def test_init():
meta = Meta()
meta.td = [200, 400]
assert meta.td[0] == 200
assert meta.si is None
meta['si'] = 'a string'
assert isinstance(meta.si, str)
assert meta.si.startswith('a')
def test_init():
meta = Meta()
meta.td = [200, 400]
assert meta.td[0] == 200
assert meta.si is None
meta["si"] = "a string"
assert isinstance(meta.si, str)
assert meta.si.startswith("a")
def test_readonly():
meta = Meta()
meta.chaine = "a string"
assert meta.chaine == 'a string'
meta.readonly = True
with raises(ValueError):
meta.chaine = "a modified string"
assert meta.chaine != "a modified string"
def test_invalid_key():
meta = Meta()
meta.readonly = False # this is accepted`
with raises(KeyError):
meta['readonly'] = True # this not because readonly is reserved
with raises(KeyError):
meta[
'_data'] = True # this not because _xxx type attributes are private
def test_swap():
meta = Meta()
meta.td = [200, 400, 500]
meta.xe = [30, 40, 80]
meta.si = 2048
meta.swap(1, 2)
assert meta.td == [200, 500, 400]
assert meta.xe == [30, 80, 40]
assert meta.si == 2048
def figure(preferences=Meta(), **kwargs):
"""
Method to open a new figure
Parameters
----------
kwargs : any
keywords arguments to be passed to the matplotlib figure constructor.
preferences : Meta dictionary
per object saved plot configuration
"""
return get_figure(preferences=preferences, **kwargs)
def test_iterator():
meta = Meta()
meta.td = [200, 400]
meta.si = 2048
meta.ls = 3
meta.ns = 1024
assert sorted([val for val in meta]) == ['ls', 'ns', 'si', 'td']
def test_iterator():
meta = Meta()
meta.td = [200, 400]
meta.si = 2048
meta.ls = 3
meta.ns = 1024
assert sorted([val for val in meta]) == ["ls", "ns", "si", "td"]
def test_permute():
meta = Meta()
meta.td = [200, 400, 500]
meta.xe = [30, 40, 80]
meta.si = 2048
p = (2, 0, 1)
meta.permute(*p)
assert meta.td == [500, 200, 400]
assert meta.xe == [80, 30, 40]
assert meta.si == 2048
def test_copy():
meta = Meta()
meta.td = [200, 400]
meta.si = 2048
meta.ls = 3
meta.ns = 1024
meta2 = meta
assert meta2 is meta
meta2 = meta.copy()
assert meta2 is not meta
assert sorted([val for val in meta2]) == ['ls', 'ns', 'si', 'td']
# bug with quantity
si = 2048 * ur.s
meta.si = si
meta3 = meta.copy()
meta3.si = si / 2.
assert meta3 is not meta
def test_copy():
meta = Meta()
meta.td = [200, 400]
meta.si = 2048
meta.ls = 3
meta.ns = 1024
meta2 = meta
assert meta2 is meta
meta2 = meta.copy()
assert meta2 is not meta
assert sorted([val for val in meta2]) == ["ls", "ns", "si", "td"]
# bug with quantity
si = 2048 * ur.s
meta.si = si
meta3 = meta.copy()
meta3.si = si / 2.0
assert meta3 is not meta
def test_get_keys_items():
meta = Meta()
meta.td = [200, 400]
meta.si = 1024
assert list(meta.keys()) == ['si', 'td']
assert list(meta.items()) == [('si', 1024), ('td', [200, 400])]
def test_get_inexistent():
meta = Meta()
assert meta.existepas is None
def test_get_keys_items():
meta = Meta()
meta.td = [200, 400]
meta.si = 1024
assert list(meta.keys()) == ["si", "td"]
assert list(meta.items()) == [("si", 1024), ("td", [200, 400])]
def _read_txt(*args, **kwargs):
# read Labspec *txt files or series
dataset, filename = args
content = kwargs.get('content', False)
if content:
fid = io.StringIO(content)
# TODO: get the l list of string
else:
fid = open(filename, 'r', encoding='utf-8')
try:
lines = fid.readlines()
except UnicodeDecodeError:
fid = open(filename, 'r', encoding='latin-1')
lines = fid.readlines()
fid.close()
# Metadata
meta = Meta()
i = 0
while lines[i].startswith('#'):
key, val = lines[i].split('=')
key = key[1:]
if key in meta.keys():
key = f'{key} {i}'
meta[key] = val.strip()
