def replicate(self, current_time): #, convenient_id=None): """ This method replicates the pathogen once. Mutation is guaranteed to be called, but not guaranteed to happen, as it depends on the substitution rate of each of the genomic segments. INPUTS: - INT: current_time This number will be set to the "creation_time" of the new pathogen. OUTPUTS: - OBJECT: new_pathogen The replicated pathogen. """ new_pathogen = copy(self) new_pathogen.parent = dict() new_pathogen.parent[self] = self.get_segment_numbers() new_pathogen.creation_time = current_time new_pathogen.id = generate_id() new_pathogen.segments = deepcopy(self.segments) new_pathogen.mutate() # print new_pathogen.parent return new_pathogen
def replicate(self, current_time): #, convenient_id=None):
"""
This method replicates the pathogen once. Mutation is guaranteed to be
called, but not guaranteed to happen, as it depends on the
substitution rate of each of the genomic segments.
INPUTS:
- INT: current_time
This number will be set to the "creation_time" of the new
pathogen.
OUTPUTS:
- OBJECT: new_pathogen
The replicated pathogen.
"""
new_pathogen = copy(self)
new_pathogen.parent = dict()
new_pathogen.parent[self] = self.get_segment_numbers()
new_pathogen.creation_time = current_time
new_pathogen.id = generate_id()
new_pathogen.segments = deepcopy(self.segments)
new_pathogen.mutate()
# print new_pathogen.parent
return new_pathogen
def reassort_with(self, other, current_time): """ This method takes in another pathogen and returns a progeny with genomic segments that are randomly selected from segments from both pathogens. Reassortment with another pathogen is akin to replication, but the step at which segments are added is different. INPUTS: - OBJECT: other The other pathogen with which to reassort. - INT: current_time The time at which the reassortment event happened. OUTPUTS: - OBJECT: new_pathogen The reassortant progeny from the two pathogens. """ new_pathogen = copy(self) new_pathogen.creation_time = current_time new_pathogen.id = generate_id() new_pathogen.segments = [] # Assign parent differently from the replicate() function. parent = dict() parent[self] = [] parent[other] = [] # Iterate over all of the segments. for segment in self.segments: i = choice([0, 1]) segment_number = segment.segment_number segments_to_choose_from = (segment, \ other.get_segment(segment_number)) new_pathogen.segments.append(deepcopy(segments_to_choose_from[i])) if i == 0: parent[self].append(segments_to_choose_from[0].segment_number) if i == 1: parent[other].append(segments_to_choose_from[1].segment_number) # Clean the dictionary if the progeny is entirely derived from one # parent. if parent[self] == []: del parent[self] if parent[other] == []: del parent[other] new_pathogen.parent = parent new_pathogen.mutate() # print new_pathogen.parent return new_pathogen
def reassort_with(self, other, current_time):
"""
This method takes in another pathogen and returns a progeny with
genomic segments that are randomly selected from segments from both
pathogens. Reassortment with another pathogen is akin to
replication, but the step at which segments are added is different.
INPUTS:
- OBJECT: other
The other pathogen with which to reassort.
- INT: current_time
The time at which the reassortment event happened.
OUTPUTS:
- OBJECT: new_pathogen
The reassortant progeny from the two pathogens.
"""
new_pathogen = copy(self)
new_pathogen.creation_time = current_time
new_pathogen.id = generate_id()
new_pathogen.segments = []
# Assign parent differently from the replicate() function.
parent = dict()
parent[self] = []
parent[other] = []
# Iterate over all of the segments.
for segment in self.segments:
i = choice([0, 1])
segment_number = segment.segment_number
segments_to_choose_from = (segment, \
other.get_segment(segment_number))
new_pathogen.segments.append(deepcopy(segments_to_choose_from[i]))
if i == 0:
parent[self].append(segments_to_choose_from[0].segment_number)
if i == 1:
parent[other].append(segments_to_choose_from[1].segment_number)
