def write_csv(dest, data, sep=',', fmt='%.6E', header=None, comment=None):
"""
:param dest: None, file, filename or io.BytesIO instance
:param data: array to save
:param sep: separator to use (default comma)
:param fmt: formatting string (default '%12.8E')
:param header:
optional list with the names of the columns to display
:param comment:
optional first line starting with a # character
"""
close = True
if dest is None: # write on a temporary file
fd, dest = tempfile.mkstemp(suffix='.csv')
os.close(fd)
if hasattr(dest, 'write'):
# file-like object in append mode
# it must be closed by client code
close = False
elif not hasattr(dest, 'getvalue'):
# not a BytesIO, assume dest is a filename
dest = open(dest, 'wb')
try:
# see if data is a composite numpy array
data.dtype.fields
except AttributeError:
# not a composite array
autoheader = []
else:
autoheader = build_header(data.dtype)
if comment:
dest.write(encode('# %s\n' % comment))
someheader = header or autoheader
if header != 'no-header' and someheader:
dest.write(encode(sep.join(htranslator.write(someheader)) + u'\n'))
if autoheader:
all_fields = [col.split(':', 1)[0].split('~') for col in autoheader]
for record in data:
row = []
for fields in all_fields:
val = extract_from(record, fields)
if fields[0] in ('lon', 'lat', 'depth'):
row.append('%.5f' % val)
else:
row.append(scientificformat(val, fmt))
dest.write(encode(sep.join(row) + u'\n'))
else:
for row in data:
dest.write(
encode(
sep.join(scientificformat(col, fmt)
for col in row) + u'\n'))
if hasattr(dest, 'getvalue'):
return dest.getvalue()[:-1] # a newline is strangely added
elif close:
dest.close()
return dest.name
def serialize(self, data):
"""
:param data:
A sequence of data where each datum has the following attributes:
* matrix: N-dimensional numpy array containing the disaggregation
histogram.
* dim_labels: A list of strings which label the dimensions of a
given histogram. For example, for a Magnitude-Distance-Epsilon
histogram, we would expect `dim_labels` to be
``['Mag', 'Dist', 'Eps']``.
* poe: The disaggregation Probability of Exceedance level for which
these results were produced.
* iml: Intensity measure level, interpolated from the source hazard
curve at the given ``poe``.
"""
with open(self.dest, 'wb') as fh, floatformat('%.6E'):
root = et.Element('nrml')
diss_matrices = et.SubElement(root, 'disaggMatrices')
_set_metadata(diss_matrices, self.metadata, _ATTR_MAP)
transform = lambda val: ', '.join(map(scientificformat, val))
_set_metadata(diss_matrices,
self.metadata,
self.BIN_EDGE_ATTR_MAP,
transform=transform)
for result in data:
diss_matrix = et.SubElement(diss_matrices, 'disaggMatrix')
# Check that we have bin edges defined for each dimension label
# (mag, dist, lon, lat, eps, TRT)
for label in result.dim_labels:
bin_edge_attr = self.DIM_LABEL_TO_BIN_EDGE_MAP.get(label)
assert self.metadata.get(bin_edge_attr) is not None, (
"Writer is missing '%s' metadata" % bin_edge_attr)
result_type = ','.join(result.dim_labels)
diss_matrix.set('type', result_type)
dims = ','.join(str(x) for x in result.matrix.shape)
diss_matrix.set('dims', dims)
diss_matrix.set('poE', scientificformat(result.poe))
diss_matrix.set('iml', scientificformat(result.iml))
for idxs, value in numpy.ndenumerate(result.matrix):
prob = et.SubElement(diss_matrix, 'prob')
index = ','.join([str(x) for x in idxs])
prob.set('index', index)
prob.set('value', scientificformat(value))
nrml.write(list(root), fh)
def serialize(self, data):
"""
:param data:
A sequence of data where each datum has the following attributes:
* matrix: N-dimensional numpy array containing the disaggregation
histogram.
* dim_labels: A list of strings which label the dimensions of a
given histogram. For example, for a Magnitude-Distance-Epsilon
histogram, we would expect `dim_labels` to be
``['Mag', 'Dist', 'Eps']``.
* poe: The disaggregation Probability of Exceedance level for which
these results were produced.
* iml: Intensity measure level, interpolated from the source hazard
curve at the given ``poe``.
