run black on all notebooks

.pre-commit-config.yaml

@@ -1,15 +1,15 @@
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v3.3.0
+  rev: v3.4.0
   hooks:
     -   id: check-toml
     -   id: check-yaml
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 0.5.4
+  rev: 0.5.5
   hooks:
     - id: nbqa-black
       additional_dependencies: [black==20.8b1]
-      files: ^Rethinking_2/
+      exclude: ^BCM/CaseStudies/MemoryRetention\.ipynb$
     - id: nbqa-isort
       additional_dependencies: [isort==5.6.4]
     - id: nbqa-pyupgrade

BCM/CaseStudies/ExtrasensoryPerception.ipynb

@@ -19,7 +19,7 @@
     "from theano import tensor as tt\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
-    "warnings.simplefilter(action='ignore', category=(FutureWarning, UserWarning))\n",
+    "warnings.simplefilter(action=\"ignore\", category=(FutureWarning, UserWarning))\n",
     "RANDOM_SEED = 8927\n",
     "np.random.seed(RANDOM_SEED)"
    ]
@@ -111,9 +111,7 @@
     "\n",
     "    cov = pm.Deterministic(\n",
     "        \"cov\",\n",
-    "        tt.stacklists(\n",
-    "            [[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]\n",
-    "        ),\n",
+    "        tt.stacklists([[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]),\n",
     "    )\n",
     "\n",
     "    tau1 = pm.Deterministic(\"tau1\", tt.nlinalg.matrix_inverse(cov))\n",
@@ -166,7 +164,11 @@
     "my_pdf = stats.kde.gaussian_kde(r)\n",
     "x = np.linspace(-1, 1, 200)\n",
     "axes[1].plot(\n",
-    "    x, my_pdf(x), \"r\", lw=2.5, alpha=0.6,\n",
+    "    x,\n",
+    "    my_pdf(x),\n",
+    "    \"r\",\n",
+    "    lw=2.5,\n",
+    "    alpha=0.6,\n",
     ")  # distribution function\n",
     "axes[1].plot(x, np.ones(x.size) * 0.5, \"k--\", lw=2.5, alpha=0.6, label=\"Prior\")\n",
     "\n",
@@ -175,9 +177,7 @@
     "BF01 = posterior / prior\n",
     "print(\"the Bayes Factor is %.3f\" % (1 / BF01))\n",
     "\n",
-    "axes[1].plot(\n",
-    "    [0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1\n",
-    ")\n",
+    "axes[1].plot([0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1)\n",
     "# ax1.hist(r, bins=100, normed=1,alpha=.3)\n",
     "axes[1].set_xlabel(\"Correlation r\")\n",
     "axes[1].set_ylabel(\"Density\")\n",
@@ -193,8 +193,7 @@
     "\n",
     "# Compare to exact solution Jeffreys (numerical integration):\n",
     "BF_Jeffex = sp.integrate.quad(\n",
-    "    lambda rho: ((1 - rho ** 2) ** ((n - 1) / 2))\n",
-    "    / ((1 - rho * freqr[0, 1]) ** ((n - 1) - 0.5)),\n",
+    "    lambda rho: ((1 - rho ** 2) ** ((n - 1) / 2)) / ((1 - rho * freqr[0, 1]) ** ((n - 1) - 0.5)),\n",
     "    -1,\n",
     "    1,\n",
     ")\n",
@@ -239,6 +238,7 @@
    "source": [
     "# Data\n",
     "# Proportion correct on erotic pictures, block 1 and block 2:\n",
+    "# fmt: off\n",
     "prc1er=np.array([0.6000000, 0.5333333, 0.6000000, 0.6000000, 0.4666667, \n",
     "                 0.6666667, 0.6666667, 0.4000000, 0.6000000, 0.6000000,\n",
     "                 0.4666667, 0.6666667, 0.4666667, 0.6000000, 0.3333333,\n",
@@ -280,6 +280,7 @@
     "                 0.5333333, 0.6000000, 0.6666667, 0.4000000, 0.4000000,\n",
     "                 0.5333333, 0.8000000, 0.6000000, 0.4000000, 0.2000000,\n",
     "                 0.6000000, 0.6666667, 0.4666667, 0.4666667, 0.4666667])\n",
+    "# fmt: on\n",
     "Nt = 60\n",
     "xobs = np.vstack([prc1er, prc2er]).T * Nt\n",
     "n, n2 = np.shape(xobs)  # number of participants\n",
@@ -345,9 +346,7 @@
     "\n",
     "    cov = pm.Deterministic(\n",