# Clean the dictionary if the progeny is entirely derived from one
# parent.
if parent[self] == []:
del parent[self]
if parent[other] == []:
del parent[other]
new_pathogen.parent = parent
new_pathogen.mutate()
# print new_pathogen.parent
return new_pathogen
def send_code():
id = generate_id()
params = {
'email': request.form['email'],
'sms': request.form['sms'],
'phone': request.form['phone'],
'emailAddress': request.form['emailAddress'],
'captcha': request.form['captcha']
}
captcha_response = req.post(
"https://www.google.com/recaptcha/api/siteverify",
data={
'secret': os.environ.get("recaptcha_id_pro"),
'response': params['captcha']
})
if not json.loads(captcha_response.text)['success']:
abort(403)
if params['sms'] == "true" and params['email'] == "true":
if verify_phone_not_present(
params['phone']) and verify_email_not_present(
params['emailAddress']):
insert_id(id, params['emailAddress'], params['phone'])
send_message(params['phone'], id)
send_email(params['emailAddress'], id)
else:
if not verify_email_not_present(params['emailAddress']):
send_recovery_email(params['emailAddress'],
get_id_by_email(params['emailAddress']))
if not verify_phone_not_present(params['phone']):
send_recovery_message(params['phone'],
get_id_by_phone(params['phone']))
elif params['sms'] == "true":
if verify_phone_not_present(params['phone']):
insert_id(id, "-", params['phone'])
send_message(params['phone'], id)
else:
send_recovery_message(params['phone'],
get_id_by_phone(params['phone']))
elif params['email'] == "true":
if verify_email_not_present(params['emailAddress']):
insert_id(id, params['emailAddress'], "-")
send_email(params['emailAddress'], id)
else:
send_recovery_email(params['emailAddress'],
get_id_by_email(params['emailAddress']))
else:
abort(400)
return json.dumps(params)
def generate():
if request.method == 'POST':
id_number = generate_id(
request.data.get('first_name', ''),
request.data.get('father_name', ''),
request.data.get('last_name', ''),
request.data.get('mother_name', ''),
request.data.get('gender', ''),
request.data.get('birthday_day', ''),
request.data.get('birthday_month', ''),
request.data.get('birthday_year', '')
)
return {'hash': id_number}, status.HTTP_201_CREATED
else:
return {'message': 'Please use POST'}
def __init__(self, segments, creation_time, progeny_size, \ parent=dict(), convenient_id=None): """ Initialize the pathogen. ATTRIBUTES: - LIST: segments a list of Segment objects that specify the genomic segments that are present in the pathogen. - INT: creation_time an integer number that specifies the time within the simulation that the pathogen was created. - DICTIONARY: parent A dictionary that specifies the parent of the pathogen. The (key, value) pairs are (parent, segments transmitted). - If the dictionary is empty, then the pathogen is a seed pathogen. - If the dictionary has one (key, value) pair, then the pathogen was replicated from one other pathogen. - If the dictionary has two (key, value) pairs, then the pathogen was a reassortant of two parental pathogens. - STRING: id a string generated form the generate_id function that uniquely identifies the pathogen. - TUPLE: progeny_size a two-tuple that describes the (mean, var) of the burst size per pathogen. These are the Normal distribution parameters. In practice, while a floating point number is drawn from the Normal distribution, it will be rounded to an integer number. """ super(Pathogen, self).__init__() self.segments = segments self.creation_time = creation_time self.parent = parent self.id = generate_id() # self.convenient_id = self.id[0:5] self.progeny_size = progeny_size
def __init__(self, segments, creation_time, progeny_size, \
parent=dict(), convenient_id=None):
"""
Initialize the pathogen.
ATTRIBUTES:
- LIST: segments
a list of Segment objects that specify the genomic segments
that are present in the pathogen.
- INT: creation_time
an integer number that specifies the time within the
simulation that the pathogen was created.
- DICTIONARY: parent
A dictionary that specifies the parent of the pathogen.
The (key, value) pairs are (parent, segments transmitted).
