From 5c5549e60aa63136d80acbadda69afc18fabe3ae Mon Sep 17 00:00:00 2001 From: AidaasinAyda Date: Fri, 27 Sep 2024 17:43:44 +0000 Subject: [PATCH 1/3] interim analysis volbrain --- ...Concatenate_volbrain_csvs_deeplesion.ipynb | 1777 ++++++++++++++++- .../Script_regions_most_burden_WMH_Volbrain.R | 64 + 2 files changed, 1832 insertions(+), 9 deletions(-) create mode 100644 r_scripts/Script_regions_most_burden_WMH_Volbrain.R diff --git a/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb b/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb index c48c4f6..c70ae58 100644 --- a/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb +++ b/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [] }, @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "tags": [] }, @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "tags": [] }, @@ -85,11 +85,1770 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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0job1729725Female13.012-Sep-2024AA424.25106633.365941423.37650633.297160...0.0000000.00000076.03580073.91648351.8309890.0000000.00000056.18544852.769953BRICK_037
0job1729988Female17.013-Sep-2024AB397.35049531.547784396.99189131.519312...79.7147000.00000085.4460120.0000000.0000000.00000055.0704240.0000000.000000BRICK_038
0job1729989Male9.013-Sep-2024AB399.87334329.013965399.58699228.993188...0.0000000.0000000.0000000.0000000.0000000.00000055.2380970.0000000.000000BRICK_039
0job1729990Male6.013-Sep-2024AB475.03261131.341326474.58403531.311730...0.00000066.27543180.91253354.99217783.9547120.0000000.0000000.0000000.000000BRICK_040
0job1729991Female12.013-Sep-2024AC393.30614830.348634391.73941430.227740...80.51643476.79309368.01733781.10602565.50860979.04259181.4746070.0000000.000000BRICK_041
0job1729992Male6.013-Sep-2024AB340.35547529.733598339.82549429.687299...81.03594575.97287777.3673050.00000051.4084520.00000068.7915920.0000000.000000BRICK_042
0job1729995Female8.013-Sep-2024AB379.96847430.656052379.91606830.651824...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000BRICK_044
0job1729996Female16.013-Sep-2024AB446.13761931.323962444.38340431.200796...54.95305373.41090276.95758364.31024766.2572170.0000000.0000000.0000000.000000BRICK_047
0job1729997Female15.013-Sep-2024AA444.87475032.878659444.34071932.839191...0.0000000.00000072.0375610.00000071.6574830.0000000.0000000.0000000.000000BRICK_048
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44 rows × 639 columns

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" + ], + "text/plain": [ + " Subject Sex Age Report date Quality control T1 \\\n", + "0 job1729059 Female 16.0 10-Sep-2024 A \n", + "0 job1729067 Male 15.0 10-Sep-2024 A \n", + "0 job1729071 Female 13.0 10-Sep-2024 A \n", + "0 job1729073 Male 9.0 10-Sep-2024 A \n", + "0 job1729088 Male 9.0 10-Sep-2024 A \n", + "0 job1729376 Male 9.0 11-Sep-2024 A \n", + "0 job1729091 Female 14.0 10-Sep-2024 A \n", + "0 job1729092 Male 17.0 10-Sep-2024 A \n", + "0 job1729093 Male 6.0 10-Sep-2024 A \n", + "0 job1729095 Female 11.0 10-Sep-2024 A \n", + "0 job1729099 Male 13.0 10-Sep-2024 A \n", + "0 job1729377 Female 8.0 11-Sep-2024 A \n", + "0 job1729378 Female 7.0 11-Sep-2024 A \n", + "0 job1729380 Female 13.0 11-Sep-2024 A \n", + "0 job1729381 Male 16.0 11-Sep-2024 A \n", + "0 job1729392 Male 11.0 11-Sep-2024 A \n", + "0 job1729419 Male 12.0 11-Sep-2024 A \n", + "0 job1729420 Male 17.0 11-Sep-2024 A \n", + "0 job1729421 Female 10.0 11-Sep-2024 A \n", + "0 job1729422 Male 6.0 11-Sep-2024 A \n", + "0 job1729715 Female 11.0 12-Sep-2024 B \n", + "0 job1729716 Male 14.0 12-Sep-2024 A \n", + "0 job1729717 Male 6.0 12-Sep-2024 A \n", + "0 job1729718 Female 19.0 12-Sep-2024 A \n", + "0 job1729719 Male 15.0 12-Sep-2024 A \n", + "0 job1729720 Male 10.0 12-Sep-2024 A \n", + "0 job1729721 Male 16.0 12-Sep-2024 A \n", + "0 job1729722 Male 12.0 12-Sep-2024 A \n", + "0 job1729724 Male 18.0 12-Sep-2024 A \n", + "0 job1729725 Female 13.0 12-Sep-2024 A \n", + "0 job1729988 Female 17.0 13-Sep-2024 A \n", + "0 job1729989 Male 9.0 13-Sep-2024 A \n", + "0 job1729990 Male 6.0 13-Sep-2024 A \n", + "0 job1729991 Female 12.0 13-Sep-2024 A \n", + "0 job1729992 Male 6.0 13-Sep-2024 A \n", + "0 job1729995 Female 8.0 13-Sep-2024 A \n", + "0 job1729996 Female 16.0 13-Sep-2024 A \n", + "0 job1729997 Female 15.0 13-Sep-2024 A \n", + "0 job1730000 Male 17.0 13-Sep-2024 A \n", + "0 job1731971 Female 13.0 18-Sep-2024 A \n", + "0 job1731972 Female 17.0 18-Sep-2024 A \n", + "0 job1731973 Male 16.0 18-Sep-2024 A \n", + "0 job1731974 Female 11.0 18-Sep-2024 A \n", + "0 job1731975 Male 8.0 18-Sep-2024 A \n", + "\n", + " Quality control FLAIR White Matter (WM) volume cm3 \\\n", + "0 A 426.365775 \n", + "0 A 400.018627 \n", + "0 B 460.715082 \n", + "0 B 455.632899 \n", + "0 B 417.806187 \n", + "0 B 434.294205 \n", + "0 A 378.981688 \n", + "0 A 460.770814 \n", + "0 C 443.976699 \n", + "0 B 433.407448 \n", + "0 B 512.351809 \n", + "0 B 467.760143 \n", + "0 B 480.858912 \n", + "0 B 429.728084 \n", + "0 B 370.635463 \n", + "0 A 454.228531 \n", + "0 A 496.509481 \n", + "0 B 384.970116 \n", + "0 A 425.252759 \n", + "0 C 470.310834 \n", + "0 C 513.153592 \n", + "0 B 447.557590 \n", + "0 B 506.302024 \n", + "0 B 438.656616 \n", + "0 B 495.629206 \n", + "0 B 416.374907 \n", + "0 B 523.505456 \n", + "0 A 488.762908 \n", + "0 A 437.439972 \n", + "0 A 424.251066 \n", + "0 B 397.350495 \n", + "0 B 399.873343 \n", + "0 B 475.032611 \n", + "0 C 393.306148 \n", + "0 B 340.355475 \n", + "0 B 379.968474 \n", + "0 B 446.137619 \n", + "0 A 444.874750 \n", + "0 A 490.636204 \n", + "0 A 396.981191 \n", + "0 A 418.327543 \n", + "0 B 435.455361 \n", + "0 B 431.976037 \n", + "0 B 409.397647 \n", + "\n", + " White Matter (WM) volume % Normal appearing WM volume cm3 \\\n", + "0 31.822560 425.491947 \n", + "0 33.516918 399.391610 \n", + "0 31.271578 458.996232 \n", + "0 31.341535 453.964613 \n", + "0 30.485584 417.656813 \n", + "0 31.854803 433.575530 \n", + "0 32.289125 378.248562 \n", + "0 31.860121 459.173720 \n", + "0 32.419402 423.977238 \n", + "0 29.697125 432.829845 \n", + "0 32.539724 508.991336 \n", + "0 32.133063 467.521944 \n", + "0 31.931989 480.473674 \n", + "0 32.453276 429.286283 \n", + "0 31.472633 369.968868 \n", + "0 31.560729 453.336053 \n", + "0 32.719800 495.022202 \n", + "0 32.114932 384.208336 \n", + "0 30.820985 422.184102 \n", + "0 31.698281 468.447478 \n", + "0 35.192737 479.844653 \n", + "0 33.232237 442.020885 \n", + "0 30.095723 505.539130 \n", + "0 32.907728 434.787976 \n", + "0 31.976793 492.756541 \n", + "0 30.675810 415.559231 \n", + "0 33.691990 522.373744 \n", + "0 31.146099 487.907616 \n", + "0 32.079465 436.313402 \n", + "0 33.365941 423.376506 \n", + "0 31.547784 396.991891 \n", + "0 29.013965 399.586992 \n", + "0 31.341326 474.584035 \n", + "0 30.348634 391.739414 \n", + "0 29.733598 339.825494 \n", + "0 30.656052 379.916068 \n", + "0 31.323962 444.383404 \n", + "0 32.878659 444.340719 \n", + "0 31.638234 490.271963 \n", + "0 30.334198 396.698180 \n", + "0 32.302329 416.537360 \n", + "0 32.102694 435.312417 \n", + "0 31.868168 431.500221 \n", + "0 32.309511 409.004493 \n", + "\n", + " Normal appearing WM volume % ... \\\n", + "0 31.757340 ... \n", + "0 33.464381 ... \n", + "0 31.154909 ... \n", + "0 31.226779 ... \n", + "0 30.474685 ... \n", + "0 31.802090 ... \n", + "0 32.226663 ... \n", + "0 31.749690 ... \n", + "0 30.959031 ... \n", + "0 29.657547 ... \n", + "0 32.326299 ... \n", + "0 32.116700 ... \n", + "0 31.906406 ... \n", + "0 32.419911 ... \n", + "0 31.416029 ... \n", + "0 31.498717 ... \n", + "0 32.621789 ... \n", + "0 32.051383 ... \n", + "0 30.598579 ... \n", + "0 31.572694 ... \n", + "0 32.908367 ... \n", + "0 32.821124 ... \n", + "0 30.050375 ... \n", + "0 32.617505 ... \n", + "0 31.791455 ... \n", + "0 30.615716 ... \n", + "0 33.619155 ... \n", + "0 31.091596 ... \n", + "0 31.996849 ... \n", + "0 33.297160 ... \n", + "0 31.519312 ... \n", + "0 28.993188 ... \n", + "0 31.311730 ... \n", + "0 30.227740 ... \n", + "0 29.687299 ... \n", + "0 30.651824 ... \n", + "0 31.200796 ... \n", + "0 32.839191 ... \n", + "0 31.614746 ... \n", + "0 30.312572 ... \n", + "0 32.164095 ... \n", + "0 32.092156 ... \n", + "0 31.833066 ... \n", + "0 32.278483 ... \n", + "\n", + " Thalamic radiation anterior right disconnection probability \\\n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 82.339908 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 70.249569 \n", + "0 64.933191 \n", + "0 74.608780 \n", + "0 0.000000 \n", + "0 71.202289 \n", + "0 0.000000 \n", + "0 86.653255 \n", + "0 83.624568 \n", + "0 73.548745 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 71.474180 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 53.974962 \n", + "0 78.493463 \n", + "0 0.000000 \n", + "0 75.334350 \n", + "0 67.968335 \n", + "0 0.000000 \n", + "0 70.816902 \n", + "0 66.428315 \n", + "0 63.392530 \n", + "0 0.000000 \n", + "0 79.714700 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 80.516434 \n", + "0 81.035945 \n", + "0 0.000000 \n", + "0 54.953053 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 84.699533 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 79.183824 \n", + "\n", + " Thalamic radiation posterior left disconnection probability \\\n", + "0 70.046951 \n", + "0 0.000000 \n", + "0 74.674181 \n", + "0 50.140846 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 67.054474 \n", + "0 77.757461 \n", + "0 72.411269 \n", + "0 0.000000 \n", + "0 71.458382 \n", + "0 58.591551 \n", + "0 0.000000 \n", + "0 87.464792 \n", + "0 69.530518 \n", + "0 59.932308 \n", + "0 74.887506 \n", + "0 75.826814 \n", + "0 74.957397 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 75.936782 \n", + "0 74.579370 \n", + "0 71.830988 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 69.006262 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 66.275431 \n", + "0 76.793093 \n", + "0 75.972877 \n", + "0 0.000000 \n", + "0 73.410902 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 61.439752 \n", + "0 62.166102 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 76.579478 \n", + "\n", + " Thalamic radiation posterior right disconnection probability \\\n", + "0 71.912365 \n", + "0 0.000000 \n", + "0 63.192490 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 55.577466 \n", + "0 56.951711 \n", + "0 90.187796 \n", + "0 77.737267 \n", + "0 72.651147 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 50.610328 \n", + "0 89.281331 \n", + "0 72.138795 \n", + "0 64.383963 \n", + "0 75.269756 \n", + "0 72.935839 \n", + "0 59.061034 \n", + "0 0.000000 \n", + "0 66.572772 \n", + "0 76.917384 \n", + "0 62.159626 \n", + "0 67.618155 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 73.680934 \n", + "0 76.035800 \n", + "0 85.446012 \n", + "0 0.000000 \n", + "0 80.912533 \n", + "0 68.017337 \n", + "0 77.367305 \n", + "0 0.000000 \n", + "0 76.957583 \n", + "0 72.037561 \n", + "0 0.000000 \n", + "0 69.843194 \n", + "0 77.451799 \n", + "0 64.532652 \n", + "0 0.000000 \n", + "0 74.324468 \n", + "\n", + " Thalamic radiation superior left disconnection probability \\\n", + "0 61.019451 \n", + "0 0.000000 \n", + "0 61.391380 \n", + "0 64.734830 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 55.613684 \n", + "0 67.498637 \n", + "0 66.361504 \n", + "0 66.485426 \n", + "0 70.359370 \n", + "0 0.000000 \n", + "0 59.069977 \n", + "0 0.000000 \n", + "0 51.079816 \n", + "0 0.000000 \n", + "0 64.580074 \n", + "0 60.713617 \n", + "0 58.153366 \n", + "0 72.502639 \n", + "0 54.971311 \n", + "0 55.978093 \n", + "0 0.000000 \n", + "0 55.705463 \n", + "0 60.709084 \n", + "0 64.591227 \n", + "0 79.858641 \n", + "0 58.557407 \n", + "0 0.000000 \n", + "0 73.916483 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 54.992177 \n", + "0 81.106025 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 64.310247 \n", + "0 0.000000 \n", + "0 60.229527 \n", + "0 0.000000 \n", + "0 55.688577 \n", + "0 54.225352 \n", + "0 55.317686 \n", + "0 61.549298 \n", + "\n", + " Thalamic radiation superior right disconnection probability \\\n", + "0 70.375589 \n", + "0 0.000000 \n", + "0 67.839608 \n", + "0 68.595711 \n", + "0 72.508677 \n", + "0 0.000000 \n", + "0 64.225355 \n", + "0 69.885059 \n", + "0 71.773830 \n", + "0 0.000000 \n", + "0 75.608585 \n", + "0 75.370172 \n", + "0 74.925667 \n", + "0 57.417841 \n", + "0 0.000000 \n", + "0 55.680752 \n", + "0 72.172295 \n", + "0 62.149193 \n", + "0 63.615571 \n", + "0 65.985916 \n", + "0 52.109200 \n", + "0 77.265547 \n", + "0 59.342724 \n", + "0 68.282871 \n", + "0 74.977405 \n", + "0 76.426947 \n", + "0 0.000000 \n", + "0 67.893802 \n", + "0 74.366200 \n", + "0 51.830989 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 83.954712 \n", + "0 65.508609 \n", + "0 51.408452 \n", + "0 0.000000 \n", + "0 66.257217 \n", + "0 71.657483 \n", + "0 0.000000 \n", + "0 58.528953 \n", + "0 61.882407 \n", + "0 0.000000 \n", + "0 77.216326 \n", + "0 80.023477 \n", + "\n", + " Uncinate fasciculus left disconnection probability \\\n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 55.993742 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 50.704229 \n", + "0 0.000000 \n", + "0 59.473966 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 66.794709 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 71.275175 \n", + "0 58.078730 \n", + "0 0.000000 \n", + "0 56.150236 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 79.042591 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 64.520457 \n", + "\n", + " Uncinate fasciculus right disconnection probability \\\n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 50.516433 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 55.070424 \n", + "0 0.000000 \n", + "0 63.779268 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 62.919336 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 88.368191 \n", + "0 69.531139 \n", + "0 55.646609 \n", + "0 0.000000 \n", + "0 50.516433 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 55.070424 \n", + "0 55.238097 \n", + "0 0.000000 \n", + "0 81.474607 \n", + "0 68.791592 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 67.486459 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 99.624413 \n", + "\n", + " Vertical occipital fasciculus left disconnection probability \\\n", + "0 51.643193 \n", + "0 0.000000 \n", + "0 56.619720 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 74.924519 \n", + "0 0.000000 \n", + "0 53.708923 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 71.281416 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 68.041502 \n", + "0 0.000000 \n", + "0 67.780625 \n", + "0 67.312564 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 53.145543 \n", + "0 56.185448 \n", + "0 0.000000 \n", + "0 0.000000 \n", + "0 0.000000 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"0 BRICK_053 \n", + "0 BRICK_054 \n", + "0 BRICK_056 \n", + "\n", + "[44 rows x 639 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "concat_df = pd.concat(dataframes)\n", "concat_df" @@ -97,13 +1856,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "tags": [] }, "outputs": [], "source": [ - "concat_df.to_csv('../secret_data/volbrains.csv')" + "concat_df.to_csv('Z:/Aida_experiment/volbrainsnew.csv')" ] }, { @@ -139,7 +1898,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.8" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/r_scripts/Script_regions_most_burden_WMH_Volbrain.R b/r_scripts/Script_regions_most_burden_WMH_Volbrain.R new file mode 100644 index 0000000..afb5231 --- /dev/null +++ b/r_scripts/Script_regions_most_burden_WMH_Volbrain.R @@ -0,0 +1,64 @@ +## Experiment with Volbrain data. At this point, we do not have analysed all of the T0 scans + +library(dplyr) +library(tidyr) +voldf <- read.csv("Z:/Aida_experiment/volbrainsnew.csv") + +# Define participants to exclude +excluded_participants <- c("BRICK_003", "BRICK_009", "BRICK_024", "BRICK_025", "BRICK_040") + +#Turn Paricipant.Id into Participant.Id +voldf <- voldf %>% + rename( Participant.Id = Paricipant.Id) + + +# Filter voldf based on your criteria +voldf_clean <- voldf %>% + filter(Quality.control.T1 != "C", # Exclude rows where Quality.control.T1 is "C" + Quality.control.FLAIR != "C", # Exclude rows where Quality.control.FLAIR is "C" + !(Participant.Id %in% excluded_participants)) # Exclude specific participants + +# View the cleaned DataFrame +head(voldf_clean) + + +#We would like to know where the lesion burden is highest in the patients +# Calculate average lesion count and volume for each region using voldf_clean +lesion_avg_summary <- data.frame( + Region = c("Periventricular", "Deep white", "Juxtacortical", "Infratentorial", "Cerebellar", "Medular"), + + Avg_Lesion_Count = c( + mean(voldf_clean$Periventricular.lesion.count, na.rm = TRUE), + mean(voldf_clean$Deep.white.lesion.count, na.rm = TRUE), + mean(voldf_clean$Juxtacortical.lesion.count, na.rm = TRUE), + mean(voldf_clean$Infratentorial.lesion.count, na.rm = TRUE), + mean(voldf_clean$Cerebellar.lesion.count, na.rm = TRUE), + mean(voldf_clean$Medular.lesion.count, na.rm = TRUE) + ), + + Avg_Lesion_Volume_Absolute = c( + mean(voldf_clean$Periventricular.lesion.volume..absolute..cm3, na.rm = TRUE), + mean(voldf_clean$Deep.white.lesion.volume..absolute..cm3, na.rm = TRUE), + mean(voldf_clean$Juxtacortical.lesion.volume..absolute..cm3, na.rm = TRUE), + mean(voldf_clean$Infratentorial.lesion.volume..absolute..cm3, na.rm = TRUE), + mean(voldf_clean$Cerebellar.lesion.volume..absolute..cm3, na.rm = TRUE), + mean(voldf_clean$Medular.lesion.volume..absolute..cm3, na.rm = TRUE) + ), + + Avg_Lesion_Volume_Normalized = c( + mean(voldf_clean$Periventricular.lesion.volume..normalized..., na.rm = TRUE), + mean(voldf_clean$Deep.white.lesion.volume..normalized..., na.rm = TRUE), + mean(voldf_clean$Juxtacortical.lesion.volume..normalized..., na.rm = TRUE), + mean(voldf_clean$Infratentorial.lesion.volume..normalized..., na.rm = TRUE), + mean(voldf_clean$Cerebellar.lesion.volume..normalized..., na.rm = TRUE), + mean(voldf_clean$Medular.lesion.volume..normalized..., na.rm = TRUE) + ) +) + +# Sort by average lesion count and/or average absolute lesion volume +lesion_avg_summary_sorted <- lesion_avg_summary[order(-lesion_avg_summary$Avg_Lesion_Count, -lesion_avg_summary$Avg_Lesion_Volume_Absolute), ] + +# Print the sorted summary +print(lesion_avg_summary_sorted) + + From eb5f968b018835655677ebfaba00e1595ba6de25 Mon Sep 17 00:00:00 2001 From: AidaasinAyda Date: Tue, 15 Oct 2024 17:03:34 +0000 Subject: [PATCH 2/3] Made it more castor-friendly for upload --- .gitignore | 3 +- brickstudy.Rproj | 13 + ...Concatenate_volbrain_csvs_deeplesion.ipynb | 1803 +---------------- notebooks/experi/.