def run():
# Make sure necessary directories exist
if not os.path.exists('tracks'):
os.makedirs('tracks')
if not os.path.exists('figures'):
os.makedirs('figures')
overallstartdate = datetime(2004, 1, 1, 0, 1)
overallstopdate = datetime(2004, 1, 2, 0, 1)
# overallstopdate = datetime(2014, 7, 1, 4, 1)
date = overallstartdate
# Start from the beginning and add days on for loop
# keep running until we hit the next month
while date < overallstopdate:
name = date.isoformat()[0:13]
# If the particle trajectories have not been run, run them
if not os.path.exists('tracks/' + name + '.nc') and \
not os.path.exists('tracks/' + name + 'gc.nc'):
# Read in simulation initialization
tp, lon0, lat0 = init(name)
# Run tracpy
# Save directly to grid coordinates
lonp, latp, zp, t, T0, U, V = tracpy.run.run(tp, date, lon0, lat0)
# Increment by 24 hours for next loop, to move through more quickly
date = date + timedelta(hours=24)
def run():
# Make sure necessary directories exist
if not os.path.exists('tracks'):
os.makedirs('tracks')
if not os.path.exists('tracks/driftcards'):
os.makedirs('tracks/driftcards')
if not os.path.exists('figures'):
os.makedirs('figures')
if not os.path.exists('figures/driftcards'):
os.makedirs('figures/driftcards')
grid = tracpy.inout.readgrid(loc)
ndriftmodel = 1000 # do 1000 drifters for each set of driftcards
# Read in deployment info and eliminate outside domain
ndrift = 3490 # number of driftcards deployed
# These are drift cards that start in numerical domain
startdate, startlon, startlat, idnum = readThenReduce(grid, ndrift)
# Find unique start dates and indices for start dates
dates, idates = np.unique(startdate, return_index=True)
# Loop through deployments
for i, date in enumerate(dates):
idate = idates[i] # Index in arrays
lon0 = np.ones(ndriftmodel)*startlon[idate]
lat0 = np.ones(ndriftmodel)*startlat[idate]
# pdb.set_trace()
# Initialize parameters
nsteps, N, ndays, ff, tseas, ah, av, z0, zpar, \
do3d, doturb, dostream, T0, U, V = init(grid, date, lon0, lat0)
# pdb.set_trace()
date = netCDF.num2date(date, units)
# # start drifters in chunks of exact same date/time
# # idrift = 0
# # tdrift = startdate[idrift] #drifter time
# idrifts = find(startdate==startdate[find(~np.isnan(startdate))[0]]) # all drifters with same start date/time
# lon0 = startlon[idrifts]
# lat0 = startlat[idrifts]
# date = netCDF.num2date(startdate[idrifts][0], units)
# pdb.set_trace()
name = 'driftcards/' + date.isoformat()
# Run tracpy
lonp, latp, zp, t, grid, T0, U, V \
= tracpy.run.run(loc, nsteps, ndays, ff, date, tseas, ah, av, \
lon0, lat0, z0, zpar, do3d, doturb, name, N=N, \
grid=grid, dostream=dostream, T0=T0, U=U, V=V)
def run(rootdir, ndrifters):
# Start date in date time formatting
date = datetime(2013, 12, 19, 0)
# Simulation initialization
itime = time.time()
tp, x0, y0 = init(rootdir, ndrifters)
print '\nAdditional time for initialization:%4.4f\n' % (time.time() - itime)
xp, yp, zp, t, T0, U, V = tracpy.run.run(tp, date, x0, y0)
def main():
black = (0, 0, 0)
size = width, height = 800, 600
print 'Initializing pygame...'
screen = init(size)
settings = Settings()
player = Player(None)
window = MainMenu(player)
if PROFILE:
pr = cProfile.Profile()
pr.enable()
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
elif event.type == pygame.KEYDOWN:
window.key_pressed(event.key)
elif event.type == pygame.KEYUP:
window.key_released(event.key)
window.update()
screen.fill(black)
window.display(screen)
pygame.display.flip()
if window.child is not None:
window = window.child
if window.done:
window = window.parent
if window is None:
running = False
if PROFILE:
pr.disable()
sort_by = 'cumulative'
ps = pstats.Stats(pr).strip_dirs().sort_stats(sort_by)
ps.print_stats()
ps.print_callers()
pygame.quit()
def main():
global font
black = (0, 0, 0)
red = (255, 0, 0)
blue = (0, 0, 255)
white = (255, 255, 255)
