def location_exists(self, given_location_id, try_cached):
loc_exists = False
loc_array = None
utils.d_print(4, "(given_location_id): ", given_location_id)
try:
# first check if we can try the cached array
if not (try_cached and self.cached_locations):
# if try_cached is set to false OR if the cached_locations are empty, then refresh.
self.cached_locations = self.api_outbound.get_locations(
self.org_id, [], [])
loc_array = self.cached_locations
print(loc_array)
for location in loc_array:
if given_location_id == location['id']:
loc_exists = True
utils.d_print(1, "loc_exists: ", loc_exists)
except:
utils.print_exception(1)
return loc_exists
def __generate_epc_class(self, gtin, lot):
try:
gtin_str = str(gtin)
utils.d_print(8, "GTINisDigit: ", gtin_str.isdigit())
if gtin_str.isdigit():
# this is a pure GTIN
prefix_str = ''
ret_str = 'urn:epc:class:lgtin:'
for company_prefix in self.company_prefix_list:
prefix_str = str(company_prefix)
if prefix_str in gtin_str:
ret_str += prefix_str + '.'
break
utils.d_print(8, "gtin_str: ", gtin_str)
utils.d_print(8, "prefix_str: ", prefix_str)
indicator, itemref = gtin_str[:-1].split(prefix_str)
ret_str += indicator + itemref + '.'
ret_str += lot
else:
# we have an IFT id
prefix_itemref = gtin_str.split(':class:')[1]
ret_str = 'urn:ibm:ift:product:lot:class:' + prefix_itemref + '.' + str(lot)
utils.d_print(5, "GTIN, Lot, EPC Class: ", gtin, lot, ret_str)
return ret_str
except:
utils.print_exception(1)
def get_unix_mgr(server, protocol):
"""Get secured manager for UNIX OS.
'server' is the mail server name.
'protocol' is the used POP3 connection protocol."""
try :
from gnomekeyringmgr import GnomeKeyringMgr
return GnomeKeyringMgr('MailDispatcher', server, protocol)
except ImportError, ex:
utils.print_exception(ex,
_('Secure storage manager initialization error'))
def __generate_biztxn_list(self, biz_txn_params):
USE_GS1_TYPES = True # for now, we will support only GS1 types
try:
utils.d_print(7, "biztxn params passed:", biz_txn_params)
return_str = ""
if biz_txn_params != None and biz_txn_params != "" :
x = json.loads(biz_txn_params)
embed_str = ""
for biztxn in x:
if 'po' in biztxn:
if USE_GS1_TYPES:
embed_str += '<bizTransaction type= "urn:epcglobal:cbv:btt:po">'
# FORMAT: urn:epcglobal:cbv:bt:1234567890123:T1234
embed_str += "urn:epcglobal:cbv:bt:" + self.headquarters_gln + ":" + biztxn['po']
else:
# urn:ibm:ift:bt:<Company Prefix>.<Location Reference>.<Transaction Id>
# Note that head quarters GLN here (and in the config file is not fully qualified)
# i.e., urn:ibm:ift:location:loc:21345589.HQ is represented as 21345589.HQ only
embed_str += '<bizTransaction>'
embed_str += "urn:ibm:ift:bt:" + self.headquarters_gln + "." + biztxn['po']
embed_str += "</bizTransaction>"
elif 'da' in biztxn:
if USE_GS1_TYPES:
embed_str += '<bizTransaction type= "urn:epcglobal:cbv:btt:desadv">'
# FORMAT: urn:epcglobal:cbv:bt:1234567890123:T1234
embed_str += "urn:epcglobal:cbv:bt:" + self.headquarters_gln + ":" + biztxn['da']
else:
# urn:ibm:ift:bt:<Company Prefix>.<Location Reference>.<Transaction Id>
# Note that head quarters GLN here (and in the config file is not fully qualified)
# i.e., urn:ibm:ift:location:loc:21345589.HQ is represented as 21345589.HQ only
embed_str += '<bizTransaction>' # type=" + ' "urn:epcglobal:cbv:btt:desadv">'
embed_str += "urn:ibm:ift:bt:" + self.headquarters_gln + "." + biztxn['da']
embed_str += '</bizTransaction>'
elif 'prodorder' in biztxn:
if USE_GS1_TYPES:
embed_str += '<bizTransaction type= "urn:epcglobal:cbv:btt:prodorder">'
# FORMAT: urn:epcglobal:cbv:bt:1234567890123:T1234
embed_str += "urn:epcglobal:cbv:bt:" + self.headquarters_gln + ":" + biztxn['prodorder']
else:
# urn:ibm:ift:bt:<Company Prefix>.<Location Reference>.<Transaction Id>
# Note that head quarters GLN here (and in the config file is not fully qualified)
# i.e., urn:ibm:ift:location:loc:21345589.HQ is represented as 21345589.HQ only
embed_str += '<bizTransaction>' # type=" + ' "urn:epcglobal:cbv:btt:prodorder">'
embed_str += "urn:ibm:ift:bt:" + self.headquarters_gln + "." + biztxn['prodorder']
embed_str += '</bizTransaction>'
else:
utils.d_print(2, "Unknown Biz Txn Type", biz_txn_params)
if embed_str != "":
return_str = '<bizTransactionList>' + embed_str + '</bizTransactionList>'
return return_str
except:
utils.print_exception(1)
return
def _cochlear_trim_sai_marginals(filename_and_indexes):
try:
filename, norm_segstart, norm_segend, audio_id, NAP_detail = filename_and_indexes
sai_video_filename = '{}_sai_video_{}'.format(filename, NAP_detail)
if os.path.isfile('{}.npy'.format(sai_video_filename)):
return sai_video_filename
if NAP_detail == 'high':
try:
NAP = utils.csv_to_array(filename + 'cochlear' + NAP_detail)
except:
NAP = brain.cochlear(filename,
stride=1,
rate=44100,
apply_filter=0,
suffix='cochlear' + NAP_detail)
if NAP_detail == 'low':
try:
NAP = utils.csv_to_array(filename + 'cochlear' + NAP_detail)
except:
NAP = brain.cochlear(
filename,
stride=IO.NAP_STRIDE,
rate=IO.NAP_RATE,
apply_filter=0,
suffix='cochlear' + NAP_detail
) # Seems to work best, in particular when they are all the same.
num_channels = NAP.shape[1]
input_segment_width = 2048
sai_params = CreateSAIParams(num_channels=num_channels,
input_segment_width=input_segment_width,
trigger_window_width=input_segment_width,
sai_width=1024)
sai = pysai.SAI(sai_params)
NAP = utils.trim_right(NAP[np.int(np.rint(NAP.shape[0] *
norm_segstart)):np.
int(np.rint(NAP.shape[0] * norm_segend))],
threshold=.05)
sai_video = [
np.copy(sai.RunSegment(input_segment.T))
for input_segment in utils.chunks(NAP, input_segment_width)
]
del NAP
np.save(sai_video_filename,
np.array([sai_rectangles(frame) for frame in sai_video]))
return sai_video_filename
except:
print utils.print_exception(
'Calculation SAI video failed for file {}, NAP detail {}'.format(
filename, NAP_detail))
return False
def push_to_bts(self):
company_prefixes = []
inbound_api = None
try:
# 1. load the workbook
wb = load_workbook(filename = self.filename)
# 2. set up the inbound API
org = organization.Organization("Name", utils.global_args.client)
org_id = org.get_id()
inbound_api = api_inbound.APIInbound(5, org_id, utils.global_args.env, utils.global_args.header_entitled_org)
# get company prefixes, head quarters GLN, etc.
org_params = org.get_properties()
# if we are asked to use a different HQ GLN, use it.
if utils.global_args.hq_gln:
org_params['hq_gln'] = utils.global_args.hq_gln
# 3. start parsing the excel sheet
# a. first gather global params
sheet_names = wb.sheetnames
utils.d_print(2, "Sheet Names in the Excel Sheet: ", sheet_names)
