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
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)
info = logictree.collect_info(ssmLT)
files = info.h5paths + info.smpaths
oq = mock.Mock(inputs={'source_model_logic_tree': ssmLT},
random_seed=42,
number_of_logic_tree_samples=0,
sampling_method='early_weights')
oq._input_files = readinput.get_input_files(oq)
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.extend([os.path.abspath(ssmLT), os.path.abspath(checkfile)])
general.zipfiles(files, archive_zip, log=log, cleanup=True)
return archive_zip
def reduce_source_model(smlt_file, source_ids, remove=True):
"""
Extract sources from the composite source model.
:param smlt_file: path to a source model logic tree file
:param source_ids: dictionary source_id -> records (src_id, code)
:param remove: if True, remove sm.xml files containing no sources
:returns: the number of sources satisfying the filter vs the total
"""
total = good = 0
to_remove = set()
paths = logictree.collect_info(smlt_file).smpaths
for dic in parallel.Starmap.apply(reduce_sm, (paths, source_ids)):
path = dic['path']
model = dic['model']
good += dic['good']
total += dic['total']
shutil.copy(path, path + '.bak')
if model:
with open(path, 'wb') as f:
nrml.write([model], f, xmlns=dic['xmlns'])
elif remove: # remove the files completely reduced
to_remove.add(path)
if good:
for path in to_remove:
os.remove(path)
parallel.Starmap.shutdown()
return good, total
def checksum(thing):
"""
Get the checksum of a calculation from the calculation ID (if already
done) or from the job.ini/job.zip file (if not done yet). If `thing`
is a source model logic tree file, get the checksum of the model by
ignoring the job.ini, the gmpe logic tree file and possibly other files.
"""
try:
job_id = int(thing)
job_file = None
except ValueError:
job_id = None
job_file = thing
if not os.path.exists(job_file):
sys.exit('%s does not correspond to an existing file' % job_file)
if job_id:
dstore = datastore.read(job_id)
checksum = dstore['/'].attrs['checksum32']
elif job_file.endswith('.xml'): # assume it is a smlt file
inputs = {p: p for p in logictree.collect_info(job_file).smpaths}
checksum = readinput.get_checksum32(inputs)
else:
oq = readinput.get_oqparam(job_file)
checksum = readinput.get_checksum32(oq.inputs)
print(checksum)
def reduce_source_model(smlt_file, source_ids, remove=True):
"""
Extract sources from the composite source model
"""
for path in logictree.collect_info(smlt_file).smpaths:
root = nrml.read(path)
model = Node('sourceModel', root[0].attrib)
origmodel = root[0]
if root['xmlns'] == 'http://openquake.org/xmlns/nrml/0.4':
for src_node in origmodel:
if src_node['id'] in source_ids:
model.nodes.append(src_node)
else: # nrml/0.5
for src_group in origmodel:
sg = copy.copy(src_group)
sg.nodes = []
weights = src_group.get('srcs_weights')
if weights:
assert len(weights) == len(src_group.nodes)
else:
weights = [1] * len(src_group.nodes)
src_group['srcs_weights'] = reduced_weigths = []
for src_node, weight in zip(src_group, weights):
if src_node['id'] in source_ids:
sg.nodes.append(src_node)
reduced_weigths.append(weight)
if sg.nodes:
model.nodes.append(sg)
shutil.copy(path, path + '.bak')
if model:
with open(path, 'wb') as f:
nrml.write([model], f, xmlns=root['xmlns'])
logging.warn('Reduced %s' % path)
elif remove: # remove the files completely reduced
os.remove(path)
def get_input_files(oqparam):
"""
:param oqparam: an OqParam instance
:param hazard: if True, consider only the hazard files
:returns: input path names in a specific order
"""