~WISC_convertR.ipynb | 204 ++ notebooks/experi/.~tbe.ipynb | 801 ++++++++ 5 files changed, 1048 insertions(+), 1776 deletions(-) create mode 100644 brickstudy.Rproj create mode 100644 notebooks/experi/.~WISC_convertR.ipynb create mode 100644 notebooks/experi/.~tbe.ipynb diff --git a/.gitignore b/.gitignore index d4a4324..0eba20f 100644 --- a/.gitignore +++ b/.gitignore @@ -92,4 +92,5 @@ conda-pkg/meta.yaml !tests/sample_synthetic_data/*.csv secret_data/* secret_data/ -tmp_dcm2bids/* \ No newline at end of file +tmp_dcm2bids/* +.Rproj.user diff --git a/brickstudy.Rproj b/brickstudy.Rproj new file mode 100644 index 0000000..8e3c2eb --- /dev/null +++ b/brickstudy.Rproj @@ -0,0 +1,13 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX diff --git a/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb b/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb index c70ae58..43b5de1 100644 --- a/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb +++ b/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 79, "metadata": { "tags": [] }, @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 80, "metadata": { "tags": [] }, @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 82, "metadata": { "tags": [] }, @@ -79,1776 +79,17 @@ " if entry_name.endswith(\".csv\"):\n", " entry = zf.read(entry_name)\n", " df = pd.read_csv(BytesIO(entry), sep=';')\n", - " df['Paricipant Id'] = participant_id\n", + " df['Participant Id'] = participant_id\n", " dataframes.append(df)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "
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SubjectSexAgeReport dateQuality control T1Quality control FLAIRWhite Matter (WM) volume cm3White Matter (WM) volume %Normal appearing WM volume cm3Normal appearing WM volume %...Thalamic radiation anterior right disconnection probabilityThalamic radiation posterior left disconnection probabilityThalamic radiation posterior right disconnection probabilityThalamic radiation superior left disconnection probabilityThalamic radiation superior right disconnection probabilityUncinate fasciculus left disconnection probabilityUncinate fasciculus right disconnection probabilityVertical occipital fasciculus left disconnection probabilityVertical occipital fasciculus right disconnection probabilityParicipant Id
0job1729059Female16.010-Sep-2024AA426.36577531.822560425.49194731.757340...0.00000070.04695171.91236561.01945170.3755890.0000000.00000051.64319368.024312BRICK_001
0job1729067Male15.010-Sep-2024AA400.01862733.516918399.39161033.464381...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000BRICK_002
0job1729071Female13.010-Sep-2024AB460.71508231.271578458.99623231.154909...0.00000074.67418163.19249061.39138067.8396080.0000000.00000056.6197200.000000BRICK_003
0job1729073Male9.010-Sep-2024AB455.63289931.341535453.96461331.226779...82.33990850.1408460.00000064.73483068.59571155.99374250.5164330.0000000.000000BRICK_004
0job1729088Male9.010-Sep-2024AB417.80618730.485584417.65681330.474685...0.0000000.0000000.0000000.00000072.5086770.0000000.0000000.0000000.000000BRICK_005
0job1729376Male9.011-Sep-2024AB434.29420531.854803433.57553031.802090...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000BRICK_006
0job1729091Female14.010-Sep-2024AA378.98168832.289125378.24856232.226663...70.2495690.00000055.57746655.61368464.22535550.70422955.0704240.0000000.000000BRICK_007
0job1729092Male17.010-Sep-2024AA460.77081431.860121459.17372031.749690...64.9331910.00000056.95171167.49863769.8850590.0000000.0000000.00000070.324558BRICK_008
0job1729093Male6.010-Sep-2024AC443.97669932.419402423.97723830.959031...74.60878067.05447490.18779666.36150471.77383059.47396663.77926874.92451959.300852BRICK_009
0job1729095Female11.010-Sep-2024AB433.40744829.697125432.82984529.657547...0.00000077.75746177.73726766.4854260.0000000.0000000.0000000.0000000.000000BRICK_010
0job1729099Male13.010-Sep-2024AB512.35180932.539724508.99133632.326299...71.20228972.41126972.65114770.35937075.6085850.0000000.00000053.7089230.000000BRICK_011
0job1729377Female8.011-Sep-2024AB467.76014332.133063467.52194432.116700...0.0000000.0000000.0000000.00000075.3701720.0000000.0000000.0000000.000000BRICK_012
0job1729378Female7.011-Sep-2024AB480.85891231.931989480.47367431.906406...86.65325571.4583820.00000059.06997774.9256670.0000000.0000000.0000000.000000BRICK_013
0job1729380Female13.011-Sep-2024AB429.72808432.453276429.28628332.419911...83.62456858.5915510.0000000.00000057.41784166.79470962.9193360.0000000.000000BRICK_014
0job1729381Male16.011-Sep-2024AB370.63546331.472633369.96886831.416029...73.5487450.00000050.61032851.0798160.0000000.0000000.00000071.28141669.875398BRICK_015
0job1729392Male11.011-Sep-2024AA454.22853131.560729453.33605331.498717...0.00000087.46479289.2813310.00000055.6807520.0000000.0000000.0000000.000000BRICK_016
0job1729419Male12.011-Sep-2024AA496.50948132.719800495.02220232.621789...0.00000069.53051872.13879564.58007472.1722950.0000000.0000000.0000000.000000BRICK_017
0job1729420Male17.011-Sep-2024AB384.97011632.114932384.20833632.051383...71.47418059.93230864.38396360.71361762.1491930.0000000.00000068.04150267.448652BRICK_019
0job1729421Female10.011-Sep-2024AA425.25275930.820985422.18410230.598579...0.00000074.88750675.26975658.15336663.6155710.0000000.0000000.00000058.009966BRICK_021
0job1729422Male6.011-Sep-2024AC470.31083431.698281468.44747831.572694...0.00000075.82681472.93583972.50263965.9859160.00000088.36819167.78062573.117297BRICK_023
0job1729715Female11.012-Sep-2024BC513.15359235.192737479.84465332.908367...53.97496274.95739759.06103454.97131152.10920071.27517569.53113967.31256451.868546BRICK_024
0job1729716Male14.012-Sep-2024AB447.55759033.232237442.02088532.821124...78.4934630.0000000.00000055.97809377.26554758.07873055.6466090.0000000.000000BRICK_025
0job1729717Male6.012-Sep-2024AB506.30202430.095723505.53913030.050375...0.0000000.00000066.5727720.00000059.3427240.0000000.0000000.00000053.896713BRICK_026
0job1729718Female19.012-Sep-2024AB438.65661632.907728434.78797632.617505...75.33435075.93678276.91738455.70546368.28287156.15023650.5164330.00000065.774650BRICK_028
0job1729719Male15.012-Sep-2024AB495.62920631.976793492.75654131.791455...67.96833574.57937062.15962660.70908474.9774050.0000000.0000000.0000000.000000BRICK_029
0job1729720Male10.012-Sep-2024AB416.37490730.675810415.55923130.615716...0.00000071.83098867.61815564.59122776.4269470.0000000.0000000.0000000.000000BRICK_030
0job1729721Male16.012-Sep-2024AB523.50545633.691990522.37374433.619155...70.8169020.0000000.00000079.8586410.0000000.0000000.0000000.0000000.000000BRICK_031
0job1729722Male12.012-Sep-2024AA488.76290831.146099487.90761631.091596...66.4283150.0000000.00000058.55740767.8938020.0000000.0000000.0000000.000000BRICK_032
0job1729724Male18.012-Sep-2024AA437.43997232.079465436.31340231.996849...63.39253069.00626273.6809340.00000074.3662000.0000000.00000053.14554366.787393BRICK_035
0job1729725Female13.012-Sep-2024AA424.25106633.365941423.37650633.297160...0.0000000.00000076.03580073.91648351.8309890.0000000.00000056.18544852.769953BRICK_037
0job1729988Female17.013-Sep-2024AB397.35049531.547784396.99189131.519312...79.7147000.00000085.4460120.0000000.0000000.00000055.0704240.0000000.000000BRICK_038
0job1729989Male9.013-Sep-2024AB399.87334329.013965399.58699228.993188...0.0000000.0000000.0000000.0000000.0000000.00000055.2380970.0000000.000000BRICK_039
0job1729990Male6.013-Sep-2024AB475.03261131.341326474.58403531.311730...0.00000066.27543180.91253354.99217783.9547120.0000000.0000000.0000000.000000BRICK_040
0job1729991Female12.013-Sep-2024AC393.30614830.348634391.73941430.227740...80.51643476.79309368.01733781.10602565.50860979.04259181.4746070.0000000.000000BRICK_041
