notebook.community

Edit and run 





In [1]:



    


%matplotlib inline
import numpy as np
import pandas as p
import matplotlib.pyplot as plt
import seaborn as sns
import ast
import copy
import itertools
sns.set_style("white")
sns.set(style="ticks")
from matplotlib import rc
rc('text', usetex=True)



    











In [ ]:



    


def myFloat(myList):
    return map(float, myList)



    











In [2]:



    


all_data = p.read_csv('../data/newleaf_compete_compiled_data.csv',index_col=0,converters={'motiffreq':ast.literal_eval,'motiffreq_sd':ast.literal_eval,'motiffreq_se':ast.literal_eval,'motif':ast.literal_eval,'bias':ast.literal_eval})



    











In [3]:



    


cooperate_list = []
compete_list = []
zero_motif = []
for row in range(len(all_data)):
    if all_data['bias'][row] == [0.5,0.5]:
        cooperate_list.append(True)
        compete_list.append(True)
        if all_data['motif'][row][0].count('0') > 2:
            zero_motif.append(all_data['motif'][row][0])
        else:
            zero_motif.append(all_data['motif'][row][1])
    elif all_data['motif'][row][0].count('0') > 2:
        cooperate_list.append(False)
        compete_list.append(True)
        zero_motif.append(all_data['motif'][row][0])
    elif all_data['motif'][row][0].count('1') > 2:
        cooperate_list.append(True)
        compete_list.append(False)
        zero_motif.append(all_data['motif'][row][1])

all_data['cooperate'] = cooperate_list
all_data['compete'] = compete_list
all_data['zero_motif'] = zero_motif



    











In [4]:



    


all_data



    


















    
Out[4]:










  
    

      
      bias
      cells
      cellswithmotif
      cellswithmotif_sd
      cellswithmotif_se
      elong
      maxstrandlen
      motif
      motiffreq
      motiffreq_sd
      ...
      nonemptystrands_sd
      nonemptystrands_se
      nr_samples
      rounds
      sampler_start
      strands
      trials
      cooperate
      compete
      zero_motif
    

  
  
    

      0
      [0.5, 0.5]
      100
      ['0.78303999999999996', '0.0055196376692678243']
      ['0.0062646947252040473', '0.00551963766926782...
      ['0.0014008283263840615', '0.00123422850396513...
      0.0500
      7
      [00000, 11111]
      [0.026563515868995979, 0.026720859127929774]
      [0.00042602582969270464, 0.00036847211422622603]
      ...
      ['0.0010062715090868679']
      ['0.0002250091498139534']
      50
      7000
      1000
      100
      20
      True
      True
      00000
    

    

      1
      [0.6, 0.4]
      100
      ['0.69462999999999997', '0.016145696020921505']
      ['0.019978015416952689', '0.016145696020921505']
      ['0.0044672200527845016', '0.00361028738468283...
      0.0500
      7
      [00000, 11111]
      [0.033151330556673783, 0.033194263137421166]
      [0.0022195408256048465, 0.002173387547884883]
      ...
      ['0.0009955282818684822']
      ['0.00022260689117814975']
      50
      7000
      1000
      100
      20
      False
      True
      00000
    

    

      2
      [0.7, 0.3]
      100
      ['0.59461000000000008', '0.092301004869936268']
      ['0.085305673316608918', '0.092301004869936268']
      ['0.019074928440232748', '0.020639132128071662']
      0.0500
      7
      [00000, 11111]
      [0.059753199164690428, 0.056418272547945736]
      [0.013836420154208122, 0.013273114868409215]
      ...
      ['0.00082135366316830665']
      ['0.00018366026244127988']
      50
      7000
      1000
      100
      20
      False
      True
      00000
    

    

      3
      [0.8, 0.2]
      100
      ['0.54421999999999993', '0.2062390678314853']
      ['0.20483661684376647', '0.2062390678314853']
      ['0.045802859954374026', '0.046116457528739122']
      0.0500
      7
      [00000, 11111]
      [0.10786570810624171, 0.093566021158762808]
      [0.043617299348785853, 0.042976838311198681]
      ...
      ['0.00089810940870250957']
      ['0.00020082336890909526']
      50
      7000
      1000
      100
      20
      False
      True
      00000
    

    

      4
      [0.9, 0.1]
      100
      ['0.67671000000000014', '0.25972034498667984']
      ['0.25773335426366534', '0.25972034498667984']
      ['0.05763093002025909', '0.058075234652991282']
      0.0500
      7
      [00000, 11111]
      [0.21846523167609955, 0.10677944122347405]
      [0.0863052776627459, 0.089292350976521773]
      ...
      ['0.00082901914935663813']
      ['0.00018537431726104939']
      50
      7000
      1000
      100
      20
      False
      True
      00000
    

    

      5
      [1.0, 0.0]
      100
      ['0.54250999999999994', '0.3316526924359276']
      ['0.32899100580411011', '0.3316526924359276']
      ['0.0735646252964018', '0.074159796520756438']
      0.0500
      7
      [00000, 11111]
      [0.25619102220790013, 0.22239374932433537]
      [0.15703337288481853, 0.15791405269472841]
      ...
      ['0.0010179160279708609']
      ['0.00022761294339294222']
      50
      7000
      1000
      100
      20
      False
      True
      00000
    

    

      6
      [0.6, 0.4]
      100
      ['0.82796000000000003', '0.0065966355060743153']
      ['0.0062615014173918269', '0.00659663550607431...
      ['0.0014001142810499407', '0.00147505254143708...
      0.0500
      7
      [11111, 00000]
      [0.028911430269514288, 0.029062897288985673]
      [0.00034060666127784821, 0.00042786563802803268]
      ...
      ['0.0010599447910150633']
      ['0.00023701086051064897']
      50
      7000
      1000
      100
      20
      True
      False
      00000
    

    

