def read_file(filename):
txt = plot_files.lines_without_comments(filename)
filetype = plot_files.determine_type(txt)
if filetype == "paren_complex":
parse_pair = plot_files.parse_pair
df = pd.read_csv(txt, delimiter=' ', names=["id", "identities", "error"],
converters={1: parse_pair, 2: parse_pair}, index_col=0)
else:
df = pd.read_csv(txt, delimiter=' ', names=["id", "identities", "error"], index_col=0)
return df
def read_level_fits(filename):
"""
Read in fit values to the levels. Fits should be single exp
Should be in the format
# Level, Amp, Error(Amp), Mass, Error(Mass)
0, 1.74082463996023, 0.0154835440133309, 0.153284504901198, 0.000705009866455281
final column chi^2 optional
"""
txt = plot_files.lines_without_comments(filename)
df = pd.read_csv(txt, delimiter=',', names=["level", "amp", "amp_error", "mass", "mass_error", "chisqr"], index_col=0)
return df
def read_zrots(filename):
txt = plot_files.lines_without_comments(filename)
df = pd.read_csv(txt, delimiter=',', names=["level", "amp", "error"], index_col=0)
return df
def read_coeffs_file(filename):
txt = plot_files.lines_without_comments(filename)
parse_pair = plot_files.parse_pair
df = pd.read_csv(txt, delimiter=' ', names=["id", "identities", "error"],
converters={1: parse_pair, 2: parse_pair}, index_col=0)
return df
def read_file(filename):
txt = plot_files.lines_without_comments(filename)
df = pd.read_csv(txt, delimiter=' ', names=["time", "correlator", "error"],
converters={1: parse_pair, 2: parse_pair}, skipinitialspace=True, index_col=0)
return df
