def logIn(self, username, pwd):
"""
Logs in a user
:param username: username
:param pwd: password
:return: None @TODO: Maybe return True/False?
(Exceptions implemented, don't think it's worth doing)
@TODO: Detailed comments
"""
conn = create_connection("Acc.db")
cur = conn.cursor()
try:
cur.execute("SELECT * FROM Accounts WHERE Accounts.username=?", (username,))
except sqlite3.OperationalError:
raise Exception("User doesn't exist")
rows = cur.fetchall()
for row in rows:
salt = row[4]
stPwd = row[5]
pwdhash = hashlib.pbkdf2_hmac('sha512', pwd.encode('utf-8'), salt.encode('ascii'), 100000)
pwdhash = binascii.hexlify(pwdhash).decode('ascii')
print()
if pwdhash != stPwd:
raise Exception("Invalid password")
self.name = row[1]
self.surname = row[2]
self.username = row[3]
self.email = row[6]
self.height = row[7]
self.weight = Weight(row[8], row[9])
self.uID = row[10]
self.DBInfo = DBInfo(self.uID)
class Linear(Module):
"""
Linear Layer (feed-forward layer)
"""
def __init__(self, in_dim, out_dim):
super(Linear, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.weight = Weight((in_dim, out_dim))
self.bias = Weight((1, out_dim))
def fprop(self, input_data):
# high_dimension_input = input_data.ndim > 2
#
# # Reshape input
# if high_dimension_input:
# input_data = input_data.reshape(input_data.shape[0], -1)
self.output = np.dot(input_data, self.weight.D) + self.bias.D
# # Reshape output
# if high_dimension_input:
# self.output = self.output.reshape(self.output.shape[0], -1)
return self.output
# TODO: Rename input_data -> prev_layer_output
def bprop(self, input_data, grad_output):
# orig_input_data_shape = input_data.shape
# high_dimension_input = input_data.ndim > 2
#
# # Reshape input and grad_output
# if high_dimension_input:
# input_data = input_data.reshape(input_data.shape[0], -1)
# grad_output = grad_output.reshape(grad_output.shape[0], -1)
# self.weight.grad = self.weight.grad + np.dot(input_data.T, grad_output)
# self.bias.grad = self.bias.grad + grad_output.sum(axis=0)
self.weight.grad = np.dot(input_data.T, grad_output)
self.bias.grad = grad_output.sum(axis=0)
self.grad_input = np.dot(grad_output, self.weight.D.T)
# if high_dimension_input:
# self.grad_input = self.grad_input.reshape(orig_input_data_shape)
return self.grad_input
def update(self, params):
self.weight.update(params)
self.bias.update(params)
def share(self, m):
self.weight = m.weight
self.bias = m.bias
class Linear(Module):
"""
Linear Layer (feed-forward layer)
"""
def __init__(self, in_dim, out_dim):
super(Linear, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.weight = Weight((in_dim, out_dim))
self.bias = Weight((1, out_dim))
def fprop(self, input_data):
# high_dimension_input = input_data.ndim > 2
#
# # Reshape input
# if high_dimension_input:
# input_data = input_data.reshape(input_data.shape[0], -1)
self.output = np.dot(input_data, self.weight.D) + self.bias.D
# # Reshape output
# if high_dimension_input:
# self.output = self.output.reshape(self.output.shape[0], -1)
return self.output
# TODO: Rename input_data -> prev_layer_output
def bprop(self, input_data, grad_output):
# orig_input_data_shape = input_data.shape
# high_dimension_input = input_data.ndim > 2
#
# # Reshape input and grad_output
# if high_dimension_input:
# input_data = input_data.reshape(input_data.shape[0], -1)
# grad_output = grad_output.reshape(grad_output.shape[0], -1)
# self.weight.grad = self.weight.grad + np.dot(input_data.T, grad_output)
# self.bias.grad = self.bias.grad + grad_output.sum(axis=0)
self.weight.grad = np.dot(input_data.T, grad_output)
self.bias.grad = grad_output.sum(axis=0)
self.grad_input = np.dot(grad_output, self.weight.D.T)
