replace nbqa-black with black-jupyter

.pre-commit-config.yaml

@@ -1,9 +1,11 @@
 repos:
+- repo: https://github.com/psf/black
+  rev: 21.8b0
+  hooks:
+    - id: black-jupyter
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 0.10.0
+  rev: 1.1.0
   hooks:
-    - id: nbqa-black
-      additional_dependencies: [black==20.8b1]
     - id: nbqa-isort
       additional_dependencies: [isort==5.6.4]
     - id: nbqa-pyupgrade

examples/conf.py

@@ -79,8 +79,8 @@
     "navbar_end": ["search-field.html", "navbar-icon-links.html"],
     "external_links": [
         {"name": "Learning", "url": "https://pymc3.readthedocs.io/en/latest/learn.html"},
-        {"name": "API", "url": "https://pymc3.readthedocs.io/en/latest/api.html"}
-    ]
+        {"name": "API", "url": "https://pymc3.readthedocs.io/en/latest/api.html"},
+    ],
 }
 
 html_favicon = "../_static/PyMC3.ico"
@@ -103,16 +103,11 @@
 # post_auto_excerpt = 2
 
 # MyST config
-myst_enable_extensions = [
-    "colon_fence",
-    "deflist",
-    "dollarmath",
-    "amsmath"
-]
+myst_enable_extensions = ["colon_fence", "deflist", "dollarmath", "amsmath"]
 
 # bibtex config
-bibtex_bibfiles = ['references.bib']
-bibtex_default_style = 'unsrt'
+bibtex_bibfiles = ["references.bib"]
+bibtex_default_style = "unsrt"
 bibtex_reference_style = "author_year"
 
 # OpenGraph config

examples/generalized_linear_models/GLM-hierarchical-binominal-model.ipynb

@@ -361,7 +361,7 @@
    ],
    "source": [
     "def logp_ab(value):\n",
-    "    \"\"\" prior density\"\"\"\n",
+    "    \"\"\"prior density\"\"\"\n",
     "    return tt.log(tt.pow(tt.sum(value), -5 / 2))\n",
     "\n",
     "\n",

examples/pymc3_howto/LKJ_correlation.py

@@ -53,9 +53,7 @@ def run(n=1000):
         n = 50
     with model:
         trace = pm.sample(n)
-    az.plot_trace(
-        trace, var_names=["mu", "r"]
-    )
+    az.plot_trace(trace, var_names=["mu", "r"])
 
 
 if __name__ == "__main__":

examples/samplers/SMC-ABC_Lotka-Volterra_example.ipynb

@@ -304,7 +304,7 @@
     "\n",
     "# Lotka - Volterra equation\n",
     "def dX_dt(X, t, a, b, c, d):\n",
-    "    \"\"\" Return the growth rate of fox and rabbit populations. \"\"\"\n",
+    "    \"\"\"Return the growth rate of fox and rabbit populations.\"\"\"\n",
     "\n",
     "    return np.array([a * X[0] - b * X[0] * X[1], -c * X[1] + d * b * X[0] * X[1]])\n",
     "\n",

examples/samplers/samplers_mvnormal.py

@@ -77,7 +77,7 @@ def run(steppers, p):
         for name in names:
             df_effectiven.at[p, name] = rate[name]
             df_runtime.at[p, name] = runtime[name]
-            df_performance.at[p, name] =  rate[name] / runtime[name]
+            df_performance.at[p, name] = rate[name] / runtime[name]
 
     print("\r\nEffective sample size [0...1]")
     print(df_effectiven.T.to_string(float_format="{:.3f}".format))

examples/survival_analysis/censored_data.ipynb

@@ -499,12 +499,12 @@
     "\n",
     "# Helper functions for unimputed censored model\n",
     "def left_censored_likelihood(mu, sigma, n_left_censored, lower_bound):\n",
-    "    \"\"\" Likelihood of left-censored data. \"\"\"\n",
+    "    \"\"\"Likelihood of left-censored data.\"\"\"\n",
     "    return n_left_censored * normal_lcdf(mu, sigma, lower_bound)\n",
     "\n",
     "\n",
     "def right_censored_likelihood(mu, sigma, n_right_censored, upper_bound):\n",
-    "    \"\"\" Likelihood of right-censored data. \"\"\"\n",
+    "    \"\"\"Likelihood of right-censored data.\"\"\"\n",
     "    return n_right_censored * normal_lccdf(mu, sigma, upper_bound)"
    ]
   },

