHC(imt): """ Converts from GMRotI50 to Larger of two horizontal components using global equation of: Boore, D and Kishida, T (2016). Relations between some horizontal- component ground-motion intensity measures used in practice. Bulletin of the Seismological Society of America, 107(1), 334-343. doi:10.1785/0120160250 No standard deviation modification required. """ # get period t if isinstance(imt, SA): t = imt.period else: t = 0.01 T1 = 0.08 T2 = 0.56 T3 = 4.40 T4 = 8.70 R1 = 1.106 R2 = 1.158 R3 = 1.178 R4 = 1.241 R5 = 1.241 Ratio = max(R1, max(min(R1+(R2-R1)/np.log(T2/T1)*np.log(t/T1), R2+(R3-R2)/np.log(T3/T2)*np.log(t/T2)), min(R3+(R4-R3)/np.log(T4/T3)*np.log(t/T3), R5))) SF = np.log(Ratio) return SF
python
def convert_to_LHC(imt): if isinstance(imt, SA): t = imt.period else: t = 0.01 T1 = 0.08 T2 = 0.56 T3 = 4.40 T4 = 8.70 R1 = 1.106 R2 = 1.158 R3 = 1.178 R4 = 1.241 R5 = 1.241 Ratio = max(R1, max(min(R1+(R2-R1)/np.log(T2/T1)*np.log(t/T1), R2+(R3-R2)/np.log(T3/T2)*np.log(t/T2)), min(R3+(R4-R3)/np.log(T4/T3)*np.log(t/T3), R5))) SF = np.log(Ratio) return SF
[ "def", "convert_to_LHC", "(", "imt", ")", ":", "# get period t", "if", "isinstance", "(", "imt", ",", "SA", ")", ":", "t", "=", "imt", ".", "period", "else", ":", "t", "=", "0.01", "T1", "=", "0.08", "T2", "=", "0.56", "T3", "=", "4.40", "T4", "=", "8.70", "R1", "=", "1.106", "R2", "=", "1.158", "R3", "=", "1.178", "R4", "=", "1.241", "R5", "=", "1.241", "Ratio", "=", "max", "(", "R1", ",", "max", "(", "min", "(", "R1", "+", "(", "R2", "-", "R1", ")", "/", "np", ".", "log", "(", "T2", "/", "T1", ")", "*", "np", ".", "log", "(", "t", "/", "T1", ")", ",", "R2", "+", "(", "R3", "-", "R2", ")", "/", "np", ".", "log", "(", "T3", "/", "T2", ")", "*", "np", ".", "log", "(", "t", "/", "T2", ")", ")", ",", "min", "(", "R3", "+", "(", "R4", "-", "R3", ")", "/", "np", ".", "log", "(", "T4", "/", "T3", ")", "*", "np", ".", "log", "(", "t", "/", "T3", ")", ",", "R5", ")", ")", ")", "SF", "=", "np", ".", "log", "(", "Ratio", ")", "return", "SF" ]
Converts from GMRotI50 to Larger of two horizontal components using global equation of: Boore, D and Kishida, T (2016). Relations between some horizontal- component ground-motion intensity measures used in practice. Bulletin of the Seismological Society of America, 107(1), 334-343. doi:10.1785/0120160250 No standard deviation modification required.
[ "Converts", "from", "GMRotI50", "to", "Larger", "of", "two", "horizontal", "components", "using", "global", "equation", "of", ":", "Boore", "D", "and", "Kishida", "T", "(", "2016", ")", ".", "Relations", "between", "some", "horizontal", "-", "component", "ground", "-", "motion", "intensity", "measures", "used", "in", "practice", ".", "Bulletin", "of", "the", "Seismological", "Society", "of", "America", "107", "(", "1", ")", "334", "-", "343", ".", "doi", ":", "10", ".", "1785", "/", "0120160250", "No", "standard", "deviation", "modification", "required", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/bradley_2013.py#L389-L421
gem/oq-engine
openquake/hazardlib/gsim/bradley_2013.py
Bradley2013._get_mean
def _get_mean(self, sites, C, ln_y_ref, exp1, exp2, v1): """ Add site effects to an intensity. Implements eq. 5 """ # we do not support estimating of basin depth and instead # rely on it being available (since we require it). z1pt0 = sites.z1pt0 # we consider random variables being zero since we want # to find the exact mean value. eta = epsilon = 0 ln_y = ( # first line of eq. 13b ln_y_ref + C['phi1'] * np.log(np.clip(sites.vs30, -np.inf, v1) / 1130) # second line + C['phi2'] * (exp1 - exp2) * np.log((np.exp(ln_y_ref) + C['phi4']) / C['phi4']) # third line + C['phi5'] * (1.0 - 1.0 / np.cosh( C['phi6'] * (z1pt0 - C['phi7']).clip(0, np.inf))) + C['phi8'] / np.cosh(0.15 * (z1pt0 - 15).clip(0, np.inf)) # fourth line + eta + epsilon ) return ln_y
python
def _get_mean(self, sites, C, ln_y_ref, exp1, exp2, v1): z1pt0 = sites.z1pt0 eta = epsilon = 0 ln_y = ( ln_y_ref + C['phi1'] * np.log(np.clip(sites.vs30, -np.inf, v1) / 1130) + C['phi2'] * (exp1 - exp2) * np.log((np.exp(ln_y_ref) + C['phi4']) / C['phi4']) + C['phi5'] * (1.0 - 1.0 / np.cosh( C['phi6'] * (z1pt0 - C['phi7']).clip(0, np.inf))) + C['phi8'] / np.cosh(0.15 * (z1pt0 - 15).clip(0, np.inf)) + eta + epsilon ) return ln_y
[ "def", "_get_mean", "(", "self", ",", "sites", ",", "C", ",", "ln_y_ref", ",", "exp1", ",", "exp2", ",", "v1", ")", ":", "# we do not support estimating of basin depth and instead", "# rely on it being available (since we require it).", "z1pt0", "=", "sites", ".", "z1pt0", "# we consider random variables being zero since we want", "# to find the exact mean value.", "eta", "=", "epsilon", "=", "0", "ln_y", "=", "(", "# first line of eq. 13b", "ln_y_ref", "+", "C", "[", "'phi1'", "]", "*", "np", ".", "log", "(", "np", ".", "clip", "(", "sites", ".", "vs30", ",", "-", "np", ".", "inf", ",", "v1", ")", "/", "1130", ")", "# second line", "+", "C", "[", "'phi2'", "]", "*", "(", "exp1", "-", "exp2", ")", "*", "np", ".", "log", "(", "(", "np", ".", "exp", "(", "ln_y_ref", ")", "+", "C", "[", "'phi4'", "]", ")", "/", "C", "[", "'phi4'", "]", ")", "# third line", "+", "C", "[", "'phi5'", "]", "*", "(", "1.0", "-", "1.0", "/", "np", ".", "cosh", "(", "C", "[", "'phi6'", "]", "*", "(", "z1pt0", "-", "C", "[", "'phi7'", "]", ")", ".", "clip", "(", "0", ",", "np", ".", "inf", ")", ")", ")", "+", "C", "[", "'phi8'", "]", "/", "np", ".", "cosh", "(", "0.15", "*", "(", "z1pt0", "-", "15", ")", ".", "clip", "(", "0", ",", "np", ".", "inf", ")", ")", "# fourth line", "+", "eta", "+", "epsilon", ")", "return", "ln_y" ]
Add site effects to an intensity. Implements eq. 5
[ "Add", "site", "effects", "to", "an", "intensity", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/bradley_2013.py#L107-L137
gem/oq-engine
openquake/hazardlib/gsim/bradley_2013.py
Bradley2013._get_v1
def _get_v1(self, imt): """ Calculates Bradley's V1 term. Equation 2 (page 1814) and 6 (page 1816) based on SA period """ if imt == PGA(): v1 = 1800. else: T = imt.period v1a = np.clip((1130 * (T / 0.75)**-0.11), 1130, np.inf) v1 = np.clip(v1a, -np.inf, 1800.) return v1
python
def _get_v1(self, imt): if imt == PGA(): v1 = 1800. else: T = imt.period v1a = np.clip((1130 * (T / 0.75)**-0.11), 1130, np.inf) v1 = np.clip(v1a, -np.inf, 1800.) return v1
[ "def", "_get_v1", "(", "self", ",", "imt", ")", ":", "if", "imt", "==", "PGA", "(", ")", ":", "v1", "=", "1800.", "else", ":", "T", "=", "imt", ".", "period", "v1a", "=", "np", ".", "clip", "(", "(", "1130", "*", "(", "T", "/", "0.75", ")", "**", "-", "0.11", ")", ",", "1130", ",", "np", ".", "inf", ")", "v1", "=", "np", ".", "clip", "(", "v1a", ",", "-", "np", ".", "inf", ",", "1800.", ")", "return", "v1" ]
Calculates Bradley's V1 term. Equation 2 (page 1814) and 6 (page 1816) based on SA period
[ "Calculates", "Bradley", "s", "V1", "term", ".", "Equation", "2", "(", "page", "1814", ")", "and", "6", "(", "page", "1816", ")", "based", "on", "SA", "period" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/bradley_2013.py#L243-L257
gem/oq-engine
openquake/hazardlib/gsim/bradley_2013.py
Bradley2013LHC.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ # extracting dictionary of coefficients specific to required # intensity measure type. C = self.COEFFS[imt] # intensity on a reference soil is used for both mean # and stddev calculations. ln_y_ref = self._get_ln_y_ref(rup, dists, C) # exp1 and exp2 are parts of eq. 7 exp1 = np.exp(C['phi3'] * (sites.vs30.clip(-np.inf, 1130) - 360)) exp2 = np.exp(C['phi3'] * (1130 - 360)) # v1 is the period dependent site term. The Vs30 above which, the # amplification is constant v1 = self._get_v1(imt) mean = self._get_mean(sites, C, ln_y_ref, exp1, exp2, v1) mean += convert_to_LHC(imt) stddevs = self._get_stddevs(sites, rup, C, stddev_types, ln_y_ref, exp1, exp2) return mean, stddevs
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): C = self.COEFFS[imt] ln_y_ref = self._get_ln_y_ref(rup, dists, C) exp1 = np.exp(C['phi3'] * (sites.vs30.clip(-np.inf, 1130) - 360)) exp2 = np.exp(C['phi3'] * (1130 - 360)) v1 = self._get_v1(imt) mean = self._get_mean(sites, C, ln_y_ref, exp1, exp2, v1) mean += convert_to_LHC(imt) stddevs = self._get_stddevs(sites, rup, C, stddev_types, ln_y_ref, exp1, exp2) return mean, stddevs
[ "def", "get_mean_and_stddevs", "(", "self", ",", "sites", ",", "rup", ",", "dists", ",", "imt", ",", "stddev_types", ")", ":", "# extracting dictionary of coefficients specific to required", "# intensity measure type.", "C", "=", "self", ".", "COEFFS", "[", "imt", "]", "# intensity on a reference soil is used for both mean", "# and stddev calculations.", "ln_y_ref", "=", "self", ".", "_get_ln_y_ref", "(", "rup", ",", "dists", ",", "C", ")", "# exp1 and exp2 are parts of eq. 7", "exp1", "=", "np", ".", "exp", "(", "C", "[", "'phi3'", "]", "*", "(", "sites", ".", "vs30", ".", "clip", "(", "-", "np", ".", "inf", ",", "1130", ")", "-", "360", ")", ")", "exp2", "=", "np", ".", "exp", "(", "C", "[", "'phi3'", "]", "*", "(", "1130", "-", "360", ")", ")", "# v1 is the period dependent site term. The Vs30 above which, the", "# amplification is constant", "v1", "=", "self", ".", "_get_v1", "(", "imt", ")", "mean", "=", "self", ".", "_get_mean", "(", "sites", ",", "C", ",", "ln_y_ref", ",", "exp1", ",", "exp2", ",", "v1", ")", "mean", "+=", "convert_to_LHC", "(", "imt", ")", "stddevs", "=", "self", ".", "_get_stddevs", "(", "sites", ",", "rup", ",", "C", ",", "stddev_types", ",", "ln_y_ref", ",", "exp1", ",", "exp2", ")", "return", "mean", ",", "stddevs" ]
See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.
[ "See", ":", "meth", ":", "superclass", "method", "<", ".", "base", ".", "GroundShakingIntensityModel", ".", "get_mean_and_stddevs", ">", "for", "spec", "of", "input", "and", "result", "values", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/bradley_2013.py#L345-L369
gem/oq-engine
openquake/hazardlib/gsim/frankel_1996.py
FrankelEtAl1996MblgAB1987NSHMP2008.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. :raises ValueError: if imt is instance of :class:`openquake.hazardlib.imt.SA` with unsupported period. """ assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES for stddev_type in stddev_types) if imt not in self.IMTS_TABLES: raise ValueError( 'IMT %s not supported in FrankelEtAl1996NSHMP. ' % repr(imt) + 'FrankelEtAl1996NSHMP does not allow interpolation for ' + 'unsupported periods.' ) mean = self._compute_mean(imt, rup.mag, dists.rhypo.copy()) mean = clip_mean(imt, mean) stddevs = self._compute_stddevs(imt, dists.rhypo.shape, stddev_types) return mean, stddevs
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): assert all(stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES for stddev_type in stddev_types) if imt not in self.IMTS_TABLES: raise ValueError( 'IMT %s not supported in FrankelEtAl1996NSHMP. ' % repr(imt) + 'FrankelEtAl1996NSHMP does not allow interpolation for ' + 'unsupported periods.' ) mean = self._compute_mean(imt, rup.mag, dists.rhypo.copy()) mean = clip_mean(imt, mean) stddevs = self._compute_stddevs(imt, dists.rhypo.shape, stddev_types) return mean, stddevs
[ "def", "get_mean_and_stddevs", "(", "self", ",", "sites", ",", "rup", ",", "dists", ",", "imt", ",", "stddev_types", ")", ":", "assert", "all", "(", "stddev_type", "in", "self", ".", "DEFINED_FOR_STANDARD_DEVIATION_TYPES", "for", "stddev_type", "in", "stddev_types", ")", "if", "imt", "not", "in", "self", ".", "IMTS_TABLES", ":", "raise", "ValueError", "(", "'IMT %s not supported in FrankelEtAl1996NSHMP. '", "%", "repr", "(", "imt", ")", "+", "'FrankelEtAl1996NSHMP does not allow interpolation for '", "+", "'unsupported periods.'", ")", "mean", "=", "self", ".", "_compute_mean", "(", "imt", ",", "rup", ".", "mag", ",", "dists", ".", "rhypo", ".", "copy", "(", ")", ")", "mean", "=", "clip_mean", "(", "imt", ",", "mean", ")", "stddevs", "=", "self", ".", "_compute_stddevs", "(", "imt", ",", "dists", ".", "rhypo", ".", "shape", ",", "stddev_types", ")", "return", "mean", ",", "stddevs" ]
See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. :raises ValueError: if imt is instance of :class:`openquake.hazardlib.imt.SA` with unsupported period.
[ "See", ":", "meth", ":", "superclass", "method", "<", ".", "base", ".", "GroundShakingIntensityModel", ".", "get_mean_and_stddevs", ">", "for", "spec", "of", "input", "and", "result", "values", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/frankel_1996.py#L102-L127
gem/oq-engine
openquake/hazardlib/gsim/frankel_1996.py
FrankelEtAl1996MblgAB1987NSHMP2008._compute_mean
def _compute_mean(self, imt, mag, rhypo): """ Compute mean value from lookup table. Lookup table defines log10(IMT) (in g) for combinations of Mw and log10(rhypo) values. ``mag`` is therefore converted from Mblg to Mw using Atkinson and Boore 1987 conversion equation. Mean value is finally converted from base 10 to base e. """ mag = np.zeros_like(rhypo) + self._convert_magnitude(mag) # to avoid run time warning in case rhypo is zero set minimum distance # to 10, which is anyhow the minimum distance allowed by the tables rhypo[rhypo < 10] = 10 rhypo = np.log10(rhypo) # create lookup table and interpolate it at magnitude/distance values table = RectBivariateSpline( self.MAGS, self.DISTS, self.IMTS_TABLES[imt].T ) mean = table.ev(mag, rhypo) # convert mean from base 10 to base e return mean * np.log(10)
python
def _compute_mean(self, imt, mag, rhypo): mag = np.zeros_like(rhypo) + self._convert_magnitude(mag) rhypo[rhypo < 10] = 10 rhypo = np.log10(rhypo) table = RectBivariateSpline( self.MAGS, self.DISTS, self.IMTS_TABLES[imt].T ) mean = table.ev(mag, rhypo) return mean * np.log(10)
[ "def", "_compute_mean", "(", "self", ",", "imt", ",", "mag", ",", "rhypo", ")", ":", "mag", "=", "np", ".", "zeros_like", "(", "rhypo", ")", "+", "self", ".", "_convert_magnitude", "(", "mag", ")", "# to avoid run time warning in case rhypo is zero set minimum distance", "# to 10, which is anyhow the minimum distance allowed by the tables", "rhypo", "[", "rhypo", "<", "10", "]", "=", "10", "rhypo", "=", "np", ".", "log10", "(", "rhypo", ")", "# create lookup table and interpolate it at magnitude/distance values", "table", "=", "RectBivariateSpline", "(", "self", ".", "MAGS", ",", "self", ".", "DISTS", ",", "self", ".", "IMTS_TABLES", "[", "imt", "]", ".", "T", ")", "mean", "=", "table", ".", "ev", "(", "mag", ",", "rhypo", ")", "# convert mean from base 10 to base e", "return", "mean", "*", "np", ".", "log", "(", "10", ")" ]
Compute mean value from lookup table. Lookup table defines log10(IMT) (in g) for combinations of Mw and log10(rhypo) values. ``mag`` is therefore converted from Mblg to Mw using Atkinson and Boore 1987 conversion equation. Mean value is finally converted from base 10 to base e.