# if arguments indicate specific rows to be pushed, push the same.
if utils.global_args.specific_rows:
self.specific_rows = utils.global_args.specific_rows.split(',')
else:
self.specific_rows = None
if 'if' in utils.global_args.isheets:
# b. next push if there are any facilities
facilities = wb['Facilities']
self.parse_and_push_worksheet(org_params, facilities, 'Facility', inbound_api)
if 'ip' in utils.global_args.isheets:
# c. next push any products
products = wb['Products']
self.parse_and_push_worksheet(org_params, products, 'Product',inbound_api)
if 'ie' in utils.global_args.isheets:
# d. next push any events
events = wb['Events']
self.parse_and_push_worksheet(org_params, events, 'Event',inbound_api)
if 'ipl' in utils.global_args.isheets:
# e. next push any events
payloads = wb['Payloads']
self.parse_and_push_worksheet(org_params, payloads, 'Payload',inbound_api)
except:
utils.print_exception(1)
return
def __generate_time_in_iso_format(self, given_time):
try:
# '2020-01-15:23:10' to '2013-06-08T14:58:56.591Z'
x = given_time.split(':')
if len(x) != 3:
utils.d_print(0, "Input Time: ", given_time)
utils.quit("Time not in required format! Format: YYYY-MM-DD:HH:MM")
iso_time = x[0] + 'T' + x[1] + ':' + x[2] + ':00.000Z'
return iso_time
except:
utils.print_exception(1)
def __generate_bizloc(self, gln):
try:
utils.d_print(7, "bizloc gln passed:", gln)
return_str = '<bizLocation><id>'
if "urn:ibm:ift:location" not in str(gln):
return_str += self.__gln_2_epcgln(gln)
else:
return_str += gln
return_str += '</id></bizLocation>'
return return_str
except:
utils.print_exception(1)
def __generate_bizstep(self, bizstep):
try:
utils.d_print(7, "bizstep value passed:", bizstep)
return_str = '<bizStep>'
if "http:" not in bizstep:
return_str += 'urn:epcglobal:cbv:bizstep:' + bizstep
else:
return_str += bizstep
return_str += '</bizStep>'
return return_str
except:
utils.print_exception(1)
def __generate_epc_serial(self, gtin_str, serial):
try:
return_str = ''
if 'urn:ibm:ift:' in gtin_str:
# we have an IFT id
prefix_itemref = gtin_str.split(':class:')[1]
return_str = 'urn:ibm:ift:product:serial:obj:' + prefix_itemref + '.' + str(serial)
else:
utils.quit("GS1 Serials not yet implemented.")
return return_str
except:
utils.print_exception(1)
def load_app_settings():
""" Load setting from configuration file. """
app_path = get_app_path()
if not os.access(app_path, os.F_OK):
return (None, None, None)
pkl_file = open(app_path, 'rb')
try :
server_info = pickle.load(pkl_file)
deletion_filters = pickle.load(pkl_file)
storing_filters = pickle.load(pkl_file)
except (EOFError, IndexError), ex:
utils.print_exception(ex, _('Settings loading error'))
return (None, None, None)
def __generate_agg_xml(self):
try:
self.xml_string += "<AggregationEvent>"
self.__add_attribute('eventTime', self.__generate_time_in_iso_format(self.event_params[self.EV_TIME]))
self.__add_attribute('eventTimeZoneOffset', self.EV_DEFAULT_TIME_ZONE)
self.xml_string += "<baseExtension>"
self.__add_attribute('eventID', self.event_params[self.EV_ID])
self.xml_string += "</baseExtension>"
self.xml_string += self.__generate_bizloc(self.event_params[self.EV_BIZLOC])
# output is a pallet id etc., the parent
# for now, only Logistic Units are supported as parents; infact, only 1 logistic unit.
x = json.loads(self.event_params[self.EV_OUTPUT])
lu_details = x[0]
self.__add_attribute('parentID', self.__generate_lu(lu_details))
if self.event_params[self.EV_INPUT_TYPE] == 'E':
self.__generate_epc_list('childEPCs', self.event_params[self.EV_INPUT])
elif self.event_params[self.EV_INPUT_TYPE] == 'Q':
self.__add_attribute('childEPCs', '')
else:
utils.quit('Unknown Input Type ' + self.event_params[self.EV_INPUT_TYPE])
self.__add_attribute('action', 'ADD')
self.xml_string += self.__generate_bizstep(self.event_params[self.EV_BIZSTEP])
self.xml_string += '<extension>'
# generate the inputs, the children
if self.event_params[self.EV_INPUT_TYPE] == 'Q':
self.__generate_quantity_list('childQuantityList', self.event_params[self.EV_INPUT])
self.xml_string += self.__generate_src_dest('source', self.event_params[self.EV_SRC])
self.xml_string += self.__generate_src_dest('destination', self.event_params[self.EV_DEST])
self.xml_string += '</extension>'
# add biz txn documents here
self.xml_string += self.__generate_biztxn_list(self.event_params[self.EV_BIZ_TXN_TYPE_LIST])
self.xml_string += '</AggregationEvent>'
utils.d_print(2, "## Aggregation: ", self.xml_string)
except:
utils.print_exception(1)
return
def __gln_2_epcgln(self, gln):
try:
return_str = "urn:epc:id:sgln:"
for company_prefix in self.company_prefix_list:
if str(company_prefix) in str(gln):
return_str += str(company_prefix) + '.'
break
location_ref = str(gln).split(str(company_prefix))[1]
return_str += location_ref[:-1]
return_str += '.0'
return return_str
except:
utils.print_exception(1)
def __generate_lu(self, lu_details):
try:
utils.d_print(7, "LU Gen", lu_details)
return_str = ''
if 'lu' in lu_details:
lud = lu_details['lu']
if 'urn:ibm:ift:' in lud:
return_str = lud
else:
utils.quit("GS1 LUs not yet implemented.")
else:
utils.d_print(7, "LU Generation: ", lu_details)
utils.quit("'lu' key not present.")
return return_str
except:
utils.print_exception(1)
def __generate_xfm_xml(self):
try:
self.xml_string += "<extension> <TransformationEvent>"
self.__add_attribute('eventTime', self.__generate_time_in_iso_format(self.event_params[self.EV_TIME]))
self.__add_attribute('eventTimeZoneOffset', self.EV_DEFAULT_TIME_ZONE)
self.xml_string += "<baseExtension>"
self.__add_attribute('eventID', self.event_params[self.EV_ID])
self.xml_string += "</baseExtension>"
self.xml_string += self.__generate_bizloc(self.event_params[self.EV_BIZLOC])
self.xml_string += self.__generate_bizstep(self.event_params[self.EV_BIZSTEP])
# input for transformation
if self.event_params[self.EV_INPUT_TYPE] == 'Q':
self.__generate_quantity_list('inputQuantityList', self.event_params[self.EV_INPUT])
elif self.event_params[self.EV_INPUT_TYPE] == 'E':
self.__generate_epc_list('inputEPCList', self.event_params[self.EV_INPUT])
else:
utils.quit('Unknown Input Type ' + self.event_params[self.EV_INPUT_TYPE])
# output for transformation
if self.event_params[self.EV_OUTPUT_TYPE] == 'Q':
self.__generate_quantity_list('outputQuantityList', self.event_params[self.EV_OUTPUT])
elif self.event_params[self.EV_OUTPUT_TYPE] == 'E':
self.__generate_epc_list('outputEPCList', self.event_params[self.EV_OUTPUT])
else:
utils.quit('Unknown Output Type ' + self.event_params[self.EV_OUTPUT_TYPE])
self.xml_string += "<extension>"
self.xml_string += self.__generate_src_dest('source', self.event_params[self.EV_SRC])
self.xml_string += self.__generate_src_dest('destination', self.event_params[self.EV_DEST])
self.xml_string += "</extension>"
# add biz txn documents here
self.xml_string += self.__generate_biztxn_list(self.event_params[self.EV_BIZ_TXN_TYPE_LIST])
self.xml_string += "</TransformationEvent></extension> "
utils.d_print(2, "## Transformation: ", self.xml_string)
except:
utils.print_exception(1)
return
def __generate_obs_xml(self):
try:
self.xml_string += "<ObjectEvent>"
self.__add_attribute('eventTime', self.__generate_time_in_iso_format(self.event_params[self.EV_TIME]))
self.__add_attribute('eventTimeZoneOffset', self.EV_DEFAULT_TIME_ZONE)
self.xml_string += "<baseExtension>"
self.__add_attribute('eventID', self.event_params[self.EV_ID])
self.xml_string += "</baseExtension>"
self.xml_string += self.__generate_bizloc(self.event_params[self.EV_BIZLOC])
self.xml_string += self.__generate_bizstep(self.event_params[self.EV_BIZSTEP])
self.__add_attribute('action', 'OBSERVE')
if self.event_params[self.EV_INPUT_TYPE] == 'E':
self.__generate_epc_list('epcList', self.event_params[self.EV_INPUT])
elif self.event_params[self.EV_INPUT_TYPE] == 'Q':
self.__add_attribute('epcList', '')
else:
utils.quit('Unknown Input Type ' + self.event_params[self.EV_INPUT_TYPE])
self.xml_string += '<extension>'
# input for observation
if self.event_params[self.EV_INPUT_TYPE] == 'Q':
self.__generate_quantity_list('quantityList', self.event_params[self.EV_INPUT])
elif self.event_params[self.EV_INPUT_TYPE] != 'E':
utils.quit('Unknown Input Type ' + self.event_params[self.EV_INPUT_TYPE])
self.xml_string += self.__generate_src_dest('source', self.event_params[self.EV_SRC])
self.xml_string += self.__generate_src_dest('destination', self.event_params[self.EV_DEST])
self.xml_string += '</extension>'
