Monday, March 2, 2015

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Group leader of CAE (Computer Aided Engineering) at Samsung Electronics
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Thursday, February 26, 2015

no title about days in Boston

I have no external keyboard which includes Korean character now in Boston. I will be helpful for me to write article in English.

Actually, I started to live very close from the university. It is very convenient to live and go university. I come university by bus. It takes less than hour. If there is no traffic jam, it takes only half hour. 

no title for chemistry machine learning

I think no title should be used in blog articles as having been used in artist pictures.

In this article, know-how for chemistry information will be discussed. I am using MAC Book Air. In MAC OS X, '.profile' or '.bash_profile' is using in stead of just '.bashrc' which is standard in Linux. Hence, in order to add library path for Python we need to modify '.profile' or '.bash_profile' but not '.bashrc'.

In Linux including Ubuntu, the Python library can be set using '.bashrc' file."

Thursday, February 19, 2015

Code Chemistry - Introduction

Previously, real experimental is really important for chemistry research. It is only way to prove truth of new chemistry theory. It is still true, while computer simulation can reduce the number of experimental trials by programming and executing the reaction invoked by the new theory in a thinking machine.


We use RDKit for showing about examples for code chemistry. If 'import rdkit' is not working we can refer to the following link:

I found that pythonpath must be set for importing new library. The pythonpath can be set up externally or internally in Python.

Example: Draw Benzene

First, benzene can be defined as follows. Before defining molecule, the basic library of rdkit can be loaded using the import command.
   from rdkit import Chem
   m = Chem.MolFromSmiles( 'C1=CC=CC=C1')

Second, the 2D coordination of the molecule can be calculated. For coordination calculation, AllChem sub-tool should be included.
   from rdkit.Chem import AllChem
   AllChem.Compute2DCoords( m)

Then, the molecular graph is drawn and save it so as to see in the picture manipulation tool.
   from rdkit.Chem import Draw      
   Draw.MolToFile(m, 'benzene.png')

Now it is the time to read image files and show on display.
   import matplotlib.pyplot as plt
   img_m = plt.imread( 'benzene.png')
   plt.imshow( img_m)

The same process can be applied for H atom included molecule.
   mH = Chem.AddHs( m)

Sunday, February 15, 2015

Some google services looks not offering to apply large-size fonts

I am using google+ and google blog in my pad. I found that there is no option to increase the font size for both services. In my case, depending on situation the font size variation is helpful. 

Monday, February 2, 2015

[Javascript example] 9 to 9 multiplication

Gugudan Table

Maximum First Level:
Maximum Second Level:

Wednesday, January 21, 2015

Automatic showing of blog article update in Google+

I saw that from today blog article will be delivered to the Google+ automatically. Previously, it asked me whether I will notice the information on Google+ or not about creating new page. Now, the notification method is changed from the manual type to the automatic type.

The automation mode is considered to be more convenient than the conventional manual mode. Now most new google-blog articles will be published on the SnS service of Google+ with hyper links of each blog article page. The full contents of each article will be shown in blog, while the link and summary are shown in the time of the SNS.

The summary can be replaced by the title since the title represents the short summary of an article. 

Saturday, June 28, 2014

Market Prediction using Python Fitting Tools (Numpy, Scipy)

In [1]:
import numpy

# Generate artificial data = straight line with a=0 and b=1
# plus some noise.
# xdata = numpy.array([0.0,1.0,2.0,3.0,4.0,5.0])
xdata = numpy.array([0.0, 1.0, 2.0, 3.0, 4.0])

# we will repace the original data into real data
# ydata = numpy.array([0.1,0.9,2.2,2.8,3.9,7.1])
ydata = numpy.array([10.4, 10.5, 11.5, 13.0, 15.1])

# Initial guess.
x0    = numpy.array([0.0, 0.0, 0.0])
In [2]:
plot( xdata, ydata, 'o-')
[<matplotlib.lines.Line2D at 0xaf4fa72c>]
In [3]:
sigma = numpy.ones(5)
print sigma
[ 1.  1.  1.  1.  1.]

In [4]:
# The objective is defined by 2D polymer.
def func(x, a, b, c):
    return a + b*x + c*x*x
In [5]:
import scipy.optimize as optimization

results = optimization.curve_fit(func, xdata, ydata, x0, sigma)
abc = results[0]
print 'results = ', results
print 'abc =', abc
results =  (array([ 10.36285714,  -0.09571429,   0.32142857]), array([[ 0.00556735, -0.00484898,  0.00089796],
       [-0.00484898,  0.00781224, -0.00179592],
       [ 0.00089796, -0.00179592,  0.00044898]]))
abc = [ 10.36285714  -0.09571429   0.32142857]

In [16]:
# In order to see the future, more data is appended.
fit_xdata = numpy.append( xdata, [5.0, 6.0])
fit_y = func( fit_xdata, abc[0], abc[1], abc[2])
print fit_y
[ 10.36285714  10.58857143  11.45714286  12.96857143  15.12285714  17.92
  21.36      ]

In [17]:
plot( xdata, ydata, 'o')
plot( fit_xdata, fit_y, '-')
title( 'Market Prediction based on Curve-Fitting')
xlabel( 'Quater')
ylabel( 'Grwoth(%)')