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MarcoGorelliMarcoGorelli
committed
run black formatter on BCM
1 parent 393bdb1 commit e27eb30

15 files changed

+747
-257
lines changed

‎BCM/CaseStudies/ExtrasensoryPerception.ipynb

Lines changed: 235 additions & 43 deletions
Original file line numberDiff line numberDiff line change
@@ -19,7 +19,7 @@
1919
"from theano import tensor as tt\n",
2020
"\n",
2121
"%config InlineBackend.figure_format = 'retina'\n",
22-
"warnings.simplefilter(action='ignore', category=(FutureWarning, UserWarning))\n",
22+
"warnings.simplefilter(action=\"ignore\", category=(FutureWarning, UserWarning))\n",
2323
"RANDOM_SEED = 8927\n",
2424
"np.random.seed(RANDOM_SEED)"
2525
]
@@ -111,9 +111,7 @@
111111
"\n",
112112
" cov = pm.Deterministic(\n",
113113
" \"cov\",\n",
114-
" tt.stacklists(\n",
115-
" [[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]\n",
116-
" ),\n",
114+
" tt.stacklists([[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]),\n",
117115
" )\n",
118116
"\n",
119117
" tau1 = pm.Deterministic(\"tau1\", tt.nlinalg.matrix_inverse(cov))\n",
@@ -166,7 +164,11 @@
166164
"my_pdf = stats.kde.gaussian_kde(r)\n",
167165
"x = np.linspace(-1, 1, 200)\n",
168166
"axes[1].plot(\n",
169-
" x, my_pdf(x), \"r\", lw=2.5, alpha=0.6,\n",
167+
" x,\n",
168+
" my_pdf(x),\n",
169+
" \"r\",\n",
170+
" lw=2.5,\n",
171+
" alpha=0.6,\n",
170172
") # distribution function\n",
171173
"axes[1].plot(x, np.ones(x.size) * 0.5, \"k--\", lw=2.5, alpha=0.6, label=\"Prior\")\n",
172174
"\n",
@@ -175,9 +177,7 @@
175177
"BF01 = posterior / prior\n",
176178
"print(\"the Bayes Factor is %.3f\" % (1 / BF01))\n",
177179
"\n",
178-
"axes[1].plot(\n",
179-
" [0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1\n",
180-
")\n",
180+
"axes[1].plot([0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1)\n",
181181
"# ax1.hist(r, bins=100, normed=1,alpha=.3)\n",
182182
"axes[1].set_xlabel(\"Correlation r\")\n",
183183
"axes[1].set_ylabel(\"Density\")\n",
@@ -193,8 +193,7 @@
193193
"\n",
194194
"# Compare to exact solution Jeffreys (numerical integration):\n",
195195
"BF_Jeffex = sp.integrate.quad(\n",
196-
" lambda rho: ((1 - rho ** 2) ** ((n - 1) / 2))\n",
197-
" / ((1 - rho * freqr[0, 1]) ** ((n - 1) - 0.5)),\n",
196+
" lambda rho: ((1 - rho ** 2) ** ((n - 1) / 2)) / ((1 - rho * freqr[0, 1]) ** ((n - 1) - 0.5)),\n",
198197
" -1,\n",
199198
" 1,\n",
200199
")\n",
@@ -239,6 +238,7 @@
239238
"source": [
240239
"# Data\n",
241240
"# Proportion correct on erotic pictures, block 1 and block 2:\n",
241+
"# fmt: off\n",
242242
"prc1er=np.array([0.6000000, 0.5333333, 0.6000000, 0.6000000, 0.4666667, \n",
243243
" 0.6666667, 0.6666667, 0.4000000, 0.6000000, 0.6000000,\n",
244244
" 0.4666667, 0.6666667, 0.4666667, 0.6000000, 0.3333333,\n",
@@ -280,6 +280,7 @@
280280
" 0.5333333, 0.6000000, 0.6666667, 0.4000000, 0.4000000,\n",
281281
" 0.5333333, 0.8000000, 0.6000000, 0.4000000, 0.2000000,\n",
282282
" 0.6000000, 0.6666667, 0.4666667, 0.4666667, 0.4666667])\n",
283+
"# fmt: on\n",
283284
"Nt = 60\n",
284285
"xobs = np.vstack([prc1er, prc2er]).T * Nt\n",
285286
"n, n2 = np.shape(xobs) # number of participants\n",
@@ -345,9 +346,7 @@
345346
"\n",
346347
" cov = pm.Deterministic(\n",
347348
" \"cov\",\n",
348-
" tt.stacklists(\n",
349-
" [[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]\n",
350-
" ),\n",
349+
" tt.stacklists([[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]),\n",
351350
" )\n",
352351
"\n",
353352
" tau1 = pm.Deterministic(\"tau1\", tt.nlinalg.matrix_inverse(cov))\n",
@@ -398,7 +397,11 @@
398397
"my_pdf = stats.kde.gaussian_kde(r)\n",
399398
"x1 = np.linspace(0, 1, 200)\n",
400399
"plt.plot(\n",
401-
" x1, my_pdf(x1), \"r\", lw=2.5, alpha=0.6,\n",
400+
" x1,\n",
401+
" my_pdf(x1),\n",
402+
" \"r\",\n",
403+
" lw=2.5,\n",
404+
" alpha=0.6,\n",
402405
") # distribution function\n",
403406
"plt.plot(x1, np.ones(x1.size), \"k--\", lw=2.5, alpha=0.6, label=\"Prior\")\n",
404407
"\n",
@@ -407,18 +410,15 @@
407410
"BF01 = posterior / prior\n",
408411
"print(\"the Bayes Factor is %.5f\" % (1 / BF01))\n",
409412
"\n",
410-
"plt.plot(\n",
411-
" [0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1\n",
412-
")\n",
413+
"plt.plot([0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1)\n",
