[pre-commit.ci] pre-commit autoupdate (#416)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 21.12b0 → 22.1.0](psf/black@21.12b0...22.1.0)
- [github.com/dfm/black_nbconvert: v0.3.0 → v0.4.0](dfm/black_nbconvert@v0.3.0...v0.4.0)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update pyproject.toml

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Dan Foreman-Mackey <foreman.mackey@gmail.com>

.pre-commit-config.yaml

@@ -16,11 +16,11 @@ repos:
     exclude: docs/tutorials
 
 - repo: https://github.com/psf/black
-  rev: "21.12b0"
+  rev: "22.1.0"
   hooks:
   - id: black
 
 - repo: https://github.com/dfm/black_nbconvert
-  rev: v0.3.0
+  rev: v0.4.0
   hooks:
   - id: black_nbconvert

docs/tutorials/autocorr.ipynb

@@ -450,7 +450,7 @@
     "\n",
     "\n",
     "def log_prob(p):\n",
-    "    return np.logaddexp(-0.5 * np.sum(p ** 2), -0.5 * np.sum((p - 4.0) ** 2))\n",
+    "    return np.logaddexp(-0.5 * np.sum(p**2), -0.5 * np.sum((p - 4.0) ** 2))\n",
     "\n",
     "\n",
     "sampler = emcee.EnsembleSampler(32, 3, log_prob)\n",

docs/tutorials/line.ipynb

@@ -148,9 +148,9 @@
    "source": [
     "A = np.vander(x, 2)\n",
     "C = np.diag(yerr * yerr)\n",
-    "ATA = np.dot(A.T, A / (yerr ** 2)[:, None])\n",
+    "ATA = np.dot(A.T, A / (yerr**2)[:, None])\n",
     "cov = np.linalg.inv(ATA)\n",
-    "w = np.linalg.solve(ATA, np.dot(A.T, y / yerr ** 2))\n",
+    "w = np.linalg.solve(ATA, np.dot(A.T, y / yerr**2))\n",
     "print(\"Least-squares estimates:\")\n",
     "print(\"m = {0:.3f} ± {1:.3f}\".format(w[0], np.sqrt(cov[0, 0])))\n",
     "print(\"b = {0:.3f} ± {1:.3f}\".format(w[1], np.sqrt(cov[1, 1])))\n",
@@ -218,7 +218,7 @@
     "def log_likelihood(theta, x, y, yerr):\n",
     "    m, b, log_f = theta\n",
     "    model = m * x + b\n",
-    "    sigma2 = yerr ** 2 + model ** 2 * np.exp(2 * log_f)\n",
+    "    sigma2 = yerr**2 + model**2 * np.exp(2 * log_f)\n",
     "    return -0.5 * np.sum((y - model) ** 2 / sigma2 + np.log(sigma2))"
    ]
   },

docs/tutorials/monitor.ipynb

@@ -58,7 +58,7 @@
     "# We'll also use the \"blobs\" feature to track the \"log prior\" for each step\n",
     "def log_prob(theta):\n",
     "    log_prior = -0.5 * np.sum((theta - 1.0) ** 2 / 100.0)\n",
-    "    log_prob = -0.5 * np.sum(theta ** 2) + log_prior\n",
+    "    log_prob = -0.5 * np.sum(theta**2) + log_prior\n",
     "    return log_prob, log_prior\n",
     "\n",
     "\n",
@@ -341,7 +341,7 @@
     "# this time, with a subtly different prior\n",
     "def log_prob2(theta):\n",
     "    log_prior = -0.5 * np.sum((theta - 2.0) ** 2 / 100.0)\n",
-    "    log_prob = -0.5 * np.sum(theta ** 2) + log_prior\n",
+    "    log_prob = -0.5 * np.sum(theta**2) + log_prior\n",
     "    return log_prob, log_prior\n",
     "\n",
     "\n",

docs/tutorials/parallel.ipynb

@@ -85,7 +85,7 @@
     "    while True:\n",
     "        if time.time() >= t:\n",
     "            break\n",
-    "    return -0.5 * np.sum(theta ** 2)"
+    "    return -0.5 * np.sum(theta**2)"
    ]
   },
   {
@@ -348,7 +348,7 @@
     "    while True:\n",
     "        if time.time() >= t:\n",
     "            break\n",
-    "    return -0.5 * np.sum(theta ** 2)\n",
+    "    return -0.5 * np.sum(theta**2)\n",
     "\n",
     "\n",
     "data = np.random.randn(5000, 200)\n",
@@ -459,7 +459,7 @@
     "    while True:\n",
     "        if time.time() >= t:\n",
     "            break\n",
-    "    return -0.5 * np.sum(theta ** 2)\n",
+    "    return -0.5 * np.sum(theta**2)\n",
     "\n",
     "\n",
     "with Pool() as pool:\n",

