diff --git a/EDA/owncloud.ipynb b/EDA/owncloud.ipynb
index 32aa864..68572b0 100644
--- a/EDA/owncloud.ipynb
+++ b/EDA/owncloud.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "aab6b326-a583-47ad-8bb7-723c2fddcc63",
"metadata": {
"scrolled": true
@@ -18,18 +18,10 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "4f42846c-27c3-4394-a40a-e22d73c2902e",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "143.946026802063\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"start = time.time()\n",
"\n",
@@ -48,18 +40,10 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "3714dec2-85d0-4f76-af46-ea45ebec2fa3",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.5121121406555176\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"start = time.time()\n",
"df_performance = pd.read_hdf(\"tmp22.h5\", \"PERFORMANCE\")\n",
@@ -69,312 +53,30 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "f50e97d0",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "22\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(22)"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "c131c816",
"metadata": {},
- "outputs": [
- {
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- "10 n-back test 05 0.782609 1.316484 \n",
- "11 n-back test 06 0.363636 1.703583 \n",
- "3 k-drive test 01 NaN NaN \n",
- "4 k-drive test 02 NaN NaN \n",
- "5 k-drive test 03 NaN NaN \n",
- "\n",
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- "3 NaN NaN NaN NaN \n",
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- "\n",
- " RECALL SONGS RECALL \n",
- "6 NaN NaN \n",
- "7 NaN NaN \n",
- "8 NaN NaN \n",
- "9 NaN NaN \n",
- "10 NaN NaN \n",
- "11 NaN NaN \n",
- "3 1.000000 NaN \n",
- "4 0.914286 0.454545 \n",
- "5 0.676471 0.347826 "
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df_performance"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"id": "6ae47e52-ad86-4f8d-b929-0080dc99f646",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0.05357074737548828\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"start = time.time()\n",
"df_4_col = pd.read_hdf(\"tmp.h5\", \"SIGNALS\", mode=\"r\", columns=[\"STUDY\"], start=0, stop=1)\n",
@@ -384,121 +86,40 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"id": "7c139f3a-ede8-4530-957d-d1bb939f6cb5",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
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- " \n",
- " \n",
- " | \n",
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- ],
+ "outputs": [],
"source": [
"df_4_col.head()"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"id": "a68d58ea-65f2-46c4-a2b2-8c3447c715d7",
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- "(1, 1)"
- ]
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- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
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- ],
+ "outputs": [],
"source": [
"df_4_col.shape"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "95aa4523-3784-4ab6-bf92-0227ce60e863",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Index: 1 entries, 0 to 0\n",
- "Data columns (total 1 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 STUDY 1 non-null object\n",
- "dtypes: object(1)\n",
- "memory usage: 16.0+ bytes\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"df_4_col.info()"
]
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "defbcaf4-ad1b-453f-9b48-ab0ecfc4b5d5",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "STUDY 0\n",
- "dtype: int64"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df_4_col.isna().sum()"
]
diff --git a/dataset_creation/open_parquet_test.ipynb b/dataset_creation/open_parquet_test.ipynb
index 309686f..38145c0 100644
--- a/dataset_creation/open_parquet_test.ipynb
+++ b/dataset_creation/open_parquet_test.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "2b3fface",
"metadata": {},
"outputs": [],
@@ -12,18 +12,10 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "74f1f5ec",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(7320, 25)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"df= pd.read_parquet(r\"C:\\Users\\micha\\FAUbox\\WS2526_Fahrsimulator_MSY (Celina Korzer)\\AU_dataset\\output_windowed.parquet\")\n",
"print(df.shape)\n",
@@ -32,508 +24,40 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "05775454",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