size = width, height = 800, 600
screen = init(size)
settings = Settings()
global_font = pygame.font.Font(pygame.font.get_default_font(), FONT_SIZE)
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
elif event.type == pygame.KEYDOWN:
if event.key == pygame.K_ESCAPE:
running = False
screen.fill(black)
pygame.draw.line(screen, blue, (0, 0), (width, height))
pygame.draw.line(screen, blue, (0, height), (width, 0))
pos = pygame.mouse.get_pos()
vec = vector(pos) - vector(size) / 2
dir = get_dir(size, vec)
intersection = get_intersection(size, vec)
dims = vector(10, 10)
rect = intersection - dims / 2
rect = rect.list() + dims.list()
center = (width / 2, height / 2)
pygame.draw.rect(screen, red, rect, 1)
pygame.draw.line(screen, red, center, intersection.list())
draw_string(screen, dir, (10, 10), white)
pygame.display.flip()
pygame.quit()
def run():
# Make sure necessary directories exist
if not os.path.exists('tracks'):
os.makedirs('tracks')
if not os.path.exists('tracks/gyre'):
os.makedirs('tracks/gyre')
if not os.path.exists('figures'):
os.makedirs('figures')
if not os.path.exists('figures/gyre'):
os.makedirs('figures/gyre')
# Location of TXLA model output
loc = 'projects/gyre/'# 'http://barataria.tamu.edu:8080/thredds/dodsC/NcML/txla_nesting6.nc'
# Read in grid
grid = tracpy.inout.readgrid(loc)
# Read in simulation initialization
nsteps, ndays, ff, tseas, ah, av, lon0, lat0, \
z0, zpar, do3d, doturb, dostream, name = init()
# Create gyre field velocities if don't exist yet
uv(grid, ndays)
# Start from the beginning and add days on for loop
date = datetime(2012, 5, 23, 0, 0)
# pdb.set_trace()
# Run tracpy
loc = 'projects/gyre/' # loc for model output
lonp, latp, zp, t, grid \
= tracpy.run.run(loc, nsteps, ndays, ff, date, tseas, ah, av, \
lon0, lat0, z0, zpar, do3d, doturb, name, \
grid=grid, dostream=dostream)
# Read in and plot tracks
d = netCDF.Dataset('tracks/' + name + '.nc')
lonp = d.variables['lonp'][:]
latp = d.variables['latp'][:]
tracpy.plotting.tracks(lonp, latp, name, grid=grid)
tracpy.plotting.hist(lonp, latp, name, grid=grid, which='hexbin')
d.close()
def fitShape(self, img):
if img is None:
print "invalid img!"
return
if self.__mPfCalcLocalProfile is None:
print "unkown local profile Caculator!You should define or specify it."
print "use ::setLocalProfileFun(pfCalcLocalProfile)"
return
self.__mImg = img
minProfileDist = 1000000
scale = 1.0
(h, w, channel) = img.shape
minSumDist = 10000000
bestScale = 0.0
bestShape = None
for i in range(3):
curImg = cv2.resize(img, (int(scale * w), int(scale * h)))
curMeanShape = copy.deepcopy(self.meanShape) * scale
curPcaMatrix = copy.deepcopy(self.pcaMatrix) * scale
initShape = init(curImg, self.face_cascade, self.eye_cascade,
curMeanShape)
targetShape, sumDist = self.__match(curImg, initShape,
curMeanShape,
self.average_profile[i],
self.sgVec[i], curPcaMatrix,
self.__mSeachWindowWd, 1.0)
if sumDist < minSumDist:
bestScale = scale
minSumDist = sumDist
bestShape = targetShape
# print "scale:",scale," ",sumDist
scale /= float(2.0)
# print "Best Scale: ",bestScale
self.__fittingResult = bestShape / float(bestScale)
return minSumDist
def main():
num_args = len(sys.argv)
if num_args < 2:
print "Usage: python main.py [instance] [plot] [iterations]"
sys.exit()
f = sys.argv[1]
output = os.path.splitext(f)[0] + '.out'
draw = False
iterations = 10
if num_args > 3:
draw = (sys.argv[2] == "True")
iterations = int(sys.argv[3])
tour_manager = init(f)
def run(num):
# print "Running iteration %d" % (num + 1)
return ga.ga_sol(tour_manager, 100, 50, 300)
result = map(run, range(iterations))
process_result(result, draw, iterations, output)