# if high_dimension_input:
# self.grad_input = self.grad_input.reshape(orig_input_data_shape)
return self.grad_input
def update(self, params):
self.weight.update(params)
self.bias.update(params)
def share(self, m):
self.weight = m.weight
self.bias = m.bias
def setq_weight_detail():
if request.method == 'POST':
get_data = json.loads(request.get_data(as_text=True))
param = {'setq_id': get_data['setq_id']}
w = Weight()
data = w.allweight(param)
if data == None:
resData = {'resCode': 1, 'data': [], 'message': '未查询到该问题集合的权重记录'}
return jsonify(resData)
resData = {'resCode': 0, 'data': data, 'message': '查询成功'}
return jsonify(resData)
else:
resData = {"resCode": 1, "data": [], "message": '请求方式错误'}
return jsonify(resData)
def __init__(self, *pargs):
self.weights = None
self.input = 0.0
self.sigmoid = 1
self.bias = 1.0
self.error = 0.0
self.learning_rate = 0.01
self.output = None
if pargs:
inputs, rand = pargs
self.weights = []
for input in inputs.get_layer():
w = Weight()
w.input = input
w.value = rand.uniform(-1, 1)
self.weights.append(w)
def __init__(self,
control_directory=".",
control_file="hypoDD.inp",
cc_dt_input="",
ct_dt_input="dt.ct",
initial_hypocenters="event.dat",
station_input="station.dat",
initial_hypocenters_output="hypoDD.loc",
relocated_hypocenters_output="hypoDD.reloc",
station_residual_output="hypoDD.sta",
data_residual_output="hypoDD.res",
takeoff_angle_output="hypoDD.src",
idat=3,
ipha=3,
dist=400,
obscc=0,
obsct=8,
istart=2,
isolv=2,
weights=[
Weight(niter=10,
wtccp=-9,
wtccs=-9,
wrcc=-9,
wdcc=-9,
wtctp=1,
wtcts=0.5,
wrct=3,
wdct=3,
damp=10)
],
velocity_model=VelocityModel(),
cid=0,
evid=[]):
"""Initialization method for the HypoDDControl object.
"""
self.control_directory = control_directory
self.control_file = control_file
self.cc_dt_input = cc_dt_input
self.ct_dt_input = ct_dt_input
self.initial_hypocenters = initial_hypocenters
self.station_input = station_input
self.initial_hypocenters_output = initial_hypocenters_output
self.relocated_hypocenters_output = relocated_hypocenters_output
self.station_residual_output = station_residual_output
self.data_residual_output = data_residual_output
self.takeoff_angle_output = takeoff_angle_output
self.idat = idat
self.ipha = ipha
self.dist = dist
self.obscc = obscc
self.obsct = obsct
self.istart = istart
self.isolv = isolv
self.weights = weights
self.velocity_model = velocity_model
self.cid = cid
self.evid = evid
def online_learning(self):
count = 0
""" initinalize """
for value in self.feature.values():
split_word = value.split(' ')
[self.weight.update({self.flabel + word: 0}) for word in split_word]
cfeature = FeatureCreator()
""" update weight """
while self.iteration >= count:
count = count + 1
for key, value in self.feature.items():
self.phi = cfeature.create(value, self.data, self.flabel)
self.label = TrainOnePrediction(self.weight, self.phi)
if self.label is not key:
update_weight = Weight(self.weight, self.phi, key)
self.weight = update_weight.update()
def new_weight():
if request.method == 'POST':
get_data = json.loads(request.get_data(as_text=True))
param = {
'user_id': get_data['u_id'],
'setq_id': get_data['q_id'],
'weight1': get_data['w1'],
'weight2': get_data['w2']
}
w = Weight()
data = w.new_weight(param)
if data == True:
resData = {"resCode": 0, "data": data, "message": '权重已更新'}
return jsonify(resData)
resData = {"resCode": 1, "data": [], "message": '更新失败'}
return jsonify(resData)
else:
resData = {"resCode": 1, "data": [], "message": '请求方式错误'}
return jsonify(resData)
def getWeight(self, intLumi=None):
# if intLumi is None:
# intLumi = Weight.FBINV
#COLIN THIS WEIGHT STUFF IS REALLY BAD!!