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- " | \n",
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- " STUDY | \n",
- " LEVEL | \n",
- " PHASE | \n",
- " AU01_sum | \n",
- " AU02_sum | \n",
- " AU04_sum | \n",
- " AU05_sum | \n",
- " AU06_sum | \n",
- " ... | \n",
- " AU14_sum | \n",
- " AU15_sum | \n",
- " AU17_sum | \n",
- " AU20_sum | \n",
- " AU23_sum | \n",
- " AU24_sum | \n",
- " AU25_sum | \n",
- " AU26_sum | \n",
- " AU28_sum | \n",
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- " \n",
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- " \n",
- " | 2 | \n",
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- " \n",
- " | 3 | \n",
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- " 10.0 | \n",
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- "
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- " \n",
- "
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- "
5 rows × 25 columns
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- "
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- ],
- "text/plain": [
- " subjectID start_time STUDY LEVEL PHASE AU01_sum AU02_sum \\\n",
- "0 0 192000 k-drive 1 baseline 441.0 354.0 \n",
- "1 0 197120 k-drive 1 baseline 459.0 357.0 \n",
- "2 0 202120 k-drive 1 baseline 487.0 342.0 \n",
- "3 0 207120 k-drive 1 baseline 545.0 374.0 \n",
- "4 0 212120 k-drive 1 baseline 571.0 375.0 \n",
- "\n",
- " AU04_sum AU05_sum AU06_sum ... AU14_sum AU15_sum AU17_sum AU20_sum \\\n",
- "0 3.0 81.0 29.0 ... 302.0 511.0 653.0 65.0 \n",
- "1 4.0 71.0 22.0 ... 222.0 549.0 683.0 54.0 \n",
- "2 5.0 70.0 18.0 ... 141.0 558.0 710.0 27.0 \n",
- "3 4.0 70.0 13.0 ... 84.0 594.0 742.0 13.0 \n",
- "4 7.0 68.0 10.0 ... 80.0 547.0 735.0 12.0 \n",
- "\n",
- " AU23_sum AU24_sum AU25_sum AU26_sum AU28_sum AU43_sum \n",
- "0 798.0 1096.0 84.0 230.0 114.0 5.0 \n",
- "1 810.0 1093.0 86.0 247.0 108.0 5.0 \n",
- "2 828.0 1092.0 86.0 257.0 95.0 3.0 \n",
- "3 858.0 1091.0 97.0 279.0 99.0 2.0 \n",
- "4 894.0 1138.0 69.0 245.0 98.0 8.0 \n",
- "\n",
- "[5 rows x 25 columns]"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "99e17328",
"metadata": {},
- "outputs": [
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- " | 7315 | \n",
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- " n-back | \n",
- " 6 | \n",
- " test | \n",
- " 17.0 | \n",
- " 11.0 | \n",
- " 407.0 | \n",
- " 0.0 | \n",
- " 86.0 | \n",
- " ... | \n",
- " 191.0 | \n",
- " 693.0 | \n",
- " 594.0 | \n",
- " 14.0 | \n",
- " 73.0 | \n",
- " 312.0 | \n",
- " 414.0 | \n",
- " 242.0 | \n",
- " 83.0 | \n",
- " 40.0 | \n",
- "
\n",
- " \n",
- " | 7317 | \n",
- " 29 | \n",
- " 7152440 | \n",
- " n-back | \n",
- " 6 | \n",
- " test | \n",
- " 14.0 | \n",
- " 9.0 | \n",
- " 409.0 | \n",
- " 0.0 | \n",
- " 87.0 | \n",
- " ... | \n",
- " 187.0 | \n",
- " 703.0 | \n",
- " 597.0 | \n",
- " 14.0 | \n",
- " 64.0 | \n",
- " 314.0 | \n",
- " 411.0 | \n",
- " 248.0 | \n",
- " 98.0 | \n",
- " 38.0 | \n",
- "
\n",
- " \n",
- " | 7318 | \n",
- " 29 | \n",
- " 7157440 | \n",
- " n-back | \n",
- " 6 | \n",
- " test | \n",
- " 14.0 | \n",
- " 9.0 | \n",
- " 417.0 | \n",
- " 0.0 | \n",
- " 94.0 | \n",
- " ... | \n",
- " 169.0 | \n",
- " 711.0 | \n",
- " 603.0 | \n",
- " 15.0 | \n",
- " 63.0 | \n",
- " 327.0 | \n",
- " 398.0 | \n",
- " 245.0 | \n",
- " 100.0 | \n",
- " 35.0 | \n",
- "
\n",
- " \n",
- " | 7319 | \n",
- " 29 | \n",
- " 7162440 | \n",
- " n-back | \n",
- " 6 | \n",
- " test | \n",
- " 13.0 | \n",
- " 9.0 | \n",
- " 436.0 | \n",
- " 0.0 | \n",
- " 100.0 | \n",
- " ... | \n",
- " 178.0 | \n",
- " 720.0 | \n",
- " 621.0 | \n",
- " 17.0 | \n",
- " 65.0 | \n",
- " 337.0 | \n",
- " 377.0 | \n",
- " 246.0 | \n",
- " 101.0 | \n",
- " 31.0 | \n",
- "
\n",
- " \n",
- "
\n",
- "
5 rows × 25 columns
\n",
- "
"
- ],
- "text/plain": [
- " subjectID start_time STUDY LEVEL PHASE AU01_sum AU02_sum \\\n",
- "7315 29 7142440 n-back 6 test 14.0 15.0 \n",
- "7316 29 7147440 n-back 6 test 17.0 11.0 \n",
- "7317 29 7152440 n-back 6 test 14.0 9.0 \n",
- "7318 29 7157440 n-back 6 test 14.0 9.0 \n",
- "7319 29 7162440 n-back 6 test 13.0 9.0 \n",
- "\n",
- " AU04_sum AU05_sum AU06_sum ... AU14_sum AU15_sum AU17_sum \\\n",
- "7315 388.0 0.0 83.0 ... 191.0 697.0 584.0 \n",
