def load(self, fname):
# Storing the name of the file for editting purposes
self.fname = fname
# Auxiliary dictionary to speed up the data convertion into pandas
aux_dict = dict(raw_time=[], time=[], event=[], experiment=[], item=[],
count=[], comment=[])
# Importing the file
out_ouf_metadata = False
with open(fname) as f:
for line in f:
if '\n' in line[0]:
pass
# Filtering lines with comments
elif '#' in line[0]:
if not out_ouf_metadata:
self.header.append(line)
self._read_metada(line)
else:
self.WTF.append(line)
# Storing events
elif is_elapsed_time(line.split()[0]):
aux_dict = self._read_events(line, aux_dict)
# Useful data from the header
else:
# We can say we are out of the metadate here because
# start_time and end_time are mandatory in the files
out_ouf_metadata = True
self._read_header_line(line.split())
# Closing the file
f.close()
# Creating the pandas dataframe
self.events = pd.DataFrame(aux_dict)
# Sorting by the time
self.events = self.events.sort(['time'])
# Sorting the columns in the dataframe
cols = ['raw_time', 'time', 'event', 'experiment', 'item', 'count',
'comment']
self.events = self.events[cols]
def merge_includes(self):
self.merged_events = self.events
# Getting the path to load correctly the files
path = os.path.dirname(os.path.abspath(self.fname))
for f in self.include_files:
print ("Reading " + f[0] + "...")
fname = os.path.join(path, f[0].strip('"'))
# There is an existing time
if len(f) > 1 and is_elapsed_time(f[1]):
ref_date = self._to_datetime(f[1])
itl = ITL(fname, ref_date=ref_date)
else:
itl = ITL(fname)
itl.check_consistency()
# Recursing over the itl files
itl.merge_includes()
# Merging the dataframes
self.merged_events = \
pd.concat([self.merged_events, itl.merged_events],
ignore_index=True)
self.merged_events = self.order_colums_in_dataframe(self.merged_events)
def load(self, fname):
# Storing the name of the file for editting purposes
self.fname = fname
# Auxiliary dictionary to speed up the data convertion into pandas
aux_dict = dict(raw_time=[], time=[], experiment=[], mode=[],
action=[], parameters=[], comment=[])
# Importing the file
out_ouf_metadata = False
with open(fname) as f:
line = ""
line_comments = list()
for l in f:
# Formatting just in case there is no space between parenthesis
l = l.replace('(', ' ( ').replace(')', ' ) ')
# Concatening lines if '\' found
if '\\' in l and '#' not in l[0] and \
'\\' not in l[l.index('\\') + 1]:
line += l[:l.index('\\')]
line_comments.append(l[l.index('\\') + 1: - 1])
# Continues with the next iteration of the loop
continue
# If there was no concatenation of lines
if len(line) == 0:
line = l
# If we were concatenating, we concatenate the last one
else:
line += l[:l.index(')') + 1]
line_comments.append(l[l.index(')'):])
if '\n' in line[0]:
pass
elif 'Comment:' in line:
pass
# Filtering lines with comments
elif '#' in line[0]:
if not out_ouf_metadata:
self.header.append(line)
self._read_metada(line)
else:
self.WTF.append(line)
# Storing events
elif len(line.split()) > 0 and \
is_elapsed_time(line.split()[0]):
aux_dict = \
self._read_events(line, aux_dict, line_comments)
# Useful data from the header
else:
# We can say we are out of the metadate here because
# start_time and end_time are mandatory in the files
out_ouf_metadata = True
self._read_header_line(line.split())
# Preparing values for next iteration
line = ""
line_comments = list()
# Closing the file
f.close()
# Creating the pandas dataframe
self.events = pd.DataFrame(aux_dict)
self.events = self.order_colums_in_dataframe(self.events)
