Dec
30
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
Dec
30
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.
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.
Dec
22
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
Dec
19
Using 'Home-Made' Fortran Binary as Python module
Python is easy to use, but it's slow, especially for loop computation.
Dec
19
3D Waterwave Simulation using Python
I used Numpy Matplotlib with Animation and 3d Plot module on my OS X Yosemite.
Dec
18
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.FuncAnimatio
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.FuncAnimatio
Dec
18
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
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
Dec
17
Matplotlib Animation in Python
Here is the update from before
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.
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.
Dec
16
Playing with Matplotlib Animation in Python
Coding like this
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()
The result
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()
The result
