def get_ttl(self, key):
with self.database.cursor(self.Cursor) as cursor:
cursor.execute(
self._escape_sql_args_formatter("""
SELECT `expired`
FROM `storage`
WHERE `key`=? AND `expired`>?
LIMIT 1;"""), (key, get_timestamp()))
result = cursor.fetchone()
if not result: return -2
ttl = int(result[0]) - get_timestamp()
return int(ttl)
def purge_expired(self):
with self.database.cursor(self.Cursor) as cursor:
cursor.execute(
self._escape_sql_args_formatter("""
DELETE
FROM `storage`
WHERE `expired`<=?;"""), (get_timestamp(), ))
def publish(self,
body,
serializer=None,
exchange=None,
routing_key=None,
mandatory=False,
immediate=False,
**properties):
"""Publish a Task formatted message
:param body: The body of the task message
:param exchange: Exchange to use, None if default
:param routing_key: Routing key for the message
:param mandatory: <tosee>
:param immediate: <tosee>
:param properties: Headers of the message
"""
# Add the current time.
body['time'] = get_timestamp()
# Encode the message.
content_type, content_encoding, content = serialize.encode(body)
properties['content_type'] = content_type
properties['content_encoding'] = content_encoding
# Send the message Task.
return self.basic_publish(body=content,
exchange=exchange or '',
routing_key=routing_key or '',
mandatory=mandatory,
immediate=immediate,
**properties)
def set(self, key, pyobj, expires=86400, encoding="utf-8"):
data = self.serialize(pyobj, encoding=encoding)
with self.database.cursor(self.Cursor) as cursor:
cursor.execute(
self._escape_sql_args_formatter("""
REPLACE INTO `storage`
(`key`, `value`, `expired`)
VALUES (?, ?, ?);"""),
(key, self._translate_blob(data), get_timestamp() + expires))
def get_all_keys_by_wildcard(self, wildcard="*"):
wc = wildcard.replace("*", "%").replace("?", "_")
with self.database.cursor(self.Cursor) as cursor:
cursor.execute(
self._escape_sql_args_formatter("""
SELECT `key`
FROM `storage`
WHERE `expired`>? AND `key` LIKE ?;"""), (get_timestamp(), wc))
result = [k for k, *_ in cursor.fetchall()]
return result
def is_expired(self, key):
with self.database.cursor(self.Cursor) as cursor:
cursor.execute(
self._escape_sql_args_formatter("""
SELECT 1
FROM `storage`
WHERE `key`=? AND `expired`>?
LIMIT 1;"""), (key, get_timestamp()))
result = not bool(cursor.fetchone())
return result
def get(self, key, encoding=None):
with self.database.cursor(self.Cursor) as cursor:
cursor.execute(
self._escape_sql_args_formatter("""
SELECT `value`
FROM `storage`
WHERE `key`=? AND `expired`>?
LIMIT 1;"""), (key, get_timestamp()))
result = cursor.fetchone()
if not result:
return
t = result[0]
t = self.unserialize(t, encoding=encoding)
return t
def get_top_song_metadata():
""" Fetch the JSON metadata about the latest top song from the fileserver. """
cache_buster = '?v=%s' % get_timestamp()
response = requests.get(settings.MEDIA_URL + 'musikk/top_meta.json' + cache_buster)
return response.json()
def getAlgorithm(request,
id_record): # ADD THE TYPE ODF THE SIGNAL ALSO IN URLS!!!
# read parameters from url
# get data type list
allow_ml = False
id_file = -1
if request.method == 'POST':
mydict = dict(request.POST.iterlists())
# if 'choose_signal' in mydict:
# print request.POST
print id_record
type_sig = ''
id_num = id_record
try:
print "RUNNING FOR ", id_num
data, cols = QueryDb(id_num)
# print cols
time = selcol(data, cols, "TIME")
labs = selcol(data, cols, "LAB")
type_sig = get_signal_type(cols)
# print type_sig
params = dict()
if (mydict['type'][0] == 'contigous'):
windows, winlab = wd.get_windows_contiguos(
time, labs, float(mydict['length'][0]),
float(mydict['step'][0]))
if (
mydict['type'][0] == 'no_mix'
): # for the values, make reference to .forms --> windowing.!!!!