def fitShape(self,img): if img is None: print "invalid img!" return if self.__mPfCalcLocalProfile is None: print "unkown local profile Caculator!You should define or specify it." print "use ::setLocalProfileFun(pfCalcLocalProfile)" return self.__mImg=img minProfileDist=1000000 scale=1.0 (h,w,channel)=img.shape minSumDist=10000000 bestScale=0.0 bestShape=None for i in range(3): curImg=cv2.resize(img,(int(scale*w),int(scale*h))) curMeanShape=copy.deepcopy(self.meanShape)*scale curPcaMatrix=copy.deepcopy(self.pcaMatrix)*scale initShape=init(curImg,self.face_cascade,self.eye_cascade,curMeanShape) targetShape,sumDist=self.__match(curImg,initShape,curMeanShape,self.average_profile[i],self.sgVec[i],curPcaMatrix,self.__mSeachWindowWd,1.0) if sumDist<minSumDist: bestScale=scale minSumDist=sumDist bestShape=targetShape # print "scale:",scale," ",sumDist scale/=float(2.0) # print "Best Scale: ",bestScale self.__fittingResult=bestShape/float(bestScale) return minSumDist
def _main():
Lag,Nx,Ny,lx,ly,Load,Name,ds,bcd,mesh,phi_mat,Vvec=init(sys.argv[1])
eps_er, E, nu = [0.001, 1.0, 0.3] # Elasticity parameters
mu,lmbda = Constant(E/(2*(1 + nu))),Constant(E*nu/((1+nu)*(1-2*nu)))
# Create folder for saving files
rd = os.path.join(os.path.dirname(__file__),\
Name +'/LagVol=' +str(np.int_(Lag))+'_Nx='+str(Nx))
if not os.path.isdir(rd): os.makedirs(rd)
# Line search parameters
beta0_init,ls,ls_max,gamma,gamma2 = [0.5,0,3,0.8,0.8]
beta0 = beta0_init
beta = beta0
# Stopping criterion parameters
ItMax,It,stop = [int(1.5*Nx), 0, False]
# Cost functional and function space
J = np.zeros( ItMax )
V = FunctionSpace(mesh, 'CG', 1)
VolUnit = project(Expression('1.0',degree=2),V) # to compute volume
U = [0]*len(Load) # initialize U
# Get vertices coordinates
gdim = mesh.geometry().dim()
dofsV = V.tabulate_dof_coordinates().reshape((-1, gdim))
dofsVvec = Vvec.tabulate_dof_coordinates().reshape((-1, gdim))
px,py = [(dofsV[:,0]/lx)*2*Nx, (dofsV[:,1]/ly)*2*Ny]
pxvec,pyvec = [(dofsVvec[:,0]/lx)*2*Nx, (dofsVvec[:,1]/ly)*2*Ny]
dofsV_max, dofsVvec_max =((Nx+1)*(Ny+1) + Nx*Ny)*np.array([1,2])
# Initialize phi
phi = Function( V )
phi = _comp_lsf(px,py,phi,phi_mat,dofsV_max)
# Define Omega = {phi<0}
class Omega(SubDomain):
def inside(self, x, on_boundary):
return .0 <= x[0] <= lx and .0 <= x[1] <= ly and phi(x) < 0
domains = MeshFunction("size_t", mesh, mesh.topology().dim())
dX = Measure('dx')
n = FacetNormal(mesh)
# Define solver to compute descent direction th
theta,xi = [TrialFunction(Vvec), TestFunction( Vvec)]
av = assemble((inner(grad(theta),grad(xi)) +0.1*inner(theta,xi))*dX\
+ 1.0e4*(inner(dot(theta,n),dot(xi,n)) * (ds(0)+ds(1)+ds(2))) )
solverav = LUSolver(av)
#---------- MAIN LOOP ----------------------------------------------
while It < ItMax and stop == False:
# Update and tag Omega = {phi<0}, then solve elasticity system.
omega = Omega()
domains.set_all(0)
omega.mark(domains, 1)
dx = Measure('dx')(subdomain_data = domains)
for k in range(0,len(Load)):
U[k] = _solve_pde(Vvec,dx,ds,eps_er,bcd,mu,lmbda,Load[k])
# Update cost functional
compliance = 0
for u in U:
eU = sym(grad(u))
S1 = 2.0*mu*inner(eU,eU) + lmbda*tr(eU)**2
compliance += assemble( eps_er*S1* dx(0) + S1*dx(1) )
vol = assemble( VolUnit*dx(1) )
J[It] = compliance + Lag * vol
# ------- LINE SEARCH ------------------------------------------
if It > 0 and J[It] > J[It-1] and ls < ls_max:
ls += 1
beta *= gamma
phi_mat,phi = [phi_mat_old,phi_old]
phi_mat = _hj(th_mat, phi_mat, lx,ly,Nx, Ny, beta)
phi = _comp_lsf(px,py,phi,phi_mat,dofsV_max)
print('Line search iteration : %s' % ls)
else:
print('************ ITERATION NUMBER %s' % It)
print('Function value : %.2f' % J[It])
print('Compliance : %.2f' % compliance)
print('Volume fraction : %.2f' % (vol/(lx*ly)))
# Decrease or increase line search step
if ls == ls_max: beta0 = max(beta0 * gamma2, 0.1*beta0_init)
if ls == 0: beta0 = min(beta0 / gamma2, 1)
# Reset beta and line search index
ls,beta,It = [0,beta0, It+1]
# Compute the descent direction th
th = _shape_der(Vvec,U,eps_er,mu,lmbda,dx,solverav,Lag)
th_array = th.vector().get_local()
