def _load(self, filename, *args, **kargs):
"""Leeds MOKE file loader routine.
Args:
filename (string or bool): File to load. If None then the existing filename is used,
if False, then a file dialog will be used.
Returns:
A copy of the itself after loading the data.
"""
if filename is None or not filename:
self.get_filename("r")
else:
self.filename = filename
with io.open(self.filename, mode="rb") as f:
line = bytes2str(f.readline()).strip()
if line != "#Leeds CM Physics MOKE":
raise Core.StonerLoadError(
"Not a Core.DataFile from the Leeds MOKE")
while line.startswith("#") or line == "":
parts = line.split(":")
if len(parts) > 1:
key = parts[0][1:]
data = ":".join(parts[1:]).strip()
self[key] = data
line = bytes2str(f.readline()).strip()
column_headers = [x.strip() for x in line.split(",")]
self.data = np.genfromtxt(f, delimiter=",")
self.setas = "xy.de"
self.column_headers = column_headers
return self
def _load(self, filename=None, *args, **kargs):
"""Sheffield Focussed MOKE file loader routine.
Args:
filename (string or bool): File to load. If None then the existing filename is used,
if False, then a file dialog will be used.
Returns:
A copy of the itself after loading the data.
"""
if filename is None or not filename:
self.get_filename("r")
else:
self.filename = filename
with io.open(self.filename, mode="rb") as f:
try:
value = [float(x.strip()) for x in bytes2str(f.readline()).split("\t")]
except Exception:
f.close()
raise Core.StonerLoadError("Not an FMOKE file?")
label = [x.strip() for x in bytes2str(f.readline()).split("\t")]
if label[0] != "Header:":
f.close()
raise Core.StonerLoadError("Not a Focussed MOKE file !")
del label[0]
for k, v in zip(label, value):
self.metadata[k] = v # Create metatdata from first 2 lines
column_headers = [x.strip() for x in bytes2str(f.readline()).split("\t")]
self.data = np.genfromtxt(f, dtype="float", delimiter="\t", invalid_raise=False)
self.column_headers = column_headers
return self
def _load(self, filename=None, *args, **kargs):
"""Leeds MOKE file loader routine.
Args:
filename (string or bool): File to load. If None then the existing filename is used,
if False, then a file dialog will be used.
Returns:
A copy of the itself after loading the data.
"""
if filename is None or not filename:
self.get_filename("r")
else:
self.filename = filename
with io.open(self.filename, mode="rb") as f:
line = bytes2str(f.readline()).strip()
if line != "#Leeds CM Physics MOKE":
raise Core.StonerLoadError("Not a Core.DataFile from the Leeds MOKE")
while line.startswith("#") or line == "":
parts = line.split(":")
if len(parts) > 1:
key = parts[0][1:]
data = ":".join(parts[1:]).strip()
self[key] = data
line = bytes2str(f.readline()).strip()
column_headers = [x.strip() for x in line.split(",")]
self.data = np.genfromtxt(f, delimiter=",")
self.setas = "xy.de"
self.column_headers = column_headers
return self
def _load(self, filename, *args, **kargs):
"""Sheffield Focussed MOKE file loader routine.
Args:
filename (string or bool): File to load. If None then the existing filename is used,
if False, then a file dialog will be used.
Returns:
A copy of the itself after loading the data.
"""
if filename is None or not filename:
self.get_filename("r")
else:
self.filename = filename
with io.open(self.filename, mode="rb") as f:
try:
value = [float(x.strip()) for x in bytes2str(f.readline()).split("\t")]
except Exception:
f.close()
raise Core.StonerLoadError("Not an FMOKE file?")
label = [x.strip() for x in bytes2str(f.readline()).split("\t")]
if label[0] != "Header:":
f.close()
raise Core.StonerLoadError("Not a Focussed MOKE file !")
del label[0]
for k, v in zip(label, value):
self.metadata[k] = v # Create metatdata from first 2 lines
column_headers = [x.strip() for x in bytes2str(f.readline()).split("\t")]
self.data = np.genfromtxt(f, dtype="float", delimiter="\t", invalid_raise=False)
self.column_headers = column_headers
return self
def _load(self, filename, *args, **kargs):
"""Loads data from a hdf5 file.