# use the existing Weight class or not? guess so...
return Weight(genNEvents=self.nGenEvents,
xSection=self.xSection,
intLumi=self.intLumi,
genEff=1 / self.effCorrFactor,
addWeight=self.addWeight)
def convert_unit(self, value, input_unit, output_unit):
if self.measurement is 'length':
return Length(value, input_unit, output_unit).convert()
if self.measurement is 'temperature':
return Temperature(value, input_unit, output_unit).convert()
if self.measurement is 'area':
return Area(value, input_unit, output_unit).convert()
if self.measurement is 'volume':
return Volume(value, input_unit, output_unit).convert()
if self.measurement is 'weight':
return Weight(value, input_unit, output_unit).convert()
return "Not a valid measurement is entered! Select from : length, temperature, volume, area, weight"
def __init__(self, name=None, surname=None, username=None, passwd=None, email=None, height=None, weight=None,
state='metric'):
"""
Constructor for a first time user if username is given, or an empty constructor otherwise
:param name: name
:param surname: surname
:param username: username -REQUIRED FOR A NEW USER @TODO: add name, surname and email to required parameters
:param email: email
:param height: height @TODO: add a height class for imperial/metric conversion on the fly
:param weight: weight Class already implemented, @TODO: Test thoroughly
:param state: metric/imperial measurement standard @TODO: Rename/Refactor to something more descriptive
@TODO: Reorganize the code into smaller, more readable parts
@TODO: Detailed comments
"""
if username is not None:
self.name = name
self.surname = surname
self.username = username
self.uID = uuid.uuid4()
self.email = email
if len(passwd) < 8:
raise Exception("Password too short. Minimum length is 8 characters")
pwd = hash_password(passwd)
salt = pwd[:64]
stPwd = pwd[64:]
self.DBInfo = DBInfo(uID=self.uID)
conn = create_connection("Acc.db")
create_table(conn, create_commands)
cur = conn.cursor()
cur.execute(arg, (name, surname, username, salt, stPwd, email, height, weight, state, str(self.uID)))
conn.commit()
if height is None:
self.height = -1
else:
self.height = height
if weight is None:
self.weight = None
else:
self.weight = Weight(weight, state=state)
def getWeight(self, intLumi=None):
'''Returns the normalization weight for a given integrated luminosity
'''
# if intLumi is None:
# intLumi = Weight.FBINV
#COLIN THIS WEIGHT STUFF IS REALLY BAD!!
# use the existing Weight class or not? guess so...
return Weight(genNEvents=self.nGenEvents,
xSection=self.xSection,
intLumi=self.intLumi,
genEff=1 / self.effCorrFactor,
addWeight=self.addWeight)
def create_weights(nodes, num_of_features, hidden_nodes):
weights = []
total_layers = 1 + len(hidden_nodes) + 1
weight_index = 0
for i in range(total_layers - 1):
for j in range(len(nodes)):
if nodes[j].get_level() == i:
for k in range(len(nodes)):
if nodes[k].get_level() == i + 1:
if nodes[k].get_is_bias_unit() == False:
#there is a connection from nodes [j] to nodes [k]
#--------------------------------
range_min = 0
range_max = 1
init_epsilon = math.sqrt(6) / (
math.sqrt(num_of_features + 1))
rand = range_min + (
range_max - range_min) * random.random()
rand = rand * (2 * init_epsilon) - init_epsilon
#--------------------------------
weight = Weight() # initialise weight
weight.set_weight_index(weight_index)
weight.set_from_index(nodes[j].get_index())
weight.set_to_index(nodes[k].get_index())
weight.set_value(
rand) # use random starting value
weight_index = weight_index + 1
weights.append(weight)
print("from " + str(nodes[j].get_label()) +
" to " + str(nodes[k].get_label()) +
": " + str(rand))
return weights
def calc_info():
for doc in read_doc('corpus/jisuanji'):
cat_info['jsj'] += 1
for word in jieba.cut(doc, cut_all=False):
word_info[word]['jsj'] += 1
for doc in read_doc('corpus/huanjing'):
cat_info['hj'] += 1
for word in jieba.cut(doc, cut_all=False):
word_info[word]['hj'] += 1
wt = Weight(cat_info, word_info)
word_list = []
for word, score in sorted(wt.ECE_ID4().items(),
key=lambda x: x[1],
reverse=True):
word_list.append(word)
word_dict = {w: i for i, w in enumerate(word_list)}
fp = open('data/info.pk', 'wb')
pickle.dump(word_list, fp)
pickle.dump(word_dict, fp)
return word_list, word_dict
def report_weight_reading(body):
""" Receives a weight reading """
session = DB_SESSION()
w = Weight(body['user_id'],
body['user_name'],