[ "Compute", "mean", "value", "from", "lookup", "table", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/frankel_1996.py#L129-L152
gem/oq-engine
openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py
_get_recurrence_model
def _get_recurrence_model(input_model): """ Returns the annual and cumulative recurrence rates predicted by the recurrence model """ if not isinstance(input_model, (TruncatedGRMFD, EvenlyDiscretizedMFD, YoungsCoppersmith1985MFD)): raise ValueError('Recurrence model not recognised') # Get model annual occurrence rates annual_rates = input_model.get_annual_occurrence_rates() annual_rates = np.array([[val[0], val[1]] for val in annual_rates]) # Get cumulative rates cumulative_rates = np.array([np.sum(annual_rates[iloc:, 1]) for iloc in range(0, len(annual_rates), 1)]) return annual_rates, cumulative_rates
python
def _get_recurrence_model(input_model): if not isinstance(input_model, (TruncatedGRMFD, EvenlyDiscretizedMFD, YoungsCoppersmith1985MFD)): raise ValueError('Recurrence model not recognised') annual_rates = input_model.get_annual_occurrence_rates() annual_rates = np.array([[val[0], val[1]] for val in annual_rates]) cumulative_rates = np.array([np.sum(annual_rates[iloc:, 1]) for iloc in range(0, len(annual_rates), 1)]) return annual_rates, cumulative_rates
[ "def", "_get_recurrence_model", "(", "input_model", ")", ":", "if", "not", "isinstance", "(", "input_model", ",", "(", "TruncatedGRMFD", ",", "EvenlyDiscretizedMFD", ",", "YoungsCoppersmith1985MFD", ")", ")", ":", "raise", "ValueError", "(", "'Recurrence model not recognised'", ")", "# Get model annual occurrence rates", "annual_rates", "=", "input_model", ".", "get_annual_occurrence_rates", "(", ")", "annual_rates", "=", "np", ".", "array", "(", "[", "[", "val", "[", "0", "]", ",", "val", "[", "1", "]", "]", "for", "val", "in", "annual_rates", "]", ")", "# Get cumulative rates", "cumulative_rates", "=", "np", ".", "array", "(", "[", "np", ".", "sum", "(", "annual_rates", "[", "iloc", ":", ",", "1", "]", ")", "for", "iloc", "in", "range", "(", "0", ",", "len", "(", "annual_rates", ")", ",", "1", ")", "]", ")", "return", "annual_rates", ",", "cumulative_rates" ]
Returns the annual and cumulative recurrence rates predicted by the recurrence model
[ "Returns", "the", "annual", "and", "cumulative", "recurrence", "rates", "predicted", "by", "the", "recurrence", "model" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py#L62-L77
gem/oq-engine
openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py
_check_completeness_table
def _check_completeness_table(completeness, catalogue): """ Generates the completeness table according to different instances """ if isinstance(completeness, np.ndarray) and np.shape(completeness)[1] == 2: return completeness elif isinstance(completeness, float): return np.array([[float(np.min(catalogue.data['year'])), completeness]]) elif completeness is None: return np.array([[float(np.min(catalogue.data['year'])), np.min(catalogue.data['magnitude'])]]) else: raise ValueError('Completeness representation not recognised')
python
def _check_completeness_table(completeness, catalogue): if isinstance(completeness, np.ndarray) and np.shape(completeness)[1] == 2: return completeness elif isinstance(completeness, float): return np.array([[float(np.min(catalogue.data['year'])), completeness]]) elif completeness is None: return np.array([[float(np.min(catalogue.data['year'])), np.min(catalogue.data['magnitude'])]]) else: raise ValueError('Completeness representation not recognised')
[ "def", "_check_completeness_table", "(", "completeness", ",", "catalogue", ")", ":", "if", "isinstance", "(", "completeness", ",", "np", ".", "ndarray", ")", "and", "np", ".", "shape", "(", "completeness", ")", "[", "1", "]", "==", "2", ":", "return", "completeness", "elif", "isinstance", "(", "completeness", ",", "float", ")", ":", "return", "np", ".", "array", "(", "[", "[", "float", "(", "np", ".", "min", "(", "catalogue", ".", "data", "[", "'year'", "]", ")", ")", ",", "completeness", "]", "]", ")", "elif", "completeness", "is", "None", ":", "return", "np", ".", "array", "(", "[", "[", "float", "(", "np", ".", "min", "(", "catalogue", ".", "data", "[", "'year'", "]", ")", ")", ",", "np", ".", "min", "(", "catalogue", ".", "data", "[", "'magnitude'", "]", ")", "]", "]", ")", "else", ":", "raise", "ValueError", "(", "'Completeness representation not recognised'", ")" ]
Generates the completeness table according to different instances
[ "Generates", "the", "completeness", "table", "according", "to", "different", "instances" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py#L80-L93
gem/oq-engine
openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py
plot_recurrence_model
def plot_recurrence_model( input_model, catalogue, completeness, dmag=0.1, filename=None, figure_size=(8, 6), filetype='png', dpi=300, ax=None): """ Plot a calculated recurrence model over an observed catalogue, adjusted for time-varying completeness """ annual_rates, cumulative_rates = _get_recurrence_model(input_model) # Get observed annual recurrence if not catalogue.end_year: catalogue.update_end_year() cent_mag, t_per, n_obs = get_completeness_counts(catalogue, completeness, dmag) obs_rates = n_obs / t_per cum_obs_rates = np.array([np.sum(obs_rates[i:]) for i in range(len(obs_rates))]) if ax is None: fig, ax = plt.subplots(figsize=figure_size) else: fig = ax.get_figure() ax.semilogy(cent_mag, obs_rates, 'bo') ax.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-') ax.semilogy(cent_mag, cum_obs_rates, 'rs') ax.semilogy(annual_rates[:, 0], cumulative_rates, 'r-') ax.grid(which='both') ax.set_xlabel('Magnitude') ax.set_ylabel('Annual Rate') ax.legend(['Observed Incremental Rate', 'Model Incremental Rate', 'Observed Cumulative Rate', 'Model Cumulative Rate']) ax.tick_params(labelsize=12) _save_image(fig, filename, filetype, dpi)
python
def plot_recurrence_model( input_model, catalogue, completeness, dmag=0.1, filename=None, figure_size=(8, 6), filetype='png', dpi=300, ax=None): annual_rates, cumulative_rates = _get_recurrence_model(input_model) if not catalogue.end_year: catalogue.update_end_year() cent_mag, t_per, n_obs = get_completeness_counts(catalogue, completeness, dmag) obs_rates = n_obs / t_per cum_obs_rates = np.array([np.sum(obs_rates[i:]) for i in range(len(obs_rates))]) if ax is None: fig, ax = plt.subplots(figsize=figure_size) else: fig = ax.get_figure() ax.semilogy(cent_mag, obs_rates, 'bo') ax.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-') ax.semilogy(cent_mag, cum_obs_rates, 'rs') ax.semilogy(annual_rates[:, 0], cumulative_rates, 'r-') ax.grid(which='both') ax.set_xlabel('Magnitude') ax.set_ylabel('Annual Rate') ax.legend(['Observed Incremental Rate', 'Model Incremental Rate', 'Observed Cumulative Rate', 'Model Cumulative Rate']) ax.tick_params(labelsize=12) _save_image(fig, filename, filetype, dpi)
[ "def", "plot_recurrence_model", "(", "input_model", ",", "catalogue", ",", "completeness", ",", "dmag", "=", "0.1", ",", "filename", "=", "None", ",", "figure_size", "=", "(", "8", ",", "6", ")", ",", "filetype", "=", "'png'", ",", "dpi", "=", "300", ",", "ax", "=", "None", ")", ":", "annual_rates", ",", "cumulative_rates", "=", "_get_recurrence_model", "(", "input_model", ")", "# Get observed annual recurrence", "if", "not", "catalogue", ".", "end_year", ":", "catalogue", ".", "update_end_year", "(", ")", "cent_mag", ",", "t_per", ",", "n_obs", "=", "get_completeness_counts", "(", "catalogue", ",", "completeness", ",", "dmag", ")", "obs_rates", "=", "n_obs", "/", "t_per", "cum_obs_rates", "=", "np", ".", "array", "(", "[", "np", ".", "sum", "(", "obs_rates", "[", "i", ":", "]", ")", "for", "i", "in", "range", "(", "len", "(", "obs_rates", ")", ")", "]", ")", "if", "ax", "is", "None", ":", "fig", ",", "ax", "=", "plt", ".", "subplots", "(", "figsize", "=", "figure_size", ")", "else", ":", "fig", "=", "ax", ".", "get_figure", "(", ")", "ax", ".", "semilogy", "(", "cent_mag", ",", "obs_rates", ",", "'bo'", ")", "ax", ".", "semilogy", "(", "annual_rates", "[", ":", ",", "0", "]", ",", "annual_rates", "[", ":", ",", "1", "]", ",", "'b-'", ")", "ax", ".", "semilogy", "(", "cent_mag", ",", "cum_obs_rates", ",", "'rs'", ")", "ax", ".", "semilogy", "(", "annual_rates", "[", ":", ",", "0", "]", ",", "cumulative_rates", ",", "'r-'", ")", "ax", ".", "grid", "(", "which", "=", "'both'", ")", "ax", ".", "set_xlabel", "(", "'Magnitude'", ")", "ax", ".", "set_ylabel", "(", "'Annual Rate'", ")", "ax", ".", "legend", "(", "[", "'Observed Incremental Rate'", ",", "'Model Incremental Rate'", ",", "'Observed Cumulative Rate'", ",", "'Model Cumulative Rate'", "]", ")", "ax", ".", "tick_params", "(", "labelsize", "=", "12", ")", "_save_image", "(", "fig", ",", "filename", ",", "filetype", ",", "dpi", ")" ]
Plot a calculated recurrence model over an observed catalogue, adjusted for time-varying completeness
[ "Plot", "a", "calculated", "recurrence", "model", "over", "an", "observed", "catalogue", "adjusted", "for", "time", "-", "varying", "completeness" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py#L96-L132
gem/oq-engine
openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py
plot_trunc_gr_model
def plot_trunc_gr_model( aval, bval, min_mag, max_mag, dmag, catalogue=None, completeness=None, filename=None, figure_size=(8, 6), filetype='png', dpi=300, ax=None): """ Plots a Gutenberg-Richter model """ input_model = TruncatedGRMFD(min_mag, max_mag, dmag, aval, bval) if not catalogue: # Plot only the modelled recurrence annual_rates, cumulative_rates = _get_recurrence_model(input_model) if ax is None: fig, ax = plt.subplots(figsize=figure_size) else: fig = ax.get_figure() ax.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-') ax.semilogy(annual_rates[:, 0], cumulative_rates, 'r-') ax.xlabel('Magnitude') ax.set_ylabel('Annual Rate') ax.set_legend(['Incremental Rate', 'Cumulative Rate']) _save_image(fig, filename, filetype, dpi) else: completeness = _check_completeness_table(completeness, catalogue) plot_recurrence_model( input_model, catalogue, completeness, dmag, filename=filename, figure_size=figure_size, filetype=filetype, dpi=dpi, ax=ax)
python
def plot_trunc_gr_model( aval, bval, min_mag, max_mag, dmag, catalogue=None, completeness=None, filename=None, figure_size=(8, 6), filetype='png', dpi=300, ax=None): input_model = TruncatedGRMFD(min_mag, max_mag, dmag, aval, bval) if not catalogue: annual_rates, cumulative_rates = _get_recurrence_model(input_model) if ax is None: fig, ax = plt.subplots(figsize=figure_size) else: fig = ax.get_figure() ax.semilogy(annual_rates[:, 0], annual_rates[:, 1], 'b-') ax.semilogy(annual_rates[:, 0], cumulative_rates, 'r-') ax.xlabel('Magnitude') ax.set_ylabel('Annual Rate') ax.set_legend(['Incremental Rate', 'Cumulative Rate']) _save_image(fig, filename, filetype, dpi) else: completeness = _check_completeness_table(completeness, catalogue) plot_recurrence_model( input_model, catalogue, completeness, dmag, filename=filename, figure_size=figure_size, filetype=filetype, dpi=dpi, ax=ax)
[ "def", "plot_trunc_gr_model", "(", "aval", ",", "bval", ",", "min_mag", ",", "max_mag", ",", "dmag", ",", "catalogue", "=", "None", ",", "completeness", "=", "None", ",", "filename", "=", "None", ",", "figure_size", "=", "(", "8", ",", "6", ")", ",", "filetype", "=", "'png'", ",", "dpi", "=", "300", ",", "ax", "=", "None", ")", ":", "input_model", "=", "TruncatedGRMFD", "(", "min_mag", ",", "max_mag", ",", "dmag", ",", "aval", ",", "bval", ")", "if", "not", "catalogue", ":", "# Plot only the modelled recurrence", "annual_rates", ",", "cumulative_rates", "=", "_get_recurrence_model", "(", "input_model", ")", "if", "ax", "is", "None", ":", "fig", ",", "ax", "=", "plt", ".", "subplots", "(", "figsize", "=", "figure_size", ")", "else", ":", "fig", "=", "ax", ".", "get_figure", "(", ")", "ax", ".", "semilogy", "(", "annual_rates", "[", ":", ",", "0", "]", ",", "annual_rates", "[", ":", ",", "1", "]", ",", "'b-'", ")", "ax", ".", "semilogy", "(", "annual_rates", "[", ":", ",", "0", "]", ",", "cumulative_rates", ",", "'r-'", ")", "ax", ".", "xlabel", "(", "'Magnitude'", ")", "ax", ".", "set_ylabel", "(", "'Annual Rate'", ")", "ax", ".", "set_legend", "(", "[", "'Incremental Rate'", ",", "'Cumulative Rate'", "]", ")", "_save_image", "(", "fig", ",", "filename", ",", "filetype", ",", "dpi", ")", "else", ":", "completeness", "=", "_check_completeness_table", "(", "completeness", ",", "catalogue", ")", "plot_recurrence_model", "(", "input_model", ",", "catalogue", ",", "completeness", ",", "dmag", ",", "filename", "=", "filename", ",", "figure_size", "=", "figure_size", ",", "filetype", "=", "filetype", ",", "dpi", "=", "dpi", ",", "ax", "=", "ax", ")" ]
Plots a Gutenberg-Richter model
[ "Plots", "a", "Gutenberg", "-", "Richter", "model" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/plotting/seismicity/occurrence/recurrence_plot.py#L135-L163
gem/oq-engine
openquake/hazardlib/nrml.py
get_tag_version
def get_tag_version(nrml_node): """ Extract from a node of kind NRML the tag and the version. For instance from '{http://openquake.org/xmlns/nrml/0.4}fragilityModel' one gets the pair ('fragilityModel', 'nrml/0.4'). """ version, tag = re.search(r'(nrml/[\d\.]+)\}(\w+)', nrml_node.tag).groups() return tag, version
python
def get_tag_version(nrml_node): version, tag = re.search(r'(nrml/[\d\.]+)\}(\w+)', nrml_node.tag).groups() return tag, version
[ "def", "get_tag_version", "(", "nrml_node", ")", ":", "version", ",", "tag", "=", "re", ".", "search", "(", "r'(nrml/[\\d\\.]+)\\}(\\w+)'", ",", "nrml_node", ".", "tag", ")", ".", "groups", "(", ")", "return", "tag", ",", "version" ]
Extract from a node of kind NRML the tag and the version. For instance from '{http://openquake.org/xmlns/nrml/0.4}fragilityModel' one gets the pair ('fragilityModel', 'nrml/0.4').
[ "Extract", "from", "a", "node", "of", "kind", "NRML", "the", "tag", "and", "the", "version", ".", "For", "instance", "from", "{", "http", ":", "//", "openquake", ".", "org", "/", "xmlns", "/", "nrml", "/", "0", ".", "4", "}", "fragilityModel", "one", "gets", "the", "pair", "(", "fragilityModel", "nrml", "/", "0", ".", "4", ")", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/nrml.py#L151-L158
gem/oq-engine
openquake/hazardlib/nrml.py
to_python
def to_python(fname, *args): """ Parse a NRML file and return an associated Python object. It works by calling nrml.read() and node_to_obj() in sequence. """ [node] = read(fname) return node_to_obj(node, fname, *args)
python
def to_python(fname, *args): [node] = read(fname) return node_to_obj(node, fname, *args)
[ "def", "to_python", "(", "fname", ",", "*", "args", ")", ":", "[", "node", "]", "=", "read", "(", "fname", ")", "return", "node_to_obj", "(", "node", ",", "fname", ",", "*", "args", ")" ]
Parse a NRML file and return an associated Python object. It works by calling nrml.read() and node_to_obj() in sequence.
[ "Parse", "a", "NRML", "file", "and", "return", "an", "associated", "Python", "object", ".", "It", "works", "by", "calling", "nrml", ".", "read", "()", "and", "node_to_obj", "()", "in", "sequence", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/nrml.py#L161-L167
gem/oq-engine
openquake/hazardlib/nrml.py
read_source_models
def read_source_models(fnames, converter, monitor): """ :param fnames: list of source model files :param converter: a SourceConverter instance :param monitor: a :class:`openquake.performance.Monitor` instance :yields: SourceModel instances """ for fname in fnames: if fname.endswith(('.xml', '.nrml')): sm = to_python(fname, converter) elif fname.endswith('.hdf5'): sm = sourceconverter.to_python(fname, converter) else: raise ValueError('Unrecognized extension in %s' % fname) sm.fname = fname yield sm
python
def read_source_models(fnames, converter, monitor): for fname in fnames: if fname.endswith(('.xml', '.nrml')): sm = to_python(fname, converter) elif fname.endswith('.hdf5'): sm = sourceconverter.to_python(fname, converter) else: raise ValueError('Unrecognized extension in %s' % fname) sm.fname = fname yield sm
[ "def", "read_source_models", "(", "fnames", ",", "converter", ",", "monitor", ")", ":", "for", "fname", "in", "fnames", ":", "if", "fname", ".", "endswith", "(", "(", "'.xml'", ",", "'.nrml'", ")", ")", ":", "sm", "=", "to_python", "(", "fname", ",", "converter", ")", "elif", "fname", ".", "endswith", "(", "'.hdf5'", ")", ":", "sm", "=", "sourceconverter", ".", "to_python", "(", "fname", ",", "converter", ")", "else", ":", "raise", "ValueError", "(", "'Unrecognized extension in %s'", "%", "fname", ")", "sm", ".", "fname", "=", "fname", "yield", "sm" ]
:param fnames: list of source model files :param converter: a SourceConverter instance :param monitor: a :class:`openquake.performance.Monitor` instance :yields: SourceModel instances
[ ":", "param", "fnames", ":", "list", "of", "source", "model", "files", ":", "param", "converter", ":", "a", "SourceConverter", "instance", ":", "param", "monitor", ":", "a", ":", "class", ":", "openquake", ".", "performance", ".", "Monitor", "instance", ":", "yields", ":", "SourceModel", "instances" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/nrml.py#L307-L326
gem/oq-engine
openquake/hazardlib/nrml.py
read
def read(source, chatty=True, stop=None): """ Convert a NRML file into a validated Node object. Keeps the entire tree in memory. :param source: a file name or file object open for reading """ vparser = ValidatingXmlParser(validators, stop) nrml = vparser.parse_file(source) if striptag(nrml.tag) != 'nrml': raise ValueError('%s: expected a node of kind nrml, got %s' % (source, nrml.tag)) # extract the XML namespace URL ('http://openquake.org/xmlns/nrml/0.5') xmlns = nrml.tag.split('}')[0][1:] if xmlns != NRML05 and chatty: # for the moment NRML04 is still supported, so we hide the warning logging.debug('%s is at an outdated version: %s', source, xmlns) nrml['xmlns'] = xmlns nrml['xmlns:gml'] = GML_NAMESPACE return nrml
python
def read(source, chatty=True, stop=None): vparser = ValidatingXmlParser(validators, stop) nrml = vparser.parse_file(source) if striptag(nrml.tag) != 'nrml': raise ValueError('%s: expected a node of kind nrml, got %s' % (source, nrml.tag)) xmlns = nrml.tag.split('}')[0][1:] if xmlns != NRML05 and chatty: logging.debug('%s is at an outdated version: %s', source, xmlns) nrml['xmlns'] = xmlns nrml['xmlns:gml'] = GML_NAMESPACE return nrml
[ "def", "read", "(", "source", ",", "chatty", "=", "True", ",", "stop", "=", "None", ")", ":", "vparser", "=", "ValidatingXmlParser", "(", "validators", ",", "stop", ")", "nrml", "=", "vparser", ".", "parse_file", "(", "source", ")", "if", "striptag", "(", "nrml", ".", "tag", ")", "!=", "'nrml'", ":", "raise", "ValueError", "(", "'%s: expected a node of kind nrml, got %s'", "%", "(", "source", ",", "nrml", ".", "tag", ")", ")", "# extract the XML namespace URL ('http://openquake.org/xmlns/nrml/0.5')", "xmlns", "=", "nrml", ".", "tag", ".", "split", "(", "'}'", ")", "[", "0", "]", "[", "1", ":", "]", "if", "xmlns", "!=", "NRML05", "and", "chatty", ":", "# for the moment NRML04 is still supported, so we hide the warning", "logging", ".", "debug", "(", "'%s is at an outdated version: %s'", ",", "source", ",", "xmlns", ")", "nrml", "[", "'xmlns'", "]", "=", "xmlns", "nrml", "[", "'xmlns:gml'", "]", "=", "GML_NAMESPACE", "return", "nrml" ]
Convert a NRML file into a validated Node object. Keeps the entire tree in memory. :param source: a file name or file object open for reading
[ "Convert", "a", "NRML", "file", "into", "a", "validated", "Node", "object", ".", "Keeps", "the", "entire", "tree", "in", "memory", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/nrml.py#L329-L349
gem/oq-engine
openquake/hazardlib/nrml.py
write
def write(nodes, output=sys.stdout, fmt='%.7E', gml=True, xmlns=None): """ Convert nodes into a NRML file. output must be a file object open in write mode. If you want to perform a consistency check, open it in read-write mode, then it will be read after creation and validated. :params nodes: an iterable over Node objects :params output: a file-like object in write or read-write mode :param fmt: format used for writing the floats (default '%.7E') :param gml: add the http://www.opengis.net/gml namespace :param xmlns: NRML namespace like http://openquake.org/xmlns/nrml/0.4 """ root = Node('nrml', nodes=nodes) namespaces = {xmlns or NRML05: ''} if gml: namespaces[GML_NAMESPACE] = 'gml:' with floatformat(fmt): node_to_xml(root, output, namespaces) if hasattr(output, 'mode') and '+' in output.mode: # read-write mode output.seek(0) read(output)
python
def write(nodes, output=sys.stdout, fmt='%.7E', gml=True, xmlns=None): root = Node('nrml', nodes=nodes) namespaces = {xmlns or NRML05: ''} if gml: namespaces[GML_NAMESPACE] = 'gml:' with floatformat(fmt): node_to_xml(root, output, namespaces) if hasattr(output, 'mode') and '+' in output.mode: output.seek(0) read(output)
[ "def", "write", "(", "nodes", ",", "output", "=", "sys", ".", "stdout", ",", "fmt", "=", "'%.7E'", ",", "gml", "=", "True", ",", "xmlns", "=", "None", ")", ":", "root", "=", "Node", "(", "'nrml'", ",", "nodes", "=", "nodes", ")", "namespaces", "=", "{", "xmlns", "or", "NRML05", ":", "''", "}", "if", "gml", ":", "namespaces", "[", "GML_NAMESPACE", "]", "=", "'gml:'", "with", "floatformat", "(", "fmt", ")", ":", "node_to_xml", "(", "root", ",", "output", ",", "namespaces", ")", "if", "hasattr", "(", "output", ",", "'mode'", ")", "and", "'+'", "in", "output", ".", "mode", ":", "# read-write mode", "output", ".", "seek", "(", "0", ")", "read", "(", "output", ")" ]
Convert nodes into a NRML file. output must be a file object open in write mode. If you want to perform a consistency check, open it in read-write mode, then it will be read after creation and validated. :params nodes: an iterable over Node objects :params output: a file-like object in write or read-write mode :param fmt: format used for writing the floats (default '%.7E') :param gml: add the http://www.opengis.net/gml namespace :param xmlns: NRML namespace like http://openquake.org/xmlns/nrml/0.4
[ "Convert", "nodes", "into", "a", "NRML", "file", ".", "output", "must", "be", "a", "file", "object", "open", "in", "write", "mode", ".", "If", "you", "want", "to", "perform", "a", "consistency", "check", "open", "it", "in", "read", "-", "write", "mode", "then", "it", "will", "be", "read", "after", "creation", "and", "validated", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/nrml.py#L352-L373
gem/oq-engine
openquake/hazardlib/nrml.py
to_string
def to_string(node): """ Convert a node into a string in NRML format """ with io.BytesIO() as f: write([node], f) return f.getvalue().decode('utf-8')
python
def to_string(node): with io.BytesIO() as f: write([node], f) return f.getvalue().decode('utf-8')
[ "def", "to_string", "(", "node", ")", ":", "with", "io", ".", "BytesIO", "(", ")", "as", "f", ":", "write", "(", "[", "node", "]", ",", "f", ")", "return", "f", ".", "getvalue", "(", ")", ".", "decode", "(", "'utf-8'", ")" ]
Convert a node into a string in NRML format
[ "Convert", "a", "node", "into", "a", "string", "in", "NRML", "format" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/nrml.py#L376-L382
gem/oq-engine
openquake/hazardlib/gsim/derras_2014.py
DerrasEtAl2014.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ C = self.COEFFS[imt] # Get the mean mean = self.get_mean(C, rup, sites, dists) if imt.name == "PGV": # Convert from log10 m/s to ln cm/s mean = np.log((10.0 ** mean) * 100.) else: # convert from log10 m/s/s to ln g mean = np.log((10.0 ** mean) / g) # Get the standard deviations stddevs = self.get_stddevs(C, mean.shape, stddev_types) return mean, stddevs
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): C = self.COEFFS[imt] mean = self.get_mean(C, rup, sites, dists) if imt.name == "PGV": mean = np.log((10.0 ** mean) * 100.) else: mean = np.log((10.0 ** mean) / g) stddevs = self.get_stddevs(C, mean.shape, stddev_types) return mean, stddevs
[ "def", "get_mean_and_stddevs", "(", "self", ",", "sites", ",", "rup", ",", "dists", ",", "imt", ",", "stddev_types", ")", ":", "C", "=", "self", ".", "COEFFS", "[", "imt", "]", "# Get the mean", "mean", "=", "self", ".", "get_mean", "(", "C", ",", "rup", ",", "sites", ",", "dists", ")", "if", "imt", ".", "name", "==", "\"PGV\"", ":", "# Convert from log10 m/s to ln cm/s", "mean", "=", "np", ".", "log", "(", "(", "10.0", "**", "mean", ")", "*", "100.", ")", "else", ":", "# convert from log10 m/s/s to ln g", "mean", "=", "np", ".", "log", "(", "(", "10.0", "**", "mean", ")", "/", "g", ")", "# Get the standard deviations", "stddevs", "=", "self", ".", "get_stddevs", "(", "C", ",", "mean", ".", "shape", ",", "stddev_types", ")", "return", "mean", ",", "stddevs" ]
See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.
