def make_matrices(self, db, header):
matrices, vectors, scalars = match_matrix_names(header)
for matrix, value in matrices.items():
a = [(i, j) for x, i, j, y in value]
a.sort()
arrays = []
for i, j in a:
vname = "%s[%s,%s]" % (matrix, i, j)
arrays.append(np.array(db.vardata[vname])[:, np.newaxis])
dims = db.vardata["%s[%s,%s]" % (matrix, i, j)].dims
X = np.concatenate(arrays, 1)
I, J = map(set, zip(*a))
X.shape = X.shape[:1] + (len(I), len(J))
db.vardata[matrix] = make_matrix(X, dims)
idx = min([db.vardata.order.index("%s[%s,%s]" % (matrix, i, j)) for i, j in a])
for i, j in a:
del db.vardata["%s[%s,%s]" % (matrix, i, j)]
del db.vardata.order[db.vardata.order.index(matrix)]
db.vardata.order.insert(idx, matrix)
for matrix, value in vectors.items():
a = [(i,) for x, i, y in value]
a.sort()
fmt = "%s[%%s]" % (matrix,)
arrays = [np.array(db.vardata[fmt % i])[:, np.newaxis] for i, in a]
dims = db.vardata["%s[%s]" % (matrix, i)].dims
X = np.concatenate(arrays, 1)
X.shape = X.shape[:1] + (len(a),)
db.vardata[matrix] = make_vector(X, dims)
idx = min([db.vardata.order.index("%s[%s]" % (matrix, i)) for i, in a])
for (i,) in a:
del db.vardata["%s[%s]" % (matrix, i)]
del db.vardata.order[db.vardata.order.index(matrix)]
db.vardata.order.insert(idx, matrix)
def combine_elements_to_matrix(data, name):
reg = re.compile("%s([0-9])([0-9])" % name)
i_idx = set()
j_idx = set()
vnames = []
for v in data.vardata:
res = reg.match(v)
if res:
i_idx.add(int(res.groups()[0]))
j_idx.add(int(res.groups()[1]))
vnames.append(v)
if vnames and len(i_idx) * len(j_idx) == len(vnames):
i_indices = sorted(i_idx)
j_indices = sorted(j_idx)
ex = data["%s%d%d" % (name, list(i_idx)[0], list(j_idx)[0])]
shape = ex.shape
x = np.empty(shape + (
len(i_indices),
len(j_indices),
),
dtype=ex.dtype)
for i, j in itertools.product(i_indices, j_indices):
val = data["%s%d%d" % (name, i, j)]
x[..., i_indices.index(i), j_indices.index(j), ] = val
else:
msg = "Can not build complete matrix using elements with name %r"
raise Exception(msg % name)
data[name] = make_matrix(np.array(x), dims=ex.dims)
return data
def proc_info(info, f, data, fn, noisedata):
info_list = info.upper().split()
if len(info_list) == 5:
info = freq_mult, twoporttype, datatype, rtype, z0 = info_list
elif len(info_list) == 3:
info = freq_mult, twoporttype, datatype = info_list
z0 = 1.
else:
msg = ("# format should only have 5 values this one has %d" %
len(info_list))
raise TouchstoneError(msg)
z0 = float(z0)
f = f * freqmultdict[freq_mult]
if fn is not None:
fn = fn * freqmultdict[freq_mult]
convfunc = dict(RI=re_im_to_complex,
DB=dB_angle_to_complex,
MA=mag_angle_to_complex)
if datatype not in convfunc:
pars = datatype
msg = "Unknown dataformat: %s, valid formats: RI, DB, MAG" % pars
raise TouchstoneError(msg)
out = np.empty((data.shape[0], data.shape[1] // 2), dtype=np.complex128)
out[...] = convfunc[datatype](data[:, ::2], data[:, 1::2])
if out.shape[1] == 1:
out.shape = (out.shape[0], 1, 1)
elif out.shape[1] == 4:
out.shape = (out.shape[0], 2, 2)
out = out.transpose(0, 2, 1)
else:
N = int(sqrt(out.shape[1]))
out.shape = (out.shape[0], N, N)
dims = (DimSweep("freq", f, unit="Hz"), )
if fn is not None and len(f) == len(fn) and np.allclose(f, fn):
noisedims = dims
elif fn is None:
noisedims = None
else:
noisedims = (DimSweep("freqn", fn, unit="Hz"), )
out = make_matrix(out, dims)
f = hfarray(f, dims)
db = DataBlock()
db[info[1]] = out
if noisedata is not None:
db.Rn = hfarray(noisedata[..., -1], dims=noisedims) * z0
db.Fmin = 10 ** (hfarray(noisedata[..., 0], dims=noisedims) / 10)
db.Gopt = hfarray(convfunc[datatype](noisedata[:, 1], noisedata[:, 2]),
dims=noisedims)
db.Z0 = hfarray(z0, unit="Ohm")
return db
def proc_info(info, f, data, fn, noisedata):
info_list = info.upper().split()
if len(info_list) == 5:
info = freq_mult, twoporttype, datatype, rtype, z0 = info_list
elif len(info_list) == 3:
info = freq_mult, twoporttype, datatype = info_list
z0 = 1.
