def test_flatten(self):
dvt = DumbVT()
self.assertEqual(
set(flatten_obj('foo', dvt)),
set([('foo.vt2.vt3.a', 1.), ('foo.vt2.vt3.b', 12.),
('foo.vt2.x', -1.), ('foo.vt2.y', -2.), ('foo.v1', 1.),
('foo.v2', 2.)]))
def test_flatten(self):
dvt = DumbVT()
self.assertEqual(set(flatten_obj('foo', dvt)),
set([('foo.vt2.vt3.a', 1.), ('foo.vt2.vt3.b', 12.),
('foo.vt2.x', -1.), ('foo.vt2.y', -2.),
('foo.v1', 1.), ('foo.v2', 2.),
('foo.vt2.vt3.data', ''),
('foo.vt2.data', ''), ('foo.data', '')]))
def test_flatten(self):
a = array([[1,2],[3,4],[5,6]])
self.assertEqual(flatten_obj('foo',a),
[('foo[0][0]',1),
('foo[0][1]',2),
('foo[1][0]',3),
('foo[1][1]',4),
('foo[2][0]',5),
('foo[2][1]',6),])
def test_flatten(self):
a = array([[1, 2], [3, 4], [5, 6]])
self.assertEqual(flatten_obj('foo', a), [
('foo[0][0]', 1),
('foo[0][1]', 2),
('foo[1][0]', 3),
('foo[1][1]', 4),
('foo[2][0]', 5),
('foo[2][1]', 6),
])
def _flatten_vartree(name, vt):
ret = []
for n, v in vt._items(set()):
ret.extend([('.'.join((name, k)), v) for k, v in flatten_obj(n, v)])
return ret
def caseset_query_to_csv(data, filename='cases.csv', delimiter=',', quotechar='"'):
"""
Post-processing function that takes a case_data_set and outputs a csv
file. Should be able to pass tests of current csv case recorder (column
ordering, meta column, etc...) Assume query by case (not variable).
Inputs:
data - results of fetch on Query object
"""
cds = data.cds
drivers = {}
for driver in cds.drivers:
drivers[driver['_id']] = driver['name']
# Determine inputs & outputs, map pseudos to expression names.
expressions = cds.simulation_info['expressions']
metadata = cds.simulation_info['variable_metadata']
inputs = []
outputs = []
pseudos = {}
for name in sorted(data[0].keys()):
# All inputs and outputs that change.
if name in metadata:
if metadata[name]['iotype'] == 'in':
inputs.append(name)
else:
outputs.append(name)
# Include objectives and constraints from all simulation levels.
elif '_pseudo_' in name and not name.endswith('.out0'):
for exp_name, exp_dict in expressions.items():
if exp_dict['pcomp_name'] == name:
pseudos[name] = '%s(%s)' % (exp_dict['data_type'], exp_name)
break
else:
raise RuntimeError('Cannot find %r in expressions' % name)
outputs.append(name)
# Allow private vars from components.
elif '.' in name:
outputs.append(name)
# Open CSV file
outfile = open(filename, 'wb')
csv_writer = csv.writer(outfile, delimiter=delimiter,
quotechar=quotechar,
quoting=csv.QUOTE_NONNUMERIC)
# No automatic data type conversion is performed unless the
# QUOTE_NONNUMERIC format option is specified (in which case unquoted
# fields are transformed into floats).
# Write the data
# data is a list of lists where the inner list is the values and metadata
# for a case
sorted_input_keys = []
sorted_input_values = []
sorted_output_keys = []
sorted_output_values = []
for i, row in enumerate( data ):
input_keys = []
input_values = []
for name in inputs:
obj = row[ row.name_map[ name ] ]
for key, value in flatten_obj(name, obj):
input_keys.append(key)
input_values.append(value)
output_keys = []
output_values = []
for name in outputs:
obj = row[ row.name_map[ name ] ]
if name in pseudos:
name = pseudos[name]
for key, value in flatten_obj(name, obj):
output_keys.append(key)
output_values.append(value)
# This should not be necessary, however python's csv writer
# is not writing boolean variables correctly as strings.
for index, item in enumerate(input_values):
if isinstance(item, bool):
input_values[index] = str(item)
for index, item in enumerate(output_values):
if isinstance(item, bool):
output_values[index] = str(item)