body['weight'],
body['timestamp'])
session.add(w)
session.commit()
session.close()
logger.debug(f"Stored event Weight request with a unique id of {body['user_id']}")
def report_weight(body):
session = DB_SESSION()
wt = Weight(body['client_id'], body['device_id'], body['weight'],
body['timestamp'])
session.add(wt)
session.commit()
session.close()
logger.debug(("DEBUG: Stored event Weight request with a unique id of " +
str(body['client_id'])))
return NoContent, 201
def __init__(self, in_dim, out_dim):
super(Linear, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.weight = Weight((in_dim, out_dim))
self.bias = Weight((1, out_dim))
def getWeight(self, intLumi=None):
return Weight(genNEvents=-1,
xSection=None,
genEff=-1,
intLumi=self.intLumi,
addWeight=1.)
def connect(self, next_neuron) -> None:
new_weight = Weight(input_neuron=self, output_neuron=next_neuron)
self.weights.append(new_weight)
return new_weight
def createConnection(self, inputs, hidden_layers, outputs, nodes):
total_layers = 1 + len(hidden_layers) + 1
# iterate over all layers
for i in range(total_layers - 1):
#print ("i = ", i)
# iterante over all nodes
for j in range(len(nodes)):
# find node located in layer i
if nodes[j].getLayerNumber() == i:
# iterate over all nodes
for k in range(len(nodes)):
# if k'th node is in the next layer (as that of j'th node)
if nodes[k].getLayerNumber() == i + 1:
# and if k'th node is not a bias node
if nodes[k].getIsBiasUnit() == False:
# output of j'th node goes to the input of k'th node
weight = Weight()
weight.setIndex(self.weight_index)
weight.setFromNode(nodes[j].getIndex())
weight.setToNode(nodes[k].getIndex())
weight.setValue(self.generateRand(inputs))
self.weight_index = self.weight_index + 1
self.weights.append(weight)
print("Weight from " +
str(nodes[j].getLabel()) + " to " +
str(nodes[k].getLabel()) + " ---> " +
str(weight.getValue()))
return self.weights
from u_net import *
import glob
from os import listdir
from os.path import isfile, join
orig_width = 340
orig_height = 260
threshold = 0.5
weights_dir = 'weights/'
onlyfiles = [f for f in listdir(weights_dir) if isfile(join(weights_dir, f))]
for counter, value in enumerate(onlyfiles):
try:
w = Weight(value)
print('%d - %s' % (counter, value))
except:
print('%d - NOT VALID %s' % (counter, value))
index_weights = int(input('Which weights open? ') or '0')
w = Weight(onlyfiles[index_weights])
weights = w.name
params = w.params
input_size = params['inputsize']
input_shape = (input_size, input_size, 3)
if params['net'] == 'normal': # normal
input_size, model = get_unet_128(input_shape=input_shape)
def __init__(self, in_dim, out_dim):
super(Linear, self).__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.weight = Weight((in_dim, out_dim))
self.bias = Weight((1, out_dim))
class Account():
"""
Main class for now
"""
def __init__(self, name=None, surname=None, username=None, passwd=None, email=None, height=None, weight=None,
state='metric'):
"""
Constructor for a first time user if username is given, or an empty constructor otherwise
:param name: name
:param surname: surname
:param username: username -REQUIRED FOR A NEW USER @TODO: add name, surname and email to required parameters
:param email: email
:param height: height @TODO: add a height class for imperial/metric conversion on the fly
:param weight: weight Class already implemented, @TODO: Test thoroughly
:param state: metric/imperial measurement standard @TODO: Rename/Refactor to something more descriptive
@TODO: Reorganize the code into smaller, more readable parts
@TODO: Detailed comments
"""
if username is not None:
self.name = name
self.surname = surname
self.username = username
self.uID = uuid.uuid4()
self.email = email
if len(passwd) < 8:
raise Exception("Password too short. Minimum length is 8 characters")
pwd = hash_password(passwd)
salt = pwd[:64]
stPwd = pwd[64:]
self.DBInfo = DBInfo(uID=self.uID)
conn = create_connection("Acc.db")
create_table(conn, create_commands)
cur = conn.cursor()
cur.execute(arg, (name, surname, username, salt, stPwd, email, height, weight, state, str(self.uID)))
conn.commit()
if height is None:
self.height = -1
else:
self.height = height
if weight is None:
self.weight = None
else:
self.weight = Weight(weight, state=state)
def logIn(self, username, pwd):
"""
Logs in a user
:param username: username
:param pwd: password
:return: None @TODO: Maybe return True/False?
