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authorNiclas Dobbertin <niclas.dobbertin@mailbox.org>2024-02-26 21:21:50 +0100
committerNiclas Dobbertin <niclas.dobbertin@mailbox.org>2024-02-26 21:21:50 +0100
commitd5e763d6e552b7fe60a9e82d5759e671f8c188f3 (patch)
tree6f3a13b44cdd16ddc167a63524074fa95ef4eec5 /experiment
parent22074d8dbea46fadbd12dafb27a4aa9006d3c7d1 (diff)
avg rtsum
Diffstat (limited to 'experiment')
-rw-r--r--experiment/analysis/RT.pngbin35757 -> 30922 bytes
-rw-r--r--experiment/analysis/analysis.ipynb15
-rw-r--r--experiment/analysis/tools.py6
3 files changed, 14 insertions, 7 deletions
diff --git a/experiment/analysis/RT.png b/experiment/analysis/RT.png
index 3d36478..1a75fca 100644
--- a/experiment/analysis/RT.png
+++ b/experiment/analysis/RT.png
Binary files differ
diff --git a/experiment/analysis/analysis.ipynb b/experiment/analysis/analysis.ipynb
index 3da688b..83a92c0 100644
--- a/experiment/analysis/analysis.ipynb
+++ b/experiment/analysis/analysis.ipynb
@@ -128,13 +128,13 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 18,
"id": "eb3f2e96-2246-4b08-a7d1-999161ab3fd3",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
@@ -155,8 +155,11 @@
"tools.plot_average_vps(axes[1], \"blocked\", test_blocked_blocked)\n",
"\n",
"axes[0].set_title(\"Train\")\n",
- "axes[1].set_title(\"Test\")\n",
- "plt.xlabel(\"Block\")\n",
+ "axes[0].set_xlabel(\"Block\")\n",
+ "axes[0].set_ylabel(\"RTsum\")\n",
+ "axes[1].set_title(\"Transfer\")\n",
+ "#plt.xlabel(\"Block\")\n",
+ "axes[1].set_xlabel(\"Block\")\n",
"plt.ylabel(\"RTsum\")\n",
"plt.legend()\n",
"plt.savefig(\"RT.png\")\n",
@@ -165,7 +168,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 10,
"id": "497bd4dc-943a-41f3-a694-3f4b8f049dee",
"metadata": {},
"outputs": [
@@ -263,7 +266,7 @@
"vp14 0.982222 0.986667"
]
},
- "execution_count": 13,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/experiment/analysis/tools.py b/experiment/analysis/tools.py
index cde322f..2287a17 100644
--- a/experiment/analysis/tools.py
+++ b/experiment/analysis/tools.py
@@ -50,7 +50,11 @@ def blocked_time(vp):
sum_time = 0
block_i += 1
sum_time += sum_time_over_trial(vp[f"{key_stem}_{trial}"])
- result[block_i] = sum_time
+ if block_i == 1:
+ avg = sum_time / 4
+ else:
+ avg = sum_time / 5
+ result[block_i] = avg
return result