[ "See", ":", "meth", ":", "superclass", "method", "<", ".", "base", ".", "GroundShakingIntensityModel", ".", "get_mean_and_stddevs", ">", "for", "spec", "of", "input", "and", "result", "values", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/derras_2014.py#L75-L93
gem/oq-engine
openquake/hazardlib/gsim/derras_2014.py
DerrasEtAl2014.get_mean
def get_mean(self, C, rup, sites, dists): """ Returns the mean ground motion in terms of log10 m/s/s, implementing equation 2 (page 502) """ # W2 needs to be a 1 by 5 matrix (not a vector w_2 = np.array([ [C["W_21"], C["W_22"], C["W_23"], C["W_24"], C["W_25"]] ]) # Gets the style of faulting dummy variable sof = self._get_sof_dummy_variable(rup.rake) # Get the normalised coefficients p_n = self.get_pn(rup, sites, dists, sof) mean = np.zeros_like(dists.rjb) # Need to loop over sites - maybe this can be improved in future? # ndenumerate is used to allow for application to 2-D arrays for idx, rval in np.ndenumerate(p_n[0]): # Place normalised coefficients into a single array p_n_i = np.array([rval, p_n[1], p_n[2][idx], p_n[3], p_n[4]]) # Executes the main ANN model mean_i = np.dot(w_2, np.tanh(np.dot(self.W_1, p_n_i) + self.B_1)) mean[idx] = (0.5 * (mean_i + C["B_2"] + 1.0) * (C["tmax"] - C["tmin"])) + C["tmin"] return mean
python
def get_mean(self, C, rup, sites, dists): w_2 = np.array([ [C["W_21"], C["W_22"], C["W_23"], C["W_24"], C["W_25"]] ]) sof = self._get_sof_dummy_variable(rup.rake) p_n = self.get_pn(rup, sites, dists, sof) mean = np.zeros_like(dists.rjb) for idx, rval in np.ndenumerate(p_n[0]): p_n_i = np.array([rval, p_n[1], p_n[2][idx], p_n[3], p_n[4]]) mean_i = np.dot(w_2, np.tanh(np.dot(self.W_1, p_n_i) + self.B_1)) mean[idx] = (0.5 * (mean_i + C["B_2"] + 1.0) * (C["tmax"] - C["tmin"])) + C["tmin"] return mean
[ "def", "get_mean", "(", "self", ",", "C", ",", "rup", ",", "sites", ",", "dists", ")", ":", "# W2 needs to be a 1 by 5 matrix (not a vector", "w_2", "=", "np", ".", "array", "(", "[", "[", "C", "[", "\"W_21\"", "]", ",", "C", "[", "\"W_22\"", "]", ",", "C", "[", "\"W_23\"", "]", ",", "C", "[", "\"W_24\"", "]", ",", "C", "[", "\"W_25\"", "]", "]", "]", ")", "# Gets the style of faulting dummy variable", "sof", "=", "self", ".", "_get_sof_dummy_variable", "(", "rup", ".", "rake", ")", "# Get the normalised coefficients", "p_n", "=", "self", ".", "get_pn", "(", "rup", ",", "sites", ",", "dists", ",", "sof", ")", "mean", "=", "np", ".", "zeros_like", "(", "dists", ".", "rjb", ")", "# Need to loop over sites - maybe this can be improved in future?", "# ndenumerate is used to allow for application to 2-D arrays", "for", "idx", ",", "rval", "in", "np", ".", "ndenumerate", "(", "p_n", "[", "0", "]", ")", ":", "# Place normalised coefficients into a single array", "p_n_i", "=", "np", ".", "array", "(", "[", "rval", ",", "p_n", "[", "1", "]", ",", "p_n", "[", "2", "]", "[", "idx", "]", ",", "p_n", "[", "3", "]", ",", "p_n", "[", "4", "]", "]", ")", "# Executes the main ANN model", "mean_i", "=", "np", ".", "dot", "(", "w_2", ",", "np", ".", "tanh", "(", "np", ".", "dot", "(", "self", ".", "W_1", ",", "p_n_i", ")", "+", "self", ".", "B_1", ")", ")", "mean", "[", "idx", "]", "=", "(", "0.5", "*", "(", "mean_i", "+", "C", "[", "\"B_2\"", "]", "+", "1.0", ")", "*", "(", "C", "[", "\"tmax\"", "]", "-", "C", "[", "\"tmin\"", "]", ")", ")", "+", "C", "[", "\"tmin\"", "]", "return", "mean" ]
Returns the mean ground motion in terms of log10 m/s/s, implementing equation 2 (page 502)
[ "Returns", "the", "mean", "ground", "motion", "in", "terms", "of", "log10", "m", "/", "s", "/", "s", "implementing", "equation", "2", "(", "page", "502", ")" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/derras_2014.py#L95-L118
gem/oq-engine
openquake/hazardlib/gsim/boore_1997.py
BooreEtAl1997GeometricMean.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ # extracting dictionary of coefficients specific to required # intensity measure type. C = self.COEFFS[imt] mean = (self._compute_style_of_faulting_term(rup, C) + self._compute_magnitude_scaling(rup.mag, C) + self._compute_distance_scaling(dists.rjb, C) + self._compute_site_term(sites.vs30, C)) stddevs = self._get_stddevs(C, stddev_types, num_sites=len(sites.vs30)) return mean, stddevs
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): C = self.COEFFS[imt] mean = (self._compute_style_of_faulting_term(rup, C) + self._compute_magnitude_scaling(rup.mag, C) + self._compute_distance_scaling(dists.rjb, C) + self._compute_site_term(sites.vs30, C)) stddevs = self._get_stddevs(C, stddev_types, num_sites=len(sites.vs30)) return mean, stddevs
[ "def", "get_mean_and_stddevs", "(", "self", ",", "sites", ",", "rup", ",", "dists", ",", "imt", ",", "stddev_types", ")", ":", "# extracting dictionary of coefficients specific to required", "# intensity measure type.", "C", "=", "self", ".", "COEFFS", "[", "imt", "]", "mean", "=", "(", "self", ".", "_compute_style_of_faulting_term", "(", "rup", ",", "C", ")", "+", "self", ".", "_compute_magnitude_scaling", "(", "rup", ".", "mag", ",", "C", ")", "+", "self", ".", "_compute_distance_scaling", "(", "dists", ".", "rjb", ",", "C", ")", "+", "self", ".", "_compute_site_term", "(", "sites", ".", "vs30", ",", "C", ")", ")", "stddevs", "=", "self", ".", "_get_stddevs", "(", "C", ",", "stddev_types", ",", "num_sites", "=", "len", "(", "sites", ".", "vs30", ")", ")", "return", "mean", ",", "stddevs" ]
See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.
[ "See", ":", "meth", ":", "superclass", "method", "<", ".", "base", ".", "GroundShakingIntensityModel", ".", "get_mean_and_stddevs", ">", "for", "spec", "of", "input", "and", "result", "values", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_1997.py#L71-L87
gem/oq-engine
openquake/hazardlib/gsim/boore_1997.py
BooreEtAl1997GeometricMean._compute_distance_scaling
def _compute_distance_scaling(self, rjb, C): """ Compute distance-scaling term (Page 141, Eq 1) """ # Calculate distance according to Page 141, Eq 2. rdist = np.sqrt((rjb ** 2.) + (C['h'] ** 2.)) return C['B5'] * np.log(rdist)
python
def _compute_distance_scaling(self, rjb, C): rdist = np.sqrt((rjb ** 2.) + (C['h'] ** 2.)) return C['B5'] * np.log(rdist)
[ "def", "_compute_distance_scaling", "(", "self", ",", "rjb", ",", "C", ")", ":", "# Calculate distance according to Page 141, Eq 2.", "rdist", "=", "np", ".", "sqrt", "(", "(", "rjb", "**", "2.", ")", "+", "(", "C", "[", "'h'", "]", "**", "2.", ")", ")", "return", "C", "[", "'B5'", "]", "*", "np", ".", "log", "(", "rdist", ")" ]
Compute distance-scaling term (Page 141, Eq 1)
[ "Compute", "distance", "-", "scaling", "term", "(", "Page", "141", "Eq", "1", ")" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_1997.py#L106-L112
gem/oq-engine
openquake/hazardlib/gsim/boore_1997.py
BooreEtAl1997GeometricMean._compute_magnitude_scaling
def _compute_magnitude_scaling(self, mag, C): """ Compute magnitude-scaling term (Page 141, Eq 1) """ dmag = mag - 6. return (C['B2'] * dmag) + (C['B3'] * (dmag ** 2.))
python
def _compute_magnitude_scaling(self, mag, C): dmag = mag - 6. return (C['B2'] * dmag) + (C['B3'] * (dmag ** 2.))
[ "def", "_compute_magnitude_scaling", "(", "self", ",", "mag", ",", "C", ")", ":", "dmag", "=", "mag", "-", "6.", "return", "(", "C", "[", "'B2'", "]", "*", "dmag", ")", "+", "(", "C", "[", "'B3'", "]", "*", "(", "dmag", "**", "2.", ")", ")" ]
Compute magnitude-scaling term (Page 141, Eq 1)
[ "Compute", "magnitude", "-", "scaling", "term", "(", "Page", "141", "Eq", "1", ")" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_1997.py#L114-L119
gem/oq-engine
openquake/hazardlib/gsim/boore_1997.py
BooreEtAl1997GeometricMean._compute_style_of_faulting_term
def _compute_style_of_faulting_term(self, rup, C): """ Computes the coefficient to scale for reverse or strike-slip events Fault type (Strike-slip, Normal, Thrust/reverse) is derived from rake angle. Rakes angles within 30 of horizontal are strike-slip, angles from 30 to 150 are reverse, and angles from -30 to -150 are normal. See paragraph 'Predictor Variables' pag 103. Note that 'Unspecified' case is used to refer to all other rake angles. """ if np.abs(rup.rake) <= 30.0 or (180.0 - np.abs(rup.rake)) <= 30.0: # strike-slip return C['B1ss'] elif rup.rake > 30.0 and rup.rake < 150.0: # reverse return C['B1rv'] else: # unspecified (also includes Normal faulting!) return C['B1all']
python
def _compute_style_of_faulting_term(self, rup, C): if np.abs(rup.rake) <= 30.0 or (180.0 - np.abs(rup.rake)) <= 30.0: return C['B1ss'] elif rup.rake > 30.0 and rup.rake < 150.0: return C['B1rv'] else: return C['B1all']
[ "def", "_compute_style_of_faulting_term", "(", "self", ",", "rup", ",", "C", ")", ":", "if", "np", ".", "abs", "(", "rup", ".", "rake", ")", "<=", "30.0", "or", "(", "180.0", "-", "np", ".", "abs", "(", "rup", ".", "rake", ")", ")", "<=", "30.0", ":", "# strike-slip", "return", "C", "[", "'B1ss'", "]", "elif", "rup", ".", "rake", ">", "30.0", "and", "rup", ".", "rake", "<", "150.0", ":", "# reverse", "return", "C", "[", "'B1rv'", "]", "else", ":", "# unspecified (also includes Normal faulting!)", "return", "C", "[", "'B1all'", "]" ]
Computes the coefficient to scale for reverse or strike-slip events Fault type (Strike-slip, Normal, Thrust/reverse) is derived from rake angle. Rakes angles within 30 of horizontal are strike-slip, angles from 30 to 150 are reverse, and angles from -30 to -150 are normal. See paragraph 'Predictor Variables' pag 103. Note that 'Unspecified' case is used to refer to all other rake angles.
[ "Computes", "the", "coefficient", "to", "scale", "for", "reverse", "or", "strike", "-", "slip", "events", "Fault", "type", "(", "Strike", "-", "slip", "Normal", "Thrust", "/", "reverse", ")", "is", "derived", "from", "rake", "angle", ".", "Rakes", "angles", "within", "30", "of", "horizontal", "are", "strike", "-", "slip", "angles", "from", "30", "to", "150", "are", "reverse", "and", "angles", "from", "-", "30", "to", "-", "150", "are", "normal", ".", "See", "paragraph", "Predictor", "Variables", "pag", "103", ".", "Note", "that", "Unspecified", "case", "is", "used", "to", "refer", "to", "all", "other", "rake", "angles", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/boore_1997.py#L121-L141
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014.get_mean_and_stddevs
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): """ See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values. """ # extract dictionaries of coefficients specific to required # intensity measure type and for PGA C = self.COEFFS[imt] C_PGA = self.COEFFS[PGA()] # Get mean and standard deviation of PGA on rock (Vs30 1100 m/s^2) pga1100 = np.exp(self.get_mean_values(C_PGA, sites, rup, dists, None)) # Get mean and standard deviations for IMT mean = self.get_mean_values(C, sites, rup, dists, pga1100) if imt.name == "SA" and imt.period <= 0.25: # According to Campbell & Bozorgnia (2013) [NGA West 2 Report] # If Sa (T) < PGA for T < 0.25 then set mean Sa(T) to mean PGA # Get PGA on soil pga = self.get_mean_values(C_PGA, sites, rup, dists, pga1100) idx = mean <= pga mean[idx] = pga[idx] # Get standard deviations stddevs = self._get_stddevs(C, C_PGA, rup, sites, pga1100, stddev_types) return mean, stddevs
python
def get_mean_and_stddevs(self, sites, rup, dists, imt, stddev_types): C = self.COEFFS[imt] C_PGA = self.COEFFS[PGA()] pga1100 = np.exp(self.get_mean_values(C_PGA, sites, rup, dists, None)) mean = self.get_mean_values(C, sites, rup, dists, pga1100) if imt.name == "SA" and imt.period <= 0.25: pga = self.get_mean_values(C_PGA, sites, rup, dists, pga1100) idx = mean <= pga mean[idx] = pga[idx] stddevs = self._get_stddevs(C, C_PGA, rup, sites, pga1100, stddev_types) return mean, stddevs
[ "def", "get_mean_and_stddevs", "(", "self", ",", "sites", ",", "rup", ",", "dists", ",", "imt", ",", "stddev_types", ")", ":", "# extract dictionaries of coefficients specific to required", "# intensity measure type and for PGA", "C", "=", "self", ".", "COEFFS", "[", "imt", "]", "C_PGA", "=", "self", ".", "COEFFS", "[", "PGA", "(", ")", "]", "# Get mean and standard deviation of PGA on rock (Vs30 1100 m/s^2)", "pga1100", "=", "np", ".", "exp", "(", "self", ".", "get_mean_values", "(", "C_PGA", ",", "sites", ",", "rup", ",", "dists", ",", "None", ")", ")", "# Get mean and standard deviations for IMT", "mean", "=", "self", ".", "get_mean_values", "(", "C", ",", "sites", ",", "rup", ",", "dists", ",", "pga1100", ")", "if", "imt", ".", "name", "==", "\"SA\"", "and", "imt", ".", "period", "<=", "0.25", ":", "# According to Campbell & Bozorgnia (2013) [NGA West 2 Report]", "# If Sa (T) < PGA for T < 0.25 then set mean Sa(T) to mean PGA", "# Get PGA on soil", "pga", "=", "self", ".", "get_mean_values", "(", "C_PGA", ",", "sites", ",", "rup", ",", "dists", ",", "pga1100", ")", "idx", "=", "mean", "<=", "pga", "mean", "[", "idx", "]", "=", "pga", "[", "idx", "]", "# Get standard deviations", "stddevs", "=", "self", ".", "_get_stddevs", "(", "C", ",", "C_PGA", ",", "rup", ",", "sites", ",", "pga1100", ",", "stddev_types", ")", "return", "mean", ",", "stddevs" ]
See :meth:`superclass method <.base.GroundShakingIntensityModel.get_mean_and_stddevs>` for spec of input and result values.
[ "See", ":", "meth", ":", "superclass", "method", "<", ".", "base", ".", "GroundShakingIntensityModel", ".", "get_mean_and_stddevs", ">", "for", "spec", "of", "input", "and", "result", "values", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L91-L120
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014.get_mean_values
def get_mean_values(self, C, sites, rup, dists, a1100): """ Returns the mean values for a specific IMT """ if isinstance(a1100, np.ndarray): # Site model defined temp_vs30 = sites.vs30 temp_z2pt5 = sites.z2pt5 else: # Default site and basin model temp_vs30 = 1100.0 * np.ones(len(sites.vs30)) temp_z2pt5 = self._select_basin_model(1100.0) *\ np.ones_like(temp_vs30) return (self._get_magnitude_term(C, rup.mag) + self._get_geometric_attenuation_term(C, rup.mag, dists.rrup) + self._get_style_of_faulting_term(C, rup) + self._get_hanging_wall_term(C, rup, dists) + self._get_shallow_site_response_term(C, temp_vs30, a1100) + self._get_basin_response_term(C, temp_z2pt5) + self._get_hypocentral_depth_term(C, rup) + self._get_fault_dip_term(C, rup) + self._get_anelastic_attenuation_term(C, dists.rrup))
python
def get_mean_values(self, C, sites, rup, dists, a1100): if isinstance(a1100, np.ndarray): temp_vs30 = sites.vs30 temp_z2pt5 = sites.z2pt5 else: temp_vs30 = 1100.0 * np.ones(len(sites.vs30)) temp_z2pt5 = self._select_basin_model(1100.0) *\ np.ones_like(temp_vs30) return (self._get_magnitude_term(C, rup.mag) + self._get_geometric_attenuation_term(C, rup.mag, dists.rrup) + self._get_style_of_faulting_term(C, rup) + self._get_hanging_wall_term(C, rup, dists) + self._get_shallow_site_response_term(C, temp_vs30, a1100) + self._get_basin_response_term(C, temp_z2pt5) + self._get_hypocentral_depth_term(C, rup) + self._get_fault_dip_term(C, rup) + self._get_anelastic_attenuation_term(C, dists.rrup))
[ "def", "get_mean_values", "(", "self", ",", "C", ",", "sites", ",", "rup", ",", "dists", ",", "a1100", ")", ":", "if", "isinstance", "(", "a1100", ",", "np", ".", "ndarray", ")", ":", "# Site model defined", "temp_vs30", "=", "sites", ".", "vs30", "temp_z2pt5", "=", "sites", ".", "z2pt5", "else", ":", "# Default site and basin model", "temp_vs30", "=", "1100.0", "*", "np", ".", "ones", "(", "len", "(", "sites", ".", "vs30", ")", ")", "temp_z2pt5", "=", "self", ".", "_select_basin_model", "(", "1100.0", ")", "*", "np", ".", "ones_like", "(", "temp_vs30", ")", "return", "(", "self", ".", "_get_magnitude_term", "(", "C", ",", "rup", ".", "mag", ")", "+", "self", ".", "_get_geometric_attenuation_term", "(", "C", ",", "rup", ".", "mag", ",", "dists", ".", "rrup", ")", "+", "self", ".", "_get_style_of_faulting_term", "(", "C", ",", "rup", ")", "+", "self", ".", "_get_hanging_wall_term", "(", "C", ",", "rup", ",", "dists", ")", "+", "self", ".", "_get_shallow_site_response_term", "(", "C", ",", "temp_vs30", ",", "a1100", ")", "+", "self", ".", "_get_basin_response_term", "(", "C", ",", "temp_z2pt5", ")", "+", "self", ".", "_get_hypocentral_depth_term", "(", "C", ",", "rup", ")", "+", "self", ".", "_get_fault_dip_term", "(", "C", ",", "rup", ")", "+", "self", ".", "_get_anelastic_attenuation_term", "(", "C", ",", "dists", ".", "rrup", ")", ")" ]
Returns the mean values for a specific IMT
[ "Returns", "the", "mean", "values", "for", "a", "specific", "IMT" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L122-L144
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_magnitude_term
def _get_magnitude_term(self, C, mag): """ Returns the magnitude scaling term defined in equation 2 """ f_mag = C["c0"] + C["c1"] * mag if (mag > 4.5) and (mag <= 5.5): return f_mag + (C["c2"] * (mag - 4.5)) elif (mag > 5.5) and (mag <= 6.5): return f_mag + (C["c2"] * (mag - 4.5)) + (C["c3"] * (mag - 5.5)) elif mag > 6.5: return f_mag + (C["c2"] * (mag - 4.5)) + (C["c3"] * (mag - 5.5)) +\ (C["c4"] * (mag - 6.5)) else: return f_mag
python
def _get_magnitude_term(self, C, mag): f_mag = C["c0"] + C["c1"] * mag if (mag > 4.5) and (mag <= 5.5): return f_mag + (C["c2"] * (mag - 4.5)) elif (mag > 5.5) and (mag <= 6.5): return f_mag + (C["c2"] * (mag - 4.5)) + (C["c3"] * (mag - 5.5)) elif mag > 6.5: return f_mag + (C["c2"] * (mag - 4.5)) + (C["c3"] * (mag - 5.5)) +\ (C["c4"] * (mag - 6.5)) else: return f_mag
[ "def", "_get_magnitude_term", "(", "self", ",", "C", ",", "mag", ")", ":", "f_mag", "=", "C", "[", "\"c0\"", "]", "+", "C", "[", "\"c1\"", "]", "*", "mag", "if", "(", "mag", ">", "4.5", ")", "and", "(", "mag", "<=", "5.5", ")", ":", "return", "f_mag", "+", "(", "C", "[", "\"c2\"", "]", "*", "(", "mag", "-", "4.5", ")", ")", "elif", "(", "mag", ">", "5.5", ")", "and", "(", "mag", "<=", "6.5", ")", ":", "return", "f_mag", "+", "(", "C", "[", "\"c2\"", "]", "*", "(", "mag", "-", "4.5", ")", ")", "+", "(", "C", "[", "\"c3\"", "]", "*", "(", "mag", "-", "5.5", ")", ")", "elif", "mag", ">", "6.5", ":", "return", "f_mag", "+", "(", "C", "[", "\"c2\"", "]", "*", "(", "mag", "-", "4.5", ")", ")", "+", "(", "C", "[", "\"c3\"", "]", "*", "(", "mag", "-", "5.5", ")", ")", "+", "(", "C", "[", "\"c4\"", "]", "*", "(", "mag", "-", "6.5", ")", ")", "else", ":", "return", "f_mag" ]
Returns the magnitude scaling term defined in equation 2
[ "Returns", "the", "magnitude", "scaling", "term", "defined", "in", "equation", "2" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L146-L159
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_geometric_attenuation_term
def _get_geometric_attenuation_term(self, C, mag, rrup): """ Returns the geometric attenuation term defined in equation 3 """ return (C["c5"] + C["c6"] * mag) * np.log(np.sqrt((rrup ** 2.) + (C["c7"] ** 2.)))
python
def _get_geometric_attenuation_term(self, C, mag, rrup): return (C["c5"] + C["c6"] * mag) * np.log(np.sqrt((rrup ** 2.) + (C["c7"] ** 2.)))