windows, winlab = wd.get_windows_no_mix(
time, labs, float(mydict['length'][0]),
float(mydict['step'][0]))
if (mydict['type'][0] == 'full_label'):
windows, winlab = wd.get_windows_full_label(time, labs)
params.update({
"windowing.type": str(mydict["type"][0]),
"windowing.length": str(mydict["length"][0]),
"windowing.step": str(mydict["step"][0])
})
# extract features from result
# store feats. in the db
params.update({"signal_type": type_sig})
if type_sig == "GSR": # GSR
data_in = selcol(data, cols, "PHA")
funcs, pars = list(
Preprocessed_Recording.objects.filter(
pk=id_num).values_list('applied_preproc_funcs_names',
'preproc_funcs_parameters'))[0]
DELTA = float(pars[funcs.index(u"GSR.preproc")][u"DELTA_PEAK"])
feat_dict = extfeat_GSR(data_in, time, DELTA, windows)
data_out, cols_out = dict_to_arrays(feat_dict)
data_out = np.column_stack((data_out, winlab))
columns_out = np.r_[cols_out, ["LAB"]]
elif type_sig == "inertial":
col_acc = ["ACCX", "ACCY", "ACCZ"]
col_gyr = ["GYRX", "GYRY", "GYRZ"]
col_mag = ["MAGX", "MAGY", "MAGZ"]
try:
acc = selcol(data, cols, col_acc)
thereIsAcc = True
except IndexError as e:
print e
thereIsAcc = False
try:
gyr = selcol(data, cols, col_gyr)
thereIsGyr = True
except IndexError as e:
print e
thereIsGyr = False
try:
mag = selcol(data, cols, col_mag)
thereIsMag = True
except IndexError as e:
print e
thereIsMag = False
columns_out = np.array(["LAB"])
data_out = winlab[:]
if thereIsAcc:
feats_acc, fcol_acc = extfeat_ACC(acc, time, col_acc,
windows)
data_out = np.column_stack([feats_acc, data_out])
columns_out = np.r_[fcol_acc, columns_out]
if thereIsGyr:
feats_gyr, fcol_gyr = extfeat_GYR(gyr, time, col_gyr,
windows)
data_out = np.column_stack([feats_gyr, data_out])
columns_out = np.r_[fcol_gyr, columns_out]
if thereIsMag:
feats_mag, fcol_mag = extfeat_MAG(mag, time, col_mag,
windows)
data_out = np.column_stack([feats_mag, data_out])
columns_out = np.r_[fcol_mag, columns_out]
elif type_sig == "IBI":
data_in = selcol(data, cols, ["TIME", "IBI"])
cols_in = ["TIME", "IBI"]
data_out, winlab = extfeat_IBI(data_in, cols_in, windows,
winlab)
columns_out = np.array([
'RRmean', 'RRSTD', 'pNN50', 'pNN25', 'pNN10', 'RMSSD',
'SDSD'
])
# print data_out.shape, winlab.shape
data_out = np.column_stack((data_out, winlab))
columns_out = np.r_[columns_out, ["LAB"]]
st = datetime.datetime.fromtimestamp(
get_timestamp()).strftime('%Y%m%d_%H%M%S')
fname = MEDIA_ROOT + type_sig + "_" + id_num + "_" + st + ".csv"
print fname
# print(fname)
toCsv(data_out, columns_out, fname)
id_file = WritePathtoDB(fname, id_num, params)
success = True
# check distinct label
print "COLUMNS", columns_out, type(columns_out), np.where(
columns_out == "LAB")[0]
allow_ml = False if len(
np.unique(data_out[:, np.where(
columns_out == "LAB")])) == 1 else True
except Exception as e:
print "COULD NOT PROCESS " + id_num + ": " + e.message
if type_sig is not None:
messages.error(
request, "Error processing " + id_num + " (" + type_sig +
"). Review your parameters! It will not be saved.")
else:
messages.error(
request,
"Error processing. Review your parameters! It will not be saved."
)
success = False
# else:
# success=False
# messages.error(request, "Choose at least one preprocessed signal")
else:
success = False
form = windowing()
# form_signal = form_select_signal(id_record)
template = "extfeat/choose_alg.html"
# print urlTmp['id_num']
context = {
'form': form,
'id_record': id_record,
'success': success,
'allow_ml': allow_ml,
'id_file': id_file
}
return render(request, template, context)
def getAlgorithm(request, id_record): # ADD THE TYPE ODF THE SIGNAL ALSO IN URLS!!!
