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andrewheusserandrewheusser
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Merge pull request #109 from paxtonfitzpatrick/master
updated examples
2 parents f561ac9 + 5f04f69 commit dbb0302

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+847
-815
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‎docs/tutorial/.ipynb_checkpoints/naturalistic-analyses-checkpoint.ipynb

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‎docs/tutorial/naturalistic-analyses.ipynb

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‎examples/crack_egg.py

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@@ -18,21 +18,6 @@
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egg = quail.load('example')
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#crack egg
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cracked_egg = quail.crack_egg(egg, subjects=[0], lists=[0])
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cracked_egg = egg.crack(subjects=[0], lists=[0])
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cracked_egg.info()
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pres = cracked_egg.get_pres_items().as_matrix()[0]
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rec = cracked_egg.get_rec_items().as_matrix()[0]
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def distmat(egg, feature, distdict):
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f = [xi[feature] for xi in egg.get_pres_features()]
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return cdist(f, f, distdict[feature])
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for idx in range(len(rec)-1):
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ind1 = np.where(pres==rec[idx])[0][0]
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ind2 = np.where(pres==rec[idx+1])[0][0]
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dists = dist[ind1, :]
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cdist = dist[ind1, ind2]
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rank = np.mean(np.where(np.sort(dists)[::-1] == cdist))

‎examples/create_egg.py

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@@ -17,40 +17,69 @@
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# generate some fake data
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next_presented = ['CAT', 'DOG', 'SHOE', 'HORSE']
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next_recalled = ['HORSE', 'DOG', 'CAT']
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next_features = [{
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presented = [{
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'item' : 'CAT',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'C',
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'length' : 3
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},
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{
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'item' : 'DOG',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'D',
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'length' : 3
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},
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{
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'item' : 'SHOE',
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'category' : 'object',
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'size' : 'smaller',
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'starting letter' : 'S',
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'length' : 4
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},
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{
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'item' : 'HORSE',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'H',
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'length' : 5
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}
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]
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recalled = [{
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'item' : 'HORSE',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'H',
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'length' : 5
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},
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{
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'item' : 'DOG',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'D',
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'length' : 3
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},
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{
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'item' : 'CAT',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'C',
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'length' : 3
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}
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]
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# set some custom distance functions
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dist_funcs = {
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'category' : 'lambda a, b: int(a!=b)',
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'size' : 'lambda a, b: int(a!=b)',
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'starting letter' : 'lambda a, b: int(a!=b)',
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'length' : 'lambda a, b: np.linalg.norm(np.subtract(a,b))'
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}
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egg = quail.Egg(pres=[next_presented], rec=[next_recalled], features=[next_features], dist_funcs=dist_funcs)
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egg.analyze('lagcrp').plot()
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egg = quail.Egg(pres=[presented], rec=[recalled], dist_funcs=dist_funcs)
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fegg = egg.analyze('lagcrp')
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fegg.plot()

‎examples/create_multisubject_egg.py

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egg = quail.Egg(pres=presented_words, rec=recalled_words)
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# analyze and plot
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egg.analyze('accuracy').plot(plot_style='violin', title='Average Recall Accuracy')
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fegg = egg.analyze('accuracy')
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fegg.plot(plot_style='violin', title='Average Recall Accuracy')

‎examples/decode_speech.py

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# decode speech
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recall_data = quail.decode_speech('../data/sample.wav', save=True,
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speech_context=['DONKEY', 'PETUNIA'],
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keypath='/Users/andyheusser/Documents/cdl/credentials/efficient-learning-553bf474f805.json')
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keypath='path/to/key.json')
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# print results
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print(recall_data)