Args:
h5file (string or h5py.Group):
Either a string or an h5py Group object to load data from
Returns:
itself after having loaded the data
"""
if filename is None or not filename:
self.get_filename("r")
filename = self.filename
else:
self.filename = filename
if isinstance(filename, string_types
): # We got a string, so we'll treat it like a file...
f = _open_filename(filename)
elif isinstance(filename, h5py.File) or isinstance(
filename, h5py.Group):
f = filename
else:
_raise_error(
f,
message=
f"Couldn't interpret {filename} as a valid HDF5 file or group or filename"
)
if "type" not in f.attrs:
_raise_error(
f,
message=
f"HDF5 Group does not specify the type attribute used to check we can load it."
)
typ = bytes2str(f.attrs["type"])
if typ != self.__class__.__name__ and "module" not in f.attrs:
_raise_error(
f,
message=
f"HDF5 Group is not a {self.__class__.__name__} and does not specify a module to use to load.",
)
loader = None
if typ == self.__class__.__name__:
loader = getattr(self.__class__, "read_HDF")
else:
mod = importlib.import_module(bytes2str(f.attrs["module"]))
cls = getattr(mod, typ)
loader = getattr(cls, "read_JDF")
if loader is None:
_raise_error(
f,
message=
"Could not et loader for {bytes2str(f.attrs['module'])}.{typ}")
return loader(f, *args, **kargs)
def _load(self, filename=None, *args, **kargs):
"""Data loader function for 340 files."""
if filename is None or not filename:
self.get_filename("r")
else:
self.filename = filename
with io.open(self.filename, "rb") as data:
keys = []
vals = []
for line in data:
line = bytes2str(line)
if line.strip() == "":
break
parts = [p.strip() for p in line.split(":")]
if len(parts) != 2:
raise Core.StonerLoadError(
"Header doesn't contain two parts at {}".format(
line.strip()))
else:
keys.append(parts[0])
vals.append(parts[1])
else:
raise Core.StonerLoadError("Overan the end of the file")
if keys != [
"Sensor Model",
"Serial Number",
"Data Format",
"SetPoint Limit",
"Temperature coefficient",
"Number of Breakpoints",
]:
raise Core.StonerLoadError(
"Header did not contain recognised keys.")
for (k, v) in zip(keys, vals):
v = v.split()[0]
self.metadata[k] = string_to_type(v)
headers = bytes2str(next(data)).strip().split()
column_headers = headers[1:]
dat = np.genfromtxt(data)
self.data = dat[:, 1:]
self.column_headers = column_headers
return self
def _load(self, filename=None, *args, **kargs):
"""Data loader function for 340 files."""
if filename is None or not filename:
self.get_filename("r")
else:
self.filename = filename
with io.open(self.filename, "rb") as data:
keys = []
vals = []
for line in data:
line = bytes2str(line)
if line.strip() == "":
break
parts = [p.strip() for p in line.split(":")]
if len(parts) != 2:
raise Core.StonerLoadError("Header doesn't contain two parts at {}".format(line.strip()))
else:
keys.append(parts[0])
vals.append(parts[1])
else:
raise Core.StonerLoadError("Overan the end of the file")
if keys != [
"Sensor Model",
"Serial Number",
"Data Format",
"SetPoint Limit",
"Temperature coefficient",
"Number of Breakpoints",
]:
raise Core.StonerLoadError("Header did not contain recognised keys.")
for (k, v) in zip(keys, vals):
v = v.split()[0]
self.metadata[k] = self.metadata.string_to_type(v)
headers = bytes2str(next(data)).strip().split()
column_headers = headers[1:]
dat = np.genfromtxt(data)
self.data = dat[:, 1:]
self.column_headers = column_headers
return self
def _extract(self, archive, member):
"""Responsible for actually reading the zip file archive.
Args:
archive (zipfile.ZipFile): An open zip archive
member (string): The name of one member of the zip file
Return:
A datafile like instance
"""
tmp = DataFile()
info = archive.getinfo(member)
data = bytes2str(
archive.read(info)) # In Python 3 this would be a bytes
self.__init__(tmp << data)
self.filename = path.join(archive.filename, member)
return self
def _load(self, filename=None, *args, **kargs):
"""Reads an Rigaku ras file including handling the metadata nicely
Args:
filename (string or bool): File to load. If None then the existing filename is used,
if False, then a file dialog will be used.
Returns:
A copy of the itself after loading the data.