0job1729992Male6.013-Sep-2024AB340.35547529.733598339.82549429.687299...81.03594575.97287777.3673050.00000051.4084520.00000068.7915920.0000000.000000BRICK_042
0job1729995Female8.013-Sep-2024AB379.96847430.656052379.91606830.651824...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000BRICK_044
0job1729996Female16.013-Sep-2024AB446.13761931.323962444.38340431.200796...54.95305373.41090276.95758364.31024766.2572170.0000000.0000000.0000000.000000BRICK_047
0job1729997Female15.013-Sep-2024AA444.87475032.878659444.34071932.839191...0.0000000.00000072.0375610.00000071.6574830.0000000.0000000.0000000.000000BRICK_048
0job1730000Male17.013-Sep-2024AA490.63620431.638234490.27196331.614746...0.0000000.0000000.00000060.2295270.0000000.0000000.0000000.0000000.000000BRICK_050
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0job1731972Female17.018-Sep-2024AA418.32754332.302329416.53736032.164095...84.69953362.16610277.45179955.68857761.8824070.00000067.4864590.0000000.000000BRICK_052
0job1731973Male16.018-Sep-2024AB435.45536132.102694435.31241732.092156...0.0000000.00000064.53265254.2253520.0000000.0000000.0000000.0000000.000000BRICK_053
0job1731974Female11.018-Sep-2024AB431.97603731.868168431.50022131.833066...0.0000000.0000000.00000055.31768677.2163260.0000000.0000000.0000000.000000BRICK_054
0job1731975Male8.018-Sep-2024AB409.39764732.309511409.00449332.278483...79.18382476.57947874.32446861.54929880.02347764.52045799.6244130.0000000.000000BRICK_056
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44 rows × 639 columns

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" - ], - "text/plain": [ - " Subject Sex Age Report date Quality control T1 \\\n", - "0 job1729059 Female 16.0 10-Sep-2024 A \n", - "0 job1729067 Male 15.0 10-Sep-2024 A \n", - "0 job1729071 Female 13.0 10-Sep-2024 A \n", - "0 job1729073 Male 9.0 10-Sep-2024 A \n", - "0 job1729088 Male 9.0 10-Sep-2024 A \n", - "0 job1729376 Male 9.0 11-Sep-2024 A \n", - "0 job1729091 Female 14.0 10-Sep-2024 A \n", - "0 job1729092 Male 17.0 10-Sep-2024 A \n", - "0 job1729093 Male 6.0 10-Sep-2024 A \n", - "0 job1729095 Female 11.0 10-Sep-2024 A \n", - "0 job1729099 Male 13.0 10-Sep-2024 A \n", - "0 job1729377 Female 8.0 11-Sep-2024 A \n", - "0 job1729378 Female 7.0 11-Sep-2024 A \n", - "0 job1729380 Female 13.0 11-Sep-2024 A \n", - "0 job1729381 Male 16.0 11-Sep-2024 A \n", - "0 job1729392 Male 11.0 11-Sep-2024 A \n", - "0 job1729419 Male 12.0 11-Sep-2024 A \n", - "0 job1729420 Male 17.0 11-Sep-2024 A \n", - "0 job1729421 Female 10.0 11-Sep-2024 A \n", - "0 job1729422 Male 6.0 11-Sep-2024 A \n", - "0 job1729715 Female 11.0 12-Sep-2024 B \n", - "0 job1729716 Male 14.0 12-Sep-2024 A \n", - "0 job1729717 Male 6.0 12-Sep-2024 A \n", - "0 job1729718 Female 19.0 12-Sep-2024 A \n", - "0 job1729719 Male 15.0 12-Sep-2024 A \n", - "0 job1729720 Male 10.0 12-Sep-2024 A \n", - "0 job1729721 Male 16.0 12-Sep-2024 A \n", - "0 job1729722 Male 12.0 12-Sep-2024 A \n", - "0 job1729724 Male 18.0 12-Sep-2024 A \n", - "0 job1729725 Female 13.0 12-Sep-2024 A \n", - "0 job1729988 Female 17.0 13-Sep-2024 A \n", - "0 job1729989 Male 9.0 13-Sep-2024 A \n", - "0 job1729990 Male 6.0 13-Sep-2024 A \n", - "0 job1729991 Female 12.0 13-Sep-2024 A \n", - "0 job1729992 Male 6.0 13-Sep-2024 A \n", - "0 job1729995 Female 8.0 13-Sep-2024 A \n", - "0 job1729996 Female 16.0 13-Sep-2024 A \n", - "0 job1729997 Female 15.0 13-Sep-2024 A \n", - "0 job1730000 Male 17.0 13-Sep-2024 A \n", - "0 job1731971 Female 13.0 18-Sep-2024 A \n", - "0 job1731972 Female 17.0 18-Sep-2024 A \n", - "0 job1731973 Male 16.0 18-Sep-2024 A \n", - "0 job1731974 Female 11.0 18-Sep-2024 A \n", - "0 job1731975 Male 8.0 18-Sep-2024 A \n", - "\n", - " Quality control FLAIR White Matter (WM) volume cm3 \\\n", - "0 A 426.365775 \n", - "0 A 400.018627 \n", - "0 B 460.715082 \n", - "0 B 455.632899 \n", - "0 B 417.806187 \n", - "0 B 434.294205 \n", - "0 A 378.981688 \n", - "0 A 460.770814 \n", - "0 C 443.976699 \n", - "0 B 433.407448 \n", - "0 B 512.351809 \n", - "0 B 467.760143 \n", - "0 B 480.858912 \n", - "0 B 429.728084 \n", - "0 B 370.635463 \n", - "0 A 454.228531 \n", - "0 A 496.509481 \n", - "0 B 384.970116 \n", - "0 A 425.252759 \n", - "0 C 470.310834 \n", - "0 C 513.153592 \n", - "0 B 447.557590 \n", - "0 B 506.302024 \n", - "0 B 438.656616 \n", - "0 B 495.629206 \n", - "0 B 416.374907 \n", - "0 B 523.505456 \n", - "0 A 488.762908 \n", - "0 A 437.439972 \n", - "0 A 424.251066 \n", - "0 B 397.350495 \n", - "0 B 399.873343 \n", - "0 B 475.032611 \n", - "0 C 393.306148 \n", - "0 B 340.355475 \n", - "0 B 379.968474 \n", - "0 B 446.137619 \n", - "0 A 444.874750 \n", - "0 A 490.636204 \n", - "0 A 396.981191 \n", - "0 A 418.327543 \n", - "0 B 435.455361 \n", - "0 B 431.976037 \n", - "0 B 409.397647 \n", - "\n", - " White Matter (WM) volume % Normal appearing WM volume cm3 \\\n", - "0 31.822560 425.491947 \n", - "0 33.516918 399.391610 \n", - "0 31.271578 458.996232 \n", - "0 31.341535 453.964613 \n", - "0 30.485584 417.656813 \n", - "0 31.854803 433.575530 \n", - "0 32.289125 378.248562 \n", - "0 31.860121 459.173720 \n", - "0 32.419402 423.977238 \n", - "0 29.697125 432.829845 \n", - "0 32.539724 508.991336 \n", - "0 32.133063 467.521944 \n", - "0 31.931989 480.473674 \n", - "0 32.453276 429.286283 \n", - "0 31.472633 369.968868 \n", - "0 31.560729 453.336053 \n", - "0 32.719800 495.022202 \n", - "0 32.114932 384.208336 \n", - "0 30.820985 422.184102 \n", - "0 31.698281 468.447478 \n", - "0 35.192737 479.844653 \n", - "0 33.232237 442.020885 \n", - "0 30.095723 505.539130 \n", - "0 32.907728 434.787976 \n", - "0 31.976793 492.756541 \n", - "0 30.675810 415.559231 \n", - "0 33.691990 522.373744 \n", - "0 31.146099 487.907616 \n", - "0 32.079465 436.313402 \n", - "0 33.365941 423.376506 \n", - "0 31.547784 396.991891 \n", - "0 29.013965 399.586992 \n", - "0 31.341326 474.584035 \n", - "0 30.348634 391.739414 \n", - "0 29.733598 339.825494 \n", - "0 30.656052 379.916068 \n", - "0 31.323962 444.383404 \n", - "0 32.878659 444.340719 \n", - "0 31.638234 490.271963 \n", - "0 30.334198 396.698180 \n", - "0 32.302329 416.537360 \n", - "0 32.102694 435.312417 \n", - "0 31.868168 431.500221 \n", - "0 32.309511 409.004493 \n", - "\n", - " Normal appearing WM volume % ... \\\n", - "0 31.757340 ... \n", - "0 33.464381 ... \n", - "0 31.154909 ... \n", - "0 31.226779 ... \n", - "0 30.474685 ... \n", - "0 31.802090 ... \n", - "0 32.226663 ... \n", - "0 31.749690 ... \n", - "0 30.959031 ... \n", - "0 29.657547 ... \n", - "0 32.326299 ... \n", - "0 32.116700 ... \n", - "0 31.906406 ... \n", - "0 32.419911 ... \n", - "0 31.416029 ... \n", - "0 31.498717 ... \n", - "0 32.621789 ... \n", - "0 32.051383 ... \n", - "0 30.598579 ... \n", - "0 31.572694 ... \n", - "0 32.908367 ... \n", - "0 32.821124 ... \n", - "0 30.050375 ... \n", - "0 32.617505 ... \n", - "0 31.791455 ... \n", - "0 30.615716 ... \n", - "0 33.619155 ... \n", - "0 31.091596 ... \n", - "0 31.996849 ... \n", - "0 33.297160 ... \n", - "0 31.519312 ... \n", - "0 28.993188 ... \n", - "0 31.311730 ... \n", - "0 30.227740 ... \n", - "0 29.687299 ... \n", - "0 30.651824 ... \n", - "0 31.200796 ... \n", - "0 32.839191 ... \n", - "0 31.614746 ... \n", - "0 30.312572 ... \n", - "0 32.164095 ... \n", - "0 32.092156 ... \n", - "0 31.833066 ... \n", - "0 32.278483 ... \n", - "\n", - " Thalamic radiation anterior right disconnection probability \\\n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 82.339908 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 70.249569 \n", - "0 64.933191 \n", - "0 74.608780 \n", - "0 0.000000 \n", - "0 71.202289 \n", - "0 0.000000 \n", - "0 86.653255 \n", - "0 83.624568 \n", - "0 73.548745 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 71.474180 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 53.974962 \n", - "0 78.493463 \n", - "0 0.000000 \n", - "0 75.334350 \n", - "0 67.968335 \n", - "0 0.000000 \n", - "0 70.816902 \n", - "0 66.428315 \n", - "0 63.392530 \n", - "0 0.000000 \n", - "0 79.714700 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 80.516434 \n", - "0 81.035945 \n", - "0 0.000000 \n", - "0 54.953053 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 84.699533 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 79.183824 \n", - "\n", - " Thalamic radiation posterior left disconnection probability \\\n", - "0 70.046951 \n", - "0 0.000000 \n", - "0 74.674181 \n", - "0 50.140846 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 67.054474 \n", - "0 77.757461 \n", - "0 72.411269 \n", - "0 0.000000 \n", - "0 71.458382 \n", - "0 58.591551 \n", - "0 0.000000 \n", - "0 87.464792 \n", - "0 69.530518 \n", - "0 59.932308 \n", - "0 74.887506 \n", - "0 75.826814 \n", - "0 74.957397 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 75.936782 \n", - "0 74.579370 \n", - "0 71.830988 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 69.006262 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 66.275431 \n", - "0 76.793093 \n", - "0 75.972877 \n", - "0 0.000000 \n", - "0 73.410902 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 61.439752 \n", - "0 62.166102 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 76.579478 \n", - "\n", - " Thalamic radiation posterior right disconnection probability \\\n", - "0 71.912365 \n", - "0 0.000000 \n", - "0 63.192490 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 55.577466 \n", - "0 56.951711 \n", - "0 90.187796 \n", - "0 77.737267 \n", - "0 72.651147 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 50.610328 \n", - "0 89.281331 \n", - "0 72.138795 \n", - "0 64.383963 \n", - "0 75.269756 \n", - "0 72.935839 \n", - "0 59.061034 \n", - "0 0.000000 \n", - "0 66.572772 \n", - "0 76.917384 \n", - "0 62.159626 \n", - "0 67.618155 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 73.680934 \n", - "0 76.035800 \n", - "0 85.446012 \n", - "0 0.000000 \n", - "0 80.912533 \n", - "0 68.017337 \n", - "0 77.367305 \n", - "0 0.000000 \n", - "0 76.957583 \n", - "0 72.037561 \n", - "0 0.000000 \n", - "0 69.843194 \n", - "0 77.451799 \n", - "0 64.532652 \n", - "0 0.000000 \n", - "0 74.324468 \n", - "\n", - " Thalamic radiation superior left disconnection probability \\\n", - "0 61.019451 \n", - "0 0.000000 \n", - "0 61.391380 \n", - "0 64.734830 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 55.613684 \n", - "0 67.498637 \n", - "0 66.361504 \n", - "0 66.485426 \n", - "0 70.359370 \n", - "0 0.000000 \n", - "0 59.069977 \n", - "0 0.000000 \n", - "0 51.079816 \n", - "0 0.000000 \n", - "0 64.580074 \n", - "0 60.713617 \n", - "0 58.153366 \n", - "0 72.502639 \n", - "0 54.971311 \n", - "0 55.978093 \n", - "0 0.000000 \n", - "0 55.705463 \n", - "0 60.709084 \n", - "0 64.591227 \n", - "0 79.858641 \n", - "0 58.557407 \n", - "0 0.000000 \n", - "0 73.916483 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 54.992177 \n", - "0 81.106025 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 64.310247 \n", - "0 0.000000 \n", - "0 60.229527 \n", - "0 0.000000 \n", - "0 55.688577 \n", - "0 54.225352 \n", - "0 55.317686 \n", - "0 61.549298 \n", - "\n", - " Thalamic radiation superior right disconnection probability \\\n", - "0 70.375589 \n", - "0 0.000000 \n", - "0 67.839608 \n", - "0 68.595711 \n", - "0 72.508677 \n", - "0 0.000000 \n", - "0 64.225355 \n", - "0 69.885059 \n", - "0 71.773830 \n", - "0 0.000000 \n", - "0 75.608585 \n", - "0 75.370172 \n", - "0 74.925667 \n", - "0 57.417841 \n", - "0 0.000000 \n", - "0 55.680752 \n", - "0 72.172295 \n", - "0 62.149193 \n", - "0 63.615571 \n", - "0 65.985916 \n", - "0 52.109200 \n", - "0 77.265547 \n", - "0 59.342724 \n", - "0 68.282871 \n", - "0 74.977405 \n", - "0 76.426947 \n", - "0 0.000000 \n", - "0 67.893802 \n", - "0 74.366200 \n", - "0 51.830989 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 83.954712 \n", - "0 65.508609 \n", - "0 51.408452 \n", - "0 0.000000 \n", - "0 66.257217 \n", - "0 71.657483 \n", - "0 0.000000 \n", - "0 58.528953 \n", - "0 61.882407 \n", - "0 0.000000 \n", - "0 77.216326 \n", - "0 80.023477 \n", - "\n", - " Uncinate fasciculus left disconnection probability \\\n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 55.993742 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 50.704229 \n", - "0 0.000000 \n", - "0 59.473966 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 66.794709 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 71.275175 \n", - "0 58.078730 \n", - "0 0.000000 \n", - "0 56.150236 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 79.042591 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 64.520457 \n", - "\n", - " Uncinate fasciculus right disconnection probability \\\n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 50.516433 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 55.070424 \n", - "0 0.000000 \n", - "0 63.779268 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 62.919336 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 88.368191 \n", - "0 69.531139 \n", - "0 55.646609 \n", - "0 0.000000 \n", - "0 50.516433 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 55.070424 \n", - "0 55.238097 \n", - "0 0.000000 \n", - "0 81.474607 \n", - "0 68.791592 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 67.486459 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 99.624413 \n", - "\n", - " Vertical occipital fasciculus left disconnection probability \\\n", - "0 51.643193 \n", - "0 0.000000 \n", - "0 56.619720 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 74.924519 \n", - "0 0.000000 \n", - "0 53.708923 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 71.281416 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 68.041502 \n", - "0 0.000000 \n", - "0 67.780625 \n", - "0 67.312564 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 53.145543 \n", - "0 56.185448 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "\n", - " Vertical occipital fasciculus right disconnection probability \\\n", - "0 68.024312 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 70.324558 \n", - "0 59.300852 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 69.875398 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 67.448652 \n", - "0 58.009966 \n", - "0 73.117297 \n", - "0 51.868546 \n", - "0 0.000000 \n", - "0 53.896713 \n", - "0 65.774650 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 66.787393 \n", - "0 52.769953 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 63.599374 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "0 0.000000 \n", - "\n", - " Paricipant Id \n", - "0 BRICK_001 \n", - "0 BRICK_002 \n", - "0 BRICK_003 \n", - "0 BRICK_004 \n", - "0 BRICK_005 \n", - "0 BRICK_006 \n", - "0 BRICK_007 \n", - "0 BRICK_008 \n", - "0 BRICK_009 \n", - "0 BRICK_010 \n", - "0 BRICK_011 \n", - "0 BRICK_012 \n", - "0 BRICK_013 \n", - "0 BRICK_014 \n", - "0 BRICK_015 \n", - "0 BRICK_016 \n", - "0 BRICK_017 \n", - "0 BRICK_019 \n", - "0 BRICK_021 \n", - "0 BRICK_023 \n", - "0 BRICK_024 \n", - "0 BRICK_025 \n", - "0 BRICK_026 \n", - "0 BRICK_028 \n", - "0 BRICK_029 \n", - "0 BRICK_030 \n", - "0 BRICK_031 \n", - "0 BRICK_032 \n", - "0 BRICK_035 \n", - "0 BRICK_037 \n", - "0 BRICK_038 \n", - "0 BRICK_039 \n", - "0 BRICK_040 \n", - "0 BRICK_041 \n", - "0 BRICK_042 \n", - "0 BRICK_044 \n", - "0 BRICK_047 \n", - "0 BRICK_048 \n", - "0 BRICK_050 \n", - "0 BRICK_051 \n", - "0 BRICK_052 \n", - "0 BRICK_053 \n", - "0 BRICK_054 \n", - "0 BRICK_056 \n", - "\n", - "[44 rows x 639 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "concat_df = pd.concat(dataframes)\n", "concat_df" @@ -1856,30 +97,42 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 84, "metadata": { "tags": [] }, "outputs": [], "source": [ - "concat_df.to_csv('Z:/Aida_experiment/volbrainsnew.csv')" + "#move the participant id row to the front. Now it's last. This is important for upload in castor.\n", + "concat_df = concat_df.loc[:, [concat_df.columns[-1]] + list(concat_df.columns[:-1])]\n" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "tags": [] - }, + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#Now make the column names castor appropriate. Do not change the \"Participant Id\" column of course. dl stands for DeepLesion algorithm\n", + "concat_df.columns = [\n", + " col if col == \"Participant Id\" else \"dl_\" + col.replace(\" \", \"_\") + \"_T0\"\n", + " for col in concat_df.columns\n", + "]\n", + "\n", + "#For castor, male is 1 and female is 2. Replace these values\n", + "concat_df[\"dl_Sex_T0\"] = concat_df[\"dl_Sex_T0\"].replace({\"Female\": 2, \"Male\": 1})\n" + ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 86, + "metadata": { + "tags": [] + }, "outputs": [], - "source": [] + "source": [ + "concat_df.to_csv('Z:/castor_proof_files/volbrains_castor.csv')" + ] } ], "metadata": { diff --git a/notebooks/experi/.~WISC_convertR.ipynb b/notebooks/experi/.~WISC_convertR.ipynb new file mode 100644 index 0000000..1e1cbda --- /dev/null +++ b/notebooks/experi/.~WISC_convertR.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "0", + "metadata": {}, + "outputs": [], + "source": [ + "#Laad de benodigde libraries\n", + "library(readxl) # Load the package into your R session\n", + "library(writexl)\n", + "library(dplyr)\n", + " #Importeer excel file van export gemstracker WISCV\n", + " # Replace \"data.xlsx\" with the actual file name and path if it's located in a different directory\n", + " WISCV_gemstracker <- read.csv(\"../../secret_data/WISC_V_BRICK_T0dd08042024.csv\", sep=\";\")\n", + " \n", + " #show column names of the new df\n", + " print(colnames(WISCV_gemstracker))\n", + "\n", + " #verander de column names van gemstracker naar castor, voor Baseline.