413414
"plt.xlim([0, 1])\n",
414415
"plt.xlabel(\"Correlation r\")\n",
415416
"plt.ylabel(\"Density\")\n",
416417
"\n",
417418
"# Compare to exact solution Jeffreys (numerical integration):\n",
418419
"freqr = sp.corrcoef(xobs[:, 0] / Nt, xobs[:, 1] / Nt)\n",
419420
"BF_Jeffex = sp.integrate.quad(\n",
420-
" lambda rho: ((1 - rho ** 2) ** ((n - 1) / 2))\n",
421-
" / ((1 - rho * freqr[0, 1]) ** ((n - 1) - 0.5)),\n",
421+
" lambda rho: ((1 - rho ** 2) ** ((n - 1) / 2)) / ((1 - rho * freqr[0, 1]) ** ((n - 1) - 0.5)),\n",
422422
" 0,\n",
423423
" 1,\n",
424424
")\n",
@@ -463,23 +463,215 @@
463463
}
464464
],
465465
"source": [
466-
"k2 = np.array([36, 32, 36, 36, 28, 40, 40, 24, 36, 36, 28, 40, 28, \n",
467-
" 36, 20, 24, 24, 16, 20, 32, 40, 32, 36, 24, 28, 44,\n",
468-
" 40, 36, 40, 32, 32, 40, 28, 20, 24, 32, 24, 24, 20, \n",
469-
" 28, 24, 28, 28, 32, 20, 44, 16, 36, 32, 28, 24, 32,\n",
470-
" 40, 28, 32, 32, 28, 24, 28, 40, 28, 20, 20, 20, 24,\n",
471-
" 24, 36, 28, 20, 20, 40, 32, 20, 36, 28, 28, 24, 20,\n",
472-
" 28, 32, 48, 24, 32, 32, 40, 40, 40, 36, 36, 32, 20,\n",
473-
" 28, 40, 32, 20, 20, 16, 16, 28, 40])\n",
474-
" \n",
475-
"x2 = np.array([50, 80, 79, 56, 50, 80, 53, 84, 74, 67, 50, 45, 62, \n",
476-
" 65, 71, 71, 68, 63, 67, 58, 72, 73, 63, 54, 63, 70, \n",
477-
" 81, 71, 66, 74, 70, 84, 66, 73, 78, 64, 54, 74, 62, \n",
478-
" 71, 70, 79, 66, 64, 62, 63, 60, 56, 72, 72, 79, 67, \n",
479-
" 46, 67, 77, 55, 63, 44, 84, 65, 41, 62, 64, 51, 46,\n",
480-
" 53, 26, 67, 73, 39, 62, 59, 75, 65, 60, 69, 63, 69, \n",
481-
" 55, 63, 86, 70, 67, 54, 80, 71, 71, 55, 57, 41, 56, \n",
482-
" 78, 58, 76, 54, 50, 61, 60, 32, 67])\n",
466+
"k2 = np.array(\n",
467+
" [\n",
468+
" 36,\n",
469+
" 32,\n",
470+
" 36,\n",
471+
" 36,\n",
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" 28,\n",
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" 40,\n",
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" 40,\n",
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" 24,\n",
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" 28,\n",
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" 36,\n",
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" 24,\n",
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" 28,\n",
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" 40,\n",
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" 28,\n",
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" ]\n",
569+
")\n",
570+
"\n",
571+
"x2 = np.array(\n",
572+
" [\n",
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" 50,\n",
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" 80,\n",
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" 79,\n",
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" 56,\n",
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" ]\n",
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")\n",
483675
"\n",
484676
"nsubjs = len(k2)\n",
485677
"ntrials = 60\n",
@@ -541,9 +733,7 @@
541733
"\n",
542734
" cov = pm.Deterministic(\n",
543735
" \"cov\",\n",
544-
" tt.stacklists(\n",
545-
" [[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]\n",
546-
" ),\n",
736+
" tt.stacklists([[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]),\n",
547737
" )\n",
548738
"\n",
549739
" tau1 = pm.Deterministic(\"tau1\", tt.nlinalg.matrix_inverse(cov))\n",
@@ -595,7 +785,11 @@
595785
"my_pdf = stats.kde.gaussian_kde(r)\n",
596786
"x1 = np.linspace(-1, 1, 200)\n",
597787
"plt.plot(\n",
598-
" x1, my_pdf(x1), \"r\", lw=2.5, alpha=0.6,\n",
788+
" x1,\n",
789+
" my_pdf(x1),\n",
790+
" \"r\",\n",
791+
" lw=2.5,\n",
792+
" alpha=0.6,\n",
599793
") # distribution function\n",
600794
"plt.plot(x1, np.ones(x1.size) * 0.5, \"k--\", lw=2.5, alpha=0.6, label=\"Prior\")\n",
601795
"\n",
@@ -604,9 +798,7 @@
604798
"BF01 = posterior / prior\n",
605799
"print(\"the Bayes Factor is %.5f\" % (1 / BF01))\n",
606800
"\n",
607-
"plt.plot(\n",
608-
" [0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1\n",
609-
")\n",
801+
"plt.plot([0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1)\n",
610802
"# ax1.hist(r, bins=100, normed=1,alpha=.3)\n",
611803
"plt.xlim([-1, 1])\n",
612804
"plt.xlabel(\"Correlation r\")\n",

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