def import_jv_data(folder, file_pattern='ch*.csv', by_scan=True, **kwargs):
"""
Imports JV data.
:param folder: Folder path containing data files.
:param file_pattern: File pattern of data files, in glob format. [Default: ch*.csv]
:param by_scan: Breaks data into forward and reverse scans, and sets the index to voltage.
[Default: True]
:param kwargs: Arguments passed to standard_functions#import_data
:returns: DataFrame containg imported data.
"""
jv = []
for file in std.get_files(folder, file_pattern):
try:
df = import_jv_datum_channel(file,
channel_pattern='ch-<>',
by_scan=by_scan)
except ValueError as err:
logging.warning('{}: {}'.format(file, err))
continue
jv.append(df)
jv = pd.concat(jv, axis=1)
return jv
def import_data( folder_path, file_pattern = '*.sIV', metadata = None, interpolate = 'linear', fillna = 0 ):
"""
Imports data from Andor output files
:param folder_path: The file path containing the data files
:param file_pattern: A glob pattern to filter the imported files [Default: '*']
:param metadata: Metadata from the file name is turned into MultiIndex columns.
+ If list, use standard keywords to include in index [ 'sample', 'power', 'wavelength', 'time' ]
+ If Dictionary, keys indicate level name, value is pattern to match
+ Reseserved key 'standard' can be provided with a list value to get standard metadata
:param interpolate: How to interpolate data for a common index [Default: linear]
Use None to prevent reindexing
:param fillna: Value to fill NaN values with [Default: 0]
:returns: A Pandas DataFrame with MultiIndexed columns
"""
# get dataframes from files
df = []
files = std.get_files( folder_path, file_pattern )
for file in files:
data = import_datum( file ) # run local import datum function
df.append( data )
if interpolate is not None:
df = std.common_reindex( df, how = 'interpolate', fillna = fillna, add_values = [ 0 ] )
df = pd.concat( df, axis = 1 )
return df
def import_data(
folder_path,
file_pattern = '*.txt',
metadata = None,
time = False,
cps = False,
interpolate = 'linear',
fillna = 0
):
"""
Imports data from Andor output files
:param folder_path: The file path containing the data files
:param file_pattern: A glob pattern to filter the imported files [Default: '*']
:param metadata: Metadata from the file name is turned into MultiIndex columns.
+ If list, use standard keywords to include in index
[ 'sample', 'power', 'wavelength', 'time', 'temperature' ]
+ If Dictionary, keys indicate level name, value is pattern to match
+ Reseserved key 'standard' can be provided with a list value to get standard metada
:param time: Read the integration time in from the file as a header. [Default: False]
:param cps: Converts the data to counts per second.
A valid time string of the form XsX must be present.
:param interpolate: How to interpolate data for a common index [Default: linear]
Use None to prevent reindexing
:param fillna: Value to fill NaN values with [Default: 0]
:param reindex: Reindex the DataFrame using
:returns: A Pandas DataFrame with MultiIndexed columns
:raises: RuntimeError if no files are found
"""
# get dataframes from files
df = []
files = std.get_files( folder_path, file_pattern )
if len( files ) == 0:
# no files found
raise RuntimeError( 'No files found matching {}'.format( os.path.join( folder_path, file_pattern ) ) )
for file in files:
data = import_datum(
file,
metadata = metadata,
cps = cps,
time = time
) # run local import datum function
df.append( data )
if interpolate is not None:
df = std.common_reindex( df, how = interpolate, fillna = fillna )
df = pd.concat( df, axis = 1 )
return df
def import_data(
folder_path,
file_pattern = '*.csv',
metadata = None,
interpolate = 'linear',
fillna = 0
):
"""
Imports data from TimeSpec II experiments output files.
:param folder_path: The file path containing the data files
:param file_pattern: A glob pattern to filter the imported files [Default: '*']
:param metadata: Metadata from the file name is turned into MultiIndex columns.
[Default: None]
:param interpolate: How to interpolate data for a common index [Default: linear]
Use None to prevent reindexing
:param fillna: Value to fill NaN values with [Default: 0]
:returns: A Pandas DataFrame with MultiIndexed columns
:raises: RuntimeError if no files are found
"""
# get dataframes from files
df = []
files = std.get_files( folder_path, file_pattern )
if len( files ) == 0:
# no files found
raise RuntimeError( 'No files found matching {}'.format( os.path.join( folder_path, file_pattern ) ) )
for file in files:
data = import_datum( file, metadata = metadata ) # run local import datum function
df.append( data )
if interpolate is not None:
df = std.common_reindex( df, how = interpolate, fillna = fillna )
df = pd.concat( df, axis = 1 ).sort_index( axis = 1 )
return df