def parse_data(self, datastream):
vardata = DataDict()
while True:
for lineparser in self.parse_stream:
linedata = datastream.pop(0).split()
for func, varname in lineparser:
vardata.setdefault(varname, []).append(func(linedata))
if not datastream:
break
return vardata
def parse_data(self, datastream):
vardata = DataDict()
while True:
for lineparser in self.parse_stream:
linedata = datastream.pop(0).split()
for func, varname in lineparser:
vardata.setdefault(varname, []).append(func(linedata))
if not datastream:
break
return vardata
def merge_blocks_to_association_list(blocks):
data = DataDict()
coords = []
# Create DataDict with one entry per variable in the blocks
# For each variable add a dictionary that is indexed by the DimPartials
for b in blocks:
coord = []
for name, dim in b.ivardata.items():
if isinstance(dim, DimPartial):
coord.append((name, array(dim.data)[0]))
coord.sort()
coord = tuple(coord)
coords.append(coord)
for v in b.vardata.keys():
data.setdefault(v, []).append((coord, b.vardata[v]))
return data
def merge_blocks_to_association_list(blocks):
data = DataDict()
coords = []
# Create DataDict with one entry per variable in the blocks
# For each variable add a dictionary that is indexed by the DimPartials
for b in blocks:
coord = []
for name, dim in b.ivardata.items():
if isinstance(dim, DimPartial):
coord.append((name, array(dim.data)[0]))
coord.sort()
coord = tuple(coord)
coords.append(coord)
for v in b.vardata.keys():
data.setdefault(v, []).append((coord, b.vardata[v]))
return data
def parse_blocks(self, stream):
for comments, header, data in stream:
db = DataBlock()
db.comments = Comments(comments)
header = header[0].strip().split("\t")
Nhead = len(header)
#data = np.array(data)
if Nhead != len(data[0]):
msg = "Different number of header variables "\
"from data columns"
raise SPDataIOError(msg)
output = DataDict()
for varname, column in zip(header, zip(*data)):
output.setdefault(varname, []).append(column)
for varname in output:
data = output[varname]
if len(data) > 1:
output[varname] = np.array(output[varname],
order="F").T
else:
output[varname] = np.array(output[varname][0])
freq = DimSweep(header[0], output[header[0]])
db[header[0]] = freq
for x in output.keys()[1:]:
if output[x].ndim == 1:
db[x] = hfarray(output[x], dims=(freq,))
else:
repdim = DimRep("rep", output[x].shape[1])
db[x] = hfarray(output[x],
dims=(freq, repdim)).squeeze()
remove = []
for vname in db.comments.property:
if vname[:1] == "@":
unit = db.comments.property[vname].unit
data = [float(db.comments.property[vname])]
db[vname[1:]] = DimPartial(vname[1:], data=data, unit=unit)
remove.append(vname)
for v in remove:
del db.comments.property[v]
db.comments.fullcomments = [com for com in db.comments.fullcomments
if not com.startswith("@")]
yield db
def parse_blocks(self, stream):
for comments, header, data in stream:
db = DataBlock()
db.comments = Comments(comments)
header = header[0].strip().split("\t")
Nhead = len(header)
#data = np.array(data)
if Nhead != len(data[0]):
msg = "Different number of header variables "\
"from data columns"
raise SPDataIOError(msg)
output = DataDict()
for varname, column in zip(header, zip(*data)):
output.setdefault(varname, []).append(column)
for varname in output:
data = output[varname]
if len(data) > 1:
output[varname] = np.array(output[varname], order="F").T
else:
output[varname] = np.array(output[varname][0])
freq = DimSweep(header[0], output[header[0]])
db[header[0]] = freq
for x in output.keys()[1:]:
if output[x].ndim == 1:
db[x] = hfarray(output[x], dims=(freq, ))
else:
repdim = DimRep("rep", output[x].shape[1])
db[x] = hfarray(output[x], dims=(freq, repdim)).squeeze()
remove = []
for vname in db.comments.property:
if vname[:1] == "@":
unit = db.comments.property[vname].unit
data = [float(db.comments.property[vname])]
db[vname[1:]] = DimPartial(vname[1:], data=data, unit=unit)
remove.append(vname)
for v in remove:
del db.comments.property[v]
db.comments.fullcomments = [
com for com in db.comments.fullcomments
if not com.startswith("@")
]
yield db
