def testdata(model_path, test_music, phase, save_path, start=0, end=1024):
savefile_name = model_path.name + ".wav"
end = start + end
wav_L = util.separate(test_music[0],
model_path,
mask=test_music[2],
start=start,
end=end)
wav_R = util.separate(test_music[1],
model_path,
mask=test_music[2],
start=start,
end=end)
wav_Stereo = np.array((wav_L, wav_R))
del wav_L, wav_R
util.SaveStereoAudio("target-%s" % savefile_name,
wav_Stereo,
phase[:, :, start:end],
save_path=save_path)
wav_L = util.separate(test_music[0],
model_path,
mask=test_music[2],
multi=1,
start=start,
end=end)
wav_R = util.separate(test_music[1],
model_path,
mask=test_music[2],
multi=1,
start=start,
end=end)
wav_Stereo = np.array((wav_L, wav_R))
del wav_L, wav_R
util.SaveStereoAudio("inst-%s" % savefile_name,
wav_Stereo,
phase[:, :, start:end],
save_path=save_path)
def main(nodelist, job_id):
"""main called when run as script"""
log.debug(f"main {nodelist} {job_id}")
nodes = util.to_hostnames(nodelist)
if job_id is not None:
_, exclusive = separate(is_exclusive_node, nodes)
if exclusive:
hostlist = util.to_hostlist(exclusive)
log.info(f"epilog suspend {hostlist} job_id={job_id}")
epilog_suspend_nodes(exclusive, job_id)
else:
# suspend is allowed to delete exclusive nodes
log.info(f"suspend {nodelist}")
suspend_nodes(nodes)
def bakker_kMeans(data, **kwargs):
"""
This is an implementation of the k-means algorithm designed specifically
for flow cytometry data in the following paper:
T.C.B. Schut, B.G.D. Grooth, and J. Greve,
"Cluster analysis of flow cytometric list mode data on a personal computer",
Cytometry, vol. 14, 1993, pp. 649-659.
@type data: array
@param data: The data to be clustered
@type kwargs: dict
@param kwargs: The following args are accepted:
- numClusters: The number of clusters to form
@rtype: tuple
@return: A list where each element indicates the cluster membership of the
corresponding index in the original data and a message string
"""
k = 1
initClusters = 200
msg = ''
if 'numClusters' in kwargs.keys():
k = int(kwargs['numClusters'])
if 'initClusters' in kwargs.keys():
initClusters = int(kwargs['numClusters'])
# Log transform
logData = tm.getMethod('log')(data)
# Choose large # (200 as suggested by authors) of non-random initial centers
centers = util.kinit(logData, initClusters)
# Run k-means
_, ids = kmeans2(logData, np.array(centers), minit='matrix')
# Merge clusters w/special comparison metric until user cluster # achieved
clusters = util.separate(logData, ids)
finalIDs = merge(k, ids, clusters)
return finalIDs, msg
def delete_instances(instances):
"""Call regionInstances.bulkInsert to create instances"""
if len(instances) == 0:
return
invalid, valid = separate(lambda inst: bool(lkp.instance(inst)), instances)
log.debug("instances do not exist: {}".format(",".join(invalid)))
if lkp.cfg.enable_reconfigure:
count = len(instances)
hostlist = util.to_hostlist(valid)
log.info("delete {} subscriptions ({})".format(count, hostlist))
execute_with_futures(subscription_delete, valid)
requests = {inst: delete_instance_request(inst) for inst in valid}
done, failed = batch_execute(requests)
if failed:
failed_nodes = [f"{n}: {e}" for n, (_, e) in failed.items()]
node_str = "\n".join(str(el) for el in truncate_iter(failed_nodes, 5))
log.error(f"some nodes failed to delete: {node_str}")
wait_for_operations(done.values())
def main(nodelist, job_id, force=False):
"""main called when run as script"""
log.debug(f"main {nodelist} {job_id}")
# nodes are split between normal and exclusive
# exclusive nodes are handled by PrologSlurmctld
nodes = expand_nodelist(nodelist)
if force:
exclusive = normal = nodes