[ "def", "_get_geometric_attenuation_term", "(", "self", ",", "C", ",", "mag", ",", "rrup", ")", ":", "return", "(", "C", "[", "\"c5\"", "]", "+", "C", "[", "\"c6\"", "]", "*", "mag", ")", "*", "np", ".", "log", "(", "np", ".", "sqrt", "(", "(", "rrup", "**", "2.", ")", "+", "(", "C", "[", "\"c7\"", "]", "**", "2.", ")", ")", ")" ]
Returns the geometric attenuation term defined in equation 3
[ "Returns", "the", "geometric", "attenuation", "term", "defined", "in", "equation", "3" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L161-L166
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_style_of_faulting_term
def _get_style_of_faulting_term(self, C, rup): """ Returns the style-of-faulting scaling term defined in equations 4 to 6 """ if (rup.rake > 30.0) and (rup.rake < 150.): frv = 1.0 fnm = 0.0 elif (rup.rake > -150.0) and (rup.rake < -30.0): fnm = 1.0 frv = 0.0 else: fnm = 0.0 frv = 0.0 fflt_f = (self.CONSTS["c8"] * frv) + (C["c9"] * fnm) if rup.mag <= 4.5: fflt_m = 0.0 elif rup.mag > 5.5: fflt_m = 1.0 else: fflt_m = rup.mag - 4.5 return fflt_f * fflt_m
python
def _get_style_of_faulting_term(self, C, rup): if (rup.rake > 30.0) and (rup.rake < 150.): frv = 1.0 fnm = 0.0 elif (rup.rake > -150.0) and (rup.rake < -30.0): fnm = 1.0 frv = 0.0 else: fnm = 0.0 frv = 0.0 fflt_f = (self.CONSTS["c8"] * frv) + (C["c9"] * fnm) if rup.mag <= 4.5: fflt_m = 0.0 elif rup.mag > 5.5: fflt_m = 1.0 else: fflt_m = rup.mag - 4.5 return fflt_f * fflt_m
[ "def", "_get_style_of_faulting_term", "(", "self", ",", "C", ",", "rup", ")", ":", "if", "(", "rup", ".", "rake", ">", "30.0", ")", "and", "(", "rup", ".", "rake", "<", "150.", ")", ":", "frv", "=", "1.0", "fnm", "=", "0.0", "elif", "(", "rup", ".", "rake", ">", "-", "150.0", ")", "and", "(", "rup", ".", "rake", "<", "-", "30.0", ")", ":", "fnm", "=", "1.0", "frv", "=", "0.0", "else", ":", "fnm", "=", "0.0", "frv", "=", "0.0", "fflt_f", "=", "(", "self", ".", "CONSTS", "[", "\"c8\"", "]", "*", "frv", ")", "+", "(", "C", "[", "\"c9\"", "]", "*", "fnm", ")", "if", "rup", ".", "mag", "<=", "4.5", ":", "fflt_m", "=", "0.0", "elif", "rup", ".", "mag", ">", "5.5", ":", "fflt_m", "=", "1.0", "else", ":", "fflt_m", "=", "rup", ".", "mag", "-", "4.5", "return", "fflt_f", "*", "fflt_m" ]
Returns the style-of-faulting scaling term defined in equations 4 to 6
[ "Returns", "the", "style", "-", "of", "-", "faulting", "scaling", "term", "defined", "in", "equations", "4", "to", "6" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L168-L189
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_hanging_wall_term
def _get_hanging_wall_term(self, C, rup, dists): """ Returns the hanging wall scaling term defined in equations 7 to 16 """ return (C["c10"] * self._get_hanging_wall_coeffs_rx(C, rup, dists.rx) * self._get_hanging_wall_coeffs_rrup(dists) * self._get_hanging_wall_coeffs_mag(C, rup.mag) * self._get_hanging_wall_coeffs_ztor(rup.ztor) * self._get_hanging_wall_coeffs_dip(rup.dip))
python
def _get_hanging_wall_term(self, C, rup, dists): return (C["c10"] * self._get_hanging_wall_coeffs_rx(C, rup, dists.rx) * self._get_hanging_wall_coeffs_rrup(dists) * self._get_hanging_wall_coeffs_mag(C, rup.mag) * self._get_hanging_wall_coeffs_ztor(rup.ztor) * self._get_hanging_wall_coeffs_dip(rup.dip))
[ "def", "_get_hanging_wall_term", "(", "self", ",", "C", ",", "rup", ",", "dists", ")", ":", "return", "(", "C", "[", "\"c10\"", "]", "*", "self", ".", "_get_hanging_wall_coeffs_rx", "(", "C", ",", "rup", ",", "dists", ".", "rx", ")", "*", "self", ".", "_get_hanging_wall_coeffs_rrup", "(", "dists", ")", "*", "self", ".", "_get_hanging_wall_coeffs_mag", "(", "C", ",", "rup", ".", "mag", ")", "*", "self", ".", "_get_hanging_wall_coeffs_ztor", "(", "rup", ".", "ztor", ")", "*", "self", ".", "_get_hanging_wall_coeffs_dip", "(", "rup", ".", "dip", ")", ")" ]
Returns the hanging wall scaling term defined in equations 7 to 16
[ "Returns", "the", "hanging", "wall", "scaling", "term", "defined", "in", "equations", "7", "to", "16" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L191-L200
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_hanging_wall_coeffs_rx
def _get_hanging_wall_coeffs_rx(self, C, rup, r_x): """ Returns the hanging wall r-x caling term defined in equation 7 to 12 """ # Define coefficients R1 and R2 r_1 = rup.width * cos(radians(rup.dip)) r_2 = 62.0 * rup.mag - 350.0 fhngrx = np.zeros(len(r_x)) # Case when 0 <= Rx <= R1 idx = np.logical_and(r_x >= 0., r_x < r_1) fhngrx[idx] = self._get_f1rx(C, r_x[idx], r_1) # Case when Rx > R1 idx = r_x >= r_1 f2rx = self._get_f2rx(C, r_x[idx], r_1, r_2) f2rx[f2rx < 0.0] = 0.0 fhngrx[idx] = f2rx return fhngrx
python
def _get_hanging_wall_coeffs_rx(self, C, rup, r_x): r_1 = rup.width * cos(radians(rup.dip)) r_2 = 62.0 * rup.mag - 350.0 fhngrx = np.zeros(len(r_x)) idx = np.logical_and(r_x >= 0., r_x < r_1) fhngrx[idx] = self._get_f1rx(C, r_x[idx], r_1) idx = r_x >= r_1 f2rx = self._get_f2rx(C, r_x[idx], r_1, r_2) f2rx[f2rx < 0.0] = 0.0 fhngrx[idx] = f2rx return fhngrx
[ "def", "_get_hanging_wall_coeffs_rx", "(", "self", ",", "C", ",", "rup", ",", "r_x", ")", ":", "# Define coefficients R1 and R2", "r_1", "=", "rup", ".", "width", "*", "cos", "(", "radians", "(", "rup", ".", "dip", ")", ")", "r_2", "=", "62.0", "*", "rup", ".", "mag", "-", "350.0", "fhngrx", "=", "np", ".", "zeros", "(", "len", "(", "r_x", ")", ")", "# Case when 0 <= Rx <= R1", "idx", "=", "np", ".", "logical_and", "(", "r_x", ">=", "0.", ",", "r_x", "<", "r_1", ")", "fhngrx", "[", "idx", "]", "=", "self", ".", "_get_f1rx", "(", "C", ",", "r_x", "[", "idx", "]", ",", "r_1", ")", "# Case when Rx > R1", "idx", "=", "r_x", ">=", "r_1", "f2rx", "=", "self", ".", "_get_f2rx", "(", "C", ",", "r_x", "[", "idx", "]", ",", "r_1", ",", "r_2", ")", "f2rx", "[", "f2rx", "<", "0.0", "]", "=", "0.0", "fhngrx", "[", "idx", "]", "=", "f2rx", "return", "fhngrx" ]
Returns the hanging wall r-x caling term defined in equation 7 to 12
[ "Returns", "the", "hanging", "wall", "r", "-", "x", "caling", "term", "defined", "in", "equation", "7", "to", "12" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L202-L218
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_f1rx
def _get_f1rx(self, C, r_x, r_1): """ Defines the f1 scaling coefficient defined in equation 9 """ rxr1 = r_x / r_1 return C["h1"] + (C["h2"] * rxr1) + (C["h3"] * (rxr1 ** 2.))
python
def _get_f1rx(self, C, r_x, r_1): rxr1 = r_x / r_1 return C["h1"] + (C["h2"] * rxr1) + (C["h3"] * (rxr1 ** 2.))
[ "def", "_get_f1rx", "(", "self", ",", "C", ",", "r_x", ",", "r_1", ")", ":", "rxr1", "=", "r_x", "/", "r_1", "return", "C", "[", "\"h1\"", "]", "+", "(", "C", "[", "\"h2\"", "]", "*", "rxr1", ")", "+", "(", "C", "[", "\"h3\"", "]", "*", "(", "rxr1", "**", "2.", ")", ")" ]
Defines the f1 scaling coefficient defined in equation 9
[ "Defines", "the", "f1", "scaling", "coefficient", "defined", "in", "equation", "9" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L220-L225
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_f2rx
def _get_f2rx(self, C, r_x, r_1, r_2): """ Defines the f2 scaling coefficient defined in equation 10 """ drx = (r_x - r_1) / (r_2 - r_1) return self.CONSTS["h4"] + (C["h5"] * drx) + (C["h6"] * (drx ** 2.))
python
def _get_f2rx(self, C, r_x, r_1, r_2): drx = (r_x - r_1) / (r_2 - r_1) return self.CONSTS["h4"] + (C["h5"] * drx) + (C["h6"] * (drx ** 2.))
[ "def", "_get_f2rx", "(", "self", ",", "C", ",", "r_x", ",", "r_1", ",", "r_2", ")", ":", "drx", "=", "(", "r_x", "-", "r_1", ")", "/", "(", "r_2", "-", "r_1", ")", "return", "self", ".", "CONSTS", "[", "\"h4\"", "]", "+", "(", "C", "[", "\"h5\"", "]", "*", "drx", ")", "+", "(", "C", "[", "\"h6\"", "]", "*", "(", "drx", "**", "2.", ")", ")" ]
Defines the f2 scaling coefficient defined in equation 10
[ "Defines", "the", "f2", "scaling", "coefficient", "defined", "in", "equation", "10" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L227-L232
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_hanging_wall_coeffs_rrup
def _get_hanging_wall_coeffs_rrup(self, dists): """ Returns the hanging wall rrup term defined in equation 13 """ fhngrrup = np.ones(len(dists.rrup)) idx = dists.rrup > 0.0 fhngrrup[idx] = (dists.rrup[idx] - dists.rjb[idx]) / dists.rrup[idx] return fhngrrup
python
def _get_hanging_wall_coeffs_rrup(self, dists): fhngrrup = np.ones(len(dists.rrup)) idx = dists.rrup > 0.0 fhngrrup[idx] = (dists.rrup[idx] - dists.rjb[idx]) / dists.rrup[idx] return fhngrrup
[ "def", "_get_hanging_wall_coeffs_rrup", "(", "self", ",", "dists", ")", ":", "fhngrrup", "=", "np", ".", "ones", "(", "len", "(", "dists", ".", "rrup", ")", ")", "idx", "=", "dists", ".", "rrup", ">", "0.0", "fhngrrup", "[", "idx", "]", "=", "(", "dists", ".", "rrup", "[", "idx", "]", "-", "dists", ".", "rjb", "[", "idx", "]", ")", "/", "dists", ".", "rrup", "[", "idx", "]", "return", "fhngrrup" ]
Returns the hanging wall rrup term defined in equation 13
[ "Returns", "the", "hanging", "wall", "rrup", "term", "defined", "in", "equation", "13" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L234-L241
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_hanging_wall_coeffs_mag
def _get_hanging_wall_coeffs_mag(self, C, mag): """ Returns the hanging wall magnitude term defined in equation 14 """ if mag < 5.5: return 0.0 elif mag > 6.5: return 1.0 + C["a2"] * (mag - 6.5) else: return (mag - 5.5) * (1.0 + C["a2"] * (mag - 6.5))
python
def _get_hanging_wall_coeffs_mag(self, C, mag): if mag < 5.5: return 0.0 elif mag > 6.5: return 1.0 + C["a2"] * (mag - 6.5) else: return (mag - 5.5) * (1.0 + C["a2"] * (mag - 6.5))
[ "def", "_get_hanging_wall_coeffs_mag", "(", "self", ",", "C", ",", "mag", ")", ":", "if", "mag", "<", "5.5", ":", "return", "0.0", "elif", "mag", ">", "6.5", ":", "return", "1.0", "+", "C", "[", "\"a2\"", "]", "*", "(", "mag", "-", "6.5", ")", "else", ":", "return", "(", "mag", "-", "5.5", ")", "*", "(", "1.0", "+", "C", "[", "\"a2\"", "]", "*", "(", "mag", "-", "6.5", ")", ")" ]
Returns the hanging wall magnitude term defined in equation 14
[ "Returns", "the", "hanging", "wall", "magnitude", "term", "defined", "in", "equation", "14" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L243-L252
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_hypocentral_depth_term
def _get_hypocentral_depth_term(self, C, rup): """ Returns the hypocentral depth scaling term defined in equations 21 - 23 """ if rup.hypo_depth <= 7.0: fhyp_h = 0.0 elif rup.hypo_depth > 20.0: fhyp_h = 13.0 else: fhyp_h = rup.hypo_depth - 7.0 if rup.mag <= 5.5: fhyp_m = C["c17"] elif rup.mag > 6.5: fhyp_m = C["c18"] else: fhyp_m = C["c17"] + ((C["c18"] - C["c17"]) * (rup.mag - 5.5)) return fhyp_h * fhyp_m
python
def _get_hypocentral_depth_term(self, C, rup): if rup.hypo_depth <= 7.0: fhyp_h = 0.0 elif rup.hypo_depth > 20.0: fhyp_h = 13.0 else: fhyp_h = rup.hypo_depth - 7.0 if rup.mag <= 5.5: fhyp_m = C["c17"] elif rup.mag > 6.5: fhyp_m = C["c18"] else: fhyp_m = C["c17"] + ((C["c18"] - C["c17"]) * (rup.mag - 5.5)) return fhyp_h * fhyp_m
[ "def", "_get_hypocentral_depth_term", "(", "self", ",", "C", ",", "rup", ")", ":", "if", "rup", ".", "hypo_depth", "<=", "7.0", ":", "fhyp_h", "=", "0.0", "elif", "rup", ".", "hypo_depth", ">", "20.0", ":", "fhyp_h", "=", "13.0", "else", ":", "fhyp_h", "=", "rup", ".", "hypo_depth", "-", "7.0", "if", "rup", ".", "mag", "<=", "5.5", ":", "fhyp_m", "=", "C", "[", "\"c17\"", "]", "elif", "rup", ".", "mag", ">", "6.5", ":", "fhyp_m", "=", "C", "[", "\"c18\"", "]", "else", ":", "fhyp_m", "=", "C", "[", "\"c17\"", "]", "+", "(", "(", "C", "[", "\"c18\"", "]", "-", "C", "[", "\"c17\"", "]", ")", "*", "(", "rup", ".", "mag", "-", "5.5", ")", ")", "return", "fhyp_h", "*", "fhyp_m" ]
Returns the hypocentral depth scaling term defined in equations 21 - 23
[ "Returns", "the", "hypocentral", "depth", "scaling", "term", "defined", "in", "equations", "21", "-", "23" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L269-L286
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_fault_dip_term
def _get_fault_dip_term(self, C, rup): """ Returns the fault dip term, defined in equation 24 """ if rup.mag < 4.5: return C["c19"] * rup.dip elif rup.mag > 5.5: return 0.0 else: return C["c19"] * (5.5 - rup.mag) * rup.dip
python
def _get_fault_dip_term(self, C, rup): if rup.mag < 4.5: return C["c19"] * rup.dip elif rup.mag > 5.5: return 0.0 else: return C["c19"] * (5.5 - rup.mag) * rup.dip
[ "def", "_get_fault_dip_term", "(", "self", ",", "C", ",", "rup", ")", ":", "if", "rup", ".", "mag", "<", "4.5", ":", "return", "C", "[", "\"c19\"", "]", "*", "rup", ".", "dip", "elif", "rup", ".", "mag", ">", "5.5", ":", "return", "0.0", "else", ":", "return", "C", "[", "\"c19\"", "]", "*", "(", "5.5", "-", "rup", ".", "mag", ")", "*", "rup", ".", "dip" ]
Returns the fault dip term, defined in equation 24
[ "Returns", "the", "fault", "dip", "term", "defined", "in", "equation", "24" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L288-L297
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_anelastic_attenuation_term
def _get_anelastic_attenuation_term(self, C, rrup): """ Returns the anelastic attenuation term defined in equation 25 """ f_atn = np.zeros(len(rrup)) idx = rrup >= 80.0 f_atn[idx] = (C["c20"] + C["Dc20"]) * (rrup[idx] - 80.0) return f_atn
python
def _get_anelastic_attenuation_term(self, C, rrup): f_atn = np.zeros(len(rrup)) idx = rrup >= 80.0 f_atn[idx] = (C["c20"] + C["Dc20"]) * (rrup[idx] - 80.0) return f_atn
[ "def", "_get_anelastic_attenuation_term", "(", "self", ",", "C", ",", "rrup", ")", ":", "f_atn", "=", "np", ".", "zeros", "(", "len", "(", "rrup", ")", ")", "idx", "=", "rrup", ">=", "80.0", "f_atn", "[", "idx", "]", "=", "(", "C", "[", "\"c20\"", "]", "+", "C", "[", "\"Dc20\"", "]", ")", "*", "(", "rrup", "[", "idx", "]", "-", "80.0", ")", "return", "f_atn" ]
Returns the anelastic attenuation term defined in equation 25
[ "Returns", "the", "anelastic", "attenuation", "term", "defined", "in", "equation", "25" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L299-L306
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._select_basin_model
def _select_basin_model(self, vs30): """ Select the preferred basin model (California or Japan) to scale basin depth with respect to Vs30 """ if self.CONSTS["SJ"]: # Japan Basin Model - Equation 34 of Campbell & Bozorgnia (2014) return np.exp(5.359 - 1.102 * np.log(vs30)) else: # California Basin Model - Equation 33 of # Campbell & Bozorgnia (2014) return np.exp(7.089 - 1.144 * np.log(vs30))
python
def _select_basin_model(self, vs30): if self.CONSTS["SJ"]: return np.exp(5.359 - 1.102 * np.log(vs30)) else: return np.exp(7.089 - 1.144 * np.log(vs30))
[ "def", "_select_basin_model", "(", "self", ",", "vs30", ")", ":", "if", "self", ".", "CONSTS", "[", "\"SJ\"", "]", ":", "# Japan Basin Model - Equation 34 of Campbell & Bozorgnia (2014)", "return", "np", ".", "exp", "(", "5.359", "-", "1.102", "*", "np", ".", "log", "(", "vs30", ")", ")", "else", ":", "# California Basin Model - Equation 33 of", "# Campbell & Bozorgnia (2014)", "return", "np", ".", "exp", "(", "7.089", "-", "1.144", "*", "np", ".", "log", "(", "vs30", ")", ")" ]
Select the preferred basin model (California or Japan) to scale basin depth with respect to Vs30
[ "Select", "the", "preferred", "basin", "model", "(", "California", "or", "Japan", ")", "to", "scale", "basin", "depth", "with", "respect", "to", "Vs30" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L308-L319
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_basin_response_term