examples/survival_analysis/cox_model.ipynb

@@ -139,7 +139,7 @@
     "inter1= [ -.01434325, -.01460965, 0, 0, 0, -.01113493, 0, 0, 0, -.0553269, -.03238896, 0, 0, -.07062459, -.07464545, -.07032613, 0, 0, -.01408955, 0, -.00219072, 0, 0, 0, 0, 0, .07300876, .01394272, 0, 0, 0, 0, 0, 0, .05120398, 0, -.00550709, -.02062663, -.03077685, -.01688493, 0, .01149963, 0, .01149963, .01149963, 0, 0, 0, 0, 0, 0, 0, 0, 0, .01149963, .0034338, .0376236, .00733331, 0, .03832785, .03832785, -.02622275, -.02622275, -.02622275, -.01492678, 0, 0, -.02897806, -.02847666, 0, 0, -.04224754, -.04743705, -.0510477, -.031893, 0, 0, 0, -.01503116, .003101, -.00083466, .02395027, -.07952866, 0, 0, -.06586029, 0, -.0613939, -.081205, -.07540084, -.08488011, -.08488011, 0, -.07492433, -.08907269, -.09451609, 0, -.08980743, 0, -.0771635, 0, 0, -.0771635, -.08204606, 0, -.05263504, 0, -.05109092, -.04696729, 0, -.04696729, 0, -.05303248, -.05348096, 0, 0, .00584956, -.00792241, -.01719816, 0, -.01576016, 0, -.04014061, 0, 0, 0, 0, 0, .0471441, 0, .04233112, 0, .04233112, 0, 0, .0493324, .04512087, .03205975, .02913185, 0, .05324252, 0, 0, 0, 0, .05054695, 0, .14026688, .01734403, .06078221, 0, 0, 0, -.03138622, 0, .01637333, 0, 0, 0, 0, .01897239, .01591935, 0, -.0619156, 0, -.06851645, 0, -.03889525, -.05023452, -.05013452, 0, 0, -.01362136, 0, 0, -.02634164, 0, 0, 0, 0, -.00890537, -.00611669, 0, 0, 0, -.01513384, 0, -.03551984, 0, -.01978032, 0, .06706496, .10551275, 0, .03092981, .06556855, 0, 0, 0, .09362991, 0, 0, 0, 0, 0, 0, .02610553, .03546937, 0, 0, .034415, 0, 0, 0, .07546701, 0, 0, 0, 0, -.02919447, -.01016712, 0, 0, 0, 0, -.04845615, -.05010044, 0, 0, 0, 0, 0, 0, -.07666632, 0, 0, -.07226554, -.08216553, -.0777643, 0, 0, -.04727952, 0, -.06870384, -.05999847, 0, 0, 0, .02772475, .02883079, .03642944, 0, .04148949, 0, 0, 0, .04268012, .03225577, 0, -.05140995, -.05399637, 0, 0, .02432223, 0, .0490674, .0490674, .0490674, 0, 0, 0, 0, 0, 0, 0, 0, .10476315, 0, 0, 0, 0, 0, .07008056, 0, 0, .01667466, 0, .05253941, .04293926, 0, .02692172, 0, 0, .08742411, .04533176, 0, .01831875, 0, .09834951, .09952456, 0, .02945534, .038731, 0, .04435538, 0, -.02357505, 0, 0, -.02357505, .09324722, 0, 0, 0, -.03490683, 0, -.05054474, 0, -.0474724, -.04905931, 0, .02879751, 0, 0, 0, 0, 0, 0, 0, .04439012, 0, .02989959, .02989959, .05468828, .04463226, 0, 0, 0, 0, 0, .01231324, -.01399783, .04595331, .00145386, 0, .06459354, -.0007196, 0, -.07614055, -.08435525, 0, -.10299519, 0, 0, 0, -.00210284, -.00797183, 0, 0, 0, 0, -.03545086, 0, 0, 0, 0, -.061286, -.07666647, 0, -.05902354, -.07652324, -.07645561, 0, 0, 0, -.03292062, 0, 0, 0, 0, -.075417, 0, -.07922532, 0, -.08583414, -.07450142, -.08066016, 0, 0, -.06249051, 0, 0, 0, 0, -.0618688, 0, -.06524737, -.04419825, -.04489509, 0, 0, 0, -.04520512, -.04187583, 0, 0, -.03753508, 0, 0, 0, 0, 0, 0, 0, 0, .06862645, 0, 0, -.00120631, .01947345, 0, 0, .03561932, 0, .03158225, .03608047, 0, 0, 0, -.02899643],\n",
     "\n",