\n", + "\n", + "#head(WISCV_gemstracker)\n", + "\n", + " # Change column names\n", + " colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"gr2o_patient_nr\"] <- \"Participant Id\"\n", + " colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"DatumWISCV\"] <- \"Datum_WISC_V\"\n", + " colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"StartWISCV\"] <- \"Start_WISC_V\"\n", + " colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"StopWISCV\"] <- \"Stop_WISC_V\"\n", + " colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"WISCVVolt\"] <- \"WISC_V_voltooid\"\n", + " colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"VolgordeWISC\"] <- \"Volgorde_NPO_3\"\n", + " colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"AfnemerWISCV\"] <- \"Afnemer_WISC_V\"\n", + " colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"WISCVOpm\"] <- \"Opmerkingen_WISC_V\"\n", + " colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"WISCVOpmUit\"] <- \"Uitleg_Opmerkingen_WISC_V\"\n", + " \n", + " \n", + "\n", + "#head(WISCV_gemstracker)\n", + "\n", + " #voeg extra kolom toe om alleen de verplichte BRICK-waarden te exporteren naar castor en niet de hele WISCV. Als je toch alle velden wil invullen, kan\n", + " #je of alle velden een \"o\" geven, of dit handmatig wijzigen per participant, in de excel die op het einde gegenereerd wordt\n", + "\n", + " WISCV_gemstracker$BRICK_of_uitgebreid <- 1\n", + " \n", + " #De volgende kolommen die wel in de Castor-export van de WICV staan, voegen we niet toe: Participant status, site abbreviation en participation creation date. \n", + " #Dit zal niet zorgen voor problemen, zolang de participanten al vóór de Gemstracker-import zijn aangemaakt in Castor. We gaan niet via deze weg nieuwe patienten importeren. Dit kan wel, maar dan heb je wel deze kolommen nodig.\n", + "\n", + " #Waarden in de kolommen aanpassen op Castor Format\n", + " \n", + " #1.Te beginnen met de datum:\n", + " \n", + " # Convert the column \"Datum_WISC_V\" to Date format\n", + " WISCV_gemstracker$Datum_WISC_V <- as.Date(WISCV_gemstracker$Datum_WISC_V, format = \"%Y-%m-%d\")\n", + " \n", + " # Change the date format to \"02-08-2023\" in the same column\n", + " WISCV_gemstracker$Datum_WISC_V <- format(WISCV_gemstracker$Datum_WISC_V, \"%d-%m-%Y\")\n", + " \n", + " \n", + "\n", + "#head(WISCV_gemstracker)\n", + "\n", + " #2.Hierbij veranderen we de afnemer meerkeuze kolom naar een kolom met 1 waarde in de Castor dataframe (\"Afnemer_WISC_V_W\")\n", + " # Let op! Dit moet aangepast worden als er meer afnemers bij komen, maar dan verandert de 6 in een 7 etc. Goed opletten als de labelsets worden aangepast in Castor en LS!\n", + " \n", + " # Convert \"AfnemerWISCV_SQ000\" to \"AfnemerWISCV_SQ006\" columns to numeric values\n", + " # Convert \"AfnemerWISCV_SQ000\" to \"AfnemerWISCV_SQ006\" columns to numeric values\n", + " for (i in 0:6) {\n", + " column_name <- paste(\"AfnemerWISCV_SQ00\", i, sep = \"\")\n", + " WISCV_gemstracker$Afnemer_WISC_V[WISCV_gemstracker[column_name] == \"Y\"] <- i\n", + " }\n", + "\n", + "#sapply(WISCV_gemstracker, class)\n", + "\n", + "#head(WISCV_gemstracker)\n", + "\n", + " \n", + " #3. Hier veranderen we de waarden in de volgorde_NPO kolom voor in Castor\n", + " \n", + " # Convert \"VolgordeWISC_SQ001\" to \"VolgordeWISC_SQ004\" columns to numeric values\n", + " for (i in 1:4) {\n", + " col_name <- paste(\"VolgordeWISC_SQ00\", i, sep = \"\")\n", + " WISCV_gemstracker$Volgorde_NPO_3[WISCV_gemstracker[, col_name] == \"Y\"] <- i\n", + " }\n", + " \n", + "\n", + " #4. Alle waarden onder kolom: Opmerkingen_WISC_V en WISC_V_voltooid gaan van Y naar 1 en van N naar 0\n", + " \n", + " # Convert \"Y\" to 1 and \"N\" to 0 in the \"Opmerkingen_WISC_V\" column\n", + " WISCV_gemstracker$Opmerkingen_WISC_V[WISCV_gemstracker$Opmerkingen_WISC_V == \"Y\"] <- 1\n", + " WISCV_gemstracker$Opmerkingen_WISC_V[WISCV_gemstracker$Opmerkingen_WISC_V == \"N\"] <- 0\n", + " \n", + "\n", + " WISCV_gemstracker$WISC_V_voltooid[WISCV_gemstracker$WISC_V_voltooid == \"Y\"] <- 1\n", + " WISCV_gemstracker$WISC_V_voltooid[WISCV_gemstracker$WISC_V_voltooid == \"N\"] <- 0\n", + " \n", + "\n", + " ## Nog te testen: Haal het uur en de minuten uit twee afzonderlijke kolommen en zet ze samen in de start kolom.\n", + " # Convert numeric columns to characters\n", + " WISCV_gemstracker$StartWISCV_SQ001 <- as.character(WISCV_gemstracker$StartWISCV_SQ001)\n", + " WISCV_gemstracker$StartWISCV_SQ002 <- as.character(WISCV_gemstracker$StartWISCV_SQ002)\n", + " \n", + "\n", + " # Create Start_WISC_V column\n", + " WISCV_gemstracker$Start_WISC_V <- paste(WISCV_gemstracker$StartWISCV_SQ001, WISCV_gemstracker$StartWISCV_SQ002, sep = \":\")\n", + " \n", + " # Repeat the same process for Stop columns\n", + " WISCV_gemstracker$StopWISCV_SQ001 <- as.character(WISCV_gemstracker$StopWISCV_SQ001)\n", + " WISCV_gemstracker$StopWISCV_SQ002 <- as.character(WISCV_gemstracker$StopWISCV_SQ002)\n", + " \n", + "\n", + " # Create Stop_WISC_V column\n", + " WISCV_gemstracker$Stop_WISC_V <- paste(WISCV_gemstracker$StopWISCV_SQ001, WISCV_gemstracker$StopWISCV_SQ002, sep = \":\")\n", + "\n", + " #Volgorde kolommen aanpassen\n", + " WISCV_gemstracker <- WISCV_gemstracker %>%\n", + " select(\"Participant Id\", \"BRICK_of_uitgebreid\", \"Datum_WISC_V\", \"Start_WISC_V\", \n", + " \"Stop_WISC_V\", \"Volgorde_NPO_3\", \"WISC_V_voltooid\", \n", + " \"Opmerkingen_WISC_V\", \"Uitleg_Opmerkingen_WISC_V\", everything())\n", + " \n", + " \n", + "\n", + "#length(colnames(WISCV_gemstracker))\n", + "\n", + " #Elke field name is uniek. In de baseline meting, hebben bijn alle velden in Castor een _1. In FU1 en FU2 zal dit zeker _2 en _3 worden\n", + " #behalve participant id en \"Volgorde_NPO_3\", moeten alle kolommen eraan geloven.\n", + "\n", + " # # Create a list to hold the new column names\n", + " # new_column_names <- vector(\"character\", length(names(WISCV_gemstracker)))\n", + " \n", + " #Delete de kolommen uit de gemstracker export die je niet nodig hebt (dplyr)\n", + "\n", + " # List of columns to be removed\n", + " columns_to_remove <- c(\"respondentid\", \"organizationid\", \"gto_id_relation\", \"forgroup\", \n", + " \"consentcode\", \"resptrackid\", \"gto_round_order\", \"gto_round_description\", \n", + " \"gtr_track_name\", \"gr2t_track_info\", \"gto_completion_time\", \"gto_start_time\", \n", + " \"gto_valid_from\", \"gto_valid_until\", \"startlanguage\", \"lastpage\", \n", + " \"gto_id_token\", \"surveyversion\", \"AfnemerWISCV_SQ000\", \"AfnemerWISCV_SQ001\", \n", + " \"AfnemerWISCV_SQ002\", \"AfnemerWISCV_SQ003\", \"AfnemerWISCV_SQ004\", \n", + " \"AfnemerWISCV_SQ005\", \"AfnemerWISCV_SQ006\", \n", + " \"VolgordeWISC_SQ001\", \"VolgordeWISC_SQ002\", \"VolgordeWISC_SQ003\", \n", + " \"VolgordeWISC_SQ004\", \"Sub\", \"StartWISCV_SQ001\", \"StartWISCV_SQ002\", \"StopWISCV_SQ001\", \"StopWISCV_SQ002\")\n", + " \n", + " # Remove the specified columns\n", + " WISCV_gemstracker <- WISCV_gemstracker %>%\n", + " select(-one_of(columns_to_remove))\n", + "\n", + "# Create a list to hold the new column names\n", + "new_column_names <- vector(\"character\", length(names(WISCV_gemstracker)))\n", + " \n", + " \n", + " # Iterate through each column name\n", + " for (i in seq_along(names(WISCV_gemstracker))) {\n", + " if (names(WISCV_gemstracker)[i] != \"Participant Id\" && names(WISCV_gemstracker)[i] != \"Volgorde_NPO_3\") {\n", + " new_column_names[i] <- paste(names(WISCV_gemstracker)[i], \"_1\", sep = \"\")\n", + " } else {\n", + " new_column_names[i] <- names(WISCV_gemstracker)[i]\n", + " }\n", + " }\n", + " \n", + " # Assign the new column names to the dataframe\n", + " names(WISCV_gemstracker) <- new_column_names\n", + " \n", + " \n", + " \n", + " # Print the updated column names\n", + " print(names(WISCV_gemstracker))\n", + " \n", + " #exporteer nieuwe df naar excel file\n", + " write_xlsx(WISCV_gemstracker, path = \"WISCV_gemstracker_poging_makeda.xlsx\")\n", + " \n", + " # Export to CSV\n", + " write.csv(WISCV_gemstracker, file = \"WISCV_gemstracker_poging_makeda_csv.csv\", row.names = FALSE)\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.1.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/experi/.~tbe.ipynb b/notebooks/experi/.~tbe.ipynb new file mode 100644 index 0000000..b59e1e4 --- /dev/null +++ b/notebooks/experi/.