      7
      [0.7, 0.3]
      100
      ['0.85846', '0.0051188279908588449']
      ['0.0048025409940988546', '0.00511882799085884...
      ['0.0010738808127534454', '0.00114460473526890...
      0.0500
      7
      [11111, 00000]
      [0.032173203290633397, 0.032173750227490958]
      [0.00043543160814900626, 0.00044826116985947868]
      ...
      ['0.00091752190164592689']
      ['0.00020516413429251686']
      50
      7000
      1000
      100
      20
      True
      False
      00000
    

    

      8
      [0.8, 0.2]
      100
      ['0.88006999999999991', '0.0045554802161791776']
      ['0.0049288031001451238', '0.00455548021617917...
      ['0.00110211387796362', '0.0010186363433532077']
      0.0500
      7
      [11111, 00000]
      [0.034808408279791384, 0.03481641286167856]
      [0.00043322381305633829, 0.0004028772578036081]
      ...
      ['0.0011062522135570991']
      ['0.00024736551497732878']
      50
      7000
      1000
      100
      20
      True
      False
      00000
    

    

      9
      [0.9, 0.1]
      100
      ['0.89627000000000012', '0.0042572291458177524']
      ['0.0049832820510181631', '0.00425722914581775...
      ['0.0011142957417131188', '0.00095194537658418...
      0.0500
      7
      [11111, 00000]
      [0.036533329478025005, 0.036612933270406982]
      [0.00041372739691955724, 0.00042091426135845711]
      ...
      ['0.00096489590630288453']
      ['0.00021575728377045176']
      50
      7000
      1000
      100
      20
      True
      False
      00000
    

    

      10
      [1.0, 0.0]
      100
      ['0.9094199999999999', '0.0043890317838903613']
      ['0.0046134152208532142', '0.00438903178389036...
      ['0.0010315910042259992', '0.00098141734241860...
      0.0500
      7
      [11111, 00000]
      [0.037742519337525135, 0.037787546801743933]
      [0.00038586453795342039, 0.00040985167159946065]
      ...
      ['0.00099527282691732146']
      ['0.00022254976971455132']
      50
      7000
      1000
      100
      20
      True
      False
      00000
    

    

      11
      [0.5, 0.5]
      100
      ['0.85012999999999983', '0.0042654894209222909']
      ['0.0047147746499700679', '0.00426548942092229...
      ['0.0010542556615925848', '0.00095379243024884...
      0.0500
      7
      [00010, 11101]
      [0.038149833353219552, 0.037976858459404669]
      [0.00053781785042562446, 0.0004670799952957574]
      ...
      ['0.00091333202615474763']
      ['0.00020422724965096315']
      50
      7000
      1000
      100
      20
      True
      True
      00010
    

    

      12
      [0.6, 0.4]
      100
      ['0.81020999999999999', '0.009287470053787503']
      ['0.0078180496289036328', '0.009287470053787503']
      ['0.0017481690421695527', '0.00207674143792624...
      0.0500
      7
      [00010, 11101]
      [0.04059556908247814, 0.039551057074896562]
      [0.00099669246682975278, 0.0012575364381482888]
      ...
      ['0.00092018330239136421']
      ['0.0002057592415907335']
      50
      7000
      1000
      100
      20
      False
      True
      00010
    

    

      13
      [0.7, 0.3]
      100
      ['0.69741000000000009', '0.034174638257046711']
      ['0.040177828462971958', '0.034174638257046711']
      ['0.0089840355631531192', '0.00764168142492213...
      0.0500
      7
      [00010, 11101]
      [0.045864225106528489, 0.044751929918765122]
      [0.0059342934126664438, 0.005402261290233786]
      ...
      ['0.0010991884779236005']
      ['0.00024578601567216973']
      50
      7000
      1000
      100
      20
      False
      True
      00010
    

    

      14
      [0.8, 0.2]
      100
      ['0.52214000000000005', '0.11007021168327061']
      ['0.11070969424580668', '0.11007021168327061']
      ['0.024755440210184103', '0.024612447562158461']
      0.0500
      7
      [00010, 11101]
      [0.044811125194977861, 0.052872263142612543]
      [0.011757223149597382, 0.013438078597081914]
      ...
      ['0.00075119996671989633']
      ['0.00016797341902812678']
      50
      7000
      1000
      100
      20
      False
      True
      00010
    

    

      15
      [0.9, 0.1]
      100
      ['0.51803999999999983', '0.1832004309492748']
      ['0.18412810323250495', '0.1832004309492748']
      ['0.041172295539597982', '0.040964861710983472']
      0.0500
      7
      [00010, 11101]
      [0.040729215222032655, 0.037338836349428688]
      [0.016677774122160392, 0.016266734038424314]
      ...
      ['0.00075363176684636532']
      ['0.0001685171860671745']
      50
      7000
      1000
      100
      20
      False
      True
      00010
    

    

      16
      [1.0, 0.0]
      100
      ['0.41252000000000005', '0.055599446040405828']
      ['0.065494225699675238', '0.055599446040405828']
      ['0.014644954079818756', '0.012432414085767894']
      0.0500
      7
      [00010, 11101]
      [0.0093124310491074606, 0.0098316844311660614]
      [0.0015289404588059582, 0.0013223845144083311]
      ...
      ['0.00091845715741128791']
      ['0.00020537326383928645']
      50
      7000
      1000
      100
      20
      False
      True
      00010
    

    

      17
      [0.5, 0.5]
      100
      ['0.8542700000000002', '0.0061652169467099919']
      ['0.005295384782997372', '0.0061652169467099919']
      ['0.0011840840341800095', '0.001378584418887724']
      0.0500
      7
      [00011, 11100]
      [0.038153589465268908, 0.037952345891485509]
      [0.00068710431990004245, 0.00049769138626010222]
      ...
      ['0.00089307863035681552']
      ['0.00019969845267302466']
      50
      7000
      1000
      100
      20
      True
      True
      00011
    