fnames = set() # files entering in the checksum
uri = oqparam.shakemap_uri
if isinstance(uri, dict) and uri:
# local files
for key, val in uri.items():
if key == 'fname' or key.endswith('_url'):
val = val.replace('file://', '')
fname = os.path.join(oqparam.base_path, val)
if os.path.exists(fname):
uri[key] = fname
fnames.add(fname)
# additional separate shapefiles
if uri['kind'] == 'shapefile' and not uri['fname'].endswith('.zip'):
fnames.update(get_shapefiles(os.path.dirname(fname)))
for key in oqparam.inputs:
fname = oqparam.inputs[key]
# collect .hdf5 tables for the GSIMs, if any
if key == 'gsim_logic_tree':
fnames.update(gsim_lt.collect_files(fname))
fnames.add(fname)
elif key == 'source_model': # UCERF
f = oqparam.inputs['source_model']
fnames.add(f)
fname = nrml.read(f).sourceModel.UCERFSource['filename']
fnames.add(os.path.join(os.path.dirname(f), fname))
elif key == 'exposure': # fname is a list
for exp in asset.Exposure.read_headers(fname):
fnames.update(exp.datafiles)
fnames.update(fname)
elif isinstance(fname, dict):
for key, val in fname.items():
if isinstance(val, list): # list of files
fnames.update(val)
else:
fnames.add(val)
elif isinstance(fname, list):
for f in fname:
if f == oqparam.input_dir:
raise InvalidFile('%s there is an empty path in %s' %
(oqparam.inputs['job_ini'], key))
fnames.update(fname)
elif key == 'source_model_logic_tree':
info = logictree.collect_info(fname)
fnames.update(info.smpaths)
fnames.update(info.h5paths)
fnames.add(fname)
else:
fnames.add(fname)
return sorted(fnames)
def reduce(fname, reduction_factor):
"""
Produce a submodel from `fname` by sampling the nodes randomly.
Supports source models, site models and exposure models. As a special
case, it is also able to reduce .csv files by sampling the lines.
This is a debugging utility to reduce large computations to small ones.
"""
if fname.endswith('.csv'):
with open(fname) as f:
line = f.readline() # read the first line
if csv.Sniffer().has_header(line):
header = line
all_lines = f.readlines()
else:
header = None
f.seek(0)
all_lines = f.readlines()
lines = general.random_filter(all_lines, reduction_factor)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
_save_csv(fname, lines, header)
print('Extracted %d lines out of %d' % (len(lines), len(all_lines)))
return
elif fname.endswith('.npy'):
array = numpy.load(fname)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
arr = numpy.array(general.random_filter(array, reduction_factor))
numpy.save(fname, arr)
print('Extracted %d rows out of %d' % (len(arr), len(array)))
return
node = nrml.read(fname)
model = node[0]
if model.tag.endswith('exposureModel'):
total = len(model.assets)
model.assets.nodes = general.random_filter(
model.assets, reduction_factor)
num_nodes = len(model.assets)
elif model.tag.endswith('siteModel'):
total = len(model)
model.nodes = general.random_filter(model, reduction_factor)
num_nodes = len(model)
elif model.tag.endswith('sourceModel'):
reduce_source_model(fname, reduction_factor)
return
elif model.tag.endswith('logicTree'):
for smpath in logictree.collect_info(fname).smpaths:
reduce_source_model(smpath, reduction_factor)
return
else:
raise RuntimeError('Unknown model tag: %s' % model.tag)
save_bak(fname, node, num_nodes, total)
def reduce(fname, reduction_factor):
"""
Produce a submodel from `fname` by sampling the nodes randomly.
Supports source models, site models and exposure models. As a special
case, it is also able to reduce .csv files by sampling the lines.
This is a debugging utility to reduce large computations to small ones.
