def test_interval(self):
"""
Try getting the interval between two chords and check it comes out
as expected.
"""
# Some randomly chosen tests
tests = [
(0, "C", "C", True),
(2, "F", "G", False),
(4, "D", "F#", False),
(6, "B", "F", True),
(8, "F", "Db", False),
(10, "Ab", "F#", False),
]
for interval, lower, upper, invertible in tests:
c0 = Chord(lower)
c1 = Chord(upper)
self.assertEqual(interval, Chord.interval(c0, c1))
# Try inverting the interval and check it's only the same in the
# cases where the interval is its own inverse
if invertible:
self.assertEqual(interval, Chord.interval(c1, c0))
else:
self.assertNotEqual(interval, Chord.interval(c1, c0))
def observation_from_chord_pair(crd1, crd2, chordmap):
if crd2 is None:
interval = 0
else:
interval = Chord.interval(Chord.from_name(str(crd1)), Chord.from_name(str(crd2)))
if not isinstance(crd1, Chord) and not isinstance(crd1, DbChord):
crd1 = Chord.from_name(crd1)
return "%d-%s" % (interval, chordmap[crd1.type])
def observation_from_chord_pair(crd1, crd2):
if crd2 is None:
interval = 0
else:
interval = Chord.interval(Chord.from_name(str(crd1)), Chord.from_name(str(crd2)))
if not isinstance(crd1, Chord):
crd1 = Chord.from_name(str(crd1))
return "%d-%s" % (interval, crd1.type)
def test_from_name(self):
"""
from_name covers a lot of possible chord instances. Here we just test
a sample of textual chords and check the instance gets the right
attributes out of the name.
It's by no means exhaustive!
"""
tests = [
# Name, root, type, additions, tetrad type
("C", 0, "", "", ""),
("F#m7", 6, "m7", "", "m7"),
("G7(9)", 7, "7", "9", "7"),
("A(9)", 9, "", "9", "7"),
("Dsus4", 2, "sus4", "", "sus4"),
("Esus4,7", 4, "sus4,7", "", "sus4,7"),
("Esus4(9)", 4, "sus4", "9", "sus4,7"),
("Fm,M7(+11)", 5, "m,M7", "+11", "m,M7"),
]
for name, root, ctype, additions, tetrad in tests:
c = Chord.from_name(name)
self.assertEqual(root, c.root)
self.assertEqual(ctype, c.type)
self.assertEqual(additions, c.additions)
self.assertEqual(tetrad, c.tetrad_type)
def test_from_name(self):
"""
from_name covers a lot of possible chord instances. Here we just test
a sample of textual chords and check the instance gets the right
attributes out of the name.
It's by no means exhaustive!
"""
tests = [
# Name, root, type, additions, tetrad type
("C", 0, "", "", ""),
("F#m7", 6, "m7", "", "m7"),
("G7(9)", 7, "7", "9", "7"),
("A(9)", 9, "", "9", "7"),
("Dsus4", 2, "sus4", "", "sus4"),
("Esus4,7", 4, "sus4,7", "", "sus4,7"),
("Esus4(9)", 4, "sus4", "9", "sus4,7"),
("Fm,M7(+11)", 5, "m,M7", "+11", "m,M7"),
]
for name, root, ctype, additions, tetrad in tests:
c = Chord.from_name(name)
self.assertEqual(root, c.root)
self.assertEqual(ctype, c.type)
self.assertEqual(additions, c.additions)
self.assertEqual(tetrad, c.tetrad_type)
def test_set_root(self):
"""
Try setting the root or numeral after a chord is created and check
that both values get correctly set.
"""
c = Chord(self.ALLOWED_NUMERALS[0][0])
for numeral, root, trg_num in self.ALLOWED_NUMERALS:
# Try setting the root and check root and numeral are correct
c.root = root
self.assertEqual(root, c.root)
self.assertEqual(trg_num, c.root_numeral)
for numeral, root, trg_num in self.ALLOWED_NUMERALS:
# Try setting the numeral and check root and numeral are correct
c.root_numeral = numeral
self.assertEqual(root, c.root)
self.assertEqual(trg_num, c.root_numeral)
def test_set_root(self):
"""
Try setting the root or numeral after a chord is created and check
that both values get correctly set.