    

      18
      [0.6, 0.4]
      100
      ['0.84681000000000017', '0.0075397877954223565']
      ['0.0067062582711971738', '0.00753978779542235...
      ['0.0014995649369067099', '0.00168594780464876...
      0.0500
      7
      [00011, 11100]
      [0.037034600395558621, 0.036993522382078162]
      [0.00075115748880579404, 0.00059462727315147401]
      ...
      ['0.00087339120100900756']
      ['0.00019529620964063238']
      50
      7000
      1000
      100
      20
      False
      True
      00011
    

    

      19
      [0.7, 0.3]
      100
      ['0.81234000000000006', '0.012649407100730087']
      ['0.012297007766119366', '0.012649407100730087']
      ['0.0027496945284885737', '0.00282849341523010...
      0.0500
      7
      [00011, 11100]
      [0.032476044023593678, 0.03264610542732195]
      [0.00080306510076232898, 0.0011679425116202457]
      ...
      ['0.00094824488398300799']
      ['0.00021203400199024073']
      50
      7000
      1000
      100
      20
      False
      True
      00011
    

    

      20
      [0.8, 0.2]
      100
      ['0.74502000000000002', '0.017567071469086731']
      ['0.015581899755806421', '0.017567071469086731']
      ['0.0034842187072570527', '0.00392811659704750...
      0.0500
      7
      [00011, 11100]
      [0.025721537585108457, 0.025752892845518332]
      [0.00075282952473154662, 0.00076810550509678183]
      ...
      ['0.00079662846421654006']
      ['0.00017813153987994424']
      50
      7000
      1000
      100
      20
      False
      True
      00011
    

    

      21
      [0.9, 0.1]
      100
      ['0.66626999999999981', '0.020785367449241768']
      ['0.02712734966781679', '0.020785367449241768']
      ['0.0060658597906644678', '0.00464774945538160...
      0.0500
      7
      [00011, 11100]
      [0.019248237613798531, 0.019400087873206132]
      [0.00077188025075358344, 0.00057182341538650737]
      ...
      ['0.0009495246179009821']
      ['0.00021232015919361096']
      50
      7000
      1000
      100
      20
      False
      True
      00011
    

    

      22
      [1.0, 0.0]
      100
      ['0.64610000000000001', '0.019339314879281529']
      ['0.019480400406562456', '0.019339314879281529']
      ['0.004355949953798819', '0.0043244022708346635']
      0.0500
      7
      [00011, 11100]
      [0.016359765568783706, 0.016443219617371645]
      [0.00048064081323673203, 0.00049344098382933749]
      ...
      ['0.0010940138710272501']
      ['0.0002446289383944619']
      50
      7000
      1000
      100
      20
      False
      True
      00011
    

    

      23
      [0.5, 0.5]
      100
      ['0.85124000000000011', '0.0070003642762359215']
      ['0.005153096156680941', '0.0070003642762359215']
      ['0.001152267330093149', '0.0015653290388924635']
      0.0500
      7
      [00100, 11011]
      [0.038113438035414574, 0.038085144538984955]
      [0.00051673634626110459, 0.00051258769219834866]
      ...
      ['0.00087854231542939187']
      ['0.00019644803384101826']
      50
      7000
      1000
      100
      20
      True
      True
      00100
    

    

      24
      [0.6, 0.4]
      100
      ['0.80144000000000004', '0.0093544588298842685']
      ['0.010638345736062534', '0.0093544588298842685']
      ['0.0023788064233980859', '0.00209172058363443...
      0.0500
      7
      [00100, 11011]
      [0.039714403769462364, 0.039943403283594983]
      [0.001296344235387037, 0.0013376150050147984]
      ...
      ['0.00091797718381234441']
      ['0.00020526593847982217']
      50
      7000
      1000
      100
      20
      False
      True
      00100
    

    

      25
      [0.7, 0.3]
      100
      ['0.68777000000000021', '0.030241724487866103']
      ['0.035812918060387042', '0.030241724487866103']
      ['0.0080080119255655346', '0.00676225517116886...
      0.0500
      7
      [00100, 11011]
      [0.046078239793168198, 0.045377798030211532]
      [0.005093972805991458, 0.004876139696051972]
      ...
      ['0.0005935217266452911']
      ['0.00013271549269019191']
      50
      7000
      1000
      100
      20
      False
      True
      00100
    

    

      26
      [0.8, 0.2]
      100
      ['0.53837000000000002', '0.065467973849814523']
      ['0.067164820404732706', '0.065467973849814523']
      ['0.015018510412154726', '0.014639083987736388']
      0.0500
      7
      [00100, 11011]
      [0.047081500626485688, 0.051387280275620298]
      [0.0076342820204130628, 0.0086286584284456704]
      ...
      ['0.0010975497938590193']
      ['0.00024541959477596484']
      50
      7000
      1000
      100
      20
      False
      True
      00100
    

    

      27
      [0.9, 0.1]
      100
      ['0.45997000000000005', '0.096485669402248536']
      ['0.099713154097140072', '0.096485669402248536']
      ['0.022296539081211687', '0.021574851563799922']
      0.0500
      7
      [00100, 11011]
      [0.037734737970515721, 0.041318507432286891]
      [0.0093052259486816457, 0.0092431286328221139]
      ...
      ['0.0012547098270118079']
      ['0.00028056164652354041']
      50
      7000
      1000
      100
      20
      False
      True
      00100
    

    

      28
      [1.0, 0.0]
      100
      ['0.39433000000000001', '0.025812591888456311']
      ['0.02603607305259379', '0.025812591888456311']
      ['0.005821842921275017', '0.005771871013804798']
      0.0500
      7
      [00100, 11011]
      [0.012189813778062757, 0.012547316634654382]
      [0.00080162314363986422, 0.0010210658556869985]
      ...
      ['0.00097832519644544058']
      ['0.00021876016433528407']
      50
      7000
      1000
      100
      20
      False
      True
      00100
    

