From 49e4821a8c97e66cca8db72ed01454bad0948748 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Mon, 23 Sep 2019 13:27:09 +0200
Subject: [PATCH] style
---
figures.ipynb | 63 ++++++++++++++++++++++++++-------------------------
1 file changed, 32 insertions(+), 31 deletions(-)
diff --git a/figures.ipynb b/figures.ipynb
index 37a3a6b..c497a6d 100644
--- a/figures.ipynb
+++ b/figures.ipynb
@@ -117,7 +117,7 @@
" ip, _ = phase_diagram_setup(fname)\n",
" zs = ip(xy)\n",
" return np.round(zs)\n",
- " \n",
+ "\n",
"\n",
"def density(x, eps=0):\n",
" e = [0.8, 0.2]\n",
@@ -225,7 +225,6 @@
"metadata": {},
"outputs": [],
"source": [
- "\n",
"fig, axs = plt.subplots(2, len(funcs_1D), figsize=(fig_width, 1.5 * fig_height))\n",
"n_points = 50\n",
"for i, ax in enumerate(axs.T.flatten()):\n",
@@ -243,9 +242,7 @@
" ax.set_title(rf\"\\textrm{{{d['title']}}}\")\n",
" elif kind == \"adaptive\":\n",
" loss = adaptive.learner.learner1D.curvature_loss_function()\n",
- " learner = adaptive.Learner1D(\n",
- " f, bounds=bounds, loss_per_interval=loss\n",
- " )\n",
+ " learner = adaptive.Learner1D(f, bounds=bounds, loss_per_interval=loss)\n",
" adaptive.runner.simple(learner, goal=lambda l: l.npoints >= n_points)\n",
" xs, ys = zip(*sorted(learner.data.items()))\n",
" xs_dense = np.linspace(*bounds, 1000)\n",
@@ -338,7 +335,7 @@
"metadata": {},
"outputs": [],
"source": [
- "learner = adaptive.Learner1D(funcs_1D[0]['function'], bounds=funcs_1D[0]['bounds'])\n",
+ "learner = adaptive.Learner1D(funcs_1D[0][\"function\"], bounds=funcs_1D[0][\"bounds\"])\n",
"times = []\n",
"for i in range(10000):\n",
" t_start = time.time()\n",
@@ -500,7 +497,7 @@
"# ys_hom = f(xs_hom) - (i + 3) * step\n",
"# axs.plot(xs_hom, ys_hom, zorder=1, c=\"k\")\n",
"# axs.scatter(xs_hom, ys_hom, alpha=1, zorder=2, c=\"k\")\n",
- " \n",
+ "\n",
"axs.axis(\"off\")\n",
"plt.savefig(\"figures/algo.pdf\", bbox_inches=\"tight\", transparent=True)\n",
"plt.show()"
@@ -522,10 +519,11 @@
"from matplotlib.patches import Polygon\n",
"\n",
"fig, axs = plt.subplots(5, 1, figsize=(fig_width, 1.5 * fig_height))\n",
- "f = lambda x: np.sin(x)**2\n",
- "xs = np.array([0, 1.3, 3, 5, 7, 8])\n",
+ "f = lambda x: np.sin(x) ** 2\n",
+ "xs = np.array([0, 1.3, 3, 5, 7, 8])\n",
"ys = f(xs)\n",
"\n",
+ "\n",
"def plot(xs, ax):\n",
" ys = f(xs)\n",
" xs_dense = np.linspace(xs[0], xs[-1], 300)\n",
@@ -534,9 +532,10 @@
" ax.plot(xs, ys, c=\"k\")\n",
" ax.scatter(xs, ys, zorder=10, s=14, c=\"k\")\n",
"\n",
+ "\n",
"plot(xs, axs[0])\n",
"plot(xs, axs[1])\n",
- " \n",
+ "\n",
"for i, ax in enumerate(axs):\n",
" ax.axis(\"off\")\n",
" ax.set_ylim(-1.5, 1.5)\n",
@@ -565,6 +564,7 @@
" [xs[i - 1], xs[i + 1]], [ys[i - 1], ys[i + 1]], c=\"k\", ls=\"--\", alpha=0.3\n",
" )\n",
"\n",
+ "\n",
"plot_tri(xs, axs[1])\n",
"\n",
"for i in [2, 3, 4]:\n",
@@ -621,25 +621,28 @@
" err_learner = err_1D(xs, ys, xs_rand, learner.function)\n",
" return err_lin, err_learner\n",
"\n",
+ "\n",
"def err_2D(zs, zs_rand):\n",
" abserr = np.abs(zs - zs_rand)\n",
" return np.average(abserr ** 2) ** 0.5\n",
"\n",
+ "\n",
"def get_err_2D(learner, N):\n",
" xys_rand = np.vstack(\n",
" [\n",
- " np.random.uniform(*learner.bounds[0], size=int(100_000**0.5)),\n",
- " np.random.uniform(*learner.bounds[1], size=int(100_000**0.5)),\n",
+ " np.random.uniform(*learner.bounds[0], size=int(100_000 ** 0.5)),\n",
+ " np.random.uniform(*learner.bounds[1], size=int(100_000 ** 0.5)),\n",