else:
msg = ("# format should only have 5 values this one has %d" %
len(info_list))
raise TouchstoneError(msg)
z0 = float(z0)
f = f * freqmultdict[freq_mult]
if fn is not None:
fn = fn * freqmultdict[freq_mult]
convfunc = dict(RI=re_im_to_complex,
DB=dB_angle_to_complex,
MA=mag_angle_to_complex)
if datatype not in convfunc:
pars = datatype
msg = "Unknown dataformat: %s, valid formats: RI, DB, MAG" % pars
raise TouchstoneError(msg)
out = np.empty((data.shape[0], data.shape[1] // 2), dtype=np.complex128)
out[...] = convfunc[datatype](data[:, ::2], data[:, 1::2])
if out.shape[1] == 1:
out.shape = (out.shape[0], 1, 1)
elif out.shape[1] == 4:
out.shape = (out.shape[0], 2, 2)
out = out.transpose(0, 2, 1)
else:
N = int(sqrt(out.shape[1]))
out.shape = (out.shape[0], N, N)
dims = (DimSweep("freq", f, unit="Hz"), )
if fn is not None and len(f) == len(fn) and np.allclose(f, fn):
noisedims = dims
elif fn is None:
noisedims = None
else:
noisedims = (DimSweep("freqn", fn, unit="Hz"), )
out = make_matrix(out, dims)
f = hfarray(f, dims)
db = DataBlock()
db[info[1]] = out
if noisedata is not None:
db.Rn = hfarray(noisedata[..., -1], dims=noisedims) * z0
db.Fmin = 10**(hfarray(noisedata[..., 0], dims=noisedims) / 10)
db.Gopt = hfarray(convfunc[datatype](noisedata[:, 1], noisedata[:, 2]),
dims=noisedims)
db.Z0 = hfarray(z0, unit="Ohm")
return db
def build_data(data):
out = build_meas_state(data)
for k, v in build_measmnt(data).items():
out[k] = hfarray(v)
data = data["data"][0, 0]
type = data["type"][0, 0][0]
Z0 = float(data["reference"][0, 0][0][0])
freq = DimSweep("freq", data["freq"][0, 0][:, 0], unit="Hz")
arraydata = data["data"][0, 0][0]["mtrx"][0].transpose((2, 0, 1))
if arraydata.dtype.name.startswith("unicode"):
arraydata = np.char.encode(arraydata, "cp1252", errors="replace")
if np.isrealobj(arraydata):
arraydata = array(arraydata, np.float64)
S = make_matrix(arraydata, (freq, ))
Scov = data["datacov"][0, 0]
out[type] = S
out["Z0"] = hfarray(Z0)
if "Date" in out:
try:
out["Date"] = hfarray(
array(datetime.datetime.strptime(out["Date"].flatten()[0],
"%d-%b-%Y %H:%M:%S"),
dtype="M8[us]"))
except ValueError:
pass
if Scov.size:
Scov = make_matrix(Scov[0]["mtrx"][0].transpose((2, 0, 1)), (freq, ))
out["%scov" % type] = Scov
for k, v in out.vardata.items():
if k in ["V1", "V2", "V1_SET", "V2_SET"]:
v.unit = "V"
for k, v in out.vardata.items():
if k in ["I1", "I2", "I1_SET", "I2_SET"]:
v.unit = "A"
for k, v in out.vardata.items():
if k in ["Z0"]:
v.unit = "Ohm"
return out
def build_data(data):
out = build_meas_state(data)
for k, v in build_measmnt(data).items():
out[k] = hfarray(v)
data = data["data"][0, 0]
type = data["type"][0, 0][0]
Z0 = float(data["reference"][0, 0][0][0])