# Sort the columns alphabetically.
if len(input_keys) > 0:
sorted_input_keys, sorted_input_values = \
(list(item) for item in zip(*sorted(zip(input_keys,
input_values))))
if len(output_keys) > 0:
sorted_output_keys, sorted_output_values = \
(list(item) for item in zip(*sorted(zip(output_keys,
output_values))))
if outfile is None:
raise RuntimeError('Attempt to record on closed recorder')
if i == 0:
headers = ['timestamp', '/INPUTS']
headers.extend(sorted_input_keys)
headers.append('/OUTPUTS')
headers.extend(sorted_output_keys)
headers.extend(['/METADATA', 'uuid', 'parent_uuid', 'msg'])
csv_writer.writerow(headers)
header_size = len(headers)
timestamp = row[ row.name_map[ 'timestamp' ] ]
csv_data = [timestamp]
csv_data.append('')
csv_data.extend(sorted_input_values)
csv_data.append('')
csv_data.extend(sorted_output_values)
exc = row[ row.name_map[ 'error_message' ] ]
msg = '' if exc is None else str(exc)
case_uuid = row[ row.name_map[ '_id' ] ]
parent_uuid = row[ row.name_map[ '_parent_id' ] ]
csv_data.extend(['', case_uuid, parent_uuid, msg])
if header_size != len(csv_data):
raise RuntimeError("number of data points (%d) doesn't match header"
" size (%d) in CSV recorder"
% (len(csv_data), header_size))
csv_writer.writerow(csv_data)
outfile.close()
def _flatten_vartree(name, vt):
ret = []
for n, v in vt._items(set()):
ret.extend([('.'.join([name, k]), v) for k, v in flatten_obj(n, v)])
return ret
def record(self, driver, inputs, outputs, exc, case_uuid, parent_uuid):
"""Store the case in a csv file. The format for a line of data
follows:
Field 1 - timestamp
Field 2 - [Empty]
Field 3 - Input 1
...
Field i+2 - Input i
Field i+3 - [Empty]
Field i+4 - Output 1
...
Field i+j+4 - Output j
Field i+j+5 - [Empty]
Field i+j+6 - uuid
Field i+j+7 - parent_uuid
Field i+j+8 - msg
"""
sorted_input_keys = []
sorted_input_values = []
sorted_output_keys = []
sorted_output_values = []
in_cfg, out_cfg = self._cfg_map[driver]
input_keys = []
input_values = []
for name, obj in zip(in_cfg, inputs):
for key, value in flatten_obj(name, obj):
input_keys.append(key)
input_values.append(value)
output_keys = []
output_values = []
for name, obj in zip(out_cfg, outputs):
for key, value in flatten_obj(name, obj):
output_keys.append(key)
output_values.append(value)
# This should not be necessary, however python's csv writer
# is not writing boolean variables correctly as strings.
for index, item in enumerate(input_values):
if isinstance(item, bool):
input_values[index] = str(item)
for index, item in enumerate(output_values):
if isinstance(item, bool):
output_values[index] = str(item)
# Sort the columns alphabetically.
if len(input_keys) > 0:
sorted_input_keys, sorted_input_values = \
(list(item) for item in zip(*sorted(zip(input_keys,
input_values))))
if len(output_keys) > 0:
sorted_output_keys, sorted_output_values = \
(list(item) for item in zip(*sorted(zip(output_keys,
output_values))))
if self.outfile is None:
raise RuntimeError('Attempt to record on closed recorder')
if self._write_headers or self._header_size == 0:
headers = ['timestamp', '/INPUTS']
headers.extend(sorted_input_keys)
headers.append('/OUTPUTS')
headers.extend(sorted_output_keys)
headers.extend(['/METADATA', 'uuid', 'parent_uuid', 'msg'])
self.csv_writer.writerow(headers)
self._write_headers = False
self._header_size = len(headers)
msg = '' if exc is None else str(exc)
data = [time.time()]
data.append('')
data.extend(sorted_input_values)
data.append('')
data.extend(sorted_output_values)
data.extend(['', case_uuid, parent_uuid, msg])
if self._header_size != len(data):
raise RuntimeError("number of data points (%d) doesn't match header"
" size (%d) in CSV recorder"
% (len(data), self._header_size))
self.csv_writer.writerow(data)
def caseset_query_to_csv(data,
filename='cases.csv',
delimiter=',',
quotechar='"'):
"""
Post-processing function that takes a case_data_set and outputs a csv
file. Should be able to pass tests of current csv case recorder (column
ordering, meta column, etc...) Assume query by case (not variable).