" ]\n",
" )\n",
"\n",
" xys_hom = np.array(\n",
" [\n",
" (x, y)\n",
- " for x in np.linspace(*learner.bounds[0], int(N**0.5))\n",
- " for y in np.linspace(*learner.bounds[1], int(N**0.5))\n",
+ " for x in np.linspace(*learner.bounds[0], int(N ** 0.5))\n",
+ " for y in np.linspace(*learner.bounds[1], int(N ** 0.5))\n",
" ]\n",
" )\n",
+ " N = len(xys_hom)\n",
"\n",
" try:\n",
" # Vectorized\n",
@@ -654,12 +657,13 @@
" zs = ip(xys_rand.T)\n",
" err_lin = err_2D(zs, zs_rand)\n",
"\n",
- " ip = interpolate.LinearNDInterpolator(learner.points[:len(xys_hom)], learner.values[:len(xys_hom)])\n",
+ " ip = interpolate.LinearNDInterpolator(learner.points[:N], learner.values[:N])\n",
" zs = ip(xys_rand.T)\n",
" err_learner = err_2D(zs, zs_rand)\n",
"\n",
" return err_lin, err_learner\n",
"\n",
+ "\n",
"N_max = 10000\n",
"Ns = np.geomspace(4, N_max, 20).astype(int)\n",
"\n",
@@ -831,6 +835,7 @@
" adaptive.runner.simple(l, goal=lambda l: l.npoints > 1000)\n",
" return l.plot_isosurface(level=level)\n",
"\n",
+ "\n",
"interact_manual(iso, level=(-6, 9, 0.1), unit_cell=lattices.keys())"
]
},
@@ -844,8 +849,10 @@
" x, y = xy\n",
" return x ** 2 + y ** 3\n",
"\n",
+ "\n",
"bounds = [(-1, 1), (-1, 1)]\n",
- "npoints = 17**2\n",
+ "npoints = 17 ** 2\n",
+ "\n",
"\n",
"def isoline_loss_function(y_iso, sigma, priority=1):\n",
" from adaptive.learner.learnerND import default_loss\n",
@@ -855,7 +862,9 @@
"\n",
" def loss(simplex, values, value_scale):\n",
" distance = np.mean([abs(y_iso * value_scale - y) for y in values])\n",
- " return priority * gaussian(distance, 0, sigma) + default_loss(simplex, values, value_scale)\n",
+ " return priority * gaussian(distance, 0, sigma) + default_loss(\n",
+ " simplex, values, value_scale\n",
+ " )\n",
"\n",
" return loss\n",
"\n",
@@ -883,7 +892,7 @@
" kind = \"homogeneous\" if i == 0 else \"adaptive\"\n",
"\n",
" if kind == \"homogeneous\":\n",
- " xs, ys = [np.linspace(*bound, int(npoints**0.5)) for bound in bounds]\n",
+ " xs, ys = [np.linspace(*bound, int(npoints ** 0.5)) for bound in bounds]\n",
" data = {xy: f(xy) for xy in itertools.product(xs, ys)}\n",
" learner.data = data\n",
" elif kind == \"adaptive\":\n",
@@ -893,17 +902,16 @@
" adaptive.runner.simple(learner, goal=lambda l: l.npoints >= npoints)\n",
"\n",
" if with_tri:\n",
- " xy = np.array([learner.tri.get_vertices(s)\n",
- " for s in learner.tri.simplices])\n",
- " print(f'{len(xy)} triangles')\n",
+ " xy = np.array([learner.tri.get_vertices(s) for s in learner.tri.simplices])\n",
+ " print(f\"{len(xy)} triangles\")\n",
" triang = mtri.Triangulation(*xy.reshape(-1, 2).T)\n",
" ax.triplot(triang, c=\"w\", lw=0.2, alpha=0.8)\n",
"\n",
" # Isolines\n",
- " vertices, lines = learner._get_iso(level, which='line')\n",
+ " vertices, lines = learner._get_iso(level, which=\"line\")\n",
" paths = np.array([[vertices[i], vertices[j]] for i, j in lines]).T\n",
- " print('{} line segments'.format(len(paths.T)))\n",
- " ax.plot(*paths, c='k')\n",
+ " print(\"{} line segments\".format(len(paths.T)))\n",
+ " ax.plot(*paths, c=\"k\")\n",
"\n",
" values = np.array(list(learner.data.values()))\n",
" ax.imshow(\n",
@@ -918,13 +926,6 @@
"axs[1].set_ylabel(r\"$\\textrm{adaptive}$\")\n",
"plt.savefig(\"figures/isoline.pdf\", bbox_inches=\"tight\", transparent=True)"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
--
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