The Mendelian Separation Law model

Law of segregation separation theorem

Segregation separation

Algorithm:

Simulating Mendelian separation theorem

#coding =utf-8
#law of segregation Mendelian separation theorem

Import Math,random,pylab

#试验次数
n=1000

#三类实验对象
#显性遗传因子
Dominant_hereditary_factor= ' D '
#隐性遗传因子
Recessive_hereditary_factor= ' d '
#遗传因子列表
List_hereditary_factor=[dominant_hereditary_factor,recessive_hereditary_factor]

#纯种高茎
High_pure=[dominant_hereditary_factor,dominant_hereditary_factor]
#纯种矮茎
Low_pure=[recessive_hereditary_factor,recessive_hereditary_factor]
#杂种高茎
Cross_high=[dominant_hereditary_factor,recessive_hereditary_factor]

#配子时, randomly selected a genetic factor

def random_hereditary_factor (List_hereditary_factor):
#真随机数
R=random. Systemrandom ()
#随机抽出一个遗传因子
Random_hereditary_factor=r.choice (List_hereditary_factor)
Return Random_hereditary_factor

#配子过程
def Son (LIST1,LIST2):
Son=[]
#纯高茎中抽取一个遗传因子
Factor1=random_hereditary_factor (List1)
Son.append (Factor1)
#纯矮茎中抽取一个遗传因子
Factor2=random_hereditary_factor (LIST2)
Son.append (Factor2)
return son

#配子性状判断, for example, high or short
def character_analysis (LIST1,LIST2):
Son=son (LIST1,LIST2)
#print ' son: ' son
#如果线性遗传因子在配子中, return to dominant traits
If Dominant_hereditary_factor in Son:
Character= "Dominant_character"

#否则返回隐性性状
Else
Character= "Recessive_character"

return character

#实验n次, the ratio of high stem to dwarf stem was observed.
def count_test (N,LIST1,LIST2):
Count_dominant=0
Count_recessive=0
For I in range (n):
Analysis1=character_analysis (LIST1,LIST2)
If analysis1== "Dominant_character":
Count_dominant+=1
If analysis1== "Recessive_character":
Count_recessive+=1

Ration=count_recessive*1.0/count_dominant
return ration

def Print (N,ratio_purehigh_purelow,ratio_crosshigh_crosshigh,ratio_crosshigh_purelow):
print ' N: ', n
print ' Ratio_purehigh_purelow: ', Ratio_purehigh_purelow
print ' Ratio_crosshigh_crosshigh: ', Ratio_crosshigh_crosshigh
print ' Ratio_crosshigh_purelow: ', Ratio_crosshigh_purelow

#绘图前准备, get a set of proportional coefficients for multiple experiments
def list_ratio (N,LIST1,LIST2):
List_ration=[]
For I in range (n):
Ration=count_test (N,LIST1,LIST2)
List_ration.append (Ration)

Return list_ration

#实验1: The ratio of purebred tall stems to purebred dwarf stems
#list_ratio1 =list_ratio (n,high_pure,low_pure)
#实验2: The ratio of high stem to hybrid high stem
#list_ratio2 =list_ratio (N,cross_high,cross_high)
#实验3: The number of hybrids with high stems and purebred dwarf stems
#list_ratio3 =list_ratio (n,cross_high,low_pure)

#实验1: The ratio of purebred tall stems to purebred dwarf stems
Ratio_purehigh_purelow=count_test (N,high_pure,low_pure)

#实验2: The ratio of high stem to hybrid high stem
#ratio_crossHigh_crossHigh =count_test (N,cross_high,cross_high)

#实验3: The number of hybrids with high stems and purebred dwarf stems
#ratio_crossHigh_pureLow =count_test (n,cross_high,low_pure)

#Print (N,ratio_purehigh_purelow,ratio_crosshigh_crosshigh,ratio_crosshigh_purelow)

Description of the Mendelian separation theorem of statistics

Hybrid high stems and hybrid high stems

Purebred tall stems and thoroughbred dwarf stems

Hybrid high stem and thoroughbred dwarf stem

#coding =utf-8
# There is a problem with the mode function
Import Math,numpy,pylab,distribution_status,statistics_functions,quartile,law_of_segregation
#list1 =[2.96,2.84,3.01,3.15,2.95,2.82,3.14]
#list1 =[19,15,29,25,24,23,21,38,22,18,30,20,19,19,16,23,27,22,34,24,41,20,31,17,23]
#list1 =[5.5,6.5,6.7,6.8,7.1,7.3,7.4,7.8,7.8]
#list1 =[164,167,168,165,170,165,164,168,164,162,163,166,167,166,165]
#list1 =[129,130,129,130,131,130,129,127,128,128,127,128,128,125,132]
#list1 =[125,126,126,127,126,128,127,126,127,127,125,126,116,126,125]
List1=law_of_segregation.list_ratio1
N=len (List1)
Mean=statistics_functions. Mean (List1)
Median=statistics_functions. Median (List1)
Mode=statistics_functions. Mode (List1)
Deviation=statistics_functions. Deviation (List1)
Standarderror=statistics_functions. Standard_error_of_the_mean (List1)
Coefficient_of_variation=statistics_functions. Coefficient_of_variation (List1)

Max1=max (List1)
Min1=min (List1)

Q_l=quartile. Quartile (List1, ' q_l ')
Q_u=quartile. Quartile (List1, ' Q_u ')
Quartile_deviation=quartile. Quartile_deviation (List1)

Skew=distribution_status. Skew (List1)
Kurtosis=distribution_status. Kurtosis (List1)

Print "Central tendency:"
Print "N:", n
Print "range[%d:%d]"% (MIN1,MAX1)
Print "mean:", mean
Print "median:", median
Print "mode:", mode
Print "Quartile 1/4:", q_l
Print "Quartile 3/4:", Q_u
Print "Quartile_deviation:", quartile_deviation

Print "-" *20
Print "Dispersion:"
print "deviation:", deviation
Print "Standard Error:", StandardError
print "Coefficient of variation:", coefficient_of_variation

Print "-" *20
Print "Distribution Status:"
Print "Skew:", skew
Print "Kurtosis:", kurtosis

#绘图
def Draw (List1):
For I in List1:
X=I[0]
Y=I[1]
Pylab.plot (x, y, ' r--')
Pylab.xlabel (' x ')
Pylab.ylabel (' y ')
Pylab.title ("Descriptive statistics")
Pylab.grid (True)
# Pad margins so this markers don ' t get clipped by the axes, let dot not coincide with axis
Pylab.margins (0.05)
Pylab.show ()

def draw_hist (List1):
#facecolor表面颜色
Pylab.hist (list1,bins=100,normed=1,facecolor= ' green ', alpha=0.5)
Pylab.xlabel (' x ')
Pylab.ylabel (' frenquency ')
Pylab.title (' hist ')
Pylab.margins (0.01)
Pylab.show ()

#数组中不重复数字
#list_noneRepeat =statistics_functions. List_nonerepeat (List1)
#不重复数字频率
#frequency =statistics_functions. Frequence (List1)

#dict_mean_frequency =dict (Zip (list_nonerepeat,frequency))

#list_mean_frequency =dict_mean_frequency.items ()

#Draw (list_mean_frequency)

Draw_hist (List1)

The Mendelian Separation Law model