"""
c = Chord(self.ALLOWED_NUMERALS[0][0])
for numeral, root, trg_num in self.ALLOWED_NUMERALS:
# Try setting the root and check root and numeral are correct
c.root = root
self.assertEqual(root, c.root)
self.assertEqual(trg_num, c.root_numeral)
for numeral, root, trg_num in self.ALLOWED_NUMERALS:
# Try setting the numeral and check root and numeral are correct
c.root_numeral = numeral
self.assertEqual(root, c.root)
self.assertEqual(trg_num, c.root_numeral)
def test_init_type(self):
"""
Try creating chords with a particular type and check (a) that they
successfully create a chord and (b) that the chord has the right type.
"""
for ctype in Chord.TYPE_SYMBOLS.values():
c = Chord("C", type=ctype)
self.assertEqual(c.type, ctype)
def interval_observation_from_chord_string_pair(chord1, chord2, type_mapping=None):
"""
Given two strings representing chords, produces a string representing
a chord observation of the form x-t, where x is the interval between
the chords (numeric) and t is the type of the first chord.
"""
from jazzparser.data import Chord
chord1 = Chord.from_name(chord1)
if chord2 is None:
interval = ""
else:
chord2 = Chord.from_name(chord2)
interval = "%d" % Chord.interval(chord1,chord2)
# Apply a mapping to the chord type if one was given
if type_mapping is not None:
ctype = type_mapping[chord1.type]
else:
ctype = chord1.type
return "%s-%s" % (interval, ctype)
def test_interval(self):
"""
Try getting the interval between two chords and check it comes out
as expected.
"""
# Some randomly chosen tests
tests = [(0, "C", "C", True), (2, "F", "G", False),
(4, "D", "F#", False), (6, "B", "F", True),
(8, "F", "Db", False), (10, "Ab", "F#", False)]
for interval, lower, upper, invertible in tests:
c0 = Chord(lower)
c1 = Chord(upper)
self.assertEqual(interval, Chord.interval(c0, c1))
# Try inverting the interval and check it's only the same in the
# cases where the interval is its own inverse
if invertible:
self.assertEqual(interval, Chord.interval(c1, c0))
else:
self.assertNotEqual(interval, Chord.interval(c1, c0))
def interval_observation_from_chord_string_pair(chord1,
chord2,
type_mapping=None):
"""
Given two strings representing chords, produces a string representing
a chord observation of the form x-t, where x is the interval between
the chords (numeric) and t is the type of the first chord.
"""
from jazzparser.data import Chord
chord1 = Chord.from_name(chord1)
if chord2 is None:
interval = ""
else:
chord2 = Chord.from_name(chord2)
interval = "%d" % Chord.interval(chord1, chord2)
# Apply a mapping to the chord type if one was given
if type_mapping is not None:
ctype = type_mapping[chord1.type]
else:
ctype = chord1.type
return "%s-%s" % (interval, ctype)
def _tree_probs(trace):
""" Add counts to the model from a derivation trace """
parent = trace.result
# Get prob for the parent category
parent_rep = model_category_repr(parent.category)
print "%s : %s" % (parent_rep, model._parent_dist.prob(parent_rep))
if len(trace.rules) == 0:
# Leaf node - lexical generation
# Get prob for this parent expanding as a leaf
print "%s -leaf- : %s" % (
parent_rep,
model._expansion_type_dist[parent_rep].prob('leaf'))
# Interpret the word as a chord
chord = Chord.from_name(trace.word)
chord = category_relative_chord(chord, parent.category)
observation = model.chord_observation(chord)
# Count this parent producing this word
# The chord root is now relative to the base pitch of the category
print "%s -leaf-> %s : %s" % \
(parent_rep, observation, model._lexical_dist[parent_rep].prob(observation))
else:
# Internal node - rule application
# There should only be one rule application, but just in case...
for rule, args in trace.rules:
if rule.arity == 1:
# Unary rule
raise ModelTrainingError, "we don't currently support "\
"unary rule application, but one was found in "\
"the training data"
if rule.arity == 2:
# Binary rule
expansion = 'right'
print "%s -right- : %s" % \
(parent_rep, model._expansion_type_dist[parent_rep].prob(expansion))
# Count this parent expanding to the head daughter
head_rep = model_category_repr(args[1].result.category,
parent.category)
print "%s -right-> %s : %s" % \
(parent_rep, head_rep,
model._head_expansion_dist[(expansion,parent_rep)].prob(head_rep))
# Count this parent with this head expansion expanding
# to the non-head daughter
non_head_rep = model_category_repr(args[0].result.category,
parent.category)
print "%s -right-> %s | %s : %s" % \
(parent_rep, head_rep, non_head_rep,
model._non_head_expansion_dist[(
head_rep, expansion, parent_rep)].prob(non_head_rep))
# Recurse to count derivations from the daughters
for arg in args:
_tree_probs(arg)
def test_from_numerals(self):
"""
Try creating chords using all possbile numerals and check the numeral
and root get set correctly.
"""
for numeral, root, trg_num in self.ALLOWED_NUMERALS:
# Try creating a Chord with each numeral
c = Chord(numeral)
# Check it has the right numeral
self.assertEqual(trg_num, c.root_numeral)
# and the right root number
self.assertEqual(root, c.root)
def test_back_conversion(self): """ Creates a tonal space path, converts it to state labels and converts it back again. This should produce the original path if all goes well. Note that the result of the back conversion will always have the path shifted so it starts as close as possible to the origin. This is correct behaviour: the state labels don't encode the enharmonic block that the path starts in and it is merely by convention that we assume the start point. Each path-chord sequence pair also gives the expected output, which may differ from the original path only in this respect. @todo: update this test """ # Just return for now: I've not had a chance to update this # lf_chords_to_states no longer exists return self.longMessage = True # Run the test on a whole set of paths for (coords,chords,output) in self.PATHS: # Build a CoordinateList for the path ens = [EnharmonicCoordinate.from_harmonic_coord((x,y)) for (x,y,fun) in coords] pcs = [PathCoordinate.from_enharmonic_coord(en) for en in ens] time = 0 for pc,(__,__,fun) in zip(pcs,coords): pc.function = fun pc.duration = 1 pc.time = time time += 1 path = Semantics(CoordinateList(items=pcs)) # Build the list of chords chords = [Chord.from_name(crd).to_db_mirror() for crd in chords] for chord in chords: chord.duration = 1 # Try converting it to states states = lf_chords_to_states(path, chords) # Now try converting it back back = states_chords_to_lf(zip(states,chords)) # Check that we got the same coordinates out in_coords = [(x,y) for (x,y,fun) in output] in_funs = [fun for (x,y,fun) in output] out_coords = [point.harmonic_coord for point in back.lf] out_funs = [point.function for point in back.lf] self.assertEqual(in_coords, out_coords, msg="coordinates converted to states and back produced something different.\nState labels:\n%s" % (states)) self.assertEqual(in_funs, out_funs, msg="coordinates converted to states and back produced different functions.\nState labels:\n%s" % (states))
def generalise_chord_name(chord_name):
"""
The grammar generalises over chord names, using X to mean "any
roman numeral chord root". When a chord name comes as input to
the parser, say "IIm", we look up not "IIm", but "Xm".
Given any chord name, this function returns the generalised
chord name to look up in the grammar.