"""
from ast import literal_eval
if filename is None or not filename:
self.get_filename("rb")
else:
self.filename = filename
sh = re.compile(r"^\*([^\s]+)\s+(.*)$") # Regexp to grab the keys
ka = re.compile(r"(.*)\-(\d+)$")
header = dict()
i = 0
with io.open(self.filename, "rb") as f:
for i, line in enumerate(f):
line = bytes2str(line).strip()
if i == 0 and line != "*RAS_DATA_START":
raise Core.StonerLoadError("Not a Rigaku file!")
if line == "*RAS_HEADER_START":
break
i2 = None
for i2, line in enumerate(f):
line = bytes2str(line).strip()
m = sh.match(line)
if m:
key = m.groups()[0].lower().replace("_", ".")
try:
value = m.groups()[1].decode("utf-8", "ignore")
except AttributeError:
value = m.groups()[1]
header[key] = value
if "*RAS_INT_START" in line:
break
keys = list(header.keys())
keys.sort()
for key in keys:
m = ka.match(key)
value = header[key].strip()
try:
newvalue = literal_eval(value.strip('"'))
except Exception:
newvalue = literal_eval(value)
if m:
key = m.groups()[0]
if key in self.metadata and not (isinstance(self[key], (np.ndarray, list))):
if isinstance(self[key], str):
self[key] = list([self[key]])
else:
self[key] = np.array(self[key])
if key not in self.metadata:
if isinstance(newvalue, str):
self[key] = list([newvalue])
else:
self[key] = np.array([newvalue])
else:
if isinstance(self[key][0], str) and isinstance(self[key], list):
self[key].append(newvalue)
else:
self[key] = np.append(self[key], newvalue)
else:
self.metadata[key] = newvalue
with io.open(self.filename, "rb") as data:
self.data = np.genfromtxt(
data, dtype="float", delimiter=" ", invalid_raise=False, comments="*", skip_header=i + i2 + 1
)
column_headers = ["Column" + str(i) for i in range(self.data.shape[1])]
column_headers[0:2] = [self.metadata["meas.scan.unit.x"], self.metadata["meas.scan.unit.y"]]
for key in self.metadata:
if isinstance(self[key], list):
self[key] = np.array(self[key])
self.setas = "xy"
self.column_headers = column_headers
return self
def _load(self, filename=None, *args, **kargs):
"""Reads an Rigaku ras file including handling the metadata nicely
Args:
filename (string or bool): File to load. If None then the existing filename is used,
if False, then a file dialog will be used.
Returns:
A copy of the itself after loading the data.
"""
from ast import literal_eval
pos = 0
reopen = False
filetype = io.IOBase
if filename is None or not filename:
self.get_filename("rb")
elif isinstance(filename, filetype):
self.filename = filename.name
pos = filename.tell()
reopen = True
else:
self.filename = filename
sh = re.compile(r"^\*([^\s]+)\s+(.*)$") # Regexp to grab the keys
ka = re.compile(r"(.*)\-(\d+)$")
header = dict()
i = 0
with io.open(self.filename, "rb") as f:
f.seek(pos)
for i, line in enumerate(f):
line = bytes2str(line).strip()
if pos == 0 and (i == 0 and line != "*RAS_DATA_START"):
raise StonerLoadError("Not a Rigaku file!")
if pos != 0 or line == "*RAS_HEADER_START":
break
for line in f:
line = bytes2str(line).strip()
m = sh.match(line)
if m:
key = m.groups()[0].lower().replace("_", ".")
