From 75d79a13473150b04dd899be2b892915e408962f Mon Sep 17 00:00:00 2001 From: "Dobbertin, Niclas" Date: Mon, 26 Feb 2024 22:09:23 +0100 Subject: update --- experiment/analysis/RT.png | Bin 30922 -> 31064 bytes experiment/analysis/analysis.ipynb | 167 +++++++++++++++++++------------------ 2 files changed, 84 insertions(+), 83 deletions(-) (limited to 'experiment/analysis') diff --git a/experiment/analysis/RT.png b/experiment/analysis/RT.png index 1a75fca..859eb36 100644 Binary files a/experiment/analysis/RT.png and b/experiment/analysis/RT.png differ diff --git a/experiment/analysis/analysis.ipynb b/experiment/analysis/analysis.ipynb index 83a92c0..e7b93d6 100644 --- a/experiment/analysis/analysis.ipynb +++ b/experiment/analysis/analysis.ipynb @@ -33,7 +33,7 @@ "metadata": {}, "outputs": [], "source": [ - "data_path = Path(\"/home/niclas/repos/uni/master_thesis/experiment/data\")\n", + "data_path = Path(\"/home/niclas/repos/uni/thesis/experiment/data\")\n", "\n", "procedures = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"overall\"]\n" ] @@ -47,7 +47,7 @@ { "data": { "text/plain": [ - "['random', 'fixed', 'blocked']" + "['random', 'blocked', 'fixed']" ] }, "execution_count": 3, @@ -128,13 +128,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "id": "eb3f2e96-2246-4b08-a7d1-999161ab3fd3", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -162,6 +162,7 @@ "axes[1].set_xlabel(\"Block\")\n", "plt.ylabel(\"RTsum\")\n", "plt.legend()\n", + "fig.tight_layout()\n", "plt.savefig(\"RT.png\")\n", "plt.show()" ] @@ -199,14 +200,14 @@ " \n", " \n", " \n", - " vp12\n", - " 0.822222\n", - " 0.820000\n", + " vp14\n", + " 0.982222\n", + " 0.986667\n", " \n", " \n", - " vp19\n", - " 0.966667\n", - " 0.800000\n", + " vp18\n", + " 0.962222\n", + " 0.970000\n", " \n", " \n", " vp15\n", @@ -214,11 +215,6 @@ " 0.980000\n", " \n", " \n", - " vp17\n", - " 0.911111\n", - " 0.960000\n", - " \n", - " \n", " vp20\n", " 0.906667\n", " 0.980000\n", @@ -229,24 +225,29 @@ " 0.943333\n", " \n", " \n", - " vp16\n", - " 0.957778\n", - " 0.926667\n", - " \n", - " \n", " vp13\n", " 0.857778\n", " 0.946667\n", " \n", " \n", - " vp18\n", - " 0.962222\n", - " 0.970000\n", + " vp17\n", + " 0.911111\n", + " 0.960000\n", " \n", " \n", - " vp14\n", - " 0.982222\n", - " 0.986667\n", + " vp12\n", + " 0.822222\n", + " 0.820000\n", + " \n", + " \n", + " vp19\n", + " 0.966667\n", + " 0.800000\n", + " \n", + " \n", + " vp16\n", + " 0.957778\n", + " 0.926667\n", " \n", " \n", "\n", @@ -254,16 +255,16 @@ ], "text/plain": [ " train test\n", - "vp12 0.822222 0.820000\n", - "vp19 0.966667 0.800000\n", + "vp14 0.982222 0.986667\n", + "vp18 0.962222 0.970000\n", "vp15 0.973333 0.980000\n", - "vp17 0.911111 0.960000\n", "vp20 0.906667 0.980000\n", "vp10 0.924444 0.943333\n", - "vp16 0.957778 0.926667\n", "vp13 0.857778 0.946667\n", - "vp18 0.962222 