updated Demonstrate script

Author: mdhumphries

Demonstrate_Bayesian_Strategy_Analysis.py

@@ -4,10 +4,12 @@
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
+import strategymodels
+
 from Functions.set_Beta_prior import set_priors
-from strategy_models.go_left import go_left
 from Functions.update_strategy_posterior_probability import update_strategy_posterior_probability
 from Functions.Summaries_of_Beta_distribution import summaries_of_Beta_Distribution
+from Functions.plotSessionStructure import plotSessionStructure
 
 # initiate TestData variable so that rat 2 testdata can be loaded
 TestData = pd.read_csv('data.csv')
@@ -18,15 +20,8 @@
 
 decay_rate = 0.9  # Set Decay rate (gamma)
 
-# MAIN LOOP: FOR EACH TRIAL TO UPDATE STRATEGY INDEX
-
 no_Trials = np.size(TestData.TrialIndex)
 
-Alpha = 0   # initialises storage
-Beta = 0 
-MAPprobability = 0 
-precision = 0
-
 success_total = 0  # initialise variables to zero
 failure_total = 0
 
@@ -35,7 +30,7 @@
 #%% run strategy analysis
 for trial in range(len(TestData)):
     rows_of_data = TestData.iloc[0:trial+1]     # select all rows of data up to the curren trial; is trial+1 as dataframe includes column row as row 0????
-    trial_type = go_left(rows_of_data)          # test whether go-left was used       
+    trial_type = strategymodels.go_left(rows_of_data)          # test whether go-left was used       
 
     [success_total, failure_total, Alpha, Beta] = update_strategy_posterior_probability(trial_type, decay_rate,
                                                                                         success_total, failure_total,
@@ -58,22 +53,7 @@
 plt.xlabel('Trials'), plt.ylabel('P(Strategy)')  # labelling the axis
 plt.axhline(y=0.5, color='firebrick', linestyle='--', linewidth=0.75,
             label="Chance")  # shows the line at which Chance is exceeded
-sessionLines = TestData[TestData['NewSessionTrials'] == 1].index  # indices list  when new session was started
-plt.vlines(sessionLines, 0, 1, colors='lightgray', linestyles='--', linewidth=0.75,
-           label="New Sessions")  # vertical lines indicate the new session trials
-ruleLines = np.array(TestData[TestData['RuleChangeTrials'] == 1].index)  # indices list  when new session was started
-ruleLines = np.insert(ruleLines, 0, 0)  # sets array for x values of rule change
 
-for x in ruleLines:  # to get shade change for rule change and labels
-    minx = x / no_Trials
-    plt.axhspan(1, 1.25, xmin=minx, alpha=0.3, edgecolor='k')  # change transparency for separation
-    plt.axvline(x, 0.8, 1)  # dividing lines
+plotSessionStructure(TestData)
 
-#  creating labels
-plt.text(1, 1.125, "Right Arm", label='Go to the Right')
-plt.text(120, 1.125, "Lit Arm", label='Go to the Lit Arm')
-plt.text(225, 1.125, "Left Arm", label='Go to the Left')
-plt.text(330, 1.125, "Unlit Arm", label='Go to the Dark Arm')
-plt.text(150, 1.3, "Rule for Reward")
-plt.legend()  # add legend
 plt.show()