# add biz txn documents here
self.xml_string += self.__generate_biztxn_list(self.event_params[self.EV_BIZ_TXN_TYPE_LIST])
self.xml_string += '</ObjectEvent>'
utils.d_print(2, "## Observation: ", self.xml_string)
except:
utils.print_exception(1)
return
def __generate_quantity_list(self, qty_list_name, qty_list):
try:
utils.d_print(7, "Gen Qty list: ", qty_list)
self.xml_string += '<' + qty_list_name + '>'
x = json.loads(qty_list)
utils.d_print(8, "Qty List JSON String: ", x)
for x_item in x:
self.xml_string += '<quantityElement>'
utils.d_print(7, "x_item[GTIN]: ", x_item['gtin'])
utils.d_print(7, "x_item[LOT ]: ", x_item['lot'])
self.__add_attribute('epcClass', self.__generate_epc_class(x_item['gtin'], x_item['lot']))
self.__add_attribute('quantity', x_item['qty'])
self.__add_attribute('uom', x_item['uom'])
self.xml_string += '</quantityElement>'
utils.d_print(7, "XML_String:", self.xml_string)
self.xml_string += '</' + qty_list_name + '>'
except:
utils.print_exception(1)
return
def __generate_src_dest(self, loc_type, gln):
try:
return_str = ''
if gln != None:
return_str += '<' + loc_type + 'List>'
return_str += '<' + loc_type + ' type="urn:epcglobal:cbv:sdt:owning_party">'
if "urn:ibm:ift:location" in str(gln):
return_str += gln
else:
return_str += self.__gln_2_epcgln(gln)
return_str += '</' + loc_type + '>'
return_str += '</' + loc_type + 'List>'
return return_str
except:
utils.print_exception(1)
def __generate_com_xml(self):
try:
self.xml_string += "<ObjectEvent>"
# convert time to required string and add
self.__add_attribute('eventTime', self.__generate_time_in_iso_format(self.event_params[self.EV_TIME]))
self.__add_attribute('eventTimeZoneOffset', self.EV_DEFAULT_TIME_ZONE)
self.xml_string += "<baseExtension>"
self.__add_attribute('eventID', self.event_params[self.EV_ID])
self.xml_string += "</baseExtension>"
self.__add_attribute('action', 'ADD')
self.xml_string += self.__generate_bizstep(self.event_params[self.EV_BIZSTEP])
self.xml_string += self.__generate_bizloc(self.event_params[self.EV_BIZLOC])
if self.event_params[self.EV_OUTPUT_TYPE] == 'Q':
self.__add_attribute('epcList', '')
else:
utils.quit("Add EPCs in Commission event to handle type 'E'")
self.xml_string += "<extension>"
if self.event_params[self.EV_OUTPUT_TYPE] == 'Q':
self.__generate_quantity_list('quantityList', self.event_params[self.EV_OUTPUT])
# add source and desination lists here
self.xml_string += self.__generate_src_dest('source', self.event_params[self.EV_SRC])
self.xml_string += self.__generate_src_dest('destination', self.event_params[self.EV_DEST])
self.xml_string += "</extension>"
# add biz txn documents here
self.xml_string += self.__generate_biztxn_list(self.event_params[self.EV_BIZ_TXN_TYPE_LIST])
self.xml_string += "</ObjectEvent>"
utils.d_print(2, "## Commission: ", self.xml_string)
except:
utils.print_exception(1)
return
def inc_nb_votes():
""" Increment the number of votes of a couple question/answer.
"""
try:
args = utils.fieldsToValuesPOST(['question_id', 'answer_id'], request)
arg_question_id = args['question_id']
arg_answer_id = args['answer_id']
answer_binding = data.AnswersBinding.query.filter_by(question_id=arg_question_id,
answer_id=arg_answer_id).first()
if answer_binding:
question = data.Questions.query.filter_by(id=arg_question_id).first()
if question:
now = datetime.now()
if not (question.datetime_start < now and question.datetime_expiry > now):
return json.dumps({"success": False, "message": "Question expired"})
answer_binding.nb_votes += 1
db.session.commit()
return json.dumps({"success": True, "message": answer_binding.nb_votes})
except Exception as e:
utils.print_exception(e)
return json.dumps({"success": False, "message": str(e)})
def create_question():
try:
args = utils.fieldsToValuesPOST(['question', 'answers',
'datetime_start', 'datetime_expiry'], request)
arg_question = args['question']
arg_answers = args['answers']
arg_datetime_start = args['datetime_start']
arg_datetime_expiry = args['datetime_expiry']
arg_answers = [a for a in arg_answers.split(';')]
does_question_exist = data.Questions.query.filter_by(content=arg_question).first()
if not does_question_exist:
question = data.Questions(arg_question, arg_datetime_start, arg_datetime_expiry)
db.session.add(question)
db.session.commit()
question_id = question.id
else:
question_id = does_question_exist.id
for answer in arg_answers:
does_answer_exist = data.Answers.query.filter_by(content=answer).first()
if not does_answer_exist:
ans = data.Answers(answer)
db.session.add(ans)
db.session.commit()
answer_id = ans.id
else:
answer_id = does_answer_exist.id
does_answer_binding_exist = data.AnswersBinding.query.filter_by(question_id=question_id,
answer_id=answer_id).first()
if not does_answer_binding_exist:
db.session.add(data.AnswersBinding(question_id, answer_id))
db.session.commit()
db.session.commit()
return json.dumps({"success": True, "message": "Question and answers correctly created."})
except Exception as e:
utils.print_exception(e)
return json.dumps({"success": False, "message": str(e)})
def _cochlear_trim_sai_marginals(filename_and_indexes):
try:
filename, norm_segstart, norm_segend, audio_id, NAP_detail = filename_and_indexes
sai_video_filename = '{}_sai_video_{}'.format(filename, NAP_detail)
if os.path.isfile('{}.npy'.format(sai_video_filename)):
return sai_video_filename
if NAP_detail == 'high':
try:
NAP = utils.csv_to_array(filename+'cochlear'+NAP_detail)
except:
NAP = brain.cochlear(filename, stride=1, rate=44100, apply_filter=0, suffix='cochlear'+NAP_detail)
if NAP_detail == 'low':
try:
NAP = utils.csv_to_array(filename+'cochlear'+NAP_detail)
except:
NAP = brain.cochlear(filename, stride=IO.NAP_STRIDE, rate=IO.NAP_RATE, apply_filter=0, suffix='cochlear'+NAP_detail) # Seems to work best, in particular when they are all the same.
num_channels = NAP.shape[1]
input_segment_width = 2048
sai_params = CreateSAIParams(num_channels=num_channels,
input_segment_width=input_segment_width,
trigger_window_width=input_segment_width,
sai_width=1024)
sai = pysai.SAI(sai_params)
NAP = utils.trim_right(NAP[ np.int(np.rint(NAP.shape[0]*norm_segstart)) : np.int(np.rint(NAP.shape[0]*norm_segend)) ], threshold=.05)
sai_video = [ np.copy(sai.RunSegment(input_segment.T)) for input_segment in utils.chunks(NAP, input_segment_width) ]
del NAP
np.save(sai_video_filename, np.array([ sai_rectangles(frame) for frame in sai_video ]))
return sai_video_filename
except:
print utils.print_exception('Calculation SAI video failed for file {}, NAP detail {}'.format(filename, NAP_detail))
return False
def __generate_epc_list(self, epc_name, epc_serial_params):
try:
utils.d_print(7, "epc values:", epc_name, epc_serial_params)
x = json.loads(epc_serial_params)
return_str = '<' + epc_name + '>'
for product_details in x:
print (product_details)
if 'gtin' in product_details:
gtin_str = product_details['gtin']
for serial_no in product_details['serial']:
return_str += '<epc>'
return_str += self.__generate_epc_serial(gtin_str, serial_no)
return_str += '</epc>'
elif 'lu' in product_details:
return_str += '<epc>'
return_str += self.__generate_lu(product_details)
return_str += '</epc>'
else:
utils.quit("Unknown key name (not gtin or lu) in EPC details")
return_str += '</' + epc_name + '>'
self.xml_string += return_str
utils.d_print(7, "return_str: ", return_str)
except:
utils.print_exception(1)
def product_exists(self, check_type, check_value, try_cached):
product_exists = False
product_array = None
utils.d_print(4, "(check_type, check_value): ", check_type,
check_value)
try:
# first check if we can try the cached array
if not (try_cached and self.cached_products):
# if try_cached is set to false OR if the cached_locations are empty, then refresh with all products
self.cached_products = self.api_outbound.get_products(
self.org_id, None, None, None)
product_array = self.cached_products
for product in product_array:
# if not isinstance(product, dict):
# continue
if check_type == 'id':
if product['id'] == check_value:
product_exists = True
elif check_type == 'description':
if product['description'] == check_value:
product_exists = True
else:
utils.d_print(1, "Uknown Checktype", check_type, " Val: ",
check_value)
utils.d_print(1, "product_exists: ", product_exists)
except:
utils.print_exception(1)
return product_exists
def get_all_questions_answers():
""" Get all running questions and answers.
A valid question is a question whose date is superior to the current date,
and did not expire yet.
