def get_tier(filename, tier_name, verbose=True):
""" Get a tier from a file.
:param filename: (str) Name of the annotated file.
:param tier_name: (str) Name of the tier
:param verbose: (bool) Print message
:returns: (Tier)
"""
# Read an annotated file.
if verbose:
print("Read file: {:s}".format(filename))
trs = trsaio.read(filename)
if verbose:
print(" ... [ OK ] ")
# Get the expected tier
if verbose:
print("Get tier {:s}".format(tier_name))
tier = trs.Find(tier_name, case_sensitive=False)
if tier is None:
print("Tier not found.")
sys.exit(1)
if verbose:
print(" ... [ OK ] ")
return tier
def get_tier(filename, tier_name, verbose=True):
""" Get a tier from a file.
:param filename: (str) Name of the annotated file.
:param tier_name: (str) Name of the tier
:param verbose: (bool) Print message
:returns: (Tier)
"""
# Read an annotated file.
if verbose:
print("Read file: {:s}".format(filename))
trs = trsaio.read(filename)
if verbose:
print(" ... [ OK ] ")
# Get the expected tier
if verbose:
print("Get tier {:s}".format(tier_name))
tier = trs.Find(tier_name, case_sensitive=False)
if tier is None:
print("Tier not found.")
sys.exit(1)
if verbose:
print(" ... [ OK ] ")
return tier
def printFreqAnn(filename, pattern):
""" Print the number of occurrences of a pattern in each tier of a file. """
print("Take a look at file {:s}:".format(filename))
trs = trsaio.read(filename)
pattern = pattern.strip()
for tier in trs:
c = 0
for ann in tier:
if ann.GetLabel().GetValue() == pattern:
c += 1
print(" Tier {:s}: {:d}".format(tier.GetName(), c))
def printFreqAnn(filename, pattern):
""" Print the number of occurrences of a pattern in each tier of a file. """
print("Take a look at file {:s}:".format(filename))
trs = trsaio.read(filename)
pattern = pattern.strip()
for tier in trs:
c = 0
for ann in tier:
if ann.GetLabel().GetValue() == pattern:
c += 1
print(" Tier {:s}: {:d}".format(tier.GetName(), c))
def printInfo(filename):
""" Print information about tiers of a file. """
# Read the file.
print("Take a look at file {:s}:".format(filename))
trs = trsaio.read(filename)
for tier in trs:
# Get the tier type
tier_type = "Unknown"
if tier.IsPoint() is True:
tier_type = "Point"
elif tier.IsInterval() is True:
tier_type = "Interval"
# Print all information
print(" * Tier: {:s}".format(tier.GetName()))
print(" - Type: {:s}".format(tier_type))
print(" - Number of annotations: {:d}".format(len(tier)))
print(" - From time: {:.4f}".format(tier.GetBeginValue()))
print(" - To time: {:.4f} ".format(tier.GetEndValue()))
if len(sys.argv) <= 1:
sys.argv.append('-h')
args = parser.parse_args()
# ----------------------------------------------------------------------------
# Load input data
mapping = TierMapping(args.m)
mapping.set_reverse(False) # from sampa to ipa direction
mapping.set_keep_miss(True) # keep unknown entries as given
mapping.set_miss_symbol("") # not used!
mapping.set_delimiters([]) # longest matching
# read content
trs_input = aio.read(args.i)
# ----------------------------------------------------------------------------
# Convert input file
trs = Transcription(name=trs_input.GetName()+"-IPA")
for n in args.n.split(','):
print(" -> Tier {:s}:".format(n))
tier = trs_input.Find(n, case_sensitive=False)
if tier is not None:
new_tier = mapping.map_tier(tier)
new_tier.SetName(n+"-IPA")
new_tier.metadata = tier.metadata
trs.Append(new_tier)
else:
# The input file name
filename = 'F_F_B003-P9-merge.TextGrid'
# The list of tiers we want to select
tier_names = ['PhonAlign', 'TokensAlign', 'toto']
# The output file name
output_filename = 'F_F_B003-P9-selection.TextGrid'
# ----------------------------------------------------------------------------
# Main
# ----------------------------------------------------------------------------
# Read an annotated file.
print("Read the file {:s}".format(filename))
trs = trsaio.read(filename)
print(" Number of tiers: {:d}.".format(trs.GetSize()))
# Create a new Transcription to add selected tiers.
new_trs = Transcription()
# Select some tiers, add into the new Transcription
for name in tier_names:
tier = trs.Find(name, case_sensitive=False)
if tier is not None:
new_trs.Append(tier)
print(" Tier {:s} successfully added.".format(tier.GetName()))
else:
print(" Error: the file does not contain a tier with name = {:s}".format(name))
# Save the Transcription into a file.
# ----------------------------------------------------------------------------
# Convert input file if not TextGrid
filename = args.i
fname, fext = os.path.splitext(filename)
if fname.endswith("-palign") is False:
print("ERROR: MarsaTag plugin requires SPPAS alignment files "
"(i.e. with -palign in its name).")
sys.exit(1)
if fext.lower() != "textgrid":
# read content
trs_input = aio.read(filename)
tier = trs_input.Find("TokensAlign", case_sensitive=False)
if tier is None:
print("ERROR: A tier with name TokensAlign is required.")
sys.exit(1)
# write as textgrid
trs = Transcription(name="TokensAlign")
trs.Append(tier)
filename = fname + ".TextGrid"
aio.write(filename, trs)
# ----------------------------------------------------------------------------
# Get MarsaTag path
if len(args.p) == 0:
if len(sys.argv) <= 1:
sys.argv.append('-h')
args = parser.parse_args()
# ----------------------------------------------------------------------------
# Load input data
mapping = TierMapping(args.m)
mapping.set_reverse(False) # from sampa to praat direction
mapping.set_keep_miss(True) # keep unknown entries as given
mapping.set_miss_symbol("") # not used!
mapping.set_delimiters([]) # longest matching
# read content
trs_input = aio.read(args.i)
# ----------------------------------------------------------------------------
# Convert input file
trs = Transcription(name=trs_input.GetName()+"-IPA")
for n in args.n.split(','):
print(" -> Tier {:s}:".format(n))
tier = trs_input.Find(n, case_sensitive=False)
if tier is not None:
new_tier = mapping.map_tier(tier)
new_tier.SetName(n+"-IPA")
trs.Append(new_tier)
else:
print(" [IGNORED] Wrong tier name.")
:copyright: Copyright (C) 2017 Fixme
:summary: Open an annotated file and save it as CSV file.
"""
import sys
import os.path
SPPAS_IS_HERE = os.path.join("..", "..")
sys.path.append(SPPAS_IS_HERE)
import sppas.src.annotationdata.aio as trsaio
# ----------------------------------------------------------------------------
# Variables
# ----------------------------------------------------------------------------
input_filename = 'F_F_B003-P9-merge.TextGrid'
output_filename = input_filename.replace('.TextGrid', '.csv')
# ----------------------------------------------------------------------------
# Main
# ----------------------------------------------------------------------------
if __name__ == '__main__':
# Read an annotated file, put content in a Transcription object.
trs = trsaio.read(input_filename)
# Save the Transcription object into a file.
trsaio.write(output_filename, trs)