def _get_basin_response_term(self, C, z2pt5): """ Returns the basin response term defined in equation 20 """ f_sed = np.zeros(len(z2pt5)) idx = z2pt5 < 1.0 f_sed[idx] = (C["c14"] + C["c15"] * float(self.CONSTS["SJ"])) *\ (z2pt5[idx] - 1.0) idx = z2pt5 > 3.0 f_sed[idx] = C["c16"] * C["k3"] * exp(-0.75) *\ (1.0 - np.exp(-0.25 * (z2pt5[idx] - 3.0))) return f_sed
python
def _get_basin_response_term(self, C, z2pt5): f_sed = np.zeros(len(z2pt5)) idx = z2pt5 < 1.0 f_sed[idx] = (C["c14"] + C["c15"] * float(self.CONSTS["SJ"])) *\ (z2pt5[idx] - 1.0) idx = z2pt5 > 3.0 f_sed[idx] = C["c16"] * C["k3"] * exp(-0.75) *\ (1.0 - np.exp(-0.25 * (z2pt5[idx] - 3.0))) return f_sed
[ "def", "_get_basin_response_term", "(", "self", ",", "C", ",", "z2pt5", ")", ":", "f_sed", "=", "np", ".", "zeros", "(", "len", "(", "z2pt5", ")", ")", "idx", "=", "z2pt5", "<", "1.0", "f_sed", "[", "idx", "]", "=", "(", "C", "[", "\"c14\"", "]", "+", "C", "[", "\"c15\"", "]", "*", "float", "(", "self", ".", "CONSTS", "[", "\"SJ\"", "]", ")", ")", "*", "(", "z2pt5", "[", "idx", "]", "-", "1.0", ")", "idx", "=", "z2pt5", ">", "3.0", "f_sed", "[", "idx", "]", "=", "C", "[", "\"c16\"", "]", "*", "C", "[", "\"k3\"", "]", "*", "exp", "(", "-", "0.75", ")", "*", "(", "1.0", "-", "np", ".", "exp", "(", "-", "0.25", "*", "(", "z2pt5", "[", "idx", "]", "-", "3.0", ")", ")", ")", "return", "f_sed" ]
Returns the basin response term defined in equation 20
[ "Returns", "the", "basin", "response", "term", "defined", "in", "equation", "20" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L321-L332
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_shallow_site_response_term
def _get_shallow_site_response_term(self, C, vs30, pga_rock): """ Returns the shallow site response term defined in equations 17, 18 and 19 """ vs_mod = vs30 / C["k1"] # Get linear global site response term f_site_g = C["c11"] * np.log(vs_mod) idx = vs30 > C["k1"] f_site_g[idx] = f_site_g[idx] + (C["k2"] * self.CONSTS["n"] * np.log(vs_mod[idx])) # Get nonlinear site response term idx = np.logical_not(idx) if np.any(idx): f_site_g[idx] = f_site_g[idx] + C["k2"] * ( np.log(pga_rock[idx] + self.CONSTS["c"] * (vs_mod[idx] ** self.CONSTS["n"])) - np.log(pga_rock[idx] + self.CONSTS["c"]) ) # For Japan sites (SJ = 1) further scaling is needed (equation 19) if self.CONSTS["SJ"]: fsite_j = np.log(vs_mod) idx = vs30 > 200.0 if np.any(idx): fsite_j[idx] = (C["c13"] + C["k2"] * self.CONSTS["n"]) *\ fsite_j[idx] idx = np.logical_not(idx) if np.any(idx): fsite_j[idx] = (C["c12"] + C["k2"] * self.CONSTS["n"]) *\ (fsite_j[idx] - np.log(200.0 / C["k1"])) return f_site_g + fsite_j else: return f_site_g
python
def _get_shallow_site_response_term(self, C, vs30, pga_rock): vs_mod = vs30 / C["k1"] f_site_g = C["c11"] * np.log(vs_mod) idx = vs30 > C["k1"] f_site_g[idx] = f_site_g[idx] + (C["k2"] * self.CONSTS["n"] * np.log(vs_mod[idx])) idx = np.logical_not(idx) if np.any(idx): f_site_g[idx] = f_site_g[idx] + C["k2"] * ( np.log(pga_rock[idx] + self.CONSTS["c"] * (vs_mod[idx] ** self.CONSTS["n"])) - np.log(pga_rock[idx] + self.CONSTS["c"]) ) if self.CONSTS["SJ"]: fsite_j = np.log(vs_mod) idx = vs30 > 200.0 if np.any(idx): fsite_j[idx] = (C["c13"] + C["k2"] * self.CONSTS["n"]) *\ fsite_j[idx] idx = np.logical_not(idx) if np.any(idx): fsite_j[idx] = (C["c12"] + C["k2"] * self.CONSTS["n"]) *\ (fsite_j[idx] - np.log(200.0 / C["k1"])) return f_site_g + fsite_j else: return f_site_g
[ "def", "_get_shallow_site_response_term", "(", "self", ",", "C", ",", "vs30", ",", "pga_rock", ")", ":", "vs_mod", "=", "vs30", "/", "C", "[", "\"k1\"", "]", "# Get linear global site response term", "f_site_g", "=", "C", "[", "\"c11\"", "]", "*", "np", ".", "log", "(", "vs_mod", ")", "idx", "=", "vs30", ">", "C", "[", "\"k1\"", "]", "f_site_g", "[", "idx", "]", "=", "f_site_g", "[", "idx", "]", "+", "(", "C", "[", "\"k2\"", "]", "*", "self", ".", "CONSTS", "[", "\"n\"", "]", "*", "np", ".", "log", "(", "vs_mod", "[", "idx", "]", ")", ")", "# Get nonlinear site response term", "idx", "=", "np", ".", "logical_not", "(", "idx", ")", "if", "np", ".", "any", "(", "idx", ")", ":", "f_site_g", "[", "idx", "]", "=", "f_site_g", "[", "idx", "]", "+", "C", "[", "\"k2\"", "]", "*", "(", "np", ".", "log", "(", "pga_rock", "[", "idx", "]", "+", "self", ".", "CONSTS", "[", "\"c\"", "]", "*", "(", "vs_mod", "[", "idx", "]", "**", "self", ".", "CONSTS", "[", "\"n\"", "]", ")", ")", "-", "np", ".", "log", "(", "pga_rock", "[", "idx", "]", "+", "self", ".", "CONSTS", "[", "\"c\"", "]", ")", ")", "# For Japan sites (SJ = 1) further scaling is needed (equation 19)", "if", "self", ".", "CONSTS", "[", "\"SJ\"", "]", ":", "fsite_j", "=", "np", ".", "log", "(", "vs_mod", ")", "idx", "=", "vs30", ">", "200.0", "if", "np", ".", "any", "(", "idx", ")", ":", "fsite_j", "[", "idx", "]", "=", "(", "C", "[", "\"c13\"", "]", "+", "C", "[", "\"k2\"", "]", "*", "self", ".", "CONSTS", "[", "\"n\"", "]", ")", "*", "fsite_j", "[", "idx", "]", "idx", "=", "np", ".", "logical_not", "(", "idx", ")", "if", "np", ".", "any", "(", "idx", ")", ":", "fsite_j", "[", "idx", "]", "=", "(", "C", "[", "\"c12\"", "]", "+", "C", "[", "\"k2\"", "]", "*", "self", ".", "CONSTS", "[", "\"n\"", "]", ")", "*", "(", "fsite_j", "[", "idx", "]", "-", "np", ".", "log", "(", "200.0", "/", "C", "[", "\"k1\"", "]", ")", ")", "return", "f_site_g", "+", "fsite_j", "else", ":", "return", "f_site_g" ]
Returns the shallow site response term defined in equations 17, 18 and 19
[ "Returns", "the", "shallow", "site", "response", "term", "defined", "in", "equations", "17", "18", "and", "19" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L334-L369
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_stddevs
def _get_stddevs(self, C, C_PGA, rup, sites, pga1100, stddev_types): """ Returns the inter- and intra-event and total standard deviations """ # Get stddevs for PGA on basement rock tau_lnpga_b, phi_lnpga_b = self._get_stddevs_pga(C_PGA, rup) num_sites = len(sites.vs30) # Get tau_lny on the basement rock tau_lnyb = self._get_taulny(C, rup.mag) # Get phi_lny on the basement rock phi_lnyb = np.sqrt(self._get_philny(C, rup.mag) ** 2. - self.CONSTS["philnAF"] ** 2.) # Get site scaling term alpha = self._get_alpha(C, sites.vs30, pga1100) # Evaluate tau according to equation 29 tau = np.sqrt( (tau_lnyb ** 2.) + ((alpha ** 2.) * (tau_lnpga_b ** 2.)) + (2.0 * alpha * C["rholny"] * tau_lnyb * tau_lnpga_b)) # Evaluate phi according to equation 30 phi = np.sqrt( (phi_lnyb ** 2.) + (self.CONSTS["philnAF"] ** 2.) + ((alpha ** 2.) * (phi_lnpga_b ** 2.)) + (2.0 * alpha * C["rholny"] * phi_lnyb * phi_lnpga_b)) stddevs = [] for stddev_type in stddev_types: assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES if stddev_type == const.StdDev.TOTAL: stddevs.append(np.sqrt((tau ** 2.) + (phi ** 2.)) + np.zeros(num_sites)) elif stddev_type == const.StdDev.INTRA_EVENT: stddevs.append(phi + np.zeros(num_sites)) elif stddev_type == const.StdDev.INTER_EVENT: stddevs.append(tau + np.zeros(num_sites)) return stddevs
python
def _get_stddevs(self, C, C_PGA, rup, sites, pga1100, stddev_types): tau_lnpga_b, phi_lnpga_b = self._get_stddevs_pga(C_PGA, rup) num_sites = len(sites.vs30) tau_lnyb = self._get_taulny(C, rup.mag) phi_lnyb = np.sqrt(self._get_philny(C, rup.mag) ** 2. - self.CONSTS["philnAF"] ** 2.) alpha = self._get_alpha(C, sites.vs30, pga1100) tau = np.sqrt( (tau_lnyb ** 2.) + ((alpha ** 2.) * (tau_lnpga_b ** 2.)) + (2.0 * alpha * C["rholny"] * tau_lnyb * tau_lnpga_b)) phi = np.sqrt( (phi_lnyb ** 2.) + (self.CONSTS["philnAF"] ** 2.) + ((alpha ** 2.) * (phi_lnpga_b ** 2.)) + (2.0 * alpha * C["rholny"] * phi_lnyb * phi_lnpga_b)) stddevs = [] for stddev_type in stddev_types: assert stddev_type in self.DEFINED_FOR_STANDARD_DEVIATION_TYPES if stddev_type == const.StdDev.TOTAL: stddevs.append(np.sqrt((tau ** 2.) + (phi ** 2.)) + np.zeros(num_sites)) elif stddev_type == const.StdDev.INTRA_EVENT: stddevs.append(phi + np.zeros(num_sites)) elif stddev_type == const.StdDev.INTER_EVENT: stddevs.append(tau + np.zeros(num_sites)) return stddevs
[ "def", "_get_stddevs", "(", "self", ",", "C", ",", "C_PGA", ",", "rup", ",", "sites", ",", "pga1100", ",", "stddev_types", ")", ":", "# Get stddevs for PGA on basement rock", "tau_lnpga_b", ",", "phi_lnpga_b", "=", "self", ".", "_get_stddevs_pga", "(", "C_PGA", ",", "rup", ")", "num_sites", "=", "len", "(", "sites", ".", "vs30", ")", "# Get tau_lny on the basement rock", "tau_lnyb", "=", "self", ".", "_get_taulny", "(", "C", ",", "rup", ".", "mag", ")", "# Get phi_lny on the basement rock", "phi_lnyb", "=", "np", ".", "sqrt", "(", "self", ".", "_get_philny", "(", "C", ",", "rup", ".", "mag", ")", "**", "2.", "-", "self", ".", "CONSTS", "[", "\"philnAF\"", "]", "**", "2.", ")", "# Get site scaling term", "alpha", "=", "self", ".", "_get_alpha", "(", "C", ",", "sites", ".", "vs30", ",", "pga1100", ")", "# Evaluate tau according to equation 29", "tau", "=", "np", ".", "sqrt", "(", "(", "tau_lnyb", "**", "2.", ")", "+", "(", "(", "alpha", "**", "2.", ")", "*", "(", "tau_lnpga_b", "**", "2.", ")", ")", "+", "(", "2.0", "*", "alpha", "*", "C", "[", "\"rholny\"", "]", "*", "tau_lnyb", "*", "tau_lnpga_b", ")", ")", "# Evaluate phi according to equation 30", "phi", "=", "np", ".", "sqrt", "(", "(", "phi_lnyb", "**", "2.", ")", "+", "(", "self", ".", "CONSTS", "[", "\"philnAF\"", "]", "**", "2.", ")", "+", "(", "(", "alpha", "**", "2.", ")", "*", "(", "phi_lnpga_b", "**", "2.", ")", ")", "+", "(", "2.0", "*", "alpha", "*", "C", "[", "\"rholny\"", "]", "*", "phi_lnyb", "*", "phi_lnpga_b", ")", ")", "stddevs", "=", "[", "]", "for", "stddev_type", "in", "stddev_types", ":", "assert", "stddev_type", "in", "self", ".", "DEFINED_FOR_STANDARD_DEVIATION_TYPES", "if", "stddev_type", "==", "const", ".", "StdDev", ".", "TOTAL", ":", "stddevs", ".", "append", "(", "np", ".", "sqrt", "(", "(", "tau", "**", "2.", ")", "+", "(", "phi", "**", "2.", ")", ")", "+", "np", ".", "zeros", "(", "num_sites", ")", ")", "elif", "stddev_type", "==", "const", ".", "StdDev", ".", "INTRA_EVENT", ":", "stddevs", ".", "append", "(", "phi", "+", "np", ".", "zeros", "(", "num_sites", ")", ")", "elif", "stddev_type", "==", "const", ".", "StdDev", ".", "INTER_EVENT", ":", "stddevs", ".", "append", "(", "tau", "+", "np", ".", "zeros", "(", "num_sites", ")", ")", "return", "stddevs" ]
Returns the inter- and intra-event and total standard deviations
[ "Returns", "the", "inter", "-", "and", "intra", "-", "event", "and", "total", "standard", "deviations" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L371-L407
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_stddevs_pga
def _get_stddevs_pga(self, C, rup): """ Returns the inter- and intra-event coefficients for PGA """ tau_lnpga_b = self._get_taulny(C, rup.mag) phi_lnpga_b = np.sqrt(self._get_philny(C, rup.mag) ** 2. - self.CONSTS["philnAF"] ** 2.) return tau_lnpga_b, phi_lnpga_b
python
def _get_stddevs_pga(self, C, rup): tau_lnpga_b = self._get_taulny(C, rup.mag) phi_lnpga_b = np.sqrt(self._get_philny(C, rup.mag) ** 2. - self.CONSTS["philnAF"] ** 2.) return tau_lnpga_b, phi_lnpga_b
[ "def", "_get_stddevs_pga", "(", "self", ",", "C", ",", "rup", ")", ":", "tau_lnpga_b", "=", "self", ".", "_get_taulny", "(", "C", ",", "rup", ".", "mag", ")", "phi_lnpga_b", "=", "np", ".", "sqrt", "(", "self", ".", "_get_philny", "(", "C", ",", "rup", ".", "mag", ")", "**", "2.", "-", "self", ".", "CONSTS", "[", "\"philnAF\"", "]", "**", "2.", ")", "return", "tau_lnpga_b", ",", "phi_lnpga_b" ]
Returns the inter- and intra-event coefficients for PGA
[ "Returns", "the", "inter", "-", "and", "intra", "-", "event", "coefficients", "for", "PGA" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L409-L416
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_taulny
def _get_taulny(self, C, mag): """ Returns the inter-event random effects coefficient (tau) Equation 28. """ if mag <= 4.5: return C["tau1"] elif mag >= 5.5: return C["tau2"] else: return C["tau2"] + (C["tau1"] - C["tau2"]) * (5.5 - mag)
python
def _get_taulny(self, C, mag): if mag <= 4.5: return C["tau1"] elif mag >= 5.5: return C["tau2"] else: return C["tau2"] + (C["tau1"] - C["tau2"]) * (5.5 - mag)
[ "def", "_get_taulny", "(", "self", ",", "C", ",", "mag", ")", ":", "if", "mag", "<=", "4.5", ":", "return", "C", "[", "\"tau1\"", "]", "elif", "mag", ">=", "5.5", ":", "return", "C", "[", "\"tau2\"", "]", "else", ":", "return", "C", "[", "\"tau2\"", "]", "+", "(", "C", "[", "\"tau1\"", "]", "-", "C", "[", "\"tau2\"", "]", ")", "*", "(", "5.5", "-", "mag", ")" ]
Returns the inter-event random effects coefficient (tau) Equation 28.
[ "Returns", "the", "inter", "-", "event", "random", "effects", "coefficient", "(", "tau", ")", "Equation", "28", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L418-L428
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_philny
def _get_philny(self, C, mag): """ Returns the intra-event random effects coefficient (phi) Equation 28. """ if mag <= 4.5: return C["phi1"] elif mag >= 5.5: return C["phi2"] else: return C["phi2"] + (C["phi1"] - C["phi2"]) * (5.5 - mag)
python
def _get_philny(self, C, mag): if mag <= 4.5: return C["phi1"] elif mag >= 5.5: return C["phi2"] else: return C["phi2"] + (C["phi1"] - C["phi2"]) * (5.5 - mag)
[ "def", "_get_philny", "(", "self", ",", "C", ",", "mag", ")", ":", "if", "mag", "<=", "4.5", ":", "return", "C", "[", "\"phi1\"", "]", "elif", "mag", ">=", "5.5", ":", "return", "C", "[", "\"phi2\"", "]", "else", ":", "return", "C", "[", "\"phi2\"", "]", "+", "(", "C", "[", "\"phi1\"", "]", "-", "C", "[", "\"phi2\"", "]", ")", "*", "(", "5.5", "-", "mag", ")" ]
Returns the intra-event random effects coefficient (phi) Equation 28.
[ "Returns", "the", "intra", "-", "event", "random", "effects", "coefficient", "(", "phi", ")", "Equation", "28", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L430-L440
gem/oq-engine
openquake/hazardlib/gsim/campbell_bozorgnia_2014.py
CampbellBozorgnia2014._get_alpha
def _get_alpha(self, C, vs30, pga_rock): """ Returns the alpha, the linearised functional relationship between the site amplification and the PGA on rock. Equation 31. """ alpha = np.zeros(len(pga_rock)) idx = vs30 < C["k1"] if np.any(idx): af1 = pga_rock[idx] +\ self.CONSTS["c"] * ((vs30[idx] / C["k1"]) ** self.CONSTS["n"]) af2 = pga_rock[idx] + self.CONSTS["c"] alpha[idx] = C["k2"] * pga_rock[idx] * ((1.0 / af1) - (1.0 / af2)) return alpha
python
def _get_alpha(self, C, vs30, pga_rock): alpha = np.zeros(len(pga_rock)) idx = vs30 < C["k1"] if np.any(idx): af1 = pga_rock[idx] +\ self.CONSTS["c"] * ((vs30[idx] / C["k1"]) ** self.CONSTS["n"]) af2 = pga_rock[idx] + self.CONSTS["c"] alpha[idx] = C["k2"] * pga_rock[idx] * ((1.0 / af1) - (1.0 / af2)) return alpha
[ "def", "_get_alpha", "(", "self", ",", "C", ",", "vs30", ",", "pga_rock", ")", ":", "alpha", "=", "np", ".", "zeros", "(", "len", "(", "pga_rock", ")", ")", "idx", "=", "vs30", "<", "C", "[", "\"k1\"", "]", "if", "np", ".", "any", "(", "idx", ")", ":", "af1", "=", "pga_rock", "[", "idx", "]", "+", "self", ".", "CONSTS", "[", "\"c\"", "]", "*", "(", "(", "vs30", "[", "idx", "]", "/", "C", "[", "\"k1\"", "]", ")", "**", "self", ".", "CONSTS", "[", "\"n\"", "]", ")", "af2", "=", "pga_rock", "[", "idx", "]", "+", "self", ".", "CONSTS", "[", "\"c\"", "]", "alpha", "[", "idx", "]", "=", "C", "[", "\"k2\"", "]", "*", "pga_rock", "[", "idx", "]", "*", "(", "(", "1.0", "/", "af1", ")", "-", "(", "1.0", "/", "af2", ")", ")", "return", "alpha" ]
Returns the alpha, the linearised functional relationship between the site amplification and the PGA on rock. Equation 31.