"""
try:
current_date = datetime.now()
questions = data.Questions.query.all()
# I do not use where() clause because I did not find how to call it with flask-sqlalchemy...
questions = [question for question in questions
if question.datetime_start < current_date
and question.datetime_expiry > current_date]
ret = []
for question in questions:
bindings = data.AnswersBinding.query.filter_by(question_id=question.id)
json_answers = []
for binding in bindings:
answer = data.Answers.query.filter_by(id=binding.answer_id).first()
answer_obj = {}
answer_obj["content"] = answer.content
answer_obj["answer_id"] = answer.id
json_answers.append(answer_obj)
question_obj = {}
question_obj["datetime_start"] = str(question.datetime_start)
question_obj["datetime_expiry"] = str(question.datetime_expiry)
question_obj["answers"] = json_answers
question_obj["content"] = question.content
question_obj["question_id"] = question.id
ret.append(question_obj)
json_ret = json.dumps(ret)
return json.dumps({"success": True, "message": json_ret})
except Exception as e:
utils.print_exception(e)
return json.dumps({"success": False, "message": str(e)})
def parse_and_push_worksheet(self, org_params, b_rows, data_type, inbound_api):
# criteria that determines if a row is to be pushed
def row_to_be_skipped(row_no):
utils.d_print(6, "Row: ", row_no, "specific_rows:", self.specific_rows)
ret_val = False
# skip the first row; it is the names of various columns; we start with row_no 1.
if row_no == 0:
ret_val = True
elif self.specific_rows: # we check if this is not None
if str(row_no) not in self.specific_rows:
ret_val = True
utils.d_print(6, "Row: ", row_no, "Skip?:", ret_val)
return ret_val
try:
b_row = {}
row_no = 0
for row in b_rows.iter_rows(values_only=True):
# if this row is not to be pushed based on certain criteria, increment and continue
if row_to_be_skipped(row_no):
row_no += 1
continue
# this is a row to be pushed.
index = 0
## we check for the first column.
if row[0] is not None:
utils.d_print (2, "-------> ", row)
for cell_value in row:
# if cell_value is not None:
# the value of index should match the values of F_PARAMS in datagen
# for Strings, we have a flag to control if we want to encode as unicode bytes
unicode_encode = False
if unicode_encode and isinstance(cell_value, str):
b_row[index] = cell_value.encode('utf-8')
else:
b_row[index] = cell_value
index += 1
utils.d_pprint(3, "Business Format Row: ", b_row)
# we now have read the "business row"
# this is where we convert the row we just read using the adapter to the row
# that we want.
if data_type == 'Facility':
d_row = self.adapter.convert_facility_row(b_row)
self._check_row_length(row_no+1, len(d_row), datagen.N_FACILITY_PARAMS)
x = datagen.Facility(org_params, d_row)
elif data_type == 'Product':
d_row = self.adapter.convert_product_row(b_row)
self._check_row_length(row_no+1, len(d_row), datagen.N_PRODUCT_PARAMS)
x = datagen.Product(org_params, d_row)
elif data_type == 'Event':
d_row = self.adapter.convert_event_row(b_row)
self._check_row_length(row_no+1, len(d_row), datagen.N_EVENT_PARAMS)
x = datagen.Event(org_params, d_row)
elif data_type == 'Payload':
d_row = self.adapter.convert_payload_row(b_row)
self._check_row_length(row_no+1, len(d_row), datagen.N_PAYLOAD_PARAMS)
x = datagen.Payload(org_params, d_row)
else:
utils.quit('Unknown Data Type!')
# retrieve the XML and push it.
xml = x.get_xml()
utils.d_print(5, "xml: ", xml)
inbound_api.push_xml(xml)
row_no += 1
print ((row_no - 1), " ", data_type, " elements pushed.")
# there could be additional payloads left in the adapter
# this is the case where there is no explicit payload tab.
if self.adapter.get_built_count('Payloads') > 0:
a_payloads = self.adapter.get_built_data('Payloads')
payload_index = 0
for p_payload in a_payloads:
self._check_row_length(payload_index+1, len(p_payload), datagen.N_PAYLOAD_PARAMS)
x = datagen.Payload(org_params, p_payload)
xml = x.get_xml()
utils.d_print(5, "xml: ", xml)
if not utils.global_args.simulate:
inbound_api.push_xml(xml)
except:
utils.print_exception(1)
class Controller:
def __init__(self, init_state, host):
self.state = init_state
context = zmq.Context()
self.publisher = context.socket(zmq.PUB)
self.publisher.bind('tcp://*:{}'.format(IO.STATE))
self.event = context.socket(zmq.PUB)
self.event.bind('tcp://*:{}'.format(IO.EVENT))
snapshot = context.socket(zmq.ROUTER)
snapshot.bind('tcp://*:{}'.format(IO.SNAPSHOT))
self.association = context.socket(zmq.REQ)
self.association.connect('tcp://{}:{}'.format(host, IO.ASSOCIATION))
incoming = context.socket(zmq.PULL)
incoming.bind('tcp://*:{}'.format(IO.EXTERNAL))
poller = zmq.Poller()
poller.register(incoming, zmq.POLLIN)
poller.register(snapshot, zmq.POLLIN)
while True:
events = dict(poller.poll())
if incoming in events:
self.parse(incoming.recv_json())
if snapshot in events:
address, _, message = snapshot.recv_multipart()
snapshot.send_multipart([ address,
b'',
dumps(self.state) ])
def parse(self, message):
print '[self.] received: {}'.format(message)
black_list = []
try:
# if 'learnwav' in message or 'respondwav_single' in message or 'respondwav_sentence' in message:
# _, filename = message.split()
# if filename in black_list:
# print 'SKIPPING BAD FILE {}'.format(filename)
# return
if message == 'dream':
self.state['memoryRecording'] = False
self.state['autorespond_sentence'] = False
self.state['ambientSound'] = False
self.state['autolearn'] = False
self.state['autorespond_single'] = False
self.state['_audioLearningStatus'] = False
self.state['record'] = False
self.publisher.send_json(self.state)
self.event.send_json({'dream': True})
if message == 'reboot':
utils.reboot()
if message == 'appendCurrentSettings':
self.association.send_pyobj([message])
self.association.recv_pyobj()
if 'getCurrentSettings' in message:
msg, value = message.split()
self.association.send_pyobj([msg, value])
self.association.recv_pyobj()
if 'i_am_speaking' in message:
_, value = message.split()
self.state['i_am_speaking'] = value in ['True', '1']
if 'enable_say_something' in message:
_, value = message.split()
self.state['enable_say_something'] = value in ['True', '1']
if 'last_segment_ids' in message:
the_ids = message[17:]
self.event.send_json({'last_segment_ids': loads(the_ids) })
if 'last_most_significant_audio_id' in message:
audio_id = message[31:]
self.event.send_json({'last_most_significant_audio_id': audio_id })
if message == 'clear play_events':
self.event.send_json({'clear play_events' : 'True'})
if 'calculate_cochlear' in message:
_, wav_file = message.split()
t0 = time.time()
try:
brain.cochlear(utils.wait_for_wav(wav_file), stride=IO.NAP_STRIDE, rate=IO.NAP_RATE)
except:
print 'SHOULD {} BE BLACKLISTED?'.format(wav_file)
black_list.append(wav_file)
print 'Calculating cochlear neural activation patterns took {} seconds'.format(time.time() - t0)
if message == 'evolve':
self.state['memoryRecording'] = False
self.state['autorespond_sentence'] = False
self.state['autolearn'] = False
self.state['autorespond_single'] = False
self.state['_audioLearningStatus'] = False
self.state['record'] = False
self.publisher.send_json(self.state)
self.association.send_pyobj(['evolve'])
self.association.recv_pyobj()
if 'register' in message and 'BRAIN' in message:
_, name, free = message.split()
self.state['brains'][name] = int(free)
if 'fullscreen' in message:
_, value = message.split()
self.event.send_json({ 'fullscreen': value in ['True', '1'] })
if 'display2' in message:
_, value = message.split()
self.event.send_json({ 'display2': value in ['True', '1'] })
if message == 'startrec':
self.state['record'] = True
if message == 'stoprec':
self.state['record'] = False
if 'facerecognition' in message:
_, value = message.split()
self.state['facerecognition'] = value in ['True', '1']
if 'print_me' in message:
self.event.send_json({ 'print_me': message[7:] })
if 'play_id' in message:
self.event.send_json({ 'play_id': message[8:] })
if 'testSentence' in message:
self.event.send_json({ 'testSentence': message[13:] })
if 'assoc_setPlotting' in message:
self.event.send_json({ 'assoc_setPlotting': message[18:] in ['True', '1'] })
if 'assoc_setParam' in message:
self.event.send_json({ 'assoc_setParam': message[15:] })
if 'respond_setParam' in message:
self.event.send_json({ 'respond_setParam': message[17:] })
if 'memoryRecording' in message:
self.state['memoryRecording'] = message[16:] in ['True', '1']
if '_audioLearningStatus' in message:
