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In [1]:



    


%matplotlib inline
from pylab import *
import matplotlib.pyplot as plt



    











In [2]:



    


alpha = 0.7
phi_ext = 2 * pi * 0.5

def flux_qubit_potential(phi_m, phi_p):
    return 2 + alpha - 2 * cos(phi_p)*cos(phi_m) - alpha * cos(phi_ext - 2*phi_p)



    











In [9]:



    


phi_m = linspace(0, 2*pi, 100)
phi_p = linspace(0, 2*pi, 100)
X,Y = meshgrid(phi_p, phi_m)
Z = flux_qubit_potential(X, Y)



    











In [10]:



    


fig, ax = plt.subplots()

p = ax.pcolor(X/(2*pi), Y/(2*pi), Z, cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())
cb = fig.colorbar(p, ax=ax)



    


















    
























In [11]:



    


Z?

Repeat Map





In [22]:



    


alphabet = ['A', 'C', 'G', 'T']
monomer = 'ACCGCTAGAAAT'
monomer * 10



    


















    
Out[22]:








'ACCGCTAGAAATACCGCTAGAAATACCGCTAGAAATACCGCTAGAAATACCGCTAGAAATACCGCTAGAAATACCGCTAGAAATACCGCTAGAAATACCGCTAGAAATACCGCTAGAAAT'

















In [40]:



    


monomer = ''.join([choice(alphabet) for index in range(frequency)])
monomer



    


















    
Out[40]:








'TGGTACTTCT'

















In [42]:



    


copies = ceil(bpPerFrequency / frequency)
repeat = monomer * copies
repeat



    


















    
Out[42]:








'TGGTACTTCTTGGTACTTCTTGGTACTTCTTGGTACTTCTTGGTACTTCTTGGTACTTCTTGGTACTTCTTGGTACTTCTTGGTACTTCTTGGTACTTCT'

















In [47]:



    


'ACGAT' * ceil(3.2)



    


















    
Out[47]:








'ACGATACGATACGATACGAT'

















In [52]:



    


def debugSequence(maxFrequency, sectionLength, startFrequency=1):
    seq = ''
    for frequency in range(startFrequency, maxFrequency+1):
        monomer = ''.join([choice(alphabet) for index in range(frequency)])
        copies = ceil(sectionLength / frequency)
        repeat = monomer * copies
        seq += (repeat)
    return seq



    











In [54]:



    


debugSequence(5, 20, 3)



    


















    
Out[54]:








'TGATGATGATGATGATGATGAGTTGGTTGGTTGGTTGGTTGCATCACATCACATCACATCA'

















In [56]:



    


raw_bias = """TTT F 0.58 22.1( 80995)  TCT S 0.17 10.4( 38027)  TAT Y 0.59 17.5( 63937)  TGT C 0.46  5.2( 19138)
TTC F 0.42 16.0( 58774)  TCC S 0.15  9.1( 33430)  TAC Y 0.41 12.2( 44631)  TGC C 0.54  6.1( 22188)
TTA L 0.14 14.3( 52382)  TCA S 0.14  8.9( 32715)  TAA * 0.61  2.0(  7356)  TGA * 0.30  1.0(  3623)
TTG L 0.13 13.0( 47500)  TCG S 0.14  8.5( 31146)  TAG * 0.09  0.3(   989)  TGG W 1.00 13.9( 50991)

CTT L 0.12 11.9( 43449)  CCT P 0.18  7.5( 27340)  CAT H 0.57 12.5( 45879)  CGT R 0.36 20.0( 73197)
CTC L 0.10 10.2( 37347)  CCC P 0.13  5.4( 19666)  CAC H 0.43  9.3( 34078)  CGC R 0.36 19.7( 72212)
CTA L 0.04  4.2( 15409)  CCA P 0.20  8.6( 31534)  CAA Q 0.34 14.6( 53394)  CGA R 0.07  3.8( 13844)
CTG L 0.47 48.4(177210)  CCG P 0.49 20.9( 76644)  CAG Q 0.66 28.4(104171)  CGG R 0.11  5.9( 21552)

ATT I 0.49 29.8(109072)  ACT T 0.19 10.3( 37842)  AAT N 0.49 20.6( 75436)  AGT S 0.16  9.9( 36097)
ATC I 0.39 23.7( 86796)  ACC T 0.40 22.0( 80547)  AAC N 0.51 21.4( 78443)  AGC S 0.25 15.2( 55551)
ATA I 0.11  6.8( 24984)  ACA T 0.17  9.3( 33910)  AAA K 0.74 35.3(129137)  AGA R 0.07  3.6( 13152)
ATG M 1.00 26.4( 96695)  ACG T 0.25 13.7( 50269)  AAG K 0.26 12.4( 45459)  AGG R 0.04  2.1(  7607)