# read parameters from url
# get data type list
if (request.method == 'POST'):
mydict = dict(request.POST.iterlists())
if 'choose_signal' in mydict:
for id_num in mydict['choose_signal']:
try:
print "RUNNING FOR ", id_num
data, cols = QueryDb(id_num)
time = selcol(data, cols, "TIME")
labs = selcol(data, cols, "LAB")
type_sig=get_signal_type(cols)
params=dict()
if (mydict['type'][0] == 'contigous'):
windows, winlab = wd.get_windows_contiguos(time, labs, float(mydict['length'][0]), float(mydict['step'][0]))
if (mydict['type'][0] == 'no_mix'): # for the values, make reference to .forms --> windowing.!!!!
windows, winlab = wd.get_windows_no_mix(time, labs, float(mydict['length'][0]), float(mydict['step'][0]))
if (mydict['type'][0] == 'full_label'):
windows, winlab = wd.get_windows_full_label(time, labs)
params.update({"windowing.type":str(mydict["type"][0]), "windowing.length":str(mydict["length"][0]), "windowing.step":str(mydict["step"][0])})
# extract features from result
# store feats. in the db
params.update({"signal_type":type_sig})
if type_sig=="GSR": #GSR
data_in=selcol(data, cols, "PHA")
funcs, pars=list(Preprocessed_Recording.objects.filter(pk = id_num).values_list('applied_preproc_funcs_names', 'preproc_funcs_parameters'))[0]
DELTA=float(pars[funcs.index(u"GSR.preproc")][u"DELTA_PEAK"])
feat_dict = extfeat_GSR(data_in, time, DELTA, windows)
data_out, cols_out=dict_to_arrays(feat_dict)
data_out=np.column_stack((data_out, winlab))
columns_out=np.r_[cols_out, ["LAB"]]
elif type_sig=="inertial":
col_acc=["ACCX", "ACCY", "ACCZ"]
col_gyr=["GYRX", "GYRY", "GYRZ"]
col_mag=["MAGX", "MAGY", "MAGZ"]
try:
acc=selcol(data, cols, col_acc)
thereIsAcc=True
except IndexError as e:
print e
thereIsAcc=False
try:
gyr=selcol(data, cols, col_gyr)
thereIsGyr=True
except IndexError as e:
print e
thereIsGyr=False
try:
mag=selcol(data, cols, col_mag)
thereIsMag=True
except IndexError as e:
print e
thereIsMag=False
columns_out=np.array(["LAB"])
data_out=winlab[:]
if thereIsAcc:
feats_acc, fcol_acc= extfeat_ACC(acc, time, col_acc, windows)
data_out=np.column_stack([feats_acc, data_out])
columns_out=np.r_[fcol_acc, columns_out]
if thereIsGyr:
feats_gyr, fcol_gyr= extfeat_GYR(gyr, time, col_gyr, windows)
data_out=np.column_stack([feats_gyr, data_out])
columns_out=np.r_[fcol_gyr, columns_out]
if thereIsMag:
feats_mag, fcol_mag= extfeat_MAG(mag, time, col_mag, windows)
data_out=np.column_stack([feats_mag, data_out])
columns_out=np.r_[fcol_mag, columns_out]
elif type_sig=="IBI":
data_in=selcol(data, cols, ["TIME","IBI"])
cols_in=["TIME", "IBI"]
data_out, winlab = extfeat_IBI(data_in, cols_in, windows, winlab)
columns_out=np.array(['RRmean', 'RRSTD', 'pNN50', 'pNN25', 'pNN10', 'RMSSD', 'SDSD'])
print data_out.shape, winlab.shape
data_out=np.column_stack((data_out, winlab))
columns_out=np.r_[columns_out, ["LAB"]]
st = datetime.datetime.fromtimestamp(get_timestamp()).strftime('%Y%m%d_%H%M%S')
fname=MEDIA_ROOT+type_sig+"_"+id_num+"_"+st+".csv"
toCsv(data_out, columns_out, fname)
WritePathtoDB(fname, id_num, params)
except Exception as e:
print "COULD NOT PROCESS "+id_num+": "+e.message
if type_sig!=None:
messages.error(request, "Error processing "+id_num+" ("+type_sig+"). Review your parameters! It will not be saved.")
else:
messages.error(request, "Error processing. Review your parameters! It will not be saved.")
success=False
else:
success=True
else:
success=False
messages.error(request, "Choose at least one preprocessed signal")
else:
success=False
form = windowing()
form_signal = form_select_signal(id_record)
template = "extfeat/choose_alg.html"
# print urlTmp['id_num']
context = {'form': form,'form_signal':form_signal, 'id_record': id_record, 'success':success}
return render(request, template, context)
def get_top_song_metadata():
""" Fetch the JSON metadata about the latest top song from the fileserver. """
cache_buster = '?v=%s' % get_timestamp()
response = requests.get(MUSIC_DIR + 'top_meta.json' + cache_buster)
return response.json()
def get_top_song_metadata():
""" Fetch the JSON metadata about the latest top song from the fileserver. """
cache_buster = '?v=%s' % get_timestamp()
response = requests.get(settings.MEDIA_URL + 'musikk/top_meta.json' +
cache_buster)
return response.json()
