def from_gwu_chem_UVVIS(filelist,
sortnames=False,
shortname=True,
cut_extension=False,
name=''):
''' Format for comma delimited two column data from GWU chemistry's UVVis. These have no useful metadata
or dark data and so it is important that users either pass in a correctly sorted filelist. Once the
dataframe is created, on can do df=df.reindex(columns=[correct order]).
It uses read_csv() to and creates a list of dataframes. Afterwards, concat() merges these.
Kwds:
sortnames- Will attempt to autosort the filelist. Otherwise, order of files passed in is
directly used as columns.
shortname- If false, full file path is used as the column name. If true, only the filename is used.
cut_extension- If using the shortname, this will determine if the file extension is saved or cut from the data.'''
if shortname:
fget = lambda x: get_shortname(x, cut_extension=cut_extension)
else:
fget = lambda x: x
### Either full names or short names of filelist
working_names = [fget(afile) for afile in filelist]
dflist = [
read_csv(
afile,
sep=',',
header=None,
index_col=0,
skiprows=2,
na_values=' ', #Used to be ' \r', or is this from IR?
names=[fget(afile)]) for afile in filelist
]
### THIS IS BUSTED, PUTTING NANS EVERYWHERE EXCEPT ONE FILE, but dflist itself ws nice.
dataframe = concat(dflist, axis=1)
### concat tries to sort these, so this will preserve the sort order
if sortnames:
dataframe = dataframe.reindex(columns=sorted(working_names))
dataframe = TimeSpectra(dataframe) #this is fine
dataframe.metadata = None
dataframe.filedict = None
dataframe.baseline = None
dataframe.specunit = 'nm' #This autodetected in plots
if name:
dataframe.name = name
return dataframe
def from_gwu_chem_UVVIS(filelist, sortnames=False, shortname=True, cut_extension=False, name=''):
''' Format for comma delimited two column data from GWU chemistry's UVVis. These have no useful metadata
or dark data and so it is important that users either pass in a correctly sorted filelist. Once the
dataframe is created, on can do df=df.reindex(columns=[correct order]).
It uses read_csv() to and creates a list of dataframes. Afterwards, concat() merges these.
Kwds:
sortnames- Will attempt to autosort the filelist. Otherwise, order of files passed in is
directly used as columns.
shortname- If false, full file path is used as the column name. If true, only the filename is used.
cut_extension- If using the shortname, this will determine if the file extension is saved or cut from the data.'''
if shortname:
fget=lambda x:get_shortname(x, cut_extension=cut_extension)
else:
fget=lambda x: x
### Either full names or short names of filelist
working_names=[fget(afile) for afile in filelist]
dflist=[read_csv(afile, sep=',', header=None, index_col=0, skiprows=2, na_values=' ', #Used to be ' \r', or is this from IR?
names=[fget(afile)]) for afile in filelist]
### THIS IS BUSTED, PUTTING NANS EVERYWHERE EXCEPT ONE FILE, but dflist itself ws nice.
dataframe=concat(dflist, axis=1)
### concat tries to sort these, so this will preserve the sort order
if sortnames:
dataframe=dataframe.reindex(columns=sorted(working_names))
dataframe=TimeSpectra(dataframe) #this is fine
dataframe.metadata=None
dataframe.filedict=None
dataframe.baseline=None
dataframe.specunit='nm' #This autodetected in plots
if name:
dataframe.name=name
return dataframe
def from_timefile_datafile(datafile, timefile, extract_dark=True, name=''):
''' Converts old-style spectral data from GWU phys lab into
a dataframe with timestamp column index and wavelength row indicies.
Creates the DataFrame from a dictionary of Series, keyed by datetime.
**name becomes name of dataframe'''
tlines=open(timefile,'r').readlines()
tlines=[line.strip().split() for line in tlines]
tlines.pop(0)
time_file_dict=dict((_get_datetime_timefile(tline),tline[0]) for tline in tlines)
### Read in data matrix, separate first row (wavelengths) from the rest of the data
wavedata=np.genfromtxt(datafile, dtype='float', skip_header=1)