(Exceptions implemented, don't think it's worth doing)
@TODO: Detailed comments
"""
conn = create_connection("Acc.db")
cur = conn.cursor()
try:
cur.execute("SELECT * FROM Accounts WHERE Accounts.username=?", (username,))
except sqlite3.OperationalError:
raise Exception("User doesn't exist")
rows = cur.fetchall()
for row in rows:
salt = row[4]
stPwd = row[5]
pwdhash = hashlib.pbkdf2_hmac('sha512', pwd.encode('utf-8'), salt.encode('ascii'), 100000)
pwdhash = binascii.hexlify(pwdhash).decode('ascii')
print()
if pwdhash != stPwd:
raise Exception("Invalid password")
self.name = row[1]
self.surname = row[2]
self.username = row[3]
self.email = row[6]
self.height = row[7]
self.weight = Weight(row[8], row[9])
self.uID = row[10]
self.DBInfo = DBInfo(self.uID)
def __repr__(self):
"""
String representation of the Account class
:return: Returns the uID for now @TODO: A nice log for the developer maybe
"""
return str(self.uID)
def measure(self, wght):
"""
Measure current body mass
:param wght: New mass in preferred unit @TODO: Test with different standard
:return: None
"""
self.weight.set(wght)
self.DBInfo.add(wght)
@staticmethod
def resetPassword(email):
"""
STATIC METHOD
Resets the password for the given email after verification
:param email: email
:return:
@TODO: Improve the email server/gmail account handling (NO HARDCODED PASSWORDS)
@TODO: Detailed comments
"""
port = 465 # For SSL
# Create a secure SSL context
context = ssl.create_default_context()
strchk = get_random_string(randint(5, 10))
"""
FOR THIS TO WORK YOU HAVE TO CREATE THE FILE IN conf/application.yml in this format:
user:
email: [email protected]
password: YOURPASSWORD
"""
conf = yaml.safe_load(open('conf/application.yml'))
senderemail = conf['user']['email']
senderpwd = conf['user']['password']
with smtplib.SMTP_SSL("smtp.gmail.com", port, context=context) as server:
server.login(senderemail, senderpwd)
server.sendmail("*****@*****.**", email, "Subject: Password recovery string: \n\n" + strchk)
chckinput = input("Input the code that was sent to your email: ")
if strchk == chckinput:
newpwd = input("Enter your new password: "******"Password too short. Minimum length is 8 characters")
pwd = hash_password(newpwd)
salt = pwd[:64]
stPwd = pwd[64:]
conn = create_connection("Acc.db")
create_table(conn, create_commands)
cur = conn.cursor()
cur.execute("UPDATE Accounts SET salt = ?, pwd = ? WHERE email = ?", (salt, stPwd, email))
conn.commit()
cur.close()
else:
raise Exception("Wrong recovery code")
def resetEmail(self, newmail):
"""
Resets the email
:param newmail: New email
:return: None
@TODO: Confirmation of email
"""
conn = create_connection("Acc.db")
create_table(conn, create_commands)
cur = conn.cursor()
cur.execute("UPDATE Accounts set email = ? WHERE username = ?", (newmail, self.username))
conn.commit()
cur.close()
def display(self):
"""
Displays the collected data in a chart
:return: None
@TODO: Implement this in the GUI
@TODO: Different types of charts?