self.state['_audioLearningStatus'] = message[21:] in ['True', '1']
if 'roboActive' in message:
self.state['roboActive'] = int(message[11:])
if 'ambientSound' in message:
self.state['ambientSound'] = int(message[13:])
if 'decrement' in message:
_, name = message.split()
self.state['brains'][name] -= 1
print '{} has now {} available slots'.format(name, self.state['brains'][name])
if 'learnwav' in message:
_, filename = message.split()
self.event.send_json({ 'learn': True, 'filename': filename })
if 'respondwav_single' in message:
_, filename = message.split()
self.event.send_json({ 'respond_single': True, 'filename': filename })
if 'respondwav_sentence' in message:
_, filename = message.split()
self.event.send_json({ 'respond_sentence': True, 'filename': filename })
if 'play_sentence' in message:
print 'playSentence', message
sentence = message[13:]
self.event.send_json({ 'play_sentence':True, 'sentence': sentence })
if 'rhyme' in message:
_, value = message.split()
self.event.send_json({'rhyme': value == 'True'})
if 'urge_to_say_something' in message:
_, value = message.split()
self.event.send_json({'urge_to_say_something': value})
if 'autolearn' in message:
self.state['autolearn'] = message[10:] in ['True', '1']
if 'autorespond_single' in message:
self.state['autorespond_single'] = message[19:] in ['True', '1']
if 'autorespond_sentence' in message:
self.state['autorespond_sentence'] = message[21:] in ['True', '1']
if 'inputLevel' in message:
self.event.send_json({ 'inputLevel': message[11:] })
if 'calibrateEq' in message:
self.event.send_json({ 'calibrateEq': True })
if 'calibrateAudio' in message:
latency_ok = False
try:
lat = open('roundtrip_latency.txt', 'r')
latency = float(lat.readline())
self.event.send_json({ 'setLatency': latency })
latency_ok = True
except Exception, e:
print 'Something went wrong when reading latency from file.', e
self.event.send_json({ 'calibrateAudio': True })
if latency_ok:
self.event.send_json({ 'calibrateNoiseFloor': True })
if 'calibrateAudio memoryRecording' in message:
self.state['memoryRecording'] = True
if 'csinstr' in message:
self.event.send_json({ 'csinstr': message[8:] })
if 'selfDucking' in message:
self.event.send_json({ 'selfDucking': message[12:] })
if 'zerochannels' in message:
self.event.send_json({ 'zerochannels': message[13:] })
if 'playfile' in message:
self.event.send_json({ 'playfile': message[9:] })
if 'selfvoice' in message:
self.event.send_json({ 'selfvoice': message[10:] })
if 'save' in message:
self.event.send_json({ 'save': utils.brain_name() if len(message) == 4 else message[5:] })
if 'load' in message:
if len(message) == 4:
brain_name = utils.find_last_valid_brain()
else:
_, brain_name = message.split()
if brain_name:
self.event.send_json({ 'load': brain_name })
self.publisher.send_json(self.state)
except Exception as e:
utils.print_exception('Something went wrong when parsing the message - try again.')
def new_learn_audio(host, debug=False):
context = zmq.Context()
mic = context.socket(zmq.SUB)
mic.connect('tcp://{}:{}'.format(host, IO.MIC))
mic.setsockopt(zmq.SUBSCRIBE, b'')
dreamQ = context.socket(zmq.PUSH)
dreamQ.connect('tcp://{}:{}'.format(host, IO.DREAM))
stateQ, eventQ, brainQ = _three_amigos(context, host)
sender = context.socket(zmq.PUSH)
sender.connect('tcp://{}:{}'.format(host, IO.EXTERNAL))
counterQ = context.socket(zmq.REQ)
counterQ.connect('tcp://{}:{}'.format(host, IO.COUNTER))
poller = zmq.Poller()
poller.register(mic, zmq.POLLIN)
poller.register(stateQ, zmq.POLLIN)
poller.register(eventQ, zmq.POLLIN)
audio = deque()
NAPs = []
wavs = []
wav_audio_ids = {}
NAP_hashes = {}
audio_classifier = []
audio_recognizer = []
global_audio_recognizer = []
mixture_audio_recognizer = []
maxlen = []
deleted_ids = []
state = stateQ.recv_json()
black_list = open('black_list.txt', 'a')
audio_memory = AudioMemory()
if debug:
import matplotlib.pyplot as plt
plt.ion()
while True:
events = dict(poller.poll())
if stateQ in events:
state = stateQ.recv_json()
if mic in events:
new_audio = utils.recv_array(mic)
if state['record']:
audio.append(new_audio)
if eventQ in events:
pushbutton = eventQ.recv_json()
if 'learn' in pushbutton:
try:
t0 = time.time()
filename = pushbutton['filename']
audio_segments = utils.get_segments(filename)
print 'Learning {} duration {} seconds with {} segments'.format(filename, audio_segments[-1], len(audio_segments)-1)
new_sentence = utils.csv_to_array(filename + 'cochlear')
norm_segments = np.rint(new_sentence.shape[0]*audio_segments/audio_segments[-1]).astype('int')
audio_ids = []
new_audio_hash = []
amps = utils.get_amps(filename)
most_significant_value = -np.inf
most_significant_audio_id = []
original_NAP_length = len(NAPs)
for segment, new_sound in enumerate([ utils.trim_right(new_sentence[norm_segments[i]:norm_segments[i+1]]) for i in range(len(norm_segments)-1) ]):
# We filter out short, abrupt sounds with lots of noise.
if np.mean(new_sound) < 2 or new_sound.shape[0] == 0:
black_list.write('{} {}\n'.format(filename, segment))
print 'BLACKLISTED segment {} in file {}'.format(segment, filename)
continue
if debug:
utils.plot_NAP_and_energy(new_sound, plt)
audio_id = audio_memory.learn(new_sound, filename, [ audio_segments[segment], audio_segments[segment+1] ])
# START LEGACY
try:
wavs[audio_id].append(filename)
except:
wavs.append([filename])
wav_audio_ids[(filename, audio_id)] = [ audio_segments[segment], audio_segments[segment+1] ]
# END LEGACY
audio_ids.append(audio_id)
if amps[segment] > most_significant_value:
most_significant_audio_id = audio_id
most_significant_value = amps[segment]
black_list.flush()
print 'AUDIO IDs after blacklisting {}'. format(audio_ids)
if len(audio_ids):
# while len(NAPs) - len(deleted_ids) > AUDIO_MEMORY_SIZE:
# utils.delete_loner(counterQ, NAPs, 'audio_ids_counter', int(AUDIO_MEMORY_SIZE*PROTECT_PERCENTAGE), deleted_ids)
# maxlen = max([ m.shape[0] for memory in NAPs for m in memory if len(m) ])
# memories = [ np.ndarray.flatten(utils.zero_pad(m, maxlen)) for memory in NAPs for m in memory if len(m) ]
# targets = [ i for i,f in enumerate(NAPs) for k in f if len(k) ]
# audio_classifier = train_rPCA_SVM(memories, targets)
# all_hammings = [ utils.hamming_distance(new_audio_hash[i], new_audio_hash[j])
# for i in range(len(new_audio_hash)) for j in range(len(new_audio_hash)) if i > j ]
# print 'RHYME VALUE', np.mean(sorted(all_hammings)[int(len(all_hammings)/2):])
# rhyme = np.mean(sorted(all_hammings)[int(len(all_hammings)/2):]) < RHYME_HAMMERTIME
# sender.send_json('rhyme {}'.format(rhyme))
brainQ.send_pyobj(['audio_learn', filename, audio_ids, audio_memory, most_significant_audio_id, wavs, wav_audio_ids])
print 'Audio learned from {} in {} seconds'.format(filename, time.time() - t0)
else:
print 'SKIPPING fully blacklisted file {}'.format(filename)
except:
utils.print_exception('Audio learning aborted.')
audio.clear()
if 'dream' in pushbutton:
new_dream(audio_memory)
if 'save' in pushbutton:
utils.save('{}.{}'.format(pushbutton['save'], mp.current_process().name), [ deleted_ids, NAPs, wavs, wav_audio_ids, NAP_hashes, audio_classifier, maxlen, audio_memory ])
if 'load' in pushbutton:
deleted_ids, NAPs, wavs, wav_audio_ids, NAP_hashes, audio_classifier, maxlen, audio_memory = utils.load('{}.{}'.format(pushbutton['load'], mp.current_process().name))
def new_respond(control_host, learn_host, debug=False):
context = zmq.Context()
eventQ = context.socket(zmq.SUB)
eventQ.connect('tcp://{}:{}'.format(control_host, IO.EVENT))
eventQ.setsockopt(zmq.SUBSCRIBE, b'')
projector = context.socket(zmq.PUSH)
projector.connect('tcp://{}:{}'.format(control_host, IO.PROJECTOR))
sender = context.socket(zmq.PUSH)
sender.connect('tcp://{}:{}'.format(control_host, IO.EXTERNAL))
brainQ = context.socket(zmq.PULL)
brainQ.bind('tcp://*:{}'.format(IO.BRAIN))
counterQ = context.socket(zmq.REQ)
counterQ.connect('tcp://{}:{}'.format(control_host, IO.COUNTER))
cognitionQ = context.socket(zmq.PUSH)
cognitionQ.connect('tcp://{}:{}'.format(control_host, IO.COGNITION))
association = context.socket(zmq.REQ)
association.connect('tcp://{}:{}'.format(learn_host, IO.ASSOCIATION))
snapshot = context.socket(zmq.REQ)
snapshot.connect('tcp://{}:{}'.format(control_host, IO.SNAPSHOT))
scheduler = context.socket(zmq.PUSH)
scheduler.connect('tcp://{}:{}'.format(control_host, IO.SCHEDULER))
dreamQ = context.socket(zmq.PULL)
dreamQ.bind('tcp://*:{}'.format(IO.DREAM))
snapshot.send_json('Give me state!')