     "        \"cov\",\n",
-    "        tt.stacklists(\n",
-    "            [[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]\n",
-    "        ),\n",
+    "        tt.stacklists([[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]),\n",
     "    )\n",
     "\n",
     "    tau1 = pm.Deterministic(\"tau1\", tt.nlinalg.matrix_inverse(cov))\n",
@@ -398,7 +397,11 @@
     "my_pdf = stats.kde.gaussian_kde(r)\n",
     "x1 = np.linspace(0, 1, 200)\n",
     "plt.plot(\n",
-    "    x1, my_pdf(x1), \"r\", lw=2.5, alpha=0.6,\n",
+    "    x1,\n",
+    "    my_pdf(x1),\n",
+    "    \"r\",\n",
+    "    lw=2.5,\n",
+    "    alpha=0.6,\n",
     ")  # distribution function\n",
     "plt.plot(x1, np.ones(x1.size), \"k--\", lw=2.5, alpha=0.6, label=\"Prior\")\n",
     "\n",
@@ -407,18 +410,15 @@
     "BF01 = posterior / prior\n",
     "print(\"the Bayes Factor is %.5f\" % (1 / BF01))\n",
     "\n",
-    "plt.plot(\n",
-    "    [0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1\n",
-    ")\n",
+    "plt.plot([0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1)\n",
     "plt.xlim([0, 1])\n",
     "plt.xlabel(\"Correlation r\")\n",
     "plt.ylabel(\"Density\")\n",
     "\n",
     "# Compare to exact solution Jeffreys (numerical integration):\n",
     "freqr = sp.corrcoef(xobs[:, 0] / Nt, xobs[:, 1] / Nt)\n",
     "BF_Jeffex = sp.integrate.quad(\n",
-    "    lambda rho: ((1 - rho ** 2) ** ((n - 1) / 2))\n",
-    "    / ((1 - rho * freqr[0, 1]) ** ((n - 1) - 0.5)),\n",
+    "    lambda rho: ((1 - rho ** 2) ** ((n - 1) / 2)) / ((1 - rho * freqr[0, 1]) ** ((n - 1) - 0.5)),\n",
     "    0,\n",
     "    1,\n",
     ")\n",
@@ -463,6 +463,7 @@
     }
    ],
    "source": [
+    "# fmt: off\n",
     "k2 = np.array([36, 32, 36, 36, 28, 40, 40, 24, 36, 36, 28, 40, 28, \n",
     "       36, 20, 24, 24, 16, 20, 32, 40, 32, 36, 24, 28, 44,\n",
     "       40, 36, 40, 32, 32, 40, 28, 20, 24, 32, 24, 24, 20, \n",
@@ -480,6 +481,7 @@
     "       53, 26, 67, 73, 39, 62, 59, 75, 65, 60, 69, 63, 69, \n",
     "       55, 63, 86, 70, 67, 54, 80, 71, 71, 55, 57, 41, 56, \n",
     "       78, 58, 76, 54, 50, 61, 60, 32, 67])\n",
+    "# fmt: on\n",
     "\n",
     "nsubjs = len(k2)\n",
     "ntrials = 60\n",
@@ -541,9 +543,7 @@
     "\n",
     "    cov = pm.Deterministic(\n",
     "        \"cov\",\n",
-    "        tt.stacklists(\n",
-    "            [[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]\n",
-    "        ),\n",
+    "        tt.stacklists([[lambda1 ** -1, r * sigma1 * sigma2], [r * sigma1 * sigma2, lambda2 ** -1]]),\n",
     "    )\n",
     "\n",
     "    tau1 = pm.Deterministic(\"tau1\", tt.nlinalg.matrix_inverse(cov))\n",
@@ -595,7 +595,11 @@
     "my_pdf = stats.kde.gaussian_kde(r)\n",
     "x1 = np.linspace(-1, 1, 200)\n",
     "plt.plot(\n",
-    "    x1, my_pdf(x1), \"r\", lw=2.5, alpha=0.6,\n",
+    "    x1,\n",
+    "    my_pdf(x1),\n",
+    "    \"r\",\n",
+    "    lw=2.5,\n",
+    "    alpha=0.6,\n",
     ")  # distribution function\n",
     "plt.plot(x1, np.ones(x1.size) * 0.5, \"k--\", lw=2.5, alpha=0.6, label=\"Prior\")\n",
     "\n",
@@ -604,9 +608,7 @@
     "BF01 = posterior / prior\n",
     "print(\"the Bayes Factor is %.5f\" % (1 / BF01))\n",
     "\n",
-    "plt.plot(\n",
-    "    [0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1\n",
-    ")\n",
+    "plt.plot([0, 0], [posterior, prior], \"k-\", [0, 0], [posterior, prior], \"ko\", lw=1.5, alpha=1)\n",
     "# ax1.hist(r, bins=100, normed=1,alpha=.3)\n",
     "plt.xlim([-1, 1])\n",
     "plt.xlabel(\"Correlation r\")\n",