th_mat = [np.zeros((Ny+1,Nx+1)),np.zeros((Ny+1,Nx+1))]
for dof in range(0, dofsVvec_max,2):
if np.rint(pxvec[dof]) %2 == .0:
cx,cy= np.int_(np.rint([pxvec[dof]/2,pyvec[dof]/2]))
th_mat[0][cy,cx] = th_array[dof]
th_mat[1][cy,cx] = th_array[dof+1]
# Update level set function phi using descent direction th
phi_old, phi_mat_old = [phi, phi_mat]
phi_mat = _hj(th_mat, phi_mat, lx,ly,Nx,Ny, beta)
if np.mod(It,5) == 0: phi_mat = _reinit(lx,ly,Nx,Ny,phi_mat)
phi = _comp_lsf(px,py,phi,phi_mat,dofsV_max)
#------------ STOPPING CRITERION ---------------------------
if It>20 and max(abs(J[It-5:It]-J[It-1]))<2.0*J[It-1]/Nx**2:
stop = True
#------------ Plot Geometry --------------------------------
if np.mod(It,10)==0 or It==1 or It==ItMax or stop==True:
pp.close(); ax = pp.subplot()
ax.contourf(phi_mat,[-10.0,.0],extent = [.0,lx,.0,ly],\
cmap=cm.get_cmap('bone'))
ax.set_aspect('equal','box')
pp.show()
pp.savefig(rd+'/it_'+str(It)+'.pdf',bbox_inches='tight')
return
import os
sim_time = 60 * 1000 * 20
upper_bound = 5
lower_bound = 1
lsw = 1
lpw = 50
cur_time = 0
CORES = 4
reliability_list = []
T_setpoint = 60
U_setpoint = 0.66
exp_funcs = extfuncs(upper_bound, lower_bound, T_setpoint)
exp_system = init(CORES, sys.argv[-2])
os.system("cp ./hotspot/hotspot.sample.init ./hotspot/hotspot.init")
#exp_system.print_hardware()
while cur_time < sim_time:
for core_idx in range(0, CORES):
exp_system.core_set[core_idx].run(cur_time)
if cur_time % (lsw * 1000) == 0:
exp_system.DVFS(lsw, exp_funcs)
if cur_time % (lsw * lpw * 1000) == 0:
exp_system.get_temperature()
reliability_list.append([exp_system.get_reliability(lsw), lsw])
lsw = exp_funcs.scaling_lsw(reliability_list)
from init import *
from scf import *
import sys
import numpy
if __name__ == "__main__":
ao_ints, scf_params, e_ZZ_repul = init(sys.argv[1])
eps, C, D, F = scf(ao_ints, scf_params, e_ZZ_repul)
H = ao_ints['T'] + ao_ints['V']
energy = get_SCF_energy(H, F, D, scf_params['unrestricted']) \
+ e_ZZ_repul
print("\nFINAL SCF ENERGY: {}\n".format(energy))
# mp2
if(scf_params['method'] == "MP2"):
import mp2
print("SCS-MP2 Correlation Energy: {}\n".format(mp2.get_mp2_energy(\
eps, C, ao_ints['g4'], scf_params['nel'])))
for t in range(0, 2, 60):
t = np.random.randint(imgCnt)
t = 0
imgName = PATH_A + "\\" + fn[t]
imgNameSave = PATH_A + "\\m2_" + fn[t]
print t, " ", imgName
img = cv2.imread(imgName)
# img=cv2.imread("xicore.jpg")
# img=cv2.imread("Face_3.jpg")
# img=cv2.imread("Face_29.jpg")
# initialized shape
# drawShape(img,meanShape,(255,0,0,255))
initShape = init(img, face_cascade, left_eye_cascade, meanShape)
# drawShape(img,initShape,COLOR[0])
# cv2.imshow(imgNameSave,img)
# targetShape=updateModelPoints(img,initShape,average_profile,sgVec,3)
# drawShape(img,targetShape,(0,0,255,255))
# targetShape=updateModelParas(img,meanShape,targetShape,pcaMatrix)
targetShape = match(img, initShape, meanShape, average_profile, sgVec, pcaMatrix, 10)
drawShape(img, targetShape, COLOR[2])
# cv2.imshow(imgName,img)
cv2.imshow(imgNameSave, img)
cv2.imwrite(imgNameSave, img)
cv2.waitKey(0)
def test_double_init():
init()
init()
assert 1 == 1
drop()
def test_double_drop():
init()
drop(with_models=True)
drop(with_models=True)
assert 1 == 1
#!/usr/bin/env python3 # Import modules import boto3 import botocore import argparse import objectpath import time import pprint import os import csv import re from init import * import collections from collections import defaultdict from aws_tag_resources import tag_instances, tag_root_volumes from user_input import * from banners import welcomebanner,endbanner,banner from datetime import datetime from colorama import init, deinit, Fore from list_new_instances import list_new_instances from read_account_info import read_account_info from find_vpcs import find_vpcs from aws_add_sg_list import attach_sg_list from choose_accounts import choose_accounts # Initialize the color ouput with colorama init()