try:
value = m.groups()[1].decode("utf-8", "ignore")
except AttributeError:
value = m.groups()[1]
header[key] = value
if "*RAS_INT_START" in line:
break
keys = list(header.keys())
keys.sort()
for key in keys:
m = ka.match(key)
value = header[key].strip()
try:
newvalue = literal_eval(value.strip('"'))
except Exception:
newvalue = literal_eval(value)
if newvalue == "-":
newvalue = np.nan # trap for missing float value
if m:
key = m.groups()[0]
idx = int(m.groups()[1])
if key in self.metadata and not (isinstance(
self[key], (np.ndarray, list))):
if isinstance(self[key], str):
self[key] = list([self[key]])
if idx > 1:
self[key].extend([""] * idx - 1)
else:
self[key] = np.array(self[key])
if idx > 1:
self[key] = np.append(
self[key],
np.ones(idx - 1) * np.nan)
if key not in self.metadata:
if isinstance(newvalue, str):
listval = [""] * (idx + 1)
listval[idx] = newvalue
self[key] = listval
else:
arrayval = np.ones(idx + 1) * np.nan
arrayval = arrayval.astype(type(newvalue))
arrayval[idx] = newvalue
self[key] = arrayval
else:
if isinstance(self[key][0], str) and isinstance(
self[key], list):
if len(self[key]) < idx + 1:
self[key].extend([""] *
(idx + 1 - len(self[key])))
self[key][idx] = newvalue
else:
if idx + 1 > self[key].size:
self[key] = np.append(
self[key],
(np.ones(idx + 1 - self[key].size) *
np.nan).astype(self[key].dtype))
try:
self[key][idx] = newvalue
except ValueError:
pass
else:
self.metadata[key] = newvalue
pos = f.tell()
max_rows = 0
for max_rows, line in enumerate(f):
line = bytes2str(line).strip()
if "RAS_INT_END" in line:
break
endpos = f.tell()
f.seek(pos)
if max_rows > 0:
self.data = np.genfromtxt(f,
dtype="float",
delimiter=" ",
invalid_raise=False,
comments="*",
max_rows=max_rows)
column_headers = [
"Column" + str(i) for i in range(self.data.shape[1])
]
column_headers[0:2] = [
self.metadata["meas.scan.unit.x"],
self.metadata["meas.scan.unit.y"]
]
for key in self.metadata:
if isinstance(self[key], list):
self[key] = np.array(self[key])
self.setas = "xy"
self.column_headers = column_headers
if reopen:
filename.seek(endpos)
self["_endpos"] = endpos
return self
def _load(self, filename=None, *args, **kargs):
"""Reads an Rigaku ras file including handling the metadata nicely
Args:
filename (string or bool): File to load. If None then the existing filename is used,
if False, then a file dialog will be used.
Returns:
A copy of the itself after loading the data.
"""
from ast import literal_eval
if filename is None or not filename:
self.get_filename("rb")
else:
self.filename = filename
sh = re.compile(r"^\*([^\s]+)\s+(.*)$") # Regexp to grab the keys
ka = re.compile(r"(.*)\-(\d+)$")
header = dict()
i = 0
with io.open(self.filename, "rb") as f:
for i, line in enumerate(f):
line = bytes2str(line).strip()
if i == 0 and line != "*RAS_DATA_START":
raise Core.StonerLoadError("Not a Rigaku file!")
if line == "*RAS_HEADER_START":
break
i2 = None
for i2, line in enumerate(f):
line = bytes2str(line).strip()
m = sh.match(line)
if m:
key = m.groups()[0].lower().replace("_", ".")
try:
value = m.groups()[1].decode("utf-8", "ignore")
except AttributeError:
value = m.groups()[1]
header[key] = value
if "*RAS_INT_START" in line:
break
keys = list(header.keys())
keys.sort()
for key in keys:
m = ka.match(key)
value = header[key].strip()
try:
newvalue = literal_eval(value.strip('"'))
except Exception:
newvalue = literal_eval(value)
if m:
key = m.groups()[0]
if key in self.metadata and not (isinstance(
self[key], (np.ndarray, list))):
if isinstance(self[key], str):
self[key] = list([self[key]])
else:
self[key] = np.array(self[key])
if key not in self.metadata:
if isinstance(newvalue, str):
self[key] = list([newvalue])
else:
self[key] = np.array([newvalue])
else:
if isinstance(self[key][0], str) and isinstance(
self[key], list):
self[key].append(newvalue)
else:
self[key] = np.append(self[key], newvalue)
else:
self.metadata[key] = newvalue
with io.open(self.filename, "rb") as data:
self.data = np.genfromtxt(data,
dtype="float",
delimiter=" ",
invalid_raise=False,
comments="*",
skip_header=i + i2 + 1)
column_headers = ["Column" + str(i) for i in range(self.data.shape[1])]
column_headers[0:2] = [
self.metadata["meas.scan.unit.x"],
self.metadata["meas.scan.unit.y"]
]
for key in self.metadata:
if isinstance(self[key], list):
self[key] = np.array(self[key])
self.setas = "xy"
self.column_headers = column_headers
return self
