Merge pull request #121 from computational-cell-analytics/revision

Revision Experiments

Author: constantinpape

.gitignore

@@ -7,9 +7,16 @@ models/*/
 *.egg-info/
 run_sbatch.sbatch
 slurm/
+slurm_revision/
 scripts/cooper/evaluation_results/
 scripts/cooper/training/copy_testset.py
 scripts/rizzoli/upsample_data.py
 scripts/cooper/training/find_rec_testset.py
 synapse-net-models/
 scripts/portal/upscale_tomo.py
+analysis_results/
+scripts/cooper/revision/evaluation_results/
+scripts/cooper/revision/export_tif_to_h5.py
+scripts/cooper/revision/copy_path.py
+scripts/cooper/revision/view.py
+scripts/cooper/revision/count_AZ.py

models_az_thin/split-chemical_fixation.json

@@ -0,0 +1 @@
+{"train": ["20180305_06_MS.h5", "20171113_01_MS.h5", "20171113_05_MS.h5", "20171113_04_MS.h5", "20180305_05_MS.h5", "20171113_3.2_MS.h5", "20180305_09_MS.h5", "20180305_07_MS.h5", "20180305_04_MS.h5", "20171006_2_2_MS.h5", "20171006_05_MS.h5", "20180305_10_MS.h5", "20171006_03_MS.h5", "20171006_2_3_MS.h5"], "val": ["20171013_01_MS.h5", "20171013_1.2_MS.h5", "20171006_3_2_MS.h5", "20180305_08_MS.h5", "20180305_02_MS.h5"], "test": ["20180305_03_MS.h5", "20171113_06_MS.h5", "20171113_07_MS.h5", "20171113_02_MS.h5", "20180305_01_MS.h5"]}

