From 38f4075c97eda2960c23e99d1ec6e907901537c5 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Mon, 23 Sep 2019 18:11:49 +0200
Subject: [PATCH] improve error fig
---
figures.ipynb | 49 ++++++++++++++++++++++++++++++-------------------
1 file changed, 30 insertions(+), 19 deletions(-)
diff --git a/figures.ipynb b/figures.ipynb
index d954248..063bea0 100644
--- a/figures.ipynb
+++ b/figures.ipynb
@@ -20,15 +20,22 @@
"metadata": {},
"outputs": [],
"source": [
+ "import functools\n",
+ "import itertools\n",
+ "import pickle\n",
+ "\n",
+ "import holoviews.plotting.mpl\n",
+ "import matplotlib; matplotlib.use(\"agg\")\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.tri as mtri\n",
"import numpy as np\n",
- "import matplotlib\n",
+ "from scipy import interpolate\n",
"\n",
- "matplotlib.use(\"agg\")\n",
+ "import adaptive\n",
"\n",
- "import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
- "# %config InlineBackend.figure_format = 'svg'\n",
+ "%config InlineBackend.figure_format = 'svg'\n",
"\n",
"golden_mean = (np.sqrt(5) - 1) / 2 # Aesthetic ratio\n",
"fig_width_pt = 246.0 # Columnwidth\n",
@@ -53,16 +60,7 @@
"}\n",
"\n",
"plt.rcParams.update(params)\n",
- "plt.rc(\"text.latex\", preamble=[r\"\\usepackage{xfrac}\", r\"\\usepackage{siunitx}\"])\n",
- "\n",
- "import adaptive\n",
- "from scipy import interpolate\n",
- "import functools\n",
- "import itertools\n",
- "import adaptive\n",
- "import holoviews.plotting.mpl\n",
- "import time\n",
- "import matplotlib.tri as mtri"
+ "plt.rc(\"text.latex\", preamble=[r\"\\usepackage{xfrac}\", r\"\\usepackage{siunitx}\"])"
]
},
{
@@ -335,6 +333,8 @@
"metadata": {},
"outputs": [],
"source": [
+ "import time\n",
+ "\n",
"learner = adaptive.Learner1D(funcs_1D[0][\"function\"], bounds=funcs_1D[0][\"bounds\"])\n",
"times = []\n",
"for i in range(10000):\n",
@@ -665,8 +665,8 @@
" return err_lin, err_learner\n",
"\n",
"\n",
- "N_max = 100\n",
- "Ns = np.geomspace(4, N_max, 20).astype(int)\n",
+ "N_max = 10_000\n",
+ "Ns = np.geomspace(4, N_max, 50).astype(int)\n",
"\n",
"loss_1D = adaptive.learner.learner1D.curvature_loss_function()\n",
"loss_2D = adaptive.learner.learnerND.curvature_loss_function()\n",
@@ -688,7 +688,10 @@
" err[i][d[\"title\"]] = (err_hom, err_adaptive)\n",
"\n",
" d[\"err_hom\"] = err_hom\n",
- " d[\"err_adaptive\"] = err_adaptive"
+ " d[\"err_adaptive\"] = err_adaptive\n",
+ "\n",
+ "with open(\"error_line_loss.pickle\", \"wb\") as f:\n",
+ " pickle.dump(err, f)"
]
},
{
@@ -697,10 +700,16 @@
"metadata": {},
"outputs": [],
"source": [
+ "import pickle, os\n",
+ "with open('error_line_loss.pickle', 'rb') as f:\n",
+ " err = pickle.load(f)\n",
+ " \n",
"fig, axs = plt.subplots(2, 1, figsize=(fig_width, 1.6 * fig_height))\n",
+ "plt.subplots_adjust(hspace=0.3)\n",
+ "\n",
+ "axs[1].set_xlabel(\"$N$\")\n",
"\n",
"for i, ax in enumerate(axs):\n",
- " ax.set_xlabel(\"$N$\")\n",
" ax.set_ylabel(r\"$\\text{Err}_{1}(\\tilde{f})$\")\n",
"\n",
" for j, (title, (err_hom, err_adaptive)) in enumerate(err[i].items()):\n",
@@ -708,8 +717,8 @@
" label = \"abc\"[j]\n",
" ax.loglog(Ns, err_hom, ls=\"--\", c=color)\n",
" ax.loglog(Ns, err_adaptive, label=f\"$\\mathrm{{({label})}}$ {title}\", c=color)\n",
+ " ax.legend()\n",
"\n",
- "plt.legend()\n",
"plt.savefig(\"figures/line_loss_error.pdf\", bbox_inches=\"tight\", transparent=True)\n",
"plt.show()"
]
@@ -721,9 +730,11 @@
"outputs": [],
"source": [
"# Error reduction\n",
+ "print(\"1D\")\n",
"for title, (err_hom, err_adaptive) in err[0].items():\n",
" print(title, err_hom[-1] / err_adaptive[-1])\n",
"\n",
+ "print(\"2D\")\n",
"for title, (err_hom, err_adaptive) in err[1].items():\n",
" print(title, err_hom[-1] / err_adaptive[-1])"
]
--
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