print t, " ", imgName
img = cv2.imread(imgName)
(h, w, channel) = img.shape
# img=cv2.imread("xicore.jpg")
# img=cv2.imread("Face_3.jpg")
# img=cv2.imread("Face_23.jpg")
# img=cv2.imread("Face_9.jpg")
#initialized shape
# drawShape(img,meanShape,(255,0,0,255))
(h, w, channel) = img.shape
scaleIndex = 0
scale = 1.0
img = cv2.resize(img, (int(scale * w), int(scale * h)))
# meanShape*=scale
# pcaMatrix*=scale
initShape = init(img, face_cascade, left_eye_cascade, meanShape)
# targetShape=updateModelPoints(img,initShape,average_profile,sgVec,3)
# drawShape(img,targetShape,(0,0,255,255))
# targetShape=updateModelParas(img,meanShape,targetShape,pcaMatrix)
targetShape, minProfileDist = match(img, initShape, meanShape,
average_profile[0], sgVec[0],
pcaMatrix, 15, scale)
drawShape(img, targetShape, COLOR[2])
cv2.namedWindow(imgNameSave, flags=cv.CV_WINDOW_AUTOSIZE)
#cv2.imshow(imgName,img)
cv2.imshow(imgNameSave, img)
cv2.imwrite(imgNameSave, img)
cv2.waitKey(0)
Energy = np.zeros(nt)
Magnetization = np.zeros(nt)
SpecificHeat = np.zeros(nt)
Susceptibility = np.zeros(nt)
T = np.linspace(1, 3, nt) #temperature
for m in range(len(T)):
E1 = M1 = E2 = M2 = 0
E1 = np.float64(E1)
M1 = np.float64(M1)
E2 = np.float64(E2)
M2 = np.float64(M2)
init(100,1)
## This is to equilibrate the system
eqSteps = 2000
config = initialstate(N)
config_init = deepcopy(config)
for i in range(eqSteps):
mcmove(config, 1.0/T[m])
## This part does the main calculations and the measurements
mcSteps = 2000
for i in range(mcSteps):
mcmove(config, 1.0/T[m]) # monte carlo moves
Ene = calcEnergy(config) # calculate the energy
Mag = calcMag(config) # calculate the magnetisation
def getSolution(config):
A = init()
U, nconf = mod2solver(A.copy(), config.copy())
firstSolution = mod2result(U.copy(), nconf.copy())
minimumSolution = minSolution(firstSolution)
return minimumSolution
from PyQt5.QtWidgets import QHBoxLayout from PyQt5.QtWidgets import QVBoxLayout from PyQt5.QtGui import QPen from PyQt5.QtGui import QPainter from window_size import * import pickle import copy window_width, window_height = get_window_size() thumbnail_label_width = int(window_width * 9 / 10) thumbnail_label_height = int(window_height * 1 / 8) rect_width = int(window_width * 3 / 4) id, dll = init() all_point = [] tmp_point = [] all_mark_text = [] tmp_all_mark_text = [] tmp_all_point = [] tmp_mark_text = [] tmp_tmp_point = [] tmp_tmp_mark_text = [] ratio = 0 original_img_height = 0 original_img_width = 0 control_button_height = 64 control_button_width = 64 thumbnail_x = 0 all_start = 0
savefig = 0
vmin = 0
vmax = 0.6 #
#ncoeff=[1e-3,5e-3,1e-2,1.5e-2,2e-2,2.5e-2,3e-2]#5e-4
ncoeff = [1e-3]
E, disp1, Tens, matTens, triangles, vertices, fxy = openfile(filename)
N = len(E)
solmatf = np.zeros((N, len(ncoeff)))
pnoiselevel = np.zeros(len(ncoeff))
psnr = np.zeros(len(ncoeff))
for j, ymeas_noise_coef in enumerate(ncoeff):
print('j is:', j)
um, Ntlevel, SNR = addnoise(disp1, ymeas_noise_coef)
pnoiselevel[j] = Ntlevel
psnr[j] = SNR
x1 = init(um, disp1, E, vertices, ymeas_noise_coef, Randinit, MA, scale)
solmatf[:, j] = proxsolve(E, um, matTens, Tens, triangles, vertices, fxy,
ymeas_noise_coef, x1, vmin, vmax, filename,
Ntlevel, SNR, Itr1, Itr2, savefig, MA) #
sio.savemat('one5E_sol_Itr1' + str(Itr1) + 'Itr2' + str(Itr2) + 'step03.mat', {
'sol': solmatf,
'pnl': pnoiselevel,
'psnr': psnr
})
Data = loadmat('one5E_sol_img2bw0.12_MA0.mat')
solmat = Data['sol']
pnl = Data['pnl'][0]
psnr = Data['psnr'][0]
x = np.array(vertices[:, 0])
y = np.array(vertices[:, 1])
def optimize_with_size(outer, major, coil, halton, m_batches, m_error,
neutrons):
gp, points, leak, leak_err = init(outer, major, coil, halton, m_batches,
m_error, neutrons)
print('GP model calculated')
print('Optimizing...', multiprocessing.cpu_count(), 'cores.')
