Datasets:

KagglingFace
/

FYP-KiTS-A-Preprocessed

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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 4 new columns ({'foreground_intensity_properties_per_channel', 'median_relative_size_after_cropping', 'shapes_after_crop', 'spacings'}) and 12 missing columns ({'numTraining', 'labels', 'release', 'training', 'name', 'licence', 'channel_names', 'reference', 'numTest', 'file_ending', 'description', 'tensorImageSize'}).

This happened while the json dataset builder was generating data using

hf://datasets/KagglingFace/FYP-KiTS-A-Preprocessed/dataset_fingerprint.json (at revision f91ee51ce513c0051bdc9bbcd7adec46d1099e0f)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              foreground_intensity_properties_per_channel: struct<0: struct<max: double, mean: double, median: double, min: double, percentile_00_5: double, percentile_99_5: double, std: double>>
                child 0, 0: struct<max: double, mean: double, median: double, min: double, percentile_00_5: double, percentile_99_5: double, std: double>
                    child 0, max: double
                    child 1, mean: double
                    child 2, median: double
                    child 3, min: double
                    child 4, percentile_00_5: double
                    child 5, percentile_99_5: double
                    child 6, std: double
              median_relative_size_after_cropping: double
              shapes_after_crop: list<item: list<item: int64>>
                child 0, item: list<item: int64>
                    child 0, item: int64
              spacings: list<item: list<item: double>>
                child 0, item: list<item: double>
                    child 0, item: double
              to
              {'channel_names': {'0': Value(dtype='string', id=None)}, 'description': Value(dtype='string', id=None), 'file_ending': Value(dtype='string', id=None), 'labels': {'Cortex': Value(dtype='string', id=None), 'Medulla': Value(dtype='string', id=None), 'Tumor': Value(dtype='string', id=None), 'background': Value(dtype='string', id=None)}, 'licence': Value(dtype='string', id=None), 'name': Value(dtype='string', id=None), 'numTest': Value(dtype='int64', id=None), 'numTraining': Value(dtype='int64', id=None), 'reference': Value(dtype='string', id=None), 'release': Value(dtype='string', id=None), 'tensorImageSize': Value(dtype='string', id=None), 'training': [{'image': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None)}]}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1316, in compute_config_parquet_and_info_response
                  parquet_operations, partial = stream_convert_to_parquet(
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 909, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 4 new columns ({'foreground_intensity_properties_per_channel', 'median_relative_size_after_cropping', 'shapes_after_crop', 'spacings'}) and 12 missing columns ({'numTraining', 'labels', 'release', 'training', 'name', 'licence', 'channel_names', 'reference', 'numTest', 'file_ending', 'description', 'tensorImageSize'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/KagglingFace/FYP-KiTS-A-Preprocessed/dataset_fingerprint.json (at revision f91ee51ce513c0051bdc9bbcd7adec46d1099e0f)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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channel_names dict	description string	file_ending string	labels dict	licence string	name string	numTest int64	numTraining int64	reference string	release string	tensorImageSize string	training list	foreground_intensity_properties_per_channel dict	median_relative_size_after_cropping float64	shapes_after_crop sequence	spacings sequence	dataset_name string	plans_name string	original_median_spacing_after_transp sequence	original_median_shape_after_transp sequence	image_reader_writer string	transpose_forward sequence	transpose_backward sequence	configurations dict	experiment_planner_used string	label_manager string	val sequence	train sequence