- "7316 407.0 0.0 86.0 ... 191.0 693.0 594.0 \n",
- "7317 409.0 0.0 87.0 ... 187.0 703.0 597.0 \n",
- "7318 417.0 0.0 94.0 ... 169.0 711.0 603.0 \n",
- "7319 436.0 0.0 100.0 ... 178.0 720.0 621.0 \n",
- "\n",
- " AU20_sum AU23_sum AU24_sum AU25_sum AU26_sum AU28_sum AU43_sum \n",
- "7315 15.0 81.0 319.0 421.0 247.0 88.0 35.0 \n",
- "7316 14.0 73.0 312.0 414.0 242.0 83.0 40.0 \n",
- "7317 14.0 64.0 314.0 411.0 248.0 98.0 38.0 \n",
- "7318 15.0 63.0 327.0 398.0 245.0 100.0 35.0 \n",
- "7319 17.0 65.0 337.0 377.0 246.0 101.0 31.0 \n",
- "\n",
- "[5 rows x 25 columns]"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"df.tail()"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "69e53731",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "RangeIndex: 7320 entries, 0 to 7319\n",
- "Data columns (total 25 columns):\n",
- " # Column Non-Null Count Dtype \n",
- "--- ------ -------------- ----- \n",
- " 0 subjectID 7320 non-null int64 \n",
- " 1 start_time 7320 non-null int64 \n",
- " 2 STUDY 7320 non-null object \n",
- " 3 LEVEL 7320 non-null int8 \n",
- " 4 PHASE 7320 non-null object \n",
- " 5 AU01_sum 7320 non-null float64\n",
- " 6 AU02_sum 7320 non-null float64\n",
- " 7 AU04_sum 7320 non-null float64\n",
- " 8 AU05_sum 7320 non-null float64\n",
- " 9 AU06_sum 7320 non-null float64\n",
- " 10 AU07_sum 7320 non-null float64\n",
- " 11 AU09_sum 7320 non-null float64\n",
- " 12 AU10_sum 7320 non-null float64\n",
- " 13 AU11_sum 7320 non-null float64\n",
- " 14 AU12_sum 7320 non-null float64\n",
- " 15 AU14_sum 7320 non-null float64\n",
- " 16 AU15_sum 7320 non-null float64\n",
- " 17 AU17_sum 7320 non-null float64\n",
- " 18 AU20_sum 7320 non-null float64\n",
- " 19 AU23_sum 7320 non-null float64\n",
- " 20 AU24_sum 7320 non-null float64\n",
- " 21 AU25_sum 7320 non-null float64\n",
- " 22 AU26_sum 7320 non-null float64\n",
- " 23 AU28_sum 7320 non-null float64\n",
- " 24 AU43_sum 7320 non-null float64\n",
- "dtypes: float64(20), int64(2), int8(1), object(2)\n",
- "memory usage: 1.3+ MB\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "3754c664",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "STUDY PHASE LEVEL\n",
- "k-drive train 1 155\n",
- " 3 156\n",
- " 2 162\n",
- " baseline 3 248\n",
- "n-back baseline 2 252\n",
- " test 5 255\n",
- " 6 256\n",
- " 1 258\n",
- " 4 258\n",
- " 2 260\n",
- " 3 260\n",
- "k-drive baseline 2 267\n",
- " 1 896\n",
- "n-back baseline 1 901\n",
- "k-drive test 1 911\n",
- " 2 912\n",
- " 3 913\n",
- "Name: count, dtype: int64"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Zeigt alle Kombinationen mit Häufigkeit\n",
"df[['STUDY', 'PHASE', 'LEVEL']].value_counts(ascending=True)"
@@ -541,21 +65,10 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"id": "f83b595c",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(1031, 25)"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"high_nback = df[\n",
" (df[\"STUDY\"]==\"n-back\") &\n",
@@ -567,19 +80,10 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": null,
"id": "c0940343",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(3080, 25)\n",
- "(3209, 25)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"low_all = df[\n",
" ((df[\"PHASE\"] == \"baseline\") |\n",
@@ -594,20 +98,10 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": null,
"id": "f7ce38d3",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "True\n",
- "7320\n",
- "7320\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print((df.shape[0]==(high_kdrive.shape[0]+high_nback.shape[0]+low_all.shape[0])))\n",
"print(df.shape[0])\n",
@@ -616,21 +110,10 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": null,
"id": "48ba0379",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(4240, 25)"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"high_all = pd.concat([high_nback, high_kdrive])\n",
"high_all.shape"
@@ -638,20 +121,10 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": null,
"id": "77dda26c",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Gesamt: 7320==7320\n",
- "Anzahl an low load Samples: 3080\n",
- "Anzahl an high load Samples: 4240\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(f\"Gesamt: {df.shape[0]}=={low_all.shape[0]+high_all.shape[0]}\")\n",
"print(f\"Anzahl an low load Samples: {low_all.shape[0]}\")\n",