"""
from jazzparser.data import Chord
# Try building a chord from the chord name
chord = Chord.from_name(chord_name)
# Only interested in the tetrad type
return "X%s" % chord.tetrad_type
def _generate(parent, depth=0, pitch=0):
# Transform the parent category so it's relative to itself
# All generated categories will be relative to this,
# so we need to make the parent self-relative at the
# start of each recursion
parent_rep = model_category_repr(parent)
parent_pitch = (pitch + base_pitch(parent)) % 12
logger.debug("%sGenerating from parent: %s" %
(" " * depth, parent_rep))
if max_depth is not None and depth >= max_depth and \
len(self._lexical_dist[parent_rep].samples()) != 0:
# Don't go any deeper than this if we can stop here
# Only possible if the parent has generated a leaf before
exp = 'leaf'
logger.debug("%sForcing leaf" % (" " * depth))
else:
# Otherwise freely generate an expansion type
exp = generate_from_prob_dist(
self._expansion_type_dist[parent_rep])
logger.debug("%sExpansion: %s" % (" " * depth, exp))
exp_parent = (exp, parent_rep)
if exp == 'leaf':
# Generate a leaf node (word)
word = generate_from_prob_dist(self._lexical_dist[parent_rep])
logger.debug("%sWord: %s, pitch: %d" %
(" " * depth, word, parent_pitch))
chord = Chord.from_name(word)
chord.root = (chord.root + parent_pitch) % 12
return [chord]
else:
# First generate a head node
head = generate_from_prob_dist(
self._head_expansion_dist[exp_parent])
logger.debug("%sHead: %s" % (" " * depth, head))
# Continue to expand this recursively to a word sequence
head_generated = _generate(head, depth=depth+1, \
pitch=parent_pitch)
head_exp_parent = (head, exp, parent_rep)
# Now generate a non-head node
non_head = generate_from_prob_dist(
self._non_head_expansion_dist[head_exp_parent])
logger.debug("%sNon-head: %s" % (" " * depth, non_head))
# Continue to expand this too
non_head_generated = _generate(non_head, depth=depth+1, \
pitch=parent_pitch)
return non_head_generated + head_generated
def generalise_chord_name(chord_name):
"""
The grammar generalises over chord names, using X to mean "any
roman numeral chord root". When a chord name comes as input to
the parser, say "IIm", we look up not "IIm", but "Xm".
Given any chord name, this function returns the generalised
chord name to look up in the grammar.
"""
from jazzparser.data import Chord
# Try building a chord from the chord name
chord = Chord.from_name(chord_name)
# Only interested in the tetrad type
return "X%s" % chord.tetrad_type
def _add_counts(trace):
""" Add counts to the model from a derivation trace """
parent = trace.result
# Add a count for the parent category
parent_rep = model_category_repr(parent.category)
self._parent_counts.inc(parent_rep)
if len(trace.rules) == 0:
# Leaf node - lexical generation
# Count this parent expanding as a leaf
self._expansion_type_counts[parent_rep].inc('leaf')
# Interpret the word as a chord
chord = Chord.from_name(trace.word)
chord = category_relative_chord(chord, parent.category)
observation = self.chord_observation(chord)
# Count this parent producing this word
# The chord root is now relative to the base pitch of the category
self._lexical_counts[parent_rep].inc(observation)
else:
# Internal node - rule application
# There should only be one rule application, but just in case...
for rule,args in trace.rules:
if rule.arity == 1:
# Unary rule
raise ModelTrainingError, "we don't currently support "\
"unary rule application, but one was found in "\
"the training data"
if rule.arity == 2:
# Binary rule
# Assume all heads come from the right
expansion = 'right'
self._expansion_type_counts[parent_rep].inc(expansion)
# Count this parent expanding to the head daughter
head_rep = model_category_repr(args[1].result.category,
parent.category)
self._head_expansion_counts[
(expansion,parent_rep)].inc(head_rep)
# Count this parent with this head expansion expanding
# to the non-head daughter
non_head_rep = model_category_repr(
args[0].result.category, parent.category)
self._non_head_expansion_counts[
(head_rep,expansion,parent_rep)
].inc(non_head_rep)
# Recurse to count derivations from the daughters
for arg in args:
_add_counts(arg)
def _add_counts(trace):
""" Add counts to the model from a derivation trace """
parent = trace.result
# Add a count for the parent category
parent_rep = model_category_repr(parent.category)
self._parent_counts.inc(parent_rep)
if len(trace.rules) == 0:
# Leaf node - lexical generation
# Count this parent expanding as a leaf
self._expansion_type_counts[parent_rep].inc('leaf')
# Interpret the word as a chord
chord = Chord.from_name(trace.word)
chord = category_relative_chord(chord, parent.category)
observation = self.chord_observation(chord)
# Count this parent producing this word
# The chord root is now relative to the base pitch of the category
self._lexical_counts[parent_rep].inc(observation)
else:
# Internal node - rule application
# There should only be one rule application, but just in case...
for rule, args in trace.rules:
if rule.arity == 1:
# Unary rule
raise ModelTrainingError, "we don't currently support "\
"unary rule application, but one was found in "\
"the training data"
if rule.arity == 2:
# Binary rule
# Assume all heads come from the right
expansion = 'right'
self._expansion_type_counts[parent_rep].inc(expansion)
# Count this parent expanding to the head daughter
head_rep = model_category_repr(args[1].result.category,
parent.category)
self._head_expansion_counts[(expansion,
parent_rep)].inc(head_rep)
# Count this parent with this head expansion expanding
# to the non-head daughter
non_head_rep = model_category_repr(
args[0].result.category, parent.category)
self._non_head_expansion_counts[(
head_rep, expansion, parent_rep)].inc(non_head_rep)
# Recurse to count derivations from the daughters
for arg in args:
_add_counts(arg)
def _generate(parent, depth=0, pitch=0):
# Transform the parent category so it's relative to itself
# All generated categories will be relative to this,
# so we need to make the parent self-relative at the
# start of each recursion
parent_rep = model_category_repr(parent)
parent_pitch = (pitch + base_pitch(parent)) % 12
logger.debug("%sGenerating from parent: %s" % (" "*depth,parent_rep))
if max_depth is not None and depth >= max_depth and \
len(self._lexical_dist[parent_rep].samples()) != 0:
# Don't go any deeper than this if we can stop here
# Only possible if the parent has generated a leaf before
exp = 'leaf'
logger.debug("%sForcing leaf" % (" "*depth))
else:
# Otherwise freely generate an expansion type
exp = generate_from_prob_dist(self._expansion_type_dist[parent_rep])
logger.debug("%sExpansion: %s" % (" "*depth, exp))
exp_parent = (exp,parent_rep)
if exp == 'leaf':
# Generate a leaf node (word)
word = generate_from_prob_dist(self._lexical_dist[parent_rep])
logger.debug("%sWord: %s, pitch: %d" % (" "*depth, word, parent_pitch))
chord = Chord.from_name(word)
chord.root = (chord.root + parent_pitch) % 12
return [chord]
else:
# First generate a head node
head = generate_from_prob_dist(self._head_expansion_dist[exp_parent])
logger.debug("%sHead: %s" % (" "*depth, head))
# Continue to expand this recursively to a word sequence
head_generated = _generate(head, depth=depth+1, \
pitch=parent_pitch)
head_exp_parent = (head,exp,parent_rep)
# Now generate a non-head node
non_head = generate_from_prob_dist(
self._non_head_expansion_dist[head_exp_parent])
logger.debug("%sNon-head: %s" % (" "*depth, non_head))
# Continue to expand this too
non_head_generated = _generate(non_head, depth=depth+1, \
pitch=parent_pitch)
return non_head_generated + head_generated
def inside_probability(self, expansion, parent, left, right=None):
"""
Probability of a (non-leaf) subtree, computed from the probability
of its expansions. This doesn't include the probabilities
of the subtrees of the daughters. To get the full inside probability,
multiply the returned value with the daughters' insider probabilities.