    

      29
      [0.5, 0.5]
      100
      ['0.85397999999999996', '0.0063294470532582721']
      ['0.0066926526878361148', '0.00632944705325827...
      ['0.0014965226359798232', '0.00141530738710712...
      0.0500
      7
      [00101, 11010]
      [0.037949438011491962, 0.037980511003081793]
      [0.00054637093520814618, 0.0005000977376221357]
      ...
      ['0.0010110386540582957']
      ['0.00022607511583542427']
      50
      7000
      1000
      100
      20
      True
      True
      00101
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      323
      [0.55, 0.45]
      100
      ['0.96274799999999972', '0.0023930699947975029']
      ['0.0026165045385016681', '0.00239306999479750...
      ['0.00037002962043598153', '0.0003384312042350...
      0.1000
      7
      [11110, 00001]
      [0.059342205555559378, 0.05928462664998959]
      [0.00041407199985723294, 0.00039256571269818513]
      ...
      ['0.00054846800599485141']
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      00001
    

    

      324
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      50
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      00001
    

    

      325
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      ['0.86150800000000016', '0.0058821506271090861']
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      [11110, 00001]
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      [0.00036528531944925077, 0.00041104488903725343]
      ...
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      ['0.000116433037785678']
      50
      15000
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      True
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      00001
    

    

      326
      [0.55, 0.45]
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      ['0.59083199999999991', '0.0064629603124264851']
      ['0.0072909105055541674', '0.00646296031242648...
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      [11110, 00001]
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      ...
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      50
      15000
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      00001
    

    

      327
      [0.55, 0.45]
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      50
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      00001
    

    

      328
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      329
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      330
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      331
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      332
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      334
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      335
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      00001
    

    

      336
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      00001
    

    

      337
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      00001
    

    

      338
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      339
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      00001
    

    

      340
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      341
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      342
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      343
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      00001
    

    

      344
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      [0.0013812882814484087, 0.0013778019519159205]
      ...
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      00001
    

    

      345
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      ['0.00091457531127840392', '0.0008411993342840...
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      [0.0011826605490361942, 0.0013627925922206638]
      ...
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      ['0.00013371556726125833']
      50
      15000
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      50
      True
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      00001
    

    

      346
      [0.95, 0.05]
      100
      ['0.61447599999999991', '0.011212582218204687']
      ['0.0090856273311203089', '0.011212582218204687']
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      ...
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      ['0.00015398812681502244']
      50
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      True
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      00001
    

    

      347
      [0.95, 0.05]
      100
      ['0.23733600000000007', '0.0090577780939919087']
      ['0.0077728182791057168', '0.00905777809399190...
      ['0.0010992425028172805', '0.00128096326254892...
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      [11110, 00001]
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      [0.00037864314678126744, 0.00031718354189214288]
      ...
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      ['0.00022455846989147447']
      50
      15000
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      True
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      00001
    

    

      348
      [1.0, 0.0]
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      ['0.48103200000000002', '0.010019469846254354']
      ['0.011150344209933589', '0.010019469846254354']
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      [0.00077214625161338923, 0.00060623140238257132]
      ...
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      00001
    

    

      349
      [1.0, 0.0]
      100
      ['0.48445200000000005', '0.0091094895575986965']
      ['0.0093814975350420757', '0.00910948955759869...
      ['0.0013267441049426263', '0.00128827636786521...
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      [11110, 00001]
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      [0.0005128159112497662, 0.00053084809425854769]
      ...
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      50
      True
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      00001
    

    

      350
      [1.0, 0.0]
      100
      ['0.48018000000000005', '0.0091039332159237665']
      ['0.0072865080800065055', '0.00910393321592376...
      ['0.0010304678549086341', '0.00128749058248982...
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      [11110, 00001]
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      [0.00081698342558340713, 0.00066061870342397811]
      ...
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      00001
    

    

      351
      [1.0, 0.0]
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      [11110, 00001]
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      [0.00057430096756925966, 0.00058119723513719713]
      ...
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      352
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      [11110, 00001]
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      [0.00028987383500209959, 0.00033964245252247601]
      ...
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      00001
    

  



353 rows × 22 columns



















In [5]:



    


high_bias = []

for row in range(len(all_data)):
    high_bias.append(all_data['bias'][row][0])
all_data['high_bias'] = high_bias



    











In [6]:



    


compete_data = all_data[(all_data['compete'] == True) & (all_data['elong'] == 0.05)]
cooperate_data = all_data[(all_data['cooperate'] == True) & (all_data['elong'] == 0.05)]



    











In [7]:



    


np.unique(compete_data['motif'].values)
np.unique(cooperate_data['motif'].values)
print np.unique(compete_data['motif'].values)
print np.unique(cooperate_data['motif'].values)



    


















    






[['00000', '11111'] ['00001', '11110'] ['00010', '11101']
 ['00011', '11100'] ['00100', '11011'] ['00101', '11010']
 ['01000', '10111'] ['01001', '10110'] ['01010', '10101']
 ['01100', '10011'] ['10000', '01111'] ['10001', '01110']
 ['10010', '01101'] ['10100', '01011'] ['11000', '00111']]
[['00000', '11111'] ['00001', '11110'] ['00010', '11101']
 ['00011', '11100'] ['00100', '11011'] ['00101', '11010']
 ['00111', '11000'] ['01000', '10111'] ['01001', '10110']
 ['01010', '10101'] ['01011', '10100'] ['01100', '10011']
 ['01101', '10010'] ['01110', '10001'] ['01111', '10000']
 ['10000', '01111'] ['10001', '01110'] ['10010', '01101']
 ['10011', '01100'] ['10100', '01011'] ['10101', '01010']
 ['10110', '01001'] ['10111', '01000'] ['11000', '00111']
 ['11010', '00101'] ['11011', '00100'] ['11100', '00011']
 ['11101', '00010'] ['11110', '00001'] ['11111', '00000']]

















