def do_file(self, stream):
tokenstream = Stream(self.tokenize(stream))
stream_of_block_tokens = self.group_blocks(tokenstream)
blocks = list(self.parse_blocks(stream_of_block_tokens))
if not blocks:
args = "No blocks found in file. Perhaps file is empty"\
" or of the wrong kind. (Tried to parse %s)"
msg = args % (self.__class__.__name__)
raise ParseError(msg)
blocks = self._make_complex(blocks)
blocks = self._properties_to_vars(blocks)
blocks = self._guess_unit(blocks)
blocks = self._normalize(blocks)
blocks = self._combine_matrices(blocks)
#import pdb;pdb.set_trace()
blocks = self._merge(blocks)
#import pdb;pdb.set_trace()
if isinstance(blocks, DataBlock):
if "FILEINDEX" not in blocks:
blocks["FILEINDEX"] = DimPartial("FILEINDEX",
[self.file_index])
else:
for b in blocks.values():
if isinstance(b, (list, tuple)):
for x in b:
if "FILEINDEX" not in x:
x["FILEINDEX"] = DimPartial(
"FILEINDEX", [self.file_index])
else:
if "FILEINDEX" not in b:
b["FILEINDEX"] = DimPartial("FILEINDEX",
[self.file_index])
self.file_index += 1
return blocks
def proc_var(self, var):
varname, value = var
varname, vartype = varname.split('(')
vartype = vartype.strip(")")
if varname.startswith("SWEEP."):
varname = varname[len("sweep."):]
if vartype == "real":
value = float(value)
else:
raise Exception("%r not an implemented var type" % vartype)
i = DimPartial(varname, np.array([value]))
return i
def build_meas_state(data):
state_names = data["measstate"][0, 0]["props"][0, 0][0]
state_data = data["measstate"][0, 0]["values"][0, 0][0]
d = DataBlock()
for k, v in zip(state_names, state_data):
if k[0] == "Index":
d["Index"] = DimPartial("Index", v[0])
else:
if v[0].ndim == 1:
d[k[0]] = hfarray(make_real(v[0][0]))
else:
d[k[0]] = hfarray(make_real(v[0]))
if d[k[0]].dtype.name.startswith("unicode"):
d[k[0]] = hfarray(np.char.encode(d[k[0]],
"cp1252",
errors="replace"),
dims=d[k[0]].dims)
return d
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 read_muwave_matlabdata(filename, drop_empty=True, **kw):
filenames = hftools.utils.glob(filename)
out = []
if len(filenames) > 1:
for idx, fname in enumerate(filenames):
d = read_single_muwave_matlabdata(fname, drop_empty=drop_empty)
d["Index2"] = DimPartial("Index2", [idx])
out.append(d)
d = hftools.file_formats.merge_blocks(out)
else:
d = read_single_muwave_matlabdata(filenames[0], drop_empty=drop_empty)
order = [
d.ivardata[dim] for dim in ["freq", "Index", "Index2"]
if dim in d.S.dims
]
d.S = d.S.reorder_dimensions(*order)
if drop_empty:
for k in d.vardata.keys():
if np.all("" == d[k]):
del d[k]
return d
a = [
(1, 1, 1),
(1, 1, 2),
(1, 2, 1),
(1, 2, 2),
(2, 1, 1),
(2, 1, 2),
(2, 2, 1),
(2, 2, 2),
]
fi = DimSweep("f", array([10, 20, 30]))
for i, k, j in a:
db = DataBlock()
db.I = DimPartial("I", array(i))
db.K = DimPartial("K", array(k))
db.J = DimPartial("J", array(j))
db.x = hfarray([11, 12, 13], dims=(fi, )) * (i + k * 10 + j * 100)
db.y = hfarray([11, 12, 13], dims=(fi, )) * (i + k * 10 + j * 100)
blocks.append(db)
for i, k, j in itertools.product([1, 2], [3, 4], [5, 6, 7]):
db = DataBlock()
db.I = DimPartial("I", array(i))
db.K = DimPartial("K", array(k))
db.J = DimPartial("J", array(j))
db.x = hfarray([11, 12, 13], dims=(fi, )) * (i + k * 10 + j * 100)
db.y = hfarray([11, 12, 13], dims=(fi, )) * (i + k * 10 + j * 100)
blocks2.append(db)