models_az_thin/split-endbulb_of_held.json

@@ -0,0 +1 @@
+{"train": ["M2_eb7_model.h5", "Wt22_eb1_10K_model2.h5", "WT40_eb5_model.h5", "KO8_eb4_model.h5", "M3_eb4_model.h5", "WT20_eb11_model2.h5", "WT13_syn6_model2.h5", "WT20_eb7_AZ1_model2.h5", "M2_eb10_model.h5", "WT11_syn1_model2.h5", "M8_eb8_model.h5", "WT22_eb10_model2.h5", "WT13_eb3_model2.h5", "WT20_syn7_model2.h5", "M7_eb6_model.h5", "WT20_eb7_AZ2_model2.h5", "WT13_syn5_model2.h5", "WT13_syn9_model2.h5", "M2_eb3_model.h5", "WT20_syn3_model2.h5", "WT20_syn1_model2.h5", "WT21_syn4_model2.h5", "WT20_eb4_model2.h5", "M2_eb9_model.h5", "M7_eb3_model.h5", "WT21_eb5_model2.h5", "WT22_eb6_AZ1_model2.h5", "WT13_syn10_model2.h5", "WT19_syn6_model2.h5", "M2_eb2_AZ2_model.h5", "WT41_eb4_model.h5", "WT13_syn4_model2.h5", "WT40_eb10_model.h5", "M1_eb8_model.h5", "WT19_syn9_model2.h5", "WT22_eb5_model2.h5", "WT39_eb7_model.h5", "KO9_eb13_model.h5", "WT39_eb5_model.h5", "WT11_eb5_model2.h5", "M7_eb15_model.h5", "M7_eb2_model.h5", "M7_eb9_model.h5", "WT22_syn7_model2.h5", "M1_eb7_model.h5", "WT11_syn6_model2.h5", "M7_eb5_model.h5", "WT22_syn5_model2.h5", "WT21_eb3_model2.h5", "WT19_syn3_model2.h5", "WT22_syn9_model2.h5", "M5_eb3_model.h5", "WT22_syn6_model2.h5", "WT39_eb9_model.h5", "WT13_eb5_model2.h5", "WT20_eb9_model2.h5", "WT20_eb2_AZ2_12K_model2.h5", "M7_eb12_model.h5", "M1_eb1_model.h5", "WT40_eb1_model.h5", "M2_eb6_model.h5", "M8_eb14_model.h5", "KO9_eb1_model.h5", "WT20_eb1_AZ2_12K_model2.h5", "M7_eb7_model.h5", "M8_eb9_model.h5", "WT40_eb11_model.h5", "M1_eb3_model.h5", "M8_eb12_model.h5", "M7_eb11_model.h5", "KO9_eb10_model.h5", "KO9_eb4_model.h5", "KO8_eb2_model.h5", "WT39_eb4_model.h5", "M1_eb5_model.h5", "M10_eb9_model.h5"], "val": ["WT41_eb6_model.h5", "Wt22_syn2_10K_model2.h5", "M1_eb6_model.h5", "KO9_eb11_model.h5", "WT20_eb5_model2.h5", "WT16_syn2_model2.h5", "KO9_eb9_model.h5", "WT21_eb7_model2.h5", "M7_eb4_model.h5", "M1_eb9_model.h5", "WT40_eb8_model.h5", "M2_eb5_model.h5", "WT13_eb4_model2.h5", "WT39_eb10_model.h5", "WT39_eb8_model.h5", "WT20_syn6_model2.h5", "WT13_syn11_model2.h5", "WT41_eb2_model.h5", "WT39_eb2_model.h5", "M2_eb14_model.h5", "M2_eb8_model.h5", "WT20_syn5_model2.h5", "M10_eb12_model.h5", "M5_eb1_model.h5", "KO9_eb12_model.h5"], "test": ["WT11_eb2_model2.h5", "WT21_syn5_model2.h5", "WT20_eb8_AZ2_model2.h5", "M10_eb8_model.h5", "WT21_eb9_model2.h5", "M2_eb1_model.h5", "WT22_syn10_model2.h5", "WT11_syn3_model2.h5", "WT11_eb1_model2.h5", "WT13_syn7_model2.h5", "WT21_eb4_model2.h5", "WT40_eb9_model.h5", "M6_eb2_model.h5", "WT22_syn1_10K_model2.h5", "WT19_syn1_model2.h5", "M7_eb10_model.h5", "KO9_eb6_model.h5", "WT11_eb7_model2.h5", "WT40_eb3_model.h5", "KO9_eb14_model.h5", "WT20_syn2_model2.h5", "WT22_eb9_model2.h5", "WT13_syn1_model2.h5", "WT39_eb3_model.h5", "WT21_syn3_model2.h5", "M8_eb6_model.h5"]}