docs/tutorials/quickstart.ipynb

@@ -101,7 +101,7 @@
     "np.random.seed(42)\n",
     "means = np.random.rand(ndim)\n",
     "\n",
-    "cov = 0.5 - np.random.rand(ndim ** 2).reshape((ndim, ndim))\n",
+    "cov = 0.5 - np.random.rand(ndim**2).reshape((ndim, ndim))\n",
     "cov = np.triu(cov)\n",
     "cov += cov.T - np.diag(cov.diagonal())\n",
     "cov = np.dot(cov, cov)"

pyproject.toml

@@ -4,7 +4,6 @@ build-backend = "setuptools.build_meta"
 
 [tool.black]
 line-length = 79
-target-version = ['py35']
 exclude = '''
 /(
     \.eggs

src/emcee/ensemble.py

@@ -642,7 +642,7 @@ def walkers_independent(coords):
     if np.any(C_colmax == 0):
         return False
     C /= C_colmax
-    C_colsum = np.sqrt(np.sum(C ** 2, axis=0))
+    C_colsum = np.sqrt(np.sum(C**2, axis=0))
     C /= C_colsum
     return np.linalg.cond(C.astype(float)) <= 1e8
 
@@ -655,11 +655,11 @@ def walkers_independent_cov(coords):
 
 
 def _scaled_cond(a):
-    asum = np.sqrt((a ** 2).sum(axis=0))[None, :]
+    asum = np.sqrt((a**2).sum(axis=0))[None, :]
     if np.any(asum == 0):
         return np.inf
     b = a / asum
-    bsum = np.sqrt((b ** 2).sum(axis=1))[:, None]
+    bsum = np.sqrt((b**2).sum(axis=1))[:, None]
     if np.any(bsum == 0):
         return np.inf
     c = b / bsum

src/emcee/tests/integration/test_longdouble.py

@@ -7,7 +7,7 @@
 
 def test_longdouble_doesnt_crash_bug_312():
     def log_prob(x, ivar):
-        return -0.5 * np.sum(ivar * x ** 2)
+        return -0.5 * np.sum(ivar * x**2)
 
     ndim, nwalkers = 5, 20
     ivar = 1.0 / np.random.rand(ndim).astype(np.longdouble)

src/emcee/tests/integration/test_proposal.py

@@ -15,11 +15,11 @@
 
 
 def normal_log_prob_blobs(params):
-    return -0.5 * np.sum(params ** 2), params
+    return -0.5 * np.sum(params**2), params
 
 
 def normal_log_prob(params):
-    return -0.5 * np.sum(params ** 2)
+    return -0.5 * np.sum(params**2)
 
 
 def uniform_log_prob(params):

src/emcee/tests/unit/test_backends.py

@@ -23,7 +23,7 @@
 
 
 def normal_log_prob(params):
-    return -0.5 * np.sum(params ** 2)
+    return -0.5 * np.sum(params**2)
 
 
 def normal_log_prob_blobs(params):

src/emcee/tests/unit/test_blobs.py

@@ -15,7 +15,7 @@ def __init__(self, blob_function):
         self.blob_function = blob_function
 
     def __call__(self, params):
-        return -0.5 * np.sum(params ** 2), self.blob_function(params)
+        return -0.5 * np.sum(params**2), self.blob_function(params)
 
 
 @pytest.mark.parametrize("backend", backends.get_test_backends())

src/emcee/tests/unit/test_sampler.py

@@ -14,7 +14,7 @@
 
 
 def normal_log_prob(params):
-    return -0.5 * np.sum(params ** 2)
+    return -0.5 * np.sum(params**2)
 
 
 @pytest.mark.parametrize(
@@ -211,7 +211,7 @@ def test_restart(backend):
 
 def test_vectorize():
     def lp_vec(p):
-        return -0.5 * np.sum(p ** 2, axis=1)
+        return -0.5 * np.sum(p**2, axis=1)
 
     np.random.seed(42)
     nwalkers, ndim = 32, 3

src/emcee/tests/unit/test_state.py

@@ -31,7 +31,7 @@ def test_overwrite(seed=1234):
     np.random.seed(seed)
 
     def ll(x):
-        return -0.5 * np.sum(x ** 2)
+        return -0.5 * np.sum(x**2)
 
     nwalkers = 64
     p0 = np.random.normal(size=(nwalkers, 1))