~tbe.ipynb @@ -0,0 +1,801 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0", + "metadata": {}, + "source": [ + "# Instructions for Freesurfer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os \n", + "import shutil\n", + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4636ad3-773b-4c76-9eb1-2dd6d45877cc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e72dd35-bd1d-4ba1-9e95-1be3b6b139fd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7096d2a-302e-4a3f-8eff-044557c6c614", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "stats1 = pd.read_csv('../../secret_data/aseg_stats.txt', sep ='\\t')\n", + "stats1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e59403cb-eb4c-4fb8-b502-0f123545d9da", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "stats2 = pd.read_csv('Z:/rr2/all/aseg_stats.txt', sep ='\\t')\n", + "stats2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8de3524c-0023-40bd-8734-54f9f94ab461", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_stats = pd.concat([stats2, stats1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e8305e6-6578-4a9e-97f2-6ef148f21882", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#total_stats.to_csv('../../secret_data/brain_volumes_from_freesurfer_no_qc.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3265aaf8-3a7d-49bb-8bc9-671046862f51", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_stats.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2aa660f-bcca-45d2-ac11-869e45554171", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_stats = total_stats.rename(columns= {'Measure:volume':'Participant ID'})\n", + "total_stats['Participant ID'] = total_stats['Participant ID'].str.replace('/', '')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e58bc675-159d-4d2b-94f9-32ace51d6d4e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_stats.head(4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9a97c11-a970-4a4a-9bb5-024ffe47d540", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_stats.to_csv('../../secret_data/brain_volumes_from_freesurfer_no_qc.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f436652-1422-4cee-bfb4-601f3865f392", + "metadata": {}, + "outputs": [], + "source": [ + "#total" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38f32f47-2b98-4b48-8780-6642b63790fd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_stats_numeric = total_stats.drop(['Participant ID'], axis=1)\n", + "total_stats_numeric.corr() \n", + "#.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "393cb751-d05d-46ea-a6d7-ffca593c46dc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sns.heatmap(total_stats_numeric.corr(method='spearman', numeric_only=False))" + ] + }, + { + "cell_type": "markdown", + "id": "61cbccff-2a1a-4a85-bbb8-10a74ca493be", + "metadata": {}, + "source": [ + "Let's ask if left and right correlate...or other things." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "320667dc-cd16-47a7-8240-566384ab0a9c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "left_to_right = total_stats_numeric[['Left-Lateral-Ventricle', 'Left-Inf-Lat-Vent',\n", + " 'Left-Cerebellum-White-Matter', 'Left-Cerebellum-Cortex',\n", + " 'Left-Thalamus-Proper', 'Left-Caudate', 'Left-Putamen', 'Left-Pallidum',\n", + " 'Left-Hippocampus',\n", + " 'Left-Amygdala', 'Left-Accumbens-area', 'Left-VentralDC',\n", + " 'Left-vessel', 'Left-choroid-plexus', 'Right-Lateral-Ventricle',\n", + " 'Right-Inf-Lat-Vent', 'Right-Cerebellum-White-Matter',\n", + " 'Right-Cerebellum-Cortex', 'Right-Thalamus-Proper', 'Right-Caudate',\n", + " 'Right-Putamen', 'Right-Pallidum', 'Right-Hippocampus',\n", + " 'Right-Amygdala', 'Right-Accumbens-area', 'Right-VentralDC',\n", + " 'Right-vessel', 'Right-choroid-plexus', \n", + " 'Left-WM-hypointensities',\n", + " 'Right-WM-hypointensities', \n", + " 'Left-non-WM-hypointensities', 'Right-non-WM-hypointensities',]]\n", + "sns.heatmap(left_to_right.corr(method='spearman', numeric_only=False))" + ] + }, + { + "cell_type": "markdown", + "id": "9530e548-e8a9-4274-9f86-71ec558c21e1", + "metadata": {}, + "source": [ + "Yes, left and right correlate. Now let's ask whether wmh increase with age...." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1f18974-8d4a-4479-8080-e7d133d4d607", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_stats_numeric.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1e5fe32-2bed-4a9c-a6ee-cf0fd71a16f6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "total_stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39599499-1409-4c24-ad32-d4ddbdac4b61", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "wmh_info = total_stats[[ 'Participant ID','3rd-Ventricle', '4th-Ventricle', 'Brain-Stem', 'Left-Hippocampus',\n", + " 'Left-Amygdala', 'CSF', 'Right-Lateral-Ventricle',\n", + " 'Right-Inf-Lat-Vent', 'Right-Cerebellum-White-Matter',\n", + " 'Right-Cerebellum-Cortex', 'Right-Thalamus-Proper', 'Right-Caudate',\n", + " 'Right-Putamen', 'Right-Hippocampus',\n", + " 'Right-Amygdala',\n", + " 'Right-choroid-plexus',\n", + " 'WM-hypointensities', ]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdbc8251-d3b5-47b7-a224-f8559076b977", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "wmh_info['Participant ID'] = wmh_info['Participant ID'].astype(str)\n", + "wmh_info['Participant ID'] = wmh_info['Participant ID'].str.replace(\"-\", \"_\")\n", + "wmh_info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bfb076f-8d68-46c6-a946-87cfdfc09d56", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for_sheet_names = pd.ExcelFile(\"../../secret_data/BRICK_datums_scans_clean_17072024_versie_2.xlsx\")\n", + "sheet_names = for_sheet_names.sheet_names\n", + "print(sheet_names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "167618c8-bfae-40a3-bf8e-bbb61e87b7f5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "demo_kit = '../../secret_data/BRICK_datums_scans_clean_17072024_versie_2.xlsx'\n", + "demography = pd.read_excel('../../secret_data/BRICK_datums_scans_clean_17072024_versie_2.xlsx', sheet_name='Dag van MRI+NPO overzicht ')\n", + "demography['Participant ID'] = demography['BRICK-nummer']\n", + "demography = demography[['Participant ID','Genotype', 'Leeftijd_bij_scan']]\n", + "demography" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6309fd11-74d1-4e35-8e6f-766b7b6d4d60", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "fused_demo_wh = demography.merge(wmh_info, on='Participant ID')\n", + "fused_demo_wh.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e12a3a5c-3975-4561-900a-ad5f0f6de889", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Do wm-hypointensities increase with age or genotype?\n", + "simple_question = fused_demo_wh[['Genotype', 'Leeftijd_bij_scan', 'WM-hypointensities']]\n", + "# recode genotype to number\n", + "simple_question['Genotype_code'] = simple_question['Genotype'].astype('category').cat.codes\n", + "simple_question.to_csv('../../secret_data/forshow1.csv')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea9db2ee-7a5a-47b1-a1ac-433f09ed27af", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "simple_question_matrix= simple_question[['Genotype_code', 'Leeftijd_bij_scan', 'WM-hypointensities']]\n", + "simple_question_matrix.corr(method='spearman').to_csv('../../secret_data/forshow2.csv')\n", + "#simple_question_matrix.corr(method='spearman')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6611e73-f8fe-48d0-967f-85f68ab9ab05", + "metadata": {}, + "outputs": [], + "source": [ + "# OK, BUT DID IT matter for neuropsychological stuff?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4605efa3-89ae-476c-b4a1-d07ce4626913", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4bf8fd0e-b484-45f9-a184-dc7a09ac5609", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "WISC_V_BRICK_T0dd08042024 = pd.read_csv(\"../../secret_data/WISCV_export_gemstracker_BRICK_T0_100424.csv\", sep=\",\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27cf1309-b3d5-4543-8bb9-3dae3b109bcd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "WISC_V_BRICK_T0dd08042024.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0b5808e-32ca-4970-b0ab-93552ac46b07", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# for f in WISC_V_BRICK_T0dd08042024.columns:\n", + "# print(f)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a70ab6e-c4fd-40b5-8d17-88ab9866b783", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "WISC_V_BRICK_T0dd08042024_for_castor = WISC_V_BRICK_T0dd08042024.rename(\n", + " columns={\"gr2o_patient_nr\": \"Participant Id\",\n", + " \"DatumWISCV\": \"Datum_WISC_V\",\n", + " \"StartWISCV\": \"Start_WISC_V\",\n", + " \"StopWISCV\": \"Stop_WISC_V\",\n", + " \"WISCVVolt\": \"WISC_V_voltooid\",\n", + " \"VolgordeWISC\":\"Volgorde_NPO_3\",\n", + " \"AfnemerWISCV\":\"Afnemer_WISC_V\",\n", + " \"WISCVOpm\": \"Opmerkingen_WISC_V\",\n", + " \"WISCVOpmUit\":\"Uitleg_Opmerkingen_WISC_V\",} )\n", + "\n", + "\n", + " # colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"gr2o_patient_nr\"] <- \"Participant Id\"\n", + " # colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"DatumWISCV\"] <- \"Datum_WISC_V\"\n", + " # colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"StartWISCV\"] <- \"Start_WISC_V\"\n", + " # colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"StopWISCV\"] <- \"Stop_WISC_V\"\n", + " # colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"WISCVVolt\"] <- \"WISC_V_voltooid\"\n", + " # colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"VolgordeWISC\"] <- \"Volgorde_NPO_3\"\n", + " # colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"AfnemerWISCV\"] <- \"Afnemer_WISC_V\"\n", + " # colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"WISCVOpm\"] <- \"Opmerkingen_WISC_V\"\n", + " # colnames(WISCV_gemstracker)[colnames(WISCV_gemstracker) == \"WISCVOpmUit\"] <- \"Uitleg_Opmerkingen_WISC_V\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8233cfd4-73b7-461f-93fc-5cfbb0cb80a3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "WISC_V_BRICK_T0dd08042024_for_castor['BRICK_of_uitgebreid'] = 1\n", + "WISC_V_BRICK_T0dd08042024_for_castor.