models_az_thin/split-endbulb_of_held_cropped.json

@@ -0,0 +1 @@
+{"train": ["Wt22_eb1_10K_model2_cropped.h5", "WT22_eb6_AZ1_model2_cropped.h5", "WT21_eb3_model2_cropped.h5", "M7_eb11_model_cropped.h5", "WT20_syn3_model2_cropped.h5", "WT13_syn7_model2_cropped.h5", "WT13_eb4_model2_cropped.h5", "WT39_eb2_model_cropped.h5", "WT20_syn1_model2_cropped.h5", "WT40_eb3_model_cropped.h5", "WT20_eb7_AZ1_model2_cropped.h5", "WT21_syn5_model2_cropped.h5", "WT13_syn4_model2_cropped.h5", "KO9_eb13_model_cropped.h5", "M10_eb9_model_cropped.h5", "WT20_syn5_model2_cropped.h5", "M10_eb8_model_cropped.h5", "M7_eb12_model_cropped.h5", "WT39_eb7_model_cropped.h5", "WT20_eb4_model2_cropped.h5", "M1_eb8_model_cropped.h5", "WT40_eb11_model_cropped.h5", "KO9_eb14_model_cropped.h5", "WT39_eb10_model_cropped.h5", "KO9_eb6_model_cropped.h5", "WT13_syn1_model2_cropped.h5", "WT13_syn9_model2_cropped.h5", "WT13_eb3_model2_cropped.h5", "WT41_eb4_model_cropped.h5", "WT40_eb5_model_cropped.h5", "WT11_eb5_model2_cropped.h5", "WT22_eb9_model2_cropped.h5", "M2_eb2_AZ2_model_cropped.h5", "WT41_eb6_model_cropped.h5", "WT13_eb5_model2_cropped.h5", "WT13_syn11_model2_cropped.h5", "WT22_syn5_model2_cropped.h5", "WT20_syn6_model2_cropped.h5", "WT22_syn9_model2_cropped.h5", "WT11_syn6_model2_cropped.h5", "M8_eb12_model_cropped.h5", "WT39_eb4_model_cropped.h5", "M8_eb8_model_cropped.h5", "WT21_eb9_model2_cropped.h5", "WT39_eb3_model_cropped.h5", "M2_eb1_model_cropped.h5", "M2_eb9_model_cropped.h5", "WT39_eb5_model_cropped.h5", "WT22_eb10_model2_cropped.h5", "M7_eb4_model_cropped.h5", "WT20_eb7_AZ2_model2_cropped.h5", "WT40_eb10_model_cropped.h5", "WT19_syn9_model2_cropped.h5", "WT22_syn6_model2_cropped.h5", "WT11_eb1_model2_cropped.h5", "M10_eb12_model_cropped.h5", "KO9_eb11_model_cropped.h5", "WT19_syn6_model2_cropped.h5", "M7_eb5_model_cropped.h5", "WT39_eb9_model_cropped.h5", "M2_eb14_model_cropped.h5", "Wt22_syn2_10K_model2_cropped.h5", "WT20_syn2_model2_cropped.h5", "M7_eb9_model_cropped.h5", "M5_eb3_model_cropped.h5", "WT22_syn1_10K_model2_cropped.h5", "M1_eb7_model_cropped.h5", "M1_eb6_model_cropped.h5", "M7_eb7_model_cropped.h5", "WT21_eb7_model2_cropped.h5", "M2_eb8_model_cropped.h5", "WT20_eb1_AZ2_12K_model2_cropped.h5", "WT20_eb9_model2_cropped.h5", "WT41_eb2_model_cropped.h5", "WT20_eb5_model2_cropped.h5", "KO9_eb12_model_cropped.h5", "M3_eb4_model_cropped.h5", "WT19_syn1_model2_cropped.h5", "M2_eb3_model_cropped.h5", "KO9_eb9_model_cropped.h5", "WT13_syn5_model2_cropped.h5", "M1_eb1_model_cropped.h5", "M2_eb5_model_cropped.h5", "WT20_eb11_model2_cropped.h5", "WT13_syn6_model2_cropped.h5", "KO9_eb10_model_cropped.h5", "M2_eb7_model_cropped.h5", "M1_eb5_model_cropped.h5", "WT13_syn10_model2_cropped.h5", "WT22_eb5_model2_cropped.h5", "KO8_eb2_model_cropped.h5", "M2_eb10_model_cropped.h5", "KO9_eb1_model_cropped.h5", "M7_eb10_model_cropped.h5", "WT21_syn3_model2_cropped.h5", "WT40_eb8_model_cropped.h5"], "val": ["WT20_eb2_AZ2_12K_model2_cropped.h5", "WT19_syn3_model2_cropped.h5", "M6_eb2_model_cropped.h5", "M8_eb9_model_cropped.h5", "KO8_eb4_model_cropped.h5", "WT21_eb4_model2_cropped.h5", "WT22_syn10_model2_cropped.h5", "WT20_syn7_model2_cropped.h5", "M7_eb3_model_cropped.h5", "M7_eb6_model_cropped.h5", "WT16_syn2_model2_cropped.h5", "WT39_eb8_model_cropped.h5", "KO9_eb4_model_cropped.h5", "M1_eb9_model_cropped.h5", "M2_eb6_model_cropped.h5", "WT11_syn1_model2_cropped.h5", "WT11_eb7_model2_cropped.h5", "M1_eb3_model_cropped.h5", "WT21_eb5_model2_cropped.h5", "WT11_syn3_model2_cropped.h5", "M8_eb6_model_cropped.h5", "WT21_syn4_model2_cropped.h5", "M7_eb15_model_cropped.h5", "WT22_syn7_model2_cropped.h5"]}