{ "0": "CT" }	kidney and kidney tumor segmentation	.nii.gz	{ "Cortex": "3", "Medulla": "2", "Tumor": "1", "background": "0" }		FYP-KiTS	0	40	Final year project KiTS data for nnunet v2	0.0	4D	[ { "image": "./imagesTr/case_00000.nii.gz", "label": "./labelsTr/case_00000.nii.gz" }, { "image": "./imagesTr/case_00001.nii.gz", "label": "./labelsTr/case_00001.nii.gz" }, { "image": "./imagesTr/case_00002.nii.gz", "label": "./labelsTr/case_00002.nii.gz" }, { "image": "./imagesTr/case_00003.nii.gz", "label": "./labelsTr/case_00003.nii.gz" }, { "image": "./imagesTr/case_00004.nii.gz", "label": "./labelsTr/case_00004.nii.gz" }, { "image": "./imagesTr/case_00005.nii.gz", "label": "./labelsTr/case_00005.nii.gz" }, { "image": "./imagesTr/case_00006.nii.gz", "label": "./labelsTr/case_00006.nii.gz" }, { "image": "./imagesTr/case_00007.nii.gz", "label": "./labelsTr/case_00007.nii.gz" }, { "image": "./imagesTr/case_00008.nii.gz", "label": "./labelsTr/case_00008.nii.gz" }, { "image": "./imagesTr/case_00009.nii.gz", "label": "./labelsTr/case_00009.nii.gz" }, { "image": "./imagesTr/case_00010.nii.gz", "label": "./labelsTr/case_00010.nii.gz" }, { "image": "./imagesTr/case_00011.nii.gz", "label": "./labelsTr/case_00011.nii.gz" }, { "image": "./imagesTr/case_00012.nii.gz", "label": "./labelsTr/case_00012.nii.gz" }, { "image": "./imagesTr/case_00013.nii.gz", "label": "./labelsTr/case_00013.nii.gz" }, { "image": "./imagesTr/case_00014.nii.gz", "label": "./labelsTr/case_00014.nii.gz" }, { "image": "./imagesTr/case_00015.nii.gz", "label": "./labelsTr/case_00015.nii.gz" }, { "image": "./imagesTr/case_00016.nii.gz", "label": "./labelsTr/case_00016.nii.gz" }, { "image": "./imagesTr/case_00017.nii.gz", "label": "./labelsTr/case_00017.nii.gz" }, { "image": "./imagesTr/case_00018.nii.gz", "label": "./labelsTr/case_00018.nii.gz" }, { "image": "./imagesTr/case_00019.nii.gz", "label": "./labelsTr/case_00019.nii.gz" }, { "image": "./imagesTr/case_00020.nii.gz", "label": "./labelsTr/case_00020.nii.gz" }, { "image": "./imagesTr/case_00021.nii.gz", "label": "./labelsTr/case_00021.nii.gz" }, { "image": "./imagesTr/case_00022.nii.gz", "label": "./labelsTr/case_00022.nii.gz" }, { "image": "./imagesTr/case_00023.nii.gz", "label": "./labelsTr/case_00023.nii.gz" }, { "image": "./imagesTr/case_00024.nii.gz", "label": "./labelsTr/case_00024.nii.gz" }, { "image": "./imagesTr/case_00025.nii.gz", "label": "./labelsTr/case_00025.nii.gz" }, { "image": "./imagesTr/case_00026.nii.gz", "label": "./labelsTr/case_00026.nii.gz" }, { "image": "./imagesTr/case_00027.nii.gz", "label": "./labelsTr/case_00027.nii.gz" }, { "image": "./imagesTr/case_00028.nii.gz", "label": "./labelsTr/case_00028.nii.gz" }, { "image": "./imagesTr/case_00029.nii.gz", "label": "./labelsTr/case_00029.nii.gz" }, { "image": "./imagesTr/case_00030.nii.gz", "label": "./labelsTr/case_00030.nii.gz" }, { "image": "./imagesTr/case_00031.nii.gz", "label": "./labelsTr/case_00031.nii.gz" }, { "image": "./imagesTr/case_00032.nii.gz", "label": "./labelsTr/case_00032.nii.gz" }, { "image": "./imagesTr/case_00033.nii.gz", "label": "./labelsTr/case_00033.nii.gz" }, { "image": "./imagesTr/case_00034.nii.gz", "label": "./labelsTr/case_00034.nii.gz" }, { "image": "./imagesTr/case_00035.nii.gz", "label": "./labelsTr/case_00035.nii.gz" }, { "image": "./imagesTr/case_00036.nii.gz", "label": "./labelsTr/case_00036.nii.gz" }, { "image": "./imagesTr/case_00037.nii.gz", "label": "./labelsTr/case_00037.nii.gz" }, { "image": "./imagesTr/case_00038.nii.gz", "label": "./labelsTr/case_00038.nii.gz" }, { "image": "./imagesTr/case_00039.nii.gz", "label": "./labelsTr/case_00039.nii.gz" } ]	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