GTT V 0.28 19.8( 72584)  GCT A 0.18 17.1( 62479)  GAT D 0.63 32.7(119939)  GGT G 0.35 25.5( 93325)
GTC V 0.20 14.3( 52439)  GCC A 0.26 24.2( 88721)  GAC D 0.37 19.2( 70394)  GGC G 0.37 27.1( 99390)
GTA V 0.17 11.6( 42420)  GCA A 0.23 21.2( 77547)  GAA E 0.68 39.1(143353)  GGA G 0.13  9.5( 34799)
GTG V 0.35 24.4( 89265)  GCG A 0.33 30.1(110308)  GAG E 0.32 18.7( 68609)  GGG G 0.15 11.3( 41277)"""



    











In [66]:



    


biases = [('TTT', 80995),
('TCT', 38027),
('TAT', 63937),
('TGT', 19138),
('TTC', 58774),
('TCC', 33430),
('TAC', 44631),
('TGC', 22188),
('TTA', 52382),
('TCA', 32715),
('TAA',  7356),
('TGA',  3623),
('TTG', 47500),
('TCG', 31146),
('TAG',   989),
('TGG', 50991),
('CTT', 43449),
('CCT', 27340),
('CAT', 45879),
('CGT', 73197),
('CTC', 37347),
('CCC', 19666),
('CAC', 34078),
('CGC', 72212),
('CTA', 15409),
('CCA', 31534),
('CAA', 53394),
('CGA', 13844),
('CTG',177210),
('CCG', 76644),
('CAG',104171),
('CGG', 21552),
('ATT',109072),
('ACT', 37842),
('AAT', 75436),
('AGT', 36097),
('ATC', 86796),
('ACC', 80547),
('AAC', 78443),
('AGC', 55551),
('ATA', 24984),
('ACA', 33910),
('AAA',129137),
('AGA', 13152),
('ATG', 96695),
('ACG', 50269),
('AAG', 45459),
('AGG',  7607),
('GTT', 72584),
('GCT', 62479),
('GAT',119939),
('GGT', 93325),
('GTC', 52439),
('GCC', 88721),
('GAC', 70394),
('GGC', 99390),
('GTA', 42420),
('GCA', 77547),
('GAA',143353),
('GGA', 34799),
('GTG', 89265),
('GCG',110308),
('GAG', 68609),
('GGG', 41277),]



    











In [86]:



    


def weighted_choice(items, probs, bincount=10000):
  '''Puts items in bins in proportion to probs
  then uses random.choice() to select items.
 
  Larger bincount for more memory use but
  higher accuracy (on avarage).'''
 
  bins = []
  for item,prob in zip(items, probs):
    bins += [item]*int(bincount*prob)
  while True:
    yield random.choice(bins)



    











In [95]:



    


codons, weights = ([entry[0] for entry in biases], [entry[1] for entry in biases])



    











In [92]:



    


total = sum(weights)



    











In [97]:



    


probabilities = [w / total for w in weights]
probabilities



    


















    
Out[97]:








[0.022114108197632605,
 0.010382532161631892,
 0.017456753328378739,
 0.005225258382446976,
 0.01604709667519796,
 0.0091274107913680846,
 0.012185625816020012,
 0.0060580015147734092,
 0.014301885494269908,
 0.0089321939587079548,
 0.0020084126168502434,
 0.00098918962898972697,
 0.012968950421477237,
 0.0085038090489964208,
 0.00027002719930191552,
 0.013922100019822017,
 0.01186290372342662,
 0.007464654832067109,
 0.012526367923935878,
 0.019985016084228827,
 0.010196871397703377,
 0.0053694185050267651,
 0.009304334578170554,
 0.019716081007067669,
 0.0042071275167272154,
 0.0086097448966497517,
 0.01457819239588117,
 0.0037798347291564394,
 0.048383741140841707,
 0.02092615233902529,
 0.028441863881172744,
 0.005884354094393209,
 0.029779986534134006,
 0.010332021512621928,
 0.020596331452516988,
 0.0098555832287171331,
 0.023697958332263964,
 0.021991790517867937,
 0.021417334271830294,
 0.015167119260283832,
 0.0068213948911618376,
 0.009258465448258802,
 0.035258344222701181,
 0.0035908975988056552,
 0.02640068760009982,
 0.013724971973415562,
 0.012411695099156499,
 0.0020769432811826808,
 0.019817648366157976,
 0.017058674808073184,
 0.03274700936003281,
 0.025480574696512909,
 0.014317448234775682,
 0.024223542112502779,
 0.019219711494094075,
 0.02713650489243416,
 0.011581955302717144,
 0.021172698912300954,
 0.039139746310947923,
 0.0095011895940418176,
 0.024372070723645591,
 0.0301174522756276,
 0.018732351988781721,
 0.011269881400996124]

















In [102]:



    


itera = weighted_choice(codons, probabilities)
itera * 100
# a = [iter.next() for x in range(10)]
# a



    


















    






---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-102-1a9ae58870f2> in <module>()
      1 itera = weighted_choice(codons, probabilities)
----> 2 itera * 100
      3 # a = [iter.next() for x in range(10)]
      4 # a

TypeError: unsupported operand type(s) for *: 'generator' and 'int'
















In [ ]:

Content source: josiahseaman/DNAResearch

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