freq = DimSweep("freq", data["freq"][0, 0][:, 0], unit="Hz")
arraydata = data["data"][0, 0][0]["mtrx"][0].transpose((2,0,1))
if arraydata.dtype.name.startswith("unicode"):
arraydata = np.char.encode(arraydata, "cp1252", errors="replace")
if np.isrealobj(arraydata):
arraydata = array(arraydata, np.float64)
S = make_matrix(arraydata, (freq,))
Scov = data["datacov"][0, 0]
out[type] = S
out["Z0"] = hfarray(Z0)
if "Date" in out:
try:
out["Date"] = hfarray(array(datetime.datetime.strptime(out["Date"].flatten()[0], "%d-%b-%Y %H:%M:%S"), dtype="M8[us]"))
except ValueError:
pass
if Scov.size:
Scov = make_matrix(Scov[0]["mtrx"][0].transpose((2,0,1)), (freq,))
out["%scov"%type] = Scov
for k,v in out.vardata.items():
if k in ["V1", "V2", "V1_SET", "V2_SET"]:
v.unit = "V"
for k,v in out.vardata.items():
if k in ["I1", "I2", "I1_SET", "I2_SET"]:
v.unit = "A"
for k,v in out.vardata.items():
if k in ["Z0"]:
v.unit = "Ohm"
return out
def _make_matrix_from_block(self, block):
matrices = {}
for matcher in self.match_matrix_element_name:
matrices.update(
match_matrix_elements(block.vardata.keys(), matcher))
for vname, (elems, i_indices, j_indices) in matrices.items():
ex = block[elems[0]]
shape = ex.shape
x = np.empty(shape + (len(i_indices) * len(j_indices), ),
dtype=ex.dtype)
for idx, elem in enumerate(elems):
x[..., idx] = block[elem]
del block[elem]
x.shape = shape + (
len(i_indices),
len(j_indices),
)
block[vname] = make_matrix(x, dims=ex.dims)
def make_matrices(self, db, header):
matrices, vectors, scalars = match_matrix_names(header)
for matrix, value in matrices.items():
a = [(i, j) for x, i, j, y in value]
a.sort()
arrays = []
for i, j in a:
vname = "%s[%s,%s]" % (matrix, i, j)
arrays.append(np.array(db.vardata[vname])[:, np.newaxis])
dims = db.vardata["%s[%s,%s]" % (matrix, i, j)].dims
X = np.concatenate(arrays, 1)
I, J = map(set, zip(*a))
X.shape = X.shape[:1] + (len(I), len(J))
db.vardata[matrix] = make_matrix(X, dims)
idx = min([
db.vardata.order.index("%s[%s,%s]" % (matrix, i, j))
for i, j in a
])
for i, j in a:
del db.vardata["%s[%s,%s]" % (matrix, i, j)]
del db.vardata.order[db.vardata.order.index(matrix)]
db.vardata.order.insert(idx, matrix)
for matrix, value in vectors.items():
a = [(i, ) for x, i, y in value]
a.sort()
fmt = "%s[%%s]" % (matrix, )
arrays = [np.array(db.vardata[fmt % i])[:, np.newaxis] for i, in a]
dims = db.vardata["%s[%s]" % (matrix, i)].dims
X = np.concatenate(arrays, 1)
X.shape = X.shape[:1] + (len(a), )
db.vardata[matrix] = make_vector(X, dims)
idx = min(
[db.vardata.order.index("%s[%s]" % (matrix, i)) for i, in a])
for i, in a:
del db.vardata["%s[%s]" % (matrix, i)]
del db.vardata.order[db.vardata.order.index(matrix)]
db.vardata.order.insert(idx, matrix)