"""
parent_rep = model_category_repr(parent.category)
# Get the probability of the expansion type
exp_prob = self._expansion_type_dist[parent_rep].prob(expansion)
if expansion == 'leaf':
# Get the probability of the word given parent
# If the model doesn't generate words, this probability is 1
if not self.lexical:
word_prob = 1.0
else:
# In this case the word is given as the left branch
word = left
# Word should be a chord label: interpret it as such
chord = Chord.from_name(word)
chord_obs = self.chord_observation(
category_relative_chord(chord, category=parent.category))
word_prob = self._lexical_dist[parent_rep].prob(chord_obs)
return exp_prob * word_prob
else:
# We currently only recognise one other case: right-head
assert right is not None, "pcfg model only supports binary branches"
head = right
non_head = left
# Get the probability of the head (right) daughter given the parent
condition = (expansion, parent_rep)
head_rep = model_category_repr(head.category, parent.category)
head_prob = self._head_expansion_dist[condition].prob(head_rep)
# Get the probability of the non-head daughter given the
# parent and the head daughter
condition = (head_rep, expansion, parent_rep)
non_head_rep = model_category_repr(non_head.category,
parent.category)
non_head_prob = \
self._non_head_expansion_dist[condition].prob(non_head_rep)
return exp_prob * head_prob * non_head_prob
def inside_probability(self, expansion, parent, left, right=None):
"""
Probability of a (non-leaf) subtree, computed from the probability
of its expansions. This doesn't include the probabilities
of the subtrees of the daughters. To get the full inside probability,
multiply the returned value with the daughters' insider probabilities.
"""
parent_rep = model_category_repr(parent.category)
# Get the probability of the expansion type
exp_prob = self._expansion_type_dist[parent_rep].prob(expansion)
if expansion == 'leaf':
# Get the probability of the word given parent
# If the model doesn't generate words, this probability is 1
if not self.lexical:
word_prob = 1.0
else:
# In this case the word is given as the left branch
word = left
# Word should be a chord label: interpret it as such
chord = Chord.from_name(word)
chord_obs = self.chord_observation(
category_relative_chord(chord,
category=parent.category))
word_prob = self._lexical_dist[parent_rep].prob(chord_obs)
return exp_prob * word_prob
else:
# We currently only recognise one other case: right-head
assert right is not None, "pcfg model only supports binary branches"
head = right
non_head = left
# Get the probability of the head (right) daughter given the parent
condition = (expansion, parent_rep)
head_rep = model_category_repr(head.category, parent.category)
head_prob = self._head_expansion_dist[condition].prob(head_rep)
# Get the probability of the non-head daughter given the
# parent and the head daughter
condition = (head_rep, expansion, parent_rep)
non_head_rep = model_category_repr(non_head.category, parent.category)
non_head_prob = \
self._non_head_expansion_dist[condition].prob(non_head_rep)
return exp_prob * head_prob * non_head_prob
def __init__(self,
inputs,
durations=None,
times=None,
roman=False,
*args,
**kwargs):
super(ChordInput, self).__init__(*args, **kwargs)
self.inputs = inputs
self.durations = durations
self.times = times
self.roman = roman
# Compute the durations from times or vice versa
if durations is None and times is None:
raise ValueError, "cannot create a ChordInput with neither "\
"times nor durations given"
elif times is None:
self.times = [
sum(durations[:i], Fraction(0)) for i in range(len(durations))
]
elif durations is None:
from jazzparser.utils.base import group_pairs
self.durations = [
time1 - time0 for (time1, time0) in group_pairs(times)
] + [Fraction(1)]
# Convert all strings to internal chord representation
# Done now so we check the chords can all be understood before doing
# anything else
self.chords = [
Chord.from_name(name, roman=roman).to_db_mirror()
for name in inputs
]
for chord, dur in zip(self.chords, self.durations):
chord.duration = dur
def __init__(self, inputs, durations=None, times=None, roman=False, *args, **kwargs):
super(ChordInput, self).__init__(*args, **kwargs)
self.inputs = inputs
self.durations = durations
self.times = times
self.roman = roman
# Compute the durations from times or vice versa
if durations is None and times is None:
raise ValueError, "cannot create a ChordInput with neither " "times nor durations given"
elif times is None:
self.times = [sum(durations[:i], Fraction(0)) for i in range(len(durations))]
elif durations is None:
from jazzparser.utils.base import group_pairs
self.durations = [time1 - time0 for (time1, time0) in group_pairs(times)] + [Fraction(1)]
# Convert all strings to internal chord representation
# Done now so we check the chords can all be understood before doing
# anything else
self.chords = [Chord.from_name(name, roman=roman).to_db_mirror() for name in inputs]
for chord, dur in zip(self.chords, self.durations):
chord.duration = dur
# Output audio files from the harmonical
if (options.harmonical is not None or \
options.enharmonical is not None) and len(results) > 0:
path = grammar.formalism.sign_to_coordinates(results[0])
# Assuming we used a temporal formalism, the times should be
# available as a list from the semantics
times = results[0].semantics.get_path_times()
point_durations = [next-current for current,next in group_pairs(times)] + [0]
# Get 3d coordinates as well
path3d = zip(add_z_coordinates(path, pitch_range=2), point_durations)
path2d = zip(path,point_durations)
# Get chord types out of the input
chords = tagger.get_string_input()
chord_durs = [tagger.get_word_duration(i) for i in range(tagger.input_length)]
chord_types = [(Chord.from_name(c).type,dur) for c,dur in zip(chords,chord_durs)]
if options.midi:
# Maybe set this as a CL option or a setting
# 73 - flute
# 0 - piano
# 4 - e-piano
instrument = 73
# TODO: make these filenames different for multiple inputs
if options.harmonical is not None:
filename = os.path.abspath(options.harmonical)
render_path_to_midi_file(filename, path3d, chord_types=chord_types, tempo=options.tempo, instrument=instrument, bass_root=True, root_octave=-1)
messages.append("Output JI MIDI data to %s" % filename)
if options.enharmonical is not None:
filename = os.path.abspath(options.enharmonical)
render_path_to_midi_file(filename, path3d, chord_types=chord_types, equal_temperament=True, tempo=options.tempo, instrument=instrument, bass_root=True, root_octave=-1)
def get_signs_for_word(self, word, tags=None, extra_features=None):
"""
Given a word string, returns a list of the possible signs
(as CCGSigns) that the grammar can assign to it.
word may also be a Chord object.
For now, this assumes that the input is a single chord in
roman numeral notation and that spelling issues have already
been resolved (e.g. that 6s have been removed).
If tags is given it should be a list of strings. Signs will be
restricted to those whose entry's tag name/POS is in the list.
If you need to get an instantiated category from a lexical entry,
use the methods on L{EntriesItem} directly, or L{get_signs_for_tag}.
"""
if isinstance(word, Chord):
chord = word.to_db_mirror()
elif isinstance(word, basestring):
chord = Chord.from_name(word, permissive=True).to_db_mirror()
elif isinstance(word, DbChord):
chord = word
else:
raise GrammarLookupError, "Tried to get signs for a word of type "\
"'%s': %s" % (type(word), word)
# Get a chord type string to look up in the grammar
chord_lookup = "X%s" % chord.type
# Check whether we know this word
if not chord_lookup in self.morph_items:
# Word not recognised
raise GrammarLookupError, "The word \"%s\" was not found in the "\
"lexicon. (Looked up %s in %s)" \
% (word, chord_lookup,
",".join(["%s" % item for item in self.morph_items.keys()]))
# Get the list of interpretations of this word
morphs = self.morph_items[chord_lookup]
# Limit to tag list if one was given
if tags is not None:
morphs = [m for m in morphs if m.pos in tags]
# Build a sign for each morph-family pair
category_list = []
for morph in morphs:
# Look for families corresponding to the POS
if not morph.pos in self.families:
raise GrammarLookupError, "There is no family in the lexicon "\
"for the POS %s." % morph.pos
for family in self.families[morph.pos]:
# Build a CCGCategory for each entry in each family found
for entry in family.entries:
sign = entry.sign.copy()
sign.tag = entry.tag_name
features = {
'root' : chord.root,
'morph' : morph
}
if extra_features is not None:
features.update(extra_features)
sign.apply_lexical_features(features)
category_list.append(sign)
return category_list
def observation_from_chord_pair(crd1, crd2):
if crd1 is None or crd2 is None:
return "0"
return "%d" % Chord.interval(Chord.from_name(str(crd1)), Chord.from_name(str(crd2)))
def get_signs_for_word(self, word, tags=None, extra_features=None):
"""
Given a word string, returns a list of the possible signs
(as CCGSigns) that the grammar can assign to it.
word may also be a Chord object.