models_az_thin/split-stem.json

@@ -0,0 +1 @@
+{"train": ["36859_H3_SP_10_rec_2kb1dawbp_crop.h5", "36859_H3_SP_01_rec_2kb1dawbp_crop.h5", "36859_H2_SP_02_rec_2Kb1dawbp_crop.h5", "36859_H2_SP_03_rec_2Kb1dawbp_crop.h5"], "val": ["36859_H3_SP_05_rec_2kb1dawbp_crop.h5", "36859_H2_SP_01_rec_2Kb1dawbp_crop.h5"], "test": ["36859_H3_SP_07_rec_2kb1dawbp_crop.h5", "36859_J1_STEM750_66K_SP_03_rec_2kb1dawbp_crop.h5"]}

models_az_thin/split-stem_cropped.json

@@ -0,0 +1 @@
+{"train": ["36859_J1_66K_TS_CA3_PS_23_rec_2Kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_15_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_66K_TS_CA3_PS_43_rec_2Kb1dawbp_crop_crop1.h5", "36859_H2_SP_01_rec_2Kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_10_rec_2kb1dawbp_crop_crop1.h5", "36859_H3_SP_05_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_07_rec_2kb1dawbp_crop_crop2.h5", "36859_H2_SP_01_rec_2Kb1dawbp_crop_crop2.h5", "36859_J1_STEM750_66K_SP_08_rec_2kb1dawbp_crop_crop3.h5", "36859_H3_SP_07_rec_2kb1dawbp_crop_crop2.h5", "36859_H3_SP_07_rec_2kb1dawbp_crop_cropped_noAZ.h5", "36859_J1_STEM750_66K_SP_03_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_06_rec_2kb1dawbp_crop_crop1.h5", "36859_H2_SP_03_rec_2Kb1dawbp_crop_crop1.h5", "36859_H3_SP_07_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_02_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_17_rec_2kb1dawbp_crop_crop1.h5", "36859_H2_SP_11_rec_2Kb1dawbp_crop_crop1.h5", "36859_H2_SP_04_rec_2Kb1dawbp_crop_crop1.h5", "36859_H2_SP_04_rec_2Kb1dawbp_crop_crop2.h5", "36859_H3_SP_10_rec_2kb1dawbp_crop_cropped_noAZ.h5", "36859_H2_SP_06_rec_2Kb1dawbp_crop_crop1.h5", "36859_J1_66K_TS_CA3_PS_26_rec_2Kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_14_rec_2kb1dawbp_crop_crop2.h5", "36859_H3_SP_01_rec_2kb1dawbp_crop_crop1.h5", "36859_H2_SP_01_rec_2Kb1dawbp_crop_crop3.h5", "36859_J1_STEM750_66K_SP_13_rec_2kb1dawbp_crop_crop2.h5", "36859_H2_SP_10_rec_crop_crop1.h5", "36859_H2_SP_02_rec_2Kb1dawbp_crop_cropped_noAZ.h5", "36859_J1_66K_TS_CA3_PS_32_rec_2Kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_01_rec_2kb1dawbp_crop_crop1.h5", "36859_H2_SP_03_rec_2Kb1dawbp_crop_cropped_noAZ.h5", "36859_H3_SP_10_rec_2kb1dawbp_crop_crop1.h5", "36859_H2_SP_02_rec_2Kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_08_rec_2kb1dawbp_crop_crop2.h5", "36859_J1_STEM750_66K_SP_03_rec_2kb1dawbp_crop_crop2.h5", "36859_H3_SP_05_rec_2kb1dawbp_crop_crop2.h5", "36859_H2_SP_07_rec_2Kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_13_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_14_rec_2kb1dawbp_crop_crop1.h5"], "val": ["36859_J1_STEM750_66K_SP_03_rec_2kb1dawbp_crop_crop3.h5", "36859_H2_SP_01_rec_2Kb1dawbp_crop_cropped_noAZ.h5", "36859_J1_STEM750_66K_SP_08_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_12_rec_2kb1dawbp_crop_crop1.h5", "36859_H3_SP_05_rec_2kb1dawbp_crop_cropped_noAZ.h5", "36859_H3_SP_09_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_11_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_07_rec_2kb1dawbp_crop_crop1.h5", "36859_J1_STEM750_66K_SP_12_rec_2kb1dawbp_crop_crop2.h5", "36859_J1_STEM750_66K_SP_10_rec_2kb1dawbp_crop_crop2.h5"]}