Inputs:
data - results of fetch on Query object
"""
cds = data.cds
drivers = {}
for driver in cds.drivers:
drivers[driver['_id']] = driver['name']
# Determine inputs & outputs, map pseudos to expression names.
expressions = cds.simulation_info['expressions']
metadata = cds.simulation_info['variable_metadata']
inputs = []
outputs = []
pseudos = {}
for name in sorted(data[0].keys()):
# All inputs and outputs that change.
if name in metadata:
if metadata[name]['iotype'] == 'in':
inputs.append(name)
else:
outputs.append(name)
# Include objectives and constraints from all simulation levels.
elif '_pseudo_' in name and not name.endswith('.out0'):
for exp_name, exp_dict in expressions.items():
if exp_dict['pcomp_name'] == name:
pseudos[name] = '%s(%s)' % (exp_dict['data_type'],
exp_name)
break
else:
raise RuntimeError('Cannot find %r in expressions' % name)
outputs.append(name)
# Allow private vars from components.
elif '.' in name:
outputs.append(name)
# Open CSV file
outfile = open(filename, 'wb')
csv_writer = csv.writer(outfile,
delimiter=delimiter,
quotechar=quotechar,
quoting=csv.QUOTE_NONNUMERIC)
# No automatic data type conversion is performed unless the
# QUOTE_NONNUMERIC format option is specified (in which case unquoted
# fields are transformed into floats).
# Write the data
# data is a list of lists where the inner list is the values and metadata
# for a case
sorted_input_keys = []
sorted_input_values = []
sorted_output_keys = []
sorted_output_values = []
for i, row in enumerate(data):
input_keys = []
input_values = []
for name in inputs:
obj = row[row.name_map[name]]
for key, value in flatten_obj(name, obj):
input_keys.append(key)
input_values.append(value)
output_keys = []
output_values = []
for name in outputs:
obj = row[row.name_map[name]]
if name in pseudos:
name = pseudos[name]
for key, value in flatten_obj(name, obj):
output_keys.append(key)
output_values.append(value)
# This should not be necessary, however python's csv writer
# is not writing boolean variables correctly as strings.
for index, item in enumerate(input_values):
if isinstance(item, bool):
input_values[index] = str(item)
for index, item in enumerate(output_values):
if isinstance(item, bool):
output_values[index] = str(item)
# Sort the columns alphabetically.
if len(input_keys) > 0:
sorted_input_keys, sorted_input_values = \
(list(item) for item in zip(*sorted(zip(input_keys,
input_values))))
if len(output_keys) > 0:
sorted_output_keys, sorted_output_values = \
(list(item) for item in zip(*sorted(zip(output_keys,
output_values))))
if outfile is None:
raise RuntimeError('Attempt to record on closed recorder')
if i == 0:
headers = ['timestamp', '/INPUTS']
headers.extend(sorted_input_keys)
headers.append('/OUTPUTS')
headers.extend(sorted_output_keys)
headers.extend(['/METADATA', 'uuid', 'parent_uuid', 'msg'])
csv_writer.writerow(headers)
header_size = len(headers)
timestamp = row[row.name_map['timestamp']]
csv_data = [timestamp]
csv_data.append('')
csv_data.extend(sorted_input_values)
csv_data.append('')
csv_data.extend(sorted_output_values)
exc = row[row.name_map['error_message']]
msg = '' if exc is None else str(exc)
case_uuid = row[row.name_map['_id']]
parent_uuid = row[row.name_map['_parent_id']]
csv_data.extend(['', case_uuid, parent_uuid, msg])
if header_size != len(csv_data):
raise RuntimeError(
"number of data points (%d) doesn't match header"
" size (%d) in CSV recorder" % (len(csv_data), header_size))
csv_writer.writerow(csv_data)
outfile.close()