if __name__ == "__main__":
class my_problem():
global outer
global coil
def fitness(self, x):
leak, error = gp([(x[0], x[1])])
ci1 = -(x[1] - x[0])
return (leak[0], ci1)
def get_nic(self):
return 1
def get_nobj(self):
return 1
def get_bounds(self):
lb = [coil, coil]
ub = [coil + outer - 0.01, coil + outer - 0.001]
return (lb, ub)
gen = 10
size = 1000
algo = algorithm(
sga(gen=1, mutation='uniform', crossover='sbx', cr=0.50, m=0.10))
prob = unconstrain(prob=my_problem(), method='death penalty')
gp_leak = []
pbar = tqdm(total=len(points))
for k in range(len(points)):
pop = population(prob, size)
for i in range(gen):
pop = algo.evolve(pop)
leakage, leakage_error = shield(pop.champion_x, major, coil, False,
outer, m_batches, m_error,
neutrons)
points[k] = pop.champion_x
leak[k] = leakage
leak_err[k] = leakage_error
gp_leak.append(pop.champion_f[0])
gp = GpRegressor(points, leak, leak_err)
pbar.update(1)
pbar.close()
opt = leak.index(min(leak))
shield(points[opt], major, coil, False, outer, m_batches, m_error,
neutrons)
print(bcolors.BOLD + '---------------')
print('OPTIMIZATION FINISHED')
print('BEST SOLUTION FOUND')
print('////////////////////')
print('R1:', points[opt][0], 'R2:', points[opt][1])
print('Score (GP) %:', gp_leak[opt])
print('Score (MC) %:', leak[opt])
print('Score error (MC) %:', leak_err[opt])
print('Relative error (MC) %:', leak_err[opt] / leak[opt] * 100)
print('////////////////////' + bcolors.ENDC)
Df[p, Nr - 1 - i,
j] = xray_transform(obj, ys[:, p], theta[Nr - 1 - i, j],
phase)
return Df
if __name__ == '__main__':
# reference coordiante system
i = np.array([1.0, 0.0, 0.0], dtype=TYPE)
j = np.array([0.0, 1.0, 0.0], dtype=TYPE)
k = np.array([0.0, 0.0, 1.0], dtype=TYPE)
# geometry parameters: source trajectory and detector parameters
conf = {}
conf = init(conf)
# object parameters
obj = {}
obj = init_obj(obj, plot=False)
# geometry parameters: source trajectory
R = conf['source_trajectory']['radius']
P = conf['source_trajectory']['pitch']
N = conf['source_trajectory']['number_turns']
ds = conf['source_trajectory']['delta_s']
D = conf['detector']['DSD']
H = conf['detector']['height']
Nr = conf['detector']['number_rows']
Nc = conf['detector']['number_columns']
delta_w = conf['detector']['pixel_height'] # detector element height
arc_alpha = conf['detector']['pixel_width'] # detector element width (arc)
print(args.cdns)
return scripts, args.cdns, args.base, args.shuffle
def copy_file_to_file(from_handler, to_handler):
# gets two handlers of files. copies contents of the first one to second
for line in from_handler:
print(line, file=to_handler, end='')
scripts, cdns, base, shuffle = get_args()
output_file_name = 'Tizex_analyze.js'
file_out = open(output_file_name, 'w') # new file
# output_file = open(output_file_name, 'w')
init(file_out) # this function creates a tmp file which beautified version of original code
for i in range(len(scripts)):
script = scripts[i]
if i in cdns:
if type(script) == str:
# when only js file is specified in args type of script is string
f = open(script)
for line in f:
print(line, file=file_out)
elif script.attrs.get('src'):
# beautifulSoup obj. a script with src attribute.
src = script.attrs.get('src').strip()
if src.startswith('http') or (base and base.startswith('http')):
if base and not src.startswith('http'):
src = os.path.join(base, src)
import pygame,sys
import mpc
from init import *
from pygame.locals import *
import PlayScreen
def init():
pygame.init()
pygame.font.init
pygame.time.set_timer(pygame.USEREVENT, 500)
# -------------- Main -------------
init()
mpc = mpc.mpc()
playScreen = PlayScreen.PlayScreen(mpc)
screen = None
if FULLSCREEN==True:
screen=pygame.display.set_mode(SCREEN_SIZE,pygame.FULLSCREEN,32)
else:
screen=pygame.display.set_mode(SCREEN_SIZE,0,32)
while True:
for event in pygame.event.get():
if event.type == QUIT:
pygame.quit()
sys.exit()
elif event.type == KEYDOWN:
if event.key == K_q:
def game(name):
global ground
totalDir = 0
init()
ground = pygame.image.load("images/foreground1.png").convert_alpha()
groundx = 0
timerBuff = 0
buffText = ""
buffT = ""
font = pygame.font.Font("fonts/vcr.ttf", 40)
loadbackground(0)
perso = Perso()
all_sprite_list.add(perso)
getSpritesVisible(all_sprite_visible, 0, all_sprite_list, perso)
lvl = 0
transition = 0
score = 0
ctrldir = 0
ctrlnuage = 0
dir = 0 #dir = 1 > droite dir = -1 > gauche
vis = 0
#BOUCLE INFINIE
start_ticks = pygame.time.get_ticks() #starter tick
continuer = 1
while continuer:
# Limitation de vitesse de la boucle
pygame.time.Clock().tick(fps)
#pour gerer la colision
oldpbottom = perso.hitb.rect.bottom
oldpy = perso.hitb.rect.y
#events
for event in pygame.event.get():
if event.type == QUIT:
raise Exception("QUIT")
if event.type == KEYDOWN:
if event.key == K_RIGHT:
ctrldir = perso.vh
elif event.key == K_LEFT:
ctrldir = -perso.vh
if event.key == K_UP:
ctrlnuage = 1
elif event.key == K_DOWN:
ctrlnuage = -1
elif event.key == K_r:
init()
elif event.key == K_ESCAPE:
raise Exception("BACK")
if event.type == KEYUP:
if event.key == K_RIGHT and ctrldir == perso.vh or event.key == K_LEFT and ctrldir == -perso.vh:
ctrldir = 0
if event.key == K_UP and ctrlnuage == 1 or event.key == K_DOWN and ctrlnuage == -1:
ctrlnuage = 0
dir = ctrldir
perso.bondir(ctrlnuage)
#update des pos du fond et des elems
for i in range(len(fonds)):
fondsx[i] = fondsx[i] - (dir * vitessebackground[i])
groundx = groundx - dir
score = max(score + dir, 0)
totalDir = totalDir + dir
for e in all_sprite_list:
e.deplacer(dir)
#collision platform
corrdir = 0
block_hit_list = pygame.sprite.spritecollide(perso.hitb,
platforms_list, False)
for block in block_hit_list:
if perso.hitb.rect.right <= block.rect.x + dir * 2:
corrdir = (block.rect.x - perso.hitb.rect.right)
elif perso.hitb.rect.x >= block.rect.right + dir * 2:
corrdir = -(perso.hitb.rect.x - block.rect.right)
elif oldpbottom <= block.rect.y:
perso.v = -vitesse
perso.rect.y = block.rect.y - 110
#print(perso.rect.x)
perso.hitb.updt(perso)
elif oldpy >= block.rect.bottom:
perso.v = -perso.v
perso.rect.top = block.rect.bottom - 30
perso.hitb.updt(perso)
perso.changeimg()
if len(block_hit_list) > 0:
# update des pos du fond et des elems
for i in range(len(fonds)):
fondsx[i] = fondsx[i] - (corrdir * vitessebackground[i])
groundx = groundx - corrdir
score = score + corrdir
totalDir = totalDir + corrdir
for e in all_sprite_list:
e.deplacer(corrdir)
#collision buffs
block_hit_list = pygame.sprite.spritecollide(perso.hitb,
blocksBuff_list, True)
for block in block_hit_list:
if block.typeBuff == "r" or block.typeBuff == "a" or block.typeBuff == "g":
if perso.addBuff(block.typeBuff
) == 1: #retourne vrai si le buff est activé
if block.typeBuff == "r" or block.typeBuff == "a" or block.typeBuff == "g":
if ctrldir > 0:
ctrldir = perso.vh
elif ctrldir < 0:
ctrldir = -perso.vh
else:
timerBuff = pygame.time.get_ticks()
if block.typeBuff == "-":
score = score + malusPoints
buffT = str(malusPoints)
elif block.typeBuff == "+":
score = score + bonusPoints
buffT = "+ " + str(bonusPoints)
elif block.typeBuff == "++":
score = score + bonusPoints1
buffT = "+ " + str(bonusPoints1)
elif block.typeBuff == "t": #tp
transition = 600
#affichage des fonds
for i in range(len(fonds)):
fondxi = fondsx[i]
if fondxi <= -2 * width or fondxi >= 2 * width:
fondxi = 0
fondsx[i] = 0
if fondxi < -width:
fenetre.blit(fonds[i], ((fondxi) + (2 * width), 0))
elif fondxi > 0:
fenetre.blit(fonds[i], ((fondxi) - (2 * width), 0))
fenetre.blit(fonds[i], (fondxi, 0))
# affichage du sol
if groundx <= -2 * width or groundx >= 2 * width:
groundx = 0
if groundx < -width:
fenetre.blit(ground, (groundx + (2 * width), height - 148))
elif groundx > 0:
fenetre.blit(ground, (groundx - (2 * width), height - 148))
fenetre.blit(ground, (groundx, height - 148))
#pygame.draw.rect(fenetre, (153, 70, 0), pygame.Rect(0, height - 148, width, 148))
#affichage des sprites
all_sprite_visible.draw(fenetre)
#verif et affichage du buff
buffText = ""
if perso.buff != "":
duraBuff = 5 - (pygame.time.get_ticks() - perso.startBuff
) / 1000 # calculate how many seconds
if duraBuff < 0: #on enleve le buff
perso.deBuff()
if ctrldir > 0:
ctrldir = perso.vh
elif ctrldir < 0:
ctrldir = -perso.vh
if perso.buff == "r":
color = (0, 200, 0)
buffText = font.render("SLOW", True, color)
elif perso.buff == "g":
color = (200, 200, 200)
buffText = font.render("LEVITATION", True, color)
else:
color = (0, 0, 200)
buffText = font.render("SPEED", True, color)
buffHud = pygame.Rect(0, 0, duraBuff * 50, 20)
buffHud.centerx = width / 2
pygame.draw.rect(fenetre, color, buffHud)
text_r = buffText.get_rect()
text_r.centerx = width / 2 # align to right to 150px
text_r.top = 25
fenetre.blit(buffText, text_r)
#affichage bonus
if (2 - (pygame.time.get_ticks() - timerBuff) / 1000) > 0:
textB = font.render(buffT, True, (255, 255, 255))
textB_rect = textB.get_rect()
textB_rect.right = width - 10 # align to right to 150px
textB_rect.y = 50
fenetre.blit(textB, textB_rect)
#affichage du niveau
text = font.render("FLOOR " + str(-lvl), True, (255, 255, 255))
text_rect = text.get_rect()
text_rect.centerx = width / 2 # align to right to 150px
text_rect.y = height - 80
fenetre.blit(text, text_rect)
#affichege du score
text = font.render(str(int(score)), True, (255, 255, 255))
text_rect = text.get_rect()
text_rect.right = width - 10 # align to right to 150px
text_rect.y = 10
fenetre.blit(text, text_rect)
#affichage du timer
seconds = 180 - (pygame.time.get_ticks() -
start_ticks) / 1000 # calculate how many seconds