prelog = "force "
else:
normal, exclusive = separate(is_exclusive_node, nodes)
prelog = ""
if job_id is None or force:
if normal:
hostlist = util.to_hostlist(normal)
log.info(f"{prelog}resume {hostlist}")
resume_nodes(normal)
else:
if exclusive:
hostlist = util.to_hostlist(exclusive)
log.info(f"{prelog}exclusive resume {hostlist} {job_id}")
prolog_resume_nodes(job_id, exclusive)
def parse_text(self,
text,
default_speaker,
separate_comma=False,
n_gram=2,
separate_sentence=False,
parse_speaker=True,
normalize=True):
"""
Parse the input text into suitable data structure
:param n_gram: concat sentences of this max length in a line
:param text: source
:param default_speaker: the default speaker if no speaker in specified
:param separate_comma: split by comma
:param separate_sentence: split sentence if multiple clauses exist
:param parse_speaker: bool for turn on/off parse speaker
:param normalize: to convert common punctuation besides comma to comma
"""
lines = re.split(r'\r\n|\n\r|\r|\n', text)
line_speaker_dict = {}
# TODO: allow speakers not in model_list and later are forced to be replaced
if parse_speaker:
# re.match(r'^.*(?=:)', text)
for i, line in enumerate(lines):
if re.search(r':|\|', line):
# ?: non capture group of : and |
speaker, line = re.split(r'\s*(?::|\|)\s*', line, 1)
# add entry only if the voice model exist in the folder,
# the unrecognized one will be changed to default in later code
if speaker in self.model_list:
line_speaker_dict[i] = speaker
lines[i] = line
if normalize:
lines = [normalize_text(line) for line in lines]
# separate by spacy sentencizer
lines = [
separate(line, n_gram, comma=separate_comma) for line in lines
]
sentence_dicts = []
for i, line in enumerate(lines):
for j, sent in enumerate(line):
if sentence_dicts:
if sent[0].is_punct and not any(sent[0].text == punct
for punct in ['“', '‘']):
sentence_dicts[-1][
'punct'] = sentence_dicts[-1]['punct'] + sent.text
continue
sentence_dict = {
'text':
sent.text,
'begin':
True if j == 0 else False,
'punct':
'',
'speaker':
line_speaker_dict.get(i, self.model_list[default_speaker])
}
while not sentence_dict['text'][-1].isalpha():
sentence_dict['punct'] = sentence_dict[
'punct'] + sentence_dict['text'][-1]
sentence_dict['text'] = sentence_dict['text'][:-1]
# Reverse the punctuation order since I add it based on the last item
sentence_dict['punct'] = sentence_dict['punct'][::-1]
sentence_dict[
'text'] = sentence_dict['text'] + sentence_dict['punct']
sentence_dicts.append(sentence_dict)
speaker_dict = {}
for i, sentence_dict in enumerate(sentence_dicts):
if sentence_dict['speaker'] not in speaker_dict:
speaker_dict[sentence_dict['speaker']] = []
speaker_dict[sentence_dict['speaker']].append(i)
self.speaker_dict = speaker_dict
self.sentence_dicts = sentence_dicts
def parse_text(self,
text,
default_speaker,
force_parse=False,
separate_comma=False,
n_gram=2,
separate_sentence=False,
parse_speaker=True,
normalize=True):
"""
Parse the input text into suitable data structure
:param force_parse: forced to replace all speaker that are not in model list as the default speaker
:param n_gram: concat sentences of this max length in a line
:param text: source
:param default_speaker: the default speaker if no speaker in specified
:param separate_comma: split by comma
:param separate_sentence: split sentence if multiple clauses exist
:param parse_speaker: bool for turn on/off parse speaker
:param normalize: to convert common punctuation besides comma to comma
"""
lines = re.split(r'\r\n|\n\r|\r|\n', text)
line_speaker_dict = {}
self.speaker_list = []
self.speaker_map_dict = {}
if parse_speaker:
# re.match(r'^.*(?=:)', text)
for i, line in enumerate(lines):
if re.search(r':|\|', line):
# ?: non capture group of : and |
speaker, line = re.split(r'\s*(?::|\|)\s*', line, 1)
# add entry only if the voice model exist in the folder,
# the unrecognized one will need to mapped so as to be able to be synthesized
if force_parse:
if speaker in self.model_list:
line_speaker_dict[i] = speaker
else:
if speaker not in self.speaker_list:
self.speaker_list.append(speaker)
line_speaker_dict[i] = speaker
lines[i] = line
for i, speaker in enumerate(self.speaker_list):
if speaker not in self.model_list:
self.speaker_map_dict[speaker] = self.model_list[i % len(
self.model_list)]
# separate by spacy sentencizer
lines = [
separate(fix_text(line), n_gram, comma=separate_comma)
for line in lines
]
self.sentence_dicts = []
for i, line in enumerate(lines):
for j, sent in enumerate(line):
if self.sentence_dicts:
# might be buggy, forgot why I wrote this at all
while sent[0].is_punct and not any(
sent[0].text == punct for punct in ['“', '‘']):
self.sentence_dicts[-1]['punct'] = self.sentence_dicts[
-1]['punct'] + sent.text[0]
sent = sent[1:]
continue
sentence_dict = {
'text': sent.text,
'begin': True if j == 0 else False,
'punct': '',
'speaker': line_speaker_dict.get(i, default_speaker)
}
while not sentence_dict['text'][-1].isalpha():
sentence_dict['punct'] = sentence_dict[
'punct'] + sentence_dict['text'][-1]
sentence_dict['text'] = sentence_dict['text'][:-1]
# Reverse the punctuation order since I add it based on the last item
sentence_dict['punct'] = sentence_dict['punct'][::-1]
sentence_dict[
'text'] = sentence_dict['text'] + sentence_dict['punct']
self.sentence_dicts.append(sentence_dict)
self.update_speaker_dict()
def __init__(self, parent, isolate = False):
hGap = 20
vGap = 10
clusterIDs = DataStore.getCurrentDataSet().getCurrentClustering()
clustering = separate(DataStore.getCurrentDataSet().data, clusterIDs)
numClusters = len(clustering)
numColumns = 3
self.radioList = []
self.isolate = isolate
#TODO: still not perfect, larger cluster sizes gives an increasing space
# at the bottom
# The magic # includes the header width, the vgap for it, and the widths and
# border pads for the two sizers
dlgWidth = 275
dlgHeight = ((numClusters+1) * (vGap+20)) + 100
if isolate:
dlgWidth += 50
#dlgHeight += 20
title = 'Clustering Info'
if (isolate):
title = 'Isolate clusters'
wx.Dialog.__init__(self, parent, wx.ID_ANY, title, size=(dlgWidth, dlgHeight))
self.CenterOnParent()
# create main data display sizer
# one row for each cluster plus header row
# cols: cluster color, % of total
self.formSizer = None
if isolate:
self.formSizer = wx.FlexGridSizer(numClusters+1, numColumns+1, hgap=hGap, vgap=vGap)
else:
self.formSizer = wx.FlexGridSizer(numClusters, numColumns, hgap=hGap, vgap=vGap)
# header row
if isolate:
self.formSizer.Add(wx.StaticText(self, -1, 'Select', (5,10)), 1, wx.EXPAND)
self.formSizer.Add(wx.StaticText(self, -1, 'Cluster', (5, 10)), 1, wx.EXPAND)
self.formSizer.Add(wx.StaticText(self, -1, '% of Total', (20, 10)), 1, wx.EXPAND)
self.formSizer.Add(wx.StaticText(self, -1, '# of Events', (20, 10)), 1, wx.EXPAND)
# data rows
for i in range(len(clustering)):
cluster = clustering[i]
if isolate:
self.radioList.append(wx.CheckBox(self, wx.ID_ANY))
self.formSizer.Add(self.radioList[i], 0, wx.EXPAND)
# cluster color box
label = wx.StaticText(self, -1, '', (20, 10))
label.SetBackgroundColour(plot.methods.plotColors[i])
self.formSizer.Add(label, 1, wx.EXPAND)
# % of total
percent = float(len(cluster))/len(DataStore.getCurrentDataSet().data)*100
label = wx.StaticText(self, -1, '%6.2f' % percent + ' %', (30, 10))