state = snapshot.recv_json()
poller = zmq.Poller()
poller.register(eventQ, zmq.POLLIN)
poller.register(brainQ, zmq.POLLIN)
poller.register(dreamQ, zmq.POLLIN)
sound_to_face = []
wordFace = {}
face_to_sound = []
faceWord = {}
register = {}
video_producer = {}
voiceType1 = 1
voiceType2 = 6
wordSpace1 = 0.3
wordSpaceDev1 = 0.3
wordSpace2 = 0.1
wordSpaceDev2 = 0.3
audio_ids = []
wavs = []
wav_audio_ids = []
NAP_hashes = {}
most_significant_audio_id = []
if debug:
import matplotlib.pyplot as plt
plt.ion()
while True:
events = dict(poller.poll())
if brainQ in events:
cells = brainQ.recv_pyobj()
mode = cells[0]
wav_file = cells[1]
if wav_file not in register:
register[wav_file] = [False, False, False]
if mode == 'audio_learn':
register[wav_file][0] = cells
if mode == 'video_learn':
register[wav_file][1] = cells
if mode == 'face_learn':
register[wav_file][2] = cells
if all(register[wav_file]):
_, _, audio_ids, audio_memory, most_significant_audio_id, wavs, wav_audio_ids = register[wav_file][0]
_, _, tarantino = register[wav_file][1]
_, _, face_id, face_recognizer = register[wav_file][2]
print 'Audio - video - face recognizers related to {} arrived at responder, total processing time {} seconds'.format(wav_file, time.time() - utils.filetime(wav_file))
for audio_id in audio_ids: # If audio_ids is empty, none of this will happen
video_producer[(audio_id, face_id)] = tarantino
if audio_id < len(sound_to_face) and not face_id in sound_to_face[audio_id]: # sound heard before, but not said by this face
sound_to_face[audio_id].append(face_id)
if audio_id == len(sound_to_face):
sound_to_face.append([face_id])
wordFace.setdefault(audio_id, [[face_id,0]])
found = 0
for item in wordFace[audio_id]:
if item[0] == face_id:
item[1] += 1
found = 1
if found == 0:
wordFace[audio_id].append([face_id,1])
# We can't go from a not known face to any of the sounds, that's just the way it is.
print 'face_id for audio segment learned', face_id
if face_id is not -1:
if face_id < len(face_to_sound) and not audio_id in face_to_sound[face_id]: #face seen before, but the sound is new
face_to_sound[face_id].append(audio_id)
if face_id == len(face_to_sound):
face_to_sound.append([audio_id])
faceWord.setdefault(face_id, [[audio_id,0]])
found = 0
for item in faceWord[face_id]:
if item[0] == audio_id:
item[1] += 1
found = 1
if found == 0:
faceWord[face_id].append([audio_id,1])
del register[wav_file]
similar_ids = []
for audio_id in audio_ids:
# I SUSPECT THIS IS WRONG, SINCE THERE IS NO SORTING OF THESE HAMMING DISTANCES IN ASSOCIATION.PY
new_audio_hash = audio_memory.audio_ids[audio_id][-1].crude_hash
similar_ids_for_this_audio_id = [ utils.hamming_distance(new_audio_hash, random.choice(h).crude_hash) for h in audio_memory.audio_ids.itervalues() ]
similar_ids.append(similar_ids_for_this_audio_id)
if len(audio_ids):
association.send_pyobj(['analyze',wav_file,wav_audio_ids,audio_ids,wavs,similar_ids,wordFace,faceWord])
association.recv_pyobj()
sender.send_json('last_most_significant_audio_id {}'.format(most_significant_audio_id))
cognitionQ.send_pyobj(face_recognizer) # A possiblity of recognizing a face that is not connecting to any soundfiles
if eventQ in events:
pushbutton = eventQ.recv_json()
if 'respond_single' in pushbutton:
try:
filename = pushbutton['filename']
audio_segments = utils.get_segments(filename)
print 'Single response to {} duration {} seconds with {} segments'.format(filename, audio_segments[-1], len(audio_segments)-1)
new_sentence = utils.csv_to_array(filename + 'cochlear')
norm_segments = np.rint(new_sentence.shape[0]*audio_segments/audio_segments[-1]).astype('int')
segment_id = utils.get_most_significant_word(filename)
NAP = utils.trim_right(new_sentence[norm_segments[segment_id]:norm_segments[segment_id+1]])
if debug:
plt.imshow(NAP.T, aspect='auto')
plt.draw()
best_match,_,_,_,_ = audio_memory.find(NAP)
soundfile = best_match.wav_file
segstart, segend = best_match.segment_idxs
voiceChannel = 1
speed = 1
amp = -3 # voice amplitude in dB
_,dur,maxamp,_ = utils.getSoundInfo(soundfile)
start = 0
voice1 = 'playfile {} {} {} {} {} {} {} {} {}'.format(1, voiceType1, start, soundfile, speed, segstart, segend, amp, maxamp)
voice2 = ''
print 'Recognized as sound {}'.format(best_match.audio_id)
# sound_to_face, video_producer
projection = _project(best_match.audio_id, sound_to_face, NAP, video_producer)
scheduler.send_pyobj([[ dur, voice1, voice2, projection, FRAME_SIZE ]])
print 'Respond time from creation of wav file was {} seconds'.format(time.time() - utils.filetime(filename))
except:
utils.print_exception('Single response aborted.')
if 'play_sentence' in pushbutton:
try:
sentence = pushbutton['sentence']
sentence = eval(sentence)
print '*** (play) Play sentence', sentence
start = 0
nextTime1 = 0
play_events = []
for i in range(len(sentence)):
word_id = sentence[i]
soundfile = np.random.choice(wavs[word_id])
speed = 1
segstart, segend = wav_audio_ids[(soundfile, word_id)]
NAP = _extract_NAP(segstart, segend, soundfile)
amp = -3 # voice amplitude in dB
_,totaldur,maxamp,_ = utils.getSoundInfo(soundfile)
dur = segend-segstart
if dur <= 0: dur = totaldur
# play in both voices
voice1 = 'playfile {} {} {} {} {} {} {} {} {}'.format(1, voiceType1, start, soundfile, speed, segstart, segend, amp, maxamp)
voice2 = 'playfile {} {} {} {} {} {} {} {} {}'.format(2, voiceType1, start, soundfile, speed, segstart, segend, amp, maxamp)
wordSpacing1 = wordSpace1 + np.random.random()*wordSpaceDev1
print 'PLAY RESPOND SPACING', wordSpacing1
nextTime1 += (dur/speed)+wordSpacing1
projection = _project(audio_id, sound_to_face, NAP, video_producer)
play_events.append([ dur+wordSpacing1, voice1, voice2, projection, FRAME_SIZE ])
scheduler.send_pyobj(play_events)
except:
utils.print_exception('Sentence play aborted.')