@TODO: Detailed comments
"""
import matplotlib.pyplot as plt
wghts = self.DBInfo.allWeights()
w = []
for i in wghts:
w.append(i[0])
print(i)
dates = self.DBInfo.allDates()
k = []
import numpy as np
for i in dates:
k.append(i[0])
# i = i[0].split()
# k.append(i[1][3:])
print(type(i))
print(k)
plt.plot(k, w, linestyle='-', linewidth=1, color='red')
plt.title(self.username)
# plt.xticks(np.arange(0, 60, step=5))
plt.show()
from weight import Weight
from graph import Graph
data_file = 'data.json'
w = Weight('data.json')
date = w.get_date()
months = w.months
while True:
w.cls()
w.show_records(date)
print('___________________________________________________')
print('a=add record; s=show records; g=show graph; q=quit')
i = input('>> ').lower()
# Add record
if i == 'a':
# w.cls()
try:
record = float(input('Add weight for today: '))
w.add_record(record, date)
except ValueError as e:
print('Enter a number, not a string.')
elif i == 's':
for i in range(len(w.months)):
print(f'{i+1}. {w.months[i]}')
select_month = int(input('\nChoose month: '))
def project():
if request.method == 'POST':
get_data = json.loads(request.get_data(as_text=True))
# 1.准备数据
# 具体来说，获取数据库中所有该问题集合的问题，和所有用户的回答
# q_list[]
# ans1[]
# ans2[]
# ans1[i][j] 问题i 用户j
# weight[i] 用户i的权重
user_list = []
q_list = []
ans1 = []
ans2 = []
# 1.1找出所有问题id
param = {'id_setq': get_data['setq_id']}
q = Q()
data = q.allq_id(param)
for i in data:
q_list.append(i['idq'])
ans1.append([])
ans2.append([])
print('q_list = ', q_list)
# 1.2根据问题id确定回答问题的用户id
# for i in range(len(q_list)):
param = {'id_q': q_list[0]}
a = Ans()
data = a.allans_userid(param)
for item in data:
if item['user_id'] not in user_list:
user_list.append(item['user_id'])
print('user_list = ', data)
# 1.3对应问题id和用户id的所有回答，ans1和ans2
for i in range(len(q_list)):
for j in user_list:
param = {'id_q': q_list[i], 'id_user': j}
a = Ans()
data = a.allans_q_user(param)
for item in data:
ans1[i].append(item['ans_one'])
ans2[i].append(item['ans_two'])
print('ans1 = ', ans1)
print('ans2 = ', ans2)
# 2.聚合结果
# MV-one
# MV-two
Data = []
q_MV_one = MV(ans1)
Data.append(q_MV_one)
for i in range(len(q_list)):
param = {'id_q': q_list[i], 'ans': q_MV_one[i]}
q = Q()
q.set_q_MV_one(param)
q_MV_two = MV(ans2)
Data.append(q_MV_two)
for i in range(len(q_list)):
param = {'id_q': q_list[i], 'ans': q_MV_two[i]}
q = Q()
q.set_q_MV_two(param)
# TD-one
# TD-two
# TD要返回对应的weight列表
q_TD_one, weight_one = TD(user_list, q_list, ans1)
Data.append(q_TD_one)
for i in range(len(q_list)):
param = {'id_q': q_list[i], 'ans': q_TD_one[i]}
q = Q()
q.set_q_TD_one(param)
q_TD_two, weight_two = TD(user_list, q_list, ans2)
Data.append(q_TD_two)
for i in range(len(q_list)):
param = {'id_q': q_list[i], 'ans': q_TD_two[i]}
q = Q()
q.set_q_TD_two(param)
# 更新权重
for i in range(len(user_list)):
param = {
'user_id': user_list[i],
'setq_id': get_data['setq_id'],
'weight1': weight_one[i],
'weight2': weight_two[i]
}
w = Weight()
w.new_weight(param)
resData = {"resCode": 0, "data": Data, "message": '返回聚合结果'}
return jsonify(resData)
class HypoDDControl:
"""Control parameters for hypoDD.