min = int(seconds / 60)
seconds = int(seconds % 60)
if seconds <= 0 and min == 0:
saveNew(name, int(score))
raise Exception("END")
if seconds >= 10:
seconds = str(seconds)
else:
seconds = "0" + str(seconds)
text = font.render(str(min) + ":" + seconds, True, (255, 255, 255))
fenetre.blit(text, (10, 10))
#transition
if transition > 0:
while transition > -height - 148:
#affichage du fond
if transition == 0:
tp(int(block.dest))
totalDir = totalDir + block.dest
init()
for e in all_sprite_list:
e.deplacer(totalDir)
else:
for i in range(len(fonds)):
fondxi = fondsx[i]
if fondxi <= -2 * width or fondxi >= 2 * width:
fondxi = 0
fondsx[i] = 0
if fondxi < -width:
fenetre.blit(fonds[i], ((fondxi) + (2 * width), 0))
elif fondxi > 0:
fenetre.blit(fonds[i], ((fondxi) - (2 * width), 0))
fenetre.blit(fonds[i], (fondxi, 0))
#affichage du sol
if transition > -height:
if groundx < -width:
fenetre.blit(ground,
(groundx + (2 * width), transition))
elif groundx > 0:
fenetre.blit(ground,
(groundx - (2 * width), transition))
fenetre.blit(ground, (groundx, transition))
else:
if groundx < -width:
fenetre.blit(ground,
(groundx +
(2 * width), transition + 2 * height))
elif groundx > 0:
fenetre.blit(ground,
(groundx -
(2 * width), transition + 2 * height))
fenetre.blit(ground, (groundx, transition + 2 * height))
pygame.display.flip()
if transition == 0:
lvl = int(totalDir / 100000)
loadbackground(lvl)
#all_sprite_visible.draw(fenetre)
transition -= 60
pygame.time.Clock().tick(fps)
pygame.display.flip()
getSpritesVisible(all_sprite_visible, score, all_sprite_list,
perso)
pygame.display.flip()
if dir != 0:
getSpritesVisible(all_sprite_visible, score, all_sprite_list,
perso)
return int(score)
def test_drop():
init()
drop(with_models=True)
assert len(dm().all()) == cnt_base_decks
assert len(ms().all()) == cnt_base_models
jika test == Benar : vprint ( " SQLi vuln! -> " + url, " cyan " , " info " ) uji elif == " keluar " : istirahat lain : lulus cetak " - " * 20 def main (): '' ' Fungsi utama ' '' if len (sys.argv) == 3 dan sys.argv [ 1 ] di [ ' -t ' , ' --target ' ]: url = sys.argv [ - 1 ] vprint ( " Situs web Target: " + url, " yellow " , " info " ) rIp (url) merangkak() sC () lain : pemakaian() if __name__ == " __main__ " : init ( autoreset = True ) logo() vprint ( " Program dimulai " , " hijau " , " info " ) utama() vprint ( " Program mematikan " , " kuning " , " info " )
# convert an array of values into a dataset matrix
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset) - look_back - 1):
a = dataset[i:(i + look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return numpy.array(dataX), numpy.array(dataY)
numpy.random.seed(7)
# load the dataset
df = init()
dataset = pandas.DataFrame(df, columns=['btc_market_price'])
dataset = dataset.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets, 80:20 split
train_size = int(len(dataset) * 0.80)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]
# reshape into X=t and Y=t+1
look_back = 3
'''
Using Multiple Linear Regression to predict the next day's closing values of Bitcoin's market price in USD.
'''
import pandas, sklearn, numpy
from sklearn import linear_model
from init import *
ipdata = init()
def correlation(dataset_arg, threshold, toprint=False):
'''
Return a copy of 'dataset_arg' without the columns that have correlation > threshold
'''
dataset = dataset_arg.copy(deep=True)
col_corr = set() # Set of all the names of deleted columns
corr_matrix = dataset.corr()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if corr_matrix.iloc[i, j] >= threshold:
colname = corr_matrix.columns[i] # getting the name of column
col_corr.add(colname)
if colname in dataset.columns:
del dataset[
colname] # deleting the column from the dataset
if toprint:
print(dataset)
return dataset
def test_init():
init()
assert len(dm().all()) == cnt_base_decks + len(conf.decks)
assert len(ms().all()) == cnt_base_models + len(conf.models)
assert len(tg().all()) == len(conf.tags)
drop()
from DLDA import *
from init import *
from numpy import matrix
training_file="../Data/Training_Data.txt"
training=matrix(init(training_file))
testing_file="../Data/Testing_Data.txt"
testing=matrix(init(testing_file))
total_gene=70
ex_range=3
ex_search=exhaustive_search(total_gene,ex_range)
minerror=1.0
for i in ex_search:
parameters = DLDA_training(training[:,i],len(i)-2)
mean0=parameters[0]
mean1=parameters[1]
cov0 = parameters[2]
cov1=parameters[3]
N0=parameters[4]
N1=parameters[5]
label = DLDA_testing(testing[:,i], mean0,mean1,cov0,cov1,N0,N1)
error =0.0
for j in range(0,len(testing)):
if testing[j,-1]!=label[j]:
error =error+1
error=float(error/len(testing))
if error<minerror:
minerror = error
feture_selected = i
print error
print feture_selected