‎examples/fingerprint_optimalpresenter.py

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from quail import Fingerprint, OptimalPresenter
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# generate some fake data
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next_presented = ['CAT', 'DOG', 'SHOE', 'BAT']
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next_recalled = ['DOG', 'CAT', 'BAT', 'SHOE']
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next_features = [{
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next_presented = [{
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'item' : 'CAT',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'C',
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'length' : 3
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},
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{
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'item' : 'DOG',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'D',
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'length' : 3
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},
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{
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'item' : 'SHOE',
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'category' : 'object',
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'size' : 'smaller',
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'starting letter' : 'S',
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'length' : 4
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},
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{
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'item' : 'BAT',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'B',
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'length' : 3
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}]
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egg = quail.Egg(pres=[next_presented], rec=[next_recalled], features=[next_features])
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next_recalled = [{
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'item' : 'DOG',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'D',
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'length' : 3
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},
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{
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'item' : 'CAT',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'C',
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'length' : 3
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},
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{
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'item' : 'BAT',
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'category' : 'animal',
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'size' : 'bigger',
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'starting letter' : 'B',
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'length' : 3
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},
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{
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'item' : 'SHOE',
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'category' : 'object',
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'size' : 'smaller',
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'starting letter' : 'S',
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'length' : 4
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}
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]
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egg = quail.Egg(pres=[next_presented], rec=[next_recalled])
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# initialize fingerprint
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fingerprint = Fingerprint(init=egg)

‎examples/plot_fingerprint.py

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egg = quail.load('example')
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# analyze and plot
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egg.analyze('fingerprint', listgroup=['average']*8, features=['temporal']).plot(title='Memory Fingerprint')
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fegg = egg.analyze('fingerprint', listgroup=['average']*8, features=['temporal'])
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fegg.plot(title='Memory Fingerprint')

‎examples/plot_lagcrp.py

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egg = quail.load('example')
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# analyze and plot
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egg.analyze('lagcrp', listgroup=['average']*8).plot(title='Lag-CRP')
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fegg = egg.analyze('lagcrp', listgroup=['average']*8)
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fegg.plot(title='Lag-CRP')

‎examples/plot_pfr.py

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import quail
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#load data
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egg = quail.load_example_data()
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egg = quail.load('example')
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# analysis
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analyzed_data = quail.analyze(egg, analysis='pfr', listgroup=['average']*8)
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# analyze and plot
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fegg = egg.analyze('pfr', listgroup=['average']*8)
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# plot
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quail.plot(analyzed_data, title='Probability of First Recall')
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fegg.plot(title='Probability of First Recall')

‎examples/plot_pnr.py

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import quail
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#load data
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egg = quail.load_example_data()
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egg = quail.load('example')
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# analysis
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analyzed_data = quail.analyze(egg, analysis='pnr', listgroup=['average']*8,
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# analyze and plot
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fegg = egg.analyze('pnr', listgroup=['average']*8,
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position=5)
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# plot
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quail.plot(analyzed_data, title='Probability of Recall')
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fegg.plot(title='Probability of Recall')

‎examples/plot_spc.py

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import quail
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#load data
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egg = quail.load_example_data()
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egg = quail.load('example')
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# analyze and plot
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egg.analyze('spc', listgroup=['average']*8).plot(title='Serial Position Curve')
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fegg = egg.analyze('spc', listgroup=['average']*8)
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fegg.plot(title='Serial Position Curve')

‎examples/plot_temporal.py

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import quail
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#load data
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egg = quail.load_example_data()
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egg = quail.load('example')
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#analysis
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analyzed_data = quail.analyze(egg, analysis='temporal', listgroup=['early']*4+['late']*4)
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#analyze and plot
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fegg = egg.analyze('temporal', listgroup=['early']*4+['late']*4)
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#plot
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quail.plot(analyzed_data, title='Temporal Clustering')
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fegg.plot(title='Temporal Clustering')

‎quail/data/naturalistic.egg

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‎quail/fingerprint.py

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@@ -103,7 +103,7 @@ def update(self, egg, permute=False, nperms=1000,
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pass_features=True,
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permute=permute,
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n_perms=nperms,
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parallel=parallel).as_matrix(), 0)
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parallel=parallel).values, 0)
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if self.state is not None:
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