In [10]:



    


for motif in np.unique(cooperate_data['zero_motif'].values):
    plt.figure()
    ax = plt.subplot(111)
    mirrored = motif
    moment = cooperate_data[cooperate_data['zero_motif'] == motif]
    biaslist = np.sort(moment['high_bias'].values)
    meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][0])    for bias in biaslist]
    cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][0])  for bias in biaslist]

    (__,caps,__) = plt.errorbar(biaslist,meanlist,yerr=cilist,label=mirrored,color='r',linewidth=2,alpha=0.9)
    for cap in caps:
        cap.set_markeredgewidth(1)
    meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][1])    for bias in biaslist]
    cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][1])  for bias in biaslist]
    (__,caps,__) = plt.errorbar(biaslist,meanlist,yerr=cilist,label=mirrored.replace('0','2').replace('1','0').replace('2','1'),color='r',linestyle='--',alpha=0.7)
    for cap in caps:
        cap.set_markeredgewidth(1)
    
    motif = motif[::-1]
    moment = cooperate_data[cooperate_data['zero_motif'] == motif]
    biaslist = np.sort(moment['high_bias'].values)
    meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][0])    for bias in biaslist]
    cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][0])  for bias in biaslist]

    (__,caps,__) = plt.errorbar(biaslist,meanlist,yerr=cilist,label=motif,color='b',linewidth=2,alpha=0.9)
    for cap in caps:
        cap.set_markeredgewidth(1)
    meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][1])    for bias in biaslist]
    cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][1])  for bias in biaslist]
    (__,caps,__) = plt.errorbar(biaslist,meanlist,yerr=cilist,label=motif.replace('0','2').replace('1','0').replace('2','1'),color='b',linestyle='--',alpha=0.7)
    for cap in caps:
        cap.set_markeredgewidth(1)
    plt.xlim(0.49,1.01)
    plt.ylim(0.00,0.07)
    ax.xaxis.set_ticks_position('bottom')
    plt.xlabel(r"$b$",fontsize=20)
    plt.ylabel('Motif Frequency at Steady State',fontsize=18)
    plt.tick_params(labelsize=18)
    plt.legend(fontsize=14,loc='upper left')
    plt.title('Cooperate',fontsize=20)
#     plt.savefig('newleaf_revision_cooperate_colorswap_{m1}_{m2}.pdf'.format(m1=mirrored,m2=motif),bbox_inches='tight')



    


















    






---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-10-084fc584ec5d> in <module>()
     42     plt.ylabel('Motif Frequency at Steady State',fontsize=18)
     43     plt.tick_params(labelsize=18)
---> 44     plt.legend(fontsize=14,loc='upper left')
     45     plt.title('Cooperate',fontsize=20)
     46     plt.savefig('newleaf_revision_cooperate_colorswap_{m1}_{m2}.pdf'.format(m1=mirrored,m2=motif),bbox_inches='tight')

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/pyplot.pyc in legend(*args, **kwargs)
   3517 @docstring.copy_dedent(Axes.legend)
   3518 def legend(*args, **kwargs):
-> 3519     ret = gca().legend(*args, **kwargs)
   3520     draw_if_interactive()
   3521     return ret

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc in legend(self, *args, **kwargs)
    511             raise TypeError('Invalid arguments to legend.')
    512 
--> 513         self.legend_ = mlegend.Legend(self, handles, labels, **kwargs)
    514         self.legend_._remove_method = lambda h: setattr(self, 'legend_', None)
    515         return self.legend_

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/legend.pyc in __init__(self, parent, handles, labels, loc, numpoints, markerscale, scatterpoints, scatteryoffsets, prop, fontsize, borderpad, labelspacing, handlelength, handleheight, handletextpad, borderaxespad, columnspacing, ncol, mode, fancybox, shadow, title, framealpha, bbox_to_anchor, bbox_transform, frameon, handler_map)
    366 
    367         # init with null renderer
--> 368         self._init_legend_box(handles, labels)
    369 
    370         if framealpha is None:

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/legend.pyc in _init_legend_box(self, handles, labels)
    640                 # original artist/handle.
    641                 handle_list.append(handler.legend_artist(self, orig_handle,
--> 642                                                          fontsize, handlebox))
    643 
    644         if len(handleboxes) > 0:

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/legend_handler.pyc in legend_artist(self, legend, orig_handle, fontsize, handlebox)
    116         artists = self.create_artists(legend, orig_handle,
    117                                       xdescent, ydescent, width, height,
--> 118                                       fontsize, handlebox.get_transform())
    119 
    120         # create_artists will return a list of artists.

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/legend_handler.pyc in create_artists(self, legend, orig_handle, xdescent, ydescent, width, height, fontsize, trans)
    471                       for x, y in zip(xdata_marker, ydata_marker)]
    472             coll = mcoll.LineCollection(verts)
--> 473             self.update_prop(coll, barlinecols[0], legend)
    474             handle_barlinecols.append(coll)
    475 

IndexError: tuple index out of range









    

















    

















    

















    

















    

















    

















    

















    

















    

















    
























In [12]:



    


for motif in np.unique(compete_data['zero_motif'].values):
    plt.figure()
    ax = plt.subplot(111)
    mirrored = motif
    moment = compete_data[compete_data['zero_motif'] == motif]
    biaslist = np.sort(moment['high_bias'].values)
    meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][0])    for bias in biaslist]
    cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][0])  for bias in biaslist]