i += 1
# read spec
rawdata = np.genfromtxt(lines[i:], delimiter='\t')
# populate the dataset
if rawdata.shape[1] == 2:
data = rawdata[:, 1][np.newaxis]
_x = Coord(rawdata[:, 0], title='Raman shift', units='1/cm')
_y = Coord(None, title='Time', units='s')
date_acq, _y = _transf_meta(_y, meta)
else:
data = rawdata[1:, 1:]
_x = Coord(rawdata[0, 1:], title='Raman shift', units='1/cm')
_y = Coord(rawdata[1:, 0], title='Time', units='s')
date_acq, _y = _transf_meta(_y, meta)
# try to transform to linear coord
_x.linear = True
# if success linear should still be True
if _x.linear:
_x = LinearCoord(_x)
# set dataset metadata
dataset.data = data
dataset.set_coordset(y=_y, x=_x)
dataset.title = 'raman Intensity'
dataset.units = 'absorbance'
dataset.name = filename.stem
dataset.meta = meta
# date_acq is Acquisition date at start (first moment of acquisition)
dataset.description = 'Spectrum acquisition : ' + str(date_acq)
# Set the NDDataset date
dataset._date = datetime.datetime.now(datetime.timezone.utc)
dataset._modified = dataset.date
# Set origin, description and history
dataset.history = f'{dataset.date}:imported from LabSpec6 text file {filename}'
return dataset
def _meta_default(self):
return Meta()
def _ranges_default(self):
ranges = Meta()
for dim in self.dims:
ranges[dim] = dict(masks={}, baselines={}, integrals={}, others={})
return ranges
def _read_topspin(*args, **kwargs):
debug_("Bruker TOPSPIN file reading")
dataset, path = args
# content = kwargs.get('content', None)
# is-it a processed dataset (1r, 2rr ....
processed = True if path.match("pdata/*/*") else False
# ------------------------------------------------------------------------
# start reading ....
# ------------------------------------------------------------------------
parents = path.parents
# Get data and acquisition parameters
if not processed:
# a fid or a ser has been selected
f_expno = parents[0]
expno = f_expno.name
procno = kwargs.get("procno", "1")
f_procno = f_expno / "pdata" / procno
f_name = parents[1]
else:
# a processes spectra has been selected (1r, ....)
f_procno = parents[0]
procno = f_procno.name
f_expno = parents[2]
expno = f_expno.name
f_name = parents[3]
acqus_files = _get_files(f_expno, "acqu")
procs_files = _get_files(f_procno, "proc")
if not processed:
dic, data = read_fid(f_expno,
acqus_files=acqus_files,
procs_files=procs_files)
# apply a -90 phase shift to be compatible with topspin
data = data * np.exp(-1j * np.pi / 2.0)
# Look the case when the reshaping was not correct
# for example, this happen when the number
# of accumulated row was incomplete
if path.name in ["ser"] and data.ndim == 1:
# we must reshape using the acqu parameters
td1 = dic["acqu2"]["TD"]
try:
data = data.reshape(td1, -1)
except ValueError:
try:
td = dic["acqu"]["TD"] // 2
data = data.reshape(-1, td)
except ValueError:
raise KeyError("Inconsistency between TD's and data size")
# reduce to td
ntd = dic["acqus"]["TD"] // 2
data = data[..., :ntd]
# Eliminate the digital filter
if kwargs.get("remove_digital_filter",
True) and dic["acqus"]["DECIM"] > 1:
data = _remove_digital_filter(dic, data)
else:
dic, datalist = read_pdata(
f_procno,
acqus_files=acqus_files,
procs_files=procs_files,
all_components=True,
)
if isinstance(datalist, list):
if datalist[0].ndim == 2:
data, dataRI, dataIR, dataII = datalist
# make quaternion
shape = data.shape
data = as_quat_array(
list(
zip(
data.flatten(),
dataRI.flatten(),
dataIR.flatten(),
dataII.flatten(),
)))
data = data.reshape(shape)
elif datalist[0].ndim == 1:
# make complex
data, dataI = datalist
data = data + dataI * 1.0j
else:
return None
else:
data = datalist
# ........................................................................................................
# we now make some rearrangement of the dic to have something more user friendly
# we assume that all experiments have similar (important) parameters so that the experiments are compatibles
meta = Meta() # This is the parameter dictionary
datatype = path.name.upper() if not processed else f"{data.ndim}D"
keys = sorted(dic.keys())
# we need the ndim of the data
parmode = int(dic["acqus"].get("PARMODE", data.ndim - 1))
if parmode + 1 != data.ndim:
raise KeyError(
f"The NMR data were not read properly as the PARMODE+1 parameter ({parmode + 1}) doesn't fit"
f" the actual number of dimensions ({data.ndim})")
# read the acqu and proc
valid_keys = list(zip(*nmr_valid_meta))[0]
keys_units = dict(nmr_valid_meta)
for item in keys:
if item[:4] in ["acqu", "proc"]:
dim = parmode
if len(item) > 4 and item[4] in ["2", "3"]:
dim = parmode + 1 - int(item[4])
for key in sorted(dic[item]):
if key.startswith("_") or key.lower() not in valid_keys:
continue
value = dic[item][key]
units = ur(keys_units[key.lower()]) if keys_units[
key.lower()] else None
if units is not None:
if isinstance(value, (float, int)):
value = value * units # make a quantity
elif isinstance(value, list) and isinstance(
value[0], (float, int)):
value = np.array(value) * units
if key.lower() not in meta:
meta[key.lower()] = [None] * data.ndim
try:
meta[key.lower()][dim] = value
except Exception:
pass
else:
meta[item.lower()] = dic[item]
# Warning: from now all parameter keys are lowercase.
# correct some initial values
meta.encoding = [0] * (parmode + 1)
meta.iscomplex = [False] * (parmode + 1)
if not processed:
meta.isfreq = [False]
meta.encoding[-1] = AQ_mod[meta.aq_mod[-1]]
meta.iscomplex[-1] = meta.aq_mod[-1] > 0
if datatype in ["SER"]:
meta.isfreq.insert(0, False)
if meta.fnmode[-2] == 0:
# For historical reasons,
# MC2 is interpreted when the acquisition status
# parameter FnMODE has the value undefined, i.e. 0
if meta.mc2 is not None:
meta.fnmode[-2] = meta.mc2[-2] + 1
meta.encoding[-2] = FnMODE[meta.fnmode[-2]]
meta.iscomplex[-2] = meta.fnmode[-2] > 1
if parmode == 2:
meta.isfreq.insert(0, False)
if meta.fnmode[-3] == 0 and meta.mc2 is not None:
meta.fnmode[-3] = meta.mc2[-3] + 1
meta.encoding[-3] = FnMODE[meta.fnmode[-3]]
meta.iscomplex[-3] = meta.fnmode[-3] > 1
# correct TD, so it is the number of complex points, not the number of data
# not for the last dimension which is already correct
meta.tdeff = meta.td[:]
meta.td = list(data.shape)
for axis in range(parmode + 1):
if meta.iscomplex[axis]:
if axis != parmode: # already done for last axis
meta.td[axis] = meta.td[axis] // 2
meta.tdeff[axis] = meta.tdeff[axis] // 2
meta.sw_h = [(meta.sw[axis].m * meta.sfo1[axis] * 1e-6).to("Hz")
for axis in range(parmode + 1)]
if processed:
meta.si = [si for si in data.shape]
meta.isfreq = [True] * (parmode + 1) # at least we assume this
meta.phc0 = [0] * data.ndim
# this transformation is to make data coherent with bruker processing
if meta.iscomplex[-1]:
data = np.conj(data * np.exp(np.pi * 1j / 2.0))
# normalised amplitudes to ns=1 and rg=1
def _norm(dat):
meta.ns = meta.get(
"ns",
[1] * data.ndim) # sometimes these parameters are not present
meta.rg = meta.get("rg", [1.0] * data.ndim)
fac = float(meta.ns[-1]) * float(meta.rg[-1])
meta.rgold = [meta.rg[-1]]
meta.rg[-1] = 1.0
meta.nsold = [meta.ns[-1]] # store the old value of NS
meta.ns[-1] = 1
dat /= fac
return dat
data = _norm(data)
# add some additional information in meta
meta.expno = [int(expno)]
# and the metadata (and make them readonly)
meta.datatype = datatype
meta.pathname = str(path)