null	null	null	null	null	null	null	null	null	null	null	null	{ "0": { "max": 669, "mean": 122.23634338378906, "median": 126, "min": -161, "percentile_00_5": -118, "percentile_99_5": 302, "std": 73.87754821777344 } }	1	[ [ 122, 512, 512 ], [ 97, 512, 512 ], [ 80, 512, 512 ], [ 106, 512, 512 ], [ 34, 512, 512 ], [ 151, 512, 512 ], [ 87, 512, 512 ], [ 31, 512, 512 ], [ 100, 512, 512 ], [ 96, 512, 512 ], [ 83, 512, 512 ], [ 92, 512, 512 ], [ 77, 512, 512 ], [ 28, 512, 512 ], [ 145, 512, 512 ], [ 73, 512, 512 ], [ 77, 512, 512 ], [ 97, 512, 512 ], [ 109, 512, 512 ], [ 125, 512, 512 ], [ 109, 512, 512 ], [ 135, 512, 512 ], [ 42, 512, 512 ], [ 99, 512, 512 ], [ 148, 512, 512 ], [ 139, 512, 512 ], [ 68, 512, 512 ], [ 149, 512, 512 ], [ 39, 512, 512 ], [ 143, 512, 512 ], [ 164, 512, 512 ], [ 102, 512, 512 ], [ 37, 512, 512 ], [ 39, 512, 512 ], [ 38, 512, 512 ], [ 126, 512, 512 ], [ 34, 512, 512 ], [ 142, 512, 512 ], [ 131, 512, 512 ], [ 141, 512, 512 ] ]	[ [ 1, 0.748046875, 0.748046875 ], [ 1, 0.626953125, 0.626953125 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 0.625, 0.8300780057907104, 0.8300780057907104 ], [ 1.25, 0.703125, 0.703125 ], [ 3.75, 0.703125, 0.703125 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 1.5, 0.65625, 0.65625 ], [ 3.75, 0.703125, 0.703125 ], [ 0.800000011920929, 0.783203125, 0.783203125 ], [ 1.5, 0.76171875, 0.76171875 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 1.25, 0.7402340173721313, 0.7402340173721313 ], [ 1, 0.587890625, 0.587890625 ], [ 1, 0.70703125, 0.70703125 ], [ 0.800000011920929, 0.73046875, 0.73046875 ], [ 3, 0.68359375, 0.68359375 ], [ 1.2000000476837158, 0.73828125, 0.73828125 ], [ 0.800000011920929, 0.806640625, 0.806640625 ], [ 0.800000011920929, 0.654296875, 0.654296875 ], [ 1.5, 0.58984375, 0.58984375 ], [ 0.800000011920929, 0.763671875, 0.763671875 ], [ 3, 0.701171875, 0.701171875 ], [ 1.2000000476837158, 0.65625, 0.65625 ], [ 0.800000011920929, 0.880859375, 0.880859375 ], [ 1.2000000476837158, 0.810546875, 0.810546875 ], [ 3, 0.765625, 0.765625 ], [ 3, 0.83984375, 0.83984375 ], [ 3, 0.701171875, 0.701171875 ], [ 0.800000011920929, 0.689453125, 0.689453125 ], [ 3, 0.685546875, 0.685546875 ], [ 0.800000011920929, 0.822265625, 0.822265625 ], [ 0.800000011920929, 0.548828125, 0.548828125 ], [ 0.800000011920929, 0.73828125, 0.73828125 ] ]	null	null	null	null	null	null	null	null	null	null	null	null
null	null	null	null	null	null	null	null	null	null	null	null	{ "0": { "max": 669, "mean": 122.23634338378906, "median": 126, "min": -161, "percentile_00_5": -118, "percentile_99_5": 302, "std": 73.87754821777344 } }	null	null	null	Dataset996_KiTS	nnUNetPlans	[ 1.25, 0.7402340173721313, 0.7402340173721313 ]	[ 98, 512, 512 ]	SimpleITKIO	[ 0, 1, 2 ]	[ 0, 1, 2 ]	{ "2d": { "data_identifier": "nnUNetPlans_2d", "preprocessor_name": "DefaultPreprocessor", "batch_size": 12, "patch_size": [ 512, 512 ], "median_image_size_in_voxels": [ 512, 512 ], "spacing": [ 0.7402340173721313, 0.7402340173721313 ], "normalization_schemes": [ "CTNormalization" ], "use_mask_for_norm": [ false ], "resampling_fn_data": "resample_data_or_seg_to_shape", "resampling_fn_seg": "resample_data_or_seg_to_shape", "resampling_fn_data_kwargs": { "is_seg": false, "order": 3, "order_z": 0, "force_separate_z": null }, "resampling_fn_seg_kwargs": { "is_seg": true, "order": 1, "order_z": 0, "force_separate_z": null }, "resampling_fn_probabilities": "resample_data_or_seg_to_shape", "resampling_fn_probabilities_kwargs": { "is_seg": false, "order": 1, "order_z": 0, "force_separate_z": null }, "architecture": { "network_class_name": "dynamic_network_architectures.architectures.unet.PlainConvUNet", "arch_kwargs": { "n_stages": 8, "features_per_stage": [ 32, 64, 128, 256, 512, 512, 512, 512 ], "conv_op": "torch.nn.modules.conv.Conv2d", "kernel_sizes": [ [ 3, 3 ], [ 3, 3 ], [ 3, 3 ], [ 3, 3 ], [ 3, 3 ], [ 3, 3 ], [ 3, 3 ], [ 3, 3 ] ], "strides": [ [ 1, 1 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ], [ 2, 2 ] ], "n_conv_per_stage": [ 2, 2, 2, 2, 2, 2, 2, 2 ], "n_conv_per_stage_decoder": [ 2, 2, 2, 2, 2, 2, 2 ], "conv_bias": true, "norm_op": "torch.nn.modules.instancenorm.InstanceNorm2d", "norm_op_kwargs": { "eps": 0.00001, "affine": true }, "dropout_op": null, "dropout_op_kwargs": null, "nonlin": "torch.nn.LeakyReLU", "nonlin_kwargs": { "inplace": true } }, "_kw_requires_import": [ "conv_op", "norm_op", "dropout_op", "nonlin" ] }, "batch_dice": true }, "3d_lowres": { "data_identifier": "nnUNetPlans_3d_lowres", "preprocessor_name": "DefaultPreprocessor", "batch_size": 2, "patch_size": [ 40, 256, 224 ], "median_image_size_in_voxels": [ 66, 370, 370 ], "spacing": [ 1.7302923384055575, 1.0246569991089323, 1.0246569991089323 ], "normalization_schemes": [ "CTNormalization" ], "use_mask_for_norm": [ false ], "resampling_fn_data": "resample_data_or_seg_to_shape", "resampling_fn_seg": "resample_data_or_seg_to_shape", "resampling_fn_data_kwargs": { "is_seg": false, "order": 3, "order_z": 0, "force_separate_z": null }, "resampling_fn_seg_kwargs": { "is_seg": true, "order": 1, "order_z": 0, "force_separate_z": null }, "resampling_fn_probabilities": "resample_data_or_seg_to_shape", "resampling_fn_probabilities_kwargs": { "is_seg": false, "order": 1, "order_z": 0, "force_separate_z": null }, "architecture": { "network_class_name": "dynamic_network_architectures.architectures.unet.PlainConvUNet", "arch_kwargs": { "n_stages": 6, "features_per_stage": [ 32, 64, 128, 256, 320, 320 ], "conv_op": "torch.nn.modules.conv.Conv3d", "kernel_sizes": [ [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ] ], "strides": [ [ 1, 1, 1 ], [ 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2 ], [ 1, 2, 2 ], [ 1, 2, 2 ] ], "n_conv_per_stage": [ 2, 2, 2, 2, 2, 2 ], "n_conv_per_stage_decoder": [ 2, 2, 2, 2, 2 ], "conv_bias": true, "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d", "norm_op_kwargs": { "eps": 0.00001, "affine": true }, "dropout_op": null, "dropout_op_kwargs": null, "nonlin": "torch.nn.LeakyReLU", "nonlin_kwargs": { "inplace": true } }, "_kw_requires_import": [ "conv_op", "norm_op", "dropout_op", "nonlin" ] }, "batch_dice": false, "next_stage": "3d_cascade_fullres" }, "3d_fullres": { "data_identifier": "nnUNetPlans_3d_fullres", "preprocessor_name": "DefaultPreprocessor", "batch_size": 2, "patch_size": [ 40, 256, 224 ], "median_image_size_in_voxels": [ 91.5, 512, 512 ], "spacing": [ 1.25, 0.7402340173721313, 0.7402340173721313 ], "normalization_schemes": [ "CTNormalization" ], "use_mask_for_norm": [ false ], "resampling_fn_data": "resample_data_or_seg_to_shape", "resampling_fn_seg": "resample_data_or_seg_to_shape", "resampling_fn_data_kwargs": { "is_seg": false, "order": 3, "order_z": 0, "force_separate_z": null }, "resampling_fn_seg_kwargs": { "is_seg": true, "order": 1, "order_z": 0, "force_separate_z": null }, "resampling_fn_probabilities": "resample_data_or_seg_to_shape", "resampling_fn_probabilities_kwargs": { "is_seg": false, "order": 1, "order_z": 0, "force_separate_z": null }, "architecture": { "network_class_name": "dynamic_network_architectures.architectures.unet.PlainConvUNet", "arch_kwargs": { "n_stages": 6, "features_per_stage": [ 32, 64, 128, 256, 320, 320 ], "conv_op": "torch.nn.modules.conv.Conv3d", "kernel_sizes": [ [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ], [ 3, 3, 3 ] ], "strides": [ [ 1, 1, 1 ], [ 2, 2, 2 ], [ 2, 2, 2 ], [ 