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a43f2147-a876-4cad-99dc-c88d84373bb6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "len(WISC_V_BRICK_T0dd08042024_for_castor)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72049f6a-a0ec-4f6c-a86d-b8313488760b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "len(WISC_V_BRICK_T0dd08042024_for_castor['Participant Id'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6487d863-ba5e-46e6-a6a1-259b393bd938", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "scores_from_wisc = WISC_V_BRICK_T0dd08042024_for_castor[[\n", + " 'Participant Id',\n", + " 'WgITot',\n", + " 'VsITot',\n", + " 'KRITot',\n", + " 'AWITot',\n", + " 'NVITot',\n", + " 'AVITot',\n", + " 'CCITot',\n", + " 'Tot',]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "637756c0-a2ec-4cee-a3c1-755f7182f0c6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "scores_from_wisc = scores_from_wisc.rename(columns={'Participant Id': 'Participant ID'})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef7f3337-4993-4a12-92c3-8f766fc0d938", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pd.merge?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a31a2d9f-c941-44bc-9e03-f1f4203fd86a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "scores_from_wisc.columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ceb02ba6-a9e7-421f-9c50-a8904e1346bc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#big_fuse = fused_demo_wh.merge(scores_from_wisc, on='Participant ID')\n", + "big_fuse = pd.merge(fused_demo_wh,scores_from_wisc, left_on='Participant ID', right_on='Participant ID', how='outer')\n", + "big_fuse.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70ddf08d-dc63-4b81-b111-43c28fcdf912", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "big_fuse_matrix = big_fuse[['Genotype','Leeftijd_bij_scan', 'Brain-Stem', 'Right-Hippocampus','Left-Hippocampus', 'Left-Amygdala',\n", + " 'Right-Lateral-Ventricle', 'Right-Inf-Lat-Vent',\n", + " 'Right-Cerebellum-White-Matter', 'Right-Cerebellum-Cortex',\n", + " 'Right-Thalamus-Proper', 'Right-Caudate', 'Right-Putamen',\n", + " 'Right-Amygdala', 'Right-choroid-plexus',\n", + " 'WM-hypointensities', 'WgITot', 'VsITot', 'KRITot', 'AWITot', 'NVITot',\n", + " 'AVITot', 'CCITot', 'Tot']]\n", + "big_fuse_matrix['Genotype_code'] = big_fuse_matrix['Genotype'].astype('category').cat.codes\n", + "big_fuse_matrix = big_fuse_matrix.drop(['Genotype'], axis=1)\n", + "big_fuse_matrix.corr(method='spearman')#.to_csv('../../secret_data/forshow3.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d21e0255-ee48-4a9c-b160-2a1b90b49de0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c8d00b5-b2ef-4b7c-aa3e-26f93bc561c6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "1", + "metadata": { + "tags": [] + }, + "source": [ + "1. first use python to move files a few at a time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64dfc2a1-5dae-4cc3-baa6-ee216e77a725", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9a8a93c-dea4-4c7a-ae5f-f759549b4c0b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4255f5e6-d2f9-401c-9bcc-54439132aa9a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "324d3b96-c0b6-438f-9323-670616e6069e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "find /mnt/data/output -iwholename '*Freesurfer*/*.nii.gz' -type f" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "shutil.copy?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "glob.glob?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "_files = glob.glob('C:/presentations/**', recursive=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#_files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for fileName_relative in glob.glob('C:/experimental', *, recursive=True): ## first get full file name with directores using for loop\n", + "\n", + " print(\"Full file name with directories: \", fileName_relative)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9", + "metadata": {}, + "outputs": [], + "source": [ + "def find_surf_and_move(src, dst, filestring):\n", + " if not os.path.exists(dst):\n", + " os.mkdir(dst)\n", + " files = glob.glob(os.path.join(src,'**'), recursive=True)\n", + "\n", + " # for path, subdirs, files in os.walk(src):\n", + " # print(files)\n", + " # for name in files:\n", + " # if filestring in name:\n", + " # #if fnmatch(name, pattern):\n", + " # full_name = os.path.join(path, name)\n", + " # #destination_folder = \n", + " # print(name)\n", + "\n", + " #shutil.copytree(real_name, dst)\n", + " \n", + " \n", + " \n", + " \n", + "# files = os.listdir()\n", + " \n", + "# if filestring is in file:\n", + " \n", + "# shutil.copytree(src, dst) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "find_surf_and_move(file_path, 'C:/experimental', 'cvasl')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "root = file_path\n", + "#pattern = \"*.\"\n", + "\n", + "for path, subdirs, files in os.walk(root):\n", + " for name in files:\n", + " if 'cvasl' in name:\n", + " #if fnmatch(name, pattern):\n", + " print(os.path.join(path, name))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From fbc8c74252e9c990fddf4a23e615d22fec338957 Mon Sep 17 00:00:00 2001 From: drcandacemakedamoore Date: Sat, 19 Oct 2024 10:37:53 +0000 Subject: [PATCH 3/3] kernel clear and add optional linux vs/ win paths --- ...Concatenate_volbrain_csvs_deeplesion.ipynb | 68 ++++++++++++++----- 1 file changed, 52 insertions(+), 16 deletions(-) diff --git a/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb b/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb index 43b5de1..974d6e4 100644 --- a/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb +++ b/notebooks/Concatenate_volbrain_csvs_deeplesion.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "metadata": { "tags": [] }, @@ -30,26 +30,31 @@ "import zipfile\n", "import os\n", "from zipfile import ZipFile\n", - "import glob" + "import glob\n", + "from io import BytesIO" ] }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ - "#navigate to zipfolders and intended folder for output\n", - "path_volbrain = 'Z:/VolBrain'\n", - "output_folder = \"Z:/VolBrain/Separate_CSV_Deeplesion\"\n" + "#navigate to zipfolders and intended folder for output (change to commented out if on Windows)\n", + "# path_volbrain = 'Z:/processed_data/VolBrain'\n", + "# output_folder = \"Z:/processed_data/VolBrain/Separate_CSV_Deeplesion\"\n", + "path_volbrain = '/mnt/data/processed_data/VolBrain'\n", + "output_folder = \"/mnt/data/processed_data/VolBrain/Separate_CSV_Deeplesion\"" ] }, { "cell_type": "code", - "execution_count": 81, - "metadata": {}, + "execution_count": null, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "#create a list to store the CSV headers in\n", @@ -58,15 +63,13 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ "dataframes = []\n", - "from io import BytesIO\n", - "\n", "\n", "for zip_file in glob.glob(os.path.join(path_volbrain, '*.zip')):\n", " # split by underscores\n", @@ -97,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "metadata": { "tags": [] }, @@ -110,7 +113,20 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "concat_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "#Now make the column names castor appropriate. Do not change the \"Participant Id\" column of course. dl stands for DeepLesion algorithm\n", @@ -125,13 +141,33 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "concat_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "tags": [] }, "outputs": [], "source": [ - "concat_df.to_csv('Z:/castor_proof_files/volbrains_castor.csv')" + "# concat_df.to_csv('Z:/castor_proof_files/volbrains_castor.csv')\n", + "#(change to commented out if on Windows)\n", + "concat_df.to_csv('/mnt/data/castor_proof_files/volbrains_castor.csv')" ] } ], @@ -151,7 +187,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.10" + "version": "3.11.9" } }, "nbformat": 4,