proc /proc procfs rw 0 0
gdm_enable=“YES”
gnome_enable="YES"
$chsh -s /bin/tcsh
(it didn’t enable tab completion actually , it change shell with tab completion feature :) )
We could so use
$chsh -s /bin/csh
tcsh shell support tab completion too.
Another thing is, normal user can’t use sudo command (because it’s not installed, :P )
So, install sudo first (I used pkg command instead of pkg_add)
#pkg install sudo
edit /usr/local/etc/sudoers as root and visudo command (don’t edit it using regular vi editor, or ANY editor)
%visudo
add this
username ALL=(ALL) ALL
and life become more easier...
import numpy as np
import aravir as ar
import time
n = 1000
u = np.ones((n,n))
v = np.ones((n,n))
e = np.ones((n,n))
t = time.clock()
d = ar.add3(u,v)
tfortran= time.clock()-t
t = time.clock()
for i in range (n):
for j in range (n):
e[i,j] = u[i,j]+v[i,j]
tnative = time.clock()-t
print 'fortran ', d
print 'native', e
print 'tfortran = ', tfortran, ', tnative = ', tnative
subroutine add3(a, b, c, n)
double precision a(n,n)
double precision b(n,n)
double precision c(n,n)
integer n
cf2py intent(in) :: a,b
cf2py intent(out) :: c,d
cf2py intent(hide) :: n
do 1700 i=1, n
do 1600 j=1, n
c(i,j) = a(i,j)
$ +b(i,j)
1600 continue
1700 continue
end
$ f2py -c aravir.f -m aravir
$ python cobamodul.py fortran [[ 2. 2. 2. ..., 2. 2. 2.] [ 2. 2. 2. ..., 2. 2. 2.] [ 2. 2. 2. ..., 2. 2. 2.] ..., [ 2. 2. 2. ..., 2. 2. 2.] [ 2. 2. 2. ..., 2. 2. 2.] [ 2. 2. 2. ..., 2. 2. 2.]] native [[ 2. 2. 2. ..., 2. 2. 2.] [ 2. 2. 2. ..., 2. 2. 2.] [ 2. 2. 2. ..., 2. 2. 2.] ..., [ 2. 2. 2. ..., 2. 2. 2.] [ 2. 2. 2. ..., 2. 2. 2.] [ 2. 2. 2. ..., 2. 2. 2.]] tfortran = 0.069974 , tnative = 1.202547
subroutine subs(a, b, n)
double precision a(n)
double precision b(n)
integer n
cf2py intent(in) :: a
cf2py intent(out) :: b
cf2py intent(hide) :: n
! b(1) = a(1)
do 100 i=2, n
b(i) = a(i)-1
100 continue
end
$ f2py -c aravir.f -m aravir
import numpy as np import aravir as ar a = np.linspace(0,1,100) b = ar.subs(a) print a print b
import numpy as np
n = 8;
g = 9.8;
dt = 0.02;
dx = 1.0;
dy = 1.0;
h = np.ones((n+2,n+2))
u = np.zeros((n+2,n+2))
v = np.zeros((n+2,n+2))
hx = np.zeros((n+1,n+1))
ux = np.zeros((n+1,n+1))
vx = np.zeros((n+1,n+1))
hy = np.zeros((n+1,n+1))
uy = np.zeros((n+1,n+1))
vy = np.zeros((n+1,n+1))
nsteps = 0
h[1,1] = .5;
def reflective():
h[:,0] = h[:,1]
h[:,n+1] = h[:,n]
h[0,:] = h[1,:]
h[n+1,:] = h[n,:]
u[:,0] = u[:,1]
u[:,n+1] = u[:,n]
u[0,:] = -u[1,:]
u[n+1,:] = -u[n,:]
v[:,0] = -v[:,1]
v[:,n+1] = -v[:,n]
v[0,:] = v[1,:]
v[n+1,:] = v[n,:]
def proses():
#hx = (h[1:,:]+h[:-1,:])/2-dt/(2*dx)*(u[1:,:]-u[:-1,:])
for i in range (n+1):
for j in range(n):
hx[i,j] = (h[i+1,j+1]+h[i,j+1])/2 - dt/(2*dx)*(u[i+1,j+1]-u[i,j+1])
ux[i,j] = (u[i+1,j+1]+u[i,j+1])/2- dt/(2*dx)*((pow(u[i+1,j+1],2)/h[i+1,j+1]+ g/2*pow(h[i+1,j+1],2))- (pow(u[i,j+1],2)/h[i,j+1]+ g/2*pow(h[i,j+1],2)))
vx[i,j] = (v[i+1,j+1]+v[i,j+1])/2 - dt/(2*dx)*((u[i+1,j+1]*v[i+1,j+1]/h[i+1,j+1]) - (u[i,j+1]*v[i,j+1]/h[i,j+1]))
for i in range (n):
for j in range(n+1):
hy[i,j] = (h[i+1,j+1]+h[i+1,j])/2 - dt/(2*dy)*(v[i+1,j+1]-v[i+1,j])
uy[i,j] = (u[i+1,j+1]+u[i+1,j])/2 - dt/(2*dy)*((v[i+1,j+1]*u[i+1,j+1]/h[i+1,j+1]) - (v[i+1,j]*u[i+1,j]/h[i+1,j]))
vy[i,j] = (v[i+1,j+1]+v[i+1,j])/2 - dt/(2*dy)*((pow(v[i+1,j+1],2)/h[i+1,j+1] + g/2*pow(h[i+1,j+1],2)) - (pow(v[i+1,j],2)/h[i+1,j] + g/2*pow(h[i+1,j],2)))
for i in range (1,n+1):