2, 2, 2 ], [ 1, 2, 2 ], [ 1, 2, 2 ] ], "n_conv_per_stage": [ 2, 2, 2, 2, 2, 2 ], "n_conv_per_stage_decoder": [ 2, 2, 2, 2, 2 ], "conv_bias": true, "norm_op": "torch.nn.modules.instancenorm.InstanceNorm3d", "norm_op_kwargs": { "eps": 0.00001, "affine": true }, "dropout_op": null, "dropout_op_kwargs": null, "nonlin": "torch.nn.LeakyReLU", "nonlin_kwargs": { "inplace": true } }, "_kw_requires_import": [ "conv_op", "norm_op", "dropout_op", "nonlin" ] }, "batch_dice": true }, "3d_cascade_fullres": { "inherits_from": "3d_fullres", "previous_stage": "3d_lowres" } }	ExperimentPlanner	LabelManager	null	null
null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	[ "case_00002", "case_00003", "case_00005", "case_00019", "case_00021", "case_00025", "case_00028", "case_00032" ]	[ "case_00000", "case_00001", "case_00004", "case_00006", "case_00007", "case_00008", "case_00009", "case_00010", "case_00011", "case_00012", "case_00013", "case_00014", "case_00015", "case_00016", "case_00017", "case_00018", "case_00020", "case_00022", "case_00023", "case_00024", "case_00026", "case_00027", "case_00029", "case_00030", "case_00031", "case_00033", "case_00034", "case_00035", "case_00036", "case_00037", "case_00038", "case_00039" ]
null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	[ "case_00000", "case_00004", "case_00008", "case_00012", "case_00015", "case_00030", "case_00033", "case_00035" ]	[ "case_00001", "case_00002", "case_00003", "case_00005", "case_00006", "case_00007", "case_00009", "case_00010", "case_00011", "case_00013", "case_00014", "case_00016", "case_00017", "case_00018", "case_00019", "case_00020", "case_00021", "case_00022", "case_00023", "case_00024", "case_00025", "case_00026", "case_00027", "case_00028", "case_00029", "case_00031", "case_00032", "case_00034", "case_00036", "case_00037", "case_00038", "case_00039" ]
null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	[ "case_00006", "case_00007", "case_00010", "case_00013", "case_00017", "case_00020", "case_00024", "case_00026" ]	[ "case_00000", "case_00001", "case_00002", "case_00003", "case_00004", "case_00005", "case_00008", "case_00009", "case_00011", "case_00012", "case_00014", "case_00015", "case_00016", "case_00018", "case_00019", "case_00021", "case_00022", "case_00023", "case_00025", "case_00027", "case_00028", "case_00029", "case_00030", "case_00031", "case_00032", "case_00033", "case_00034", "case_00035", "case_00036", "case_00037", "case_00038", "case_00039" ]
null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	[ "case_00009", "case_00011", "case_00016", "case_00018", "case_00022", "case_00023", "case_00027", "case_00031" ]	[ "case_00000", "case_00001", "case_00002", "case_00003", "case_00004", "case_00005", "case_00006", "case_00007", "case_00008", "case_00010", "case_00012", "case_00013", "case_00014", "case_00015", "case_00017", "case_00019", "case_00020", "case_00021", "case_00024", "case_00025", "case_00026", "case_00028", "case_00029", "case_00030", "case_00032", "case_00033", "case_00034", "case_00035", "case_00036", "case_00037", "case_00038", "case_00039" ]
null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	[ "case_00001", "case_00014", "case_00029", "case_00034", "case_00036", "case_00037", "case_00038", "case_00039" ]	[ "case_00000", "case_00002", "case_00003", "case_00004", "case_00005", "case_00006", "case_00007", "case_00008", "case_00009", "case_00010", "case_00011", "case_00012", "case_00013", "case_00015", "case_00016", "case_00017", "case_00018", "case_00019", "case_00020", "case_00021", "case_00022", "case_00023", "case_00024", "case_00025", "case_00026", "case_00027", "case_00028", "case_00030", "case_00031", "case_00032", "case_00033", "case_00035" ]

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