models_az_thin/split-tem.json

@@ -0,0 +1 @@
+{"train": ["20190805_09002_B4_SC_08_SP.h5", "WT_MF_DIV28_3.2_MS_09204_K1.h5", "20190524_09204_F4_SC_09_SP.h5", "20190524_09204_F4_SC_06_SP.h5", "WT_MF_DIV14_6.2_MS_E2_09175_CA3_2.h5", "WT_MF_DIV14_07_MS_C2_09175_CA3.h5", "WT_Unt_SC_09175_C4_04_DIV15_mtk_04.h5", "M13_CTRL_22723_O3_06_DIV29_06_MS.h5", "WT_Unt_SC_09175_E2_04_DIV14_mtk_03.h5", "WT_MF_DIV14_01_MS_E2_09175_CA3.h5", "M13_CTRL_22723_J1_03_DIV29_03_MS.h5", "20190807_23032_D4_SC_03_SP.h5", "M13_DKO_09201_U1_05_DIV31_05_MS.h5", "WT_Unt_SC_09175_E4_02_DIV14_mtk_02.h5", "20190805_09002_B4_SC_01_SP.h5", "WT_Unt_SC_09175_C4_08_DIV15_mtk_08.h5", "WT_MF_DIV14_01_MS_B2_09175_CA3.h5", "20190805_09002_B4_SC_10_SP.h5", "WT_Unt_SC_09175_D4_01_DIV14_mtk_01.h5", "WT_Unt_SC_09175_E2_03_DIV14_mtk_03.h5", "WT_Unt_SC_09175_C4_02_DIV15_mtk_02.h5", "20190805_09002_B4_SC_12_SP.h5", "20190805_09002_B4_SC_02_SP.h5", "M13_DKO_09201_U1_04_DIV31_04_MS.h5", "WT_MF_DIV14_02_MS_D2_09175_CA3.h5", "WT_Unt_SC_09175_E2_05_DIV14_mtk_05.h5", "20190807_23032_D4_SC_07_SP.h5", "M13_CTRL_09201_S2_03_DIV31_03_MS.h5", "WT_MF_DIV14_01.2_MS_D1_09175_CA3.h5", "20190807_23032_D4_SC_01_SP.h5", "WT_Unt_SC_09175_E4_03_DIV14_mtk_03.h5", "WT_MF_DIV28_3.3_MS_09204_K1.h5", "WT_Unt_SC_09175_D5_04_DIV14_mtk_04.h5", "20190524_09204_F4_SC_05_SP.h5", "M13_DKO_22723_A1_03_DIV29_03_MS.h5", "20190805_09002_B4_SC_7.2_SP.h5", "M13_DKO_22723_A4_08_DIV29_08_MS.h5", "WT_Unt_SC_09175_E4_05_DIV14_mtk_05.h5", "M13_CTRL_09201_S1_01_DIV31_01.h5", "WT_Unt_SC_09175_C4_03_DIV15_mtk_03.h5", "WT_MF_DIV28_03_MS_09204_M1.h5", "M13_DKO_09201_U1_5.2_DIV31_5.2_MS.h5", "WT_Unt_SC_09175_E2_01_DIV14_mtk_01.h5", "WT_MF_DIV28_2.3_MS_09002_B1.h5", "WT_MF_DIV28_01_MS_09204_F1.h5", "M13_CTRL_09201_S2_05_DIV31_05_MS.h5", "20190807_23032_D4_SC_04_SP.h5", "M13_DKO_09201_Q1_04_DIV31_04_MS.h5", "WT_MF_DIV28_04_MS_09204_M1.h5", "WT_Unt_SC_09175_D4_04_DIV14_mtk_04.h5", "WT_MF_DIV14_02_MS_B2_09175_CA3.h5", "20190807_23032_D4_SC_10_SP.h5", "M13_DKO_09201_U1_03_DIV31_03_MS.h5", "WT_MF_DIV14_01_MS_D1_09175_CA3.h5", "WT_MF_DIV28_05_MS_09204_F1.h5", "WT_Unt_SC_09175_D4_05_DIV14_mtk_05.h5", "WT_MF_DIV14_01_MS_D2_09175_CA3.h5", "WT_MF_DIV28_03_MS_09204_K1.h5", "M13_DKO_09201_O1_01_DIV31_01_MS.h5", "WT_MF_DIV14_04_MS_C2_09175_CA3.h5", "20190524_09204_F4_SC_04_SP.h5", "WT_MF_DIV14_02_MS_E1_09175_CA3.h5", "20190807_23032_D4_SC_09_SP.h5", "M13_CTRL_22723_O2_04_DIV29_04_MS.h5", "WT_MF_DIV28_03_MS_09204_F1.h5", "WT_MF_DIV28_01_MS_09002_B1.h5", "20190524_09204_F4_SC_03_SP.h5", "M13_DKO_22723_A1_4.2_DIV29_4.2_MS.h5", "M13_DKO_23037_K1_01_DIV29_01_MS.h5", "M13_DKO_23037_K1_1.2_DIV29_1.2_MS.h5", "20190524_09204_F4_SC_11_SP.h5", "WT_MF_