[ "Returns", "the", "alpha", "the", "linearised", "functional", "relationship", "between", "the", "site", "amplification", "and", "the", "PGA", "on", "rock", ".", "Equation", "31", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hazardlib/gsim/campbell_bozorgnia_2014.py#L442-L454
gem/oq-engine
openquake/hmtk/seismicity/utils.py
decimal_year
def decimal_year(year, month, day): """ Allows to calculate the decimal year for a vector of dates (TODO this is legacy code kept to maintain comparability with previous declustering algorithms!) :param year: year column from catalogue matrix :type year: numpy.ndarray :param month: month column from catalogue matrix :type month: numpy.ndarray :param day: day column from catalogue matrix :type day: numpy.ndarray :returns: decimal year column :rtype: numpy.ndarray """ marker = np.array([0., 31., 59., 90., 120., 151., 181., 212., 243., 273., 304., 334.]) tmonth = (month - 1).astype(int) day_count = marker[tmonth] + day - 1. dec_year = year + (day_count / 365.) return dec_year
python
def decimal_year(year, month, day): marker = np.array([0., 31., 59., 90., 120., 151., 181., 212., 243., 273., 304., 334.]) tmonth = (month - 1).astype(int) day_count = marker[tmonth] + day - 1. dec_year = year + (day_count / 365.) return dec_year
[ "def", "decimal_year", "(", "year", ",", "month", ",", "day", ")", ":", "marker", "=", "np", ".", "array", "(", "[", "0.", ",", "31.", ",", "59.", ",", "90.", ",", "120.", ",", "151.", ",", "181.", ",", "212.", ",", "243.", ",", "273.", ",", "304.", ",", "334.", "]", ")", "tmonth", "=", "(", "month", "-", "1", ")", ".", "astype", "(", "int", ")", "day_count", "=", "marker", "[", "tmonth", "]", "+", "day", "-", "1.", "dec_year", "=", "year", "+", "(", "day_count", "/", "365.", ")", "return", "dec_year" ]
Allows to calculate the decimal year for a vector of dates (TODO this is legacy code kept to maintain comparability with previous declustering algorithms!) :param year: year column from catalogue matrix :type year: numpy.ndarray :param month: month column from catalogue matrix :type month: numpy.ndarray :param day: day column from catalogue matrix :type day: numpy.ndarray :returns: decimal year column :rtype: numpy.ndarray
[ "Allows", "to", "calculate", "the", "decimal", "year", "for", "a", "vector", "of", "dates", "(", "TODO", "this", "is", "legacy", "code", "kept", "to", "maintain", "comparability", "with", "previous", "declustering", "algorithms!", ")" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L105-L126
gem/oq-engine
openquake/hmtk/seismicity/utils.py
decimal_time
def decimal_time(year, month, day, hour, minute, second): """ Returns the full time as a decimal value :param year: Year of events (integer numpy.ndarray) :param month: Month of events (integer numpy.ndarray) :param day: Days of event (integer numpy.ndarray) :param hour: Hour of event (integer numpy.ndarray) :param minute: Minute of event (integer numpy.ndarray) :param second: Second of event (float numpy.ndarray) :returns decimal_time: Decimal representation of the time (as numpy.ndarray) """ tmo = np.ones_like(year, dtype=int) tda = np.ones_like(year, dtype=int) tho = np.zeros_like(year, dtype=int) tmi = np.zeros_like(year, dtype=int) tse = np.zeros_like(year, dtype=float) # # Checking inputs if any(month < 1) or any(month > 12): raise ValueError('Month must be in [1, 12]') if any(day < 1) or any(day > 31): raise ValueError('Day must be in [1, 31]') if any(hour < 0) or any(hour > 24): raise ValueError('Hour must be in [0, 24]') if any(minute < 0) or any(minute > 60): raise ValueError('Minute must be in [0, 60]') if any(second < 0) or any(second > 60): raise ValueError('Second must be in [0, 60]') # # Initialising values if any(month): tmo = month if any(day): tda = day if any(hour): tho = hour if any(minute): tmi = minute if any(second): tse = second # # Computing decimal tmonth = tmo - 1 day_count = MARKER_NORMAL[tmonth] + tda - 1 id_leap = leap_check(year) leap_loc = np.where(id_leap)[0] day_count[leap_loc] = MARKER_LEAP[tmonth[leap_loc]] + tda[leap_loc] - 1 year_secs = ((day_count.astype(float) * SECONDS_PER_DAY) + tse + (60. * tmi.astype(float)) + (3600. * tho.astype(float))) dtime = year.astype(float) + (year_secs / (365. * 24. * 3600.)) dtime[leap_loc] = year[leap_loc].astype(float) + \ (year_secs[leap_loc] / (366. * 24. * 3600.)) return dtime
python
def decimal_time(year, month, day, hour, minute, second): tmo = np.ones_like(year, dtype=int) tda = np.ones_like(year, dtype=int) tho = np.zeros_like(year, dtype=int) tmi = np.zeros_like(year, dtype=int) tse = np.zeros_like(year, dtype=float) if any(month < 1) or any(month > 12): raise ValueError('Month must be in [1, 12]') if any(day < 1) or any(day > 31): raise ValueError('Day must be in [1, 31]') if any(hour < 0) or any(hour > 24): raise ValueError('Hour must be in [0, 24]') if any(minute < 0) or any(minute > 60): raise ValueError('Minute must be in [0, 60]') if any(second < 0) or any(second > 60): raise ValueError('Second must be in [0, 60]') if any(month): tmo = month if any(day): tda = day if any(hour): tho = hour if any(minute): tmi = minute if any(second): tse = second tmonth = tmo - 1 day_count = MARKER_NORMAL[tmonth] + tda - 1 id_leap = leap_check(year) leap_loc = np.where(id_leap)[0] day_count[leap_loc] = MARKER_LEAP[tmonth[leap_loc]] + tda[leap_loc] - 1 year_secs = ((day_count.astype(float) * SECONDS_PER_DAY) + tse + (60. * tmi.astype(float)) + (3600. * tho.astype(float))) dtime = year.astype(float) + (year_secs / (365. * 24. * 3600.)) dtime[leap_loc] = year[leap_loc].astype(float) + \ (year_secs[leap_loc] / (366. * 24. * 3600.)) return dtime
[ "def", "decimal_time", "(", "year", ",", "month", ",", "day", ",", "hour", ",", "minute", ",", "second", ")", ":", "tmo", "=", "np", ".", "ones_like", "(", "year", ",", "dtype", "=", "int", ")", "tda", "=", "np", ".", "ones_like", "(", "year", ",", "dtype", "=", "int", ")", "tho", "=", "np", ".", "zeros_like", "(", "year", ",", "dtype", "=", "int", ")", "tmi", "=", "np", ".", "zeros_like", "(", "year", ",", "dtype", "=", "int", ")", "tse", "=", "np", ".", "zeros_like", "(", "year", ",", "dtype", "=", "float", ")", "#", "# Checking inputs", "if", "any", "(", "month", "<", "1", ")", "or", "any", "(", "month", ">", "12", ")", ":", "raise", "ValueError", "(", "'Month must be in [1, 12]'", ")", "if", "any", "(", "day", "<", "1", ")", "or", "any", "(", "day", ">", "31", ")", ":", "raise", "ValueError", "(", "'Day must be in [1, 31]'", ")", "if", "any", "(", "hour", "<", "0", ")", "or", "any", "(", "hour", ">", "24", ")", ":", "raise", "ValueError", "(", "'Hour must be in [0, 24]'", ")", "if", "any", "(", "minute", "<", "0", ")", "or", "any", "(", "minute", ">", "60", ")", ":", "raise", "ValueError", "(", "'Minute must be in [0, 60]'", ")", "if", "any", "(", "second", "<", "0", ")", "or", "any", "(", "second", ">", "60", ")", ":", "raise", "ValueError", "(", "'Second must be in [0, 60]'", ")", "#", "# Initialising values", "if", "any", "(", "month", ")", ":", "tmo", "=", "month", "if", "any", "(", "day", ")", ":", "tda", "=", "day", "if", "any", "(", "hour", ")", ":", "tho", "=", "hour", "if", "any", "(", "minute", ")", ":", "tmi", "=", "minute", "if", "any", "(", "second", ")", ":", "tse", "=", "second", "#", "# Computing decimal", "tmonth", "=", "tmo", "-", "1", "day_count", "=", "MARKER_NORMAL", "[", "tmonth", "]", "+", "tda", "-", "1", "id_leap", "=", "leap_check", "(", "year", ")", "leap_loc", "=", "np", ".", "where", "(", "id_leap", ")", "[", "0", "]", "day_count", "[", "leap_loc", "]", "=", "MARKER_LEAP", "[", "tmonth", "[", "leap_loc", "]", "]", "+", "tda", "[", "leap_loc", "]", "-", "1", "year_secs", "=", "(", "(", "day_count", ".", "astype", "(", "float", ")", "*", "SECONDS_PER_DAY", ")", "+", "tse", "+", "(", "60.", "*", "tmi", ".", "astype", "(", "float", ")", ")", "+", "(", "3600.", "*", "tho", ".", "astype", "(", "float", ")", ")", ")", "dtime", "=", "year", ".", "astype", "(", "float", ")", "+", "(", "year_secs", "/", "(", "365.", "*", "24.", "*", "3600.", ")", ")", "dtime", "[", "leap_loc", "]", "=", "year", "[", "leap_loc", "]", ".", "astype", "(", "float", ")", "+", "(", "year_secs", "[", "leap_loc", "]", "/", "(", "366.", "*", "24.", "*", "3600.", ")", ")", "return", "dtime" ]
Returns the full time as a decimal value :param year: Year of events (integer numpy.ndarray) :param month: Month of events (integer numpy.ndarray) :param day: Days of event (integer numpy.ndarray) :param hour: Hour of event (integer numpy.ndarray) :param minute: Minute of event (integer numpy.ndarray) :param second: Second of event (float numpy.ndarray) :returns decimal_time: Decimal representation of the time (as numpy.ndarray)
[ "Returns", "the", "full", "time", "as", "a", "decimal", "value" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L137-L197
gem/oq-engine
openquake/hmtk/seismicity/utils.py
haversine
def haversine(lon1, lat1, lon2, lat2, radians=False, earth_rad=6371.227): """ Allows to calculate geographical distance using the haversine formula. :param lon1: longitude of the first set of locations :type lon1: numpy.ndarray :param lat1: latitude of the frist set of locations :type lat1: numpy.ndarray :param lon2: longitude of the second set of locations :type lon2: numpy.float64 :param lat2: latitude of the second set of locations :type lat2: numpy.float64 :keyword radians: states if locations are given in terms of radians :type radians: bool :keyword earth_rad: radius of the earth in km :type earth_rad: float :returns: geographical distance in km :rtype: numpy.ndarray """ if not radians: cfact = np.pi / 180. lon1 = cfact * lon1 lat1 = cfact * lat1 lon2 = cfact * lon2 lat2 = cfact * lat2 # Number of locations in each set of points if not np.shape(lon1): nlocs1 = 1 lon1 = np.array([lon1]) lat1 = np.array([lat1]) else: nlocs1 = np.max(np.shape(lon1)) if not np.shape(lon2): nlocs2 = 1 lon2 = np.array([lon2]) lat2 = np.array([lat2]) else: nlocs2 = np.max(np.shape(lon2)) # Pre-allocate array distance = np.zeros((nlocs1, nlocs2)) i = 0 while i < nlocs2: # Perform distance calculation dlat = lat1 - lat2[i] dlon = lon1 - lon2[i] aval = (np.sin(dlat / 2.) ** 2.) + (np.cos(lat1) * np.cos(lat2[i]) * (np.sin(dlon / 2.) ** 2.)) distance[:, i] = (2. * earth_rad * np.arctan2(np.sqrt(aval), np.sqrt(1 - aval))).T i += 1 return distance
python
def haversine(lon1, lat1, lon2, lat2, radians=False, earth_rad=6371.227): if not radians: cfact = np.pi / 180. lon1 = cfact * lon1 lat1 = cfact * lat1 lon2 = cfact * lon2 lat2 = cfact * lat2 if not np.shape(lon1): nlocs1 = 1 lon1 = np.array([lon1]) lat1 = np.array([lat1]) else: nlocs1 = np.max(np.shape(lon1)) if not np.shape(lon2): nlocs2 = 1 lon2 = np.array([lon2]) lat2 = np.array([lat2]) else: nlocs2 = np.max(np.shape(lon2)) distance = np.zeros((nlocs1, nlocs2)) i = 0 while i < nlocs2: dlat = lat1 - lat2[i] dlon = lon1 - lon2[i] aval = (np.sin(dlat / 2.) ** 2.) + (np.cos(lat1) * np.cos(lat2[i]) * (np.sin(dlon / 2.) ** 2.)) distance[:, i] = (2. * earth_rad * np.arctan2(np.sqrt(aval), np.sqrt(1 - aval))).T i += 1 return distance
[ "def", "haversine", "(", "lon1", ",", "lat1", ",", "lon2", ",", "lat2", ",", "radians", "=", "False", ",", "earth_rad", "=", "6371.227", ")", ":", "if", "not", "radians", ":", "cfact", "=", "np", ".", "pi", "/", "180.", "lon1", "=", "cfact", "*", "lon1", "lat1", "=", "cfact", "*", "lat1", "lon2", "=", "cfact", "*", "lon2", "lat2", "=", "cfact", "*", "lat2", "# Number of locations in each set of points", "if", "not", "np", ".", "shape", "(", "lon1", ")", ":", "nlocs1", "=", "1", "lon1", "=", "np", ".", "array", "(", "[", "lon1", "]", ")", "lat1", "=", "np", ".", "array", "(", "[", "lat1", "]", ")", "else", ":", "nlocs1", "=", "np", ".", "max", "(", "np", ".", "shape", "(", "lon1", ")", ")", "if", "not", "np", ".", "shape", "(", "lon2", ")", ":", "nlocs2", "=", "1", "lon2", "=", "np", ".", "array", "(", "[", "lon2", "]", ")", "lat2", "=", "np", ".", "array", "(", "[", "lat2", "]", ")", "else", ":", "nlocs2", "=", "np", ".", "max", "(", "np", ".", "shape", "(", "lon2", ")", ")", "# Pre-allocate array", "distance", "=", "np", ".", "zeros", "(", "(", "nlocs1", ",", "nlocs2", ")", ")", "i", "=", "0", "while", "i", "<", "nlocs2", ":", "# Perform distance calculation", "dlat", "=", "lat1", "-", "lat2", "[", "i", "]", "dlon", "=", "lon1", "-", "lon2", "[", "i", "]", "aval", "=", "(", "np", ".", "sin", "(", "dlat", "/", "2.", ")", "**", "2.", ")", "+", "(", "np", ".", "cos", "(", "lat1", ")", "*", "np", ".", "cos", "(", "lat2", "[", "i", "]", ")", "*", "(", "np", ".", "sin", "(", "dlon", "/", "2.", ")", "**", "2.", ")", ")", "distance", "[", ":", ",", "i", "]", "=", "(", "2.", "*", "earth_rad", "*", "np", ".", "arctan2", "(", "np", ".", "sqrt", "(", "aval", ")", ",", "np", ".", "sqrt", "(", "1", "-", "aval", ")", ")", ")", ".", "T", "i", "+=", "1", "return", "distance" ]
Allows to calculate geographical distance using the haversine formula. :param lon1: longitude of the first set of locations :type lon1: numpy.ndarray :param lat1: latitude of the frist set of locations :type lat1: numpy.ndarray :param lon2: longitude of the second set of locations :type lon2: numpy.float64 :param lat2: latitude of the second set of locations :type lat2: numpy.float64 :keyword radians: states if locations are given in terms of radians :type radians: bool :keyword earth_rad: radius of the earth in km :type earth_rad: float :returns: geographical distance in km :rtype: numpy.ndarray
[ "Allows", "to", "calculate", "geographical", "distance", "using", "the", "haversine", "formula", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L200-L252
gem/oq-engine
openquake/hmtk/seismicity/utils.py
greg2julian
def greg2julian(year, month, day, hour, minute, second): """ Function to convert a date from Gregorian to Julian format :param year: Year of events (integer numpy.ndarray) :param month: Month of events (integer numpy.ndarray) :param day: Days of event (integer numpy.ndarray) :param hour: Hour of event (integer numpy.ndarray) :param minute: Minute of event (integer numpy.ndarray) :param second: Second of event (float numpy.ndarray) :returns julian_time: Julian representation of the time (as float numpy.ndarray) """ year = year.astype(float) month = month.astype(float) day = day.astype(float) timeut = hour.astype(float) + (minute.astype(float) / 60.0) + \ (second / 3600.0) julian_time = ((367.0 * year) - np.floor( 7.0 * (year + np.floor((month + 9.0) / 12.0)) / 4.0) - np.floor(3.0 * (np.floor((year + (month - 9.0) / 7.0) / 100.0) + 1.0) / 4.0) + np.floor((275.0 * month) / 9.0) + day + 1721028.5 + (timeut / 24.0)) return julian_time
python
def greg2julian(year, month, day, hour, minute, second): year = year.astype(float) month = month.astype(float) day = day.astype(float) timeut = hour.astype(float) + (minute.astype(float) / 60.0) + \ (second / 3600.0) julian_time = ((367.0 * year) - np.floor( 7.0 * (year + np.floor((month + 9.0) / 12.0)) / 4.0) - np.floor(3.0 * (np.floor((year + (month - 9.0) / 7.0) / 100.0) + 1.0) / 4.0) + np.floor((275.0 * month) / 9.0) + day + 1721028.5 + (timeut / 24.0)) return julian_time
[ "def", "greg2julian", "(", "year", ",", "month", ",", "day", ",", "hour", ",", "minute", ",", "second", ")", ":", "year", "=", "year", ".", "astype", "(", "float", ")", "month", "=", "month", ".", "astype", "(", "float", ")", "day", "=", "day", ".", "astype", "(", "float", ")", "timeut", "=", "hour", ".", "astype", "(", "float", ")", "+", "(", "minute", ".", "astype", "(", "float", ")", "/", "60.0", ")", "+", "(", "second", "/", "3600.0", ")", "julian_time", "=", "(", "(", "367.0", "*", "year", ")", "-", "np", ".", "floor", "(", "7.0", "*", "(", "year", "+", "np", ".", "floor", "(", "(", "month", "+", "9.0", ")", "/", "12.0", ")", ")", "/", "4.0", ")", "-", "np", ".", "floor", "(", "3.0", "*", "(", "np", ".", "floor", "(", "(", "year", "+", "(", "month", "-", "9.0", ")", "/", "7.0", ")", "/", "100.0", ")", "+", "1.0", ")", "/", "4.0", ")", "+", "np", ".", "floor", "(", "(", "275.0", "*", "month", ")", "/", "9.0", ")", "+", "day", "+", "1721028.5", "+", "(", "timeut", "/", "24.0", ")", ")", "return", "julian_time" ]
Function to convert a date from Gregorian to Julian format :param year: Year of events (integer numpy.ndarray) :param month: Month of events (integer numpy.ndarray) :param day: Days of event (integer numpy.ndarray) :param hour: Hour of event (integer numpy.ndarray) :param minute: Minute of event (integer numpy.ndarray) :param second: Second of event (float numpy.ndarray) :returns julian_time: Julian representation of the time (as float numpy.ndarray)
[ "Function", "to", "convert", "a", "date", "from", "Gregorian", "to", "Julian", "format" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L255-L289
gem/oq-engine
openquake/hmtk/seismicity/utils.py
piecewise_linear_scalar
def piecewise_linear_scalar(params, xval): '''Piecewise linear function for a scalar variable xval (float). :param params: Piecewise linear parameters (numpy.ndarray) in the following form: [slope_i,... slope_n, turning_point_i, ..., turning_point_n, intercept] Length params === 2 * number_segments, e.g. [slope_1, slope_2, slope_3, turning_point1, turning_point_2, intercept] :param xval: Value for evaluation of function (float) :returns: Piecewise linear function evaluated at point xval (float) ''' n_params = len(params) n_seg, remainder = divmod(n_params, 2) if remainder: raise ValueError( 'Piecewise Function requires 2 * nsegments parameters') if n_seg == 1: return params[1] + params[0] * xval gradients = params[0:n_seg] turning_points = params[n_seg: -1] c_val = np.array([params[-1]]) for iloc in range(1, n_seg): c_val = np.hstack( [c_val, (c_val[iloc - 1] + gradients[iloc - 1] * turning_points[iloc - 1]) - (gradients[iloc] * turning_points[iloc - 1])]) if xval <= turning_points[0]: return gradients[0] * xval + c_val[0] elif xval > turning_points[-1]: return gradients[-1] * xval + c_val[-1] else: select = np.nonzero(turning_points <= xval)[0][-1] + 1 return gradients[select] * xval + c_val[select]
python
def piecewise_linear_scalar(params, xval): n_params = len(params) n_seg, remainder = divmod(n_params, 2) if remainder: raise ValueError( 'Piecewise Function requires 2 * nsegments parameters') if n_seg == 1: return params[1] + params[0] * xval gradients = params[0:n_seg] turning_points = params[n_seg: -1] c_val = np.array([params[-1]]) for iloc in range(1, n_seg): c_val = np.hstack( [c_val, (c_val[iloc - 1] + gradients[iloc - 1] * turning_points[iloc - 1]) - (gradients[iloc] * turning_points[iloc - 1])]) if xval <= turning_points[0]: return gradients[0] * xval + c_val[0] elif xval > turning_points[-1]: return gradients[-1] * xval + c_val[-1] else: select = np.nonzero(turning_points <= xval)[0][-1] + 1 return gradients[select] * xval + c_val[select]
[ "def", "piecewise_linear_scalar", "(", "params", ",", "xval", ")", ":", "n_params", "=", "len", "(", "params", ")", "n_seg", ",", "remainder", "=", "divmod", "(", "n_params", ",", "2", ")", "if", "remainder", ":", "raise", "ValueError", "(", "'Piecewise Function requires 2 * nsegments parameters'", ")", "if", "n_seg", "==", "1", ":", "return", "params", "[", "1", "]", "+", "params", "[", "0", "]", "*", "xval", "gradients", "=", "params", "[", "0", ":", "n_seg", "]", "turning_points", "=", "params", "[", "n_seg", ":", "-", "1", "]", "c_val", "=", "np", ".", "array", "(", "[", "params", "[", "-", "1", "]", "]", ")", "for", "iloc", "in", "range", "(", "1", ",", "n_seg", ")", ":", "c_val", "=", "np", ".", "hstack", "(", "[", "c_val", ",", "(", "c_val", "[", "iloc", "-", "1", "]", "+", "gradients", "[", "iloc", "-", "1", "]", "*", "turning_points", "[", "iloc", "-", "1", "]", ")", "-", "(", "gradients", "[", "iloc", "]", "*", "turning_points", "[", "iloc", "-", "1", "]", ")", "]", ")", "if", "xval", "<=", "turning_points", "[", "0", "]", ":", "return", "gradients", "[", "0", "]", "*", "xval", "+", "c_val", "[", "0", "]", "elif", "xval", ">", "turning_points", "[", "-", "1", "]", ":", "return", "gradients", "[", "-", "1", "]", "*", "xval", "+", "c_val", "[", "-", "1", "]", "else", ":", "select", "=", "np", ".", "nonzero", "(", "turning_points", "<=", "xval", ")", "[", "0", "]", "[", "-", "1", "]", "+", "1", "return", "gradients", "[", "select", "]", "*", "xval", "+", "c_val", "[", "select", "]" ]