BCM/CaseStudies/HeuristicDecisionMaking.ipynb

@@ -386,9 +386,7 @@
     "        \"zi\",\n",
     "        p=phi,\n",
     "        shape=ns,\n",
-    "        testval=np.asarray(\n",
-    "            [1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-    "        ),\n",
+    "        testval=np.asarray([1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
     "    )\n",
     "    zi_ = tt.reshape(tt.repeat(zi, nq), (ns, nq))\n",
     "\n",
@@ -610,9 +608,7 @@
     "    unique_ord = np.vstack({tuple(row) for row in subj_s})\n",
     "    num_display = 10\n",
     "    print(\"Subject %s\" % (subj_id))\n",
-    "    print(\n",
-    "        \"There are %s search orders sampled in the posterior.\" % (unique_ord.shape[0])\n",
-    "    )\n",
+    "    print(\"There are %s search orders sampled in the posterior.\" % (unique_ord.shape[0]))\n",
     "\n",
     "    mass_ = []\n",
     "    for s_ in unique_ord:\n",
@@ -733,9 +729,7 @@
     "        \"zi\",\n",
     "        p=phi,\n",
     "        shape=ns,\n",
-    "        testval=np.asarray(\n",
-    "            [1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
-    "        ),\n",
+    "        testval=np.asarray([1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
     "    )\n",
     "    zi_ = tt.reshape(tt.repeat(zi, nq), (ns, nq))\n",
     "\n",

BCM/CaseStudies/MultinomialProcessingTrees.ipynb

@@ -19,7 +19,7 @@
     "from theano import tensor as tt\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
-    "warnings.simplefilter(action='ignore', category=(FutureWarning, UserWarning))\n",
+    "warnings.simplefilter(action=\"ignore\", category=(FutureWarning, UserWarning))\n",
     "RANDOM_SEED = 8927\n",
     "np.random.seed(RANDOM_SEED)"
    ]
@@ -300,6 +300,7 @@
     "Nsubj = 21\n",
     "Nitem = 20\n",
     "\n",
+    "# fmt: off\n",
     "response_1=np.array([2,4,4,10,2,1,3,14,2,2,5,11,6,0,4,10,1,\n",
     "                     0,4,15,1,0,2,17,1,2,4,13,4,1,6,9,5,1,4,\n",
     "                     10,1,0,9,10,5,0,3,12,0,1,6,13,1,5,7,7,1,\n",
@@ -315,6 +316,7 @@
     "                     3,0,11,6,1,2,16,1,2,1,10,1,3,6,7,13,0,\n",
     "                     0,8,4,3,5,16,1,1,2,5,4,7,4,15,0,5,0,6,\n",
     "                     3,6,5,17,2,0,1,17,1,0,2,8,3,6,3]).reshape(21,-1)\n",
+    "# fmt: on\n",
     "\n",
     "kall = [response_1, response_2, response_6]"
    ]
@@ -526,21 +528,15 @@
     "indiv_trial2 = []\n",
     "\n",
     "with model2:\n",
-    "    indiv_trial2.append(\n",
-    "        pm.sample(2000, tune=2000, init=\"adapt_diag\", target_accept=0.95)\n",
-    "    )\n",
+    "    indiv_trial2.append(pm.sample(2000, tune=2000, init=\"adapt_diag\", target_accept=0.95))\n",
     "\n",
     "kshared.set_value(kall[1])\n",
     "with model2:\n",
-    "    indiv_trial2.append(\n",
-    "        pm.sample(2000, tune=2000, init=\"adapt_diag\", target_accept=0.95)\n",
-    "    )\n",
+    "    indiv_trial2.append(pm.sample(2000, tune=2000, init=\"adapt_diag\", target_accept=0.95))\n",
     "\n",
     "kshared.set_value(kall[2])\n",
     "with model2:\n",
-    "    indiv_trial2.append(\n",
-    "        pm.sample(2000, tune=2000, init=\"adapt_diag\", target_accept=0.95)\n",
-    "    )"
+    "    indiv_trial2.append(pm.sample(2000, tune=2000, init=\"adapt_diag\", target_accept=0.95))"
    ]
   },
   {