     "inter2= [-.78348798, -.63418788, 0, 0, 0, .11481193, 0, 0, 0, -.88128799, -1.109488, 0, 0, -.30888793, .29651192, -.36688802, 0, 0, -.59088796, 0, .50561196, 0, 0, 0, 0, 0, 2.1662121, .08891205, 0, 0, 0, 0, 0, 0, -.23918791, 0, -.9575879, -.07728811, .29641202, 1.2273121, 0, 1.5764117, 0, .72131211, 1.279212, 0, 0, 0, 0, 0, 0, 0, 0, 0, .36481193, 1.5480118, -.03078791, 1.389112, 0, .70901209, -.16668792, 1.435812, .47001198, 2.0838118, 1.1673121, 0, 0, 1.4470119, .23301201, 0, 0, -.61948794, -.41388795, .263212, .66171199, 0, 0, 0, 1.6920118, 1.334012, 1.2101121, .41591194, -.48498794, 0, 0, .09911207, 0, -.46908805, .0205119, .0535119, -.14228792, -.55708808, 0, -.54008788, -.30998799, -.10958811, 0, -.01338812, 0, -.51788801, 0, 0, .13271193, -.11208793, 0, -.54508799, 0, .16641192, .95871216, 0, 1.6281118, 0, -.49718806, -.41348812, 0, 0, -.11718794, -.57058805, -.59488791, 0, -.65658802, 0, -.52698797, 0, 0, 0, 0, 0, 1.3500118, 0, 1.665812, 0, 1.963912, 0, 0, 1.9371119, .90991193, -.39558789, .39521196, 0, -.05268808, 0, 0, 0, 0, -.12458798, 0, -.28228804, .79281193, -.26358792, 0, 0, 0, -.72828788, 0, .355912, 0, 0, 0, 0, -.43538806, -1.566388, 0, -.28388807, 0, -.69028801, 0, -.78128809, -.54648799, -.92738789, 0, 0, .61571199, 0, 0, 1.012012, 0, 0, 0, 0, .43991187, .9404121, 0, 0, 0, .61671191, 0, 2.6073117, 0, -.60438794, 0, -.18108793, -.48178813, 0, -.22628804, -.07398792, 0, 0, 0, 1.830512, 0, 0, 0, 0, 0, 0, -.36918804, 1.3247118, 0, 0, 1.163012, 0, 0, 0, 1.3241119, 0, 0, 0, 0, -.90038794, -1.250888, 0, 0, 0, 0, -1.048188, -.90138787, 0, 0, 0, 0, 0, 0, -.00878807, 0, 0, .46301201, -.22048803, -.71518797, 0, 0, -.4952881, 0, -.83718795, .57951194, 0, 0, 0, 1.054112, .61721212, 2.2717118, 0, 2.0280118, 0, 0, 0, 2.6503119, 2.3914118, 0, -.36418793, -.9259879, 0, 0, .16971211, 0, 1.9360118, 2.5344119, 2.0171118, 0, 0, 0, 0, 0, 0, 0, 0, .77211195, 0, 0, 0, 0, 0, 1.952312, 0, 0, .25901201, 0, -.5028879, .03641204, 0, .38571194, 0, 0, -.44528791, -.55918807, 0, .39721206, 0, -.34038803, -.05988808, 0, -.03518792, .045512, 0, -.039288, 0, .01431207, 0, 0, .030412, -.31918809, 0, 0, 0, -.324388, 0, -1.232188, 0, -.23678799, -.89188808, 0, -.6766879, 0, 0, 0, 0, 0, 0, 0, -.67818803, 0, 1.099412, 1.2767119, -.64068788, -.50678796, 0, 0, 0, 0, 0, .68371207, .11251191, -.17128797, .17081194, 0, -.48708794, .09591202, 0, -.20108791, -.02158805, 0, -.3012881, 0, 0, 0, -.87638801, -.54488796, 0, 0, 0, 0, -.14738794, 0, 0, 0, 0, -.75718802, -.37418792, 0, 1.0981121, 1.1441121, .47381189, 0, 0, 0, -.33498809, 0, 0, 0, 0, -.91838807, 0, -.34488794, 0, .12971191, .99381214, -.91608804, 0, 0, .98171192, 0, 0, 0, 0, -.01528808, 0, -.41458794, .25691202, .18601207, 0, 0, 0, -.35608789, .79691201, 0, 0, 1.548912, 0, 0, 0, 0, 0, 0, 0, 0, 1.1663117, 0, 0, -.009088, -.49578807, 0, 0, -.2677879, 0, -.25468799, .68631202, 0, 0, 0, -.36198804])\n",
-    "#fmt: off"
+    "# fmt: off"
    ]
   },
   {
@@ -149,19 +149,18 @@
    "outputs": [],
    "source": [
     "def load_data_cox(dta):\n",
-    "    array = lambda x : np.array(dta[x], dtype=float)\n",
-    "    t = array('t')\n",
-    "    obs_t = array('obs_t')\n",
-    "    pscenter = array('pscenter')\n",
-    "    hhcenter = array('hhcenter')\n",
-    "    ncomact = array('ncomact')\n",
-    "    rleader = array('rleader')\n",
-    "    dleader = array('dleader')\n",
-    "    inter1 = array('inter1')\n",
-    "    inter2 = array('inter2')\n",
-    "    fail = array('FAIL')\n",