    (__,caps,__) = plt.errorbar(biaslist,meanlist,yerr=cilist,label=mirrored,color='r',linewidth=2,alpha=0.9)
    for cap in caps:
        cap.set_markeredgewidth(1)
    meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][1])    for bias in biaslist]
    cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][1])  for bias in biaslist]
    (__,caps,__) = plt.errorbar(biaslist,meanlist,yerr=cilist,label=mirrored.replace('0','2').replace('1','0').replace('2','1'),color='r',linestyle='--',alpha=0.7)
    for cap in caps:
        cap.set_markeredgewidth(1)

    motif = motif[::-1]
    moment = compete_data[compete_data['zero_motif'] == motif]
    biaslist = np.sort(moment['high_bias'].values)
    meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][0])    for bias in biaslist]
    cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][0])  for bias in biaslist]

    (__,caps,__) = plt.errorbar(biaslist,meanlist,yerr=cilist,label=motif,color='b',linewidth=2,alpha=0.9)
    for cap in caps:
        cap.set_markeredgewidth(1)
    meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][1])    for bias in biaslist]
    cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][1])  for bias in biaslist]
    (__,caps,__) = plt.errorbar(biaslist,meanlist,yerr=cilist,label=motif.replace('0','2').replace('1','0').replace('2','1'),color='b',linestyle='--',alpha=0.7)
    for cap in caps:
        cap.set_markeredgewidth(1)
    plt.xlim(0.49,1.01)
    plt.ylim(0.00,0.07)
    ax.xaxis.set_ticks_position('bottom')
    plt.xlabel(r"$b$",fontsize=20)
    plt.ylabel('Motif Frequency at Steady State',fontsize=18)
    plt.tick_params(labelsize=18)
    plt.legend(fontsize=14,loc='upper left')
    plt.title('Compete',fontsize=20)
#     plt.savefig('newleaf_revision_compete_nolim_colorswap_{m1}_{m2}.pdf'.format(m1=mirrored,m2=motif),bbox_inches='tight')
#     plt.savefig('newleaf_revision_compete_colorswap_{m1}_{m2}.pdf'.format(m1=mirrored,m2=motif),bbox_inches='tight')



    


















    






---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-12-ae62093f2c14> in <module>()
     41     plt.ylabel('Motif Frequency at Steady State',fontsize=18)
     42     plt.tick_params(labelsize=18)
---> 43     plt.legend(fontsize=14,loc='upper left')
     44     plt.title('Compete',fontsize=20)
     45 #     plt.savefig('newleaf_revision_compete_nolim_colorswap_{m1}_{m2}.pdf'.format(m1=mirrored,m2=motif),bbox_inches='tight')

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/pyplot.pyc in legend(*args, **kwargs)
   3517 @docstring.copy_dedent(Axes.legend)
   3518 def legend(*args, **kwargs):
-> 3519     ret = gca().legend(*args, **kwargs)
   3520     draw_if_interactive()
   3521     return ret

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc in legend(self, *args, **kwargs)
    511             raise TypeError('Invalid arguments to legend.')
    512 
--> 513         self.legend_ = mlegend.Legend(self, handles, labels, **kwargs)
    514         self.legend_._remove_method = lambda h: setattr(self, 'legend_', None)
    515         return self.legend_

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/legend.pyc in __init__(self, parent, handles, labels, loc, numpoints, markerscale, scatterpoints, scatteryoffsets, prop, fontsize, borderpad, labelspacing, handlelength, handleheight, handletextpad, borderaxespad, columnspacing, ncol, mode, fancybox, shadow, title, framealpha, bbox_to_anchor, bbox_transform, frameon, handler_map)
    366 
    367         # init with null renderer
--> 368         self._init_legend_box(handles, labels)
    369 
    370         if framealpha is None:

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/legend.pyc in _init_legend_box(self, handles, labels)
    640                 # original artist/handle.
    641                 handle_list.append(handler.legend_artist(self, orig_handle,
--> 642                                                          fontsize, handlebox))
    643 
    644         if len(handleboxes) > 0:

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/legend_handler.pyc in legend_artist(self, legend, orig_handle, fontsize, handlebox)
    116         artists = self.create_artists(legend, orig_handle,
    117                                       xdescent, ydescent, width, height,
--> 118                                       fontsize, handlebox.get_transform())
    119 
    120         # create_artists will return a list of artists.

/Users/grantkinsler/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/legend_handler.pyc in create_artists(self, legend, orig_handle, xdescent, ydescent, width, height, fontsize, trans)
    471                       for x, y in zip(xdata_marker, ydata_marker)]
    472             coll = mcoll.LineCollection(verts)
--> 473             self.update_prop(coll, barlinecols[0], legend)
    474             handle_barlinecols.append(coll)
    475 

IndexError: tuple index out of range









    

















    

















    

















    

















    

















    

















    

















    

















    

















    
























In [77]:



    


cooperate_data_no00000[cooperate_data_no00000['high_bias']==0.9]



    


















    
Out[77]:










  
    

      
      bias
      cells
      cellswithmotif
      cellswithmotif_sd
      cellswithmotif_se
      elong
      maxstrandlen
      motif
      motiffreq
      motiffreq_sd
      ...
      nonemptystrands_se
      nr_samples
      rounds
      sampler_start
      strands
      trials
      cooperate
      compete
      zero_motif
      high_bias
    

  
  
    

      38
      [0.9, 0.1]
      100
      ['0.85284000000000015', '0.007370610558155976']
      ['0.0050725141695218924', '0.007370610558155976']
      ['0.0011342486499881842', '0.00164811862437144...
      0.05
      7
      [00111, 11000]
      [0.035013655233347624, 0.035020681293004764]
      [0.00047653477131490271, 0.00049215463287092642]
      ...
      ['0.00020354263803930346']
      50
      7000
      1000
      100
      20
      True
      False
      11000
      0.9
    

    