Attributes:
control_directory: The directory where the control file is located.
control_file: The name of the control file.
cc_dt_input: Filename of cross-corr diff. time input.
ct_dt_input: Filename of catalog travel time input.
initial_hypocenters: Filename of initial hypocenter input.
station_input: Filename of station input.
initial_hypocenters_output: Filename of initial hypocenter output.
relocated_hypocenters_output: Filename of relocated hypocenter output.
station_residual_output: Filename of station residual output.
data_residual_output: Filename of data residual output.
takeoff_angle_output: Filename of takeoff angle output.
idat: Data type: 1 = cross correlation data only; 2 = absolute (catalog)
data only; 3 = x-corr & catalog.
ipha: Phase: 1 = P-wave; 2 = S-wave; 3 = P- & S-wave.
dist: Max. distance between centroid of event cluster and stations.
obscc, obsct: Min. number of x-corr, catalog links per event pair to form a
continuous cluster. 0 = no clustering performed. If IDAT = 3,
the sum of OBSCC and OBSCT is taken and used for both.
istart: Initial locations: 1 = start from cluster centroid; 2 = start from
catalog locations.
isolv: Least squares solution: 1 = singular value decomposition (SVD);
2 = conjugate gradients (LSQR).
weights: Data weighting by iteration. See class Weight.
velocity_model: The velocity model to be used. See class VelocityModel.
cid: Index of cluster to be relocated (0 = all).
evid: ID of events to be relocated (8 per line). Blank for all events.
The ID numbers are in free format.
"""
control_directory = "."
control_file = "hypoDD.inp"
cc_dt_input = ""
ct_dt_input = "dt.ct"
initial_hypocenters = "event.dat"
station_input = "station.dat"
initial_hypocenters_output = "hypoDD.loc"
relocated_hypocenters_output = "hypoDD.reloc"
station_residual_output = "hypoDD.sta"
data_residual_output = "hypoDD.res"
takeoff_angle_output = "hypoDD.src"
idat = 3
ipha = 3
dist = 400
obscc = 0
obsct = 8
istart = 2
isolv = 2
weights = [
Weight(niter=10,
wtccp=-9,
wtccs=-9,
wrcc=-9,
wdcc=-9,
wtctp=1,
wtcts=0.5,
wrct=3,
wdct=3,
damp=10)
]
velocity_model = VelocityModel()
cid = 0
evid = []
def __init__(self,
control_directory=".",
control_file="hypoDD.inp",
cc_dt_input="",
ct_dt_input="dt.ct",
initial_hypocenters="event.dat",
station_input="station.dat",
initial_hypocenters_output="hypoDD.loc",
relocated_hypocenters_output="hypoDD.reloc",
station_residual_output="hypoDD.sta",
data_residual_output="hypoDD.res",
takeoff_angle_output="hypoDD.src",
idat=3,
ipha=3,
dist=400,
obscc=0,
obsct=8,
istart=2,
isolv=2,
weights=[
Weight(niter=10,
wtccp=-9,
wtccs=-9,
wrcc=-9,
wdcc=-9,
wtctp=1,
wtcts=0.5,
wrct=3,
wdct=3,
damp=10)
],
velocity_model=VelocityModel(),
cid=0,
evid=[]):
"""Initialization method for the HypoDDControl object.