"""
if fname.endswith('.csv'):
with open(fname) as f:
line = f.readline() # read the first line
if csv.Sniffer().has_header(line):
header = line
all_lines = f.readlines()
else:
header = None
f.seek(0)
all_lines = f.readlines()
lines = general.random_filter(all_lines, reduction_factor)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
_save_csv(fname, lines, header)
print('Extracted %d lines out of %d' % (len(lines), len(all_lines)))
return
elif fname.endswith('.npy'):
array = numpy.load(fname)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
arr = numpy.array(general.random_filter(array, reduction_factor))
numpy.save(fname, arr)
print('Extracted %d rows out of %d' % (len(arr), len(array)))
return
node = nrml.read(fname)
model = node[0]
if model.tag.endswith('exposureModel'):
total = len(model.assets)
model.assets.nodes = general.random_filter(model.assets,
reduction_factor)
num_nodes = len(model.assets)
elif model.tag.endswith('siteModel'):
total = len(model)
model.nodes = general.random_filter(model, reduction_factor)
num_nodes = len(model)
elif model.tag.endswith('sourceModel'):
reduce_source_model(fname, reduction_factor)
return
elif model.tag.endswith('logicTree'):
for smpath in logictree.collect_info(fname).smpaths:
reduce_source_model(smpath, reduction_factor)
return
else:
raise RuntimeError('Unknown model tag: %s' % model.tag)
save_bak(fname, node, num_nodes, total)
def get_input_files(oqparam, hazard=False):
"""
:param oqparam: an OqParam instance
:param hazard: if True, consider only the hazard files
:returns: input path names in a specific order
"""
fnames = set() # files entering in the checksum
for key in oqparam.inputs:
fname = oqparam.inputs[key]
if hazard and key not in ('site_model', 'source_model_logic_tree',
'gsim_logic_tree', 'source'):
continue
# collect .hdf5 tables for the GSIMs, if any
elif key == 'gsim_logic_tree':
gsim_lt = get_gsim_lt(oqparam)
for gsims in gsim_lt.values.values():
for gsim in gsims:
for k, v in gsim.kwargs.items():
if k.endswith(('_file', '_table')):
fnames.add(v)
fnames.add(fname)
elif key == 'source_model': # UCERF
f = oqparam.inputs['source_model']
fnames.add(f)
fname = nrml.read(f).sourceModel.UCERFSource['filename']
fnames.add(os.path.join(os.path.dirname(f), fname))
elif key == 'exposure': # fname is a list
for exp in asset.Exposure.read_headers(fname):
fnames.update(exp.datafiles)
fnames.update(fname)
elif isinstance(fname, dict):
fnames.update(fname.values())
elif isinstance(fname, list):
for f in fname:
if f == oqparam.input_dir:
raise InvalidFile('%s there is an empty path in %s' %
(oqparam.inputs['job_ini'], key))
fnames.update(fname)
elif key == 'source_model_logic_tree':
for smpaths in logictree.collect_info(fname).smpaths.values():
fnames.update(smpaths)
fnames.add(fname)
else:
fnames.add(fname)
return sorted(fnames)
def get_input_files(oqparam, hazard=False):
"""
:param oqparam: an OqParam instance
:param hazard: if True, consider only the hazard files
:returns: input path names in a specific order
"""
fnames = [] # files entering in the checksum
for key in oqparam.inputs:
fname = oqparam.inputs[key]
if hazard and key not in ('site_model', 'source_model_logic_tree',
'gsim_logic_tree', 'source'):
continue
# collect .hdf5 tables for the GSIMs, if any
elif key == 'gsim_logic_tree':
gsim_lt = get_gsim_lt(oqparam)
for gsims in gsim_lt.values.values():
for gsim in gsims:
table = getattr(gsim, 'GMPE_TABLE', None)
if table:
fnames.append(table)
fnames.append(fname)
elif key == 'source_model': # UCERF
f = oqparam.inputs['source_model']
fnames.append(f)
fname = nrml.read(f).sourceModel.UCERFSource['filename']
fnames.append(os.path.join(os.path.dirname(f), fname))
elif key == 'exposure': # fname is a list
for exp in asset.Exposure.read_headers(fname):
fnames.extend(exp.datafiles)
fnames.extend(fname)
elif isinstance(fname, dict):
fnames.extend(fname.values())
elif isinstance(fname, list):
for f in fname:
if f == oqparam.input_dir:
raise InvalidFile('%s there is an empty path in %s' %
(oqparam.inputs['job_ini'], key))
fnames.extend(fname)
elif key == 'source_model_logic_tree':
for smpaths in logictree.collect_info(fname).smpaths.values():
fnames.extend(smpaths)
fnames.append(fname)
else:
fnames.append(fname)
return sorted(fnames)
def get_params(job_inis, **kw):
"""
Parse one or more INI-style config files.