Output.csv

@@ -218,7 +218,7 @@ Alpha,Beta,MAPprobability,Precision
 5.300203773019574,6.69979622580384,0.43,52.71712088773742
 4.870183395717617,7.129816603223456,0.387,53.911593626064956
 5.483165056145856,6.51683494290111,0.448,52.38872192443417
-6.034848550531271,5.9651514486109996,0.503,52.001754220140775
+6.034848550531271,5.9651514486109996,0.503,52.00175422014077
 6.531363695478143,5.4686363037499,0.553,52.41105902631707
 5.978227325930329,6.02177267337491,0.498,52.0006847441459
 5.480404593337297,6.5195954060374195,0.448,52.39291687376341
@@ -229,23 +229,23 @@ Alpha,Beta,MAPprobability,Precision
 6.18463410831974,5.815365891311036,0.518,52.04928742645532
 5.666170697487766,6.333829302179933,0.467,52.161471634341666
 6.1995536277389895,5.80044637196194,0.52,52.05758385728475
-5.679598264965091,6.320401734765746,0.468,52.14870677564352
+5.679598264965091,6.320401734765746,0.468,52.14870677564354
 6.211638438468582,5.788361561289172,0.521,52.06477845938611
 5.690474594621724,6.3095254051602545,0.46900000000000003,52.13875567695228
 6.221427135159552,5.77857286464423,0.522,52.07091766186058
-6.699284421643597,5.3007155781798065,0.5700000000000001,52.71605788558549
-7.129355979479238,4.870644020361826,0.613,53.90997800052084
+6.699284421643597,5.3007155781798065,0.5700000000000001,52.716057885585485
+7.129355979479238,4.870644020361826,0.613,53.90997800052083
 6.516420381531314,5.483579618325644,0.552,52.38809392501376
-6.964778343378183,5.035221656493079,0.596,53.38016987249572
+6.964778343378183,5.035221656493079,0.596,53.3801698724957
 7.368300509040364,4.631699490843772,0.637,54.852716516871105
 7.731470458136328,4.268529541759395,0.673,56.723817640635886
 8.058323412322695,3.9416765875834554,0.706,58.93593301760396
-8.352491071090427,3.64750892882511,0.735,61.445977948011674
+8.352491071090427,3.64750892882511,0.735,61.44597794801169
 8.617241963981384,3.382758035942599,0.762,64.21945599021568
 7.855517767583246,4.14448223234834,0.686,57.49906028610618
-8.16996599082492,3.8300340091135054,0.717,59.8250355502601
+8.16996599082492,3.8300340091135054,0.717,59.82503555026008
 8.452969391742428,3.5470306082021548,0.745,62.43550256847977
-8.707672452568186,3.2923275473819396,0.771,65.29812701163428
+8.707672452568186,3.2923275473819396,0.771,65.29812701163429
 8.936905207311368,3.0630947926437457,0.794,68.38459133925393
 9.143214686580231,2.8567853133793712,0.8140000000000001,71.66866629009075
 9.328893217922207,2.6711067820414343,0.833,75.12499781248921
@@ -258,59 +258,59 @@ Alpha,Beta,MAPprobability,Precision
 10.20071480656322,1.7992851934193939,0.92,101.9941428768469
 9.280643325906897,2.7193566740774546,0.8280000000000001,74.17568887377108
 9.452578993316209,2.547421006669709,0.845,77.74183783687629
-9.607321093984588,2.3926789060027382,0.861,81.43650328954836
-9.746588984586129,2.2534110154024645,0.875,85.2339821539392
+9.607321093984588,2.3926789060027382,0.861,81.43650328954834
+9.746588984586129,2.2534110154024645,0.875,85.23398215393918
 8.871930086127517,3.128069913862218,0.787,67.454555135619
 9.084737077514765,2.9152629224759963,0.808,70.68312669184965
-9.276263369763289,2.7237366302283963,0.8280000000000001,74.09137633391158
+9.276263369763289,2.7237366302283963,0.8280000000000001,74.0913763339116
 9.448637032786959,2.5513629672055567,0.845,77.65410719967797
 9.603773329508263,2.3962266704850013,0.86,81.34597062921584
 9.743395996557437,2.256604003436501,0.874,85.1412724255392
-9.869056396901694,2.130943603092851,0.887,89.01398778815889
+9.869056396901694,2.130943603092851,0.887,89.0139877881589
 9.982150757211524,2.0178492427835657,0.898,92.93793179675056
 10.083935681490372,1.9160643185052093,0.908,96.88704136032035
 10.175542113341335,1.8244578866546886,0.918,100.83572965321693
 10.257987902007201,1.7420120979892197,0.926,104.7592776310976
-9.332189111806482,2.6678108881902975,0.833,75.19124446235213
+9.332189111806482,2.6678108881902975,0.833,75.19124446235212
 9.498970200625834,2.501029799371268,0.85,78.79713965677631
 9.649073180563251,2.3509268194341413,0.865,82.52416405662672
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-9.905749276256234,2.0942507237416543,0.891,90.23807845854381
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 9.01517434863061,2.9848256513674887,0.802,69.56851705539694
 9.21365691376755,2.7863430862307403,0.8210000000000001,72.91875033841015
 9.392291222390796,2.607708777607666,0.839,76.43199675073127
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+9.82798030112289,2.172019698875989,0.883,87.69560071617009
 9.945182271010601,2.05481772898839,0.895,91.60512952893302
 10.050664043909542,1.9493359560895511,0.905,95.54861460825909
 10.145597639518588,1.854402360480596,0.915,99.50026517374877
 10.231037875566729,1.7689621244325364,0.923,103.43502356110339
 10.307934088010056,1.6920659119892827,0.931,107.32896346858584
 10.37714067920905,1.6228593207903543,0.9380000000000001,111.15967436062158