"""
self.control_directory = control_directory
self.control_file = control_file
self.cc_dt_input = cc_dt_input
self.ct_dt_input = ct_dt_input
self.initial_hypocenters = initial_hypocenters
self.station_input = station_input
self.initial_hypocenters_output = initial_hypocenters_output
self.relocated_hypocenters_output = relocated_hypocenters_output
self.station_residual_output = station_residual_output
self.data_residual_output = data_residual_output
self.takeoff_angle_output = takeoff_angle_output
self.idat = idat
self.ipha = ipha
self.dist = dist
self.obscc = obscc
self.obsct = obsct
self.istart = istart
self.isolv = isolv
self.weights = weights
self.velocity_model = velocity_model
self.cid = cid
self.evid = evid
def write_control_file(self):
print "preparing hypoDD control"
print "working directory is {}".format(self.control_directory)
full_filename = "{}/{}".format(self.control_directory,
self.control_file)
print "writing ph2dt control file: {}".format(full_filename)
nlay = len(self.velocity_model.layers)
nset = len(self.weights)
with open(full_filename, "w") as f:
control_file_format_1 = (
"* {}\n"
"*--- INPUT FILE SELECTION\n"
"* filename of cross-corr diff. time input "
"(blank if not available):\n"
"{}\n"
"* filename of catalog travel time input "
"(blank if not available):\n"
"{}\n"
"* filename of initial hypocenter input:\n"
"{}\n"
"* filename of station input:\n"
"{}\n"
"*\n"
"*--- OUTPUT FILE SELECTION\n"
"* filename of initial hypocenter output "
"(if blank: output to hypoDD.loc):\n"
"{}\n"
"* filename of relocated hypocenter output "
"(if blank: output to hypoDD.reloc):\n"
"{}\n"
"* filename of station residual output "
"(if blank: no output written):\n"
"{}\n"
"* filename of data residual output "
"(if blank: no output written):\n"
"{}\n"
"* filename of takeoff angle output "
"(if blank: no output written):\n"
"{}\n"
"*\n"
"*--- DATA SELECTION:\n"
"* IDAT IPHA DIST\n"
"{} {} {}\n"
"*\n"
"*--- EVENT CLUSTERING:\n"
"* OBSCC OBSCT\n"
"{} {}\n"
"*\n"
"*--- SOLUTION CONTROL:\n"
"* ISTART ISOLV NSET\n"
"{} {} {}\n"
"*\n"
"*--- DATA WEIGHTING AND REWEIGHTING:\n"
"* NITER WTCCP WTCCS WRCC WDCC WTCTP WTCTS "
"WRCT WDCT DAMP\n")
f.write(
control_file_format_1.format(
self.control_file, self.cc_dt_input, self.ct_dt_input,
self.initial_hypocenters, self.station_input,
self.initial_hypocenters_output,
self.relocated_hypocenters_output,
self.station_residual_output, self.data_residual_output,
self.takeoff_angle_output, self.idat, self.ipha, self.dist,
self.obscc, self.obsct, self.istart, self.isolv, nset))
control_file_format_2 = "{} {} {} {} {} {} {} {} {} {}\n"
for weight in self.weights:
f.write(
control_file_format_2.format(weight.niter, weight.wtccp,
weight.wtccs, weight.wrcc,
weight.wdcc, weight.wtctp,
weight.wtcts, weight.wrct,
weight.wdct, weight.damp))
control_file_format_3 = ("*\n"
"*--- MODEL SPECIFICATIONS:\n"
"* NLAY RATIO\n"
"{} {}\n"
"* TOP:\n")
f.write(
control_file_format_3.format(nlay, self.velocity_model.ratio))
control_file_format_4 = " {}"
for layer in self.velocity_model.layers:
f.write(control_file_format_4.format(layer.top))
f.write("\n* VEL:\n".format())
for layer in self.velocity_model.layers:
f.write(control_file_format_4.format(layer.vel))
control_file_format_5 = ("\n*\n"
"*--- CLUSTER/EVENT SELECTION:\n"
"* CID\n"
"{}\n")
f.write(control_file_format_5.format(self.cid))
f.write("* ID\n".format())
n = len(self.evid)
k = n // 8
m = n - 8 * k
control_file_format_6 = "{}\n"
for i in range(1, k + 1):
f.write(
control_file_format_6.format(" ".join(
str(x) for x in self.evid[i * 8 - 8:i * 8])))
f.write(
control_file_format_6.format(" ".join(
str(x) for x in self.evid[n - m:n])))