:param job_inis:
List of configuration files (or list containing a single zip archive)
:param kw:
Optionally override some parameters
:returns:
A dictionary of parameters
"""
input_zip = None
if len(job_inis) == 1 and job_inis[0].endswith('.zip'):
input_zip = job_inis[0]
job_inis = extract_from_zip(
job_inis[0],
['job_hazard.ini', 'job_haz.ini', 'job.ini', 'job_risk.ini'])
not_found = [ini for ini in job_inis if not os.path.exists(ini)]
if not_found: # something was not found
raise IOError('File not found: %s' % not_found[0])
cp = configparser.ConfigParser()
cp.read(job_inis)
# directory containing the config files we're parsing
job_ini = os.path.abspath(job_inis[0])
base_path = decode(os.path.dirname(job_ini))
params = dict(base_path=base_path, inputs={'job_ini': job_ini})
if input_zip:
params['inputs']['input_zip'] = os.path.abspath(input_zip)
for sect in cp.sections():
_update(params, cp.items(sect), base_path)
_update(params, kw.items(), base_path) # override on demand
# populate the 'source' list
inputs = params['inputs']
smlt = inputs.get('source_model_logic_tree')
if smlt:
inputs['source'] = logictree.collect_info(smlt).smpaths
elif 'source_model' in inputs:
inputs['source'] = [inputs['source_model']]
return params
def reduce_source_model(smlt_file, source_ids, remove=True):
"""
Extract sources from the composite source model
"""
found = 0
to_remove = []
for paths in logictree.collect_info(smlt_file).smpaths.values():
for path in paths:
logging.info('Reading %s', path)
root = nrml.read(path)
model = Node('sourceModel', root[0].attrib)
origmodel = root[0]
if root['xmlns'] == 'http://openquake.org/xmlns/nrml/0.4':
for src_node in origmodel:
if src_node['id'] in source_ids:
model.nodes.append(src_node)
else: # nrml/0.5
for src_group in origmodel:
sg = copy.copy(src_group)
sg.nodes = []
weights = src_group.get('srcs_weights')
if weights:
assert len(weights) == len(src_group.nodes)
else:
weights = [1] * len(src_group.nodes)
src_group['srcs_weights'] = reduced_weigths = []
for src_node, weight in zip(src_group, weights):
if src_node['id'] in source_ids:
found += 1
sg.nodes.append(src_node)
reduced_weigths.append(weight)
if sg.nodes:
model.nodes.append(sg)
shutil.copy(path, path + '.bak')
if model:
with open(path, 'wb') as f:
nrml.write([model], f, xmlns=root['xmlns'])
elif remove: # remove the files completely reduced
to_remove.append(path)
if found:
for path in to_remove:
os.remove(path)
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 = [os.path.abspath(ssmLT), os.path.abspath(checkfile)]
for fs in logictree.collect_info(ssmLT).smpaths.values():
files.extend(fs)
general.zipfiles(files, archive_zip, log=log, cleanup=True)
return archive_zip
def renumber_sm(smlt_file):
"""
Renumber the sources belonging to the same source model, even if split
in multiple files, to avoid duplicated source IDs. NB: it changes the
XML files in place, without making a backup, so be careful.
"""
logging.basicConfig(level=logging.INFO)
smpaths = logictree.collect_info(smlt_file).smpaths
smap = parallel.Starmap(read_sm, [(path, ) for path in smpaths])
smodel, srcs = {}, []
for sm, fname, sources in smap:
smodel[fname] = sm
srcs.extend(sources)
parallel.Starmap.shutdown()
dic = general.groupby(srcs, operator.attrgetter('value'))
n = 1
for sources in dic.values():
for src in sources:
src.node['id'] = str(n)
n += 1
for fname, root in smodel.items():
logging.info('Saving %s', fname)
with open(fname, 'wb') as f:
nrml.write(root, f, xmlns=root['xmlns'])
def reduce_source_model(smlt_file, source_ids, remove=True):
"""
Extract sources from the composite source model.