0.970000\n", - "vp14 0.982222 0.986667" + "vp17 0.911111 0.960000\n", + "vp12 0.822222 0.820000\n", + "vp19 0.966667 0.800000\n", + "vp16 0.957778 0.926667" ] }, "execution_count": 10, @@ -315,24 +316,24 @@ " \n", " \n", " \n", - " vp12\n", + " vp14\n", " 0.992\n", - " 0.592\n", - " 0.392\n", " 0.976\n", - " 0.960\n", - " 1.000\n", - " 0.016\n", + " 0.992\n", + " 0.976\n", + " 0.400\n", + " 0.600\n", + " 0.968\n", " \n", " \n", - " vp19\n", - " 1.000\n", - " 0.992\n", - " 0.000\n", - " 0.576\n", - " 0.992\n", - " 0.992\n", - " 0.848\n", + " vp18\n", + " 0.976\n", + " 0.976\n", + " 0.960\n", + " 0.392\n", + " 0.600\n", + " 0.984\n", + " 0.904\n", " \n", " \n", " vp15\n", @@ -345,16 +346,6 @@ " 0.928\n", " \n", " \n", - " vp17\n", - " 0.392\n", - " 0.968\n", - " 0.584\n", - " 1.000\n", - " 1.000\n", - " 0.992\n", - " 0.648\n", - " \n", - " \n", " vp20\n", " 0.992\n", " 0.376\n", @@ -375,16 +366,6 @@ " 0.712\n", " \n", " \n", - " vp16\n", - " 0.976\n", - " 0.600\n", - " 0.376\n", - " 0.976\n", - " 0.992\n", - " 1.000\n", - " 0.752\n", - " \n", - " \n", " vp13\n", " 0.384\n", " 0.960\n", @@ -395,24 +376,44 @@ " 0.568\n", " \n", " \n", - " vp18\n", - " 0.976\n", - " 0.976\n", - " 0.960\n", + " vp17\n", " 0.392\n", - " 0.600\n", - " 0.984\n", - " 0.904\n", + " 0.968\n", + " 0.584\n", + " 1.000\n", + " 1.000\n", + " 0.992\n", + " 0.648\n", " \n", " \n", - " vp14\n", + " vp12\n", " 0.992\n", + " 0.592\n", + " 0.392\n", " 0.976\n", + " 0.960\n", + " 1.000\n", + " 0.016\n", + " \n", + " \n", + " vp19\n", + " 1.000\n", " 0.992\n", + " 0.000\n", + " 0.576\n", + " 0.992\n", + " 0.992\n", + " 0.848\n", + " \n", + " \n", + " vp16\n", " 0.976\n", - " 0.400\n", " 0.600\n", - " 0.968\n", + " 0.376\n", + " 0.976\n", + " 0.992\n", + " 1.000\n", + " 0.752\n", " \n", " \n", "\n", @@ -420,16 +421,16 @@ ], "text/plain": [ " 1 2 3 4 5 6 overall\n", - "vp12 0.992 0.592 0.392 0.976 0.960 1.000 0.016\n", - "vp19 1.000 0.992 0.000 0.576 0.992 0.992 0.848\n", + "vp14 0.992 0.976 0.992 0.976 0.400 0.600 0.968\n", + "vp18 0.976 0.976 0.960 0.392 0.600 0.984 0.904\n", "vp15 0.992 0.992 0.960 0.392 0.592 1.000 0.928\n", - "vp17 0.392 0.968 0.584 1.000 1.000 0.992 0.648\n", "vp20 0.992 0.376 0.952 0.976 0.976 0.560 0.784\n", "vp10 0.968 0.360 0.592 0.984 0.984 0.992 0.712\n", - "vp16 0.976 0.600 0.376 0.976 0.992 1.000 0.752\n", "vp13 0.384 0.960 0.928 0.560 0.992 0.968 0.568\n", - "vp18 0.976 0.976 0.960 0.392 0.600 0.984 0.904\n", - "vp14 0.992 0.976 0.992 0.976 0.400 0.600 0.968" + "vp17 0.392 0.968 0.584 1.000 1.000 0.992 0.648\n", + "vp12 0.992 0.592 0.392 0.976 0.960 1.000 0.016\n", + "vp19 1.000 0.992 0.000 0.576 0.992 0.992 0.848\n", + "vp16 0.976 0.600 0.376 0.976 0.992 1.000 0.752" ] }, "execution_count": 11, @@ -475,7 +476,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.2" + "version": "3.11.7" } }, "nbformat": 4, -- cgit v1.2.3