def from_gwu_chem_UVVIS(filelist, sortnames=False, shortname=True, cut_extension=False, name=None):
''' 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.darkseries=None
dataframe.specunit='nm' #This autodetected in plots
dataframe.name=name
return dataframe
def from_spec_files(file_list, specframe=None, skiphead=17, skipfoot=1,\
check_for_overlapping_time=True, extract_dark=True, name=None):
''' Takes in raw files directly from Ocean optics spectrometer and creates a spectral dataframe.
This is somewhat customized to my analysis, but has general arguments for future adaptations.
Built to work with 2-column data only!!!
Dataframe is constructed from a list of dictionaries.
Each dataframe gets an appended headerdata attribute (dataframe.headerdata) which is a dictionary,
keyed by columns and stores (infile, header, footer) data so no info is lost between files.
Constructed to work for non-equally spaced datafiles, or non-identical data (aka wavelengths can have nans).
**kwargs
- check_for_overlapping will raise errors if any files have identical times. Otherwise, time
is overwritten. Really only useful for testing or otherwise cornercase instances.
- Extract dark will attempt to find a filename with caseinsenstive string match to "dark". If dark
not found, will print warning. If multiple darks found, will raise error.
-skiphead/skipfoot are mostly for reminder that this filetype has a 17 line header and a 1 line footer.'''
### Need to add functionality for spectral dataframe (join/concat)
if specframe:
raise NotImplemented
dict_of_series={} #Dict of series eventually merged to dataframe
time_file_dict={} #Dict of time:filename (darkfile intentionally excluded)
### If looking for a darkfile, this will find it. Bit redundant but I'm lazy..###
if extract_dark:
darkfile=extract_darkfile(file_list, return_null=True)
if darkfile:
with open(darkfile) as f:
header=[f.next().strip() for x in xrange(skiphead)]
wavedata=np.genfromtxt(darkfile, dtype=spec_dtype, skip_header=skiphead, skip_footer=skipfoot)
darktime=_get_datetime_specsuite(header)
darkseries=Series(wavedata['intensity'], index=wavedata['wavelength'], name=darkfile)
file_list.remove(darkfile)
f.close()
else:
darkseries=None
for infile in file_list:
###Read in only the header lines, not all the lines of the file
###Strips and splits in one go
with open(infile) as f:
header=[f.next().strip() for x in xrange(skiphead)]
###Store wavelength, intensity data in a 2-column datatime for easy itemlookup
###Eg wavedata['wavelength']
wavedata=np.genfromtxt(infile, dtype=spec_dtype, skip_header=skiphead, skip_footer=skipfoot)
### Extract time data from header
datetime=_get_datetime_specsuite(header)
### Make sure timepoints aren't overlapping with any others
if check_for_overlapping_time:
try:
time_file_dict[datetime]
except KeyError:
pass
else:
raise KeyError('Duplicate time %s found in between files %s, %s'\
%(datetime,infile, time_file_dict[datetime]) )
time_file_dict[datetime]=infile
dict_of_series[datetime]=Series(wavedata['intensity'], index=wavedata['wavelength'])
f.close()
### Make dataframe, add filenames, darkseries and metadata attributes (note, DateTimeIndex auto sorts!!)
dataframe=TimeSpectra(dict_of_series)
dataframe.specunit='nm'
dataframe.filedict=time_file_dict
dataframe.darkseries=darkseries #KEEP THIS AS DARK SERIES RECALL IT IS SEPARATE FROM BASELINE OR REFERENCE..
dataframe.name=name
### Take metadata from first file in filelist that isn't darkfile
for infile in file_list:
if infile==darkfile:
pass
else:
with open(infile) as f:
header=[f.next().strip() for x in xrange(skiphead)]
meta_partial=_get_metadata_fromheader(header)
break
meta_general=get_headermetadata_dataframe(dataframe, time_file_dict)
meta_general.update(meta_partial)
dataframe.metadata=meta_general
return dataframe
def from_timefile_datafile(datafile, timefile, extract_dark=True, name=None):
''' 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:
####Fine darkseries 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]
darkseries=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:
darkseries=None
dataframe=TimeSpectra(data, columns=sorted_times, index=wavelengths)
### Add field attributes to dataframe
dataframe.darkseries=darkseries
dataframe.filedict=time_file_dict
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