if 'respond_sentence' in pushbutton:
print 'SENTENCE Respond to', pushbutton['filename'][-12:]
try:
filename = pushbutton['filename']
audio_segments = utils.get_segments(filename)
print 'Sentence response to {} duration {} seconds with {} segments'.format(filename, audio_segments[-1], len(audio_segments)-1)
new_sentence = utils.csv_to_array(filename + 'cochlear')
norm_segments = np.rint(new_sentence.shape[0]*audio_segments/audio_segments[-1]).astype('int')
segment_id = utils.get_most_significant_word(filename)
print '**Sentence selected to respond to segment {}'.format(segment_id)
NAP = utils.trim_right(new_sentence[norm_segments[segment_id]:norm_segments[segment_id+1]])
best_match,_,_,_,_ = audio_memory.find(NAP)
audio_id = best_match.audio_id
soundfile = best_match.wav_file
numWords = len(audio_segments)-1
print numWords
association.send_pyobj(['setParam', 'numWords', numWords ])
association.recv_pyobj()
association.send_pyobj(['makeSentence', audio_id])
print 'respond_sentence waiting for association output...',
sentence, secondaryStream = association.recv_pyobj()
print '*** (respond) Play sentence', sentence, secondaryStream
start = 0
nextTime1 = 0
nextTime2 = 0
enableVoice2 = 1
play_events = []
for i in range(len(sentence)):
word_id = sentence[i]
soundfile = np.random.choice(wavs[word_id])
voiceChannel = 1
speed = 1
# segment start and end within sound file, if zero, play whole file
segstart, segend = wav_audio_ids[(soundfile, word_id)]
NAP = _extract_NAP(segstart, segend, soundfile)
amp = -3 # voice amplitude in dB
#totaldur, maxamp = utils.getSoundParmFromFile(soundfile)
_,totaldur,maxamp,_ = utils.getSoundInfo(soundfile)
dur = segend-segstart
if dur <= 0: dur = totaldur
voice1 = 'playfile {} {} {} {} {} {} {} {} {}'.format(voiceChannel, voiceType1, start, soundfile, speed, segstart, segend, amp, maxamp)
#start += dur # if we want to create a 'score section' for Csound, update start time to make segments into a contiguous sentence
wordSpacing1 = wordSpace1 + np.random.random()*wordSpaceDev1
nextTime1 += (dur/speed)+wordSpacing1
#print 'voice 2 ready to play', secondaryStream[i], i
if enableVoice2:
word_id2 = secondaryStream[i]
#print 'voice 2 playing', secondaryStream[i]
soundfile2 = np.random.choice(wavs[word_id2])
voiceChannel2 = 2
start2 = 0.7 # set delay between voice 1 and 2
speed2 = 0.7
amp2 = -10 # voice amplitude in dB
try:
segstart2, segend2 = wav_audio_ids[(soundfile2, word_id2)]
dur2 = segend2-segstart2
#totalDur2, maxamp2 = utils.getSoundParmFromFile(soundfile2)
_,totalDur2,maxamp2,_ = utils.getSoundInfo(soundfile)
if dur2 <= 0: dur2 = totalDur2
voice2 = 'playfile {} {} {} {} {} {} {} {} {}'.format(voiceChannel2, voiceType2, start2, soundfile2, speed2, segstart2, segend2, amp2, maxamp2)
wordSpacing2 = wordSpace2 + np.random.random()*wordSpaceDev2
nextTime2 += (dur2/speed2)+wordSpacing2
except:
voice2 = ''
utils.print_exception('VOICE 2 tried to access an illegal soundfile/audio_id combination.')
#enableVoice2 = 0
# trig another word in voice 2 only if word 2 has finished playing (and sync to start of voice 1)
if nextTime1 > nextTime2: enableVoice2 = 1
projection = _project(audio_id, sound_to_face, NAP, video_producer)
print 'SENTENCE RESPOND SPACING', wordSpacing1
play_events.append([ dur+wordSpacing1, voice1, voice2, projection, FRAME_SIZE ])
scheduler.send_pyobj(play_events)
print 'Sentence respond time from creation of wav file was {} seconds'.format(time.time() - utils.filetime(filename))
except:
utils.print_exception('Sentence response aborted.')
if 'testSentence' in pushbutton:
print 'testSentence', pushbutton
association.send_pyobj(['makeSentence',int(pushbutton['testSentence'])])
print 'testSentence waiting for association output...'
sentence, secondaryStream = association.recv_pyobj()
print '*** Test sentence', sentence, secondaryStream
if 'assoc_setParam' in pushbutton:
try:
parm, value = pushbutton['assoc_setParam'].split()
association.send_pyobj(['setParam', parm, value ])
association.recv_pyobj()
except:
utils.print_exception('Assoc set param aborted.')
if 'respond_setParam' in pushbutton:
items = pushbutton['respond_setParam'].split()
if items[0] == 'voiceType':
chan = items[1]
if chan == '1': voiceType1 = int(items[2])
if chan == '2': voiceType2 = int(items[2])
if items[0] == 'wordSpace':
chan = items[1]
print 'wordSpace chan', chan, items
if chan == '1': wordSpace1 = float(items[2])
if chan == '2': wordSpace2 = float(items[2])
if items[0] == 'wordSpaceDev':
chan = items[1]
print 'wordSpaceDev1 chan', chan, items
if chan == '1': wordSpaceDev1 = float(items[2])
if chan == '2': wordSpaceDev2 = float(items[2])
if 'play_id' in pushbutton:
try:
items = pushbutton['play_id'].split(' ')
if len(items) < 3: print 'PARAMETER ERROR: play_id audio_id voiceChannel voiceType'
play_audio_id = int(items[0])
voiceChannel = int(items[1])
voiceType = int(items[2])
print 'play_audio_id', play_audio_id, 'voice', voiceChannel
print 'wavs[play_audio_id]', wavs[play_audio_id]
#print wavs
soundfile = np.random.choice(wavs[play_audio_id])
speed = 1
#print 'wav_audio_ids', wav_audio_ids
segstart, segend = wav_audio_ids[(soundfile, play_audio_id)]
#segstart = 0 # segment start and end within sound file
#segend = 0 # if zero, play whole file
amp = -3 # voice amplitude in dB
#dur, maxamp = utils.getSoundParmFromFile(soundfile)
_,dur,maxamp,_ = utils.getSoundInfo(soundfile)
start = 0
sender.send_json('playfile {} {} {} {} {} {} {} {} {}'.format(voiceChannel, voiceType, start, soundfile, speed, segstart, segend, amp, maxamp))
except:
utils.print_exception('play_id aborted.')
if 'print_me' in pushbutton:
# just for inspecting the contents of objects while running
print 'printing '+pushbutton['print_me']
if 'brain ' in pushbutton['print_me']:
print_variable = pushbutton['print_me'].split('brain ')[-1]
try:
print eval(print_variable)
except Exception, e:
print e, 'print_me in brain failed.'
elif 'association ' in pushbutton['print_me']:
print_variable = pushbutton['print_me'].split('association ')[-1]
association.send_pyobj(['print_me',print_variable])
if 'dream' in pushbutton:
play_events = []
for audio_segment in audio_memory.all_segments():
segstart, segend = audio_segment.segment_idxs
dur = segend - segstart
NAP = _extract_NAP(segstart, segend, audio_segment.wav_file)
speed = 1
amp = -3
maxamp = 1
start = 0
voice1 = 'playfile {} {} {} {} {} {} {} {} {}'.format(1, 6, np.random.rand()/3, audio_segment.wav_file, speed, segstart, segend, amp, maxamp)
projection = _project(audio_segment.audio_id, sound_to_face, NAP, video_producer)
voice2 = 'playfile {} {} {} {} {} {} {} {} {}'.format(2, 6, np.random.randint(3,6), audio_segment.wav_file, speed, segstart, segend, amp, maxamp)
play_events.append([ dur, voice1, voice2, projection, FRAME_SIZE ])
print 'Dream mode playing back {} memories'.format(len(play_events))
scheduler.send_pyobj(play_events)
if 'save' in pushbutton:
utils.save('{}.{}'.format(pushbutton['save'], mp.current_process().name), [ sound_to_face, wordFace, face_to_sound, faceWord, video_producer, wavs, wav_audio_ids, audio_classifier, maxlen, NAP_hashes, face_id, face_recognizer, audio_memory ])
if 'load' in pushbutton:
sound_to_face, wordFace, face_to_sound, faceWord, video_producer, wavs, wav_audio_ids, audio_classifier, maxlen, NAP_hashes, face_id, face_recognizer, audio_memory = utils.load('{}.{}'.format(pushbutton['load'], mp.current_process().name))
def new_dream(audio_memory):
#import matplotlib.pyplot as plt
#plt.ion()
try:
print 'Dreaming - removing wrongly binned filenames'
mega_filenames_and_indexes = []
for audio_id, audio_segments in audio_memory.audio_ids.iteritems():
NAP_detail = 'low'
filenames_and_indexes = []
for audio_segment in audio_segments:
segstart, segend = audio_segment.segment_idxs
audio_times = utils.get_segments(audio_segments.wav_file)
norm_segstart = segstart/audio_times[-1]
norm_segend = segend/audio_times[-1]
filenames_and_indexes.append([ soundfile, norm_segstart, norm_segend, audio_id, NAP_detail ])
mega_filenames_and_indexes.extend(filenames_and_indexes)
k = 2
print 'Examining audio_id {}'.format(audio_id)
if len(audio_segments) == 1:
print 'Just one member in this audio_id, skipping analysis'
continue
sparse_codes = mysai.experiment(filenames_and_indexes, k)
# plt.matshow(sparse_codes, aspect='auto')
# plt.colorbar()
# plt.draw()
coarse = np.mean(sparse_codes, axis=1)
coarse.shape = (len(coarse), 1)
codebook,_ = kmeans(coarse, k)
instances = [ vq(np.atleast_2d(s), codebook)[0] for s in coarse ]
freqs = itemfreq(instances)
sorted_freqs = sorted(freqs, key=lambda x: x[1])
print 'Average sparse codes: {} Class count: {}'.format(list(itertools.chain.from_iterable(coarse)), sorted_freqs)
if len(sorted_freqs) == 1:
print 'Considered to be all the same.'