self.formSizer.Add(label, 1, wx.EXPAND | wx.ALIGN_CENTER)
# number of events
label = wx.StaticText(self, -1, str(len(cluster)), (30, 10))
self.formSizer.Add(label, 1, wx.EXPAND | wx.ALIGN_CENTER)
# create the button row
self.buttonSizer = None
if isolate:
self.buttonSizer = self.CreateButtonSizer(wx.OK | wx.CANCEL)
else:
self.buttonSizer = self.CreateButtonSizer(wx.OK)
# main sizer
self.sizer = wx.BoxSizer(wx.VERTICAL)
self.sizer.Add(self.formSizer, 1, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.TOP, 20)
self.sizer.Add(self.buttonSizer, 0, wx.EXPAND | wx.LEFT | wx.RIGHT | wx.TOP | wx.BOTTOM, 20)
self.SetSizer(self.sizer)
def resume_nodes(nodelist, placement_groups=None, exclusive=False):
"""resume nodes in nodelist"""
def ident_key(n):
# ident here will refer to the combination of partition and group
return "-".join(
(
lkp.node_partition_name(n),
lkp.node_group_name(n),
)
)
# support already expanded list
nodes = nodelist
if isinstance(nodes, str):
nodelist = expand_nodelist(nodelist)
nodes = sorted(nodelist, key=ident_key)
if len(nodes) == 0:
return
grouped_nodes = {
ident: chunk
for ident, nodes in groupby(nodes, ident_key)
for chunk in chunked(nodes, n=BULK_INSERT_LIMIT)
}
log.debug(f"grouped_nodes: {grouped_nodes}")
# make all bulkInsert requests and execute with batch
inserts = {
ident: create_instances_request(nodes, placement_groups, exclusive)
for ident, nodes in grouped_nodes.items()
}
started, failed = batch_execute(inserts)
if failed:
failed_reqs = [f"{e}" for _, (_, e) in failed.items()]
log.error("bulkInsert API failures: {}".format("\n".join(failed_reqs)))
for ident, (_, exc) in failed.items():
down_nodes(grouped_nodes[ident], exc._get_reason())
# wait for all bulkInserts to complete and log any errors
bulk_operations = [wait_for_operation(op) for op in started.values()]
for bulk_op in bulk_operations:
if "error" in bulk_op:
error = bulk_op["error"]["errors"][0]
log.error(
f"bulkInsert operation error: {error['code']} operationName:'{bulk_op['name']}'"
)
# Fetch all insert operations from all bulkInserts. Group by error code and log
successful_inserts, failed_inserts = separate(
lambda op: "error" in op, get_insert_operations(bulk_operations)
)
# Apparently multiple errors are possible... so join with +.
# grouped_inserts could be made into a dict, but it's not really necessary. Save some memory.
grouped_inserts = util.groupby_unsorted(
failed_inserts,
lambda op: "+".join(err["code"] for err in op["error"]["errors"]),
)
for code, failed_ops in grouped_inserts:
# at least one insert failure
failed_nodes = [parse_self_link(op["targetLink"]).instance for op in failed_ops]
hostlist = util.to_hostlist(failed_nodes)
count = len(failed_nodes)
log.error(
f"{count} instances failed to start due to insert operation error: {code} ({hostlist})"
)
down_nodes(hostlist, code)
if log.isEnabledFor(logging.DEBUG):
msg = "\n".join(
err["message"] for err in next(failed_ops)["error"]["errors"]
)
log.debug(f"{code} message from first node: {msg}")
# If reconfigure enabled, create subscriptions for successfully started instances
if lkp.cfg.enable_reconfigure and len(successful_inserts):
started_nodes = [
parse_self_link(op["targetLink"]).instance for op in successful_inserts
]
count = len(started_nodes)
hostlist = util.to_hostlist(started_nodes)
log.info("create {} subscriptions ({})".format(count, hostlist))
execute_with_futures(subscription_create, nodes)
def partition_mounts(mounts):
"""partition into cluster-external and internal mounts"""
def internal_mount(mount):
return mount.server_ip == lkp.control_host
return separate(internal_mount, mounts)