DIV28_1.2_MS_09204_F1.h5", "M13_DKO_09201_O3_06_DIV31_06_MS.h5", "WT_Unt_SC_09175_C4_01_DIV15_mtk_01.h5", "WT_MF_DIV14_3.2_MS_D2_09175_CA3.h5", "WT_MF_DIV28_07_MS_09002_B2.h5", "WT_MF_DIV28_06_MS_09204_F1.h5", "M13_CTRL_09201_S2_04_DIV31_04_MS.h5", "WT_MF_DIV28_1.2_MS_09002_B1.h5", "WT_MF_DIV28_3.4_MS_09204_K1.h5", "20190524_09204_F4_SC_10_SP.h5", "20190805_09002_B4_SC_11_SP.h5", "20190524_09204_F4_SC_01_SP.h5", "WT_Unt_SC_09175_C4_05_DIV15_mtk_05.h5", "WT_MF_DIV28_04_MS_09002_B2.h5"], "val": ["WT_MF_DIV28_02_MS_09002_B1.h5", "M13_CTRL_09201_S2_06_DIV31_06_MS.h5", "20190805_09002_B4_SC_05_SP.h5", "WT_MF_DIV14_04_MS_B2_09175_CA3.h5", "M13_CTRL_22723_O3_07_DIV29_07_MS.h5", "WT_MF_DIV14_03.2_MS_D1_09175_CA3.h5", "WT_MF_DIV14_04_MS_E1_09175_CA3.h5", "20190524_09204_F4_SC_07_SP.h5", "WT_MF_DIV28_08_MS_09204_F2.h5", "WT_MF_DIV14_06_MS_C2_09175_CA3.h5", "WT_Unt_SC_09175_B5_03_DIV16_mtk_05.h5", "WT_MF_DIV28_2.2_MS_09002_B1.h5", "WT_Unt_SC_09175_D4_02_DIV14_mtk_02.h5", "WT_MF_DIV28_04_MS_09204_F1.h5", "20190805_09002_B4_SC_04_SP.h5", "M13_DKO_09201_Q1_01_DIV31_01_MS.h5", "WT_MF_DIV28_02_MS_09204_M1.h5", "M13_CTRL_22723_O2_05_DIV29_05_MS_.h5", "20190807_23032_D4_SC_08_SP.h5", "WT_Unt_SC_09175_D5_05_DIV14_mtk_05.h5", "WT_Unt_SC_09175_D5_02_DIV14_mtk_02.h5", "WT_MF_DIV14_05_MS_B2_09175_CA3.h5", "WT_Unt_SC_09175_D5_01_DIV14_mtk_01.h5", "20190524_09204_F4_SC_02_SP.h5", "WT_Unt_SC_09175_E4_04_DIV14_mtk_04.h5", "M13_DKO_09201_O3_6.2_DIV31_6.2_MS.h5", "M13_DKO_22723_A1_05_DIV29_05_MS.h5", "WT_MF_DIV14_04_MS_E2_09175_CA3_2.h5", "WT_MF_DIV14_03_MS_C2_09175_CA3.h5"], "test": ["WT_MF_DIV28_4.2_MS_09204_M1.h5", "M13_DKO_09201_Q1_03_DIV31_03_MS.h5", "WT_MF_DIV14_3.1_MS_D2_09175_CA3.h5", "WT_Unt_SC_09175_B5_03_DIV16_mtk_04.h5", "WT_MF_DIV28_10_MS_09002_B3.h5", "WT_MF_DIV14_06_MS_E2_09175_CA3_2.h5", "WT_MF_DIV14_03.3_MS_D1_09175_CA3.h5", "20190805_09002_B4_SC_09_SP.h5", "20190805_09002_B4_SC_7.1_SP.h5", "WT_MF_DIV14_03.1_MS_D1_09175_CA3.h5", "WT_Unt_SC_09175_E4_01_DIV14_mtk_01.h5", "WT_MF_DIV28_09_MS_09002_B3.h5", "WT_Unt_SC_09175_B5_01_DIV16_mtk_01.h5", "M13_DKO_22723_A1_06_DIV29_06_MS.h5", "WT_MF_DIV14_01_MS_orig_C2_09175_CA3.h5", "20190807_23032_D4_SC_05_SP.h5", "M13_DKO_22723_A4_10_DIV29_10_MS.h5", "WT_MF_DIV14_05_MS_C2_09175_CA3.h5", "WT_MF_DIV28_07_MS_09204_F2.h5", "WT_Unt_SC_09175_D5_03_DIV14_mtk_03.h5", "M13_CTRL_22723_O2_05_DIV29_5.2.h5", "WT_MF_DIV14_02_MS_C2_09175_CA3.h5", "WT_Unt_SC_09175_B5_03_DIV16_mtk_03.h5", "WT_MF_DIV14_03_MS_B2_09175_CA3.h5", "WT_Unt_SC_09175_B5_02_DIV16_mtk_02.h5", "WT_Unt_SC_09175_C4_06_DIV15_mtk_06.h5", "WT_MF_DIV14_05_MS_E2_09175_CA3_2.h5", "M13_CTRL_09201_S2_02_DIV31_02_MS.h5", "WT_MF_DIV28_08_MS_09002_B3.h5"]}