BCM/CaseStudies/NumberConceptDevelopment.ipynb

@@ -19,7 +19,7 @@
     "from theano import tensor as tt\n",
     "\n",
     "%config InlineBackend.figure_format = 'retina'\n",
-    "warnings.simplefilter(action='ignore', category=(FutureWarning, UserWarning))\n",
+    "warnings.simplefilter(action=\"ignore\", category=(FutureWarning, UserWarning))\n",
     "RANDOM_SEED = 8927\n",
     "np.random.seed(RANDOM_SEED)"
    ]
@@ -93,9 +93,7 @@
     "            # Will be 1 for HN-Knowers\n",
     "            ind4 = int(i1 == nz)\n",
     "            ind5[i, j, k] = (\n",
-    "                ind3\n",
-    "                + ind4 * (2 + ind2)\n",
-    "                + (1 - ind4) * (1 - ind3) * (ind1 * ind2 + ind1 + 1)\n",
+    "                ind3 + ind4 * (2 + ind2) + (1 - ind4) * (1 - ind3) * (ind1 * ind2 + ind1 + 1)\n",
     "            )"
    ]
   },

BCM/CaseStudies/PsychophysicalFunctions.ipynb

@@ -143,9 +143,7 @@
     }
    ],
    "source": [
-    "xmean = np.array(\n",
-    "    [318.888, 311.0417, 284.4444, 301.5909, 296.2000, 305.7692, 294.6429, 280.3571]\n",
-    ")\n",
+    "xmean = np.array([318.888, 311.0417, 284.4444, 301.5909, 296.2000, 305.7692, 294.6429, 280.3571])\n",
     "nstim = np.array([27, 24, 27, 22, 25, 26, 28, 28])\n",
     "nsubjs = 8\n",
     "\n",
@@ -660,9 +658,7 @@
     "    sigma_p = pm.Uniform(\"sigma_p\", lower=0, upper=3)\n",
     "    mu_p = pm.Normal(\"mu_p\", mu=0, tau=0.001)\n",
     "\n",
-    "    probitphi = pm.Normal(\n",
-    "        \"probitphi\", mu=mu_p, sd=sigma_p, shape=nsubjs, testval=np.ones(nsubjs)\n",
-    "    )\n",
+    "    probitphi = pm.Normal(\"probitphi\", mu=mu_p, sd=sigma_p, shape=nsubjs, testval=np.ones(nsubjs))\n",
     "    phii = pm.Deterministic(\"phii\", Phi(probitphi))\n",
     "\n",
     "    pi_ij = pm.Uniform(\"pi_ij\", lower=0, upper=1, shape=xij.shape)\n",
@@ -820,9 +816,7 @@
     "    sigma_p = pm.Uniform(\"sigma_p\", lower=0, upper=3)\n",
     "    mu_p = pm.Normal(\"mu_p\", mu=0, tau=0.001)\n",
     "\n",
-    "    probitphi = pm.Normal(\n",
-    "        \"probitphi\", mu=mu_p, sd=sigma_p, shape=nsubjs, testval=np.ones(nsubjs)\n",
-    "    )\n",
+    "    probitphi = pm.Normal(\"probitphi\", mu=mu_p, sd=sigma_p, shape=nsubjs, testval=np.ones(nsubjs))\n",
     "    phii = pm.Deterministic(\"phii\", Phi(probitphi))\n",
     "\n",
     "    pi_ij = pm.Uniform(\"pi_ij\", lower=0, upper=1, shape=xij.shape)\n",
@@ -831,9 +825,7 @@
     "    # zij_ = pm.Uniform('zij_',lower=0, upper=1, shape=xij.shape)\n",
     "\n",
     "    zij = pm.Bernoulli(\"zij\", p=phii[sbjid], shape=xij.shape)\n",
-    "    thetaij = pm.Deterministic(\n",
-    "        \"thetaij\", tt.switch(tt.eq(zij, 0), tlogit(linerpredi), pi_ij)\n",
-    "    )\n",
+    "    thetaij = pm.Deterministic(\"thetaij\", tt.switch(tt.eq(zij, 0), tlogit(linerpredi), pi_ij))\n",
     "\n",
     "    rij_ = pm.Binomial(\"rij\", p=thetaij, n=nij, observed=rij)"
    ]