:param smlt_file: path to a source model logic tree file
:param source_ids: dictionary source_id -> records (src_id, code)
:param remove: if True, remove sm.xml files containing no sources
:returns: the number of sources satisfying the filter vs the total
"""
if isinstance(source_ids, dict): # in oq reduce_sm
def ok(src_node):
code = tag2code[re.search(r'\}(\w\w)', src_node.tag).group(1)]
arr = source_ids.get(src_node['id'])
if arr is None:
return False
return (arr['code'] == code).any()
else: # list of source IDs, in extract_source
def ok(src_node):
return src_node['id'] in source_ids
good, total = 0, 0
to_remove = set()
for paths in logictree.collect_info(smlt_file).smpaths.values():
for path in paths:
logging.info('Reading %s', path)
root = nrml.read(path)
model = Node('sourceModel', root[0].attrib)
origmodel = root[0]
if root['xmlns'] == 'http://openquake.org/xmlns/nrml/0.4':
for src_node in origmodel:
total += 1
if ok(src_node):
good += 1
model.nodes.append(src_node)
else: # nrml/0.5
for src_group in origmodel:
sg = copy.copy(src_group)
sg.nodes = []
weights = src_group.get('srcs_weights')
if weights:
assert len(weights) == len(src_group.nodes)
else:
weights = [1] * len(src_group.nodes)
src_group['srcs_weights'] = reduced_weigths = []
for src_node, weight in zip(src_group, weights):
total += 1
if ok(src_node):
good += 1
sg.nodes.append(src_node)
reduced_weigths.append(weight)
if sg.nodes:
model.nodes.append(sg)
shutil.copy(path, path + '.bak')
if model:
with open(path, 'wb') as f:
nrml.write([model], f, xmlns=root['xmlns'])
elif remove: # remove the files completely reduced
to_remove.add(path)
if good:
for path in to_remove:
os.remove(path)
return good, total
def main(what, report=False):
"""
Give information about the passed keyword or filename
"""
if os.environ.get('OQ_DISTRIBUTE') not in ('no', 'processpool'):
os.environ['OQ_DISTRIBUTE'] = 'processpool'
if what == 'calculators':
for calc in sorted(base.calculators):
print(calc)
elif what == 'gsims':
for gs in gsim.get_available_gsims():
print(gs)
elif what == 'imts':
for im in gen_subclasses(imt.IMT):
print(im.__name__)
elif what == 'views':
for name in sorted(view):
print(name)
elif what == 'exports':
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
elif what == 'extracts':
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
elif hasattr(func, 'func'): # for partial objects
fm = FunctionMaker(func.func)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
elif what == 'parameters':
params = []
for val in vars(OqParam).values():
if hasattr(val, 'name'):
params.append(val)
params.sort(key=lambda x: x.name)
for param in params:
print(param.name)
elif what == 'mfds':
for cls in gen_subclasses(BaseMFD):
print(cls.__name__)
elif what == 'sources':
for cls in gen_subclasses(BaseSeismicSource):
print(cls.__name__)
elif os.path.isdir(what) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(what)
print(mon)
elif what.endswith('.xml'):
node = nrml.read(what)
if node[0].tag.endswith('sourceModel'):
print(source_model_info([node]))
elif node[0].tag.endswith('logicTree'):
sm_nodes = []
for smpath in logictree.collect_info(what).smpaths:
sm_nodes.append(nrml.read(smpath))
print(source_model_info(sm_nodes))
else:
print(node.to_str())
elif what.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(what))
else:
print(readinput.get_oqparam(what).json())
if mon.duration > 1:
print(mon)
elif what:
print("No info for '%s'" % what)
def main(what, report=False):
"""
Give information about the passed keyword or filename
"""
if os.environ.get('OQ_DISTRIBUTE') not in ('no', 'processpool'):
os.environ['OQ_DISTRIBUTE'] = 'processpool'
if what == 'calculators':
for calc in sorted(base.calculators):
print(calc)
elif what == 'gsims':
for gs in gsim.get_available_gsims():
print(gs)
elif what == 'portable_gsims':
for gs in gsim.get_portable_gsims():
print(gs)
elif what == 'imts':
for im in vars(imt).values():
if inspect.isfunction(im) and is_upper(im):
print(im.__name__)
elif what == 'views':