- If the dictionary is empty, then the pathogen is a seed pathogen.
- If the dictionary has one (key, value) pair, then the pathogen was replicated from one other pathogen.
- If the dictionary has two (key, value) pairs, then the pathogen was a reassortant of two parental pathogens.
- STRING: id
a string generated form the generate_id function that uniquely
identifies the pathogen.
- TUPLE: progeny_size
a two-tuple that describes the (mean, var) of the burst size
per pathogen. These are the Normal distribution parameters. In
practice, while a floating point number is drawn from the
Normal distribution, it will be rounded to an integer number.
"""
super(Pathogen, self).__init__()
self.segments = segments
self.creation_time = creation_time
self.parent = parent
self.id = generate_id()
# self.convenient_id = self.id[0:5]
self.progeny_size = progeny_size
def __init__(self, length=1000, sequence=None, id=None): """ Initialize the sequence with a random sequence of length 1000 if sequence is not specified. Otherwise, initialize sequence with a sequence that is specified. """ super(Sequence, self).__init__() self.sequence = None if sequence == None: self.sequence = self.generate_sequence(length) else: self.sequence = sequence if id == None: self.id = generate_id() else: self.id = id
def index(request):
if request.method == 'POST':
if 'RDS_HOSTNAME' in os.environ:
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.mysql',
'NAME': os.environ['RDS_DB_NAME'],
'USER': os.environ['RDS_USERNAME'],
'PASSWORD': os.environ['RDS_PASSWORD'],
'HOST': os.environ['RDS_HOSTNAME'],
'PORT': os.environ['RDS_PORT'],
}
}
cnx = mysql.connector.connect(
user=DATABASES['default']['USER'],
password=DATABASES['default']['PASSWORD'],
database=DATABASES['default']['NAME'],
host=DATABASES['default']['HOST'],
port=DATABASES['default']['PORT'])
cursor = cnx.cursor()
#Begin Database Work
try: #this is to make a table
cursor.execute('DROP TABLE jobs')
cursor.execute(
'CREATE TABLE IF NOT EXISTS joblist (email VARCHAR(100), id VARCHAR(200), submit_time VARCHAR(200), jobstarted BOOLEAN, finish_time VARCHAR(200), jobfinished BOOLEAN);'
)
cnx.commit()
except:
pass
#End Database Work
uid = str(generate_id())
cursor.execute(
"INSERT INTO joblist (email,id,submit_time,jobstarted,finish_time,jobfinished) VALUES ('"
+ request.POST.get('email', '') + "','" + uid + "','" +
str(time.time()) + "',FALSE,null,FALSE);")
cnx.commit()
cursor.execute("SELECT * FROM joblist")
res = ''
for entry in cursor:
res += str(entry) + '<br>'
cursor.close()
cnx.close()
sigma_cfg = make_cfg(request.POST, request.FILES, uid)
if (request.POST.get("read_type", "ERROR") == "single"):
f = default_storage.open(
uid + '/' + request.FILES["single_file"].name, 'wb')
f.write(request.FILES["single_file"].read())
f.close()
else:
f = default_storage.open(
uid + '/' + request.FILES["paired_file1"].name, 'wb')
f.write(request.FILES["paired_file1"].read())
f.close()
f = default_storage.open(
uid + '/' + request.FILES["paired_file2"].name, 'wb')
f.write(request.FILES["paired_file2"].read())
f.close()
f = default_storage.open(uid + '/sigma_config.cfg', 'w')
f.write(sigma_cfg)
f.close()
#Thread(target=debug_call, args=(uid,)).start()
#subprocess.Popen(['sh','/opt/python/current/app/run_job.sh', uid], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
return HttpResponse(
'Your job is now in the queue.<br><br>Your unique id for this job is: '
+ str(uid) +
'<br><br>If you did not enter your email SAVE THIS LINK: YOUR SITE DOMAIN/results/'
+ str(uid))
return HttpResponse('DB Error!')
else:
return render(request, 'sigma.html')