"""
with open(self.dest, 'wb') as fh, floatformat('%.6E'):
root = et.Element('nrml')
diss_matrices = et.SubElement(root, 'disaggMatrices')
_set_metadata(diss_matrices, self.metadata, _ATTR_MAP)
transform = lambda val: ', '.join(map(scientificformat, val))
_set_metadata(diss_matrices, self.metadata, self.BIN_EDGE_ATTR_MAP,
transform=transform)
for result in data:
diss_matrix = et.SubElement(diss_matrices, 'disaggMatrix')
# Check that we have bin edges defined for each dimension label
# (mag, dist, lon, lat, eps, TRT)
for label in result.dim_labels:
bin_edge_attr = self.DIM_LABEL_TO_BIN_EDGE_MAP.get(label)
assert self.metadata.get(bin_edge_attr) is not None, (
"Writer is missing '%s' metadata" % bin_edge_attr
)
result_type = ','.join(result.dim_labels)
diss_matrix.set('type', result_type)
dims = ','.join(str(x) for x in result.matrix.shape)
diss_matrix.set('dims', dims)
diss_matrix.set('poE', scientificformat(result.poe))
diss_matrix.set('iml', scientificformat(result.iml))
for idxs, value in numpy.ndenumerate(result.matrix):
prob = et.SubElement(diss_matrix, 'prob')
index = ','.join([str(x) for x in idxs])
prob.set('index', index)
prob.set('value', scientificformat(value))
nrml.write(list(root), fh)
def serialize(self, data):
"""
Write the hazard curves to the given as GeoJSON. The GeoJSON format
is customized to contain various bits of metadata.
See :meth:`HazardCurveXMLWriter.serialize` for expected input.
"""
oqmetadata = {}
for key, value in self.metadata.items():
if key == 'imls':
oqmetadata['IMLs'] = value
if value is not None:
if key == 'imls':
oqmetadata['IMLs'] = list(value)
else:
oqmetadata[_ATTR_MAP.get(key)] = scientificformat(value)
features = []
feature_coll = {
'type': 'FeatureCollection',
'features': features,
'oqtype': 'HazardCurve',
'oqnrmlversion': '0.4',
'oqmetadata': oqmetadata,
}
for hc in data:
poes = list(hc.poes)
lon = hc.location.x
lat = hc.location.y
feature = {
'type': 'Feature',
'geometry': {
'type': 'Point',
'coordinates': [float(lon), float(lat)],
},
'properties': {
'poEs': list(poes)
},
}
features.append(feature)
with open(self.dest, 'w') as fh:
json.dump(feature_coll,
fh,
sort_keys=True,
indent=4,
separators=(',', ': '))
def write_csv(dest, data, sep=',', fmt='%.6E', header=None, comment=None):
"""
:param dest: None, file, filename or io.BytesIO instance
:param data: array to save
:param sep: separator to use (default comma)
:param fmt: formatting string (default '%12.8E')
:param header:
optional list with the names of the columns to display
:param comment:
optional comment dictionary
"""
if comment is not None:
comment = ', '.join('%s=%r' % item for item in comment.items())
close = True
if dest is None: # write on a temporary file
fd, dest = tempfile.mkstemp(suffix='.csv')
os.close(fd)
if hasattr(dest, 'write'):
# file-like object in append mode
# it must be closed by client code
close = False
elif not hasattr(dest, 'getvalue'):
# not a BytesIO, assume dest is a filename
dest = open(dest, 'wb')
try:
# see if data is a composite numpy array
data.dtype.fields
except AttributeError:
# not a composite array
autoheader = []
else:
autoheader = build_header(data.dtype)
if comment:
dest.write(encode('# %s\n' % comment))
someheader = header or autoheader
if header != 'no-header' and someheader:
dest.write(encode(sep.join(someheader) + u'\n'))
if autoheader:
all_fields = [col.split(':', 1)[0].split('~')
for col in autoheader]
for record in data:
row = []
for fields in all_fields:
val = extract_from(record, fields)
if fields[0] in ('lon', 'lat', 'depth'):
row.append('%.5f' % val)
else:
row.append(scientificformat(val, fmt))
dest.write(encode(sep.join(row) + u'\n'))
else:
for row in data:
dest.write(encode(sep.join(scientificformat(col, fmt)
for col in row) + u'\n'))
if hasattr(dest, 'getvalue'):
return dest.getvalue()[:-1] # a newline is strangely added
elif close:
dest.close()
return dest.name
def format(val):
return scientificformat(val, fmt)
def format(val):
col = scientificformat(val, fmt)
if sep in col and not col.startswith('"'):
return '"%s"' % col
return col