-    "    return (t, obs_t, pscenter, hhcenter, ncomact,\n",
-    "            rleader, dleader, inter1, inter2, fail)"
+    "    array = lambda x: np.array(dta[x], dtype=float)\n",
+    "    t = array(\"t\")\n",
+    "    obs_t = array(\"obs_t\")\n",
+    "    pscenter = array(\"pscenter\")\n",
+    "    hhcenter = array(\"hhcenter\")\n",
+    "    ncomact = array(\"ncomact\")\n",
+    "    rleader = array(\"rleader\")\n",
+    "    dleader = array(\"dleader\")\n",
+    "    inter1 = array(\"inter1\")\n",
+    "    inter2 = array(\"inter2\")\n",
+    "    fail = array(\"FAIL\")\n",
+    "    return (t, obs_t, pscenter, hhcenter, ncomact, rleader, dleader, inter1, inter2, fail)"
    ]
   },
   {
@@ -170,8 +169,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "(t, obs_t, pscenter, hhcenter, ncomact, rleader,\n",
-    "     dleader, inter1, inter2, fail) = load_data_cox(dta)"
+    "(t, obs_t, pscenter, hhcenter, ncomact, rleader, dleader, inter1, inter2, fail) = load_data_cox(dta)"
    ]
   },
   {
@@ -210,33 +208,33 @@
    "outputs": [],
    "source": [
     "with Model() as model:\n",
-    "    \n",
+    "\n",
     "    T = len(t) - 1\n",
     "    nsubj = len(obs_t)\n",
     "\n",
     "    # risk set equals one if obs_t >= t\n",
     "    Y = np.array([[int(obs >= time) for time in t] for obs in obs_t])\n",
     "    # counting process. jump = 1 if obs_t \\in [t[j], t[j+1])\n",
-    "    dN = np.array([[Y[i,j]*int(t[j+1] >= obs_t[i])*fail[i] for j in range(T)] for i in\n",
-    "                    range(nsubj)])\n",
+    "    dN = np.array(\n",
+    "        [[Y[i, j] * int(t[j + 1] >= obs_t[i]) * fail[i] for j in range(T)] for i in range(nsubj)]\n",
+    "    )\n",
+    "\n",
+    "    c = Gamma(\"c\", 0.0001, 0.00001)\n",
+    "    r = Gamma(\"r\", 0.001, 0.0001)\n",
+    "\n",
+    "    dL0_star = r * np.diff(t)\n",
     "\n",
-    "    c = Gamma('c', .0001, .00001)\n",
-    "    r = Gamma('r', .001, .0001)\n",
-    "    \n",
-    "    dL0_star = r*np.diff(t)\n",
-    "    \n",
     "    # prior mean hazard\n",
-    "    mu = dL0_star * c \n",
-    "    \n",
-    "    dL0 = Gamma('dL0', mu, c, shape=T)\n",
+    "    mu = dL0_star * c\n",
+    "\n",
+    "    dL0 = Gamma(\"dL0\", mu, c, shape=T)\n",
     "\n",
-    "    beta = Normal('beta', np.zeros(7),\n",
-    "                  np.ones(7)*100, shape=7)\n",
+    "    beta = Normal(\"beta\", np.zeros(7), np.ones(7) * 100, shape=7)\n",
     "\n",
     "    linear_model = tt.exp(tt.dot(X.T, beta))\n",
     "    idt = Y[:, :-1] * tt.outer(linear_model, dL0)\n",
     "\n",
-    "    dn_like = Poisson('dn_like', idt, observed=dN)"
+    "    dn_like = Poisson(\"dn_like\", idt, observed=dN)"
    ]
   },
   {
@@ -365,7 +363,7 @@
    ],
    "source": [
     "with model:\n",
-    "    trace = sample(2000, n_init=10000, init='advi_map')"
+    "    trace = sample(2000, n_init=10000, init=\"advi_map\")"
    ]
   },
   {
@@ -390,7 +388,7 @@
     }
    ],
    "source": [
-    "az.plot_trace(trace, var_names=['c', 'r']);"
+    "az.plot_trace(trace, var_names=[\"c\", \"r\"]);"
    ]
   },
   {
@@ -415,7 +413,7 @@
     }
    ],
    "source": [
-    "az.plot_forest(trace, var_names=['beta']);"
+    "az.plot_forest(trace, var_names=[\"beta\"]);"
    ]
   },
   {

