Monday, February 16, 2015
Scripting in SwishMax
The movies below's result of my tinkering with SwishMax this morning; consist of me abusing the script to force the project done in just one frame, :)
Tuesday, December 30, 2014
Gnome 3 on Freebsd (self documentation)
install it using
#pkg install gnome3
/etc/fstab
proc /proc procfs rw 0 0
/etc/rc.conf
gdm_enable=“YES”
gnome_enable="YES"
The menu is missing
install Parallel Tools
#cd /usr/ports/emulators/parallels-tools/ && make install clean
but it need kernel source.
Install via svn
#pkg install devel/subversion
# svnlite checkout http://svn0.eu.freebsd.org/base/releng/10.0/ /usr/src
run it again
#cd /usr/ports/emulators/parallels-tools/ && make install clean
Playing with FreeBSD
Got boot-only iso and managed to install it on my Parallel Desktop on my Macbook Air with Yosemite.
Of course it just CLI.
Strange, the root shell have tab completion feature, but the normal users didn’t.
(DuckDuckGo-ing)
The answer come from 2003 and 2005 Mailing-List, :)
To enable tab completion
$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...
Monday, December 22, 2014
Compare Native Loop Time in Python with "homemade" Fortran Module
This code print d and e as result of two matrix addition, e's using python native code, d's using fortran module compiled with F2PY
The code
The fortran module I imported to python
save it as aravir.f and compile using
And here the result
The Desktop, :)
The code
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
The fortran module I imported to python
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
save it as aravir.f and compile using
$ f2py -c aravir.f -m aravir
And here the result
$ 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
The Desktop, :)
Friday, December 19, 2014
Using 'Home-Made' Fortran Binary as Python module
Python is easy to use, but it's slow, especially for loop computation. So I compute it using fortran like this
save it as aravir.py and do the following command
To use the module on the python I use the code below
:)
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
save it as aravir.py and do the following command
$ f2py -c aravir.f -m aravir
To use the module on the python I use the code below
import numpy as np
import aravir as ar
a = np.linspace(0,1,100)
b = ar.subs(a)
print a
print b
:)
3D Waterwave Simulation using Python
I used Numpy Matplotlib with Animation and 3d Plot module on my OS X Yosemite.
The code is still messy and clearly not efficient (there's slow loop here and there) but it works, :)
Here The Result
The Code
and the snapshot
The code is still messy and clearly not efficient (there's slow loop here and there) but it works, :)
Here The Result
The Code
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()
and the snapshot
Thursday, December 18, 2014
3D Surface Plot Animation using Matplotlib in Python
And here's the animation
The result
The snapshot
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()
The result
The snapshot
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My sky is high, blue, bright and silent.
Nugroho's (almost like junk) blog
By: Nugroho Adi Pramono