# add two parameters needed for phasing
meta.pivot = [0] * data.ndim
meta.exptc = [0] * data.ndim
# make the corresponding axis
# debug_('Create coords...')
coords = []
axe_range = list(range(parmode + 1))
for axis in axe_range:
if not meta.isfreq[axis]:
# the axis is in time units
dw = (1.0 / meta.sw_h[axis]).to("us")
# coordpoints = np.arange(meta.td[axis])
# coord = Coord(coordpoints * dw,
# title=f"F{axis + 1} acquisition time") # TODO: use AQSEQ for >2D data
coord = LinearCoord(
offset=0.0,
increment=dw,
units="us",
size=meta.td[axis],
title=f"F{axis + 1} acquisition time",
)
coord.meta.larmor = meta.sfo1[axis]
coords.append(coord)
else:
size = meta.si[axis]
sizem = max(size - 1, 1)
deltaf = -meta.sw_h[axis] / sizem
first = meta.sfo1[axis] - meta.sf[axis] - deltaf * sizem / 2.0
# coord = Coord(np.arange(size) * deltaf + first)
coord = LinearCoord(offset=first, increment=deltaf, size=size)
coord.meta.larmor = meta.sfo1[
axis] # needed for ppm transformation
coord.ito("ppm")
if meta.nuc1 is not None:
nuc1 = meta.nuc1[axis]
regex = r"([^a-zA-Z]+)([a-zA-Z]+)"
m = re.match(regex, nuc1)
mass = m[1]
name = m[2]
nucleus = "^{" + mass + "}" + name
else:
nucleus = ""
coord.title = rf"$\delta\ {nucleus}$"
coords.append(coord)
dataset.data = data
for axis, cplex in enumerate(meta.iscomplex[::-1]):
if cplex and axis > 0:
dataset.set_quaternion(inplace=True)
dataset.meta.update(meta)
dataset.meta.readonly = True
dataset.set_coordset(*tuple(coords))
dataset.title = "intensity"
dataset.origin = "topspin"
dataset.name = f"{f_name.name} expno:{expno} procno:{procno} ({datatype})"
dataset.filename = f_name
return dataset
def test_instance():
meta = Meta()
assert isinstance(meta, Meta)
def _read_txt(*args, **kwargs):
# read Labspec *txt files or series
dataset, filename = args
content = kwargs.get("content", False)
if content:
pass
# fid = io.StringIO(content)
# TODO: get the l list of string
else:
fid = open(filename, "r", encoding="utf-8")
try:
lines = fid.readlines()
except UnicodeDecodeError:
fid = open(filename, "r", encoding="latin-1")
lines = fid.readlines()
fid.close()
if len(lines) == 0:
return
# Metadata
meta = Meta()
i = 0
while lines[i].startswith("#"):
key, val = lines[i].split("=")
key = key[1:]
if key in meta.keys():
key = f"{key} {i}"
meta[key] = val.strip()
i += 1
# .txt extension is fairly common. We determine non labspc files based
# on the absence of few keys. Two types of files (1D or 2D) are considered:
labspec_keys_1D = ["Acq. time (s)", "Dark correction"]
labspec_keys_2D = ["Exposition", "Grating"]
if all(keywd in meta.keys() for keywd in labspec_keys_1D):
pass
elif all(keywd in meta.keys() for keywd in labspec_keys_2D):
pass
else:
# this is not a labspec txt file"
return
# read spec
rawdata = np.genfromtxt(lines[i:], delimiter="\t")
# populate the dataset
if rawdata.shape[1] == 2:
data = rawdata[:, 1][np.newaxis]
_x = Coord(rawdata[:, 0], title="Raman shift", units="1/cm")
_y = Coord(None, title="Time", units="s")
date_acq, _y = _transf_meta(_y, meta)
else:
data = rawdata[1:, 1:]
_x = Coord(rawdata[0, 1:], title="Raman shift", units="1/cm")
_y = Coord(rawdata[1:, 0], title="Time", units="s")
date_acq, _y = _transf_meta(_y, meta)
# try to transform to linear coord
_x.linear = True
# if success linear should still be True
if _x.linear:
_x = LinearCoord(_x)
# set dataset metadata
dataset.data = data
dataset.set_coordset(y=_y, x=_x)
dataset.title = "Counts"
dataset.units = None
dataset.name = filename.stem
dataset.meta = meta
# date_acq is Acquisition date at start (first moment of acquisition)
dataset.description = "Spectrum acquisition : " + str(date_acq)
# Set the NDDataset date
dataset._date = datetime.datetime.now(datetime.timezone.utc)
dataset._modified = dataset.date
# Set origin, description and history
dataset.history = f"{dataset.date}:imported from LabSpec6 text file {filename}"
return dataset
def test_equal():
meta1 = Meta()
meta2 = Meta()
assert meta1 == meta2