      61
      [0.9, 0.1]
      100
      ['0.77416999999999991', '0.0077548436476824042']
      ['0.0088808276641313029', '0.00775484364768240...
      ['0.0019858134353458261', '0.00173403575511002...
      0.05
      7
      [01011, 10100]
      [0.02409304917479518, 0.023842220313185399]
      [0.00041483839891352369, 0.00053169693663315592]
      ...
      ['0.00029268368249698494']
      50
      7000
      1000
      100
      20
      True
      False
      10100
      0.9
    

    

      70
      [0.9, 0.1]
      100
      ['0.70740000000000003', '0.0065688583482976575']
      ['0.0072512067961133329', '0.00656885834829765...
      ['0.0016214191315017869', '0.00146884138013605...
      0.05
      7
      [01101, 10010]
      [0.018581994485631755, 0.018645445723336856]
      [0.00026518105940538021, 0.00025683055938335468]
      ...
      ['0.00019379874096597932']
      50
      7000
      1000
      100
      20
      True
      False
      10010
      0.9
    

    

      75
      [0.9, 0.1]
      100
      ['0.68302999999999991', '0.0080145867017582233']
      ['0.0073954783482882364', '0.00801458670175822...
      ['0.0016536792312900361', '0.00179211606766972...
      0.05
      7
      [01110, 10001]
      [0.019731838450871196, 0.019541093906271866]
      [0.00050201531115208063, 0.00050492063073901859]
      ...
      ['0.00019164838115674285']
      50
      7000
      1000
      100
      20
      True
      False
      10001
      0.9
    

    

      89
      [0.9, 0.1]
      100
      ['0.79094999999999993', '0.0073982700680631907']
      ['0.004811600565300468', '0.0073982700680631907']
      ['0.0010759065944588261', '0.001654303478809129']
      0.05
      7
      [10011, 01100]
      [0.027318708609762404, 0.027319540231588845]
      [0.00051172975103048698, 0.00044234877070414548]
      ...
      ['0.00017901375785116984']
      50
      7000
      1000
      100
      20
      True
      False
      01100
      0.9
    

    

      99
      [0.9, 0.1]
      100
      ['0.76337999999999995', '0.0069101085374978083']
      ['0.0073348210612120572', '0.00691010853749780...
      ['0.0016401158495667305', '0.00154514724217467...
      0.05
      7
      [10101, 01010]
      [0.023526984856746036, 0.023507327523864828]
      [0.0003659361483050524, 0.00037454493561796324]
      ...
      ['0.00018598483809171347']
      50
      7000
      1000
      100
      20
      True
      False
      01010
      0.9
    

    

      104
      [0.9, 0.1]
      100
      ['0.60818000000000005', '0.0098134397639156393']
      ['0.010677246836146487', '0.0098134397639156393']
      ['0.0023875049738168101', '0.00219435184052148...
      0.05
      7
      [10110, 01001]
      [0.01407222720905112, 0.013820651541368611]
      [0.00033939370767995024, 0.00031514773187306177]
      ...
      ['0.00021559644129715979']
      50
      7000
      1000
      100
      20
      True
      False
      01001
      0.9
    

    

      109
      [0.9, 0.1]
      100
      ['0.87238000000000004', '0.0054175271111458409']
      ['0.0058500940163385739', '0.00541752711114584...
      ['0.0013081207895297817', '0.00121139588904701...
      0.05
      7
      [10111, 01000]
      [0.034051324912520289, 0.034145286019669506]
      [0.00038312238363362856, 0.00035459198246088778]
      ...
      ['0.00016717758671543859']
      50
      7000
      1000
      100
      20
      True
      False
      01000
      0.9
    

    

      120
      [0.9, 0.1]
      100
      ['0.59147999999999989', '0.0092771709049688338']
      ['0.0080496956464204301', '0.00927717090496883...
      ['0.0017999666663580192', '0.00207443847823935...
      0.05
      7
      [11010, 00101]
      [0.014617205058125433, 0.014622699055582059]
      [0.00037031655773894551, 0.00036113388194471162]
      ...
      ['0.00020607398671351052']
      50
      7000
      1000
      100
      20
      True
      False
      00101
      0.9
    

    

      125
      [0.9, 0.1]
      100
      ['0.85666999999999993', '0.0050637041777734371']
      ['0.0054601373609095057', '0.00506370417777343...
      ['0.0012209238305479957', '0.00113227867594510...
      0.05
      7
      [11011, 00100]
      [0.030994816164120854, 0.031099084946655708]
      [0.00036852640871603182, 0.00040974197959439234]
      ...
      ['0.00019211482373831349']
      50
      7000
      1000
      100
      20
      True
      False
      00100
      0.9
    

    

      130
      [0.9, 0.1]
      100
      ['0.63441000000000003', '0.0082213137636268085']
      ['0.012302597286752072', '0.0082213137636268085']
      ['0.0027509443832982104', '0.001838341643982418']
      0.05
      7
      [11100, 00011]
      [0.028810293307396866, 0.0285515601595475]
      [0.0011740252129996825, 0.00075615628957589214]
      ...
      ['0.00018066906763472736']
      50
      7000
      1000
      100
      20
      True
      False
      00011
      0.9
    

    

      135
      [0.9, 0.1]
      100
      ['0.84727999999999992', '0.0060254792340526751']
      ['0.0037917805843692789', '0.00602547923405267...
      ['0.00084786791424135838', '0.0013473381164355...
      0.05
      7
      [11101, 00010]
      [0.030098184153307346, 0.029882490346718422]
      [0.0005511777567757905, 0.00044135102546340031]
      ...
      ['0.00022055087735032506']
      50
      7000
      1000
      100
      20
      True
      False
      00010
      0.9
    

    