-10.439426611288146,1.560573388711319,0.9440000000000001,114.90660727681917
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 10.495483950159333,1.5045160498401873,0.9500000000000001,118.55136273368429
-10.5459355551434,1.4540644448561686,0.9550000000000001,122.07790740279881
+10.5459355551434,1.4540644448561686,0.9550000000000001,122.07790740279879
 10.59134199962906,1.4086580003705516,0.9590000000000001,125.47271239115283
 10.632207799666155,1.3677922003334966,0.963,128.72481178164438
 10.66898701969954,1.331012980300147,0.967,131.8257852037574
 10.702088317729586,1.2979116822701322,0.97,134.76967229067563
 10.731879485956629,1.2681205140431189,0.973,137.55282977076868
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-10.78282238362487,1.2171776163749264,0.978,142.632809153164
-10.804540145262383,1.1954598547374338,0.98,144.93209181124323
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+10.78282238362487,1.2171776163749264,0.978,142.63280915316398
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 10.824086130736145,1.1759138692636903,0.982,147.07507966950106
 10.84167751766253,1.1583224823373213,0.984,149.0664378101318
-10.857509765896278,1.1424902341035892,0.986,150.91177223717548
+10.857509765896278,1.1424902341035892,0.986,150.91177223717543
 9.87175878930665,2.12824121069323,0.887,89.10261722380483
 9.984582910375984,2.015417089623907,0.898,93.02742073562489
 10.086124619338387,1.9138753806615165,0.909,96.97680209535673
-10.177512157404548,1.8224878425953648,0.918,100.92518976117528
+10.177512157404548,1.8224878425953648,0.918,100.92518976117525
 10.259760941664094,1.7402390583358285,0.926,104.84788903569286
-10.333784847497686,1.6662151525022455,0.933,108.72147847681366
+10.333784847497686,1.6662151525022455,0.933,108.72147847681363
 10.400406362747917,1.599593637252021,0.9400000000000001,112.52418294349124
 10.460365726473126,1.539634273526819,0.9460000000000001,116.2362004044769
-10.514329153825814,1.485670846174137,0.9510000000000001,119.8399653191584
+10.514329153825814,1.485670846174137,0.9510000000000001,119.83996531915837
 10.562896238443233,1.4371037615567233,0.9560000000000001,123.32033750054764
 10.60660661459891,1.393393385401051,0.961,126.66471145234812
 10.64594595313902,1.3540540468609459,0.965,129.8630467869815
@@ -338,21 +338,21 @@ Alpha,Beta,MAPprobability,Precision
 10.965133787685096,1.0348662123149013,0.996,164.97103598462877
 10.968620408916586,1.0313795910834112,0.997,165.47611036668067
 10.971758368024927,1.02824163197507,0.997,165.9336345968057
-10.974582531222435,1.025417468777563,0.997,166.34782385825866
-10.977124278100192,1.0228757218998068,0.998,166.72256859628868
+10.974582531222435,1.025417468777563,0.997,166.34782385825864
+10.977124278100192,1.0228757218998068,0.998,166.72256859628865
 10.979411850290173,1.0205881497098261,0.998,167.0614501090752
 10.981470665261156,1.0185293347388433,0.998,167.36775740035435
 10.98332359873504,1.0166764012649592,0.998,167.64450472953752
 10.984991238861538,1.0150087611384633,0.998,167.89444941051255
 10.986492114975384,1.0135078850246169,0.999,168.120109508795
-10.987842903477846,1.0121570965221551,0.999,168.3237811696369
-10.989058613130062,1.0109413868699397,0.999,168.50755537867585
-10.990152751817057,1.0098472481829457,0.999,168.67333401337868
+10.987842903477846,1.0121570965221551,0.999,168.32378116963687
+10.989058613130062,1.0109413868699397,0.999,168.50755537867582
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 10.991137476635352,1.008862523364651,0.999,168.82284508951886
 10.992023728971818,1.007976271028186,0.999,168.9576571437698
 10.992821356074636,1.0071786439253674,0.999,169.07919272259343
 10.993539220467172,1.0064607795328306,0.999,169.18874097022754
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 10.99476676857841,1.0052332314215928,0.999,169.3764343527415
 10.99529009172057,1.0047099082794335,0.999,169.45659173195799
 10.995761082548514,1.0042389174514903,0.999,169.52880545864426
@@ -364,25 +364,25 @@ Alpha,Beta,MAPprobability,Precision
 10.997747265470666,1.0022527345293375,0.999,169.83408643350313
 10.9979725389236,1.0020274610764037,0.999,169.86878860839823
 10.99817528503124,1.0018247149687634,0.999,169.90003403124143
-10.998357756528115,1.001642243471887,0.999,169.92816582544694
+10.998357756528115,1.001642243471887,0.999,169.9281658254469
 10.998521980875305,1.0014780191246984,0.999,169.9534932851317
-10.998669782787774,1.0013302172122285,0.999,169.97629516675184
+10.998669782787774,1.0013302172122285,0.999,169.9762951667518
 10.998802804508996,1.0011971954910055,0.999,169.99682266879097
 10.998922524058097,1.001077475941905,0.999,170.01530212745763
 10.999030271652288,1.0009697283477146,0.999,170.03193745416306
 10.99912724448706,1.000872755512943,0.999,170.04691233846205
 10.999214520038354,1.0007854799616487,0.999,170.06039223817461
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