run_sbatch_revision.sbatch

@@ -0,0 +1,16 @@
+#! /bin/bash
+#SBATCH -c 4 #4 #8
+#SBATCH --mem 256G #120G #32G #64G #256G
+#SBATCH -p grete:shared #grete:shared #grete-h100:shared
+#SBATCH -t 3:00:00 #6:00:00 #48:00:00 
+#SBATCH -G A100:1 #V100:1 #2 #A100:1  #gtx1080:2 #v100:1 #H100:1
+#SBATCH --output=/user/muth9/u12095/synapse-net/slurm_revision/slurm-%j.out 
+#SBATCH -A nim00007 
+#SBATCH --constraint 80gb 
+
+source ~/.bashrc
+conda activate synapse-net
+python /user/muth9/u12095/synapse-net/scripts/cooper/revision/updated_data_analysis/run_data_analysis.py \
+ -i /mnt/lustre-emmy-hdd/projects/nim00007/data/synaptic-reconstruction/cooper/20241102_TOMO_DATA_Imig2014/exported/SNAP25 \
+ -o /mnt/lustre-emmy-hdd/projects/nim00007/data/synaptic-reconstruction/cooper/20241102_TOMO_DATA_Imig2014/afterRevision_analysis/boundaryT0_9_constantins_presynapticFiltering/weaker_SVfilter/man_subset --store \
+ -s ./analysis_results/weaker_SVfilter/man_subset

scripts/baselines/cryo_ves_net/evaluate_cooper.py

@@ -6,6 +6,7 @@
 import numpy as np
 import pandas as pd
 from elf.evaluation.matching import matching
+from elf.evaluation.dice import symmetric_best_dice_score
 from tqdm import tqdm
 
