def convert(path):
contents = opusFC.listContents(path)
opusd = opusdictionary.OpusDict()
opath = path + "test.nxs"
name_dict = get_name_dict()
with h5py.File(opath, 'w') as f:
nxEntry = f.create_group("entry1")
nxEntry.attrs["NX_class"] = "NXentry"
for db in contents:
data = opusFC.getOpusData(path, db)
if isinstance(data, opusFC.ImageDataReturn):
add_image_data(nxEntry, data)
if isinstance(data, opusFC.MultiRegionDataReturn):
add_multi_data(nxEntry, data)
if isinstance(data, opusFC.ImageTRCDataReturn):
add_image_traces(nxEntry, data)
#nxColl = nxEntry.create_group("parameters")
#nxColl.attrs["NX_class"] = "NXcollection"
#for k,v in data.parameters.items():
# name = k
# if (k in opusd):
# name = opusd[k]
# nxColl.create_dataset(name,data=v)
images = opusFC.getVisImages(path)
count = 1
for im in images:
nxe = f.create_group("microscope" + str(count))
nxe.attrs["NX_class"] = "NXentry"
add_visible_image(nxe, im)
count = count + 1
print("Created file " + opath)
def sheets(self):
try:
import opusFC
except ImportError:
# raising an exception here would just show an generic error in File widget
return ()
dbs = []
for db in opusFC.listContents(self.filename):
dbs.append(db[0] + " " + db[1] + " " + db[2])
return dbs
def sheets(self):
try:
import opusFC
except ImportError:
# raising an exception here would just show an generic error in File widget
return ()
dbs = []
for db in opusFC.listContents(self.filename):
dbs.append(db[0] + " " + db[1] + " " + db[2])
return dbs
def parameters():
fl = file_list()
count = 0
for f in fl:
try:
dbs = opusFC.isOpusFile(f)
#control structure for the loop
if dbs == True:
conte = opusFC.listContents(f)
data = opusFC.getOpusData(f, conte[0])
count += 1
print(count)
#print conte, f
data = opusFC.getOpusData(f, conte[0])
print data.parameters, f
SNM.append(data.parameters['SNM'])
INS.append(data.parameters['INS'])
DAT.append(data.parameters['DAT'])
TIM.append(data.parameters['TIM'])
DUR.append(data.parameters['DUR'])
CNM.append(data.parameters['CNM'])
RES.append(data.parameters['RES'])
ZFF.append(data.parameters['ZFF'])
NPT.append(data.parameters['NPT'])
LWN.append(data.parameters['LWN'])
FXV.append(data.parameters['FXV'])
LXV.append(data.parameters['LXV'])
minY.append(data.minY)
maxY.append(data.maxY)
continue
else:
print('identified as non-Opus', f)
except ValueError:
print('Cant process a non-Opus file', f)
continue
LWN = []
LXV = []
FXV = []
minY = []
maxY = []
dbs=[]
# loop of all files
count = 0
for f in file_list:
try:
dbs = opusFC.listContents(f)
count += 1
print(count,f)
print(dbs)
for i in dbs:
data = opusFC.getOpusData(f, i)
print(data.parameters)
except ValueError:
print('Cant process a non-Opus file', f)
continue
def sheets(self):
import opusFC
dbs = []
for db in opusFC.listContents(self.filename):
dbs.append(db[0] + " " + db[1] + " " + db[2])
return dbs
def export_spectra(in_folder=None,
out_folder=DATA_KSSL,
nb_decimals=4,
max_wavenumber=4000,
valid_name=['XN', 'XS'],
nb_chunks=1):
"""Exports KSSL MIRS spectra into a series of .csv files
Parameters
----------
in_folder: string, optional
Specify the path of the folder containing the KSSL MIRS spectra
out_folder: string, optional
Specify the path of the folder that will contain exported files
nb_decimals: int, optional
Specify floating point precision (to save memory)
max_wavenumber: int, optional
Specify the max wavenumber to be considered in spectra
valid_name: list of str, optional
Specify valid spectra file names
nb_chunks: int, optional
Specify tne number of chunks/files to be created
Returns
-------
None
"""
in_folder = Path(in_folder)
out_folder = Path(out_folder)
if not in_folder.exists():
raise IOError('in_folder not found.')
if not out_folder.exists():
out_folder.mkdir(parents=True)
columns = None
valid_files = [
f for f in in_folder.rglob('*.0')
if re.search(r'X.', f.name)[0] in valid_name
]
for (l_bound, u_bound) in list(chunk(len(valid_files), nb_chunks)):
columns = None
rows_list = []
for i, f in enumerate(tqdm(valid_files[l_bound:u_bound])):
dbs = opusFC.listContents(f)
if dbs:
data = opusFC.getOpusData(str(f), dbs[0])
row = [f.name] + list(data.y[data.x <= max_wavenumber])
rows_list.append(row)
if columns is None:
columns = list(
(data.x[data.x <= max_wavenumber]).astype(int))
df = pd.DataFrame(rows_list, columns=['id'] + list(columns))
df = df.round(nb_decimals)
df.to_csv(out_folder /
'spectra_{}_{}.csv'.format(l_bound, u_bound - 1),
index=False)
def sheets(self):
import opusFC
dbs = []
for db in opusFC.listContents(self.filename):
dbs.append(db[0] + " " + db[1] + " " + db[2])
return dbs