for name in sorted(view):
print(name)
elif what == 'exports':
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
items = [(DISPLAY_NAME.get(exporter, '?'), exporter, formats)
for exporter, formats in dic.items()]
n = 0
for dispname, exporter, formats in sorted(items):
print(dispname, '"%s"' % exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
elif what == 'extracts':
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
elif hasattr(func, 'func'): # for partial objects
fm = FunctionMaker(func.func)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
elif what == 'parameters':
docs = OqParam.docs()
names = set()
for val in vars(OqParam).values():
if hasattr(val, 'name'):
names.add(val.name)
params = sorted(names)
for param in params:
print(param)
print(docs[param])
elif what == 'mfds':
for cls in gen_subclasses(BaseMFD):
print(cls.__name__)
elif what == 'venv':
print(sys.prefix)
elif what == 'sources':
for cls in gen_subclasses(BaseSeismicSource):
print(cls.__name__)
elif what == 'consequences':
known = scientific.KNOWN_CONSEQUENCES
print('The following %d consequences are implemented:' % len(known))
for cons in known:
print(cons)
elif os.path.isdir(what) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(what)
print(mon)
elif what.endswith('.xml'):
node = nrml.read(what)
if node[0].tag.endswith('sourceModel'):
print(source_model_info([node]))
elif node[0].tag.endswith('logicTree'):
bset = node[0][0]
if bset.tag.endswith("logicTreeBranchingLevel"):
bset = bset[0]
if bset.attrib['uncertaintyType'] == 'sourceModel':
sm_nodes = []
for smpath in logictree.collect_info(what).smpaths:
sm_nodes.append(nrml.read(smpath))
print(source_model_info(sm_nodes))
elif bset.attrib['uncertaintyType'] == 'gmpeModel':
print(logictree.GsimLogicTree(what))
else:
print(node.to_str())
elif what.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(what))
else:
print(readinput.get_oqparam(what).json())
if mon.duration > 1:
print(mon)
elif what:
print("No info for '%s'" % what)
def info(calculators, gsims, views, exports, extracts, parameters,
report, input_file=''):
"""
Give information. You can pass the name of an available calculator,
a job.ini file, or a zip archive with the input files.
"""
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if extracts:
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
if parameters:
params = []
for val in vars(OqParam).values():
if hasattr(val, 'name'):
params.append(val)
params.sort(key=lambda x: x.name)
for param in params:
print(param.name)
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
node = nrml.read(input_file)
if node[0].tag.endswith('sourceModel'):
if node['xmlns'].endswith('nrml/0.4'):
raise InvalidFile(
'%s is in NRML 0.4 format, please run the following '
'command:\noq upgrade_nrml %s' % (
input_file, os.path.dirname(input_file) or '.'))
print(source_model_info([node[0]]))
elif node[0].tag.endswith('logicTree'):
nodes = [nrml.read(sm_path)[0]
for sm_path in logictree.collect_info(input_file).smpaths]
print(source_model_info(nodes))
else:
print(node.to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file)
def info(calculators, gsims, views, exports, extracts, report, input_file=''):
"""
Give information. You can pass the name of an available calculator,
a job.ini file, or a zip archive with the input files.
"""
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if extracts:
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
node = nrml.read(input_file)
if node[0].tag.endswith('sourceModel'):
if node['xmlns'].endswith('nrml/0.4'):
raise InvalidFile(
'%s is in NRML 0.4 format, please run the following '
'command:\noq upgrade_nrml %s' %
(input_file, os.path.dirname(input_file) or '.'))
print(source_model_info([node[0]]))
elif node[0].tag.endswith('logicTree'):
nodes = [
nrml.read(sm_path)[0]
for sm_path in logictree.collect_info(input_file).smpaths
]
print(source_model_info(nodes))
else:
print(node.to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file)