For now, this assumes that the input is a single chord in
roman numeral notation and that spelling issues have already
been resolved (e.g. that 6s have been removed).
If tags is given it should be a list of strings. Signs will be
restricted to those whose entry's tag name/POS is in the list.
If you need to get an instantiated category from a lexical entry,
use the methods on L{EntriesItem} directly, or L{get_signs_for_tag}.
"""
if isinstance(word, Chord):
chord = word.to_db_mirror()
elif isinstance(word, basestring):
chord = Chord.from_name(word, permissive=True).to_db_mirror()
elif isinstance(word, DbChord):
chord = word
else:
raise GrammarLookupError, "Tried to get signs for a word of type "\
"'%s': %s" % (type(word), word)
# Get a chord type string to look up in the grammar
chord_lookup = "X%s" % chord.type
# Check whether we know this word
if not chord_lookup in self.morph_items:
# Word not recognised
raise GrammarLookupError, "The word \"%s\" was not found in the "\
"lexicon. (Looked up %s in %s)" \
% (word, chord_lookup,
",".join(["%s" % item for item in self.morph_items.keys()]))
# Get the list of interpretations of this word
morphs = self.morph_items[chord_lookup]
# Limit to tag list if one was given
if tags is not None:
morphs = [m for m in morphs if m.pos in tags]
# Build a sign for each morph-family pair
category_list = []
for morph in morphs:
# Look for families corresponding to the POS
if not morph.pos in self.families:
raise GrammarLookupError, "There is no family in the lexicon "\
"for the POS %s." % morph.pos
for family in self.families[morph.pos]:
# Build a CCGCategory for each entry in each family found
for entry in family.entries:
sign = entry.sign.copy()
sign.tag = entry.tag_name
features = {'root': chord.root, 'morph': morph}
if extra_features is not None:
features.update(extra_features)
sign.apply_lexical_features(features)
category_list.append(sign)
return category_list
def observation_from_chord(crd):
chord = Chord.from_name(crd)
return chord.type
def test_back_conversion(self):
"""
Creates a tonal space path, converts it to state labels and converts
it back again. This should produce the original path if all goes
well.
Note that the result of the back conversion will always have the
path shifted so it starts as close as possible to the origin. This is
correct behaviour: the state labels don't encode the enharmonic
block that the path starts in and it is merely by convention that we
assume the start point.
Each path-chord sequence pair also gives the expected output, which
may differ from the original path only in this respect.
@todo: update this test
"""
# Just return for now: I've not had a chance to update this
# lf_chords_to_states no longer exists
return
self.longMessage = True
# Run the test on a whole set of paths
for (coords, chords, output) in self.PATHS:
# Build a CoordinateList for the path
ens = [
EnharmonicCoordinate.from_harmonic_coord((x, y))
for (x, y, fun) in coords
]
pcs = [PathCoordinate.from_enharmonic_coord(en) for en in ens]
time = 0
for pc, (__, __, fun) in zip(pcs, coords):
pc.function = fun
pc.duration = 1
pc.time = time
time += 1
path = Semantics(CoordinateList(items=pcs))
# Build the list of chords
chords = [Chord.from_name(crd).to_db_mirror() for crd in chords]
for chord in chords:
chord.duration = 1
# Try converting it to states
states = lf_chords_to_states(path, chords)
# Now try converting it back
back = states_chords_to_lf(zip(states, chords))
# Check that we got the same coordinates out
in_coords = [(x, y) for (x, y, fun) in output]
in_funs = [fun for (x, y, fun) in output]
out_coords = [point.harmonic_coord for point in back.lf]
out_funs = [point.function for point in back.lf]
self.assertEqual(
in_coords,
out_coords,
msg=
"coordinates converted to states and back produced something different.\nState labels:\n%s"
% (states))
self.assertEqual(
in_funs,
out_funs,
msg=
"coordinates converted to states and back produced different functions.\nState labels:\n%s"
% (states))
def observation_from_chord(crd):
chord = Chord.from_name(crd)
return chord.type