Piecewise linear function for a scalar variable xval (float). :param params: Piecewise linear parameters (numpy.ndarray) in the following form: [slope_i,... slope_n, turning_point_i, ..., turning_point_n, intercept] Length params === 2 * number_segments, e.g. [slope_1, slope_2, slope_3, turning_point1, turning_point_2, intercept] :param xval: Value for evaluation of function (float) :returns: Piecewise linear function evaluated at point xval (float)
[ "Piecewise", "linear", "function", "for", "a", "scalar", "variable", "xval", "(", "float", ")", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L292-L330
gem/oq-engine
openquake/hmtk/seismicity/utils.py
sample_truncated_gaussian_vector
def sample_truncated_gaussian_vector(data, uncertainties, bounds=None): ''' Samples a Gaussian distribution subject to boundaries on the data :param numpy.ndarray data: Vector of N data values :param numpy.ndarray uncertainties: Vector of N data uncertainties :param int number_bootstraps: Number of bootstrap samples :param tuple bounds: (Lower, Upper) bound of data space ''' nvals = len(data) if bounds: # if bounds[0] or (fabs(bounds[0]) < PRECISION): if bounds[0] is not None: lower_bound = (bounds[0] - data) / uncertainties else: lower_bound = -np.inf * np.ones_like(data) # if bounds[1] or (fabs(bounds[1]) < PRECISION): if bounds[1] is not None: upper_bound = (bounds[1] - data) / uncertainties else: upper_bound = np.inf * np.ones_like(data) sample = hmtk_truncnorm.rvs(lower_bound, upper_bound, size=nvals) else: sample = np.random.normal(0., 1., nvals) return data + uncertainties * sample
python
def sample_truncated_gaussian_vector(data, uncertainties, bounds=None): nvals = len(data) if bounds: if bounds[0] is not None: lower_bound = (bounds[0] - data) / uncertainties else: lower_bound = -np.inf * np.ones_like(data) if bounds[1] is not None: upper_bound = (bounds[1] - data) / uncertainties else: upper_bound = np.inf * np.ones_like(data) sample = hmtk_truncnorm.rvs(lower_bound, upper_bound, size=nvals) else: sample = np.random.normal(0., 1., nvals) return data + uncertainties * sample
[ "def", "sample_truncated_gaussian_vector", "(", "data", ",", "uncertainties", ",", "bounds", "=", "None", ")", ":", "nvals", "=", "len", "(", "data", ")", "if", "bounds", ":", "# if bounds[0] or (fabs(bounds[0]) < PRECISION):", "if", "bounds", "[", "0", "]", "is", "not", "None", ":", "lower_bound", "=", "(", "bounds", "[", "0", "]", "-", "data", ")", "/", "uncertainties", "else", ":", "lower_bound", "=", "-", "np", ".", "inf", "*", "np", ".", "ones_like", "(", "data", ")", "# if bounds[1] or (fabs(bounds[1]) < PRECISION):", "if", "bounds", "[", "1", "]", "is", "not", "None", ":", "upper_bound", "=", "(", "bounds", "[", "1", "]", "-", "data", ")", "/", "uncertainties", "else", ":", "upper_bound", "=", "np", ".", "inf", "*", "np", ".", "ones_like", "(", "data", ")", "sample", "=", "hmtk_truncnorm", ".", "rvs", "(", "lower_bound", ",", "upper_bound", ",", "size", "=", "nvals", ")", "else", ":", "sample", "=", "np", ".", "random", ".", "normal", "(", "0.", ",", "1.", ",", "nvals", ")", "return", "data", "+", "uncertainties", "*", "sample" ]
Samples a Gaussian distribution subject to boundaries on the data :param numpy.ndarray data: Vector of N data values :param numpy.ndarray uncertainties: Vector of N data uncertainties :param int number_bootstraps: Number of bootstrap samples :param tuple bounds: (Lower, Upper) bound of data space
[ "Samples", "a", "Gaussian", "distribution", "subject", "to", "boundaries", "on", "the", "data" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L333-L363
gem/oq-engine
openquake/hmtk/seismicity/utils.py
hmtk_histogram_1D
def hmtk_histogram_1D(values, intervals, offset=1.0E-10): """ So, here's the problem. We tend to refer to certain data (like magnitudes) rounded to the nearest 0.1 (or similar, i.e. 4.1, 5.7, 8.3 etc.). We also like our tables to fall on on the same interval, i.e. 3.1, 3.2, 3.3 etc. We usually assume that the counter should correspond to the low edge, i.e. 3.1 is in the group 3.1 to 3.2 (i.e. L <= M < U). Floating point precision can be a bitch! Because when we read in magnitudes from files 3.1 might be represented as 3.0999999999 or as 3.1000000000001 and this is seemingly random. Similarly, if np.arange() is used to generate the bin intervals then we see similar floating point problems emerging. As we are frequently encountering density plots with empty rows or columns where data should be but isn't because it has been assigned to the wrong group. Instead of using numpy's own historgram function we use a slower numpy version that allows us to offset the intervals by a smaller amount and ensure that 3.0999999999, 3.0, and 3.10000000001 would fall in the group 3.1 - 3.2! :param numpy.ndarray values: Values of data :param numpy.ndarray intervals: Data bins :param float offset: Small amount to offset the bins for floating point precision :returns: Count in each bin (as float) """ nbins = len(intervals) - 1 counter = np.zeros(nbins, dtype=float) x_ints = intervals - offset for i in range(nbins): idx = np.logical_and(values >= x_ints[i], values < x_ints[i + 1]) counter[i] += float(np.sum(idx)) return counter
python
def hmtk_histogram_1D(values, intervals, offset=1.0E-10): nbins = len(intervals) - 1 counter = np.zeros(nbins, dtype=float) x_ints = intervals - offset for i in range(nbins): idx = np.logical_and(values >= x_ints[i], values < x_ints[i + 1]) counter[i] += float(np.sum(idx)) return counter
[ "def", "hmtk_histogram_1D", "(", "values", ",", "intervals", ",", "offset", "=", "1.0E-10", ")", ":", "nbins", "=", "len", "(", "intervals", ")", "-", "1", "counter", "=", "np", ".", "zeros", "(", "nbins", ",", "dtype", "=", "float", ")", "x_ints", "=", "intervals", "-", "offset", "for", "i", "in", "range", "(", "nbins", ")", ":", "idx", "=", "np", ".", "logical_and", "(", "values", ">=", "x_ints", "[", "i", "]", ",", "values", "<", "x_ints", "[", "i", "+", "1", "]", ")", "counter", "[", "i", "]", "+=", "float", "(", "np", ".", "sum", "(", "idx", ")", ")", "return", "counter" ]
So, here's the problem. We tend to refer to certain data (like magnitudes) rounded to the nearest 0.1 (or similar, i.e. 4.1, 5.7, 8.3 etc.). We also like our tables to fall on on the same interval, i.e. 3.1, 3.2, 3.3 etc. We usually assume that the counter should correspond to the low edge, i.e. 3.1 is in the group 3.1 to 3.2 (i.e. L <= M < U). Floating point precision can be a bitch! Because when we read in magnitudes from files 3.1 might be represented as 3.0999999999 or as 3.1000000000001 and this is seemingly random. Similarly, if np.arange() is used to generate the bin intervals then we see similar floating point problems emerging. As we are frequently encountering density plots with empty rows or columns where data should be but isn't because it has been assigned to the wrong group. Instead of using numpy's own historgram function we use a slower numpy version that allows us to offset the intervals by a smaller amount and ensure that 3.0999999999, 3.0, and 3.10000000001 would fall in the group 3.1 - 3.2! :param numpy.ndarray values: Values of data :param numpy.ndarray intervals: Data bins :param float offset: Small amount to offset the bins for floating point precision :returns: Count in each bin (as float)
[ "So", "here", "s", "the", "problem", ".", "We", "tend", "to", "refer", "to", "certain", "data", "(", "like", "magnitudes", ")", "rounded", "to", "the", "nearest", "0", ".", "1", "(", "or", "similar", "i", ".", "e", ".", "4", ".", "1", "5", ".", "7", "8", ".", "3", "etc", ".", ")", ".", "We", "also", "like", "our", "tables", "to", "fall", "on", "on", "the", "same", "interval", "i", ".", "e", ".", "3", ".", "1", "3", ".", "2", "3", ".", "3", "etc", ".", "We", "usually", "assume", "that", "the", "counter", "should", "correspond", "to", "the", "low", "edge", "i", ".", "e", ".", "3", ".", "1", "is", "in", "the", "group", "3", ".", "1", "to", "3", ".", "2", "(", "i", ".", "e", ".", "L", "<", "=", "M", "<", "U", ")", ".", "Floating", "point", "precision", "can", "be", "a", "bitch!", "Because", "when", "we", "read", "in", "magnitudes", "from", "files", "3", ".", "1", "might", "be", "represented", "as", "3", ".", "0999999999", "or", "as", "3", ".", "1000000000001", "and", "this", "is", "seemingly", "random", ".", "Similarly", "if", "np", ".", "arange", "()", "is", "used", "to", "generate", "the", "bin", "intervals", "then", "we", "see", "similar", "floating", "point", "problems", "emerging", ".", "As", "we", "are", "frequently", "encountering", "density", "plots", "with", "empty", "rows", "or", "columns", "where", "data", "should", "be", "but", "isn", "t", "because", "it", "has", "been", "assigned", "to", "the", "wrong", "group", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L366-L401
gem/oq-engine
openquake/hmtk/seismicity/utils.py
hmtk_histogram_2D
def hmtk_histogram_2D(xvalues, yvalues, bins, x_offset=1.0E-10, y_offset=1.0E-10): """ See the explanation for the 1D case - now applied to 2D. :param numpy.ndarray xvalues: Values of x-data :param numpy.ndarray yvalues: Values of y-data :param tuple bins: Tuple containing bin intervals for x-data and y-data (as numpy arrays) :param float x_offset: Small amount to offset the x-bins for floating point precision :param float y_offset: Small amount to offset the y-bins for floating point precision :returns: Count in each bin (as float) """ xbins, ybins = (bins[0] - x_offset, bins[1] - y_offset) n_x = len(xbins) - 1 n_y = len(ybins) - 1 counter = np.zeros([n_y, n_x], dtype=float) for j in range(n_y): y_idx = np.logical_and(yvalues >= ybins[j], yvalues < ybins[j + 1]) x_vals = xvalues[y_idx] for i in range(n_x): idx = np.logical_and(x_vals >= xbins[i], x_vals < xbins[i + 1]) counter[j, i] += float(np.sum(idx)) return counter.T
python
def hmtk_histogram_2D(xvalues, yvalues, bins, x_offset=1.0E-10, y_offset=1.0E-10): xbins, ybins = (bins[0] - x_offset, bins[1] - y_offset) n_x = len(xbins) - 1 n_y = len(ybins) - 1 counter = np.zeros([n_y, n_x], dtype=float) for j in range(n_y): y_idx = np.logical_and(yvalues >= ybins[j], yvalues < ybins[j + 1]) x_vals = xvalues[y_idx] for i in range(n_x): idx = np.logical_and(x_vals >= xbins[i], x_vals < xbins[i + 1]) counter[j, i] += float(np.sum(idx)) return counter.T
[ "def", "hmtk_histogram_2D", "(", "xvalues", ",", "yvalues", ",", "bins", ",", "x_offset", "=", "1.0E-10", ",", "y_offset", "=", "1.0E-10", ")", ":", "xbins", ",", "ybins", "=", "(", "bins", "[", "0", "]", "-", "x_offset", ",", "bins", "[", "1", "]", "-", "y_offset", ")", "n_x", "=", "len", "(", "xbins", ")", "-", "1", "n_y", "=", "len", "(", "ybins", ")", "-", "1", "counter", "=", "np", ".", "zeros", "(", "[", "n_y", ",", "n_x", "]", ",", "dtype", "=", "float", ")", "for", "j", "in", "range", "(", "n_y", ")", ":", "y_idx", "=", "np", ".", "logical_and", "(", "yvalues", ">=", "ybins", "[", "j", "]", ",", "yvalues", "<", "ybins", "[", "j", "+", "1", "]", ")", "x_vals", "=", "xvalues", "[", "y_idx", "]", "for", "i", "in", "range", "(", "n_x", ")", ":", "idx", "=", "np", ".", "logical_and", "(", "x_vals", ">=", "xbins", "[", "i", "]", ",", "x_vals", "<", "xbins", "[", "i", "+", "1", "]", ")", "counter", "[", "j", ",", "i", "]", "+=", "float", "(", "np", ".", "sum", "(", "idx", ")", ")", "return", "counter", ".", "T" ]
See the explanation for the 1D case - now applied to 2D. :param numpy.ndarray xvalues: Values of x-data :param numpy.ndarray yvalues: Values of y-data :param tuple bins: Tuple containing bin intervals for x-data and y-data (as numpy arrays) :param float x_offset: Small amount to offset the x-bins for floating point precision :param float y_offset: Small amount to offset the y-bins for floating point precision :returns: Count in each bin (as float)
[ "See", "the", "explanation", "for", "the", "1D", "case", "-", "now", "applied", "to", "2D", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L404-L432
gem/oq-engine
openquake/hmtk/seismicity/utils.py
bootstrap_histogram_1D
def bootstrap_histogram_1D( values, intervals, uncertainties=None, normalisation=False, number_bootstraps=None, boundaries=None): ''' Bootstrap samples a set of vectors :param numpy.ndarray values: The data values :param numpy.ndarray intervals: The bin edges :param numpy.ndarray uncertainties: The standard deviations of each observation :param bool normalisation: If True then returns the histogram as a density function :param int number_bootstraps: Number of bootstraps :param tuple boundaries: (Lower, Upper) bounds on the data :param returns: 1-D histogram of data ''' if not number_bootstraps or np.all(np.fabs(uncertainties < PRECISION)): # No bootstraps or all uncertaintes are zero - return ordinary # histogram #output = np.histogram(values, intervals)[0] output = hmtk_histogram_1D(values, intervals) if normalisation: output = output / float(np.sum(output)) else: output = output return output else: temp_hist = np.zeros([len(intervals) - 1, number_bootstraps], dtype=float) for iloc in range(0, number_bootstraps): sample = sample_truncated_gaussian_vector(values, uncertainties, boundaries) #output = np.histogram(sample, intervals)[0] output = hmtk_histogram_1D(sample, intervals) temp_hist[:, iloc] = output output = np.sum(temp_hist, axis=1) if normalisation: output = output / float(np.sum(output)) else: output = output / float(number_bootstraps) return output
python
def bootstrap_histogram_1D( values, intervals, uncertainties=None, normalisation=False, number_bootstraps=None, boundaries=None): if not number_bootstraps or np.all(np.fabs(uncertainties < PRECISION)): output = hmtk_histogram_1D(values, intervals) if normalisation: output = output / float(np.sum(output)) else: output = output return output else: temp_hist = np.zeros([len(intervals) - 1, number_bootstraps], dtype=float) for iloc in range(0, number_bootstraps): sample = sample_truncated_gaussian_vector(values, uncertainties, boundaries) output = hmtk_histogram_1D(sample, intervals) temp_hist[:, iloc] = output output = np.sum(temp_hist, axis=1) if normalisation: output = output / float(np.sum(output)) else: output = output / float(number_bootstraps) return output
[ "def", "bootstrap_histogram_1D", "(", "values", ",", "intervals", ",", "uncertainties", "=", "None", ",", "normalisation", "=", "False", ",", "number_bootstraps", "=", "None", ",", "boundaries", "=", "None", ")", ":", "if", "not", "number_bootstraps", "or", "np", ".", "all", "(", "np", ".", "fabs", "(", "uncertainties", "<", "PRECISION", ")", ")", ":", "# No bootstraps or all uncertaintes are zero - return ordinary", "# histogram", "#output = np.histogram(values, intervals)[0]", "output", "=", "hmtk_histogram_1D", "(", "values", ",", "intervals", ")", "if", "normalisation", ":", "output", "=", "output", "/", "float", "(", "np", ".", "sum", "(", "output", ")", ")", "else", ":", "output", "=", "output", "return", "output", "else", ":", "temp_hist", "=", "np", ".", "zeros", "(", "[", "len", "(", "intervals", ")", "-", "1", ",", "number_bootstraps", "]", ",", "dtype", "=", "float", ")", "for", "iloc", "in", "range", "(", "0", ",", "number_bootstraps", ")", ":", "sample", "=", "sample_truncated_gaussian_vector", "(", "values", ",", "uncertainties", ",", "boundaries", ")", "#output = np.histogram(sample, intervals)[0]", "output", "=", "hmtk_histogram_1D", "(", "sample", ",", "intervals", ")", "temp_hist", "[", ":", ",", "iloc", "]", "=", "output", "output", "=", "np", ".", "sum", "(", "temp_hist", ",", "axis", "=", "1", ")", "if", "normalisation", ":", "output", "=", "output", "/", "float", "(", "np", ".", "sum", "(", "output", ")", ")", "else", ":", "output", "=", "output", "/", "float", "(", "number_bootstraps", ")", "return", "output" ]
Bootstrap samples a set of vectors :param numpy.ndarray values: The data values :param numpy.ndarray intervals: The bin edges :param numpy.ndarray uncertainties: The standard deviations of each observation :param bool normalisation: If True then returns the histogram as a density function :param int number_bootstraps: Number of bootstraps :param tuple boundaries: (Lower, Upper) bounds on the data :param returns: 1-D histogram of data
[ "Bootstrap", "samples", "a", "set", "of", "vectors" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L435-L483
gem/oq-engine
openquake/hmtk/seismicity/utils.py
bootstrap_histogram_2D
def bootstrap_histogram_2D( xvalues, yvalues, xbins, ybins, boundaries=[None, None], xsigma=None, ysigma=None, normalisation=False, number_bootstraps=None): ''' Calculates a 2D histogram of data, allowing for normalisation and bootstrap sampling :param numpy.ndarray xvalues: Data values of the first variable :param numpy.ndarray yvalues: Data values of the second variable :param numpy.ndarray xbins: Bin edges for the first variable :param numpy.ndarray ybins: Bin edges for the second variable :param list boundaries: List of (Lower, Upper) tuples corresponding to the bounds of the two data sets :param numpy.ndarray xsigma: Error values (standard deviatons) on first variable :param numpy.ndarray ysigma: Error values (standard deviatons) on second variable :param bool normalisation: If True then returns the histogram as a density function :param int number_bootstraps: Number of bootstraps :param returns: 2-D histogram of data ''' if (xsigma is None and ysigma is None) or not number_bootstraps: # No sampling - return simple 2-D histrogram #output = np.histogram2d(xvalues, yvalues, bins=[xbins, ybins])[0] output = hmtk_histogram_2D(xvalues, yvalues, bins=(xbins, ybins)) if normalisation: output = output / float(np.sum(output)) return output else: if xsigma is None: xsigma = np.zeros(len(xvalues), dtype=float) if ysigma is None: ysigma = np.zeros(len(yvalues), dtype=float) temp_hist = np.zeros( [len(xbins) - 1, len(ybins) - 1, number_bootstraps], dtype=float) for iloc in range(0, number_bootstraps): xsample = sample_truncated_gaussian_vector(xvalues, xsigma, boundaries[0]) ysample = sample_truncated_gaussian_vector(yvalues, ysigma, boundaries[0]) # temp_hist[:, :, iloc] = np.histogram2d(xsample, # ysample, # bins=[xbins, ybins])[0] temp_hist[:, :, iloc] = hmtk_histogram_2D(xsample, ysample, bins=(xbins, ybins)) if normalisation: output = np.sum(temp_hist, axis=2) output = output / np.sum(output) else: output = np.sum(temp_hist, axis=2) / float(number_bootstraps) return output
python
def bootstrap_histogram_2D( xvalues, yvalues, xbins, ybins, boundaries=[None, None], xsigma=None, ysigma=None, normalisation=False, number_bootstraps=None): if (xsigma is None and ysigma is None) or not number_bootstraps: output = hmtk_histogram_2D(xvalues, yvalues, bins=(xbins, ybins)) if normalisation: output = output / float(np.sum(output)) return output else: if xsigma is None: xsigma = np.zeros(len(xvalues), dtype=float) if ysigma is None: ysigma = np.zeros(len(yvalues), dtype=float) temp_hist = np.zeros( [len(xbins) - 1, len(ybins) - 1, number_bootstraps], dtype=float) for iloc in range(0, number_bootstraps): xsample = sample_truncated_gaussian_vector(xvalues, xsigma, boundaries[0]) ysample = sample_truncated_gaussian_vector(yvalues, ysigma, boundaries[0]) temp_hist[:, :, iloc] = hmtk_histogram_2D(xsample, ysample, bins=(xbins, ybins)) if normalisation: output = np.sum(temp_hist, axis=2) output = output / np.sum(output) else: output = np.sum(temp_hist, axis=2) / float(number_bootstraps) return output
[ "def", "bootstrap_histogram_2D", "(", "xvalues", ",", "yvalues", ",", "xbins", ",", "ybins", ",", "boundaries", "=", "[", "None", ",", "None", "]", ",", "xsigma", "=", "None", ",", "ysigma", "=", "None", ",", "normalisation", "=", "False", ",", "number_bootstraps", "=", "None", ")", ":", "if", "(", "xsigma", "is", "None", "and", "ysigma", "is", "None", ")", "or", "not", "number_bootstraps", ":", "# No sampling - return simple 2-D histrogram", "#output = np.histogram2d(xvalues, yvalues, bins=[xbins, ybins])[0]", "output", "=", "hmtk_histogram_2D", "(", "xvalues", ",", "yvalues", ",", "bins", "=", "(", "xbins", ",", "ybins", ")", ")", "if", "normalisation", ":", "output", "=", "output", "/", "float", "(", "np", ".", "sum", "(", "output", ")", ")", "return", "output", "else", ":", "if", "xsigma", "is", "None", ":", "xsigma", "=", "np", ".", "zeros", "(", "len", "(", "xvalues", ")", ",", "dtype", "=", "float", ")", "if", "ysigma", "is", "None", ":", "ysigma", "=", "np", ".", "zeros", "(", "len", "(", "yvalues", ")", ",", "dtype", "=", "float", ")", "temp_hist", "=", "np", ".", "zeros", "(", "[", "len", "(", "xbins", ")", "-", "1", ",", "len", "(", "ybins", ")", "-", "1", ",", "number_bootstraps", "]", ",", "dtype", "=", "float", ")", "for", "iloc", "in", "range", "(", "0", ",", "number_bootstraps", ")", ":", "xsample", "=", "sample_truncated_gaussian_vector", "(", "xvalues", ",", "xsigma", ",", "boundaries", "[", "0", "]", ")", "ysample", "=", "sample_truncated_gaussian_vector", "(", "yvalues", ",", "ysigma", ",", "boundaries", "[", "0", "]", ")", "# temp_hist[:, :, iloc] = np.histogram2d(xsample,", "# ysample,", "# bins=[xbins, ybins])[0]", "temp_hist", "[", ":", ",", ":", ",", "iloc", "]", "=", "hmtk_histogram_2D", "(", "xsample", ",", "ysample", ",", "bins", "=", "(", "xbins", ",", "ybins", ")", ")", "if", "normalisation", ":", "output", "=", "np", ".", "sum", "(", "temp_hist", ",", "axis", "=", "2", ")", "output", "=", "output", "/", "np", ".", "sum", "(", "output", ")", "else", ":", "output", "=", "np", ".", "sum", "(", "temp_hist", ",", "axis", "=", "2", ")", "/", "float", "(", "number_bootstraps", ")", "return", "output" ]
