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...

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

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, :)

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

        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

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

3D Surface Plot Animation using Matplotlib in Python

And here's the animation

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

3D Surface Plot using Matplotlib in Python

It's slightly modified from before

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()

the result


the snapshot