data, wavelengths=wavedata[:,1::], wavedata[:,0] #Separate wavelength column
### Sort datetimes here before assigning/removing dark spec etc...
sorted_tfd=sorted(time_file_dict.items())
sorted_times, sorted_files=zip(*( (((i[0]), (i[1])) for i in sorted_tfd)))
### Seek darkfile. If found, take it out of dataframe. ###
if extract_dark:
darkfile=extract_darkfile(sorted_files, return_null=True)
if darkfile:
####Find baseline by reverse lookup (lookup by value) and get index position
#darkindex, darktime=[(idx, time) for idx, (time, afile) in enumerate(sorted_tfd) if afile == darkfile][0]
darkindex=sorted_files.index(darkfile)
darktime=sorted_times[darkindex]
baseline=Series(data[:,darkindex], index=wavelengths, name=darkfile)
del time_file_dict[darktime] #Intentionally remove
sorted_times=list(sorted_times) #Need to do in two steps
sorted_times.remove(darktime)
data=np.delete(data, darkindex, 1) #Delete dark column from numpy data
else:
baseline=None
dataframe=TimeSpectra(data, columns=sorted_times, index=wavelengths)
### Add field attributes to dataframe
dataframe.baseline=baseline
dataframe.filedict=time_file_dict
if name:
dataframe.name=name
### Get headermeta data from first line in timefile that isn't darkfile. Only checks one line
### Does not check for consistency
for line in tlines:
if line[0]==darkfile:
pass
else:
meta_partial=_get_headermetadata_timefile(line[0]) #DOUBLE CHECK THIS WORKS
break
### Extract remaining metadata (file/time info) and return ###
meta_general=get_headermetadata_dataframe(dataframe, time_file_dict)
meta_general.update(meta_partial)
dataframe.metadata=meta_general
dataframe.specunit='nm' #This autodetected in plots
### Sort dataframe by ascending time (could also sort spectral data) ###
dataframe.sort(axis=1, inplace=True) #axis1=columns
return dataframe
def from_timefile_datafile(datafile, timefile, extract_dark=True, name=''):
''' Converts old-style spectral data from GWU phys lab into
a dataframe with timestamp column index and wavelength row indicies.
Creates the DataFrame from a dictionary of Series, keyed by datetime.
**name becomes name of dataframe'''
tlines = open(timefile, 'r').readlines()
tlines = [line.strip().split() for line in tlines]
tlines.pop(0)
time_file_dict = dict(
(_get_datetime_timefile(tline), tline[0]) for tline in tlines)
### Read in data matrix, separate first row (wavelengths) from the rest of the data
wavedata = np.genfromtxt(datafile, dtype='float', skip_header=1)
data, wavelengths = wavedata[:,
1::], wavedata[:,
0] #Separate wavelength column
### Sort datetimes here before assigning/removing dark spec etc...
sorted_tfd = sorted(time_file_dict.items())
sorted_times, sorted_files = zip(*((((i[0]), (i[1])) for i in sorted_tfd)))
### Seek darkfile. If found, take it out of dataframe. ###
if extract_dark:
darkfile = extract_darkfile(sorted_files, return_null=True)
if darkfile:
####Find baseline by reverse lookup (lookup by value) and get index position
#darkindex, darktime=[(idx, time) for idx, (time, afile) in enumerate(sorted_tfd) if afile == darkfile][0]
darkindex = sorted_files.index(darkfile)
darktime = sorted_times[darkindex]
baseline = Series(data[:, darkindex], index=wavelengths, name=darkfile)
del time_file_dict[darktime] #Intentionally remove
sorted_times = list(sorted_times) #Need to do in two steps
sorted_times.remove(darktime)
data = np.delete(data, darkindex,
1) #Delete dark column from numpy data
else:
baseline = None
dataframe = TimeSpectra(data, columns=sorted_times, index=wavelengths)
### Add field attributes to dataframe
dataframe.baseline = baseline
dataframe.filedict = time_file_dict
if name:
dataframe.name = name
### Get headermeta data from first line in timefile that isn't darkfile. Only checks one line
### Does not check for consistency
for line in tlines:
if line[0] == darkfile:
pass
else:
meta_partial = _get_headermetadata_timefile(
line[0]) #DOUBLE CHECK THIS WORKS
break
### Extract remaining metadata (file/time info) and return ###
meta_general = get_headermetadata_dataframe(dataframe, time_file_dict)
meta_general.update(meta_partial)
dataframe.metadata = meta_general
dataframe.specunit = 'nm' #This autodetected in plots
### Sort dataframe by ascending time (could also sort spectral data) ###
dataframe.sort(axis=1, inplace=True) #axis1=columns
return dataframe