continue
fewest_class = sorted_freqs[0][0]
ousted_audio_segments = [ audio_segment for audio_segment, i in zip(audio_segments, instances) if i == fewest_class ]
print 'Class {} has fewest members, deleting audio_segments {}'.format(fewest_class, ousted_audio_segments)
filter(audio_memory.forget, ousted_audio_segments)
print 'Creating mega super self-organized class'
for row in mega_filenames_and_indexes:
row[-1] = 'high'
high_resolution_k = 256
clusters = 24
sparse_codes = mysai.experiment(mega_filenames_and_indexes, high_resolution_k)
sparse_codes = np.array(sparse_codes)
# plt.matshow(sparse_codes, aspect='auto')
# plt.colorbar()
# plt.draw()
codebook,_ = kmeans(sparse_codes, clusters)
instances = [ vq(np.atleast_2d(s), codebook)[0] for s in sparse_codes ]
cluster_list = {}
for mega, instance in zip(mega_filenames_and_indexes, instances):
soundfile,_,_,audio_id,_ = mega
cluster_list[(soundfile, audio_id)] = instance
print cluster_list
except:
utils.print_exception('NIGHTMARE!')
def brand_rank_prediction(file, model_dict):
file_name = os.path.basename(file)
try:
logger.info("{file_name} predict".format(file_name=file_name))
predict_begin_time = time.time()
tp = pd.read_csv(file, sep='\t', encoding='gb18030',
names=['brandName', 'category', 'cityCount', 'averageGrade', 'midgrade', 'averageCommentCount',
'midCommentCount', 'averageprice', 'midPrice', 'categoryTotalCount', 'fiveRank',
'tenRank', 'twentyRank', 'fortyRank', 'fivePercentRank', 'tenPercentRank',
'twentyPercentRank', 'fortyPercentRank', 'hundredPercentRank'],
quoting=csv.QUOTE_NONE)
parent_category = tp['category'][0]
if not parent_category in model_dict:
brand_rank_result = tp.loc[:, ['brandName', 'hundredPercentRank', 'categoryTotalCount']]
else:
tp = feature_covert(tp)
if file_name == 'binguanfandian-feature':
features = tp.loc[:, ['averageCommentCount_log', 'averageprice_log']]
elif file_name == 'jinrongyinxing-feature':
features = tp.loc[:,
['cityCount_log', 'averageGrade_log', 'averageCommentCount_log', 'averageprice_log',
'categoryTotalCount_log', 'is_atm_sign']]
else:
features = tp.loc[:,
['cityCount_log', 'averageGrade_log', 'averageCommentCount_log', 'averageprice_log',
'categoryTotalCount_log']]
model = model_dict.get(parent_category)
y_train_pred = model.predict(features)
tp['y_train_pred'] = y_train_pred
tp['pre_rank_level'] = tp['y_train_pred'].apply(func=convert_level)
brand_rank_result = tp.loc[:, ['brandName', 'pre_rank_level', 'categoryTotalCount']]
path = local_featurePoiRank_path + file_name + "-brandRank"
brand_rank_result.to_csv(path, encoding='gb18030', sep='\t', index=False, header=False, quoting=csv.QUOTE_NONE)
logger.info("{file_name} rank predict finished,used time:{use_time}".format(
file_name=file_name, use_time=time.time() - predict_begin_time))
except Exception as e:
logger.info('{file_name} exception,info:{info}'.format(file_name=file_name, info=utils.print_exception()))
def _show_error(self, exception, message):
"""Show error.
'exception' is the exception information.
'message' is the corresponding message."""
print_exception(exception, message)
self._show_connection_error(exception)
def rank_prediction(file, model_dict):
file_name = os.path.basename(file)
try:
# global local_featurePoiRank_path
logger.info("{file_name} predict".format(file_name=file_name))
tp = pd.read_csv(file, sep='\t', encoding='gb18030',
names=['name', 'dataId', 'city', 'parentCategory', 'subCategory', 'brand',
'categoryScore', 'tagScore', 'matchCountScore', 'gradeScore',
'commentScore', 'priceScore', 'areaScore', 'leafCountScore', 'doorCountScore',
'parkCountScore', 'innerCountScore', 'buildCountScore',
'point', 'keyword', 'matchCount', 'grade', 'comment', 'price', 'area',
'leafCount', 'doorCount', 'parkCount', 'innerCount', 'buildCount',
'hotCount', 'hitcount', 'viewcount', 'citySize'],
quoting=csv.QUOTE_NONE, iterator=True, chunksize=1000)
featurePoi = pd.concat(tp, ignore_index=True)
parentCategory = featurePoi['parentCategory'][0]
if not parentCategory in model_dict:
return
model = model_dict.get(parentCategory)
begin_time = time.time()
W = weight.loc[weight.parentCategory == parentCategory, 'subCategoryScore':]
X = featurePoi.iloc[:, 6:18]
featureValues = np.multiply(X, W)
featurePoi.loc[:, ('sumWeight1')] = featureValues.sum(axis=1)
y_train_pred = model.predict(featurePoi.loc[:, ['sumWeight1', 'citySize']])
featurePoi['y_train_pred'] = y_train_pred
featurePoi['pre_rank_level'] = featurePoi['y_train_pred'].apply(func=convert_level)
path = local_featurePoiRank_path + file_name + "-rank"
featurePoi.to_csv(path, encoding='gb18030', sep='\t', index=False, header=False,quoting=csv.QUOTE_NONE)
logger.info("{file_name} rank predict finished,used time:{use_time}".format(
file_name=file_name, use_time=time.time() - begin_time))
except Exception as e:
logger.info('{file_name} exception,info:{info}'.format(file_name=file_name,info=utils.print_exception()))
def write_test_file(name, all_tests):
for test in all_tests:
unicode = requires_unicode(str(test))
if not unicode and not isinstance(test.expected(), bool):
unicode = requires_unicode(test.expected().render())
if unicode:
test.add_condition(r'UNICODE_LITERALS_OK')
tests_by_group = {}
for test in all_tests:
if test.group() not in tests_by_group:
tests_by_group[test.group()] = {}
cond = test.condition()
if cond not in tests_by_group[test.group()]:
tests_by_group[test.group()][cond] = []
tests_by_group[test.group()][cond].append(test)
all_tests = tests_by_group
test_file_path = Path(
utils.entry_script_dir(), '..', 'tests',
rf'conformance_{sanitize(name.strip())}.cpp').resolve()
with StringIO() as test_file_buffer:
write = lambda txt, end='\n': print(
txt, file=test_file_buffer, end=end)
# preamble
write(
r'// This file is a part of toml++ and is subject to the the terms of the MIT license.'
)
write(r'// Copyright (c) Mark Gillard <*****@*****.**>')
write(
r'// See https://github.com/marzer/tomlplusplus/blob/master/LICENSE for the full license text.'
)
write(r'// SPDX-License-Identifier: MIT')
write(r'//-----')
write(
r'// this file was generated by generate_conformance_tests.py - do not modify it directly'
)
write(r'')
write(r'#include "tests.h"')
# test data
write(r'')
write('namespace')
write('{', end='')
for group, conditions in all_tests.items():
for condition, tests in conditions.items():
write('')
if condition != '':
write(f'#if {condition}')
write('')
for test in tests:
write(f'\t{test}')
if condition != '':
write('')
write(f'#endif // {condition}')
write('}')
# tests
write('')
write(f'TEST_CASE("conformance - {name}")')
write('{', end='')
for group, conditions in all_tests.items():
for condition, tests in conditions.items():
if condition != '':
write('')
write(f'#if {condition}')
for test in tests:
write('')
expected = test.expected()
if isinstance(expected, bool):
if expected:
write(
f'\tparsing_should_succeed(FILE_LINE_ARGS, {test.identifier()}); // {test.name()}'
)
else:
write(
f'\tparsing_should_fail(FILE_LINE_ARGS, {test.identifier()}); // {test.name()}'
)
else:
s = expected.render('\t\t')
write(
f'\tparsing_should_succeed(FILE_LINE_ARGS, {test.identifier()}, [](toml::table&& tbl) // {test.name()}'
)
write('\t{')
write(f'\t\tconst auto expected = {s};')
write('\t\tREQUIRE(tbl == expected);')
write('\t});')
if condition != '':
write('')
write(f'#endif // {condition}')
write('}')
write('')
test_file_content = test_file_buffer.getvalue()
# clang-format
print(f"Running clang-format for {test_file_path}")
try:
test_file_content = utils.apply_clang_format(
test_file_content, cwd=test_file_path.parent)
except Exception as ex:
print(rf'Error running clang-format:', file=sys.stderr)
utils.print_exception(ex)
# write to disk
print(rf'Writing {test_file_path}')
with open(test_file_path, 'w', encoding='utf-8',
newline='\n') as test_file:
test_file.write(test_file_content)