Calculates a 2D histogram of data, allowing for normalisation and bootstrap sampling :param numpy.ndarray xvalues: Data values of the first variable :param numpy.ndarray yvalues: Data values of the second variable :param numpy.ndarray xbins: Bin edges for the first variable :param numpy.ndarray ybins: Bin edges for the second variable :param list boundaries: List of (Lower, Upper) tuples corresponding to the bounds of the two data sets :param numpy.ndarray xsigma: Error values (standard deviatons) on first variable :param numpy.ndarray ysigma: Error values (standard deviatons) on second variable :param bool normalisation: If True then returns the histogram as a density function :param int number_bootstraps: Number of bootstraps :param returns: 2-D histogram of data
[ "Calculates", "a", "2D", "histogram", "of", "data", "allowing", "for", "normalisation", "and", "bootstrap", "sampling" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L486-L558
gem/oq-engine
openquake/hmtk/seismicity/utils.py
lonlat_to_laea
def lonlat_to_laea(lon, lat, lon0, lat0, f_e=0.0, f_n=0.0): """ Converts vectors of longitude and latitude into Lambert Azimuthal Equal Area projection (km), with respect to an origin point :param numpy.ndarray lon: Longitudes :param numpy.ndarray lat: Latitude :param float lon0: Central longitude :param float lat0: Central latitude :param float f_e: False easting (km) :param float f_e: False northing (km) :returns: * easting (km) * northing (km) """ lon = np.radians(lon) lat = np.radians(lat) lon0 = np.radians(lon0) lat0 = np.radians(lat0) q_0 = TO_Q(lat0) q_p = TO_Q(np.pi / 2.) q_val = TO_Q(lat) beta = np.arcsin(q_val / q_p) beta0 = np.arcsin(q_0 / q_p) r_q = WGS84["a"] * np.sqrt(q_p / 2.) dval = WGS84["a"] * ( np.cos(lat0) / np.sqrt(1.0 - (WGS84["e2"] * (np.sin(lat0) ** 2.))) / (r_q * np.cos(beta0))) bval = r_q * np.sqrt( 2. / (1.0 + (np.sin(beta0) * np.sin(beta)) + (np.cos(beta) * np.cos(beta0) * np.cos(lon - lon0)))) easting = f_e + ((bval * dval) * (np.cos(beta) * np.sin(lon - lon0))) northing = f_n + (bval / dval) * ((np.cos(beta0) * np.sin(beta)) - (np.sin(beta0) * np.cos(beta) * np.cos(lon - lon0))) return easting, northing
python
def lonlat_to_laea(lon, lat, lon0, lat0, f_e=0.0, f_n=0.0): lon = np.radians(lon) lat = np.radians(lat) lon0 = np.radians(lon0) lat0 = np.radians(lat0) q_0 = TO_Q(lat0) q_p = TO_Q(np.pi / 2.) q_val = TO_Q(lat) beta = np.arcsin(q_val / q_p) beta0 = np.arcsin(q_0 / q_p) r_q = WGS84["a"] * np.sqrt(q_p / 2.) dval = WGS84["a"] * ( np.cos(lat0) / np.sqrt(1.0 - (WGS84["e2"] * (np.sin(lat0) ** 2.))) / (r_q * np.cos(beta0))) bval = r_q * np.sqrt( 2. / (1.0 + (np.sin(beta0) * np.sin(beta)) + (np.cos(beta) * np.cos(beta0) * np.cos(lon - lon0)))) easting = f_e + ((bval * dval) * (np.cos(beta) * np.sin(lon - lon0))) northing = f_n + (bval / dval) * ((np.cos(beta0) * np.sin(beta)) - (np.sin(beta0) * np.cos(beta) * np.cos(lon - lon0))) return easting, northing
[ "def", "lonlat_to_laea", "(", "lon", ",", "lat", ",", "lon0", ",", "lat0", ",", "f_e", "=", "0.0", ",", "f_n", "=", "0.0", ")", ":", "lon", "=", "np", ".", "radians", "(", "lon", ")", "lat", "=", "np", ".", "radians", "(", "lat", ")", "lon0", "=", "np", ".", "radians", "(", "lon0", ")", "lat0", "=", "np", ".", "radians", "(", "lat0", ")", "q_0", "=", "TO_Q", "(", "lat0", ")", "q_p", "=", "TO_Q", "(", "np", ".", "pi", "/", "2.", ")", "q_val", "=", "TO_Q", "(", "lat", ")", "beta", "=", "np", ".", "arcsin", "(", "q_val", "/", "q_p", ")", "beta0", "=", "np", ".", "arcsin", "(", "q_0", "/", "q_p", ")", "r_q", "=", "WGS84", "[", "\"a\"", "]", "*", "np", ".", "sqrt", "(", "q_p", "/", "2.", ")", "dval", "=", "WGS84", "[", "\"a\"", "]", "*", "(", "np", ".", "cos", "(", "lat0", ")", "/", "np", ".", "sqrt", "(", "1.0", "-", "(", "WGS84", "[", "\"e2\"", "]", "*", "(", "np", ".", "sin", "(", "lat0", ")", "**", "2.", ")", ")", ")", "/", "(", "r_q", "*", "np", ".", "cos", "(", "beta0", ")", ")", ")", "bval", "=", "r_q", "*", "np", ".", "sqrt", "(", "2.", "/", "(", "1.0", "+", "(", "np", ".", "sin", "(", "beta0", ")", "*", "np", ".", "sin", "(", "beta", ")", ")", "+", "(", "np", ".", "cos", "(", "beta", ")", "*", "np", ".", "cos", "(", "beta0", ")", "*", "np", ".", "cos", "(", "lon", "-", "lon0", ")", ")", ")", ")", "easting", "=", "f_e", "+", "(", "(", "bval", "*", "dval", ")", "*", "(", "np", ".", "cos", "(", "beta", ")", "*", "np", ".", "sin", "(", "lon", "-", "lon0", ")", ")", ")", "northing", "=", "f_n", "+", "(", "bval", "/", "dval", ")", "*", "(", "(", "np", ".", "cos", "(", "beta0", ")", "*", "np", ".", "sin", "(", "beta", ")", ")", "-", "(", "np", ".", "sin", "(", "beta0", ")", "*", "np", ".", "cos", "(", "beta", ")", "*", "np", ".", "cos", "(", "lon", "-", "lon0", ")", ")", ")", "return", "easting", ",", "northing" ]
Converts vectors of longitude and latitude into Lambert Azimuthal Equal Area projection (km), with respect to an origin point :param numpy.ndarray lon: Longitudes :param numpy.ndarray lat: Latitude :param float lon0: Central longitude :param float lat0: Central latitude :param float f_e: False easting (km) :param float f_e: False northing (km) :returns: * easting (km) * northing (km)
[ "Converts", "vectors", "of", "longitude", "and", "latitude", "into", "Lambert", "Azimuthal", "Equal", "Area", "projection", "(", "km", ")", "with", "respect", "to", "an", "origin", "point" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L585-L625
gem/oq-engine
openquake/hmtk/seismicity/utils.py
area_of_polygon
def area_of_polygon(polygon): """ Returns the area of an OpenQuake polygon in square kilometres """ lon0 = np.mean(polygon.lons) lat0 = np.mean(polygon.lats) # Transform to lamber equal area projection x, y = lonlat_to_laea(polygon.lons, polygon.lats, lon0, lat0) # Build shapely polygons poly = geometry.Polygon(zip(x, y)) return poly.area
python
def area_of_polygon(polygon): lon0 = np.mean(polygon.lons) lat0 = np.mean(polygon.lats) x, y = lonlat_to_laea(polygon.lons, polygon.lats, lon0, lat0) poly = geometry.Polygon(zip(x, y)) return poly.area
[ "def", "area_of_polygon", "(", "polygon", ")", ":", "lon0", "=", "np", ".", "mean", "(", "polygon", ".", "lons", ")", "lat0", "=", "np", ".", "mean", "(", "polygon", ".", "lats", ")", "# Transform to lamber equal area projection", "x", ",", "y", "=", "lonlat_to_laea", "(", "polygon", ".", "lons", ",", "polygon", ".", "lats", ",", "lon0", ",", "lat0", ")", "# Build shapely polygons", "poly", "=", "geometry", ".", "Polygon", "(", "zip", "(", "x", ",", "y", ")", ")", "return", "poly", ".", "area" ]
Returns the area of an OpenQuake polygon in square kilometres
[ "Returns", "the", "area", "of", "an", "OpenQuake", "polygon", "in", "square", "kilometres" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/hmtk/seismicity/utils.py#L628-L638
gem/oq-engine
openquake/commonlib/oqvalidation.py
OqParam.input_dir
def input_dir(self): """ :returns: absolute path to where the job.ini is """ return os.path.abspath(os.path.dirname(self.inputs['job_ini']))
python
def input_dir(self): return os.path.abspath(os.path.dirname(self.inputs['job_ini']))
[ "def", "input_dir", "(", "self", ")", ":", "return", "os", ".", "path", ".", "abspath", "(", "os", ".", "path", ".", "dirname", "(", "self", ".", "inputs", "[", "'job_ini'", "]", ")", ")" ]
:returns: absolute path to where the job.ini is
[ ":", "returns", ":", "absolute", "path", "to", "where", "the", "job", ".", "ini", "is" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqvalidation.py#L170-L174
gem/oq-engine
openquake/commonlib/oqvalidation.py
OqParam.get_reqv
def get_reqv(self): """ :returns: an instance of class:`RjbEquivalent` if reqv_hdf5 is set """ if 'reqv' not in self.inputs: return return {key: valid.RjbEquivalent(value) for key, value in self.inputs['reqv'].items()}
python
def get_reqv(self): if 'reqv' not in self.inputs: return return {key: valid.RjbEquivalent(value) for key, value in self.inputs['reqv'].items()}
[ "def", "get_reqv", "(", "self", ")", ":", "if", "'reqv'", "not", "in", "self", ".", "inputs", ":", "return", "return", "{", "key", ":", "valid", ".", "RjbEquivalent", "(", "value", ")", "for", "key", ",", "value", "in", "self", ".", "inputs", "[", "'reqv'", "]", ".", "items", "(", ")", "}" ]
:returns: an instance of class:`RjbEquivalent` if reqv_hdf5 is set
[ ":", "returns", ":", "an", "instance", "of", "class", ":", "RjbEquivalent", "if", "reqv_hdf5", "is", "set" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqvalidation.py#L176-L183
gem/oq-engine
openquake/commonlib/oqvalidation.py
OqParam.check_gsims
def check_gsims(self, gsims): """ :param gsims: a sequence of GSIM instances """ imts = set(from_string(imt).name for imt in self.imtls) for gsim in gsims: restrict_imts = gsim.DEFINED_FOR_INTENSITY_MEASURE_TYPES if restrict_imts: names = set(cls.__name__ for cls in restrict_imts) invalid_imts = ', '.join(imts - names) if invalid_imts: raise ValueError( 'The IMT %s is not accepted by the GSIM %s' % (invalid_imts, gsim)) if 'site_model' not in self.inputs: # look at the required sites parameters: they must have # a valid value; the other parameters can keep a NaN # value since they are not used by the calculator for param in gsim.REQUIRES_SITES_PARAMETERS: if param in ('lon', 'lat'): # no check continue param_name = self.siteparam[param] param_value = getattr(self, param_name) if (isinstance(param_value, float) and numpy.isnan(param_value)): raise ValueError( 'Please set a value for %r, this is required by ' 'the GSIM %s' % (param_name, gsim))
python
def check_gsims(self, gsims): imts = set(from_string(imt).name for imt in self.imtls) for gsim in gsims: restrict_imts = gsim.DEFINED_FOR_INTENSITY_MEASURE_TYPES if restrict_imts: names = set(cls.__name__ for cls in restrict_imts) invalid_imts = ', '.join(imts - names) if invalid_imts: raise ValueError( 'The IMT %s is not accepted by the GSIM %s' % (invalid_imts, gsim)) if 'site_model' not in self.inputs: for param in gsim.REQUIRES_SITES_PARAMETERS: if param in ('lon', 'lat'): continue param_name = self.siteparam[param] param_value = getattr(self, param_name) if (isinstance(param_value, float) and numpy.isnan(param_value)): raise ValueError( 'Please set a value for %r, this is required by ' 'the GSIM %s' % (param_name, gsim))
[ "def", "check_gsims", "(", "self", ",", "gsims", ")", ":", "imts", "=", "set", "(", "from_string", "(", "imt", ")", ".", "name", "for", "imt", "in", "self", ".", "imtls", ")", "for", "gsim", "in", "gsims", ":", "restrict_imts", "=", "gsim", ".", "DEFINED_FOR_INTENSITY_MEASURE_TYPES", "if", "restrict_imts", ":", "names", "=", "set", "(", "cls", ".", "__name__", "for", "cls", "in", "restrict_imts", ")", "invalid_imts", "=", "', '", ".", "join", "(", "imts", "-", "names", ")", "if", "invalid_imts", ":", "raise", "ValueError", "(", "'The IMT %s is not accepted by the GSIM %s'", "%", "(", "invalid_imts", ",", "gsim", ")", ")", "if", "'site_model'", "not", "in", "self", ".", "inputs", ":", "# look at the required sites parameters: they must have", "# a valid value; the other parameters can keep a NaN", "# value since they are not used by the calculator", "for", "param", "in", "gsim", ".", "REQUIRES_SITES_PARAMETERS", ":", "if", "param", "in", "(", "'lon'", ",", "'lat'", ")", ":", "# no check", "continue", "param_name", "=", "self", ".", "siteparam", "[", "param", "]", "param_value", "=", "getattr", "(", "self", ",", "param_name", ")", "if", "(", "isinstance", "(", "param_value", ",", "float", ")", "and", "numpy", ".", "isnan", "(", "param_value", ")", ")", ":", "raise", "ValueError", "(", "'Please set a value for %r, this is required by '", "'the GSIM %s'", "%", "(", "param_name", ",", "gsim", ")", ")" ]
:param gsims: a sequence of GSIM instances
[ ":", "param", "gsims", ":", "a", "sequence", "of", "GSIM", "instances" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqvalidation.py#L316-L344
gem/oq-engine
openquake/commonlib/oqvalidation.py
OqParam.ses_ratio
def ses_ratio(self): """ The ratio risk_investigation_time / investigation_time / ses_per_logic_tree_path """ if self.investigation_time is None: raise ValueError('Missing investigation_time in the .ini file') return (self.risk_investigation_time or self.investigation_time) / ( self.investigation_time * self.ses_per_logic_tree_path)
python
def ses_ratio(self): if self.investigation_time is None: raise ValueError('Missing investigation_time in the .ini file') return (self.risk_investigation_time or self.investigation_time) / ( self.investigation_time * self.ses_per_logic_tree_path)
[ "def", "ses_ratio", "(", "self", ")", ":", "if", "self", ".", "investigation_time", "is", "None", ":", "raise", "ValueError", "(", "'Missing investigation_time in the .ini file'", ")", "return", "(", "self", ".", "risk_investigation_time", "or", "self", ".", "investigation_time", ")", "/", "(", "self", ".", "investigation_time", "*", "self", ".", "ses_per_logic_tree_path", ")" ]
The ratio risk_investigation_time / investigation_time / ses_per_logic_tree_path
[ "The", "ratio" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqvalidation.py#L356-L365
gem/oq-engine
openquake/commonlib/oqvalidation.py
OqParam.all_cost_types
def all_cost_types(self): """ Return the cost types of the computation (including `occupants` if it is there) in order. """ # rt has the form 'vulnerability/structural', 'fragility/...', ... costtypes = sorted(rt.rsplit('/')[1] for rt in self.risk_files) if not costtypes and self.hazard_calculation_id: with util.read(self.hazard_calculation_id) as ds: parent = ds['oqparam'] self._risk_files = get_risk_files(parent.inputs) costtypes = sorted(rt.rsplit('/')[1] for rt in self.risk_files) return costtypes
python
def all_cost_types(self): costtypes = sorted(rt.rsplit('/')[1] for rt in self.risk_files) if not costtypes and self.hazard_calculation_id: with util.read(self.hazard_calculation_id) as ds: parent = ds['oqparam'] self._risk_files = get_risk_files(parent.inputs) costtypes = sorted(rt.rsplit('/')[1] for rt in self.risk_files) return costtypes
[ "def", "all_cost_types", "(", "self", ")", ":", "# rt has the form 'vulnerability/structural', 'fragility/...', ...", "costtypes", "=", "sorted", "(", "rt", ".", "rsplit", "(", "'/'", ")", "[", "1", "]", "for", "rt", "in", "self", ".", "risk_files", ")", "if", "not", "costtypes", "and", "self", ".", "hazard_calculation_id", ":", "with", "util", ".", "read", "(", "self", ".", "hazard_calculation_id", ")", "as", "ds", ":", "parent", "=", "ds", "[", "'oqparam'", "]", "self", ".", "_risk_files", "=", "get_risk_files", "(", "parent", ".", "inputs", ")", "costtypes", "=", "sorted", "(", "rt", ".", "rsplit", "(", "'/'", ")", "[", "1", "]", "for", "rt", "in", "self", ".", "risk_files", ")", "return", "costtypes" ]
Return the cost types of the computation (including `occupants` if it is there) in order.
[ "Return", "the", "cost", "types", "of", "the", "computation", "(", "including", "occupants", "if", "it", "is", "there", ")", "in", "order", "." ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqvalidation.py#L377-L389
gem/oq-engine
openquake/commonlib/oqvalidation.py
OqParam.min_iml
def min_iml(self): """ :returns: a numpy array of intensities, one per IMT """ mini = self.minimum_intensity if mini: for imt in self.imtls: try: mini[imt] = calc.filters.getdefault(mini, imt) except KeyError: raise ValueError( 'The parameter `minimum_intensity` in the job.ini ' 'file is missing the IMT %r' % imt) if 'default' in mini: del mini['default'] return F32([mini.get(imt, 0) for imt in self.imtls])
python
def min_iml(self): mini = self.minimum_intensity if mini: for imt in self.imtls: try: mini[imt] = calc.filters.getdefault(mini, imt) except KeyError: raise ValueError( 'The parameter `minimum_intensity` in the job.ini ' 'file is missing the IMT %r' % imt) if 'default' in mini: del mini['default'] return F32([mini.get(imt, 0) for imt in self.imtls])
[ "def", "min_iml", "(", "self", ")", ":", "mini", "=", "self", ".", "minimum_intensity", "if", "mini", ":", "for", "imt", "in", "self", ".", "imtls", ":", "try", ":", "mini", "[", "imt", "]", "=", "calc", ".", "filters", ".", "getdefault", "(", "mini", ",", "imt", ")", "except", "KeyError", ":", "raise", "ValueError", "(", "'The parameter `minimum_intensity` in the job.ini '", "'file is missing the IMT %r'", "%", "imt", ")", "if", "'default'", "in", "mini", ":", "del", "mini", "[", "'default'", "]", "return", "F32", "(", "[", "mini", ".", "get", "(", "imt", ",", "0", ")", "for", "imt", "in", "self", ".", "imtls", "]", ")" ]
:returns: a numpy array of intensities, one per IMT
[ ":", "returns", ":", "a", "numpy", "array", "of", "intensities", "one", "per", "IMT" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqvalidation.py#L392-L407
gem/oq-engine
openquake/commonlib/oqvalidation.py
OqParam.set_risk_imtls
def set_risk_imtls(self, risk_models): """ :param risk_models: a dictionary taxonomy -> loss_type -> risk_function Set the attribute risk_imtls. """ # NB: different loss types may have different IMLs for the same IMT # in that case we merge the IMLs imtls = {} for taxonomy, risk_functions in risk_models.items(): for risk_type, rf in risk_functions.items(): imt = rf.imt from_string(imt) # make sure it is a valid IMT imls = list(rf.imls) if imt in imtls and imtls[imt] != imls: logging.debug( 'Different levels for IMT %s: got %s, expected %s', imt, imls, imtls[imt]) imtls[imt] = sorted(set(imls + imtls[imt])) else: imtls[imt] = imls self.risk_imtls = imtls if self.uniform_hazard_spectra: self.check_uniform_hazard_spectra()
python
def set_risk_imtls(self, risk_models): imtls = {} for taxonomy, risk_functions in risk_models.items(): for risk_type, rf in risk_functions.items(): imt = rf.imt from_string(imt) imls = list(rf.imls) if imt in imtls and imtls[imt] != imls: logging.debug( 'Different levels for IMT %s: got %s, expected %s', imt, imls, imtls[imt]) imtls[imt] = sorted(set(imls + imtls[imt])) else: imtls[imt] = imls self.risk_imtls = imtls if self.uniform_hazard_spectra: self.check_uniform_hazard_spectra()
[ "def", "set_risk_imtls", "(", "self", ",", "risk_models", ")", ":", "# NB: different loss types may have different IMLs for the same IMT", "# in that case we merge the IMLs", "imtls", "=", "{", "}", "for", "taxonomy", ",", "risk_functions", "in", "risk_models", ".", "items", "(", ")", ":", "for", "risk_type", ",", "rf", "in", "risk_functions", ".", "items", "(", ")", ":", "imt", "=", "rf", ".", "imt", "from_string", "(", "imt", ")", "# make sure it is a valid IMT", "imls", "=", "list", "(", "rf", ".", "imls", ")", "if", "imt", "in", "imtls", "and", "imtls", "[", "imt", "]", "!=", "imls", ":", "logging", ".", "debug", "(", "'Different levels for IMT %s: got %s, expected %s'", ",", "imt", ",", "imls", ",", "imtls", "[", "imt", "]", ")", "imtls", "[", "imt", "]", "=", "sorted", "(", "set", "(", "imls", "+", "imtls", "[", "imt", "]", ")", ")", "else", ":", "imtls", "[", "imt", "]", "=", "imls", "self", ".", "risk_imtls", "=", "imtls", "if", "self", ".", "uniform_hazard_spectra", ":", "self", ".", "check_uniform_hazard_spectra", "(", ")" ]
:param risk_models: a dictionary taxonomy -> loss_type -> risk_function Set the attribute risk_imtls.
[ ":", "param", "risk_models", ":", "a", "dictionary", "taxonomy", "-", ">", "loss_type", "-", ">", "risk_function" ]
train
https://github.com/gem/oq-engine/blob/8294553a0b8aba33fd96437a35065d03547d0040/openquake/commonlib/oqvalidation.py#L409-L433