 INPUT_ROOT = "/mnt/lustre-emmy-hdd/projects/nim00007/data/synaptic-reconstruction/cooper/vesicles_processed_v2/testsets"  # noqa
@@ -25,11 +26,11 @@
 ]
 
 
-def evaluate_dataset(ds_name):
+def evaluate_dataset(ds_name, force):
     result_folder = "./results/cooper"
     os.makedirs(result_folder, exist_ok=True)
     result_path = os.path.join(result_folder, f"{ds_name}.csv")
-    if os.path.exists(result_path):
+    if os.path.exists(result_path) and not force:
         results = pd.read_csv(result_path)
         return results
 
@@ -44,6 +45,9 @@ def evaluate_dataset(ds_name):
         mask_key = None
 
     pred_files = sorted(glob(os.path.join(OUTPUT_ROOT, ds_name, "**/*.h5"), recursive=True))
+    if ds_name == "04":
+        pred_names = [os.path.basename(path) for path in pred_files]
+        input_files = [path for path in input_files if os.path.basename(path) in pred_names]
     assert len(input_files) == len(pred_files), f"{len(input_files)}, {len(pred_files)}"
 
     results = {
@@ -52,12 +56,13 @@ def evaluate_dataset(ds_name):
         "precision": [],
         "recall": [],
         "f1-score": [],
+        "sbd-score": [],
     }
     for inf, predf in tqdm(zip(input_files, pred_files), total=len(input_files), desc=f"Evaluate {ds_name}"):
         fname = os.path.basename(inf)
         sub_res_path = os.path.join(result_folder, f"{ds_name}_{fname}.json")
 
-        if os.path.exists(sub_res_path):
+        if os.path.exists(sub_res_path) and not force:
             print("Loading scores from", sub_res_path)
             with open(sub_res_path, "r") as f:
                 scores = json.load(f)
@@ -89,6 +94,8 @@ def evaluate_dataset(ds_name):
                 gt[mask == 0] = 0
 
             scores = matching(seg, gt)
+            sbd_score = symmetric_best_dice_score(seg, gt)
+            scores["sbd"] = sbd_score
 
             with open(sub_res_path, "w") as f:
                 json.dump(scores, f)
@@ -98,16 +105,19 @@ def evaluate_dataset(ds_name):
         results["precision"].append(scores["precision"])
         results["recall"].append(scores["recall"])
         results["f1-score"].append(scores["f1"])
+        results["sbd-score"].append(scores["sbd"])
 
     results = pd.DataFrame(results)
     results.to_csv(result_path, index=False)
     return results
 
 
 def main():
+    force = False
+
     all_results = {}
     for ds in DATASETS:
-        result = evaluate_dataset(ds)
+        result = evaluate_dataset(ds, force=force)
         all_results[ds] = result
 
     groups = {
@@ -123,16 +133,24 @@ def main():
     }
 
     for name, datasets in groups.items():
-        f1_scores = []
+        f1_scores, sbd_scores = [], []
 
         for ds in datasets:
             this_f1_scores = all_results[ds]["f1-score"].values.tolist()
+            this_sbd_scores = all_results[ds]["sbd-score"].values.tolist()
             f1_scores.extend(this_f1_scores)
+            sbd_scores.extend(this_sbd_scores)
 
         mean_f1 = np.mean(f1_scores)
         std_f1 = np.std(f1_scores)
+        print("F1-Score")
         print(name, ":", mean_f1, "+-", std_f1)
 
+        mean_sbd = np.mean(sbd_scores)
+        std_sbd = np.std(sbd_scores)
+        print("SBD-Score")
+        print(name, ":", mean_sbd, "+-", std_sbd)
+
 
 if __name__ == "__main__":
     main()