      140
      [0.9, 0.1]
      100
      ['0.90540399999999988', '0.0044769632564942811']
      ['0.0039471488444192326', '0.00447696325649428...
      ['0.00055821114284829686', '0.0006331382155580...
      0.05
      7
      [01111, 10000]
      [0.041925285875835695, 0.041934133104306326]
      [0.00040019901444195393, 0.00039149795168601237]
      ...
      ['0.00012833606495447799']
      50
      15000
      1000
      100
      50
      True
      False
      10000
      0.9
    

    

      314
      [0.9, 0.1]
      100
      ['0.90128399999999997', '0.0046295071011933772']
      ['0.0054146601001355613', '0.00462950710119337...
      ['0.00076574857492521715', '0.0006547111729610...
      0.05
      7
      [11110, 00001]
      [0.067006625869633629, 0.066624470107593495]
      [0.0011698704082052089, 0.0010022435384090551]
      ...
      ['0.00012441660339359431']
      50
      15000
      1000
      100
      50
      True
      False
      00001
      0.9
    

  



14 rows × 23 columns



















In [167]:



    


sns.set_palette('Greys_d',13)
fig,ax = plt.subplots()
finished = []
cooperate_data_no00000 = cooperate_data
Y = np.swapaxes(np.asarray(map(myFloat,cooperate_data_no00000[cooperate_data_no00000['high_bias']==0.9]['motiffreq'].values)),0,1)[0]
X = cooperate_data_no00000[cooperate_data_no00000['high_bias']==0.9]['zero_motif'].values
new_order = [x for (y,x) in sorted(zip(Y,X), key=lambda pair: pair[0])]
print new_order[::-1]
print len(new_order)
for motif in new_order[::-1]:
    if motif not in finished:
        mirrored = motif
        moment = cooperate_data_no00000[cooperate_data_no00000['zero_motif'] == motif]
        biaslist = np.sort(moment['high_bias'].values)
        meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][0])    for bias in biaslist]
        cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][0])  for bias in biaslist]
        if motif == '10000':
            (ebar, caps, _) = plt.errorbar(biaslist,meanlist,yerr=cilist,alpha=0.9,capsize=5,color='b')
        elif motif == '00001':
            (ebar, caps, _) = plt.errorbar(biaslist,meanlist,yerr=cilist,alpha=0.9,capsize=5,color='r')
        else:
            (ebar, caps, _) = plt.errorbar(biaslist,meanlist,yerr=cilist,alpha=0.9,capsize=5)
        for cap in caps:
                cap.set_markeredgewidth(1)

        finished.append(motif)

ax.xaxis.set_ticks_position('bottom')
plt.xlim(0.49,1.01)
plt.ylim(0,0.07)
plt.xlabel(r"$b$",fontsize=20)
plt.ylabel('Motif Frequency at Steady State',fontsize=18)
plt.tick_params(labelsize=18)
plt.title('Cooperate',fontsize=20)
plt.legend(new_order[::-1],loc='lower left',ncol=3,fontsize=12)
# plt.savefig('newleaf_revision_cooperate_all_with00000_ylim_greyscale.pdf',bbox_inches='tight')



    


















    






['00001', '10000', '00000', '11000', '01000', '00100', '00010', '00011', '01100', '10100', '01010', '10001', '10010', '00101', '01001']
15
00001
10000
00000
11000
01000
00100
00010
00011
01100
10100
01010
10001
10010
00101
01001
['00001', '10000', '00000', '11000', '01000', '00100', '00010', '00011', '01100', '10100', '01010', '10001', '10010', '00101', '01001']










    
























In [166]:



    


sns.set_palette('Greys_d',13)
fig,ax = plt.subplots()
finished = []
compete_data_no00000 = compete_data
Y = np.swapaxes(np.asarray(map(myFloat,compete_data_no00000[compete_data_no00000['high_bias']==0.9]['motiffreq'].values)),0,1)[0]
X = compete_data_no00000[compete_data_no00000['high_bias']==0.9]['zero_motif'].values
new_order = [x for (y,x) in sorted(zip(Y,X), key=lambda pair: pair[0])]
for motif in new_order[::-1]:
    if motif not in finished:
        mirrored = motif
        moment = compete_data_no00000[compete_data_no00000['zero_motif'] == motif]
        biaslist = np.sort(moment['high_bias'].values)
        meanlist = [float(moment[moment['high_bias'] == bias]['motiffreq'].values[0][0])    for bias in biaslist]
        cilist = [1.96*float(moment[moment['high_bias'] == bias]['motiffreq_se'].values[0][0])  for bias in biaslist]
        if motif == '10000':
            (ebar, caps, _) = plt.errorbar(biaslist,meanlist,yerr=cilist,alpha=0.9,capsize=5,color='b')
        elif motif == '00001':
            (ebar, caps, _) = plt.errorbar(biaslist,meanlist,yerr=cilist,alpha=0.9,capsize=5,color='r')
        else:
            (ebar, caps, _) = plt.errorbar(biaslist,meanlist,yerr=cilist,alpha=0.9,capsize=5)
        for cap in caps:
                cap.set_markeredgewidth(1)

        color = ebar.get_color()
        finished.append(motif)

plt.xlim(0.49,1.01)
plt.ylim(0.0,0.07)
ax.xaxis.set_ticks_position('bottom')
plt.xlabel(r"$b$",fontsize=20)
plt.ylabel('Motif Frequency at Steady State',fontsize=18)
plt.tick_params(labelsize=18)
ax.legend(finished,loc='lower left',ncol=3,fontsize=12)
plt.title('Compete',fontsize=20)
# plt.savefig('newleaf_revision_compete_all_ylim_with00000_greyscale.pdf',bbox_inches='tight')



    


















    






00000
10000
01000
00001
00010
00100
11000
00011
10100
00101
10001
01100
01001
10010
01010
['00000', '10000', '01000', '00001', '00010', '00100', '11000', '00011', '10100', '00101', '10001', '01100', '01001', '10010', '01010']

Content source: grantkinsler/motifproject

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