latqcdtools.interfaces.interfaces

convertTable(source, target, sourceDelimiter='', targetDelimiter=''):
'''
Convert a source table into a target table. The assumption for the source file is that
is that the only lines are table lines, i.e. there's no intervening \hline or something like that.
The table type is determined by the file extensions of source and target.

Args:
    source (str): source filename 
    target (str): target filename 
'''

readGPL(filename, discardTag=True, raggedWarn=True, floatT=<class 'numpy.float64'>):
'''
Load GPL files from Peter Lepage's g-2 tools as 2d array. Can also load GPL-like files, where one allows the
tag (column 0) on each line to be different. Optionally ignore tag, which is just a label. Implemented in this way
rather than using genfromtxt to allow the possibility of ragged tables. 
'''

readJSON(filename, ignoreExtension=False) -> dict:
'''
Load a JSON file. Returns a dict, where each key level corresponds to an organizational level of the JSON. 
'''

readPickle(filename):
'''
Load a Pickle file.

Args:
    filename (str)
'''

readWML(filename) -> list:
'''
Does its best to read a table from Wikipedia Markup Language. Returns a list of lists,
where each row corresponds to either a line of the table or a line of markup code. You
will have to do some processing by hand, since so many people edit Wikipedia and have
inconsistent styles.

Args:
    filename (str): Name of file 

Returns:
    list: list of rows and commands in markup table 
'''

readYAML(filename, ignoreExtension=False) -> dict:
'''
Load a YAML file. Returns a dict, where each key level corresponds to an organizational level of the YAML. 
'''

writeJSON(data, filename):
'''
Write dictionary to JSON file.

Args:
    data (dict)
    filename (str)
'''

writeYAML(data, filename):
'''
Write dictionary to YAML file.

Args:
    data (dict)
    filename (str)
'''

class csvTable(delimiter):

class genericTable(delimiter=None, pre='', post=''):

class latexTable():

class markdownTable():

class redmineTable():