From 4230f7219f0570eed63f6e133df68f6857b04ba7 Mon Sep 17 00:00:00 2001 From: Kees van Kempen Date: Tue, 18 Oct 2022 19:32:22 +0200 Subject: [PATCH] 06: PLOT 1c --- Exercise sheet 6/exercise_sheet_06.ipynb | 29 ++++++++++++++++++++---- 1 file changed, 24 insertions(+), 5 deletions(-) diff --git a/Exercise sheet 6/exercise_sheet_06.ipynb b/Exercise sheet 6/exercise_sheet_06.ipynb index dd8f281..c2c7467 100644 --- a/Exercise sheet 6/exercise_sheet_06.ipynb +++ b/Exercise sheet 6/exercise_sheet_06.ipynb @@ -474,7 +474,7 @@ " t_0 = time.time()\n", " return dt\n", "\n", - "with h5py.File('xy_data.hdf5','a') as f:\n", + "with h5py.File(\"xy_data.hdf5\", \"a\") as f:\n", " if not \"cluster-size\" in f:\n", " state = xy_aligned_init_config(width)\n", " cluster_sizes = np.zeros(len(temperatures))\n", @@ -528,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "e84152c5", "metadata": { "deletable": false, @@ -544,11 +544,30 @@ "task": false } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Plotting\n", - "# YOUR CODE HERE\n", - "raise NotImplementedError()" + "with h5py.File(\"xy_data.hdf5\", \"r\") as f:\n", + " plt.scatter(temperatures, f[\"cluster-size\"], marker='x')\n", + " plt.xlabel(\"T\")\n", + " plt.ylabel(\"average cluster size\")\n", + " plt.title(\"Average cluster size for the $w \\\\times w$ XY model \\n\"\n", + " \"(w = {}, #equilibrations = {}, #measurements = {})\"\n", + " .format(width, equilibration_moves, measurement_moves))\n", + " plt.show()" ] }, {