for j in range(1,n+1):
h[i,j] = h[i,j] - (dt/dx)*(ux[i,j-1]-ux[i-1,j-1]) - (dt/dy)*(vy[i-1,j]-vy[i-1,j-1])
u[i,j] = u[i,j] - (dt/dx)*((pow(ux[i,j-1],2)/hx[i,j-1] + g/2*pow(hx[i,j-1],2)) - (pow(ux[i-1,j-1],2)/hx[i-1,j-1] + g/2*pow(hx[i-1,j-1],2))) - (dt/dy)*((vy[i-1,j]*uy[i-1,j]/hy[i-1,j]) - (vy[i-1,j-1]*uy[i-1,j-1]/hy[i-1,j-1]))
v[i,j] = v[i,j] - (dt/dx)*((ux[i,j-1]*vx[i,j-1]/hx[i,j-1]) - (ux[i-1,j-1]*vx[i-1,j-1]/hx[i-1,j-1])) - (dt/dy)*((pow(vy[i-1,j],2)/hy[i-1,j] + g/2*pow(hy[i-1,j],2)) - (pow(vy[i-1,j-1],2)/hy[i-1,j-1] + g/2*pow(hy[i-1,j-1],2)))
#dh = dt/dt*(ux[1:,:]-ux[:-1,:])+ dt/dy*(vy[:,1:]-vy[:,:-1])
reflective()
return h,u,v
'''
for i in range (17):
#print h
proses(1)
'''
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from mpl_toolkits.mplot3d import Axes3D
a = n
x = np.arange(n+2)
y = np.arange(n+2)
x,y = np.meshgrid(x,y)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
def plotset():
ax.set_xlim3d(0, a)
ax.set_ylim3d(0, a)
ax.set_zlim3d(0.5,1.5)
ax.set_autoscalez_on(False)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
cset = ax.contour(x, y, h, zdir='x', offset=0 , cmap=cm.coolwarm)
cset = ax.contour(x, y, h, zdir='y', offset=n , cmap=cm.coolwarm)
cset = ax.contour(x, y, h, zdir='z', offset=.5, cmap=cm.coolwarm)
plotset()
surf = ax.plot_surface(x, y, h,rstride=1, cstride=1,cmap=cm.coolwarm,linewidth=0,antialiased=False, alpha=0.7)
fig.colorbar(surf, shrink=0.5, aspect=5)
from matplotlib import animation
def data(k,h,surf):
proses()
ax.clear()
plotset()
surf = ax.plot_surface(x, y, h,rstride=1, cstride=1,cmap=cm.coolwarm,linewidth=0,antialiased=False, alpha=0.7)
return surf,
ani = animation.FuncAnimation(fig, data, fargs=(h,surf), interval=10, blit=False)
#ani.save('air.mp4', bitrate=512)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
def data(i, z, line):
z = np.sin(x+y+i)
ax.clear()
line = ax.plot_surface(x, y, z,color= 'b')
return line,
n = 2.*np.pi
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.linspace(0,n,100)
y = np.linspace(0,n,100)
x,y = np.meshgrid(x,y)
z = np.sin(x+y)
line = ax.plot_surface(x, y, z,color= 'b')
ani = animation.FuncAnimation(fig, data, fargs=(z, line), interval=30, blit=False)
plt.show()
import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation from mpl_toolkits.mplot3d import Axes3D n = 2.*np.pi fig = plt.figure() ax = fig.add_subplot(111, projection='3d') x = np.linspace(0,n,100) y = np.linspace(0,n,100) x,y = np.meshgrid(x,y) z = np.sin(x+y) line = ax.plot_surface(x, y, z,color= 'b') plt.show()
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
def simData():
t_max = n
dt = 1./8
k = 0.0
t = np.linspace(0,t_max,100)
while k < t_max:
x = np.sin(np.pi*t+np.pi*k)
k = k + dt
yield x, t
def simPoints(simData):
x, t = simData[0], simData[1]
line.set_data(t, x)
return line
n = 2.
fig = plt.figure()
ax = fig.add_subplot(111)
line, = ax.plot([], [], 'b')
ax.set_ylim(-1, 1)
ax.set_xlim(0, n)
ani = animation.FuncAnimation(fig, simPoints, simData, blit=False,\
interval=100, repeat=True)
plt.show()
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
fig = plt.figure()
n = 10
x = np.linspace(0,2*np.pi,100)
def init():
pass
def animate(k):
h = np.sin(x+np.pik)
plt.plot(x,h)
ax = plt.axes(xlim=(0, 2*np.pi), ylim=(-1.1, 1.1))
anim = animation.FuncAnimation(fig, animate,init_func=init,frames=360,interval=20,blit=False)
plt.show()
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