examples/survival_analysis/weibull_aft.ipynb

@@ -146,7 +146,7 @@
    "outputs": [],
    "source": [
     "def weibull_lccdf(x, alpha, beta):\n",
-    "    \"\"\" Log complementary cdf of Weibull distribution. \"\"\"\n",
+    "    \"\"\"Log complementary cdf of Weibull distribution.\"\"\"\n",
     "    return -((x / beta) ** alpha)"
    ]
   },
@@ -595,7 +595,7 @@
     "\n",
     "\n",
     "def gumbel_sf(y, mu, sigma):\n",
-    "    \"\"\" Gumbel survival function. \"\"\"\n",
+    "    \"\"\"Gumbel survival function.\"\"\"\n",
     "    return 1.0 - tt.exp(-tt.exp(-(y - mu) / sigma))"
    ]
   },

scripts/check_toc_is_complete.py

@@ -21,5 +21,7 @@
     parser.add_argument("paths", nargs="*", type=Path)
     args = parser.parse_args()
     for path in args.paths:
-        notebook_name = path.relative_to('examples').with_suffix('').as_posix()
-        assert notebook_name in toc_keys, f"Notebook '{notebook_name}' not added to table of contents!"
+        notebook_name = path.relative_to("examples").with_suffix("").as_posix()
+        assert (
+            notebook_name in toc_keys
+        ), f"Notebook '{notebook_name}' not added to table of contents!"